Stage Based KM

Journal of Knowledge ManagementAntecedents of the stage-based knowledge management evolutionHsiu-Fen Lin

Article information:To cite this document:Hsiu-Fen Lin, (2011),"Antecedents of the stage-based knowledge management evolution", Journal of Knowledge Management, Vol. 15 Iss 1pp. 136 - 155Permanent link to this document:http://dx.doi.org/10.1108/13673271111108747

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Antecedents of the stage-basedknowledge management evolution

Hsiu-Fen Lin

Abstract

Purpose To enhance ones understanding of the evolution of knowledge management (KM), this study

seeks todevelopa researchmodel to examine the impactof individual (knowledgeself-efficacy,openness

incommunication, reciprocalbenefits),organizational (topmanagementsupport, organizational rewards,

and sharing culture), and information technology contexts (KM system infrastructure and KM system

quality) on the KM evolution along three stages (KM initiation, implementation, and institutionalization).

Design/methodology/approach Survey data from 241 managers (in charge of KM practices in their

companies) in large Taiwanese firms were collected and used to test the research model using the

structural equation modeling (SEM) approach.

Findings The results reveal that the attributes for individual-organizational-technological contexts

have different impacts on three stages of KM evolution. In particular, knowledge self-efficacy, top

management support, and KM system quality have positive effects on all three KM evolution stages.

Research limitations/implications Future research should include structured interviews and case

studies of managers dealing with ongoing or recently completed KM planning projects to help

understand the practical usefulness of the research model.

Practical implications Creating an organizational climate characterized by top management support

and knowledge-sharing culture is likely to assist both management and employees in socializing and

interacting with one another, thus driving KM effectiveness. Managers should strive to enable

employees to propose ideas for new opportunities and foster a positive social interaction culture for

implementing KM initiatives.

Originality/value Theoretically, this study aims to provide a research model that is capable of

understanding the antecedents of the stage-based KM evolution. From a managerial perspective, the

findings of this study provide valuable guidelines to policy-makers and practitioners in implementing KM

and accelerating KM evolution.

Keywords Knowledge management, Individual perception, Organizations,Communication technologies

Paper type Research paper

Introduction

In a dynamic, rapidly changing business environment, the ability of an organization to

strategically leverage knowledge becomes its main source of sustainable competitive

advantage. Knowledge management (KM) is widely recognized as evolving into a tool for

strategic management for most organizations (Teece et al., 1997), but failure to widely

promote KM limits its benefits (Anantatmula, 2008). To fully realize value from knowledge

exploitation, the evolution of business knowledge and KM with advancement of

organizations over time becomes an important research topic (Kambil, 2009; Lee and

Kim, 2001; Lin, 2007a). This study focuses on KM practices, which refers to a process

involving the management of all knowledge to meet existing and emerging needs, identify

and exploit existing and acquiring knowledge assets and develop new opportunities (Jarrar,

2002). KM practices aim to see individual knowledge become group and organizational

knowledge over time, which in turn improves the stock of knowledge available to the firm.

PAGE 136 j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL. 15 NO. 1 2011, pp. 136-155, Q Emerald Group Publishing Limited, ISSN 1367-3270 DOI 10.1108/13673271111108747

Hsiu-Fen Lin is based in the

Department of Shipping

and Transportation

Management, National

Taiwan Ocean University,

Taiwan.

The author would like to thankthe National Science Council ofthe Republic of China, Taiwanfor financially supporting thisresearch under Contract No.NSC97-2410-H-019-008.

Received: 5 April 2010Accepted: 19 July 2010

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The development of KM practices is an important undertaking because KM is the businessprocess of collecting and creating useful knowledge (i.e. knowledge acquisition), storingthat knowledge in the repository to enable employees to access that knowledge easily (i.e.knowledge conversion), exploiting and usefully applying knowledge (i.e. knowledgeapplication), and preventing inappropriate knowledge use (i.e. knowledge protection) (Goldet al., 2001). Moreover, social activities (such as social networks and informal communitynetworks) are a critical resource to facilitate and improve KM processes within and betweenorganizations (Widen-Wulff and Ginman, 2004; Rhodes et al., 2008). Kim and Lee (2006)also argue that without social networks, formal KM processes are insufficient to encourageemployees to share, contribute and reuse knowledge in work environments. These KMprocesses highlight the continuous reconfiguration of the firm knowledge-based assets, andadapt to changing market conditions to achieve organizational renewal and innovativeness.KM evolution attracts substantial research attention because it involves enhancingorganizational learning capacity and competitive advantage to ensure long-term firmsurvival (Cepeda and Vera, 2007; Johannessen and Olsen, 2003).

In this study, KM evolution defines as a series of stages from firm initial evaluation of KMpractices at the pre-implementation stage (KM initiation), to their formal implementation (KMimplementation), and finally to such practices becoming institutionalized as daily activitiesthroughout the organization and increasing overall organizational effectiveness. Theliterature reviewed by Lee and Kim (2001) and Lin (2007a) suggests that a stage-based KMevolution analysis would provide insight for understanding KM implementation. However,much existing research focuses on assessing the linkage between the contextualantecedents and a single stage of KM evolution, such as KM adoption decisions (Bocket al., 2005; Lin, 2007b), KM implementation (Anantatmula and Kanungo, 2010; Chen andHuang, 2007), and KM effectiveness (Chen and Huang, 2009; Wu and Tsai, 2005; Zaim et al.,2007). Researchers rarely examine empirically exactly how contextual factors impact thevarious stages of KM.

