Abstract
Objective
The aim of this pilot study is to identify the main predictors and barriers that influence user intention to adopt teledentistry services among the Saudi population.
Methods
A total of 426 respondents completed a questionnaire. Our study used the technology acceptance model to predict how users will adopt teledentistry. An analysis of the factors that influence patient users’ intention to utilize teledentistry was conducted using partial least square structural equation modeling.
Results
Among the factors that influenced the intention to use teledentistry services, social influence positively affected the intention (β = 0.18, t = 2.19, p-value = 0.03), whereas perceived risk negatively affected it (β = −0.21, t = 2.69, p-value =0.01).
Conclusion
The perception of risk was a major barrier to the adoption of teledentistry services, while social influence was a significant positive predictor. Using the findings of the study, policymakers and health service providers can gain a better understanding of the factors influencing the implementation of teledentistry.
Keywords: Teledentistry, technology acceptance, technology acceptance model, usage intention
Introduction
During the past few years, information and communication technologies have revolutionized the delivery of health care.1,2 Telemedicine refers to the use of computer technology, telecommunications, and clinical expertise to provide services such as diagnostics, consultations, and treatment remotely.3,4 Teledentistry is the application of telemedicine in the field of dentistry.3 A variety of modalities can be used to deliver teledentistry services, such as synchronous (live video), asynchronous (store and forward), remote patient monitoring, or mobile health (mHealth).5 With teledentistry, oral health care can be more readily accessible and delivered more efficiently at a lower cost.6 Additionally, it might reduce rural and urban oral health care disparities.6
In 1994, a large teledentistry project was implemented to serve U.S. soldiers and their dependents worldwide.7 Teledentistry has been shown to reduce overall costs, improve patient care, and provide more complete data analyses than traditional referral processes.7 Teledentistry has been adopted somewhat slowly; however, many dental practices and hospitals have been encouraging teledentistry since the start of the coronavirus disease 2019 (COVID-19) pandemic.8,9 As dental procedures significantly increase the risk of transmission, face-to-face consultations have been severely restricted.10 Risk assessment, triage, and the management of emergency dental care have frequently been accomplished remotely by telephone or video link as the first step in the urgent dental care system.9
In the sight of the American Dental Association, with the help of teledentistry dental practitioners can now reach a greater number of patients, thus enabling greater access to dental care.5 A high level of patient satisfaction has generally been documented with telemedicine consultations.11–13 For example, the majority of patients at Massachusetts General Hospital rated telemedicine follow-up visits as more convenient than in-person appointments.14 According to a recent survey of private U.S. health plan members, the majority of those who participate in U.S. private health plans will use video or online services if they are available.14
Previous studies have examined the acceptance of teledentistry among dental professionals and dental students in Saudi Arabia.15–20 However, there is a lack of published evidence showing that patients are ready to accept teledentistry.21,22 The aim of this pilot study was to determine the perceptions of the Saudi population regarding teledentistry and to identify any existing gaps in terms of patient preferences.
Methods
This study was reviewed and approved by the Institutional Review Board at King Abdulaziz University Faculty of Dentistry (#286-09-21). The survey was distributed from December 2021 to March 2022. Participant eligibility criteria included living in Saudi Arabia and being 18 or older. All participants provided informed consent. The main outcome of this study is the intention to use teledentistry.
In this study, we used the technology acceptance model (TAM) to predict how users will adopt teledentistry and to identify any design flaws in the information system before it is widely used.23 The TAM theory was developed in the 1980s as an information system model that describes how users accept and use technology over time.24 It was thought that the key to increasing the adoption of information technology (IT) was to increase acceptance, which could be measured by asking individuals about their future intentions to use it.25 It is possible to increase the adoption and use of IT by understanding the factors that shape one's intentions.25 The questionnaire used was adopted from previous studies.23,25–29 To fit the goal of the study, minor adjustments to the questionnaire were made. The questionnaire consists of two parts. The first part concerns the demographic characteristics of the participants—gender, age, marital status, nationality, education, employment status, income, region, living area, study/work in the dental field, dental insurance, chronic disease, and last dental visit. The second part deals with eight constructs of TAM adopted from previous studies—facilitating conditions, perceived ease of use, perceived risk, perceived usefulness, resistance to use, social influence, technological anxiety, and trust.23,25,29 All constructs and their definitions are shown in Table 1.
