Document Type : Original Article

Authors

1 MSc Student in Health Information Technology, Student Research Committee, School of Paramedicine, Hormozgan University of medical sciences, Bandar Abbas, Iran.

2 Dept. of Health Information management, School of Paramedicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

3 Dept. Oral and Dental Disease Research Center, Dept. of Dental Public Health, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.

4 Dept. of Health Information Management, Dept. of Health Information Management, School of Paramedicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

10.30476/dentjods.2023.96932.1977

Abstract

Statement of the Problem: Health information technology is used in dentistry worldwide. Despite the limited specialized resources for providing orthodontic treatment in Iran, the need to examine the technology acceptance model (TAM) seems necessary and is a significant step in the successful acceptance of teleorthodontic technology.
Purpose: The present study has identified and investigated the factors affecting the acceptance of teleorthodontic technology among orthodontists based on the TAM3 with the aim of successful implementation and deployment of this technology.
Materials and Method: In this descriptive-analytical research, 300 Iranian orthodontists who were members of the Iranian Orthodontic Association were selected by census sampling. The data was gathered through a modified and accommodated questionnaire called the acceptance model 3. The validity was confirmed. Moreover, the reliability was calculated based on Cronbach's alpha, which was equal to 0.870. Multiple linear regression analysis was also utilized to investigate the relationships between dependent, independent, and mediator variables. Besides, the final model was designed by the Amos software.
Results: The results of 251 orthodontic specialists proved that subjective norm, job relevance, output quality, results in demonstrability, and job relevance on output quality could significantly affect perceived usefulness. Similarly, the perception of external control was identified to have a significant influence on perceived ease of use. On the other hand, the perceived usefulness does not play a mediating role between perception and subjective norm. Furthermore, perceived usefulness was confirmed as a mediating factor in relationship to both perceived ease of use and behavioral intention.
Conclusion: The findings of the present study revealed valuable scientific evidence to identify and apply the key factors affecting the acceptance and use of modern teleorthodontic technology in Iran. Besides, the structure of the TAM3 was recognized as fruitful and worthwhile for predicting the acceptance of this new technology and also in identifying key effective factors.

Highlights

Nasrin Davaridolatabadi (Google Scholar)

