Pub Date : 2024-01-08DOI: 10.1080/12460125.2023.2298617
V. Seymour, M. Xenitidou, L. Timotijevic, C. E. Hodgkins, E. Ratcliffe, B. Gatersleben, N. Gilbert, C. R. Jones
{"title":"Public acceptance of smart home technologies in the UK: a citizens’ jury study","authors":"V. Seymour, M. Xenitidou, L. Timotijevic, C. E. Hodgkins, E. Ratcliffe, B. Gatersleben, N. Gilbert, C. R. Jones","doi":"10.1080/12460125.2023.2298617","DOIUrl":"https://doi.org/10.1080/12460125.2023.2298617","url":null,"abstract":"","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"7 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445346","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 : 2023-12-26DOI: 10.1080/12460125.2023.2294398
Amit Sood, Amit Kumar Bhardwaj, Rajendra Kumar Sharma
{"title":"Perceptions of facilitators towards adoption of AI-based solutions for sustainable agriculture","authors":"Amit Sood, Amit Kumar Bhardwaj, Rajendra Kumar Sharma","doi":"10.1080/12460125.2023.2294398","DOIUrl":"https://doi.org/10.1080/12460125.2023.2294398","url":null,"abstract":"","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"18 3","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139155413","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 : 2023-11-22DOI: 10.1080/12460125.2023.2246251
Thanachart Ritbumroong
{"title":"I am therefore, I do: a fit perspective of decision-making styles and business intelligence usage","authors":"Thanachart Ritbumroong","doi":"10.1080/12460125.2023.2246251","DOIUrl":"https://doi.org/10.1080/12460125.2023.2246251","url":null,"abstract":"","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"196 ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139250275","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 : 2023-10-10DOI: 10.1080/12460125.2023.2263687
Femi Olan, Richard B. Nyuur, Emmanuel Ogiemwonyi Arakpogun, Ziad Elsahn
The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.
{"title":"AI: A knowledge sharing tool for improving employees’ performance","authors":"Femi Olan, Richard B. Nyuur, Emmanuel Ogiemwonyi Arakpogun, Ziad Elsahn","doi":"10.1080/12460125.2023.2263687","DOIUrl":"https://doi.org/10.1080/12460125.2023.2263687","url":null,"abstract":"The utilisation of artificial intelligence (AI) is progressively emerging as a significant mechanism for innovation in human resource management (HRM). The capacity to facilitate the transformation of employee performance across numerous responsibilities. AI development, there remains a dearth of comprehensive exploration into the potential opportunities it presents for enhancing workplace performance among employees. To bridge this gap in knowledge, the present work carried out a survey with 300 participants, utilises a fuzzy set-theoretic method that is grounded on the conceptualisation of AI, KS, and HRM. The findings of our study indicate that the exclusive adoption of AI technologies does not adequately enhance HRM engagements. In contrast, the integration of AI and KS offers a more viable HRM approach for achieving optimal performance in a dynamic digital society. This approach has the potential to enhance employees’ proficiency in executing their responsibilities and cultivate a culture of creativity inside the firm.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136358849","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 : 2023-10-06DOI: 10.1080/12460125.2023.2263676
Vimlesh Kumar Ojha, Sanjeev Goyal, Mahesh Chand
ABSTRACTData-driven decision making (DDDM) in advanced manufacturing systems (AMS) is the use of data to make smart decisions that improve manufacturing operations. Companies can make themselves more competitive, cut costs, and improve their production by using data analytics. The investigation of critical success factors aids companies in identifying vital areas that demand attention for the implementation of DDDM in AMS. This comprehension enables companies to devise effective strategies for the successful adoption of DDDM within AMS. In this research, twelve critical success factors that affect the use of DDDM in AMS were discovered and statistically analysed using an integrated methodology of ISM, MICMAC, and DEMATEL to create a hierarchical model. This research paper suggests that companies should focus on developing a skilled workforce and creating a data-driven culture to successfully adopt DDDM in AMS. Additionally, the findings highlight the importance of top management support and government initiatives in promoting the adoption of DDDM in manufacturing.KEYWORDS: Advanced manufacturing Systemscritical success factors (CSFs)DDDMadoptionbig data (BD)ISM-DEMATEL Article highlight Produces a roadmap for the implementation of DDDM in AMS.Exploring the key drivers that enable the effective implementation of DDDM in AMS through the identification of critical success factors (CSFs).Analysing the CSFs and modelling them on the basis of their prominence using an integrated ISM-MICMAC-DEMATEL methodology.Abbreviations DDDM=Data-driven decision makingAMS=Advanced Manufacturing SystemsCSFs=Critical Success FactorsBDA=Big data analyticsDT=Digital transformationLR=Literature reviewIoT=Internet of ThingsCPS=Cyber-physical systemsSME=Small & medium-sized4IR=Fourth industrial revolution or Industry 4.0SM=Smart ManufacturingISM=Interpretive structural modellingAcknowledgmentsIndustry professionals from India’s manufacturing sector were a huge help to the authors in identifying and comparing factors and validating findings, and the authors are grateful for their assistance.Disclosure statementIt should be noted that the research discussed in this publication was not influenced by any financial or personal conflicts of interest of the authors.Data availability statementAll data generated or analysed during this research are included in this article.
