In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations

Increasing acceptance of medical AI: The role of medical staff participation in AI development

BACKGROUND

Medical artificial intelligence (AI) in varying degrees has exerted significant influence on many medical fields, especially in the midst of the COVID-19 pandemic. However, little is known regarding how to address the reluctance of medical staff to use AI technology. While recent research has highlighted the importance of medical staff participation in the development of AI, the current understanding of influence of medical staff participation on acceptance of AI is limited.

OBJECTIVES

To provide insights into the mechanism that how medical staff participation impacts on the medical staff's acceptance of AI and to examine the moderating effect of speciesism.

METHODS

This study was conducted from 6th August to 3rd September. Data was collected from doctors and nurses and a total of 288 valid questionnaires were obtained. Smart PLS 3.2.8 was used as partial least square (PLS) software to validate the research model.

RESULTS

The study determined that medical staff participation had a significant impact on acceptance of medical AI-IDT (β = 0.35, p ≤ 0.001) and acceptance of medical AI-ADT (β = 0.44, p ≤ 0.001). The results also show that AI self-efficacy and AI anxiety have significant mediating effects and speciesism has significant moderating effects among the theoretical model.

CONCLUSIONS

This study provides insights into ways to explore influence factors of acceptance of AI based on user participation perspective. The results indicate that medical staff participation enhances acceptance of medical AI through the cognitive path (i.e., AI self-efficacy) and the affective path (i.e., AI anxiety). These results have practical implications for how organizations assist the staff to accommodate themselves to AI technology in the future.

Employees' Appraisals and Trust of Artificial Intelligences' Transparency and Opacity

Artificial intelligence (AI) is being increasingly used as a decision agent in enterprises. Employees' appraisals and AI affect the smooth progress of AI-employee cooperation. This paper studies (1) whether employees' challenge appraisals, threat appraisals and trust in AI are different for AI transparency and opacity. (2) This study investigates how AI transparency affects employees' trust in AI through employee appraisals (challenge and threat appraisals), and (3) whether and how employees' domain knowledge about AI moderates the relationship between AI transparency and appraisals. A total of 375 participants with work experience were recruited for an online hypothetical scenario experiment. The results showed that AI transparency (vs. opacity) led to higher challenge appraisals and trust and lower threat appraisals. However, in both AI transparency and opacity, employees believed that AI decisions brought more challenges than threats. In addition, we found the parallel mediating effect of challenge appraisals and threat appraisals. AI transparency promotes employees' trust in AI by increasing employees' challenge appraisals and reducing employees' threat appraisals. Finally, employees' domain knowledge about AI moderated the relationship between AI transparency and appraisals. Specifically, domain knowledge negatively moderated the positive effect of AI transparency on challenge appraisals, and domain knowledge positively moderated the negative effect of AI transparency on threat appraisals.

How and why non-balanced reciprocity differently influence employees’ compliance behavior: The mediating role of thriving and the moderating roles of perceived cognitive capabilities of artificial intelligence and conscientiousness

Previous studies have paid more attention to the impact of non-balanced reciprocity in the organization on employees’ behaviors and outcomes, and have expected that the reciprocity norm could improve employees’ compliance behavior. However, there are two distinct types of non-balanced reciprocity, and whether generalized reciprocity affects employees’ compliance behavior rather than negative reciprocity and its mechanisms has not been further explored so far. Building on the social exchange theory and cognitive appraisal theory, we established and examined a model in a scenario-based experiment across a two-stage survey of 316 participants. In this article, we propose that generalized reciprocity (relative to negative reciprocity) positively influences employees’ compliance behavior, and thriving at work mediates its relationship. Furthermore, we argue that the positive association between generalized reciprocity and thriving at work is moderated by the perceived cognitive capabilities of artificial intelligence (AI). This association is amplified for people high in the perceived cognitive capabilities of AI. We also propose that the positive association between thriving at work and compliance behavior is moderated by conscientiousness, such that the association is amplified for people high in conscientiousness. These findings have theoretical and practical implications.

Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.