Table 4 Future research directions... | Emerald Publishing

Table 4

Future research directions proposed by extant literature

Category	Research focus	Representative studies	Research Priority
Theories	Investigate antecedents, evolution, and cross-cultural variations in cognitive appraisals of AI by FLE	Kong et al. (2021), Presbitero and Teng-Calleja (2023), Shamim et al. (2023), Chiu et al. (2021), Liang et al. (2022)	High Priority: Understanding cognitive and emotional responses is critical for effective AI integration in global contexts. Cross-cultural comparisons could illuminate cultural moderators influencing AI acceptance and appraisals
	Examine the role of Conservation of Resources Theory in mitigating FLE negative psychological responses to AI.	Li et al. (2019), You et al. (2018), Liang et al. (2022), Verma and Singh (2022), Khaliq et al. (2022)	Moderate Priority: Enhancing resilience during transitions can improve retention and performance. Exploring how organizational resources buffer stressors adds valuable insights into effective integration strategies
	Investigate the intersection of ORA and gamified designs for upskilling FLE to support AI adoption	Li et al. (2019), Verma and Singh (2022)	Moderate Priority: Training frameworks that incorporate gamification and ORA strategies can significantly improve engagement and reduce adoption resistance
Context	Explore FLE psychological responses to AI integration in underrepresented and diverse global contexts	Wang et al. (2023a, b), Shamim et al. (2023), Verma and Singh (2022), Presbitero and Teng-Calleja (2023)	High Priority: Regional disparities in AI adoption and FLE psychological outcomes necessitate broader geographical research, especially in underrepresented countries
Characteristics	Broaden the scope of AI research to include underexplored industries and regions	Liang et al. (2022), Qiu et al. (2022), Wang et al. (2023a, b), Verma and Singh (2022), Leung et al. (2023)	High Priority: Expanding research to diverse sectors like healthcare, hospitality, and retail could provide industry-specific insights into FLE stressors and satisfaction
	Investigate long-term psychological impacts of AI on FLE, including job satisfaction, stress, and training effectiveness	Wang et al. (2023a, b), Leung et al. (2023), Verma and Singh (2022), Presbitero and Teng-Calleja (2023)	High Priority: Longitudinal research on psychological outcomes is essential for developing robust, sustainable AI integration strategies
	Identify organizational and individual factors influencing FLE acceptance of and engagement with AI.	Khaliq et al. (2022), Kong et al. (2021), Liang et al. (2022), Motamarri et al. (2020)	Moderate Priority: Leadership styles, innovation climates, and personalized support systems require further exploration to understand their impact on engagement
	Examine customers’ perceptions as an outcome variable to determine long-term effects on service quality	Do et al. (2023), Qiu et al. (2022), Motamarri et al. (2020), Kong et al. (2021)	Moderate Priority: Linking FLE responses to customer satisfaction and service quality will bridge gaps in understanding human-AI collaboration impacts
Methods	Employ longitudinal and cross-national studies to assess dynamic FLE psychological responses to AI integration	Willems et al. (2023), Verma and Singh (2022), Shamim et al. (2023), Wang et al. (2023a, b)	High Priority: Longitudinal methods are key to understanding evolving psychological impacts, while cross-national approaches can address global variability
Methods	Focus on qualitative methods to explore nuanced FLE responses to AI across various service industries	Liang et al. (2022), Shamim et al. (2023), Verma and Singh (2022), Do et al. (2023)	Moderate Priority: Qualitative methods can complement quantitative research, providing deeper insights into individual and contextual variables affecting FLE attitudes and behaviours

Category	Research focus	Representative studies	Research Priority
Theories	Investigate antecedents, evolution, and cross-cultural variations in cognitive appraisals of AI by FLE	Kong et al. (2021), Presbitero and Teng-Calleja (2023), Shamim et al. (2023), Chiu et al. (2021), Liang et al. (2022)	High Priority:Understanding cognitive and emotional responses is critical for effective AI integration in global contexts. Cross-cultural comparisons could illuminate cultural moderators influencing AI acceptance and appraisals
	Examine the role of Conservation of Resources Theory in mitigating FLE negative psychological responses to AI.	Li et al. (2019), You et al. (2018), Liang et al. (2022), Verma and Singh (2022), Khaliq et al. (2022)	Moderate Priority:Enhancing resilience during transitions can improve retention and performance. Exploring how organizational resources buffer stressors adds valuable insights into effective integration strategies
	Investigate the intersection of ORA and gamified designs for upskilling FLE to support AI adoption	Li et al. (2019), Verma and Singh (2022)	Moderate Priority:Training frameworks that incorporate gamification and ORA strategies can significantly improve engagement and reduce adoption resistance
Context	Explore FLE psychological responses to AI integration in underrepresented and diverse global contexts	Wang et al. (2023a, b), Shamim et al. (2023), Verma and Singh (2022), Presbitero and Teng-Calleja (2023)	High Priority:Regional disparities in AI adoption and FLE psychological outcomes necessitate broader geographical research, especially in underrepresented countries
Characteristics	Broaden the scope of AI research to include underexplored industries and regions	Liang et al. (2022), Qiu et al. (2022), Wang et al. (2023a, b), Verma and Singh (2022), Leung et al. (2023)	High Priority:Expanding research to diverse sectors like healthcare, hospitality, and retail could provide industry-specific insights into FLE stressors and satisfaction
	Investigate long-term psychological impacts of AI on FLE, including job satisfaction, stress, and training effectiveness	Wang et al. (2023a, b), Leung et al. (2023), Verma and Singh (2022), Presbitero and Teng-Calleja (2023)	High Priority:Longitudinal research on psychological outcomes is essential for developing robust, sustainable AI integration strategies
	Identify organizational and individual factors influencing FLE acceptance of and engagement with AI.	Khaliq et al. (2022), Kong et al. (2021), Liang et al. (2022), Motamarri et al. (2020)	Moderate Priority:Leadership styles, innovation climates, and personalized support systems require further exploration to understand their impact on engagement
	Examine customers’ perceptions as an outcome variable to determine long-term effects on service quality	Do et al. (2023), Qiu et al. (2022), Motamarri et al. (2020), Kong et al. (2021)	Moderate Priority:Linking FLE responses to customer satisfaction and service quality will bridge gaps in understanding human-AI collaboration impacts
Methods	Employ longitudinal and cross-national studies to assess dynamic FLE psychological responses to AI integration	Willems et al. (2023), Verma and Singh (2022), Shamim et al. (2023), Wang et al. (2023a, b)	High Priority:Longitudinal methods are key to understanding evolving psychological impacts, while cross-national approaches can address global variability
Methods	Focus on qualitative methods to explore nuanced FLE responses to AI across various service industries	Liang et al. (2022), Shamim et al. (2023), Verma and Singh (2022), Do et al. (2023)	Moderate Priority:Qualitative methods can complement quantitative research, providing deeper insights into individual and contextual variables affecting FLE attitudes and behaviours

Source(s): Authors’ own work