Top Management Decision Sharing and Adherence to Plans

Top Management Decision Sharing and Adherence to Plans Jeffrey G. Covin GEORGIA INSTITUTE OF TECHNOLOGY

Dennis P. Slevin UNIVERSITY OF PITTSBURGH

Randall L. Schultz UNIVERSITY OF IOWA

This article describes a study of the impact of top management decision sharing on the tendency of firms to adhere to their strategic plans, and the effects of strategic mission and environmental hostility on this decision sharing-adherence to plans relationship. As a secondary focus, the research also examined the impact of environmental hostility on the relationship between adherence to plans and firm financial performance. For the purposes of this study, adherence to plans was defined as an organizational outcome reflected in whether firms characteristically persist with predetermined and intended business plans or whether they regularly and extensively adjust their choice of business strategies and tactics in unplanned ways. Data were collected using questionnaires which were sent to the senior executives (presidents and/or CEOs) of 109 independent, nondiversified manufacturing firms. Approximately 20 industry segments are represented in the sample. Moderated regression analysis and subgroup analysis were used to analyze the data. Tests for monotonicity were conducted to clarify the regression analysis findings. Results indicate that firms with a participative or shared approach to top management decision making are no more likely to adhere to their plans than are firms which do not practice decision sharing. However, top management decision sharing was found to be positively related to adherence to plans among firms with ‘‘harvest’’ strategies and among firms operating in benign environments. Conversely, top management decision sharing was found to be negatively related to adherence to plans among firms with ‘‘build’’ strategies and among firms operating in hostile environments. Additional analyses revealed that adherence to plans is more positively related to financial performance among firms in hostile environments than among firms in more benign environments. The implications of these results and the limitations of the research are discussed. J BUSN RES 1997. 40.21–36  1997 Elsevier Science Inc.

Address correspondence to Jeffrey G. Covin, DuPree School of Management, Georgia Institute of Technology, Atlanta, GA 30332-0520. E-mail: jeff.covin@ mgt.gatech.edu Journal of Business Research 40, 21–36 (1997)  1997 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

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rganizations vary greatly in their tendencies to adhere to their strategic plans. Research has shown that firms can be extremely flexible in their implementation of plans, modifying them as organizational or environmental conditions seem to warrant (see Ginsberg, 1988). However, many firms choose to adhere closely to their plans or intended strategies, deviating very little from predetermined strategic paths. Such adherence to plans may be attributable, for example, to top managers’ ‘‘commitment to the status quo,’’ which Hambrick, Geletkanycz, and Fredrickson (1993, p. 402) define as ‘‘a belief in the enduring correctness of current organizational strategies and profiles.’’ Still, the reasons why firms adhere to or deviate from their plans are poorly understood, largely because of the paucity of research on this topic. Research into the phenomenon of adherence versus nonadherence to plans is important for several reasons. First, research on this topic should further illuminate the processes and contexts within which strategies are both formulated and implemented. Regarding strategy formulation, Mintzberg and Waters (1985) have argued that additional research is needed on the types of strategies realized as a function of the structures, processes, and operating environments of organizations. Inasmuch as firms which adhere to their plans have, to use Mintzberg and Waters’ term, ‘‘realized strategies,’’ whereas firms which deviate from their plans have ‘‘unrealized strategies,’’ research into the phenomenon of adherence versus nonadherence to plans is directly responsive to Mintzberg and Waters’ (1985) call for research on common strategy types. Additionally, it is possible and appropriate to conceive of adherence to plans as an organizational outcome manifested in the strategy implementation process. That is, we know whether or not plans are being adhered to as strategies are being implemented. Therefore, research into the phenomenon of adherence versus nonadherence to plans could assist management scholars in explaining and predicting the various implementation models ISSN 0148-2963/97/$17.00 PII S0148-2963(96)00207-X

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and processes observed in organizations (see, for example, Nutt [1987] and Bourgeois and Brodwin [1984]). Moreover, there are at least two pragmatic reasons for studying the phenomenon of adherence versus nonadherence to plans. First, whether or not firms characteristically adhere to their plans has implications for the desired quantity and allocation of resources in the total planning effort. For example, firms which commonly and extensively deviate from their plans may be well advised to invest in short-term, tactical, or contingency plans rather than in the development of detailed, long-term strategic plans. Second, the decision to adhere to or deviate from plans is a pervasive and potentially significant uncertainty among firms operating in competitive and dynamic environments. Both adherence and nonadherence to plans have potential trade-offs which create ambiguity over the ‘‘correct’’ strategic path. Yet managers must decide when and how much their firms’ plans should be adhered to versus altered as these firms navigate their environments over time, experiencing both forces that encourage and forces that discourage deviation from plans. Research that helps to clarify the sources of this ‘‘adherence-to-plans uncertainty’’ may yield clues into its effective management. Theory and limited research evidence suggest that the process through which strategies emerge may be a determinant of a firm’s tendency to adhere to its strategic plans. In particular, top management decision sharing may increase the likelihood that firms will adhere to their plans. When major operating and strategic decisions result from consensus-oriented team decision making, where multiple individuals share decision responsibility, those involved in making these decisions may be more committed to the chosen course of action. As observed in much of the research on participative decision making, involvement in the decision process often results in a sense of commitment to the final decision (e.g., Locke, Latham, and Erez, 1988). Such commitment by the decision makers, in turn, may increase the likelihood that organizations will subsequently adhere to their plans (Gush and MacMillan, 1986; Ghemawat, 1991). Still, this presumed linkage between adherence to plans and top management decision sharing has not been empirically investigated in any direct sense. The research described in this article examined the conditions under which top management decision sharing is related to adherence to plans, which was operationalized in this study as an organizational outcome reflected in whether firms characteristically persist with predetermined and intended business plans or whether they regularly and extensively adjust their choice of business strategies and tactics in unplanned ways. The term ‘‘plan’’ is used herein to denote a preconceived expectation or intention regarding the firm’s future operational and strategic behavior. It need not be a formal written document, but may exist as a generally understood direction that the firm should be taking or as an implicit preference for certain courses of action. The underlying assumption of this research was that the strength of the relationship between top management decision sharing and adherence to plans will

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vary among firms with different strategic missions and operating under different environmental conditions. As a secondary focus, the current research also examined the impact of the environment on the relationship between adherence to plans and firm financial performance. The following section on Theoretical Background reviews literature relevant to the current research and develops the study’s hypotheses. The sample, measures, and analytical techniques employed in this research are then discussed in the Methods section. The Results section details the findings of the data analysis. Finally, the conclusions and limitations of the current research and proposed future research directions are presented in the Discussion section.

Theoretical Background Top Management Decision Sharing and Adherence to Plans The presumed linkage between top management decision sharing and adherence to plans has not been directly investigated in prior research. Nonetheless, a linkage between these constructs is strongly suggested by the results obtained in studies of similar organizational phenomena. To position the current research relative to other studies, definitions will first be given of what is meant by top management decision sharing and adherence to plans, then related variables and studies will be introduced. The phenomenon of top management decision sharing falls within the broad construct of participative decision making. However, top management decision sharing represents a particular form of participative decision making. Specifically, top management decision sharing exists when high-level managerial choices involving business strategy, capital budget decisions, and functional level strategic decisions (regarding production, marketing, etc.) are made by top management groups or committees rather than by single individuals who have responsibility for the decision area. This form of participative decision making is distinguished by its occurrence at the top or strategic level of organizations, its corresponding focus on major operating and strategic decisions, and its largely horizontal process (i.e., decisions are shared with peers, or those who are treated as peers, rather than with subordinates in a vertical participation arrangement). According to the framework suggested by Cotton, Vollrath, Froggatt, Lengnick-Hall, and Jennings (1988), studies of participative decision making can be categorized on the basis of five ‘‘properties’’: formal-informal, direct-indirect, level of influence, content, and short-term versus long-term. Top management decision sharing, as defined above, can be described as formal and affording a high level of long-term, direct influence over significant, organizational level decisions to members of the decision-making team. Examples of studies in which participative decision making has been observed or operationalized to include most or all of these properties are those of Dean and Sharfman (1993), Khandwalla (1977), Quinn (1980), and Hickson, Butler, Cray, Mallory, and Wilson (1986).

