موازنه گستردگی/عمق؛ مدل‌سازی تصمیم‌گیری مدیریتی در شرایط عدم قطعیت با رویکرد رفتاری – شناختی
محورهای موضوعی : مطالعات رفتاری در مدیریت
علیرضا ولیان
1
(گروه مدیریت بازرگانی، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی،)
حسین رحمان سرشت
2
(استاد مدیریت استراتژیک، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبائی)
کلید واژه: عدم قطعیت, تصمیم‌گیری مدیریتی, موازنه گستردگی/عمق, علوم اعصاب شناختی سازمانی,
چکیده مقاله :
این پژوهش باهدف مدل‌سازی الگوی تصمیم‌گیری مدیریتی در شرایط عدم قطعیت به‌منظور توسعه شواهد تجربی لازم برای گسترش به‌کارگیری رویکردهای رفتاری - شناختی در مطالعات علوم اعصاب شناختی سازمانی صورت‌گرفته است. این پژوهش از نظر دیدگاه فلسفی اثبات‌گرا، با رویکرد استدلال استقرایی و استراتژی تجربی و در قالب یک طرح شبه آزمایشگاهی اجرا شده است. 62 نفر (33 زن) در قالب دو گروه آزمودنی شامل افراد باسابقه مدیریت حرفه‌ای در سطوح میانی سازمان (29 نفر) و افراد فاقد سابقه مدیریتی (31) نفر، یک آزمون رفتاری تصمیم‌گیری مدیریتی در شرایط عدم قطعیت در چارچوب موازنه گستردگی/عمق را انجام دادند. مدل‌سازی داده‌های به‌دست‌آمده از آزمون رفتاری با استفاده از روش رگرسیون خطی نشان داد که مدل‌های توانی بهتر از مدل‌های خطی داده‌ها را در هر دو گروه توصیف می‌کند. علاوه بر این، بررسی الگوی تصمیم‌گیری مدیریتی در شرایط عدم قطعیت تفاوت معنی‌داری در بین مدیران و افراد فاقد سابقه مدیریتی نشان نداد. نتایج به‌دست‌آمده از این مطالعه نشان می‌دهد که از منظر موازنه گستردگی/عمق تفاوت معنی‌داری بین مدیران و نامدیران در موقعیتهای تصمیم‌گیری در شرایط عدم قطعیت وجود ندارد و بنابراین موازنه گستردگی/عمق را می‌توان به‌عنوان یک میان‌بر شناختی در موقعیتهای تصمیم‌گیری در شرایط عدم قطعیت نظر گرفت.
The present research aims to develop the theoretical framework necessary to apply cognitive-behavioral approaches to decision-making as one of the crucial functions of managers in organizations through creating empirical evidence in managerial decision-making patterns under conditions of uncertainty. According to the cognitive-behavioral approach in this study, a behavioral task of decision-making under uncertainty is used in the Breadth/Depth balance framework. Sixty-two people (33 women) in two groups of subjects, including people with professional management experience (29) and people without management experience (31 people) have undertaken the task. Modeling the data obtained from the behavioral test using the linear regression method showed that the modeling using power models describes the data better than the linear model in both groups. In addition, examining the managerial decision-making pattern in the conditions of uncertainty using the Wilcoxon statistical test did not show any significant difference between managers and non-managers. The results show no significant difference between managers and non-managers in the Breadth/Depth where both groups tend to full breadth in low-capacity conditions, and as the capacity increases, they transit to more depth. Therefore, the trade-off can be considered a cognitive heuristic, due to which people show close to optimal behavior. Further efforts to improve the quality of managerial decision-making, especially in the Breadth/Depth frameworks formulated with the balance of breadth/depth, should be focused on analysis and intervention in essential cognitive functions.
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Arend, Richard J. (2020): Strategic decision-making under ambiguity: a new problem space and a proposed optimization approach. In Business Research 13 (3), pp. 1231–1251.