Table 1.
List of the proposed constructs.
| Constructs | Definitions |
|---|---|
| FC | 55. FC1: I would be able to have all the necessary resources to use the teledentistry services 56. FC2: I would acquire sufficient knowledge to use teledentistry services FC3: Teledentistry will fit well with my health care routine |
| PEOU | 57. PEOU1: I would find learning to use teledentistry would not be very difficult for me. 58. PEOU2: I would find it easy to interact with doctors using teledentistry. PEOU3: Interacting with teledentistry systems would be clear and understandable for me. |
| PR | 59. PR1: Learning how to use teledentistry services and adapting would be a loss of my time. 60. PR2: Using teledentistry systems would be a loss of money and resources. PR3: Using teledentistry would not be compatible with my moral values and image. |
| PU | 61. PU1: Using teledentistry would improve the quality of my health care. 62. PU2: Using teledentistry would improve my access to health care services. PU3: Using teledentistry would be useful in my daily routine. |
| RC | 63. RC1: I would not want teledentistry to alter my traditional way of using health care services. 64. RC2: I would not want teledentistry to interfere with or change the way I interact with doctors. RC3: I do not want teledentistry services to change the way I deal with my health problems and choices. |
| SI | 65. SI1: People around me who mean a lot to me would prefer if I would use teledentistry services. SI2: People who significantly influence my behavior would prefer if I use teledentistry services. |
| TA | 66. TA1: Using teledentistry would make me feel nervous. TA2: Using teledentistry would make me confused and uncomfortable. |
| T | 67. T1: Teledentistry services would be trustworthy in improving my health care routine. 68. T2: Teledentistry systems will require me to be cautious with this technology. T3: I feel satisfied and confident that I will be able to rely on the benefits of teledentistry. |
| UI | 69. UI1: Assuming that I am given the chance to access teledentistry, I intend to use teledentistry services. 70. UI2: Whenever I would need remote medical care from professionals, I would gladly use teledentistry services. UI3: I intend to inform my relatives and friends about teledentistry. |
FC: facilitating conditions; PEOU: perceived ease of use; PR: perceived risk; PU: perceived usefulness; RC: resistance to use; SI: social influence; TA: technology anxiety; T: trust; UI: usage intention.
The questionnaire was developed in English. It was translated into Arabic using the forward and backward translation method. The questionnaire was provided in English and Arabic via the QuestionPro platform.30 Participants were recruited using a non-probability snowball sampling method. The questionnaire was distributed through social media platforms (WhatsApp, Twitter, Instagram, and Snapchat). Pre-testing was conducted to ensure the validity and effectiveness of the questionnaire. All participants were required to sign a consent form.
Sample size calculation
The sample size was calculated using the online Raosoft sample size calculator.31 It was calculated using a 35 million population size,32 50% response distribution, a 95% confidence level, and a 5% margin of error. This study required a minimum sample size of 385 participants. A total of 770 participants will be recruited to account for any missing data or non-response rate.
Statistical analysis
Univariate analysis was conducted to calculate the frequencies and percentage of the participants’ characteristics. Partial least square structural equation modeling was used to examine the factors that affect users’ intention to use teledentistry.33 The standardized path coefficient, t-statistics, and the bootstrapping method were used to test the structural model. The significance level was set at a p-value of < 0.05. Data was analyzed using IBM-SPSS (version 20; Armonk, NY, USA) and SmartPLS 3.3.5 software.
