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Strategic Consensus and Firm Performance: Beyond
Strategic Consensus and Firm Performance: Beyond
Extant research on strategic consensus focuses specifically on managers (i.e., top and middle managers). Our study advances this literature by including non-supervisory employees’ shared understanding of strategic priorities in the relationship between consensus and firm performance. Results show that an organization-wide (i.e., top managers’, middle managers’, and non-supervisory employees’) shared understanding of strategic priorities positively contributes to firm performance, whereas top and middle manager consensus is unrelated to performance. We also find that a participative firm culture facilitates organization-wide consensus, which suggests that a participative culture plays a significant role in developing a consensus within an organization’s membership.
Keywords: Strategic consensus; Top managers; Middle managers; Non-supervisory employees; Firm performance; Participative Culture
Strategic consensus refers to the shared understanding among members of an organization about strategic priorities that are fundamental to the strategy process and firm performance (Bourgeois, 1980; Rapert, Velliquette, & Garretson, 2002). However, most research so far takes a narrow view that focuses only on the shared understanding between managers rather than the shared understanding between the organization-wide membership (see Kellermans, Walter, Lechner, & Floyd, 2005 for an overview), which includes non-supervisory employees. Additionally, prior empirical studies present mixed results regarding the relationship between strategic consensus and firm performance. Some find a positive relationship between top management team (TMT) consensus and firm performance (e.g., Dess, 1987), while others find it to be negative (Bourgeois, 1985) or non-significant (West & Schwenk, 1996). With respect to strategic consensus between top and middle managers, Bowman and Ambrosini (1997) show a positive relationship between consensus and firm performance while Wooldridge and Floyd (1990) show no relationship.
We posit that one possible reason for the mixed results is the narrow focus on managers’ shared understanding, which overlooks the shared understanding of strategic priorities held by an important subset of the organization, non-supervisory employees. Moreover, the over reliance on managers is used to study the antecedents to strategic consensus in organizations. This is problematic if strategic consensus is, in fact, considered the shared understanding among members in an organization because managers are not the only organizational members.
To address these research gaps, we examine the performance impact of organization-wide strategic consensus, which includes the consensus on strategic priorities between top managers (TM), middle managers (MM), and non-supervisory employees (NE). Furthermore, given the importance of understanding the development of consensus, we also assess how a formalized firm structure and a participative firm culture affect the level of organization-wide strategic consensus found in organizations. Figure 1 depicts our conceptual model.
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THEORY AND HYPOTHESES
Strategic Consensus and Firm Performance
Ansoff (1965) suggests that the consensus on strategic priorities of an organization is fundamental to the strategy formulation process and therefore the organization’s success. Often the formulation of strategy involves the CEO working in conjunction with the rest of the dominant coalition, a.k.a. the Top Management Team (TMT), to provide the organization with a strategic direction. Thus, the consensus literature became part of the upper echelons research that focused on the fine-grained aspects of the TMT process, which attempts to specify the intervening process mechanisms between top management decision-making and organizational outcomes (Lawrence, 1997). Strategic priorities were developed and agreed upon by the TMT with little concern for other organizational members’ shared understanding of strategic priorities because it was assumed that consensus at the TMT level “filtered down” to lower levels when it came to strategy implementation (e.g., Dess, 1987).
In recognition of the equivocal results regarding the relationship between TMT consensus and firm performance, many researchers suggest that strategic consensus should include both strategy formulation and implementation (e.g., Wooldridge & Floyd, 1990). In other words, to affect firm performance, consensus had to occur not only with the strategic direction the organization should take, but also had to occur with the processes that the organization should use to pursue the strategic directives provided by the TMT. Because middle managers play a primary role in strategic implementation efforts, studies explicitly included middle managers into the consensus research and recognized that disagreements on strategic priorities could occur between top and middle management (e.g., Floyd & Wooldridge, 1997; Wooldridge & Floyd, 1990). The degree of shared understanding on priorities between top and middle managers is termed the scope of strategic consensus. Unfortunately, examinations of strategic consensus scope between TM and MM again yield inconclusive results (e.g., Bowman & Ambrosini, 1997; Wooldridge & Floyd, 1990).
Amason (1996) suggests that understanding provides a common direction for team members, and Wooldridge and Floyd (1989, 1990) suggest that organizational members must both understand and commit to decisions if those decisions are to be correctly implemented. Therefore, strategic consensus includes more than just consensus between top and middle managers; instead, strategic consensus concerns the organization-wide shared understanding of strategic priorities. This is consistent with the view of Markóczy (2001) and Mintzberg (1978) who suggest that the implementation of strategic priorities involves establishing a widespread understanding of, and commitment towards, these priorities. In essence, the ultimate success of any formulated strategy depends on whether middle managers make decisions consistent with the formulated strategy and non-supervisory employees can effectively take appropriate actions to implement the policies set forth by middle managers.
