Employee-driven innovation (EDI) burgeons as an important mechanism to drive the exploration activities by making the general employees responsible for innovation. However, little is known about the conditions under which EDI is most effective. To get a better understanding of EDI, we examine how Stedin, an established global player within the energy distribution industry based in the Netherlands, involves its general employees in innovation activities. Stedin actively supports EDI through strategic programmes designed to stimulate employee innovation. Our findings highlight that collaboration is a main driver of EDI at Stedin. In the early implementation phases, dynamic, heterogeneous, informal and distant collaborations are essential, while the later phases benefit from more stability and intimacy. The insights from our detailed case study provide actionable guidelines for organising EDI initiatives in practice.

Although previous literature has identified personal branding as an important concept in marketing, little is understood about the effects of personal brand equity (PBE) during the personnel selection process. To address this research gap, we performed two experimental studies and one field study in the domains of sales and engineering to examine the effect of candidates’ PBE on hiring outcomes through recruiters’ perceptions. This research draws upon signaling theory and an integration of the accessibility-diagnosticity model with the competence-based view of careers and regards PBE as the interpreted outcome of personal branding signals, reflecting how recruiters perceive and evaluate the value conveyed by job candidates. We unveil that candidates’ PBE positively predicts hiring recommendation and that credibility mediates this relationship. Moreover, job hierarchy and objective job qualifications appear to negatively moderate the relationship between candidates’ PBE and hiring recommendation. Our findings also indicate that objective job qualifications negatively interact with candidates’ PBE in predicting their credibility. The present research contributes to personal branding and selection research by offering novel insights into the role of PBE during the interview process, thereby providing guidance for job candidates and practitioners.

Purpose: Previous literature has identified human resources as a key source of competitive advantage in organizations of all sizes. However, Small and Medium-sized Enterprises (SMEs) face difficulty in comprehensively implementing all recommended Human Resource Management (HRM) functions. In this study, we shed light on the field of HRM in SMEs by focusing on the context of Greek Small and Medium-sized Hotels (SMHs), which represent a dominant private sector employer across the country. Design/methodology/approach: Using a fuzzy-set qualitative comparative analysis (fsQCA) and 34 in-depth interviews with SMHs' owners/managers, we explore the HRM conditions leading to high levels of performance, while taking into consideration the influence of internal key determinants. Findings: We uncover three alternative successful HRM strategies that maximize business performance, namely the Compensation-based performers, the HRM developers and the HRM investors. Each strategy fits discreet organizational characteristics related to company size, ownership type and organizational structure. Originality/value: To the best of the authors' knowledge this is among the first empirical studies that examine different and equifinal performance-enhancing configurations of HRM practices in SMHs.

The present research aims to highlight the underlying factors that drive students’ adoption of the ChatGPT chatbot in higher education. This study extends the meta-UTAUT framework by including additional exogenous factors of anthropomorphism, trust, design novelty, and institutional policy. Empirical examination with Structural Equation Modelling among 355 students in Dutch higher education institutions revealed attitude and behavioural intention as significant positive predictors of students’ ChatGPT use behaviour. Institutional policy negatively moderated the effect of behavioural intention on use behaviour. Behavioural intention was significantly and positively influenced by attitude, performance expectancy, social influence, and facilitating conditions. Anthropomorphism, design novelty, trust, performance expectancy, and effort expectancy were unveiled as significant positive antecedents of attitude. The central theoretical contributions of this research include investigating students’ use behaviour instead of behavioural intention, establishing attitude as a core construct, underlining additional antecedents of attitude, and highlighting the importance of institutional policy. The present study contributes to prior research on technology adoption, especially in the area of artificial intelligence in education. The findings yield valuable insights for chatbot designers, product managers, and higher education policy writers.

While the rise of artificial intelligence (AI) tools holds promise for delivering benefits, it is important to acknowledge the associated risks of their deployment. In this article, we conduct a focused literature review to address two central research inquiries concerning ChatGPT and similar AI tools. Firstly, we examine the potential pitfalls linked with the development and implementation of ChatGPT across the individual, organizational, and societal levels. Secondly, we explore the role of a multi-stakeholder responsible research and innovation framework in guiding chatbots’ sustainable development and utilization. Drawing inspiration from responsible research and innovation and stakeholder theory principles, we underscore the necessity of comprehensive ethical guidelines to navigate the design, inception, and utilization of emerging AI innovations. The findings of the focused review shed light on the potential perils of ChatGPT implementation across various societal levels, including issues such as devaluation of relationships, unemployment, privacy concerns, bias, misinformation, and digital inequities. Furthermore, the proposed multi-stakeholder Responsible Research and Innovation framework can empower AI stakeholders to proactively anticipate and deliberate upon AI’s ethical, social, and environmental implications, thus substantially contributing to the pursuit of responsible AI implementation.

Purpose: Staffing is a tool that employers use to decrease information asymmetry when hiring employees. However, how staffing effectiveness evolves according to employee demographic characteristics has not been thoroughly elucidated to date. The purpose of this paper is to develop and test a model linking staffing practices, age and employee performance. Design/methodology/approach: Using a stratified sample of 1,254 employees, the authors hypothesize: the main effects of staffing practices on employee performance, the main effects of age on employee performance and the moderating effects of age on the relationship between staffing practices and employee performance. Findings: The results show significant positive effects of staffing and age on employee performance and a negative moderating effect of age on the abovementioned relationship. Practical implications: The organizations are urged to invest in recruitment and selection practices and implement focused practices that appeal to an aging workforce. Originality/value: The paper is the first to explore the topic of aging workforce and the efficiency of staffing practices on employee performance in the context of the Greek labor force. The authors discuss the results, theoretical contributions, practical implications and future research directions in light of the challenge of managing an aging workforce.