Results
Sample characteristics
Of the 966 people who received the questionnaire, 426 completed it; 89 were ineligible (younger than 18) and were excluded from the study. Table 2 shows the study sample's characteristics. The study sample tended to be Saudi women who are married, hold a bachelor's degree, and are employed in the governmental sector. Approximately 45% of the study sample fell in the age range of 30–49, and more than one-third have a monthly income of >10,000–20,000 SAR. The majority live in urban areas, with 47% living in the Western region. About 38% have dental insurance, and more than two-thirds have had a dental visit in the past year.
Table 2.
Characteristics of the study population (n = 426).
| Characteristics | Frequency (%)a |
|---|---|
| Gender | |
| Male | 145 (34.4) |
| Female | 277 (65.6) |
| Age | |
| 18–29 | 94 (22.1) |
| 30–49 | 190 (44.7) |
| 50+ | 141 (33.2) |
| Marital status | |
| Single | 77 (18.1) |
| Married | 305 (71.6) |
| Divorced | 32 (7.5) |
| Widowed | 12 (2.8) |
| Nationality | |
| Saudi | 383 (92.0) |
| Non-Saudi | 34 (8.0) |
| Level of education | |
| High school or less | 31 (7.4) |
| Diploma degree | 21 (5.0) |
| Bachelor's degree | 268 (63.7) |
| Postgraduate degree | 101 (24.0) |
| Employment status | |
| Unemployed | 70 (16.5) |
| Retired | 84 (19.9) |
| Employee: Government sector | 162 (38.3) |
| Employee: Private sector | 86 (20.3) |
| Student | 21 (5.0) |
| Income per month | |
| Less than 4000 SAR | 70 (16.7) |
| 4000–10,000 SAR | 122 (29.2) |
| 10,001–20,000 SAR | 141 (33.7) |
| More than 20,000 SAR | 85 (20.3) |
| Region | |
| Western | 200 (47.1) |
| Northern | 50 (11.8) |
| Eastern | 59 (13.9) |
| Southern | 36 (8.5) |
| Central | 80 (18.8) |
| Living area | |
| Urban area | 405 (95.7) |
| Rural area | 18 (4.3) |
| Study or work in the dental field | |
| Yes | 32 (7.5) |
| No | 392 (92.5) |
| Dental insurance | |
| Yes | 160 (37.9) |
| No | 262 (62.1) |
| Chronic disease | |
| Yes | 135 (31.7) |
| No | 291 (68.3) |
| Last dental visit | |
| <1 year | 289 (68.2) |
| 1–2 years | 75 (17.6) |
| >2 years | 60 (14.2) |
Not all variables add up to total subject number due to missing values.
The measurement model
Exploratory factor analysis revealed that T2 (Teledentistry systems will require me to be cautious with this technology) and T3 (I feel satisfied and confident that I will be able to rely on the benefits of teledentistry) had an Eigenvalue of less than 1. Therefore, they were not retained in the analysis.34
To assess the construct validity of the variables, a confirmatory factor analysis was conducted (Table 2). All items had factor loadings greater than 0.5 in relation to their constructs, indicating construct validity.35 A Cronbach's alpha was calculated for all constructs to ensure internal consistency, with all scales exceeding the recommended value of 0.7.36 A composite reliability of 0.80 and an average variance extracted (AVE) of over 0.5 were met for all constructs to ensure convergent validity (Table 3).37
Table 3.
