Pub Date : 2022-05-03DOI: 10.1080/12460125.2022.2070953
P. Keenan, C. Heavin
ABSTRACT This research-in-progress article uses a bibliometric approach to explore the research landscape by the gender of publishing authors in the Decision Support Systems (DSS) field over 10 years, from 2011 to 2020. The Web of Science (WOS) provides a valuable information resource on academic disciplines as it contains both the articles published and the articles cited. This research presents information on the gender breakdown of authors publishing on the topic of DSS globally. We examined publication trends over time, considering the main categories and research areas by authors’ gender. As a result, some initial recommendations to guide future research efforts of both DSS academics and practitioners are provided.
{"title":"DSS research: a bibliometric analysis by gender","authors":"P. Keenan, C. Heavin","doi":"10.1080/12460125.2022.2070953","DOIUrl":"https://doi.org/10.1080/12460125.2022.2070953","url":null,"abstract":"ABSTRACT This research-in-progress article uses a bibliometric approach to explore the research landscape by the gender of publishing authors in the Decision Support Systems (DSS) field over 10 years, from 2011 to 2020. The Web of Science (WOS) provides a valuable information resource on academic disciplines as it contains both the articles published and the articles cited. This research presents information on the gender breakdown of authors publishing on the topic of DSS globally. We examined publication trends over time, considering the main categories and research areas by authors’ gender. As a result, some initial recommendations to guide future research efforts of both DSS academics and practitioners are provided.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"31 1","pages":"107 - 116"},"PeriodicalIF":3.4,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46934279","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 : 2022-05-02DOI: 10.1080/12460125.2022.2071404
Nuria Mollá, C. Heavin, A. Rabasa
ABSTRACT Traditionally, Decision Support Systems (DSS) data were stored statically and persistently in a database. Increasing volume and intensity of information and data streams create new opportunities and challenges for DSS experts, data scientists, and decision makers. Novel data stream contexts require that we move beyond static DSS modelling techniques to support data-driven decision-making. Implementing incremental and/or adaptive algorithms may help to solve some of the challenges arising from data streams. This research investigates the use of these algorithms to better understand how their performance compares with more traditional approaches. We show that an adaptive DSS engine has the potential to identify errors and improve the accuracy of the model. We briefly identify how this approach could be applied to unexpected highly uncertain decision scenarios. Future research considers new opportunities to pursue a multidisciplinary approach to adaptive DSS design, development, and implementation leveraging emerging machine learning techniques in tackling complex decision problems.
{"title":"Data-driven decision making: new opportunities for DSS in data stream contexts","authors":"Nuria Mollá, C. Heavin, A. Rabasa","doi":"10.1080/12460125.2022.2071404","DOIUrl":"https://doi.org/10.1080/12460125.2022.2071404","url":null,"abstract":"ABSTRACT Traditionally, Decision Support Systems (DSS) data were stored statically and persistently in a database. Increasing volume and intensity of information and data streams create new opportunities and challenges for DSS experts, data scientists, and decision makers. Novel data stream contexts require that we move beyond static DSS modelling techniques to support data-driven decision-making. Implementing incremental and/or adaptive algorithms may help to solve some of the challenges arising from data streams. This research investigates the use of these algorithms to better understand how their performance compares with more traditional approaches. We show that an adaptive DSS engine has the potential to identify errors and improve the accuracy of the model. We briefly identify how this approach could be applied to unexpected highly uncertain decision scenarios. Future research considers new opportunities to pursue a multidisciplinary approach to adaptive DSS design, development, and implementation leveraging emerging machine learning techniques in tackling complex decision problems.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"31 1","pages":"255 - 269"},"PeriodicalIF":3.4,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44537575","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 : 2022-04-29DOI: 10.1080/12460125.2022.2070946
G. Phillips-Wren, Mary Daly, F. Burstein
ABSTRACT Decision support systems (DSS) have been traditionally developed to assist with unstructured and semi-structured problems. Early DSS researchers explored a broad range of techniques for supporting human cognition as part of decision making. Cognition during decision making was viewed in terms of two competing, and sometimes cooperating, systems: one that was automatic and fast, and one that was deliberative and slow. The aim of this research is to trace historical studies on cognitive aspects of decision support and determine the theoretical underpinnings of DSS support for cognition. We analysed articles drawing on the seminal literature to derive the relevant dimensions, including the classical Gorry & Scott Morton (1989) framework. This analysis identified opportunities for future research relevant to providing better support for cognition by highlighting some design parameters for information systems.