Organization-Wide Strategic Consensus
According to Dess and Priem (1995) and Dooley et al. (2000), consensus promotes a unified direction for the firm, increases strategic commitment, and resolves differences, which should enhance implementation. Although middle managers are those individuals responsible for strategy implementation, they must rely on non-supervisory employees to carry out the actions necessary for successful implementation. McDermott and Boyer (1999) examined the consensus between middle managers and non-supervisory employees and found inconsistencies between their views on strategy, which brings into question the view that strategic priorities necessarily “filter down” in organizations. Unfortunately, they did not examine top managers’ views of strategic priorities or test the relationship between consensus and firm performance. One reason provided for the inconsistent views was the middle managers’ inability to communicate strategic priorities to their subordinates.
However, research in human resource management (HRM) suggests that the communication of goals alone may not be enough to improve firm performance. Huselid (1995) suggests that HRM practices, such as employee involvement, can play a valuable role in the development and execution of an organization’s strategic business plan if these practices are aligned with the organization’s strategy. Most of the literature in this area focuses on customer service oriented organizations; for example, Liao and Chuang (2004) show that employee involvement is positively related to individual service performance. However, Huselid (1995), moving beyond just customer service, found that the use of high involvement work systems leads to greater individual productivity and, more importantly, greater overall firm performance. Therefore, successful implementation of formulated strategies requires that middle managers involve non-supervisory level employees through practices that offer employees a better understanding of the organization’s strategy.
The view that strategic consensus refers to the shared understanding among members of an organization about strategic priorities would suggest that the impact of consensus will be most significant if all members of the organization share the same understanding rather than just a subset of the organization. This is not to say that all members agree on the means by which these priorities should be satisfied given that groupthink situations can lead to poor decisions (e.g., Schweiger, Sandberg, & Rechner, 1989), but that all members understand the organization’s priorities. Whyte (1989) suggests that it is not groupthink alone that leads to poor decision-making, but the combination of groupthink and a poor framework for decision-making. Therefore, the inclusion of all members of the organization would allow for more successful decision-making because more information is available for decision makers.
Such an organization-wide view stems from research examining the Japanese style of management where consensus building and broad decision-making are encouraged (Ouchi, 1981; Pascale & Athos, 1981). According to Ouchi (1981), broad decision-making makes available all the knowledge and skills required to make an informed decision. Pascale and Athos (1981) suggest that following a decision-making process that leads to consensus allows for managerial deficiencies in knowledge or skill to be counterbalanced by resources in other areas of the firm. Thus, strategic formulation and implementation are processes that include members from a variety of subsets in the organization. Pascale (1984: 64) speaks directly to this concept in his review of the success of Japanese organizations entering the competitive U.S. market:
What saved Japan’s near-failures was the cumulative impact of “little brains” in the form of salesmen and dealers and production workers, all contributing incrementally to the quality and market position these companies enjoy today. Middle and upper management saw their primary task as guiding and orchestrating this input from below rather than steering the organization from above...
Strategic consensus does not preclude the involvement of non-supervisory employees, but suggests that the information they provide is valuable for the development of strategy.
In environments where strategic priorities involve a focus on market orientation, the shared understanding of non-supervisory employees may be extremely critical. Levitt (1969) suggests that the “augmented product” is a potential source of product differentiation and an important issue in the relationship between service and strategy. The augmented product consists of service issues such as installation, delivery, warranty, and repairs (Bowen, Siehl, & Schneider, 1989), which are all issues directly serviced by non-supervisory employees. According to Bowen et al. (1989), these types of service (i.e., distribution, product, and marketing) fall under the broad definition of marketing orientation. Because non-supervisory employees deal directly with these market orientation issues and it is their effort that is under final evaluation by the customer, non-supervisory employees need a clear understanding of organization’s strategic priorities. This further suggests that consensus is an organization-wide phenomenon rather than one limited to just top and middle management. Therefore, an organization-wide shared understanding among all levels of the organization on strategic priorities should lead to better firm performance.
H1: Organization-wide consensus has a positive effect on firm performance.
