A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Mohammad Dana Lagzi; Seyed Mojtaba Sajadi; Mohammadreza Taghizadeh-Yazdi

doi:10.1016/j.jretconser.2024.103908

A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Mohammad Dana Lagzi
, Seyed Mojtaba Sajadi^*
, Mohammadreza Taghizadeh-Yazdi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

13 Downloads (Pure)

Abstract

Assessing the retail industry's efficiency is pivotal for economic growth and corporate productivity. This study employs a novel approach, utilizing a regression-based Stochastic Data Envelopment Analysis (SDEA) model, Balanced Scorecard (BSC), and Decision Tree. The integration of these methods is a pioneering effort in the retail sector. This is a data-driven decision-making framework, aiding managers in predicting efficient and inefficient Decision-Making Units (DMUs). Results from a case study in 44 retail store chains in Iran indicate that the accuracy of the SDEA model is 99%. The Decision Tree highlights low branch efficiency due to a low customer count, a unique finding in comparison to prior studies.

Original language	English
Article number	103908
Number of pages	12
Journal	Journal of Retailing and Consumer Services
Volume	80
Early online date	23 May 2024
DOIs	https://doi.org/10.1016/j.jretconser.2024.103908
Publication status	Published - Sept 2024

Bibliographical note

Data Access Statement

Data will be made available on request.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Keywords

Stochastic data envelopment analysis
Performance measurement
Balanced scorecard
Decision tree
Retail industry
Chain stores

Access to Document

10.1016/j.jretconser.2024.103908Licence: CC BY 4.0

MD Lagzi et al_Hybrid stochastic data envelopment analysis and decision tree for retail performance prediction
Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).
Final published version, 2.46 MBLicence: CC BY 4.0

Cite this

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title = "A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry",

abstract = "Assessing the retail industry's efficiency is pivotal for economic growth and corporate productivity. This study employs a novel approach, utilizing a regression-based Stochastic Data Envelopment Analysis (SDEA) model, Balanced Scorecard (BSC), and Decision Tree. The integration of these methods is a pioneering effort in the retail sector. This is a data-driven decision-making framework, aiding managers in predicting efficient and inefficient Decision-Making Units (DMUs). Results from a case study in 44 retail store chains in Iran indicate that the accuracy of the SDEA model is 99\%. The Decision Tree highlights low branch efficiency due to a low customer count, a unique finding in comparison to prior studies.",

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KW - Stochastic data envelopment analysis

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KW - Balanced scorecard

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A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Abstract

Bibliographical note

Data Access Statement

UN SDGs

Keywords

Access to Document

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