International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 44
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
An Adaptive Model of the Effect of Environment,
Structure, and Diversification on Strategic
Performance 1Kamel E. Ghorab, Ph. D.,
2Fadia M. Hegazy, Ph.D.
1Professor of Management Information Systems Alhosn University P. O. Box 38772, Abu Dhabi, UAE
1Tel.: (971) 2 407 - 0731
2Associate Professor and Chair of Management Information Systems Alhosn University P. O. Box 38772, Abu Dhabi, UAE
2Tel. (971) 2 407 – 0570
2e-mail: [email protected],
1e-mail: [email protected]
Abstract-- The current study is an attempt to investigate the
environment-strategy-structure-performance relationships in an
integrative sense. This investigation is performed in light of the
adaptive strategy model. Statistical analysis is performed using the
PLS method on a structural equations framework. Analysis of data
from 112 large manufacturing firms has yielded significant findings.
(1) analyzing strategic performance in terms of slack generation,
slack investment, and stakeholders' satisfaction provides additional
dimensions to modeling the environment-strategy-structure-
performance relationships, (2) higher levels of environmental
munificence were associated with lower levels of diversification, and
higher levels of slack generation, (3) higher levels of environmental
instability were associated with lower levels of diversification,
divisionalization and slack investment, (4) higher levels of
diversification and divisionalization were associated with higher
levels of slack generation and investment, (5) size did not mediate
the strategy-structure relationship, (6) both a firm's ability to
generate slack and its ability to invest it were positively associated
with its stakeholders' satisfaction.
INTRODUCTION
Investigation of the relationship between strategy and structure is
central to strategic management. Since Chandler's [22] study on
the relation between strategy and structure, research in this area
has branched into two streams. The first stream of studies has
concerned itself with investigating the dynamics and
performance implications of degree of diversification-
organization interface [24] [58] [60] [62] [64] [70] [78] [79]
[86]. The second stream of studies has focused on linkages
among environmental characteristics, organizational strategy,
structure, size, and performance outcomes [2] [6] [8] [9] [13]
[18] [23] [37x] [38] [47z] [50] [67]. The merits of these two
schools of thought suggest the potential of integrative research.
Although many researchers, [56] [32] 57] for example, have
called for the examination of the simultaneous relationships
among strategy, structure, and environmental variables, few have
tried to examine those relationships in a systemic manner.
In response to those suggestions, Keats & Hitt [48] have
developed and tested an integrative model of relationships
among environmental dimensions, diversification, firm size,
structural divisionalization, and economic performance. Their
methodology was based on structural equations analysis. Their
results provided support for portions of all three models of
environment-organizational strategy-economic performance
interface described by Romanelli & Tushman [69] - the external
control, strategic management, and inertial models. However,
the strategic management model received the weakest support.
In the study conclusion, the authors called for future research to
assess their model and improve on it.
The current study is an attempt to investigate the relationships in
the strategic management model. It focuses on reexamining
strategic management as it relates to organizational functioning.
It will attempt to answer a major question: Will the above-
described model (Keat & Hitt‟s) hold if a different setting is
employed and more variables are introduced to it? The model
assumptions will not be examined, however its conclusions will
be.
Since the main interest of the current study is in examining the
strategic management model in particular, therefore we decided
to base our analysis on a well established framework of strategic
management described by Chaffee [19]. An adequate
introduction of his framework will be detailed in the next section
on review of related literature.
THEORETICAL FOUNDATION
Models of Strategy Virtually everyone writing on strategy agrees that no consensus
on its definition exists [15] [37] [41] [45] [52] [72] [74] [77].
Analysis by Chaffee [19], reveals that the strategy definitions in
the literature cluster into three distinct groups based on each
group primary focus: a linear strategy model, an adaptive
strategy model, and an interpretive strategy model.
Linear Strategy. It is widely adopted and focused on sequential
action involved in strategic planning [22]. It portrays top
management as having substantial flexibility to change the
organization. Through its strategy, an organization deals with its
environment to its advantage [15] [65] [66]. Because this model
was developed primarily for profit-seeking businesses, two of its
important measures of performance are profit and productivity.
This model suggests dominance of the environment-
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 45
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
organization-performance (profitability and productivity)
relationships in a sequential time-frame fashion.
Adaptive Strategy. According to this model, the organization is
expected continually to assess its internal as well as external
environment variables. It proposes that the organization and its
parts change in order to be aligned with the environment
conditions [43]. This model suggests dominance of
environment-organization-performance (generation and
investment of slack and stockholders' satisfaction) relationships
in a simultaneous time frame fashion.
Interpretive Strategy. This model defines strategy as orienting
frames of reference that allow the organization and its
environment to be understood by organizational stakeholders
and motivates them to act in ways that are expected to produce
favorable results [82]. It is based on a social contract, rather than
an organismic or biological view of the organization [49].
Moreover, it emphasizes to deal with the environment through
symbolic actions and communications. Still in its attempts to
deal with structural complexity, notably conflicting and
changing demands for organizational output, it emphasizes
attitudinal and cognitive complexity among diverse stakeholders
in the organization. This model suggests dominance of
stakeholders' satisfaction-traditional performance (generation of
slack)-strategy (investment of slack) relationships and
environment-stakeholders' satisfaction relationships.
Given the current study purpose, it is decided to employ the
adaptive model as a theoretical basis to the empirical
investigation. It is considerably difficult, given available
resources, to collect the required data to examine aforementioned
relationships based on the interpretive model. The relationships
under investigation in the adaptive model are more complex and
dynamic than in the linear model.
Strategy and Structure A basic premise of thinking about strategy concerns the
inseparability of organization and environment [11] [51] [80x].
The organization uses strategy to deal with changing
environment. Examining the variables included in Keats &
Hitt‟s (1988) model, the hypothesized relationships among these
variables, and the employed analytical approach, it is found that
most of these aspects agree with the current study. Therefore,
these aspects will be employed in the adaptive strategy model
investigated here.
