Pub Date : 2023-12-20DOI: 10.1109/EMR.2023.3335694
Soila Lemmetty;Alexander Brem
Digital transformation, artificial intelligence, crises, and societal challenges are playing an increasing role in creating opportunities but also pressures for innovation. The digital transformation and the accompanying development of artificial intelligence have fundamentally changed how our society works—and this process is not at the end, but at the beginning (Dabrowska et al., 2022). They have enabled information sharing, automation, and the exploitation of a wider knowledge base while creating new business models. At the same time, a series of recent crises, such as pandemics, wars, and environmental problems, have made visible how major disruptions and problems in the functioning of societies and work organizations can occur unexpectedly, forcing us to innovate new solutions quickly and flexibly (Viardot et al., 2023).
数字化转型、人工智能、危机和社会挑战正在发挥越来越大的作用，为创新创造机遇，同时也带来压力。数字化转型和与之相伴的人工智能发展从根本上改变了我们的社会运行方式--这一进程不是结束，而是开始（Dabrowska et al.）它们实现了信息共享、自动化和对更广泛知识库的利用，同时创造了新的商业模式。与此同时，最近发生的一系列危机，如流行病、战争和环境问题，让我们看到了社会和工作组织运作中的重大干扰和问题是如何在意想不到的情况下发生的，迫使我们快速灵活地创新新的解决方案（Viardot 等人，2023 年）。
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Pub Date : 2023-12-20DOI: 10.1109/EMR.2023.3316563
{"title":"Call for Papers: Special Issue on Technology Management in Smart Cities Infrastructure and Applications","authors":"","doi":"10.1109/EMR.2023.3316563","DOIUrl":"https://doi.org/10.1109/EMR.2023.3316563","url":null,"abstract":"","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"51 4","pages":"225-225"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10366447","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-20DOI: 10.1109/EMR.2023.3316555
{"title":"Some People Were Born to Lead","authors":"","doi":"10.1109/EMR.2023.3316555","DOIUrl":"https://doi.org/10.1109/EMR.2023.3316555","url":null,"abstract":"","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"51 4","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10366451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-20DOI: 10.1109/EMR.2023.3342118
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/EMR.2023.3342118","DOIUrl":"https://doi.org/10.1109/EMR.2023.3342118","url":null,"abstract":"","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"51 4","pages":"226-226"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10366450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-20DOI: 10.1109/EMR.2023.3342200
Peter E. D. Love;Jane Matthews;Weili Fang;Stuart Porter;Hanbin Luo;Lieyun Ding
Explainable artificial intelligence (XAI) is a burgeoning concept. It is gaining prominence as an approach to better understand how artificial intelligence solutions' outputs can improve decision making. Evaluation frameworks to enable organizations to understand XAIs what, why, how, and when are yet to be developed. Thus, we aim to fill this void by developing a conceptual content , context , process, and outcome (CCPO) evaluation framework to justify XAIs adoption and effective management using construction organizations as a backdrop for the article's setting. After introducing and describing the proposed novel CCPO framework for operationalizing XAI, we discuss its implications for future research. The contributions of our article are twofold: First, it highlights the need for organizations to embrace and enact XAI so that decision makers and stakeholders can better understand why and how a specific prediction materializes; and second, it provides a frame of reference for organizations to realize the business value and benefits of XAI.
{"title":"Learning to Comprehend and Trust Artificial Intelligence Outcomes: A Conceptual Explainable AI Evaluation Framework","authors":"Peter E. D. Love;Jane Matthews;Weili Fang;Stuart Porter;Hanbin Luo;Lieyun Ding","doi":"10.1109/EMR.2023.3342200","DOIUrl":"https://doi.org/10.1109/EMR.2023.3342200","url":null,"abstract":"Explainable artificial intelligence (XAI) is a burgeoning concept. It is gaining prominence as an approach to better understand how artificial intelligence solutions' outputs can improve decision making. Evaluation frameworks to enable organizations to understand XAIs what, why, how, and when are yet to be developed. Thus, we aim to fill this void by developing a conceptual \u0000<italic>content</i>\u0000, \u0000<italic>context</i>\u0000, \u0000<italic>process,</i>\u0000 and \u0000<italic>outcome</i>\u0000 (CCPO) evaluation framework to justify XAIs adoption and effective management using construction organizations as a backdrop for the article's setting. After introducing and describing the proposed novel CCPO framework for operationalizing XAI, we discuss its implications for future research. The contributions of our article are twofold: First, it highlights the need for organizations to embrace and enact XAI so that decision makers and stakeholders can better understand \u0000<italic>why</i>\u0000 and \u0000<italic>how</i>\u0000 a specific prediction materializes; and second, it provides a frame of reference for organizations to realize the business value and benefits of XAI.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"230-247"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.1109/EMR.2023.3338444
Juan Carlos Andrango Vicuña;Asghar Afshar Jahanshahi
The frequent interaction between customers and firms generates tie strength (TS) among them, which may be impaired due to service failures, as organizations are not exempt from such scenarios. With using survey-based data from 348 Ecuadorian costumers with service failure experience, this research aims to demonstrate the moderating effect of TS (high versus low) on the relationship between perceived justice dimensions and complaint and cumulative satisfaction (CUS), thus extending our understanding of justice theory and relationship marketing. The results reveal that when the customer's TS to the firm is strong (versus weak), the moderating effect influences the relationship between distributive justice perception and CUS. Additionally, under the influence of strong ties, the relationship between both procedural and interactional justice and complaint satisfaction is strengthened. On the contrary, low levels of ties affect the evaluations of the recovery processes and procedures, as well as the handling of complaints regarding time and speed implemented by firms, without achieving an impact on customer behavior. These findings have important implications, as they highlight the need for managers to consider the type of relationship with the existing customers when designing and implementing recovery strategies.
{"title":"Understanding the Impact of Tie Strength on Customer's Perceived Justice and Satisfaction: Insights From Service Failures in Customer–Firm Interactions","authors":"Juan Carlos Andrango Vicuña;Asghar Afshar Jahanshahi","doi":"10.1109/EMR.2023.3338444","DOIUrl":"https://doi.org/10.1109/EMR.2023.3338444","url":null,"abstract":"The frequent interaction between customers and firms generates tie strength (TS) among them, which may be impaired due to service failures, as organizations are not exempt from such scenarios. With using survey-based data from 348 Ecuadorian costumers with service failure experience, this research aims to demonstrate the moderating effect of TS (high versus low) on the relationship between perceived justice dimensions and complaint and cumulative satisfaction (CUS), thus extending our understanding of justice theory and relationship marketing. The results reveal that when the customer's TS to the firm is strong (versus weak), the moderating effect influences the relationship between distributive justice perception and CUS. Additionally, under the influence of strong ties, the relationship between both procedural and interactional justice and complaint satisfaction is strengthened. On the contrary, low levels of ties affect the evaluations of the recovery processes and procedures, as well as the handling of complaints regarding time and speed implemented by firms, without achieving an impact on customer behavior. These findings have important implications, as they highlight the need for managers to consider the type of relationship with the existing customers when designing and implementing recovery strategies.","PeriodicalId":35585,"journal":{"name":"IEEE Engineering Management Review","volume":"52 1","pages":"212-229"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140619477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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