Table 1. Conceptual mapping of AI... | Emerald Publishing

Table 1.

Conceptual mapping of AI adoption barriers within TOE–TPB–institutional framework

Code	Barrier	Primary theoretical domain	Illustrative rationale
F1	Data quality issues	TOE – technological	Poor or biased data reduce personalization accuracy and system reliability, limiting technological readiness
F2	System integration issues	TOE – technological	Lack of interoperability between legacy and AI systems weakens infrastructure scalability
F3	Change resistance	TPB – behavioral	Negative attitudes and low perceived control impede behavioral intention to adopt AI
F4	Skill deficiency	TOE / TPB (organizational–behavioral)	Limited AI literacy constrains both organizational capability and user confidence
F5	Ethical concerns	Institutional – normative	Algorithmic bias and fairness concerns reflect external legitimacy and moral expectations
F6	ROI uncertainty	Institutional – cognitive/coercive	Pressure for measurable outcomes creates institutionalized performance constraints
F7	Data privacy concerns	Institutional – regulative	Compliance with GDPR/DPDPA and societal trust requirements influences adoption legitimacy
F8	Limited awareness	TPB – cognitive	Lack of understanding lowers perceived usefulness and subjective norms supporting adoption
F9	High costs	TOE – organizational	Financial and resource constraints restrict innovation capacity and scalability
F10	Infrastructure limitations	TOE – technological	Inadequate computing power and connectivity inhibit AI deployment at scale

Code	Barrier	Primary theoretical domain	Illustrative rationale
F1	Data quality issues	TOE – technological	Poor or biased data reduce personalization accuracy and system reliability, limiting technological readiness
F2	System integration issues	TOE – technological	Lack of interoperability between legacy and AI systems weakens infrastructure scalability
F3	Change resistance	TPB – behavioral	Negative attitudes and low perceived control impede behavioral intention to adopt AI
F4	Skill deficiency	TOE / TPB (organizational–behavioral)	Limited AI literacy constrains both organizational capability and user confidence
F5	Ethical concerns	Institutional – normative	Algorithmic bias and fairness concerns reflect external legitimacy and moral expectations
F6	ROI uncertainty	Institutional – cognitive/coercive	Pressure for measurable outcomes creates institutionalized performance constraints
F7	Data privacy concerns	Institutional – regulative	Compliance with GDPR/DPDPA and societal trust requirements influences adoption legitimacy
F8	Limited awareness	TPB – cognitive	Lack of understanding lowers perceived usefulness and subjective norms supporting adoption
F9	High costs	TOE – organizational	Financial and resource constraints restrict innovation capacity and scalability
F10	Infrastructure limitations	TOE – technological	Inadequate computing power and connectivity inhibit AI deployment at scale