Copyright 1982 Gerardine L. DeSanctis

^Sr\

A. LXArtlNATlON Of ka EXr^ECIANCY THEOflY MODEL Of

DECISION SUPPOfiT SYSTEM USE

by

GEfiAEDINE L . DESANCIIS, d . A - , M.A,

A DiSSEiiTATiON

I I I

iiUSINEiiii ADiilNI:Si:;ATION

S u b m i t t e d t o t h e 3 r d d u d t e F a c u l t y of T e x a s T e c h U n i v e r s i t y i n

P a r t i a l F u l f i i i m e L t o i t i i e R e < a U i r e a e n t s t o r

t h e D e g r e e of

DOCTOR OF BUSINESS AJ i .I NI ST h A l l ON

A p p r o v e d

A c c e p t e d

May, 1982

7:-

CCi\^ ACKHOiLEDCEHBITS

I would like to express ay gratitude to several persons

whose tiae and efforts Bade this dissertation project

possible. I thank Dr. Mike Crino for carefully supervising

the research in all its stages and for being a aentor and

friend to ae throughout ay doctoral education. My sincere

appreciation is also given to Dr. Jia Courtney, who

co-chaired the dissertation coaaittee, for stiaulating ay

interest in behavioral research in HIS and for working

closely with me for the past two years. Dr. Carlton

Whitehead is thanked for his insightful coaaents and

constant encourageaent as I wrote the thesis. I am grateful

to Dr. Richard Barton for his help in refining the

simulation game used in the study, to Dr. Gary Kelley for

his recommendations regarding the data analysis procedures,

and to Dr. Jim Wilcox for providing objective comments on

the manuscript. The project would not have been possible

without the Department of Information Systems, Health

Sciences Center, Texas Tech University, which provided the

computer resources reguired to conduct this study. The

assistance of Mr. Gary Mims is particularly appreciated-

Finally, I would like to thank my husband, Michael, for

tiis love and understanding during the many difficult periods

of this research project.

ii

TABLE OF COITEVTS

ACKHOWLEDGEHENTS ii ABSTRACT vi LIST OF TABLES viii LIST OF FIGURES ix

Chapter page

I. BACKGROUND AND PURPOSE OF THE RESEARCH 1

Introduction . . . . . . . . . . . . . . . . . 1 Definitions • • . • • . . . . 4

Management Information System (HIS) . . . . . 4 Decision Support System (DSS) 4

Statement of the Problem . . . • • • . . . • • • 6 Purpose of the Study . . . . . . . . . . . . . . 1 4 Overview of the Study . . . . . . . .14 Organization of the Dissertation 15 Summary . . . . . . . . . . . . . 16

II. REVIEW OF THE LITERATURE 17

Expectancy Theory . . . . . . . . . . . . . . . 17 Overview . . . . . . . . . . . . . . . . . . 17 C r i t i c i s m s . . . . . . . . . . . . . . . . . 2 7

Conceptual C r i t i c i s a s . . . . . . . . • • 2 7 Methodolog ica l I s s u e s . . . 3 5

Behav iora l Research in MIS . . . . . . 4 4 Use as a Dependent Variable . . . . . . . . . 4 6 Independent V a r i a b l e s . 48

User A t t i t u d e s 48 I n d i v i d u a l D i f f e r e n c e s . . . . 5 1 Contex tua l F a c t o r s - 5 7

MIS Frameworks 58 Models of Decis ion Support System Use . . . . 63

Non-expectancy Based Models . 6 3 Expectancy Based Models . . . . 6 7

Summary . . . . . . . . . . . . . . . 6 7

I I I . PROPOSED MODEL OF USER BEHAVIOR 72

Model Overview 74 Research Hypotheses . . 8 3 Summary . . . . . . . . . . . 8 4

111

I V . METHODOLOGY 86

S u b j e c t s . 8 6 Procedure . . . . . • . • . • • • • 8 9 The Task Environment • • • • 95 Reward C o n d i t i o n s • . . • • • • • 9 9 O p e r a t i o n a l i z a t i o n of Var iab les • • . • • • . 100

Independent V a r i a b l e s • • • • 100 Dependent V a r i a b l e s . . . . • . . . . • • • 102 P e r s o n a l i t y V a r i a b l e s 103

Locus of Control . . . . • • • . • • • . 103 C o g n i t i v e S t y l e • 105

Confounding V a r i a b l e s . • 107 Advantages of the Design • • • • • • 108 Suaaary • • . • . . . . • • . . . 109

V. RESULTS . . . . • • I l l

Hypothes i s #1 • • • 116 W i t h i n - S u b j e c t s Analyses 116 A c r o s s - S u b j e c t s Analyses • . . • • • • • . 120

Hypothes i s #2 • 128 Hypothes i s #3 131 Summary . . • • • • • 132

VI. CONCLOSIONS 134

D i s c u s s i o n of R e s u l t s • • • 134 T h e o r e t i c a l l a p l i c a t i o n s . . . 140 P r a c t i c a l l a p l i c a t i o n s . • . • • • . . . • • • 141 L i m i t a t i o n s of the Study 142 Recommendations f o r Further Research 145 Summary • 147

REFERENCES 149

Appendix page

A. SUBJECT CONSENT FORM 169

B. AGREEMENT TO CONFIDENTIALITY 171

C. BACKGROUND QUESTIONNAIRE . . 172

D. LOCUS OF CONTROL SCALE 174

E. DECISION FORM 176

F. BML/SLIH QUIZ 177

IV

G. EXPERIHENTAL QUESTIONNAIRE 179

H. POST EXPERIMENT QUESTIONNAIRE . 182

I. DESCRIPTION OF THE BUSINESS MANAGEMENT LABORATORY 183

J. DESCRIPTION OF THE SLIM SYSTEH 185

K. DESCRIPTION OF DUHHY FIRM STRATEGIES AND SAMPLE OUTPUT 193

ABSTRACT

This dissertation developed a conceptual model of user

behavior based on expectancy theory, a psychological theory

of motivation^ A portion of the proposed model was examined

in a controlled laboratory study. The major independent

variable in the study was predicted motivation, or ''force"

to use a decision support systea^ Aaount of DSS use was the

dependent variable^

A business siaulation "game** and its accompanying

interactive support program served as the research contexts

Eighty-eight undergraduate business students participated in

the study^ The procedure required each subject to play the

role of a manager in a competitive industry consisting of

three firms: the student's fira and two phantoa firas.

Over a three-week period, the subject was required to make

two "practice** decisions and five "real" decisions.

Subjects were trained in the use of the DSS which

accompanied the simulation, but they were not required to

use the systea beyond the practice decision period. The

subjects received aonetary and grade-based rewards which

were contingent upon their level of performance in the

simulation.

VI

Results of the study suggest soae support for the

hypothesized aodel of user behavior^ The strength of the

force—^behavior relationship, as in previous expectancy

theory research, was not strong^ However, the presence of

significant across-subjects correlations between aotivation

to use a DSS and actual use of the system imply that

expectancy theory constructs aay offer soae explanatory

power in a coaprehensive theory of user behavior. Tests for

hypotheses of the influence of the two personality

variables, locus of control and cognitive style, on

components of the expectancy aodel yielded no significant

findings. The results are discussed in teras of their

implications for the expectancy theory literature, for the

MIS literature, and for development of behavioral-science

based theory within the field of MIS-

vii

LIST OP TABLES

Table £J5e

1. I n d i v i d u a l D i f f e r e n c e V a r i a b l e s i n HIS Research . . 10

2 . Sample C h a r a c t e r i s t i c s . . . 8 7

3^ Heans and Standard D e v i a t i o n s for the Research V a r i a b l e s 112

4 . Analyses of Variance for E f f e c t s of Subject Group on H o t i v a t i o n a l Force Scores to Ose SLIM . . . . . 114

5. D e s c r i p t i v e S t a t i s t i c s for Post-Experiment Quest ion #3 114

6. T e s t s f o r Contaa inat ion of Systea Ose Measures Due

t o I n s u f f i c i e n t Access t o SLIM 115

7. Accuracy of W i t h i n - S u b j e c t s Choice t o Ose SLIM • • 119

8. C o r r e l a t i o n s Between P r e d i c t o r S c o r e s and Ose of SLIM 121

9. D e s c r i p t i v e S t a t i s t i c s for Post -Exper imenta l Quest ions # 1 , #2 , and #4 123

10. T e s t s f o r the Covarying In f luence on Post-Experiment Quest ion #1 On Aaount of Sys tea Use . . . - • • 124

I K C o r r e l a t i o n s Between P r e d i c t o r S c o r e s and Use of SLIM for S u b j e c t s Who Recognized Use of SLIM t o be Opt ional . . . . • . • • . • . . . 125

12. P r e d i c t i o n of Tiae Spent Using Expectancy-Based Pred ic tor Scores for Two P r e d i c t o r Measures a t Two Tiae P e r i o d s 127

13. Use—^Performance and Perforaance—>Outcoae E x p e c t a n c i e s of I n t e r n a l s vs . Externa l s . . . . 130

14^ Use—>Perforaance Expec tanc ie s for High versus Low A n a l y t i c s '^^

V l l l

LIST OF FIGORBS

Figure page

K Han-machine i n t e r a c t i o n • • • • 3

2 . Bas i c components of a DSS. • • • • • . . . • • . . • 6

3^ Overview of MIS/DSS l i t e r a t u r e • . • 8

4 . Lucas (1975) model of user behavior^ • • • • . • . • 6 4

5. Schewe (1976) model of user b e h a v i o r . . . . . . . . 6 6

6^ Mehra and A lexander ' s model of s y s t e a use^ . . . . . 67

7. Robey (1979) aodel of user behav ior . 69

8. A m o t i v a t i o n a l model of DSS use . • . • • . . . . . . 7 5

9 . Model to be t e s t e d 80

10. Out l ine of exper imenta l procedure. . • • . . . • • • 9 0

I K Sample ad hoc q u e r i e s . • 190

12. Sample SLIM batch f i l e 191

1 3 . Sample SLIH w h a t - i f e x e r c i s e . 192

I X

Chapter I

BACKGROOMD AND POEPOSE Of THE BESEABCH

1.1 Introduction

The study of human response to information system

technology has its roots in earlier literature concerned

with the dynamics of the relationship between man and the

machines he creates. Beginning with the industrial

revolution, but particularly since World War II, concern for

the effects of industrial mechanization on worker motivation

and perforaance has resulted in numerous theoretical

developments and empirical investigations, largely within

the fields of industrial engineering (human factors) and

general psychology. Early writing in this area dealt with

the psychological, social, and economic implications of

automation and mass production processes for individuals and

organizations.

With the development of computers in the 1950»s and the

emergence of the discipline known as Management Information

Systems (MIS) in the 1960's, the aan-aachine relationship

took a more specific focus-- the study of human reaction to

computer technology. As a separate discipline of study, MIS

has borrowed extensively from the areas of organizational

2

behavior and general psychology in attempting to deal with

problems related to information systems design, development,

and iapleaentation. Indeed, the presence of a social-

psychological orientation is one feature distinguishing BIS

as a discipline separate from purely technical fields such

as coaputer science and management science (Keen, 1980)•

The recent development of Decision Support Systems

(DSS) has led to an even more specific focus for the

human-machine issue, as we now consider the individual

within an interactive decision-making context. The subject

of study is not as general as man and machine, or even man

and computer. In the case of a DSS, the **man'* takes on a

particular role, that of decision-maker. And the level of

human interaction with the machine is much more

sophisticated than previously. The machine is not merely

automating human physical movements (as in production

operations) or displaying large sums of data (as in

transactions processing). In the case of a DSS, the machine

converses with the person, usually a aid or high-level

manager, responding to his queries and providing a wide

variety of information in a variety of formats, to

facilitate his making semistructured or unstructured

decisions. The focus of study is thus on the interface

between the human user and the decision models or other

software which the machine provides. A body of literature

is now emerging which addresses behavioral issues pertaining

to the use of DSS within organ iza t ions . Keeping in mind the

h i s t o r i c a l development of l i t e r a t u r e re lated to human

i n t e r a c t i o n with machines, one can appropriately conclude

that the researcher i n t e r e s t e d in understanding huaan

behavior within the context of a DSS can not only draw froa

the e x i s t i n g DSS l i t e r a t u r e , but a lso froa the aore general

area of MIS and the even broader organizat ional behavior,

huaan f a c t o r s , and psycholog ica l l i t e r a t u r e . These l a t t e r

f i e l d s may of fer a t h e o r e t i c a l foundation to the study of

the aanager /dec is ion support s y s t e a r e l a t i o n s h i p (see Figure

1 ) .

MAN-MACHINE INTERACTION

Psychological Literature (Huaan Factors)

Hanageaent Literature (Organizational Behavior)

Hanageaent Inforaat ion Systeas

(user behavior)

Decision Support Systeas

(user as dec i s ion aaker)

Figure 1: Man-aachine interact ion ,

1.2 Definitions

1-2 1 Hanageaent Infornatigs Systen (Bisi

A HIS is that subsystea of the organization which

systematically provides inforaation resources to support

record-keeping, report-writing, and decision-aaking (Davis,

1974). At a conceptual level, the HIS includes all

informational and decision-making activity associated with

operating the organization. Until the early 1970's the

mechanized aspect, or hardware, of the MIS typically

consisted of an electronic data processing system (BDP) .

The purpose of the EDP was merely to provide information for

use in making structured decisions at the operational level

of the organization. In recent years HIS technology has

expanded to include sophisticated data bases, model bases,

interactive software, and a variety of communication

devices. These developments have allowed computerized

decision support to expand into the managerial and strategic

levels of the firm.

1.2«2 Decision Sa££ort Sxsten iPSS)

A DSS refers to that segment of the HIS which directly

facilitates semistructured or unstructured decision-making

(Alter, 1977a; Keen 8 Wagner, 1979; Vazsonyi, 1978) • A DSS

can therefore be distinguished from an EDP system which is

designed to automate transaction processing (Alter, 1979)^

Definitions of DSS vary in the literature, and there is not

a consensus on the precise meaning of the ten. For

purposes of this study a DSS is defined as an interactive,

computer-based system which supports managers in making

semistructured and unstructured decisions. Hajor components

of a DSS include a data base, a set of models, and a query

language (see Figure 2)• The data base typically consists

of the transactional information of the organization,

together with external data relevant to managerial

decision-making^ The model base may include traditional

management science or in-house developed models useful in

making operational, tactical, and strategic level decisions^

The data base and model base are managed by software

programs which assure the necessary organization of data^

Both are directed by a command, or query, language that

allows the user to access and to manipulate both data and

models to support decision-making (Sprague & Watson, 1979)^

A DSS is characterized by its ability to: (1) select data

from a data base, (2) aggregate data into meaningful report

formats, and (3) calculate the anticipated consequences of a

proposed decision or possible changes in the organizational

environment (Blanning, 1979).

^ «.

I DATA BASE f ^ I I

.A J . . I USER INTERFACE | i (Query Language) j

— 1 ~ • 4. 1 4. I DECISION HAKER |

HODEL BASE 1

Figure 2: Basic components of a DSS.

K 3 Statenent of the Problem

Host researchers and practitioners would probably agree

that our understanding of human behavior within the context

of a MIS or DSS is far from complete^ Behavioral research

to date has dealt with such issues as the conversational

attributes of the query language, the number and type of

reports generated, the effects of user characteristics on

decision performance, and alternative methods of data

display for various types of users. The major categories of

independent variables that have been studied include: (1)

individual characteristics, (2) technological attributes,

and (3) situational factors (see Figure 3). Existing

studies point to the importance of the experience level of

the user, his cognitive style, personality type, and

decision-making ability in determining the quality of

resulting decisions and user confidence in decisions.

Characteristics of the software affecting these dependent

variables have been identified, and situational factors have

also been studied to some extents For the most part,

however, this literature is fragmented, with little

theoretical foundation to integrate the variety of reported

investigations^ Frameworks have been presented for

classifying research studies and developing potential

hypotheses (Chervany, Dickson, 5 Kozar, 1972; Gorry & Scott

Morton, 1971; Ives, Hamilton, 6 Davis, 1980; Lucas, 1975;

Mason S Hitroff, 1973; Hock, 1973; Nolan & Wetherbe, 1980),

but explanations of user behavior have not, for the most

part, been based in theory. There is a definite need for

further study of the role of individual, technical, and

situational factors in DSS use, and more importantly, for

attempts to examine these variables within appropriate

theoretical models (Keen, 1980; Van Horn, 1973). The

present investigation will focus on that segment of the DSS

literature dealing with individual and situational

attributes, and examine user behavior within a behavioral

science model of motivation.

Huch of the behavioral literature in DSS, and MIS more

generally, has been devoted to the study of individual

differences in users which might account for varied

8

Individual Factors

Personality Variables

achievement anxiety defensiveness locus of control risk-taking tolerance for ambiguity

Cognitive Variables

a b i l i t y knowledge cognit ive s t y l e cognit ive complexity information overload

Demographic Variables

age education experience sex

I

DEPENDENT VARIABLES

Use of the system Performance User satisfaction User confidence in decisions Nature of queries Nature of report requests

i • * •

H 7 13

I

Technological Factors I ,

I in teract ive vs . batch i CRT vs . printer I report format

"•••

II II II \ \

11 f •

Situational Factors

type of decision organization characteristics organization policies

Figure 3: Overview of MIS/DSS literature.

9

approaches to the decision process, use of the system,

satisfaction with systea output, and confidence in resulting

decisions (Zand, 1979a). Individual differences which have

been examined include demographic characteristics,

personality characteristics, and cognitive style. Soae

behavioral literature has focused on user attributes and

perceptions. A summary list of individual difference

studies is presented in Table 1. These studies have

typically looked at several deaographic variables and

perhaps one personality, cognitive style, or attitude

diaension^ Few studies have exaained deaographic,

personality, cognitive style, and attitudinal

characteristics in one experiaent^

One factor not yet addressed in the aajority of

behavioral studies is the aotivational state of the user.

The iaportance of aotivation in decision-aaker behavior has

been recognized in the HIS literature (Chorba & New, 1980;

Ein-dor & Segev, 1978; Hock, 1973; Zand, 1979a), however, it

has not been explicitly studied as an experiaental variable.

Hotivational research in the area of DSS is relevant for

several reasons.

First, individual difference or situational studies

which ignore aotivational variables risk drawing iaproper

conclusions from their results. Apart fr9m individual

difference or situational characteristics, behavioral

10

1

L -r

1 , 1

i , . 1

t r •

TABLE 1 1

Individual Difference Variables in HIS Research |

Deaographic

Variable

age

education

experience

sex

Personality

Variable

need for achievement locus of control risk propensity

anxiety defensiveness tolerance for ambiguity dogmatism

Characteristics |

Ch

Research J

Taylor,1975 J Taylor & Dunnette,1974 | Lucas, 1975 | Vasarhelyi,1973 J Taylor,1975; | Taylor & Dunnette,1974 i Tiessen,1976 | Smith,1975 | Vasarhelyi,1973 )

1 f

aracteristics | , 1 1

Research |

Wynne & Dickson,1975 | Zmud,1979c | Henderson & Nutt,1980 | Taylor & Dunnette,1974 | Wynne & Dickson,1975 1 Taylor & Dunnette,1974 1 Zmud, 1979c | Taylor & Dunnette,1974 |

Cognitive Characteristics I II • ^

Variable

general intelligence cognitive complexity

cognitive style analytic vs. heuristic

abstract vs. concrete perceptive vs. receptive

Research {

Taylor & Dunnette,1974 | Hiller & Gordon, 1975 | Schroeder, Driver, & | Streufert, 1967 |

Barkin,1974 | Benbasat S Taylor,1978 | Doktor & Hamilton,1973 1 Huysman, 1968 | Lusk & KersBick,1979 | Vasarhelyi, 1973 | Schroeder et al. | McKenny 6 Keen, 1974 |

K

9

7

4

11

l~

1 _ _ r

1——

Table

Jungian c o g n i t i v e t y p e s

A t t i t u d e s

Veuriable

p o s i t i v e v s . n e g a t i v e a t t i t u d e s

user e x p e c t a t i o n s

1 cont inued 1

and

Gingras ,1977 | H e l l r e i g e l & Slocum,19751 Henderson & N u t t , 1 9 8 0 t Hanoocherci , 1978 | S teckroth et a l ^ , 1 9 8 0 |

P e r c e p t i o n s 1

Research I

Brady, 1967; Guthr i e ,1973 ) Lucas , 1975 I Rodriguez ,1977 J Ginzberg ,1975 ,1981 | S c h u l t z & S l e v i n , 1 9 7 5 | Haish ,1979 I

response to a DSS may also be a function of the user's

motivation to use the system and his desire to make "good"

decisions. "Noise" in results of studies dealing with user

attributes may occur because of failure within experiments

to control for subject interest in the decision task, or for

subject enthusiasm. Zmud (1979a) points out that such

factors must be taken into account whenever decision

behavior is being investigated. "Failure to do so

undoubtedly confounds experimental data regardless of

whether or not significant results are obtained" (Zmud,

1979a, p. 148). In short, when examining decision behavior

the motivation of the decision maker becomes as critical as

those variables directly under study.

S H ? 4

12

A second r a t i o n a l e for studying the nature of

motivat ional processes r e l a t e s to the impl i ca t ions for

design and implementation of dec i s ion support systems. I f

we could improve our understanding of what motivates people

to use or not use an a v a i l a b l e information system, then

perhaps we could des ign systems i n ways that would maximize

the i r appeal to po ten t ia l users. with regard to

implementation, motivation has been shown to in f luence the

e f f e c t i v e n e s s of t ra in ing programs i n various work s k i l l s

(cf . Froman, 1977). I n t u i t i v e l y we might expect motivation

to a f f e c t l ikewise the learning process of a naive DSS user . w

Research i n t o the e f f e c t s of motivation on user l earn ing

might suggest methods of improving user tra in ing through #

manipulation of motivational var iab le s .

r In a rather e x t e n s i v e d i scuss ion of user expec ta t ions J

H and system s u c c e s s , Ein-dor and Segev (1978) have J

hypothesized that p o s i t i v e user expectat ions are necessary

t o achieve HIS s u c c e s s . However, the authors o f f er no

t h e o r e t i c a l model or data to support the ir p r o p o s i t i o n .

Research focusing on motivation var iab le s might add i n s i g h t

i n t o how users formulate t h e i r a t t i t u d e s toward a system and

the e f f e c t s of those expec ta t ions on t h e i r behavior.

F i n a l l y , motivat ional research i s p a r t i c u l a r l y re levant

in l i g h t of arguments presented by Argyris (197 1) , Huysman

(1970) , and others (Driver 6 Hock, 1975; HcArthur, 1980;

?

13

Zmud, 1979b) which state that management resistance to MIS

and operations research/nanagement science techniques is due

to managers feeling threatened by methods developed by

persons with thought processes alien to their own.

Designers and users of information systems have been found

to possess divergent psychological characteristics (Gingras,

1977) . Designers tend to be "thinking," analytic types

while users are frequently "feeling," intuitive types. An

understanding of motivational processes may indicate ways to

reduce or overcome differences between these two groups.

It is the thesis of the current study that a >

motivational component is needed in theoretical models of

user behavior if complete understanding of the role of « r

individual differences is to be achieved. Insufficient ^

attention has been given to motivational variables in HIS j

and DSS research (Zmud, 1979a)• Several writers have J 4

acknowledged the importance of user preconceptions and

a t t i t u d e s toward the HIS and DSS, par t i cu lar ly as they

r e l a t e to sys ten use (Ein-dor 5 Segev, 1978; Lucas, 1975b) •

Numerous s t u d i e s of user a t t i t u d e s have been conducted. But

very l i t t l e sys temat ic empirical research has examined the

importance of user motivation i n determining actual use of

the information system or the inf luence of user percept ions

on the q u a l i t y of decis ion-making. Even l e s s a t t e n t i o n has

been given t o the process in which user preconceptions or

e x p e c t a t i o n s , and hence mot ivat ion, change over time as the

user i n t e r a c t s with the DSS and makes a s e r i e s of d e c i s i o n s .

I

14

1.4 Purpose of tke Stody

This dissertation will develop and test a conceptual

model of user behavior based on expectancy theory, a

psychological theory of motivation. The body of

organizational behavior literature devoted to expectancy

theory, together with literature related to behavioral

aspects of information system use will serve as the basis

for the proposed model. The primary purpose of the model

will be to explain DSS usage and, to a lesser extent,

decision performance^ The dissertation represents an

original attempt to examine the interrelationships among

attitudes, individual differences, usage, and performance.

A recent review of the individual difference literature in ,

HIS calls for such a study (Zmud, 1979a). While this ^

research may have implications for information system ^

implementation and methods for increasing DSS use in J

organizations, the primary focus is on explaining the usage

process as it naturally occurs.

1-5 Overview of the Stndj

This research project will take the form of a

controlled laboratory study. The major dependent variable

will be amount of DSS use. The independent variable will be

predicted "effort," as measured by a matrix algebra approach

to expectancy assessment. Two individual difference

variables, locus of control and cognitive style, will be

?

15

examined for their potential influence on certain model

relationships^ The study will attempt to control for

subject age, experience, knowledge, and other variables

which night have potential contaminating effects on the

dependent variables of interest. A business simulation

"game" and its accompanying interactive support program will

serve as the experimental context. The proposed model of

user behavior will be tested for its ability to explain user

behavior within a given individual as well as across

individuals. Results of the study will be primarily

discussed from the point of view of potential theoretical

contributions to DSS literature. Secondary attention will t

be given to the practical implications of the findings for f

HIS managers and implementers.

0

1^6 Organization of the Dissertat ion ^ 7 p

Expectancy theory literature is reviewed in Chapter 2, A

and HIS literature relevant to the study of information

system use is then discussed. Specific attention is given

to two models within the HIS/Operations Research literature

which have attempted to apply expectancy concepts to the

understanding of user behavior. The proposed conceptual

model to be tested is then presented in Chapter 3, followed

by a detailed description of the research instruments and

procedures Chapter 4. Chapter 5 summarizes the results of

within and across-subjects data analyses^ Finally, Chapter

16

6 discusses the significance and implications of this study^

All questionnaires and materials used in the data gathering

process are contained in various appendices^ In addition, a

brief description of the business simulation and its

accompanying DSS is provided.

^•^ Sunnary

This chapter has presented a rationale for the

application of organization behavior theory to explain the

behavior of persons who use information systems^ A brief

look at the individual difference literature in HIS reveals

that a theoretical model of DSS use which includes a I J.

motivational component is needed^ In addition to a ^ f

theoretical model, empirical investigations into the precise '

operation of motivation in user behavior are needed. The " i

purpose of this dissertation is to provide a theoretical ^ 5

model of DSS use and an e m p i r i c a l t e s t of the v a l i d i t y of 4 the model.

Chapter I I

RSTIEV OP THB LITBRATORB

2 . 1 Expectancy Theory

2 . 1 . 1 Over view

The expectancy theory of mot iva t ion i s roo ted in t he

c o g n i t i v e psychology t r a d i t i o n of Lewin (1938), Tolman

(1932) , Atkinson (1958) and o t h e r s wel l known in the e a r l y I

p s y c h o l o g i c a l l i t e r a t u r e ^ The theory was f i r s t a r t i c u l a t e d

by Vroom in 1964 wi th in the domain of o r g a n i z a t i o n a l j*

psychology^ Vroom*s o r i g i n a l work has been desc r ibed a s : f i

"perhaps the most widely accepted theory of work and ^ p

motivation among today's industrial psychologists" (Wahba 6 A

House, 1974, p^ 21)• Components of the theory are similar

to central concepts in major theories of learning,

decision-making, attitude formation, personality

development, and motivation (Hitchell, 1974). These

theories, as well as expectancy theory, share the common

assumption that:

the strength of a tendency to act in a certain way depends on the strength of an expectancy that the act will be followed by a given consequence (or outcome) and on the value or attractiveness of that consequence (or outcome) to the actor (Lawler, 1973, p- 45).

17

18

Expectancy theory has been character ized as having "more

supporters than any other c o g n i t i v e theory in psychology"

( H i t c h e l l , 1974,p. 1053), and a s "the most popular theory

of motivation current ly under research" (Stahl 6 Harre l l ,

1981, p. 30 3) . The theory has been shown to predict more

c l o s e l y job motivation and job s a t i s f a c t i o n than other major

t h e o r i e s of motivation, including Haslow's need hierarchy

and Herzberg's two- fac tor theory (Goodman, Rose, 6 Furcon,

1970; Wofford, 1971) •

Vroom (1964) presented two models in h i s o u t l i n e of

expectancy theory. His valence model pos tu la tes that a

person's s a t i s f a c t i o n with a job i s a function of the

ins t rumenta l i ty (I) of the job for a t t a i n i n g other outcoaes

and the valence (V) of those outcoaes^ S yabo l i ca l ly ,

n VJ = f 5 (Vk lik)

k = 1

where VJ = va lence , or a t t r a c t i v e n e s s , of outcoae j Vk = va lence , or a t t r a c t i v e n e s s , of outcoae k i jk = the cognized i n s t r u a e n t a l i t y of outcoae j for

the a t ta inaent of outcoae k

An "outcoae" i s anything a person des ires to obtain^

"Valence" r e f e r s to the s trength of a person's p o s i t i v e or

negat ive f e e l i n g toward the outcoae. " I n s t r u a e n t a l i t y , "

according t o Vroom, represents a perceived corre la t ion

between a f i r s t - l e v e l outcome ( e . g . , performance) and a

s e c o n d - l e v e l outcome ( e . g . , P^y) • The e ^ o r t model

p o s t u l a t e s that the force on a person to perform an act i s a

19

monotonically increasing function of the algebraic sum of

the products of the valence of all outcoaes, and the

strength of the person's expectancies (E) that the act will

be followed by the attainment of those outcomes.

Symbolically,

n =5 (EiJ Vj) j = 1

where Fi = the force to perform act i E = the strength of the expectancy that act i will

be followed by outcome j Vi = the valence of outcome j.

Vroom defined "expectancy" as the perceived probability that

a given behavior will be followed by a particular outcome.

The valence model has primarily been used to predict job

satisfaction, occupational preference, and the valence of

effective performance. On the other hand, the effort model

has been used most frequently to predict J3b effort and

performance (Hitchell, 1974). Host studies report some

support for the effort model, but there is more consistent

support for the valence model.

A number of modifications and redefinitions of

expectancy theory have been made since Vroom's original work

in an attempt to improve the predictive power of the models.

The most widely used formula to determine motivation (or

force) to behave is that proposed by Lawler and Porter

(1967) in which the force to act is said to equal the

expectancy that effort will result in performance times the

20

sum of the expectancy that performance will result in

outcomes times the valence of those outcomes. Symbolically,

H = (E — » p) • 2 ((p — > 0) (V))

where H = a o t i v a t i o n , or f o r c e , t o a c t E = e f f o r t P = p e r f o r a a n c e 0 = outcoae V = v a l e n c e .

The aode l assumes that the h igher the

e f f o r t - t o - p e r f o r m a n c e expectancy and the more c l o s e l y

performance i s s een to be r e l a t e d to p o s i t i v e l y v a l e n t

outcomes , t h e g r e a t e r w i l l be the i n d i v i d u a l ' s e f f o r t t o

perform. According t o Lawler (1973) the model w i l l tend t o

be a more a c c u r a t e p r e d i c t o r of behavior as an i n d i v i d u a l

g a i n s e x p e r i e n c e in a g iven s i t u a t i o n and d e v e l o p s aore

a p p r o p r i a t e e x p e c t a n c i e s . Independence among the model

components and between e f f o r t and performance c r i t e r i a i s

assumed (Behl ing & S t a r k e , 1973; Schmidt, 1973) , a l though

p o s i t i v e c o r r e l a t i o n s between some of the v a r i a b l e s have

been noted i n the l i t e r a t u r e ( e . g . , Sheridan, Slocum, 6

R i c h a r d s , 1974 ) .

An e x t e n s i v e number of s t u d i e s have been conducted

us ing a v a r i e t y of methodolog ies t o examine expec tancy

t h e o r y . The l i t e r a t u r e i n c l u d e s : f i e l d s t u d i e s ( e . g . ,

Hackman B P o r t e r , 1968) and labora tory s t u d i e s ( e . g . , Arvey,

1972) ; o b s e r v a t i o n a l re search ( e . g . , Sher idan, Slocum, 6

Hin, 1975) and s t u d i e s with exper imenta l manipulation ( e . g . .

21

Pritchard 5 DeLeo, 1973) ; concurrent studies (e.g., Ferris,

1978) and longitudinal studies (e.g., Reinharth S Wahba,

1976); within-subjects designs (e.g., Dillard, 1981a), aDd

across-subjects designs (e,g^, Graen, 1969). Target

populations for these studies have ranged from university

students (e.g., Vroom, 1966) and office workers (e.g^,

Hackman 6 Porter, 1968) to auditors (e.g., Ferris, Dillard,

S Nethercott, 1980), drug abuse professionals (Froman,

1977), and career naval officers (Parker S Dyer, 1976). The

model has even been tested in cross-cultural settings

(Ferris et al., 1980; Hatsui S Ohtsuka, 1978; Hatsui &

Terai, 1975). Attempts to demonstrate the existance of a

systematic relationship between expectancy theory variables

and behavioral response typically use a survey procedure

with a correlational design. The major components are

measured using questionnaires. The scores are combined

according to the expectancy model, and the resulting

composite is then correlated with work behavior. This

strategy has yielded correlations between motivation and

effort or performance ranging from 0.0 to 0.4 (Peters,

1977). Although low correlations have created doubts about

the theoretical utility of the model (Campbell & Pritchard,

1979), the consistency of the positive findings over many

work settings, subject populations, operationalizations of

the major variables, and statistical analysis procedures

indicates basic support for the cognitive processes

22

specified by the model (Peters, 1977). The number of

studies demonstrating the predictive validity of expectancy

theory far exceeds the number of findings showing no support

for the model. Expectancy theory has been able to

successfully predict job effort (Arvey S Neel, 1976;

Hitchell & Nebeker, 1973; Mitchell 8 Pollard, 1973; Peters,

1977; Vroom, 1964), job or task performance (Galbraith &

Cummings, 1967; Hackman & Porter, 1968; Lawler 5 Porter,

1967; Hoitra, 1976), interview choice (Kuleck, 1976),

occupational preference (Bartol, 1976; Dillard, 1979;

Hitchell 5 Beach, 1976; Sheridan, Richards, 5 Slocum, 1973;

Vroom, 1966), academic performance (Henson,1976), and job

satisfaction (Kesselman, Wood, & Hagen, 1973; Mitchell 6

Albright, 1972; Sheridan, Slocum, C Richards, 1974). In

addition, the model has been used to meaningfully predict

job change behavior (Dillard, 1979), retirement decisions

(Bran & Jacobson, 1977; Parker & Dyer, 1976), interpersonal

perceptions (Ayers, Nacci, & Tedeschi, 1973; Squier, 1971),

investment decision-making (Fry, 1975), and leader behavior

(Hatsui S Ohtsuka, 1978; Nebeker & Hitchell, 1973). On the

other hand, the theory has been unsuccessful, or only weakly

successful, in predicting reaction time (Geller, Tuso, &

Wellington, 1975), interpersonal attraction (Johnson 6

Johnson, 1972), persistence in an adult education program

(Zeigler, 1980), and global measures of work aotivation and

performance (Arvey 5 Neel, 1974; Ferris, 1978; Reinharth S

Wahba, 1975).

23

Vroom (1964) originally conceived the aodel to be a

predictor of effort (the force to perform an act), not

performance (the act itself). In his logic, effort is a

behavior, while performance is an outcome. But later

authors have extended and reconceptualized the model to

predict the quantity and quality of job perforaance, as well

as many other first-level outcomes, such as job

satisfaction, occupational choice, and various types of

decision-making. When predicting performance, expectancy

theorists agree that one must account for the individual's

ability as well as his motivational level. Both Vroom

(1964) and Porter & Lawler (1968) postulated the following

relationship:

Performance = Hotivation • Ability.

A large percentage of expectancy theory studies have

examined performance as a criterion variable. In some

instances the subject's ability has been assessed, but many

studies have attempted to predict performance on the basis

of motivation alone. Without directly accounting for

individual ability the expectancy model has been successfnl

in predicting job performance ratings (Dachler & Hobley,

1973; Kopelman, 1977; Pritchard & Sanders, 1973), quantity

of policies sold by insurance salespersons (Hatsui S

Terai, 1975), and performance in repetitive tasks such as

arithmetic problem solving (Hotowidlo, Loehr, & Dunnette,

1978), data coding (Berger, Cummings, ft Henemaa, 1975), and

24

word evaluation (Taylor & Huesman, 1974). Some of these

studies may have indirectly controlled for ability through

sample selection procedures^ But in no case did the

author (s) state that the study was designed with this

intention^ The empirical work of Georgopoulus, Mahoney, and

Jones (1957), on which Vroom's theory is partially based,

suggested that worker productivity could be predicted using

only the worker's perception of the relationship between

productivity and goal attainments An obvious problem arises

in testing this hypothesis in field settings where

performance is frequently measured using supervisors'

ratings^ A supervisor's perception of a subordinate's

motivation could contaminate performance ratings, causing an

artificial inflation in the correlation between motivation

and performance.