{"title":"Data-driven decision making in advanced manufacturing Systems: modeling and analysis of critical success factors","authors":"Vimlesh Kumar Ojha, Sanjeev Goyal, Mahesh Chand","doi":"10.1080/12460125.2023.2263676","DOIUrl":"https://doi.org/10.1080/12460125.2023.2263676","url":null,"abstract":"ABSTRACTData-driven decision making (DDDM) in advanced manufacturing systems (AMS) is the use of data to make smart decisions that improve manufacturing operations. Companies can make themselves more competitive, cut costs, and improve their production by using data analytics. The investigation of critical success factors aids companies in identifying vital areas that demand attention for the implementation of DDDM in AMS. This comprehension enables companies to devise effective strategies for the successful adoption of DDDM within AMS. In this research, twelve critical success factors that affect the use of DDDM in AMS were discovered and statistically analysed using an integrated methodology of ISM, MICMAC, and DEMATEL to create a hierarchical model. This research paper suggests that companies should focus on developing a skilled workforce and creating a data-driven culture to successfully adopt DDDM in AMS. Additionally, the findings highlight the importance of top management support and government initiatives in promoting the adoption of DDDM in manufacturing.KEYWORDS: Advanced manufacturing Systemscritical success factors (CSFs)DDDMadoptionbig data (BD)ISM-DEMATEL Article highlight Produces a roadmap for the implementation of DDDM in AMS.Exploring the key drivers that enable the effective implementation of DDDM in AMS through the identification of critical success factors (CSFs).Analysing the CSFs and modelling them on the basis of their prominence using an integrated ISM-MICMAC-DEMATEL methodology.Abbreviations DDDM=Data-driven decision makingAMS=Advanced Manufacturing SystemsCSFs=Critical Success FactorsBDA=Big data analyticsDT=Digital transformationLR=Literature reviewIoT=Internet of ThingsCPS=Cyber-physical systemsSME=Small & medium-sized4IR=Fourth industrial revolution or Industry 4.0SM=Smart ManufacturingISM=Interpretive structural modellingAcknowledgmentsIndustry professionals from India’s manufacturing sector were a huge help to the authors in identifying and comparing factors and validating findings, and the authors are grateful for their assistance.Disclosure statementIt should be noted that the research discussed in this publication was not influenced by any financial or personal conflicts of interest of the authors.Data availability statementAll data generated or analysed during this research are included in this article.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351391","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 : 2023-10-04DOI: 10.1080/12460125.2023.2263677
Tamara Ćurlin, Božidar Jaković, Zoran Wittine
ABSTRACTDecision support methods are used widely in different industries to advance decision-making and strategy development with the purpose of achieving company’s goals. In the hotel industry, decision support strategies became a tool for gaining competitive advantage through personalisation and providing curated offers to potential guests. This paper aims to explore state of the art on investigations and establish relevance and key focuses within the topic. In order to achieve paper goals a combination of methods and analytic tools were utilised, such as bibliometric analysis, citation analysis and cluster analysis. Results which emerged from the analysis point out that information technology is the most critical perspective of the topic. Decision support in the hotel industry’s further development is dependent on technological advancement. Future investigations could concentrate on providing more profound knowledge on the individual focus areas, and expand the investigation, for instance, on the tourism sector or hospitality industry.KEYWORDS: Decision supporthotel industryonline reviewsbibliometric analysis Disclosure statementNo potential conflict of interest was reported by the author(s).
