How to cite this paper
Hammad, A., Bataineh, A., Alshurideh, M & Salhab, H. (2022). Factors affecting healthcare providers to accept digital marketing: The moderating role of subjective norms.International Journal of Data and Network Science, 6(4), 1085-1098.
Refrences
Alhashmi, S. F., Salloum, S. A., & Mhamdi, C. (2019). Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model. International Journal of Information Technology Lan-guage studies, 3(3), 27-42.
Al-Swidi, A., Huque, S. M. R., Hafeez, M. H., & Shariff, M. N. M. (2014). The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal, 116(10), 1561-1580.
Arbuckle, J. L. (2014). IBM SPSS Amos 23 user’s guide. IBM, Amos Development Corporation. Retrieved from: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/amos/23.0/ en/Manuals/IBM_SPSS_Amos_User_Guide.pdf
Bedi, S. S., Kaur, S., & Lal, A. K. (2017). Understanding web experience and perceived web enjoyment as antecedents of online purchase intention. Global Business Review, 18(2), 465 477.
Bhatti, A., & Akram, H. (2020). The moderating role of subjective norms between online shopping behaviour and its de-terminants. International Journal of Social Sciences and Economic Review, 1-09.
Churchil, G.A., & Brown, T.J. (2014). Basic Marketing Research (8th Ed.). Cengage Learning.
Davis, F. D. (1987). User acceptance of information systems: the technology acceptance model (TAM).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3) 319-340.
Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: an Indian perspective. International journal of medical informatics, 141, 104164.
Fishbein, M. (2008). Reasoned action, theory of. The International Encyclopedia of Communication.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Read-ing, MA: Addison-Wesley.
Fishbein, M. & Ajzen, I. (1980). Predicting and understanding consumer behavior: Attitude-behavior correspondence. In Ajzen, I. & Fishbein, M. (eds.). Understanding Attitudes and Predicting Social Behavior (pp. 148-172). Englewood Cliffs, NJ: Prentice Hall
Gagnon, M. P., Ngangue, P., Payne-Gagnon, J., & Desmartis, M. (2016). m-Health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, 23(1), 212-220.
Hair, J.F., Jr., Black, W.C., Babin, B.J. & Anderson, R.E. (2010). Multivariate Data Analysis.7th ed., Prentice Hall, Upper Saddle River, N.J.
Hollebeek, L. D., & Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and val-ue: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45, 27-41.
Jahanmir, S. F., & Cavadas, J. (2018). Factors affecting late adoption of digital innovations. Journal of business re-search, 88, 337-343.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Kaur, G. (2017). The importance of digital marketing in the tourism industry. International Journal of Research-Granthaalayah, 5(6), 72-77.
Kiili, C., Leu, D. J., Marttunen, M., Hautala, J., & Leppänen, P. H. (2019). Exploring early adolescents’ evaluation of aca-demic and commercial online resources related to health. Reading and Writing, 31(2).
Laher, S. (2010). Using exploratory factor analysis in personality research: Best-practice recommendations. SA Journal of Industrial Psychology, 36(1), 1-7. doi:10.4102/sajip.v36i1.873
Mezo, P. G., & Short, M. M. (2012). Construct validity and confirmatory factor analysis of the self-control and self-management scale. Canadian Journal of Behavioural Science, 44(1), 1–8.
Minton, E. A., Spielmann, N., Kahle, L. R., & Kim, C. H. (2018). The subjective norms of sustainable consumption: A cross-cultural exploration. Journal of Business Research, 82, 400-408.
Nuseira, M. T., & Aljumahb, A. (2020). The Role of Digital Marketing in Business Performance with the Moderating Ef-fect of Environment Factors among SMEs of UAE. International Journal of Innovation, Creativity and Change. 11(3).
Rasmi, M., Alazzam, M. B., Alsmadi, M. K., Almarashdeh, I. A., Alkhasawneh, R. A., & Alsmadi, S. (2018). Healthcare professionals’ acceptance Electronic Health Records system: Critical literature review (Jordan case re-search). International Journal of Healthcare Management., 13(1), 48-60. DOI: 10.1080/20479700.2017.1420609
Schnall, R., Higgins, T., Brown, W., Carballo-Dieguez, A., & Bakken, S. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informat-ics, 216, 467.
SHOTER, A. M., BATAINEH, A. Q., & SALHAB, H. A. (2016). Building a Model for Determining the Factors Affecting Mobile Marketing Acceptance and Adoption. IRMBR-International Review of Management and Business Research, 5, 22.
Yim, M. Y. C., & Yoo, C. Y. (2020). Are digital menus really better than traditional menus? The mediating role of con-sumption visions and menu enjoyment. Journal of Interactive Marketing, 50, 65-80.
Yoga, I. M. S., Korry, N. P. D. P., & Yulianti, N. M. D. R. (2019). Information technology adoption on digital marketing communication channel. International journal of social sciences and humanities, 3(2), 95-104.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sci-ences, 39(2), 273-315.
