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|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4841||2008||18 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Human Resource Management Review, Volume 18, Issue 1, March 2008, Pages 1–18
Instead of merely combining theories of self-regulation, the current paper articulates a dynamic process theory of the underlying cognitive subsystems that explain relationships among long-used constructs like goals, expectancies, and valence. Formal elements of the theory are presented in an attempt to encourage the building of computational models of human actors, thinkers, and learners in organizational contexts. Discussion focuses on the application of these models for understanding the dynamics of individuals interacting in their organizations.
The field of human resource management (HRM) is premised on the notion that HRM is facilitated by understanding the nature of the resource (i.e., humans). Part of this understanding relates to individual differences in knowledge, skills, abilities, and other characteristics (e.g., personality), and part to the processes and parameters that affect motivation (Campbell & Pritchard, 1976). Of these two parts, the latter has arguably been the more difficult and disarrayed (Mitchell, 1997). Yet, most comprehensive theories of motivation were abandoned or grossly simplified, often by their originators, because of the overwhelming complexity of the nature they sought to understand. For example, McClelland, Atkinson, Clark, and Lowell (1953) introduced a comprehensive approach to understand motivation, but ended up focusing on need for achievement as an important aspect of the approach. Likewise, narrower approaches, focusing on either individual differences in persons or context variables in the environment, ruled the theoretical and empirical landscape of the day (Cronbach, 1957). However, Cronbach and others (e.g., Mischel, 1968) pointed out that neither approach alone was sufficient, leading researchers to consider interactions between person and environment variables (e.g., Magnusson & Ender, 1977). Indeed, most observers in the field have recognized for some time the dynamic (i.e., over time) interaction of persons with their environments and the reciprocal influences occurring due to these interactions (e.g., Bandura, 1986, Katz and Kahn, 1978 and Lewin, 1951). Nonetheless, work stemming from these approaches maintained their focus on the causes of behavior, ignoring the role of feedback processes that could close loops of causation. The result was static, open loop conceptualizations of human behavior or dynamic conceptualizations but parsed into parts that could be more easily conceptualized. Both, as noted by Landy and Conte (2004) in the opening quote, are of only limited value to those seeking to understand how and why whole persons behave as they do in whole settings. Recognizing these limitations, more recent efforts have been made to combine or integrate our knowledge, particularly in the area of motivation in applied settings (see, Kanfer, 1990, for a comprehensive review of many of these efforts). Interestingly, the majority of these theories share a view of the human as a self-regulator (Vancouver & Day, 2005). Self-regulation refers to the maintenance of internally represented desired states within the self (Vancouver, 2000). Internally represented desired states are called goals in psychology (Austin & Vancouver, 1996); hence, human self-regulation theories are those that attempt to describe the consequences or processes of goal striving (Kanfer, 1990). More comprehensive theories also describe the processes involved in the establishment, planning, and revision of goals (Austin and Vancouver, 1996 and Vancouver, 2005). The most common integrative approach is to articulate verbal theories of relationships (e.g., Locke & Latham, 2004) or underlying processes (e.g., Bandura, 1986 and Bandura, 1997). Although these approaches provide clues regarding research questions and potential insights toward innovative interventions, they are often imprecise or ambiguous (Luce, 1995). Indeed, these verbal theories can be contrasted with the more formal (i.e., mathematical) theories of the past (e.g., Hull, 1943). Interestingly, that contrast is considered an advantage of the more recent theories over the more formal theories of the past because the mathematically specified theories were seen as oversimplified and unable to account for a substantial amount of phenomena, or too abstract to be of much use (Pinder, 1998). Yet, the mathematics and formalization provide a foundation for precise prediction and unambiguous understanding needed more than ever given the complexity of the problem (Harrison, Lin, Carroll, & Carley, 2007). That is, mathematics is a tool theoreticians (and practitioners) need to fully understand the implications of the dynamic interactions among the various parts and the emerging properties and processes that arise from those dynamic interactions (Forrester, 1961). More specifically, it allows one to construct computational models and simulate the behavior of those models over time to see if the models can a) account for observed behavior of human beings, b) provide clues for understanding how individuals process information and self-regulate, c) make accurate