Decision Sharing and Adherence to Plans

Adherence to plans was defined above as an organizational outcome reflected in whether firms characteristically persist with predetermined and intended business plans or whether they regularly and extensively adjust their choice of business strategies and tactics in unplanned ways. Firms that adhere to their plans may or may not undergo strategic change. Strategic change (at the business level) refers to ‘‘alterations in competitive decisions within particular product/market domains’’ (Ginsberg, 1988, p. 560) and is often assessed by examining the change in emphasis placed on decisions and tactics related to product price, delivery time, quality of customer service, etc. (e.g., Lant, Milliken, and Batra, 1992). Generally speaking, firms that make frequent and extensive changes in their strategies will not be adhering to their plans. (Two dimensions are key here—frequency of deviation and magnitude of deviation.) Nonetheless, because firms can plan to change their strategies, a change in strategy does not necessarily signify a deviation from plans. Similarly, firms that adhere to their plans are not necessarily exhibiting ‘‘strategic persistence,’’ which has been defined as ‘‘the extent to which a firm’s strategy remains stable over time’’ (Finkelstein and Hambrick, 1990, p. 487). A firm’s plan may be to change the basis on which it competes, for example, by changing its marketing strategy over a product’s life cycle. However, if this firm cannot or does not enact those changes (because, for example, of insufficient resources), it will be exhibiting strategic persistence but not adhering to its plans. Thus, adherence to plans is a different construct than strategic change and strategic persistence, and it does not necessarily imply either of these latter phenomena. Because there will be times when firms need to deviate from their plans as well as times when firms should stick to their plans, adherence to plans per se is neither inherently good nor bad. It can be argued that adherence to plans reflects ability to execute a course of action as intended, and that there may be merit in having such an ability. Nonetheless, the ultimate value of this ability will be contingent upon the situationally determined appropriateness of the actions called for in the plan. Why would one expect top management decision sharing to be associated with a tendency for firms to adhere to their plans? The answer lies in theory suggested by empirical studies of related constructs. Two such studies are particularly relevant. In Korsgaard, Schweiger, and Sapienza’s (1995) study of 20 teams of middle- and upper-level managers in one Fortune 500 corporation, it was found that team members were more committed to the ‘‘final decision’’ in decisionmaking teams where the members’ input was carefully considered by the team leader. Such commitment, as speculated above, may increase the likelihood that organizations will adhere to their plans. Similarly, Kim and Mauborgne (1993) studied the relationship between the perception of procedural justice in strategic decision making by subsidiary top managers and these managers’ compliance with the resulting strategic decisions. Their sample was composed of 19 multinational corporations with five to eight subsidiary top managers re-

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sponding per corporation. Results indicated that subsidiary top managers’ perceptions of procedural justice (which reflect the existence of participative strategic decision making) were positively associated with these managers’ compliance with higher-level corporate strategic decisions (r 5 .56). While the preceding studies do not definitively indicate that top management decision sharing will be associated with greater adherence to plans (as these variables are operationalized in the current research), they do reveal the plausibility of this notion. The general argument in support of this idea is that participative decision making, where decision responsibility is shared among multiple individuals, can and often does result in more mutually acceptable and thus less modified decisions. This argument is supported by years of research on participation at lower levels of organizations (see Sagie, 1994; Miller and Monge, 1986; and Glew, O’Leary-Kelly, Griffin, and Van Fleet, 1995). Importantly, and consistent with this argument, Guth and MacMillan (1986) observed that middle-level managers are less likely to seek to modify strategic plans formulated at higher levels if these managers are personally committed to the plans, as can be encouraged by involving these managers in the strategic planning process. In short, it is hypothesized: H1: Top management decision sharing is positively related to adherence to plans.

Top Management Decision Sharing– Strategic Mission Alignment The aforementioned arguments notwithstanding, it is possible that the relationship between top management decision sharing and adherence to plans will be moderated by a firm’s strategic mission. Gupta and Govindarajan (1984) use the term ‘‘strategic mission’’ to denote the fundamental strategic thrust of a firm wherein it chooses to engage in actions designed primarily to increase sales revenue and build market share, generate cash flow and short-term profits, or some combination of the two. ‘‘Build’’ strategies and ‘‘harvest’’ strategies are at opposite ends of the strategic mission continuum, with ‘‘hold’’ strategies in the middle. Build strategies are pursued when a firm aims to increase its market share and is willing, if necessary, to sacrifice short-term profits. Hold strategies are designed to enable the firm to maintain market share and realize moderate or ‘‘reasonable’’ rates of return. When a firm is willing to sacrifice market share for short-term profitability and cash flow maximization, a harvest strategy is used. In short, strategic mission represents the firm’s overall strategic philosophy or orientation concerning the likely trade-offs between market share growth and short-term profits. It is more concerned with the broad objectives sought by the firm than with the specific means used to achieve those objectives. Research and theory suggest that top management decision sharing may have a more positive relationship with adherence to plans among firms with harvest strategies than firms with build strategies. Harvest strategies were observed by Bart (1987), in a study of strategic practices in five large consumer

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packaged goods firms, to require less strategic change than build strategies as they were being executed. To quote one of Bart’s (1987, p. 144) interviewees, ‘‘Typically, all a manager has to do [when implementing a harvest strategy] . . . is that which was done last year.’’ As such, the managers in harvest strategy scenarios were more able to adhere to their business plans. Additionally, managers may have fewer strategic options under harvest than build strategies. That is, there may be many more ways to pursue a build strategy than a harvest strategy. Therefore, under a harvest strategy scenario it may be easier to reach agreement on a particular course of action through decision sharing. Such agreement will tend to promote adherence to plans. Collectively, the preceding suggests that adherence to plans may be particularly great when decision sharing is practiced under harvest strategy conditions. The combination of minimal top management decision sharing and build strategies may also promote adherence to plans. Firms with build strategies are commonly argued to require strong leaders with clear and definite visions for the future (e.g., Gerstein and Reisman, 1983; Bart, 1987; Herbert and Deresky, 1987). Although top management decision sharing is not necessarily inconsistent with strong leadership, such participative management may result in negotiated or compromise decisions that are inconsistent with the practice (or suggest the absence) of leadership. Strong leaders are not characteristically thought of as individuals whose positions on strategic issues are heavily influenced by others’ opinions. Rather, they have their own personal visions and philosophies that guide their decisions (Drucker, 1970). In short, build strategies may be most closely adhered to when implemented in the context of a clear strategic vision. And the exercise of individual leadership, rather than the practice of decision sharing, may be the best means for ensuring the emergence of such vision. Thus, it is hypothesized: H2: Top management decision sharing is positively related to adherence to plans among firms at the harvest end of the strategic mission spectrum and negatively related to adherence to plans among firms at the build end of the strategic mission spectrum.

Top Management Decision Sharing– Environmental Hostility Alignment A second variable that may moderate the relationship between top management decision sharing and adherence to plans is the external environment in which a firm operates. The external environment is often described and assessed in terms of its generic dimensions including, for example, dynamism, complexity, and munificence (Dess and Beard, 1984; Keats and Hitt, 1988). One environmental dimension with theoretical ties to the constructs of top management decision sharing and adherence to plans is environmental hostility. Hostile environments are characterized by high levels of competitive intensity, a paucity of readily exploitable market opportunities, tremendous competitive, market, and/or product-related uncertainties, and

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a general vulnerability to influence from forces and elements external to the firm’s immediate environment. They are harsh, overwhelming settings in which survival, and not necessarily competitive excellence, is often viewed as a major accomplishment (Edelstein, 1992; Covin and Slevin, 1989). Top management decision sharing may be most positively associated with adherence to plans among firms in low hostility or ‘‘benign’’ environments. This is because benign environments are conducive to firm profitability and growth, the realization of which often causes firms to persist with their past strategies (Schwenk and Tang, 1989). In other words, benign environments, because of the strong performance levels often experienced therein, can lead managers to believe in the efficacy of their firms’ strategies. When the top managers of firms believe that their strategies are ‘‘good,’’ they will more likely adhere to them during implementation. Thus, the combination of high levels of top management decision sharing and low levels of environmental hostility is likely to promote adherence to plans. The possibility that adherence to plans will be greatest when top management decision sharing and environmental hostility are inversely related is also defensible under the scenario of low decision sharing and high hostility. Specifically, directive leadership and quick decisions are often advocated for firms facing hostile environments. For example, Mintzberg (1979, p. 282) has argued that ‘‘When an organization faces extreme hostility . . . it must respond quickly and in an integrated fashion, it turns to its leader for direction [because] hostility demands the speed and coordination of a centralized response.’’ If autocratic visionaries in these organizations can elicit confidence in their strategic plans, then close adherence to these plans will likely result. Based on the preceding arguments, it is hypothesized: H3: Top management decision sharing is positively related to adherence to plans among firms in benign environments and negatively related to adherence to plans among firms in more hostile environments.