Several studies in which ability has not been assessed

have been unable to predict job performance using expectancy

theory (Arvey, 1972; Batlis & Waters, 1973; Reinharth &

Wahba, 1976). Lawler S Porter (1967) did measure ability

and were successful in predicting performance ratings of

workers using predicted effort. Galbraith and Cummings

(1967) also found support for the hypothesized interaction

between motivation and ability in determining performance.

In 1972, Heneman & Schwab reviewed nine field studies of

expectancy theory where performance was a dependent measure

and ability was assessed and concluded the following:

25

"Generally, valence, instrumentality and role perceptions

were significantly related to performance while ability was

not" (p. 5). But in recent studies the expectancy model has

been unable to meaningfully predict performance even when

ability has been included as an independent variable (Lawler

& Suttle, 1973; Sheridan, Slocum S Richards, 1974). In

fact, in some cases ability has accounted for a larger

proportion of the variance in performance than motivation

(Arvey, 1972; Henson, 1976; Mento, Cartledge S Locke, 1980;

Hitchell & Nebeker, 1973)• The question of the importance

of ability and its relationship to motivation in determining

task performance is thus unaswerable at this points

Conclusions regarding the predictive power of expectancy

theory for job performance are difficult to formulate due to

the contradictory nature of the existing literature.

Performance may be a function of ability only, motivation

only, or some combination of these variables. Host likely,

the relationship is contingent upon individual and

situational factors^

A careful review of the expectancy theory literature

does indicate that the model is more closely linked to

effort than to performance (Campbell & Pritchard, 1976;

Mitchell, 1974; Peters, 1977). There is also some evidence

that prediction of work effort can be enhanced if one

considers additional variables beyond the components of the

model itself. The simplest form of expectancy theory states

26

t h a t a person's motivation to perform in a p a r t i c u l a r way

w i l l be inf luenced by h i s or her expec tanc ie s regarding

try ing to perfora i n t h a t way (E—»P) , h i s or her

expec tanc ie s about the outcomes a s soc ia ted with performing

at t h a t l e v e l (P—>0), and the a t t r a c t i v e n e s s of the

outcomes invo lved . The accuracy of the model p o t e n t i a l l y

improves i f one accounts for fac tors which inf luence these

r e l a t i o n s h i p s (Lawler, 1973) . Expectancies have been shown

to be a function of, for example, the u s e r ' s a b i l i t y (Vroom,

1964), ro le percept ions (Porter 6 Lawler, 1968), demographic

c h a r a c t e r i s t i c s (Kuleck, 1976) , and personal i ty f a c t o r s

( B a t l i s & Waters, 1973; Henson, 1976; Parker S Dyer, 1976;

R e i c h e l t , 1974; Terborg, Richardson, 6 Pritchard, 1980)•

S i t u a t i o n a l f a c t o r s , such as the psychological c l imate of

the organizat ion (James, Hartman, Stebbins , 6 Jones, 1977),

communications from others (Campbell 6 Pritchard, 1979),

perceived environmental uncertainty ( F e r r i s , 1978), and the

extent to which rewards are contingent upon performance

(Arnold, 1981), a l s o appear to inf luence the r e l a t i o n s h i p s

hypothesized by the model. Therefore, to meaningfully t e s t

expectancy theory or apply i t s cons truc t s , one must account

for v a r i a b l e s which p o t e n t i a l l y moderate expectancy and

i n s t r u m e n t a l i t y r e l a t i o n s h i p s . Inclusion of addi t iona l

psycho log ica l and s i t u a t i o n a l components may improve the

p r e d i c t i v e power of the model. The problem for the

researcher i s to determine which var iab les are relevant in a

given s i t u a t i o n .

27

2.1.2 Criticisns

Several comprehensive reviews of expectancy theory

research are available in the literature (Campbell C »

Pritchard, 1979; Connolly, 1976; Heneman 5 Schwab, 1972;

House & Wahba, 1972; Hitchell, 1974; Hitchall S Bigland,

1971; Wahba 5 House, 1974). Although each presents its own

unique criticisms and conclusions, several major problems

with the model and the research have been consistently

identified. From a theoretical standpoint, expectancy

theory has been criticized for: (1) being unnecessarily

complex, (2) falsely assuming that people are rational, (3)

failing to include certain variables known to be important

in human cognition processes, (4) lacking construct

validity, and (5) postulating relationships which might be

wore accurately explained with alternative aodels. The

empirical literature on expectancy theory has been

criticized for: (1) overuse of across-subjects designs, (2)

inappropriate use of concurrent rather than longitudinal

studies, and (3) adopting faulty measurement methods for the

theoretical variables^ Each of these criticisms will now be

briefly considered.

2-1•2.1 Conceptual Criticisns

Several writers have commented that the complexity of

expectancy theory detracts from its usefulness and makes

empirical tests of the theory almost impossible to conduct

28

(Hollenback, 1976; Ivancevich, Szilagyi, 6 Wallace, 1977;

Lawler & Suttle, 1973)• Some authors have suggested that

the relationships within the aodel be sinplified. For

exaaple, a direct aeasure of expected effort would be aore

parsiaonious and aight be equally as accurate as the more

elaborate expectancy equation (Hitchell & Pollard, 1973).

In some studies the expectancy and instrumentality

components have been combined into a more global path-goal

concept (Evans, 1970; Georgopoulos et al., 1957; Porter &

Lawler, 1968). Other investigators have reacted more

strongly to the complexity problem and recommended that the

theory be abandoned until more sophisticated methodologies

become available (Campbell & Pritchard, 1979; Wolf 5

Connolly, 1981). Since it is not customary among scientists

to allow research to be method-driven, abandonment of

expectancy theory is probably not the wisest course at this

point. Hotivation within human beings may truly be a

complex process. Therefore, a theory which addresses the

complexity of cognitive processes should not be rejected,

even if empirical testing of the theory is difficult

(Dillard, 1981b).

Like other cognitive theories, expectancy theory

assumes that behavior follows from a systematic process of

conscious choice by an individual. The expectancy model

assumes that individuals are rational and anticipatory, that

they intelligently estimate the value of outcomes and

29

at tempt t o n a x i n i z e t h e i r p o t e n t i a l t o o b t a i n d e s i r a b l e

outcomes . C r i t i c s of expectancy theory q u e s t i o n whether:

(1) behavior i s t r u l y r a t i o n a l and (2) g iven r a t i o n a l i t y ,

behav ior i s based on a maximization p r i n c i p l e . These i s s u e s

are d i f f i c u l t t o r e s o l v e s i n c e they c e n t e r on the b a s i c

nature of man and are as much p h i l o s o p h i c a l q u e s t i o n s as

t h e y a r e p s y c h o l o g i c a l o n e s . The r a t i o n a l view of man a s

e spoused by c o g n i t i v e p s y c h o l o g i s t s i s countered by operant

c o n d i t i o n i n g and p s y c h o a n a l y t i c t h e o r i e s which assume t h a t

behav ior i s more fundamenta l ly determined by p e r s o n a l i t y and

environmenta l e v e n t s . None of t h e s e t h e o r i e s i s g e n e r a l l y

a c c e p t e d among p s y c h o l o g i s t s a s super ior t o the o t h e r s in

i t s a b i l i t y t o e x p l a i n b e h a v i o r , s i n c e s o c i a l s c i e n c e has

been unable t o u n e q u i v o c a l l y determine the prime cause of

human a c t i o n (Perv in , 1970) .

There c e r t a i n l y i s some e m p i r i c a l support for the

n o t i o n that p e o p l e s ' g o a l s and i n t e n t i o n s r e l a t e to t h e i r

a c t u a l behavior (Locke, Bryan, and Kendal l , 1968) . P o s i t i v e

f i n d i n g s w i t h i n the g o a l theory and expectancy theory

l i t e r a t u r e support t h i s v i e w p o i n t . On the o ther hand, the

work of Simon (1977) and Simon and Newel l (1971) s u g g e s t s

t h a t d e c i s i o n - m a k e r s e x h i b i t r a t i o n a l i t y only wi th in c e r t a i n

b o u n d a r i e s . According t o t h e i r r e s e a r c h , c o g n i t i v e

l i m i t a t i o n s o f p e o p l e prevent them from r o u t i n e l y o p t i m i z i n g

in m u l t i - a l t e r n a t i v e s i t u a t i o n s ; t h e r e f o r e , people c o n s t r u c t

s i m p l i f i e d models of problems and behave r a t i o n a l l y w i t h i n

30

the constraints of these models. Addressing this point in a

controlled laboratory study, Feldaan, Reitz, and Hilternan

(1976) attempted to determine if subjects used either

optimization or satisficing approaches when selecting a

given level of performance in a repetitive task^ Their

findings were unable to support either cognitive theory^

Other research indicates that a person's choice of an effort

level may involve use of a marginal gain or comparative

principle rather than maximization of expected benefit

(Kopelman, 1977; Kopelman, Liebman & Tukl, 1978) • The

presence of a large group of studies supporting expectancy

theory implies at least some validity to the the

optimization assumption. Nevertheless, theoretical and

empirical work which counters this assumption does suggest

the possibility that expectancy theory overintellectualizes

the cognitive processes used by people in making a selection

among alternative behaviors.

In a related vein, some critics have suggested that the

expectancy model is not so much invalid as incomplete. For

example, the theory does not account for the fact that an

individual's behavior may be influenced by the expectations

of other people. Inclusion of a measure of group norms

seems to improve the predictive power of the model (Hitchell

& Bigland, 1971; Hitchell & Nebeker, 1973; Hitchell S

Pollard, 1973). Further, by showing that personality or

situational differences affect expectancies, the rational

31

man assumptions of the theory may be modified (Henson,

1976). Literature examining the role of moderator variables

in expectancy theory relationships is relevant to this

argument. The crucial problem here is determining exactly

which moderator variables are relevant for a given

individual in a particular situation.

The most serious theoretical issues confronting

expectancy theory deal with the construct validity of the

model components themselves, and the possibility that

alternative models might better explain the postulated

relationships among these componenents^ For example,

Campbell and Pritchard (1976) point out that it is difficult

to measure and predict "effort" if we do not know what the

construct means in the first place:

Organizational psychology is without any clear specification of the meaning of effort and consequently there is no operationalization of the variable that possesses even a modicum of construct validity (p. 92).

There is also the issue of whether instrumentality should be

defined as a perceived correlation or as a subjective

probability^ Most researchers have measured the concept in

probabilistic terms (Hitchell, 1974) , although Vroom

conceived instrumentality to be a perceived correlation^

Hollenback (1979) has argued that Vroom's definition is less

meaningful than a probabilistic interpretation since

probability estimates are easier for respondents to

comprehend and allow for a second outcome to be seen as

32

c e r t a i n regardless of the probabi l i ty of the f i r s t l e v e l

outcome^ (For example, i f a person i s cer ta in she w i l l get

paid $3.00 per hour regardless of her performance, the

c o r r e l a t i o n between performance and the second outcome i s

unc lear , whi le the probabi l i ty of a p—^o r e l a t i o n s h i p i s

obvious ly I.OO^)

There i s a l s o some t h e o r e t i c a l question as to the

c a t e g o r i z a t i o n of outcomes and their treatment within the

aodel . There i s d e f i n i t i o n a l confusion concerning the

d i s t i n c t i o n between i n t r i n s i c ( se l f -administered) and

e x t r i n s i c (external ly-adminis tered) outcomes (Parker & Dyer,

1976) , although t h e o r e t i c a l l y the two types of outcomes

should be separate ly studied ( H i t c h e l l , 1974; Wahba & House,

1974) . In most i n s t a n c e s where the two groups have been

separate ly examined, i n s t r i n s i c rewards have shown stronger

p r e d i c t i v e power than e x t r i n s i c rewards (Campbell, Dunnette,

Lawler, 5 Weick, 1970; Graen, 1969; Lawler 6 S u t t l e , 1973;

Mitchel l S Albright , 1972) , although at l e a s t one study has

found e x t r i n s i c outcomes to y i e l d superior r e s u l t s (Kopelman

5 Thompson, 1976). The exc lus ion of negat ive ly valent

outcomes apparently increases the predic t ive v a l i d i t y of the

model (Leon, 1981; Parker 5 Dyer, 1976), although Vroom f e l t

that both types of rewards should be inc luded.

Empirical r e s u l t s a l s o suggest that the highest

variance in dependent v a r i a b l e s i s explained in s t u d i e s

33

where the list of outcomes is of moderate (as opposed to

long or short) length. If the researcher uses too few

outcomes, she may omit some that are important to the

individual. On the other hand, a lengthy outcome list will

tend to introduce error into the model (Leon, 1979;

Hitchell, 1974; Parker 6 Dyer, 1976; Schwab, Olian-Gottlieb,

& Heneman, 1979). From a theoretical perspective, it is

only important that the list be complete in the sense of

containing all possible secondary outcomes of relevance to

the individual in the particular situation.

Even more controversial than these definitional

problems are questions regarding the theoretical

relationships among the model components. Vroom (1964) and

Lawler and Porter (1967) hypothesized multiplicative

relationships among expectancy, instrumentality, and

valence. A multiplicative model implies that motivation

will be zero if either the E—>P or P—>0 expectancies are

zero. The logic here is that a person will not initiate a

behavior if he thinks that either he cannot complete it

successfully or that he will not receive valued rewards for

completing it (Lawler, 1973). If the model components were

additive rather than multiplicative then motivation could be

above zero if either the E—>P or P—^0 expectancies were

zero, as long as both were not zero. Comparative studies

have found the multiplicative model to be superior (Arnold,

1981; Hakman & Porter, 1968), inferior (Stahl & Harrell,

34

1981) and no different from the additive model (Hitchell S

Nebeker, 1973). Further, some studies have been able to

verify the presence of an interaction between

performance-outcome instrumentality and outcome valence

(Arnold, 1981; Hatsui & Terai, 1975), while others have not

(Kuleck, 1976; Pritchard & DeLeo, 1973). Of course, the

nature of the relationship aaong model components may vary

across indiviuals. For example, some individuals' cognitive

processes may follow an additive formula while others' are

multiplicative (Stahl & Harrell, 1981).

Some investigators have rejected consideration of the

total model predictor score in favor of an approach which

treats the model components as separate independent

predictors. These studies have typically used stepwise

regression or discriminant analysis procedures to

demonstrate that individual components of expectancy theory

can explain a larger proportion of the variance in dependent

variables than a predicted effort score generated by the

complete multiplicative model. (Dillard, 1979, 1981a;

Zeigler, 1980). But other comparative studies have found

the aggregate model score to be superior to use of

individual components (House, Shapiro, & Wahba, 1974; Lawler

6 Suttle, 1973). Reinharth S Wahba (1976) tested nine

alternative models to the original expectancy theory model,

including expectancy variables individually, additive,

multiplicative, and combined versions. None of the

35

approaches was superior to the others in predicting effort

and performance. Wolf and Connolly (1981) point out that

alternative conbinations of expectancy theory components can

lead to predictions similar to the conplete model simply

because the theory postulates a disproportionately large

number of independent variables relative to dependent

variables. The excess of degrees of freedom resulting from

this situation means that: "an alnost unlimited variety of

modes of combination of independent variables would yield

predictions as good as, or better than, the expectancy

model" (p. 4 1). Expectancy studies should be careful to

have sufficiently large sample sizes so that alternative

models may be tested to explain a certain type of choice

behavior. In any event, the researcher must keep in mind

that the goal of behavioral research is to explain the

behavior of most people, or certain types of people, in a

given situation. The fact that a model does not apply to

all people in all situations should not be alarming.

2.1.2.2 aethodoloqical Issues

in his original formulation of expectancy theory Vroom

stated the following:

It is also assumed that people choose from among alternative acts, the one corresponding to the strongest positive (or weakest negative) force. This formulation is similar to the notion in decision theory that people choose in a way that maximizes subjective expected utility (Vroom, 1964, p. 19).

36

The theory clearly intends to describe the cognitive process

used by a single individual when making a choice among

alternative behaviors. Vroom's comments imply that

empirical tests of the model involve measuring a given

individual's expectancies and instrumentalities toward

multiple behaviors in a particular situation. Hore

specifically, the investigator must assess: (1) the degree

to which each of two or more effort levels leads to each of

two or more performance levels, (2) the degree to which each

of two or more performance levels will lead to either

obtaining or not obtaining each of a set of outcomes, and

(3) the degree of attractiveness of obtaining and not

obtaining each outcome (Hollenback, 1979; Hitchell, 1974).

Hany reviewers have criticized the empirical expectancy

theory literature for overuse of across-subjects designs in

which a group of subjects' perceptions are measured toward a

single level of effort, a single level of performance, and

one state of an outcome. A typical survey might ask a

respondent to rate the degree to which she expects hard work

(high effort) to lead to success on a task (high

performance), and the probability associated with high

success and obtaining each of a set of outcomes. The

researcher then hopes for adequate variability within the

sample so that some subjects see strong effort-performance

relationships while others report weak relationships. The

model is then tested using this distribution of responses.

37

This a c r o s s - s u b j e c t s a n a l y s i s i m p l i c i t l y assumes that a l l

s u b j e c t s use the sane c o g n i t i v e approach when formulating

t h e i r ex pec t a nc i e s . C r i t i c s of the method contend that snch

an assunption i s t h e o r e t i c a l l y incorrect and empir ica l ly

unsubstant iated . Some reviewers f e e l that the r e s u l t s of

a c r o s s - s u b j e c t s des igns have no v a l i d i t y and, t h e r e f o r e ,

should not be conducted (Wolf & Connolly, 1981). But others

argue that v i t h i n - s u b j e c t s s tud ie s are often p r a c t i c a l l y

i n f e a s i b l e s ince perfornance es t imates of expectancy and

ins t rumenta l i ty must be obtained for every subject for a t

l e a s t two e f f o r t l e v e l s ( e . g . , high-low) and two performance

l e v e l s ( e . g . , succes s -nonsucces s ) , making quest ionnaires

unduly long and time consuming (Steers & Porter, 1979).

One might argue that between-subjects designs are

conservat ive t e s t s of the theory, s i n c e heterogenei ty of

c o g n i t i v e processes within a sample i s poss ib le ( D i l l a r d ,

1981b). Therefore, p o s i t i v e f indings from such s t u d i e s

o f fer greater support than wi th in - subjec t s s tudies for the

construct v a l i d i t y of the model. In contrast to the ear ly

expectancy theory l i t e r a t u r e (1964-1974), the more recent

expectancy l i t e r a t u r e (1975-present) does contain

w i th in - subjec t t e s t s of the model (Di l lard , 1981a; Lawler,

Kuleck, Rhode, S Sorensen, 1975; Hatsui, Kagawa, Nagamatsu,

& Ohtsuka, 1977; Hatsui 6 Ohtsuka, 1978; Nebeker 6 Hoy,

1976; Parker 5 Dyer, 1976; Pavett , 1978). These r e s u l t s

have g e n e r a l l y been supportive of the theory.

^^^^^

38

Where p o s s i b l e , r e s e a r c h e r s should conduct

w i t h i n - s u b j e c t t e s t s s i n c e such an approach i s more

t h e o r e t i c a l y pure. Where a b e t w e e n - s u b j e c t s approach i s

aore p r a c t i c a l l y f e a s i b l e , t h e i n v e s t i g a t o r can s e l e c t a

s a n p l e which i s a s hoaogeneous a s p o s s i b l e on background

c h a r a c t e r i s t i c s known t o be r e l e v a n t t o the t h e o r e t i c a l

c o n s t r u c t s , so t h a t the assunpt ion of c o m p a r a b i l i t y of

s u b j e c t s i s at l e a s t p a r t i a l l y met. " R e s u l t s of

( b e t w e e n - s u b j e c t s ) d e s i g n s should be i n t e r p r e t e d with

c a u t i o n but should not be t o t a l l y r e j e c t e d " ( D i l l a r d , 1981b,

p . 4 9 ) .

With regard to exper imenta l procedure , an a d d i t i o n a l

problem in some expec tancy research has been i n c o r r e c t use

of c o n c u r r e n t , as opposed to l o n g i t u d i n a l , d e s i g n s . The

expec tancy model i s temporal i n the s e n s e t h a t t ime must

p a s s be fore the m o t i v a t i o n a l force t o a c t i n f l u e n c e s a

p e r s o n ' s behavior (Campbell e t a l . , 1970; Goodman, 1973;

M i t c h e l l , 1974; Por ter S Lawler, 1 % 8 ) . F i e l d s t u d i e s which

sample independent and dependent v a r i a b l e s a l l a t the s a i e

p o i n t i n t ime are t h e r e f o r e i n a p p r o p r i a t e . A time l a g i s

n e c e s s a r y between measurement of the independent v a r i a b l e s

and t h e p a r t i c u l a r behav ior of i n t e r e s t (Reinharth & Wahba,

1 9 7 6 ) . Only a smal l percentage of expectancy theory s t u d i e s

have examined t h e i r data over t ime and compared l o n g i t u d i n a l

t o c o n c u r r e n t r e s u l t s . C o n c l u s i o n s drawn from t h e s e s t u d i e s

have been c o n t r a d i c t o r y . For example, Lawler (1968) found

^ i ^ ^ .

39

longitudinal data to better predict behavior than concurrent

data. Kopelman and Thonpson (1976) found longitudinal

correlations of one year to be higher than concurrent

correlations but longitudinal correlations of four years to

be no higher than static ones. Finally, Lawler and Suttle

(1973) and Sheridan et al. (1974) found little evidence of

temporal effects in their data. Theoretically then,

expectancy theory research should allow for time passage

between independent and dependent variable measurement. Bat

it is unclear how long this time interval should be and

whether or not a time lag will add meaningful predictive

power to the model.

Apart from experimental design questions, a number of

measurement issues have plagued the empirical literature on

expectancy theory^ There is unresolved controversy

concerning how each of the model components should be

measured^ Vroom defined "expectancy" as the subjective

probability that an outcome will follow from a certain level

of efforts Host studies have assessed the concept in

likelihood terms, using Likert-type rating scales. But

there is a notable variety across studies in the way these

scales have been designed. Effort-performance expectancies

have been measured using 5-point (e.g., Janes et al., 1977),

7-point (e.g., Lawler 6 Suttle, 1973), and 11-point Likert

scales (e.g^, Berger et al^, 1975; Kopelman et al^, 1978) •

Some questionnaires have used zero as the lowest

40

a n c h o r - p o i n t on the expectancy s c a l e ( e ^ g . , Berger e t a l ^ ,

1 9 7 5 ) , whi le o t h e r s have s t a r t e d with *1 .00 as the l o w - p o i n t

(e^g^, H i t c h e l l S Nebeker, 1 9 7 3 ) • In most c a s e s t h e s c a l e s

have used o n e - p o i n t i n t e r v a l s , bnt • 1 0 - p o i n t , • 2 5 - p o i n t ,

and 10 -po in t i n t e r v a l s have a l s o been used^

S imi lar v a r i a b i l i t y i n survey d e s i g n has occurred in

t h e measurement of i n s t r u m e n t a l i t y . As i n the c a s e of

e x p e c t a n c y , the most popular approach to measuring

i n s t r u m e n t a l i t y has been L i k e r t - t y p e r a t i n g s c a l e s . These

s c a l e s have ranged from - 1 . 0 0 t o • I . 0 0 , from 0 . 0 t o • 4 ^ 0 0 ,

from -3^00 t o • 3 ^ 0 0 , and from 0 .0 to 100 . Rarely has an

i n v e s t i g a t o r e x p l a i n e d h i s r a t i o n a l e f o r s e l e c t i o n of one

s c a l e range over a n o t h e r . F i v e - p o i n t , 7 - p o i n t , 9 - p o i n t ,

1 1 - p o i n t , and 1 7 - p o i n t s c a l e s have a l l been used. As

d i s c u s s e d e a r l i e r , most r e s e a r c h e r s have measured

i n s t r u m e n t a l i t y i n p r o b a b i l i s t i c terms d e s p i t e Vroom's

d e f i n i t i o n o f t h e concept as a perce ived c o r r e l a t i o n

( H i t c h e l l , 1 9 7 4 ) . The p r o b a b i l i s t i c approach has been shown

t o be more e m p i r i c a l l y p r a c t i c a l and perhaps more

t h e o r e t i c a l l y sound (Hollenback, 1979) . Using a

p r o b a b i l i s t i c approach w i l l r e s u l t i n the same p r e d i c t e d

c h o i c e for a s u b j e c t a s the c o r r e l a t i o n a l method. And s i n c e

c h o i c e i s t h e b e h a v i o r of i n t e r e s t in expectancy t h e o r y ,

r e j e c t i o n of the c o r r e l a t i o n a l approach should not be a

r e s e a r c h problem (Hol lenback , 1 9 7 9 ) .

41

Various types of Likert s c a l e s have a l s o been used in

the a s se s snent of valence , although ranking and paired

conparison techniques have been used as well (e .g^,

Galbraith & Cunnings, 1967; Sheridan e t a l . , 1974). A

c o n t r o v e r s i a l i s sue here invo lves the dimension along which

va lence i s measured. Vroom (1964) conceptual ized valence as

a n t i c i p a t e d s a t i s f a c t i o n with second- l eve l outcomes.

Reviewers of expectancy theory l i t e r a t u r e note that many

s t u d i e s have anchored the i r valence s c a l e s with an

"inportance" dimension, which p o t e n t i a l l y represents a

d i f f e r e n t concept (Connolly, 1976; Hi t che l l , 1974) .

Empirical ly , valence measures based on " d e s i r a b i l i t y " have

y ie lded higher p r e d i c t i v e power than those using

"importance" (Schwab e t a l . , 1979). Sca les requiring the

respondent to rate the degree of "at tract iveness" have been

found to be superior to s c a l e s using behavioral anchors,

paired comparisons, or rat ings of general value of outcomes

( I l g e n , Nebeker, S Pritchard, 1981). Following a

comprehensive review of the l i t e r a t u r e , Hitchel l (1974)

recommended the use of "at tract ion" over "importance" as the

valence dimension. According to H i t c h e l l , the

" a t t r a c t i v e n e s s " l a b e l i s more t h e o r e t i c a l l y v a l i d .

Valence s c a l e s using only p o s i t i v e numbers as anchors

have re su l t ed in greater explained variance than those using

some combination of negat ive , zero , and pos i t i ve anchors

(Leon, 1981; Schwab e t a l . , 1979). However, from a

42

t h e o r e t i c a l perspec t ive the value of the anchor po ints i s

not as important as the o v e r a l l approach used to measure

v a l e n c e . Measurement of valence requires a conparison

process . According t o Vroon (1964) , "an outcone i s

p o s i t i v e l y va lent when the person prefers a t ta in ing i t t o

not a t t a i n i n g i t ( i . e . , he prefers x to not x)" (p. 15) . If

the researcher wants t o be true to the theory, then

p o s i t i v e no t iva t ion should mean tha t high performance i s more a t t r a c t i v e than low performance, not simply that high performance has a large nunber a s s o c i a t e d with i t (Hollenback, 1979, p. 582) .

Consequently, valence i s best assessed by asking the subject

to conpare the a t t r a c t i v e n e s s of obtaining an outcome to the

a t t r a c t i v e n e s s of not obtaining i t . Host s t u d i e s have

t r e a t e d valence toward an outcome as a s ing le s c a l e value

that can range from low to h igh , -an approach which i s

i n c o n s i s t e n t with the theory (Hollenback, 1979).

As pointed out e a r l i e r , a wi th in-subjects approach to

expectancy theory requires that at l e a s t two l e v e l s of

e f f o r t , two l e v e l s of performance, and two l e v e l s of each

outcome be a s se s sed for each respondent. Only recent ly have

expectancy theory researchers adopted t h i s approach, and

there i s not a consensus as to the bes t way in which to

design experimental ques t ionnaires for measurement of these

v a r i a b l e s . A promising technique has been developed by

Hollenback (1979) . Based on matrix algebra p r i n c i p l e s , the

method has s e v e r a l a t t r a c t i v e f e a t u r e s : (1) mult iple l e v e l s

43

of e f f o r t and performance can be considered when a s s e s s i n g

expectanc ies and i n s t r u m e n t a l i t i e s ; (2) both expectanc ies

and i n s t r u m e n t a l i t i e s are determined in p r o b a b i l i s t i c terms

with the c o n s t r a i n t that expectancies ( ins trumenta l i tes )

must sum to 1.00 for a s i n g l e l e v e l of e f f o r t (performance);

and (3) valence i s ca l cu la ted as a s c a l e d i s tance , or the

d i f f erence between the a t t r a c t i v e n e s s of obtaining an

outcome and not obtaining i t . A f i n a l advantage of

Hollenback's matrix method i s the use of expectancy and

ins t rumenta l i ty measures which have absolute zero p o i n t s .

Schmidt (1973) has argued that r a t i o s c a l e s are necessary

for the m u l t i p l i c a t i v e operat ions of the expectancy model.

Hany s t u d i e s have incorrec t ly confined the ir measures to

ordinal and i n t e r v a l s c a l e s (Hi tche l l , 1974).

The empirical l i t e r a t u r e on expectancy theory conta ins

a notable var i e ty in neasurement approaches. The l a c t of

we l l -deve loped , widely-accepted techniques makes i t

d i f f i c u l t f or any one researcher to determine the most

appropriate way in which t o measure the model components.

To sone ex tent the var i e ty of surveys used across s t u d i e s

adds ex terna l v a l i d i t y to expectancy theory f ind ings . But

the p r o l i f e r a t i o n of neasures a l s o adds confusion. There i s

l i t t l e or no psychometric data on most of the ques t ionnaires

used. H i t c h e l l (1974) in h i s review of the l i t e r a t u r e found

some empir ica l support for the r e l i a b i l i t y and construct

v a l i d i t y of expectancy instruments . But other research

44

indicates that the reliability and validity of these

measures are extremely low (DeLeo & Pritchard, 1974) • In

addition, an overwhelning najority of expectancy theory

studies rely on subject ratings or rankings for independent

and dependent variable assessnent^ These measures are

susceptible to method variance, rater bias and halo effects

typically found in self-report scales. This problem cannot

be overcome in the assessment of predicted effort, since the

theory relies on perceptions of subjects as independent

variables. But researchers can, and should, reduce bias in

dependent measures by using objective, qmantitative measures

of work effort and performance where such data is available

(Schwab et al., 1979; Williams S Seller, 1973)•

In conclusion, further work is needed in the area of

measurement of independent and dependent expectancy theory

constructs^ neasurement instrunents are needed which are

theoretically valid and psychometrically sound. Expectancy

research can be improved if investigators design their

studies in ways that are truly consistent with the theory,

practically feasible, and make cross-study comparisons

possible.

2.2 BekaTioral Researgh in BIS

In a very general sense, the literature of HIS presents

two basic thrusts, or orientations. On the one hand there

is the "technical" literature which focuses on hardware and

45

software elenents of systens and has its roots in conputer

science, electrical engineering, operations research, and

nanagenent science. Couterbalancing this category is the

"behavioral" literature which focuses on hunan aspects of

computer use at individual, group, and organizational levels

of analysis. This latter body of literature draws from the

fields of psychology, sociology, organizational behavior,

and organization theory. The two categories are equally

devoted to the issues of design, developnent,

inplenentation, and utilization of infornation systens in

organizations, although each adopts a unique perspective on

the issues.

Of relevance to this dissertation is that segnent of

behavioral HIS literature which deals with the man-machine

interface at the individual level of analysis. A

fundamental premise underlying this area of study is that

success of a HIS depends more heavily on the individual

behavior of users than on the tecdinical attributes of the

systen (Cheney, 1977; Keen, 1979; Watkins, 1980).

Behavioral researchers continually point out that system

problems in organizations are more often user-based than

technically caused (Bostrom S Heinen, 1977; Diebold, 1979;

Dickson 6 Simmons, 1970; Faerber S Ratliff, 1980; Keen,

1975; Kling, 1977; Lucas, 1975b). Overwhelning concern in

HIS with addressing human aspects of systems has resulted in

the adoption of a socio-technical orientation by some

46

researchers (Bostrom, 1980; Bostrom 6 Heinen, 1977; Keen,

1981), and the emergence of a feeling anong major writers in

the field that "the mainstream of HIS seems no longer

technical but organizational, managerial, and behavioral"

(Keen, 1980, p. 14).

2.2.1 Use as a Dependent Variable

The ultimate objective of most behavioral research is

to provide insight into how systems might be better

designed, installed, or used in organizations. At the

individual level of analysis this objective has resulted in

a body of literature devoted to identifying causal and

associative relationships between system success and the

attributes of people who use information systems. An

obTious question that arises here is, "What is a successful

management information system," or, for purposes of this

research, "What is a successful decision support system?"

It is generally agreed that a DSS should, by definition,

have all or most of the following characteristics: (1)

flexibility, (2) ease of use, (3) interactive capability,

and (4) capacity to support many types of decision-making

(cf. Sprague, 1980). Once such a system is developed and

implemented, its "success" might be assessed through one or

more of the following criteria: (1) user satisfaction with

the system, (2) user attitudes toward the system, (3)

decision performance, or (4) degree of utilization of the

0^f\

47

system (cf . King 6 Rodriguez, 1978; Zmud, 1979a) . A l l of

t h e s e have been used i n HIS r e s e a r c h . But t h e r e i s

d i sagreement regarding which of these v a r i a b l e s c o n s t i t u t e s

the "bes t" measure of s y s t e n s u c c e s s . Soae w r i t e r s argue

t h a t q u a l i t y of d e c i s i o n - n a k i n g i s the nost a p p r o p r i a t e

s u c c e s s measure s i n c e t h i s i s the reason o r g a n i z a t i o n s

i n s t a l l computer s y s t e m s i n the f i r s t place^ But o b j e c t i v e

measures of d e c i s i o n perforaance are o f t e n u n a v a i l a b l e t o

r e s e a r c h e r s ^ T h e r e f o r e , t h e most popular dependent

v a r i a b l e s i n implementat ion research have been user

a t t i t u d e s , or some measure of a c t u a l or intended use of a

system (Lucas, 1975a) • Severa l r e v i e w e r s of HIS r e s e a r c h

have noted t h e advantages of system usage as a dependent

v a r i a b l e (Keen, 1980; King 6 C l e l a n d , 1975; King &

Rodriguez , 1978; Lucas , 1975b) • The b a s i c argument here i s

t h a t a HIS can support o r g a n i z a t i o n a l dec i s ion-making only

i f i t i s used^ According to Lucas ( 1 9 7 5 ) , " s u c c e s s f u l

implementa t ion i s d e f i n e d as high l e v e l s of use of a

v o l u n t a r y system" ( p ^ l ) - Bin-dor and SegeT (1978) contend

t h a t u se i s "a pr ine c r i t e r i o n of HIS s u c c e s s " (p^1066)^ In

s h o r t .

It is evident that those doing research in the field of Hanagement Information Systems (MIS) feel that utilization of a system is one of the 'key' variables associated with the success or desirability of the system (Barkin 6 Dickson, 1977, p^ 36).

Empirical studies in which system use has been a dependent

variable include those by Schewe (1976), Gerrity (1971),

48

Robey (1979), Alavi (1978), Cheney and Andrews (1978), and

King and Rodriguez (1978, 1981). In nearly all of these

studies "use" has been defined in terms of the amount of

data selected from the system. In some cases extent of

usage has been measured as the actual number of entered

inquiries, but the najority of studies have relied on

self-report rating scales of use since more objective

measures are frequently unavailable.

2.2«2 Independent Variables

If one examines the behavioral HIS literature with

attention to independent variables, several general

categories of variables are evident: (1) user attitudes, or

expectations, (2) individual difference variables, and (3)

contextual factors. Huch of this literature is relevant to

the study of user motivation. Horeover, when considered as

a whole, the findings of these studies point to the

potential viability of an expectancy theory perspective of

the notivation process.

2.2.2.1 Dser Attitudes

Preconceived attitudes toward HIS have repeatedly been

shown to relate to HIS usage (King 6 Rodriguez, 1978; Lucas,

1975b, 1978; Robey, 1979; Robey 6 Zeller, 1978; Vasarhelyi,

1973). In most instances, favorable attitudes have been

associated with greater system use. For example, Lucas

49

(1975b) found that a t t i t u d e s toward the conpater ' s p o t e n t i a l

predicted use in a s e r i e s of nine t e s t s across three

samples . A survey of 12 manufacturing and R & D f i r n s by

Brady (1967) suggested that top nanagers expect the conputer

to have s i g n i f i c a n t p o s i t i v e e f f e c t s on decis ion-making. On

the other hand, midd le - l eve l managers do not as r e a d i l y

recognize the impact of a HIS on the ir jobs . Survey data

reported by Guthrie (1973) i n d i c a t e s that middle managers

may not perce ive information systems as an import.ant need

requiring t h e i r time or , u l t i m a t e l y , bringing then rewards.

