Janice A. Black
New Mexico State University, USA
Gerard Farias
Loyola University, USA
Many business environments today can be described as chaotic or complex systems characterized by nonlinearity, aperiodicity, and unpredictability (Johnson and Burton, 1994; D'Aveni, 1994; Ilinitch, D'Aveni, and Lewin, 1996). However, much of our current understanding of business strategy arises from traditional economic models that are largely linear, periodic, and predictable (Peteraf, 1993). These models assume that marketplaces move toward equilibrium, unless barriers to competition (i.e., imperfect information) make supranormal returns possible (Peteraf, 1993). Beyond barriers restricting movement toward equilibrium, there are also events of discontinuous change (Nadler and Tushman, 1995) that often move the market toward higher dynamism and complexity (D'Aveni, 1994; Ilinith et al., 1996). Such movement away from equilibrium is not rare.
Strategy researchers began to seek dynamic strategies that would be able to incorporate conditions of nonlinearity, ambiguity, and uncertainty in the 1970s (Cohen, March, and Olsen, 1972; Mintzberg, 1979) and recently have attacked problems associated with complex dynamic systems (Liebeskind et al., 1996; Hanssen-Bauer Snow, Smith, and Zeithaml, 1996). Still, many important research issues are only now being articulated. The lack of a well-defined conceptual framework that can explain the simultaneous presence of both equilibria-oriented markets and attendant strategies, as well as disequilibria-oriented markets and strategies, is one such issue.
This article utilizes the theoretical bases of disequilibrium-based Austrian economic theory (Scarth, 1988; Jacobsen, 1997, Stacey, 1995; Young, 1995; Smith and Grim, 1996) and complexity theory (Senge, 1990; Wheatley, 1992; Waldorf, 1992; Stacey, 1995; McKelvey, 1997). Both of these theoretical bases propose movement between structured and unstructured states (Stacey, 1995; Young, 1995). We propose to link the information processing and organization design literature with Austrian economics and, by utilizing complexity theory, create a model that explains the simultaneous presence of both equilibrium and disequilibrium characteristics in marketplaces.
Several authors have presented basic premises of Austrian economics to management researchers (Jacobsen, 1992; Young, 1995; Stacey, 1994; Hunt, 1995; McWilliams and Smart, 1995). This school of economics assumes that causal links are nonlinear and that relative firm performance is only partially the outcome of plans and managerial intentions—making specific predictions of outcomes problematic. Such assumptions mirror market realities and allow us to explore market complexity and hypercompetition as something other than aberrations (Stacey, 1995; Young et al., 1996).
While traditional economics assumes that changes in market structure are exogenous to the model, the process of structural change in markets is integral to the Austrian economic model (Young, 1995; Kirzner, 1979; Hayek, 1945, among others). The market order or structure is the byproduct of each entrepreneur's actions (Mises, 1949; Kirzner, 1979). For instance, imperfect information is an example of “market failure” in traditional economic analysis, whereas imperfect information flows are a core process characteristic in the Austrian view. For our purposes, an entrepreneur can represent either an individual or a collective of individuals who make market interaction choices (i.e., a firm). Our definitions of market-structuring actions include the identification of opportunities and linkages of the main elements in the market supply or value chain (i.e., input or supply choices, production choices, customer choices, etc.; see Lawler, 1996, among others).
While the overall order emerges from all actions taken, following both Kirzner (1982) and Lachmann (1978), we have found it useful to focus on two types of entrepreneurial actions: structuring and refining.
Structuring actions relate to setting boundaries for what it means to compete in a particular industry or market. Refining actions pinpoint the most effective and efficient ways to operate in that newly defined or redefined market. First and early movers in a market therefore create structure in that market and in the process set standards. Followers, attracted by the high returns, follow the beaten path and adopt the structure and standards set. Note that the structuring process may involve a completely new marketplace (a new product or service) or redefine an existing marketplace. For example, Amazon.com pioneered a restructuring of the already existing retail book market. Barnesandnoble.com and others have adopted this structure.