Figure 1. Adaptive Strategy Model Hypothesized Relationships
Environment Strategy Performance
Time (t) (t+1) (t+2)
H1 H10 H14
H16 H18 H8
H3 H5 H6 H9
H11 H13
H17 H2 H7
H19 H15
H4 H12
Munificence
Diversification
Slack
Generation
Instability
Size
Complexity
Divisionalization
Slack
Investment
Stakeholders’
Satisfaction
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 46
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
Figure 1 represents a schematic presentation of a general system
model of the relationships between the organization and its
environment. Related literature is the primary source of these
proposed relationships. The identifiers in parentheses reflect the
numbered hypothesized relationship paths on the Figure.
H1: It is expected environmental that munificence will have a
negative effect on diversification strategy. An organization may
attempt to buffer itself in response to perceived volatility or low
munificence in a particular domain through diversification [55]
[60x].
H2: It is expected that environmental instability will have a
negative impact on divisionalization. Divisionalization allows
development of specialized knowledge to deal with specific
environmental elements (e.g., instability and complexity) and
creates decentralized decision-making authority to take needed
actions [83] [47x].
H3: It is expected that environmental instability will have a
negative effect on diversification strategy [55]. The adaptive
model tends to focus the manager‟s attention on means, and the
„goal‟ is represented by coalignment of the organization with its
environment. Rather than assuming that the organization must
deal with the environment, this model assumes that the
organization must change with the environment [44] [73].
H4: It is expected that environmental complexity will have a
positive effect on divisionalization. MacCrimmon and Taylor
[54] and Bobbitt and Ford [14] suggested that organizational
decision makers deal with environmental complexity by
structural divisionalization.
H5: It is expected that diversification strategy will have a
positive effect on the organization size. Growth in size may
result from diversification, often accomplished via acquisition of
other firms [42] [87]. Grinyer and Yasai-Ardekani [38] found
that size was correlated with both diversification strategy and
structure. They suggested that firm size may mediate the
strategy-structure relationship. Beyer and Trice [10] suggested
that the relationship between size and structural complexity
depends on firm strategy. Thus, increased diversification may
result in increased size, making a firm more complex to manage.
H6: It is expected that organization size will have a positive
effect on diversification strategy. Size may also affect
diversification-strategy decisions. As firms grow, they tend to
increase their market share and enjoy economies of scale,
thereby increasing their market power and organizational slack
[15] [16]. These in turn provide a firm with the ability and
resources to seek further diversification [53]. However, Hannan
and Freeman [40] argued that inertia also increases with size. At
some point, firm size may constrain further diversification
moves.
H7: It is expected that organization size will have a positive
impact on divisionalization. Increased size may exert
administrative pressures on a firm's structural configuration,
requiring increased decentralization and specialization of
decision making [42].
H8: Divisionalization may also positively affect a firm's
diversification. Pitts [63] argued that structure institutionalizes
strategy and thereby provides the premises for strategic decision
making. Additionally, Staw, Sandelands, and Dutton [76] and
Christensen & Stevenson [25x] suggested that executives'
commitments to established policies preclude perception of
threats or need for change. Thus, structure influences strategy.
H9: It is expected that increased divisionalization will result
positive slack generation. Bettis and Hall [8] and Montgomery
[58] questioned that linkage. Increased size may strain the
increasingly decentralized authority [12].
H10: It is expected that increased diversification will have a
positive effect on slack generation.
H11: It is expected that diversification strategy will have a
positive effect on slack investment.
H12: It is expected that divisionalization will have a positive
impact on slack investment. The transformation processes
pursued by a firm can be classified into two broad categories:
adaptive specialization and adaptive generalization [20] [21]. In
adaptive specialization, the emphasis is predominantly on
profitably exploiting the firm's current environment, and
generating a net surplus of contributions over the inducements
paid to various stakeholders of the firm for their cooperation [4].
Adaptive generalization, on the other hand, is concerned with the
investment of the firm's net surplus of 'slack' resources [28] for
improving its ability to adapt to uncertain or even unknown
future environments.
H13: It is expected that as slack generation increases, it will lead
to increased slack investment. A firm's ability to invest its slack
depends on its ability to generate this slack.
H14: It is expected that as slack generation increases, it will
improves stakeholders‟ satisfaction.
H15: It is expected that as slack investment increase, it will
improves stakeholders‟ satisfaction.
H16: It is expected that environmental munificence will have a
positive impact on slack generation. Munificent environments
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 47
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
present opportunities for promoting slack resources that can
support growth [48].
H17: It is expected that environmental instability will have a
negative impact on slack generation. A firm will invest its net
surplus of 'slack' resources [28] for improving its ability to adapt
to uncertain or even unknown future environments.
H18: The primary influence of environmental munificence is on
the firm size. It is expected that environmental munificence will
positively effect the organization size. Munificent environments
present opportunities for expansion in existing and new markets.
H19: It is expected that environmental complexity will have a
negative impact on divisionalization. Complex environments
may place constraints on firm growth. According to Lawrence
and Lorsch [50], complex environments require high internal
differentiation. Therefore, firms must use resources to train, hire,
and develop specialists to manage interdependencies in their
environment. Thus, they have fewer resources to invest in assets
or external market promotion designed to increase product sales
and market share.
In their study, Keats & Hitt [48] have found that: (1) The
influence of environmental instability was pervasive. On one
hand, it had significant negative effects on diversification,
divisionalization, and operating performance. On the other hand,
it had significant positive impacts on market performance. (2)
Neither munificence nor complexity exhibited a significant
relationship with diversification, divisionalization, or either
performance dimensions. (3) Both munificence and complexity
exhibited significant relationships with organization size. The
first had a positive effect while the second had a negative impact
on size. Divisionalization had significant positive effect on
diversification. However, size did not exhibit the mediating role
suggested by related literature [38]. (4) Diversification strategy
exhibited a significant relationship with only one of the two
performance dimensions; it had positive effect on market
performance. (5) Operating performance had a significant
negative impact on market performance.
METHODS
Study Sample
Our study sample was drawn from Rumelt's [71] diversification
strategy and corporate structure databank. Most of the firms in
the databank (252 of 262) were in manufacturing. Three criteria
were followed in selecting the sample firms: (1) the few firms
involved with retailing or services were excluded to maintain
comparability with related studies. (2) the firms that were
objects of merger or acquisition, or had exhibited a major shift in
industrial or major-product-group classification over the time
frame of the study were excluded too. (3) the accessibility of all
study relevant data.
