Catalysts and contradictions: rethinking technology acceptance through Human+ readiness in accounting

Purpose This study reconstructs the technology acceptance model (TAM) for professional settings where adoption is no longer discretionary. It develops an experience-contingent account in which technology-related beliefs are shaped by direct exposure to digital technologies (DTs) and organizes those beliefs through Human+ readiness, an individual-level construct with three components: cognitive appraisal of benefits, capability-related confidence and psychological cost, and temporal adaptation. It then identifies the conditions under which behavioral intention, the model's conventional outcome, no longer varies across individuals. Design/methodology/approach Survey data from 426 professional accountants support a comparison of users and nonusers of DTs, capturing the difference between experience-based and expectation-based evaluation. Perceived benefits (PB) carry the cognitive appraisal component, while perceived knowledge trust (PKT) and perceived stress (PS) carry capability confidence and psychological cost; all three are specified as antecedents of perceived usefulness (PU). Temporal adaptation is captured by decomposing perceived ease of use (PEU) along a temporal axis into perceived ease of understanding (PEUN), perceived ease of learning (PEL) and perceived ease of keeping pace (PEKP). Hypotheses are tested through correlation and regression analyses run separately for the two groups. Findings PB and capability-related beliefs drive PU, with PS constraining it, and PEUN, PEL and PEKP shape PEU. The weight of these dimensions varies systematically with experience: adaptation capacity (PEKP) dominates among users, and understanding and learning (PEUN, PEL) matter more for nonusers. Behavioral intention to use (BIU) is near-universal in both groups, above 98%, which suggests that adoption in the accounting profession has become structurally embedded and is no longer individually chosen. The explanatory work of the model therefore moves from intention to the capability and adaptation processes that govern effective engagement. Exposure changes the balance among the three components of readiness more than it changes the overall level. Practical implications The value of DTs is already recognized and adoption is largely nondiscretionary, so the effective levers are capability development, stress reduction and continuous adaptation support, more than just the communication of technological benefits. Keeping professionals current with ongoing technological change emerges as a condition for sustained engagement, and the evidence locates the heaviest adaptation and stress burdens among experienced users, not newcomers. Social implications The main burden of digital transformation falls on capability and adaptation, not on willingness to adopt, which draws attention to the human cost of technological change in knowledge-intensive professions. Where adoption is structurally imposed, professionals with uneven exposure or limited adaptive capacity risk being left behind, opening the way to new forms of occupational inequality and psychological strain. Organizations and professional bodies share responsibility for inclusive upskilling that also safeguards the well-being and professional identity of workers in the Human+ era. Originality/value The contribution lies in identifying a scope condition, professional embedding, under which the framework has to be rebuilt. Existing acceptance models already include capability, stress and experience effects, so this model's novelty is structural rather than conceptual. Drawing on the microfoundations of dynamic capabilities, the study treats individual belief structure as componential, with the weight of its components varying by exposure. Under professional embedding, intention ceases to vary, experience determines which set of relationships operates instead of moderating a common set, and the model's explanatory locus relocates to the conditions of effective use. The user and nonuser comparison tests this directly.