What factors influence the intention to adopt blockchain technology in accounting education?

Blockchain has gained attention in accounting for its capacity to provide secure, transparent, and efficient records, with potential to enhance financial reporting, reduce fraud, and improve efficiency. Despite its benefits, its incorporation into accounting education is limited, and curricula often lag technological advancements. Integrating blockchain into accounting courses (e.g., auditing, financial and managerial accounting) can better prepare students for industry needs, but requires faculty to act as early adopters. This study addresses uncertainty around faculty behavioral intentions to adopt blockchain in accounting education, focusing on Indian universities and colleges. Guided by the Technology Acceptance Model (TAM), the study examines perceived ease of use, perceived usefulness, attitude, and behavioral intention, and investigates how organizational support (OS) influences and moderates these relationships. The research question is: What factors influence faculty members' behavioral intentions toward adopting and integrating blockchain technology in accounting education, and how does organizational support moderate these intentions? The study aims to fill a literature gap by exploring these determinants and offering policy-relevant insights for effective integration of blockchain in accounting education.

Prior work notes widespread interest but comparatively low adoption of blockchain, with limited focus on accounting education specifically. Studies have examined blockchain in supply chains, companies, cryptocurrencies, and education administration, but few address faculty intentions in accounting education. TAM is widely used to explain technology adoption via perceived usefulness (PU), perceived ease of use (PEU), attitude (ATU), and behavioral intention (BI). Evidence generally supports that PEU positively affects PU and ATU; PU positively affects ATU and BI; and ATU positively affects BI. Organizational support (OS)—resources, training, infrastructure, and encouragement—facilitates technology integration and may directly influence BI and moderate relationships between PU/ATU and BI. Based on this, the study formulates hypotheses: H1: PEU positively affects PU. H2: PEU positively affects ATU. H3: PU positively affects ATU. H4: PU positively affects behavioral intention (BIB) to adopt and integrate blockchain in accounting education. H5: ATU positively influences BIB. H6: OS positively affects BIB. H7: OS moderates the PU→BIB relationship. H8: OS moderates the ATU→BIB relationship.

Design: Quantitative, cross-sectional survey using a moderated TAM model with organizational support as a moderator. Sampling and context: Purposive sampling of faculty members in Indian universities and colleges. Data collection: Online structured questionnaire (Google Docs; distributed via WhatsApp, Messenger, ResearchGate). Two sections: demographics and constructs. Sample: 191 valid responses. Demographics included gender (111 male, 77 female), age (below 30: 23; 30–40: 82; above 40: 83), education (doctorate: 146; postgraduate: 38; other: 4), academic rank (teaching assistant, assistant professor, associate professor, professor), and years of experience categories. Measures: TAM constructs and OS measured with established items. PEU (3 items), PU (4 items; PU4 later dropped due to low loading), ATU (4 items), BIB (3 items), OS (4 items). Sources include Davis (1989), Fathema et al. (2015), Alshurafat et al. (2021), Smith (2017), Tasnim et al. (2023), Huang et al. (2011), Li et al. (2022), and Al-Hattami (2023). Common method bias mitigation: Anonymity, careful questionnaire design; statistical assessment via full collinearity VIFs (thresholds <5 for multicollinearity and <3.3 for CMB). Analysis tool: SmartPLS 4. Measurement model assessment: Reliability (Cronbach’s alpha and composite reliability), convergent validity (AVE>0.50), discriminant validity (Fornell-Larcker). Structural model assessment: Bootstrapping (5000 subsamples) for path significance; R-squared for variance explained and Q² via blindfolding for predictive relevance.

Measurement model: All constructs showed adequate reliability (Cronbach’s alpha and composite reliability >0.70) and convergent validity (AVE >0.50). PU4 was dropped due to loading <0.60. Maximum VIF was 3.253, indicating no serious multicollinearity or common method bias. Structural model and hypotheses: All hypotheses were supported. H1 (PEU→PU): Beta=0.589, t=9.612, p<0.001. H2 (PEU→ATU): Beta=0.357, t=4.762, p<0.001. H3 (PU→ATU): Beta=0.565, t=7.920, p<0.001. H4 (PU→BIB): Beta=0.223, t=2.308, p=0.021. H5 (ATU→BIB): Beta=0.366, t=4.825, p<0.001. H6 (OS→BIB): Beta=0.215, t=3.023, p=0.003. H7 (OS×ATU→BIB): Beta=0.171, t=2.971, p=0.003. H8 (OS×PU→BIB): Beta=0.130, t=2.222, p=0.026. Model fit and predictive power: R² values: PU=0.347, ATU=0.684, BIB=0.640, indicating the model explains 64% of the variance in behavioral intention (BIB). Q² values were all >0, evidencing predictive relevance (PU Q²=0.260; ATU Q²=0.492; BIB Q²=0.508). Overall, PEU indirectly influences BIB via PU and ATU; PU and ATU directly influence BIB; OS both directly increases BIB and strengthens (moderates) the effects of PU and ATU on BIB.

Findings confirm TAM relationships in the context of blockchain integration in accounting education. Perceived ease of use enhances perceived usefulness and attitudes, which in turn foster behavioral intention to adopt blockchain in curricula. Faculty who find blockchain comprehensible and easy to implement and teach report more favorable attitudes and greater intention to integrate it. Perceived usefulness is pivotal for shaping both attitudes and intentions; when instructors see blockchain as beneficial for teaching accounting concepts and improving performance, their intention to adopt increases. Organizational support is crucial: it directly boosts intention and moderates the PU→BIB and ATU→BIB relationships, amplifying the positive effects of usefulness and attitudes when institutions provide training, resources, infrastructure, and a supportive climate. Thus, the study addresses the research question by identifying PEU, PU, ATU, and OS as key determinants and showing that OS strengthens the path from perceptions and attitudes to intention, highlighting institutional levers for effective integration.

Applying TAM to blockchain adoption in accounting education shows that perceived usefulness, perceived ease of use, and attitudes are central to faculty intentions, and organizational support is a key enabler and moderator. Enhancing perceived usefulness and ease of use, cultivating positive attitudes, and providing robust organizational support (training, resources, infrastructure, and encouragement) can promote successful blockchain integration. Policymakers and academic leaders should craft strategies that build capacity and support early adopters to embed blockchain into accounting curricula, preparing students for a digitized profession.

Limitations include a modest sample size (n=191) and a single-country context (India), which may constrain generalizability. The study focuses on TAM; incorporating additional theories could broaden insights. COVID-19 impeded in-person interviews that might have provided deeper qualitative understanding. Demographic moderators (e.g., expertise, education level) and other factors such as social influence were not analyzed. The operationalization of organizational support may not capture its full breadth, potentially omitting relevant dimensions. Inclusion of teaching assistants as respondents may introduce variability in instructional practices, affecting consistency and generalizability. Future research should expand samples across countries, examine additional moderators and theories, refine OS measurement, and consider standardized roles of teaching assistants.