Business Intelligence & Analytics and Decision Quality - Insights on Analytics Specialization and Information Processing Modes
Open accessAbstract
Leveraging the benefits of business intelligence and analytics (BI&A) and improving decision quality does not only depend on establishing BI&A technology, but also on the organization and characteristics of decision processes. This research investigates new perspectives on these decision processes and establishes a link between characteristics of BI&A support and decision makers’ modes of information processing behavior, and how these ultimately contribute to the quality of decision outcomes. We build on the heuristic–systematic model (HSM) of information processing, as a central explanatory mechanism for linking BI&A support and decision quality. This allows us examining the effects of decision makers’ systematic and heuristic modes of information processing behavior in decision making processes. We further elucidate the role of analytics experts in influencing decision makers’ utilization of analytic advice. The analysis of data from 136 BI&A-supported decisions reveals how high levels of analytics elaboration can have a negative effect on decision makers’ information processing behavior. We further show how decision makers’ systematic processing contributes to decision quality and how heuristic processing restrains it. In this context we also find that trustworthiness in the analytics expert plays an important role for the adoption of analytic advice.
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A high decision quality relies on critical analysis of all applicants and selection of the best match.
“A high decision quality relies on the critical analysis of all applicants and the selection of the applicant, who best matches the job requirements”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Kowalczyk and Gerlach, 2015
Presenting a highly aggregated summary of candidates' suitability might encourage peripheral and heuristic elaboration, decreasing the soundness of the decision.
“Hence, presenting a highly aggregated summary of the candidates’ suitability for a job position might encourage a peripheral and heuristic elaboration of the presented data as not all data is taken into consideration, which decreases the soundness of the decision (Kowalczyk and Gerlach, 2015).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Kowalczyk and Gerlach, 2015
Cites
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194 · see citing works
Bonaccio, S., & Dalal, R. S. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.07.001 · Cited by 1,011 · see citing works
Sharma, R., Mithas, S., & Kankanhalli, A. (2014) · European Journal of Information Systems · doi:10.1057/ejis.2014.17 · Cited by 516 · see citing works
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Check the box! How to deal with automation bias in AI-based personnel selection
Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1118723 · Cited by 59 · see citing works
“Hence, presenting a highly aggregated summary of the candidates’ suitability for a job position might encourage a peripheral and heuristic elaboration of the presented data as not all data is taken into consideration, which decreases the soundness of the decision (Kowalczyk and Gerlach, 2015).”
BackgroundInfluential
- Authors
- Kowalczyk, M., & Gerlach, J.
- Affiliations
- Technische Universität Darmstadt (DE)
- ORCID
- Gerlach, Jin
- Venue
- University of Münster, Münster, Germany
- Publisher
- University of Münster, Münster, Germany
- Published
- 2015
- OpenAlex
- W1124794459
- Cited by
- 9
- References
- 3
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 2 · 2022: 2 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- RePEc: Research Papers in Economics (submittedVersion)
- Publications of Darmstadt Technical University, Institute for Business Studies (BWL) (submittedVersion)
- Journal of the Association for Information Systems (submittedVersion)
- Institut für Wirtschaftsinformatik, Westfälische Wilhelms-Universität Münster
- European Conference on Information Systems
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
- Social Sciences › Business, Management and Accounting › Strategy and Management › Competitive and Knowledge Intelligence
- Keywords
- business intelligence and analytics, decision quality, heuristic-systematic model, decision-makers, trustworthiness, decision processes
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