رفتار در مدیریت عملیات: بررسی یافته های اخیر و بازبینی پیش فرض های قدیمی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|7749||2006||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Operations Management, Volume 24, Issue 6, December 2006, Pages 737–752
In this paper, we provide a perspective on why behavioral research is critical to the operations management (OM) field, what prior research exists, and what opportunities lie ahead. The use of human experiments in operations management is still fairly novel despite a small stream of publications going back more than 20 years. We develop a framework for identifying the types of behavioral assumptions typically made in analytical OM models. We then use this framework to organize the results of prior behavioral research and identify future research opportunities. Our study of prior research is based on a search of papers published between 1985 and 2005 in six targeted journals including the Journal of Operations Management, Manufacturing and Service Operations Management, Production and Operations Management, Management Science, Decision Sciences, and the Journal of Applied Psychology.
Most introductory operations management (OM) courses cover a wide range of topics including product development, process design and improvement, inventory management, forecasting, and supply chain management. Many of the latest tools and techniques taught in such courses are fairly simple and easy to apply. Despite this, there is often a disconnection between the concepts introduced in class and the actual rules-of-thumb followed in practice. There are many reasons for this gap, but most have to do with either a lack of awareness on the part of the OM decision maker or a lack of applicability of the tools themselves. Many of our techniques and theories ignore important characteristics of real systems and therefore are perceived to be difficult to apply in practice. Also, even when methods are known and do apply, they may be difficult to implement given lack of information, trust, or proper incentives. A common factor in this breakdown is people. When it comes to implementation, the success of operations management tools and techniques, and the accuracy of its theories, relies heavily on our understanding of human behavior. Lack of trust between supply chain partners, incentive misalignment, and natural risk aversion are but three behavioral issues that can negatively impact operational success. The impact of behavioral issues on economic activity is studied extensively in many fields, including economics, accounting, marketing, and management. However, its study in operations management is relatively scarce. Our goal here is to make the case for the importance of behavioral research in the field of operations management. Specifically, we hope to provide inspiration and guidance to other researchers interested in studying behavioral operations management. We do this by first offering a framework for thinking about the behavioral assumptions commonly used in operational models. We divide these assumptions into three categories: Intentions, Actions, and Reactions. This framework allows us to systematically question underlying OM model assumptions and their implications on performance. We believe this characterization is helpful for identifying the types of operational problems that could benefit from behavioral research. Next we report on the findings of a literature review of papers that investigate behavioral issues in OM. We limited our coverage to papers using human experiments as the methodology for uncovering behavioral effects. We cover papers published between 1985 and June 2005 (i.e., the past 20 years) in six select journals: Journal of Operations Management, Manufacturing and Service Operations Management, Production and Operations Management, Management Science, Decision Sciences, and the Journal of Applied Psychology. The first four journals were chosen since they are arguably the top four journals in the OM field. The remaining two were selected for their broader scope and amenability to experimental research. While relevant papers obviously exist outside this set of journals, we believe this coverage provides a sound initial investigation into the type of research that exists in this area. Our literature review reveals several interesting findings. First, the application of human experiments to operational problems spans many sub-disciplines including production control, supply chain management, quality management, and operations technology. It appears that behavioral issues arise in a wide range of settings. Second, the number of human experiments using OM-contexts is significantly higher in interdisciplinary journals (such as Management Science and Decision Sciences) than in journals focused exclusively on OM. Third, the rate of publication over the past 20 years has been relatively stable regardless of recent acknowledgements concerning the importance of incorporating behavioral issues into OM work (e.g., Boudreau et al., 2003). Based on patterns and gaps observed in prior literature, we offer our thoughts on areas within OM that are ripe for further behavioral exploration. We also discuss how one can apply our behavioral assumption framework to different OM problem domains to generate possible research questions. The paper continues in Section 2 with a brief discussion of the benefits of using behavioral experiments to test issues relevant to OM. In Section 3, we discuss the nature of behavioral assumptions made implicitly or explicitly in OM models and introduce our three assumption categories. This assumption framework is used to organize the main literature review in Section 4. We conclude in Section 5 with a discussion of possible paths for future research.
نتیجه گیری انگلیسی
Our initial discussion of assumption types (in Section 3) outlined a number of behavioral assumptions that one could test in future research. Table 1 also provides specific hypotheses that could be tested in different OM contexts. We conclude with a number of additional ideas and conclusions drawn from the papers listed in Table 2. The majority of papers surveyed focus on operational decisions within two traditional (and tactical) contexts: inventory management and production management. However, one could argue that behavioral issues are even more likely to arise in the remaining four context areas (namely product development, quality management, procurement and strategic sourcing, and supply chain management). For example, the success of a product development or quality improvement project is inherently riddled with environmental factors that may impact human behavior. Similarly, supply chain management and sourcing tasks involve reliance on multiple parties across different organizations, with different perspectives, capabilities, objectives, and information availability. OM theory concerning institutional structure and interaction effects is beginning to emerge in these areas (thanks to recent applications of empirical methods within OM). We believe these theories could be further tested, refined, and strengthened through carefully designed human experiments. There has also been an explosion of OM research in the last 10 years focusing on interaction effects among decentralized decision makers. This research combines traditional OM models of scheduling, inventory planning, quality management, supply chain management, etc., with game theoretic rules and analysis indicative of decentralized decision making environments (e.g., see Cachon and Netessine, 2004 for a review). The tight connection between game theory and experimental methods in economics suggests that experiments may be an important tool for testing these new game theoretic results in OM. In future years, we hope to see the body of behavioral work accelerate within these problem domains. In terms of the types of assumptions tested, Table 2 shows a dominance of work related to Action assumptions. This might be due to the fact that many OM theories relate to the optimal actions (i.e., operations) of individuals or systems. Theories concerning the Intentions or Reactions of individuals are often less developed. In the case of Intentions, it is also perhaps more difficult to identify behavioral insights that are truly novel to OM settings. For example, one might identify the behavioral characteristic that inventory managers are risk averse, but is this aversion unique to the OM task or simply in line with the aversion exhibited by managers in other business settings? Tests of Action and Reaction assumptions are likely to be more directly associated with OM settings; often capturing specific aspects of task dependencies or second moment phenomenon (e.g., planned variations and/or uncertainty in input, output, or demand). So, while we hope experimental research will continue to test assumptions of all types, we expect the Action category to continue to dominate and the Reaction category to gain momentum. Another way to categorize prior experimental research is to consider the environment of the experiment itself. The literature we reviewed falls into three types: (1) industrial experiments where real workers are observed performing authentic tasks, (2) laboratory experiments where subjects take part in a controlled, and often stylized, version of an authentic task, and (3) situational experiments where subjects are given a description of a situation and asked to answer questions about how they would feel or act in such a situation. The majority of publications included in Table 2 were of the second type (approximately 75%). Only six papers involved industrial experiments. We hope the number of industrial experiments, in particular, will grow in future years. In summary, we believe behavioral experiments, if properly designed and executed, can provide windows into a wide range of phenomena of interest to operations managers. We view experimental research as a means for ensuring more realistic OM theories and models, with the assumptions of many established OM theories serving as a vast and rich ground for experimentation. Our assumption framework provides one method for identifying implicit behavioral assumptions in OM models. We look forward to the emergence of other frameworks, as well as updated literature reviews, in the years to come.