Model the environment - Sample Essay
While some research conceptualizes the environment in terms of a single environmental attribute such as “velocity” (Eisenhardt, 1989) or “complexity” (Gavetti, Levinthal, and Rivkin, 2005), we model the environment as a heterogeneous flow of opportunities flow characterized by four dimensions – velocity, complexity, ambiguity, and unpredictability. We chose these dimensions because they are the attributes that research has shown to be relevant in dynamic markets (D’Aveni, 1994; Grant, 1996; Eisenhardt and Tabrizi, 1995; Rindova and Kotha, 2001).
We define velocity as the pace of opportunity flow into a given environment. The velocity of opportunity flow is a key dimension of market dynamism because it influences the nature of major organizational activities like strategic decision making (e. g. , Hickson, et al. , 1986; Eisenhardt, 1989) and product innovation (Eisenhardt and Tabrizi, 1995). The Internet bubble is an example of an environment with a high velocity of opportunities. We define complexity as the degree to which environmental opportunities have many features that must be successfully executed.
Factors like institutional norms (DiMaggio and Powell, 1983), geographic or material-resource constraints (Dill, 1958), and technical challenges (Tyre and Orlikowski, 1993) increase the complexity of opportunities because they increase the number of opportunity features that must be correctly executed. The implication of higher complexity is that opportunities become more difficult to capture. Biotechnology is an example of a high complexity environment because many facets of an opportunity must be correct to achieve success (Hill and Rothermael, 2003).
Ambiguity is the degree to which the key features of opportunities are difficult to interpret. Ambiguity is an important aspect of the environment because ambiguous environments are equivocal, and so are challenging to perceive (March and Olsen, 1976; Hickson, et al. , 1986). Nascent markets such as nanotechnology are environments with high ambiguity (Santos and Eisenhardt, 2006). Unpredictability is the degree to which past opportunities are dissimilar from present ones and so are unforeseeable. In predictable environments, leaders can use anticipated patterns to capture opportunities (Dess and Beard, 1984).
In contrast, leaders have few or no patterns to exploit in unpredictable environments (Baum and Wally, 1999). Growth markets, for example, are often unpredictable (Eisenhardt and Schoonhoven, 1990). Operationalization of Market Dynamism Velocity is operationalized as the rate of opportunity flow into the environment. Specifically, a Poisson distribution is used to model a stochastic flow of opportunities into the environment with a velocity lambda, ?. A Poisson distribution, p(k), describes the probability of k opportunities arriving in t timesteps and is determined by the single rate parameter ?
: p(k) = (? t)e-? t / k! (1) Poisson is a well-known probability distribution that is used to model arrival flows (Cinlar, 1975; Glynn and Whitt, 1992). It is particularly attractive here and in many simulations because it makes few underlying assumptions about the timing of opportunities (Law and Kelton, 1991). Although lambda can range from 0 to infinity, we fix an upper bound because of the inherent limits of bounded rationality and resource-constraints that limit the number of opportunities that can be introduced into the environment (Shane, 2000).
Complexity is operationalized as the number of features of an opportunity that must be correctly executed to capture the opportunity. Similar to computational complexity (i. e. , number of states needed to complete a computation) (Simon, 1962; Sipser, 1997), greater complexity makes an opportunity more difficult to capture because the organization must get many features right to achieve success. Specifically, an integer indicating the number of opportunity features that must be correct in order to capture a given opportunity is used to operationalize complexity.
Since each opportunity has 10 features, complexity ranges from 0 to 10. Ambiguity is operationalized as the difference between the actual features of an opportunity and its features as perceived by the firm. The actual features of opportunities are modeled as a 10-element bit string (i. e. , vector) of 1s and 0s – e. g. , 0100100110. The perceived features of the same opportunity are also a 10-element bit string of 1s and 0s, but differ from the actual features by those features for which perception does not match reality – e. g. , 0110100110.