The 112 firms thus selected represent a broad range of industries,
all with two-digit Standard Industrial Classification (SIC) codes
between 20 and 39. The sizes of the firms ranged from $ 162
million to $ 23 billion in sales (median = $ 1.45 billion) and
from $ 96 million to $ 17 billion in net assets (median = $ 1.0
billion).
Table I shows the composition of selected sample compared
with that of the Fortune 500. The range of diversification
represented is slightly more constrained than that of the entire
Fortune 500. In two cases we have to use trend analysis in order
to extrapolate three missing values of R & D expenditures in
order to complete the set of data.
Table I
Composition of Selected Sample
Category Percent in Current Study Sample Percent in Fortune 500
Single-business 11.8 14.4
Dominant 36.6 22.6
Related 46.4 42.3
Unrelated 5.5 20.7
Construct and Variable Measurement
Since the constructs in Figures 2 are not directly measurable they
must be implied from measured variables. Accordingly, a 'latent
variable' design with multiple indicators for each construct was
chosen. Constructs are represented by a combination of variables
that can be empirically measured. This
latent variable design has recently been applied in the
management literature (e.g. Bagozzi [3]; Chin [24x]; Fornell &
Larcker [35]; Peterson [62x]; Venkatraman & Ramanujam [81];
Keats & Hitt [48]).
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 48
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
Measures of Environmental Characteristics
The environmental dimensions were operationally defined in
terms of the SIC codes. Related literature has supported the use
of industry as a suitable aggregate for the study of organizational
actions [29].
Following the same method used by Keats & Hitt [48], the
selection of environmental indicators was based on examination
of Dess and Beard's [29] factor loadings. The variables that had
strong factor loadings on their respective factors (munificence,
instability, complexity) and relatively weak loadings on the
others were adapted, thus enhancing construct validity. Data for
the environmental measures of industry munificence and
instability were obtained from Annual Survey of Manufactures
and from COMPUSTAT tapes for industry complexity.
Environmental indicators will computed over the period 1969-
1978.
Munificence. Starbuck [75] and Aldrich [1] state that
organizations seek out environments that permit organizational
growth and stability. Dess and Beard [29] study suggested that
industry growth in sales (rs), price-cost margin (rpc), total
employment (remp), and value added (rva) were loaded highly on
the munificence factor. Following that result, the current study
designated these four indicators for environmental munificence
to estimate the adaptive strategy model. They were specified as
the rate of growth (regression slope coefficient of the dependent
variable against time over a ten-year period 1964-1973 or 1969-
1978) divided by the mean value of the dependent variable (to
adjust for absolute industry size) . Total sales = Value of
shipments, price-cost margin = value added by manufacture
minus total wages. Data for these variables are taken from
Census of Manufactures 1964-1978.
Instability. Much of the literature in organization theory and
business policy theory has dealt with instability and suggests that
turnover, absence of pattern, and unpredictability are the best
measures of environmental stability-instability. Aldrich [1]
stated that environmental turbulence "leads to internally induced
change .... that are obscure to administrators and difficult to plan
for" (1979:p. 69). Dess and Beard [29] study suggested that
volatility of sales (vars), price-cost margin (varpc), employment
(varemp), and value added (varva) in the dominant industry over
the period of study were loaded highly on the instability factor.
Following that result the current study designated these four
indicators for environmental instability to estimate the strategic
management model. Standard error of the regression slope
coefficient divided by mean value to measure the dispersion
about the regression line obtained when each dependent variable
was regressed on time over the period 1964-1973 or 1969-1978.
Data for these variables are taken from Census of Manufacturers
1964-1978.
Complexity. Three elements are of concern when dealing with
environmental complexity. They are: the number, diversity, and
distribution of task-environments elements [1] [29]. In this
research, the indicator for the complexity dimension for the
study period is computed based on Grossack's [39] dynamic
measure of industry concentration. Regression coefficient of
terminal-year market shares of all firms in a given industry upon
their shares in the initial year. Data for these variables are
extracted from COMPUSTAT database.
Measures of Organizational Characteristics
Diversification Strategy. Rumelt's [70] system of
diversification categories is adopted here. Related literature has
reported strong support of Rumelt‟s ratio-based measure [25]
[58] [30]. The values for this variable are obtained from Rumelt's
1978 data bank. The level of diversification for each firm as of
1974 is ranked from 1 to 9, according to the following order of
categories: single business, single vertically integrated business,
dominant-product vertically integrated business, dominant
constrained, dominant linked, dominant unrelated, related
constrained, related linked, unrelated businesses, multibusiness
and unrelated portfolio.
Size. For a study of this nature, related literature suggests for a
study to use size measures that reflect firm sales and assets [46].
Thus, in the present study, size is measured in terms of dollar
sales volume (SV) and net assets (NTA) for 1974. Values for
those measure are obtained from COMPUSTAT tapes.
Structural Divisionalization. This study employs Rumelt's
1974 system of structural divisionalization. The values for this
variable are obtained from Rumelt's l978 data bank. The level of
divisionalization for each firm as of 1974 is ranked from 1 to 5,
according to the following order of categories: simple functional
organization, functional organization with subsidiaries for
separate products, geographic divisions, product divisions, and
the highly decentralized holding company form with a small
head office.
Measures of Performance
Measures Of The Quality Of A Firm's Transformation.
Performance measures that help evaluate the quality of a firm's
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 49
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
transformations are the true discriminators of 'excellence' [31].
As aforementioned, the transformation processes pursued by a
firm can be classified into two broad categories: adaptive
specialization and adaptive generalization. Generation of slack
can often be quantified through financial measures; however,
evaluating how well a firm has invested its slack is more
difficult.
This study uses a few of the publicly reported financial measures
for evaluating the manner in which a firm has managed its slack
resources. Five variables are selected to assess a firm's ability to
generate slack. Profitability is an obvious determinant of a firm's
slack resources. This study uses cashflow by investment ratio
(CFBYIN) as a measure of profitability. Productivity is another
important measure of a firm's ability to generate slack. Sales
revenue per employee (SABYEM) is a crude measure of the
firm's labor productivity, and the firm's sales revenue per dollar
of total assets (SABYTA) is a measure of its capital productivity.
