决策过程中的信息、情境线索和个人策略会发生什么变化?潜在决策特征在现实决策中的贡献

IF 1.5 Q3 MANAGEMENT
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引用次数: 0

摘要

摘要 对信息处理策略在确定个人决策态度方面所起作用的研究很少,而且一般来说,几乎都是通过自我报告的方法进行探讨。本研究通过一项基于真实决策情景的新任务和对实际决策行为的观察,探讨了在决策过程中如何使用不同的信息处理策略。研究考虑了信息处理的三个层次:(i) 与决策细节有关的低层次信息;(ii) 与任务和要实现的目标有关的中层次信息;(iii) 包括情境方面和背景特征在内的高层次信息。此外,还进行了一般决策风格问卷调查。通过层次聚类分析,分析出了三种新出现的特征,分别是关注低层次、中层 次和高层次的信息,这与在支持决策过程中收集和处理信息时注重细节、注重任务和注重情境 的方法有关。事实证明,这些新出现的特征在主要的一般决策风格方面也存在差异,这一发现证明了潜在分类的稳健性。这些发现使我们能够勾勒出一个模型,在这个模型中,不同的信息处理策略为确定决策者的不同特征提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What happens to information, situational cues, and individual strategies in decision-making? The contribution of latent decisional profiles in realistic decisions

Abstract

The role of information-processing strategies in defining individual attitudes toward decision-making has been little investigated and, in general, has almost always been explored via self-reported measures. This study investigates how different strategies for information processing are used to make decisions, via a new task based on realistic decision-making scenarios and observation of actual decisional behavior. Three levels of information processing are considered: (i) low-level information related to decision details; (ii) mid-level information concerning the task and the goals to be accomplished; (iii) high-level information including situational aspects and features of the context. General decision-making style questionnaire was also administered. Hierarchical cluster analysis parsed out three emerging profiles characterized by attention to low-level, mid-level, and high-level pieces of information, which was linked to detail-focused, task-oriented, and situation-aware approaches to gather and process information in supporting decision. Such emerging profiles also proved to differ in terms of primary general decision-making styles, a finding that depose in favor of the robustness of the latent classification. These findings allowed us to delineate a model in which different information-processing strategies provide the basis for identifying different profiles of decision-makers.

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来源期刊
Decision
Decision MANAGEMENT-
CiteScore
2.30
自引率
0.00%
发文量
27
期刊介绍: The aim of the Journal, Decision, is to publish qualitative, quantitative, survey-based, simulation-based research articles at the national and sub-national levels. While there is no stated regional focus of the journal, we are more interested in examining if and how individuals, firms and governments in emerging economies may make decisions differently. Published for the management scholars, business executives and managers, the Journal aims to advance the management research by publishing empirically and theoretically grounded articles in management decision making process. The Editors aim to provide an efficient and high-quality review process to the authors. The Journal accepts submissions in several formats such as original research papers, case studies, review articles and book reviews (book reviews are only by invitation). The Journal welcomes research-based, original and insightful articles on organizational, individual, socio-economic-political, environmental decision making with relevance to theory and practice of business. It also focusses on the managerial decision-making challenges in private, public, private-public partnership and non-profit organizations. The Journal also encourages case studies that provide a rich description of the business or societal contexts in managerial decision-making process including areas – but not limited to – conflict over natural resources, product innovation and copyright laws, legislative or policy change, socio-technical embedding of financial markets, particularly in developing economy, an ethnographic understanding of relations at a workplace, or social network in marketing management, etc. Research topics covered in the Journal include (but not limited to): Finance and Accounting Organizational Theory and Behavior Decision Science Public Policy-Economic Insights Operation Management Innovation and Entrepreneurship Information Technology and Systems Management Optimization and Modelling Supply Chain Management Data Analytics Marketing Management Human Resource Management
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