{"title":"The perspectives of decision support in hotel industry: systematic review and bibliometric analysis","authors":"Tamara Ćurlin, Božidar Jaković, Zoran Wittine","doi":"10.1080/12460125.2023.2263677","DOIUrl":"https://doi.org/10.1080/12460125.2023.2263677","url":null,"abstract":"ABSTRACTDecision support methods are used widely in different industries to advance decision-making and strategy development with the purpose of achieving company’s goals. In the hotel industry, decision support strategies became a tool for gaining competitive advantage through personalisation and providing curated offers to potential guests. This paper aims to explore state of the art on investigations and establish relevance and key focuses within the topic. In order to achieve paper goals a combination of methods and analytic tools were utilised, such as bibliometric analysis, citation analysis and cluster analysis. Results which emerged from the analysis point out that information technology is the most critical perspective of the topic. Decision support in the hotel industry’s further development is dependent on technological advancement. Future investigations could concentrate on providing more profound knowledge on the individual focus areas, and expand the investigation, for instance, on the tourism sector or hospitality industry.KEYWORDS: Decision supporthotel industryonline reviewsbibliometric analysis Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135592136","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 : 2023-10-04DOI: 10.1080/12460125.2023.2260518
Marvin Braun, Maike Greve, Alfred Benedikt Brendel, Lutz M. Kolbe
ABSTRACTArtificial Intelligence (AI) fundamentally changes the way we work by introducing new capabilities. Human tasks shift towards a supervising role where the human confirms or disconfirms the presented decision. In this study, we utilise the signal detection theory to investigate and explain how the performance of human error detection is influenced by specific information design. We conducted two online experiments in the context of AI-supported information extraction and measured the ability of participants to validate the extracted information. In the first experiment, we investigated the mechanism of information provided prior to conducting the error detection task. In the second experiment, we manipulated the design of the presented information during the task and investigated its effect. Both manipulations significantly impacted the error detection performance of humans. Hence our study provides important insights for developing AI-based decision support systems and contributes to the theoretical understanding of human-AI collaboration.KEYWORDS: Supervisionartificial intelligenceerror detectiondecision makingsignal detection theory AcknowledgmentsWe acknowledge that a previous version of study 2 has received valuable feedback on the European Conference on Information Systems 2022.Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics statementThe present research constitutes a non-interventional study, specifically focused on surveys and data analysis, wherein no direct intervention, manipulation, or experimentation on human participants is involved. As a result, this study falls under the category where ethical approval is not required.
{"title":"Humans supervising Artificial intelligence – Investigation of Designs to optimize error detection","authors":"Marvin Braun, Maike Greve, Alfred Benedikt Brendel, Lutz M. Kolbe","doi":"10.1080/12460125.2023.2260518","DOIUrl":"https://doi.org/10.1080/12460125.2023.2260518","url":null,"abstract":"ABSTRACTArtificial Intelligence (AI) fundamentally changes the way we work by introducing new capabilities. Human tasks shift towards a supervising role where the human confirms or disconfirms the presented decision. In this study, we utilise the signal detection theory to investigate and explain how the performance of human error detection is influenced by specific information design. We conducted two online experiments in the context of AI-supported information extraction and measured the ability of participants to validate the extracted information. In the first experiment, we investigated the mechanism of information provided prior to conducting the error detection task. In the second experiment, we manipulated the design of the presented information during the task and investigated its effect. Both manipulations significantly impacted the error detection performance of humans. Hence our study provides important insights for developing AI-based decision support systems and contributes to the theoretical understanding of human-AI collaboration.KEYWORDS: Supervisionartificial intelligenceerror detectiondecision makingsignal detection theory AcknowledgmentsWe acknowledge that a previous version of study 2 has received valuable feedback on the European Conference on Information Systems 2022.Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics statementThe present research constitutes a non-interventional study, specifically focused on surveys and data analysis, wherein no direct intervention, manipulation, or experimentation on human participants is involved. As a result, this study falls under the category where ethical approval is not required.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135591440","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 : 2023-09-30DOI: 10.1080/12460125.2023.2263683
Natalia Vieira Dantas, Ana Paula Henriques de Gusmão, Marcella Teixeira Gonzaga
ABSTRACTWith the advent of the General Personal Data Protection Law (LGPD) in Brazil, companies need to adapt to the law and, therefore, must identify weaknesses in relation to the protection of personal data. Next, actions are defined that include investments in technology, infrastructure and training. These actions require resources, either for the implementation of some technology or readjustment of the system, time for implementation, restructuring of processes, and involvement of key people. Considering these actions can also impact the organizational strategy, this work proposes a model to support the prioritization of actions necessary to comply with the LGPD. The model is based on the FITradeoff (Flexible and Interactive Tradeoff) multicriteria method. The proposed model was applied in a Brazilian organization that is adapting to the LGPD and needed formal support in relation to decisions. The recommendations obtained demonstrated alignment with the company's needs and strategies.KEYWORDS: Data protection; compliancemulticriteria decisionfitradeoff AcknowledgmentsThe authors would like to acknowledge the National Council for the Improvement of Higher Education (CAPES) and the Brazilian Research Council (CNPq) Brazilian National Research Council (CNPq), under Grant 311197/2020-5, for the support received to develop this research.Disclosure statementNo potential conflict of interest was reported by the author(s).
