When decision support systems fail: Insights for strategic information systems from Formula 1

Aversa, Paolo; Cabantous, Laure; Haefliger, Stefan

doi:10.1016/j.jsis.2018.03.002

Cited by 58 publications

(36 citation statements)

References 77 publications

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“…The Analytic Hierarchy Process (AHP) is used as Decision Support Systems (DSS) (Sprague & Carlson, 1982;Aversa, Cabantous, & Haefliger, 2018;Keenan & Jankowski, 2019) with its different generator systems. The Multiple Criteria Decision Making (MCDM) is a well-known multicriteria methodological approach for the decision-making process.…”

Section: Methodsmentioning

confidence: 99%

Multicriteria analysis of the compliance for the improvement of information security

Solana-González

Vanti²,

Fontana

2019

JISTEM

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Information security is a current issue of protection of information assets that considers significant variables of a strategic, organizational and IT governance nature, and that requires to analyze the compliance with international standards that regulate business actions. In this way, the work analyzes institutional compliance to improve information security applying the Analytic Hierarchy Process methodology to the specific practices defined in ISO/IEC 27002:2013. Expert Choice has been used as Decision Support Systems that has generated as a result the ranking of priorities of the criteria and alternatives used in the decisional process. It has been later applied in a medium-sized Brazilian industrial company. The results identify that the main security practice is the one related to the independent critical analysis of information security.

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Section: Methodsmentioning

confidence: 99%

Multicriteria analysis of the compliance for the improvement of information security

Solana-González

Vanti²,

Fontana

2019

JISTEM

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“…Nevertheless, the benefits associated with Decision Support Systems (DSS) powered by BD are not entirely foolproof. In this regard, Aversa et al (2018) believe that the characteristics of the social, physical and technical environment, the distribution of decision-making activities among individuals and corporate artefacts as well as the performativity of DSS models may also negatively influence the assumptions and methods of data-driven decision-making in organisations.…”

Section: Green Cluster: Big Data and Knowledge Managementmentioning

confidence: 99%

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

Ciampi

Marzi

Demi

et al. 2020

JKM

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Purpose Designing knowledge management (KM) systems capable of transforming big data into information characterised by strategic value is a major challenge faced nowadays by firms in almost all industries. However, in the managerial field, big data is now mainly used to support operational activities while its strategic potential is still largely unexploited. Based on these considerations, this study proposes an overview of the literature regarding the relationship between big data and business strategy. Design/methodology/approach A bibliographic coupling method is applied over a dataset of 128 peer-reviewed articles, published from 2013 (first year when articles regarding the big data-business strategy relationship were published) to 2019. Thereafter, a systematic literature review is presented on 116 papers, which were found to be interconnected based on the VOSviewer algorithm. Findings This study discovers the existence of four thematic clusters. Three of the clusters relate to the following topics: big data and supply chain strategy; big data, personalisation and co-creation strategies and big data, strategic planning and strategic value creation. The fourth cluster concerns the relationship between big data and KM and represents a ‘bridge’ between the other three clusters. Research limitations/implications Based on the bibliometric analysis and the systematic literature review, this study identifies relevant understudied topics and research gaps, which are suggested as future research directions. Originality/value This is the first study to systematise and discuss the literature concerning the relationship between big data and firm strategy.

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“…In this regard, we mention the work of Aversa, Cabantous and Haefliger (2018), in which, by means of a Formula 1 race, the authors determine that decision support system (DSS) potential failure is exacerbated under pressure and time constraints.…”

Section: Independent Journal Of Management and Production (Ijmandp)mentioning

confidence: 99%

Big data analytics: achievements, challenges, and research trends

Henriques

Meirelles

Cunha

2020

Ind. Jour. Manag. & Prod.

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Big data applications combined with analytical tools foster prediction techniques that impact societal, economic, and political changes. After almost a decade of studies, this paper proposes to identify major debates on big data analytics, presenting its evolution over the past years and identifying its research tendencies. We limited our research to the top eight journals in information systems. Our findings suggest that big data analytics is apparently reaching a plateau, which might be confirmed by publications in the following years. The paper contributes to the current debate on big data by identifying ongoing studies in the research community. In addition, it provides a critical analysis of the field development, from its perceived benefits to its unimagined consequences. Finally, we conclude that other perspectives on big data analytics might include a new wave of studies and that new paths beyond productivity gains can be explored.

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When decision support systems fail: Insights for strategic information systems from Formula 1

Cited by 58 publications

References 77 publications

Multicriteria analysis of the compliance for the improvement of information security

Multicriteria analysis of the compliance for the improvement of information security

The big data-business strategy interconnection: a grand challenge for knowledge management. A review and future perspectives

Big data analytics: achievements, challenges, and research trends

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