Antecedents of Strategic Consensus
The potential impact of organization-wide strategic consensus on firm performance suggests that it is critical to identify the factors that lead to an organization-wide strategic consensus. Prior strategic consensus research focused specifically on antecedents to top management consensus by examining the decision-making process (e.g., Wooldridge & Floyd, 1989), firm structure (e.g., Dess & Priem, 1995), and top management demographic characteristics (e.g., Iaquinto & Fredrickson, 1997), all of which show some positive influence on a shared understanding of strategic priorities. However, the examination of the antecedents to organization-wide consensus is under-explored given the neglect of non-supervisory employees.
As McDermott and Boyer (1999) suggest, the failure to create consensus between organizational levels may be the result of poor communication between managers and employees. Thus, for organization-wide strategic consensus to occur, the organization must be able to communicate strategic priorities between levels. In addition to communication, employee involvement may also strengthen their understanding of strategic priorities (Ouchi 1981; Pascale 1984). As both organizational structure and culture variables may play fundamental roles in communication and employee involvement, we focus on the roles of organizational structure and culture in developing organization-wide consensus.
Formalized firm structure. The formalization of organizational tasks is a key attribute in the modern firm structure (Pugh, Hickson, Hinings, MacDonald, Turner, & Lup, 1963). Wally and Baum (1994) suggest that formalization concerns the statement and operation of procedures, rules, and roles within an organization. Weber (1947) suggests a positive relationship between formalization and performance because formalized structures reduce friction and ambiguity while increasing precision, task knowledge, and continuity. Fredrickson (1986) suggests that formalization leads to clear norms of behavior and clear expectations for performance. This suggests that formalization may lead to a greater shared understanding between individuals in a firm given clearly defined norms and performance expectations.
The level of formalization in a firm’s structure may influence the amount of agreement on strategic priorities between individuals at multiple organizational levels. For example, Miller (1987) shows that greater formalization leads to higher involvement and participation from individuals in various organizational groups during the strategy making process. Jaworski and Kohli (1993) similarly suggest a positive relationship between formalization and information sharing within an organization. Organizational members must understand the firm’s strategic priorities to make decisions that are consistent with the firm’s strategic goals. Slevin and Covin (1997) suggest that greater formalization, with the accompanying rules, policies, and procedures, minimizes uncertainty on strategic priorities for managers. Managers must have a shared understanding of the firm’s strategic priorities to create rules and regulations that reflect the firm’s goals for employees, which will likely lead to an organization-wide strategic consensus.
H2: A formalized structure positively affects organization-wide consensus.
Participative firm culture. Rapert et al. (2002) examined the role of communication within the TMT and found that higher communication was positively associated with consensus. Intuitively, communication should play a major role in consensus between other groups as well. However, communication is embedded in a firm’s culture (Rapert et al., 2002) and thus culture may play a more fundamental role in the formation of consensus.
According to Ravasi and Schultz (2006), firm culture is broadly defined as shared mental assumptions that define appropriate behaviors in organizations and thereby guide interpretation and action for various situations. There is general agreement that culture is a set of cognitions shared by members of a social unit (e.g., O’Reilly, Chatman, & Caldwell, 1991; Smircich, 1983). According to O’Reilly and Chatman (1996), firms with strong cultures are those that have widely shared and strongly held norms and values. Sørensen (2002) suggests that organizations benefit from having employees that are dedicated to common goals and that strong cultures can motivate employees towards fulfilling these goals.
As such, culture may indirectly affect firm performance through the development of strategic consensus. However, culture can take many forms; for example Menon, Bharadwaj, Adidam, and Edison (1999) refer to an innovative culture. With respect to strategic consensus, a culture that encourages interaction between organizational members should increase the shared understanding of strategic priorities between such members. Quinn (1988) suggests culture can be used to encourage participation by an organization’s members, which Zhou, Tse, and Li (2006) refer to as a participative culture. Among employees, culture can emphasize unity, cooperation, and belonging as well as encourage employees to participate in decision-making. Deshpande and Webster (1989) suggest that culture helps employees understand the motivation for certain actions within the firm rather than simply just accepting the actions as given. Therefore, a participative culture can help develop a shared understanding between organizational members and thus a greater organization-wide consensus.
H3: A participative culture positively affects organization-wide consensus.
We obtained the sample through a multistage, stratified random sampling procedure. Using average employee income, we grouped Chinese national and provincial capital cities into three categories according to their stage of economic development and then selected three cities from each category. The highly developed region sample consisted of Beijing, Shanghai, and Guangzhou; the medium developed region consisted of Nanjing, Wuhan, and Chengdu; and the developing region consisted of Xian, Changchun, and Guiyang. Next, using the State Statistical Bureau’s national industrial statistical databank, we selected 20 manufacturing firms with at least 100 employees from each city. These firms are spread evenly across four types of ownership: state-owned enterprise (SOE), joint ventures, collectively owned, and shared stock.
