M.B.A. doctoral thesis in Business Administration (Information Systems) will be put forth for public defence at the Faculty of Social Sciences, Business and Economics, and Law at Åbo Akademi University.

The thesis is entitled .

The public defence of the doctoral thesis takes place on Tuesday 10 June 2025, at 1PM in auditorium Stora auditoriet, ASA, Vänrikinkatu 3, Turku. You can also follow the defence online. Professor , Stockholm University, Sweden, will serve as opponent and Professor , Åbo Akademi University, as custos.

This thesis investigates the multifaceted impact of e-commerce on customer satisfaction and trust, leveraging advanced Natural Language Processing (NLP) techniques and machine learning (ML) models. The research introduces three novel datasets tailored for Aspect-Based Sentiment Analysis (ABSA) and Aspect Extraction (AE), derived from customer reviews on platforms such as Trustpilot. Key aspects identified include Shipping, Trust, Customer Service, Pricing, and Refund Process. These aspects are systematically analysed to understand their influence on customer experiences.

State-of-the-art transformer-based models, such as BERT and RoBERTa, are employed to perform sentiment classification and aspect extraction. The study demonstrates that these models outperform traditional approaches, such as Support Vector Machines (SVM) and Naïve Bayes (NB), by effectively capturing complex sentiment nuances and extracting relevant aspects from unstructured text data.

In addition, the research integrates Fuzzy-set Qualitative Comparative Analysis (FsQCA) with Large Language Models (LLMs) to uncover intricate causal relationships between various stages of the customer journey and trust outcomes. This novel combination provides deeper insights into how specific aspects of the e-commerce experience influence trust.

The findings offer actionable insights for e-commerce businesses, emphasizing the importance of improving key areas such as shipping, customer service, and refund processes to enhance customer satisfaction and trust. By leveraging advanced NLP techniques and ML models, this research provides a comprehensive framework for analyzing customer feedback and generating managerial insights from unstructured review data.

This thesis contributes to the field by providing a detailed analysis of customer feedback, demonstrating the practical application of cutting-edge technologies in sentiment analysis (SA), and offering evidence-based recommendations for e-commerce businesses to foster customer loyalty and trust.

Laleh Davoodi was born in 1979 in Iran. She can be reached by email [email protected].

The doctoral thesis can be read online through the Doria publication archive.

Click here for a press photo of the doctoral student.

To follow the defence, you need the Zoom software or the Google Chrome browser. You do not need to create a Zoom account to follow the defence. If you install the application, you participate by clicking on the meeting link, after which you should allow the link to open in the Zoom app.