One recent observation is that many markets have not wound down to a stable equilibrium but have rather kicked into a “hyper” phase (D'Aveni, 1994; special issue of Organization Science, 1997). The Austrian economics perspective allows for this type of market change to occur as the direct result of the competitive actions taken by firms within the marketplace. For example, a hypercompetitive shift can occur when a change in technology makes the industry more dynamically resourceful (i.e., able to produce new strategic assets; Thomas, 1996). Alternatively, such a shift may occur when the economic acts in the focal marketplace increase in number and rate to an information overload stage (Black and Farias, 1996). In information overload, ambiguity in the marketplace increases and its boundaries become indeterminable. Disequilibria conditions again exist, but the increased ambiguity is a result of a complex market system problem (hypercompetition or information overload), rather than a simple market system problem (lack of market structure). The market has moved from one patterned period into a “chaotic” session where opportunities for redefinition abound. When new patterns emerge, they may be different in specifics but will be recognizable.
It is not only the market structure or lack thereof that provides the potential for rent creation. Austrian economics supports the idea that efforts to refine or shape market structure and/or market interactions are also potential sources of rent creation (Coyne and Subramanium, 1996). From this perspective, a state of equilibrium is not the main characteristic of the marketplace. Rather, markets are characterized by a series of disequilibria that, as this fitting dynamic occurs and information is shared, move closer to states of equilibria (Kirzner, 1982), unless the earlier mentioned shift to hypercompetition occurs (D'Aveni, 1994).
The term “complexity” is not new to the management literature. Senge (1990) distinguishes between detail complexity and dynamic complexity. Most of the literature focuses on detail complexity, which essentially is concerned with the number of variables: the more the number of variables, the more complex the problem. However, a complex system is not the same thing as a complicated system (Devaney, 1993). A complicated system meets Senge's definition of detail complexity and is one with many parts and subparts with a wide range of linear relationships. It can look very intricate, but it has a static pattern. Dynamic complexity refers to the nonlinearity and low predictability in a system.
Dynamic nonlinear systems are being addressed by a number of system researchers (Lichtenstein, 1998a, 1998b) and have been gathered together under the heading of complexity theory. In this framework, a complex system can look very simple but will have nonlinear relationships among its constituent elements (e.g., a feedback loop). A system with embedded nonlinear relationships becomes dynamic until it reaches a state of equilibrium (Devaney, 1993; Cramer, 1993). At equilibrium, the system neither uses nor produces anything. Most complex systems are in a far-from-equilibrium state and so are dynamic (Cramer, 1993).
Complex systems have the characteristic of having nonlinear relationships between system elements that may interact, creating an unpredictable reoccurrence of a patterned result (Johnson and Burton, 1994; D'Aveni, 1994; Waldorf, 1992; Stacey, 1995: Wheatley, 1992; Senge, 1990; McKelvey, 1997). Most applications of complexity theory include dynamism in the system over time, with the reoccurring patterns being recognizable to earlier iterations but not identical to those earlier patterns. Common examples of such dynamic complex systems include those from meteorology. One can certainly recognize a cloud when one sees it, but the specific water droplet formation is never the same. A static version of this is the snowflake (no one flake is identical to any other).
Recall that dynamic systems are predictable only over the short term (Hunt, 1995; Cramer, 1993) and retain an ordered stability typically when in close proximity to an attractor variable.These strange attractors act as order coalescent points for the complex system (Devaney, 1993; Hunt, 1995). Patterns emerge at these spots after much iteration. Such patterns imply not only the presence of an attractor but a particular attractor, which comes in many types (Favre et al., 1995). These range from attractors that are independent of time and or are in a stationary state to those that have a regular repeating pattern over time, to those whose pattern changes slightly as it repeats irregularly.
An interesting type of attractor is one whose revealing pattern includes a bifurcation point. The point represents a critical value where equally viable alternatives exist, but which then contribute to the forming of a new attractor with a different order pattern. In other words, there is a qualitative change in the system (Favre et al., 1995). While recognizable, the nature of the order in the system is different from its earlier state. The bifurcation point occurs even when one follows the existing “rules of order.” Such a bifurcation point can result in the system being poised on the edge of entering chaotic behavior (Cramer, 1993). It only takes three bifurcations before the system becomes unpredictable and turbulent (i.e., change and multiple possibilities are prevalent; Favre et al., 1995). The area of time during these iterations (while a system is moving toward a chaotic state but before it reaches true chaos) has been termed “the edge of chaos” (Cramer, 1993; Brown and Eisenhardt, 1998).
The edge of chaos, where data generated from deep underlying nonlinear rules appears chaotic, has been targeted as an area where a great deal of organizational activity occurs, metaphorically speaking (among others Brown and Eisenhardt, 1998; Stacey, 1995). Yet further application of complexity theory is predicated on a better translation into organizational and economic literatures of complexity theory assumptions. Bryan (1988, 1994) has begun transferring some of the concepts in his positive feedback economics. We suggest that Austrian economics also provides a linking mechanism between complexity theory and organizational activity, specifically entrepreneurial actions and attendant strategies for a full range of actions. This linkage is possible because the deep underlying rules guiding the system are the two main “drivers” of Austrian economics: the creation and diffusion of market information (Black and Farias, 1998).
