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臺大管理論叢
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27
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3
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3
explicitly considers the dynamic, disruptive nature of process development and management
(Young, Smith, and Grimm, 1996). Our core argument is that the likelihood of effective
process development and management for a focal firm depends not only on its own
operational excellence (Micro-level), but also the (non-)responses of its rivals (Macro-level)
(i.e., the multilevel interactions between inner-firm capability development trade-off and
inter-firm competition).
We follow the work of dynamic computational theory proponents such as Sterman,
Henderson, Beinhocker, and Newman (2007), Vancouver, Weinhardt, and Schmidit (2010),
and Rahmandad (2012); to model process competition, and of scholars who espouse
simulation methods for theory development in entrepreneurship, management and
organization (see, Adner, Polos, Ryall, and Sorenson, 2009; Davis, Eisenhardt, and Bingham,
2007; Harrison, Lin, Carroll, and Carley, 2007; Yang and Chandra, 2013; Keyhani,
Lévesque, and Madhok, 2015). Dynamic computational theory refers to the mathematical
and empirical specifications of a theoretical account of how key constructs (or variables)
influence each other over time (Vancouver et al., 2010). Such theory can be simulated to
examine how variables in a multilevel, interconnected system changes from a given set of
starting values (Jayanthi and Sinha, 1998). Moreover, in line with Bendoly, Croson,
Goncalves, and Schultz (2010) and Nair, Narasimhan, and Choi (2009), we take the low
church approach of capability theorizing, one that relies on a behavioral standpoint, as
opposed to the high church approach that derives theory from equilibrium and rationality
assumptions (Rahmandad, 2012). Although the findings are somewhat restricted by the
model settings, this research can help decision makers make informed choices on process
capability development and contribute to the process management and operations strategy
literatures. The theory we develop depicts a dynamic, causal mechanism through which firms
are “aware” of, “motivated” by, and “capable” of developing new best practices or
improving the existing best practice in their focal industry.
Our main contribution is the simulations that produce new insights from established
constructs and their relationships. Specifically, we re-examine the history of process
innovation in car manufacturing (i.e., CP to MPS then to TPS) and use an in-depth review of
the existing empirical and theoretical literatures coupled with the system dynamics
methodology (Sterman, 2000; Repenning, 2002; Größler, Thun, and Milling, 2008; Bendoly
et al., 2010; Cui, Zhao, and Ravichandran, 2011). We identify the key constructs of
fundamental dynamics of process competition from the literature and assess their
relationships parsimoniously using dynamic computational theory. Hence, our model is well