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Deviation from fit: an advantage when environments change.

The literature on strategy-environment fit has shown that strategies' effectiveness depends on environmental conditions (Pennings 1987, Prescott 1986, Venkatraman/Prescott 1990). This literature shows that deviating from strategies that fit an environment harms performances. Yet discovering that a particular strategy fits a given environment best, does not imply that all firms can or will converge on that strategy.

Firms with strategies that fit their environments, may erect barriers that prevent others from imitating their strategies (Mascarenhas/Aaker 1989, Hatten/Hatten 1987). Or, even though external forces present few barriers, internal resource profiles might prevent some firms from following strategies that fit an environment (Barney 1991). Conflicting demands of different environmental elements represent other reasons why firms might deviate from ideal strategies (Gresov 1989).

Since implementing strategies takes time, firms may benefit sometimes by deviating from fitting strategies. The changes in internal systems and structures needed to maintain internal fit (Miller 1992) face resistance (Meyer/Rowan 1977, Fombrun/Ginsberg 1990), delaying the adaptation process. The possibility of first-mover advantages (Lieberman/Montgomery 1988) gives firms reason to respond early to environmental changes. Consequently, when firms anticipate environmental changes, they benefit by deviating from strategies that fit current environments.

This paper extends the discussion on strategy-environment fit by examining one instance when firms benefit by deviating from fit - namely when environments change. It differs from previous studies that have discussed firms deviating from fit (Gresov 1989) by arguing that firms actually benefit, though in future periods, from their deviation. The paper examines this proposition with evidence from the Savings and Loan Association (SLA) industry in the United States. The industry provides a useful site to test these propositions since it experienced two, anticipated, instances of environmental change in the early 1980s when regulations about the products the industry could offer changed dramatically.

This paper is structured as follows. The following section develops principal arguments. The subsequent section describes methods. Results and discussions are followed by brief conclusions.

Theory

The idea that firms deliberately deviate from strategies that fit their environments, is not new. Strategic groups (Caves/Porter 1977, Hatten/Schendel 1977, Mascarenhas/Aaker 1989) pose barriers to the mobility of firms between them, limiting some firms' abilities to follow chosen strategies viably. Consequently firms often settle for strategies that do not yield optimal results in their environments.

Firms' resources also limit strategies they may follow viably. Firms that attempt strategies not supported by their resources, run the risk that others with more appropriate resources might imitate their strategies with greater effectiveness (Barney 1991). Once again, strategies suited to an environment may not be viable for all firms attempting to compete in that environment.

Gresov (1989) demonstrated that firms deliberately deviate from ideal designs when facing multiple, often conflicting contingencies. Rather than adopt designs that might be completely unsuited to any one contingency, firms choose to deviate from fit in order to minimize cumulative penalties. Such a rationale might similarly cause firms to deviate from fitting strategies.

Future Performances - Another Reason to Abandon Fit

This paper extends the preceding arguments by suggesting that firms also abandon fit when they anticipate that environments will change. We view strategies in this paper as processes involving the establishment of objectives for the firm and the deployment of resources to achieve them (Chandler 1962, Shapiro 1989).

If firms could implement strategies instantaneously, they would always try to adopt the ideal strategies for their environments. Firms whose strategies were closest to ideal to begin with, would try to bar others' attempts at imitation. Fitting firms would maintain their strategy until the instant their environments changed. Or, if first-mover advantages of any significant size existed, until the moment some other firm implemented a strategy more suited to a changing environment.

But strategies cannot be implemented instantaneously. Often, new strategies require firms to adapt internally and maintain fit between strategies and internal features such as systems and structures (Miller 1992). Inertial forces in organizations pose resistance to the adaptation of both strategies and structures (Fombrun/Ginsberg 1990, Meyer/Rowan 1977). Overcoming inertial forces takes time and requires compromises that might result in the emergent strategies looking nothing like intended (Mintzberg 1978, Mintzberg/Waters 1982).

Lags in strategy implementation imply that returns to fit should be evaluated not only over the current, but also future periods. Firms that deviate from fit in their current environments, may be doing so deliberately in order to benefit in the future. Though their current deviation costs them, their future returns may compensate them. This is especially true when environments change.