{"title":"Support for cognition in decision support systems: an exploratory historical review","authors":"G. Phillips-Wren, Mary Daly, F. Burstein","doi":"10.1080/12460125.2022.2070946","DOIUrl":"https://doi.org/10.1080/12460125.2022.2070946","url":null,"abstract":"ABSTRACT Decision support systems (DSS) have been traditionally developed to assist with unstructured and semi-structured problems. Early DSS researchers explored a broad range of techniques for supporting human cognition as part of decision making. Cognition during decision making was viewed in terms of two competing, and sometimes cooperating, systems: one that was automatic and fast, and one that was deliberative and slow. The aim of this research is to trace historical studies on cognitive aspects of decision support and determine the theoretical underpinnings of DSS support for cognition. We analysed articles drawing on the seminal literature to derive the relevant dimensions, including the classical Gorry & Scott Morton (1989) framework. This analysis identified opportunities for future research relevant to providing better support for cognition by highlighting some design parameters for information systems.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"31 1","pages":"18 - 30"},"PeriodicalIF":3.4,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48055823","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 : 2022-04-29DOI: 10.1080/12460125.2022.2070952
Zsombor Szádoczki, S. Duleba
ABSTRACT The aggregation of evaluators’ preferences is a key problem in group decision making. We examine the recently proposed distance-based techniques and compare their efficiency to the traditional aggregation of individual preferences (AIP) methods in simulated Analytic Hierarchy Process (AHP) cases. We use the Kendall W statistic to measure the rank correlation among the individual priority vectors of the group and the common priority vector for the different aggregation approaches. Extensive simulations (altogether 88000 cases) show that both the Euclidean Distance-Based Aggregation Method (EDBAM) and the Aitchison Distance-Based Aggregation Method significantly outperform the traditional techniques in case of smaller and mid-sized priority vectors (at most six items to be compared). However, EDBAM outperform the AIP methods for all dimensions that is conventionally used in AHP, and its computation time is also low.
{"title":"Distance-based aggregation in group AHP","authors":"Zsombor Szádoczki, S. Duleba","doi":"10.1080/12460125.2022.2070952","DOIUrl":"https://doi.org/10.1080/12460125.2022.2070952","url":null,"abstract":"ABSTRACT The aggregation of evaluators’ preferences is a key problem in group decision making. We examine the recently proposed distance-based techniques and compare their efficiency to the traditional aggregation of individual preferences (AIP) methods in simulated Analytic Hierarchy Process (AHP) cases. We use the Kendall W statistic to measure the rank correlation among the individual priority vectors of the group and the common priority vector for the different aggregation approaches. Extensive simulations (altogether 88000 cases) show that both the Euclidean Distance-Based Aggregation Method (EDBAM) and the Aitchison Distance-Based Aggregation Method significantly outperform the traditional techniques in case of smaller and mid-sized priority vectors (at most six items to be compared). However, EDBAM outperform the AIP methods for all dimensions that is conventionally used in AHP, and its computation time is also low.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"31 1","pages":"98 - 106"},"PeriodicalIF":3.4,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44177835","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 : 2022-04-20DOI: 10.1080/12460125.2022.2062849
T. Chowdhury, J. Oredo
ABSTRACT Alongside the revolutionary benefits of AI, it can cause numerous problems across the system development process. AI ecosytem players have recently started to interrogate the ethical biases implicit in AI-enabled applications and agents. The contestable nature of ethics and the complexity of AI-enabled applications has led to incoherent literature around AI ethical biases. The numerous conceptions of AI ethics and a multiplicity of ethical biases has compounded matters for researchers, practitioners, and policy makers. The current study proposes a conceptual framework to organize AI ethical biases. A narrative literature review was conducted to identify and group the biases into data biases, method biases and implementation biases. The CRISP-DM framework was used to classify the ethical biases. The emerging conceptual framework has four clusters that represents: System development phases, scope of ethical bias, exemplars, and possible solutions. The study extends the existing AI ethical frameworks and provides a unified communication artefact for practitioners.
{"title":"AI ethical biases: normative and information systems development conceptual framework","authors":"T. Chowdhury, J. Oredo","doi":"10.1080/12460125.2022.2062849","DOIUrl":"https://doi.org/10.1080/12460125.2022.2062849","url":null,"abstract":"ABSTRACT Alongside the revolutionary benefits of AI, it can cause numerous problems across the system development process. AI ecosytem players have recently started to interrogate the ethical biases implicit in AI-enabled applications and agents. The contestable nature of ethics and the complexity of AI-enabled applications has led to incoherent literature around AI ethical biases. The numerous conceptions of AI ethics and a multiplicity of ethical biases has compounded matters for researchers, practitioners, and policy makers. The current study proposes a conceptual framework to organize AI ethical biases. A narrative literature review was conducted to identify and group the biases into data biases, method biases and implementation biases. The CRISP-DM framework was used to classify the ethical biases. The emerging conceptual framework has four clusters that represents: System development phases, scope of ethical bias, exemplars, and possible solutions. The study extends the existing AI ethical frameworks and provides a unified communication artefact for practitioners.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45872286","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 : 2022-04-12DOI: 10.1080/12460125.2022.2062848
Philipp Korherr, D. Kanbach, S. Kraus, Paul Jones
ABSTRACT Research shows that many firms still make business critical decisions intuitively, despite clear evidence that analytics-based decision-making is likely more effective in creating corporate and social value. With the aim of providing actionable guidance to firms on how to accomplish the shift to analytics-based decision-making, this paper sheds light on the management factors that prove critical in this context. An in-depth single-site case study was conducted with a large publicly listed German manufacturing company. Building on 22 semi-structured interviews, this empirical study identifies six factors that play a critical role in establishing analytics-based decision-making: management behaviour, top management and strategy, analytics infrastructure, organisation and governance, HR management and development, and culture. This study forms the basis for further scientific research on the role of firm management in the transitional phase. Furthermore, it provides firm leaders with a systemised and practical framework to structure firm efforts to establish data-based decision making.
{"title":"The role of management in fostering analytics: the shift from intuition to analytics-based decision-making","authors":"Philipp Korherr, D. Kanbach, S. Kraus, Paul Jones","doi":"10.1080/12460125.2022.2062848","DOIUrl":"https://doi.org/10.1080/12460125.2022.2062848","url":null,"abstract":"ABSTRACT Research shows that many firms still make business critical decisions intuitively, despite clear evidence that analytics-based decision-making is likely more effective in creating corporate and social value. With the aim of providing actionable guidance to firms on how to accomplish the shift to analytics-based decision-making, this paper sheds light on the management factors that prove critical in this context. An in-depth single-site case study was conducted with a large publicly listed German manufacturing company. Building on 22 semi-structured interviews, this empirical study identifies six factors that play a critical role in establishing analytics-based decision-making: management behaviour, top management and strategy, analytics infrastructure, organisation and governance, HR management and development, and culture. This study forms the basis for further scientific research on the role of firm management in the transitional phase. Furthermore, it provides firm leaders with a systemised and practical framework to structure firm efforts to establish data-based decision making.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47904397","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 : 2022-04-08DOI: 10.1080/12460125.2022.2043576
S. Srinivasan, Amit Agrahari, Ashwani Kumar
ABSTRACT Analytics success has delivered superior customer value, higher revenue growth, and enhanced profitability to organizations. However, a large percentage of organizations in each industry has failed to realize such analytics success. Past studies have identified the antecedents of success and failure of business analytics projects. Executive Sponsorship was rated as among the top-success enablers. There has been limited attention to how executive sponsors influence business analytics success. To demystify Executive Sponsor’s role, we applied Deductive Thematic Analysis to the narratives recorded from key informants for 21 successful and 20 failed analytics projects covering 14 industries. This study identified four influence domains that span the project lifecycle and recommends the Executive Sponsor’s involvement in each of these. The influence domains are Project Initiation, Organization culture, User trust, and Deployment enablers. Our informants found the influence domains comprehensive to explain the outcomes of their projects. The influence domains can help articulate the role of the Executive Sponsor better and set expectations. They can also be used to analyze projects, identify causes of failure, and plan corrective actions.