The measurement model.
| Items | Loadings | Cronbach's Alpha | CR | AVE | |
|---|---|---|---|---|---|
| FC | FC1 | 1.746 | 0.817 | 0.891 | 0.732 |
| FC2 | 2.219 | ||||
| FC3 | 1.761 | ||||
| PEOU | PEOU1 | 1.675 | 0.826 | 0.896 | 0.742 |
| PEOU2 | 2.147 | ||||
| PEOU3 | 1.988 | ||||
| PR | PR1 | 1.615 | 0.809 | 0.887 | 0.723 |
| PR2 | 1.977 | ||||
| PR3 | 1.813 | ||||
| PU | PU1 | 2.348 | 0.856 | 0.912 | 0.776 |
| PU2 | 2.472 | ||||
| PU3 | 1.846 | ||||
| RC | RC1 | 1.901 | 0.845 | 0.906 | 0.763 |
| RC2 | 2.331 | ||||
| RC3 | 1.992 | ||||
| SI | SI1 | 1.765 | 0.794 | 0.906 | 0.829 |
| SI2 | 1.765 | ||||
| TA | TA1 | 2.818 | 0.891 | 0.948 | 0.901 |
| TA2 | 2.818 | ||||
| T | T1 | 1 | 1 | 1 | 1 |
| UI | UI1 | 2.735 | 0.893 | 0.933 | 0.823 |
| UI2 | 2.689 | ||||
| UI3 | 2.539 |
FC: facilitating conditions; PEOU: perceived ease of use; PR: perceived risk; PU: perceived usefulness; RC: resistance to use; SI: social influence; TA: technology anxiety; T: trust; UI: usage intention; CR: composite reliability; AVE: average variance extracted.
The correlation matrix
The square root of the AVE was compared with correlations between the constructs under study to ensure discriminant validity.37 For each construct, the square root of AVE was larger than correlations with other factors37 (Table 4).
Table 4.
The correlation matrix.
| FC | PEOU | PR | PU | RU | SI | TA | T | UI | |
|---|---|---|---|---|---|---|---|---|---|
| FC | 0.855 a | - | |||||||
| PEOU | 0.676 | 0.861 a | - | ||||||
| PR | −0.543 | −0.579 | 0.851 a | - | |||||
| PU | 0.686 | 0.75 | −0.549 | 0.881 a | - | ||||
| RU | −0.257 | −0.201 | 0.302 | −0.256 | 0.874 a | - | |||
| SI | 0.624 | 0.516 | −0.284 | 0.514 | −0.232 | 0.91 a | - | ||
| TA | −0.456 | −0.534 | 0.594 | −0.535 | 0.309 | −0.273 | 0.949 a | - | |
| T | 0.693 | 0.706 | −0.496 | 0.726 | −0.176 | 0.529 | −0.515 | 1 a | - |
| UI | 0.726 | 0.711 | −0.63 | 0.731 | −0.304 | 0.594 | −0.541 | 0.706 | 0.907 a |
FC: facilitating conditions; PEOU: perceived ease of use; PR: perceived risk; PU: perceived usefulness; RC: resistance to use; SI: social influence; TA: technology anxiety; T: trust; UI: usage intention; AVE: average variance extracted
Square root of AVE for each latent variable.
The structural model testing
To verify the model fit, the normed fit index (NFI) and standardized root mean square residual (SRMR) were calculated. The NFI was 0.815, and it has been suggested that a value higher than 0.8 is considered a good fit.38 The SRMR was 0.051, which is considered a good fit according to Hu and Bentler.39
Table 5 presents the relationship between the independent variables and the dependent variable (usage intention). Hypothesis testing demonstrated the positive effects of facilitating conditions, perceived ease of use, perceived usefulness, social influence, and trust, although the only significant factor was social influence (β = 0.18, t = 2.19, p-value = 0.03). The factors that had negative influence were perceived risk, resistance to use, and technology anxiety, with perceived risk being the only significant factor (β = −0.21, t = 2.69, p-value = 0.01).
Table 5.
