Table 1.
Literature review on healthcare workers’ adoption intention.
| Authors | Context | Theoretical Basis | Region | Key Findings |
|---|---|---|---|---|
| Alsyouf et al. (2022) [23] | Nurses’ continuance intention of EHR | UTAUT, ECT, FFM | Jordan | Performance expectancy as a mediating variable on the relationships between the different personality dimensions and continuance intention, specifically conscientiousness as a moderator. |
| Pikkemaat et al. (2021) [24] | Physicians’ adoption intention of telemedicine | TPB | Sweden | Attitudes and perceived behavioral control being significant predictors for physicians to use telemedicine. |
| Hossain et al. (2019) [25] | Physicians’ adoption intention of EHR | Extended UTAUT | Bangladesh | Social influence, facilitating conditions, and personal innovativeness in information technology had a significant influence on physicians’ adoption intention to adopt the EHR system. |
| Alsyouf and Ishak (2018) [26] | Nurses’ continuance intention to use EHR | UTAUT and TMS | Jordan | Effort expectancy, performance expectancy, and facilitating conditions positively influence nurses’ continuance intention to use and top management support as significant and negatively related to nurses’ continuance adoption intention. |
| Fan et al. (2018) [27] | Healthcare workers’ adoption intention of AIMDSS | UTAUT, TTF, trust theory | China | Initial trust mediates the relationship between UTAUT factors and behavioral intentions. |
| Bawack and Kamdjoug (2018) [28] | Clinicians’ adoption intention of HIS | Extended UTAUT | Cameroon | Performance expectancy, effort expectancy, social influence, and facilitating conditions have a positive direct effect on clinicians’ adoption intention of HIS. |
| Adenuga et al. (2017) [29] | Clinicians’ adoption intention of telemedicine | UTAUT | Nigeria | Performance expectancy, effort expectancy, facilitating condition, and reinforcement factor have significant effects on clinicians’ adoption intention of telemedicine. |
| Liu and Cheng (2015) [30] | Physicians’ adoption intention of MEMR | The dual-factor model | Taiwan | Physicians’ intention to use MEMRs is significantly and directly related to perceived ease of use and perceived usefulness, but perceived threat has a negative influence on physicians’ adoption intention. |
| Hsieh (2015) [31] | Healthcare professionals’ adoption intention of health clouds | TPB and Status quo bias theory | Taiwan | Attitude, subjective norm, and perceived behavior control are shown to have positive and direct effects on healthcare professionals’ intention to use the health cloud. |
| Wu et al. (2011) [32] | Healthcare professionals’ adoption intention of mobile healthcare | TAM and TPB | Taiwan | Perceived usefulness, attitude, perceived behavioral control, and subjective norm have a positive effect on healthcare professionals’ adoption intention of mobile healthcare. |
| Egea and González (2011) [33] | Physicians’ acceptance of EHCR | Extended TAM | Southern Spain | Trust fully mediated the influences of perceived risk and information integrity perceptions on physicians’ acceptance of EHCR systems. |
Note: EHR, electronic health record; AIMDSS, medical diagnosis support system; HIS, health information system; MEMR, mobile electronic medical records; EHCR, electronic health care records. UTAUT, unified theory of acceptance and use of technology; ECT, the theory of expectation confirmation; FFM, five-factor model; TPB, theory of planned behavior; TMS, top management support; TTF, task technology fit; TAM, technology-acceptance model.