The tension between these two drivers is revealed in the fitting dynamic and results in market growth. Although Austrian economics includes in the framework all market participants (i.e., suppliers, customers, regulators, etc.) as contributing to the level of market information available, for simplicity these participants' contributions are implicitly added in the revealed data portion of the model. These two driving forces result in entrepreneurial market-organizing efforts of market structuring or refining.
These separate activities have been identified as enterprising and honing activities respectively (Black, Farias and Mandel, 1996; Black, 1997, 1998; Black and Farias, 1998). The enterprising orientation is defined as market structuring: the inclination to put together the elements of a market and thereby enact a definition of the market (whether an initial definition or a revised definition). The second, honing orientation is defined as market refining: the inclination to refine the details of the activities within a defined market structure by acquiring and using the information available from all relevant economic agents in the market.
With these two orientations acting as the deep mathematical rules for the system, we can see how the organizing efforts of entrepreneurs result in two simultaneous drivers: the enactment of market structure and the revealing of information to all market participants.
The existence of a market structure pattern also provides information about a particular market. When the market boundaries are either not yet defined or are being defined, there is a feedback loop reinforcing the enterprising orientation market-organizing efforts. Participants in the economic activity of this market will loop through these steps until a stable pattern results (i.e., industry standards are set). We anticipate that the setting of industry standards is the equivalent of a bifurcation point in a complex system. At this bifurcation point, many organizing actions are switched from an enterprising orientation to a honing orientation based on those acceptable industry standards. A new organizing logic is in place. Enterprising activities done in this market context are now done with the logic and intent to destabilize this market pattern.
Thus we now have an expanded model that shows how the economic environment moves from one ordered state to another We would expect the honing cycle to repeat again and again until the next critical bifurcation point is reached. That bifurcation point is evidenced in one of two scenarios:
Scenario 1 The market will stabilize into the reduced returns associated with the perfect competition model of traditional neoclassical economics, with the attendant generic strategies suggest by Porter (1980).
Scenario 2 It will transition into a hypercompetitive state with strategies associated with a market in transition and rapid change (among others D'Aveni, 1994; Eisenhardt and Brown, 1997).
At this hypercompetitive state, the market has again entered the edge of chaos but with a twist: There is now organizational history regarding a successful pattern. There is organizational inertia (Kelly and Amburgey, 1989). Learning and change literatures suggest that organizations will be tempted to reuse the exact pattern of resources and processes that have previously brought them success (Tushman and Romanelli, 1985; Gresov, Haveman and Oliva, 1992; Levitt and March, 1988).
Thus in this more complex, edge-of-chaos environment, we expect that there will continue to be some resources expended on honing organizing efforts. These efforts may even be a rational approach to reducing the complexity and chaos by tightening and simplifying relationships of their particular supply or value chain subsystem. Alternatively, when faced with chaotic market patterns, others may choose to re-emphasize their enterprising activities. These entrepreneurs again choose to set the definitions of the market structure in a more directed fashion.
To illustrate, we will use an example discussed earlier. Amazon.corn introduced a major change in the retail book market. Its success revealed the existence of a market for internet-based book retailing. This information is not available to Amazon.com alone. Any entrepreneur wishing to enter this market now has information revealed by the success of Amazon.com. Other book retailers (e.g., Barnesandnoble.com) use this information and structure the market further. Several other companies selling a variety of products (e.g., Buy.com) recognize the value of the market and the potential for rent. Further structuring takes place. Firms have two choices. They might choose to compete by destabilizing the market through the introduction of changes that induce new market restructuring. On the other hand, they might choose to compete by developing more efficient ways to deliver the product or service. As discussed earlier, the former strategy reflects the enterprising orientation and the latter the honing orientation. Enterprising actors interpret and enact their environments in unique ways. Honing actors adopt the interpretations of the enterprising actors.
