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Mobile Health Applications for Healthy Living: Factors Influencing the Intention to Use Mobile Health Applications

Yıl 2024, Sayı: 69, 95 - 104, 30.12.2024
https://doi.org/10.18070/erciyesiibd.1526435

Öz

This study aims to identify the factors affecting the intention to use mobile health applications and to reveal how users' health awareness influences their intention to use mobile health applications. The study was conducted with individuals aged 18 and over who use applications related to physical activity (pedometer, home exercise, regular sleep, etc.) and healthy eating (calorie counter, water intake tracking, etc.). A questionnaire was used as the data collection tool in the research. SPSS and the "Process Macro" package program were used for data analysis. The collected data were evaluated with descriptive statistics, validity and reliability analysis, simple linear regression analysis, and "bootstrap model 4" analysis. The research concluded that perceived usefulness, perceived ease of use, subjective norm, and behavior change techniques have a mediating effect on the relationship between health awareness and satisfaction, and that satisfaction level has a mediating effect on the relationship between perceived usefulness, perceived ease of use, subjective norm, behavior change techniques, and the intention to use mobile health applications. Additionally, it was found that health awareness positively affects the intention to use. The study emphasized the significant potential of mobile health applications in the delivery of healthcare services and suggested that hospital managers, policymakers, public and private sector representatives, and internal and external stakeholders take strategic steps and develop actions regarding the future role of mobile health applications.

