STUDENT & ALUMNI PEER REVIEWED JOURNAL ABSTRACTS
Harrison, D. E., Ajjan, H., Ferrell, L., Hochstein, B. W., & Johnson, R. (2025)
Houston, C. M., & Hamrick, J.D. (2025)
Iyer, P., Nikolov, A. N., Sleep, S., Eskridge, B., Moke, D. M., & Hutchings, J. (2025)
Jozani, M., Williams, J. A., Aleroud, A., & Bhagat, S. (2025)
Bridging the Gap: Aligning Developers and Managers in Implementing AI Ethics
Failure of Cooperative Self- Regulation: An Exploration of Cooperative Regulatory Violations
Navigating the AI Wave for Sales Management: The Mediating Role of Marketing Agility
Emotional and Informational Dynamics in Question- Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach
Journal of Marketing Theory and Practice, 1-20
Annals of Public and Cooperative Economics, 96(2), 225-256
Industrial Marketing Management, 127, 62-73
Information Systems Frontiers, 27(5), 1899-1923
Concerns over the ethics of artificial intelligence (AI) are increasing. Establishing a baseline for AI ethical decision-making should include the views of managers and developers of the technology along with ethics and compliance experts in organizations. An understanding of these views provides the foundation of AI ethics princi - ples. In practice, ethical principles are discussed but not necessarily always considered. We address this relevant gap across two studies. Study 1 explores the role of prin - ciples via manager and executive interviews and uncovers a disconnect, finding that ethical principles for AI are not as widely implemented as one might expect. To under - stand ways that principles can be more uniformly adopted, Study 2 surveys AI developers and provides evidence that attitudes toward technology, self-efficacy, and subjective norms affect the development of ethical AI programs. Our findings suggest AI managers need to provide leadership in communicating principles translated into rules for developers.
Cooperative organizations are built on strong principles and values rooted in equity and social responsibility. How - ever, we observe a group of cooperatives that have become embroiled in material legal violations. They egregiously commit 602 violations over 20 years related to the safety and just treatment of their members, employees, commu - nity, customers and the environment, with penalties for misconduct totaling $2.3 billion. This directly opposes the ideals espoused by cooperatives, and their susceptibility to misconduct challenges the assumption that cooperatives are immune to the ethical failures seen in public firms. We find that when government agencies bring enforcement actions against misconduct, it reduces future negative be - haviour. We also find that some cooperatives make changes to the leaders who are present at the time of wrongdoing, and these cooperatives have a decrease in the severity of misconduct in the future. Collectively, these findings sug - gest that a number of cooperatives harm the groups they claim to serve but that government enforcement and coop - erative disciplining carried out in response to regulatory violations can mitigate future misconduct.
Sales management has undergone significant transforma - tions in response to the changing external environment and the adoption of emerging technologies such as arti - ficial intelligence (AI) tools, including Seamless AI and Einstein (Sales AI). These technological advancements have reshaped the industrial sales management landscape and customer journeys. To thrive in this evolving envi - ronment, industrial firms must embrace marketing agility, characterized by proactiveness, responsiveness, flexibility, and speed. This study explores business-to-business (B2B) firms’ challenges when integrating AI into their sales man - agement processes. Based on the dynamic capabilities lit - erature, we argue that marketing agility plays a mediating role in the adoption of AI technology and sales team per - formance in uncertain business contexts. Additionally, we investigate the moderating effects of marketing influence and internal organizational climate on the relationship among AI technology adoption, agility, and performance. We collected data from B2B sales managers from compa - nies generating at least $5 million in annual revenues and employing more than 100 individuals. Our findings under - score the necessity for aligning strategic decisions, such as technology adoption (and use), with marketing agility to affect firm performance positively. Theoretically, this study contributes to the emerging literature on the facilita - tors of marketing agility in B2B firms.
Online health communities (OHCs) offer emotional and informational support to their users. However, past re - search has primarily treated these supports as separate, but they coexist in messages, making it essential to consider the emotional valence of text to understand the support being provided. This study examines how aligning ques - tions and responses in OHCs reduces information gaps, and enhances support quality and perceived helpfulness. We use a labeled data set of question-response pairs to develop multimodal machine learning models to predict support interactions. Using explainable AI, we reveal the emotions within support exchanges, underscoring how emotional valence in the text determines informational support in OHCs and provide insight into the interaction between emotional and informational support. This study refines social support theory and establishes a foundation for decision aids and emotion-sensitive AI systems to de - liver personalized social support tailored to users’ infor - mational and emotional needs.
Made with FlippingBook - Online catalogs