Guthrie concluded that a nanager must perceive a need for a

HIS and a l s o expect that the system w i l l increase h i s

" s a t i s f a c t i o n s " before he w i l l devote s i g n i f i c a n t time and

"ef fort" to system use . Using the Schultz and S lev in

measure of user a t t i t u d e s , Rodriguez (1977) found a t t i t u d e s

toward performance, along with goals and urgency, t o be

p o s i t i v e l y r e l a t e d t o s u b j e c t s ' perceived worth of a DSS and

t h e i r actual use of i t^ He o b j e c t i v e l y measured use by

tracking the number of i n t e r a c t i v e quer ies i n i t i a t e d by

subjects^ Considered toge ther , the r e s u l t s of Brady,

Guthrie, and Rodriguez suggest that use of a system within

an organiza t ion may be a function of managers' percept ions

regarding i t s impact on the jobs they perform, the rewards

they r e c e i v e from the job, and the organizat iona l l e v e l in

which the task (or dec i s ion) i s performed^

50

In an extensive discussion of requirements for syster

success, Swanson (1974) hypothesizes that appreciation of

the HIS by users is necessary for later use of the system.

In his view "appreciation" refers to the user's assessment

of the relative value of the MIS as a vehicle for achieving

his own objectives --a concept similar to perceived

performance—^outcome expectancy. Ein-dor and Segev (1978)

likewise hypothesize the inportance of user expectations in

later system success. In their view, users must have

positive and realistic expectations of a new system: "HIS

projects will succeed to the extent that expectations are

constrained from below by motivation and from above by

reality" (p. 1072)• Users' expectations develop as a

function of their own past experience with computers, their

cognitive style, as well as through the influence of experts

who install the system in the organization.

The results of a number of implementation studies have

verified that a system is more likely to fail when users

hold unrealistic expectations (Ginzberg, 1975; Shultz &

Slevin, 1975). The best available empirical work on user

expectations was recently conducted by Ginzberg (1981).

Using a longitudinal design in a field setting, Ginzberg

found that the expectations of bank enployees before system

implementation correlated significantly with their

post-inplementation attitudes and actual use of the system.

Users who held realistic expectations prior to installation

51

were more satisfied and used the systen more than users with

unrealistic expectations. Ginzberg's research indicates

that user expectations are indeed a critical variable in HIS

success. He calls for further research on this independent

variable and also reconnends that nore attention be given to

development of good instrunents for measuring user

expectations^

2.2«2.2 Individual Differei <;es

The individual differences literature in MIS is quite

extensive and beyond the realn of consideration in this

review. User characteristics have been by far the most

popular group of independent variables in behavioral HIS

research at the individual level of analysis. Table 1

(Chapter 1) presents a listing of the variables which have

been examined and their reference citations. For a

comprehensive review of this literature, the reader is

referred to Zmud (1979a)• Of relevance to this disseration

is the subset of individual difference studies in which user

attributes have been shown to relate to user expectations,

systen use, or decision quality. Demographic, personality,

and cognitive characteristics are all relevant here.

Experience has been one of the most frequently examined

variables in human/computer interaction studies (cf.

Benbasat et al., 1980; Chafin S Hartin, 1979; Vasarhelyi,

1973; Walther & O'Neil, 1974). Amount of previous exposure

52

to computers apparently a f f e c t s users ' a t t i t u d e s toward the

HIS (Guthrie, 1973; Koester 6 Luthans, 1979) , as wel l as the

way in which they use the systen (Benbasat S Hasul i s , 1980;

Lucas, 1978; Watkins, 1980). Experienced HIS users have

been found t o be l e s s l i k e l y t o change t h e i r a t t i t u d e s

toward the system than inexperienced users (Vasarhelyi ,

1973, 1977). Experienced persons a l s o tend to use the

sys ten nore frequent ly (Walther & O'Neil , 1974; Werner,

1974) , to be more l i k e l y t o use systen options (Benbasat e t

a l . , 1980), and to make bet ter dec i s ions than inexperienced

u s e r s , p a r t i c u l a r l y during i n i t i a l dec is ion periods

(Benbasat, 1974; Smith, 1975; Taylor, 1975; Tiessen, 1976) .

With regard t o other background var iab le s , educat ional

l e v e l has been shown t o r e l a t e negat ive ly to system use

(Lucas, 1975b), yet p o s i t i v e l y with decis ion performance

(Vasarhe ly i ,1973) . Only one study has examined age as an

independent v a r i a b l e . Tayor (1975) found age to in f luence

d e c i s i o n q u a l i t y more than experience. In a sample of 76

managers between the ages of 23 and 57, older managers took

more time to make d e c i s i o n s but made be t ter d e c i s i o n s than

younger managers. F i n a l l y , Vasarhelyi (1977) t e s t e d the

e f f e c t of sex on various dependent measures of system

succes s and found no meaningful d i f f erences between male and

female users .

53

The literature examining personality and cognitive

characteristics is nore extensive than that exanining

denographic variables. Personality attributes, such as need

for achievenent and degree of defensiveness have been shown

to affect perfornance and type of use in gaming experiments

(Wynne 6 Dickson, 1975). Subjects characterized by high

internal locus of control, low dogmatism, and high

risk-taking propensity have been found to request more

information than those with external locus of control, high

dogmatism, or low risk-taking tendency (Taylor & Dunnette,

1974; Zmud, 1979c)• Counterbalancing these studies showing

the significance of personality variables in user behavior

is the work of Gingras (1977) in which personality type was

found to have no influence on users' perceptions of HIS

reports or self-reported levels of use.

Since Hason and Hitroff (1973) first challenged the

assumption that "all users think alike," cognitive style has

become a popular variable in behavioral research dealing

with DSS use. An individual's "cognitive style" refers to

his characteristic mode of perceptual and thinking behavior

when presented with a problem or task (Zmud, 1979a).

Cognitive style is exhibited in the individual's selective

processes (filtering infornation), organizing processes

(patterns or integrating selected infornation) , aoderating

or controlling processes (e.g., aotives), and adapting

processes (overcoaing situational constraints of the task)

54

(Bieri, 1971). The variable is inportant in inforaation

systen design in that a person's cognitive style nay

deternine how he reacts to the systen (e.g., his use of it),

and the kind of support he needs when aaking decisions

(e.g., types of reports). A variety of dimensions and

measurement instruments have been used to examine cognitive

style in HIS and related accounting research. These

include: (1) analytic vs. heuristic (Barkin, 1974; Benbasat

& Taylor, 1978; Doktor S Hanilton, 1973; Huysman, 1970; Lusk

6 Kersnik, 1979; Vasarhelyi, 1973); (2) abstract vs.

concrete (Schroeder, Driver, 6 struefert, 1967) ; (3)

perceptive vs. receptive (HcKenny & Keen, 1974); and (4)

Jungian cognitive styles (Gingras, 1977; Hellreigel &

slocum, 1975; Henderson & Nutt, 1980; Hanoochehri, 1978;

steckroth et al., 1980).

The analytic-heuristic typology has had the most

extensive use in behavioral studies of interactive

information systems, particularly in laboratory

experimentation using business games as research vehicles.

Empirical evidence suggests that use of decision aids or

problem solving models is closely associated with the

analytic-heuristic dimension of cognitive style. High

analytics tend to take a planned approach to problem^

solving, applying formal analysj^, whereas low analytics

tend to use a trial and error approach to problem solving,

relying on _spontaneous action_a_nd feedback. In contrast to

55

low analytics (heuristics), high analytics have been found

to utilize more information when making decisions

(Vasarhelyi, 1973) , to have more positive attitudes towards

computers (Benbasat & Taylor, 1978; Vasarhelyi, 1973), and

to be less concerned with systen inflexibility (Vasarhelyi,

1973). Low analytics will tend to select more data from a

system, but much of the data is likely to be irrelevant to

the decision at hand (Barkin & Dickson, 1977). High

analytics may not perjforn better than low analytics, but

they are more likely to use models when making decisions and

to prefer more structured, sjimmarized feedback (Bariff &

Lusk, 1977; Benbasat & Dexter, 1980; Benbasat & Schroeder,

.1977). Some researchers have found high analytics to

outperform low analytics in gaming situations (Benbasat 6

Dexter, 1979, 1980; Lusk, 1973) and to be more likely to use

stable, fixed strategies, as opposed to flexible, changing

strategies, when playing simulation games (Benbasat &

Dexter, 1979). In general,

(j^ liu kuM. ^ *s ** nunber of parameters, the number of decision (H a^lhi^c^f^ /^y-^^'^^^'^'i^^f and the QttB' ^ ^^ output variables Vfci Hu r»-i i Increases, low analytics can be expected to have

\\^AAjoh'^ '^'^^y^xacz^^'xiLq difficulties coping wlththe integration and / n.L'rc^ Structuring of data (Benbasat 6 Dexter, 198 0, p. 14).

• ^ ^^^ These findings are consistent with Wltkln's theory that the

^ high analytic is more capable of using structured approaches

to solving problems (cf. Wltkln et al., 1971). The

theoretical constructs of Wltkln, as well as empirical

findings mentioned above, suggest that high analytics, in

56

contrast to low analytics, will be more likely to perceive

an Information system as useful, particularly one which

employs quantitative techniques or displays data In

systenatlc ways. In other words, high analytics can be

expected to hold more positive expectations regarding the

utility of the conputer systen to facilitate good

perforaance. Low analytics aay select aore data froa the

systea, but high analytics, due to their better ability to

lapose structure on a field of stiauli, will aore easily

Identify coaaands and variables that are relevant to a

decision task.

The extensive research on Individual differences

clearly Indicates the inportant role of background,

personality, and cognitive variables In explaining user

behavior. One laplication of this area of research is that

information systeas should be designed In ways that cater to

the needs of particular types of users, or else be flexible

enough to meet the needs of a wide variety of users (Bariff

& Lusk, 1977; Driver e Hock, 1975; Hason 6 Hitroff, 1973;

McKenney & Keen, 1974; Sprague, 1980) . For the researcher

interested in studying the role of notivation In user

behavior, the Implication Is that Individual differences

must be considered.

57

2.2.2«3 Contextual Factors

A final class of variables relevant to the study of

user notivation Is the situational factors surrounding use

of an information system^ In conceptual discussions of HIS

and DSS, many writers have acknowledged the important role

of the situation In determining behavior (Ein-dor & Segev,

1978; Keen, 1979; Hason £ Hitroff, 1973; Sanders S Courtney,

1981; Schonberger, 1980; Wetherbe & Whitehead, 1977)^ But

the empirical literature in this area Is very meager,

particularly with respect to behavior at the Individual

level of analysis^ From a motivational perspective, the

most relevant and well-studied situational variable is

reward conditions surrounding a system. Rewards have been

recognized as an important component of the task environment

surrounding man/computer Interaction (Humford, 1975). The

important role of top management policy, for example. Is

well-recognized in the implementation literature (Kanter,

1977). Several gaming experiments have offered small

monetary prizes for subjects demonstrating good performance

In a decision task (e.g., Benbasat & Hasulis, 1980; Lucas 6

Neilsen, 1980), though none of the studies considered the

effects of varying levels of rewards on decision

performance. The most clearcut recognition of the

importance of monetary rewards in determining user behavior

is given by Lucas (1978). On the basis of several field

studies he concluded that organizational climate.

58

particularly as reflected In reward practices. Influences

attitudes and perceptions toward a computer Information

system^

2^2.3 HIS Praneworks

In an effort to summarize the HIS literature and

suggest future research directions, several authors have

outlined frameworks for Information system variables. Each

of the major frameworks Is now briefly discussed^

The majority of Information systems of the 1960's and

early 1970's used formal, repetitive models and focused on

structured problems at the operational control level of the

organization^ These systems were typically designed without

concern for potential variability across users,

organizations, or problem context. Underlying the design of

these systems were assmuptions that "all users think alike,"

"users think like designers," and "there Is one best

approach to problem-solving," In one of the first proposed

research frameworks for the HIS field, Hason and Hitroff

(1973) criticized such simplistic systems and called for the

use of multivariate research designs to investigate

variables within one or more of the following categories:

(1) user psychological attributes, (2) problem type, (3)

type of information system, (4) organizational context, and

(5) mode of Information presentation^ In effect, these

authors were identifying a need for HIS research to match

59

the development of more complex Information technology with

wider applications during the 1970's^

Earlier Gorry and Scott Horton (1971) provided a

nanagement-based perspective of HIS by combining Anthony's

(1965) model of management (operational control, management

control, and strategic planning) with Simon's (1960) model

of decision-making (structured or unstructured) • In their

view, an infornation systen must be tailored to the specific

type of decision and context at hand. The authors

distinguish between a HIS and a DSS, defining the latter as

any Information system which facilitates unstructured or

semistructured decision-making at any level of management.

The Gorry and Scott Horton framework has served as a

conceptual basis for many studies dealing with decision

support systems^

Dickson, Senn, and Chervany (1977) summarize and

integrate a series of behavioral studies In HIS conducted at

the University of Hlnnesota between 1973 and 1977.

According to the model they present, three classes of

Independent variables Influence decision effectiveness

within an Information systems context: (1| characteristics

of the decision-maker, (2) characteristics of the decision

environment, and (3) attributes of the information system.

"Decision-maker" variables include indirectly acquired

attributes, such as aptitudes and personality

60

characteristics, as well as directly acquired attributes,

like learning and experience. The "decision environment"

variables refer to the functional area la the organization,

organization level, and various envlronaental

characteristics, such as coapetitiveness, stability and tiae

pressure. within the category of variables relating to the

system Itself, HIS research might examine the content and

nature of report formats or other declslon-alds available to

the user^ Finally, quality of decision effectiveness can be

measured In terms of profit, cost, or time to make a given

decision. According to the framework, several approaches

can be used In research Investigating these classes of

variables, including case studies, field studies, and

controlled experiments. In the authors* view, laboratory

studies, particularly experimental gaming, "offer the most

Immediate promise for improvement in the current

state-of-the-art on information analysis and design" (p.

915)^

Using a systems approach, Nolan and Wetherbe (1980)

have outlined a framework for HIS research which goes beyond

the "micro" view of earlier frameworks to consider

organizational and environmental variables which influence,

and are influenced by, the HIS^ In their view, the HIS must

be considered in the context of Its Inputs (Information

requests, organizational resources) , transformations

(hardware, software, database, procedures, and personnel)

61

and outputs (transaction processing. Information reporting,

decision support, and programmed decisions). They classify

existing studies Into these three categories and their

respective subsystens and suggest that future HIS research

be conducted within a systems theory perspective.

The most recent framework for classifying past research

In HIS and suggesting future hypotheses has been outlined by

Ives, Hanilton, and Davis (1980), In their view,

envlronnent. Information system, and processing

characteristics are the three major groups of variables

relevant to HIS research, "Environment" refers to the

general external environment, the specific 3rganizatlonal

environment, the user environment, and the environment

surrounding HIS development and operation. Elements of the

"information" subsystem include the content, form, and

presentation time of data from the system. Finally,

"process" variables flow out of the Interactions between the

HIS and the environment. There are three major processes of

Interest to the researcher: development, operations, and

use. The authors note that research dealing with the

Influence of one or more variables from the environmental

characteristics group on variables from the process group is

quite common In MIS, constituting 24,8% of doctoral

dissertations in MIS between 1973 and 1979. They call for

greater research effort In the process group variables

(e.g., participation and utilization). They note that

62

although use has been a popular dependent measure in past

research, relatively little attention has been devoted to

the use process Itself, such as how and why the user is

satisfied.

The research frameworks discussed above aid In

organizing the empirical literature on Information systems.

They point to variables, or classes of variables, important

in research and stimulate thought and discussion within the

field. What frameworks fall to do Is to provide

conceptualization regarding causal relationships among these

variables. Overemphasis on frameworks In the past has

resulted In models where "the boxes are arbitrary and the

arrows are largely atheoretical" (Keen, 1980, p. 12).

Because of this, according to Keen, "MIS research Is a theme

rather than a substantive field" (1980, p. 9). MIS

frameworks have served as preliminary steps in the movement

of the field toward theory-development (Lucas, Clowes, 6

Kaplan, 1974). But the time has come for MIS to move away

from frameworks and toward theory. The application of

expectancy theory principles to an MIS context represents an

attempt In this direction. Several writers have called for

application of behavioral science theory to MIS design,

implementation, and utilization issues (e.g.. Cooper &

Swanson, 1979; Powell, 1976). The importance of cognitive

psychology as an underlying field for decision research has

been recognized in the past (Lasky, 1972), particularly

63

through the work of Simon (1977). Applying Keen's

terminology, cognitive psychology Is a "reference

discipline" for HIS, -an established field to which HIS

looks for research methodology and theoretical rationale.

For this reason, expectancy theory may be a particularly

appropriate starting point for the developnent of a theory

of Individual user behavior.

2.2*^ Models of Decision Support Systen Use

Several nodels of infornation system use are present in

the HIS literature. Three of these have no underlying

theoretical base. Two are based in expectancy theory.

2.2.4.1 Hon-expectancY Based Models

LUCAS (1975b) . Lucas presents one of the most

integrative models of HIS use available in the literature

(see Figure 4). Following a large number of studies on user

behavior, he Identified five major determinants of systen

use: (1) user attitudes and perceptions, (2) technical

quality of the system, (3) situational and personal factors,

(4) decision style, and (5) performance. According to

Lucas, positive attitudes about information systems in

general lead to high levels of system use, an assumption not

incongruent with expectancy theory. Questionnaire and

computer file data from salesmen in a manufacturing firm

yielded a weak positive association between use and

64

performance, with use being a function of personal,

situational, and decision style variables. The study used

only self-report measures of use, Lucas calls for

laboratory studies to further test the relationships

postulated by the model. His model does mot Include a

motivational component per se, bnt "attitudes and

perceptions" are similar to, although more general than,

"expectations," Expectancy theory may help to clarify the

components of the Lucas model and their Interrelationships,

I Quality of 1 I System t

• •

I I

T Attitudes I

and I t Perceptions) 4. - ^ >

^ •

Use of ) Information I

System

I Analys i s , 1 Action I 4 T •

I 4 M. f

i Performance f -4. •

I S i t u a t i o n a l | I and l^ j Personal | t Factors I 4 — _ ^ --4.

Figure 4: Lucas (1975) model of user behavior.

65

SCHEME (1976) . Schewe presents a model in which user

percept ions determine b e l i e f s about the HIS and f e e l i n g s of

favorableness or unfavorableness ( a t t i t u d e s ) , and u l t imate ly

system use (see Figure 5 ) , Like Lucas, he hypothes izes that

the presence of favorable a t t i t u d e s i s centra l to obtaining

high u t i l i z a t i o n of the system. Survey data c o l l e c t e d In 10

food process ing firms (only two of which had i n t e r a c t i v e

systems) revealed that users ' perceptions of the sys tem's

c a p a b i l i t y and the surrounding environment ( e . g , , top

managenent support , norale) were re lated to actual use , but

a t t i t u d e s toward sys ten outcones ( e , g , , dec i s ion

e f f e c t i v e n e s s ) were not s i g n i f i c a n t l y re lated to use . He

concluded that s i t u a t i o n a l cons tra in t s override the

in f luence of a t t i t u d e s on behavior. Unlike Lucas, Schewe

used an o b j e c t i v e measure of use (number of reports

reques ted) . His r e s u l t s did not s trongly support h i s model,

nor are they c o n s i s t e n t with expectancy theory pred ic t i ons

of user behavior, perhaps because he f a i l e d to account for

ind iv idua l d i f f e r e n c e s known to be Important in HIS use .

The o v e r a l l conclus ion fron the Lucas and Schewe

s t u d i e s i s t h a t behavior i s a function of user percept ions

about the information system. As Zmud (1979a) points out ,

however, ne i ther study o f f er s a meaningful psycholog ica l

explanat ion for i t s f ind ings .

66

• «.

I Evaluative | I I I Process | 4. ,«.

• I Beliefs IPer- I I A Iceptuall ^i {Pro- 1 ^ B 1cesses | • •

About HIS Dimensions About

I HIS-related t objects, the 1 atmosphere, t and signlf-t leant others

Constraints: Beliefs

A. About HIS Dimensions

B. About HIS-r e l a t e d o b j e c t s , the atmosphere, and s i g n i f i c a n t o thers

{ A t t i t u d e I

Toward I ]Use of I I the I 1 HIS

. ^ I System | I T^ l 1

I Usage | • 4

Figure 5: Schewe (1976) model of user behavior .

MEHRA AND ALEXANDER (1979) • These authors o f f e r a

s i m p l e model of user behavior In which use i s seen a s a

f u n c t i o n of : (1) p r e d i s p o s i t i o n toward system u s e , (2)

system c a p a b i l i t y , and (3) job demands ( s e e Figure 6)^ They

p r e s e n t data from 139 system users In 10 d i f f e r e n t

o r g a n i z a t i o n s to support t h e i r model. Use was s u b j e c t i v e l y

measured In t h i s s t u d y . The r e s u l t i n g model can be

c r i t i c i z e d f o r be ing o v e r l y s i m p l i s t i c and for f a i l i n g to

c o n s i d e r the r e l a t i o n s h i p between performance and use of a

s y s t e m . On the o t h e r hand, the model p o i n t s out the

67

importance of experience and communications fron others in

determining "pred i spos i t i on toward use ." Expectancy theory

night nore opera t iona l ly def ine t h i s cons truc t as

expectancies regarding the e f for t - to -per fornance

re la t ionsh ip^

I Experience i with sys ten

4. - _

I Reports I fron others |

4 > — • f

Pred i spos i t i on 1 I toward

system usage |

I System Capabil ity

4. 4.

Figure 6: Hehra and Alexander's model of system use.

2 . 2 . 4 . 2 BxpectancT Based Models

VERTINSII, BARTH, and HITCHELL (1975) . The purpose of

t h i s stndy was to exanine the OR/HS process and i t s e f f e c t s

on ind iv idua l users and t h e i r organiza t ions . Questionnaire

and interv iew data fron managers in a variety of bus iness

firms were c o n s i s t e n t with an expectancy theory aodel in

which use of OR/HS i s seen to be a function of : (1) the

expectat ion that use of OR/HS techniques w i l l lead to

perforaance; (2) the expectat ion that good perforaance w i l l

68

lead to des ired outcoaes ; and (3) the valence of a v a i l a b l e

outcomes. In a d d i t i o n , they suggest t h a t Use—^Performance

expectanc ies nay be inf luenced by past experience and

p e r s o n a l i t y v a r i a b l e s . The authors develop t h i s expectancy

theory nodel of OR/HS laplementat lon as an ad hoc

explanat ion of t h e i r f i n d i n g s . The study was not designed

to t e s t an expectancy model a p r i o r i , and thus the r e s u l t s

only Imply the d i r e c t i o n of expectancy theory r e l a t i o n s h i p s

without e x p l i c i t l y t e s t i n g t h e i r v a l i d i t y .

E2§II (.1979) • Robey surveyed 66 members of the

s a l e s f o r c e of a large Indus tr ia l manufacturing firm who used

a HIS to record, update, and maintain customer accounts . He

found a s trong c o r r e l a t i o n between se l f - repor ted performance

and use of the HIS (r= .79) , where use was o b j e c t i v e l y

measured as the number of customer records updated per time

period. User a t t i t u d e s toward the organizat ional context

( i . e . , "degree of s a t i s f a c t i o n with the consequences of MIS

use") were weakly re la ted (although s i g n i f i c a n t l y ) to

perceived worth of the system (r=.08 to r = . 3 6 ) . Robey

concluded that use i s more a function of a t t i t u d e s toward

the organ iza t iona l environment than of perceived worth of

the s y s t e a I t s e l f . Attitude toward performance was the

a t t i t u d e aos t s t rong ly re la ted to s y s t e a use. Robey

o u t l i n e d an expectancy theory i n t e r p r e t a t i o n of these

r e s u l t s In which use of the HIS depends upon: (1) the

expected l i k e l i h o o d that perfornance w i l l r e s u l t froa use;

69

(2) the perceived likelihood that rewards will result froa

perforaance; and (3) the value of the rewards received froa

perforaance (see Figure 7)•

4. 4.

I User i t C h a r a c t e r i s t i c s |

•

4. 4,

i Use of Systen | I (Effort) r

f A

4 . - — —

J Job "l Perfornance

4. X • 4.

i Systen and | ) Envlronaental J I Characteristics ) 4. 4.

J Extrinsic | ^ y^\ Rewards | r • T • 1 • 1 •

I I n t r i n s i c | i Rewards |

4. 4.

J Cognit ive Assessaent I of Above S i tua t ion f

Figure 7: Robey (1979) aodel of user behavior.

Robey's aodel c o n s t i t u t e s the only a o t i v a t i o n a l

approach to HIS use discussed In the l i t e r a t u r e to date^

Subsequent research in t h i s area could laprove Robey's

conceptual scheae and aethodology by: (1) applying

expectancy theory to the research s e t t i n g a pr ior i , rather

than ad hoc; (2) exaaining the Perf oraiace-->Outcoae

^ \

70

relationship; Robey only neasured Effort—^Performance

expectancies and did not identify outcomes or assess their

valences; (3) using an objective measure of performance

rather than an indirect measure; and (4) using a DSS setting

rather than a transaction processing system^

2- 3 Sunnary

This chapter has presented an overview of the

expectancy theory and behavioral HIS literatures^ Within

the area of study concerned with organizational behavior,

expectancy theory has been, and continues to be, the most

well-researched theory of human motivation^ Despite its

popularity, however, it is a theory surrounded by

controversy. For example, as a cognitive process model it

relies on several questionable assumptions regarding the

nature of man. To the extent that these assumptions are

untestable, the true construct validity of the theory is

difficult to determine. The model has also been criticized

for being overly complex and for failing to include certain

variables, such as expectations of others, known to be

important in human cognitive processes. Reviewers of

expectancy theory research have identified common

methodological problems in empirical tests of the theory,

the most notable of which Include failure to use

longitudinal, within-subjects designs, and the application

of measurement instruments with unknown reliability and

71

v a l i d i t y . Despite t h e s e c r i t i c i s m s , the model has been used

s u c c e s s f u l l y to expla in numerous types of behaviors In a

v a r i e t y of research s e t t i n g s .

within the HIS l i t e r a t u r e , two empirical s t u d i e s have

used expectancy-based models to explain voluntary use of

t ransact ion processing systems. In both Ins tances

expectancy theory was applied ad hoc to the research

f i n d i n g s . These s t u d i e s , however, represent a recogn i t ion

among HIS researchers of the need to study human motivation

In the context of information system use . Of relevance to

the understanding of user motivation processes I s HIS

research deal ing with user a t t i t u d e s , user background and

psycho log ica l c h a r a c t e r i s t i c s , and s i t u a t i o n a l f a c t o r s

surrounding computer use , HIS research "frameworks" point

to the Importance of studying these groups of var iab les as

they r e l a t e to the use of information systems in

o r g a n i z a t i o n s . An expectancy theory perspect ive of the

information system use process deserves further

cons iderat ion in MIS research because, unlike other models

of user behavior. I t I s t h e o r e t i c a l l y based and has a large

body of empir ical l i t e r a t u r e as soc ia ted with i t . The

f i n d i n g s of e x i s t i n g behavioral s tud ie s in HIS can aid in

hypothes iz ing concepts and r e l a t i o n s h i p s In an

expectancy-based model of user behavior.

Chapter I I I

PROPOSED HODBL OP OSEB BBBAVIOS

The l i t e r a t u r e presented In Chapters 1 and 2 s u g g e s t s

t h e f o l l o w i n g c o n c l u s i o n s :

1. There Is a need for more carefu l study of the

motivation processes surrounding use of information

systems in organ iza t ions .

2. There Is a need for theory-development within the

f i e l d of HIS. Theories based in cogn i t i ve psychology

of fer p o t e n t i a l here, s ince c o g n i t i v e psychology i s a

reference d i s c i p l i n e for behavioral research In HIS.

Ex i s t ing frameworks point to the need for HIS research to

study var iab le s r e l a t e d to user a t t r i b u t e s , the

o r g a n i z a t i o n a l c o n t e x t , and the psychological process

involved in using an information system. Development of an

expectancy theory model of user behavior would include

v a r i a b l e s from each of these c a t e g o r i e s , as well as account

for the causa l r e l a t i o n s h i p s among these var iab le s , and

between system use and dec i s ion performance. Application of

expectancy theory p r i n c i p l e s might a l so aid in

o p e r a t l o n a l l z l n g some concepts In e x i s t i n g models of system

72

73

use and in clarifying their Interrelationships. An

empirical test of an expectancy-based model would represent

an extension of earlier work In the area done by Vertinsky

et al. (1975) and Robey (1979). An enormous amount of the

behavioral literature In HIS In the past has been devoted to

the study of individual differences. An expectancy nodel

takes a nore appropriate view in Its recognition that

behavior Is a function not only of the person but of the

situation.

The nost recent direction In HIS Implementation research involves the adoption of an expectancy approach toward both HIS development and HIS usage. Here, Individual differences are believed to influence HIS knowledge and HIS attitudes, which in turn influence MIS expectancies (joint appraisals of the utilities of specific outcomes and the likelihood that Implementation behaviors will result in these outcomes) .... While related literatures provide a great amount of support for expectancy models of organization behavior, very little research of this nature has been done In an MIS context (Zmud, 1980, p. 215).

Expectancy theory Is selected as the basis for the

proposed model of user behavior because It is consistent

with, and yet expands, prior behavioral research in HIS.

This Is not to suggest that Vroom's theory Is the only

potentially applicable motivation theory, but it is one

which can provide an orderly starting point for

investigation, systematic comparison, and integration of

alternative models of user behavior. Within the

organizational behavior literature, negative empirical

findings and theoretical problems associated with expectancy

74

theory have caused soae reviewers to conclude that the

theory Is not a viable explanation of how people decide on a

particular level of effort to expend In their work. Despite

such problems, however, the model continues to receive

support and acceptance.

In short, the expectancy-type model appears to have enjoyed substantial If uneven support. In spite of some shortcomings in the relevant studies, and seems to merit further work (Connolly, 1976, p, 46),

3.1 Model Overview

An expectancy theory based model of user behavior is

shown in Figure 8. The model concepts and relationships

have been developed based on the literature presented in the

previous chapter. The model indicates that the motivation

to expend effort to use a DSS is primarily determined by

three variables.

The first variable (box 1) refers to the manager's

expectation that use of an information system will lead to

decision-making of a given quality (I.e., perfornance

level)- This expectancy can vary from certainty that use of

the DSS will lead to effective decision-making (high

performance) to certainty that use would lead to ineffective

decision-making (low performance). Prior experience with

computerized information systems, educational level,

cognitive style, and personality factors influence the

individual's use—^performance expectancy. The work of

75

• 4>

1 l o c u s of I I c o n t r o l I

— • 4. 4-

| e d u c a - | t t i o n I 4. , 4.

4. — 4 .

1 e x p e r i e n c e ! 4. - .^ - - . f

• I I

4. 4.

l o c u s I of c o n t r o l |

"J 2a 2b

I (Use—>Perf . ) | • \ >( (Perf .—^Outcomes) (Valence) ) | . . — , ^ ^

• 1 • • ^ «. 4> ' 4-

I c o g n l t l v e ] jcommunlcatlonst I s t y l e I I from o t h e r s 1

I o r g a n i z a t i o n a l | I c h a r a c t e r i s t i c s |

I A b i l i t y f— 4. 4.

4. 4.

j C o g n i t i v e L I S t y l e I 4. 4.

1

4. . . & . . . — - 4 .

I use of DSS I 4. ^ ^ »

I

.1

1 Education | "4. 4.

i System Qual i ty 1 4. . . . . . . . 4.

4. 4.

1 Experience 1

4. . — -

I Qual i ty of I I Dec ls lon-Haking | 4. f

I I

1 1 1 • -

Outcomes 1 -intrinsic | -extrinsic I . . . . . . . . . . . . ^ 4.

\ ^

Figure 8: A motivational model of DSS use.

76

Guthr ie (1973) and Koester and Luthans (1979) s u g g e s t s t h a t

more e x p e r i e n c e d u s e r s w i l l t end to have nore p o s i t i v e ,

r e a l i s t i c e x p e c t a n c i e s than l e s s exper ienced u s e r s .

Educat ion has been shown t o r e l a t e to system

use (Lucas, 19 7 5b) . The model h y p o t h e s i z e s t h a t t h i s

r e l a t i o n s h i p i s a f u n v c t i o n of d i f f e r e n c e s In

use—^performance e x p e c t a n c i e s a c r o s s i n d i v i d u a l s . The

expec tancy theory l i t e r a t u r e s u g g e s t s t h a t communications

f r o n o t h e r s and p e r s o n e l e x p e r i e n c e with the s t i m u l u s

s i t u a t i o n may a l s o have an e f f e c t on the u s e r ' s

e x p e c t a n c i e s .

The second major v a r i a b l e (box 2a) r e f e r s t o the

manager's b e l i e f t h a t a g iven d e c i s i o n q u a l i t y w i l l l e a d t o

p a r t i c u l a r outcomes or p a y o f f s . Thus, t h i s type of

expec tancy I n v o l v e s b e l i e f s about reward c o n t i n g e n c i e s which

may be provided by the o r g a n i z a t i o n ( e x t r i n s i c ) or through

some s o r t o f s e l f s a t i s f a c t i o n or i n t e r n a l r e i n f o r c i n g

p r o c e s s ( i n t r i n s i c ) . The perce ived s t rength of the

r e l a t i o n s h i p between performance and o b t a i n i n g rewards i s a

f u n c t i o n of t h e i n d i v i d u a l ' s p e r s o n a l i t y ( locus of c o n t r o l )

and t h e o r g a n i z a t i o n a l c o n d i t i o n s surrounding the MIS ( e . g . ,

d o e s t h e o r g a n i z a t i o n reward good performance; how does the

o r g a n i z a t i o n reward performance?) .

The t h i r d major v a r i a b l e (box 2b) in the model r e f e r s

to t h e manager's p e r c e p t i o n s regarding the degree of

77

a t t r a c t i v e n e s s of d i f f e r e n t kinds of outcomes. These

outcomes may be I n t r i n s i c , such as f e e l i n g s of

acconpl ishnent and f u l f i l l n e n t , or e x t r i n s i c , such as pay or

pronotion v a r i a b l e s . The a t t r a c t i v e n e s s of a given outcome

may range from very high t o very low.

The model s t a t e s that the extent of use of an

information system can be predicted in the fo l lowing way.

F i r s t , the I n d i v i d u a l ' s s u b j e c t i v e probabi l i ty that d e c i s i o n

performance w i l l lead to outcomes i s weighted by the valence

of the array of re l evant outcomes. Second, the weighted

p r o b a b i l i t y - v a l e n c e combinations are summed. Third, t h i s

weighted sun I s then conblned with the i n d i v i d u a l ' s

s u b j e c t i v e p r o b a b i l i t y that use of the sys ten w i l l r e s u l t in

performance. The r e s u l t i s a motivational score which can

be used to predic t the extent to which the indiv idual uses

the in fornat ion system.

The ac tua l (as opposed to expected) qual i ty of the

I n d i v i d u a l ' s decision-making I s a funct ion not only of

system use, but a l s o the u s e r ' s decision-making a b i l i t y ,

c o g n i t i v e s t y l e , exper ience , and educational l e v e l , as wel l

as the the o b j e c t i v e qual i ty of the system. Obviously, the

be t t er the qua l i ty of the information system, the stronger

w i l l be the p o s i t i v e r e l a t i o n s h i p between system use and

good decis ion-making. Holding motivation constant , the

greater the i n d i v i d u a l ' s knowledge or s k i l l l e v e l the b e t t e r

78

h i s performance w i l l be (Porter & Lawler , 1968; Vroon,

1 9 6 4 ) . Hore e x p e r i e n c e d u s e r s are nore l i k e l y t o make

b e t t e r d e c i s i o n s than i n e x p e r i e n c e d u s e r s , p a r t i c u l a r l y

during i n i t i a l d e c i s i o n p e r i o d s (Benbasat , 1974; Smith ,

1975; T a y l o r , 1975; T i e s s e n , 1 9 7 6 ) . Education i s

h y p o t h e s i z e d t o r e l a t e p o s i t i v e l y t o d e c i s i o n q u a l i t y

( V a s a r h e l y i , 1973) . F i n a l l y , c o g n i t i v e s t y l e may i n f l u e n c e

t h e DSS Use—^Qual i ty of Dec ls lon-Haking r e l a t i o n s h i p , with

p e r s o n s of d i f f e r e n t c o g n i t i v e s t y l e s using the system in

mean ingfu l ly d l f e r e n t ways ( V a s a r h e l y i , 1973) .