The structural model.
| Path coefficient | T-statistics | P-value | Lower 95% CI | Upper 95% CI | |
|---|---|---|---|---|---|
| Facilitating conditions | 0.16 | 0.67 | 0.5 | −0.20 | 0.45 |
| Perceived ease of use | 0.11 | 0.14 | 0.9 | −0.31 | 0.39 |
| Perceived risk | −0.21 | 2.69 | 0.01* | −0.53 | −0.14 |
| Perceived usefulness | 0.19 | 1.49 | 0.1 | −0.05 | 0.61 |
| Resistance to use | −0.05 | 0.48 | 0.6 | −0.11 | 0.07 |
| Social influence | 0.18 | 2.19 | 0.03* | 0.06 | 0.46 |
| Technology anxiety | −0.04 | 0.03 | 1.0 | −0.12 | 0.14 |
| Trust | 0.16 | 1.21 | 0.2 | −0.08 | 0.25 |
* p-value <0.05.
Discussion
The World Health Organization has published a set of guiding principles for the implementation of telemedicine services during the COVID-19 pandemic, and the differences in accepting teledentistry across countries is expected to vary. Therefore, this pilot study used TAM to predict the acceptance and use of teledentistry among the general population in Saudi Arabia. This model is used to identify the main predictors and barriers that influence the user intension to adopt teledentistry services.
Social influence is defined as factors that motivate or inhibit people, including the extent to which individuals consider their peers’ opinions, particularly those of friends and acquaintances, regarding how they should behave.28 Our findings reveal that social influence is a positive significant predictor for the acceptance of teledentistry services by the general population. It is consistent with previous studies that social influence is found to be a positive determinant of users’ intension to use teledentisty.23,40,41 It is likely that an individual's willingness to use teledentistry services will increase if their family members or peers consider these services to be effective in improving their oral health. It is possible that uncertainty created by newly introduced innovations makes potential adopters seek the opinions of their peers and local social circle.42 Furthermore, most respondents in this study were aged 30–49 (generation Y), and social networks account for a larger share of sources of information about dental and health care.43
Evidence has shown that social influence is a potentially important determinant of users’ willingness to adopt new technologies.44,45 Additionally, studies on technology acceptance in health care often apply Rogers’ diffusion of innovation theory. According to this theory, apparent elements of innovation, dissemination through social channels, and time are key factors influencing the spread and success of the innovations.46 It is therefore important to incorporate social influence into major technological acceptance models for both scholars and health care organizations to gain a better understanding of the issue. Consequently, health care policymakers need to engage well-known and trusted community members and local public figures when introducing new technology-based health services so they can facilitate the diffusion of these services among the general population and facilitate the acceptance of these services. As technology use will continue to be driven by social influence, future multi-theoretical studies are needed to better understand how social networks influence behavior and referent effects, so that the subjective norm can be enhanced and users’ adoption of teledentistry services can be predicted.47
Perceived risk is an individual's perception of uncertainty when deciding to take a particular action or engage in a particular activity.48 Our findings revealed that perceived risk was a major barrier to users’ intention to use teledentistry. The effect of perceived risk on users’ adoption of technology-based health care services has also been observed in other studies.23,49 Technology-based services are associated with uncertainties and risks that are distinct to patients and health care providers, and perceived risk plays a significant role in the decision to use them.50,51 Previous studies have identified several aspects of perceived risk, such as psychological risk caused by reduced mental satisfaction, the financial risk caused by cost barriers, and performance risk associated with a probabilistic perception of insufficient health information when using online services.23 Perceived risk has been shown to contribute to the expectation of destructive consequences, which then negatively influences usage intentions.52 The respondents’ insecurity and perceived sense of risk regarding teledentistry services may be reasonable, as the impersonal nature of these services is the main barrier when seeking online oral health care.53
The use of teledentistry is becoming increasingly popular in order to manage patients more effectively. Teledentistry can be particularly useful in remote areas that are difficult to reach. Patients were able to learn about their treatment and diagnosis through teledentistry, as well as educational programs that raised awareness about oral health.54
This study has a few limitations. First, it used an online questionnaire that was completed exclusively by individuals who are computer literate and who have Internet access. Therefore, the results cannot be generalized, as the bias towards interest in this subject could not be avoided, and the opinions of individuals in resource-constrained places were not explored. Furthermore, the responses were collected during the COVID-19 pandemic, which may have influenced the respondents’ acceptance decisions. Last, due to the cross-sectional nature of this study, causality cannot be determined. However, this was the first study to utilize a theoretical framework to assess the facilitators of and the barriers to predicting the Saudi population's future intention to adopt teledentistry services. In addition, the results of this study suggest that policymakers and health care authorities should formulate strategic plans by identifying the key aspects in adopting teledentistry services from the patient perspective to ensure effective diffusion.
