Business Analytics Education and Business Research Methods: A Framework for Validating Qualitative Results Using Sequential Mixed Methods
Year: 2025
Type: article
Abstract: This paper contributes to advancing qualitative and mixed-methods research in business by offering a comprehensive framework that rigorously validates qualitative findings through an explanatory sequential mixed-methods approach. Qualitative research broadly investigates and exposes facts from an epistemological perspective. However, challenges related to the generalisation and validity of the qualitative results require further inquiry, specifically in computer science and business technology research, such as business data analytics. Thus, this article aims to guide researchers in sequentially validating qualitative research methods using Grounded Theory Methodology GTM, bias reduction, and Structural equation modelling (SEM). The study suggests three sequential stages of mixed-methods research design — the research methods employed (GTM) to extract qualitative categories. Data was collected from postgraduate students using semi-structured interviews that were analysed using a rigorous (GTM) coding technique. Accordingly, results underwent a process of bias reduction as an initial phase of validation of qualitative results, and finally, results were validated using quantitative SEM analysis. The proposed framework meticulously demonstrates how an ordered set of stages in a research design can validate qualitative results while steering a study in conceptualising Business Analytics (BA) as an academic field. The research makes significant contributions to the practical and theoretical implications of qualitative results, mixed methods research design, and validation processes in business computing education research fields, thereby enhancing the understanding and application of these methods in real-world scenarios.
Author: Dalia Al-Eisawi
Institution: Princess Sumaya University for Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4411260734