By sharing individual employee knowledge within an organization, knowledge of individualemployees becomes organizational knowledge, enabling organizational learning (Cabreraet al., 2006). Thus, successful KM evolution requires individual employees to share arepository of experiences. Beyond individual-level factors, Bounfour (2003) observed thatan organization must assess its readiness for the KM evolution, including managementsupport and organizational learning culture. Additionally, influences from informationtechnology can also affect the KM evolution. For example, Hazlett et al. (2003) found thatinformation technology can assist organizational employees in performing their KM tasksmore efficiently. However, these contextual factors (individual, organizational, andinformation technology) focus on a single-stage of KM evolution and examined separatelyin different research models. The literature lacks a unified theoretical research model forguiding empirical research. The individual-organizational-technological contexts affectingthe multi-stage evolution of KM practices thus need to be identified and understood.

This study develops a research model to examine the impact of three important contextualfactors (individual, organizational, and information technology) on three stages of KMevolution (KM initiation, implementation and institutionalization). The research model andhypothesized relationships are empirically tested using the structural equation modeling(SEM) approach, supported by LISREL 8.8 software. This study has theoretical andmanagerial implications. Theoretically, this study aims to provide a research model thatcapable of understanding the antecedents of the stage-based KM evolution. From amanagerial perspective, the findings of this study provide valuable guidelines topolicy-makers and practitioners in implementing KM and accelerating KM evolution.

Theoretical background and research model

The stages of KM evolution

KM evolution can be viewed as an administrative innovation, owning to its fundamental rolein generating new ideas and developing new business opportunities through thesocialization and learning process of knowledge workers (Darroch and McNaughton,

VOL. 15 NO. 1 2011 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 137

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2002; Hall and Andriani, 2003). KM evolution is complex and dynamic, and varies acrosstime with distinct sets of antecedents and also involves different loci of organizational impact(Lee and Kim, 2001). To better understand KM evolution problems and their solution,multi-stage rather than single-stage analysis provides better insight into KM practices (Lin,2007a). A stage-based KM evolution model helps an organization to assess its relativeprogress in implementing KM. Various KM evolution models are proposed and validated withmultiple KM research. These models are developed by different perspectives. For example,Lee and Kim (2001) propose that organizational capability of KM grows through the followingfour stages: initiation, propagation, integration, and networking. Xu and Quaddus (2005)regard the adoption of KM systems as an innovation diffusion process and proposed asix-stage model. The six stages are initiation, adoption, pilot implementation, organicgrowth, organizational implementation, and diffusion. Arguing that KM is adaptable overtime through the dimensions of KM process, KM effectiveness, and social-technical support,Lin (2007a) suggests a KM evolution stage model which consists of three stages:

1. KM initiation.

2. development.

3. mature.

According to the innovation diffusion literature (Rogers, 1995), innovation evolution beginsfrom initial firm awareness and evaluation of administrative innovation. In the first or theinitiation stage, the firm starts to recognize the importance of KM and prepare for KM efforts.The KM literature (Kaser and Miles, 2002; Lin and Lee, 2006; Song, 2002) suggests thatwhen decision makers perceive KM to have clear overall organizational benefits, they aremore likely to promote KM within their organization. Applying this perspective to KM, KMinitiation, the first stage of KM evolution, is defined as the rating assigned to the potentialbenefits of KM before the firm began implementing KM.

KM implementation follows KM initiation. Consistent with innovation diffusion and the KMliterature (Gold et al., 2001; Rogers, 1995; Xu and Quaddus, 2005), KM implementation, thesecond stage of KM evolution, is defined as the degree to which the activities of knowledgeacquisition, knowledge conversion, knowledge application, and knowledge protection areimplemented within the organization. During the KM implementation stage, the firm hassuccessfully implemented KM to facilitate and motivate knowledge activities. The importantconcept in KM implementation includes knowledge transfer strategies, human resourcepolicies, and KM system deployment.

Institutionalization is often characterized as the final stage in an innovation diffusion process(Goodman and Steckler, 1989). KM institutionalization, the third stage of KM evolution, isdefined as the extent to which KM practices have been successfully implemented toimprove overall organizational effectiveness. The stage of KM institutionalization representsthe steady state in which KM can effectively adapt to change and enhances organizationalperformance. During this stage, firms with proficiency in acquiring, converting, utilizing, andprotecting knowledge are more skilled in developing profitable KM effectiveness.

Based on the above theoretical considerations and literature review, this study specifiesinitiation, implementation, and institutionalization as three stages of KM evolution. This isconsistent with the KM stage model of Lin (2007a) that analyze the KM evolution byconsidering a sequence from initiation to development and then to mature stage. Next, thisstudy attempts to identify factors influencing the three KM evolution stages, which discussesbelow.

Antecedents of KM evolution

KM involves a dynamic capacity of the firm that evolves over time (Easterby-Smith andPrieto, 2008; Gold et al., 2001; Zahra and George, 2002). KM evolution is a dynamic processof strengthening organizational effectiveness by maximizing the utilization of knowledge thatis shared among employees (Grant, 1996). KM evolution must begin simultaneously for bothemployees and the organization, because individual involvement is essential toorganizational learning and knowledge sharing (Lin, 2007a; Yeh et al., 2006). Davenportand Prusak (1998) also argue that information technology can be viewed as a key

PAGE 138 j JOURNAL OF KNOWLEDGE MANAGEMENTj VOL. 15 NO. 1 2011

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determinant of the effectiveness of organizational execution of KM. The KM system canenable rapid knowledge search, access and retrieval, and can facilitate the success of KM

practices. The literature recognizes that KM embeds within the often-complex social and

technical interactions involving individuals, groups, or organizations that are attempting to

create an effective KM environment (Lee and Choi, 2003). Hence, reviewing the above KM

literature, this study investigates that the individual-organizational-technological contexts

are appropriate to study contextual factors that influence the stage-based KM evolution.

First, referring to the individual context, most authors agree that the success or failure of KM

depends on employee beliefs and attitudes about sharing knowledge (Wasko and Faraj,

2005; Mohamed et al., 2008). Moffett et al. (2003) suggest that individual motivators may

enable employee willingness to participate and engage in the obtaining and sharing of

knowledge. Employees are motivated when they think that KM practices will be worth theeffort and able to maintain good interpersonal relationships with others. Therefore, the

expectation of individual and reciprocal relationships can encourage employees to

participate in KM practices, in turn facilitating the KM evolution.

