Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Post-acceptance IS use is the key to leveraging value from IS investments. However, it also poses many demands on the user. Drawing on the challenge-hindrance stressor framework, this study develops a theory to explain how and why IS use stressors influence post-acceptance use. We identify two different types of IS use stressors: challenge IS use stressors and hindrance IS use stressors. We hypothesize that they are appraised through challenge IS use appraisal and hindrance IS use appraisal, respectively, through which they influence routine use and innovative use. We evaluate our hypotheses by surveying 178 users working in one organization and analyze the data collected using consistent partial least square (PLSc). We find that challenge IS use stressors positively influence routine use and innovative use via challenge IS use appraisal. Hindrance IS use stressors negatively influence routine use via hindrance IS use appraisal. We then dive deeper into these findings using a two-step fuzzy set qualitative comparative analysis (fsQCA), identifying the presence of challenge IS use stressors and challenge IS use appraisal as necessary conditions for high innovative use. We also reveal that the presence of hindrance IS use stressors and hindrance IS use appraisal only influences routine use and innovative use in the absence of challenge IS use stressors and challenge IS use appraisal. We discuss the practical relevance and transferability of our findings based on a comprehensive applicability check. Our findings advance IS scholarship of IS use stress and post-acceptance use by showing how routine use and innovative use emanate from IS use stressors.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

IT offers significant benefits both to individuals and organisations, such as during the Covid-19 pandemic where technology played a primary role in aiding remote working environments; however, IT use comes with consequences such as ‘technostress’ – stress arising from extended use of technology. Addressing the paucity of research related to this topic, in this study, we examine the role of mindfulness and IT mindfulness to both mitigate the impact of technostress and alleviate its negative consequences; revealing that mindfulness can reduce technostress and increase job satisfaction, while IT mindfulness can enhance user satisfaction and improve task performance. Moreover, our work sheds light on the under-researched relationship between mindfulness and IT mindfulness; showing that the latter has a stronger influence on IT related outcomes; revealing the valuable role of mindfulness and IT mindfulness in the workplace and offering important implications to theory and practice.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

Background The rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees. Objective This study employs the framework of hindrance and challenge stressors to examine the impact of leadership AI awareness on employees’ voice behavior (VB). Methods We analyzed survey data from 487 employees in knowledge-intensive industries across China. Results Leadership AI awareness, under hindering stressors, positively correlates with employees’ job insecurity (JI), which inversely affects their VB; JI acts as a mediator. Conversely, under challenging stressors, leadership AI awareness enhances employees’ intrinsic motivation, which positively correlates with VB. Intrinsic motivation also mediates this relationship. Additionally, AI self-efficacy moderates the influence of leadership AI awareness on employees’ VB. Conclusions This study elucidates a dual-path mechanism through which leadership AI awareness affects employees’ VB and delineates the boundary conditions for this influence. The findings offer managerial insights for stimulating employees’ VB amidst AI integration in the workplace.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

Background The rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees. Objective This study employs the framework of hindrance and challenge stressors to examine the impact of leadership AI awareness on employees’ voice behavior (VB). Methods We analyzed survey data from 487 employees in knowledge-intensive industries across China. Results Leadership AI awareness, under hindering stressors, positively correlates with employees’ job insecurity (JI), which inversely affects their VB; JI acts as a mediator. Conversely, under challenging stressors, leadership AI awareness enhances employees’ intrinsic motivation, which positively correlates with VB. Intrinsic motivation also mediates this relationship. Additionally, AI self-efficacy moderates the influence of leadership AI awareness on employees’ VB. Conclusions This study elucidates a dual-path mechanism through which leadership AI awareness affects employees’ VB and delineates the boundary conditions for this influence. The findings offer managerial insights for stimulating employees’ VB amidst AI integration in the workplace.

