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研究生: 洪佩萱
Hung, Pei-Hsuan
論文名稱: 使用者對行動裝置健康體能管理應用程式之接受程度與持續使用行為意向
Users’ Satisfaction and Continuous Intention to Use the Application of Health and Fitness Management Mobile Devices
指導教授: 洪榮昭
Hong, Jon-Chao
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 147
中文關鍵詞: 健康體能管理應用程式科技創新意識科技接受模式理論期望確認理論持續使用行為意向
英文關鍵詞: Health and Fitness Application, Technology Innovativeness, Technology Acceptance Model, Expectation Confirmation Theory, Continuous Intention
DOI URL: http://doi.org/10.6345/NTNU201900791
論文種類: 學術論文
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  • 健康體能管理應用程式之使用者在全球仍持續成長,在台灣對於健康體能管理應用程式之使用亦持續增加,使用健康體能管理應用程式是否確實受益於使用者尚不明確,本研究以科技接受模式理論及期望確認理論,以透過個人科技創新意識及使用健康體能管理應用程程式的易用性和有用性的交互感知,預測個人之使用意圖。本研究以問卷調查法為研究方法,以十五歲以上之一般大眾且瞭解健康健能管理應用程式的接受程度和持續使用情況者為研究對象,總共回收379份問卷。研究假設之驗證結果係基於驗證性因素分析及結構方程模型,研究結果為具有顯著性:(1)具有高科技創新意識者,相對具有高易用性認知及實用性認知;(2)具有高易用性認知及實用性認知者,相對具有高滿意度;(3)具有高滿意度者,相對具有高持續使用行為意向,基於適配的假設模型,本研究結果顯示,管理者可以目標對於具有高科技創新意識的人行銷推廣健康體能管理應用程式。

    The users of the health and fitness management applications grow successively and globally, and the numbers of users also grow up simultaneously in Taiwan. The issue of using mobile Apps is really benefited to those users is unclear. Thus, this study adapted expectancy confirmation model and technology acceptance model to explore how the usage intention can be predicted by individual technology innovativeness, and the interactive perceptions of ease of use and usefulness in using Apps of health and fitness management. The research method is questionnaire survey with 379 total respondents aged 15 and older, which understanding the acceptable and subsequent use of the health and fitness management application. The verification of the hypothesis is based on the confirmatory factor analysis and structural equation modelling. The research statistics come up with significant results: (1) The higher level of individual technogy innovativeness participants had, the higher level of ease of use and usefulness they would have; (2) The higher level of ease of use and usefulness participants had, the higher level of satisfaction they would have; and (3) the higher level of satisfaction participants had, the higher level of intention to use they would have. According to the fit model, the implication of this study suggested that managers can target those people with high level of technology innovativeness to promote the marketing sharing.

    摘 要 i Abstract ii 目 次 iii 表 次 v 圖 次 vii 第一章 導論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 5 第三節 名詞解釋 7 第四節 研究方法與流程 9 第五節 研究範圍與限制 12 第二章 文獻探討 15 第一節 健康體能管理應用程式 15 第二節 科技創新意識 20 第三節 科技接受模式理論 22 第四節 期望確認理論 26 第三章 研究設計 35 第一節 研究架構 35 第二節 研究假設 37 第三節 研究對象 42 第四節 研究工具 42 第五節 資料處理與分析方法 49 第四章 研究結果與討論 51 第一節 樣本特徵分析 51 第二節 描述性統計分析 55 第三節 項目分析 60 第四節 信效度分析 72 第五節 研究結果 80 第六節 研究討論 88 第五章 結論與建議 111 第一節 研究結論 111 第二節 研究建議 114 參考文獻 117 附錄 正式問卷內容 143

    一、中文文獻
    Flurry(2018)。Flurry:運動及健康管理風潮帶動下,去年國內相關app使用量成長110%。iThome電腦周刊。取自 https://www.ithome.com.tw/news/124461
    王雲東(2014)。第五章 變項的操作型定義與測量。社會研究方法(頁80-87)。新北市:威仕曼文化事業股份有限公司。
    吳明隆 (2009)。第二章 CFA區別效度檢驗。結構方程模式:方法與實務運用(頁56)。高雄市:麗文文化。
    吳明隆(2006)。第二章 模式適配度統計量之介紹。結構方程模式:SIMPLIS 的應用(頁49)。臺北市:台灣五南圖書出版股份有限公司。
    吳明隆(2007)。第三章 單因子變異數分析。SPSS 操作與應用:變異數分析實務(頁181-223)。臺北市:五南圖書出版股份有限公司。
    吳明隆(2007)。結構方程模型Stata的操作與應用。臺北市:台灣五南圖書出版股份有限公司。
    吳明隆(2013)。結構方程模型:AMOS的操作與應用。重慶:重慶大學出版社。
    邱皓政(2017)。多元迴歸的自變數比較與多元共線性之影響: 效果量、優勢性與相對權數指標的估計與應用。臺大管理論叢,27(3),65-108。
    國家發展委員會(2017)。106年個人家戶數位機會調查報告【原始數據】。未出版之統計數據。取自 https://www.ndc.gov.tw/cp.aspx?n=55c8164714dfd9e9
    張紹勳(2017)。第三章Full SEM分析實例:員工教育訓練績效評估模型、第四章 SEM實例分析、Builder介面操作。Stata在結構方程模型及試題反應理論的應用(頁222-224、290)。台北:台灣五南圖書出版股份有限公司。
    教育部體育署(2017)。運動現況統計【原始數據】。未出版之統計數據。取自https://isports.sa.gov.tw/Apps/TIS08/TIS0801M_01V1.aspx?MENU_CD=M07&ITEM_CD=T01&MENU_PRG_CD=12&LEFT_MENU_ACTIVE_ID=26
    陳正昌(2013)。第二十五章 驗證性因素分析。SPSS與統計分析(頁701)。臺北市:台灣五南圖書出版股份有限公司。
