Chapter 4 Supply disruption management: The early bird catches the
4.4 How managers actually intend to behave in supply disruption response
4.5.2 Implications for practice
In addition to the theoretical implications discussed above, the results of our research also have important implications for practice. Managers responding to supply disruptions need to be aware of the conditions that characterize their environment. The level of response uncertainty that firms face considerably affects the ramifications of delaying action or immediately responding to a supply disruption. In complex environments, only if response uncertainty is perceived to be high and long-lasting, waiting for more information can be advisable, based on our results. Furthermore, managers are required to trade off the accuracy of their actions against the speed of their reaction. On the one hand, delaying a response results in more precise first improvements and requires less changes to be made. On the other hand, immediate responses on average result in quicker recovery of firm performance. Moreover, the long-term performance of firms is, on average, not weakened but rather strengthened by response uncertainty if detrimental decisions can be reversed. In complex environments, firms may also benefit from quick responses in the short run and may increase their market share as Nokia did in did in the aftermath of the aforementioned disruption.
Furthermore, previous research on decision making behavior highlighted the influence of cognitive biases on the decision to act or not to act. Managers should be aware of these biases to avoid being misled in their decision of when to respond to a supply disruption. Some decision makers tend to be biased towards analysis (Kerstholt,
1996). A tendency for RAF can, for example, even be fostered by a status-quo bias (Samuelson & Zeckhauser, 1988). This bias represents the tendency in human decision making under uncertainty to value the current state more than a potentially superior but uncertain alternative. In the worst case, this may lead to the phenomenon of analysis paralysis meaning that action is delayed further and further. Similarly, a tendency for RFA is intensified by an action bias. If managers take action, “at least they will be able to say that they tried to do something” (Bar-Eli et al., 2007, p. 616). Furthermore, action is considered more appropriate than inaction in response to bad performance (Zeelenberg, Van den Bos, Van Dijk, & Pieters, 2002). Hence, taking action might often appear to be an attractive option for managers although they should rather delay a response.
Surprisingly, based on the results of the vignette-based experiments, we are able to demonstrate that managers tend to refrain from taking immediate action if the degree of either response uncertainty, complexity, or path dependence increase. However, managers should rather take immediate action in complex environments, as delineated by our simulation experiments. This incongruity has important practical implications for the management of supply disruptions. Managers seem not to be aware of the benefits associated with quick responses in complex decision making environments and should demonstrate a higher willingness to take immediate action when exposed to complexity.
4.5.3 Limitations and future research opportunities
The contribution of this research is constrained by several limitations. As a main assumption of the NK model and the model developed in the third section, each decision
i is assumed to interact with exactly the same number of decisions as every other decision.
Nevertheless, some decisions might actually interact with more of the other decisions than others do. Furthermore, we have limited our model to decision makers with centralized authority, because we assumed that this is a basic characteristic of disruption management processes. We did not consider many factors that might also characterize organizational search processes such as the presence of a hierarchy (Mihm et al., 2010) and the need to coordinate (Lounamaa & March, 1987). In addition, we assumed that a supply disruption does not change the underlying complexity of an environment. However, it might be possible that a severe supply disruption alters the level of interaction between the operational activities of a firm. Another limitation concerns our conceptualization of performance. Although we refer to a firm’s operating performance as main determinant
of a firm’s recovery efforts, our model’s measure of operating performance is abstract rather than specific.
Moreover, although complementing the simulation experiments, the vignette-based experiments introduces further limitations. First and foremost, the participants were exposed to vignettes containing information about a specific one-shot supply disruption situation. In real disruption recovery processes, subjects are likely to receive such information in a more fragmented fashion, perhaps over multiple time periods. In order to reduce the complexity of the decision making task for our respondents, we refrained from a multi-stage setting taking, e.g., the duration of response uncertainty into account. Thus, research accounting for the dynamics of disruption recovery processes would add to the validity and generalizability of our findings. In addition, we rely on the intentions of managers instead of their actual behavior. However, prior research shows that intentions may serve as reliable indicators of actual behavior (e.g., Ajzen, 1991; T. L. Webb & Sheeran, 2006).
Finally, our work suggests several opportunities for further research. The delineated and defined disruption response strategies RAF and RFA could be empirically investigated through large-scale studies or further (dynamic) experiments. The developed propositions could be used to derive testable hypotheses on the performance of RAF and RFA. Moreover, our research indicates that the use of the NK model can provide rich insights into the management of supply disruptions. The NK framework will remain a powerful tool to analyze organizational decision making under complexity that does not allow for analytical optimization. Future research could build on our suggestions on how to represent supply disruption recovery and apply the NK model methodology to further research questions of the field by adjusting or extending our model. In addition, our research demonstrates the importance and relevance of behavioral aspects in the supply (chain) disruption context and the need to further investigate disruption management processes, typically characterized by limited time and unreliable or sparse information. Therefore, we hope that this work encourages further research on supply disruption management. Given the fact that firms will most likely never be able to fully control their environment and perfectly predict changes, managers will continue to face severe supply disruptions and require additional insights on how best to respond to them.