Conclusion
Social influence was found to be a significant positive predictor of the adoption of teledentistry services, whereas perceived risk was found to be a major barrier. These empirically based insights are important to help policymakers implement effective strategies to ensure the large-scale successful acceptance of teledentistry services.
Acknowledgments
The authors acknowledge Omar Khalid Abdulwassi and Youssef Ghassan Ajab Noor who participated in the data collection.
Footnotes
Author contributions: LB and MA researched literature and conceived the study. OA and YA collected the data. LB analyzed the data. The manuscript was drafted and critically reviewed by LB and MA. LB and MA approved the final version of the manuscript.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Ethical approval: This study was reviewed and approved by the Institutional Review Board at King Abdulaziz University Faculty of Dentistry, Jeddah, Saudi Arabia (#286-09-21). All methods were performed in accordance with the principles of the Declaration of Helsinki.
Informed consent: All participants provided informed consent.
Guarantor: LB
ORCID iD: Lina Bahanan https://orcid.org/0000-0001-6508-5939
References
- 1.Sharifi M, Ayat M, Jahanbakhsh Met al. et al. E-health implementation challenges in Iranian medical centers: A qualitative study in Iran. Telemed e-Health 2013 1; 19: 122–128. [DOI] [PubMed] [Google Scholar]
- 2.Mullett CJ, Evans RS, Christenson JCet al. et al. Development and impact of a computerized pediatric antiinfective decision support program. Pediatrics 2001 1; 108: e75–e75. [DOI] [PubMed] [Google Scholar]
- 3.Patel RN, Antonarakis GS. Factors influencing the adoption and implementation of teledentistry in the UK, with a focus on orthodontics. Community Dent Oral Epidemiol 2013; 41: 424–431. [DOI] [PubMed] [Google Scholar]
- 4.Byrne E, Watkinson S. Patient and clinician satisfaction with video consultations during the COVID-19 pandemic: An opportunity for a new way of working. J Orthod 2020 28; 48: 64–73. [DOI] [PubMed] [Google Scholar]
- 5.Jampani ND, Nutalapati R, Dontula BSKet al. et al. Applications of teledentistry: A literature review and update. J Int Soc Prev Community Dent 2011; 1: 37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Rocca MA, Kudryk VL, Pajak JCet al. et al. The evolution of a teledentistry system within the department of defense. Proc AMIA Symp 1999: 921–924. [PMC free article] [PubMed] [Google Scholar]
- 7.Mariño R, Ghanim A. Teledentistry: A systematic review of the literature. J Telemed Telecare 2013; 19: 179–183. [DOI] [PubMed] [Google Scholar]
- 8.Menhadji P, Patel R, Asimakopoulou K, et al. Patients’ and dentists’ perceptions of tele-dentistry at the time of COVID-19. A questionnaire-based study: Tele-dentistry at the time of COVID-19. J Dent 2021;113: 103782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Quinn B, Field J, Gorter R, , et al. COVID-19: The immediate response of European academic dental institutions and future implications for dental education. Eur J Dent Educ 2020; 24: 811–814. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.American Dental Association. ADA Policy on Teledentistry. https://www.ada.org/about/governance/current-policies/ada-policy-on-teledentistry#:∼:text=Teledentistry%20cannot%20be%20used%20to,for%20the%20purposes%20of%20teledentistry. (2020, accessed 6 September 2021).