However, two distinct but intertwined loops operate. As long as the actors in a particular market continue to earn rents, both the actors and the market grow. Eventually, however, these rents are eroded and a new tension created. The enterprising rent seekers seek to destabilize the market and redefine it, or move to create new markets. The honing organizations seek to stabilize and further structure the market. Note, however, that the availability of information increases as characteristics of a market are revealed through the structuring process. In other words, the market has become less equivocal or ambiguous. Actors have to deal with the relatively tame problem of uncertainty. However, as the number of actors increases, conditions of information overload are created and present opportunities for redefining the marketplace. The market has reached conditions of equivocality once more. The interpretation and enactment of this environment generate a new cycle of marketplace dynamics.
Recall that the primary orientation in use before the first bifurcation point (which occurred when industry standards and norms coalesced) was the enterprising orientation. These market activities occurred at the very beginning of a market and hence under conditions of high ambiguity. These conditions imply that firms that act will be those that have a bias of high levels of enterprising orientation.
The first bifurcation point occurs when an industry norm or standard coalesces. There is then a shift in the logic of order in the market. The shift is toward refining the now defined market. This implies that there will be a shift toward an emphasis on a honing orientation rather than an enterprising orientation. Firms will then focus their resources on developing their efficiency or uncertainty-reduction skills. This increase in uncertainty-reduction skills enables them able to move to higher honing levels. However, they typically have few or no slack resources available to develop their enterprising capabilities. The combination of the need to focus their attention on uncertainty-reduction skills and inattention to or lack of development of their equivocality-reduction skills (due to no immediate need for such skills) increases the tendency of firms' orientations to drift toward only reinforcing their uncertainty-reduction skills. Once a high level of honing orientation is reached, a firm is able to keep pace with the efficiency pressures of a complex market. Furthermore, by being able to keep pace, these high-honing firms now generate or have the slack available to utilize and/or develop greater amounts of their enterprising-fitting dynamic orientation.
As market information is dispersed and firms reach high levels of honing, there is a potential shakeout of firms that have not been able to develop the necessary skills to move to higher honing levels. If the market continues to develop better and better honing skills, it may become mature and continue to develop as predicted by traditional economic models. If, however, in their competition for better fit firms begin to take incremental steps in increasing their enterprising skills, another bifurcation point may be reached. As firms increase their use of an enterprising-fitting orientation and associated equivocality-reduction skills, they finetune their ability to handle ambiguity and may ultimately be able to attain high levels of both honing and enterprising. While this may be due to the available slack as indicated above, if there are a number of high-honing firms their proactive efforts may spark a hypercompetitive environment.
This movement to a hypercompetitive environment is the second bifurcation point. When this happens (which can be simply due to the proactive tendency involved in a high honing orientation), firms may choose to engage in economic activities resulting in increased information density and an increased pace in response to others' actions. This “more to consider and respond to with less response time” is typical of hypercompetitive environments (D'Aveni, 1994). If a firm has no enterprising skills, then the act of engaging in ambiguity reduction will probably be forced on externally as the result of the hypercompetitive actions and/or complex markets that are leading to the change. At this point, firms may choose to stir up the water by introducing destabilizing actions that will cause greater levels of ambiguity to occur, creating opportunities for the firm to earn returns from market-structuring activities as well as from its market-refining efforts from the honing orientation. An alternative scenario is one where firms actually leave the previous market and create a new market, which would be a third bifurcation.
However, once the density of firms in new markets increases, enterprising firms will face pressure to create efficiencies in their operations or move on to other new markets with lower densities. In making the choice of whether or not to invest in “honing skills,” some firms will also need to be aware of an additional pressure. Specifically, the pressure to remain in their markets and develop honing skills may emanate from stakeholders in a firm who value the more certain returns available from honing existing markets. Movement into and out of enterprising activities may be part of an overall strategy of planned cycling. A firm may choose to cycle between the two dynamic choices in such a way as to move into a new market, hone its position there until a certain floor level of returns is obtained, and then expand into another market.
Note that if the firm neglects to invest in either skill base, those skills may languish and it may drift to a strategic orientation that is below the level of its current orientation. These tendencies suggest that firms' strategies will be forced to move eventually in some direction (by choice, market pressure, or drift). Thus, changes in a firm's strategy should be more the norm than the exception, regardless of its starting orientation. This implies that changes in a firm's business-level strategy may be more prevalent than previously implied. This also implies that such changes may be the result of strategic thought that is not a “stuck in the middle,” waffling perspective, but a deliberative effort to receive the rents associated with the level of market complexity.
