An Adaptive Model of the Effect of Environment, Structure ...ijens.org/Vol_14_I_01/147901-4848-IJBAS-IJENS.pdfStrategy and Structure A basic premise of thinking about strategy concerns

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-

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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



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].


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



present opportunities for promoting slack resources that can

support growth [48].


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]).



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



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.



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.



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



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).



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 )



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



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].



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.

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