Kaynakça

  • Ahadzadeh, A.S., Sharif, S.P., Ong, F.S. ve Khong, K.W. (2015). Integrating health belief model and technology acceptance model: an investigation of healthrelated internet use. Journal of Medical Internet Research, 17(2), 1-17.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50, 179-211.
  • Ajzen, I. ve Madden, T. J. (1986). Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474.
  • Alay, D. (2022). Sağlık bilgi sistemleri kapsamında mobil sağlık uygulamalarının teknoloji kabul modeliyle değerlendirilmesi. (Yayımlanmamış yüksek lisans tezi). Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü.
  • Balapour, A., Reychav, I., Sabherwal, R. ve Azuri, J. (2019). Mobile technology identity and self-efficacy: implications for the adoption of clinically supported mobile health apps. International Journal of Information Management, 49: 58-68.
  • Bentler, P. M. ve Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1): 78-117.
  • Bıyık, E. (2024). Sağlık hizmeti kullanıcılarının mobil sağlık uygulamalarını kabulünü ve kullanımını etkileyen faktörlerin birleştirilmiş teknoloji kabul ve kullanım teorisi kapsamında incelenmesi. Doktora Tezi, Ankara Hacı Bayram Veli Üniversitesi Lisansüstü Eğitim Enstitüsü, Ankara.
  • Bryman, A. ve Cramer, D. (2001). Quantitative data analysis with SPSS release 10 for windows: a guide for social scientists. London: Routledge.
  • Büyüköztürk, Ş. (2018). Sosyal bilimler için veri analizi el kitabı: istatistik, araştırma deseni SPSS uygulamaları ve yorum. (24. Baskı). Ankara: Pagem Akademi Yayıncılık.
  • Can, S., Arslan, E. ve Ersöz, G. (2014). Güncel bakış açısı ile fiziksel aktivite. SPORMETRE Beden Eğitimi ve Spor Bilimleri Dergisi, 12(1), 1-10.
  • Chau, P. Y. K. Ve Hu, P. J.-H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Information & Management, 39(4), 297-311.
  • Cheung, M. L. Chau, K.Y., Lam, M.H., Tse, G., Ho, K.Y., Flint, S.W., Broom, D.R. Tso, E.K., Lee, K.Y. (2019), Examining Consumers’ Adoption of Wearable Healthcare Technology: The Role of Health Attributes, International Journal of Environmental Research and Public Health, 16(13), s. 1-16.
  • Cho, J., Park, D. ve Lee, H. E. (2014). Cognitive factors of using health apps : systematic analysis of relationships among health consciousness , health ınformation orientation , ehealth literacy , and health app use efficacy. Journal Of Medıcal Internet Research, 16(5), 1-10.
  • Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75–83.
  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. (Doctoral dissertation). Massachusetts Institute of Technology, Massachusetts.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D., Bagozzi, R. P. ve Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003.
  • Dal, Ö. (2021). Sağlık hizmetlerinde büyük veri: mobil sağlık uygulamalarının kullanımını etkileyen faktörlerin genişletilmiş teknoloji kabul modeli ile incelenmesi. (Doktora Tezi). Beykent Üniversitesi Lisansüstü Eğitim Enstitüsü İşletme Anabilim Dalı.
  • Deng, Z. (2013). Understanding public users’ adoption of mobile health service. International Journal Of Mobile Communications, 11(4), 351-373.
  • Duarte, P. ve Pinho, J. C. (2019). A mixed methods utaut2-based approach to assess mobile health adoption. Journal Of Business Research, 102, 140–150.
  • Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B. ve Weerakkody, V. (2016). A generalised adoption model for services: a cross-country comparison of mobile health (M-Health). Government Information Quarterly, 174-187.
  • Ekiyor, A. ve Yalçın, G. (2016). Mobıle health applications with smart phones. International Journal of Management and Applied Science, 2(9), 140-143.
  • Erdem, H. (2011). Kurumsal kaynak planlama sistemlerinin kullanımında etkili olan faktörlerin genişletilmiş teknoloji kabul modeli ile incelenmesi. (Doktora Tezi). İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü.
  • Fishbein, M. ve Ajzen, I. (1975). Belief, attitude, ıntention and behavior: an ıntroduction to theory and research, reading. MA: Addison-Wesley.
  • George, D.ve Mallery, M. (2010). SPSS for windows step by step: a simple guide and reference, 17.0 update. (10a ed.) Boston: Pearson.
