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3955. Analysing business intelligence adoption through natural language processing: a study of online customer testimonials
Invited abstract in session TA-28: Fairness and responsible AI, stream Advancements of OR-analytics in statistics, machine learning and data science.
Tuesday, 8:30-10:00Room: 065 (building: 208)
Authors (first author is the speaker)
| 1. | Masoud Fakhimi
|
| Surrey Business School, University of Surrey | |
| 2. | Ashkan Lotfipoor
|
| Surrey Business School, University of Surrey | |
| 3. | Alex Hagen-Zanker
|
| School of Sustainability, Civil and Environmental Engineering, University of Surrey |
Abstract
Enterprises increasingly recognize the potential of business intelligence (BI) systems to enable data-informed decision-making. This study offers a novel, large-scale analysis of online customer testimonials to illuminate successful BI adoption patterns. Our research leverages natural language processing (NLP) techniques for systematic and thorough analysis of these rich, yet unstructured, data sources. Employing a qualitative approach, we used Python to scrape approximately 1000 testimonials from 'Customer Stories' sections of BI system websites (e.g., Power BI, Tableau, Knime). A large language model (LLM), Llama 2, was used to systematically extract and analyse key variables: motivations, implementation considerations, challenges, security concerns, monetization, collaboration, dynamic capabilities, and behaviour change. Our findings reveal common motivations for BI adoption, typical implementation pathways, and recurring challenges that organizations encounter. Additionally, the study uncovered how successful BI implementations monetize their data-driven insights and foster collaboration across teams. This customer-centric perspective will provide actionable guidance for businesses considering BI investments, enabling them to anticipate potential roadblocks and maximize the return on their BI initiatives.
Keywords
- Artificial Intelligence
- Decision Support Systems
- Machine Learning
Status: accepted
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