Firms facing changing environments have to decide whether and when to deviate from fit with current environments. The contingencies they face follow from the level of fit with current environments, and anticipated fit with future ones. These contingencies appear in the form of a 2x2 table in Figure 1.

Quadrant I contains firms whose strategies currently fit their environments, and will continue to fit in the future. When technologies are stable, firms build barriers to imitation that yield competitive advantages over the medium term. Primary industries, for instance, often enjoy scale economies not easily overcome. Firms in this quadrant benefit by maintaining their current strategies.

Firms in quadrant II face environments changing in ways that reduce current strategies' effectiveness. In the absence of inertia (Fombrun/Ginsberg 1990) these firms would try and adapt their strategies to future needs. But when would the adaptation occur? The answer depends on the nature of first-mover advantages, and the resources the firm possesses.

If first-mover advantages exist (Lieberman/Montgomery 1988) firms benefit by anticipating future environments. However, the precise timing of the internal changes made by the firm depends on its resources (Robinson/Fornell/Sullivan 1992) and the threat posed to the firm's core business (Mitchell 1989). Under some conditions, even firms with fitting strategies might deviate from them in anticipation of new environments.

Quadrant III in Figure 1 contains firms who anticipate their current inappropriate strategies becoming appropriate and even ideal with environmental change. Firms that made investments in technology or capacity before markets were ready, might wait rather than abandon their strategies. Firms deviating from fit that anticipated more appropriate environments shortly, might continue deviating rather than converge upon currently profitable strategies.

Firms in quadrant IV have no choice but to change strategies. However whether they converge on currently fitting strategies or strategies more suited to future environments will depend upon anticipated environments. If environments are expected to change from their current conditions, then firms in this quadrant too will maintain their deviation from currently fitting strategies.

Hence firms receive incentives to deviate from fitting strategies. These incentives come in the form of improved future performances. Firms seeking to implement strategies more suited to future environments, therefore, may deviate from fit even before environments actually change.

This paper argues, therefore, that:

When environments change, deviating from fitting strategies will be positively associated with future performances.

Managing Deviation from Fit

Even firms that accept the advantages of deviating from current fit, may encounter resistance when they actually attempt to do so. Firms in quadrant II [ILLUSTRATION FOR FIGURE I OMITTED] follow strategies that currently fit their environments. Such firms probably possess strongly institutionalized control systems, policies, and other procedures that reinforce their current fit (Miller 1990, Starbuck 1983). Such successful organizations resist change even after environments reduce the effectiveness of their current strategies (Johnson 1992). This resistance would be magnified if they attempted to deviate even before environments changed.

Firms in quadrants III and IV will display weakened resistance to change due to their relatively poorer performances - consequences of their current lack of fit. However, poorer performances create pressure on management to act. In the case of firms in quadrant III action aimed at improving current fit will actually prove detrimental in the future as firms move away from current strategies which, had they waited, would have proved successful. Thus even though firms realize the advantages of deviating from fit, they will face resistance in implementing such deviation.

Similarly, appropriately timing deviation from fit may be more feasible in some circumstances than others. In the instance examined in this paper, US Savings and Loan Associations were aware of potential changes in regulations well before they actually came into being. They also had reasonable knowledge of when these changes would take effect. In many such instances of changes in regulations, firms will similarly be able to anticipate environmental changes. However, in other instances such as changes brought about by the advent of new technologies, changes will be harder to time.

Methods

This paper's proposition has been tested in the US Savings and Loan Association industry. The industry presents an attractive site since it experienced significant regulatory change in 1980 and 1982, with the passage of two Acts that changed its identity (Haveman 1992, White 1991). Newly permissible product markets allowed SLAs to dramatically reduce their traditional dependence on home mortgages. The sample includes all SLAs insured by the Federal Savings and Loan Insurance Corporation (FSLIC) in the six states of California, Florida, Michigan, Ohio, Texas, and Wisconsin, representing approximately one-third of all such SLAs in the United States.

Performances have been measured in the years 1980, 1982, and 1985, the former two representing the height of environmental change and the latter a period by which most changes in the industry had been completed (Strunk/Case 1988, White 1991). Deviations from fit have been measured two years prior to the years performances were measured, or in 1978, 1980, and 1983.

