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This study aims to investigate the adoption intention of artificial intelligence (AI) in family businesses through the perspectives of digital entrepreneurship and entrepreneurship orientation.

The study examines contributing factors explaining the adoption intention of AI in the context of family businesses. The developed research model is examined and validated using structural equation modelling based on 631 respondents' data. Purposeful sampling is used to collect the respondents' data.

The proposed model included two endogenous (i.e. business innovativeness and adoption intention) and six exogenous variables (i.e. affordances, culture and flexible design, entrepreneurial orientation, generativity, openness and technology orientation) through ten direct paths and three indirect paths. The results depicted the significant influence of all the exogenous variables on the endogenous variable reflecting support of all the hypotheses. The business innovativeness partially mediates the relationships of culture and flexible design, entrepreneurial orientation and technology orientation with adoption intention. Further, the results demonstrated a model variance of 24.6% for business innovativeness and 64.2% for adoption intention of artificial intelligence in the family business.

The study contributes to theoretical developments in entrepreneurship and family business research and AI's theoretical progress, especially to digital entrepreneurship.

Theoretically, it contributes to the literature of entrepreneurship, particularly digital entrepreneurship. Additionally, the research model adds to the role of entrepreneurial orientation and digital entrepreneurship in the emerging family entrepreneurship literature. Considering the scarcity of research in this field, the empirically validated model explaining critical antecedents of AI adoption intention in the family business is a foundation for discussion, critique and future research.

The purpose of this paper is to identify and examine the important factors that could affect consumers' behavioural intention and use behaviour towards mobile payment services during COVID-19.

The proposed model extends meta-Unified Theory of Acceptance and Use of Technology (meta-UTAUT) model with perceived severity and self-efficacy factors affecting consumers' behavioural intention and use behaviour towards mobile payment services. A convenient sampling technique has been utilized to gather data from a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation.

The findings revealed that performance expectancy, effort expectancy and perceived severity have a significant positive impact on consumers' attitude; facilitating conditions has a significant positive impact on effort expectancy; self-efficacy has a significant positive impact on effort expectancy; attitude has a significant positive impact on behavioural intention; and behavioural intention has a significant positive impact on use behaviour. Social influence did not confirm any significant relationship.

The current research study has utilized a non-probability convenient sampling technique to gather data through a self-administered questionnaire. The data collection was restricted to the online mode to avoid any physical contact considering the COVID-19 situation. The respondents were adopters of mobile payment services. The scope of the study is the COVID-19 context or related chronic diseases context where major preventive mechanisms such as social distancing and avoidance of physical contacts are vital.

This study has extended the meta-UTAUT model with the COVID-19 context-specific constructs and relationships. The undertaken work has strengthened the explanability of the model. The inclusion of context relevant variables such as perceived severity and self-efficacy and their association with the existing meta-UTAUT framework have enriched the context of the study. The current study offers a holistic understanding of significant factors influencing Indian consumers’ adoption of mobile payment services in the COVID-19 context.

This paper aims to determine the entrepreneur's intention to accept artificial intelligence (AI) and provide advancement in the domain of digital entrepreneurship.

Extensive literature review and theories have been considered in the area of technology adoption/acceptance and digital entrepreneurship to identify the factors affecting the intention of entrepreneurs with respect to accept AI for digital entrepreneurship. Further, a model, artificial intelligence acceptance and digital entrepreneurship (AIADE) is theorized after formulating some hypotheses. The theorized model has been validated with 476 useable responses.

The findings revealed that performance expectancy, openness, social influence, hedonic motivations and generativity have a positive impact on entrepreneur's acceptance intention of AI. Additionally, affordance has no direct relationship with AI acceptance intention, but it affects AI acceptance intention through attitude. Inconvenience has a significant negative relationship with the intention to accept AI, while uncertainty was found to be positively affecting the AI acceptance intention. Effort expectancy did not confirm any significant relationship.

By considering existing theoretical models and concepts the authors contribute to the AI's theoretical progress, specifically in the domain of entrepreneurship. The authors complement and extend existing technology adoption/acceptance theories and digital entrepreneurship theories by developing a theoretical model, AIADE, explaining the entrepreneur's intention to accept AI.

The practical implications of the study show that performance expectancy (positive), openness (positive), social influence (positive), hedonic motivations (positive), generativity (positive), affordance through attitude (positive), uncertainty (positive), effort expectancy (negative) and inconvenience (negative) are the antecedents for the entrepreneurs to accept AI for digital entrepreneurship. The authors suggest that intentional improvement planning is developed by increasing entrepreneur's positive perceptions of AI affordance and explanation of its generativity and openness, and improving their attitude of using AI for digital entrepreneurship.

This is the first study that reveals the critical antecedents of entrepreneur's intention to accept AI for digital entrepreneurship. Relevant theoretical background, discussion, implications, limitations and future research recommendations are discussed.