摘要随着巴西《通用个人数据保护法》(LGPD)的出台，企业需要适应法律，因此，必须找出与个人数据保护有关的弱点。接下来，定义行动，包括对技术、基础设施和培训的投资。这些行动需要资源，要么用于实施某些技术，要么需要重新调整系统，需要时间来实施，需要重组过程，需要关键人员的参与。考虑到这些行动也会影响组织战略，本工作提出了一个模型，以支持遵守LGPD所需行动的优先次序。该模型基于fitradoff (Flexible and Interactive Tradeoff)多准则方法。提议的模型已在一个巴西组织中应用，该组织正在适应LGPD，需要在决策方面得到正式支持。获得的建议与公司的需求和战略一致。关键词:数据保护;作者要感谢国家高等教育改进委员会(CAPES)和巴西研究委员会(CNPq)巴西国家研究委员会(CNPq)在赠款311197/2020-5下为开展本研究提供的支持。披露声明作者未报告潜在的利益冲突。
{"title":"A multicriteria model to support decisions regarding data protection compliance","authors":"Natalia Vieira Dantas, Ana Paula Henriques de Gusmão, Marcella Teixeira Gonzaga","doi":"10.1080/12460125.2023.2263683","DOIUrl":"https://doi.org/10.1080/12460125.2023.2263683","url":null,"abstract":"ABSTRACTWith the advent of the General Personal Data Protection Law (LGPD) in Brazil, companies need to adapt to the law and, therefore, must identify weaknesses in relation to the protection of personal data. Next, actions are defined that include investments in technology, infrastructure and training. These actions require resources, either for the implementation of some technology or readjustment of the system, time for implementation, restructuring of processes, and involvement of key people. Considering these actions can also impact the organizational strategy, this work proposes a model to support the prioritization of actions necessary to comply with the LGPD. The model is based on the FITradeoff (Flexible and Interactive Tradeoff) multicriteria method. The proposed model was applied in a Brazilian organization that is adapting to the LGPD and needed formal support in relation to decisions. The recommendations obtained demonstrated alignment with the company's needs and strategies.KEYWORDS: Data protection; compliancemulticriteria decisionfitradeoff AcknowledgmentsThe authors would like to acknowledge the National Council for the Improvement of Higher Education (CAPES) and the Brazilian Research Council (CNPq) Brazilian National Research Council (CNPq), under Grant 311197/2020-5, for the support received to develop this research.Disclosure statementNo potential conflict of interest was reported by the author(s).","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136279764","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 : 2023-09-29DOI: 10.1080/12460125.2023.2263675
Sérgio Pedro Duarte, António Lobo, Joana Ribeiro, João Valente Neves, António Couto, Sara Ferreira
ABSTRACTThe design of effective road safety countermeasures requires a network diagnosis supported by data. Moreover, infrastructures need to be ready for the introduction of vehicle-to-infrastructure communications to support technologies, as truck platooning. Ascendi, a motorway concessionaire, developed an action plan to decrease crash frequency and casualties that rests in data recorded by automatic vehicle counting devices. To have a good network representation, new equipment will provide more data, thus enhancing the selection of countermeasures. We developed a multilevel decision-support approach to define equipment location. The process stages correspond to three levels of analysis: (1) clustering for road segment classification (network level); (2) quantification of the devices to install ensuring similar coverage (concession level); (3) device allocation according to geographical and cost criteria (segment level). An iterative and participatory process involving Ascendi resulted in a proposal for adding 43 devices to the existing 72, increasing the network coverage to 39%.KEYWORDS: Cluster analysismultilevel decisiondecision-makingdata collectionroad safetyVision Zero AcknowledgmentsThe authors acknowledge Ascendi’s support in the development of the decision-making process.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData not available due to commercial restrictions.Additional informationFundingThis work is financially supported by national funds through the FCT/MCTES (PIDDAC), under the project PTDC/ECI-TRA/4672/2020.