  • Grigsby-Toussaint, D. S., Shin, J. C., Reeves, D. M., Beattie, A., Auguste, E. ve Jean-Louis, G. (2017). Sleep apps and behavioral constructs: a content analysis. Preventive Medicine Reports, 6, 126-129.
  • Handayani, P. W., Gelshirani, N. B., Azzahro, F., Pinem, A. A. ve Hidayanto, A. N. (2020). The influence of argument quality, source credibility, and health consciousness on satisfaction, use intention, and loyalty on mobile health application use. Informatics in Medicine Unlocked, 20, 100429.
  • Hayes, A.F. (2013). Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York, NY: The Guilford Press.
  • Khan, Z.F. ve Alotaibi, S.R. (2020). Applications of artificial ıntelligence and big data analytics in m-health: a healthcare system perspective. Journal of Healthcare Engineering, 1-15.
  • Kılıç, S. (2016). Cronbach’ın Alfa güvenirlik katsayısı. Journal of Mood Disorders, 6(1), 47-48.
  • Kline, P. (1994). An easy guide to factor analysis. New York: Routledge.
  • Kopmaz, B. ve Arslanoğlu, A. (2048). Mobil sağlık ve akıllı sağlık uygulamaları. Sağlık Akademisyenleri Dergisi, 5 (4), 251-255.
  • Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., ……Swendeman, D. (2013). Mobile health technology evaluation. American Journal of Preventive Medicine, 45(2), 228–236.
  • Lin, T. T. ve Bautista, J. R. (2017). Understanding the relationships between m-health apps’ characteristics, trialability, and mhealth literacy. Journal of Health Communication, 22(4), 346-354.
  • Liu, C., Zhu, Q., Holroyd, K. A. ve Seng, E. K. (2011). Status and trends of mobile-health applications for ios devices: a developer's perspective. Journal of Systems and Software, 84(11), 2022-2033.
  • McKay, F.H., Wright, A., Shill, J., Stephens, H. ve Uccellini, M. (2019). Using health and well-being apps for behavior change: a systematic search and rating of apps. JMIR Mhealth Uhealth, 7(7), 1-11.
  • Mensah, I. K. (2022). Understanding the drivers of ghanaian citizens' adoption intentions of mobile health services. Frontiers in Public Health, 10, 1-20.
  • Michaelidou, N. ve Hassan, L. M. (2008). The role of health consciousness, food safety concern and ethical identity on attitudes and intentions towards organic food. International Journal of Consumer Studies, 32(2), 163-170.
  • Miao, R., Wu, Q., Wang, Z., Zhang, X., Song, Y., Zhang, H., Sun, Q., Jiang, Z. (2017). Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach. International Journal of Production Research, 55(19), 5801-5815.
  • Mutlu, B. (2021). Algılanan mobil sağlık hizmet kalitesinin yeniden kullanım niyeti üzerindeki etkisinde algılanan kullanım kolaylığının düzenleyici rolü ve mobil sağlık platformları üzerine bir uygulama. (Yayımlanmamış Yüksek Lisans Tezi). Marmara Üniversitesi Sosyal Bilimler Enstitüsü.
  • Nisha, N., Iqbal, M. ve Rifat, A. (2019). The changing paradigm of health and mobile phones. Journal Of Global Information Management, 27(1), 19-46.
  • Nunnally, J.C (1978). Psychometric theory. NewYork: McGraw Hill.
  • Nusairat, N., Abdellatif, H., Al-Gasawneh, J., Akhorshaideh, A., Aloqool, A., Rabah, S. ve Ahmad, A. (2021). Determinants of behavioral intentions to use mobile healthcare applications in jordan. International Journal of Data and Network Science, 5(4), 547-556.
  • Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469.
  • Öney Doğanyiğit, S. (2014). Sağlık hizmetleri iletişiminde mobil sağlık: “adımsayar” uygulaması örneği. (Yayımlanmamış Yüksek Lisans Tezi). Galatasaray Üniversitesi Sosyal Bilimler Enstitüsü.
  • Pai, F.-Y.ve Huang, K.-I. (2011). Applying the technology acceptance model to the ıntroduction of healthcare ınformation systems. Technological Forecasting And Social Change, 78(4), 650-660.
  • Rao, V. S. ve Krishna, T. M. (2014). A design of mobile health for android applications. American Journal of Engineering Research, 3(06), 20-29.
  • Saheb, T. (2020). An empirical ınvestigation of the adoption of mobile health applications: ıntegrating big data and social media services. Health And Technology, 10, 1063-1077.
  • Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A. ve King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: a review. The Journal of Educational Research, 99(6), 323-338.
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SAĞLIKLI YAŞAM İÇİN MOBİL SAĞLIK UYGULAMALARI: MOBİL SAĞLIK UYGULAMALARININ KULLANIM NİYETİNİ ETKİLEYEN FAKTÖRLER