- 11.Mair F, Whitten P. Systematic review of studies of patient satisfaction with telemedicine. Br Med J 2000; 320: 1517–1520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Collins K, Walters S, Bowns I. Patient satisfaction with teledermatology: Quantitative and qualitative results from a randomized controlled trial. J Telemed Telecare 2004; 10: 29–33. [DOI] [PubMed] [Google Scholar]
- 13.Rutherford E, Noray R, Ó hEarráin C, , et al. Potential benefits and drawbacks of virtual clinics in general surgery: Pilot cross-sectional questionnaire study. JMIR Perioper Med 2020; 3: e12491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Donelan K, Barreto EA, Sossong Set al. Patient and clinician experiences with telehealth for patient follow-up care. Am J Manag Care 2019; 25: 40–44. [PubMed] [Google Scholar]
- 15.Al-Khalifa KS, AlSheikh R. Teledentistry awareness among dental professionals in Saudi Arabia. PLoS One 2020; 15: e0240825. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Alsharif AT, Al-harbi SS. Dentists’ self-perception on teledentistry: The changing landscape driven by technological booming in the 21st century. Open Dent J 2020; 14: 291–297. [Google Scholar]
- 17.Almazrooa SA, Mansour GA, Alhamed SAet al. The application of teledentistry for Saudi patients’ care: A national survey study. J Dent Sci 2021; 16: 280–286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Aboalshamat KT. Awareness of, beliefs about, practices of, and barriers to teledentistry among dental students and the implications for Saudi Arabia Vision 2030 and coronavirus pandemic. J Int Soc Prev Community Dent 2020; 10: 431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Alassad F, Alqhtani N, Alshammery D. Implementation of teledentistry in postgraduate dental education during COVID-19 pandemic in Saudi Arabia. Ann Dent Spec 2021; 9(1): 20–26. [Google Scholar]
- 20.Alawwad SM, Zakirulla M, Alasmari NMet al. et al. Perceptions of teledentistry among dental professionals in Saud Arabia. Ann Trop Med Public Heal 2019; 22: 182. [Google Scholar]
- 21.Rahman N, Nathwani S, Kandiah T. Teledentistry from a patient perspective during the coronavirus pandemic. Br Dent J 2020; 229: 1–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Daniel SJ, Wu L, Kumar S. Teledentistry: A systematic review of clinical outcomes, utilization and costs. Am Dent Hyg Assoc 2013; 87: 345–352. [PubMed] [Google Scholar]
- 23.Kamal SA, Shafiq M, Kakria P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol Soc 2020; 60: 101212. [Google Scholar]
- 24.Chuttur M. Overview of the technology acceptance model: origins, developments and future directions. Sprouts Work Pap Inf Syst 2009; 9(37). [Google Scholar]
- 25.Holden RJ, Karsh BT. The technology acceptance model: Its past and its future in health care. J Biomed Inform 2010; 43: 159–172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yi MY, Jackson JD, Park JSet al. et al. Understanding information technology acceptance by individual professionals: Toward an integrative view. Inf Manag 2006; 43: 350–363. [Google Scholar]
- 27.Chau PYK, Hu PJH. Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Inf Manag 2002; 39: 297–311. [Google Scholar]
- 28.Venkatesh V, Morris MG, Davis GBet al. et al. User acceptance of information technology: Toward a unified view. MIS Q Manag Inf Syst 2003; 27: 425–478. [Google Scholar]
- 29.Alabdullah JH, Van Lunen BL, Claiborne DMet al. et al. Application of the unified theory of acceptance and use of technology model to predict dental students’ behavioral intention to use teledentistry. J Dent Educ 2020; 84: 1262–1269. [DOI] [PubMed] [Google Scholar]
- 30.QuestionPro. Free Online Survey Software and Tools. https://www.questionpro.com/ (2021, accessed 11 September 2021)
