An integrated academic library supply chain (IALSC) model

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-07-19 DOI:10.1108/jm2-04-2024-0097
A.K. Mahbubul Hye, Nurakmal Ahmad Ahmad, Md. Mamun Habib
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Abstract

Purpose

This exploratory study illustrated an integrated academic library supply chain (IALSC) model to design the strategic planning management tool of the academic library. The supply chain (SC) model has been widely used in manufacturing industries and has also been applied in many service industries with the same objectives. However, very few studies for academic libraries, particularly the implementation of the integrated SC model, are being executed, although it has been proven that SC management in practice can enhance stakeholder satisfaction, increase revenues and decrease total costs. The academic library also needs to be successful in providing quality products, services and information to fulfil the library users’ needs within the library budget. This research aims to develop a verified model of the integrated SC for the academic library.

Design/methodology/approach

This research used both qualitative and quantitative approaches to achieve its objectives. The proposed conceptual SC model, named as IALSC, for the academic library has been developed using the system thinking method; eventually, it has been validated through the fuzzy Delphi method, an expert judgement technique.

Findings

The research findings could contribute to academic library management in planning and formulating a roadmap for the library to increase its quality services for all stakeholders.

Originality/value

The conceptual model would have a high potential to be proposed as the strategic decision-making tool for an academic library, i.e. the flow of funds through the operations of the library, the library stakeholders’ satisfaction measurement, the decision process currently made by the library management team on the purchase of new library resources, the library resource suppliers, etc.

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综合学术图书馆供应链(IALSC)模式
目的 本探索性研究阐述了综合学术图书馆供应链(IALSC)模型,以设计学术图书馆的战略规划管理工具。供应链(SC)模型已被广泛应用于制造业,也被应用于许多具有相同目标的服务业。然而,尽管实践证明供应链管理可以提高利益相关者的满意度、增加收入和降低总成本,但针对学术图书馆的研究,尤其是综合供应链模式的实施研究,却寥寥无几。学术图书馆还需要在图书馆预算范围内成功提供优质产品、服务和信息,以满足图书馆用户的需求。本研究旨在为学术图书馆开发一个经过验证的综合 SC 模型。使用系统思维方法为学术图书馆开发了名为 IALSC 的拟议概念性 SC 模型;最后,通过模糊德尔菲法(一种专家判断技术)对其进行了验证。研究结果研究结果有助于学术图书馆管理层规划和制定图书馆路线图,以提高其为所有利益相关者提供的优质服务。原创性/价值该概念模型极有可能被提议作为学术图书馆的战略决策工具,即图书馆运营中的资金流、图书馆利益相关者满意度测量、图书馆管理团队目前就购买新图书馆资源所做的决策过程、图书馆资源供应商等。
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
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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