Yıl 2024, Sayı: 69, 95 - 104, 30.12.2024
https://doi.org/10.18070/erciyesiibd.1526435

Öz

Bu çalışma, mobil sağlık uygulamalarının kullanım niyetini etkileyen faktörleri belirlemeyi ve kullanıcıların sağlık bilincinin mobil sağlık uygulaması kullanım niyetini ne yönde etkilediğini ortaya koymayı amaçlamaktadır. Çalışma, 18 yaş ve üzeri, fiziksel aktivite (Adımsayar, Evde Egzersiz, Düzenli Uyku vb), sağlıklı beslenme (Kalori Sayacı, Su Tüketimi Takibi vb) gibi uygulamaları kullanan bireylerle yürütülmüştür. Araştırmada veri toplama aracı olarak anket formu kullanılmıştır. Verilerin analizinde SPSS ve “Process Macro” paket programı kullanılmıştır. Derlenen veriler tanımlayıcı istatistikler, geçerlik ve güvenirlik analizi, basit doğrusal regresyon analizi ve “bootstrap model 4” analizi ile değerlendirilmiştir. Araştırma sonucunda algılanan fayda, algılanan kullanım kolaylığı, subjektif norm ve davranış değiştirme tekniklerinin sağlık bilinci ile memnuniyet arasındaki ilişkide aracı etkiye sahip olduğu ve algılanan fayda, algılanan kullanım kolaylığı, subjektif norm ve davranış değiştirme teknikleri ile mobil sağlık uygulamalarını kullanım niyeti arasındaki ilişkide memnuniyet düzeyinin aracı etkiye sahip olduğu sonucuna ulaşılmıştır. Ayrıca sağlık bilincinin kullanım niyetini pozitif yönde etkilediği tespit edilmiştir. Çalışma sonucunda, mobil sağlık uygulamalarının sağlık hizmeti sunumunda önem arz eden bir potansiyeli olduğu vurgulanmış, hastane yöneticilerine, politika yapıcılara, kamu ve özel sektör temsilcilerine, iç ve dış paydaşlara mobil sağlık uygulamalarının gelecekteki yerine yönelik stratejik hamleler geliştirerek adımlar atılması önerilmiştir.