Given the nature of the study was to examine the antecedents and firm performance outcomes of strategic consensus, we employed a multiple-source, multiple-informant design to obtain our respondent list. Additionally, the multiple-informant design provides data of superior quality to the single-informant approach (cf. Van Bruggen, Lilien, & Kacker, 2002). Using employee lists provided by personnel departments, 22 employees were surveyed in each firm: two top managers (a senior marketing manager and a senior personnel manager), 10 randomly selected middle managers, and 10 randomly selected non-supervisory employees. Thus, our final sample consists of 3,960 total employees from top management, middle management, and non-supervisory employee levels in 180 manufacturing firms from nine major cities in China.
Hoskisson, Eden, Lau, and Wright (2000) suggest that in a transitional economy, collaboration with local researchers is a key means of obtaining reliable and valid information; in addition, face-to-face interviews are desirable because they raise the response rate and generate more valid information. For these reasons, a major market research company with branches in all nine cities was commissioned to conduct the survey through personal interviews. All respondents were informed of the confidentiality of their responses. Each respondent was given a cash gift equivalent to an average worker’s half-day pay. In addition to controls by the research firm, an experienced research assistant was hired independently to travel to seven of the nine cities to monitor the fieldwork. Over 30% of the participants were telephoned subsequently to check on the interview process. No mishandlings by field workers were found.
Most measures used in the survey were adapted from previous research in the literature, but some were developed especially for this study. The original questionnaire was developed in English and then translated to Chinese. Back-translation was performed to ensure conceptual equivalence (Mullen, 1995). Unless otherwise stated, all questionnaire items were measured using a five-point scale anchored by ‘1 = strongly agree’ and ‘5 = strongly disagree’. In the analysis, all items were reverse-coded so that larger numbers indicate stronger agreement to make the results more intuitively understandable. The Appendix shows measurement items and results of validity analyses.
Market orientation was chosen as the content of strategic consensus primarily because prior strategic consensus research suggests the content of consensus should match the environmental context (Kellermanns et al., 2000). Zhou, Gao, Yang, and Zhou (2005) notes the importance of market orientation for Chinese firms because of the economic environment in China and research has shown that market orientation is one of the most important strategic orientations for firms to achieve long-term success (e.g., Hurley & Hult, 1998; Noble, Sinha, & Kumar, 2002). The market orientation measure was adopted from the MARKOR scale (Kohli, Jaworski, & Kumar, 1993) and contains three components: market intelligence generation (6 items; α = 0.88), intelligence dissemination (5 items; α = 0.87), and responsiveness (9 items; α = 0.92). Given high inter-correlations among the three components (ranging from 0.73 to 0.79), overall market orientation was computed by equally weighting and averaging the three component scores (Jaworski & Kohli, 1993) from each individual surveyed. Next, the mean score of market orientation was computed for each respondent group (i.e., TM, MM, NE, and the organization-wide mean, which is composed of all three groups). Following the literature, strategic consensus between two or more groups (e.g., TM and MM) was obtained by first calculating the standard deviation between these groups’ mean scores of market orientation, and then multiplying the standard deviation by (-1) so that larger numbers indicate greater consensus (West & Schwenk, 1996). The same computation was performed to create four scopes of strategic consensus: TM and MM consensus (TM-MM), MM and NE consensus (MM-NE), TM and NE consensus (TM-NE), and organization-wide consensus. TM-MM, MM-NE, and TM-NE consensus variables were formed for pre- and post analyses to validate the data and to compare our results to previous TM-MM strategic consensus research findings.
Firm performance was measured via one objective indictor: return on assets (ROA). Using the State Statistical Bureau’s statistical databank we obtained each firm’s ROA one year after the survey data was conducted. Such subsequent performance information could best capture the potential causal impact of strategic consensus on firm performance.
Formalized structure was measured with three items developed on the basis of Walker and Ruekert’s (1987) conceptualization of a formalized firm structure, which evaluates the extent of job codification in the organization as well as the level of emphasis on observing rules. We used TMT perceptions of formalization to compute the score (α = 0.80) because top managers are most likely to be the designers of firm structure.
Using the cultural value frame literature (Quinn, 1988), participative culture was measured using five items that assess participative management, employee involvement, and internal cooperation. Deshpande, Farley, and Webster (1993) suggest that low-ranking employees may be in the best position to assess a firm’s culture and thus the average score of non-supervisory employees was used to assess culture (α = 0.88).