The Computation of Strategy-Environment Fits

Strategies

Past studies (Benston 1985, Strunk/Case 1988), and conversations with key regulators of SLAs suggested that product strategies were principally responsible for their performances after deregulation (Benston 1985, White 1991). Before deregulation, SLAs followed strict rules about the products they could offer and the prices they could charge (Garcia et al. 1983). The dramatic relaxation of many of these restrictions, coupled with reduced regulatory oversight, was held responsible for the sharp increase in SLA failures after deregulation (White 1991). Consequently this study uses SLAs' product choices as its indicator of their strategies.

SLAs' incomes depend on the assets they hold. Their product strategies manifest themselves in the particular baskets of products comprising their assets. Product strategy may be measured, therefore, as a point in n-space where n represents the number of products that SLAs can offer, and each product represented by a dimension. The coordinates of a firm in this space will be determined by the percentage of total assets in each permitted product. The maximum value a firm can have on any dimension is 100, and the minimum 0.

Environments

Studies of organization-environment fits characterize environments by competitive conditions. Prescott (1986) and Venkatraman and Prescott (1990) use market structures to represent environmental conditions. Jauch et al. (1980) used "environmental challenges" from various sources to characterize environments. The nature of competition in the SLA industry permits researchers to specify environments without defining specific characteristics. Pennings (1987) observed that the branches of banks he studied were likely to compete principally with others in the same district. Given restrictions on distances between borrowers and lenders (Brewer et al. 1980), SLAs too competed primarily with other SLAs within limited geographical regions.

Standard Metropolitan Statistical Areas (SMSAs) are used here to approximate the markets of SLAs since they define regions such that journeys beginning and ending within their borders are more likely than journeys across their borders (Bureau of the Census 1986). Since SLAs typically offered similar products at similar prices, customers received few incentives to travel out of their way to find one.

Fits

Fits will be represented by the systems model described by Drazin and Van de Ven (1985, Van de Ven/Drazin 1985), and subsequently used by Pennings (1987), Gresov (1989), Venkatraman and Prescott (1990), and others. The model identifies the ideal configuration for a given environment and evaluates cumulative deviation, a measure of misfit, from the ideal. Ideal configurations are usually found by averaging values of variables across top performing firms in each environment (Van de Ven/Drazin 1985, Venkatraman/Prescott 1990).

This study uses only the best performing firm in an environment as a proxy for the ideal performer since averaging values may yield profiles that cannot obtain in practice (Miller/Friesen 1984). To reduce the likelihood that a single anomalous year might bias results, the ideal performer was represented by the firm with the highest returns to total assets over three years. The percentage of assets that the best performer deployed in each permissible product was used as the ideal product strategy for its environment.

A list of products permitted to SLAs in 1985, appears in Table 1. The Euclidean distance from the ideal profile to that of a given firm, measures the firm's lack of fit. Thus, the distance of firm i from the ideal firm in its environment would be computed as

[d.sub.i] = [([summation over j][([P.sub.ij] - P[I.sub.j]).sup.2])].sup.1/2].

[P.sub.ij] represents the percentage of firm i's assets in product j, P[I.sub.j] represents the percentage of the ideal firm's assets in product j, and [d.sub.i] represents the distance of firm i from the ideal in its environment. Distances calculated in this fashion were averaged over two years, representing the lack of fit of the firm, at the end of the second year.

Covariates

These analyses control for the direct effects of both strategies and environments on performances. Since this study specifically investigates contingent effects of product choices on performances, it controls for the event that absolute levels of product diversity directly influence performances. An entropy measure (Jacquemin/Berry 1979) of diversity in SLAs' product portfolios, entered regression equations before fit effects.
Table 2. Computation of Environmental Munificence from Constituent
Variables

1. Population of SMSA.
2. Value of average single-family home.
3. Average rental for single-family home.
4. Personal income per capita.
5. Farm income per capita.
6. Manufacturing income per capita
7. Retail trade income per capita
8. Services income per capita.
9. Value of permits for new housing per annum.

Components Factor 1. Factor 2. Factor 3.

