TY - GEN
T1 - Data-driven decision-making and the role of personality and cognitive style
T2 - America�s Conference on Information Systems: A Tradition of Innovation, AMCIS 2017
AU - Wiedenhof, Tertia M.
AU - Plomp, Marijn G.A.
PY - 2017
Y1 - 2017
N2 - Data-driven decision-making (DDDM) is said to have huge benefits for organizations. Data are generally kept in IS, and humans use these data for informed decision-making. Therefore, DDDM is fundamentally related to the adoption and use of IS by individuals. IT adoption and use is influenced by personality and cognitive style, but not much is known on how personality and cognitive style influence the adoption and use of data-driven decision support systems. Our study aims to further our understanding of this influence. Through a structured literature review of IS literature, 55 papers were found and analyzed, resulting in 14 relevant topics and 7 key papers. Our findings indicate that personality and cognitive style may influence the adoption and use of data-driven decision support systems. We provide implications for future research to obtain a better understanding of DDDM, and practitioners can use our results to realize more successful DDDM.
AB - Data-driven decision-making (DDDM) is said to have huge benefits for organizations. Data are generally kept in IS, and humans use these data for informed decision-making. Therefore, DDDM is fundamentally related to the adoption and use of IS by individuals. IT adoption and use is influenced by personality and cognitive style, but not much is known on how personality and cognitive style influence the adoption and use of data-driven decision support systems. Our study aims to further our understanding of this influence. Through a structured literature review of IS literature, 55 papers were found and analyzed, resulting in 14 relevant topics and 7 key papers. Our findings indicate that personality and cognitive style may influence the adoption and use of data-driven decision support systems. We provide implications for future research to obtain a better understanding of DDDM, and practitioners can use our results to realize more successful DDDM.
KW - Business analytics
KW - Cognitive style
KW - Data-driven decision-making
KW - Decision support systems
KW - Personality
UR - http://www.scopus.com/inward/record.url?scp=85048357264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048357264&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85048357264
T3 - AMCIS Proceedings
BT - Data Science and Analytics for Decision Support (SIGDSA)
PB - Americas Conference on Information Systems
Y2 - 10 August 2017 through 12 August 2017
ER -