The ability of the firm to raise long-term capital resources is yet
another measure of the slack available to it. Two popular
measures of this ability are its market to book ratio (MBYB) and
its long-term debt to equity ratio (LDTBYEQ) [16].
Five variables are chosen to evaluate a firm's ability to invest its
available slack. A popular measure of a firm's investment in its
future is the percentage of its sales revenues that it allocates to R
& D expenses (RDBYSA) [61] [9]. Another measure is the
percentage of sales revenues that the firm allocates to advertising
expenses (ADBYSA) [9]. Other uses of the firm's slack are
abnormal increases in its fixed and working capital expenditures
[16], as measured by increases in the capital expenditures to
sales ratio (CEBYSA) and working capital to sales ratio
(WCBYSA). Dividend payout ratio (DIVPAY) was used as a
third measure of slack usage [16].
Measures of Multiple Stakeholders’ Satisfaction. A necessary
condition for excellence is the continued cooperation of the
firm's multiple stakeholders. Minimizing their dissatisfaction
should be a concurrent objective of 'excellent' companies.
Stockholders' satisfaction is assessed in terms of three variables:
quality of management value as a long-term investment
measured by after-tax return on shareholders' equity (ROE),
price/earning ratio (PE) and dividend to equity ratio
(DIVBYEQ), and financial soundness measured by the firm's
quick ratio or current assets excluding inventories by current
liabilities (QUICK) and total debt to equity ratio (DBYEQ), and
use of corporate assets measured by the firm's pre-tax return on
gross assets (ROA). Quality of management as a variable of
stakeholders‟ satisfaction was dropped because it is extremely
difficult to measure.
Customers' satisfaction is assessed in terms of three variables:
quality of products and services measured by cost of goods sold
as a percentage of sales revenues, and innovation measured by R
& D as a percentage of sales revenues (RDBYSA) and product
differentiation measured by advertising expenditures as a
percentage of sales revenues (ADBYSA). Order backlog as a
percentage of sales revenues is a feasible measure of a firm's
quality of products and services nonetheless it is dropped
because of lack of data.
Employees' satisfaction is assessed in terms of the firm's ability
to attract and keep its employees measured by per employee
labor and related expenses (LBYEMP).
Community satisfaction evaluated in terms of the firm's social
responsibility is extremely difficult to measure using available
data in the study databank. Therefore, it is dropped.
Testing Procedure
A structural modeling approach was chosen to evaluate both
error in construct measurement and error in hypothesized
relations. Rather than using the well-known LISREL model
[47], Wold‟s method [84] [85] of latent variables partial least-
squares (PLS) was employed. The choice was motivated by
several considerations. First, managerial data do not often
satisfy the requirements of multi-normality and interval scaling,
or attain the sample size required by maximum-likelihood
estimation (ML). Second, Wold's method of PLS avoids many
of the restrictive assumptions underlying ML techniques and
ensures against improper solutions and factor indeterminacy.
A general PLS model is composed of two parts: the structural
model and the measurement model. The structural model
specifies the relations among the constructs (or latent variables)
while the measurement model specifies the relations between the
manifest variables and the constructs which they represent. It is
assumed for estimation purposes that the unobservables are
specified as linear combinations of their respective indicators
and, for convenience, that all variables are standardized. The
measurement model enables us to evaluate whether the
constructs are measured with satisfactory accuracy.
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 50
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
RESULTS
The Measurement Model
Table II contains the PLS descriptive statistics for the
measurement model. Average variance captured (rvc) ranges
between 0.69 and 0.90. These results indicate satisfactory
convergent validity for all constructs in the theoretical model.
Moreover, the reasonably low average squared correlations
among constructs indicate that the model also satisfies the
condition of discriminant validity in the given formulation.
TABLE II
Measurement Model Descriptive Statistics
PLS Descriptive Statistics
Convergent Validitya
Munificence
Instability
Complexityc
Diversificationc
Size
Divisionalizationc
Slack Generation
Slack Investment
Stackeholders‟ Satisfaction
.83
.73
.71
.90
.87
.69
Discriminiant Validityb
Munificence
Instability
Complexityc
Diversification
c
Size
Divisionalizationc
Slack Generation
Slack Investment
Stackeholders‟ Satisfaction
.09
.15
.03
.21
.22
.32
(a) Average variance captured
(b) Average of the squared correlations of the parameter of the particular construct with all other constructs
(c) The construct is based on a single indicator.
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 51
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
Table III reports the PLS parameter estimates and the error
variance of each indicator for the measurement model. One
notes that across all indicators and constructs the measurement
model loadings are high and consistent in sign, and the residual
variances are generally small. Thus, it can be concluded that the
constructs of interest are measured in this case with more
precision.
TABLE III
Measurement Model Parameter Estimates
Constructs: Indicators Loading Error Variance
Environment:
Munificence:
rs
rpc
remp
rva
Instability:
vars
varpc
varemp
varva
Complexity:
COMP
.75
.84
.69
.80
.67
.70
.69
.75
1.00
.18
.14
.22
.16
.23
.21
.22
.18
.00
Strategy:
Diversification:
DF
Size:
SV
NTA
Divisionalization:
DV
1.00
.91
.85
1.00
.00
.08
.12
.00
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 52
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
TABLE III (continued)
Measurement Model Parameter Estimates
Constructs: Indicators Loading Error Variance
Performance:
Slack Generation:
CFBYIN
SABYEMP
SABYTA
MBYB
Slack Investment:
RDBYSA
ADBYSA
CEBYSA
WCBYSA
PAYOUT
Stackeholders‟
Satisfaction:
ROE
PE
DIVBYEQ
QUICK
TDBYEQ
ROA
CGSBYSA
RDBYSA
ADBYSA
LBYSA
.83
.71
.93
.67
.84
.95
.60
.83
.71
.87
.72
.79
.69
.66
.83
.80
.92
.71
.66
.15
.20
.06
.23
.14
.04
.32
.10
.20
.08
.19
.17
.24
.27
.11
.14
.05
.21
.29
(a) indicates a fixed or single indicator.