It is evident that the drivers of the dynamism in a disequilibriumbased economy are found in the high-enterprising and high-honing orientations. As discussed, an enterprising orientation is associated with the preference and ability to use equivocality-reduction informationprocessing skills, while a honing orientation is associated with the preference and ability to use uncertainty-reduction information-processing skills. Those firms that utilize the skills of either orientation thus finetune a specific set of skills. When equivocality-reduction skills are enhanced, they reinforce further enterprising activities. When uncertainty-reduction skills are enhanced, they reinforce further honing activities. Although the exact relationship is an empirical question, it is reasonable to assume that a learning curve (a nonlinear relationship) of some sort is present. This implies that further uses of complexity theory in interpreting market and firm actions are likely.
In addition to providing an explanation of the deep underlying rules of our economic marketplace, the model also provides firms, entrepreneurs, and managers with insight into when to expect the next critical point at which a bifurcation might occur, reordering the definition of competition in a market. While both the explanatory and predictive powers of the model have yet to be determined empirically, it does provide us with a base from which to test. Enterprising and honing actions can be coded from descriptions of actions in the marketplace. Changes from a predominance of one type to that of another can be determined. The timing and results of changes in predominance can be studied, as can the ability of a firm to handle both types of skills simultaneously or sequentially.
This model provides an explanation for the simultaneous existence of both equilibrium and disequilibrium tendencies in a marketplace. We believe that it helps to enhance our understanding of the dynamics of the marketplace, focusing both on environmental and organizational factors. The model is testable through longitudinal studies of industries as they have evolved and changed over time. It may also be tested in the context of innovations within an industry. For example, Southwest Airlines redefined the airline market in many ways. Nevertheless, many of the traditional airlines did not choose to change. Yet many of Southwest's innovations are being copied by these traditional airline companies. For instance, the idea of ticketless travel has now been adopted by most airlines in the US. This may be an example of imitation of the innovator to at least maintain competitive parity.
Because of the current timeliness of many of the issues addressed by our model, empirical work is just beginning to be published that addresses these or similar issues (Ilinitch et al., 1996). Additionally, much empirical work remains to be done to test the basic propositions of Austrian economics (Jacobson, 1991) from which this model has been developed. There has been some recent work by Young, Smith and Grimm (1996) that does provide support for some of the critical propositions used in the model. They confirmed some of the assertions of the dynamic fitting process by finding that overall industry profitability declines with increased industry competition. They also found that firms that introduce new competitive elements raise their own performance and lower others (implied support for the high-honing and highenterprising orientations). Yet even with this encouraging study, and while the suggestions of the fitting dynamic in organization studies are derived logically from previous research and theories, much empirical testing remains to be done.
The authors would like to thank Jack Brittain, Dennis Duchon, Amy Hillman, Stan Mandel, Will Mitchell, Dan Schendel, and Scott Sherman for their thoughtful and insightful comments. Any errors remain ours. This paper was originally presented at the 1999 Annual Academy of Management meeting in Chicago.
Aupperle, K. E. (1996) “Spontaneous organizational reconfiguration: a historical example based on Xenophon'ss Anabasis,” Organization Science, 7 (4): 444-60.
Barney, J. B. (1991) “Firm resources and sustained competitive advantage,” Journal of Management, 17: 99-120.
Black, J. A. (1997) “Understanding entrepreneurs and entrepreneurship in a disequilibrium based world,” submission to the Entrepreneurship Division of the Academy of Management for presentation at the 1997 Annual Academy of Management Meeting in Boston, MA.
Black. J. A. and Farias, G. (1996) “The creation of complexity: staying one step ahead of the game,” presentation at INFORMS Annual Meeting, Meeting within a Meeting on Complexity Theory, Atlanta, GA.
Black, J. A., Farias, G. and Mandel, S. (1996) “The fitting dynamic and complex markets,” presentation to the Western Academy of Management, Annual Meeting, March, Banff, Canada.
Boal, K. B. and Whitehead, C. J. (1992) “A critique and extension of the stratified systems theory perspective,” in R. L. Phillips and J. G. Hunt (eds) Strategic Leadership: A Multiorganizational-Level Perspective, Westport, CT: Quorum Books.
Brown, S. L. and Eisenhardt, K. M. (1998) Competing on the Edge: Strategy as Structured Chaos, Boston, MA: Harvard Business School Press.
Cohen, M. D., March, J. G., and Olsen, J. P. (1972) “A garbage can model of organizational choices,” Administrative Science Quarterly, 17: 1-25.
Cramer, F. (1993) Chaos and Order, D. I. Loewus (trans.), New York: VCH.
Daft, R. L. and Lengel, R. H. (1986) “Organizational information requirements, media rich- ness and structural design,” Management Science, 32: 554-71.