    黃芳銘 (2015)。第六章 適配度評鑑。結構方程模式-理論與應用(頁168)。臺北市:台灣五南圖書出版股份有限公司。
    榮泰生(2007)。第四章 信度與效度。Amos與研究方法(頁113)。臺北市:台灣五南圖書出版股份有限公司。
    鄭怡君、蔡俊傑(2016)。Bootstrap 中介效果結構方程模式分析。體育學系學刊,1(15),102-114。
    蘇文彬(2018)。Flurry:運動及健康管理風潮帶動下,去年國內相關a-p¬p使用量成長110%。iThome電腦周刊。取自 https://www.ithome.com.tw/news/124461

    二、外文文獻
    Aarts, H., Verplanken, B., & Van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355-1374.
    Abdul Aziz, N., Ong, T., Foong, S., Senik, R., & Attan, H. (2018). Green initiatives adoption and environmental performance of public listed companies in Malaysia. Sustainability, 10(6), 2003.
    Adeinat, I. (2019). Mediating effects between perspectives in strategy maps. Administrative Sciences, 9(1), 14.
    Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.
    Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.
    Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
    Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22, 16-23.
    Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
    Alalwan, A., Baabdullah, A. M., Rana, N. P., Dwivedi, Y. K., Hudaib, F., & Shammout, A. (2018). Examining the factors affecting behavioural intention to adopt mobile health in Jordan. Conference on e-Business, e-Services and e-Society, 459-467. Retrieved from https://doi.org/10.1007/978-3-030-02131-3_41
    Alotaibi, R., Houghton, L., & Sandhu, K. (2017). Factors influencing users’ intentions to use mobile government applications in Saudi Arabia: TAM Applicability. International Journal of Advanced Computer Science and Applications, 8, 200-211.
    Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125-143.
    Arpaci, I. (2017). Antecedents and consequences of cloud computing adoption in education to achieve knowledge management. Computers in Human Behavior, 70, 382-390.
    Baby, M., Gale, C., & Swain, N. (2019). A communication skills intervention to minimise patient perpetrated aggression for healthcare support workers in New Zealand: A cluster randomised controlled trial. Health & Social Care in the Community, 27(1), 170-181.
    Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
    Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815-824.
    Beatty, A. L., Magnusson, S. L., Fortney, J. C., Sayre, G. G., & Whooley, M. A. (2018). VA FitHeart, a mobile app for cardiac rehabilitation: Usability Study. JMIR Human Factors, 5(1), e3.
    Becker, S., Miron-Shatz, T., Schumacher, N., Krocza, J., Diamantidis, C., & Albrecht, U.-V. (2014). mHealth 2.0: Experiences, possibilities, and perspectives. JMIR mHealth and uHealth, 2(2), e24.
    Bettiga, D., Lamberti, L., & Lettieri, E. (2019). Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach. Health care management science, 1-12. Retrieved from https://doi.org/10.1007/s10729-019-09468-2
    Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214.
    Bhattacherjee, A. (2001b). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
    Bhattacherjee, A., & Barfar, A. (2011). Information technology continuance research: current state and future directions. Asia Pacific Journal of Information Systems, 21(2), 1-18.
    Bhattacherjee, A., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: A theoretical model and longitudinal test. MIS Quarterly, 28(2), 229-254.
    Bhattacherjee, A., Limayem, M., & Cheung, C. M. (2012). User switching of information technology: A theoretical synthesis and empirical test. Information & Management, 49(7-8), 327-333.
    Bohliqa, A., Charoensupyanant, A., Spatar, D., Ha, J., Yilmaz, S., & Daim, T. U. (2018). Technology assessment: Study of user references for weight loss mobile applications both globally and in the United States. Infrastructure and Technology Management, 297-324. Retrieved from https://doi.org/10.1007/978-3-319-68987-6_9
    Boulding, W., Lee, E., & Staelin, R. (1994). Mastering the mix: Do advertising, promotion, and sales force activities lead to differentiation? Journal of Marketing Research, 159-172.