{"title":"A multilevel decision-making approach for road infrastructure management","authors":"Sérgio Pedro Duarte, António Lobo, Joana Ribeiro, João Valente Neves, António Couto, Sara Ferreira","doi":"10.1080/12460125.2023.2263675","DOIUrl":"https://doi.org/10.1080/12460125.2023.2263675","url":null,"abstract":"ABSTRACTThe design of effective road safety countermeasures requires a network diagnosis supported by data. Moreover, infrastructures need to be ready for the introduction of vehicle-to-infrastructure communications to support technologies, as truck platooning. Ascendi, a motorway concessionaire, developed an action plan to decrease crash frequency and casualties that rests in data recorded by automatic vehicle counting devices. To have a good network representation, new equipment will provide more data, thus enhancing the selection of countermeasures. We developed a multilevel decision-support approach to define equipment location. The process stages correspond to three levels of analysis: (1) clustering for road segment classification (network level); (2) quantification of the devices to install ensuring similar coverage (concession level); (3) device allocation according to geographical and cost criteria (segment level). An iterative and participatory process involving Ascendi resulted in a proposal for adding 43 devices to the existing 72, increasing the network coverage to 39%.KEYWORDS: Cluster analysismultilevel decisiondecision-makingdata collectionroad safetyVision Zero AcknowledgmentsThe authors acknowledge Ascendi’s support in the development of the decision-making process.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData not available due to commercial restrictions.Additional informationFundingThis work is financially supported by national funds through the FCT/MCTES (PIDDAC), under the project PTDC/ECI-TRA/4672/2020.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246020","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 : 2023-09-28DOI: 10.1080/12460125.2023.2263684
Pavlos Delias, Daniela Grigori
ABSTRACTEvent data from business processes evidence their patterns, behaviors, and dysfunctions. Analytics techniques like clustering and sorting can reveal relevant insights, when data are correlated with a single case identifier. However, when multiple entities are involved, unidimensional models are challenged. We introduce a novel method for analyzing business processes involving multiple interacting entity types. Our approach employs embedding representations to capture pairwise similarities among entity types and their interrelationships. An optimization problem encompasses similarity matrices, cross-entity relationship matrices, and embeddings. An iterative algorithm refines this model, yielding embedding representations and cluster assignments for each entity type. Formulating our method across three diverse business scenarios demonstrates its practicality and potential. Our results, through a proof of concept using real-world data, underscore the value of accounting for the multifaceted nature of business processes, showing substantial improvements and qualitative distinctions compared to unidimensional models.KEYWORDS: Process analyticsmultiple entitiesclusteringnetwork embeddingsdecision supportproblem formulation Disclosure statementNo potential conflict of interest was reported by the authors.Notes1. https://www.win.tue.nl/bpi/2017/challenge.html
{"title":"Formulating the potentials of clustering of event data over multiple entities for decision support: a network embeddings approach","authors":"Pavlos Delias, Daniela Grigori","doi":"10.1080/12460125.2023.2263684","DOIUrl":"https://doi.org/10.1080/12460125.2023.2263684","url":null,"abstract":"ABSTRACTEvent data from business processes evidence their patterns, behaviors, and dysfunctions. Analytics techniques like clustering and sorting can reveal relevant insights, when data are correlated with a single case identifier. However, when multiple entities are involved, unidimensional models are challenged. We introduce a novel method for analyzing business processes involving multiple interacting entity types. Our approach employs embedding representations to capture pairwise similarities among entity types and their interrelationships. An optimization problem encompasses similarity matrices, cross-entity relationship matrices, and embeddings. An iterative algorithm refines this model, yielding embedding representations and cluster assignments for each entity type. Formulating our method across three diverse business scenarios demonstrates its practicality and potential. Our results, through a proof of concept using real-world data, underscore the value of accounting for the multifaceted nature of business processes, showing substantial improvements and qualitative distinctions compared to unidimensional models.KEYWORDS: Process analyticsmultiple entitiesclusteringnetwork embeddingsdecision supportproblem formulation Disclosure statementNo potential conflict of interest was reported by the authors.Notes1. https://www.win.tue.nl/bpi/2017/challenge.html","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135388080","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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