Abstract

Background The rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees. Objective This study employs the framework of hindrance and challenge stressors to examine the impact of leadership AI awareness on employees’ voice behavior (VB). Methods We analyzed survey data from 487 employees in knowledge-intensive industries across China. Results Leadership AI awareness, under hindering stressors, positively correlates with employees’ job insecurity (JI), which inversely affects their VB; JI acts as a mediator. Conversely, under challenging stressors, leadership AI awareness enhances employees’ intrinsic motivation, which positively correlates with VB. Intrinsic motivation also mediates this relationship. Additionally, AI self-efficacy moderates the influence of leadership AI awareness on employees’ VB. Conclusions This study elucidates a dual-path mechanism through which leadership AI awareness affects employees’ VB and delineates the boundary conditions for this influence. The findings offer managerial insights for stimulating employees’ VB amidst AI integration in the workplace.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

Background The rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees. Objective This study employs the framework of hindrance and challenge stressors to examine the impact of leadership AI awareness on employees’ voice behavior (VB). Methods We analyzed survey data from 487 employees in knowledge-intensive industries across China. Results Leadership AI awareness, under hindering stressors, positively correlates with employees’ job insecurity (JI), which inversely affects their VB; JI acts as a mediator. Conversely, under challenging stressors, leadership AI awareness enhances employees’ intrinsic motivation, which positively correlates with VB. Intrinsic motivation also mediates this relationship. Additionally, AI self-efficacy moderates the influence of leadership AI awareness on employees’ VB. Conclusions This study elucidates a dual-path mechanism through which leadership AI awareness affects employees’ VB and delineates the boundary conditions for this influence. The findings offer managerial insights for stimulating employees’ VB amidst AI integration in the workplace.

Abstract

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

Information Technology (IT) has been vastly characterized as a double-edged sword, offering significant benefits to individuals but at the same time bringing certain negative consequences, such as technostress. Technostress can severely affect individuals in the workplace, causing fatigue, loss of motivation, inability to concentrate, dissatisfaction at work and reduced productivity among others; thus significantly affecting individual well-being work as well as increasing costs for organisations. Recently, studies have shown the beneficial role of mindfulness in reducing technostress experiences of individuals; however, the evidence that exists until today is very limited, and mostly focused on evaluating the impact of mindfulness on technostress and its negative consequences. As the current research stands, at the moment it is relatively unknown how mindfulness affects the underlying mechanisms of technostress experiences of individuals. Through semi-structured interviews with 10 knowledge workers, the current study explores how mindfulness alleviates technostress within the workplace, by investigating the experiences of more mindful employees and learning from their practices. Findings offer a deeper insight into the relationship of mindfulness and technostress, revealing a toolkit of the underlying strategies that more mindful and IT mindful individuals deploy as well as their perceptions during technostress experiences at work thus shedding light on the path between mindfulness and technostress. The study contributes both to academia and practice, offering important implications to managers and practitioners that strive to improve employee well-being within organisations.

Abstract

Background The rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees. Objective This study employs the framework of hindrance and challenge stressors to examine the impact of leadership AI awareness on employees’ voice behavior (VB). Methods We analyzed survey data from 487 employees in knowledge-intensive industries across China. Results Leadership AI awareness, under hindering stressors, positively correlates with employees’ job insecurity (JI), which inversely affects their VB; JI acts as a mediator. Conversely, under challenging stressors, leadership AI awareness enhances employees’ intrinsic motivation, which positively correlates with VB. Intrinsic motivation also mediates this relationship. Additionally, AI self-efficacy moderates the influence of leadership AI awareness on employees’ VB. Conclusions This study elucidates a dual-path mechanism through which leadership AI awareness affects employees’ VB and delineates the boundary conditions for this influence. The findings offer managerial insights for stimulating employees’ VB amidst AI integration in the workplace.