D'Aveni, R. A. (1994) Hypercompetition, New York, NY: Free Press.
Devaney, R. L. (1988) “Dynamics of simple maps,” in R. L. Devaney and L. Keen (eds) Chaos and Fractals: The Mathematics behind the Computer Graphic, Providence, RI: American Mathematical Society.
Drucker, P. F. (1991) “Japan: new strategies for a new reality,” Wall Street Journal, October 2.
Favre, A., Guitton, H., Guitton, J., Lichnerowicz, A., and Wolff, E. (1995) Chaos and Determinism: Turbulence as a Paradigm for Complex Systems Converging toward Final States, B. E. Schwarzbach (trans.), London: Johns Hopkins University Press.
Galbraith, J. R. (1977) Designing Complex Organizations, Reading, MA: Addison-Wesley.
Galunic, D. C. and Eisenhardt, K. M. (1996) “The evolution of intracorporate domains: divisional charter losses in high-technology, multidivisional corporations,” Organization Science, 7 (3): 255-82.
Grant, R. M. (1996) “Prospering in dynamically competitive environments: organization capability as knowledge creation,” Organization Science, 7 (4): 413-27.
Hanssen-Bauer, J. and Snow, C. C. (1996) “Responding to hypercompetition: the structure and processes of a regional learning network organization,” Organization Science, 7 (4): 413-27.
Hunt, J. C. R. (1995) “Foreword,” in A. Favre, H. Guitton, J. Guitton, A. Lichnerowicz, and E. Wolff, Chaos and Determinism: Turbulence as a Paradigm for Complex Systems Converging Toward Final States, B. E. Schwarzbach (trans.), London: Johns Hopkins University Press.
Kirzner, I. M. (1978) “Economics and error,” in Louis M. Spadaro (ed.) New Directions in Austrian Economics, Kansas City, KS: Sheed Andrews and McMeel.
Kirzner, I. M. (1979) “Comment: X-inefficiency, error and the scope for entrepreneurship,” in Mario J. Rizzo (ed.) Time, Uncertainty and Disequilibrium, Lexington, MA: Lexington Books.
Kirzner, I. M. (1982) “Uncertainty, discovery, and human action: a study of the entrepre- neurial profile in the Misesian system,” in I. M. Kirzner (ed.) Method, Process and Austrian Economics, Lexington, MA: Lexington Books.
Lachmann, L. M. (1977) “Austrian economics in the present crisis of economic thought,” in Walter Grinder (ed.) Capital, Expectations and the Market Process, Kansas City, KS: Sheed Andrews and McMeel.
Lichtenstein, B. D. (1998) Presentation at Sun-Break Conference on Organizations, Las Cruces, NM, February.
Liebeskind, J. P., Oliver, A. L., Zucker, L., and Brewer, M. (1996) “Social networks, learning and flexibility: sourcing scientific knowledge in biotechnology firms,” Organization Science, 7 (4): 428-43.
March, J. G. and Simon, H. A. (1992) Organizations, Oxford: Blackwell.
Miles, R. E. and Snow, C. C. (1994) Fit, Failure, and the Hall of Fame: How Companies Succeed or Fail, New York, NY: Free Press.
Mintzberg, H. (1978) “Patterns in strategy formation,” Management Science, 24: 934-48.
Peteraf, M. A. (1993) “The cornerstones of competitive advantage: a resource-based view,” Strategic Management Journal, 14: 179-91.
Porter, M. E. (1980) Competitive Strategy, New York, NY: Free Press.
Scarth, W. M. (1988) Macroeconomics: an Introduction to Advanced Methods, New York: Harcourt Brace Jovanovich.
Schumpeter, J. A. (1934) The Theory of Economic Development, Cambridge, MA: Harvard University Press.
Stacey, R. D. (1995) “The science of complexity: an alternative perspective for strategic change processes,” Strategic Management Journal, 16 (6):477-95.
Summer, C. E., Bettis, R. A, Duhaime, I. H., Grant, J. H., Hambrick, D. C., Snow, C. C., and Zeithaml, C. P. (1990) “Doctoral education in the field of business policy and strat- egy,” 1990 Yearly Review of Management of the Journal of Management, 12: 167-83.
Vaill, P. (1989) Managing as a Performing Art: New Ideas for a World of Chaotic Change, San Francisco, CA: Jossey-Bass.
Weick, K. E. (1995) Sensemaking in Organizations, Thousand Oaks, CA: Sage.