Session Chair: Prof. Andoh-Baidoo, University of Texas Rio Grande Valley, USA
Dr. Oluwaseun E. Olabode, Lecturer, Marketing and Branding Research Centre, University of Bradford, UK
Research shows that big data analytics capability (BDAC) is a major determinant of firm performance. However, limited research has theoretically articulated and empirically tested the mechanisms and conditions under which BDAC influences performance. This study advances existing knowledge on BDAC–performance relationship by drawing on the knowledge-based view and contingency theory to argue that how and when BDAC influences market performance is dependent upon the intervening role of disruptive business model and the contingency role of intensity of competition. This argument is empirically tested on primary data from 360 firms in the United Kingdom. Findings show that the positive effect of BDAC on market performance is partially mediated by a disruptive business model, and this indirect positive effect is strengthened when competitive intensity increases in magnitude. These findings provide new perspectives on the business model processes and competitive conditions under which firms maximize marketplace value from investments in BDACs.
Francis Kofi Andoh-Baidoo, Bright Frimpong, Christian Bautista, and Rakesh Guduru, University of Texas Rio Grande Valley
Like other business-related disciplines, operations management (OM) and supply chain (SCM) scholars have increasingly been engaged in big data research (BDR) where computationally intensive methods are employed on large data sets. In this paper, we test four conjectures that can have negative consequences on the field of OM and SCM in terms of theory development. Using data from the top three journals in the OM and SC, we investigate the differences between BDR and non-BDR publications. Preliminary results reveal significant contrasts between BDR and non-BDR papers concerning theory development, hypothesis testing and research focus. We find that BDR publications are more theoretical than non-BDR. We offer implications of the findings.
Joseph Manga, Emmanuel Ayaburi and Francis Kofi Andoh-Baidoo, University of Texas Rio Grande Valley
Patients’ initial impression can influence the kind of reactions they receive and their subsequent participation. Prior studies use inference models to examine participation as a continuum phenomenon. In the online health communities (OHCs), distinguishing giving participation from receiving participation provides interesting insights at the granular level. Leveraging social presence theory, this study identifies and uses social presence cues in the initial posts of 536 patients to examine patients’ giving and receiving participation in a prominent OHC using decision tree induction as the analytic approach. Findings from the decision tree analytics indicate that, while intimacy is the most important predictor of giving and receiving participation, nonverbal communication is the most important predictor for participation overall. Based on the results, the study offers important contributions for theory development on user giving and receiving participation in OHCs, and a practical guide to help platform managers understand users’ motivation in online community participation.