1980

4. 0.897 0.209
1. 0.885 -0.214
8. 0.869
7. 0.681 0.434

6. -0.842
9. 0.774
5. -0.247 0.411

1982

4. 0.972
1. 0.963
8. 0.734 0.353 0.307

9. 0.867
7. 0.416 0.764
6. -0.648 0.562
5. -0.910

1985

4. 0.897 -0.291
1. 0.881 -0.299
7. 0.690 0.353
8. 0.588 0.298

6. -0.764
9. 0.386 0.711
5. 0.588

Two alternative procedures could reduce the above loadings to
manageable factors. The first would combine variables using all the
loadings larger than an arbitrary absolute size. Alternatively, a
smaller number of large, consistent loadings could be combined with
unit weights and the rest discarded, following the reasoning that
unit weights yield comparable predictive accuracy to regression
weights (Pant & Starbuck, 1990). Such a procedure would offer the
advantage of factors that could be easily understood. Hence:

Munificence (1) = (Population + Personal income per capita)

Munificence (2) = (Permits for new housing - Manufacturing income
per capita)


Munificence, or the capacity of environments to sustain organizations; dynamism or volatility, the fluctuation in the supply of critical resources; and complexity arising from heterogeneity in products all significantly affect performances (Bluedorn 1993, Dess/Beard 1984, Starbuck 1976). Complexity will be omitted from these analyses since product strategies will capture variances attributable to heterogeneity in products.

Munificence was computed from indicators that affect the revenues of SLAs. A comprehensive list of such variables, developed from the literature and conversations with regulators, appears in Table 2. These variables were factor analyzed separately in 1980, 1982, and 1985, and the results appear in the same table. Munificence was represented by the two variables calculated as shown in the footnotes to Table 2. Volatility was calculated as the variance in all SLA assets within an SMSA, calculated over six half-yearly periods before the end of the year in question. Along with environmental measures an additional control for firm sizes, measured as the log of total assets, also entered the equation.

Analyses

Hierarchical regression equations with performances dependent, tested the proposition separately in each of 1980, 1982, and 1985. Performances were measured as returns on assets, averaged over two years. Covariates entered the equation first followed by past deviations from fit, evaluated two years before the current period. The results of these regressions appear in Table 4.

Results and Discussion

Zero-order correlation coefficients between independent variables in the three periods do not indicate multicollinearity (Table 3). All correlation coefficients are below 0.5.

Results from the regression equations support the proposition that deviating from fit correlates with improved future performances during times of change. In each of the years 1980, 1982, and 1985, past deviations from strategies that fit environments prove to be positively and significantly associated with performances. In the SLA industry of the early 1980s firms' future performances improved as they deviated from fit.

The rate at which this improvement occurred varied. Deviating from fit in 1980 appears to have had the largest effect on future performances. This is perhaps understandable given that the deregulatory process set into motion in 1980 reached its culmination in 1982. The "partial deregulation" of 1980 (White 1986, 1991) was understood by many to be the first step in a process yet to be completed. Many SLAs would have understood that aligning with currently fitting [TABULAR DATA FOR TABLE 3 OMITTED] [TABULAR DATA FOR TABLE 4 OMITTED] strategies was likely to be futile. Hence preparing for anticipated changes in regulations by adapting to future environments may have been more common.

These results do not indicate that firms needed to predict the shape of new environments accurately in order to perform better subsequently. Literature suggests that forecasts are notoriously inaccurate (Armstrong 1985, Pant/Starbuck 1990) and the current results do not refute this. They indicate, rather, that fit could be a liability during environmental change much like success (Miller 1990). Firms that deviated from fit were more prepared for the eventuality of new environments. Firms that fit, may have become too dependent on the shape of their current environments.

These results also indicate that it was not product diversity per se that affected performances as much as product strategies represented by the particular product mixes firms offered. Product diversity bore a positive relation to performance only in 1982. However, deviating from fits correlated positively with performances in all three years. The particular profiles that firms adopted correlated more closely with performances than diversity alone.

Munificence generally had a positive effect on performances, supporting the representation of environments by SMSAs. The year in which this relationship was weakest, 1985, is also the year in which volatility associated positively with performances. This apparently counter-intuitive result may find an explanation in the over-regulated nature of the SLA industry before deregulation, where managers could afford to ignore the needs of their particular environments (Haveman 1992, White 1991). In such a context, those environments that were relatively more volatile may have forced learning in organizations that came in useful after deregulation.

