Assessing the nexus of Generative AI adoption, ethical considerations and organizational performance

Numerous enterprises employ Generative AI (GenAI) for a plethora of business operations, which can enhance organizational effectiveness. The adoption might be driven by multiple factors influencing the business landscape. Additionally, numerous ethical considerations could impact the deployment of GenAI. This unique study investigated how organizations adopt GenAI and its effects on their performance. Further, this research utilized institutional theory and ethical guidelines for AI design to develop a research framework examining how organizations adopt GenAI and its impact on their performance. A survey of 384 managers from information technology (IT) and information technology-enabled services (ITeS) companies was conducted. Data analysis was done using PLS-SEM to examine and validate the proposed model. The study outcome reveals that institutional pressures, i.e., coercive, normative and mimetic forces, influence the use of GenAI in organizations. It was also found that fairness, accountability, transparency, accuracy and autonomy influence the use of GenAI. Also, the results divulge that the use of GenAI influences organizational performance and is moderated by organizational innovativeness. This study provides insights to developers of GenAI, senior management of companies, the government and IT policymakers by highlighting the institutional pressures and ethical principles influencing the use of GenAI.<br/

Adoption of artificial intelligence (AI) based employee experience (EEX) chatbots

Purpose: AI-based chatbots are revamping employee communication in organizations. This paper examines the adoption of AI-based employee experience chatbots by employees. Design/methodology/approach: The proposed model is developed using behavioral reasoning theory and empirically validated by surveying 1,130 employees and data was analyzed with PLS-SEM. Findings: This research presents the “reasons for” and “reasons against” for the acceptance of AI-based employee experience chatbots. The “reasons for” are – personalization, interactivity, perceived intelligence and perceived anthropomorphism and the “reasons against” are perceived risk, language barrier and technological anxiety. It is found that “reasons for” have a positive association with attitude and adoption intention and “reasons against” have a negative association. Employees' values for openness to change are positively associated with “reasons for” and do not affect attitude and “reasons against”. Originality/value: This is the first study exploring employees' attitude and adoption intention toward AI-based EEX chatbots using behavioral reasoning theory