{"title":"Role of Executive Sponsors in business analytics success – Understanding their influence domains using Deductive Thematic Analysis","authors":"S. Srinivasan, Amit Agrahari, Ashwani Kumar","doi":"10.1080/12460125.2022.2043576","DOIUrl":"https://doi.org/10.1080/12460125.2022.2043576","url":null,"abstract":"ABSTRACT Analytics success has delivered superior customer value, higher revenue growth, and enhanced profitability to organizations. However, a large percentage of organizations in each industry has failed to realize such analytics success. Past studies have identified the antecedents of success and failure of business analytics projects. Executive Sponsorship was rated as among the top-success enablers. There has been limited attention to how executive sponsors influence business analytics success. To demystify Executive Sponsor’s role, we applied Deductive Thematic Analysis to the narratives recorded from key informants for 21 successful and 20 failed analytics projects covering 14 industries. This study identified four influence domains that span the project lifecycle and recommends the Executive Sponsor’s involvement in each of these. The influence domains are Project Initiation, Organization culture, User trust, and Deployment enablers. Our informants found the influence domains comprehensive to explain the outcomes of their projects. The influence domains can help articulate the role of the Executive Sponsor better and set expectations. They can also be used to analyze projects, identify causes of failure, and plan corrective actions.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"32 1","pages":"409 - 438"},"PeriodicalIF":3.4,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48826716","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 : 2022-04-07DOI: 10.1080/12460125.2022.2059172
K. Hopf, Andreas Weigert, T. Staake
ABSTRACT Companies are pinning high hopes on competitive advantages through data analytics. So far, value gains through analytics have been demonstrated for IT-heavy and data-rich business areas. Yet, research has paid little attention to value creation through data analytics in the plethora of companies with limited data (i.e. having transactions in the hundreds and attributes in the tens). Building on the literature of big data value creation and the resource-based view, we carried out an in-depth analytics case study with a retailer of renewable energy systems. Firms in this business area operate with expensive but few sales, so their available data are notoriously limited. Our findings demonstrate that data analytics capabilities and value creation mechanisms (democratise, contextualise, experiment with data, and execute data insights) are also effective in situations with limited data. Practice and research should therefore put not only emphasis on the volume and the variety of data but also on contextual factors related to managers (e.g. clear strategy, vision, leadership) and all employees (e.g. openness for agile working mode, data awareness).
{"title":"Value creation from analytics with limited data: a case study on the retailing of durable consumer goods","authors":"K. Hopf, Andreas Weigert, T. Staake","doi":"10.1080/12460125.2022.2059172","DOIUrl":"https://doi.org/10.1080/12460125.2022.2059172","url":null,"abstract":"ABSTRACT Companies are pinning high hopes on competitive advantages through data analytics. So far, value gains through analytics have been demonstrated for IT-heavy and data-rich business areas. Yet, research has paid little attention to value creation through data analytics in the plethora of companies with limited data (i.e. having transactions in the hundreds and attributes in the tens). Building on the literature of big data value creation and the resource-based view, we carried out an in-depth analytics case study with a retailer of renewable energy systems. Firms in this business area operate with expensive but few sales, so their available data are notoriously limited. Our findings demonstrate that data analytics capabilities and value creation mechanisms (democratise, contextualise, experiment with data, and execute data insights) are also effective in situations with limited data. Practice and research should therefore put not only emphasis on the volume and the variety of data but also on contextual factors related to managers (e.g. clear strategy, vision, leadership) and all employees (e.g. openness for agile working mode, data awareness).","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"32 1","pages":"289 - 325"},"PeriodicalIF":3.4,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42795430","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 : 2022-03-23DOI: 10.1080/12460125.2022.2057006
Christian Lennerholt, J. V. Laere, Eva Söderström
ABSTRACT Self-service business intelligence (SSBI) enables all users, including those with limited technical skills, to perform business intelligence (BI) tasks without the support of BI experts. SSBI reduces pressure on BI experts, gives more freedom to self-reliant users and speeds up decision-making. Recent research has illustrated how organisations experience numerous challenges when trying to obtain SSBI benefits. The AQUIRE framework organises 37 identified SSBI challenges in five categories: A ccess and use of data, Data Q uality, U ser I ndependence, creating R eports and E ducation. SSBI literature does poorly address how these challenges can be tackled. This research study aimed to identify strategies on how to manage those 37 SSBI challenges. The performed case study includes 24 semi-structured interviews with respondents from two organisations which have been heavily involved in SSBI implementation. The results reveal how nine identified SSBI success factors are related to the 37 AQUIRE challenges and how they can be addressed over time.