Bach, Dominik R.; Dolan, Raymond J. (2012): Knowing how much you don't know. A neural organization of uncertainty estimates. In Nature reviews neuroscience 13 (8), pp. 572–586. DOI: 10.1038/nrn3289.
Bechara, Antoine; Damasio, Hanna; Tranel, Daniel; Damasio, Antonio R. (2005): The Iowa Gambling Task and the somatic marker hypothesis. Some questions and answers. In Trends in cognitive sciences 9 (4), pp. 159–162.
Bedenk, Stephan; Mieg, Harald A. (2018): Failure in innovation decision making. In: Strategies in Failure Management: Springer, pp. 95–106.
Birollo, Gustavo; Rouleau, Linda; Teerikangas, Satu Päivi (2018): Middle Managers’ Interactions at the Heart of the Strategy-Adaptation Process in Acquisitions. In Academy of Management Proceedings 2018 (1), p. 12954. DOI: 10.5465/AMBPP.2018.12954abstract.
Bland, Amy Rachel (2012): Different varieties of uncertainty in human decision-making. In Frontiers in neuroscience 6, p. 85.
Boulding, William; Moore, Marian Chapman; Staelin, Richard; Corfman, Kim P.; Dickson, Peter Reid; Fitzsimons, Gavan, et al. (1994): Understanding managers' strategic decision-making process. In Marketing Letters 5 (4), pp. 413–426.
Brand, Matthias; Labudda, Kirsten; Markowitsch, Hans J. (2006): Neuropsychological correlates of decision-making in ambiguous and risky situations. In Neural Networks 19 (8), pp. 1266–1276. DOI: 10.1016/j.neunet.2006.03.001.
Busemeyer, J. R., & Johnson, J. G. (2004). Computational models of decision making. Blackwell handbook of judgment and decision making, 133-154.
Caspers, Svenja; Heim, Stefan; Lucas, Marc G.; Stephan, Egon; Fischer, Lorenz; Amunts, Katrin; Zilles, Karl (2012): Dissociated neural processing for decisions in managers and non-managers. In PloS one.
Cert, Patrick Easen; Wilcockson, Jane (1996): Intuition and rational decision‐making in professional thinking: a false dichotomy? In Journal of advanced nursing 24 (4), pp. 667–673.
Checkland, Peter B. (1989): Soft systems methodology. In Human systems management 8 (4), pp. 273–289.
Cohen, Michael D.; March, James G.; Olsen, Johan P. (1972): A garbage can model of organizational choice. In Administrative science quarterly, pp. 1–25.
Damghani, K. Khalili; Taghavifard, M. T.; Moghaddam, R. Tavakkoli (Eds.) (2009): Decision making under uncertain and risky situations: Citeseer (15).
Di Caprio, Debora; Santos-Arteaga, Francisco J.; Tavana, Madjid (2014): The optimal sequential information acquisition structure: a rational utility-maximizing perspective. In Applied Mathematical Modelling 38 (14), pp. 3419–3435.
Dreu, Carsten K. W. de; Weingart, Laurie R. (2003): Task versus relationship conflict, team performance, and team member satisfaction: a meta-analysis. In Journal of Applied Psychology 88 (4), p. 741.
Dutton, Jane E.; Ashford, Susan J. (1993): Selling issues to top management. In Academy of Management Review 18 (3), pp. 397–428.
Dayan, P., & Abbott, L. F. (2005). Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT Press.
Einola, Suvi (2017): Making Sense of Strategic Decision Making. In: Real-time Strategy and Business Intelligence: Springer, pp. 149–166.
Eisenhardt, Kathleen M.; Zbaracki, Mark J. (1992): Strategic decision making. In Strategic management journal 13 (S2), pp. 17–37.
Farrar, Danielle C.; Mian, Asim Z.; Budson, Andrew E.; Moss, Mark B.; Killiany, Ronald J. (2018): Functional brain networks involved in decision-making under certain and uncertain conditions. In Neuroradiology 60 (1), pp. 61–69. DOI: 10.1007/s00234-017-1949-1.