Thus, environmental ambiguity is operationalized by the proportion of perceived features that differ from actual features. For example, ambiguity = 0. 1 could produce the example (above) bit strings of opportunity features since they differ by only 1 element of 10. This is a particularly useful way to model environmental ambiguity because it makes precise the relationship between the perceived and actual features of opportunities. Ambiguity ranges from 0 to 1.
Unpredictability is operationalized by the extent to which the features of opportunities in the present are dissimilar to the features of opportunities in the past. We use an entropy measure of unpredictability (Cover and Thomas, 1991), and vary entropy by varying the probability, p, of a given feature being a ‘1’. Formally, the entropy, H, of an opportunity is simply the negative sum over all possibilities (in this case only 2 possibilities: either ‘1’ or ‘0’) of p*log(p): H = – p log2(p) (2) For example, when the probability of a feature being ‘1’ is 0.5, or p(1)=0. 5 – and, by implication, the probability of being a ‘0’ is 0. 5, or p(0)=0. 5 – the entropy is high at H=1 and unpredictability is thus high as well (Cover and Thomas, 1991).
In contrast, when there is a higher probability of elements equal to ‘1’ than ‘0,’ the opportunities have a lower entropy or H<1 and thus, less unpredictability. The implication of less unpredictability is that leaders are more able to use rules to exploit underlying patterns in the opportunity flow because these patterns are more likely to exist.
For example, a stream of opportunities with a high probability of 6 recurring 1s (1111110101… 1111110010…. 1111110110) can be matched by rules with that same pattern. iii. Thus, we operationalize unpredictability by giving the rules the same proportionality of ‘1’s as we give the opportunities. Our operationalization of unpredictability is especially useful because it captures the insight that organizations can learn underlying patterns of opportunities and incorporate them into their heuristics (Cyert and March, 1963; Bingham and Eisenhardt, 2005).
In particular, less unpredictability is associated with more pattern and more unpredictability is associated with less pattern. Unpredictability ranges from 0 to 1. See the Technical Appendix for more detail. In addition, each opportunity has an associated perceived payoff value that executives believe a priori will be gained if the opportunity is correctly captured, an actual payoff value that the firm actually receives if the opportunity is correctly captured, and a window of opportunity during which an opportunity can be captured.
There is no a priori knowledge of the exact length of the window of opportunity or the actual magnitude of the payoff, consistent with real environments (Kirzner, 1997; Shane, 2000). The operationalization of each dimension, the opportunity payoffs, and windows of opportunity are described further in the Technical Appendix. In summary, we model the environment as a flow of heterogeneous opportunities. Four dimensions (i. e. , velocity, complexity, ambiguity, and unpredictability) are defined and operationalized as parameters that describe this flow.
These dimensions take specific values in each simulation run, but can be varied across runs. Opportunities flow into the environment at a velocity ?. More or fewer features of a given opportunity must be correctly acted upon depending on complexity. The degree to which the actual features of opportunities differ from the perceived features seen by firms is specified by ambiguity. Unpredictability is the degree to which features of opportunities are dissimilar to one another over time. Organization: Rules as Structure
We model the organization as a set of rules for capturing opportunities. Following Eisenhardt and Sull (2001), we use five types of rules: boundary, priority, how-to, timing, and exit rules. These types have also emerged in empirical research (Bingham and Eisenhardt, 2005), and characterize the types of rules that appear in the literature on dynamic environments (Burgelman, 1994; 1996; Gersick, 1994; Brown and Eisenhardt, 1997; Rindova and Kotha, 2001; Galunic and Eisenhardt, 2001; Miner et al, 2001).
Each rule relates to particular actions to be taken with respect to capturing an opportunity (Table 1). Collectively, these rules partially determine which opportunities are chosen (boundary rules), in what order (priority rules), how to execute them (how-to rules), how many opportunities to address at a time (timing rules), and when to stop (exit rules) exploiting an opportunity, and so form the framework of rules (i. e. , improvisational referents) within which flexible action occurs (Weick, 1998). Operationalization of Five Rule Types
Boundary rules determine which opportunities leaders will attempt to capture. Boundary rules are critical because they define the scope of market opportunities within which firms operate (Santos and Eisenhardt, 2005). For example, pharmaceutical companies often use rules to decide which drug development opportunities to consider. These rules might specify the size of the market, therapeutic area, and other factors in deciding whether to pursue an opportunity. These rules can often be framed as if/then statements (March and Simon, 1958).
