When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption

Corentin Burnay; Sarah Bouraga; Mathieu Lega

doi:10.1007/978-3-031-33080-3_26

When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption

Corentin Burnay, Sarah Bouraga, Mathieu Lega

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

Résumé

Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Big data, business analytics, business intelligence, and more generally data-driven Decision Support Systems (data-driven DSS) intend to achieve this. Organizations invest massively in the development of data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of dashboards, which can be seen as the visible tip of the data-driven DSS iceberg and which play a major role in the adoption of the entire system. This paper advances early empirical research conducted on one possible root cause for data-driven DSS dashboard adoption or rejection, namely the dashboar
content. We study the effect of dashboards over- and underloading on traditional Technology Adoption Models, and try to uncover the trade offs to which data-driven DSS interface designers are confronted when creating new dashboards. The result is a Dashboard Adoption Model, enriching the seminal TAM model with new content-oriented variables to support the design of more supportive data-driven DSS dashboards.

langue originale	Anglais
titre	Research Challenges in Information Science
Sous-titre	Information Science and the Connected World - 17th International Conference, RCIS 2023, Proceedings
rédacteurs en chef	Selmin Nurcan, Andreas L. Opdahl, Haralambos Mouratidis, Aggeliki Tsohou
Editeur	Springer Science and Business Media Deutschland GmbH
Pages	435-451
Nombre de pages	17
ISBN (imprimé)	9783031330797
Les DOIs	https://doi.org/10.1007/978-3-031-33080-3_26
Etat de la publication	Publié - 26 mai 2023
Evénement	17th International Conference on Research Challenges in Information Sciences, RCIS 2023 - Corfu, Grèce Durée: 23 mai 2023 → 26 mai 2023

Série de publications

Nom	Lecture Notes in Business Information Processing
Volume	476 LNBIP
ISSN (imprimé)	1865-1348
ISSN (Electronique)	1865-1356

Une conférence

Une conférence	17th International Conference on Research Challenges in Information Sciences, RCIS 2023
Pays/Territoire	Grèce
La ville	Corfu
période	23/05/23 → 26/05/23

Financement

BeCoDigital]. Financial support was also received from the European Regional Development Fund (ERDF) for the Wal-e-Cities project with award number [ETR121200003138] and from the Research Public Service of Wallonia (SPW Recherche) for the project ARIAC by DIGITALWALLONIA4AI with award number [2010235]. Acknowledgement. This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822). It was also co-founded by the European Union under Grant Agreement No. 101087529. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Acknowledgements. This research is supported by the Estonian Research Council (PRG1226) and the European Research Council (PIX Project). Unit 2023-2027 (CEX2021-001201-M) funded by MCIN/AEI /10.13039/501100011033, and the RCIS community for their valuable insights that helped develop this work. Acknowledgements. This work has been developed under the project Digital Knowledge Graph – Adaptable Analytics API with the financial support of Accenture LTD, the Generalitat Valenciana through the CoMoDiD project (CIPROM/2021/023), the Spanish State Research Agency through the DELFOS (PDC2021-121243-I00) and SREC (PID2021-123824OB-I00) projects, MICIN/AEI/10.13039/501 100011033 and co-financed with ERDF and the European Union Next Generation EU/PRTR. and H2020 Programmes under grant agreements 101070455 (DYNABIC), 101095634 (ENTRUST) and 101020416 (ERATOSTHENES), and the Research Council of Norway’s BIA-IPN programme under grant agreement 309700 (FLEET).

Bailleurs de fonds	Numéro du bailleur de fonds
Accenture LTD
ENTRUST	101020416
European Union Next Generation EU/PRTR
H2020 Programmes	101070455, 101095634
Research Public Service of Wallonia	2010235
SREC	PID2021-123824OB-I00, MICIN/AEI/10.13039/501 100011033
European commission	101087529
European commission
European Research Council
Eesti Teadusagentuur	PRG1226
Eesti Teadusagentuur
Generalitat Valenciana	CIPROM/2021/023
Generalitat Valenciana
Ministerio de Ciencia e Innovación
Norges Forskningsråd	309700
Norges Forskningsråd
European Regional Development Fund	CZ.02.1.01/0.0/0.0/16_019/0000822, ETR121200003138
European Regional Development Fund
Agencia Estatal de Investigación	PDC2021-121243-I00
Agencia Estatal de Investigación

Accès au document

10.1007/978-3-031-33080-3_26

Autres fichiers et liens

Lien vers la publication sur Scopus

Reducing the Effects of Information Overload on Governments Decision-Making: Empirical Frameworks to Support Data Selection
Lega, M. (Auteur), Burnay, C. (Promoteur), Linden, I. (Copromoteur), Gnabo, J.-Y. (Président), Kolp, M. (Jury), Simonofski, A. (Jury) & CRUSOE, J. (Jury), 19 août 2024
Student thesis: Doc types › Docteur en Sciences