Kaynakça

  • Ahadzadeh, A.S., Sharif, S.P., Ong, F.S. ve Khong, K.W. (2015). Integrating health belief model and technology acceptance model: an investigation of healthrelated internet use. Journal of Medical Internet Research, 17(2), 1-17.
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50, 179-211.
  • Ajzen, I. ve Madden, T. J. (1986). Prediction of goal-directed behavior: attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22(5), 453-474.
  • Alay, D. (2022). Sağlık bilgi sistemleri kapsamında mobil sağlık uygulamalarının teknoloji kabul modeliyle değerlendirilmesi. (Yayımlanmamış yüksek lisans tezi). Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü.
  • Balapour, A., Reychav, I., Sabherwal, R. ve Azuri, J. (2019). Mobile technology identity and self-efficacy: implications for the adoption of clinically supported mobile health apps. International Journal of Information Management, 49: 58-68.
  • Bentler, P. M. ve Chou, C. P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1): 78-117.
  • Bıyık, E. (2024). Sağlık hizmeti kullanıcılarının mobil sağlık uygulamalarını kabulünü ve kullanımını etkileyen faktörlerin birleştirilmiş teknoloji kabul ve kullanım teorisi kapsamında incelenmesi. Doktora Tezi, Ankara Hacı Bayram Veli Üniversitesi Lisansüstü Eğitim Enstitüsü, Ankara.
  • Bryman, A. ve Cramer, D. (2001). Quantitative data analysis with SPSS release 10 for windows: a guide for social scientists. London: Routledge.
  • Büyüköztürk, Ş. (2018). Sosyal bilimler için veri analizi el kitabı: istatistik, araştırma deseni SPSS uygulamaları ve yorum. (24. Baskı). Ankara: Pagem Akademi Yayıncılık.
  • Can, S., Arslan, E. ve Ersöz, G. (2014). Güncel bakış açısı ile fiziksel aktivite. SPORMETRE Beden Eğitimi ve Spor Bilimleri Dergisi, 12(1), 1-10.
  • Chau, P. Y. K. Ve Hu, P. J.-H. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Information & Management, 39(4), 297-311.
  • Cheung, M. L. Chau, K.Y., Lam, M.H., Tse, G., Ho, K.Y., Flint, S.W., Broom, D.R. Tso, E.K., Lee, K.Y. (2019), Examining Consumers’ Adoption of Wearable Healthcare Technology: The Role of Health Attributes, International Journal of Environmental Research and Public Health, 16(13), s. 1-16.
  • Cho, J., Park, D. ve Lee, H. E. (2014). Cognitive factors of using health apps : systematic analysis of relationships among health consciousness , health ınformation orientation , ehealth literacy , and health app use efficacy. Journal Of Medıcal Internet Research, 16(5), 1-10.
  • Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75–83.
  • Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. (Doctoral dissertation). Massachusetts Institute of Technology, Massachusetts.
  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of technology. MIS Quarterly, 13(3), 319-340.
  • Davis, F. D., Bagozzi, R. P. ve Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003.
  • Dal, Ö. (2021). Sağlık hizmetlerinde büyük veri: mobil sağlık uygulamalarının kullanımını etkileyen faktörlerin genişletilmiş teknoloji kabul modeli ile incelenmesi. (Doktora Tezi). Beykent Üniversitesi Lisansüstü Eğitim Enstitüsü İşletme Anabilim Dalı.
  • Deng, Z. (2013). Understanding public users’ adoption of mobile health service. International Journal Of Mobile Communications, 11(4), 351-373.
  • Duarte, P. ve Pinho, J. C. (2019). A mixed methods utaut2-based approach to assess mobile health adoption. Journal Of Business Research, 102, 140–150.
  • Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B. ve Weerakkody, V. (2016). A generalised adoption model for services: a cross-country comparison of mobile health (M-Health). Government Information Quarterly, 174-187.
  • Ekiyor, A. ve Yalçın, G. (2016). Mobıle health applications with smart phones. International Journal of Management and Applied Science, 2(9), 140-143.
  • Erdem, H. (2011). Kurumsal kaynak planlama sistemlerinin kullanımında etkili olan faktörlerin genişletilmiş teknoloji kabul modeli ile incelenmesi. (Doktora Tezi). İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü.
  • Fishbein, M. ve Ajzen, I. (1975). Belief, attitude, ıntention and behavior: an ıntroduction to theory and research, reading. MA: Addison-Wesley.
  • George, D.ve Mallery, M. (2010). SPSS for windows step by step: a simple guide and reference, 17.0 update. (10a ed.) Boston: Pearson.
  • Grigsby-Toussaint, D. S., Shin, J. C., Reeves, D. M., Beattie, A., Auguste, E. ve Jean-Louis, G. (2017). Sleep apps and behavioral constructs: a content analysis. Preventive Medicine Reports, 6, 126-129.
  • Handayani, P. W., Gelshirani, N. B., Azzahro, F., Pinem, A. A. ve Hidayanto, A. N. (2020). The influence of argument quality, source credibility, and health consciousness on satisfaction, use intention, and loyalty on mobile health application use. Informatics in Medicine Unlocked, 20, 100429.
  • Hayes, A.F. (2013). Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. New York, NY: The Guilford Press.
  • Khan, Z.F. ve Alotaibi, S.R. (2020). Applications of artificial ıntelligence and big data analytics in m-health: a healthcare system perspective. Journal of Healthcare Engineering, 1-15.
  • Kılıç, S. (2016). Cronbach’ın Alfa güvenirlik katsayısı. Journal of Mood Disorders, 6(1), 47-48.
  • Kline, P. (1994). An easy guide to factor analysis. New York: Routledge.
  • Kopmaz, B. ve Arslanoğlu, A. (2048). Mobil sağlık ve akıllı sağlık uygulamaları. Sağlık Akademisyenleri Dergisi, 5 (4), 251-255.
  • Kumar, S., Nilsen, W. J., Abernethy, A., Atienza, A., Patrick, K., Pavel, M., ……Swendeman, D. (2013). Mobile health technology evaluation. American Journal of Preventive Medicine, 45(2), 228–236.
  • Lin, T. T. ve Bautista, J. R. (2017). Understanding the relationships between m-health apps’ characteristics, trialability, and mhealth literacy. Journal of Health Communication, 22(4), 346-354.
  • Liu, C., Zhu, Q., Holroyd, K. A. ve Seng, E. K. (2011). Status and trends of mobile-health applications for ios devices: a developer's perspective. Journal of Systems and Software, 84(11), 2022-2033.