FeldmanHall, Oriel; Glimcher, Paul; Baker, Augustus L.; Phelps, Elizabeth A. (2016): Emotion and decision-making under uncertainty: Physiological arousal predicts increased gambling during ambiguity but not risk. In Journal of Experimental Psychology: General 145 (10), p. 1255.
FeldmanHall, Oriel; Shenhav, Amitai (2019): Resolving uncertainty in a social world. In Nature human behavior 3 (5), pp. 426–435.
Friend, John Kimball; Hickling, Allen (2005): Planning under pressure: the strategic choice approach: Routledge.
Gigerenzer, Gerd (2004): Fast and frugal heuristics: The tools of bounded rationality. In Blackwell handbook of judgment and decision making 62, p. 88.
Gloy, K.; Herrmann, M.; Fehr, T. (2020): Decision making under uncertainty in a quasi-realistic binary decision task - An fMRI study. In Brain and cognition 140, p. 105549. DOI: 10.1016/j.bandc.2020.105549.
Gottlieb, Jacqueline; Oudeyer, Pierre-Yves (2018): Towards a neuroscience of active sampling and curiosity. In Nature reviews neuroscience 19 (12), pp. 758–770.
Greifeneder, Rainer; Scheibehenne, Benjamin; Kleber, Nina (2010): Less may be more when choosing is difficult: Choice complexity and too much choice. In Acta Psychologica 133 (1), pp. 45–50.
Guo, Yidi; Huy, Quy Nguyen; Xiao, Zhixing (2017): How middle managers manage the political environment to achieve market goals: Insights from C hina's state‐owned enterprises. In Strategic management journal 38 (3), pp. 676–696.
Haita-Falah, Corina (2017): Sunk-cost fallacy and cognitive ability in individual decision-making. In Journal of Economic Psychology 58, pp. 44–59.
Hambrick, Donald C.; Crossland, Craig (2018): A strategy for behavioral strategy: Appraisal of small, midsize, and large tent conceptions of this embryonic community. In: Behavioral strategy in perspective: Emerald Publishing Limited.
Hansen, Kathleen; Hillenbrand, Sarah; Ungerleider, Leslie (2012): Effects of prior knowledge on decisions made under perceptual vs. categorical uncertainty. In Frontiers in neuroscience 6, p. 163.
Hirsh, Jacob B.; Mar, Raymond A.; Peterson, Jordan B. (2012): Psychological entropy: a framework for understanding uncertainty-related anxiety. In Psychological Review 119 (2), p. 304.
Ivancevich, John M.; Matteson, Michael T.; Konopaske, Robert (1990): Organizational behavior and management.
Johnson, Johnnie E. V.; Powell, Philip L. (1994): Decision making, risk, and gender: Are managers different? In British journal of management 5 (2), pp. 123–138.
Jordan, Lizabeth; Thatchenkery, Tojo (2011): Leadership decision-making strategies using appreciative inquiry: A case study. In International Journal of Globalisation and Small Business 4 (2), pp. 178–190.
Kahneman, Daniel; Egan, Patrick (2011): Thinking, fast and slow: Farrar, Straus and Giroux New York.
Knight, Frank Hyneman (1921): Risk, uncertainty, and profit: Houghton Mifflin.
Krain, Amy L.; Wilson, Amanda M.; Arbuckle, Robert; Castellanos, F. Xavier; Milham, Michael P. (2006): Distinct neural mechanisms of risk and ambiguity. A meta-analysis of decision-making. In Neuroimage 32 (1), pp. 477–484.
Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature neuroscience, 21(9), 1148-1160.
Laureiro-Martinez, Daniella (2014): Cognitive control capabilities, routinization propensity, and decision-making performance. In Organization Science 25 (4), pp. 1111–1133.
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