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.

The research paper focuses on reviewing the antecedents affecting human employees' behaviour under Human-Robot collaboration (HRC). The extensive review of literature gives the antecedent outcome framework, highlighting the factors influencing human employee behaviour in not only adopting robots but also collaborating with robots in the evolved workplace to provide evidence-based recommendations. The evidence-based proposed framework applies Daniel Tranfield's systematic literature review (SLR) guidelines, highlighting the outcomes in the research paper. The findings suggest there are various existing antecedents, drivers and barriers influencing Human employee behavior, Robotic acceptance, Employee job performance, work engagement, actual usage of robots, and job satisfaction.This paper examines the antecedents that affect humans in HRC. The various drivers and barriers affecting employees at individual, team, and organizational levels in achieving the desired employee behavior are proposed through the Antecedent-Outcome framework, synthesizing the implications of the practice.

The evolved business ecosystem in Industry 5.0 requires every individual to interact with computers daily. Combined intelligence combines human and computer intelligence to align for teaming up a smarter team, reaping better organizational benefits. HCI involves AI, IOT, machine learning, neurocognition, NLP, generative AI, RPA, social robotics, etc., accentuating the need for humans to contribute equally to creating team synergy. Actual human-computer interactions require a mature environment to enable easy adoption by employees. Our research paper proposes a conceptual framework listing the various drivers and inhibitors affecting employees' decisions toward adopting intelligent computers at the workplace. The implications of the research paper can be mapped under the UN's 8 and 9 sustainable development goals. The proposed conceptual framework will give a complete understanding of the significant factors required for consideration for the successful adoption of intelligent computers by employees.