Abstract

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Abstract

Introduction The increasing digitalization of office work, especially with the rise of remote work, has amplified the impact of technostress in organizations. This study examines how technostress influences employee voice behavior. Grounded in the challenge-hindrance stressor framework, we hypothesize that certain aspects of technostress may positively affect voice behavior, psychological safety, intrinsic motivation, and affective commitment. Our findings provide insights for organizations to understand these dynamics and develop managerial strategies that foster positive workplace behaviors. Methods We conducted a cross-sectional study using an online questionnaire with office employees experienced in remote work (N = 361). Data were analyzed using three-step hierarchical regression models to assess the direct effects of technostress on voice behavior. Additionally, structural equation models (SEM) were used to explore indirect effects and the moderating roles of psychological safety, intrinsic motivation, and affective commitment. Results Our findings reveal that technostress consists of challenge and hindrance components. Techno-uncertainty and, to a lesser extent, techno-overload acted as challenge stressors, positively influencing voice behavior directly or through intrinsic motivation and affective commitment. Conversely, techno-insecurity and techno-complexity emerged as hindrance stressors. Techno-insecurity negatively affected all measured variables, while techno-complexity reduced voice behavior and psychological safety. We observed a positive linear relationship between challenge stressors and voice behavior, a negative linear relationship with hindrance stressors, and a weak U-shaped relationship between techno-insecurity and promotive voice. Discussion Our study underscores the need to analyze technostress through the challenge-hindrance stressors framework, as its components can both enhance and hinder employee motivation and voice behavior. We interpret our findings through the lens of Conservation of Resources (COR) theory, emphasizing a proactive rather than a defensive or reactive approach. Additionally, we propose managerial strategies to encourage voice behavior in technostress-prone work environments.

Abstract

Information Technology (IT) has been vastly characterized as a double-edged sword, offering significant benefits to individuals but at the same time bringing certain negative consequences, such as technostress. Technostress can severely affect individuals in the workplace, causing fatigue, loss of motivation, inability to concentrate, dissatisfaction at work and reduced productivity among others; thus significantly affecting individual well-being work as well as increasing costs for organisations. Recently, studies have shown the beneficial role of mindfulness in reducing technostress experiences of individuals; however, the evidence that exists until today is very limited, and mostly focused on evaluating the impact of mindfulness on technostress and its negative consequences. As the current research stands, at the moment it is relatively unknown how mindfulness affects the underlying mechanisms of technostress experiences of individuals. Through semi-structured interviews with 10 knowledge workers, the current study explores how mindfulness alleviates technostress within the workplace, by investigating the experiences of more mindful employees and learning from their practices. Findings offer a deeper insight into the relationship of mindfulness and technostress, revealing a toolkit of the underlying strategies that more mindful and IT mindful individuals deploy as well as their perceptions during technostress experiences at work thus shedding light on the path between mindfulness and technostress. The study contributes both to academia and practice, offering important implications to managers and practitioners that strive to improve employee well-being within organisations.

Abstract

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Abstract

PurposeIndividuals can improve their task performance by using information and communications technology (ICT). However, individuals who use ICT may also suffer from negative outcomes, such as burnout and anxiety, which lead to poorer performance and well-being. While researchers have studied the positive outcomes of ICT use in the aggregate, the same has not been done for negative outcomes.Design/methodology/approachThis study uses a meta-analysis of 52 studies to examine the relationship between ICT use and negative outcomes, and the influence of job autonomy on ICT use and the negative outcomes of ICT use. Job autonomy is relevant because a higher level of job autonomy allows individuals to decide how, how often and when they will use ICT that is causing negative outcomes for their work.FindingsThe results of the meta-analysis revealed that ICT use increased negative job outcomes and that, unexpectedly, autonomy exacerbated this effect.Research limitations/implicationsThe results of this study point to the prevalence of negative outcomes from ICT use among individuals. Researchers should study how users may potentially restrict the value that organizations may be able to obtain from the implementation of new systems, especially whether individual-level negative outcomes could coalesce into a collective resistance. There also needs to be further research into the motivating and inhibiting roles of autonomy in enhancing ICT use, while mitigating its negative impacts simultaneously.Originality/valueThe study provides an aggregate analysis of the negative impacts of ICT use among individuals and the role of autonomy in the relationship.