Zhao, J., & Wang, J. (2020). Health advertising on short-video social media: a research on user Attitudes based on the ex-tended technology acceptance model. International journal of environmental research and public health, 17(5), 1501.
Al-Swidi, A., Huque, S. M. R., Hafeez, M. H., & Shariff, M. N. M. (2014). The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal, 116(10), 1561-1580.
Arbuckle, J. L. (2014). IBM SPSS Amos 23 user’s guide. IBM, Amos Development Corporation. Retrieved from: ftp://public.dhe.ibm.com/software/analytics/spss/documentation/amos/23.0/ en/Manuals/IBM_SPSS_Amos_User_Guide.pdf
Bedi, S. S., Kaur, S., & Lal, A. K. (2017). Understanding web experience and perceived web enjoyment as antecedents of online purchase intention. Global Business Review, 18(2), 465 477.
Bhatti, A., & Akram, H. (2020). The moderating role of subjective norms between online shopping behaviour and its de-terminants. International Journal of Social Sciences and Economic Review, 1-09.
Churchil, G.A., & Brown, T.J. (2014). Basic Marketing Research (8th Ed.). Cengage Learning.
Davis, F. D. (1987). User acceptance of information systems: the technology acceptance model (TAM).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3) 319-340.
Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: an Indian perspective. International journal of medical informatics, 141, 104164.
Fishbein, M. (2008). Reasoned action, theory of. The International Encyclopedia of Communication.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Read-ing, MA: Addison-Wesley.
Fishbein, M. & Ajzen, I. (1980). Predicting and understanding consumer behavior: Attitude-behavior correspondence. In Ajzen, I. & Fishbein, M. (eds.). Understanding Attitudes and Predicting Social Behavior (pp. 148-172). Englewood Cliffs, NJ: Prentice Hall
Gagnon, M. P., Ngangue, P., Payne-Gagnon, J., & Desmartis, M. (2016). m-Health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association, 23(1), 212-220.
Hair, J.F., Jr., Black, W.C., Babin, B.J. & Anderson, R.E. (2010). Multivariate Data Analysis.7th ed., Prentice Hall, Upper Saddle River, N.J.
Hollebeek, L. D., & Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and val-ue: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45, 27-41.
Jahanmir, S. F., & Cavadas, J. (2018). Factors affecting late adoption of digital innovations. Journal of business re-search, 88, 337-343.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Kaur, G. (2017). The importance of digital marketing in the tourism industry. International Journal of Research-Granthaalayah, 5(6), 72-77.
Kiili, C., Leu, D. J., Marttunen, M., Hautala, J., & Leppänen, P. H. (2019). Exploring early adolescents’ evaluation of aca-demic and commercial online resources related to health. Reading and Writing, 31(2).
Laher, S. (2010). Using exploratory factor analysis in personality research: Best-practice recommendations. SA Journal of Industrial Psychology, 36(1), 1-7. doi:10.4102/sajip.v36i1.873
Mezo, P. G., & Short, M. M. (2012). Construct validity and confirmatory factor analysis of the self-control and self-management scale. Canadian Journal of Behavioural Science, 44(1), 1–8.
Minton, E. A., Spielmann, N., Kahle, L. R., & Kim, C. H. (2018). The subjective norms of sustainable consumption: A cross-cultural exploration. Journal of Business Research, 82, 400-408.
Nuseira, M. T., & Aljumahb, A. (2020). The Role of Digital Marketing in Business Performance with the Moderating Ef-fect of Environment Factors among SMEs of UAE. International Journal of Innovation, Creativity and Change. 11(3).
Rasmi, M., Alazzam, M. B., Alsmadi, M. K., Almarashdeh, I. A., Alkhasawneh, R. A., & Alsmadi, S. (2018). Healthcare professionals’ acceptance Electronic Health Records system: Critical literature review (Jordan case re-search). International Journal of Healthcare Management., 13(1), 48-60. DOI: 10.1080/20479700.2017.1420609
Schnall, R., Higgins, T., Brown, W., Carballo-Dieguez, A., & Bakken, S. (2015). Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Studies in health technology and informat-ics, 216, 467.
SHOTER, A. M., BATAINEH, A. Q., & SALHAB, H. A. (2016). Building a Model for Determining the Factors Affecting Mobile Marketing Acceptance and Adoption. IRMBR-International Review of Management and Business Research, 5, 22.
Yim, M. Y. C., & Yoo, C. Y. (2020). Are digital menus really better than traditional menus? The mediating role of con-sumption visions and menu enjoyment. Journal of Interactive Marketing, 50, 65-80.
Yoga, I. M. S., Korry, N. P. D. P., & Yulianti, N. M. D. R. (2019). Information technology adoption on digital marketing communication channel. International journal of social sciences and humanities, 3(2), 95-104.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sci-ences, 39(2), 273-315.
Zhao, J., & Wang, J. (2020). Health advertising on short-video social media: a research on user Attitudes based on the ex-tended technology acceptance model. International journal of environmental research and public health, 17(5), 1501.