Decision Sharing-Strategy– Environment Alignment Because adherence to plans is expected to be influenced by the fit between top management decision sharing and strategic mission as well as the fit between top management decision sharing and environmental hostility, it stands to reason that adherence to plans may be affected by the fit between all three variables. In other words, top management decision sharing, strategic mission, and environmental hostility may have a three-way interactive effect on adherence to plans. For reasons given below, it is hypothesized: H4a: In hostile environments, top management decision sharing is negatively related to adherence to plans among firms at build end of the strategic mission spectrum and positively related to adherence to plans among firms at the harvest end.

Decision Sharing and Adherence to Plans

H4b: In benign environments, top management decision sharing is positively related to adherence to plans among firms at build end of the strategic mission spectrum and negatively related to adherence to plans among firms at the harvest end. Given that (as will be discussed in the Measures section) build strategies are represented by positive scores on the strategic mission scale employed in this study, whereas harvest strategies are represented by negative scores, the preceding hypothesis can be equivalently restated as follows: In hostile environments, adherence to plans will be promoted when the strategic mission and the top management decision sharing scales are inversely related. In benign environments, adherence to plans will be promoted when the strategic mission and the top management decision sharing scales are positively related. Considering first the case of hostile environments, the possibility that adherence to plans will be greatest when the strategic mission and the top management decision-making scales are inversely related can be defended by reconsidering some of the previously presented arguments. Specifically, nonparticipative strategic decision-making styles were argued to be effective at eliciting confidence when the external environment calls for a strong, uncompromised, and unequivocal action, as in hostile environments. Such confidence can increase the likelihood that strategic plans will be implemented as intended, with minimal modification. When firms in such environments are also pursuing build strategies, the positive impact of nonparticipative strategic decision making on adherence to plans may be amplified. Build strategies tend to flourish when executed by entrepreneurial individuals who rely on their skills of persuasion to create commitment to the chosen tactics (Bart, 1987). Thus, the combination of nonparticipative strategic decision making (i.e., low top management decision sharing scores) and build strategies (i.e., high strategic mission scores) would seem to best promote adherence to plans in hostile environments. Conversely, in the case of benign environments, top management decision sharing may be essential to gaining commitment to plans among build-strategy firms. It was argued earlier that decision sharing may best promote adherence to plans in strategic contexts where there are a small number of strategic options to consider, as would likely be the case with harvest (versus build) strategies. This possibility was advanced because having a small number of strategic options may make it be easier to reach agreement through decision sharing, and decision agreement should promote commitment and adherence to plans. This logic would suggest that the positive impact of decision sharing on adherence to plans will decrease as the number of strategic options open to a firm increases. However, the preceding logic may not hold if there are more than some modest number of strategic options. If strategic options are numerous, decision sharing may enable the strategic managers to focus on the most promising strategic paths. This would seem to be the case among firms that pursue

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growth strategies in benign environments. These environments afford multiple paths along which firms can pursue profitability and growth because the range of potentially viable strategic options is not nearly as restricted as in more hostile environments (Potter, 1994; Hall, 1980). Because both growth strategies and benign environments would appear to contribute to the number of strategic options open to a firm, decision sharing under such circumstances may enable top managers to better focus on the best strategic options. Such focus may promote decision agreement and subsequent adherence to plans. In short, this argument suggests, as hypothesized above, that the combination of participative strategic decision making and build strategies—or, more generally, a positive relationship between the strategic mission and decision sharing scales—will best promote adherence to plans in benign environments.

Environmental Effects on the Adherence to Plans/Performance Relationship The preceding arguments and hypotheses treat adherence to plans as a dependent variable. Indeed, as presented in the introductory section, there are compelling reasons for researchers to empirically identify the determinants of adherence to plans. Nonetheless, the prior sections beg the question of when a firm should generally adhere to versus deviate from its plans. Quite possibly, the context within which a firm operates will influence the level of overall financial success achieved when adhering to (or deviating from) plans. Stated differently, it is arguable that contextual factors will moderate the adherence to plans-financial performance relationship. Two ‘‘contextual factors’’ were considered in the development of the prior hypotheses—strategic mission and environmental hostility. It is difficult to defensibly theorize about whether adhering to one strategy type will generally promote higher financial performance than adhering to another strategy type. Therefore, no hypothesis will be offered regarding the potential moderating effect of strategic mission on the adherence to plans-financial performance relationship. However, there are reasons to believe that environmental hostility may moderate this relationship. For example, as was previously implied, hostile environments are unforgiving. Therefore, organizations operating in these environments should leave little to chance and, instead, should carefully plan and stick to their strategies, both to avoid being caught off guard and to promote a general consciousness of how the organization has chosen to deal with threatening environmental trends and elements (Greiner and Bhambri, 1989; Potter, 1994). On the other hand, benign environments are munificent, resourceful settings where firms need not be highly focused or plan driven in order to grow and competitively excel. Furthermore, in benign environments strategic flexibility, which can imply a deviation from plans, may be viewed as critical to achieving higher financial performance as more attractive market opportunities are discovered. These arguments suggest the following hypothesis.

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H5: Adherence to plans is more positively related to financial performance among firms in hostile environments than among firms in more benign environments.

Methods Sample This study was part of a larger research project focused on various strategic issues in advanced-technology firms. Questionnaires were sent to the senior executives (presidents and/ or CEO’s) of 364 nondiversified, independent firms in southwestern Pennsylvania. These firms were all classified as ‘‘manufacturing’’ by Standard Industrial Classification (SIC) codes and as ‘‘advanced-technology facilities’’ by a monitoring survey performed in 1988 by the University Center for Social and Urban Research at the University of Pittsburgh (DeAngelis, 1989). (While the firms in the sample are all advanced-technology facilities, they operate under widely varying levels of industry technological sophistication. The phrase ‘‘advancedtechnology facility’’ was operationalized in the monitoring survey to include firms that employ advanced process technology in ‘‘low-tech’’ industries as well as firms that operate in technology-based industries.) Telephone calls were made to all nonresponding firms three weeks after the initial mailing to determine if the questionnaire had been received by the addressee and if the addressee intended to complete and return the material. Thirty-four of the 364 firms in the initial universe were excluded from the research based on information obtained through this telephone follow up. These 34 firms were excluded for various reasons: 19 were out of business, six had moved out of state, and nine were excluded for miscellaneous reasons. One hundred and twentytwo of the remaining 330 firms completed and returned the research questionnaire for a response rate of 37%. A comparison of the early respondents (i.e., those firms that returned the questionnaire before being contacted a second time) and the late respondents (i.e., those firms that returned the questionnaire only after having been asked a second time) revealed no differences (i.e., p ..10) between the subgroups in terms of age, number of employees, annual sales revenue, or any of the research variables assessed in this study. These results suggest the absence of response bias if it is assumed that the latter subgroup of firms would not have responded had they not been contacted a second time. The key informant approach was employed in the current study, following the guidelines suggested by Huber and Power (1985). Specifically, to ensure that the respondents were familiar with the research issues and could respond accurately, the most senior managers (presidents and CEOs) of the sampled firms were targeted for data collection. As noted by Hambrick (1981), general managers are typically the most knowledgeable persons regarding their companies’ strategic processes and overall business situations. Second, to minimize social

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desirability bias in the measurement of constructs, the respondents were reminded that there were no right or wrong answers to the questions being asked of them, and they were guaranteed confidentiality. Third, to motivate the respondents to participate seriously in the study, all respondents were offered summaries of the results. Finally, the data were collected using carefully structured measures and a questionnaire that was pilot tested on a sample of seven general managers (whose feedback was used to make minor changes to the content of the questionnaire prior to mailing). The general managers included in the pilot test were drawn from the same population as the other firms targeted in the study. The companies headed by these individuals ranged in size from less than 100 employees to more than 500 employees. (The general managers had all proven to be conscientious respondents in prior studies by the authors and were known by at least one of the authors on a first-name basis.) Instructions to the general managers were that they should complete the questionnaire with respect to their businesses, identify any inappropriately or ambiguously worded questions, and provide open-ended ‘‘reactions’’ to the content of the questionnaire. Based on their feedback, as well as feedback received from three other researchers, the survey questionnaire was modified to more clearly, comprehensively, and/or directly assess the variables of interest in the study. The revised questionnaire was then mailed to the respondents in the current study, and the initial seven general managers were excluded from the final research sample. One hundred and nine firms were selected for this research from the larger database of 122 firms, using two selection criteria. First, all firms must have provided complete information on the measures examined in this study. This criterion ensured that a common set of firms would be included in all analyses. Second, all firms must have indicated a strategic mission that reflected a desire to continue business operations. This criterion excluded from the research firms whose strategic mission scores placed them in the ‘‘divest’’ category. The average firm age, number of employees, and sales revenue in the sample are 23.03 years (SD 5 20.58), 371.96 employees (SD 5 1457.38), and $60.74 million (SD 5 $218.26), respectively. The sample is slightly positively skewed with respect to both firm age and number of employees. For example, the skewness values for age and number of employees are 2.02 and 5.90, respectively. The kurtosis values for age and number of employees are 4.98 and 35.88, respectively. These positive kurtosis values indicate that the distributions of age and number of employees within the sample are somewhat peaked, although not extremely so. Because of the shape of the sample distribution with respect to age and number of employees, and because these two variables might be expected to covary with some of the main variables in the study, they were treated as control variables in the analysis (as described in a following section). Approximately 20 different industries are represented in the sample. Included among the products manufactured by