Abstract

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.

Abstract

This study investigates the complex interplay between organizationally prescribed perfectionism (OPP), job insecurity, counterproductive work behavior (CWB), and self-efficacy in learning artificial intelligence (AI) in the context of modern organizations. Based on several theories, the current research suggests and tests a moderated mediation model. Using a three-wave time-lagged design with data collected from 412 workers across various South Korean corporations, we examine how OPP influences CWB both directly and indirectly through job insecurity, and how self-efficacy in AI learning moderates the OPP–job insecurity link. Our results show that OPP is positively linked to CWB, and this association is partially mediated by job insecurity. Moreover, AI learning self-efficacy functions as a moderator in the OPP–job insecurity link, such that the positive link is weaker for members with higher levels of AI learning self-efficacy. These findings extend our understanding of perfectionism in organizational settings and highlight the role of technological self-efficacy in mitigating the negative impacts of perfectionist cultures. This research may contribute to the literature on perfectionism, CWB, and technological adaptation at work, and has important implications for managing high-performance cultures in the period of rapid technological advancement.

Abstract

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

This study investigates the complex interplay between organizationally prescribed perfectionism (OPP), job insecurity, counterproductive work behavior (CWB), and self-efficacy in learning artificial intelligence (AI) in the context of modern organizations. Based on several theories, the current research suggests and tests a moderated mediation model. Using a three-wave time-lagged design with data collected from 412 workers across various South Korean corporations, we examine how OPP influences CWB both directly and indirectly through job insecurity, and how self-efficacy in AI learning moderates the OPP–job insecurity link. Our results show that OPP is positively linked to CWB, and this association is partially mediated by job insecurity. Moreover, AI learning self-efficacy functions as a moderator in the OPP–job insecurity link, such that the positive link is weaker for members with higher levels of AI learning self-efficacy. These findings extend our understanding of perfectionism in organizational settings and highlight the role of technological self-efficacy in mitigating the negative impacts of perfectionist cultures. This research may contribute to the literature on perfectionism, CWB, and technological adaptation at work, and has important implications for managing high-performance cultures in the period of rapid technological advancement.

Abstract

Introduction The increasing digitalization of office work, especially with the rise of remote work, has amplified the impact of technostress in organizations. This study examines how technostress influences employee voice behavior. Grounded in the challenge-hindrance stressor framework, we hypothesize that certain aspects of technostress may positively affect voice behavior, psychological safety, intrinsic motivation, and affective commitment. Our findings provide insights for organizations to understand these dynamics and develop managerial strategies that foster positive workplace behaviors. Methods We conducted a cross-sectional study using an online questionnaire with office employees experienced in remote work (N = 361). Data were analyzed using three-step hierarchical regression models to assess the direct effects of technostress on voice behavior. Additionally, structural equation models (SEM) were used to explore indirect effects and the moderating roles of psychological safety, intrinsic motivation, and affective commitment. Results Our findings reveal that technostress consists of challenge and hindrance components. Techno-uncertainty and, to a lesser extent, techno-overload acted as challenge stressors, positively influencing voice behavior directly or through intrinsic motivation and affective commitment. Conversely, techno-insecurity and techno-complexity emerged as hindrance stressors. Techno-insecurity negatively affected all measured variables, while techno-complexity reduced voice behavior and psychological safety. We observed a positive linear relationship between challenge stressors and voice behavior, a negative linear relationship with hindrance stressors, and a weak U-shaped relationship between techno-insecurity and promotive voice. Discussion Our study underscores the need to analyze technostress through the challenge-hindrance stressors framework, as its components can both enhance and hinder employee motivation and voice behavior. We interpret our findings through the lens of Conservation of Resources (COR) theory, emphasizing a proactive rather than a defensive or reactive approach. Additionally, we propose managerial strategies to encourage voice behavior in technostress-prone work environments.

Abstract

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Abstract

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

This study investigates the complex interplay between organizationally prescribed perfectionism (OPP), job insecurity, counterproductive work behavior (CWB), and self-efficacy in learning artificial intelligence (AI) in the context of modern organizations. Based on several theories, the current research suggests and tests a moderated mediation model. Using a three-wave time-lagged design with data collected from 412 workers across various South Korean corporations, we examine how OPP influences CWB both directly and indirectly through job insecurity, and how self-efficacy in AI learning moderates the OPP–job insecurity link. Our results show that OPP is positively linked to CWB, and this association is partially mediated by job insecurity. Moreover, AI learning self-efficacy functions as a moderator in the OPP–job insecurity link, such that the positive link is weaker for members with higher levels of AI learning self-efficacy. These findings extend our understanding of perfectionism in organizational settings and highlight the role of technological self-efficacy in mitigating the negative impacts of perfectionist cultures. This research may contribute to the literature on perfectionism, CWB, and technological adaptation at work, and has important implications for managing high-performance cultures in the period of rapid technological advancement.

Abstract

Introduction The increasing digitalization of office work, especially with the rise of remote work, has amplified the impact of technostress in organizations. This study examines how technostress influences employee voice behavior. Grounded in the challenge-hindrance stressor framework, we hypothesize that certain aspects of technostress may positively affect voice behavior, psychological safety, intrinsic motivation, and affective commitment. Our findings provide insights for organizations to understand these dynamics and develop managerial strategies that foster positive workplace behaviors. Methods We conducted a cross-sectional study using an online questionnaire with office employees experienced in remote work (N = 361). Data were analyzed using three-step hierarchical regression models to assess the direct effects of technostress on voice behavior. Additionally, structural equation models (SEM) were used to explore indirect effects and the moderating roles of psychological safety, intrinsic motivation, and affective commitment. Results Our findings reveal that technostress consists of challenge and hindrance components. Techno-uncertainty and, to a lesser extent, techno-overload acted as challenge stressors, positively influencing voice behavior directly or through intrinsic motivation and affective commitment. Conversely, techno-insecurity and techno-complexity emerged as hindrance stressors. Techno-insecurity negatively affected all measured variables, while techno-complexity reduced voice behavior and psychological safety. We observed a positive linear relationship between challenge stressors and voice behavior, a negative linear relationship with hindrance stressors, and a weak U-shaped relationship between techno-insecurity and promotive voice. Discussion Our study underscores the need to analyze technostress through the challenge-hindrance stressors framework, as its components can both enhance and hinder employee motivation and voice behavior. We interpret our findings through the lens of Conservation of Resources (COR) theory, emphasizing a proactive rather than a defensive or reactive approach. Additionally, we propose managerial strategies to encourage voice behavior in technostress-prone work environments.

Abstract

Introduction The increasing digitalization of office work, especially with the rise of remote work, has amplified the impact of technostress in organizations. This study examines how technostress influences employee voice behavior. Grounded in the challenge-hindrance stressor framework, we hypothesize that certain aspects of technostress may positively affect voice behavior, psychological safety, intrinsic motivation, and affective commitment. Our findings provide insights for organizations to understand these dynamics and develop managerial strategies that foster positive workplace behaviors. Methods We conducted a cross-sectional study using an online questionnaire with office employees experienced in remote work (N = 361). Data were analyzed using three-step hierarchical regression models to assess the direct effects of technostress on voice behavior. Additionally, structural equation models (SEM) were used to explore indirect effects and the moderating roles of psychological safety, intrinsic motivation, and affective commitment. Results Our findings reveal that technostress consists of challenge and hindrance components. Techno-uncertainty and, to a lesser extent, techno-overload acted as challenge stressors, positively influencing voice behavior directly or through intrinsic motivation and affective commitment. Conversely, techno-insecurity and techno-complexity emerged as hindrance stressors. Techno-insecurity negatively affected all measured variables, while techno-complexity reduced voice behavior and psychological safety. We observed a positive linear relationship between challenge stressors and voice behavior, a negative linear relationship with hindrance stressors, and a weak U-shaped relationship between techno-insecurity and promotive voice. Discussion Our study underscores the need to analyze technostress through the challenge-hindrance stressors framework, as its components can both enhance and hinder employee motivation and voice behavior. We interpret our findings through the lens of Conservation of Resources (COR) theory, emphasizing a proactive rather than a defensive or reactive approach. Additionally, we propose managerial strategies to encourage voice behavior in technostress-prone work environments.