{"title":"Success factors for managing the SSBI challenges of the AQUIRE framework","authors":"Christian Lennerholt, J. V. Laere, Eva Söderström","doi":"10.1080/12460125.2022.2057006","DOIUrl":"https://doi.org/10.1080/12460125.2022.2057006","url":null,"abstract":"ABSTRACT Self-service business intelligence (SSBI) enables all users, including those with limited technical skills, to perform business intelligence (BI) tasks without the support of BI experts. SSBI reduces pressure on BI experts, gives more freedom to self-reliant users and speeds up decision-making. Recent research has illustrated how organisations experience numerous challenges when trying to obtain SSBI benefits. The AQUIRE framework organises 37 identified SSBI challenges in five categories: A ccess and use of data, Data Q uality, U ser I ndependence, creating R eports and E ducation. SSBI literature does poorly address how these challenges can be tackled. This research study aimed to identify strategies on how to manage those 37 SSBI challenges. The performed case study includes 24 semi-structured interviews with respondents from two organisations which have been heavily involved in SSBI implementation. The results reveal how nine identified SSBI success factors are related to the 37 AQUIRE challenges and how they can be addressed over time.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"32 1","pages":"491 - 512"},"PeriodicalIF":3.4,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45564545","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 : 2022-02-27DOI: 10.1080/12460125.2022.2041274
Julian Märtins, D. Westmattelmann, G. Schewe
ABSTRACT To fully realize the benefits of Decision Support Systems (DSS), it is important to investigate factors influencing individuals who are affected by the DSS’ decision but are not involved in decision-making. An example of such DSS is the Video Assistant Referee (VAR) in professional football. Drawing on transparency and justice research, we examined the role of transparency, procedural justice, and social influence on individuals’ attitudes towards the VAR. A quantitative vignette-based approach (N = 824) using two scenarios (fans watching from home/in stadiums) was chosen. Results indicate that all variables are higher in the home setting. Structural equation modelling revealed that transparency, procedural justice, and social influence significantly impact individual’s attitude towards the VAR. Multigroup analyses showed that the effect size of one transparency dimension is significantly stronger at home, while social influence is stronger in stadiums. To further interpret the findings, we conducted twelve semi-structured interviews among football fans.
{"title":"Affected but not involved: Two-scenario based investigation of individuals’ attitude towards decision support systems based on the example of the video assistant referee","authors":"Julian Märtins, D. Westmattelmann, G. Schewe","doi":"10.1080/12460125.2022.2041274","DOIUrl":"https://doi.org/10.1080/12460125.2022.2041274","url":null,"abstract":"ABSTRACT To fully realize the benefits of Decision Support Systems (DSS), it is important to investigate factors influencing individuals who are affected by the DSS’ decision but are not involved in decision-making. An example of such DSS is the Video Assistant Referee (VAR) in professional football. Drawing on transparency and justice research, we examined the role of transparency, procedural justice, and social influence on individuals’ attitudes towards the VAR. A quantitative vignette-based approach (N = 824) using two scenarios (fans watching from home/in stadiums) was chosen. Results indicate that all variables are higher in the home setting. Structural equation modelling revealed that transparency, procedural justice, and social influence significantly impact individual’s attitude towards the VAR. Multigroup analyses showed that the effect size of one transparency dimension is significantly stronger at home, while social influence is stronger in stadiums. To further interpret the findings, we conducted twelve semi-structured interviews among football fans.","PeriodicalId":45565,"journal":{"name":"Journal of Decision Systems","volume":"32 1","pages":"384 - 408"},"PeriodicalIF":3.4,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46080977","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}