Keywords

  • Berndt J, Leone P, King G. Using teledentistry to provide interceptive orthodontic services to disadvantaged children. Am J Orthod Dentofacial Orthop. 2008; 134: 700-706. 
  • Husin MH, Lim YK. InWalker: smart white cane for the blind. Disabil Rehabil Assist Technol. 2020; 15: 701-707.
  • Alhammadi MS, Halboub E, Fayed MS, Labib A, El-Saaidi C. Global distribution of malocclusion traits: A systematic review. Dental Press J Orthod. 2018; 23: 40.e1-40.e10.
  • Shen L, He F, Zhang C, Jiang H, Wang J. Prevalence of malocclusion in primary dentition in mainland China, 1988-2017: a systematic review and meta-analysis. Sci Rep. 2018; 8: 4716.
  • Balachandran P, Janakiram C. Prevalence of malocclusion among 8-15 years old children, India - A systematic review and meta-analysis. J Oral Biol Craniofac Res. 2021; 11: 192-199.
  • Nasir M, Ramadhany YF. Tele-orthodontic as a recent solution in malocclusion treatment. Makassar Dent J. 2020; 9: 78-81.
  • Alogaibi YA, Murshid ZA, Alsulimani FF, Linjawi AI, Almotairi M, Alghamdi M, et al. Prevalence of malocclusion and orthodontic treatment needs among young adults in Jeddah city. J Orthod Sci. 2020; 9: 3.
  • Eslamipour F, Afshari Z, Najimi A. Prevalence of malocclusion in permanent dentition of Iranian population: A review article. Iran J Public Health. 2018; 47: 178-187.
  • Bustati N, Rajeh N. The impact of COVID-19 pandemic on patients receiving orthodontic treatment: An online questionnaire cross-sectional study. J World Fed Orthod. 2020; 9: 159-163.
  • Tella AJ, Olanloye OM, Ibiyemi O. Potential of teledentistry in the delivery of oral health services in developing countries. Ann Ib Postgrad Med. 2019; 17: 115-123.
  • Daniel SJ, Kumar S. Teledentistry: a key component in access to care. J Evid Based Dent Pract. 2014; 14 Suppl: 201-208.
  • Maspero C, Abate A, Cavagnetto D, El Morsi M, Fama A, Farronato M. Available technologies, applications and benefits of teleorthodontics: A literature review and possible applications during the COVID-19 pandemic. J Clin Med. 2020; 9: 1891.
  • Dalessandri D, Sangalli L, Tonni I, Laffranchi L, Bonetti S, Visconti L, et al. Attitude towards telemonitoring in orthodontists and orthodontic patients. Dent J (Basel). 2021; 9: 47.
  • Martina S, Amato A, Rongo R, Caggiano M, Amato M. The perception of COVID-19 among Italian dentists: an orthodontic point of view. Int J Environ Res Public Health. 2020; 17: 4384.
  • Stokel-Walker C. Why telemedicine is here to stay. BMJ. 2020; 371: m3603.
  • Kotantoula G, Haisraeli-Shalish M, Jerrold L. Teleorthodontics. Am J Orthod Dentofacial Orthop. 2017; 151: 219-221.
  • Saccomanno S, Quinzi V, Sarhan S, Laganà D, Marzo G. Perspectives of tele-orthodontics in the COVID-19 emergency and as a future tool in daily practice. Eur J Paediatr Dent. 2020; 21: 157-162.
  • Estai M, Kanagasingam Y, Tennant M, Bunt S. A systematic review of the research evidence for the benefits of teledentistry. J Telemed Telecare. 2018; 24: 147-156.
  • Jacox LA, Mihas P, Cho C, Lin FC, Ko CC. Understanding technology adoption by orthodontists: A qualitative study. Am J Orthod Dentofacial Orthop. 2019; 155: 432-442.
  • Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010; 43: 159-172.
  • Chuttur MY. Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. 1th ed. Working Papers on Information Systems: Indiana University, USA. Sprouts: 2009. p. 37.
  • Godoe P, Johansen T. Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. J European Psychology Students. 2012; 3: 38-52.
  • Mortenson MJ, Vidgen R. A computational literature review of the technology acceptance model. International Journal of Information Management. 2016; 36: 1248-1259.
  • Shachak A, Kuziemsky C, Petersen C. Beyond TAM and UTAUT: Future directions for HIT implementation research. J Biomed Inform. 2019; 100: 103315.
  • Klaic M, Galea MP. Using the Technology Acceptance Model to Identify Factors That Predict Likelihood to Adopt Tele-Neurorehabilitation. Front Neurol. 2020; 11: 580832.
  • Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences. 2008; 39: 273-315.
  • Chang SJ, Im EO. A path analysis of Internet health information seeking behaviors among older adults. Geriatr Nurs. 2014; 35: 137-141.
  • Ebnehoseini Z, Tara M, Tabesh H, Dindar FH, Hasibian S. Understanding key factors affecting on hospital electronic health record (EHR) adoption. J Family Med Prim Care. 2020; 9: 4348-4352.
  • Sridhar A, Drahota A, Walsworth K. Facilitators and barriers to the utilization of the ACT SMART Implementation Toolkit in community-based organizations: a qualitative study. Implement Sci Commun. 2021; 2: 55.
  • Usmanova G, Gresh A, Cohen MA, Kim YM, Srivastava A, Joshi CS, et al. Acceptability and Barriers to Use of the ASMAN Provider-Facing Electronic Platform for Peripartum Care in Public Facilities in Madhya Pradesh and Rajasthan, India: A Qualitative Study Using the Technology Acceptance Model-3. Int J Environ Res Public Health. 2020; 17: 8333.
  • Zhu M, Zhang Y. Medical and public health instructors' perceptions of online teaching: A qualitative study using the Technology Acceptance Model 2. Educ Inf Technol (Dordr). 2022; 27: 2385-2405.
  • Zhang H, Cocosila M, Archer N. Factors of adoption of mobile information technology by homecare nurses: a technology acceptance model 2 approach. Comput Inform Nurs. 2010; 28: 49-56.
  • Su YY, Huang ST, Wu YH, Chen CM. Factors Affecting Patients' Acceptance of and Satisfaction with Cloud-Based Telehealth for Chronic Disease Management: A Case Study in the Workplace. Appl Clin Inform. 2020; 11: 286-294.
  • De Angelis G, Brosseau L, Davies B, King J, Wells GA. The use of information and communication technologies by arthritis health professionals to disseminate a self-management program to patients: a pilot randomized controlled trial protocol. Digit Health. 2018; 4: 2055207618819571.
  • Lee SS, Tay SM, Balakrishnan A, Yeo SP, Samarasekera DD. Mobile learning in clinical settings: unveiling the paradox. Korean J Med Educ. 2021; 33: 349-367.
  • Chen CK, Tsai TH, Lin YC, Lin CC, Hsu SC, Chung CY, Pei YC, Wong AMK. Acceptance of different design exergames in elders. PLoS One. 2018; 13: e0200185.
  • Nadri H, Rahimi B, Lotfnezhad Afshar H, Samadbeik M, Garavand A. Factors Affecting Acceptance of Hospital Information Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments. Appl Clin Inform. 2018; 9: 238-247.
  • Ebrahimi S, Mehdipour Y, Karimi A, Khammarnia M, Alipour J. Determinants of physicians' technology acceptance for mobile health services in healthcare settings. Health Manag Inform Sci. 2018; 5: 9-15.
  • Domingos C, Costa P, Santos NC, Pêgo JM. Usability, Acceptability, and Satisfaction of a Wearable Activity Tracker in Older Adults: Observational Study in a Real-Life Context in Northern Portugal. J Med Internet Res. 2022; 24: e26652.
  • Ho KF, Chang PC, Kurniasari MD, Susanty S, Chung MH. Determining factors affecting nurses' acceptance of a care plan system using a modified technology acceptance model 3: structural equation model with cross-sectional data. JMIR Med Inform. 2020; 8: e15686.
  • Venkatesh V. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research. 2000; 11: 342-365.
  • Cengiz E, Bakırtaş H. Technology acceptance model 3 in understanding employee's cloud computing technology. Global Business Review. 2020: 0972150920957173.
  • Isernia S, Pagliari C, Jonsdottir J, Castiglioni C, Gindri P, Gramigna C, et al. HEAD study group. Efficiency and patient-reported outcome measures from clinic to home: The human empowerment aging and disability program for digital-health rehabilitation. Front Neurol. 2019; 10: 1206.
  • Mansourzadeh M, Mahmoodi F, Hamdollah H. Investigating the effective factors on acceptance of ICT among students based on technology acceptance Model 3. Education Strategies in Medical Sciences. 2016; 9: 357-370.