改进工作场所决策的认知系统:废物管理领域的探索性研究

IF 4.1 3区 管理学 Q2 BUSINESS
Paolo Esposito, Gianluca Antonucci, Gabriele Palozzi, Justyna Fijałkowska
{"title":"改进工作场所决策的认知系统:废物管理领域的探索性研究","authors":"Paolo Esposito, Gianluca Antonucci, Gabriele Palozzi, Justyna Fijałkowska","doi":"10.1108/md-08-2023-1320","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.</p><!--/ Abstract__block -->\n<h3>Research limitations/implications</h3>\n<p>Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.</p><!--/ Abstract__block -->","PeriodicalId":18046,"journal":{"name":"Management Decision","volume":null,"pages":null},"PeriodicalIF":4.1000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cognitive systems for improving decision-making in the workplace: an explorative study within the waste management field\",\"authors\":\"Paolo Esposito, Gianluca Antonucci, Gabriele Palozzi, Justyna Fijałkowska\",\"doi\":\"10.1108/md-08-2023-1320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.</p><!--/ Abstract__block -->\\n<h3>Research limitations/implications</h3>\\n<p>Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.</p><!--/ Abstract__block -->\\n<h3>Practical implications</h3>\\n<p>The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.</p><!--/ Abstract__block -->\",\"PeriodicalId\":18046,\"journal\":{\"name\":\"Management Decision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Management Decision\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1108/md-08-2023-1320\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Management Decision","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/md-08-2023-1320","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0

摘要

目的 人工智能(AI)有助于在复杂情况下制定预防性决策策略。本文旨在研究工人如何应对人工智能工具的干预,从而改善他们的日常工作决策和行动。我们致力于深化工人如何应对人工智能干预工具,以改善他们的日常工作决策和行动。我们在一个因环境可持续性问题而日益重要的领域,即废物管理(WM),对这些方面进行了研究。本手稿旨在:(1)研究人工智能是否能通过降低社会保障成本和保证更好的服务连续性来提高 WM 的绩效;(2)研究在实际工作环境中实施这一预测性风险模型需要哪些结构性变化。为了实现这些目标,本研究在与高水平专家进行面对面访谈的基础上,开展了一项定性调查。具体来说,人工智能可能是使运营行为与企业战略目标保持一致的工具。通过可穿戴数字基础设施获得的信息经过适当阐述后,可以做出精简工作组织的决策,减少因浪费时间和/或物质资源而造成的潜在损失。尽管如此,受访者的声望使本研究成为一个有趣的试点,涉及人工智能这样一个新兴主题,从而从受访者的知识、技能和经验中获取更多信息,实施有价值的人工智能计划,使运营行为与业务战略目标保持一致。实践意义最关键的问题是在商业环境中向员工提供反馈的 "质量",特别是在组织内部进行知识转移时。 原创性/价值这项研究关注的是一个较少被调查的背景,即人工智能在内部决策中的作用,特别是管理人员与员工之间以及员工与员工之间的互动。受算法管理的员工可以被视为复杂算法分析结果的参与者,这些分析结果通过简单的界面提供给他们,他们可以很容易地利用这些结果做出正确的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive systems for improving decision-making in the workplace: an explorative study within the waste management field

Purpose

Artificial intelligence (AI) can help in defining preventive strategies in taking decisions in complex situations. This paper aims to research how workers might deal with intervening AI tools, with the goal of improving their daily working decisions and movements. We contribute to deepening how workers might deal with intervening AI tools aiming at improving their daily working decisions and movements. We investigate these aspects within a field, which is growing in importance due to environmental sustainability issues, i.e. waste management (WM).

Design/methodology/approach

This manuscript intends to (1) investigate if AI allows better performance in WM by reducing social security costs and by guaranteeing a better continuity of service and (2) examine which structural change is required to operationalize this predictive risk model in the real working context. To achieve these goals, this study developed a qualitative inquiry based on face-to-face interviews with highly qualified experts.

Findings

There is a positive impact of AI schemes in helping to detect critical operating issues. Specifically, AI potentially represents a tool for an alignment of operational behaviours to business strategic goals. Properly elaborated information, obtained through wearable digital infrastructures, allows to take decisions to streamline the work organization, reducing potential loss due to waste of time and/or physical resources.

Research limitations/implications

Being a qualitative study, and the limited extension of data, it is not possible to guarantee its replication and generalizability. Nevertheless, the prestige of the interviewees makes this research an interesting pilot, on such an emerging theme as AI, thus eliciting stimulating insights from a deepening of information coming from respondents’ knowledge, skills and experience for implementing valuable AI schemes able to an align operational behaviours to business strategic goals.

Practical implications

The most critical issue is represented by the “quality” of the feedback provided to employees within the business environment, specifically when there is a transfer of knowledge within the organization.

Originality/value

The study focuses on a less investigated context, the role of AI in internal decision-making, particularly, for what regards the interaction between managers and workers as well as the one among workers. Algorithmically managed workers can be seen as the players of summarized results of complex algorithmic analyses offered through simpleminded interfaces, which they can easily use to take good decisions.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.20
自引率
8.70%
发文量
126
期刊介绍: ■In-depth studies of major issues ■Operations management ■Financial management ■Motivation ■Entrepreneurship ■Problem solving and proactivity ■Serious management argument ■Strategy and policy issues ■Tactics for turning around company crises Management Decision, considered by many to be the best publication in its field, consistently offers thoughtful and provocative insights into current management practice. As such, its high calibre contributions from leading management philosophers and practitioners make it an invaluable resource in the aggressive and demanding trading climate of the Twenty-First Century.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信