Decision Sharing and Adherence to Plans

firms in the sample are specialty glassware, electro-mechanical pressure switches, jewelry, computer-aided transcription devices, car care products, pacemakers and related biomedical devices, coatings for food and beverage containers, specialty steels, thermoplastic compounds, audio transducers, water treatment chemicals, orthopedic foot products, metal cutting tools, activated carbon, breathing apparatus, and printed circuits. Industrial goods are offered by 58 of the sampled firms; 26 firms offer components for incorporation into other firms’ finished goods; 13 firms offer consumer goods; and 12 firms offer ‘‘other’’ types of goods (e.g., raw materials) or did not classify their product type.

Measures A 4-item, 7-point scale was developed to measure adherence to plans (see Appendix). As operationalized in this study, firms score high on the adherence to plans scale if: (1) they almost always adhere closely to their intended plans/strategies; (2) modifications to their intended plans/ strategies are typically minimal; (3) they generally regard themselves as very effective at implementing their chosen plans/strategies; and (4) they are almost always able to implement the plans/strategies they would most like to employ. The construct validity of the adherence to plans scale was assessed by examining its correlation with a theoreticallyrelated variable which relates to a firm’s planning orientation. A strong planning orientation may be associated with stronger adherence to plans because firms with plans or, to use Mintzberg’s (1978) term, intended strategies, may be somewhat better attuned than those with more implicit strategies to the set of tactics required to accomplish the firm’s objectives. This is important because, as Rumelt (1979) has argued, prerecognition of the actions necessary to execute a strategy can greatly facilitate the implementation process. Miller and Friesen’s (1982) ‘‘futurity’’ scale was used to assess the strength of the firms’ planning orientations. This scale measures the emphasis placed on such things as the planning of long-term investments and the forecasting of sales, profits, and the nature of markets. The adherence to plans scale is, as expected, positively correlated with the futurity scale: r 5 .41, p 5 .000. This result suggests that these two theoretically related scales are tapping into a larger and common construct, which is evidence that the adherence to plans scale is measuring what it was designed to measure (see Allen and Yen, 1979). ADHERENCE TO PLANS.

A 4-item, 7-point scale was used to measure the extent to which top managers involved others when making major operating and strategic decisions (see Appendix). Higher mean scores indicate greater top management decision sharing. The first three items of this scale are adapted from Khandwalla’s (1976/1977) ‘‘participation’’ scale.

TOP MANAGEMENT DECISION SHARING.

The strategic mission measure used in this research was developed by Gupta and Govindarajan (1984) (see Appendix). This measure assesses the firm’s reliSTRATEGIC MISSION.

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ance on the strategic mission categories of build, hold, harvest, and divest. Consistent with Gupta and Govindarajan’s (1984) scoring of this measure, values of 11, 0, 21, and 22 were multiplied by the percentages the respondent allocated to these four strategy categories. This allows a single strategy index, ranging from 1.0 (pure build) to 22.0 (pure divest), to be assigned to each firm. Consistent with the aforementioned sample selection criteria, firms with strategy indices of less than 21.0 (i.e., divest strategy firms) were excluded from this research. The observed range of the index among the firms examined in this study is 1.0 to 20.70. Khandwalla’s (1976/1977) 3-item, 7-point scale was used to assess environmental hostility (see Appendix). Higher scores on this measure indicate greater levels of environmental hostility. ENVIRONMENTAL HOSTILITY.

FIRM FINANCIAL PERFORMANCE. Because different financial performance criteria are appropriate for evaluating the effectiveness of different strategies, a financial performance measure that is not ‘‘strategy specific’’ was chosen for the current research. Specifically, a slightly modified version of Gupta and Govindarajan’s (1984) ‘‘effectiveness at strategy implementation’’ scale—one employing only financial performance criteria—was used to construct weighted average financial performance scores for the sampled firms. Moreover, consistent with the argument that managers’ satisfaction with their firms’ performance takes into account industry differences in acceptable or normal performance ranges (e.g., Sapienza, 1992; Cooper, 1984), the chosen measure assessed top management satisfaction with firm financial performance. As shown in the Appendix, the respondents were first asked to indicate on a 5-point scale, ranging from 1 5 of little importance to 5 5 extremely important, the degree of importance their business unit’s top managers attach to each of nine financial performance criteria. Consistent with Gupta and Govindarajan (1984), these importance scores were mathematically adjusted to sum to 1.0 for the purpose of minimizing the impact of response bias. (This adjustment procedure ensures, for example, that individuals who might indicate that all nine financial performance criteria are extremely important will not generate higher performance scores for their firms simply because of their personal upward response bias.) The respondents were then asked to indicate on another 5-point scale, ranging from 1 5 not at all satisfied to 5 5 highly satisfied, the degree to which their business unit’s top managers are currently satisfied with their performance on each of these same financial performance criteria. The individual satisfaction scores were multiplied by the importance scores and the products of this step were summed to create a weighted average performance index for each firm. The specific equation used to calculate a firm’s performance index is as follows:

Performance 5 S (Criterion Satisfaction Score 3 Criterion Importance Score) S (All Criteria Importance Scores)

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Using the raw satisfaction data in the preceding equation will yield a higher performance score for firms whose top managers are not at all satisfied (5 1) with their firm’s performance on an extremely important (5 5) performance criterion (i.e., 1 3 5 5 5) than for firms whose top managers are not at all satisfied (5 1) with their firm’s performance on a performance criterion of little importance (5 1) (i.e., 1 3 1 5 1). Therefore, prior to the construction of the above index, the raw satisfaction data were recoded to a 22.0 to 2.0 scale in order to ensure that higher performance scores are never calculated for firms whose top managers express dissatisfaction on important performance criteria than for firms whose top managers express dissatisfaction on unimportant performance criteria. This data transformation step had no adverse impact on the measurement properties of the scale. The data are normally distributed on the firm performance scale (skewness 5 2.168; kurtosis 5 2.019; range 5 22.0 to 2.0). Moreover, all weighted financial performance criteria (e.g., sales level importance 3 sales level satisfaction) load above .70 on a single factor, suggesting that it’s appropriate to construct a single performance index from the data for each firm. The summary statistics (i.e., mean, standard deviation, and alpha coefficient) for this and the other scales are presented in the Results section.

Analytical Techniques Hypothesis 1 proposes that top management decision sharing will be linearly related to adherence to plans. Regression analysis was used to test this hypothesis. Hypotheses 2 through 5 are more complicated in that they predict that contextual factors (i.e., strategic mission and/or environmental hostility) will moderate the decision sharing-adherence to plans relationship (H2, H3, and H4) or the adherence to plans-firm performance relationship (H5). Accordingly, the moderator variable identification method advocated by Sharma, Durand, and Gur-Arie (1981) was used to test Hypotheses 2 through 5. Briefly, Sharma et al. suggest that tests for moderator variables should proceed in several steps. First, moderated regression analysis should be used to determine if the theoretically defined moderator and independent variables significantly interact such that they jointly predict changes in the criterion variable. If no significant interaction is found, subgroup analysis should be used to determine if the strength of the relationships between the criterion and independent variables differ across subgroups created by splitting the sample on the basis of the hypothesized moderator variable. The first analytical technique (moderated regression analysis) is generally recognized as a test of whether the moderator variable affects the form of the relationship between the independent and criterion variables; the second analytical technique (subgroup analysis) is generally recognized as a test of whether the moderator variable affects the strength of the relationship between the independent and criterion variables (Arnold, 1982; Prescott, 1986).