Once t h e manager makes a d e c i s i o n , i n t r i n s i c and

e x t r i n s i c outcomes may r e s u l t . Over t i m e , the u s e r ' s a c t u a l

e x p e r i e n c e i n the s i t u a t i o n may cause him t o a l t e r h i s

e x p e c t a t i o n s , the v a l u e s placed on c o n t i n g e n t outcomes , and

hence h i s m o t i v a t i o n t o use the sys tem In the f u t u r e .

Repeated use of a g iven system by a p a r t i c u l a r manager

should r e s u l t i n h i s deve loping I n c r e a s i n g l y a c c u r a t e

e s t i m a t i o n s o f use—^performance and performance—^outcome

p r o b a b i l i t i e s . As Lawler (1973) p o i n t s o u t , l e a r n i n g p l a y s

an important r o l e i n the development of e x p e c t a n c i e s .

Assuming the r e s e a r c h e r measures the u s e r ' s e x p e c t a t i o n s a t

d i f f e r e n t p o i n t s i n t i m e , the power of the model to p r e d i c t

system use should i n c r e a s e over time-

The model proposed here I s c o n s i s t e n t with both the

I n d i v i d u a l D i f f e r e n c e P e r s p e c t i v e and t h e Rat iona l Han View

79

3f decision-making described by Keen and Scott Horton

(1978) . The Ind iv idua l Dif ference perspec t ive enphas izes

the Inportance of the u s e r ' s p e r s o n a l i t y , background and

d e c i s i o n s t y l e i n deternin ing behavior. Like the Rational

Han View, expectancy theory assumes that the user l o g i c a l l y

c a l c u l a t e s expected p r o b a b i l i t i e s of outcomes r e s u l t i n g from

perfornance and then chooses to act In a way which naximizes

attainment of d e s i r a b l e outcomes.

The current research projec t wi l l not t e s t the v a l i d i t y

of the complete model, but only that portion of the model

out l ined in Figure 9 . Experience, education, and t o some

extent a b i l i t y , w i l l be treated as contro l var iables in t h i s

study. The research sample w i l l be s e l e c t e d that w i l l be as

homogeneous as p o s s i b l e with respect to these background

c h a r a c t e r i s t i c s . Experimental procedures w i l l be used to

contro l for the in f luence of communications of o thers on

use—^performance expectanc ies ; and organizat ional reward

condi t ions w i l l be held constant for each part ic ipant in the

study-

The model s t a t e s that the use—^performance and

performance—^outcome r e l a t i o n s h i p s are influenced by

p e r s o n a l i t y and c o g n i t i v e s t y l e v a r i a b l e s . As pointed out

e a r l i e r (Chapter 2 ) , the pred ic t ive power of expectancy

theory tends t o Improve when one accounts for the e f f e c t s of

Indiv idual d i f f e r e n c e s or s i t u a t i o n a l var iab les on

80

I locus 1 I locus I t of control j ) of control j • j • •- p •

I (Use—^Perf.) | » | >( (Perf .—^Outcomes) (Valence)) |

i I c o g n i t i v e s t y l e |

I Use of DSS I

Figure 9: Hodel to be t e s t e d .

expectancy r e l a t i o n s h i p s . A given variable Is sa id to

Inf luence an expectancy r e l a t i o n s h i p i f the strength of the

E—>p or P—K) r e l a t i o n s h i p , or the s t a b i l i t y of the

r e l a t i o n s h i p , var ies across types of Indiv iduals or types of

s i t u a t i o n s . The Importance of locus of control in the

proposed model of user behavior i s suggested In e x i s t i n g

expectancy theory l i t e r a t u r e , while the hypothesized

Inf luence of c o g n i t i v e s t y l e I s Implied In a v a i l a b l e HIS

l i t e r a t u r e .

81

Within the expectancy theory l i t e r a t u r e .

I n t e r n a l - e x t e r n a l l o c u s of contro l has been f reguent ly

s tud ied as a v a r i a b l e Inf luenc ing the E—>? and P—>0

expec tanc i e s (Gi les , 1977; Pavet t , 1978; Schneidernan,

1980) . The concept of locus of control r e f e r s to the ex tent

to which a person s e e s the world In terns of i n t e r n a l

c o n t r o l ( i . e . , she a c t s on the world) or external contro l

( I . e . , the world a c t s on h e r ) . Locus of control Is s i m i l a r

to the ins t rumenta l i ty concept in that both represent the

extent to which the indiv idual f e e l s i n f l u e n t i a l In her

environment. Consequently, the proposed model of user

behavior hypothes izes that persons high in Internal l ocus of

contro l w i l l report stronger P—»0 expectancies than those

low in i n t e r n a l locus of c o n t r o l , s ince the former group by

d e f i n i t i o n i s more l i k e l y to recognize an instrumental

r e l a t i o n s h i p between t h e i r behavior and events in t h e i r

l i v e s (Lawler, 1973) . With regard to E—»P e x p e c t a n c i e s ,

B a t l i s (1978) found i n t e r n a l s to have stronger E—>P

expectanc ies than e x t e r n a l s , with ex terna l s more l i k e l y than

I n t e r n a l s to change t h e i r expectancies over time. Likewise,

S z i l a g y i and Sims (1975) noted a re la t i onsh ip between E—>P

p r o b a b i l i t y and locus of contro l . In an experimental

s e t t i n g , i n t e r n a l s u b j e c t s have been found to exert greater

e f f o r t , re sources , and time in making dec i s ions than

e x t e r n a l s (Elklns & Cochran, 1978). Internals as employees

have been found to be more motivated t o work than e x t e r n a l s .

62

to a c t u a l l y perform b e t t e r , and to see working hard as being

more Instrumental In obtaining things they want (Broedling,

1975) . According to Lefcourt (1972) and Phares (1976) , high

I n t e r n a l s engage In greater information search a c t i v i t y than

t h e i r e x t e r n a l counterparts . The Implicat ion i s that

i n t e r n a l s , in c o n t r a s t to e x t e r n a l s , wi l l perceive an

Information system as more u s e f u l . With respect to computer

use , i n t e r n a l s have been shown to request more information

than e x t e r n a l s (Zmud, 1979c). As B a t l i s (1978) no te s ,

" locus of contro l represents a broad, general ized expectancy

while the effort—^performance component. . . denotes an

expectancy which I s . . . s i t u a t i o n - s p e c i f i c , " (p, 243) ,

Consequently, the I-E fac tor should be p o s i t i v e l y re lated to

the E—>P component, but the c o r r e l a t i o n i s not expected to

be extremely strong. Although several s tud ies have f a i l e d

to f ind s i g n i f i c a n t d i f f erences between i n t e r n a l s and

e x t e r n a l s within an expectancy theory framework ( B a t l i s C

Waters, 1973; Campbell, 1975; Sodikoff, 1975), a greater

number of I n v e s t i g a t i o n s have i d e n t i f i e d d i f ferences between

the two c a t e g o r i e s on expectancy theory var iables ( B a t l i s ,

1978; G i l e s , 1977; Henson, 1976; Pavet t , 1978; R e i c h e l t ,

1974) -

The model s t a t e s that cogni t ive s t y l e w i l l Influence

use—^performance expectanc ies of DSS users . The e x i s t i n g

MIS l i t e r a t u r e , p a r t i c u l a r l y the work of Benbasat and Taylor

(1978) and Vasarhelyi (1973), suggests that decision-makers

83

characterized by high analytic cognitive style, when

compared to decision- makers with low analytic ability, will

tend to have more positive expectations regarding the

utility of a computerized Information system to facilitate

good performance. In addition to differences in

expectations, high and low analytics may use the system in

different ways. The complete model (Figure 8) states that

cognitive style will affect the manner in which a

decision-maker uses the DSS (e,g,, the types of inquiries he

inputs to the system) . However, this issue will not be

examined in the current research project,

3*2 Besearch Hypotheses

This study will examine the validity of the model of user

behavior shown in Figure 9. Three hypotheses will be

tested.

HYPOTHESIS # 1 . An i n d i v i d u a l ' s use of a DSS can be

p r e d i c t e d as f o l l o w s :

USE=(Use—^Performance)*2( (Performance—^Outcomes) (Valence) )

where USE r e f e r s t o the e x t e n t of vo luntary use of a DSS, PERFORHANCB r e f e r s t o the q u a l i t y of d e c i s i o n ­

making, OUTCOHES r e f e r to reward c o n d i t i o n s a s s o c i a t e d with

DSS u s e , and VALENCE r e f e r s to the degree of a t t r a c t i v e n e s s

of each outcome.

The e q u a t i o n s t a t e s t h a t the i n d i v i d u a l ' s mot ivat ion to use

the DSS i s a f u n c t i o n of h i s or her s u b j e c t i v e p r o b a b i l i t y

84

that use will lead to a given level of decision-making,

times the sum of the subjective probabilities associated

with a given quality of decision-making and obtaining a set

of outcomes, each of which has an attractiveness rating

associated with it. Hotivation to use the DSS is related

positively to actual system use,

HYPOTHESIS #2, Locus of control influences the

Use—>P erf or ma nee and the Perf ormance—> Outcome

relationship. Persons high in internal locus of control

will have greater Use—^Performance and

Performance——^Outcome expectancies than those low in

internal locus of control.

HYPOTHESIS 13. Cognitive style Influences the

Use—^Performance relationship. High analytics will have

stronger Use—^Performance expectancies than low analytics.

3.3 Sunnary

This chapter has presented an expectancy-based

n o t l v a t i o n a l nodel of user behavior drawn from both the

expectancy theory and HIS l i t e r a t u r e s . The model extends

two pr ior s t u d i e s in which expectancy concepts were applied

to understanding voluntary use of information systems. The

proposed nodel i s c o n s i s t e n t with e x i s t i n g MIS l i t e r a t u r e

which po ints to the important role of l earning , reward

c o n d i t i o n s , a t t i t u d e s , and user c h a r a c t e r i s t i c s in

85

determining use of a decision support system. The current

study will test a portion of the proposed model. More

specifically, this research project will test the hypothesis

that motivation to use a DSS is a function of: (1) the

user's expectation that use of the systen will result in

high quality decisions, (2) the user's expectancy that

decision performance will lead to intrinsic or extrinsic

outcomes, and (3) the user's ratings of the attractiveness

of those outcomes. In addition, the study will determine

whether personality and cognitive style variables Influence

expectancy relationships.

^ ^

C h a p t e r IT

BBTH0D0L06T

4, 1 Sttblects

Eighty-eight undergraduate students In the College of

Business Administration at Texas Tech University

participated In the study. All were seniors with a major in

some area of business. On the average, the students were 22

years of age, with a cumulative grade point average of 2.95,

and 13 months of business-related work experience. All had

successfully completed an introductory computer course as

part of their undergraduate training. Twelve students had

participated in business simulation games in the past. Five

had work experience In computer programming or systems

analysis prior to participation in this study. Additional

descriptive Information of the sample is given in Table 2.

Using students as experimental subjects constitutes a

more practical, though less desirable, alternative to using

practicing managers. Although managers and students have

been found to respond similarly to decision tasks (Chorba 6

New, 1980; Khera 6 Benson, 1970), there is also evidence

that the decision behavior of students differs dramatically

from that of managers (Alpert, 1967; Fleming, 1969; Lucas 6

86

87

r • • • • • • • -

1 TABLE 2

1 Sample C h a r a c t e r i s t i c s

\ Age 1 Grade p o i n t average 1 # advanced programming c o u r s e s i t p r o d u c t i o n , OR, or MS c o u r s e s 1 # months o f f u l l - t i m e work 1 e x p e r i e n c e 1 i months o f f u l l - t i m e b u s l n e s s -i r e l a t e d work e x p e r i e n c e 1 1 . . . — . . . f — - - • i

j Hajor 1 Accounting 1 Finance 1 Management 1 Harket lng 1 Hanagement Informat ion Systems 1 General B u s i n e s s 1 Other 1 B u s i n e s s Gaming Experience t Yes 1 No 1 C u r r e n t l y p a r t i c i p a t i n g ] Sex 1 Males 1 F e n a l e s 1 TOTAL

Mean

2 2 . 4 2 . 9 5 , 2 2 7 . 5 4 5

2 1 , 1 3

13 .03

Median

22 2.9 0 1

12 .5

S

N

"~

19 27 20 14

5 1 2

13 73

2

61 27 88

.... . . . . - ^

1 Range |

20^33 1 2 . 0 - 4 , 0 1

0-4 1 0-2 J

0-99 1

0-99 1 1 V

. . , . - 1

N e i l s e n , 1980). Hence the g e n e r a l i z a b i l i t y of the r e s u l t s

of t h i s study may be hampered g iven a s t r i c t l y student-based

populat ion. Even i f managers were used in the study,

however, ex ternal v a l i d i t y problems would not be e n t i r e l y

removed because of the laboratory nature of the experiment.

88

Chervany (in Van Horn, 1973) points out that nanagers nay

behave quite differently in the artificial world of the lab

than In the real world of organizational declslon-naking.

Acknowledging these restrictions, this study sanpled

from an undergraduate, senior-level student population. In

the judgment of the researcher, the nature of the study was

sufficiently unique to warrant use of student subjects. The

predictive potential of expectancy theory for decision

support systen use had not yet been tested In any population

or any setting, and thus it seened legitimate to begin with

a student sanple and a laboratory environment, Horeover, if

the relationships and constructs of the model are valid,

they should be demonstrable in a controlled setting using

students.

Laboratory experiments studying the human/computer

interface have typically sampled from diverse student

populations, including undergraduate and graduate students,

and experienced and Inexperienced subjects in the same study

(e.g,, Benbasat et al., 1980; Vasarhelyi, 1973). Because of

prior evidence suggesting the Importance of experience and |

education in HIS use, all participants in the study were

screened to meet the following criteria: (1) completion of

at least three years of undergraduate education, (2) active

enrollment in an academic program leading to an

undergraduate business degree, and (3) no experience with

89

the Business Hanagement Laboratory s imulat ion (BML) or I t s

accompanying dec i s ion support system (SLIH).

4 .2 Procednre

A controlled laboratory study was conducted in which a

business slnulatlon game and Its accompanying DSS served as

the primary research vehicles. The utility of laboratory

studies In general and experimental gaming approaches in

particular when studying the human/conputer relationship has

been well recognized In the HIS literature (Benbasat &

Schroeder, 1977; Flsk, 1967; King 6 Rodriguez, 1981; Lucas,

1975a, Schnelderman, 1978; Senn & Dickson, 1974; Van Horn,

1973; Wolf, 1971). Data for the study was collected over a

five-month period. In all, six groups consisting of from 8

to 20 students each participated In the study, with each

group taking approximately three weeks to complete the

entire experiment. Subjects participated on a voluntary

basis and received course credit of approximately 20 percent

of the final grade in the course from which they were

solicited for the study.

An overview of the experimental procedure is shown in

Figure 10. All subjects were exposed to the same treatment

conditions. Subjects met formally with the experimenter for

one hour on each of the first two days of the study. For

the remainder of the three-week period, the students worked

on their own, although the experimenter was available for

consultation and questions throughout the study.

90

Week

I

I I

I I I

1. 2 . 3 .

4 . 5 ,

1. 2,

3 ,

1,

2 .

1

P r e b r i e f ing Consent Form C o n f i d e n t i a l ­i t y Form I -E S c a l e I n t r o d u c t i o n t o BML

BHL/SLIH QMLZ Expectancy Measures D e c i s i o n I due

Expectancy Measures D e c i s i o n IV

1 .

2 .

1.

1.

Day

2

Training In SLIM 1 St prac­t i c e d e c i s i o n

Dec i s ion I I due

D e c i s i o n V due

(run twice)

1.

3 ,

1,

2,

3,

4.

1

3 1

2nd p r a c t i c e | d e c i s i o n due |

D e c i s i o n | I I I due 1

Feedback 1 on D e c i - | s i o n s V,VI 1 Expectancy 1 Heasures | P o s t - 1 Experiment | Quest ions f Debr ie f ing |

Figure 10: Outline of experimental procedure.

Day one of the study began with a prebriefing In which

subjects were familiarized with the general nature of the

project and their role as participants. Each subject then

completed a Subject Consent Form (Appendix A) , an Agreement

to Confidentiality (Appendix B), a demographic questionnaire

(Appendix C), a cognitive style test, and a personality

measure (Appendix D). During the remainder of the period,

the subject received written and oral introductions to the

91

Business Management Laboratory, Each subject received the

following Instructions:

You are the manager of firm #1, just hired to replace the previous manager. As you can see, your firm is In bad financial condition, having lost about $30000 last quarter. Your job as the new nanager Is to turn this company around, to make it profitable and competitive in the industry. You will have a year and a half to do this. For the upcoming year and a half, beginning with the first quarter of 1981, you are going to be making a series of decisions for your firm. Your objective is to maximize retained earnings for your firm In each quarter, but particularly In the long run. Your performance as a manager will be measured In terms of your firm's retained earnings at the end of the second quarter of 1982, You will have two practice decisions, followed by five "real" decisions. You will be given feedback on the effects of your decisions after each quarter.

At the end of the second quarter of 1982, the retained earnings figure for your firm will be compared to that of other firms. If your firm obtains the highest retained earnings in the industry, you will receive an A In this project and you will receive $15, If your firm obtains the second highest net profit in the Industry, your grade will be a B, and you will receive $10, If your firn ranks third la the industry as of the second quarter of 1982, then you will receive a C in this project and there Is no cash bonus. As long as you hand In all of your decisions and conplete all questionnarlres for the project, you are guaranteed a grade of C. Remember, your objective here is to make your firm's cumulative retained earnings as large as possible by the end of quarter two of 1982.

The experlnenter emphasized that as far as course credit was

concerned, only the retained earnings figure for their firm

was important. The subjects were encouraged to be as honest

and accurate as possible on all questionnaires given during

the study, since these had no effects on their grade and

confidentiality was assured. The subjects were told that

they were not competing against each other, but rather

92

against "phantom" or "dummy" firns. They were encouraged to

work alone, to avoid consulting with one another, since each

Industry was slightly different. Day one of the study ended

with the experimenter answering the subjects' questions

regarding the game or experimental procedure.

On day two of the study, the subject again net formally

with the experimenter for one hour. Each subject was given

a copy of the SLIM User's Hanual (Courtney & Jensen, 1981)

and was asked to read this over for the first twenty minutes

of the hour. The experimenter then gave an oral

introduction to the use of SLIH, explaining how commands are

entered and reports are retrieved. This presentation was

essentially a summary of information given in the user's

manual. Bach subject then logged onto the computer system

and became familiar with actual use of basic SLIM commands

and functions. By the end of the hour, each subject

completed his first practice decision sheet (Appendix E)

and gave this to the experimenter for processing. Subjects

were told that they must use SLIM for 45 minutes, on their

own time, before handing In their second practice decision.

They were instructed to practice as many SLIM commands and

functions as possible during that period, so that they felt

comfortable with the system. The following instructions

were given:

Before handing in your second practice decision you must use SLIH for 45 minutes. Following your second practice decision you will take a quiz to be sure that you understand the simulation (BML) and how to use

93

SLIH, Then we w i l l s t a r t with the "real" d e c i s i o n s , beginning again with the f i r s t quarter of 1981,

You wi l l have up to two days to make each of your quarter ly d e c i s i o n s . Reports on the e f f e c t s of your d e c i s i o n s w i l l be a v a i l a b l e the day af ter you hand in a d e c i s i o n sheet for process ing. You may use any s t r a t e g y or approach you des i re in making your d e c i s i o n s . During the prac t i ce dec i s ions you nust use SLIH, But once we s t a r t with the "real" d e c i s i o n s I t w i l l be up to you whether or not you use the sy s t en and how much you use I t .

Not ice that the experimenter emphasized that use of SLIH

fo l lowing the p r a c t i c e dec i s ions would be opt iona l . The

laboratory s i t u a t i o n attempted to model condit ions found in

actual organizat ion s e t t i n g s . Typical ly a f l r n ' s top

nanagenent decides to I n s t a l l an I n t e r a c t i v e computer

system. Hanagers are then trained to use the system, but

actual use of the system in decision-making i s voluntary.

Following completion of the two prac t i ce d e c i s i o n s , but

before making the f i r s t "real" d e c i s i o n , each subjec t

completed an experimental quest ionnaire (Appendix G) and

took a br ie f quiz which measured basic knowledge of the BML

game and use of SLIH (Appendix F ) , If the subject scored

below 75% on e i t h e r of the two parts of the quiz, i n d i c a t i n g

l e s s than adequate knowledge of BHL or SLIH, he or she

rece ived an a d d i t i o n a l hour of training fron the

experimenter and retook the quiz . In a l l , 6 s u b j e c t s

required r e t r a i n i n g . A second low score on the quiz

re su l t ed in the s u b j e c t ' s termination from the experiment.

One student was dropped from the study due to t h i s

94

c r i t e r i o n . Assuming the s u b j e c t s u c c e s s f u l l y completed the

quiz , she made f i v e s e t s of d e c i s i o n s over the next two week

per iod , each se t separated by approximately two days, A

subjec t was permitted to take more than two days to nake a

d e c i s i o n I f necessary . Likewise , he could hand In d e c i s i o n s

a f t er one day If t h i s was more convenient. In a few

i n s t a n c e s , conputer problens occurred and the study was

extended for a day to account for t h i s . After conple t ing

each d e c i s i o n s h e e t , the student was asked to e s t l n a t e the

t o t a l anount of t i n e spent on decision-making. "Time spent

on the dec i s ion process" was defined as a l l t i n e devoted to

thinking and working on the problen. Including computer

t ime, but not only computer t ime. Across the e n t i r e length

of the s tudy , the subject had access to the I n t e r a c t i v e

computer system and was free to use or not use I t as he

chose . All queries made by the subject were s tored for

l a t e r a n a l y s i s , although the subject was not aware of t h i s

procedure. Following the th ird dec i s ion and the l a s t

d e c i s i o n , an Bxperinental Questionnaire (expectancy

measures) was adn ln l s t ered . At the end of the study, the

s u b j e c t completed a Post Experiment Questionnaire (Appendix

H) and was given a short debrief ing on the purpose of the

p r o j e c t .

95

4 . 3 The Task Bn?ironnent

The t a s k env l ronnen t for t he s tudy was a a u l t i - p e r l o d

nanagenent d e c i s i o n game. The Business Hanagenent

Labora tory (BHL) (Jensen & C h e r r l n g t o n , 1977) d e p i c t s an

o l i g o p o l i s t i c I ndus t ry i n which s t a i n l e s s s t e e l f l a t w a r e i s

manufactured and s o l d t o r e t a i l e r s (see Appendix I ) . The

d e c i s i o n s e t t i n g i s designed In such a way t h a t I t cannot be

comple te ly modeled and op t ima l d e c i s i o n s t he r e fo r e cannot be

gene ra t ed by using an a l g o r i t h m . In i t s most complete form,

the game s i m u l a t e s an i n d u s t r y na rke t ing two produc t s In two

market a r e a s . Up t o e i g h t f i r m s can compete In any one

I n d u s t r y , and nanagers wi thin t h e s e " f i rms" can make up to

50 d i f f e r e n t d e c i s i o n s per q u a r t e r .

Because t h i s s tudy was I n t e r e s t e d In the behavior of

i n d i v i d u a l s , r a t h e r than g r o u p s , each s u b j e c t was p laced in

a s e p a r a t e " i n d u s t r y , " independent of a l l o the r

p a r t i c i p a n t s . In o rde r to be sure t h a t a l l s u b j e c t s were

exposed to s i m i l a r exper imenta l envi ronments , each

" i n d u s t r y " c o n t a i n e d , in a d d i t i o n t o the p a r t i c u l a r

s u b j e c t ' s f i r m , two "dummy" firms whose s t r a t e g i e s and

d e c i s i o n p o l i c i e s were predetermined by the r e s e a r c h e r and

were held c o n s t a n t a c r o s s a l l s u b j e c t s . One dummy firm

adopted an a g g r e s s i v e , expans ionary s t r a t e g y based on

improving n e t p r o f i t through producing a high volume,

c h a r g i n g a low produc t p r i c e , and keeping o p e r a t i n g expenses

a s low as p o s s i b l e . The second dummy firm adopted a more

96

conservative, high price-low volume strategy, with less

emphasis on reducing production expenses and greater

enphasis on product pronotion. Algorlthns were developed

for deternining the decisions of the dunny firms. The

algorlthns were established so that these firns would react,

within reasonable bounds, to the decision behavior of the

subject. Although this approach resulted In less than

Identical envlronnents across subjects, a responsive

Industry represented a nore realistic decision-setting and

also discouraged collusion anong game participants. Sample

dummy team decisions and BML output are shown In Appendix K.

In addition to fornulatlon of dummy team strategies and

decision algorithms, development of the task environment for

the study also required modification of the BHL game so that

It offered an adequate level of simplicity for a single

player making decisions within limited time periods. The

major constraints placed on the game included the following:

1. Each firm produced one product which it could sell in one market area.

2. Subjects were not permitted to buy additional production space or equipment, or to expand capacity by adding a second work shift. No capacity expansion decision had to be made. A selected production level was automatically met through overtime and subcontracting.

3. Decisions regarding product research and development, quality control, number of salesrepresentatives, and salary paid to salesreps were held constant across all quarters in the game. The subject could not change these predetermined values.

97

4. Organizational environmental factors, such as the economic Index, the stock narket Index, the prime rate, and the bill rate, were held stable throughout the game.

5. Players made no financial decisions. Funding occurred automatically through "special loans," at a fixed Interest rate,

6. No short term Investment was made available, no dividends could be declared, no stocks or bonds could be Issued, and firms were not permitted to factor their accounts receivable.

For, each quarter in the gane, each "firm" completed the 7

decision sheet shown In Appendix B. These decisions were ^

selected because they were fundamental to operation of a BML

firn and because they represented a combination of

unstructured, semistructured, and structured decisions for

the "manager. "

Modification of the game and development of dummy-firm

algorithms was achieved through: (1) consul ta t ion with the

author of the BHL game and other experts on business gaming;

(2) e x t e n s i v e t r i a l and error work on the part of the

experimenter; and (3) t e s t i n g s impl i f i ed vers ions of the

game in two p i l o t s t u d i e s , one c o n s i s t i n g of 5 graduate

s tudent s and the other containing 7 undergraduate s t u d e n t s .

As In s imi lar gaming experiments (Benbasat & Dexter, 1979;

Benbasat 5 Dexter, 1980; Benbasat 6 Schroeder, 1977;

Schroeder S Benbasat, 1975), the subjec t was given th e

o b j e c t i v e of maximizing net prof i t of h i s firm over the

duration of the game. In t h i s case , however, net p r o f i t was

measured cumulat ive ly , in terms of re ta ined earnings . To

98

a v o i d t h e e f f e c t s of end-of-game s t r a t e g i e s , a s u b j e c t ' s

l a s t d e c i s i o n s e t was run for two c o n s e c u t i v e q u a r t e r s of

the game ( 1 s t , 2nd q u a r t e r s of 198 2 ) .

Accompanying t h e BHL s i m u l a t i o n I s an I n t e r a c t i v e

computer program, e n t i t l e d , "The System Laboratory f o r

In format ion Hanagement", or SLIH (Courtney & Jensen , 1 9 8 0 ) ,

For an e x t e n s i v e d e s c r i p t i o n of SLIH, s e e Appendix J , In a

very g e n e r a l s e n s e , SLIH a c t s a s a "computerized c o n s u l t a n t "

t o the BHL p a r t i c i p a n t , prov id ing h in with in format ion and

d e c i s i o n models which may be u s e f u l In d e c i s i o n - m a k i n g .

SLIH i s an e x t e r n a l re source a v a i l a b l e t o the d e c i s i o n

maker, but one which i s not a b s o l u t e l y necessary for the

d e c i s i o n p r o c e s s t o o c c u r . In the c o n t e x t of t h i s s t u d y ,

SLIH i s c o n s i d e r e d t o be a d e c i s i o n support system because

I t meets d e s c r i p t i v e c r i t e r i a of a DSS (Sprague, 1980;

Wagner, 1 9 8 1 ) :

1. I t s u p p o r t s , but does not r e p l a c e , the d e c i s i o n - m a k e r .

2. I t c o n t a i n s a data b a s e , modeling c a p a b i l i t y , and a c o n v e r s a t i o n a l user i n t e r f a c e ,

3 . I t s u p p o r t s s e m i s t r u c t u r e d as w e l l as s t r u c t u r e d d e c l s l o n-ma kl ng.

4 . The sys tem i s f l e x i b l e , easy to u s e , and has a s h o r t r e s p o n s e t i m e .

SLIH was adopted i n i t s complete o r i g i n a l form for t h i s

s t u d y . The only r e s t r i c t i o n p laced on use of t h e system was

t h a t s t u d e n t s were not t r a i n e d to d e v e l o p t h e i r own "BATCH"

99

f i l e s for s tor ing SLIH commands. Resul t s of p i l o t s t u d i e s

i n d i c a t e d that i t was too d i f f i c u l t t o teach a subject the

necessary s k i l l s for t h i s aspect of SLIH usage within the

U n i t e d t i n e span of the research p r o j e c t . Therefore, the

researcher developed a pre-packaged BATCH f i l e for the

s u b j e c t s to use . When executed, t h i s f i l e generated labor ,

m a t e r i a l s , maintenance and cos t ing information for a given

l e v e l of production. Because the student was unable to

develop BATCH f i l e s on h i s own, the nodeling c a p a b i l i t y of

SLIH was reduced for the study. In A l t e r ' s (1977b) taxonomy

of DSS, the vers ion of SLIH used was more data-or iented than

model -or iented .

4.4 Reward Conditions

Organizations reward good decision making on the basis

of performance evaluation procedures. These rewards usually

consist of pay or promotion incentives. This study was

designed to nodel the formal reward system of an

organization. Subjects received monetary and grade

compensation based on their performance (retained earnings)

in the game. The practice of using cash prizes as

incentives Is not uncommon In behavioral studies of DSS

(e.g., Benbasat 5 Dexter, 1980: Benbasat et al. , 1980;

Benbasat & Schroeder, 1977). The reward scheme used in this

experiment, however, was more substantial than that used in

other experiments where cash prizes have been as low as

$1.00.

100

s t u d e n t s , l i k e managers, may a l s o experience Interna l

reinforcement for good job performance. In the case of t h i s

experimental task, i n t r i n s i c outcones nay include such

t h i n g s as: a f e e l i n g of acconpl i shnent , learning a new

s k i l l , or s a t i s f y i n g a need to neet a cha l lenge . In t e s t i n g

the expectancy aode l , i t I s inportant that at l e a s t one

l i s t e d reward be valued by the subject If a

perfornance—^outcome contingency Is t o be perceived. To be

t h e o r e t i c a l l y pure. In other words, the study should have

provided valued rewards on an indiv idual b a s i s . Besides

being p r a c t i c a l l y d i f f i c u l t , there i s evidence that such a

procedure would not neanlngful ly Improve the p r e d i c t i v e

power of the model for group data (Hi tche l l , 1974).

Therefore, i n t r i n s i c outcomes i d e n t i f i e d by undergraduate

p i l o t s u b j e c t s were used in the experimental quest ionnaire

(see Appendix G, part C) .

4 .5 Ogera t iona l l za t lon of Variables

4 . 5 . 1 Independent Variables

Expectancy neasures were taken af ter the prac t i ce

per iod , a f t e r the third d e c i s i o n , and af ter the l a s t

d e c i s i o n . The Bxperinental Questionnaire (Appendix G) was

developed according to g u i d e l i n e s presented by Hollenback

(1979) for measurement of expectancy theory c o n s t r u c t s . The

approach o f f e r s the fo l lowing methodological advantages:

101

1. Two l e v e l s of e f f o r t and two l e v e l s of performance are used to a s s e s s expectancies and I n s t r u n e n t a l l t l e s .

2. Valence I s deternlned by neasurlng and conparlng the a t t r a c t i o n of ge t t ing an outcone with the a t t r a c t i o n of not ge t t ing that sane outcone.

3 . The expectancy and I n s t r u a e n t a l i t y aeasures have abso lute zero po in t s .

Part A of the quest ionnaire a s s e s s e s effort—^perforaance

e x p e c t a n c i e s . Part B neasures perfornance—^outcoae

expectanc ies (or "Ins trunenta l l t l e s" ) , and Part C obta ins

valence r a t i n g s . In answering the quest ionnaire the

respondent i s providing the fol lowing data:

1. A aa tr lx E of effort—^performance expectanc ies corresponding to the perceived a s s o c i a t i o n between two l e v e l s of e f f o r t with two l e v e l s of performance. For example.

E = Ose H r , 1 . 9 1

L [.5 .5J L H

Performance

2, A partitioned matrix I, with each partition containing the Instrumentalities that describe the perceived association between two levels of performance and two levels of a given outcome. For example.

L I = Performance

H

r.3 . 7 1 0 LOT

[.8 ,2 11.0 oj H L H L

Approval Cash

3. A partioned vec tor V In which each p a r t i t i o n conta ins two l e v e l s of a t t r a c t i o n of a given outcome. For example.

102

Approval H L

V = Cash H

L

6 2

7 1

By applying matrix multiplication, the approach yields two

predictor scores for the respondent, one corresponding to

high effort (or high DSS use) and the other corresponding to

low effort (or low DSS use). These scores refer to the

predicted "force," or notivation, of the subject to use

SLIH. Using the above exanple.

( B • I • V ) = F Use H

L [::]

4.5.2 Dependent Variables

The nodel of user behavior examined la this study

attempts to predict amount of voluntary use of a DSS.

System use was measured in three ways: (1) total number of

queries entered by the subject, (2) total number of "good"

queries (i.e., total queries minus typographical errors),

and (3) the number of data values requested from the SLIH

data base. Because expectancy theory intends to predict

work effort, an additional dependent measure was captured:

total time devoted to the decision task. This was a

self-report measure. After each decision period, the

subject was asked to estimate the total time spent on the

decision process.

103

Dependent measures were recorded at three po int s in

t ime: fo l lowing the 2nd p r a c t i c e d e c i s i o n ; a f t er the th ird

"real" d e c i s i o n ; and a f t e r the l a s t "real" d e c i s i o n .

4 . 5 . 3 Persona l i ty Variables

4 . 5 . 3 . 1 Locus of Control

The Abbreviated Heasure of Internal-Exteraj, Locus of

Control as developed by Valecha and Ostron (1974) was used

in t h i s study to measure locus of contro l (see Appendix D) •

The s c a l e c o n s i s t s of 11 I t ens s e l ec t ed fron Rotter ' s (1966)

Interna l -Externa l Locus of Control instrument. The s c a l e

was developed by e l lmat lng from Rot ter ' s s c a l e a l l items

which (1) were f i l l e r i tems, and (2) had content themes

r e l a t e d to s choo l , p o l i t i c s , f r i endsh ip , and other nonwork

t o p i c s . The s c a l e r e t a i n s the forced-choice format used by

Rotter in which the respondent must i n d i c a t e which of two

s ta t ements , one Internal and one e x t e r n a l . I s c l o s e s t to her

a t t i t u d e , Valecha (1972) presents normative data on the

s c a l e based on a nat ional probabi l i ty sample of 4330 males

between the ages of 16 and 26. Data on the construct

v a l i d i t y of the Instrument i s c o n s i s t e n t with R o t t e r ' s

f ind ing that high Interna l s engage In more instrumental

g o a l - d i r e c t e d a c t i v i t y than low i n t e r n a l s , who e x h i b i t

emotional non goa l -d i rec ted responses . The i n t e r n a l

c o n s i s t e n c y c o e f f i c i e n t s ( c o e f f i c i e n t alpha) for the s c a l e

104

for white males, black males, and the t o t a l normative sample

are . 6 6 , . 4 9 , and .62 r e s p e c t i v e l y . S p l i t - h a l f

r e l i a b i l i t i e s are . 8 0 , . 6 6 , and . 7 7 , which compare favorably

to r e l i a b i l i t y of . 7 0 genera l ly reported for the 29-item

Rotter I-E s c a l e .

Valecha and Ostron report that the factor s tructure of

the s c a l e i s c o n s i s t e n t with R o t t e r ' s Instrument. Pr inc ipa l

components a n a l y s i s with varimax ro ta t ion y ie lded two

f a c t o r s accounting for a t o t a l of 34.5% of the var iance .