For example, boundary rules might be “If the drug development opportunity is 1) within cardiology 2) has at least a $100 million/year projected market and 3) for which at least one senior scientist has related experience, then consider the opportunity. ” In addition, this same pharmaceutical firm might have other boundary rules related to other therapeutic areas, market sizes, or factors such as geography and scientific difficulty. Thus, boundary rules classify opportunities as either outside or inside the firm’s purview.
Boundary rules are operationalized as a 10-element bit string of 1s, 0s, and s – e. g. To determine if an opportunity is within the firm’s boundaries, each boundary rule element with a 1 or 0 is compared to the corresponding perceived feature of the opportunity. If a perceived feature of the opportunity has the 1 or 0 in the corresponding place, then this element ‘matches’. All elements in a boundary rule with a 1 or 0 need to match for the rule to classify the opportunity as inside the pool of opportunities to pursue further.
That is, 1s and 0s represent conditions that must match corresponding perceived features of the opportunity elements are not checked for a match, and so boundary rules with more ? ‘s are less structured. Priority rules rank opportunities that have successfully passed through the boundary rules. An example of a priority rule is Intel’s rule for allocating manufacturing capacity to alternative semiconductor products (i. e. , opportunities) according to their corresponding profit margins (Burgelman, 1996: 205). Applying this rule resulted in a ranking of semiconductor product manufacturing opportunities, and so prioritized the products that were made.
Consistent with Burgelman (1996), priority rules are operationalized by the rankings of the opportunities according to their perceived payoff values. How-to rules specify the actions for executing the opportunities. For example, Galunic and Eisenhardt (2001) find a how-to rule for executing “patching” opportunities in a high-performing, multi-business corporation that stated, “Always assign new product-market charters to divisions that 1) have relevant product-market experience and 2) are currently assigned to charters with shrinking market size or fading profit margins.
” How-to rules can relate to a variety of opportunities that are embedded in activities such as manufacturing, sales, internationalization and acquisitions. How-to rules are operationalized with a 10-element bit string of 1s, 0s, and ? s (e. g. , 0). For each 1 and 0, the firm applies its how-to rules to an opportunity by taking the corresponding rule-based actions. For each “? “, the firm randomly improvises a 1 or 0 (e. g. , 0111100110), and then compare this set of 10 actions to the opportunity’s features.
If the number of actions (both rule-based and improvised) that match the actual features of the opportunity equals or exceeds the value of the environmental complexity of the opportunity, then the opportunity has been captured and the firm gains the actual payoff value of that opportunity for the time period. For example, if the environmental complexity = 6 and the actions above – 0111100110 – are compared to the opportunity 0110101010, then the opportunity is successfully captured because 7 of the actions were correctly taken.
This is an effective operationalization of how-to rules (i. e. , heuristics that specify actions for executing opportunities) because it captures the idea that while certain actions are specified by the rules, others are left open to real-time improvisation (Brown and Eisenhardt, 1997; Miner, et al. , 2001). Timing rules specify the maximum number of opportunities that a firm can attend to at any given time. Timing rules have been found in several studies of organizational processes in dynamic environments.
For example, Brown and Eisenhardt (1997) find that managers use timing rules to pace the execution of multiple product development opportunities according to a temporal rhythm (e. g. , one new product every 9 months). Similarly, Bingham and Eisenhardt (2005) note that entrepreneurs use timing rules to specify the number of new country entry opportunities to exploit simultaneously. Gersick (1994) also finds that entrepreneurs use timing rules to delimit the opportunities that are simultaneously addressed. Timing rules are operationalized as the number of opportunities that can be examined at any given time.