The Structural Model
The estimates of the structural model are reported in Table IV.
Also given in this table are the jack-knife t-test significance
results (see Fenwick [33] for the underlying computational
procedure).
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 53
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
TABLE IV
Structural Model Parameter Estimates
Hypothesis Relationa PLS
H1 MUNF DF (-) -.29*
H2 INST DV (-) -.32*
H3 INST DF (-) -.45*
H4 COMP DV (+) .11
H5 DF SZ (+) -.04
H6 SZ DF (+) .08
H7 SZ DV (+) .12
H8 DV DF (+) .19
H9 DV GS (+) .27*
H10 DF GS (+) .23*
H11 DF IS (+) .20*
H12 DV IS (+) .41*
H13 GS IS (+) .56*
H14 GS SS (+) .23*
H15 IS SS (+) .87*
H16 MUNF GS (+) .67*
H17 INST IS (-) -.33*
H18 MUNF SZ (+) .58*
H19 COMP SZ (-) -.31*
(a) Sign of the hypothesized relationship (direct effect)
(*) Significant at 0.01 significance level
The statistic is approximately distributed with N - 1 degrees of freedom
(where J(p) = jack-knife estimate of p, s(p) = standard deviation of the jack-
knife estimate, and N = the number of subsamples used for jack-knifing, in
this case 111).
One important finding of estimating the model, in its given
formulation, is the insignificance of size as a mediating factor as
suggested by Grinyer and Yasai-Ardekani [38]. This agrees with
Keats & Hitt's [48] findings.
For the adaptive model, size is found to be insignificant as a
mediating factor. Only six relationships (H4,H5,H6,H7, and H8)
do not pass the 0.01 significance hurdle. Further, the
coefficients of determination R2 of the diversification, size,
generation of slack, investment of slack, and stakeholders'
satisfaction relations are 0.61, 0.52, 0.75, 0.68, 0.83,
respectively. Given that the stakeholders' satisfaction is the
central focus of the model, it is obvious that a satisfactory fit is
obtained.
[ ( J - P) /( s / N ) ] * [ ( N - 1) /( 2 N - 1) ]( p ) ( p )
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 54
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
Direct Effects
Environment and Diversification. It was hypothesized that
environment (munificence and instability) and diversification
levels would be negatively related given the relatively large
firms in the study sample (H1 and H3). The results in Table IV
support both hypotheses (-0.29 and -0.45). Judging from the size
of the coefficients one can conclude that, in the industries
studied, the direct effect of adapting to the environmental
changes through diversification is quite important. Through its
diversification, a firm interprets and adapts to its environment in
order to exploit the prevailing opportunities and hedge against
any threats in it.
Environment and Size. It was hypothesized that environment
munificence and size (H18) would be positively related. Further,
environment complexity and size (H19) would be negatively
related. The results in Table IV support both expectations (0.58
and -0.31). Munificent environments present opportunities for
expansion, on one hand. This agrees with Keats & Hitt's [48]
findings. Complex environments, on the other hand, place
constraints on firm growth. This, also, agrees with Lawrence
and Lorsch's [50] arguments.
Environment, Organizational Strategy And Generation Of
Slack. It was hypothesized that environmental munificence and
generation of slack are positively related (H16). Estimated
results in Table IV support this hypothesis (0.67). It indicates
that munificent environments induce firm ability to generate
slack. Also, it was hypothesized that the higher a firm's
diversification level, the better its ability to generate slack would
be (H10). In addition, it was argued that the higher a firm's level
of divisionalization, the better its ability to generate slack would
be (H9). Estimated results show that higher levels of both
organizational factors are indeed positively related to a firm's
ability to generate slack (0.23 and 0.27). In adapting to the
environmental changes, which may represent opportunities or
threats, a firm may choose to diversify or restructure to better
align itself to its environment.
Environment Strategy Performance
Time (t) (t+1) (t+2)
(-) (+) (+)
(+) (-) (+) (+)
(
(+)
(-) (-) (+) (-) (+)
(+)
p < .01
n. s.
Munificence
Diversification
Slack
Generation
Instability
Size
Complexity
Divisionalization
Slack
Investment
Stackeholders‟
Satisfaction
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 55
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
Environment, Organizational Strategy And Investment Of
Slack. It was hypothesized that environment instability and a
firm's investment of slack would be negatively related (H17).
The results in Table IV show support of this hypothesis (-0.33).
This indicates that uncertain environments induce firms to
conserve in investing their available slack. Also, it was
hypothesized that diversification and divisionalization would be
positively related to investment of slack (H11 and H12). The
results in Table IV show support of these hypotheses (0.20 and
0.41). This indicates that diversification posture and
organizational structure influence a firm's investment strategy.
In addition, a firm's ability to generate slack was hypothesized to
influence its ability to invest this slack in the positive direction.
This is supported by results shown in Table IV (0.56).
Generation Of Slack, Investment Of Slack And
Stakeholders' Satisfaction. It was hypothesized that generation
of slack and stakeholders' satisfaction, on one hand, would be
positively related (H14), and that investment of slack and
stakeholders' satisfaction, on the other hand, would also be
positively related (H15). The results in Table IV highly support
these two hypotheses (0.23 and 0.87). Based on the size of the
second coefficient, a sound investment of strategic slack implies
the firm success and satisfies its stakeholders.
Total Effects
The previous discussion was limited to the direct relations
among constructs without referring to the indirect effects on
endogenous variables. For example, environmental munificence
influences an organization's ability to generate slack not only
directly, but also through the diversification-generation of slack
relation (DF GS). Therefore, indirect effects need to be
considered to evaluate the total impact of one construct on
another.