Second, referring to the organizational context, organizational support is usually made tocapture efficiently the benefits of KM (Alazmi and Zairi, 2003). In the context of KM, the

different aspects of organizational support are critical drivers of KM evolution, such as top

management support (Storey and Barnett, 2000), reward systems linked to KM (Wong,

2005), and knowledge sharing culture (Syed-Ikhsan and Rowland, 2004).

Finally, referring the information technology context, KM system infrastructure (such as

groupware, online databases, intranet, and virtual communities) can be effectively used to

facilitate the codification, integration, and dissemination of organizational knowledge (Zack,

1999). Kulkarni et al. (2006) proposed that enterprises require a high-quality KM system that

is accessible and capable of easily leveraging KM practices. Firms with greater KM system

readiness and higher KM system quality thus are more likely to create sources of sustainable

growth and pursue KM best practices. In summary, in the research model (see Figure 1),factors from individual-organizational-technological contexts, were included as antecedents

of three stages of KM evolution.

Individual context. Davenport and Prusaks (1998) definition indicates that knowledge is

personal. Organizations can only begin to effectively manage knowledge resources whenemployees are willing to cooperate with colleagues to contribute knowledge to the firm. The

research considered here has focused on individual factors that promote or inhibit the KM

evolution. The three factors that may be proximal determinants of KM evolution are identified:

knowledge self-efficacy, openness in communication, and reciprocal benefits.

Knowledge self-efficacy defines as the judgments of employee regarding their capabilities

to provide knowledge that is valuable to the organization. Researchers find that employees

with high confidence in their ability to provide valuable knowledge are more likely to both

donate and collect knowledge with colleagues (Lin, 2007b). Employees who believe that

they can contribute valuable knowledge will encourage firms to effectively move across

various stages of KM evolution. Knowledge self-efficacy is thus expected to influence thethree stages of KM evolution.

H1. Knowledge self-efficacy relates positively to KM initiation, implementation and

institutionalization.

In the context of KM, openness in communication defines as the degree to which employees

are willing to exchange their ideas and knowledge with colleagues, even if those ideas

contradict popular opinion. Studies have shown that openness in communication acts as a

major facilitator in establishing a learning culture (Marquardt and Reynolds, 1994).

H2. Openness in communication quality relates positively to KM initiation,

implementation and institutionalization.

Blau (1964) highlights reciprocity behavior as a benefit of individuals engaging in social

exchange. Some studies consider the importance of reciprocal benefits to provide an

effective motivation for facilitating knowledge sharing and thus achieving long-term mutual


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cooperation (Bock et al., 2005; Kankanhalli et al., 2005). Therefore, this study posits a close

relationship between reciprocal benefits and the three stages of KM evolution.

H3. Reciprocal benefits relates positively to KM initiation, implementation and


Organizational context. Organizational characteristics play a strategic and crucial role in

influencing organizational change, innovation, and outcomes, especially in KM areas (Pan

and Scarbrough, 1998; Park et al., 2004). This study chooses three organizational factors as

antecedents of three stages of KM evolution:

1. top management support;

2. organizational rewards; and

3. sharing culture.

In this study, top management support refers to the degree to which top management

understands the importance of KM and the extent to which top management is involved in

KM practices. KM involves a process of wide-ranging organizational change initiatives, not

only a strategic management effort (Eisenhardt and Martin, 2000). Support from top

management is critical in the growth of KM practices since it encourages voluntary

employee participation in donating and collecting important knowledge. Therefore, high

levels of top management support may result in more mature KM practices and facilitate the

KM evolution.

H4. Top management support relates positively to KM initiation, implementation and


Figure 1 Research model

Individual context

Openness incommunication

Knowledgeself-efficacy

Reciprocalbenefits

Top managementsupport

Organizational context

Sharing culture

Organizationalrewards

KM systeminfrastructure

Information technology context

KM systemquality

KMinitiation

KMimplementation

KMinstitutionalization

Three stages of KM evolution

H1(+,+,+)

H2(+,+,+)

H3(+,+,+)

H4(+,+,+)

H5(+,+,+)

H6(+,+,+)

H7(+,+,+)

H8(+,+,+) Control variables

Firm size Industry type


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Organizational rewards, such as salary incentive, bonuses, promotion incentive, or jobsecurity, indicate what the organization values shape employee behaviors (Cabrera andBonache, 1999). Previous researchers suggest that reward systems can encourageemployees to share their knowledge (Al-Alawi et al., 2007; Wong, 2005). As such, this studyexpects an important relationship between organizational rewards and the three stages ofKM evolution.

H5. Organizational rewards relates positively to KM initiation, implementation andinstitutionalization.

Sharing culture comprises a set of shared understandings related to providing employeesaccess to relevant information and building and using knowledge networks withinorganizations (Hoegl et al., 2003). A firm can successfully promote KM practices not only bydirectly incorporating knowledge in its business strategy, but also by creating a knowledgesharing culture (Connelly and Kelloway, 2003; Lin and Lee, 2004). Syed-Ikhsan and Rowland(2004) also suggest that sharing culture is a crucial element to facilitate KM in businessoperations.

H6. Sharing culture relates positively to KM initiation, implementation andinstitutionalization.

Information technology context. In this study, KM system infrastructure refers to informationtechnologies that enable KM-related activities, such as groupware, online databases,intranet, and virtual communities. The capability of KM systems has evolved from merelybeing static archives of information and knowledge to being human-machine interfaces forinteractive knowledge transfer and learning. Two factors likely affect the KM evolution: KMsystem infrastructure and KM system quality.

