STUDENT & ALUMNI PEER REVIEWED JOURNAL ABSTRACTS
van Esch, P., Cui, Y., Arli, D., & Eskridge, B. (2025)
van Esch, P., Cui, Y., Arli, D., & Eskridge, B. (2025)
Vincent, V. U., Edirisinghe Vincent, N., & Prieto, L. (2025)
Williams, R., Manley, S., Aaron, J., & Daniel, F. (2025)
Consumer Perceptions of GenAI in Service Delivery: Scale Development and Validation
Virtual Social Influencer Scale Development and Validation: An Algorithmic Approach
Journal of Small Business Strategy, 35(3), 76-95 “Comprehensive Strategic Approach (CSA)”: The Evolution From Construct to an Emerging Theoretical Framework In 2018, Williams Jr, Manley, Aaron, and Daniel’s research found that strategic planning, goal setting, and financial analysis, together, formed a higher-order construct that we labeled comprehensive strategic approach (CSA). Also, they found that when small business leaders apply CSA, firm performance is enhanced. Numerous researchers have referenced this work. However, is CSA a valid concept? To address that question, we apply conceptual replication (where approaches are changed to provide more evidence) to re-test CSA. Our conceptual replication indicates, con - sistent with the original study, that strategic planning, goal setting, and financial analysis form CSA, and that CSA has a positive relationship with small business performance. Additionally, based on the many citations of the original article, this replication utilizing improved measures, and the robustness of the results in a dynamic industry both before and after COVID-19, we argue that CSA is a new theoretical framework that is useful for small business practitioners and researchers alike.
Intuition or Artificial Intelligence? When do Decision-Makers Rely on AI Decision-Aids for Ill-Structured Problems
Australasian Marketing Journal, 14413582251395758
Australasian Marketing Journal, 1-15
American Journal of Business, 40(4), 161-183
The incorporation of Generative Artificial Intelligence (GenAI) technologies into service delivery processes has surfaced as a transformative trend with serious implica - tions for several industries and sectors. In response to the growing need for comprehensive frameworks to examine the impact of AI-driven service delivery systems, we pro - pose the development of a GenAI service delivery scale. Grounded in socio-technical systems theory, the scale aims to measure consumer perceptions of GenAI in service delivery. The development of the scale holds significant implications for researchers, practitioners, and policymak - ers, providing a standardized measure of consumer percep - tions of GenAI in service delivery. Through collaborative efforts and ongoing refinement, the GenAI service delivery scale aims to advance our understanding of consumer per - ceptions of AI-driven service delivery and contribute to the progress of best practices in the field.
Influencer marketing has emerged as a prominent strat - egy for brands to engage with consumers in the digi - tal age, with virtual social influencers playing a pivotal role in shaping consumer perceptions and behaviors. To effectively evaluate influencer effectiveness and optimize marketing strategies, there is a need for a standard - ized measure. We introduce a virtual social influencer scale, which provides a comprehensive framework for assessing their performance across the dimensions of trustworthiness and connectedness. Drawing on insights from socio-technical systems theory, the use of an algorith - mic approach in scale development spawns from the psy - chology literature. This research provides the foundation of its introduction into the marketing discipline. Future research directions include refining the scale, addressing algorithmic limitations, and exploring cross-cultural vari - ations to enhance the scale’s validity and applicability in diverse contexts. The virtual social influencer scale rep - resents a significant advancement in influencer marketing research, offering valuable insights to marketers, research - ers, and practitioners in the digital marketing landscape.
With its superior analytical and decision-making capa - bilities, Artificial Intelligence (AI) is a valuable decision aid that enables organizational decision-makers to make effective and efficient decisions while reducing human errors. However, decision-makers continue to dispropor - tionately rely on their intuition, especially when making decisions for ill-structured problems characterized by uncertainty and ambiguity. In this study, we assess the conditions in which decision-makers are willing to forego their intuition and rely on an AI decision aid for an ill-structured problem.
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