An important observation is that none of the relationships
commented on earlier changes in sign. Thus, while some effects
change in absolute size, the established relationships among the
constructs still hold, suggesting that indirect effects are in
general less important than the direct effects. Some interesting
changes in magnitude are to be noted, however. First, the total
effect of environmental munificence on generation of slack
(0.48) is lower than the direct effect (0.67). This is probably due
to the indirect link (MUNF DF) and (DF GS). While
higher industry growth may mean less need to use diversification
as a buffer, it is likely to contribute positively to generation of
slack. The fact that the total effect of environmental
munificence on generation of slack is lower than the direct effect
may reflect the mixed effect of diversification on generation of
slack, which supports Montgomery's doubts and Bettis and Hall's
idea about this linkage.
Second, the total effect of diversification on investment of slack
(0.11) is lower than the direct effect (0.20). This is due to the
indirect link (GS IS). While diversification may mean
higher returns, i.e. generation of slack, that positively affects the
ways a firm invests its accumulated slack, a firm can benefit
from low levels of diversification to enable it to achieve the
advantages of specialization whereas the firms that are not
highly diversified nor specialized cannot enjoy high returns.
This agrees with PIMS study findings (1974).
DISCUSSION
Results supported a reasonable number of the relationships
depicted in the proposed model. The formulated model suggests
dynamics similar mainly to Keats & Hitt [48] and different in
part from those predicted by past research. However, Keats &
Hitt [48] used an integrative model similar to ours, whereas past
research primarily examined only parts of a model or used
inadequate analytical methods.
The suggested strategic model received strong support. Each of
the environmental dimensions and organizational variables was
found to affect at least one organizational performance variable
in a simultaneous time frame fashion. Clearly, however, firm
organizational strategy, in terms of diversification and
divisionalization, was the dominant influence in affecting its
ability to generate slack. The results show that firms in this
study that had high diversification degree and chose to
decentralize its decision making had better opportunities to
generate different kinds of slack resources. This result is
supported by growth potential in the environment which was
found significant as an influence on firm ability to generate
slack.
In addition, firm diversification and divisionalization strategies
were also dominant factors in affecting its ability to invest slack
resources. Firms in this study that have generated a great
amount of slack resources, diversified its business and chose to
decentralize its decision making had better chance to invest their
slack resources. This result is supported by low levels of
environmental instability which was found significant as an
influence on firm ability to invest its slack. However, Bourgeois
[17] warned that firms should not necessarily seek reduced
uncertainty because this may result in missing opportunities.
Instability was found significant as an influence on firm
divisionalization. Instable industries exerted pressure on firm
structure, requiring decentralization, which agrees with
Williamson [83].
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 56
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
Munificence seem to exert a dominant influence on
diversification strategy. Firms in this study were found to buffer
themselves in response to low munificence through
diversification [55] [25z].
The combination of a good ability to generate slack and a good
chance to invest it was found significant as an influence on firm
stackeholders' satisfaction [21].
Most of the model dominant theoretical relationships were
proven significant. Above all, the model exhibited a good
statistical fit.
CONCLUSIONS
The results of this study suggest that a large portion of the
adaptive strategy as described by Chaffee [19] has relevance for
organizational functioning. Unlike in Keats & Hitt‟s model, the
strategic management linkages received substantial support in
the current study.
The study suggests that better representative model of strategic
management yields more relevant information that explain the
functional relationships among environment, strategy and
performance.
The findings are limited by (1) the fact that estimating the study
models is based on the use of accounting data, (2) the proxy
measures that are chosen as to estimate the latent variables, (3)
the statistical technique that is selected to estimate the
relationships of concern to the study, (4) the time period that is
used as a timeframe for the statistical analysis, and (5) the model
that is employed to represent the relationships among
organizational constructs of interest. Future research is
suggested to assess the validity of estimated relationships based
on different strategic model, such as the interpretive model.
Considering different proxy measures to estimate the latent
variables in the model is another area to look at. The use of
different structural model is also feasible.
REFERENCES [1] Aldrich, H. E. 1979. Organizations and environments. Englewood
Cliffs, NJ:Prentice-Hall. [2] Andrews, K. R. 1980. The concept of corporate strategy. Homewood,
IL: Richard D. Irwin.
[3] Bagozzi, R. 1980. Causal models in marketing. New York: Wiley. [4] Barnard, C. I. 1938. The functions of the executive. Cambridge, M.A.:
Harvard University Press.
[5] Bentler, P. 1980. Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology: 419-456.
[6] Bettis, R. A. 1981. Performance differences in related and unrelated
diversified firms. Strategic Management Journal, 2: 379-393. [7] Bettis, R. A. 1983. Modern financial theory, corporate strategy, and
public policy: Three conundrums. Academy of Management Review,
8: 406-415.
[8] Bettis, R. A. and Hall, W. K. 1982. Diversification strategy, accounting
determined risk and acounting determined return. Academy of Management Journal, 25.
[9] Bettis, R. A. and Mahajan, V. July 1985. Risk/return performance of
diversified firms. Management Science, 31. [10] Beyer, J. M. and Trice, H. M. 1979. A reexamination of the relations
between scope and various components of organizational complexity.
Administrative Science Quarterly, 24:
48- 64.
[11] Biggadike, E. R. 1981. The contributions of marketing to strategic
management. Academy of Management Review, 6: 621-632.
[12] Blau, P. M. 1970. A formal theory of differentiation in organization. American Sociology Review, 35: 201-218.
[13] Blau, P. M. and Schoenherr, R. 1971. The structure of Organization.
New York: Basic Books. [14] Bobbitt, H. R. Jr. and Ford, J. 1980. Decision maker choice as a
determinant of organizational structure. Academy of Management
Review, 5: 13-23. [15] Bourgeois, L. J. 1980. Strategy and environment: A conceptual
integration. Academy of Management Review, 5: 25-39.
[16] Bourgeois, L. J. 1981. On the measurement of organizational slack.
Academy of Management Review, 6: 29-40.
[17] Bourgeois, L. J. 1985. Strategic goals, perceived uncertainty, and
economic performance in volatile environments. Academy of Management Journal, 28: 548-573.
[18] Burns, T. and Stalker, G. M. 1961. The management of innovation.
London: Tavistock. [19] Chaffee, E. E. 1985. Three models of strategy. Academy of
Management Review, 10(1): 89-98.
[20] Chakraverthy, B. S. 1982. Adaptation: a promising ,metaphor for strategic management. Academy of Management Review, 7: 35-44.