    Boulos, M. N. K., Brewer, A. C., Karimkhani, C., Buller, D. B., & Dellavalle, R. P. (2014). Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online Journal of Public Health Informatics, 5(3), 229.
    Boulos, M. N. K., Wheeler, S., Tavares, C., & Jones, R. (2011). How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. Biomedical Engineering Online, 10(1), 24.
    Boyle, G. J. (1991). Does item homogeneity indicate internal consistency or item redundancy in psychometric scales?. Personality and Individual Differences, 12(3), 291-294.
    Browen, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit in testing structural equation model. Mark Sci, 18(2), 31-43.
    Bryman, A. (2016). Social research methods. Oxford, England: Oxford university press.
    Bull, S. (2010). Technology-based health promotion. Thousand Oaks, CA: Sage.
    Burke, R. R. (2002). Technology and the customer interface: What consumers want in the physical and virtual store. Journal of the Academy of Marketing Science, 30(4), 411-432.
    Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (multivariate applications series). New York: Taylor & Francis Group, 396, 7384.
    Byun, H., Chiu, W., & Bae, J. S. (2018). Exploring the adoption of sports brand apps: An application of the modified technology acceptance model. International Journal of Asian Business and Information Management (IJABIM), 9(1), 52-65.
    Carmines, E., & Mclver, J. (1981). Analyzing models with unobserved models: Analysis of covariance structures. Beverly Hills, CA: Sage.
    Chae, J. (2018). A comprehensive profile of those who have health-related apps. Health Education & Behavior, 45(4), 591–598.
    Chang, H. S., Lee, S. C., & Ji, Y. G. (2016). Wearable device adoption model with TAM and TTF. International Journal of Mobile Communications, 14(5), 518-537.
    Chau, P. Y. (1996). An empirical investigation on factors affecting the acceptance of case by systems developers. Information & Management, 30(6), 269-280.
    Chen, M.-F., & Lin, N.-P. (2018). Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 28(2), 351-373.
    Chen, S. C., Liu, S. C., Li, S. H., & Yen, D. C. (2013). Understanding the mediating effects of relationship quality on technology acceptance: an empirical study of e-appointment system. Journal of Medical Systems, 37(6), 9981.
    Chen, Y., Yang, L., Zhang, M., & Yang, J. (2018). Central or peripheral? Cognition elaboration cues’ effect on users’ continuance intention of mobile health applications in the developing markets. International Journal of Medical Informatics, 116, 33-45.
    Cheng, Y. H., & Huang, T. Y. (2013). High speed rail passengers’ mobile ticketing adoption. Transportation Research Part C: Emerging Technologies, 30, 143-160.
    Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296-325.
    Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.
    Chiu, S. J., Chou, Y. T., Chen, P. T., & Chien, L. Y. (2019). Psychometric properties of the mandarin version of the family resilience assessment scale. Journal of Child and Family Studies, 28(2), 354-369.
    Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75-83.
    Cho, J. Y., Ko, D., & Lee, B. G. (2018). Strategic approach to privacy calculus of wearable device user regarding information disclosure and continuance intention. KSII Transactions on Internet & Information Systems, 12(7), 3356-3374.
    Cho, J., Lee, H. E., Kim, S. J., & Park, D. (2015). Effects of body image on college students' attitudes toward diet/fitness apps on smartphones. Cyberpsychology, Behavior, and Social Networking, 18(1), 41-45.
    Churchill Jr, G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491-504.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
    Cornet, V. P., & Holden, R. J. (2017). Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics, 77, 120-132.
    Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98.
    Coughlin, S. S., Whitehead, M., Sheats, J. Q., Mastromonico, J., & Smith, S. (2016). A review of smartphone applications for promoting physical activity. Jacobs Journal of Community Medicine, 2(1), 021.
    Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184-201.
    Dallinga, J. M., Mennes, M., Alpay, L., Bijwaard, H., & de la Faille-Deutekom, M. B. (2015). App use, physical activity and healthy lifestyle: A cross sectional study. BMC Public Health, 15(1), 833.
    Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology). Retrieved from https://dspace.mit.edu/bitstream/handle/1721.1/15192/14927137-MIT.pdf
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
    Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
    Davis, T. L., DiClemente, R., & Prietula, M. (2016). Taking mHealth forward: examining the core characteristics. JMIR mHealth and uHealth, 4(3). Retrieved from https://mhealth.jmir.org/2016/3/e97
    Dawes, J., Faulkner, M., & Sharp, B. (1998). Business orientation scales: development and psychometric assessment. 27th EMAC Conference, 5, 461-478.
    de Boer, P. S., van Deursen, A. J., & van Rompay, T. J. (2019). Accepting the internet-of-things in our homes: The role of user skills. Telematics and Informatics, 36, 147-156.
    Deng, L., