3T Framework for AI Adoption in Human Resource Management: A Strategic Assessment Tool of Talent, Trust, and Technology

Artificial intelligence (AI) is steadily entering and transforming the management, work, and organizational ecosystems. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. AI applications are increasingly assisting also Human Re-source Management (HRM) in undertaking time-critical tasks and managerial and administrative decision-making. However, more in-depth and comprehensive studies are needed to understand the specific factors affecting the full adoption of AI technology from a multi-level viewpoint and address the potential limitations of AI appropriation or its adverse outcomes in HRM.The purpose of this study is to investigate the conditions in which human talent may take advantage of the unique opportunities offered by AI. However, whereas previous studies were conducted on the individual perception of AI and technology readiness or adoption, an integrated approach aiming to combine talent management-related dimensions and managerial-related dimensions is still not avail-able. For this research gap, we build a strategic management assessment frame-work of the driving factors of Talent, Trust, and Technology (3T) in AI adoption in HRM. We investigate the impact of these trends on the human-related and technology ecosystems and provide an integrated analysis of individual micro (talent management) organizational macro (trust and technology) adoption of AI technology.The paper advances the current definition and understanding of individual human facilitators and impediments behind the ability to speed up the adoption of AI-based technology. The practical contribution can facilitate the human-centered and trustworthy design and adoption of AI

Artificial Intelligence, Human Talent, and Trust: A Management Tool for Assessing and Supporting AI Adoption in HRM

Artificial intelligence (AI) is steadily entering and transforming the management, work, and organizational ecosystems. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. AI applications are increasingly assisting also Human Resource Management (HRM) in undertaking time-critical tasks and managerial and administrative decision-making. However, more in-depth and comprehensive studies are needed to understand the specific factors affecting the full adoption of AI technology from a multi-level viewpoint and address the potential limitations of AI appropriation or its adverse outcomes in HRM. The purpose of this study is to investigate the conditions in which human talent may take advantage of the unique opportunities offered by AI. However, whereas previous studies were conducted on the individual perception of AI and technology readiness or adoption, an integrated approach aiming to combine talent management-related dimensions and managerial-related dimensions is still not available. For this research gap, we build a strategic management assessment framework of the driving factors of Talent, Trust, and Technology in AI adoption in HRM. We investigate the impact of these trends on the human-related and technology ecosystems and provide an integrated analysis of individual micro (talent management) organizational macro (trust and technology) adoption of AI technology. The paper advances the current definition and understanding of individual human facilitators and impediments behind the ability to speed up the adoption of AI-based technology. The practical contribution can facilitate the human-centered and trustworthy design and adoption of AI

Smartphone Use Behavior and Quality of Life, Influence of Two Awareness Modes

How does smartphone use behavior affect quality of life factors? In what mental states does smartphone disturb quality of life and when it improves it? The following work suggests new insights into smartphone use behavior, mainly regarding two distinct smartphone modes of use that affect quality of life in opposite ways. The aware smartphone mode of use reflects an active lifestyle, while the unaware mode of use reflects the use of the smartphone in conjunction with other activities. Using data from 215 individuals who reported their quality of life and smartphone use habits, we show that high levels of smartphone use in the unaware mode have a significant negative effect on one’s quality of life. However, the results show a mild and less clear positive effect when the individual demonstrates an aware mode of use. Using structural equation modeling, we identify three latent factors within the quality of life construct and measure the effect of each factor on quality of life. (i) The functioning latent factor, which is an individual’s ability to function well in his or her daily life, is not affected by smartphone use behavior. In contrast, (ii) the competence latent factor, which can be associated with the lack of negative emotions/pain, and (iii) the positive feelings latent factor both show a clear effect with the smartphone-unaware mode of use. Since smartphones are currently an interface between the self and cyber space, as well as between the self and other individuals online, these results are important when considering social wellbeing in relation to digital human behavior, smartphone addiction and a healthy mode of use

Actionable trust in service organizations: A multi-dimensional perspective

Purpose. The paper explores attitudinal and behavioral antecedents of trust and respective outcomes within the service industry at multiple levels of analysis. Method. Data were obtained from academic and administrative service providers (n = 76) and clients (n = 868) using paper-and-pencil and on-line questionnaires. Findings. Individual, dyadic and organizational factors throughout service delivery affect trust as a behavior. Value fit between service providers and clients contributed to trust as a behavioral action. Implications. Our findings confirm that success of service delivery is a multi-dimensional phenomenon. It confirms that actionable trust is a dominant factor in service success, thus calls for the need to pay attention to the relational aspect of service encounters. Finally, value fit between clients and service providers is crucial in achieving trust throughout the service interaction. Originality. The study provides a management tool for measuring action based trust within service organizational context