predictions of an individual's future behavior after deriving parameter values that represent the individual, and d) predict or give clues regarding the effectiveness of interventions applied to the individual ( Harrison et al., 2007 and Hulin and Ilgen, 2000). To capitalize on these benefits of a more formal and comprehensive theory of the dynamics of motivation and behavior, our end goal is to provide mathematical representations of the basic person processes by which computational models of specific persons-in-contexts (Ford, 1992) can be created. Moreover, these basic self-regulatory processes are comprehensive, including acting, thinking, learning, and feeling, as well as how these processes tie into each other and the context in which they are occurring. This theoretical integration of processes and context is critical given the interaction of these processes and context within the phenomena of interest to applied psychologists. We know of no modern attempt to integrate all these components into a formal, interconnected set of theoretical statements (i.e., equations). Given the breadth of this endeavor, the complexity of the task, and the difficulties associated with measurement and observation, our descriptions must be considered preliminary. However, we are fortunate to have much of the trail blazed and some paths even well trod by the classic, grand theories of motivation and human behavior. For instance, our discussion of a dynamic process theory of self-regulation begins with a review of Lewin's (1951) work. Lewin wrote in a time when comprehensive theories of human behavior and motivation were revered. That period ended shortly after his death, but his theorizing became the basis of several middle-range theories that subsequently emerged (Pinder, 1998) and provides a context for understanding the theory presented here. A second advantage of our dynamic process theory of self-regulation that becomes clear as one reads through the descriptions of the basic processes is that their underlying architectures are all highly similar and build off one another. This similarity provides a remarkable source of parsimony where one would expect complexity. Instead, complexity is hypothesized to arise as the parts are put together and repeated as necessary to represent the person-in-context and as elements of the complex environment are properly understood and represented. The perceived value of this perspective, along with advances in computational modeling, provides the background for why we believe now is a good time to essentially “reintroduce” Lewinian theorizing to applied psychology.1 Below we briefly review Lewin (1951). Then in separate sections we consider the four basic processes of the human experience (i.e., acting, thinking, learning, and feeling). In each section, we describe the minimum amount of mathematics needed to represent each process and how the equations relate to previous theories and equations in previous sections. The aim of each of the sections is to provide a theoretical and mathematical description that will form the foundation for the development of computational models of these processes. In doing so, we draw on a wide range of basic and applied psychological research and theory. In the final sections of this paper we describe some of the implications of our theorizing, including examples of how this theory can be used to clarify some of the ambiguities and inconsistencies in the motivation literature and examples of types of organizational problems to which our dynamic process theory might be applied.
نتیجه گیری انگلیسی
For the field of motivation, Atkinson once advocated that “The goal of this field of scientific inquiry is to develop concepts which account for the direction, vigor, and persistence of an individual's behavior that are more useful than… the fund of intuitive wisdom we so often refer to as ‘common sense’” (1964, p. 3). Theories of work motivation are clearly making progress toward that goal in that they are becoming more comprehensive, self-regulatory or goal-oriented, and dynamic. We believe these are laudable trends, but there is still much work to be done. According to Simon (1992) dynamic, process levels of explanation, particularly if it can be precisely (i.e., mathematically) represented, are the type of explanations researchers should seek. Dynamic process models not only provide a deeper level of understanding, but also potentially lead to applications to motivational problems. We present our dynamic process theory as a framework for furthering this quest. No doubt additional conceptual and empirical work is required in both directions (i.e., deeper understanding and practical applications). Moreover, because the theory describes how open systems interact with their environments over time, it represents a much more sophisticated conceptualization of person–environment interaction needed to understand the behavior of human beings nested in complex and dynamic contexts. Finally, because of the field's need to understand dynamic interactions between persons and environments, our models can inform not only applied problems, but also more basic theorizing (e.g., Hastie & Pennington, 2000). In concert, these efforts might help narrow the gap between where human resource management currently is and where it could be.