Factor I , which they l abe l an " ideo log i ca l control" f a c t o r ,

measures "general I d e o l o g i c a l b e l i e f s about the

c o n t i n g e n c i e s of reinforcement as they are bel ieved to be

e f f e c t e d by one 's own e f f o r t s or by environmental fac tors"

(p. 371) . Factor II re fers to "personal c o n t r o l , " or the

in f luence of the par t i cu lar Individual over events In h i s

l i f e . With the except ion of one item (G), the two f a c t o r s

are item-Independent. Comparisons of r e l i a b i l i t y , v a l i d i t y ,

and factor s t ruc ture of the Valecha s c a l e with the Rotter

s c a l e led Valecha and Ostrom (1974) to conclude the

f o l l o w i n g : "The present abbreviated vers ion of the Rotter

s c a l e can be regarded as a s a t i s f a c t o r y replacement of the

o r i g i n a l s c a l e " (p. 375) .

The Valecha s c a l e was chosen for use in t h i s study over

the more frequent ly used Rotter I-E s c a l e because of i t s

considerably shorter l ength . Rot ter ' s s c a l e has been

105

criticized for being unnecessarily long, and. In addition,

for being affected by social desirability and yielding

nultiple dimensions when factor analyzed (Robinson & Shaver,

1978), Several alternative scales have been developed to

measure the I-E construct. None has adequately overcome the

three criticisms of Rotter*s scale. Valecha, however, has

at least removed one major problem with the instrument.

Despite criticisms of the Rotter scale. It has been and

continues to be the most frequently used measure of locus of

control In psychological and organizational behavior

research. The I-E scale has been used by nearly all

expectancy theory and MIS researchers who have examined the

locus of control variable (e.g,, Batlis, 1978; Batlis &

Waters, 1973; Broedling, 1975; Henson, 1976; Zmud, 1979c),

Consequently, In choosing an alternative scale, this

researcher felt compelled to use an instrument similar to

Rotter's so that results may be meaningfully compared with

other studies,

4.5.3.2 Cognitive Style

The group version of the Enbedded Figures Test (GEFT)

as formulated by Oltman, Raskin, and Wltkln (cf., wltkln,

Oltman, Raskin, 5 Karp, 1971) was used to measure cognitive

style In this experiment. The GEFT has been used in nearly

all MIS studies of the analytic/ heuristic variable. The

scale requires 20 minutes to complete and consists of 18

106

complex stimulus figures, each of which contains a simpler

form that the subject must discern within a time limit in

order to respond correctly. Linlted norms are available on

male and female college students. Parallel forms

reliability was estimated at .82 for men (N=80) and women

(N=97). These reliability figures are consistent with those

computed on the EFT. Wltkln et al. (1971) observed that

there are several approaches to validation of the GEFT. One

Is to compare performance on the EFT, a well-recognized

measure of the ability to perceive an object independent of

context, with performance on the GEFT. This was

accomplished for a group of 73 male undergraduates and 68

female undergraduates. The GEFT correlated -.82 with the

EFT for males and -,63 for females. The negative

correlation reflects the fact that scores on the two tests

are computed in reverse manner. This is also true of the

GEFT and the Portable Hod and Frames Test (PRFT) . The GEFT

correlated -.39 and -.34, males and females respectively,

with performance on the PRFT, The GEFT has also been

related to the ABC scale, a measure of psychological

differentiation expressed In terms of the degree to which

body concept is articulated. Correlations of .71 and .55

with the ABC scale were found for male and female college

students.

Reviewers of the GEFT have found the scale to have

adequate reliability and validity (Goodstein, 1978; Hall,

107

1978) , and have c h a r a c t e r i z e d the Ins t rumen t as be ing a

"powerful approach t o tapping one of t h e c o g n i t i v e s t y l e

d imens ions of human p e r s o n a l i t y " (Goodste in , 1978, p . 8 3 8 ) .

In r e s e a r c h s e t t i n g s s u b j e c t s may be c l a s s i f i e d as low or

high a n a l y t i c s based on compar isons with the median s c o r e In

t h e s a n p l e . Wltkln e t a l . (1971) r epo r t ed 13 t o be t h e

median s c o r e , while s e v e r a l HIS s t u d i e s have used 14 as t he

cu to f f value (Benbasat & Dexter , 1980; Doktor & Hamil ton ,

1973; Lusk, 1973).

4.6 Confounding Variables

The des ign used in t h i s s tudy was c o n s i s t e n t with

expec tancy theo ry and HIS s t u d i e s on which t h i s r e s e a r c h I s

based . Sanple s e l e c t i o n and l a b o r a t o r y procedures a t t empted

t o l i m i t t h e I n f l u e n c e of ex t r aneous v a r i a t i o n on the

independent and dependent measures of i n t e r e s t . In a d d i t i o n

t o con tamina t ion inposed by varying background

c h a r a c t e r i s t i c s of the s u b j e c t s ( e . g . , expe r i ence and

e d u c a t i o n ) , d i f f e r e n c e s In system use might have occur red i f

s u b j e c t s f e l t " f o r c e d " t o use the sys tem, i f they were

unable to o b t a i n a c c e s s t o t e r m i n a l s , or If s u b j e c t s

p e r c e i v e d t h a t t h e i r dec i s ion performance was heav i l y

In f luenced by chance- Following the l a s t d e c i s i o n s e t , in a

Post Exper imenta l Q u e s t i o n n a i r e (Appendix H), s u b j e c t s were

asked t o i n d i c a t e : (1) whether or not they f e l t fo rced t o

use t h e i n t e r a c t i v e system In the expe r imen ta l s i t u a t i o n .

108

(2) the extent to which they felt their performance in the

game was a function of chance rather than decision quality,

and (3) how frequently they were unable to access a terminal

when they wanted to use one. Each of these questions will

be examined for its potential confounding Influence on the

dependent variable neasures.

^ •7 Advantages of the Design

Through careful experinental design, this study

attenpted to avoid methodological problems commonly found In

expectancy theory research. Both within and across-subjects

data were collected. Two levels (rather than one) of

expectancy. Instrumentality, and valence were measured for

each subject. The expectancy and instrumentality constructs

were assessed in probabilistic terms, and both were measured

on scales with a rational zero point to allow the

nultiplicatlve assumptions of the model to be tested.

Consistent with Hltchell«s (1974) suggestion, valence was

measured along an "attractiveness" dimension, rather than In

terns of "inportance," and both positive and negatively

valent rewards were included in the outcone list on the

Experinental Questionnaire. Heasures of effort and

performance were objective, rather than based on more

unreliable self-report information. Individual difference

characteristics were examined for their potential role in

the expectancy model. Finally, expectancy neasures were

109

taken prior to dependent behavioral measures, rather than

concurrently as is often done in field research.

The design used In this study also represents an

Improvement over past HIS gaming studies. To date, most

such studies have presented subjects with fairly simple,

structured problems to solve In rather limited periods of

time. The well-known Hlnnesota Experlnents, for exanple,

focused on operational-level decisions, and took place over

a period of two to three days. Typically, the subject's

task In these studies has been to "find the price/quantity

conblnatlon which will naxinize profit" (e.g., Benbasat &

Hasulis, 1980). In the current study, subjects were

Involved for a three-week period and were required to make

unstructured as well as structured decisions. Horeover, the

experimental game had no single winning strategy. Finally,

unlike other studies, this study attempted to control for

background characteristics known to Influence Infornation

system use and decision performance (e.g., age, education,

experience).

4.3 Sunnary

To test the model of user behavior presented In Chapter

3, a laboratory study using a business simulation "game" and

Its accompanying DSS was conducted. Eighty-eight

senior-level undergraduate business students participated in

the study. All were exposed to the same treatment

110

conditions. The experimental procedure required each

subject to play the role of a manager in a competitive

Industry consisting of three firms: the student's firm and

two phantom firms. Each phantom firm had pre-established

decision policies which were unknown to the student.

Decision algorlthns were developed for the phanton firms so

that they reacted, within reasonable bounds, to the decision

behavior of the student-run firn. Over a three-week period,

the subject was required to make two "practice" decisions

and five "real" decisions. Subjects were trained In the use

of a DSS which accompanied the business game, but they were

not required to use the system beyond the practice decision

period. The subjects received monetary and grade-based

rewards which were contingent upon their level of

performance in the game. Heasures of expectancies and DSS

use were taken at three points in time: after the second

practice decision, after the third real decision, and after

the last real decision.

Chapter f

RBSOLTS

The hypothesized model (Figure 9) states that the

Individual's expectancies at one point in time influence his

behavior at a later point In time. Consequently, In testing

the three hypotheses of this study. Independent variables

measured at time periods one and two were examined for their

relationship to dependent neasures taken at tines two and

three respectively. Heans and standard deviations for

independent and dependent measures taken at the relevant

time periods are shown In Table 3, In addition, descriptive

statistics for the two personality variables, locus of

control and cognitive style, are given In this table.

Although the results of any one administration of the

Experimental Questionnaire yielded a 2 X 2 matrix of

ef f ort—>perf ormance expectancies, a 2 X 1 vector of

performance—^outcome instrumentalities, and high and low

motivation scores, only the following information is

relevant to the hypothesis testing for this study: (1) the

predictor, or force, score associated with motivation to use

SLIH "extensively," (2) the difference, or range, between

the motivation to use SLIH "extensively" and the motivation

to not use SLIM "much at all," (3) the expectancy value

111

112

associated with high use of SLIM and good decision

performance, and (4) the instrumentality score corresponding

to the perceived relationship between performing well and

obtaining a set of outcones.

r " • • • —

1 Heans and S t a n d a r d

t.,.,.-r

i V a r i a b l e 1 Y 1 I n d e p e n d e n t

1 F o r c e t o u s e SLIM

\ F o r c e t o n o t u s e SLIH

[ Range ( h i g h - l o w ) s c o r e

1 High Use—>Hlgh P e r f . 1 e x p e c t a n c y J High P e r f . — > H i g h 1 outcome e x p e c t a n c y

1 Dependent

1 T o t a l # q u e r i e s

1 T o t a l #good q u e r i e s

1 #Data v a l u e s r e t r i e v e d

1 Amount of t ime s p e n t 1 on d e c i s i o n making

1 P e r s o n a l i t y V a r i a b l e s

J Locus of C o n t r o l 1 C o g n i t i v e S t y l e

TABLE 3

D e v i a t i o n s f o r V a r i a b l e s

Time P e r i o d

1 2 1 2 1 2 1 2 1 2

2 3 2 3

[ 2 3 2 3

N

80 82 80 82 80 82 83 84 80 82

88 88 38 88 88 88 88 88

88 88

t h e Reseac

Hean

2 8 . 9 3 28 .71 2 6 . 8 5 2 6 . 7 7 2-076 1.940 7 .471 7 . 1 0 2 2 .369 2 . 3 8 5

4 2 . 5 0 2 2 . 8 4 3 4 . 5 3 19 .48

173 . 12 114.80 143 .60

7 6 . 2 5

2 3 . 3 7 12 .30

ch

SD

3 . 7 6 3 - 3 6 3 . 4 9 3 - 7 9 2 - 8 0 3 - 2 6 1 , 0 9 1 , 5 2 4 . 4 3 4 . 5 9

3 1 . 9 9 2 4 , 4 4 2 6 . 7 1 2 0 , 6 5

1 3 4 . 6 0 1 3 9 . 5 0

7 1 . 9 8 4 2 . 0 9

5 . 4 2 4 . 2 3

~ i

1 1

1

J L —

^ ^

113

Before testing the major hypotheses of the study,

preliminary analyses of variance were performed to assure

equality of experimental treatment conditions across the six

groups of subjects. Results of these analyses are shown In

Table 4. Separate ANOVAs for each of three time periods for

the effects of subject group on force scores suggest that

subjects' motivation to use SLIH In their declsioa-naking

did not vary significantly over the 5-nonth period of data

collection. The Inplication Is that the experlnenter did

not create systematic differences in motivation levels

regarding the SLIH systen across subject groups.

As an additional check on the experinental procedures,

an Investigation was made into the posslbllty that subjects

did not have adequate access to the SLIH system. As

Indicated In Table 5, the sample as a whole reported that

computer terminals were sufficiently available to them.

Regression analyses were conducted to test for possible

contamination in the use measures due to subjects not having

adequate access to the DSS. As shown in Table 6, when the

variance associated with responses to question #3 on the

Post-Experiment questionnaire Is partlalled out, there Is no

impact on the model of interest. In short, inability to

access the computer was not a confounding variable In the

study.

114

I

TABLE 4

Ana lyses of Variance f o r B f f e c t s of Subject Group on H o t i v a t i o n a l Force Scores t o Use SLIH

Dependent V a r i a b l e : Source df

Force t o Use SLIH (Tina 1) HS F Pr.>F

Hodel Error Total

5 74 79

786503919 1457288530

. 5 4 748

Dependent Variable: Source df

Hodel Error Tota l

5 76 81

F o r c e t o Use SLIH (Time 2) HS F Pr.>F

476500442 8687599433

. 8 3 532

Dependent Variable; Source df

Force t o Use SLIH (Time 3) HS F Pr.>F

Model Error Total

5 81 86

84027834 1774931666

.47 797

I

TABLE 5

Descriptive Statistics for Post-Experiment Question #3

H Ques. #3: How f r e q u e n t l y during t h i s experiment

did you go t o the computer room to use SLIH only to f ind t h e r e were no t e r m i n a l s a v a i l a b l e ? (1 = not a t a l l , 7=extremely f r e q u e n t l y )

N Mean SD R ange

87 1-72 1- 29 1 - 7

115

TABLE 6

Tests for Contanlnatlon of Systen Use Heasures Due to Insufficient Access to SLIH

t Values and Associated Probabilities for Beta Coefficients

Use Heasures: Tine 2 (N-80)

Queries (p)

Force(Tl) 1.60 (. 115) Ques. 3 .56(.579)

#Good Queries (p)

2, 12(-037) .59(.556)

#Data Values (p)

1.31 (.195) .03 (.973)

Use Heasures: Tine 3 (N=82)

Queries (p)

Force (T2) 1.96 (.054) Ques. 3 -.69 (.493)

IGood Queries (p)

1.51(.136) -.62(.534)

#Data Values (p)

1.54 (.127) -1.50(. 137)

I

Results for each of the research hypotheses are now

presented. Hypothesis #1 was tes ted using both within and

a c r o s s - s u b j e c t s data , while hypotheses #2 and #3 were

exanined with a c r o s s - s u b j e c t s data on ly . The statistical

procedures applied la testing the three hypotheses are

consistent with existing research which examines the

validity of expectancy-based models of behavior.

^ \

116

5. 1 Hypothesis M

An individual's use of a DSS can be predicted as

follows:

USE=(Use—^Perfornance) •5( (Perfornance—>Outcones) (Valence)).

Motivation to use the DSS Is a function of the subjective

probability that use will lead to a given level of

decislon-naklng, tines the sun of the subjective

probabilities associated with a given quality of

decislon-naklng and obtaining a set of outcomes, each of

which has an attractiveness rating associated with It.

Hotivation to use the DSS Is related positively to actual

system use,

5.1.1 Blthin-Subjects Analyses

Comparisons were made for each subject between the

derived force scores for using SLIH extensively (high use)

and for using SLIM very little (low use). If a subject's

force score for high use of SLIH was greater than his force

score for low use of SLIH, it was predicted that the subject

would use the system "extensively" In decision-making. To

account for measurement error, a criterion of .25 standard

deviations of the range score was arbitrarily selected to

represent a meaningful difference between high and low force

scores. Predicted use was classified as "high" If the range

score for the subject (I.e., high force score minus low

force score) exceeded ^.25 standard deviations of the range

117

score for the total sample. Likewise, If the range score

for the subject was less than -.25 standard deviations of

the range score for the entire sanple, then It was predicted

that the subject would use SLIM very little. Each subject's

predicted behavior was then conpared to his actual use of

SLIH In the following period of decislon-naklng. Actual use

was classified as "high" If the number of queries (or data

values) made by the individual exceeded the median number of

queries (or data values) entered by the total sample of

subjects for that period. "Low" use was defined as making

fewer than the median number of queries (data values) made

by the total sample. Following Parker and Dyer (1976), the

accuracy of the expectancy model was assessed In terms of

the percentages of correct and Incorrect predictions of SLIH

use. These calculations are presented in Table 7. Chi

square tests are not meaningful for these classification

matrices since over 20% of the cells have counts below 5.

For almost all subjects, the motivation to use SLIH exceeded

the motivation to not use SLIH. In retrospect, the problem

Is understandable from the nature of the study. Subjects

were asked to compare their expectancy of success In the

decision task when using SLIH versus not using SLIH. When

given a choice between having a resource and not having that

resource, it is not suprislng that only a few subjects

indicated that they would be more successful without the

resource. The classification procedure Is also hampered by

118

the fact that some subjects are eliminated from the analyses

because their force scores for high and low use of SLIH are

not meaningfully different (I.e., their range scores vary

between -.25 and •.25 SDs of the nean range score for the

total sanple). If only the first row of the matrix is

examined, the predictive accuracy of the model for subjects

with "high" force scores can be tested. To do this we

conduct a simple z test of the null hypothesis that the

probability of actual use (for subjects with high predictor

scores) being high or low Is less than or equal to .50.

Results (Table 7) suggest that, although the model was more

accurate than Inaccurate In Its predictions, predictive

accuracy approached statistical significance only for number

of good queries made during time 3 of the study (z=1.63,

p=.05, one-tailed test). In each case the effect size (j)

was small (q<.15) and the power of the z test was Inadequate

for detection of the effect. For example, when comparing

high versus low users at time 2 with number of queries as

the measure of use, the power of the z test was only 38

(q=.085, n=53, p=.05) (Cohen, 1969). For the six z tests

performed, power ranged from 13 (use measure = #data values,

T3; q=.075, n=40, p-.05) to 40 (use measure = tgood queries,

T2; q=.077, n= 52, p=.05) (Cohen, 1969). For each

comparison made, a larger sample size would be required to

detect a significant effect. The implication is that

hypothesis #1 cannot be disconfirmed on the basis of these

within-subjects analyses.

119

TABLE 7

Accuracy of Wlthln-Subjects Choice t o Use SLIM

Conparison of Predicted and Actual DSS Use Levels for Three Measures of Systen Use ( c r i t i c a l z = 1.64)

Use Measure: iQuerles I

Actual Use (T2)

Predicted Use(Tl) High Low %Correct z Power

Extensive 31 22 58.5 1.23 38 Very Little 1 3 75.0 Total (N=57) 59.6

Actual Use (T3)

Predicted Use(T2) High Low %Correct z Power

Extensive 24 16 60.0 1.27 32 Very Little 1 0 0.0 Total (N = 44) 54.5

"f Use Measure: # Good Queries

Actual Use (T2)

Predicted Use(TI) High Low %Correct z Power

Extensive 30 22 57.7 1.12 40 Very Little 1 3 75.0 Total (N=56) 58.9

Actual Use (T3)

Predicted Use(T2) High Low XCorrect z Power

Extensive 24 14 63.2 1.63 29 Very Little 1 Total (N = 39)

ow

14 0

%Corr€Ct

6 3 . 2 0 . 0

6 1 . 5

y

120

— I

TABLE 7 continued

Use Heasure: # Data Values

Actual Use (T2)

Predicted Use(TI) High Low %Correct z Power High

28 2

Low

25 2

%Correct

5 2 . 8 5 0 . 0 5 2 . 6

Extensive 28 25 52.8 .406 15 Very Little Total (N=57)

Actual Use (T3)

Predicted Use(12) High Low %Correct z Power i gh

23 0

Low

17 1

%Correct

5 7 . 5 100 .0

5 8 . 5

Extensive 23 17 57.5 .949 13 Very Little Total (N^41)

I J

5.1.2 Across-Snb jects Analyses

As an across-subjects test of hypothesis #1, force

scores associated with high use of SLIH at times 1 and 2

were correlated with actual measures of use at times 2 and

3, In addition, the range (high-low) of force scores was

correlated with the dependent measures- Results are shown

In Table 8. Force scores taken after the practice period

related significantly to number of good queries made during

decisions 1, 2, and 3 of the study (T2) at a level

significantly above chance (r=.226, p<.02), and force scores

taken at time 2 correlated significantly with the number of

queries made during time 3 (r=.213, p<.03)- Using range

121

scores Improved the predictive power of the model. Both

total queries and number of good queries correlated

significantly with force scores at tine 1, and correlations

between force scores at tine 2 and behavior at tine 3 were

significant at the .05 level for all three measures of DSS

use.

K-

TABLE 8

C o r r e l a t i o n s Between P r e d i c t o r Scores and Use of SLIH

Two Time P e r i o d s f o r Two P r e d i c t o r Heasures and Three Measures of Use

P r e d i c t o r : High Force Score IGood

Force-Use (N) #Querles (p) Queries (p)

T1 - T2 (80) . 1 6 9 ( . 0 7 ) T2 - T3 (82) . 2 1 3 ( - 0 3 ) *

- 2 2 6 ( . 0 2 ) • . 1 6 5 ( . 0 7 )

P r e d i c t o r : Range (h igh- low force s c o r e s ) IGood

Force-Use (N) IQuer l e s (p) Queries (p)

T1 - T2 (80) . 2 0 1 ( . 0 4 ) • T2 - T3 (82) .111 (-02) *

.191 ( . 0 5 ) »

. 2 08 ( - 03 ) •

I Data Values (p)

. 1 5 0 ( . 0 9 ) , 159 ( . 0 8 )

I Data Values (p)

. 0 4 8 ( . 3 3 )

. 2 3 1 ( . 02 ) *

As the next s t e p i n the a c r o s s - s u b j e c t s a n a l y s i s , i t ems

1^ 2 , and 4 of the Post Experimental Q u e s t i o n n a i r e

(Appendix H) were examined f o r t h e i r p o t e n t i a l c o v a r y i n g

i n f l u e n c e on s u b j e c t s ' use of SLIM, The o b j e c t i v e here was

t o d e t e r n i n e If the v a r i a n c e a s s o c i a t e d with use of t h e DSS

0 ^ \

122

was a f u n c t i o n of v a r i a b l e s o t h e r than m o t i v a t i o n . The

r e s e a r c h e r nay not have been a b l e t o adequate ly c o n t r o l for

such problems a s : (1) the s u b j e c t f e e l i n g f o r c e d or

o b l i g a t e d t o use t h e s y s t e n , (2) the s u b j e c t p e r c e i v i n g t h a t

h i s performance in the s i m u l a t i o n was l a r g e l y a f u n c t i o n of

c h a n c e , or (3) t h e s u b j e c t ' s f a i l u r e to a c c e p t the

p r e s c r i b e d g o a l of maximizing r e t a i n e d e a r n i n g s f o r h i s

f i r m . I f t h e s e v a r i a b l e s had a s y s t e m a t i c Impact on amount

of SLIM u s e , then t h e i r t reatment a s c o v a r l a t e s In the data

a n a l y s e s would Improve the t e s t of h y p o t h e s i s I I .

Table 9 p r e s e n t s d e s c r i p t i v e s t a t i s t i c s for the

r e s p o n s e s to q u e s t i o n s 1, 2 , and 4 of the pos t experiment

s u r v e y . N o t i c e t h a t no s u b j e c t reported tha t he did not t r y

t o o b t a i n h igh r e t a i n e d e a r n i n g s for h i s firm when p l a y i n g

the BML game. Thus I t I s not necessary t o be concerned

about the c o v a r y i n g I n f l u e n c e of t h i s v a r i a b l e on DSS u s e .

I t was c r u c i a l t o the exper imenta l des ign t h a t s u b j e c t s

not f e e l coerced i n t o us ing the DSS. S u b j e c t s were

e x p l i c i t l y t o l d by the experimenter t h a t they had a c h o i c e

to use or n o t use SLIM, once the p r a c t i c e d e c i s i o n per iod

had ended. As t h e Information In Table 9 s u g g e s t s , most

s u b j e c t s did Indeed r e c o g n i z e t h i s f r e e c h o i c e . Regres s ion

r e s u l t s (Table 10) i n d i c a t e t h a t the q u e s t i o n 1 v a r i a b l e was

not a meaningful c o v a r l a t e In the p r e d i c t i o n of use of SLIM

with m o t i v a t i o n a l f o r c e s c o r e s ( t v a l u e s a s s o c i a t e d with

123

TABLE 9

Descriptive Statistics for Post-Bxperlmental Questions II, 12, and 14

I

Ques. II: To what extent did you feel that you had a free choice to use or not use the interactive system when making your decisions? (1=not at all, 7=to a great extent)

N Hean SD Range

87 6.01 1.16 3 - 7

Ques. 12: To what extent do you feel that your perfornance In the BHL gane was due to chance or randon factors, rather than to the quality of your decisions? (1=not at all due to chance, 7=entlrely due to chance)

N Hean SD Range

87 3,55 1.36 1 - 7

Ques, 14: Is there any reason that you did not try to obtain high retained earnings when participating In the gane?

"Yes" ' 0 "No" = 87

beta c o e f f i c i e n t s for question 1 were l e s s s i g n i f i c a n t than

those for f o r c e ) , except where "use" was neasured as nunber

of data va lues extracted fron the SLIH database (here t

va lues for quest ion 1 were nore s i g n i f i c a n t than those for

f o r c e ) . For a true t e s t of the model, subjects who did not

recognize t h a t use of SLIH was opt ional should be e l iminated

from the s tudy . Corre la t ions between predictor s c o r e s and

124

use for only those subjects who recognized their free choice

to use SLIH (l,e., those who responded with a value of 4 or

greater on question 1 of Appendix H) are presented In Table

11. When subjects who failed to recognize that use of SLIH

was optional are dropped fron the analysis, several of the

correlations Increased slightly, but the pattern of the

results renalned the sane. The dataset became more "pure"

from an experinental design perspective, but power was

reduced when the sample size was lowered.

TABLE 10

T e s t s for the Covarying I n f l u e n c e on Post-Experiment Quest ion 11 On Amount of System Use

t Values and Assoc ia ted P r o b a b i l i t i e s f o r Beta C o e f f i c i e n t s

h Use Heasures: Time 2 (N=80)

IGood IData Queries (p) Queries (p) Values (p)

Force(TI) 1.60(. 113) 25,14(.036) 1,55(.126) Ques. 1 -1-11 (.272) -1.08(.283) -2il9(.031)

Use Heasures: Time 3 (N=82)

IGood IData Queries (p) Queries (p) Values (p)

Force(T2) 1.92(.058) 1.46(.147) 1.44(.155) Ques. 1 -1.19(.237) -.79(.437) -1.87(.065)

125

TABLE 11

Correlations Between Predictor Scores and Use of SLIH for Subjects Who Recognized Ose of SLIH to be Optional

Predictor: High Force Score IGood IData

Force-Use (N) IQuerles (p) Queries (p) Values (p)

T1 -12 (74) .155(.09) .219(.03)* .139(.12) T2 - T3 (74) .212(.03)* .169(.08) .160(.09)

P r e d i c t o r : Range (h igh- low f o r c e s c o r e s ) IGood IData

Force-Use (N) I Q u e r l e s (p) Queries (p) Values (p)

T1 - T2 (74) . 2 4 4 ( . 0 2 ) * . 2 2 9 ( . 0 2 ) * . 0 4 6 ( - 3 5 ) T2 - T3 (74) . 2 2 7 ( . 0 2 ) * - 2 1 9 ( . 0 3 ) * - 2 4 0 ( , 0 2 ) *

With regard t o q u e s t i o n 12 of the s u r v e y , r e s u l t s

s u g g e s t t h a t , on the average , s u b j e c t s f e l t tha t t h e i r

performance was s l i g h t l y a f u n c t i o n of chanca r a t h e r than

j u s t t h e q u a l i t y of t h e i r d e c i s i o n s . R e c a l l t h a t s u b j e c t s

responded t o t h i s q u e s t i o n f o l l o w i n g feedback on t h e i r f i n a l

BHL d e c i s i o n . Consequent ly , s u b j e c t s who perforned poor ly

In t h e gane tended t o report that t h e i r perfornance was

l a r g e l y a f u n c t i o n of chance , w h i l e t h o s e who perforned w e l l

d id not (r between perfornance and q u e s t i o n 2=. 207, p = . 0 5 ) .

The s u g g e s t i o n here I s tha t r e s p o n s e s t o t h i s q u e s t i o n nay

have been at l e a s t p a r t i a l l y a f u n c t i o n of f i n a l perfornance

r a t h e r than o f f a i l u r e i n the exper imenta l des ign t o s e t up

a s i t u a t i o n where s u b j e c t s could l e g i t i a a t e l y p e r c e i v e a

126

connection between work effort and quality of

decislon-aaklng. Applying this logic. It Is not aeanlngful

to treat question 2 as a covarlate In later data analyses.

In any event, the fact that question 2 responses related

significantly to motivational force scores (r=-.238, p=. 03)

implies that this Item cannot be entered as a covarlate In

tests of the hypotheses of this study.

As a final test of the expectancy model, predictor

scores based on subjects* perceptions regarding the

relationship between "working hard" and performing well In

the BHL game, and between "spending tine" on the decision

process and perfornlng well, were correlated with total

amount of time spent on the decision task. Results are

shown In Table 12. The expectancy model was suprlslngly

unsuccessful In predicting the amount of time spent on the

decision process by subjects. One would Intuitively expect

amount of time spent on a problem to be a better measure of

work "effort" than amount of DSS use. Horeover, the

inability of the model to predict the former dependent

variable contradicts existing expectancy theory studies In

which amount of work time has been meaningfully related to

motivational force scores (e.g, Terborg, 1977), A possible

explanation for discrepancies in these findings may be

related to the self-report nature of the "time spent"

measure. Standard deviations for this dependent variable

were quite large (see Table 3) , suggesting dramatic

^r\

127

variability In the way In which subjects estimated the

amount of tine they spent on each BHL decision.

TABLE 12

P r e d i c t i o n of Tine Spent Using Expectancy-Based P r e d i c t o r S c o r e s f o r Two P r e d i c t o r Measures a t Two

Time P e r i o d s

P r e d i c t o r High Force Score "Working Hard"

Force-Use (N) (Time Spent)

T1 - T2 T2 - T3

(80) (82)

103 (,18) 088(.21)

"Spending Time" (Time Spent)

. 125(. 13)

.113 (.15)

P r e d i c t o r : Range (high- low f o r c e s c o r e s ) "Working Hard" "Spending Time"

Force-Use (N) (Time Spent) (Time Spent)

T1 - T2 T2 - T3

(80) (82)

. 1 1 3 ( . 1 6 ) - . 1 4 7 ( . 9 1 )

. 177 ( .06 ) - . 0 2 8 ( . 60 )

In conclusion, the across-subjects analyses suggest

weak to moderate support for the first research hypothesis.

Correlations between predicted and actual DSS use ranged

from .05 to .23. Where range scores were the predictors,

the correlations between forces scores and behavioral

measures were significant at the .05 level for nearly every

use measure for both time periods. No experimental

artifacts could be identified as meaningful covarlates in

the motivation—>use relationship.

128

5 . 2 Hypothesis #2

Persons high i n i n t e r n a l locus of contro l w i l l have

grea ter Use—>Performance and Performance—^Outcome

expec tanc ie s than those low in Internal locus of c o n t r o l .

Before conducting t e s t s of t h i s hypothes is , the

psychometric proper t i e s of the Instrument used to measure

locus of contro l were b r i e f l y examined. This procedure

seemed p a r t i c u l a r l y important s i n c e the Abbreviated Heasure

of I-E Locus of Control was standardized on an a l l - m a l e

sample and has not been a well-used s c a l e In research

I n v e s t i g a t i n g the locus of contro l var iab le . The observed

mean and standard dev iat ion for the s c a l e in t h i s sample

(M=23.37, SD=5.42) compared favorably to that i n the

normative group (H=22.66, SD=5.15)- The median value in the

current sample was 23; the authors of the s c a l e f a i l e d to

report a median score for the normative group. S p l i t - h a l f

r e l i a b i l i t y (r=.72) was s l i g h t l y be t ter than that reported

by the des igners of the s c a l e ( r = . 6 9 ) . The Interna l

c o n s i s t e n c y of the s c a l e appeared to be adequate

( c o e f f i c i e n t alpha = . 7 5 ) , exceeding that found in the

normative sample (a lpha=.69) . A princ ipal components

a n a l y s i s of the 11-ltem instrument with varimax ro ta t ion

y i e lded one dominant factor (eigenvalue=3.34) and two minor

f a c t o r s (e lgenvalues=1 .45 , 1.15 r e s p e c t i v e l y ) . Variance

accounted for by the f a c t o r s was 30.4% for Factor I , 13.2%

for Factor I I , and 10.5% for Factor I I I . For purposes cf

129

this study, the IE scale will be treated as unldlnenslonal.

This decision Is consistent with application of the scree

test criterion for selection of factors following principal

conponents analysis. The variance explained by this factor

far exceeds that found by the developers of the Instrunent

(Factor I explained varlance=20.5%). Horeover, when treated

as unldlnenslonal, all but two Items in the scale load

heavily (factor loadings > .33) on the one factor-

Hypothesis 12 states that IE locus of control

influences the Use—>Performance and Performance—^Outcome

relationships, with Internals having more positive

expectancies in each case. Subjects with scores above the

sample median (Hd. = 23) were classified as "externals," and

those with scores below the median were considered

"internals." One-tailed t tests for independent groups were

used to test the hypothesized relationships (see Table 13),

In all cases assumptions of equality of group variances were

met. At time 1, nean values for Use—^Performance

expectancies were greater for subjects high in internal

locus of control (internals) than for those low in internal

locus of control (externals); however, at time 2 this

relationship became reversed. In neither case was the

difference between Internals' and externals' expectancies

statistically significant. With regard to the

Performance—-^outcome expectancy, the relationship was in

the hypothesized direction, with Internals perceiving a

130

s t r o n g e r r e l a t i o n s h i p between performance and o b t a i n i n g

rewards . However, aga in t h e s e d i f f e r e n c e s were not

s i g n i f i c a n t .

TABLE 13

Use-—>Performance and Performance—^Outcome E x p e c t a n c i e s of I n t e r n a l s v s . E x t e r n a l s

Use—^Performance Expectanc ies

Group

I n t e r n a l s E x t e r n a l s

Group

I n t e r n a l s E x t e r n a l s

N nean(TI) SD t

36 7 6 . 1 9 11 .21 1.43 35 72 -37 11.31

N Hean(T2) SD t

36 7 0 . 8 1 15.65 - . 5 2 4 35 7 2 . 7 1 15.02

Prob.>t^

08

Prob, >t

70

Performance—^Outcome Expectanc ies

Group

Internals Externals

Group

Internals Externals

N Mean(T1)

38 7 6 - 4 1

36 7 4 . 1 8

N Mean(T2)

35 7 5 . 6 2 38 7 2 . 6 3

SD t

11 .40 - 9 2 9 9 .05

SD t

11 .67 1 .21 9 .46

Prob.>t

18

Prob.>t

12

^all t t e s t s are one-tai led

c

In l i gh t of e a r l i e r research suggesting the p o s s i b i l i t y

of a direct re lat ionship between locus of control and degree

131

of work effort (Broedling, 1975), the relationship between

this personality variable and systen use was Investigated.

Locus of control did have an Influence on anount of SLIH use

at Tine 2 (r=-.236, p<.05, two-tailed test), with Internals

naklng significantly nore queries (t=2.37, p=.02, two-tailed

test) and good queries (t=2.60, p=.01, two-tailed test) than

externals. By the end of the study, however. Internals and

externals were using the system at appoxlmately egual

levels. Thus, although locus of control did not Influence

the expectancy components of the expectancy model, it did

have an Independent effect on DSS use (during the early

periods of decision-making) . Hypothesis 12 of the study is

not supported, but further research might investigate the

relative influence of notivation and the locus of control

variable on use of a DSS.

5.3 Hypothesis 13

High analytics will have stronger Use—^Performance

expectancies than low analytics.

The reliability of the instrument used to measure

cognitive style appeared to be adequate for the sample used

in this study (split half r=.852, N = 88) . Also, the scale

had good internal consistency, as measured by coefficient

(alpha=.882, N=88). Finally, the median score otained in

the student sample was Identical to that reported by witkin

et al. (1971) for the normative group (Nd. = 13) . Subjects

132

scoring above this median were classified as "high

analytics," and those below were considered "low analytics."

Results of t tests for Independent groups to test this

hypothesis are given In Table 14. As predicted, high

analytics had stronger Use—^Perfornance expectancies than

low analytics. However, these differences were not

significant. Thus hypothesis 3 was not supported.