To control for the potential impact of other factors, firm age, firm size, firm location, and firm ownership were included as control variables. Firm age was operationalized as the logarithm of years that a firm has been in business and was included as a control because newer firms had less time to develop consensus, especially through culture. Firm size was measured by the logarithm of number of employees in the firm and was included as a control given larger firms may have more managerial levels and thus, may have a more difficult time developing a shared understanding across multiple managerial levels. Firm location was coded as a dummy variable: Beijing, Shanghai, and Guangzhou were the most developed areas and coded as ‘‘1’’, others were coded as ‘‘0’’. Firm ownership was also coded as a dummy variable: SOEs as “1”, all others as “0”. Prior research examining firms in China have found both firm location and firm ownership are related to firm performance (Zhou et al., 2006); thus, to ensure the variance in firm performance is explained by our consensus variable, firm location and ownership were added as controls.
Two variables used in this study are latent constructs: formalized structure and participative culture. In addition, market orientation was used for the development of strategic consensus; thus, market orientation measures were subject to assessment as well. A confirmatory factor analysis was conducted to assess the convergent validity and reliability of these constructs. Because these are firm-level constructs, aggregated scores were used for the multiple informant measures. The market orientation construct was treated as a second-order construct with the three previously noted summated indicators. In the model, each of the three latent constructs correlates with the others, but the measurement items and their error terms are not correlated. Details of these assessments are reported in the Appendix.
The measurement model shows an acceptable fit with the data (χ2 (41) = 83.52, p = .00; goodness-of-fit index (GFI) = .92; confirmatory fit index (CFI) = .98; root-mean-squared error of approximation (RMSEA) = .08). In addition, all factor loadings are significant (p < .01), suggesting unidimensionality of the measures (Anderson & Gerbing, 1988). Also, the average variance extracted (ranging between .60 and .90) all exceed .50 and the composite reliabilities (ranging from .82 to .96) all meet the .70 benchmark. These results demonstrate that our measures possess adequate reliability and convergent validity. The discriminant validity of the latent constructs was evaluated in two ways. First, all of the cross-construct correlation coefficients were significantly less than 1.0 (p < .01), signifying the discriminant validity of the measures. Second, for each construct, the average variance extracted (ranging between .60 and .90) was much higher than its highest shared variance with other constructs (ranging between .11 and .13), providing strong support of discriminant validity (Fornell & Larker, 1981). Overall, these tests show that our measures are reliable and valid.
Table 1 presents the descriptive statistics and correlations of variables in the study. Given the hierarchical structure of modern organizations, we expect: 1) greater consensus between TM-MM than between TM-NE and 2) greater consensus between MM-NE than TM-NE. We examined these expectations to further validate our data. Table 2 shows the results of the paired-sample T tests and indicate that strategic consensus between the three groups as well as across the whole organization are all significantly different from 0. The results signify the presence of different perceptions of strategic priorities in firms. As expected, both the TM-MM consensus and the MM-NE consensus are greater than the TM-NE consensus (t (179) = 7.46, p = .00 and t (179) = 8.57, p = .00, respectively).
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We tested the model depicted in Figure 1 using structural equation modeling with maximum likelihood estimation. As suggested by Arbuckle and Wothke (1999), we treat single-item variables (firm age, firm size, firm location, and firm ownership) as pseudo-exogenous variables (i.e., each variable was assumed to be perfectly measured by a single item with zero measurement error). Exogenous variables are correlated with each other to accommodate potential relationships among the independent variables. Results showed that the model fit the data very well (χ2 (10) = 11.33, p = .33; GFI = .99, CFI = .98; RMSEA = .03).
Table 3 reports the hypotheses testing results. H1 states that organization-wide strategic consensus positively affects firm performance. The path is positive and significant (( = .29, p = .02), supporting H1. This finding is particularly relevant because the data comes from different sources (consensus data was obtained through surveys and performance data was obtained from archival sources).
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H2 proposes that formalized structure positively affects organization-wide strategic consensus. The path is not significant (( = -.14, p = .53) and thus H2 is not supported. H3 states that a firm’s participative culture positively affects organization-wide strategic consensus. The path is positive and significant (( = .81, p = .00), strongly supporting H3.
