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Investigating the Impact of Business Intelligence Adoption on Managerial Decision-Making in Startups
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Alireza Torabian *   |
| M.Sc. in Information Technology Management and E-Business, Iran University of Science and Technology (IUST), Tehran, Iran |
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Abstract: (3 Views) |
| In today's fast-paced world, where organizations—particularly startups—face numerous challenges such as resource scarcity, intense competition, and rapid market fluctuations, making swift, data-driven decisions is of paramount importance. This study investigates the impact of business intelligence (BI) adoption on the decision-making processes of startup managers. As an ensemble of data analytics tools, techniques, and processes, business intelligence plays a vital role in extracting meaningful insights from raw data, thereby enabling managers to make more precise and effective decisions. This research employs a mixed-methods design (quantitative and qualitative), with data collected through questionnaires and semi-structured interviews with startup executives. Quantitative findings indicate that a majority of managers perceive BI tools as effective, noting that these systems accelerate decision-making, mitigate risks, and enhance predictability. In the qualitative phase, managers emphasized the significance of data analytics in strategic and operational decision-making while identifying challenges such as low data quality, implementation costs, and technical skill gaps as major barriers to the effective utilization of BI. The results demonstrate that the targeted application of business intelligence—especially in startup environments characterized by time constraints and limited resources—can enhance decision quality and increase the likelihood of organizational success. Furthermore, fostering a data-driven culture and developing analytical infrastructure are essential prerequisites for fully leveraging business intelligence capabilities. |
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| Keywords: Business Intelligence, Startup, Data-Driven Decision Making, Data Analytics, Management Information Systems |
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Full-Text [PDF 1073 kb]
(1 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2025/04/16 | Accepted: 2025/05/19 | Published: 2025/06/20
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