A well-developed KM system infrastructure can allow firms to expand available socialnetworks by overcoming geographical boundaries and thus achieving more effectivecollaborative activities (Pan and Leidner, 2003). Zack (1999) believes that KM system playsthe following three different roles in KM activities:

1. obtaining knowledge;

2. defining, storing, categorizing, indexing, and linking knowledge-related digital items; and

3. seeking and identifying related content.

A solid infrastructure for KM increases the knowledge base available to individualemployees and enables employees to work together, in turn promoting the KM evolution.

H7. KM system infrastructure relates positively to KM initiation, implementation, andinstitutionalization.

KM system quality refers to the quality of knowledge provided by the KM system. Knowledgeaccuracy, relevance, currency, reliability, and accessibility are examples of qualities valuedby employees (DeLone and McLean, 2003; Nelson et al., 2005). In situations involving highKM system quality, employees are better able to search for and use knowledge, helpingemployees to use KM system to effectively perform KM functions.

H8. KM system quality relates positively to KM initiation, implementation andinstitutionalization.

Control variables

This study includes two control variables to account for contextual differences: firm size andindustry type. First, firm size may be positively related to strategic renewal and innovationefforts, since large firms are more likely to possess slack resources (Cohen and Levinthal,1989). Second, industry type is used to control for industry-specific differences that mayaffect the KM evolution, as service and manufacturing industries differ in their KM stylesimplementation (Choi and Lee, 2003). The use of these variables in the researchmodel helpscontrol for firm- and industry-level differences that might affect KM initiation, implementationand institutionalization.


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Method

Survey procedure

Data were collected through mail survey of senior executives in Taiwanese companies. Adraft questionnaire was adapted from previous studies and modified for use in the KMcontext. With establishing content validity, the questionnaire is refined through rigorouspre-testing. The pre-testing focuses on instrument clarity, question wording and validity.During the pre-testing, three doctoral students, two management profession and threesenior executives (in charge of KM practices in their companies) are invited to comment onthe questions and wordings. The comments of these eight individuals then provided a basisfor revisions to the construct measures. The population in this study is the top 1,600Taiwanese firms (including 1,000 manufacturing, 500 retail/wholesale distribution, and 100financial service firms), published by 2008 Common Wealth Magazine. Random sampling isperformed to select 50 percent in each type of industry. A total of 800 questionnaires aredistributed among the managers (in charge of KM practices in their companies) of thesampled firms. To ensure that managers received the questionnaire and maximize responserate, four research assistants spent two weeks telephoning these 800 firms. The researchassistants sought the name of themanagers (currently and directly in charge of KM) to whoma questionnaire should be mailed.

Sample

Of the 800 firms, 247 responded, with 241 having complete data available for subsequentanalysis, yielding an effective response rate of 30.1 percent. Table I shows thecharacteristics of the responding firms in terms of industry, total assets, number ofemployees, and respondent title. All respondents had worked in the firm for an average of14.6 years. This finding result indicates that respondents are sufficiently knowledgeable toanswer the survey.

Additionally, this study conducts two statistical analyses to ensure the absence ofnon-response bias (Armstrong and Overton, 1977). First, this study compares theresponding and non-responding firms in terms of company assets and employee numbers.This information is available from the 2008 Common Wealth Magazine, and the independent

Table I Demographic characteristics of the responding firms

Demographic characteristics Frequency Percentage

IndustryManufacturing 151 62.6Retail/wholesale distribution 72 29.9Financial services 18 7.5

Total assets (NT$)Less than $10 billion 82 34.0$11-$50 billion 96 39.8$51-$100 billion 27 11.2Over $100 billion 36 15.0

Number of employeesFewer than 1,000 68 28.21,001-5,000 108 44.85,001-10,000 33 13.7Over 10,000 32 13.3

Respondent titleChief executive officer 43 17.8IS manager 93 38.6Business operation manager 35 14.5Administration/finance manager 50 20.8Others 20 8.3

Note: n 241


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sample t-test revealed no significant difference between the two groups (p 0.13 and 0.10,respectively). The respondents are then divided into two groups based on return dates.

Comparison of the two groups in terms of company assets and number of employees again

revealed no significant differences based on the independent sample t-test (p 0.11 and0.23, respectively). Therefore, non-response bias should not be a problem in this study.

Measures

Measurement items were developed on the basis of a comprehensive review of the literature

and modified to suit the KM context. Constructs and associated indicators in the

measurement model, as well as prior research support, is listed in the Appendix (Table II)

and discussed below.

Independent variables. Knowledge self-efficacy was measured by the extent to which

employee judgments of their capability to provide knowledge that is valuable to the

organization (Lu et al., 2006; Spreitzer, 1995). Openness in communication was measured

by five items adapted from Roberts and OReilly (1997). Higher scores indicate that

employees feel free to communicate their ideas and knowledge with colleagues. Reciprocal

benefits were measured using four items taken from Kankanhalli et al. (2005), which focused

on employee beliefs that current knowledge contribution would lead to future requests for

knowledge being met.

Top management support assessed the level of top management commitment to the KM

practices using four items (Tan and Zhao, 2003; Taylor and Wright, 2004). Organizational

rewards define as the degree to which employees believe that they will receive extrinsic

incentives (such as salary incentive, bonuses, promotion incentive, or job security) through

their knowledge contribution (Davenport and Prusak, 1998; Hargadon, 1998). This study

measures sharing culture with four items referring to the importance of employee interaction

for building knowledge sharing networks, as well as the willingness of employees to share

knowledge and experience (Gold et al., 2001).

KM system infrastructure refers to technologies that enable KM-related activities; a four-item

scale was adapted from Lee and Choi (2003). KM system infrastructure was measured by

whether KM system can facilitate employees to contribute to the knowledge with colleagues.

Finally, KM system quality wasmeasured by five items drawn from both DeLone andMcLean

(2003) and Nelson et al. (2005). KM system quality measures the extent to which the

knowledge provided by the KM system is accurate, relevant, up-to-date, reliable, and easy

to access.