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Results Table 1 shows the summary statistics and correlations among the research variables. Each multi-item scale has an alpha coefficient that exceeds the minimum standard suggested by Van de Ven and Ferry (1980). Moreover, the low correlations among the independent variables suggest that multicollinearity will not be a problem in the regression analysis. Two of the correlations shown in Table 1 are particularly noteworthy. The negative correlation between adherence to plans and strategic mission (r 5 2.21, p , .05) is consistent with Bart’s (1987) observation that, relative to growth strategies, the implementation of harvest strategies (which have lower scores on the strategic mission scale) typically requires fewer deviations from past strategic actions. Further, the negative correlation between adherence to plans and environmental hostility (r 5 2.40, p , .001) is consistent with the arguments offered in support of H3. That is, firms will be inclined to adhere to their strategies when performance is high, as is more often the case in benign than hostile environments. Table 2 shows the regression analysis results. As can be seen, firm size and age were entered first in all regression equations to control for the potentially confounding effects of these two variables. (Size was operationalized as number of employees and age was measured in years.) This step partials out size and age effects from the relationships in question and permits a more accurate assessment of the predictive power of the independent variables. Hypothesis 1 is not supported. Top management decision sharing does not have a significant main effect on adherence to plans (p 5 .441). Firms with participative approaches to strategic decision making were no more likely to adhere to their plans than were firms where decision sharing was avoided. This result is somewhat surprising given the findings of the previously-cited studies by Guth and MacMillan (1986), Korsgaard et al. (1995), and Kim and Mauborgne (1993). Nonetheless, a study by Woolridge and Floyd (1990) may help to explain the absence of a strong relationship between top management decision sharing and adherence to plans. Woolridge and Floyd investigated the impact of middle-level managers’ involvement in the strategy formulation process on the quality and implementation ease of strategic decisions. Their sample was composed of 20 organizations, all of which competed in dynamic environments. Contrary to their expectations, one of their conclusions (Woolridge and Floyd, 1990, p. 238–239) was that ‘‘low levels of involvement may reduce commitment, but . . . involvement alone does not create commitment.’’ While care must be taken to distinguish between Woolridge and Floyd’s focus on middle-level managers’ strategic involvement and the current study’s focus on strategic decision sharing among top-level managers, perhaps a similar rationale could explain the absence of a strong relationship in the current study. That is, perhaps the absence of top management decision sharing will reduce senior managers’ commitment to ‘‘others’’ decisions, but top management deci-

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29

Table 1. Summary Statistics and Correlation Matrix (n 5 109) Variable

M

SD

Alpha

1

2

3

4

Adherence to plans Top management decision sharing Strategic mission Environmental hostility Firm financial performance Firm size (employees) Firm age (years)

4.58

1.02

.77

4.18 0.33

1.58 0.42

.85 NA

2.02 2.21*

4.04

1.03

.62

2.40***

.09

.06

0.09

0.89

.93

.59***

.12

2.13

2.37***

371.96 23.03

1457.38 20.58

NA NA

.13 .17

.08 .22*

.02 2.26**

.15 2.08

5

6

.17 .14

.03

2.03

*p , .05 **p , .01 ***p , .001

sion sharing per se will not create widespread senior-level commitment to these decisions. And in the absence of widespread senior-level commitment to strategic decisions, there may be less predictable effects on adherence to plans. However, the data indicate that adherence to plans is predicted by the interactions among top management decision sharing, strategic mission, and environmental hostility. Consistent with H2, the negative and significant (p 5 .004) regression coefficient for the decision sharing-strategic mission interaction term implies that top management decision sharing has a more positive impact on adherence to plans among firms at the harvest end of the strategic mission spectrum than among firms at the build end. Consistent with H3, the negative and significant (p 5 .053) regression coefficient for the decision sharing–environmental hostility interaction term implies that top management decision sharing has a more positive impact on adherence to plans among firms in benign environments than among firms in more hostile environments. And consistent with H4, the negative and significant (p 5 .007) regression coefficient for the decision sharing-strategic mission–environmental hostility interaction term implies that in hostile environments top management decision sharing has a more positive impact on adherence to plans among firms at the harvest end of the strategic mission spectrum than among firms at the build end. In benign environments, on the other hand, top management decision sharing has a more positive impact on adherence to plans among firms at the build end of the strategic mission spectrum than among firms at the harvest end. (Following the procedure suggested by Allison (1977), all three possible bivariate interaction terms were entered into the regression equation before the three-way interaction term when testing H4.) In short, H2, 3, and 4 are supported. As shown in Table 2, the moderated regression analysis results do not support H5—the regression coefficient for the interaction term is positive, as the hypothesis implies, but insignificant (p 5 .944). Thus, environmental hostility does not moderate the form of the relationship between adherence to plans and firm financial performance. However, as argued

by Sharma et al. (1981), subgroup analysis should be performed to identify moderator effects whenever moderated regression analysis fails to do so. Accordingly, the sample was split at the median value of hostility (4.0) and correlations were computed between the adherence to plans scale and the firm financial performance scale. In the more hostile environments, the correlation between these scales is r 5 .66 (p 5 .000). In the more benign environments, this correlation is r 5 .30 (p 5 .040). Using a modified Fisher Z-transformation test (see Arnold, 1982), it was computed that these two correlation coefficients differ significantly (p , .05). This result implies that the more hostile the environment, the more appropriate it will be for firms to adhere to their plans. Therefore, while H5 is not supported by the moderated regression analysis results, it is supported by the subgroup analysis results. These latter results indicate that environmental hostility moderates the strength of the relationship between adherence to plans and firm financial performance. What makes the preceding finding particularly intriguing is the fact that firms in hostile environments tend to exhibit greater deviation from their plans. (As shown in Table 1, there is a correlation between adherence to plans and environmental hostility of r 5 2.40.) Perhaps high-performing firms in hostile environments are particularly cognizant of the need for strategic flexibility and, therefore, develop plans which are generally less detailed than those employed by low-performing firms. Having less detailed plans could, in turn, create a situation in which greater adherence to plans is possible. Alternatively, perhaps the preceding finding can be explained by the possibility that adherence to plans will more likely result from high performance in hostile than benign environments. Specifically, in benign environments high performance may be achieved relatively easily or through numerous or generally recognized strategic approaches. Therefore, some deviation from ‘‘successful’’ plans will less likely be a major issue for these firms. However, in hostile environments, the formulas for success tend to be fewer in number and somewhat harder to identify (Hall, 1980; Edelstein, 1992). In

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Table 2. Moderated Regression Analysis Results Hypothesis 1 Equation 1: R-squared 5 .044; F 5 2.452@; DF 5 2,106 AP 5 4.361 1 0.000FS 1 0.008FA (0.000) (0.005) Equation 2: R-squared 5 .050; F 5 1.827; DF 5 3,105 AP 5 4.545 1 0.000FS 1 0.009FA 2 0.049DS (0.000) (0.005) (0.063) Significance level of top management decision sharing in Equation 2: p 5 .441 Hypothesis 2 Equation 1: R-squared 5 .044; F 5 2.452@; DF 5 2,106 AP 5 4.361 1 0.000FS 1 0.008FA (0.000) (0.005) Equation 2: R-squared 5 .081; F 5 2.292@; DF 5 4,104 AP 5 4.730 1 0.000FS 1 0.007FA 2 0.045DS 2 0.445S (0.000) (0.005) (0.062) (0.236) Equation 3: R-squared 5 .153; F 5 3.714**; DF 5 5,103 AP 5 4.141 1 0.000FS 1 0.006FA 1 0.089DS 1 1.366S 2 0.413DSS (0.000) (0.005) (0.075) (0.654) (0.140) Significance level of top management decision sharing 3 strategic mission in Equation 3: p 5 .004 Hypothesis 3 Equation 1: R-squared 5 .044; F 5 2.452@; DF 5 2,106 AP 5 4.361 1 0.000FS 1 0.008FA (0.000) (0.005) Equation 2: R-squared 5 .212; F 5 7.010***; DF 5 4,104 AP 5 6.104 1 0.000FS 1 0.007FA 2 0.020DS 2 0.407H (0.000) (0.004) (0.058) (0.088) Equation 3: R-squared 5 .241; F 5 6.527***; DF 5 5,103 AP 5 4.533 1 0.000FS 1 0.007FA 1 0.365DS 1 0.001H 2 0.100DSH (0.000) (0.004) (0.205) (0.226) (0.050) Significance level of top management decision sharing 3 environmental hostility in Equation 3: p 5 .053 Hypothesis 4 Equation 1: R-squared 5 .044; F 5 2.452@; DF 5 2,106 AP 5 4.361 1 0.000FS 1 0.008FA (0.000) (0.005) Equation 2: R-squared 5 .238; F 5 6.425***; DF 5 5,103 AP 5 6.243 1 0.000FS 1 0.005FA 2 0.018DS 2 0.400S 2 0.399H (0.000) (0.005) (0.057) (0.216) (0.087) Equation 3: R-squared 5 .354; F 5 6.854***; DF 5 8,100 AP 5 3.889 1 0.000FS 1 0.005FA 1 0.491DS 1 2.361S 1 0.045H (0.000) (0.004) (0.195) (0.937) (0.218) 2 0.444DSS 2 0.095DSH 2 0.200SH (0.124) (0.048) (0.172) Equation 4: R-squared 5 .400; F 5 7.340***; DF 5 9,99 AP 5 5.033 1 0.000FS 1 0.006FA 1 0.202DS 2 1.993S 2 0.251H (0.000) (0.004) (0.216) (1.821) (0.237) 1 0.618DSS 2 0.021DSH 1 0.910SH 2 0.272DSSH (0.404) (0.053) (0.436) (0.099) Significance level of top management decision sharing 3 strategic mission 3 environmental hostility in Equation 4: p 5 .007 (continued)