- 31.Sample Size Calculator by Raosoft, Inc. http://www.raosoft.com/samplesize.html (accessed 7 October 2020)
- 32.General Authority for Statistics. Population Estimates. https://www.stats.gov.sa/en/43 (2021, accessed 8 September 2021)
- 33.Sarstedt M, Ringle CM, Smith Det al. et al. Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. J Fam Bus Strateg 2014; 5: 105–115. [Google Scholar]
- 34.Hayton JC, Allen DG, Scarpello V. Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organ Res Methods 2004; 7: 191–205. [Google Scholar]
- 35.Hair JF, Black WC, Babin BJet al. et al. Multivariate data analysis. 7th ed. Upper Saddle River, NJ, USA: PrenticeHall, 2009. [Google Scholar]
- 36.Nunnally J, Bernstein I. Psychometric theory. 3rd ed. New Delhi: Tata McGraw-Hill Ed., 2010. [Google Scholar]
- 37.Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J Mark Res 1981; 18: 382. [Google Scholar]
- 38.Bartholomew DJ, Knott M (Martin), Moustaki I. Latent variable models and factor analysis : A unified approach. 3rd ed. Chichester, UK: Wiley, 2011. [Google Scholar]
- 39.Hu LT, Bentler PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol Methods 1998; 3: 424–453. [Google Scholar]
- 40.Jin Z, Chen Y. Telemedicine in the cloud era: Prospects and challenges. IEEE Pervasive Comput 2015; 14: 54–61. [Google Scholar]
- 41.Deng Z, Hong Z, Ren Cet al. et al. What predicts patients’ adoption intention toward mhealth services in China: Empirical study. JMIR mHealth uHealth 2018; 6(8): e172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Burkhardt ME, Brass DJ. Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. Adm Sci Q 1990; 35: 104. [Google Scholar]
- 43.Bolton RN, Parasuraman A, Hoefnagels A, et al. Understanding generation Y and their use of social media: A review and research agenda. J Serv Manag 2013; 24: 245–267. [Google Scholar]
- 44.Baptista G, Oliveira T. Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Comput Human Behav 2015; 50: 418–430. [Google Scholar]
- 45.Lu J, Yao JE, Yu CS. Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. J Strateg Inf Syst 2005; 14: 245–268. [Google Scholar]
- 46.Rogers EM. Diffusion of innovations. 5th ed. New York, NY u.a.: Free Press, 2003. [Google Scholar]
- 47.Graf-Vlachy L, Buhtz K, König A. Social influence in technology adoption: Taking stock and moving forward. Manag Rev Q 2018; 68: 37–76. [Google Scholar]
- 48.Nicolaou AI, McKnight DH. Perceived information quality in data exchanges: Effects on risk, trust, and intention to use. Inf Syst Res 2006; 17: 332–351. [Google Scholar]
- 49.Prodhan UK, Rahman MZ, Jahan I. A survey on the telemedicine in Bangladesh. In: IEEE International Conference on Computing, Communication and Automation. International Conference on Computing, Communication and Automation (ICCCA), 2016; p. 857–61.
- 50.Dixon B. A roadmap for the adoption of e-health. e-Service J 2007; 5: 3. [Google Scholar]
- 51.Yarbrough AK, Smith TB. Technology acceptance among physicians: A new take on TAM. Med Care Res Rev 2007; 64: 650–672. [DOI] [PubMed] [Google Scholar]
- 52.Hsieh PJ. An empirical investigation of patients’ acceptance and resistance toward the health cloud: The dual factor perspective. Comput Human Behav 2016; 63: 959–969. [Google Scholar]
- 53.Tenore G, Podda GM, La Torre G, , et al. Evaluation of patient’s perception and acceptance of tele(oral)medicine for care during the COVID-19 pandemic: A cross-sectional pilot survey. Appl Sci 2021; 11: 7443. [Google Scholar]
- 54.Maqsood A, Sadiq MSK, Mirza Det al. et al. The teledentistry, impact, current trends, and application in dentistry: A global study. Biomed Res Int 2021; 2021: 5437237.. [DOI] [PMC free article] [PubMed] [Google Scholar]