[21] Chakraverthy, B. S. 1986. Measuring Strategic Performance. Strategic
Management Journal, 7: 437-458. [22] Chandler, A. D., Jr. 1962. Strategy and structure, Cambridge, Mass.:
The M. I. T. Press.
[23] Chandrasekaran, G. 1982. Market concentration and organization performance. Unpublished Ph. D. dissertation, State University of
New York at Buffalo.
[24] Channon, D. F. 1971. The strategy and structure of British enterprise.
Unpublished Ph. D. dissertation, Harvard Business School.
[24x] Chin, W., 1998, “The Partial Least Square Approach to Structural
Equation Modeling,” in Marcoulides, G. A. (Ed.), Modern Methods
for Business Research, London: Lawrence Erlbaum Associates, pp.
295-336.
[25] Christensen, H. K. and Montgomery, C. A. 1981. Corporate economic performance: Diversification strategy versus market structure.
Strategic Management Journal, 2: 327-343.
[25x] Christensen, C. & H. Stevenson, October 2006, "The tools of
cooperation and change," Harvard Business Review, 84 (10), pp. 73-80.
[25z] Markides, C. C., November-December 1997, "To diversify or not to
diversify," Harvard Business Review, 75 (6), pp. 93-99.
[26] Cook, T. D. and Campbell, D. T. 1979. Quasi-experimentation: Design
and analysis issues for field settings. Boston, M.A.: Houghton- Mifflin. [27] Cullen, J. B., Anderson, K. S. and Baker, D. D. 1986. Blau's theory of
structural differentiation revisited: A theory of structural change or
scale ? Academy of Management Journal, 29: 203-229.
[28] Cyert, R. M. and March, J. G. 1963. A behavioral theory of the firm.
Prentice-Hall, N.J.: Englewood Cliffs.
[29] Dess, G. G. and Beard, D. W. 1984. Dimensions of organizational task environments. Administrative Science Quarterly, 29: 52-73.
[30] Dubofsky, P. and Varadarajan, P. 1987. Diversification and measures
of performance: Additional empirical evidence. Academy of Management Journal, 30: 597-608.
[31] Evan, W. M. 1976. Organization theory and organizational
effectiveness: An exploring analysis. Organization and Administrative Sciences, 7: 15-28."
[32] Fahey, L. and Christensen, H. K. 1986. Evaluating the research on
strategy content. Journal of Management, 12: 167-183. [33] Fenwick, I. 1979. Techniques in Market Measurement: The jackknife.
Journal of Marketing Research, 16: 410 -414.
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 57
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
[34] Fornell, C. and Bookstein, F. L. 1982. Two structural equation models:
LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research: 440-452.
[35] Fornell, C. and Larcker,D. F. 1981. Evaluating structural equation
models with unobservable variables and measurement error. Journal of Marketing Research, 18: 39-50.
[36] Gleuck, F. 1980. Strategic management and business policy, New
York: McGraw-Hill Book Co. [37] Glueck, F., Kaufman, S. and Walleck, A. S. 1982. The four phases of
strategic management. Journal of Business Strategy, 2 (3): 9-21.
[37x] Gottfredson, M., Schaubert, S., & H.Saenz, February 2008, "The new
leader's guide to diagnosing the business," Harvard Business
Review, 86 (2), p. 73.
[38] Grinyer, P. H. and Yasai-Ardekani, M. 1981. Strategy, structure, size,
and bureaucracy. Academy of Management Journal, 24: 471-486. [39] Grossack, I. 1965. Towards an integration of static and dynamic
measures of industry concentration. Review of Economics and
Statistics, 7: 301-308. [40] Hannan, M. T. and Freeman, J. 1984. Structural inertia and
organizational change. American Sociological Review, 49: 149-164.
[41] Hatten, K. J. 1979. Quantitative research methods in strategic
management. In Schendel, D. E. and Hofer, C. W. (Eds.), Strategic
management: A new view of business policy and planning: 448-467.
Boston: Little, Brown. [42] Hitt, M. A. and Ireland, R. D. 1985. Corporate distinctive competence,
strategy, industry and performance. Strategic Management Journal, 6:
273-293. [43] Hofer, C. W. 1973. Some preliminary research on pattern of strategic
behavior. Academy of Management Proceedings: 46-59.
[44] Hofer, C. W. 1976. Conceptual scheme for formulating a total business strategy. Boston: HBS Case Services.
[45] Hofer, C. W. and Schendel, D. 1978. Strategy formulation: Analytical
concepts. St. Paul, MN: West. [46] Jackson, J. H., Morgan, C. P. and Paolillo, J. G. P. 1986. Organization
theory: A macro perspective for management, 3 rd. ed., Englewood
Cliffs, NJ: Prentice-Hall. [47] Joreskog, K. G. and Serbom, D. 1985. Analysis of linear structural
relationships by the method of maximum likelihood: LISREL
VVIV, University of Uppsala.
[47x] Kanter, R. M., July-August 1994, "Collaborative Advantage: The art of
the alliance," Harvard Business Review, 72 (4), pp. 105-106.
[47z] Kaplan, R. S., & D. P. Norton, 2001, The Strategy-focused
organization, Boston: Harvard Business School Press.
[48] Keats, B. W. and Hitt, M. A. 1988. A causal model of linkages among
environmental dimensions, macro organizational characteristics, and performance. Academy of Management Journal, 31: 570-598.
[49] Keeley, M. 1980. Organizational analogy: A comparison of organismic
and social contract models. Administrative Science Quarterly, 25: 337-362.
[50] Lawrence, P. R. and Lorsch, J. 1969. Organization and Environment.
Homewood, IL.: Richard D. Irwin. [51] Lenz, R. T. 1980a. Strategic capability: A concept and framework for
analysis. Academy of Management Review, 5: 225-234. [52] Lenz, R. T. 1980b. Environment, strategy, organization structure and
performance: Patterns in one industry. Strategic Management Journal,
1: 209-226.
[53] Luffman, G. A., and Reed, R. 1982. Diversification in British industry
in the 1970s. Strategic Management Journal, 3: 303-314.