Finally, size was strongly and negatively associated with performances. The liability of size has been documented extensively in the literature on organizations (Starbuck/Nystrom 1981). The inertial attributes of size tend to render large sizes liabilities when firms need to change direction. In addition, large firms usually become large because they have encountered success in the past. Success itself might contribute to the unwillingness of firms to change the systems and strategies by which they achieved success (Hedberg/Nystrom/Starbuck 1976, Miller 1990). This would have enhanced the liability of size in times of environmental change.

Conclusions

This paper aims to extend the literature on strategy-environment fit by examining when and why firms deviate from fit. Research has suggested that firms deviate from fitting strategies because they have no choice. That is, when barred from some strategies (Hatten/Hatten 1987), or when resources prove to be inadequate (Barney 1991, Peteraf 1993), firms may be forced to avoid fitting strategies. Also, when firms face multiple contingencies (Gresov 1989), they may avoid fit with some to minimize cumulative penalties.

Firms also benefit by deviating from fit under some special circumstances. When environments change such that once fitting strategies are no longer appropriate, firms must adapt. Where first-mover advantages exist, firms receive incentives to adapt strategies in anticipation of changes in environments. In other words, firms deviate from fit to perform better in the future.

This paper's results from the US SLA industry in the early 1980s, confirm these suppositions. Deviations from fits in the past proved to be positively associated with performances in all the three years examined. Strategies examined for fit were product strategies, specifically the percentages of total assets that SLAs committed to different products. These product distributions have been identified as key indicators of SLA performances (Benston 1985, Garcia et al. 1983).

These results were obtained in a unique industry at an unprecedented time in its history. The degree of change in the environment of the SLA industry was dramatic, though in keeping with changes in other industries such as transport and telecommunications (Brock 1986, Weiss/Klass 1986). Technological changes too, have totally changed the competitive environments of industries. In short, the kind of environmental changes that occurred in the SLA industry bear resemblance to other such changes. The results obtained here, therefore, bear some generalization.

At least two implication of these results deserve further attention. First, these results indicate that deviating from fits, when environments change is advantageous. However, they cannot distinguish between deviations that anticipate future environments accurately and any other deviations. They only show that some deviation appears to be correlated with improved future performances. At the very least, these results indicate the liabilities of success. Firms that have followed fitting strategies may become dependent on their environments, so that they suffer when environments change. Further research may help distinguish between these two explanations.

Second, these results also call into question the future of the concept of strategic fit. The advent of hyperturbulence (McCann/Selsky 1984) and hypercompetition (D' Aveni 1994) implies that the only constant that firms can expect is change. From a prescriptive standpoint, therefore, it does not matter which of the preceding explanations of these results is true. If firms benefit by deviating from fit when environments change, and if environments change all the time, then firms will not bother with fitting their environments. Some speculations about the dangers of thinking in terms of "fitting" environments have already been expressed (D'Aveni 1994, Hamel/Prahalad 1994). These results add weight to such concerns.

Table 1. Products Offered by SLAs Used in Calculation of Product Profiles

1. FHA/VA and other federally insured loans

2. Conventional Loans:

a) 1-4 dwelling units b) 5 or more dwelling units c) Other improved real estate d) Unimproved land

3. Mortgage backed securities:

a) Insured by an agency/instrument of the United States government b) Conventional c) Advances for borrowers taxes and insurance

4. Commercial loans:

a) Secured b) Unsecured

5. Consumer loans:

a) Loans on deposits b) Home improvement loans c) Education loans d) Consumer auto loans e) Other closed-end consumer loans f) Credit cards, other open-end credit g) Mobile home loans - retail

6. Financing leases:

a) Consumer b) Non-consumer

7. Real estate held for development/investment/resale:

a) Residential property (net) b) Non-residential property (net)

8. Cash, deposits, and investment securities:

a) Cash and demand deposits b) US Government and agency securities c) Common and preferred stock (except FHLB) d) Other investments

9. Financial futures/options:

a) Initial margins b) Maintenance margins c) Financial options fees paid

10. Equity investments in subsidiaries/service corporations

11. Leased property (net):

a) Consumer b) Non-consumer

Note

1 The author would like to acknowledge the assistance provided by Grant RP3950031 of the Academic Research Fund of the National University of Singapore. The author would also like to thank Bill Starbuck for comments on earlier drafts of this paper.

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Dr. P. Narayan Pant, Senior Lecturer in the Department of Business Policy, National University of Singapore, Singapore.
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