  • McKay, F.H., Wright, A., Shill, J., Stephens, H. ve Uccellini, M. (2019). Using health and well-being apps for behavior change: a systematic search and rating of apps. JMIR Mhealth Uhealth, 7(7), 1-11.
  • Mensah, I. K. (2022). Understanding the drivers of ghanaian citizens' adoption intentions of mobile health services. Frontiers in Public Health, 10, 1-20.
  • Michaelidou, N. ve Hassan, L. M. (2008). The role of health consciousness, food safety concern and ethical identity on attitudes and intentions towards organic food. International Journal of Consumer Studies, 32(2), 163-170.
  • Miao, R., Wu, Q., Wang, Z., Zhang, X., Song, Y., Zhang, H., Sun, Q., Jiang, Z. (2017). Factors that influence users’ adoption intention of mobile health: a structural equation modeling approach. International Journal of Production Research, 55(19), 5801-5815.
  • Mutlu, B. (2021). Algılanan mobil sağlık hizmet kalitesinin yeniden kullanım niyeti üzerindeki etkisinde algılanan kullanım kolaylığının düzenleyici rolü ve mobil sağlık platformları üzerine bir uygulama. (Yayımlanmamış Yüksek Lisans Tezi). Marmara Üniversitesi Sosyal Bilimler Enstitüsü.
  • Nisha, N., Iqbal, M. ve Rifat, A. (2019). The changing paradigm of health and mobile phones. Journal Of Global Information Management, 27(1), 19-46.
  • Nunnally, J.C (1978). Psychometric theory. NewYork: McGraw Hill.
  • Nusairat, N., Abdellatif, H., Al-Gasawneh, J., Akhorshaideh, A., Aloqool, A., Rabah, S. ve Ahmad, A. (2021). Determinants of behavioral intentions to use mobile healthcare applications in jordan. International Journal of Data and Network Science, 5(4), 547-556.
  • Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469.
  • Öney Doğanyiğit, S. (2014). Sağlık hizmetleri iletişiminde mobil sağlık: “adımsayar” uygulaması örneği. (Yayımlanmamış Yüksek Lisans Tezi). Galatasaray Üniversitesi Sosyal Bilimler Enstitüsü.
  • Pai, F.-Y.ve Huang, K.-I. (2011). Applying the technology acceptance model to the ıntroduction of healthcare ınformation systems. Technological Forecasting And Social Change, 78(4), 650-660.
  • Rao, V. S. ve Krishna, T. M. (2014). A design of mobile health for android applications. American Journal of Engineering Research, 3(06), 20-29.
  • Saheb, T. (2020). An empirical ınvestigation of the adoption of mobile health applications: ıntegrating big data and social media services. Health And Technology, 10, 1063-1077.
  • Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A. ve King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: a review. The Journal of Educational Research, 99(6), 323-338.
  • Schroeder, T., Kamalakkannan, A., Seaman, K., Nguyen, A., Siette, J., Gewald, H. ve Georgiou, A. (2024). Perception of middle-aged and older adults towards mhealth apps: a comparative factor analysis between Australia and Germany. International Journal of Medical Informatics, 189, 1-7.
  • Sun, Y., Wang, N., Guo, X. ve Peng, Z. (2013). Understanding the acceptance ofmobile health services: a comparison and ıntegration of alternative models. Journal Of Electronic Commerce Research, 14(2), 183-201.
  • Tezcan, C. (2016). Sağlığa yenilikçi bir bakış açısı: mobil sağlık. Yayın No: TÜSİAD-T/2016-03/575: 1-116. Uysal, B. ve Ulusinan, E. (2020). Güncel dijital sağlık uygulamalarının incelenmesi. Selçuk Sağlık Dergisi, 1, 46-60.
  • Venkatesh, V., Thong, J. Y. L. ve Xu, X. (2012). Consumer acceptance and use of ınformation technology: extending the unified theory of acceptance and use of technology. Forthcoming in MIS Quarterly, 36 (1), 157-178.
  • Yan, M., Filieri, R., Raguseo, E. ve Gorton, M. (2021). Mobile apps for healthy living: factors influencing continuance intention for health apps. Technological Forecasting & Social Change, 166, 2-13.
  • Yıldırım Taşer, P., Bozyiğit, F., Özcanhan, M. H. ve Utku, S. (2017). Bulut tabanlı mobil diyabet kontrol uygulaması: mobil diyabetim. Bilişim Teknolojileri Dergisi, 10(2), 153-159.
  • Yi, M. Y., Jackson, J. D., Park, J. S. ve Probst, J. C. (2006). Understanding ınformation technology acceptance by ındividual professionals: toward an ıntegrative view. Information & Management, 43(3), 350-363.
  • Zhang, X.,Liu, S., Wang, L., Zhang, Y. ve Wang, J. (2020). Mobile health service adoption ın china: ıntegration of theory of planned behavior, protection motivation theory and personal health differences. Online Information Review, 44(1), 1-23.
  • Zhao, Y., Ni, Q. ve Zhou, R. (2018). What factors influence the mobile health service adoption? a meta-analysis and the moderating role of age. International Journal Of Information Management, 43, 342-350.
  • Wahyuni, R. ve Nurbojatmiko. (2017). Explaining acceptance of e-health services: an extension of tam and health belief model approach. 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017 içinde. Institute of Electrical and Electronics Engineers Inc.
  • World Health Organization (WHO). (2011). mHealth new horizons for health through mobile technologies. who library cataloguing-in-publication data. https://www.afro.who.int/publications/mhealth-new-horizons-health-through-mobile-technologie.
  • Wu, I. L., Li, J. Y. ve Fu, C. Y. (2011). The adoption of mobile healthcare by hospital’s professionals: an ıntegrative perspective. Decision Support Systems, 51(3), 587-596.
  • Wu, J.-H. ve Wang, S.C. (2005). What drives mobile commerce?. Information & Management, 42(5), 719-729.
Toplam 62 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İşletme
Bölüm Makaleler
Yazarlar

Refika Ülke Şimdi 0000-0001-8394-2383

Özlem Gedik 0000-0003-0840-0765

Edibe Asuman Atilla 0000-0002-2823-9801

Erken Görünüm Tarihi 27 Aralık 2024
Yayımlanma Tarihi 30 Aralık 2024
Gönderilme Tarihi 1 Ağustos 2024
Kabul Tarihi 25 Eylül 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 69

Kaynak Göster

APA Ülke Şimdi, R., Gedik, Ö., & Atilla, E. A. (2024). SAĞLIKLI YAŞAM İÇİN MOBİL SAĞLIK UYGULAMALARI: MOBİL SAĞLIK UYGULAMALARININ KULLANIM NİYETİNİ ETKİLEYEN FAKTÖRLER. Erciyes Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi(69), 95-104. https://doi.org/10.18070/erciyesiibd.1526435

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