Language Consistency and Exchange: Market Reactions to Change in the Distribution of Field-level Information

Abstract

Markets are fluid. Over time, the dominant designs, processes and paradigms that define an industry invariably succumb to productive innovation or changes in fashion (Arthur, 2009; Schumpeter, 1942; Simmel, 1957). Take for example the recent upheaval of the cell phone market following Apple's release of the iPhone. When it was introduced in 2007, one could clearly differentiate Apple's product from all others; however, subsequent imitation of the iPhone produced a market in which nearly all cell phones look (and perform) alike. The iPhone was a harbinger of the new dominant design. These cycles of innovation and fashion are not limited to consumer markets. Business markets (often defined by longer term inter-firm relationships) are subject to similar transformations. For example, current practices in the biotechnology industry are quite distinct from those accompanying its emergence from university labs in the second half of the 20th century (Powell et al., 2005). Technologies that were once viewed as radical have undergone a process of legitimation and integration into mainstream healthcare delivery systems. Practices that were dominant in the 1980's gave way to newer business models in the 1990's and feedback from down-stream providers changed the way drugs were delivered to patients (Wolff, 2001).During periods of transition, market actors face great difficulty anticipating reactions to their behavior (practices, products, etc.). How they deal with this uncertainty is an interminable source of academic inquiry in the social sciences (see e.g. Alderson, 1965; Simon, 1957; Thompson, 1967) and, in a broad sense, it is the primary concern of the current work as well. However, I am focused specifically on the turmoil caused by transitions in technology, taste and attention over time--the disagreements which occur as market actors collectively shift their practices from one paradigm to the next (Powell and Colyvas, 2008). If innovations are assumed to arise locally and diffuse gradually (see e.g. Bass, 1969; Rogers, 2002), then transient differences in knowledge are a natural outcome. Those closest to, or most interested in an innovation will have greater knowledge than those furthest away or less involved. Thus, for a period following some shift in technology, taste or attention, market participants will vary in their knowledge and interpretation of the change. In the following chapters, I investigate the ramifications of this sort of knowledge heterogeneity on the exchange behavior and subsequent performance of market participants. It is the central argument of this thesis that this heterogeneity affects exchange by both limiting coordination and increasing quality uncertainty. The details of this argument are fleshed out in Chapters 1, 2 and 3 (summarized below), which build upon each other in a progression from abstract, to descriptive to specific tests of theory. However, each can also stand by itself as an independent examination of the knowledge-exchange relationship. The final chapter synthesizes my findings and highlights some implications for practitioners and further research. In Chapter 1, I review the history and development of Alderson's (1965) 'law of exchange' in the marketing literature and propose an extension based on insights from information theory. A concept called market entropy is introduced to describe the distribution of knowledge in a field and propositions are offered to explain the exchange behavior expected when this distribution changes. Chapter 2 investigates knowledge heterogeneity through its relation with written language. Drawing on social-constructionist theories of classification (Goldberg, 2012) and insights from research on the legitimation process (Powell and Colyvas, 2008), I argue for a measure of field-level consensus based on changes in the frequency distribution of descriptive words over time. This measure is operationalized using eleven years of trade journal articles from the biotech industry and is shown to support the propositions offered in Chapter 1. Chapter 3 builds on the arguments and evidence developed in Chapters 1 and 2 to test theory on the structural advantages of a firm's position in a network of strategic alliances. Prior work has documented returns to network centrality based on the premise that central firms have greater and more timely access to information about industry developments (Powell et al., 1996, 1999). However, other research claims that benefits to centrality accrue based on the signal that such a position provides about an actor's underlying quality (Malter, 2014; Podolny, 1993, 2005). I investigate this tension in the literature and offer new insights based on interactions between network position and the measure developed in Chapter 2.