TABLE 14

Use—^Perfornance Expectancies for High versus Low Analytics

Group N

High Analytics 38 Low Analytics 36

Group N

High A n a l y t i c s 38 Low A n a l y t i c s 36

Hean(Tl)

7 5 . 6 1 73 -83

Mean(T2)

7 1 . 8 7 6 9 . 11

SD t

11.34 . 6 8 4 10.92

SD t

16.45 .773 14.06

Prob.>t*

.25

Prob.>t

.22

^all t t e s t s are one-tai led

5.4 Sunnary

within-subjects t e s t s of hypothesis II yielded l i t t l e

support for the predict ive power of an expectancy model of

user behavior. However, across-subjects analyses suggested

133

some support for the model. Correlations between predictor

scores and actual use of the DSS improved when the predictor

accounted for the difference In the subject's force to use

SLIH and his force to not use the systen. The strength of

the force—>behavlor relationships In the current study are

comparable to those observed In previous expectancy theory

research. Tests for hypotheses of the Influence of two

personality variables, locus of control and cognitive style,

on components of the expectancy nodel yielded no significant

findings. In short, hypothesis 11 of the study received

sone support, while hypotheses 12 and 13 were not supported.

Chapter VI

COBCLOSIOBS

6 . 1 D i s c u s s i o n o f R e s u l t s

The results of this study provide weak to moderate

support for the model of user behavior outlined in Figure 9.

In testing this model, a methodology was selected which Is

typical of that found In expectancy theory research, relying

on survey procedures and correlational analysis. As pointed

out earlier, prior expectancy research has yielded

correlations between notivation and work behavior ranging

fron 0 to about .40 (Peters, 1977). Across-subjects

correlations found here are about equal to, or slightly

below, those reported In the najority of studies showing

support for expectancy-based models of behavior. Contrary

to expectancy-theory predictions, support for the model was

greater in between-subjects analyses than in within-subjects

analyses. As noted earlier, the design of the study did not

really lend itself to proper within-subjects analysis since

the situation presented the individual with the option of

either using a resource or not using a resource, versus a

choice among several alternative resources. Also, the

sample size was too small for detection of anything but

134

135

large differences In within-subjects analyses, and the

effects of Interest may be small In size, as In connon In

behavioral research.

The findings of this study do provide enplrlcal support

for the theoretical notion In expectancy theory that

behavior Is a function of cognitive choice. The predictive

power of the nodel Increased narkedly when the subject's

force to not use SLIH was accounted for In the predictor

score. That Is, range scores were better predictors of DSS

use than were high force scores used alone. To the extent

that Hollenback's (1979) matrix method allowed the range

score to be readily calculated, this study suggests a

potential advantage of his technique for measuring

expectancy theory concepts. The better predictive power of

the model for DSS use measures as opposed to the "time

spent" variable, contradicts previous findings demonstrating

the predictive validity of expectancy theory for amount of

time spent on a task (e.g, Terborg, 1977), However, the

results are consistent with the suggestion made by Schwab et

al. (1979) and Williams and Seller (1973) that objective,

quantitative measures of work effort are superior to

self-report scales.

The results are not consistent with research suggesting

the influence of individual difference variables on

expectancy theory relationships. Inclusion of personality

136

v a r i a b l e s did not Improve the p r e d i c t i v e power of the nodel .

In p a r t i c u l a r , lack of support for hypothesis 12 i n t h i s

study c o n t r a d i c t s e x i s t i n g expectancy theory l i t e r a t u r e

sugges t ing the Inportance of locus of contro l as a var iab le

in f luenc ing ef fort—>perfornance ( B a t l i s , 1978; e iz i lagyi &

S i n s , 1975) and perfornance—^outcone (Lawler, 1973)

e x p e c t a n c i e s . The i n p l i c a t i o n of t h i s f inding I s not so

nuch that locus of contro l and cogn i t ive s t y l e are

I r r e l e v a n t In fornulat lon of effort—^perfornance and

performance—^outcome expectanc ies In general , but rather

tha t these two persona l i ty c h a r a c t e r i s t i c s may have no

meaningful Inf luence on expectat ions within the context of

DSS use . Furthermore, i t may be that var iables other than

l o c u s of contro l and c o g n i t i v e s t y l e are c r i t i c a l to

formulation of use^->performance and performance—^outcome

expec tanc ie s of DSS users . Greater use of the DSS by

s u b j e c t s high In I n t e r n a l , as opposed to ex terna l , l ocus of

contro l i s a f inding c o n s i s t e n t with s t u d i e s demonstrating

the importance of t h i s personal i ty var iable as an

Independent predictor of work e f f o r t (Broedling, 1975;

Elklns 6 Cochran, 1978) and of Infornation search a c t i v i t y

(Lefcourt , 1972; Phares, 1976; Znud, 1979c). Future

research night I n v e s t i g a t e the r e l a t i v e , or I n t e r a c t i n g ,

i n f l u e n c e of locus of control and not ivat ion on anount of

voluntary use of Infornat ion systems In organ iza t ions . I t

may be , for example, that locus of control moderates the

137

motivation—>use relationship, and that the relationship

between notivation to use a DSS and actual use of the system

Is different for Internals than for externals. Further work

Is needed here. The current study merely suggests that

notivation and locus of control affect use. The precise

nature of the relationship anong these variables remains to

be determined.

The contribution of this study to the expectancy theory

literature lies in it its attempt to apply Vroom's model to

still another type of behavior found In organizations: use

of a DSS. Expectancy constructs have been used in the past

to explain a wide range of behaviors In a variety of

research settings. However, the particular behavior

considered In the current study was more novel and complex

than most. In this sense, the study represents an extension

of available expectancy theory research, and the results may

have implications for future studies concerned with

explaining fairly complex behaviors within expectancy

frameworks.

From the perspective of behavioral HIS literature, the

findings of this study are consistent with prior research

demonstrating a relationship between attitudes and HIS use

(King & Rodriguez, 1978; Lucas, 1975b, 1978; Vasarhelyi,

1973). In particular, the research Is consistent with the

results of Rodriguez (1977), Ginzberg (1981), and others

138

( e . g . , Robey, 1979) showing that users ' a t t i tudes toward

decis ion performance re la te pos i t ive ly to actual use of

Information systems. with regard to the Individual

di f ferences l i t e r a t u r e , the re su l t s obtained here contradict

HIS s tudies which have found differences between high and

low analyt ics in the ir a t t i tudes toward information systems

(Benbasat & Taylor, 1978; Vasarhelyi, 1973). It may be that

high and low analyt ics di f fer In their global f ee l ings about

information systems but not in their spec i f i c expectations

regarding the use—->perf ornance relat ionship. A large

amount of empirical HIS research has focused on cognit ive

s t y l e . Because th i s variable has been, and continues to be,

so well s tudied, future research dealing with the role of

notivation In user behavior probably should Include the

cognit ive s t y l e variable , despite the lack of s ign i f i cant

f indings in the current study. with regard to locus of

control , t h i s study has verified ear l i er work showing the

greater tendency on the part of internals to use information

systems (Zmud, 197 9c)- The current study suggests that

these differences In use are probably not a function of

dif fering expectations on the part of Internals versus

externals . Further research Is needed to verify th i s

finding as well as to invest igate other possible

re lat ionships between motivation and the locus of control

variable within the context of DSS use.

139

This study attempted to improve, both conceptually and

methodologically, prior studies which suggested that

expectancy concepts could be applied to the understanding of

Infornation systen use. Vertinsky et al. (1975) found

Interview data to be consistent with the notion that systen

use is a function of expectations regarding relationships

among use, performance, and outcomes. Robey (1979) used the

Schultz and Slevin (1975) Instrument to survey Industrial

sales representatives and then proposed an expectancy-based

nodel to explain his findings. The current study extended

these studies In that it: (1) integrated available

expectancy theory and HIS literatures in developing an

expectancy nodel, (2) tested the expectancy-based nodel a

priori rather than ad hoc, (3) used a survey Instrument

specifically designed for measurement of expectancy theory

constructs, (4) controlled for background characteristics

known to Influence attitudes and extent of system use by

Individuals, and (5) applied the expectancy model to a DSS

setting rather than to a transaction processing systen. To

the extent that this study demonstrated support for

expectancy-based explanations of user behavior, earlier work

done In the area by Vertinsky et al. (1975) and Robey (1979)

Is confirmed. But the rather low correlations found In the

current study Imply that expectancy theory may not offer the

strong degree of explanatory power to MIS user behavior

implied by earlier authors. Had a more stringent decision

140

rule been applied to tests of the significance of the

obtained correlations (e.g., p < .01), the first hypothesis

of the study would have received no support whatsoever.

Thus, although the results supply sone verification of the

contention that a relationship exists between expectancies

and subsequent use of Infornation systens, the strength of

that relationship and the degree to which that relationship

holds up across settings are still unresolved Issues at this

point. Therefore, one nust proceed with caution when

drawing theoretical or practical laplications froa the

findings of this study.

6.2 Theoretical Inplications

The results of this research laply that aotivation is

indeed an iaportant variable to be considered when studying

use of inforaation systeas. Moreover, one can conclude froa

this study that expectancy theory has potential as a basis

for explaining the aotivation of decision support systea

users. Eaplrlcal support for the theoretical aodel outlined

In this dissertation represents an laproveaent In current

understanding of the dynaalcs of user behavior. To date,

the aainstreaa of behavioral MIS research has been concerned

with relating variables In a palrwlse fashion (e.g.,

experience and use, or attitudes and use), with the net

result that aany specific relationships have been identified

but with very little integration. The current research

141

p r o j e c t has d e a o n s t r a t e d how a nuaber of MIS s t u d i e s a i g h t

be c o n c e p t u a l l y i n t e g r a t e d i n t o a t h e o r e t i c a l aode l and then

e n p l r l c a l l y t e s t e d . In t h i s s e n s e , t h i s d i s s e r t a t i o n has

made a c o n t r i b u t i o n t o b e h a v i o r a l MIS r e s e a r c h . The

c o n t r i b u t i o n of t h e study i s modest i n terms of i s o l a t i n g

v a r i a b l e s which can e x p l a i n l a r g e p r o p o r t i o n s of v a r i a n c e in

b e h a v i o r . One n i g h t i n i t i a l l y be d i scouraged t h a t the

h y p o t h e s i z e d nodel e x p l a i n e d only s n a i l anounts of the

v a r i a n c e in the dependent measures. However, from a

s c i e n t i f i c p e r s p e c t i v e . I t I s b e t t e r t o e x p l a i n s m a l l

amounts of v a r i a n c e w i t h i n t h e o r e t i c a l frameworks than to

e x p l a i n l a r g e r amounts with nonsystemat lc s e l e c t i o n of

v a r i a b l e s . A pre l iminary s t e p has been made In the

development of a theory of I n d i v i d u a l user b e h a v i o r .

Support for t h e proposed model of DSS was comparable t o t h a t

found In most expec tancy theory s t u d i e s . Therefore , f u r t h e r

r e s e a r c h on e x p e c t a n c y - b a s e d mode l s , and perhaps

c o g n l t l v e l y - b a s e d mot iva t ion models in g e n e r a l , i s

warranted.

6 . 3 P r a c t i c a l I l E l i c a t i o n s

Any p r a c t i c a l c o n c l u s i o n s drawn fron t h i s s tudy can

o n l y be made under t h e assumption that f u t u r e r e s e a r c h w i l l

p r o v i d e a d d i t i o n a l support for an e x p e c t a n c y - b a s e d model of

user b e h a v i o r . At t h i s p o i n t , the I m p l i c a t i o n s of t h i s

s t u d y for MIS managers, for DSS d e s i g n e r s , and f o r DSS

^ \

142

implementa t ion are l a r g e l y s p e c u l a t i v e . i n a very g e n e r a l

s e n s e , e n p l r l c a l j u s t i f i c a t i o n of the model of user b e h a v i o r

t e s t e d in the c u r r e n t s tudy i m p l i e s t h a t user e x p e c t a t i o n s

r e g a r d i n g t h e r e l a t i o n s h i p between system use and d e c i s i o n

p e r f o r n a n c e , and between d e c i s i o n q u a l i t y and a v a i l a b l e

o u t c o n e s , a f f e c t l a t e r vo luntary use of the DSS In t h e

o r g a n i z a t i o n . T h e r e f o r e , i n p l e m e n t e r s must be s e n s i t i v e t o

u s e r s ' e x p e c t a t i o n s of a system and be aware of how t h e y , a s

e x p e r t s . I n f l u e n c e these e x p e c t a t i o n s during the

i n p l e n e n t a t i o n p r o c e s s . O r g a n i z a t i o n a l managers shou ld be

s e n s i t i v e t o the Impact of reward c o n d i t i o n s on use of

computer s y s t e m s . At a g l o b a l l e v e l , an e x p e c t a n c y - t h e o r y

view o f t h e use p r o c e s s I m p l i e s t h a t system use I s a

f u n c t i o n of both the I n d i v i d u a l and the s i t u a t i o n , S y s t e a s

can be des igned t o meet the needs of i n d i v i d u a l s , or

I n d i v i d u a l s can be s e l e c t e d f o r j o b s based on t h e i r

p r o p e n s i t y t o use a DSS. But " s u c c e s s " of a system a l s o

r e q u i r e s d e s i g n i n g an o r g a n i z a t i o n a l environment which

a l l o w s p e o p l e t o p e r c e i v e t h a t u s i n g the system w i l l

f a c i l i t a t e t h e i r o b t a i n i n g rewards which they v a l u e .

6 . 4 ^ i n i t a t i o n s of t h e S tu^ i

The l i m i t a t i o n s of t h i s s tudy can be d i s c u s s e d from two

p e r s p e c t i v e s : t h r e a t s to i n t e r n a l v a l i d i t y and t h r e a t s t o

e x t e r n a l v a l i d i t y . These l i m i t a t i o n s r a i s e q u e s t i o n s

r e g a r d i n g t h e accuracy of the r e s u l t s and the

143

generalizability of the findings. From the point of view of

expectancy theory, the study may be criticized for:

1- Neasurlng only two levels of force to use SLIH (high

and low); the presence of three or more levels might

have allowed a more sensitive test of the model

since. In fact, users do not choose between high and

low levels of use but rather among multiple levels of

use.

2. The outcome list used In the Experimental

Questionnaire was based on pilot data and may not

have been appropriate for some respondents.

Theoretically, each subject should generate her own

outcome list when measuring expectancy constructs.

3. The length of the study may have been too short.

Expectancy theory Is a temporal model, meaning that

tine must pass between motivation to behave and

observation of the behavior. Results may have more

accurate had the study been extended over a period

longer than three weeks.

As originally outlined by Vroom, the purpose of

expectancy theory is to predict force, or intention to

behave, not behavior Itself. Although the experimental

procedures used In this study sought to limit discrepancies

144

between a subject's desire to use SLIH and his actual use of

the system, control may not have been perfect. It Is

conceivable that a person may have desired to use SLIH but

was unable to carry out his Intentions due to situational

constraints (e.g., work connltments, or the system was down

when the subject wanted to use It), Also, with regard to

the issue of experimental control, although the study

attempted to limit variability In the sample with respect to

background characteristics, the sample obtained was not

homogeneous on these dlnenslons. For exanple, students who

had acadenic najors In the area of HIS were permitted to

participate in the study, resulting in some subjects having

considerably more computer experience and expertise than

others.

One of the nore serious problems encountered in the

study relates to measurement of the dependent variable of

system use. Although all subjects received training in the

use of SLIH and the CMS system in which It operated, there

were several things which the student could do at the

terminal which would cause his file of stored queries to be

suddenly erased, occasionally this problem did occur.

However, there was no indication that loss of queries

occurred in a systematic fashion,

A final Issue to be considered with regard to validity

relates to the power of the statistical tests which were

145

conducted. An ad hoc check on power reveals that the sample

size was adequate for detection of a moderate-sized

experinental effect (powers.84, N=82) , but inadequate for

detection of a snail effect (power=.24, N^82). Thus it is

possible that a larger sanple size would have resulted in

nore significant findings.

The external validity problens with the study are a

function of its laboratory setting, the ganlng situation,

and the use of student subjects. All of these factors

created an artificial context for the study which may

compromise the generalization of the findings to

non-laboratory settings. This research was deliberately

designed to be fundamental, rather than applied. Support

was found for the proposed model in a highly controlled

setting. Subsequent research may take a more applied focus,

examining the behavior of practicing managers in a nore

realistic setting.

6.5 Heconnendatlons fog Further Research

Follow-up research night take several directions.

First, sone of the relationships hypothesized in the more

conplete model of user behavior (Figure 8) might be

investigated. Of particular interest here is the DSS

Use—>Quallty of Decision-Making relationship. An important

objective of decision support systems Is to Improve decision

quality. Therefore, the relationship between amount of use

146

and d e c i s i o n performance d e s e r v e s f u r t h e r c o n s i d e r a t i o n .

S e c o n d , a t t e n t i o n n i g h t be g iven t o Improving t h e p o r t i o n of

t h e model t e s t e d In the c u r r e n t s tudy (Figure 9) . This

n i g h t I n v o l v e adding new v a r i a b l e s ( e . g , o ther I n d i v i d u a l

d i f f e r e n c e v a r i a b l e s ) , removing some e x i s t i n g v a r i a b l e s

( e . g . , c o g n i t i v e s t y l e ) , or changing the nature of t h e

r e l a t i o n s h i p s among t h e v a r i a b l e s in the model ( e . g . ,

combining the components a d d i t l v e l y ra ther than

m u l t l p l l c a t l v e l y , a s some p a s t expec tancy r e s e a r c h has

d o n e ) . F i n a l l y , the p r e d i c t i v e power of the

e x p e c t a n c y - b a s e d model can be c o n t r a s t e d t o t h a t of o ther

t h e o r e t i c a l models of DSS u s e . For example , models of use

based on g o a l t h e o r y , c o g n i t i v e d i s s o n a n c e , or r e i n f o r c e m e n t

theory c o n c e p t s could be developed and compared t o the

c u r r e n t model for t h e i r r e l a t i v e p r e d i c t i v e v a l i d i t y . The

u l t i m a t e c o n t r i b u t i o n of expectancy theory t o e x p l a i n i n g

user behavior l i e s i n i t s s t r o n g e r p r e d i c t i v e power r e l a t i v e

t o a l t e r n a t i v e t h e o r e t i c a l models of t h e use p r o c e s s . Thus,

comparat ive t h e o r e t i c a l work i s needed.

A d d i t i o n a l a r e a s of s tudy inc lude the f o l l o w i n g : t h e

r o l e of feedback in t h e proposed model, the i n f l u e n c e of

e x p e c t a n c y v a r i a b l e s on type of use (as opposed t o amount of

u s e ) , and t h e impact of s o c i a l d e s i r a b i l i t y on measurement

of e x p e c t a n c y v a r i a b l e s . As noted e a r l i e r , t h e r e i s a need

f o r f u t u r e r e s e a r c h t o t e s t the hypotheses of t h i s s t u d y in

r e a l o r g a n i z a t i o n a l s e t t i n g s t o determine i f the f i n d i n g s

are g e n e r a l l z a b l e t o non- laboratory s i t u a t i o n s . in

147

addition, field-based resarch should provide insight into

the implications of the expectancy nodel for DSS design and

inplenentation.

6.6 Sunnary

The r e s u l t s of t h i s study have been discussed in terns

of t h e i r I n p l i c a t i o n s for expectancy theory research and for

behavioral research in the f i e l d of MIS. The study has

contr ibuted to the expectancy theory l i t e r a t u r e by

s u c c e s s f u l l y applying Vroom's model of work e f for t t o s t i l l

another type of behavior in organizat ions: use of a DSS.

From the perspec t ive of the MIS l i t e r a t u r e , t h i s research

has confirmed e a r l i e r f indings regarding the r e l a t i o n s h i p

between a t t i t u d e s and system use, and the r e l a t i o n s h i p

between locus of contro l and use . However, t h i s study was

unable to confirm e a r l i e r s t u d i e s which suggested the

i n f l u e n c e of c o g n i t i v e s t y l e on user a t t i t u d e s . The r e s u l t s

obtained here Imply that motivation i s an important var iab le

to be considered when studying use of information sys tems .

Moreover, one can conclude from this study that expectancy

theory has p o t e n t i a l as a bas i s for explaining the

motivation of DSS u s e r s . The study was l imi ted by s e v e r a l

design and measurement c o n s t r a i n t s . In addi t ion , the

laboratory nature of the research r e s t r i c t s one from

g e n e r a l i z i n g the f ind ings or from drawing p r a c t i c a l

i m p l i c a t i o n s from the r e s u l t s . Neverthe less , by Integrat ing

148

a l arge subset of behavioral HIS l i t e r a t u r e within a

meaningful conceptual framework, th i s study has served as a

preliminary step In the development of a theory of user

behavior. Further research on expectancy-based models of

the use process I s warranted.

RBFEBBHCBS

Alavi , M. A study of HIS design v a r i a b l e s : Decision s t y l e s and decis ion a id ing s t r a t e g y . Unpublished Ph.D, d l s e r t a t l o n . The Ohio State Univers i ty , 1978.

A lpe r t , B. Non-businessmen as su r roga tes for businessmen In behavioral experiments . Journal of Business, 1967, 15 , 20 3-297.

A l t e r , S. Why I s man-computer In t e r ac t i on Important for decis ion support systems? I n t e r f a c e s , 1977a, 7 (2) , 109-115,

A l t e r , S. Taxonomy of decision support systems, Slgan Hanagenent Review, 1977b, 19 ( F a l l ) , 39-56,

A l t e r , S . Decision support s y s t e n s : Current p rac t ice and cont inuing cha l l enges . Reading, Hass: Addison-Wesley, 1979,

Anthony, R, N, Planning and cont ro l systems: A framework for a n a l y s i s . Cambridge, Mass.: Harvard Universi ty Graduate School of Business Administrat ion, Studies In Hanagement Contro l , 1961.

Argy r i s , C. M2magement Information systems: The chal lenge to r a t i o n a l i t y and emot iona l i ty . Management Science. 197 1, 12 (6) , B275-B292.

Arnold, H. J . A t e s t of the v a l i d i t y of the m u l t i p l i c a t i v e hypothesis of expectancy-valence theor ies of work mot iva t ion . Academy of Management Journa l . 1981, 24 (1) , 128-141,

Arvey, R. D. Task performance as a function of perceived effor t -performance and performa nee-re ward con t ingenc ies . Organiza t ional Behavior and Human Performance, 1972, 8, 423-433.

Arvey, R. D-, 6 Neel, C. W. Testing expectancy theory p r e d i c t i o n s using behavlora l ly based measures of mot iva t iona l e f fo r t for engineers . Journal of Vocational Behavior, 1974, 4 (3) , 299-310.

Arvey, R- D. , 6 Neel, C. W. Motivation and obsolescence in eng inee r s . I n d u s t r i a l Gerontoloax 1976, 3 (2 ) , 113-120.

149

150

Atkinson, J. W, Towards experimental a n a l y s i s of human motivation In t erns of n o t l v e s , expec tanc i e s , and I n c e n t i v e s , in J. W, (Ed.) , Motives In fantasy, a c t i o n s . and s o c i e t y . Pr inceton, N, J , : Van^NostrandT 1958.

Ayers, L , , Naccl , P , , S Tedeschi , J. T, Attract ion and r e a c t i o n s to noncontlngent pronises . Bu l l e t in of the Psychononic S o c i e t y , 1973, 1 (1-B), 75-77 .

Bar i f f , M., 6 Lusk, E. Cognit ive and personal i ty t e s t s for the design of management information systems. Hanagenent Sc i ence , 1977, 23 , 820-829,

Barkin, S. R, An i n v e s t i g a t i o n Into some fac tors a f f e c t i n g Information system u t i l i z a t i o n . Unpublished Ph, D, d i s s e r t a t i o n . Univers i ty of Minnesota, 1974,

Barkin, S, R, , & Dickson, G, W, An Inves t iga t ion of information s y s t e n u t i l i z a t i o n . Infornation and Hanagenent, 1977, 1 , 35-45.

Barto l , K. H. Expectancy theory as a predictor of female occupat ional choice and a t t i t u d e toward bus iness , A£§i§lX of Management Journal , 1976, 19 (4 ) , 669-675.

B a t l i s , N. C. Relat ionship between locus of control and ins trumenta l i ty theory predictor of acadenic perfornance. Psycholog ica l Reports, 1978, 43 ( 1 ) , 239-245.

B a t l i s , N. C. , & Waters, L. K. Locus of control and achievenent no t iva t ion as moderators of the expectancy-academic perfornance r e l a t i o n s h i p . Journal og Educational and Psycholog ica l Measureneat, 1973, H , 895-902.

Benbasat, I . An exper inenta l evaluat ion of the e f f e c t s of Infornat ion sys ten and dec is ion naklng c h a r a c t e r i s t i c s on d e c i s i o n e f f e c t i v e n e s s . Unpublished Ph. D, d i s s e r t a t i o n . Univers i ty of Hlnnesota, 1974-

Benbasat, I . , S Dexter, A. S. Value and events approaches to accounting: An Experinental eva luat ion . The Accounting Review, 1979 ( O c t . ) , 735-749.

Benbasat, I . , S Dexter, A. S. Indiv idual d i f f erences in the use of d e c i s i o n support a i d s . HISCR-WP-80-05, Univers i ty of Hlnnesota, January 1980. ^

Benbasat, I . , Dexter, A. S . , & Hasul i s , P. S. An exper inenta l study of the human/computer i n t e r f a c e . HISCR-WP-30-16, Univers i ty of Minnesota, 1980.

151

B e n b a s a t , I . , & Schroeder, R. G, An experimental i n v e s t i g a t i o n of some HIS design v a r i a b l e s . The Hanagenent Infornat ion Systen flua£terlj, 19777~i# 3 7-5 0.

Benbasat, I . , 6 Taylor, B. H. The inpact of c o g n i t i v e s t y l e s on Infornation systems design. Hanageaent I ^ l 2 £ l i t l o n Sjrst.ens 2M£ie£LZ# 1978, 2 (2) ,43-547

Beh l ing , 0 . , 6 Starke, F. A. The pos tu la tes of expectancy theory. Acadeny of Hanagenent Journal, 1973, 16, 373-388. " ~~

Berger, C. J . , Cunnings, L. L. , 6 Henenan, H. S. Expectancy theory and operant condit ioning pred ic t ions of perfornance under variable r a t i o and continuous schedules of re inforcenent . Organizational Behavior ^nd Humajj gerfornance. 1975, l i , ~ 2 2 7 - 2 4 3 .

B i e r i , J. Cognit ive s tructures in persona l i ty . In H. H.' Schroeder S P. Suedfeld (Eds . ) , Personal i ty theory and InfornatloQ process ing . New York? Ronald Press , 1971, 178-208.

Blrnbaum, H. H. The dev i l r ides again: Correlation as an Indes of f i t . Psychological B u l l e t i n . 1973, 79, 239-242.

Blanning, R. W. The funct ions of a dec i s ion support s y s t e n . Infornat ion and Hanagenent. 1979, 2, 8 7-9 3.

Bostrom, R. P- A s o c i o - t e c h n i c a l perspect ive on MIS implementation. Paper presented at the ORSA/TIHS National Conference, Colorado Springs , Colorado, November 1980, 63pp.

Bostrom, R. P . , 6 Heinen, S. J. MIS problems and f a i l u r e s : A s o c l o - t e c h n l c a l perspect ive Part I I : The a p p l i c a t i o n of the s o c i o t e c h n i c a l theory. Hanagement Information System Quarterly, 1977, 1, ( 4 ) , 11-28.

Brady, R. H, Computers in top-management dec i s ion making. Harvard Business Review, 1967, 45 (July/August) , 67 -76 ,

^ Broedl ing , L. A. Relat ionship of in terna l - ex terna l contro l to work motivat ion and performance in an expectancy model. Journal of A££lied Psychology, 19 75, 60, 65 -70 .

Campbell, D. E. Locus of contro l and expectancy theory: P r e d i c t i o n s of acadenic e f f o r t . Unpublished Ph.D. d i s s e r t a t i o n . Univers i ty of Houston, 1975.

Canpbel l , J. P-r Dunnette, H. D. , Lawler, E. E. , I I I . , & Welck, K. E . , Jr . ManaqS£iil behavior, perfornaqce. and e f f e c t i v e n e s s . New~YorkT McGraw-Hill, 1970.

152

Campbell , J. P . , & P r i t c h a r d , R. D. Hot iva t ion theory in I n d u s t r i a l and o r g a n i z a t i o n a l p s y c h o l o g y . In H. Dunnette (Ed.) , Handbook of I n d u s t r i a l and o r g a n i z a t i o n a l p s y c h o l o g y . Chicago: Rand HcNally, 1976, ppl 6 3 - 1 3 0 .

C a n p b e l l , J . P . , & P r i t c h a r d , R, D. Research e v i d e n c e p e r t a i n i n g to e x p e c t a n c y - l n s t r u m e n t a l l t y - v a l e n c e t h e o r y . In R. H. S t e e r s S L, W. P o r t e r , H o t i v a t i o n and work b e h a v i o r . New York: McGraw-Hill, T979,

C h a f i n , R, L . , & Mart in , T, H, A man-computer I n t e r f a c e s t u d y for command and c o n t r o l conputer s y s t e n s . P r o c e e d i n g s of t h e i n t e r n a t i o n a l conference on c y b e r n e t i c s and s o c i e t y , lEEB, 1979, p , 2 1 - 2 6 .

Cheney, P. H. O r g a n i z a t i o n a l c h a r a c t e r i s t i c s and i n f o r n a t i o n s y s t e m s : An i n v e s t i g a t i o n . Unpublished Ph.D. d i s s e r t a t i o n . U n i v e r s i t y of Hlnnesota , 1977,

Cheney, P. H. , 5 Andrews, D. J . An i n v e s t i g a t i o n of f a c t o r s t h a t may a f f e c t t h e u t i l i z a t i o n of d e c i s i o n suppot s y s t e m s . Unpublished manuscript , Texas Tech U n i v e r s i t y , 1978,

Chervany, N . , Dickson , G., & Kozar, K. A. An e x p e r i m e n t a l gaming framework f o r i n v e s t i g a t i n g t h e i n f l u e n c e of management in format ion sys tems on d e c i s i o n e f f e c t i v e n e s s , MISRC-WP-71-72, U n i v e r s i t y of Minnesota , 1972.

Chorba, R, W., S New, J . L. Information support for d e c i s i o n maker l e a r n i n g i n a c o m p e t i t i v e environment: An e x p e r i m e n t a l s t u d y . D e c i s i o n S c i e n c e s , 1980, H , 6 0 3 - 6 1 5 .

Cohen, J. S t a t i s t i c a l £Ower a n a i y s l s f o r the b e h a v i o r a l s c l e n c e s r New"lork: Academic P r e s s , 1969.

C o n n o l l y , T. Some c o n c e p t u a l and methodolog ica l i s s u e s in expectancy models of work performance mot iva t ion . Acadeny of Ma§3ie i§ l l t E£liS!l / 1976, 1 , 37 -47 .

Cooper, R. B . , & Swanson, E. B. Managenent i n f o r n a t i o n r e q u i r e n e n t s : The s t a t e of the a r t . Data g a s e , 1979 ( F a l l ) , 5 - 1 6 .

Courtney , J. F . , 5 J e n s e n , R, L. SLIH: A management s i m u l a t i o n for t e a c h i n g MIS and DSS. I n t e r f a c e : The Computer Educat ion Quarter ly , 1980, 2 (1) , 5 0 - 5 7 .

Courtney , J- F . , 6 J e n s e n , R. L, SLIH Users Hanual, D a l l a s , Texas: B u s i n e s s P u b l i c a t i o n s , 1981.

153

D a c h l e r , H. P . , 6 Hobley, W- H. Construct v a l i d a t i o n of an i n s t r u m e n t a l i t y - e x p e c t a n c y task g o a l model of work m o t i v a t i o n : Some t h e o r e t i c a l boundary c o n d i t i o n s . Journal o f Applied P s ^ h o l g q x # 1973, 58 , 397 -418 .

D a v i s , G. B. M a i S l t l ^ a t I af or motion S y s t e n s : Coacee tua l F o u n d a t i o n s , s t r u c t u r e , and Developnent , New York: HcGraw-Hll l , 1974 ,

DeLeo, P, J, , 6 P r i t c h a r d , R. D. An examinat ion of some m e t h o d o l o g i c a l problems In t e s t i n g e x p e c t a n c y - v a l e n c e models with survey t e c h n i q u e s . O r g a n i z a t i o n a l Behavior and Human Performance. 1974, 1 2 , " 1 4 3 - 1 4 8 .

D i c k s o n , G. W. , Senn, J . A. , & Chervany, N. L, Research In management in format ion s y s t e m s : The Hlnnesota e x p e r i m e n t s . Hanagement S c i e n c e , 1977, 2J, 9 1 3 - 9 2 3 .

D i c k s o n , G,, G S l n o n s , J , The b e h a v i o r a l s i d e of HIS. B u s i n e s s H o r i z o n s . 1970 (August ) , 5 9 - 7 1 .

D i e b o l d , J. In format ion: Your most v i t a l a s s e t In t h e 1980s . D i r e c t o r . 1979, 3 1 , 4 1 - 4 2 .

D i l l a r d , J . R. A p p l i c a b i l i t y of an o c c u p a t i o n a l g o a l -e x p e c t a n c y model In p r o f e s s i o n a l account ing o r g a n i z a t i o n s . D e c i s i o n S c i e n c e s . 1979, 1 2 , 161-176.

D i l l a r d , J . F. An update on the a p p l i c a b i l i t y of an o c c u p a t i o n a l g o a l - e x p e c t a n c y nodel in p r o f e s s i o n a l a c c o u n t i n g o r g a n i z a t i o n s . Dec i s ion S c i e n c e s . 1981a, 12 ( 1 ) , 3 2 - 3 8 .

D i l l a r d , J . F, So where do we go from here? D e c i s i o n S c i e n c e s , 1981b, D e c i s i o n S c i e n c e s , 1981b, 1 2 , (T7# 4 6 - 5 0 ,

Doktor , R. , 6 Hamilton, W. Cogni t ive s t y l e and the a c c e p t a n c e of managenent s c i e n c e recommendations. Hanagement S c i e n c e , 197 3, 19 (8 ) , 8 8 4 - 8 9 4 ,

D r i v e r , H, J . , 6 Hock, T. J . Human informat ion p r o c e s s i n g , d e c i s i o n s t y l e t h e o r y , and account ing Information s y s t e m s . The Accounting Review, 1975, 409-508 .

E i n - d o r , P . , 6 S e g e v , E. Organizat ion c o n t e x t and the s u c c e s s of HIS. Manaement S c i e n c e , 1978, 24, 1064-1077 .

E l k l n s , G. R. , S Cochran, S. W. I n t e r n a l and e x t e r n a l l o c u s of c o n t r o l as d e t e r m i n a n t s of d e c i s i o n making in a game of s k i l l . P s y c h o l o g i c a l Reports , 1978, 42 , ( 3 , P t . 2 ) .

154

Eran, H., 6 Jacobson, D. Expectancy theory predic t ion of the preference to remain employed or to r e t i r e . Journal of gerontology . 1976, 3 ( 5 ) , 605-610.

Evans, H. G. The e f f e c t s of supervisory behavior on the path-goal r e l a t i o n s h i p . Organizational Behavior and Hunan Performance, 1970, 54,"105-1147

Faerber, L. G., 6 R a t l i f f , R. L. People problems behind HIS f a i l u r e s , I l n a n c i a l Executive, 1980, 48 (Apr i l ) , 18-24.

Feldnan, J, H,, Re i tz , H. J . , & Hilternan, R. J, A l t e r n a t i v e s to o p t l n i z a t i o n in expectancy theory-Journal of Applied Psyci^ology. 19 76, 61 , 712-720.

F e r r i s , K. R. Perceived environmental uncertainty as a mediator of expectancy theory pred ic t ions : Some prel iminary f i n d i n g s . Decis ion S c i e n c e s . 1978, 9, 379-390.

Ferr is ,K. R, , D i l l ard , J. F. , & Nethercott , L, A conparison of V-I-B nodel p r e d i c t i o n s : A cross -nat iona l study in p r o f e s s i o n a l accounting f i r n s . Accounting. Organlzatons and S o c i e t y , 19 80, 5, ( 4 ) , 3 6 1 - 3 6 8 , ~

Flsk, G. (Ed. ) . The psychology of management dec i s ion: The second symposium of the College of Hanagement Psychology of the I n s t i t u t e of Hanagement Sc iences , Phi ladelphia & Vienna, September, 1965, Lund, Sweden: Gleerup, 1967.

Fleming, J, £ , Managers as subjects In business dec i s ion research. Academy of Management Journal. 19 6 9, 12, 59-66 .

Froman, L. Some mot ivat ional determinants of tra inee e f f o r t and performance: An i n v e s t i g a t i o n of expectancy theory . Unpublished Ph.D. d i s s e r t a t i o n , Wayne State U n i v e r s i t y , 1977.

Fry, F. L, Incorporat ing expectancy theory Into s o c i a l investment d e c i s i o n s . Proceedings of the Academy of Management, 1975, 333-335.