Finally, exit rules indicate when to stop the execution of an opportunity. Exiting fading opportunities in a timely fashion is critical for firms because it frees up resources for capturing new opportunities (Burgelman, 1994). In this simulation, exit rules are operationalized by comparing the remaining actual payoff for each currently captured opportunity that is about to reach the end of its window of opportunity with the perceived payoff available for the highest priority opportunity that could be addressed, and then choosing the opportunity with the highest perceived payoff.
This operationalization of exit rules captures the idea that firms stop exploiting opportunities when they are about to end and when attractive new opportunities are available. Operationalization of the Amount of Structure and Performance Amount of Structure. We operationalize the amount of structure in two ways in order to ensure more robust results. The first operationalization is simply the number of total rules of all types that the firm uses. This operationalization is consistent with theoretical notions of structure such as Simon’s (1962) in which the amount of structure is associated with the number of components.
In the context of our research, this suggests that an increase of the number of rules increases structure through the number of specified actions, some of which are simultaneous. The second operationalization focuses on the amount of structure in the how-to rules because of their importance in capturing opportunities. For example, recent empirical research by Bingham and Eisenhardt (2005) on the emergence of rules for capturing opportunities indicates that the number of how-to rules exhibits the greatest variance across firms, and is the type of rule that is most closely associated with capturing opportunities and creating high performance.
Specifically, the second operationalization of the amount of structure, number of rules, is the total number of rule-based actions specified in the how-to rule, (ranging from 0 to 10) – i. e. , the number of how-to rule elements that have either a 1 or a 0 value. For example, the number of rules in the how-to rule element bit string 01? 0?? 011? is 6. For ease of exposition, we term a small to moderate number of rules (i. e. 3-5) “simple rules”. Performance. Performance is operationalized as the sum of all actual payoffs to every opportunity captured, across all time periods.
This operationalization is particularly appropriate for our research because it is consistent with the empirical research on dynamic markets that indicates that performance is the result of a series of advantages and their related payoffs (D’Aveni, 1994; Rindova and Kotha, 2001; Roberts, 1999). Specifically, each opportunity has an associated actual payoff that is drawn from a normal distribution, and performance is the sum of these actual payoffs of captured opportunities, across all time periods. See the Technical Appendix for details.
Simulating the Model We implemented this model in Matlab and Java software. The flow of the computer program is outlined below (see also Table 1) while key parameter and distribution details are in the Technical Appendix. Initially, the organization’s structure (i. e. , its rules) and environment (i. e. , the velocity, complexity, ambiguity, and unpredictability of the flow of opportunities) are randomly set using draws from probability distributions (Law and Kelton, 1991). In each timestep, opportunities flow into the environment at a velocity lambda.
The organization takes rule-based actions in attempts to capture some of these opportunities (10-element bit string of 0’s and 1’s). But it typically does not have rules to cover all facets of an opportunity, and so it takes some flexibly improvised actions. When these actions (both rule-based and improvised) match the opportunity (number of element matches is the same or greater than the environmental complexity), the opportunity is captured and firm performance increases by the actual payoff value of the opportunity.
A priori the length of the window of opportunity and the size of the payoff are perceived by the organization, but (as in real firms) their actual values may not correspond to these perceptions. Consistent with cognitive limits (March and Simon, 1958; Occasio, 1997), the organization has a fixed attention budget for each timestep that is decreased when rules are compared to opportunities (e. g. , boundary rules are compared to the opportunity) and when rule-based and/or improvised actions are taken. Since improvised action involves real-time sensemaking (Weick, 1993) and thoughtful convergence of design and action (Miner et al., 2001), improvised action requires more attention than rule-based action.
If the organization uses all of its attention in a given timestep, it must wait until its stock of attention is reset in the next timestep. At the end of t=200 timesteps, the simulation ends and performance is computed. We chose this number of timesteps because it is large enough to allow sufficient opportunities to flow into the environment such that any initialization effects on the findings are mitigated (Law and Kelton, 1991).