The research hypothesis test reveals that organization-wide strategic consensus positively affects firm performance. This is a contribution to the strategic consensus literature which has focused on the effects of within TM consensus or TM-MM consensus on firm performance. To compare with previous findings, we further examine the relationships of between-group consensus and firm performance. A model was estimated with TM-MM consensus, TM-NE consensus, and MM-NE consensus as exogenous variables, firm performance as an endogenous variable, and firm age, size, location, and ownership as control variables. Results are presented in Table 3. The model fits the data very well (χ2 (12) = 12.13, p = .44; GFI = .98; CFI = .99; RMSEA = .01). Path analysis reveals that both TM-MM consensus (( = -.09, p = .28) and MM-NE consensus (( = -.06, p = .52) are not related to firm performance, but TM-NE consensus (( = .21, p = .03) is positively related to firm performance. These results reinforce the inclusion of non-supervisory employees in examining strategic consensus issues.
Because organization-wide consensus and TM-NE consensus both positively affect firm performance, it is natural to question the relationship between organization-wide consensus and TM-NE consensus. Thus, we estimated a model with TM-MM, TM-NE, and MM-NE as exogenous variables and organization-wide consensus as an endogenous variable (see Table 3). The model is a just-identified standard linear regression model and has a very good fit with the data (GFI = 1.00; CFI = 1.00; and R2 = .99). All three paths are significant with TM-NE consensus contributing the most variance to organization-wide consensus. Such results appear to be consistent with our primary findings that suggest non-supervisory employees play an important role in strategic consensus.
Consistent with the definition of strategic consensus and unlike prior empirical research in this area, this study examines strategic consensus as an organization-wide phenomenon. We found that strategic consensus issues, or lack of consensus, exist at all levels of the firm (see results in Table 2); however, understanding organization-wide consensus may be very important given its positive relationship with firm performance (H1). For the prior strategic consensus research this is an important outcome because prior studies focusing specifically on top and middle management found mixed results (Bowman & Ambrosini, 1997; Wooldridge & Floyd, 1990). It could be that when ignoring non-supervisory employee views of strategic priorities the consensus of top and middle managers was understated. In essence, our results suggest that understanding strategic priorities should not be limited to the managerial levels as suggested by prior research (Dess & Origer, 1987; Kellermanns et al., 2005; Wooldridge & Floyd, 1989; 1990); instead, all organizational members should be included.
We also examined two organizational factors, formalized structure and participative culture, that would likely impact the level of organization-wide strategic consensus. Results indicate that a participative culture positively impacts organization-wide consensus (H3); however, a formalized structure does not influence the level of organization-wide consensus. This suggests that while a formalized structure may reduce the ambiguity for individual employee’s task expectation, it does not translate to an employee’s shared understanding of a firm’s strategic priorities. Instead, a culture of frequent communication and involvement is more effective in reaching a shared understanding of a firm’s strategic priorities.
It is interesting to note that although neither TM-MM consensus nor MM-NE consensus was related to firm performance, TM-NE consensus was positively related to firm performance. This finding further supports the inclusion of non-supervisory employees in the strategic consensus research. Although it seems that middle managers play a less significant role in the strategic consensus and firm performance relationship, one should be careful when interpreting the value of middle managers in the consensus research. Our post analysis shows that TM-MM consensus, MM-NE consensus, and TM-NE consensus all contribute to organization-wide consensus. These results clearly reflect the hierarchical structure found in most organizations with the middle managers’ understanding of consensus between that of top managers’ and non-supervisory employees’. It seems that when there is agreement between top managers and non-supervisory employees, there is agreement between all organizational levels; however, when there is disagreement, there is disagreement between all organizational levels.
This study advances the strategic consensus literature in several ways. First, we show evidence that strategic consensus can be formed between all levels of the organization and that organization-wide consensus impacts firm performance. In particular, we refine the strategic consensus concept by including organizational members from both managerial and non-supervisory employee levels. The inclusion of non-supervisory employees allowed us to assess the shared understanding of strategic priorities between top managers and non-supervisory employees, the shared understanding of strategic priorities between middle managers and non-supervisory employees, and the shared understanding of strategic priorities organization-wide. The importance of including the non-supervisory employees is further validated by our findings that it is the organization-wide scope and not top and middle management consensus that positively contributes to firm performance.
Second, consensus among the TMT does not automatically “filter down” as is assumed in past strategic consensus studies. For example, Dess and Origer (1987), in reviewing the consensus literature, suggests that TM consensus on formulated strategies should lead to the development of structures and systems to ensure successful implementation. Our results suggest that the development of such structures is not automatic and that differences in shared understanding can occur between TM, MM, and NE, but consensus between these levels is required for higher performance.
