A bibliometric and visualization analysis of intertemporal choice: origins, growth and future research avenues

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-04-05 DOI:10.1108/jm2-07-2023-0157

Maneesha Singh, Tanuj Nandan

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引用次数: 0

Abstract

Purpose

This study aims to conduct a bibliometric analysis on “intertemporal choice” behavior of individuals from journals in the Scopus database between 1957 and 2023. The research covered the data on the said topic since it first originated in the Scopus database and carried out performance analysis and content analysis of papers in the business management and finance disciplines.

Design/methodology/approach

Bibliometric analysis, including science mapping and performance analysis, followed by content analysis of the papers of identified clusters, was conducted. Three clusters based on cocitation analysis and six themes (three major and three minor) were identified using the bibliometrix package in R studio. The content analysis of the papers in these clusters and themes have been discussed in this study, along with the thematic evolution of intertemporal choice research over the period of time, paving a way for future research studies.

Findings

The review unpacks publication and citation trends of intertemporal choice behavior, the most significant authors, journals and papers along with the major clusters and themes of research based on cocitation and degree of centrality and relevance, respectively, i.e. discounting experiments and intertemporal choice, impulsivity, risk preference, time-inconsistent preference, etc.

Originality/value

Over the past years, the research on “intertemporal choice” has flourished because of the increasing interest of researchers and scholars from different fields and the dynamic and pervasive nature of this topic. The well-developed and scattered body of knowledge on intertemporal choice has led to the need of applying a bibliometric analysis in the intertemporal choice literature.

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时际选择的文献计量和可视化分析：起源、发展和未来研究途径

目的本研究旨在对 Scopus 数据库中 1957 年至 2023 年间期刊中的个人 "跨期选择 "行为进行文献计量分析。研究涵盖了上述主题自首次出现在 Scopus 数据库以来的数据，并对商业管理和金融学科的论文进行了绩效分析和内容分析。设计/方法/途径进行了文献计量分析，包括科学图谱和绩效分析，然后对确定的聚类论文进行了内容分析。使用 R studio 中的 bibliometrix 软件包确定了基于同源分析的三个集群和六个主题（三个主要主题和三个次要主题）。本研究讨论了这些集群和主题中论文的内容分析，以及一段时间内跨时空选择研究的主题演变，为未来的研究铺平了道路。研究结果综述解读了跨时空选择行为的出版和引用趋势，最重要的作者、期刊和论文，以及分别基于共生和中心度及相关性的主要研究集群和主题，即贴现实验和跨时空选择。原创性/价值在过去的几年中，由于来自不同领域的研究人员和学者对 "跨期选择 "的兴趣日益浓厚，以及该主题的动态性和普遍性，"跨期选择 "研究蓬勃发展。有关跨期选择的知识体系发展完善且分散，因此有必要对跨期选择文献进行文献计量分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.