these environments high-performing firms, having discovered a rare ‘‘success formula,’’ may be particularly reluctant to deviate from their plans. In the interest of full interpretation of the regression analysis results, tests for monotonicity were conducted using the regression equation data, following the procedures suggested by Gupta and Govindarajan (1984). Monotonicity tests indicate whether the independent variable (top management deci-

sion sharing) has a constant effect (i.e., either positive or negative) on the dependent variable (adherence to plans) over the observed range of the theoretically defined moderator variable (strategic mission in the case of H2, environmental hostility in the case of H3). In other words, monotonicity tests indicate whether relationships between independent and dependent variables are linear (monotonic) or U-shaped (nonmonotonic). To conduct a test for monotonicity, the regression

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Table 2. Continued Hypothesis 5 Equation 1: R-squared 5 .047; F 5 2.601@; DF 5 2,105 PERF 5 20.083 1 0.000FS 1 0.006FA (0.000) (0.004) Equation 2: R-squared 5 .387; F 5 16.248***; DF 5 4,103 PERF 5 21.246 1 0.000FS 1 0.002FA 1 0.424AP 2 0.166H (0.000) (0.003) (0.05s) (0.074) Equation 3: R-squared 5 .387; F 5 12.874***; DF 5 5,102 PERF 5 21.146 1 0.000FS 1 0.002FA 1 0.403AP 2 0.189H 1 0.005APH (0.000) (0.003) (0.310) (0.331) (0.070) Significance level of adherence to plans 3 environmental hostility in Equation 3: p 5 .944 @p , .10 *p , .05 **p , .01 ***p , .001 Abbreviations: AP 5 Adherence to Plans, PERF 5 Firm Financial Performance, DS 5 Top Management Decision Sharing, S 5 Strategic Mission, H 5 Environmental Hostility, FS 5 Firm Size (employees), FA 5 Firm Age (years). Figures in parentheses are the standard errors. Unstandardized regression coefficients are reported because, unlike standardized regression coefficients, they are not affected by the points of origin of the independent variables. See Southwood (1978) for details.

equation should be differentiated with respect to the independent variable. If the value of the moderator variable that makes the partial derivative of the regression equation equal to zero falls within the observed range of the moderator variable, then the independent variable has a nonmonotonic, or nonlinear, relationship with the dependent variable over the range of the moderator. Otherwise, the relationship is monotonic. For example, in the case of the regression test of H2, the partial derivative of the regression equation with respect to decision sharing equals .089 minus .413 multiplied by the strategic mission score. As can be computed, this equation equals zero when the strategic mission index equals .215. This value (.215) falls within the observed strategic mission range of 2.70 to 1.0. Therefore, decision sharing has a nonmonotonic relationship with adherence to plans over the strategic mission range. Moreover, by substituting strategic mission scores other than .215 into the preceding equation, it can be determined that decision sharing has a negative impact on adherence to plans among firms with strategic mission scores greater than .215. Among firms with strategic mission scores less than .215, decision sharing has a positive impact on adherence to plans. In the case of the regression test of H3, the partial derivative of the regression equation with respect to decision sharing 5 .365 2 .100 3 the environmental hostility score. This equation equals zero when the environmental hostility index equals 3.65, a value which is within the observed environmental hostility range of 1.33 to 6.67. Accordingly, decision sharing has a nonmonotonic relationship with adherence to plans over the environmental hostility range. As can be determined by substituting alternative values for environmental hostility (i.e., other than 3.65) into the preceding equation, decision sharing has a negative impact on adherence to plans among firms with hostility scores greater than 3.65, and a positive impact on adherence to plans among firms with hostility scores less than 3.65.

To interpret adequately the information contained in the complete regression equation (used to test H4), including the negative regression coefficient for the three-way interaction term, the analytical procedure suggested by Govindarajan and Fisher (1990) was followed. Specifically, one of the independent variables was held constant, then the entire regression equation was differentiated with respect to a second independent variable. The procedure used to test for monotonicity, outlined above, was then used to determine the value of the third independent variable at which the partial derivative of the regression equation with respect to the second independent variable equals zero. With this information, it was possible to determine under what joint strategic and environmental conditions top management decision sharing promotes adherence to plans. Govindarajan and Fisher (1990) note that the choice of variable to hold constant as well as the choice of variable to serve as the basis for the partial derivative are mathematically arbitrary decisions in that any combination of independent variables will yield equivalent results. Because managers typically cannot change the level of hostility in their firms’ environments, hostility was held constant in the current analysis. Specifically, in order to determine what the regression results indicate about the relationship between adherence to plans, decision sharing, and strategic mission in hostile environments, the hostility score was fixed at the top of the observed range (6.67) and the regression equation was differentiated with respect to decision sharing. This differential equation 5 .062 2 1.196 3 the strategic mission score. Using this information, it can be determined that, in highly hostile environments, decision sharing has no impact on adherence to plans when a firm’s strategic mission score is .052. However, decision sharing is negatively related to adherence to plans among firms with strategic mission scores greater than .052, and

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positively related to adherence to plans among firms with strategic mission scores less than .052. To better understand the relationship between adherence to plans, decision sharing, and strategic mission in benign environments, the hostility score was then fixed at the bottom of the observed range (1.33) and the regression equation was differentiated with respect to decision sharing. This differential equation 5 .174 1 .256 3 the strategic mission score. These results indicate that, in highly benign environments, decision sharing has no impact on adherence to plans when a firm’s strategic mission score is 2.680. However, decision sharing is positively related to adherence to plans among firms with strategic mission scores greater than 2.680, and negatively related to adherence to plans among firms with strategic mission scores less than 2.680. Note that 2.680 is within the observed strategic mission range of 2.70 to 1.0, but just barely so. This suggests that in highly benign environments, decision sharing is positively associated with adherence to plans in all but the most harvest-oriented firms.

Discussion Conclusions and Limitations To summarize, the results of this study, based on a sample of 109 manufacturing firms, indicate that:

• Top management decision sharing does not have a sig-

•

•

•

•

nificant main effect on adherence to plans. That is, such decision sharing does not, in general, promote adherence to plans. Top management decision sharing has a positive impact on adherence to plans among firms at the harvest end of the strategic mission spectrum and a negative impact on adherence to plans among firms at the build end of the strategic mission spectrum. Top management decision sharing has a positive impact on adherence to plans among firms in benign environments and a negative impact on adherence to plans among firms in more hostile environments. In hostile environments, top management decision sharing has a positive impact on adherence to plans among firms at the harvest end of the strategic mission spectrum and a negative impact on adherence to plans among firms at the build end of the strategic mission spectrum. Conversely, in benign environments, top management decision sharing has a positive impact on adherence to plans among firms at the build end of the strategic mission spectrum and a negative impact on adherence to plans among firms at the (extreme) harvest end of the strategic mission spectrum. Adherence to plans is more positively related to financial performance among firms in hostile environments than among firms in more benign environments.