Third, our results confirm, as suggested by prior consensus researchers (e.g., Bowman & Ambrosini, 1997; Wooldridge & Floyd, 1990), that middle managers play an important role in resolving consensus issues in organizations. The strong correlation between TM-NE consensus and organization-wide consensus suggest that the middle managers’ role in organizations may be to act as a communication bridge between top managers and non-supervisory employees. Middle managers may be responsible for conveying the strategic priorities of the top management to lower levels or, possibly, to bring the strategic initiatives brought forth by lower levels to top managers as suggested by the literature on the Japanese style of management (e.g., Ouchi, 1981; Pascale, 1984; Pascale & Athos, 1981). Burgelman (1983) notes that it is the middle manager who champions new strategic initiatives to top managers, which suggests the information may travel bottom to top when forming strategic priorities. Thus, future research could focus on yet another facet of the strategic consensus literature, the locus of consensus, which could examine where consensus begins and how it moves from one organizational group to another with the recognition that it could begin with non-supervisory employees.
Fourth, although there are studies on the antecedents to TM consensus in the literature, more research is needed on the factors that may affect consensus scope. We advance the literature by investigating the impact of two organizational factors and found that at least one of them, participative culture, affects how different groups in organizations agree on strategic priorities. However, our hypothesis concerning formalized structure was not supported, which suggests that further research should be done to investigate these issues. For example, concerning structure, we examined only one element of structure, formalization. Future research should examine other elements that may play a role in the level of consensus in organizations such as centralization, which like formalization has been shown to be related to TMT strategic consensus (Eisenhardt & Bourgeois, 1988). Future research may also investigate and identify other factors (e.g., demographic composition of the employees) that may enhance the scope of strategic consensus in organizations.
Our results also provide managerial implications. Given the significant positive impact of organization-wide consensus on firm performance, companies should assess the shared understanding of strategic priorities at all levels of the organization rather than merely between top and middle managers. Furthermore, our results show that a participative culture is beneficial to the development of strategic consensus. However, these increases in consensus should be leveled toward non-supervisory employees because the organization-wide consensus is associated with increased future performance. From this viewpoint, organizational culture should reflect a situation where all organizational members, especially the non-supervisory employees, provide information that may be relevant to strategic decision-making. Although top managers clearly set strategic priorities, they should redefine the decision-making process to encourage non-supervisory employee participation in the strategy making process to ensure, in the least, that organizational members share an understanding of the organization’s strategic priorities.
Limitations and Future Research
There are limitations to consider. Kellermanns et al. (2005) note that there are many methodological approaches used to assess strategic consensus in the literature; for example, researchers have used the mean of the absolute value differences between individuals (e.g., Dess, 1987) and the mean of the standard deviations, the most common method. According to Kellermanns et al. (2005), each approach has its advantages and is more appropriate under certain conditions; however, these methods are used to assess within group strategic consensus (e.g., TM consensus or MM consensus) rather than cross group consensus. Similar to our use of the standard deviation between group mean scores, prior research on between group consensus has modified within group measures to examine consensus across groups (e.g., Wooldridge & Floyd, 1990; Rapert et al., 2002). Additionally, prior research has introduced new measures such as plotting managerial perceptions on two-dimensional charts (e.g., Bowman & Ambrosini, 1997) and causal mapping (Markoczy, 2001). Because few studies have examined the shared understanding of strategic priorities between groups, future research should determine the conditions under which differing methods of between group consensus measures should be used.
Additionally, contingency theorists (e.g., Burns & Stalker, 1961) suggest that strategy should match the environment, which has implications for this study. We use data collected from China, which faces environmental conditions conducive to success for organizations with a strong marketing orientation and thus it may be that different environmental conditions require a focus on different contents of consensus. This may limit the generalizability of our study beyond that of Chinese organizations. Future research should examine strategic consensus issues in different contexts.