Abstract

Introduction The increasing digitalization of office work, especially with the rise of remote work, has amplified the impact of technostress in organizations. This study examines how technostress influences employee voice behavior. Grounded in the challenge-hindrance stressor framework, we hypothesize that certain aspects of technostress may positively affect voice behavior, psychological safety, intrinsic motivation, and affective commitment. Our findings provide insights for organizations to understand these dynamics and develop managerial strategies that foster positive workplace behaviors. Methods We conducted a cross-sectional study using an online questionnaire with office employees experienced in remote work (N = 361). Data were analyzed using three-step hierarchical regression models to assess the direct effects of technostress on voice behavior. Additionally, structural equation models (SEM) were used to explore indirect effects and the moderating roles of psychological safety, intrinsic motivation, and affective commitment. Results Our findings reveal that technostress consists of challenge and hindrance components. Techno-uncertainty and, to a lesser extent, techno-overload acted as challenge stressors, positively influencing voice behavior directly or through intrinsic motivation and affective commitment. Conversely, techno-insecurity and techno-complexity emerged as hindrance stressors. Techno-insecurity negatively affected all measured variables, while techno-complexity reduced voice behavior and psychological safety. We observed a positive linear relationship between challenge stressors and voice behavior, a negative linear relationship with hindrance stressors, and a weak U-shaped relationship between techno-insecurity and promotive voice. Discussion Our study underscores the need to analyze technostress through the challenge-hindrance stressors framework, as its components can both enhance and hinder employee motivation and voice behavior. We interpret our findings through the lens of Conservation of Resources (COR) theory, emphasizing a proactive rather than a defensive or reactive approach. Additionally, we propose managerial strategies to encourage voice behavior in technostress-prone work environments.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.

Abstract

Background The rapid advancement of Artificial Intelligence (AI) is driving transformative changes across various sectors, reshaping organizational management models and altering work dynamics for employees. Objective This study employs the framework of hindrance and challenge stressors to examine the impact of leadership AI awareness on employees’ voice behavior (VB). Methods We analyzed survey data from 487 employees in knowledge-intensive industries across China. Results Leadership AI awareness, under hindering stressors, positively correlates with employees’ job insecurity (JI), which inversely affects their VB; JI acts as a mediator. Conversely, under challenging stressors, leadership AI awareness enhances employees’ intrinsic motivation, which positively correlates with VB. Intrinsic motivation also mediates this relationship. Additionally, AI self-efficacy moderates the influence of leadership AI awareness on employees’ VB. Conclusions This study elucidates a dual-path mechanism through which leadership AI awareness affects employees’ VB and delineates the boundary conditions for this influence. The findings offer managerial insights for stimulating employees’ VB amidst AI integration in the workplace.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.

Abstract

The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being.

Abstract

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.

Abstract

The current study examines the psychological factors shaping AI adoption, focusing on anxiety, motivation, and dependency. It identifies two dimensions of AI anxiety: anticipatory anxiety, driven by fears of future disruptions, and annihilation anxiety, reflecting existential concerns about human identity and autonomy. We demonstrate a U-shaped relationship between AI anxiety and usage, where moderate engagement reduces anxiety, and high or low levels increase it. Perceived utility, interest, and attainment significantly correlate with AI engagement, while frequent AI usage is linked to high dependency but not to anxiety. These findings highlight the dual role of psychological factors in hindering and alleviating AI usage. This study enriches the understanding of emotional and motivational drivers in AI adoption and highlights the importance of balanced implementation strategies to foster sustainable and effective AI integration while mitigating the risks of over-reliance.