J. G. Covin et al.

Three primary conclusions can be drawn from this research. First, top management decision sharing may be of limited direct value as a means for facilitating adherence to plans. Thus, general managers who seek to encourage commitment and adherence to a course of action may need to involve managers below the upper echelons in the decision-making process. Adherence to plans should not be expected just because top management decision sharing has been employed. Of course, top management decision sharing may affect outcome variables other than adherence to plans. For example, Woolridge and Floyd’s (1990) study of middle-level managers’ involvement in the ‘‘strategic process’’ suggested that such involvement has a positive impact on the quality of strategic decisions. Similarly, it may be that top management decision sharing positively affects the quality of an organization’s strategic decisions. As such, the current study provides a limited perspective on the outcome variables likely to be affected by top management decision sharing. Care must be taken to not conclude that top management decision sharing, or the absence thereof, is strategically irrelevant to organizations beyond the contexts examined in the current research. While top management decision sharing may have a minimal direct effect on adherence to plans, the results of this study suggest that strategic and environmental variables can moderate the relationship between top management decision sharing and adherence to plans. Thus, a second conclusion of this research is that contingency-based models and frameworks may be helpful in predicting when managers will most likely adhere to or deviate from their plans. Finally, the observed moderating effect of environmental hostility on the adherence to plans-financial performance relationship suggests that contextual factors will impact the extent to which firms should seek to adhere to their plans. A tentative managerial prescription following from this finding is that the more hostile a firm’s environment, the more important it is for managers to carefully develop and adhere to strategic plans. Hostility appears to require that managers exercise particular caution when making midstream adjustments as their firms’ strategies are being implemented. Perhaps in their efforts to achieve superior performance, managers of hostile environment firms are prone to overreacting or too quickly reacting to ‘‘discouraging’’ market feedback. Indeed, research suggests that a strategic focus on the long term (e.g., long-term capital investments) is associated with superior financial performance in hostile industries (Covin and Slevin, 1989; Edelstein, 1992). Such a focus would suggest that hostile environment firms may benefit more commonly or greatly than benign environment firms from ‘‘staying the course,’’ adhering to well-considered plans rather than making frequent and/or extensive strategic adjustments in a reactionary fashion. The preceding findings and conclusions should be considered in light of the following observations. First, the research was based on a cross-sectional (versus longitudinal) design. Thus, causal relationships cannot be definitively inferred from

Decision Sharing and Adherence to Plans

the results, and any causal effects implied in the prior discussions should be regarded as speculative. Second, the sample for the study was a group of advanced-technology firms. While this sample had the advantage of allowing facility type to be controlled in the research (as previously mentioned, the sampled firms were all preclassified as advanced-technology facilities), it may also have limited the external validity of the findings. The possibility that different results may have emerged from a different sample cannot be ruled out. Third and finally, it should be remembered that adherence to plans is not an inherently desirable objective. While this outcome variable may, as discussed in a prior section, be regarded as reflecting a particular strategic ability (i.e., to execute a course of action as intended), adherence to plans per se should not be regarded as normative.

Future Research Directions Three potentially promising research foci follow from the current study. The pursuit of these foci should help to create a much more complete understanding of the phenomena of adherence to plans, decision sharing, or the relationship between the two. First, given the importance of understanding the normative implications of adherence to plans, future research should seek to more fully identify the conditions under which adherence to plans (or, conversely, deviation from plans) results in high performance. The tentative results presented above suggest that environmental effects on the adherence to plans-financial performance relationship should be a top-priority research focus. Additionally, research should examine the effects of strategic learning and organizational structure on the relationship between firm performance and the extensiveness of deviations from strategic plans. Regarding strategic learning, logic suggests that firms which seldom adhere to their plans may, nonetheless, excel if they are able to accumulate strategic knowledge from the trial and error experiences that occur during the strategy implementation process. Similarly, the existence of organic structural forms may increase the likelihood that any deviations from plans reflect timely and appropriate strategic repositioning choices which, in turn, contribute to high performance. This type of research would mesh well with that described in this article. Second, the nomological network within which adherence to plans exists should be empirically investigated. For example, while the concepts of strategic intent (Hamel and Prahalad, 1989), strategic persistence (Finkelstein and Hambrick, 1990), commitment to the status quo (Hambrick et al., 1993), and adherence to plans are theoretically related, research that focuses on the empirical similarities and differences between these concepts has not been conducted. Empirical studies could help to sort out how these concepts relate to one another. Such efforts may, in turn, allow cumulative models to be derived from research findings that are currently fragmented. Finally, it would be useful to identify empirically the role

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of middle- and lower-level managers in promoting or discouraging adherence to plans. Research suggests that middle managers can greatly affect the content and success of implemented strategies (Gush and MacMillan, 1986; Floyd and Woolridge, 1994). Therefore, such managers’ involvement in the strategic decision making process may have a significant impact on whether or not plans are implemented as initially envisioned. Similar effects may exist with respect to lower levels of management, although the strength of the relationship between strategic involvement and adherence to plans might be expected to vary with the levels and extensiveness of managerial involvement in the strategic decision-making process. The authors thank JBR Associate Editor Peter Davis, Teresa Covin, Shaker Zahra, and the three anonymous reviewers for their helpful comments on this article.

References Allen, M. J., and Yen, W. M.: Introduction to Measurement Theory. Brooks/Cole, Monterey, CA. 1979. Allison, P. D.: Testing for Interaction in Multiple Regression. American Journal of Sociology 83(1) (1977): 144–153. Arnold, H. J.: Moderator Variables: A Classification of Conceptual, Analytic, and Psychometric Issues. Organizational Behavior and Human Performance 29(2) (1982): 143–174. Bart, C. K.: Implementing ‘‘Growth’’ and ‘‘Harvest’’ Product Strategies. California Management Review 29(4) (1987): 139–156. Bourgeois, L. J. III, and Brodwin, D. R.: Strategic Implementation: Five Approaches to an Elusive Phenomenon. Strategic Management Journal 5(3) (1984): 241–264. Cooper, R. G.: How New Product Strategies Impact on Performance. Journal of Product Innovation Management 1(1) (1984): 5–18. Cotton, J. L., Vollrath, D. A., Froggatt, K. L., Lengnick-Hall, M. L., and Jennings, K. R.: Employee Participation: Diverse Forms and Different Outcomes. Academy of Management Review 13(1) (1988): 8–22. Covin, J. G., and Slevin, D. P.: Strategic Management of Small Firms in Hostile and Benign Environments. Strategic Management Journal 10(1) (1989): 75–87. Dean, J. W., and Sharfman, M. P.: Procedural Rationality in the Strategic Decision Making Process. Journal of Management Studies 30(4) (1993): 587–610. DeAngelis, J. P.: Monitoring Southwestern Pennsylvania’s Advanced Technology Industry: Insights into Personnel Needs. Unpublished manuscript, University of Pittsburgh’s Center for Social and Urban Research, 1989. Dess, G. G., and Beard, D. W.: Dimensions of Organizational Task Environments. Administrative Science Quarterly 29(1) (1984): 52–73. Drucker, P. F.: Entrepreneurship in the Business Enterprise. Journal of Business Policy 1(1) (1970): 1–16. Edelstein, J. Y.: Adjustment and Decline in Hostile Environments. Garland Publishing, New York. 1992. Finkelstein, S., and Hambrick, D. C.: Top-management Team Tenure and Organizational Outcomes: The Moderating Role of Managerial Discretion. Administrative Science Quarterly 35(3) (1990): 484– 503.

34

J Busn Res 1997:40:21–36

Floyd, S. W., and Woolridge, B.: Dinosaurs or Dynamos? Recognizing Middle Management’s Strategic Role. Academy of Management Executive 8(4) (1994): 47–57. Gerstein, M., and Reisman, H.: Strategic Selection: Matching Executives to Business Conditions. Sloan Management Review 24(2) (1983): 33–49. Ghemawat, P.: Commitment: The Dynamic of Strategy. Free Press, New York. 1991. Ginsberg, A.: Measuring and Modeling Changes in Strategy: Theoretical Foundations and Empirical Directions. Strategic Management Journal 9(6) (1989): 559–575. Glew, D. J., O’Leary-Kelly, A. M., Griffin, R. W., and Van Fleet, D. D.: Participation in Organizations: A Preview of the Issues and Proposed Framework for Future Analysis. Journal of Management 21(3) (1995): 395–421. Govindarajan, V., and Fisher, J.: Strategy Control Systems, and Resource Sharing: Effects on Business-unit Performance. Academy of Management Journal 33(2) (1990): 259–285. Greiner, L. E., and Bhambri, A.: New CEO Intervention and Dynamics of Deliberate Strategic Change. Strategic Management Journal 10 (Summer 1989): 67–86. Gupta, A. K., and Govindarajan, V.: Business Unit Strategy, Managerial Characteristics, and Business Unit Effectiveness at Strategy Implementation. Academy of Management Journal 27(1) (1984): 25–41. Guth, W. D., and MacMillan, I. C.: Strategy Implementation versus Middle Management Self-interest. Strategic Management Journal 7(4) (1986): 313–327. Hall, W. K.: Survival Strategies in a Hostile Environment. Harvard Business Review 58(5) (1980): 75–85. Hambrick, D.: Strategic Awareness within Top Management Teams. Strategic Management Journal 2(3) (1981): 263–279. Hambrick, D. C., Geletkanycz, M. A., and Fredrickson, J. W.: Top Management Commitment to the Status Quo: Some Tests of its Determinants. Strategic Management Journal 14(6) (1993): 401– 418. Hamel, G., and Prahalad, C. K.: Strategic Intent. Harvard Business Review 67(3) (1989): 63–76. Herbert, T. T., and Deresky, H.: Should General Managers Match their Business Strategies? Organizational Dynamics 15(3) (1987): 40–51. Hickson, D. J., Butler, R. J., Cray, D., Mallory, G. R., and Wilson, D. C.: Top Decisions: Strategic Decision-making in Organizations Jossey-Bass, San Francisco, CA. 1986. Huber, G. P., and Power, J. D.: Retrospective Reports of Strategic Level Managers. Strategic Management Journal 6(2) (1985): 171– 180. Keats, B. W., and Hitt, M. A.: A Causal Model of Linkages among Environmental Dimensions, Macro Organizational Characteristics, and Performance. Academy of Management Journal 31(3) (1988): 570–598. Khandwalla, P. N.: Some Top Management Styles, their Context and Performance. Organization and Administrative Sciences 7(4) (1976–1977): 21–51. Khandwalla, P. N.: The Design of Organizations. Harcourt Brace Jovanovich, New York. 1977. Kim, W. C., and Mauborgne, R. A.: Procedural Justice, Attitudes, and Subsidiary Top Management Compliance with Multinationals’

