It is also observed that researchers have utilised summated measures of performance as the dependent variables. We think that more disaggregated measures of performance for each individual manufacturing priority (e.g. quality, delivery) could be used in order to accurately assess not only how the lack of consensus on an individual priority affects its levels of performance, but also how this lack of consensus can potentially affect another different individual priority, as was explained in preceding paragraphs. Furthermore, Rusjan (2006) utilises return-on-assets (ROA) as the dependent variable. We feel that when studying consensus on strategic manufacturing priorities, performance measures that are more dependant on the manufacturing unit and function should be used. This is because, as Joshi et al. (2003, p. 361) write:
[…] measures of financial performance depend on factors beyond the control of the manufacturing unit or manager, including factors such as market conditions, economic conditions, technological changes, and governmental policies.
Two of the studies in Table I show inconclusive results (Joshi et al., 2003; Tarigan, 2005) and another one shows totally contradictory evidence (Lindman et al., 2001). Rusjan (2006) is the only paper that shows what could be considered as consistent results (i.e. higher consensus lead to higher performance). However, as was discussed before, there are issues with the methodology and dependent variable utilised in Rusjan (2006). We consider that these issues make its results and conclusions questionable.
The overall inconclusiveness of the four studies in Table I could be attributed in part to the inadequacy of the methodologies utilised. Interestingly, in their critical analysis of the literature on strategic consensus studies in the business strategy field, Kellermanns et al. (2006) also point to methodological issues that might be related to the inconsistency of the results found in those studies. Specifically, they argue that different measures might be more appropriate under different conditions. As was seen in our review of the literature, in the case of the study of strategic consensus along manufacturing competitive priorities, methodologies and approaches utilised in business strategy research may not be a good fit, particularly given the absence of mainstream operations management theory in them (e.g. trade-offs theory).
The review and analysis of previous studies offer good opportunities for new research ideas. Thus, we propose that a more adequate method to measure and study strategic consensus and performance within manufacturing plants should include not only a measurement of the importance of each competitive priority, but also a measurement that reflects the awareness regarding the potential relationships that exist between each pair of competitive priorities. A comparison between these two measures would then reveal whether the relationships that exist between each pair of competitive priorities are fully understood from both the strategic perspective (i.e. what is/is not important) and the operational perspective (i.e. why it is/is not important).
The following is an illustrative example of the new methodology and approach that we are proposing.
Consider “Quality” (Q), “Delivery” (D), and “Costs” (C) as the competitive priorities that can be pursued by an imaginary manufacturing firm. Let us also suppose that “costs” is in a trade-off situation with “quality” and “delivery”. Likewise, let us also suppose that there is no trade-off between “quality” and “delivery”. In this scenario, top management in this firm could then consider that attaining high levels of performance on Q and D is very important to the achievement of their strategic goals. It would be possible to do this since there is no trade-off relationship between Q and D, and achieving high levels of performance on both priorities would be possible. However, since C is in a trade-off situation with both Q and D, its strategic value would automatically become low.
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