[54] MacCrimmon, K. R. and Taylor, R. A., Jr. 1976. Decision making and problem solving. In Dunnette, M. D. (Ed.). Handbook of industrial and
organizational psychology: 1397-1453. Chicago: Rand McNally &
Co. [55] Miles, R. E., Snow, C. C. and Pfeffer, J. 1974. Organization
environment: Concepts and issues. Industrial Relations, 13: 244-264.
[56] Miller, D. and Friesen, P. H. 1978. Archetypes of strategy formulation. Management Science, 24: 921-933.
[57] Miller, D. and Toulouse, J. 1986. Strategy, structure, CEO personality,
and performance in small firms. American Journal of Small Business, 10: 47-62..
[58] Montgomery, C. A. 1982. The measurement of firm diversification:
Some new empirical evidence. Academy of Management Journal, 25: 299-307.
[59] Myers, S. C. 1984. Finance theory and financial strategy. Interfaces,
14 (1): 126-137. [60] Nathanson, D. and Cassano, J. 1982. Organization, diversity, and
performance. The Wharton Magazine: 19-26.
[60x] Neilson, G. , Martin, K. , & E. Powers, June 2008, "The Secrets to
Successful Strategy Execution," Harvard Business Review, 86 (6), pp.
61-70.
[61] Old, B. S. 1982. Corporate directors should rethink technology.
Harvard Business Review: 6-14. [62] Pavon, R. J. 1972. The strategy and structure of Italian industrial
enterprise. Unpublished Ph. D. dissertation, Harvard Business School.
[62x] Peterson, R., “A Meta-Analysis of Variance Accounted for and Factor
Loadings in Exploratory Factor Analysis,” Marketing Letters, Vol. 11,
2000, pp. 261-275.
[63] Pitts, T. H. 1980. The strategy-structure relationship: An exploration
into causality. Paper presented at annual meeting of the Academy of
Management, Detroit.
[64] Pooley, G. 1972. Strategy and structure of French enterprise.
Unpublished Ph. D. dissertation, Harvard Business School.
[65] Porter, M. E. 1980. Competitive strategy: Techniques for analyzing industries and competitors, New York: Free Press.
[66] Porter, M. E. 1985. Competitive advantage: Creating and sustaining superior performance, New York: Free Press.
[67] Prescott, J. E. 1986. Environments as moderates of the relationship
between strategy and performance. Academy of Management Journal, 29: 329-346.
[68] Pugh, D. S., Hickson, D. J., Hinings, C. R. and Turner, C. 1969. The
context of organizational structures. Administrative Science Quarterly, 14: 91-114.
[69] Romanelli, E. and Tushman, M. L. 1986. Inertia, environments, and
strategic choice: A quasi-experimental design for comparative-longitudinal research. Management Science, 32: 608-621.
[70] Rumelt., R. P. 1974. Strategy, structure, and economic performance,
Division of Research, Graduate School of Business Administration, Harvard University.
[71] Rumelt, R. P. 1978. Databank on diversification strategy and corporate
structure. Paper MGL-55, Managerial studies center, Graduate School of Management, University of California at Los
Angeles.
[72] Rumelt, R. P. 1979. Evaluation of strategy: Theory and models. In Schendel, D. E. and Hofer, C. W. (Eds.): 196-212. Strategic
Management: A new view of business policy and planning, Boston:
Little, Brown. [73] Shirley, R. C. 1982. Limiting the scope of strategy: A decision based
approach. Academy of Management Review, 7: 262-268.
[74] Spender, J. C. 1979. Commentary. In Schendel, D. E. and Hofer, C. W. (Eds.): 383-404. Strategic Management: A New View of Business
Policy and Planning. Boston: Little, Brown.
[75] Starbuck, W. H. 1976. Organizations and their environments. In Dunette M. D. (Ed.):1069-1123. Handbook of industrial and
organizational psychology. Rand McNally Co.
[76] Staw, B. M., Sandelands, L. E. and Dutton, J. E. 1981. Threat-rigidity effects in organizational behavior: A multilevel analysis.
Administrative Science Quarterly, 26: 501-524.
[77] Steiner, G. A. 1979. Strategic Planning. New York: Free Press. Stopford, J. 1968. Growth and organizational change in the
multinational field. Unpublished dissertation, Graduate School of
Business Administration, Harvard University. [78] Suzuki, Y. 1980. The strategy ans structure of top Japanese industrial
enterprise 1950-1970. Strategic Management Journal, 1: 265-391.
[79] Thanheiser, H. T. 1972. Strategy and structure of German industrial enterprise. Unpublished Ph. D. dissertation, Harvard Business School.
[80] Thompson, J. D. 1967. Organizations in action. New
York:McGraw-Hill Book Co.
[80x] Thompson, A, Strickland III, A., & J. Gamble, Crafting and Executing
Strategy: The Quest for Competitive Advantage, 17th ed., Boston: McGraw-
Hill Book Co., 2010: 54-95.
International Journal of Basic & Applied Sciences IJBAS-IJENS Vol:14 No:01 58
147901-4848- IJBAS-IJENS @ February 2014 IJENS I J E N S
[81] Venkatraman, N. and Ramanujam, V. 1987. Planning system success:
A conceptalization and an operational model. Management Science: 687-705.
[82] Weick, K. E. and Daft, R. L. 1983. The effectiveness of interpretation
systems. In Cameron, K. S. and Whetten, D. A. (Eds.): 71-93. Organizational effectiveness: A comparison of multiple models. New
York: Academic Press.
[83] Williamson, O. E. 1975. Markets and hierarchies: Analysis and antitrust implications, New York, Free Press.
[84] Wold, H. 1980. Soft modeling: Intermediate between traditional model
building and data analysis. Mathematical Statistics: 333-346. [85] Wold, H. 1982. Systems under indirect observation using PLS. In Fornell,
C. (Ed.), 1: 325-347. A second generation of multivariate analysis. New
York, Praeger. [86] Wrigley, L. 1970. Divisional autonomy and diversification, unpublished
doctoral dissertation, Harvard University, Boston.
Zook, C., April 2007, "Finding your next core business", Harvard Business
Review, 85 (4), pp. 66-75.