Dependent variables. Initially the main task of a potential adopter is to gather relevant

information on an administrative innovation and assess its potential benefits (Rogers, 1995).

Thus, KM initiation was measured by how the potential benefits of KM were rated before the

firm began implementing KM. Five items were used: gain competitive advantage, improve

employee relations and development, innovate new products/services, identify new

business opportunities, and promote organizational innovation culture (Skyme and

Amindon, 1997).

KM implementation was measured by an aggregated index: whether the firm has

implemented the 11 KM practices along four dimensions of KM process. The four

dimensions of KM process, including knowledge acquisition, knowledge conversion,

knowledge application, and knowledge protection, were adapted from the works of Gold

et al. (2001). Then, this study aggregates the 11 KM implementation items and converted

them into a five-point scale to form the dependent variable, KM implementation. This

approach has been suggested by the literature to measure technology

adoption/implementation (Grover and Goslar, 1993).

KM institutionalization was measured using four items asking respondents about the extent

to which KM practices have been successfully implemented to improve overall

organizational effectiveness, streamline corporate internal processes, coordinate the

development efforts of different units, and adapt quickly to unanticipated changes. These

items were adapted from Gold et al. (2001) and Becerra-Fernandez and Sabherwal (2001).


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TableII

Resultsofthemeasurementmodel

Corr

ela

tion

matr

ix

Const

ructs

Rang

eof

stand

ard

ized

load

ing

saC

om

posi

tere

liab

ility

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(1)Knowledgese

lf-efficacy

0.61-0.79

0.83

0.50

(2)Openness

incommunicatio

n0.62-0.88

0.88

0.36

0.60

(3)Reciprocalbenefits

0.67-0.78

0.82

0.37

0.33

0.53

(4)Topmanagementsu

pport

0.79-0.86

0.90

0.22

0.24

0.35

0.69

(5)Organizatio

nalrewards

0.78-0.92

0.94

0.21

0.03

0.07

0.10

0.75

(6)Sharingcultu

re0.69-0.87

0.85

0.31

0.11

0.23

0.14

0.06

0.59

(7)KM

system

infrastructure

0.62-0.83

0.80

0.24

0.50

0.46

0.47

0.21

0.25

0.51

(8)KM

system

quality

0.70-0.80

0.86

0.35

0.21

0.18

0.17

0.06

0.29

0.37

0.56

(9)KM

initiatio

n0.71-0.78

0.86

0.15

0.22

0.32

0.23

0.05

0.18

0.27

0.26

0.55

(10)KM

implementatio

nna

na

0.15

0.13

0.08

0.12

0.01

0.17

0.12

0.16

0.19

na

(11)KM

Institutio

nalizatio

n0.80-0.90

0.91

0.18

0.10

0.19

0.12

0.07

0.25

0.31

0.30

0.24

0.08

0.71

(12)Firm

size

na

na

0.01

0.01

0.02

0.02

0.01

0.01

0.01

0.01

0.02

0.01

0.01

na

(13)Industry

type

na

na

0.01

0.01

0.01

0.01

0.01

0.01

0.01

0.02

0.01

0.05

0.01

0.02

na

Notes:

aAllstandardizedloadingsare

significantat

p,

0.01level.

na:loadings,

composite

reliability,andave

rageva

rianceextracted(AVE)are

notapplicable

tothesingle-item

constructs.D

iagonalelementsreprese

nttheAVE,w

hile

off-diagonalelementsreprese

ntthesq

uare

correlations.Foradequate

discriminantvalid

ity,d

iagonalelementssh

ould

begreater

thancorresp

ondingoff-diagonalelements

x2

2376.69;

df

1005;norm

edx2

236;CFI

0.91;NNFI

0.90;IFI

0.91;RMSEA

0.075


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Together, these items reflect the extent to which KM practices have been incorporated intoorganizational-level effectiveness.

Control variables. Firm size was measured by the number of employees in the entireorganization, log-transformed to reduce data variance. Industry type contains twocategories, that is, service-oriented (including retail/wholesale distribution and financialservices) and manufacturing industries.

Results

This study used the structural equation modeling (SEM) to test the research model,supported by LISREL software (version 8.8) (Joreskog and Sorbom, 1996). LISREL softwarewas chosen primarily because of its emphasis on the overall variance-covariance matrix andthe overall model fit (Fornell and Bookstein, 1982). As the first step of the Anderson andGerbing (1988) procedure, the measurement model used confirmatory factor analysis (CFA)to test reliability and validity of the constructs. Then, the structural model examined theassociations hypothesized in the research model.

Results of the measurement model

For the measurement model to have sufficiently good model fit, the overall model fit wasassessed in terms of five common measures: normed x 2 (the ratio of x 2 to the degree offreedom), comparative fit index (CFI), non-normed fit index (NNFI), incremental fit index (IFI),and root mean square error of approximation (RMSEA). A very good fit is normally deemedto exist when normed x 2 is smaller than 3 (Bagozzi and Yi, 1988), CFI, NNFI and IFI aregreater than 0.9 (Bagozzi and Yi, 1988), and RMSEA is around 0.1 (Browne and Cudeck,1993).

As Table II shows, all model-fit indices exceed commonly accepted levels, demonstratingthat the measurement model show a good fit with the data collected. The measurementmodel was further assessed for construct reliability and validity (see Table II. The compositereliabilities of the constructs ranged between 0.80 and 0.94, which exceeds therecommended cut-off level of 0.70 (Nunnally and Bernstein, 1994). All constructs in themodel satisfied the requirements for convergent validity (standardized loadings greater than0.5 and significant at p, 0.01) (Hair et al., 1998; Gefen et al., 2000) and discriminant validity(average variance extracted greater than each square correlation) (Fornell and Larcker,1981), suggesting adequate reliability, convergent validity, and discriminant validity.