Contient cette citation

Burnay, C., Bouraga, S., & Lega, M. (2023). When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption. Dans S. Nurcan, A. L. Opdahl, H. Mouratidis, & A. Tsohou (eds.), Research Challenges in Information Science: Information Science and the Connected World - 17th International Conference, RCIS 2023, Proceedings (p. 435-451). (Lecture Notes in Business Information Processing; Vol 476 LNBIP). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-33080-3_26

Burnay, Corentin ; Bouraga, Sarah ; Lega, Mathieu. / When Dashboard’s Content Becomes a Barrier : Exploring the Effects of Cognitive Overloads on BI Adoption. Research Challenges in Information Science: Information Science and the Connected World - 17th International Conference, RCIS 2023, Proceedings. Editeur / Selmin Nurcan ; Andreas L. Opdahl ; Haralambos Mouratidis ; Aggeliki Tsohou. Springer Science and Business Media Deutschland GmbH, 2023. p. 435-451 (Lecture Notes in Business Information Processing).

@inproceedings{fe44ff1bcc3b47d4bea62212ec20f44f,

title = "When Dashboard{\textquoteright}s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption",

abstract = "Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Big data, business analytics, business intelligence, and more generally data-driven Decision Support Systems (data-driven DSS) intend to achieve this. Organizations invest massively in the development of data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of dashboards, which can be seen as the visible tip of the data-driven DSS iceberg and which play a major role in the adoption of the entire system. This paper advances early empirical research conducted on one possible root cause for data-driven DSS dashboard adoption or rejection, namely the dashboar content. We study the effect of dashboards over- and underloading on traditional Technology Adoption Models, and try to uncover the trade offs to which data-driven DSS interface designers are confronted when creating new dashboards. The result is a Dashboard Adoption Model, enriching the seminal TAM model with new content-oriented variables to support the design of more supportive data-driven DSS dashboards.",

keywords = "Dashboards Adoption, Decision Making, Decision Support Systems, Information Overloads, Structural Equation Modeling",

author = "Corentin Burnay and Sarah Bouraga and Mathieu Lega",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 17th International Conference on Research Challenges in Information Sciences, RCIS 2023 ; Conference date: 23-05-2023 Through 26-05-2023",

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Burnay, C , Bouraga, S & Lega, M 2023, When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption. Dans S Nurcan, AL Opdahl, H Mouratidis & A Tsohou (eds), Research Challenges in Information Science: Information Science and the Connected World - 17th International Conference, RCIS 2023, Proceedings. Lecture Notes in Business Information Processing, VOL. 476 LNBIP, Springer Science and Business Media Deutschland GmbH, p. 435-451, 17th International Conference on Research Challenges in Information Sciences, RCIS 2023, Corfu, Grèce, 23/05/23. https://doi.org/10.1007/978-3-031-33080-3_26

When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption. / Burnay, Corentin ; Bouraga, Sarah ; Lega, Mathieu.
Research Challenges in Information Science: Information Science and the Connected World - 17th International Conference, RCIS 2023, Proceedings. Ed. / Selmin Nurcan; Andreas L. Opdahl; Haralambos Mouratidis; Aggeliki Tsohou. Springer Science and Business Media Deutschland GmbH, 2023. p. 435-451 (Lecture Notes in Business Information Processing; Vol 476 LNBIP).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

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T2 - 17th International Conference on Research Challenges in Information Sciences, RCIS 2023

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AU - Bouraga, Sarah

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PY - 2023/5/26

Y1 - 2023/5/26

N2 - Decision makers in organizations strive to improve the quality of their decisions. One way to improve that process is to objectify the decisions with facts. Big data, business analytics, business intelligence, and more generally data-driven Decision Support Systems (data-driven DSS) intend to achieve this. Organizations invest massively in the development of data-driven DSS and expect them to be adopted and to effectively support decision makers. This raises many technical and methodological challenges, especially regarding the design of dashboards, which can be seen as the visible tip of the data-driven DSS iceberg and which play a major role in the adoption of the entire system. This paper advances early empirical research conducted on one possible root cause for data-driven DSS dashboard adoption or rejection, namely the dashboar content. We study the effect of dashboards over- and underloading on traditional Technology Adoption Models, and try to uncover the trade offs to which data-driven DSS interface designers are confronted when creating new dashboards. The result is a Dashboard Adoption Model, enriching the seminal TAM model with new content-oriented variables to support the design of more supportive data-driven DSS dashboards.

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Burnay C , Bouraga S , Lega M. When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption. Dans Nurcan S, Opdahl AL, Mouratidis H, Tsohou A, rédacteurs en chef, Research Challenges in Information Science: Information Science and the Connected World - 17th International Conference, RCIS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 435-451. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-031-33080-3_26

When Dashboard’s Content Becomes a Barrier: Exploring the Effects of Cognitive Overloads on BI Adoption

Résumé

Série de publications

Une conférence

Financement

Accès au document

Autres fichiers et liens

Empreinte digitale

Thèses de l'étudiant

Reducing the Effects of Information Overload on Governments Decision-Making: Empirical Frameworks to Support Data Selection

Contient cette citation