“As ideas about evolution have developed, they have moved from outcome conceptions of evolution to process conceptions … from conceptions of evolutionary processes as “efficient” instruments of adaptation to an appreciation of their “inefficiencies” … [a]nd … from an emphasis on using evolutionary theories to predict history to an emphasis on the engineering of history.” (March 1994, p. 39).
Abstract
This paper explores how the rules that guide search affect organizational adaptation in complex and turbulent environments. Our consideration of such rules extends beyond search scope—i.e., exploitation of current technologies vs. exploration of new technologies—to include focus on competition. We consider two types of competitive focus—i.e., external, where the choice of focal technology to be improved is influenced by information about other organizations and internal, where it is not influenced by others. We refer to this expanded set of rules as managerial selection and vary it to explore how it affects organizational adaptation. Employing an agent based simulation model, built on the framework of NKC fitness landscapes, we consider multiple types of interdependencies within and between technologies and across competitors. We show that in the presence of these multiple interdependencies, the ability of organizations to adapt is conditioned as much or more by the focus of search than by its scope. In particular, we observe that in simple and stable environments, organizational adaptation is enhanced by an external focus but in complex and turbulent environments, such external focus is counterproductive.
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Notes
In his model of endogenous technological change Romer (1990) adopts the premise that “technological change arises in large part because of intentional actions taken by people who respond to market incentives” (p. S72).
For this reason, we use the terms: technology, to account for the unit of variation and selection that charts an organization’s adaptive path over time; and managerial selection, to account for the semi-stable behavioral rules that drive an organization’s changes in technology. Winter (2008) argues, “in evolutionary theory … it is the rules themselves that are regarded as data and as logically antecedent to the values (actions) they yield in particular environments” (p. 4).
Two such examples are quartz watches and personal computers. Even though a research consortium of Swiss watch producers was the first to produce prototypes of the quartz watch, no Swiss watch company saw their economic value, until the use of quartz by others threatened the dominance of the Swiss firms. Similarly, in the early 1970s inventors at Xerox PARC—the R&D arm of Xerox—created the Alto computer, which employed a mouse, featured a graphical user’s interface and was networked to many others, all before anyone introduced PCs. But because the commercial viability of these technologies was unclear at the time, Xerox ended up agreeing to let Apple study them in exchange for an opportunity to make a small investment in Apple.
More precisely, for the rule to be robust it must be accompanied by an obsession to apply it moderately; that is, neither too weak to lead organizations away from sub-optimal traps nor so strong that it pulls them away from the highest peaks.
Like a preferred direction, errors in evaluation cause exploiters to mistakenly accept negative feedback and, thereby, effectively allow an organization to stumble on new opportunities for adaptation.
As Bickhard and Campbell (2003) note, “internal selections… do not necessarily select-out actual systems that have already come into existence, as in evolutionary selecting-out of an organism, but, rather, can select within a set of possibilities which one(s) will come into existence” (p. 243).
Drazin and Van de Ven (1985) point out that, intentional or not, an equilibrium between the environment and organizations is assumed to exist, at least over long periods of time, and an identity, or isomorphic relationship, between the environment and organizations, is presumed to exist for those who survive. For example, in his study of Intel Corporation, Burgelman (1991) noted that the firm’s internal selection processes were aligned with the selection pressures in the external environment, even though this choice of investment selection criteria happened to be incoherent with the firm’s intended strategy to be a leader in dynamic random access memory (i.e., DRAM) chips. That is, resources were allocated (by the choice of investment selection criteria) to Intel’s more profitable EPROM and microprocessor businesses rather than to its less profitable business in DRAMs. Burgelman saw the alignment of management’s choice of investment criteria with technologies favored by the market, and the subsequent success of this alignment, to ultimately induce the adaptation of Intel’s strategy that moved it away from doing business in DRAMs.
Note that although the NKC model as originally proposed by Kauffman (1993) did not specify any parameter like the S in our model, adding such a specification means implicitly that S would be equal to N for all of Kauffman’s specifications. That is, all N attributes of the technologies of other organizations are assumed to affect the C attributes of the focal technology, whereas our model permits us to limit the affect of other technologies to less than N (i.e., S ≤ N) attributes.
Note that in the absence of market selection, all organizations grow (i.e., Z goes to zero and the probability of growth approximates 1). In the presence of extreme market selection (i.e., Z goes to infinity), only the organization with the best technology grows.
As observed by Rivkin and Siggelkow (2007), at period 2,500 modeled organizations “have largely exhausted their improvement opportunities” (p. 1079). To keep scenarios comparable, time step 2,500 is used as a stopping point both for stable and for turbulent environments.
Runs were specified as follows. Sets of random numbers, drawn from the same random seed (see Kleijnen 1988; Conway et al. 1959), were used to (1) generate three different technology landscapes; (2) specify 50 different initial positions for each landscape; and (3) conduct 50 replication runs that generated results for each initial position. Hence, the 7,500 runs represent 50 replications of simulations that originate from 50 different initial positions for each of 3 technology landscapes.
Since N is equal to 12, 12 alternatives differ from the current technology by exactly one elementary attribute; 66 differ by two attributes, 220 differ by three attributes, 495 differ by four attributes and 792 differ by five attributes. Hence, a total of 1,585 alternatives exist that differ by as many as 5 elementary attributes.
For example, to control for any advantage organizations might accrue from a more favorable starting position, each comparison was based on simulations that originated from the same starting position. Since organizations whose competitive focus is externally focused have the power to free ride the discoveries of others, the more others there are whose competitive focus is independent (i.e., internal) the greater the advantage to those who are externally focused. However, the more free riders there are, the more they must compete with each other in improving the same best position in each round. We restricted the number of firms to just one of each type to minimize these effects.
Absent Xerox PARC’s explicitly stated mission, “to create the future without worrying about the commercial viability of the resulting technology,” it is doubtful whether the Alto desktop computer would have been invented or if things like the mouse, the GUI or even the internet would be available, as we have them today.
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Acknowledgments
For detailed discussions, valuable comments and suggestions we thank Grahame Dowling, Javier Garcia Sánchez, Modestas Gelbuda, Simas Kucinskas, Mats Lingblad, Peter Murmann, Salih Zeki Ozdemir, Jim Robins, Simon Rodan, Wouter Rosingh, Loredana Volpe and participants in seminars at the Australian School of Business, Singapore Management University and the 2008 Academy of Management Meetings in Anaheim, CA. We also thank the two anonymous referees for very helpful comments and suggestions. Support from Sapienza, University of Rome is gratefully acknowledged. This paper is the inseparable result of a co-operation between the authors. However, the contributions were led in: Sects. 4.1 and 5 by Peter Moran; Sects. 2 and 4.2 by Michele Simoni and Sects. 3 and 4.3 by Gianluca Vagnani.
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Moran, P., Simoni, M. & Vagnani, G. Becoming the best: by beating or ignoring the best? Toward an expanded view of the role of managerial selection in complex and turbulent environments. J Manag Gov 15, 447–481 (2011). https://doi.org/10.1007/s10997-010-9129-2
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DOI: https://doi.org/10.1007/s10997-010-9129-2