Results of the structural model

Table III shows the standardized paths in the structural model. Within the individual context,knowledge self-efficacy has significant and positive paths to KM initiation (pathcoefficient 0.32, p , 0.01), implementation (path coefficient 0.13, p , 0.10) and

Table III Results of the structural model

Path toPath from KM initiation KM implementation KM institutionalization Supported?

H1. Knowledge self-efficacy 0.32*** 0.13* 0.15* SupportedH2. Openness in communication 0.06 0.22** 0.24** Partially supportedH3. Reciprocal benefits 0.21** 0.09 0.06 Partially supportedH4. Top management support 0.12* 0.21*** 0.12* SupportedH5. Organizational rewards 0.08 0.03 0.01 Not supportedH6. Sharing culture 0.04 0.24*** 0.16** Partially supportedH7. KM system infrastructure 0.13 0.22* 0.48*** Partially supportedH8. KM system quality 0.23*** 0.19** 0.23*** SupportedControl variablesFirm size 0.01 0.15*** 0.02Industry 0.08* 0.01 0.01

R 2 (%) 50 32 46

Notes: *p , 0.10; **p , 0.05; ***p , 0.01


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institutionalization (path coefficient 0.15, p , 0.10). Thus, the results support H1.Openness in communication has significant and positive paths to KM implementation(path coefficient 0.22, p , 0.05) and institutionalization (path coefficient 0.24,p , 0.05), and thus the results partially support H2. Reciprocal benefits has significantand positive path to KM initiation (path coefficient 0.21, p , 0.05) while it has nosignificant paths to KM implementation and institutionalization. Thus, the findings partiallysupport H3.

Within the organizational context, this study finds significant and positive paths from topmanagement support to KM initiation (path coefficient 0.12, p , 0.10), implementation(path coefficient 0.21, p , 0.01) and institutionalization (path coefficient 0.12,p , 0.10). Accordingly, the result support H4. All three paths associated withorganizational rewards are no significant, so the results do not support H5. Sharingculture has significant paths to KM implementation (path coefficient 0.24, p , 0.01) andinstitutionalization (path coefficient 0.16, p , 0.05), thus the results partially support H6.Within the information technology context, KM system infrastructure has significant andpositive paths to KM implementation (path coefficient 0.22, p , 0.10) andinstitutionalization (path coefficient 0.48, p , 0.01). However, KM system infrastructurehas no significant path to KM initiation. Thus, the results partially support H7. Finally, KMsystem quality has significant and positive paths to KM initiation (path coefficient 0.23,p , 0.01), implementation (path coefficient 0.19, p , 0.05) and institutionalization (pathcoefficient 0.23, p , 0.01). Thus, the results support H8. Additionally, the R-square for thethree dependent variables, KM initiation, implementation and institutionalization, are 50percent, 32 percent, and 46 percent, respectively.

Finally, regarding the control variables, firm size has a significant and positive path to KMimplementation. The results indicate that larger firms have more resources and skillsavailable to implement KM practices. Next, service-oriented firms (including retail/wholesaledistribution and financial services), while compared to manufacturing firms, are more likelyto perceive potential benefits of KM activities during the initiation stage. A possible reason isthat firms in service industries that face end-consumers directly are more likely to launch KMinitiatives to facilitate more effective service innovation.

Discussion

This study tests the effects of eight individual-organizational-technological factors on threestages of KM evolution. The empirical results reveal several factors with differential effects atdifferent KM evolution stages and discusses below.

Individual context

Knowledge self-efficacy is positively correlated with the three KM evolution stages.Knowledge self-efficacy has been cited to be an important factor for facilitating KM (Kanget al., 2008). This result implies that employees require competence and confidence toengage in KM practices. That is, employees who believe in their ability to contributeorganizationally useful knowledge tend to have stronger motivation to facilitate the KMevolution.

The results show that reciprocal benefits are only significant in their impacts on the KMinitiation stage, but show no significant impact on the other two stages. This result isconsistent with that of Scott (2000), who argues that collaboration ability depends heavily ontrust as open reciprocity, and that information and knowledge sharing will not occur freelywithout such reciprocity. By emphasizing organizational social resources such as employeebeliefs regarding mutual or reciprocal benefits, firms are likely to have high absorptivecapacity to employ organizational knowledge in the KM initiation stage. When reciprocalrelationships among employees evolve to become deeper and more stable, the reciprocalexchange of social benefits will attract less attention and the focus of KM practices will shiftto other important antecedents, such as openness in communication and sharing culture.

Openness in communication significantly influences the latter two KM evolution stages, afinding that is interesting to practitioners. Open communication among employees is an


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evolutionary concept which describes the gradual formation of effective employeecommunication of willingness to share knowledge with colleagues. Since organizationalemployment of KM is immature during the initial stage, the openness of any communicationclimate is waiting to be well established.

Organizational context

The results are consistent with the hypothesis that top management support has a positiveeffect on three stages of KM evolution. As knowledge is often equated with power sinceknowledge can be a source of sustainable competitive advantage for individual employees,they may sense a threat to their power, importance and job security from sharing knowledgewith colleagues. Therefore, topmanagement support is a key driver of KMevolution. Similarly,other researchers assert that KM adoption is a type of organizational change, and thus topmanagement support determines its success or failure (Liebowitz, 1999; Lin and Lee, 2006).

This study reveals that organizational rewards are non-significant at each of three stages ofKM evolution, which is indeed surprising given the widely cited relationship between rewardsystem and KM (Ruggles, 1998; Wiig, 1997). Knowledge gathering and sharing occursmainly in informal interactions, and the difficulty of measuring knowledge utilization processand outcomes creates difficultly in making organizational rewards contingent on the KMevolution. Osterloh and Frey (2000) also acknowledge that with intrinsically motivatedemployees, the generation and transfer of tacit knowledge is more important than withextrinsically motivated employees (such as those motivated by monetary compensation).

