组织困境:投资于效力还是效率?

IF 1.8 Q3 MANAGEMENT
Michael Dreyfuss, Gavriel David Pinto
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引用次数: 0

摘要

目的每家商业公司都会面临长期(LT)与短期(ST)投资多少的两难问题(LTvST 问题)。长期运营可提高公司声誉,并在未来获得收益。与此相反,ST 业务会带来即时回报。因此,每个组织都面临着投资 LT 与 ST 活动的两难选择。前者涉及 "做什么 "或有效性,后者涉及 "怎么做 "或效率。管理者的职责就是解决这个两难问题,但他们往往做不到这一点,主要原因是缺乏相关知识。本研究旨在提出一个动态优化控制模型,以提出并解决 LTvST 问题。本研究提出了一个动态优化控制模型,以提出并解决投资短期还是长期运营的两难问题。在学术界,对效益的投资转化为对研究的投资,而对效率的投资则转化为对教学的投资。拥有良好声誉的大学和学院能够吸引更多的候选人,并从更高的学费中获益。通过研究最优解和进行敏感性分析,获得了稳态条件和深刻见解。此外,本文还应用于以教学为短期活动、研究为长期活动的大学。从应用中获得的启示适用于许多不同领域。作者认为,这篇论文对学术文献和企业管理者都有重大贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The organization dilemma: investing in effectiveness versus efficiency?
Purpose Every business company deals with the dilemma of how much to invest in long-term (LT) versus short-term (ST) problem (LTvST problem). LT operations increase the reputation of the company, and revenue is rewarded in the future. In contrast, ST operations result in immediate rewards. Thus, every organization faces the dilemma of how much to invest in LT versus ST activities. The former deals with the “what” or effectiveness, and the latter deals with the “how” or efficiency. The role of managers is to solve this dilemma; however, they often fail to do so, mainly because of a lack of knowledge. This study aims to propose a dynamic optimal control model that formulates and solves the LTvST problem. Design/methodology/approach This study proposes a dynamic optimal control model that formulates and solves the dilemma whether to invest in short- or LT operations. Findings This model is illustrated as an example of an academic institute that wants to maximize its reputation. Investing in effectiveness in the academy translates into investing in research, whereas investing in efficiency translates into investing in teaching. Universities and colleges with a good reputation attract stronger candidates and benefit from higher tuition fees. Steady-state conditions and insightful observations were obtained by studying the optimal solution and performing a sensitivity analysis. Originality/value To the best of the authors’ knowledge, this paper is the first one to explore the optimal strategy when trying to maximize the short and LT activities of a company and solve the LTvST problem. Furthermore, it is applied on universities where teaching is the ST activity and research the LT activity. The insights gleaned from the application are relevant to many different fields. The authors believe that the paper makes a significant contribution to academic literature and to business managers.
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
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.
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