Galbrai th , J. R., S Cummings, L. L. An empirical i n v e s t i g a t i o n of the motivational determinants of task performance: I n t e r a c t i v e e f f e c t s between i n s t r u m e n t a l i t y - v a l e n c e and m o t i v a t i o n - a b i l i t y . Organizat ional Behavior and Human Performance. 1967, 2 , 237-257.

Gavin, J- F. Se l f -es teem as a moderator of the r e l a t i o n s h i p between e x p e c t a n c i e s and job performance. Journal of Applied Psychology. 1973, 58, 8 3-88.

tfTV

155

G e l l e r , E. S . , Tuso, H. A . , 6 W e l l i n g t o n , C. J. The outcone of a c o a c t o r ' s p r e d i c t i o n as a d e t e r n l n a n t of c h o i c e r e a c t i o n t i n e . B u l l e t i n of the Psychononic S o c i e t y , 1975, 5 ( 4 ) , 3 0 3 - 3 0 5 .

Georgopoulos , B. S . . , Hahoney, G. H., 6 J o n e s , N. W., J r . A p a t h - g o a l approach t o p r o d u c t i v i t y . Journal of Appl ied P s y c h o l o g y . 1957, 4 1 , 3 4 5 - 3 5 3 .

G e r r i t y , T. P . , J r . Design of nan-nachlne d e c i s i o n s y s t e n s . S loan Hanagenent Review, 1971 , 12 ( W i n t e r ) , 5 9 - 7 5 .

G i l e s , W. R. Volunteer ing for job e n r i c h n e n t : A t e s t of e x p e c t a n c y theory p r e d i c t i o n s . Personne l Psyc]^ologj[, 1977, 30 , 4 2 7 - 4 3 5 .

G i n g r a s , L. The psycho logy of the d e s i g n o f I n f o r n a t i o n s y s t e n s . Unpublished P h , 0 , d i s s e r t a t i o n , U a l v e r s i t y of C a l i f o r n i a at Los Ange le s , 1977.

Ginzberg , H. A p r o c e s s approach t o nanagenent s c i e n c e I n p l e n e n t a t i o n . Unpublished Ph.D, d i s s e r t a t i o n , A l f red P. Sloan School of Hanagenent, H.I .T . , 1975.

Ginzberg , H, Early d i a g n o s i s of HIS I n p l e n e n t a t i o n f a i l u r e . Hanagenent S c i e n c e . 1981, 27 ( 4 ) , 4 5 9 - 4 7 8 .

Goodnan, P. S - , Rose , J- H,, & Furcon, J , E, Conparison of n o t l v a t i o n a l a n t e c e n d e n t s of the work performance of s c i e n t i s t s and e n g i n e e r s . Journal of Applied P s y c h o l o g y . 1970, 14 , 491-495.

G o o d s t e i n , 1. D. Group Embedded Figures Tes t : A r e v i e w . In 0 . K. Buros ( E d . ) , The e i g h t h mental measurements yearbook. Highland Park, New J e r s e y : Gryphon P r e s s , 19787

Gorry, G. A . , & S c o t t Horton, M- S. A framework for management Informat ion s y s t e n s . Sloa n Hanagenent Review. 1971 , }3 ( 1 ) , 5 5 - 7 0 .

Graen, G. I n s t r u n e n t a l i t y theory of work n o t i v a t i o n : Sone e x p e r i m e n t a l r e s u l t s and sugges t ed m o d i f i c a t i o n s . Journal og Applied P s y c h o l o g y , 1969, 53 (Monographs No. 2 ,"par t 2) .

G u t h r i e , A. Middle managers and MIS: A a t t i t u d e s u r v e y . Journal o f Economics and B u s i n e s s , 19 7 3, 26, 5 9 - 6 6 .

Hackman, J. R. , & P o r t e r , L, W, Expectancy theory p r e d i c t i o n s of work e f f e c t i v e n e s s . O r g a n i z a t i o n a l Behavior and Human Performance, 1968, 3 , 417-426 .

ir\

156

H a l l , A. E. Group Embedded F igures T e s t : A rev iew. In 0 K. Buros ( E d , ) , The e i g h t h mental measurements yearbook Highland Park, New J e r s e y : Gryphon P r e s s , T978.~

H e l l r i e g e l , D . , s Slocum, J . w . , Jr . Managerial problem-s o l v i n g s t y l e s . Bus iness Horizons , 1975 (December), 2 9 - 3 7 .

Henderson, J . c . , & N u t t , P. c . The i n f l u e n c e of d e c i s i o n s t y l e on d e c i s i o n making behav ior . Hanagement S c i e n c e , 1980, 26 , 371-3 86 .

Heneman, H. G,, I I I , 6 Schwab, D. P. Evaluat ion of r e s e a r c h on expec tancy t h e o r y p r e d i c t i o n s of employee performance. P s y c h o l o g i c a l B u l l e t i n . 1972 , 78, 1-9 ,

Henson, R. Expectancy b e l i e f s , a b i l i t y , and p e r s o n a l i t y in p r e d i c t i n g acaden ic performance. Journal of Educat iona l Research , 1976, 70 ( 1 ) , 4 1 - 4 4 ,

Hol lenback , J , H, An e n p l r l c a l conpar ison and i n t e g r a t i o n of e x p e c t a n c y - v a l e n c e and need for ach ievenent t h e o r i e s . Unpublished Ph.D. d i s s e r t a t i o n , Purdue U n i v e r s i t y , 1976.

Hol lenback , J . A matrix method for expectancy r e s e a r c h . Academy of Hanagement Review, 1979, 4 , 579-587 .

House, R. J . , S h a p i r o , H. J - , & Wahba, H- A- Expectancy theory as a p r e d i c t o r of work behavior and a t t i t u d e : A r e e v a l u a t i o n of e m p i r i c a l e v i d e n c e . Dec i s ion S c i e n c e s , 1974, 5 , 481 -506 .

House, R, J. , 6 Wahba, H. A, Expectancy theory in I n d u s t r i a l and o r g a n i z a t i o n a l psychology: An I n t e g r a t i v e nodel and a rev i ew of t h e l i t e r a t u r e . Proceedings of the B^th Annual Convent ion of the Anerican P s y c h o l o g i c a l A s s o c i a t i o n . 1972, 7, 4 6 5 - 4 6 6 .

Uuysnan, J, The inBJ.enentatioQ of o p e r a t i o n s r e s e a r c h . New York: W l l e y - I n t e r s c l e n c e , 1970.

I l g e n , D, R, , Nebeker, D. M,, 6 P r i t c h a r d , R. D. Expectancy t h e o r y n e a s u r e s : An e m p i r i c a l comparison in an e x p e r i m e n t a l s i m u l a t i o n . O r g a n i z a t i o n a l Behavior and Hiiian Performance, 1981, 28 , 189-223.

I v a n c e v i c h , J . H., S z i l a g y i , A. D . , J r . , & Wal lace , H. J . , J r , O r g a n i z a t i o n a l Behavior and Performance, Santa Monica, C a l , : Goodyear, 1977-

I v e s , B- , Hamil ton, S . , 6 D a v i s , G. B, A framework f o r r e s e a r c h i n computer-based management informat ion s y s t e m s . Hanagement S c i e n c e , 1980, 2 6 , 910-9 34,

157

James, L. R. , Hartman, A., Stebbins, H, W., & Jones, A. P. Relationships between psychological cllnate and a VIE model for work motivation- Personnel Psychology, 1977, 30, 229-254.

Jenson, H. L., S Cherrlngton, D. J, Participants manual: The Business fiaaagenent Laboratori. Dallas7~TX,: Business Publications, 19777

Johnson, D. W,, 6 Johnson, s. The effects of attitude slnilarlty, expectation of goal facilitation, and actual goal facilitation on Interpersonal attraction. Journal 2l Experinental Social Psychology, 1972, 8, 197-206."

Kanter, J. Hangement-orlented nanagenent infornation §I§iens, Englewood cllffs. New Jersey: Prantlce~Hall, 1977,

Keen, P. G, W. (Ed,), The Inplenentation of computer-based decision aids: Proceedings of a conference on the Implementation of computer-based decision aids. Cambridge, Hass,: Center for Information System Research, 1975.

Keen, P. G, W. Value analysis: Justifying decision support systens, Hanagement Information Sxstem Quarterly, 1981, 5 (1), 1-15.

Keen, P. G. V, HIS research: Reference disciplines and a cumulative tradition. Proceedings of the First ' International Conference on Inf orma tlon Systems. Philadelphia, PA., 1980, 9-18.

Keen, P. G. W., 6 Horton, H. S. Scott. Decision si fiport systems: An organizational perspective. Reading, Hass: Addison-Wesley7 T978,

Keen, P. G. W,, S Wagner, 6. R. DSS: An executive mind support system. Datamation, 1979, 25, 117-122,

Kesselman, G, A., Wood, H. T., & Hagen, E. L, A field correlational test of expectancy theory of work motivation. Proceedings of the 81st Annual Convention of the American Psychological Association. Hontreal, Canada, 1973, 8, 569-570.

Khera, I. P. r 6 Benson, J. D. Are students really poor substitutes for buslnessnen In behavioral research? Journal of Harketlng Research, 1970, 4, 529-532.

Klng,W. a., 6 Cleland, D. I. The design of MIS: An Infornation analysis approach. Management Science, 1975, 22, 286-297.

King,w. R s Rodriguez, J. J. Evaluating management in tornat ion s y s t e n s . Mana£enent Infornation Systens fiijarterly, 1978. 2 f3i . U'k^^7 ^

158

fiiiarterry, 1978, 2 (3)7 43-57

^^"^oin!: ^'' 5 »?^F^9aez, J, I . P a r t i c i p a t i v e design of s t r a t e g i c d e c i s i o n support s y s t e n s : An e n p l r l c a l a s s e s s n e n t . Hanagenent Sc i ence , 1981, 22# 717-726.

^^'^K'U^' -^* . £ l£S£ i ienta l design ^rocedur^s for the behavioral s c i e n c e s , Belnont, Cal, 7" Brooki-Cole, 1968,

Kopelnan, H, B, Across -Indiv idual , w i th ln- ind lv idua l and return on e f f o r t vers ions of expectancy theory- Decis ion S c i e n c e s , 1977, 8, 651-662.

Kopelnan, R. E. Psychologica l s tages of careers in eng ineer ing: An expectancy theory taxonony. Journal of Vocational Behavior, 1977, 10 (3 ) , 270-286. —

Kopelman, R. E. , Liebman, A. H. , S Yukl, G- A. Experimental t e s t of a return on e for t version of expectancy theory: Across-person and withln-person ana lyses . Journal of Managenent. 1978, 4 ( 1 ) , 97-105,

Kling, B. The organ iza t iona l context of user-centered software des igns . Hanagenent Inf orna tlon Systens 2 M £ t e r l j , 1977 (Decenber), 41-52.

^ ^ K o e s t e r , R., 6 Luthans, P. The Inpact of the conputer on the choice a c t i v i t y of dec i s ion makers: A r e p l i c a t i o n with ac tua l users of computerized HIS. Academy og Management Journal, 1979, 22, 416-422.

Kopelman, R. E. , 6 Thompson, P. H. Boundary condi t ions for expectancy theory pred ic t ions of work motivation and job performance. Acadeny of Hanagenent Journal, 1976, 19, 237-258. - - — - __

Kuleck, W. J . Indiv idual d i f f erences as moderators of expectancy theory pred ic t ions of job choice a t t i t u d e s . D i s s e r t a t i o n Abstracts In ternat iona l . 1976, 37 (6-B) 3130-3131,

Lasky, J. The Importance of cogni t ive psychology to the development of adaptive dec i s ion support systems. Proceedings of the 1972 Hidwest Conference of the American I n s t i t u t e for Decision Sc iences . Apri i~21-22, 'i972,"c21-C26.

^ Lawler, E. E . , I I I . A c o r r e l a t i o n - c a u s a l ana lys i s of the r e l a t i o n s h i p between expectancy a t t i t u d e s and job performance. Journal of Applied Psychology. 1968, 52, 462-484.

r\

159

Lawler, E. E. , in. Hotivation In work organizations. Honterey, Cal.: Brooks-Cole, 19737^

Lawler, E. E., III., Kuleck, W. J., Jr., Rhode, J. G., 6 Sorensen, J. E. Job choice and post decision dissonance. 2£aanizational Behavior and Hunan Perfornance. 19 75, 13, 133-145.

Lawler, E. E., III, 6 Porter, L. W. Antecedent attitudes of effective nanagerial perfornance. Organizational Behavior and Hunan Perfornance, 196 77 2, 122-142-

Lawler, E. E., III, & Suttle, J. L. Expectancy theory and job behavior. Organisational Behavior and Hunan Performance, 1973, 9, 482-503.

Lefcourt, H. H. Recent developnents In the study of locus of control. In B. A. Haher (Ed.) , Progress in experinental psychological research. New York: Acadenic Press, 1972.

Leon, F. R. The role of positive and negative outcomes in the causation of motivational forces- Journal of Applied Psychology, 1981, 66, (1), 45-53-

Lewln, K. The conceptual representation and neasurenent of psychological forces. Durham, NC: Duke University Press, 1938.

Locke, E. A., Bryin, J, ., & Kendall, L. N. Goals and Intentions as mediators of the effects of monetary Incentives on behavior. Journal of Applied Psychology. 1968, 52# 104-121. Durham, N. C,: Duke University Press,""19 38.

Lucas, H. D. , Jr. Hethodological research on the implementation of computer-based decision aids. In P. G. W. Keen (Ed.), Proceedings of a conference on the Implementation of conputer-based decision aids. Canbrldge, HA.: "center for Information System Research, HIT, 1975a.

Lucas, H. C , Jr. Performance and the use of an information system. Hanagement Science, 1975b, 20, 908-9 19.

Lucas, H. C , Jr, Empirical evidence for a descriptive model of implementation. Hanagement Information Systems Quarterly, 1978, 2, (2), 27-52.

Lucas, H. C. , Jr., Clowes, K. W. , & Kaplan, R. B. Frameworks for information systems. INFOR Journal, 1974, IZ (3).

r\

160

Lucas, H. C, , 6 N i e l s e n , N. R. The Impact of the mode of information presentat ion on learning and performance. Hanagement Sc ience . 198 0, 26, 982-993.

Lusk, E. J . Cognit ive aspects of annual reports : Fie ld independence/dependence. Journal of Accounting Research, 1973 Supplement, H , 19 1-2017

Lusk, E, J , , 6 Kersnick, H, Ef fects of c o g n i t i v e s t y l e and report fornat on task perfornance: The MIS design conseguences, Managenent Sc ience , 1979, 25, 787-798,

Haish, A. H, A u s e r ' s behavior toward h i s MIS, Managenent Infornat ion S i s t e j ^^uarterly, 1979, 3 (1 ) , 39-527

Hanoochehri, G, An experinental evaluat ion of the e f f e c t s of psycho log ica l types and problen types on preference for some management information system c h a r a c t e r i s t i c s and decls Ion-ma king performance. Unpublished Ph.D. d i s s e r t a t i o n , Loulslanna State Univers i ty , 1978.

Hason, B. 0 . , 6 Hi trof f , I , I , A program for research on management information systems, Hanagement Sc ienge , 1973, 19 , 475-487,

Hatsui , T, , Kagawa, H., Nagamatsu, J , , 6 Ohtsuka, Y. Val id i ty of expectancy theory as a wlthin-person behavioral choice model for s a l e s a c t i v i t i e s . Journal of A££lled Psychology. 1977, 62 , 764-767.

Hatsui , T . , 6 Ohtsuka, Y. Withln-person expectancy theory p r e d i c t i o n s of supervisory cons iderat ion and s tructure behavior. Journal of Applied Psychology. 1978, 63, 128-131-

Hatsui , T. , S Tashitake, T, A c r o s s - c u l t u r a l study of the v a l i d i t y of expectancy theory of work motivation. Journal of Applied Psychology, 1975, 60 , 263-265.

Hatsu i , T. , 6 Terai , T. A c r o s s - c u l t u r a l study of the v a l i d i t y of the expectancy theory of work motivat ion. Journal og Applied Ps ichoiogy , 1975, 60, 263-265.

HcArthur, D. S. Dec is ion s c i e n t i s t s , dec i s ion makers, and the gap. I n t e r f a c e s , 1980, 10, 110-113.

HcKenney, J. , 6 Keen, P. How do managers think? Harvard Business Review, 1974, 52 (3) , 79-90.

Hehra, S . , & Alexander, E. , I I I . A behavioral a n a l y s i s of management information use. Proceedings of the H t h Annual Meeting of the Anerican I n s t i t u t e for Decis ion Sciences,^November 1979, Vol7 1, 158-159.

f^

161

Mento, A. J., Cartledge, N. D,, 6 Locke, E. A. Haryland vs. nlchlgan vs. Hlnnesota: Another look at the relationship of expectancy and goal difficulty to task performance. Organizational Behavior and Human Perfornance, 1980, 25, 419-440.

Miller, D., & Gordon, L. A, Conceptual levels and the design of accounting Infornation systems. Decision Sciences. 1975, 6, 259-269.

Hitchell, T. R. Expectancy nodels of job satisfaction, occupational preference, and effort: A theoretical, methodological, and empirical appraisal. Psychological l!iii§tin, 1974, §1, 1053-1077.

Mitchell, T, R., & Albright, D. W, Expectancy theory predictions of the satisfaction, effort, performance, and retention of naval aviation officers. Organizational Behavior and Human Perfornance, 1972, 8 (1), 1-20.

Hitchell, T. R,, & Beach, L, R, A review of occupational preference and choice research using expectancy theory and decision theory. Journal og Occupational Psychology, 1976, 49 (4), 231-248."

Hitchell, T, R,, 5 Biglan, A, Instrumentality theories: Current uses In psychology. Psychological Bulletin, 1971, 76, 432-454.

Hitchell, T. R., & Nebeker, D. Expectancy theory predictions of academic effort and performance. Joijrnal of Appllg^ Psychology, 1973, 57, 61-67.

Mitchell, T, R., 6 Pollard, W. E, Instrunentality theory predictions of acadenic behavior. Journal of Social Psychology. 1973, 89, 34-45,

Hock, T, J. A longitudinal study of sone information structure alternatives. Data Base, 1973, 5 (2,3,4), 40-45.

Moltra, S, A pre-program evaluat ion model determining t r a i n i n g e f f e c t i v e n e s s , based on the expectancy theory of work mot ivat ion . Unpublished Ph.D. d i s s e r t a t i o n , Cornel l U n i v e r s i t y , 1976.

Hotowidlo, S. J . , Loegr, V,, & Dunnette, H. D. A laboratory study of the e f f e c t s of goal s p e c i f i c i t y on the r e l a t i o n s h i p between probabi l i ty of success and performance. Journal of Applied Psychology, 1978, 6 i , 172-179,

Humford, E. (Ed.) IFIP conference on hunan choise and computers. New York: American Elsevuier , 1975.

162

Nebeker, D. H., 6 H i t c h e l l , T, R, Expectancy theory p r e d i c t i o n s of leader behavior. Proceedings of the 81st Annual Convention of the American"PsYchologlcal A s s o c i a t i o n . 1973, MontreaI7"canada, 8 , 577-578.

Nolan, R. L. , & Wetherbe, J. c. Toward a comprehensive framework for HIS research, Hanagenent Infornation iJCsten Quarterly^ 1980, 4 (2) , 1-20,"

Parker, D. F . , 6 Dyer, L. Expectancy theory as a within person behavioral choice nodel: An e n p l r l c a l t e s t of some conceptual and nethodological refinements. Organizational Behavior and Human Performance, 1976, 17,

P a v e t t , C. H. Expectancy theory, persona l i ty , and the s i t u a t i o n . D i s s e r t a t i o n Abstracts In ternat iona l , 1978, 39 (6-A), 3695-3696.

Pervin , L. A. Persona l i ty : Theory, assessment, and research. New York: John Wiley, 1?70,

P e t e r s , L, H. Cognit ive models of motivat ion, expectancy theory, and e f f o r t : An a n a l y s i s and empirical t e s t . Organizational Behavior and Human Performance. 1977, 2Q, 129-148."

Phares, E. J. Locug og control In personality. Horrlstown, R. J.: General Learning Press, 1976.

Porter, L. W. , 6 Lawler, E. E., III. Managerial attitudes and performance. Homewood, 111.: Dorsey Press, 1968.

Pritchard, R. D., 6 DeLeo, P. J. Experimental test of the valence-Instrumentality relationships In job performance. Journal of Applied Ps^choiogi, 1973, 57, 264-270.

Pritchard, R. D,, S Sanders, H- S. The influence of valence, instrumentality, and expectancy on effort and performance. Journal of Applied Psxchology, 1973, 57, 55-60.

Reichelt, P. A. Moderators in expectancy theory: Influence on the relationships of motivation with effort and job performance. Unpublished Ph.D. dissertation, 1974.

Reinharth, L. , & Wahba, H. A. Expectancy theory as a predictor of work motivation, effort expenditure, and job performance. Academx of Hanagement Journal, 1975, U , 520-537.

Reinharth, L. , S Wahba, M. A. A test of alternative models of expectancy theory. Human Relations, 1976, 29 (3), 257-272.

163

Robey, D. User a t t i t u d e s and MIS use . Academy of Hanagement Journa l , 1979, 2 2 , 5 2 7 - 5 3 8 ,

Robey, D . , 6 Z e l l e r , R. F. F a c t o r s a f f e c t i n g t h e s u c c e s s and f a i l u r e of an Information system for product g u a l l t y . I n t e r f a c e s . 1978, 8 ( 2 ) , 7 0 - 7 8 .

Robinson, J . p . , & Shaver , P. R. Heasures of s o c i a l E s y c h o l o g i c a l a t t i t u d e s . Ann Arbor, H i c h i g a n 7 " " l n s t i t u t e f o r S o c i a l Research, 1978.

Rodriguez , J . I . The des ign and e v a l u a t i o n of a s t r a t e g i c i s s u e c o m p e t i t i v e in format ion system. Unpublished Ph.D, d i s s e r a t i o n . U n i v e r s i t y of P i t t s b u r g , 1977,

R o t t e r , J. B. Genera l i zed e x p e c t a n c i e s for i n t e r n a l v e r s u s e x t e r n a l c o n t r o l of re in forcement . P s y c h o l o g i c a l Honographs. 1966, 80 (1 Whole No. 6 0 9 ) .

S a n d e r s , G. L . , 5 Courtney, J . R. A key implementat ion f a c t o r : ' O r g a n i z a t i o n a l Form': A paradigm f o r MIS r e s e a r c h . Paper presented a t the Annual Heetlng of t h e Anerican I n s t i t u t e f o r Dec i s ion S c i e n c e s - Bos ton , H a s s , , 1981.

Schewe, C. D, The nanagenent Information system u s e r : An e x p l o r a t o r y b e h a v i o r a l a n a l y s i s . Academy o£ Hanagement J o u r n a l , 1976, 1 9 , 577-590 .

S c h n i d t , F, L. I m p l i c a t i o n s of a measurement problem f o r e x p e c t a n c y theory r e s e a r c h . Organ lza t lona l Behavior and HiJiaa Performance, 1973, 10 , 243-2517

Schnelderman, B. Improving the human f a c t o r s a s p e c t s of da tabase i n t e r a c t i o n s . ACH T r a n s a c t i o n s on Database S y s t e m s . 1978, 3 ( 4 ) , 4 17-439 .

^---/L Schnelderman, B. Software psychology: Human f a c t o r s In computer and Informat ion s y s t e m s . Cambridge, HA: Wlnthrop,~19807

Schonberger , R. J . HIS d e s i g n : A c o n t i n g e n c y approach. Hanagement I n f o r n a t i o n System a M I i S E i l r 1980, 4 ( 1 ) , 13 -20 .

S c h r o e d e r , R. G., & Benbasat , I . An exper imenta l e v a l u a t i o n of the r e l a t i o n s h i p of u n c e r t a i n t y i n the environment to i n f o r m a t i o n used by d e c i s i o n makers. Dec i s ion S c i e n c e s , 1975, 5 5 4 - 5 6 7 .

S c h r o e d e r , H. , D r i v e r , H., S S t r u e f e r t , S. Hun^n i n f o r n a t i o n p r o c e s s i n g . New York: H o l t , 1967.

164

S c h u l t z , R. L . , & S l e v i n , D. P. ( E d s . ) , I n p l e n e n t i n g Operat ions Research/^Hanagenent S c i e n c e . New York: Anerican E l s e v i e r , 1975.

Schwab, D. P . , O l i a n - G o t t l i e b , J . D, , & Henenan, H, G, B e t w e e n - s u b j e c t s expectancy theory r e s e a r c h : A s t a t i s t i c a l rev iew of s t u d i e s p r e d i c t i n g e f f o r t and p e r f o r n a n c e . P s y c h o l o g i c a l B u l l e t i n . 1979, 86 , 1 3 9 - 1 4 7 .

Senn, J . A, , 6 D ickson , G, W. Information system s t r u c t u r e and purchasing d e c i s i o n e f f e c t i v e n e s s . Journal of Purchas ing and H a t e r l a l s Hanageaent, 1974, 10 .

S h e r i d a n , J. E . , R ichards , H. D . , 6 S l o c u a , J . W. Expectancy theory of a o t i v a t i o n : A aode l of job s e l e c t i o n . Proceed ings o^ t h e Hidwest Academy of Hanagement. 197 3 .

S h e r i d a n , J. E . , Slocum, J , W., 6 Hin, B. H o t i v a t i o n a l d e t e r m i n a n t s of job performance. Journal of Applied P s i c h o l o g y , 1975, 60 ( 1 ) , 119-121-

S h e r l d a n , J . E . , Slocum, J . W-, & Richards , H. D. Expectancy theory as a l ead I n d i c a t o r of job behav ior . D e c i s i o n S c i e n c e s , 1974, 5 , 5 07-5 22 .

Simon, H. A. The new s c i e n c e of management d e c i s i o n . New York: Harper & Row, 1960.

Simon, H A. The new s c i e n c e of nanagenent d e g i s i p n . Englewood C l l f f s , 5 J . : P r e n t i c e - H a l l , 1977.

Smith , H. R. Experimental comparison of database Inquiry t e c h n l g u e s . Unpublished Ph.D. d i s s e r t a t i o n . U n i v e r s i t y of Hlnnesota , 1975.

Snyder , R. A . , Howard, A . , 6 Hammer, T. H. The p r e d i c t i v e power of w i t h i n v e r s u s a c r o s s - s u b j e c t s s c o r e s in expec tancy r e s e a r c h . JournajL of Psychology^ 1978, lOjO (2) , 2 8 5 - 2 9 2 .

S o d i k o f f , C. L. I n t e r n a l - e x t e r n a l l o c u s of c o n t r o l as a moderator of expectancy t h e o r y , normative b e l i e f s , and need achievement p r e d i c t i o n s of m o t i v a t i o n . Unpublished Ph.D. d i s s e r t a t i o n , 1975-

Sprague , R. H . , J r . A framework for the development of d e c i s i o n support s y s t e m s . Management Information Systems Q u a r t e r l y , 1980, 4 ( 4 ) , 1 -26 .

Sprague , R. H . , 6 Watson, H. J. Bit by b i t : Toward d e c i s i o n support s y s t e m s . C a l i f o r n i a Management Review, 1979, 22 , 6 0 - 6 8 .

&. JA.

^

165

S t a h l , M. J , , 6 Harre l l , A- H. Hodellng course e f f o r t dec i s ions with behavioral dec is ion theory . Proceedings 2 i i M 121^ i M M i Meeting of the American I n s t i t u t e for Decis ion S c l e a c e s , 1980, \, 361-363.

S t a h l , M. J . , 6 Harre l l , A. H. Hodellng e f fort d e c i s i o n s with behavioral d e c i s i o n theory: Toward an Indiv idual d i f f e r e n c e s model of expectancy theory . Organizational Behavior and Hunan Perfornance,. 198 1, 27, 303-325.

Steckroth , R, L., Slocun, J. i . , 5 S i n s , H. P. Organizational r o l e s , c o g n i t i v e r o l e s , and problen-so lv ing s t y l e s . Proceedings of the 12th Annual Heetlng 2 l i k e Anerlcaa I n s t l t u t g for Decision Sciences,"T98o7 1# 331-333.

S t e e r s , R. H. , 6 Porter , L, i . Hotivation and work behavior. New York: HcSraw-Hlll, 1979.

Squier , R. W. The e f f e c t of feedback Infornation and behavior cons i s t ency on accuracy of s o c i a l p r e d i c t i o n . Journal of Socia l Psychology. 1971, 83 (2 ) , 255-264.

Swanson, E. B. Hanagenent infornat ion sys t ens : Appreciation and Involvement. Hanagement Science, 1974, 2X, 178-188.

S z i l a g y i , A. D., S Sims, H. P. , Jr . Locus of control and expec tanc ie s across multiple occupational l e v e l s . Journal of Applied Psychology. 1975, £0, 638-640.

Taylor , R. N. Age and experience as determinants of managerial information processing and decis ion making performance. Academy of Hanagenent Journal, 1975, 18, 74 -81 .

Taylor , R. N . , 6 Dunnette, H. D, Relat ive contr ibut ion of decision-maker a t t r i b u t e s to the dec i s ion process , organ iza t iona l Behavior and Huaan Performance. 19 74, 12, 286-298.

T a y l o r , S . H,, 6 Huesmann, L, P. Replicat ion report: Expectancy confirmed again: A computer Inves t iga t ion of expectancy theory. Journal of Expermeatal Soc ia l Psychology, 1974, 10 (5 ) , 497-501.

Terborg, J- R- Val idat ion and extension of an ind iv idua l d i f f e r e n c e s model of work performance. Organizational Behavior and Human Performance, 1977, 18, 18 8-216.

r\

166

Terborg, J, R., Richardson, P. , 6 Pritchard, R. D. Person-s i t u a t i o n e f f e c t s in the predict ion of performance: An I n v e s t i g a t i o n of a b i l i t y , s e l f - e s t e e m , and reward c o n t i n g e n c i e s . Journal of Applied Psychology, 1980, 5 ( 5 ) , 574-5 83.

T i e s s e n , P, An i n v e s t i g a t i o n of some e f f e c t s of data aggregat ion and c o g n i t i v e s t y l e on decision-making performance. Unpublished Ph.D. d i s s e r t a t i o n . Univers i ty of Hlnnesota, 1976.

Tolman, B, C, EiLLEosive behavior in an ina l s and nen. New York: Century, 1932,

Valecha, G. K, Construct va l idat ion of an I n t e r n a l - e x t e r n a l l o c u s of reinforcement re la ted to work-related v a r i a b l e s -annual Meeting of the Anerican Psychological Assoc ia t i on , Honolulu, Hawaii, T9727

Valecha, G, K,, S Ostron, T, H, An abbreviated neasure of i n t e r n a l - e x t e r n a l locus of contro l . Journal of Persona l i ty Assessnent , 1974, 1^ ( 4 ) , 369-376,"

Van Horn, R, L. Empirical s t u d i e s of management information s y s t e n s . Data Base, 1973, 5, 172-180,

Vasarhely i , H. A. Han-nachine planning systems: A behavioral examination of i n t e r a c t i v e decision-making. Unpublished Ph.D. d i s s e r t a t i o n . Universi ty of C a l i f o r n i a , Los Angeles , 1973.

Vasarhely i , H. A. flan-machine planning systems: A c o g n i t i v e s t y l e examination of i n t e r a c t i v e d e c i s i o n ­making. Journal of Accounting Research, 1977, 15, 138-153-

Vazsonyi, A- Decis ion support systems: The new technology of d e c i s i o n making- I n t e r f a c e s , 1978, 9, 72-77.

Vert insky, I . , Barth R. T. , & H i t c h e l l , V. F. A study of OR/ HS implementation as a s o c i a l change process . In R. L, Schultz 6 D. P. Slevin (Eds . ) , Implementing Operations Research/Hanagenent Science. New York: American E l s e v i e r , 1975,

Vroon, V, H. Work and motivation. New York: Wiley, 1964.

Vroon, V. H. Organizational choice: A study of pre- and p o s t - d e c i s i o n p r o c e s s e s . Organizational Behavior and Hunan Perfornance. 1966, 1, 212-225,

Wagner, R. G. Decis ion support systems: The r e a l substance . I n t e r f a c e s , 1981, H ( 2 ) , 77-86.

167

Wahba, H., 6 House, R. Expectancy theory In work and m o t i v a t i o n : Sone l o g i c a l and methodo log ica l i s s u e s . M i a n R e l a t i o n s , 1974, 2Z# 121-147 .

Wal ther , G. H. , & O ' N e i l , H. F . , Jr . O n - l i n e user-computer I n t e r f a c e : The e f f e c t s on I n t e r f a c e f l e x i b i l i t y , t e r m i n a l t y p e , and e x p e r i e n c e on perfornance . Proceedings o f the N a t i o n a l Conputer C o n f e r e n c e s , 1974, 4 3 .

Watkins , P. R. P e r c e i v e d Information c o n p l e x i t y , and c o g n i t i v e s t y l e s : I n p l i c a t i o n s for d e c i s i o n support s y s t e n s . Unpublished Ph.D. d i s s e r t a t i o n . Arizona s t a t e U n i v e r s i t y , 1980.

Werner, D. J , A s tudy of the use of an e x p e r i n e n t a l i n f o r n t i o n system i n a medical environment. Hanagement I n f o r n a t i o n . 1974, 3 ( 3 ) , 133-142. b i b ; W l l l l a n s , W. E . , & S e l l e r , D, A. R e l a t i o n s h i p between neasures of e f f o r t and job p e r f o r n a n c e . Journal of Applied Psycho logy , 1973 , 57 , 4 9 - 5 4 .

Wetherbe, J« C , & Whitehead, C. J . A cont ingency view of managing t h e data proces s ing o r g a n i z a t i o n , Hanagement In format ion Systems Quarter ly , 1977, 1 ( 1 ) , 19 -25 ,

w i t k i n , H, A . , Oltman, P. K,, Raskin, E. , 6 Karp, S. A. manual for the Embedded F i g u r e s T e s t . Palo A l t o , CA: Consu l t ing P s y c h o l o g i s t s P r e s s , 1971.

Wofford, J . C. The m o t i v a t i o n a l bases of job s a t i s f a c t i o n and job performance . Personnel Psycho logy , 1971, 24 ( 3 ) , 5 0 1 - 5 1 8 ,

Wolf, G. Some r e s e a r c h and teach ing with an o n - l i n e o l i g o p o l y game us ing an a r t i f i c i a l p l a y e r . Unpublished manuscr ip t , 197 1.

Wolf, G., 6 C o n n o l l y , T. B e t w e e n - s u b j e c t s d e s i g n s in t e s t i n g e x p e c t a n c y models: A methodolog ica l n o t e . D e c i s i o n S c i e n c e s 1981, 12 (1)# 3 9 - 4 5 .

Wynne, B, E. " D e c i s i o n r u l e i n f o r n a t i o n s y s t e m s , " An e v a l u a t i o n of the e f f e c t i v e n e s s of I n d i v i d u a l man-machine i n t e r a c t i o n s e q u e n c e s . Ann Arbor, H i c h , : U n i v e r s i t y M i c r o f i l m s , 1972.

Wynne, B. B, Measuring the immeasurable or c r e d i b i l i t y in the p u b l i c s e c t o r . I n t e r f a c e s , 1977, 8 ( 1 ) , 106-109 .

Wynne, B . , & Dickson, G. W. Experienced managers' performance in e x p e r i m e n t a l man-machine d e c i s i o n sys tem s i m u l a t i o n . Academy of Management J o u r n a l , 1975, 1 8 , 2 5 - 4 0 .

168

Z e i g l e r , W. The use of expectancy theory to explain and predict p e r s i s t e n c e In adult education. Unpublished Ph.D, t h e s i s . The Ohio State Univers i ty , 1980.

Zmud, R, w. Individual d i f f erences and MIS success : A review of the empir ical l i t e r a t u r e - Hanagement S c i e n c e , 1979a, 25 , 966-979.

Zmud, R. W. Perceptions of cogni t ive s t y l e s : Acqu i s i t i on , e x h i b i t i o n and Impl icat ions for Information system des ign . Journal of Hanagement, 197 9b, 5 , 7-20.

Zmud, R. w. Locus of contro l , ambiguity to lerance , and Information system design a l t e r n a t i v e s : Correlates of dec i s ion behavior. Proceedings of the 11th Annual Heetlng of the American I n s t i t u t e for Decision S c i e n c e s . 1979c, 1 , 146-1487"

Zmud, R, W, The ro l e of indiv idual d i f f erences in HIS implementation s u c c e s s . Symposium on HIS Implementation research. Proceedings of the 12th Annual j|eetjLng of the American I n s t i t u t e for Decis ion S c i e n c e s , 1980, 1, 215.

