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How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?

Mehrbakhsh Nilashi, Abdullah M. Baabdullah, Rabab Ali Abumalloh, Keng-Boon Ooi, Garry Wei-Han Tan, Mihalis Giannakis, Yogesh Dwivedi

Annals of Operations Research

Swansea University Author: Yogesh Dwivedi

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DOI (Published version): 10.1007/s10479-023-05272-y

Abstract

Big data and predictive analytics (BDPA) techniques have been deployed in several areas of research to enhance individuals’ quality of living and business performance. The emergence of big data has made recycling and waste management easier and more efficient. The growth in worldwide food waste has...

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Published in: Annals of Operations Research
ISSN: 0254-5330 1572-9338
Published: Springer Science and Business Media LLC 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa62919
first_indexed 2023-03-12T22:39:51Z
last_indexed 2024-11-29T19:36:41Z
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spelling 2024-11-29T15:33:48.5728126 v2 62919 2023-03-12 How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry? d154596e71b99ad1285563c8fdd373d7 Yogesh Dwivedi Yogesh Dwivedi true false 2023-03-12 Big data and predictive analytics (BDPA) techniques have been deployed in several areas of research to enhance individuals’ quality of living and business performance. The emergence of big data has made recycling and waste management easier and more efficient. The growth in worldwide food waste has led to vital economic, social, and environmental effects, and has gained the interest of researchers. Although previous studies have explored the influence of big data on industrial performance, this issue has not been explored in the context of recycling and waste management in the food industry. In addition, no studies have explored the influence of BDPA on the performance and competitive advantage of the food waste and the recycling industry. Specifically, the impact of big data on environmental and economic performance has received little attention. This research develops a new model based on the resource-based view, technology-organization-environment, and human organization technology theories to address the gap in this research area. Partial least squares structural equation modeling is used to analyze the data. The findings reveal that both the human factor, represented by employee knowledge, and environmental factor, represented by competitive pressure, are essential drivers for evaluating the BDPA adoption by waste and recycling organizations. In addition, the impact of BDPA adoption on competitive advantage, environmental performance, and economic performance are significant. The results indicate that BDPA capability enhances an organization’s competitive advantage by enhancing its environmental and economic performance. This study presents decision-makers with important insights into the imperative factors that influence the competitive advantage of food waste and recycling organizations within the market. Journal Article Annals of Operations Research 0 Springer Science and Business Media LLC 0254-5330 1572-9338 Waste and recycling industry, Business performance, Big data and predictive analytics, Decision making, Competitive advantage 25 3 2023 2023-03-25 10.1007/s10479-023-05272-y COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University. 2024-11-29T15:33:48.5728126 2023-03-12T22:34:14.5119520 Faculty of Humanities and Social Sciences School of Management - Business Management Mehrbakhsh Nilashi 1 Abdullah M. Baabdullah 2 Rabab Ali Abumalloh 3 Keng-Boon Ooi 4 Garry Wei-Han Tan 5 Mihalis Giannakis 6 Yogesh Dwivedi 7 62919__27072__f9cba21823a4497d824523f21dca0195.pdf 62919.pdf 2023-04-17T14:13:18.0710138 Output 1550298 application/pdf Version of Record true © The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/
title How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
spellingShingle How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
Yogesh Dwivedi
title_short How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
title_full How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
title_fullStr How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
title_full_unstemmed How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
title_sort How can big data and predictive analytics impact the performance and competitive advantage of the food waste and recycling industry?
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Mehrbakhsh Nilashi
Abdullah M. Baabdullah
Rabab Ali Abumalloh
Keng-Boon Ooi
Garry Wei-Han Tan
Mihalis Giannakis
Yogesh Dwivedi
format Journal article
container_title Annals of Operations Research
container_volume 0
publishDate 2023
institution Swansea University
issn 0254-5330
1572-9338
doi_str_mv 10.1007/s10479-023-05272-y
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
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description Big data and predictive analytics (BDPA) techniques have been deployed in several areas of research to enhance individuals’ quality of living and business performance. The emergence of big data has made recycling and waste management easier and more efficient. The growth in worldwide food waste has led to vital economic, social, and environmental effects, and has gained the interest of researchers. Although previous studies have explored the influence of big data on industrial performance, this issue has not been explored in the context of recycling and waste management in the food industry. In addition, no studies have explored the influence of BDPA on the performance and competitive advantage of the food waste and the recycling industry. Specifically, the impact of big data on environmental and economic performance has received little attention. This research develops a new model based on the resource-based view, technology-organization-environment, and human organization technology theories to address the gap in this research area. Partial least squares structural equation modeling is used to analyze the data. The findings reveal that both the human factor, represented by employee knowledge, and environmental factor, represented by competitive pressure, are essential drivers for evaluating the BDPA adoption by waste and recycling organizations. In addition, the impact of BDPA adoption on competitive advantage, environmental performance, and economic performance are significant. The results indicate that BDPA capability enhances an organization’s competitive advantage by enhancing its environmental and economic performance. This study presents decision-makers with important insights into the imperative factors that influence the competitive advantage of food waste and recycling organizations within the market.
published_date 2023-03-25T20:20:38Z
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