Bayesian network methodology and machine learning approach: an application on the impact of digital technologies on logistics service quality

IF 5.9 3区管理学 Q1 MANAGEMENT

International Journal of Physical Distribution & Logistics Management Pub Date : 2024-05-08 DOI:10.1108/ijpdlm-05-2023-0195

Behzad Maleki Vishkaei, Pietro De Giovanni

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引用次数: 0

Abstract

Purpose

This paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.

Design/methodology/approach

Using a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.

Findings

This study was funded by the European Union—NextGenerationEU, in the framework of the GRINS-Growing Resilient, INclusive and Sustainable project (GRINS PE00000018—CUP B43C22000760006). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them.

Originality/value

This research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.

查看原文本刊更多论文

贝叶斯网络方法和机器学习方法：数字技术对物流服务质量影响的应用

目的本文旨在利用贝叶斯网络（BN）方法，辅以机器学习（ML）和假设分析，采用服务质量（SERVQUAL）框架，研究数字技术（DT）对物流服务质量（LSQ）的影响。设计/方法/途径本研究以 244 家意大利企业为样本，估算了与 DT 和 SERVQUAL 物流相关的概率分布及其相互关系。此外，BN 技术还可以应用 ML 技术来揭示隐藏的关系，并通过一系列假设分析来提取更多知识。研究结果本研究由欧盟-下一代欧盟（European Union-NextGenerationEU）在 GRINS-成长的弹性、包容性和可持续性项目（GRINS PE00000018-CUP B43C22000760006）框架内资助。本研究深入探讨了 DTIE 和 DTA 对 SERVQUAL 物流的影响，从而填补了现有文献中没有研究探讨这些技术与 SERVQUAL 维度之间错综复杂关系的空白。在研究方法上，我们开创性地将 BN 与 ML 技术和假设分析相结合，从而探索出用于物流和供应链研究的创新技术。

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

International Journal of Physical Distribution & Logistics Management MANAGEMENT-

CiteScore

11.20

自引率

10.40%

发文量

期刊介绍： IJPDLM seeks strategically focused, theoretically grounded, empirical and conceptual, quantitative and qualitative, rigorous and relevant, original research studies in logistics, physical distribution and supply chain management operations and associated strategic issues. Quantitatively oriented mathematical and modelling research papers are not suitable for IJPDLM. Desired topics include, but are not limited to: Customer service strategy Omni-channel and multi-channel distribution innovations Order processing and inventory management Implementation of supply chain processes Information and communication technology Sourcing and procurement Risk management and security Personnel recruitment and training Sustainability and environmental Collaboration and integration Global supply chain management and network complexity Information and knowledge management Legal, financial and public policy Retailing, channels and business-to-business management Organizational and human resource development Logistics and SCM education.