| | Author(s) (year) | Influencing factors | Object of study | Model/theory |
| Dunnebeil et al. (2012) [30],
Liu et al. (2013) [31] | Perceived ease of use, perceived usefulness | Doctors/patients | TAM |
| | Tsai (2014) [32] | Perceived ease of use, perceived usefulness social capital theory, social cognition theory | Elderly people | Extended TAM | | Rho et al. (2014) [33] | Perceived incentives, clinical factors, individual factors, perceived ease of use, perceived usefulness | Doctors | | Wang (2016) [34] | Service quality, price, waiting time, transportation cost, etc. | Patients | | Zhou et al. (2019) [9] | Satisfaction with medical services (MSS) (affordability, waiting time), perceived ease of use, information quality | Elderly people |
| | Diño and de Guzman (2015) [21] | Performance expectancy, effort expectancy, social influence | Elderly people | UTAUT | | Adenuga et al. (2017) [23] | Suitable incentives, performance expectancy, effort expectancy, facilitating condition | Doctors |
| | Wang et al. (2015) [38], Zhan et al. (2017) [39] | Utility of patients’ telehealth services, medical expenses, reimbursement ratio of medical insurance, hospital costs, etc. | Hospitals and patients | Game theory | | Rajan et al. (2019) [37] | Doctor’s utility (price, equilibrium arrival rate, etc.), patient’s utility (reward from seeking treatment, congestion cost, payment, etc.) | Doctors and patients |
| | Xue and Liang (2007) [22], Combi et al. (2016) [5] | Doctor: face-to-face visit habits, extra cost of telehealth, etc. Patient: cost of telehealth, reimbursement, etc. | Doctors and patients | Literature review, survey, report, etc. | | U.S. Department of Health and Human Services (2016) [7, 22] | Payment, especially more comprehensive coverage by Medicare | Policies | | Scott Kruse et al. (2018) [40] | Technically challenged staff, resistance to change, cost, reimbursement, etc. | Doctors and patients |
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