What drives digital marketers toward using generative AI: An investigation of the factors influencing adoption of ChatGPT and direction for future research
Jul 2026· Asian Management and Business Review· pp. 312-329· 0 citations· 65 references
TL;DR
The findings reveal that performance expectancy, awareness, effort expectancy, and industry influence significantly shape attitudes toward ChatGPT, whereas organizational culture does not play a significant role.
Abstract
Generative artificial intelligence (AI) is revolutionizing digital marketing by enabling content creation, customer engagement, and strategic decision-making at unprecedented speed and scale. Yet, despite its transformative potential, research on the factors driving marketers’ adoption of generative AI tools such as ChatGPT remains scarce, particularly in emerging economies. Against this backdrop, this study aims to investigate the determinants influencing digital marketers’ adoption of ChatGPT and chart directions for future research. Quantitative research design was employed, drawing on constructs from the TAM and the UTAUT, while incorporating contextual variables such as industry influence, organizational culture, and perceived credibility. Data were collected from 500 digital marketers in Bangladesh using convenience sampling and analyzed using PLS-SEM. The findings reveal that performance expectancy, awareness, effort expectancy, and industry influence significantly shape attitudes toward ChatGPT, whereas organizational culture does not play a significant role. Attitude and perceived credibility emerged as the strongest predictors of actual usage, underscoring the centrality of favorable perceptions and trust in adoption behavior. The chapter makes threefold contributions. Academically, it extends adoption models by highlighting contextual variables in the adoption of AI. Practically, it offers guidance for marketers and organizations to build trust and showcase performance benefits. At the policy level, it calls for regulatory frameworks ensuring credibility and responsible use. Collectively, the study enriches understanding of generative AI adoption and lays a foundation for future research in this rapidly evolving field.
The proliferation of AI-driven retail platforms in Indonesia has outpaced empirical understanding of how cultural factors shape their adoption and effectiveness. This study addresses a specific gap: existing hybrid TAM–SERVQUAL models treat cultural variables as contextual background rather than structural moderators, limiting their explanatory power in high-context, collectivist markets. Specifically, this research examines whether interpersonal trust, collectivism orientation, and relation-based communication preferences significantly moderate the relationships between AI-based marketing information systems and customer satisfaction, and whether these moderation effects differ between large retailers and micro, small, and medium enterprises (UMKM) in Indonesia. To fill this gap, we develop and formally validate the Culturally-Moderated AI Adoption Framework (CMAAF), which extends TAM and SERVQUAL beyond additive integration by formally specifying cultural boundary conditions that amplify or attenuate AI’s effect on satisfaction. Unlike prior hybrid models, CMAAF is validated here using confirmatory factor analysis (CFA) and structural equation modeling via SmartPLS 4.0 with a sample of 347 respondents across five major Indonesian cities. Reliability ranged from α = 0.782 to 0.891. Results confirm that AI personalization (β = 0.421), recommendation systems (β = 0.318), and chatbots (β = 0.267) significantly predict customer satisfaction (R² = 0.637), with cultural variables acting as significant structural moderators, particularly in the UMKM segment. These findings demonstrate that CMAAF provides a more precise and culture-sensitive predictive framework than existing hybrid models, with direct implications for AI deployment strategies in emerging economies.
Fordiana Ekawati, Emi Sita Eriana· bit-Tech· 0 citations
Purpose: This study aims to examine how dynamic capabilities—detecting, utilizing, and transforming—mediate the adoption of Generative Artificial Intelligence (Generative AI) to optimize content marketing strategies among culinary Micro, Small, and Medium Enterprises (MSMEs) in Jatinangor.
Research Method: A qualitative descriptive approach with a multiple case study design was employed to explore how culinary MSMEs integrate Generative AI into content marketing while addressing digital and resource constraints.
Results and Discussion: The findings show that detecting capability develops organically through a bottom-up process, with creative staff acting as information gatekeepers. This capability is reflected in tactical budgeting for premium AI accounts and independent experimentation. Transforming capability emerges by restructuring conventional workflows into a human–AI hybrid model, in which AI generates ideas and drafts, while creative staff perform cultural and local curation. This integration reduces content production time by 50%–60% without compromising brand authenticity or local identity.
Implications: Generative AI serves as a capability enhancer, increasing creative productivity and supporting digital creativity among resource-constrained MSMEs.