Sharing culture is a significant antecedent of KM implementation and institutionalizationstages. Organizational knowledge-sharing climate is the key influence of employeeengagement in the KM process or the effectiveness of knowledge sharing activities(Sondergaard et al., 2007; Syed-Ikhsan and Rowland, 2004). The findings of this study withregard to the KM evolution are consistent with these arguments. In fact, KM evolution islimited when an organization has a complete KM deployment framework, but lacks a strongsharing culture to supports it. Organizational sharing culture acts as a catalyst stimulatingthe KM evolution, and thus to facilitate and motivate KM practices, an organization mustcreate a knowledge sharing environment that promotes successful KM implementation andrealizes more KM benefits.

Information technology context

Within the information technology context, both KM system infrastructure and KM systemquality are positive factors for KM evolution. However, KM system infrastructure is shownwith no significance at KM initiation stage. This phenomenon might be explained by the factthat knowledge is embedded in the myriad communities that constitute organizations, aswell as in organizational work practices, values, and systems (DeTienne and Jackson,2001). Thus, knowledge creation and distribution do not result simply by building KM systeminfrastructure at KM initiation stage. During subsequent KM evolution stages, awell-developed KM system infrastructure and KM practices are closely linked to facilitatethe KM evolution. Additionally, the results also show that KM system quality is a significantfacilitator of all of three KM evolution stages. Higher KM system quality increases theusefulness of KM by enhancing the fit between KM system output and employee knowledgerequirements (Kulkarni et al., 2006). If the KM system provide accurate, relevant, up-to-date,reliable, and easy to access knowledge, KM system can result in faster task performanceand more mature KM practices.

Conclusions

As contemporary firms increasingly seek to enhance their business performance bypromoting the KM evolution, KM evolution becomes an important guarantor of sustainablecompetitive advantage. Drawing on theoretical perspectives on the process and contexts ofKM practices, this study develops a research model to examine the impact of threeimportant contextual factors, individual, organizational, and information technology, on theKM evolution among three stages. The empirical results identify significant factors shaping


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the KM evolution, and reveal their differential effects across different stages. Theimplications for practice and the limitations and future research are discussed below.

Implications for practice

This study proposes the following implications for managers, especially within the context of

managing KM implementation within organizations. Concerning the individual context,efforts to foster targeted reciprocal relationships of employees are necessary for the

initiation of a planned and effective KM environment. Hence, managers can improve

employee perceptions of reciprocal benefits and face the important problem of instillinginterpersonal trust into their organizations, which is the main concern in initial KM efforts and

initiatives. This study also demonstrates open communication with employees to be animportant variable to facilitate the success of KM implementation and institutionalization.

Managers must consider that KM can continue to evolve when employees believe that anorganization as offering a safe environment in which express themselves. Openness in

communication helps eliminate resistance barriers to KM implementation, and without opencommunication, successful KM evolution might not exist. Knowledge self-efficacy is an

important enabler during KM evolution. This finding suggests that managers should paymore attention to providing useful feedback to enhance employee knowledge self-efficacy.

For example, a highly self-efficacious staff can be established by recruiting and selecting

employees who are proactive, who have high cognitive attitude and self-esteem, and whoare intrinsically motivated. Managers also can enhance perceptions of knowledge

self-efficacy among valued knowledge workers by indicating to them that their knowledgecontribution significantly impacts the KM evolution.

Regarding the organizational context, creating an organizational climate characterized bytopmanagement support and knowledge sharing culture is likely to assist both management

and employees in socializing and interacting with one another, thus driving KMeffectiveness. Managers should strive to enable employees to propose ideas for new

opportunities and foster a positive social interaction culture for implementing KM initiatives.Additionally, managers should alter their management style to encourage creativity, sharing,

and utilization of new knowledge among employees. Organizational rewards may providetemporary incentives for KM initiatives, but are not a fundamental force in organizational KM

evolution. Managers thus should not emphasize organizational rewards (such as salary

incentive, bonuses, promotion incentive, or job security) as a key driver of more mature KMpractices.

Finally, managers should focus on information technology concerns during the developmentand establishment of a KM environment. Investing in KM systems can help managers to

enhance employee perceptions of supportive interest in their knowledge acquisition andmanagement skills. The results also indicate that KM evolution requires managers to invest

time and effort in ensuring that employees are satisfied with KM system quality, since KMsystem quality factors are identified as the key contributors to employee job performance

when using KM systems. That is, employees find KM systems to be a useful means offacilitating the KM evolution when KM systems are a reliable and easy means of system

access that provide accurate, relevant, and up-to-date knowledge content.

Limitations and future research

This study includes several limitations to this study. First, since the dataset are

cross-sectional and not longitudinal, limiting observation of temporal causality in theproposed model and preventing analysis of longitudinal processes, such as the KM

evolution process in a dynamic context, future research should collect longitudinal data toprovide a clearer basis for the suggestions by the proposed model of temporal causality.

Moreover, by comparing data gathered during different periods, further insights can begained regarding the KM evolution in a dynamic environment.

Second, this study proposes a research model for understanding the relationships betweenthree contextual factors and KM evolution stages, and further empirically validates the

proposed model using a large sample survey. Future research should include structured


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interviews and case studies of managers dealing with ongoing or recently completed KMplanning projects to help understand the practical usefulness of the research model.

Finally, this study, however, does not consider all determinants of different KM evolutionstages. Chang and Lee (2008) propose that external environment (e.g. environmentuncertainty, environment change frequency, environment complexity, and environmentchange scale) affect the correlation between organization knowledge accumulation andorganizational innovation. Future studies can test whether or not external environmentvariables also affect the three stages of KM evolution, thus providing a deepeningunderstanding of antecedents to the KM theory.