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Strategic Consensus and Firm Performance
Means, Standard Deviations, And Correlations
| |1 |2 |3 |
| | | |t-value | |
|Consensus of TM vs. MM |-.36 |.27 |-17.56*** | |
|Consensus of TM vs. NE |-.55 |.33 |-21.94*** | |
|Consensus of MM vs. NE |-.35 |.24 |-19.07*** | |
|Consensus Organization-wide |-.46 |.22 |-28.05*** | |
|(TM vs. MM) > (TM vs. NE) | .19 |.34 |7.46*** | |
|(MM vs. NE) > (TM vs. NE) | .20 |.31 |8.57*** | |
Notes: - *** p < .001 (two-tailed)
Structural Model Estimation
| |Paths | |Std Estimate |t-value |Sig. |
|Research Hypotheses | | | | |
| |Organization-wide SC |( Firm Performance | .29 |2.41 |.02 |
| |Formalized structure |( Organization wide SC |-.14 |-.63 |.53 |
| |Participative culture |( Organization wide SC | .81 |3.77 |.00 |
|Control Variables | | | | |
| |Firm age |( Organization wide SC |-.08 |-.44 |.66 |
| |Firm size |( Organization wide SC | .28 |1.55 |.12 |
| |Firm location |( Organization wide SC |-.13 |-.77 |.45 |
| |Firm ownership |( Organization wide SC |-.17 |-.98 |.33 |
| |Firm age |( Firm Performance |-.02 |-.25 |.80 |
| |Firm size |( Firm Performance | .01 | .10 |.92 |
| |Firm location |( Firm Performance |-.03 | -.37 |.71 |
| |Firm ownership |( Firm Performance |-.14 |-1.55 |.12 |
|Goodness-of-Fit Statistics | | | | |
| |χ2 (10) = 11.33, p = .33; GFI = .99; AGFI = .95; CFI = .98; TLI = .99; RMSEA = .03 |
| | | | | | |
|Post Analysis I | | | | |
| |TM-MM consensus |( Firm Performance |-.09 |-1.09 |.28 |
| |TM-NE consensus |( Firm Performance | .21 | 2.24 |.03 |
| |MM-NE consensus |( Firm Performance |-.06 | -.64 |.52 |
| |Firm age |( Firm Performance |-.06 | -.73 |.46 |
| |Firm size |( Firm Performance | .08 |1.06 |.29 |
| |Firm location |( Firm Performance |-.06 | -.79 |.43 |
| |Firm ownership |( Firm Performance |-.18 |-2.43 |.02 |
|Goodness-of-Fit Statistics | | | | |
| |χ2 (12) = 12.13, p = .44; GFI = .98; AGFI = .95; CFI = .99; TLI = .99; RMSEA = .01 |
| | | | | | |
|Post Analysis II | | | | |
| |TM-MM consensus |( Organization wide SC |.45 |52.48 |.00 |
| |TM-NE consensus |( Organization wide SC |.55 |57.93 |.00 |
| |MM-NE consensus |( Organization wide SC |.41 |46.41 |.00 |
|Goodness-of-Fit Statisticsa | | | | |
| |χ2 (0) = 0, p = n/a; GFI = 1.00; CFI = 1.00; IFL = 1.00; R2 = .99 |
Note: a – This is a just-identified standard linear regression model. Hence, not all fit statistics is available.
Measurement Items and Validity Assessment
|Market orientation(from all respondents) CR =.96, AVE = .90, HSV = .13 |Loading |
|1. Intelligence generation summated indicator with 6 items, including: |1.00a |
|Our company frequently asks for customers’ opinions on our products and services. | |
|Our company gets to know our customer’s product preferences timely. | |
|2. Intelligence dissemination: summated indicator with 5 items, including: |.97 |
|We frequently hold interdepartmental meetings to discuss market trends and development. | |
|Our marketing personnel regularly discuss customers’ needs with other functional departments. | |
|3. Responsiveness: summated indicator with 9 items, including: |.95 |
|The activities of different departments in our company are well coordinated. | |
|Our company regularly holds interdepartmental meetings to plan a response to changes in business environment. | |
| | |
|Formalized Structure (from top managers) CR = .82, AVE = .60, HSV = .11 | |
|1. Our company emphasizes the importance of following procedures strictly in its production. |1.00a |
|2. Our company strictly follows rules and regulations in its operation. |.77 |
|3. Our company has a complete system of rules and regulations. |.86 |
| | |
|Participative Culture (from non-supervisory employees) CR = .94, AVE = .76, HSV = .13 | |
|1. Our company promotes unity and cooperation. |1.00a |
|2. Our company gives employees opportunities to involve in the decision-making process. |.81 |
|3. Our company tries to help employees understand the dynamics of the market situation. |.94 |
|4. Our company tries to help employees understand what is happening in the company. |.92 |
|5. Our company encourages its employees to feel they belong here. |.85 |
| | |
|Goodness of fit: χ2 (41) = 83.52, p = .00; GFI = .92; AGFI = .87; CFI = .98; TLI = .97; RMSEA = .08 |
Notes: - sample size = 180; CR = composite reliability; AVE = average variance extracted; HSV = highest shared variance with other constructs.
- a Fixed factor loading.
• Firm Age
• Firm Size
• Firm Location
• Firm Ownership
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