گذشته و آینده تئوری بازاریابی کسب و کار
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|19634||2013||11 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Industrial Marketing Management, Volume 42, Issue 3, April 2013, Pages 394–404
A complex systems approach to understanding and modelling business marketing systems is described. The focus is on the dynamics and evolution of such systems and the processes and mechanisms driving this, rather than the more usual comparative static, variables based statistical models. Order emerges in a self-organising, bottom up way from the local or micro actions and interactions of those involved. We describe the development of our thinking regarding this approach and its main features, including the development of agent based simulation models and the identification and modelling of underlying mechanisms and processes. We conclude by discussing the implications of this approach for business marketing theory and research.
Patterns in business history matter, as they provide key insights into the way business systems operate and are an important determinant of their present and future. Our research has for many years focussed on uncovering these patterns. Exploration of the evolution of ideas that guide research and of their evolutionary paths is similarly beneficial (Wilkinson, 2001). This article describes the ideas that have underpinned our theories of business marketing systems in part by considering the way they have evolved. We believe we are now at a major transition point in the social sciences, which sees exploration and explanation of the deeper processes of evolution as central to scientific advancement. Advances in computing power and software enable us to tackle these important issues of theory in ways that were not previously possible. We can now test far more realistic theories and models of market systems that deal with the inherent complexities, dynamics and processes of social systems, including market systems. Business markets are complex adaptive systems in which order emerges in a self-organising, bottom up way from the actions and interactions of people and firms and other types of organisations involved. Control and power are distributed through networks of interconnected, interdependent business actors. This challenges traditional notions of management and actor-centred theories of performance. A person or firm's behaviour and performance cannot be understood as simply the product of its own resources, skills, competences, orientations and motives. Actors operate in the context of other people and firms with whom they are interconnected in various ways. Behaviour and performance depends as much, if not more, on what others do, believe and want than on their own resources, skills, competences, orientations and motives. Context matters. Context is created by the history of past interactions, interconnections, events and the like. Increasingly we recognize that the study of the history of business systems provides insight into their present state and possible future(s) (Young and Bairstow, 2011 and Young and Bairstow, 2012). Similarly, consideration of our evolution to a “complex systems science” framework for understanding and researching business markets provides insight into the nature of context, the value its study can provide and ways of effectively researching it. This article is organised as follows. First, we describe what we mean by a complex adaptive systems' view of marketing, and business marketing in particular, and its relevance for advancing theory and research. Next, we briefly trace our intellectual journey towards this perspective. We describe some of the main ideas we have encountered and the way these have shaped our thinking and research within a complex systems science approach. In the final sections we consider the implications of complex systems thinking for business marketing theory and research as well as for management and policy.
نتیجه گیری انگلیسی
In some quarters of marketing there is considerable resistance to complexity and associated simulation methods. The problem is two-fold, there is a lack of research capability and research training in these areas and thus there is limited capability to use them or even evaluate the benefits they can bring. Perhaps more worryingly, we fear there is ever-increasing myopia within marketing, that our discipline is focusing on ever-smaller pieces of the big picture of marketing, that is, the markets within which business occurs, using the ever-more-rigorous reductionist methods that have characterised much of our discipline in recent decades. This process closes us off to all but a few new areas and opportunities. It has been argued that this is in part due to traditional methodologies driving the conceptual understanding of business and other social systems, which in turn is limiting the type of problems that are considered (Andriani and McKelvey, 2009 and Laughlin, 2005). Instead of theories creating a requirement for appropriate research methods, the current limitations and assumptions of the traditional research approach are restricting the basis for theories about business and other social systems. In such a climate complexity science is rejected by these entrenched interests; they argue that simulation has limited value and/or that this approach is relatively new and/or that the theories are not strong enough. Leading complexity scientists agree that not all the principles of organisation, patterns and hidden forces that make our world what it is have been solved but this does not mean that work in this area should not proceed and not be published in marketing journals purporting to be at the cutting edge of our discipline. To summarize, we have argued that marketing theory and research needs to break out of the straightjacket of its current dominant forms of methodology and theorising. It needs to move from a linear, comparative static, variables based approach to a non-linear, dynamic, evolutionary, process and mechanism based approach, as reflected in complex systems theory and methods. Such a transformation of the research agenda would not replace existing methods and theories. On the contrary, complex systems research complements, extends and contextualizes existing research. It must of necessity be informed by it, in part be validated by it and make use of existing methods in developing, analysing and testing complex systems models of business markets. Complex systems research will help stimulate, accelerate and necessitate further research of the more familiar type as well as enable new and neglected areas of research. A future research agenda needs to include: • Conducting systematic case histories and qualitative research to identify and better understand the nature and role of different mechanisms and process operating in business markets; • Developing and testing variables based statistical models of complex systems simulations of business markets; • Developing systematic experimental designs to analyse the behaviour and performance of complex computer simulation models under different conditions; • Designing and conducting experiments with real people and organisations that can inform and test the predictions and behaviour of complex system computer simulation models. There is a fundamental shift involved; it involves moving from the assumptions of reductionism to a constructionist view. As Nobel laureate Philip Anderson (1972) notes, “the reductionist hypothesis does not by any means imply a ‘constructionist’ one: The ability to reduce everything to simple fundamental laws does not imply the ability to start from those laws and reconstruct the universe” (p. 393). We urge marketers to at least consider both alternatives in doing science, thus opening the possibilities to do more research and know more about marketing systems. As we move further into the “century of complexity” (as articulated by Stephen Hawking), we urge the continuation and development of our own and others' voyages of scientific discovery via consideration of the complex properties of business networks' evolution and an extension in the ways we research them.