J. G. Covin et al.

Corporate Strategic Decisions. Academy of Management Journal 36(3) (1993): 502–526. Korsgaard, M. A., Schweiger, D. M., and Sapienza, H. J.: Building Commitment, Attachment and Trust in Strategic Decision-making Teams: The Role of Procedural Justice. Academy of Management Journal 38(1) (1995): 60–84. Lant, T. K., Milliken, F. J., and Batra, B.: The Role of Managerial Learning and Interpretation in Strategic Persistence and Reorientation: An Empirical Investigation. Strategic Management Journal 13(8) (1992): 585–608. Locke, E. A., Latham, G. P., and Erez, M.: The Determinants of Goal Commitment. Academy of Management Review 13(1) (1988): 23–39. Miller, D., and Friesen, P. H.: Innovation in Conservative and Entrepreneurial Firms: Two Models of Strategic Momentum. Strategic Management Journal 3(1) (1982): 1–25. Miller, K. I., and Monge, P. R.: Participation, Satisfaction, and Productivity: A Meta-analytic Review. Academy of Management Journal 29(4) (1986): 727–754. Mintzberg, H.: Patterns in Strategy Formation. Management Science 24(9) (1978): 934–948. Mintzberg, H.: The Structuring of Organizations. Prentice-Hall, Englewood Cliffs, NJ 1979. Mintzberg, H., and Waters, J. A.: Of Strategies, Deliberate and Emergent. Strategic Management Journal 6(3) (1985): 257–272. Nutt, P. C.: Identifying and Appraising How Managers Install Strategy. Strategic Management Journal 8(1) (1987): 1–14. Potter, D. V.: Rare Mettle: Gold and Silver Strategies to Succeed in Hostile Markets. California Management Review 37(1) (1994): 65–82. Prescott, J. E.: Environments as Moderators of the Relationships between Strategy and Performance. Academy of Management Journal 29 (1986): 329–346. Quinn, J. B.: Strategies for Change: Logical Incrementalism. Irwin, Homewood IL. 1980. Rumelt, R. P.: Evaluation of Strategy: Theory and Models. In Strategic Management: A New View of Business Policy and Planning, D. Schendel and C. W. Hofer eds., Little Brown & Co., Boston, MA. 1979, pp. 196–211. Sagie, A.: Participative Decision Making and Performance: A Moderator Analysis. Journal of Applied Behavioral Science 30(2) (1994): 227–246. Sapienza, H. J.: When Do Venture Capitalists Add Value? Journal of Business Venturing 7(1) (1992): 9–28. Schwenk, C., and Tang, M.: Economic and Psychological Explanations for Strategic Persistence. OMEGA 17(6) (1989): 559–570. Sharma, S., Durand, R. M., and Gur-Arie, O.: Identification and Analysis of Moderator Variables. Journal of Marketing Research 18(3) (1981): 291–300. Southwood, K. E.: Substantive Theory and Statistical Interaction: Five Models. American Journal of Sociology 83(5) (1978): 1154–1203. Van de Ven, A., and Ferry, D.: Measuring and Assessing Organizations John Wiley & Sons, New York. 1980. Woolridge, B., and Floyd, S. W.: The Strategy Process, Middle Management Involvement, and Organizational Performance. Strategic Management Journal 11(3) (1990): 231–241.

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Appendix Respondents were given the following instructions for completing measures involving 7-point scale items: Circle number ‘‘1’’ if the statement on the left-hand side of the scale best describes your reaction to the item. Circle number ‘‘7’’ if the statement on the right-hand side of the scale best describes your reaction to the item. Circle numbers ‘‘2’’ through ‘‘6’’ depending upon your best estimate of an intermediate position. Adherence to Plans Scale Please circle the numbers in the following scales that best describe the strategy implementation process within your business unit. My business unit almost never adheres closely to its intended business plans/strategies Modifications to my business unit’s intended business plans/strategies are typically extensive In general, my business unit is very ineffective at implementing its chosen business plans/strategies My business unit is almost never able to implement the business plans/strategies it would most like to employ

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7

My business unit almost always adheres closely to its intended business plans/strategies Modifications to my business unit’s intended business plans/strategies are typically minimal In general, my business unit is very effetive at implementing its chosen business plans/strategies My business unit is almost always able to implement the business plans/strategies of its choice

Top Management Decision Sharing Scale To what extent is decision making at top levels in your business unit characterized by participative, group, or democratic decision making in relation to the following classes of decisions:

• Product or service-related decisions concerning production, marketing, and R&D strategies. No participation; the responsible top executives make the decisions using existing information

1 2 3 4 5 6 7

Decisions made by top management groups or committees after full discussion and attempt at reaching consensus

• Capital budget decisions—the selection and financing of long-term investments. No participation; the responsible top executives make the decisions using existing information

1 2 3 4 5 6 7

Decisions made by top management groups or committees after full discussion and attempt at reaching consensus

• Long-term strategies (of growth, diversification, etc.) and decisions related to changes in the business unit’s operating philosophy. No participation; the responsible top executives make the decisions using existing information

1 2 3 4 5 6 7

Decisions made by top management groups or committees after full discussion and attempt at reaching consensus

In general, major operating and strategic decisions are made individually without much interaction

1 2 3 4 5 6 7

In general, major operating and strategic decisions result from consensus-oriented team decision making

Strategic Mission Scale Given below are descriptions of several alternative mission strategies. Depending upon the context, each of these descriptions may represent the strategy for all, only a fraction, or none of a business unit’s products. Please indicate below what percentage of your business unit’s current total sales revenue is accounted for by products represented by each of the following descriptions. Your answers should total to 100%. Build Strategy: Increase sales and market share, be willing to accept low returns on investment in the short-to-medium term, if necessary

%

Hold Strategy: Maintain market share and obtain a reasonable return on investment

%

Harvest Strategy: Maximize profitability and cash flow in the short-to-medium term, be willing to sacrifice market share if necessary

%

Divest Strategy: Prepare for sales, liquidation, or bankruptcy

%

Other: None of the above (please specify)

% Total

100% (continued)

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Appendix, continued Environmental Hostility Scale How would you characterize the external environment within which your business unit functions? Very safe, little threat to the survival and well-being of my business unit Rich in investment and marketing opportunities An environment that my business unit can control and manipulate to its own advantage, such as a dominant firm has in an industry with little competition and few hindrances

1234567 1234567 1234567

Very risky, one false step can mean my business unit’s undoing Very stressful, exacting, hostile; very hard to keep afloat A dominating environment in which my business unit’s initiatives count for very little against the tremendous political, technological or competitive forces

Firm Financial Performance Scale Please indicate the degree of importance your business unit’s top managers attach to each of the following performance criteria by circling the appropriate number.

Sales level ($) Sales growth rate Cash flow Return on shareholder equity Gross profit margin Net profit from operations Profit to sales ratio Return on investment Ability to fund business growth from profits

Of Little Importance 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2

Moderately Important 3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4

Extremely Important 5 5 5 5 5 5 5 5 5

Please indicate the extent to which your business unit’s top managers are currently satisfied with your business unit’s performance on each of the following criteria.

Sales level ($) Sales growth rate Cash flow Return on shareholder equity Gross profit margin Net profit from operations Profit to sales ratio Return on investment Ability to fund business growth from profits

Not at all Satisfied 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2

Moderately Satisfied 3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4

Highly Satisfied 5 5 5 5 5 5 5 5 5