Br\

Appendix A

SUBJECT COHSBBT FOBS

I consent to serve as a subject In the research investigation entitled: "A Business Simulation Study," The nature and general purpose of the research procedure have been explained to me, and I have been informed that nonpartlclpatlon is an alternative I may choose. The research is to be performed by Gerry DeSanctis under the direction of Dr, Hichael Crino (742-3133) and Dr. James Courtney (742-2169) who are authorized to use the services of others In the performance of the research.

As part of this study I will complete several guestlonnalres and participate in a business simulation game for the next 3 to 4 weeks. I understand that any further Ingulrles I make concerning this procedure will be answered by Ms. DeSanctis. I understand my Identity will not be revealed In any publication, document, recording, video-tape, photograph, computer data storage, or in any other way which relates to this research. I understand that I may contact the Texas Tech University Institutional Review Board for the Protection of Human Subjects by writing them in care of the Office for Research Services, Texas Tech University, Lubbock, Texas 79409, or by calling 742-3884.

If this research causes any physical Injury to participants In this project, treatment Is not necessarily available at Texas Tech University or the student Health Center, nor is there necessarily any insurance carried by the University or Its personnel applicable to cover any such injury. Financial compensation for any such Injury must be provided through the participant's own Insurance program. Further Information about these matters may be obtained from Dr. J. Knox Jones, Jr., Vice President for Research and Graduate Studies, 742-2152, Room 118 Administration Building, Texas Tech University, Lubbock, Texas 79409,

169

170

Finally, I understand that I am free to withdraw my consent and discontinue participation at any time without prejudice following notification of the experimenter.

(Signature)

(Date)

(Signature of Project Director) (Date)

Appendix B

A5BBBHBNT TO COIFIDEITIALITT

I understand that i t Is in the best i n t e r e s t of

s c i e n t i f i c Inguiry not to d i s cuss with ny fe l low s t u d e n t s ,

now or during the next 10 months, any aspect of the

experiment in which I am p a r t i c i p a t i n g . I f u l l y r e a l i z e

t h a t such d i scuss ion may lead to poss ib le d i s t o r t i o n s of the

data and may in e f f e c t cause the e n t i r e experiment to be

abandoned.

Signature Date

171

Appendix C

BACKGBOOHO QOBSTIOBIAIBE

Nane;

Pernanent Mailing Address:

Phone:

Age:

Sex: G.P.A. :

Current Year of Study: Sophonore

Junior

Senior

Highest Degree Obtained to Date

high school diploma

B.B.A., B.S., or B.A.

_ ____ __M. B. A. , H,S., or H. A.

other (specify)

Number of months of f u l l - t i m e work experience:

Number of months of f u l l - t i m e work experience in a bus iness

or b u s i n e s s - r e l a t e d p o s i t i o n :

If working towards a degree s t a t e

degree:

major f i e l d :

172

173

Have you s u c c e s s f u l l y completed BA 2340 , In troduc t ion t o

Computers (or e g u l v a l e n t ) ? yes

no

currently enrolled

If you have completed any additional computer courses,

please list these.

Course Number Course Title

Number of months of experience as a computer programmer or

systems analyst:

If you have completed any courses In production, operations

research, or management science, please list these below.

Course Number Course Title

Have you ever played a business simulation game (such as

IHAGINIT or ADSIH) ?

yes

no

c u r r e n t l y p lay ing

Appendix D

LOCOS OF COMTBOL SCALE

A. 1. Hany of the unhappy things In people ' s l i v e s are par t ly due t o bad luck.

2, Peop le ' s misfortunes r e s u l t from the mistakes they make-

I s t h i s statement much c l o s e r or s l i g h t l y c lo ser to your opinion? a- much c lo ser b. s l i g h t l y c l o s e r

B. 1, In the long run, people get the respect they deserve In t h i s world,

2. Unfortunately, an Indiv idual ' s work often passes unrecognized no matter how hard he t r i e s .

I s t h i s statement much c l o s e r or s l i g h t l y c l o s e r to your opinion? a. much c lo ser b. slightly closer

3. 1. Without the right breaks, one cannot be an effective leader.

2. Capable people who fall to become leaders have not taken advantage of their opportunities.

Is this statement much closer or slightly closer to your opinion? a. much closer b, slightly closer

D, 1, Becoming a success Is a matter of hard work; luck has little or nothing to do with it,

2, Getting a good job depends mainly on being in the right place at the right time.

Is this statement much closer or slightly closer to your opinion? a. much closer b, slightly closer

E. 1. What happens to me Is my own doing. 2. sometimes I feel that I don't have enough control

over the direction my life is taking. Is this statement much closer or slightly closer to your opinion? a. much c loser b. slightly closer

174

: \

175

F. 1. When I make plans, I am almost certain that I can make them work.

2. It is not always wise to plan too far ahead, because many things turn out to be a matter of good or bad fortune anyway.

Is this statement much closer or slightly closer to your opinion? a, much closer b. slightly closer

G. 1, In ny case, getting what I want has little or nothing to do with luck.

2, Hany times we might just as well decide what to do by flipping a coin.

Is this statement much closer or slightly closer to your opinion? a. much c loser b. s l i g h t l y c l o s e r

H. 1. Who ge t s to be boss often depends on who was lucky enough to be In the r igh t place f i r s t ,

2, Getting people to do the r ight thing depends upon a b i l i t y ; luck has l i t t l e or nothing to do with I t ,

I s t h i s statement much c l o s e r or s l i g h t l y c l o s e r to your opinion? a. much c loser b. s l i g h t l y c l o s e r

I. 1. Host people don't realize the extent to which their lives are controlled by accidental happenings.

2, There Is really no such thing as "luck," Is this statement much closer or slightly closer to your opinion? a. much c l o s e r b. s l i g h t l y c l o s e r

J. 1. In the long run, the bad things that happen to us are balanced by the good ones,

2, Most misfortunes are the result of lack of ability. Ignorance, laziness, or all three.

Is this statement much closer or slightly closer to your opinion? a. much c l o s e r b, s l i g h t l y c l o s e r

K. 1. Many times I feel that I have little influence over the things that happen to me-

2. It is impossible for me to believe that chance or luck plays an important role in my life-

Is this statement much closer or slightly closer to your opinion? a. much c loser b. s l i g h t l y c l o s e r

r\

Appendix B

DBCISIOI FORH

Mane: Date: / /

Quarter: Year:

Decis ion Deadline:

Product P r i c e : $_

Advert is ing: $,

Production (#units)

Stage 1 Maintenance: $,

s tage 2 Maintenance: I.

Raw Material 1 (# units)

Raw Material 2 (# uni ts )

Engineering Studies $ (In even thousands)

Total time spent on the dec i s ion process

Qual i ty Control $ 1000

Research & Development $ 1000

S a l e s r e p r e s e n t a t i v e s 10

Salary per s a l e s r e p $ 2000

176

Appendix F

BHL/SLIH QUIZ

BHL/SLIH Q u e s t i o n n a i r e NAHE:

D i r e c t i o n s : S e l e c t t h e b e s t answer t o each of t h e f o l l o w i n g q u e s t i o n s .

1. The CURRENT ECONOHIC INDEX given on the BHL Industry report refers to: a. an estimate of the inflation index b. a measure that is analogous to the gross national

product (GNp) c. the economic forecast for the next quarter to come d. an estimate of the stainless steel flatware

production level this year

2. Consumer demand in the stainless steel flatware Industry is somewhat seasonal. a. true b. false

3. As a manager for your firm, the price which you set for your product is a. the retail price b- the wholesale price c. the producers' price d. b or c

4. If your firm has back orders which must be filled In the current quarter, what selling price is attached to these orders? a. the current quarter's price b. the price that prevailed at the time of the order c. the current quarter's price or the price at order

time, whichever is lower d. the current quarter's price or the price at order

time, whichever is higher

5 . The SALES VOLUHE r e p o r t e d f o r your f i rm i n any q u a r t e r r e f e r s t o a. new sales for the current quarter b. new sales plus backorders c. new sales plus backorders plus lost sales

177

178

6. Which of the fo l lowing i s not true regarding purchase of raw mater ia ls? a. If i n s u f f i c i e n t raw materials have been ordered,

mater ia ls w i l l automatical ly be purchased from a d i s t r i b u t o r .

b. I t takes two weeks to order and obtain raw mater ia l s from a d i s t r i b u t o r .

c. If materia ls are purchased from a d i s t r i b u t o r , the firm Is charged the CURRENT HARKET PRICE which I s not known (by you) In advance.

d. Raw mater ia l s purchased for s torage in your inven­tory are obtained at the FUTURES CONTRACT PRICE quoted on the current per iod's INDUSTRY REPORT.

e . Raw mater ia l s purchased for storage In inventory are a v a i l a b l e at the beginning of the period In which they are ordered,

7. In order to counteract the e f f e c t s of depreciat ion and avoid reduction in capac i ty , your firm can spend money on a. maintenance b. guallty control c. research and development

3. Which of the fo l lowing I s true concerning s p e c i a l loans? a. These occur automatical ly If the firm i s short of

cash, b. The loan ra te i s 12S annually. c . The loan assures that the firm has a minimum cash

balance of $10000, d. I t i s the only financing method ava i lab le to the

firm. e. All of the above are t rue .

9. Suppose you wanted to find out the net Income for Firm 2 In the 3rd guarter of 1980 (5th guarter of BHL gaming). Show how you would reguest t h i s information from the SLIH system.

10. When ta lk ing to SLIH each of your commands(gueries) must end with what?

11. What SLIH command would you use to f ind out your f i rm's maximum net income across a l l guarters thus far in the game?

12. Write a s e t of commands which would t e l l SLIH to c a l c u l a t e the p r i c e - t o - e a r n i n g s for your firm in the current quarter .

r\

Appendix 6

EXPBBIHEBTAL QOESTIONHAIBB

NAHE:

DATE:

PART A

1. Think of "working hard" in terms of devoting time to decision-making, carefully considering alternatives, and trying hard to make good decisions. Assuming that you work hard when making decisions, distribute a total of 100 points so that they reflect the perceived chances of your

obtaining high retained earnings for your firm obtaining low retained earnings for your firm

100 2. Assuming that you do not work hard, distribute a total

of 100 points so that they reflect the perceived chances of your

obtaining high retained earnings for your firm obtaining low retained earnings for your firm

100 3- Assuming that you spend a great deal of time on the

decision-making process distribute a total of 100 points so that they reflect the perceived chances of your

obtaining high retained earnings for your firm obtaining low retained earnings for your firm

100 4, Assuming that you do not spend a great deal of time on

the decision-making process, distribute a total of 100 points so that they reflect the perceived chances of your

obtaining high retained earnings for your firm obtaining low retained earnings for your firm

100 5- Assuming that you use SLIH extensively, distribute a

total of 100 points so that they reflect the perceived chances of your

obtaining high retained earnings for your firn obtaining low retained earnings for your firm

100

179

C^

180

5, Assuming that you do not use SLIH much at all, distribute a total of 100 points so that they reflect the perceived chances of your

obtaining high retained earnings for your firm obtaining low retained earnings for your firm

100 PART B

7, Assuming that your firm obtains high retained earnings in the BHL game, d i s t r i b u t e a t o t a l of 100 po ints so tha t they r e f l e c t the perceived chances of your then experiencing each of the fol lowing outcomes: a, rece iv ing approval from your f r i ends

not rece iv ing approval from your friends _ " Too"

b. rece iv ing add i t iona l course po ints t h i s semester not rece iv ing addi t iona l course points

c. rece iv ing a cash bonus not r e c e i v i n g a cash bonus

d- a f e e l i n g of having learned something no f e e l i n g of having learned something

e. a sense of having met a chal lenge no sense of having met a chal lenge

100

100

100

100 f. significantly reduced time available for other

Important things no significant reduction in time available for other important things

100 8, Assuming that your firm obtains ^w retained earnings in

the BHL game, distribute a total of 100 points so that they reflect the perceived chances of your then experiencing each of the following outcomes: a. receiving approval from your friends

not receiving approval from your friends 100

b. receiving additional course points this semester not receiving additional course points

100 c, receiving a cash bonus

not receiving a cash bonus 100

d. a feeling of having learned something no feeling of having learned something 100 a s ense of having met a chal lenge no sense of having met a chal lenge 100

^

181

f. s i g n i f i c a n t l y reduced time a v a i l a b l e for other inportant th ings

no s i g n i f i c a n t reduction in ,time ava i lab le for other Important things

100 PART C

Rate each o f the f o l l o w i n g In terms of I t s degree of a t t r a c t i v e n e s s t o you. ( c i r c l e )

n e i t h e r a t t r a c -

not a t f i v e e x t r e -a l l nor mely

a t t r a c - u n a t t r a c - a t t rac ­t i v e t i v e t i v e

9. rece iv ing approval fron f r i ends 1 2 3 4 5 6 7

not rece iv ing approval fron fr iends 1 2 3 4 5 6 7

10. rece iv ing add i t iona l course points

t h i s semester 1 2 3 4 5 6 7

not rece iv ing addi t iona l course

po ints t h i s semester

11. r ece iv ing a cash bonus

not rece iv ing a cash bonus

12, a f e e l i n g of having learned

something

no f e e l i n g of having learned

something

13. a sense of having met a challenge

no sense of having net a chal lenge

14, spending t ine on Inportant things

other than t h i s experlnent

not spending time on important

th ings other than t h i s experiment

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Appendix B

POST BXPEBIHEHT QOBSTIOIIAIBB

(1) To what extent did you feel that you had a free choice to use or not use the interactive system when making your decisions?

1 not at all

7 to a great extent

(2) To what extent do you feel that your performance In the BHL game was due to chance or random factors, rather than to the guallty of your decisions?

1 not at all due to

chance

entirely due to chance

(3) How freguently during this experiment did you go to the computer room to use SLIH only to find there were no terminals available?

1 not at all

extremely frequently

(4) Is there any reason that you did not try to obtain high retained earnings when participating In the game? (If so, explain)

182

Appendix I

DESCRIPTIOH OF TAB BUSIiESS HIMAGBHBIT LABOBATOBI

The Business Hanagenent Laboratory (BHL) i s a conputer

s l n u l a t l o n of s eve ra l major decision-making funct ions of a

f i r m ' s management. The dec is ions involve a s impl i f ied

vers ion of a manufacturing firm In a ra ther competi t ive

i n d u s t r y . The game can be run with one or two products in

one or two market a r e a s . The decis ions to be made by each

firm Involve such th ings as product p r i ces , nuaber and

loca t ion of salesmen, production q u a n t i t i e s , plant

expansion, and methods of financing the organiza t ion . The

number of f irms per industry i s l imited to e ight with each

producing i d e n t i c a l products . The standard repor ts provide

l i t t l e Information about compet i tors ' pos i t ions except t h e i r

p r i c e s , company income, and number and locat ion of salesmen.

Year-end r epo r t s supplement t h i s infornat ion with the

aggregate l e v e l s of various firm expenditures throughout the

i n d u s t r y . These r e p o r t s are pr imari ly used as expenditure

benchmarks enabling firms to determine t h e i r r e l a t i v e

expendi ture p o s i t i o n s In the indust ry for such th ings as

a d v e r t i s i n g , qua l i ty con t ro l , process s t u d i e s , e t c .

183

184

Although the game i s a s impl i f i ed version of a real

manufacturing concern and designed to be primarily a

teaching dev ice , the compet i t ive nature of each f irm's

a c t i o n s are very l i f e - l i k e . All firms s t a r t with s i m i l a r

f i n a n c i a l p o s i t i o n s and part i c ipants are no t i f i ed that they

are replac ing the previous nanagenent tean. Firns conpete

for narket share in the two product areas with the bus iness

s t r a t e g i e s they d e v i s e .

The format of the game regulres that d e c i s i o n s be

entered p e r i o d i c a l l y represent ing operation of the firm over

a calandar guarter . Approximately 50 dec i s ions are made in

each round of p lay . Quarterly reports are printed and

returned to the p a r t i c i p a n t s . These reports , along with

d e c i s i o n inputs and some Internal game var iab le s , are the

primary sources of data for SLIH.

^adapted from Courtney and Jensen (1980) , pp. 5-6.

Appendix J

DESCRIPTIOH OF THB SLIH SISTBH

The S y s t e n Laboratory for I n f o r n a t i o n Hanagement (SLIH)

c o n s i s t s of a dynamic data base and data d i c t i o n a r y , a

s i m p l e guery language and guery p r o c e s s o r , and a module for

data base a d m i n i s t r a t i o n . The a d m i n i s t r a t i o n module

i n c l u d e s f e a t u r e s f o r managing passwords and f o r data base

e n c r y p t i o n i f a d d i t i o n a l s e c u r i t y i s d e s i r e d .

The SLIH data base c o n t a i n s 126 i t e m s for each round of

p lay ( s i m u l a t i n g a ca lendar g u a r t e r ) . New data are

a u t o m a t i c a l l y added to the data base by BHL as i t p r o c e s s e s

s t u d e n t d e c i s i o n i n p u t and g e n e r a t e s r e s u l t s . T h e r e f o r e ,

a f t e r the a d m i n i s t r a t o r o b t a i n s the s t u d e n t s ' input and runs

BHL, s t u d e n t s may r e t r i e v e the new data from the SLIH data

base by us ing the guery language .

One firm has access t o some data about other f i r m s .

Th i s I s data that I s e s s e n t i a l l y "publ ic" in the r e a l world,

but g o e s beyond what i s i n standard BHL r e p o r t s . For

example . In a c t u a l i t y one firm could always know a n o t h e r ' s

p r i c e and SLIH a l l o w s shar ing of such d a t a . However, some

d a t a , such as f a c t o r e d accounts r e c e i v a b l e , i s s t r i c t l y

p r i v a t e and on ly the owning firm has a c c e s s to i t . Access

185

186

t o some data i s de layed to s i m u l a t e informat ion t h a t i s

r e l e a s e d i n annual r e p o r t s . A l s o , when data about a

competing f irm I s r e t r i e v e d , an error term I s added t o

s i m u l a t e d i s c r e p a n c i e s In r e p o r t i n g , data c o l l e c t i o n , e t c .

The SLIH Query Language

Two t y p e s of i n t e r a c t i v e p r o c e s s i n g are d i s c u s s e d : ad

hoc q u e r i e s and predef ined or "canned" q u e r i e s with

i n t e r a c t i v e c a p a b i l i t y and l i m i t e d modeling c a p a b i l i t y .

F igure 11 i l l u s t r a t e s some ad hoc q u e r i e s j u s t to g i v e a

f e e l f o r the l anguage . Hore d e t a i l e d examples are d i s c u s s e d

s u b s e q u e n t l y .

After t h e user has logged I n t o SLIM he/she I s ready to

e n t e r commands. The f i r s t query (what the user e n t e r s

f o l l o w s the "?") r e t r i e v e s t h e c u r r e n t v a l u e of the

B u l l - B e a r s t o c k market index (BBI). The second command

( u s i n g the a b b r e v i a t e d form of pr int ) r e t r i e v e s the number

of s a l e s r e p s from f irm 1, area 1, quarter 8 . The t h i r d

r e t r i e v a l u s e s the LIST command which outputs format In a

t a b u l a r f a s h i o n . I tems r e t r i e v e d are t h e Bul l -Bear Index ,

Economic Index ( E I ) , and the user f i r m ' s s tock p r i c e (SP)

f o r a l l q u a r t e r s ( Q 1 - * ) . The fourth command ( a f t e r one

t y p o g r a p h i c a l error) uses the HAX f u n c t i o n to r e t r i e v e the

f i r m ' s maximum s t o c k p r i c e . Not ice t h a t the d i a l o g u e I s

" u s e r - f r i e n d l y " and the e r r o r message he lps l o c a t e the

187

source of the problem, other functions are HIN, SUH,

AVERAGE, and LOG, The fifth command computes this firm's

quick ratio and the sixth command (on the same line) prints

the quick ratio.

As part of the standard decision-making process,

students usually cone up with sone queries that they wish to

use during each round of play. A procedure has been devised

to avoid typing such queries each time. This process

consists of putting the guerles onto a command file and

instructing SLIH to read and execute commands from this

"batch" file. Another command (TERHINAL) can be embedded in

the batch file to switch control back to conversational

modeling capability allowing use of "what if" type queries.

The use of predefined command files and a simple model

Is Illustrated next. In this example the student wants to

know how much cash will be available from three sources:

current sales and backorders (approximately 60X collected in

cash), accounts receivable (100% collected), and

cash-on-hand. Backorders are filled at the lower of current

price or price at time of order.

The command file is listed in Figure 12. The file

(which is created by the student—not the instructor) has

been set up so that the user can interactively define

variables (as opposed to data base items) Y(1) through Y(4)

to be changes in price and variables Z(1) through Z (4) to be

188

changes in sales volume. The manner in which this is done

is explained later. The first four lines of the command

file define variables X(1) through X(4) to be the new trial

prices (current prices plus the changes). Trial sales

volumes (X(5) through X (8)) are calculated on lines 5

through 8. Finally, revenue from sales and backorders is

computed on lines 9-12. Notice how the HIN function Is used

to select the appropriate price on backorders.

The REHARK command Is then used to augment the standard

headings of the LIST command which Is used to output

results. Finally, total sales revenue, cash from sales

revenue, accounts receivable and cash-on-hand are printed at

line 17 and control returns to the terminal at line 18.

In the sample terminal session using this command file

(Figure 13), the user first assumes no change In price or

sales volume. SLIH automatically sets all variables to zero

initially, so after logging in the user innediately types in

BATCH; and SLIH begins executing conmands from the

user-prepared batch file. The first output the user gets

corresponds to the assumption of no change In price or sales

volume. The user can then play "What if..." games by

redefining price changes and/or sales volume changes using

the COHPUTE command. The user must then REWIND the file and

enter BATCH again. In the example, the user raises the

price on product 1 In area 1 by $2.00 and lowers sales

\

189

volume 1000 u n i t s , then reruns the command f i l e and logs

o f f . Under these assumptions over $761,000 in cash in

generated.

This simple example c l e a r l y i l l u s t r a t e s how the user

can employ t h e guery language to In terac t ive ly ex trac t data

(In t h i s c a s e , p r i c e s , s a l e s volumes, backorders, accounts

r e c e i v a b l e and cash) and play simple modeling games to

e s t i m a t e cash a v a i l a b l e next guarter. An i n d i r e c t

addressing option and WHEN command can be used to crea te

more powerful aode l s . For exaaple , one teaa developed a 640

l i n e coaaand f i l e for production scheduling.^

adapted froa Courtney and Jensen (1980), pp. 5-6.

190

HOWDY, I'H CALLED SLIH. WHAT IS YOUR FIRH NUHBER? ?2 PASSWORD? xxxxxxxxx

•

PRINT BBI: BBI (Q8) = 119.44

PRI SREPS ( F I ) ; SREPS (F1,Q8,A1) = 11 .00

LIST s p ( 2 l : : i ) rBBi(2lz*) # 1 1 ( 2 1 - * ) ; SP BBI " EI F2

1.6473 111.16 105.25 1 .5231 112 .00 106.77 1 .4595 112 .78 107.00 1 .5150 114.25 107.80 1 ,5657 116 ,76 108.50 1 ,9370 121,44 109.10 1 .9579 120.44 110.00 2 . 7 9 7 0 119.44 112.12

? PRINT HAX ( S P ( 2 1 = * ) ) ; THAT'S INTERESTING, BUT WHAT KIND OF SUBSCRIPT IS THAT? NO - OR ) AFTER THE ARGUHBNT

? PRINT M I (1£(21-*) ) : MAX = 2 ,7970

? COMPUTE Q= (AR»CASH»ST^) /(STL»SPL»AP) ; PRINT Q; Q = 2.85l9

? M 2 ; S L I H ' S A Y S SO LONG, PODNEH. 2 . 8 9 7 CP SECONDS EXECUTION TIME,

Figure 11: Saaple ad hoc queries.

^

191

1. COM X(1) = PRICE (P1,A1) • Y(1); 2. COM X(2) = PRICE (P2,A1) • Y (2); 3. COM X(3) = PRICE (P1,A2) • Y(3); 4. COH X(4) = PRICE (P2,A2) • Y(4); 5. COH X(5) = SV (P2,A1) • Z(1); 6. COH X(6) = SV (P1,A1) • Z(2); 7. COH X(7) = SV (P1,A2) • Z (3) ; 8. COH X(8) = SV (P2,A2) • Z(4); 9. COH X(9) = X(1)*X(5) >B0(P1,A1)*HIN (PRICE(P1,A1) ,X(1)) ; 10. COH X(10)= X(2)*X(6)*BO(P2, Al) •HIN(PBICE(P2,A1) ,X(2)) ; 11. COH X(11)= X(3)*X(7) •B0(P1,A2) *HIN(PRICE(P2,A2) ,X(4)) ; 12. COH X(12)= X(4)*X(8) •BO(P2,A2)*HIN (PRICE(P2,A2) ,X(4)) ; 13. REHARK X IS PRICE CHANGE AND Z IS SALES VOL CHANGE 14. REHARK X IS SALES REVENUE 15. LIST PRICE (A1-2,P1-2) ,Y(1-4) ,SV(A1-2,P1-2) ,Z(1-4) ,

X(9-12); 1 6 . REHARK R IS CASH FROH SALES. SUH IS TOTAL CASH AVAILABLE 1 7 . COH R = SUH(X(9-12) ) • . 6 0 ; PRI R, AR, CASH; 1 8 . TERHINAL;

F i g u r e 12 : Sample SLIH b a t c h f i l e ,

192

HOWDY, I'H CALLED SLIH. WHAT I S YOUR FIRH NUHBER? 1 PASSWORD? xxxxxxxx ? BATCH;

Y I S PRICE CHANGE AND Z I S SALES VOL CHANGE X I S SALES REVENUE

PRICE Y SV Z X F2,Q8

4 2 . 5 0 0 . 0 0 0 9 0 7 0 . 0 0 0 . 0 0 0 3 8 5 4 7 5 . 0 0 1 1 . 0 0 0 . 0 0 0 6 8 3 3 . 0 0 0 . 0 0 0 7 5 1 6 3 . 0 0 4 4 . 0 0 0 . 0 0 0 5 5 4 7 . 0 0 0 . 0 0 0 3 4 4 0 6 8 . 0 0 1 2 . 0 0 0 . 0 0 0 2 2 5 4 . 0 0 0 . 0 0 0 2 7 0 4 8 . 0 0

R I S CASH FROM SALES, SUM I S TOTAL CASH AVAILABLE R = 4 3 9 0 5 2 . 4 0 A R ( F 2 , Q 8 ) = 2 9 1 1 5 1 . 6 4 CASH(F2,Q8) = 4 6 9 3 1 . 9 0 SUM = 7 7 7 1 3 5 . 9 5

? COMPUTE Y ( 1 ) = 2 . 0 ; COH Z ( 1 ) = - 1 0 0 0 ; REWIND; BATCH;

Y I S PRICE CHANGE AND Z I S SALES VOL CHANGE X I S SALES REVENUE

PRICE Y SV Z X F 2 , Q 8 F2 ,Q8

4 2 . 5 0 2 . 0 0 0 9 0 7 0 , 0 0 - 1 . 0 0 0 3 5 9 1 1 5 . 0 0 1 1 . 0 0 0 . 0 0 0 6 8 3 3 . 0 0 0 . 0 0 0 7 5 1 6 3 . 0 0 4 4 . 0 0 0 . 0 0 0 5 5 4 7 . 0 0 0 .000 2 4 4 0 6 8 . 0 0 1 2 , 0 0 0 . 0 0 0 2 2 5 4 . 0 0 0 .000 2 7 0 4 8 . 0 0

R I S CASH FROH SALES, SUH I S TOTAL CASH AVAILABLE R = 4 2 3 2 3 6 . 4 0 A R ( F 2 , Q 8 ) = 2 9 1 1 5 1 . 6 4 CASH(F2,Q8) = 4 6 9 3 1 , 9 0 SUH = 7 6 1 3 1 9 , 9 5

END; SLIM SAYS SO LONG, PODNER,

F i g u r e 1 3 : S a m p l e SLIH w h a t - i f e x e r c i s e .

Appendix K

OBSCRIPTIOI OF DDHHT FIRH STBATB6IBS AHD SAHPLB OUTPUT

Assune t h a t the s u b j e c t ' s f i r n I s FI and tha t f i r n s 2

and 3 are the phanton f i r n s , F2 adopts a high p r i c e , low

volume s t r a t e g y , and F3 adopts a low p r i c e , high volume

s t r a t e g y . The a l g o r i t h m s for the two dummy f irms and t h e i r

upper and lower bounds are g iven below. with the e x c e p t i o n

of the f i r s t p r a c t i c e d e c i s i o n , the dummy firms always

f o l l o w the s t u d e n t ' s f i r m . That I s , the a l g o r i t h m s are

a p p l i e d to FI d e c i s i o n s of the pr ior guarter of BHL p l a y .

Algor i thms f o r F2

D e c i s i o n S t r a t e g y Upper Bound Lower Bound

P r i c e ' 5« above FI $55 $42 A d v e r t i s i n g 10X above FI $25000 $2000 Production^ 2 below Fl 9 , 1 4 , 1 8 , 2 0 , 2 5 7 , 1 2 , 1 6 , 1 8 , 2 3 Halntenance

s t a g e 1 u n l t s * 1 . 8 * . 1 0 $4500 $1260 s t a g e 2 u n l t s * 3 . 7 * . 15 $13875 $3885

Raw Mater ia l H^ one u n i t s * 2 3 . L ' 575000 161000 two un l t s*11 275000 77000

Eng ineer ing $1000 < Fl $20000 $1000

Algori thms f o r F3

D e c i s i o n S t r a t e g y Upper Bound Lower Bound

Prlce^ 5% below Fl $50 $38 A d v e r t i s i n g 15% below Fl $20000 $1000 Product ion^ 2 above Fl 1 8 , 2 5 , 3 0 , 3 5 , 3 8 1 4 , 2 0 , 2 5 , 3 0 , 3 5 Maintenance

s t a g e 1 u n i t s * 1 . 8*. 10 $6840 $2520 s t a g e 2 u n i t s • 3 . 7 * . 1 5 $21090 $7770

193

194

Raw Material one units*23 two unlts*11

Engineering $1000 > Fl

874000 418000 $30000

322000 154000

$2000

*At the bound£irles, pr ice could vary randonly within $1 of that bound,

^in thousands of u n i t s . Boundaries varied for each of 5 guarters .

The fo l lowing s cener lo I l l u s t r a t e s a sanple s u b j e c t ' s

d e c i s i o n s and s e l e c t e d r e s u l t s for the three f i r n s over f i v e

per iods of BHL ganlng. A copy of the Industry and f i rn

repor t s for the f i r s t guarter of play are shown on the

fo l lowing two pages.

Fl Quarterly Decis ions

Price Advertising Production Halntenance stage 1 stage 2 Raw Haterlar one two

Engineering

Q1

40 8000 13000

1800 5900

600 300 8000

Q2

42.30 9500

25000

4500 13875

720 330 7000

Q3

44 9500 33000

5940 18315

740 350

9000

Q4

1100 40000

7200 22200

830 395 8000

Q5

1300 33500

6030 18590

710 340 8000

Q6

4 4 . 5 0 4 4 . 7 5 4 4 . 7 5 1300

33500

6030 18590

710 340 8000

BHL Output

Fl Harket Share .325 .439 Ret.Earnings^-302 -316

F2 Harket Share ,225 .211 Ret, Earnings^ -298 -280

F3 Market Share ,450 .351 Ret. Earnings^ -311 -354

423 222

231 233

346 393

354 133

235 140

411 367

.297 -43

.279 30

.424 -275

.315 38

.283 131

.402 -186

Hn thousands of units. In thousands of dollars.

195

T H E B U S I N E S S M A N A G E M E N T L A B O R A T C R Y

Q6 P E R I O D . R E P O R T

INDUSTRY REPORT, QUARTER I YEAR

CURRENT 6C0NCMIC INDEX = 1 0 5 . 2 5 NEXT QTR. FORECAST = 1 0 4 . 9 1 BILL RATE = 0 . 0 5 1 2 5

81

BULL-BEAR STUCK INDEX = 1 0 5 . 0 0 NEXT YR. FORECAST = 1 0 5 . 0 0

PRIME RATE = 0 .U6920 AT MARKET: RAW MATL. #1 = S 0 . 3 3 0 1 , RAH MATL. #2 -FUTURES CONTRACTS: RAM MATL. §1 - S 0 . 3 0 0 0 , RAW MATL. «2 =

TOTAL IMPORTS: PRODUCT I = 1 1 2 3 3 0 . , PRODUCT 2 = 0 . TOTAL PROMOTION: AREA 1 , PRODUCT ONE I I S O O . , PRODUCT TWO •TOTAL PROMOTION: AREA 2 , PRODUCT ONE 0 . , PRODUCT TWO

0 . FIRMS HAVE COMMISSION ON PRODUCT 1 0 . FIRMS HAVE COMMISSION CN PRODUCT 2 3 . FIRMS PAY A SALARY

0.4139 0.3820

0. 0.

CO. 1 2

- 3

CO. * I 2 3

DVD/SH STOCK-PRICE 0 . 0 1 .68 0 . 0 1.83 0 . 0 1 .89

PRODUCT PRICES AREA 1 AREA PROD. l PR00.2 PROD. 4 0 . 0 0 0 . 0 0 . 0 4 2 . 0 5 0 . 0 0 . 0 3 8 . 0 0 0 . 0 0 . 0

2 1

EARNINGS - 4 4 8 4 5 . - 4 1 1 9 9 . - 5 7 4 6 8 .

PROD.2 O.G 0 . 0 C O

A1-SALESREPS-A2 1 0 . 0 . I C . 0 . 1 0 . 0 .

196

T H E B U S I N E S

06 P E R I O D R E

••REPORT FOR FIRM 1 ,

S H A

P O R T

QUARTER

N A G E M E N T L A B O R A T O R Y

YEAR 81

PRODUCT 1 COMMISSION PRODUCT 2 COMMISSION BASE SALARY SALES TRAINEES CREDIT RATING

SECOND SHIFT

.SALES VOL. -BACKORDERS MKT. SHARE SALES LOST UNITS MFG. F INISHED GOODS I N V . / U N I T ( A V E . ) UNIT MFG. COST

0 . 0 0 . 0 2 0 0 0 .

0 . 5 . 0 0

AREA 1 1 . 0 0 0

PROD. 1 1 3 0 0 0 . 2 7 5 3 1 .

0 .325 9 1 7 7 .

1 3 0 0 0 . 0 .

0 . 0 3 4 . 3 1 5

RAW MATL. (UNITS)

LABOR HOURS/SHIR •N£W CONST START

TYPE I 600 0 0 0 . STAGE 1

3 0 3 0 . 0 .

•• INCOME STATEMENT SALES INVESTMENT INC. ( # 0 : EXPENSES

COST CF GOODS SOLC ADVERTISING Q . C . a BUDGET RESEARCH SALES EXP. ADMINISTRATION INVENTORY CHGS. MAINTENANCE DEPRECIATION INTEREST FACTOR COST MISCELLANEOUS TAXES

NET INCOME

PROD. 2 0 . 0 .

0 . 0 0 . 0 . 0 .

0 . 0 0 . 0

TYPE 2 3 0 0 0 0 0 . STAGE 2

1 8 7 3 9 . 0 .

0 . )

4 4 6 1 0 0 . 8 0 0 0 . 1 0 0 0 . 9 0 0 0 .

2 0 0 0 0 . 2 6 4 5 5 .

4 5 0 0 . 7 7 0 0 .

1 3 1 2 5 . 3 6 2 9 .

0 . 0 . 0 .

S.T.RATE AREA 2

0 . 0 PROD. 1

0 . 0 .

0 . 0 0 . 0 . 0 .

C O 0 . 0

TYPE 1 0 .

STAGE I C. 0 .

4 9 4 6 6 4 . 0 .

1 1 . 4 2 ?

PROD. 2 0 . 0 .

0 . 0 0 . 0 . 0 .

0 . 0 o.c

TYPE 2 0 .

STAGE 2 0 . 0 .

- 4 4 8 4 5 .

••BALANCE SHEET CASH ACCTS. REC. F I N . GOODS RAW MATL. UNAMORT. DISC PLANT + 5 0 . -ACCUM.OEP.( • • TOTAL

100 0 0 . 2 0 0 1 9 6 .

0 . 2 9 4 6 0 0 .

0 . 5250 CO. 2 1 0 0 0 0 . ) 8 1 9 7 9 6 .

ACCTS. PAY SPEC. LOAN S . T . LOAN TERM LOAN BONDS P D I N . CAP. RET. EARNS • • TOTAL

165004 . 4 1 6 8 3 1 .

0 . _ 0.

0, 5 4 0 0 0 0 .

- 3 0 2 0 3 9 , 8 1 9 7 9 6 .

^

^