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Appendix

Table AI Constructs and indicators

Constructs Indicators Literature support

Individual contextKnowledge self-efficacy 1. Employees have the expertise required to provide valuable

knowledge for my organization (1 , 5)Lu et al. (2006); Spreitzer(1995)

2. Employees have confidence in their ability to contribute valuableknowledge with colleagues (1 , 5)3. Employees have confidence in their ability to provide knowledgethat would improve work processes in my organization (1 , 5)4. Employees have confidence in their ability to provide knowledgethat would increase the productivity in my organization (1 , 5)5. Employees have confidence in their ability to acquire valuableknowledge from experts or colleagues (1 , 5)

Openness in communication 1. In my organization, discussions of difference of opinion areencouraged among employees (1 , 5)

Roberts and OReilly (1997)

2. In my organization, top management listens to what employeeshave to say (1 , 5)3. There is a sufficient level of mutual understanding amongemployees in job-related discussion (1 , 5)4. Openness communication among employees is helpful forjob-related tasks (1 , 5)5. The manner of communication among employees is frank andcandid (1 , 5)

Reciprocal benefits When employees contribute their knowledge with colleagues . . . Kankanhalli et al. (2005)1. they strengthen ties between existing colleagues and themselves(1 , 5)2. they expand the scope of their association with other colleagues(1 , 5)3. they expect to receive knowledge in return when necessary(1 , 5)4. they believe that their future requests for knowledge will beanswered (1 , 5)

Organizational contextTop management support 1. Top management is highly interested in promoting KM practices

(1 , 5)Tan and Zhao (2003); Taylorand Wright (2004)

2. Top management is aware of the benefits of KM practices (1 , 5)3. Top management has allocated adequate financial and otherresources for the development and promotion of KM practices(1 , 5)4. Top management has a vision to project in my organization as aleader in the promotion of KM practices (1 , 5)

Organizational rewards 1. Employees will receive a better work assignment for theirknowledge contribution (1 , 5)

Davenport and Prusak (1998);Hargadon (1998)

2. Employees will receive a higher salary in return for theirknowledge contribution (1 , 5)3. Employees will receive a higher bonus in return for theirknowledge contribution (1 , 5)4. Employees will receive increased promotion opportunities inreturn for their knowledge sharing (1 , 5)5. Employees will receive increased job security in return for theirknowledge contribution (1 , 5)

Sharing culture 1. In my organization, high levels of participation are expected insharing knowledge (1 , 5)

Gold et al. (2001)

2. In my organization, benefits of sharing knowledge outweigh thecost (1 , 5)3. My organization encourages employee learning and toleratestheir mistakes (1 , 5)4. The atmosphere of my organization facilitates informal interactionamong employees (1 , 5)

(Continued)


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Table AI

Constructs Indicators Literature support

Information technologycontextKM system infrastructure My organization . . . Lee and Choi (2003)

1. uses KM system that allows employees to collaborate withcolleagues (1 , 5)2. uses KM system that allows employees to communicate withcolleagues (1 , 5)3. uses KM system to search and access necessary knowledge(1 , 5)4. uses KM system to store specific types of knowledge(1 , 5)

KM system quality 1. The knowledge provided by the KM system is accurate(1 , 5)

DeLone and McLean (2003);Nelson et al. (2005)

2. The knowledge provided by the KM system is relevant to my job(1 , 5)3. The knowledge provided by the KM system is always up to date(1 , 5)4. The operation of the KM system is dependable (1 , 5)5. The KM system makes knowledge easy to access (1 , 5)

KM evolutionKM initiation At the time your organization was considering to promote KM

practices, what rating did the following potential benefits of KMreceive?

Skyme and Amindon (1997)

1. To gain competitive advantage (1 , 5)2. To improve employee relations and development (1 , 5)3. To Innovate new products/services (1 , 5)4. To identify new business opportunities (1 , 5)5. To promote organizational innovation culture (1 , 5)

KM implementation Check the box to ensure that appropriate KM practices areimplemented in your organization (#):1. Knowledge acquisition Gold et al. (2001)1-1. Distributing knowledge throughout the organization (Y/N)1-2. Acquiring knowledge about new products/services within ourindustry (Y/N)1-3. Providing informal procedures for an effective knowledgesharing (Y/N)2. Knowledge conversion2-1. Transferring organizational knowledge resources to employees(Y/N)2-2. Codifying and organizing knowledge (Y/N)2-3. Absorbing new knowledge from research and educationalprograms (Y/N)2-4. Absorbing useful knowledge from customers, suppliers andother trading partners (Y/N)3. Knowledge application3-1. Applying knowledge learned from experiences (Y/N)3-2. Using knowledge in development of new products/services(Y/N)3-3. Quickly links knowledge sources to solve problems andchallenges (Y/N)4. Knowledge protection4-1. Knowledge that is restricted is clearly identified (Y/N)

KM institutionalization After the implementation of KM, my organization has improved itsability to. . .

Becerra-Fernandez andSabherwal (2001);

1. Improve overall organizational effectiveness (1 , 5) Gold et al. (2001)2. Streamline corporate internal processes (1 , 5)3. Coordinate the development efforts of different units (1 , 5)4. Adapt quickly to unanticipated changes (1 , 5)

Notes: Coding in parentheses is as follows: # continuous variable; Y/N dummy variable; 1 , 5 five-point Likert scale


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About the author

Hsiu-Fen Lin is an associate professor in the Department of Shipping and TransportationManagement at National Taiwan Ocean University (ROC). She received her PhD degree inInformation Management from National Taiwan University of Science and Technology,Taiwan, in 2004. Her research interests include knowledge management, electroniccommerce, and organizational impact of information technology. Her research hasappeared in Information and Management, Management Decision, Journal of InformationScience, Behaviour & Information Technology, Internet Research, Electronic CommerceResearch and Applications and several conference proceedings. Hsiu-Fen Lin can becontacted at: [email protected]


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