Originality: This study contributes by explaining Generative AI adoption through a dynamic capabilities perspective and demonstrating how human–AI collaboration enables productive yet culturally authentic content marketing at the micro-business level.
Anthonius S. Hutabarat, Dewi Tamara, Irawan R D Budianto et al.· Advances in Human Resource M...· 0 citations
The results show that AI self-efficacy, perceived usefulness, and relative advantage have significant positive effects on lecturers’ intention to use AI, and institutional support plays a pivotal moderating role, strengthening the relationship between intention to use AI and actual adoption behaviour.
A.M. Al-Darabseh, S. F. Padlee, Siti Nur et al.· Journal of Intelligent Decis...· 0 citations
The purpose of this paper is to investigate the psychological and behavioral factors that explain why consumers remain engaged with artificial intelligence (AI) influencers. The study specifically explores the construct of “stickiness” – the ability of AI influencers to attract, engage and retain users over time-within the context of digital marketing and strategic brand management.
A qualitative research design was used, using semi-structured interviews with 37 active AI influencer followers recruited from social media platforms. Thematic analysis, grounded in Gioia principles, was used to identify and interpret the key drivers of user stickiness. NVivo software facilitated systematic coding and theme development.
The analysis uncovered seven critical themes: AI influencer realism, homophily, information quality, trans-parasocial interaction, interactive stickiness, brand engagement in self-concept and followers’ escapism. Perceived realism and homophily foster trust and emotional connection, while transparent information quality and trans-parasocial interaction are associated with sustained behavioral commitment. The study develops an emergent interpretive framework to explore how cognitive, emotional and behavioral dimensions interact to shape persistent engagement and loyalty in the AI influencer context.
Brands and marketers can leverage AI influencers by prioritizing personalization, authenticity and transparency, enhancing strategic consumer engagement and brand loyalty.
The study highlights potential risks associated with excessive AI influencer affiliation, particularly for vulnerable populations such as younger or socially isolated users, emphasizing the need for platform transparency, ethical content design and digital well-being protections.
This paper makes two distinct theoretical contributions. First, it conceptualizes trans-parasocial interaction as a novel AI-specific relational mechanism that transcends classical parasocial frameworks by introducing algorithmically maintained social presence, data-driven reciprocity and engineered homophily that are structurally absent in human influencer contexts. Second, the study re-theorizes six well-established engagement constructs, namely, realism, homophily, information quality, trust, escapism and identity-based brand engagement, for AI influencer settings, demonstrating that algorithmic design and perpetual availability fundamentally alter how these mechanisms are experienced. Together, these contributions extend influencer marketing theory beyond its human-centric foundations, offering academics and practitioners a richer framework for understanding non-human social actors.
Grounded in the Technology Acceptance Model (TAM), this study investigates what determines AI adoption behaviour and user satisfaction among staff and students in Malaysian and Indonesian universities. Survey data were gathered from 748 respondents across 12 institutions in 2025. Using binary logistic regression to identify predictors of training participation and k-means clustering to reveal latent user segments, we find that perceived usefulness and perceived ease of use are stronger predictors of both AI usage and satisfaction than formal training attendance. Malaysian respondents were 2.47 times more likely to have completed AI training than their Indonesian counterparts (p < .001), yet this structural advantage produced no significant difference in satisfaction between the two countries (Mann-Whitney U, p = .214). Three user profiles emerged: AI Skeptics (23.8%), who require demonstration of practical task value before any training engagement; AI Learners (42.9%), who benefit most from discipline-embedded mentoring and competency recognition; and AI Champions (33.3%), best deployed as peer facilitators rather than additional training recipients. For policymakers, these findings indicate that satisfaction-focused AI strategy must prioritise ease-of-use improvements and workflow integration over training volume, while Indonesian institutions specifically need structural investment in infrastructure and governance frameworks as preconditions for effective capacity-building.
Mohd. Azlishah Othman, A. Jaafar, Redzuan Abd Manap et al.· International journal of res...· 0 citations
The findings indicate that Perceived Usefulness is reflected in enhanced efficiency for reference searching and academic task completion, and Perceived Ease of Use is manifested through intuitive user interfaces, although effective prompt engineering skills remain unevenly distributed.
Rhenaldi Prihat Sukoco, Harianto Harianto, Agus Wiyaka et al.· Publika· 0 citations
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