Theorizing artificial intelligence acceptance and digital entrepreneurship model

Abstract

PurposeThis paper aims to determine the entrepreneur's intention to accept artificial intelligence (AI) and provide advancement in the domain of digital entrepreneurship.Design/methodology/approachExtensive 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.FindingsThe 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.Research limitations/implicationsBy 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.Practical implicationsThe 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.Originality/valueThis 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.

Consumer adoption of mobile payment services during COVID-19: extending meta-UTAUT with perceived severity and self-efficacy

“…Online channels (e.g. LinkedIn, Facebook sharing Google forms to collect data) do not have any time and space barrier and are considered apt to gather the survey information (Couper, 2017;Evans and Mathur, 2018;Upadhyay et al, 2021). Additionally, researchers utilized online channels to gather the survey data to avoid any physical contact or distribution of physical documents during COVID-19.…”

“…Additionally, researchers utilized online channels to gather the survey data to avoid any physical contact or distribution of physical documents during COVID-19. The survey was distributed through social networking sites (Saunders et al, 2007;Upadhyay et al, 2021) to gather responses from general public (e.g. LinkedIn and Facebook sharing Google survey form) and to staff and students of two elite educational Institutions via email.…”

“…PLS-SEM is considered as effective for synthesizing an exploratory study like this (Hair et al, 2018). PLS-SEM works effectively to test the complex model without any sample restriction (Willaby et al, 2015;Upadhyay et al, 2021). PLS-SEM analysis was conducted using SmartPLS 3.3.2 software to verify the proposed theoretical model and hypotheses (Lim et al, 2021;Ringle et al, 2015).…”

Power negotiation on the tango dancefloor: The adoption of AI in B2B marketing

“…Regardless of the driver, it is clear that B2B marketing is increasingly embracing AI, resulting in a significant upheaval of practice. This calls for rigorous investigation and theorisation (Duan et al, 2019;Upadhyay, Upadhyay, & Dwivedi, 2021) of the state of play of AI in the B2B landscape.…”

“…The current literature provides scant empirically-based, practical guidance to B2B managers (e.g., Han et al, 2021;Upadhyay et al, 2021), even though AI is expected to disrupt marketing practice. This gap is addressed by offering the following implications emerging from our study which translate into advice to focal firms for their AI adoption, as well as more general observations for AI suppliers, and tech giants.…”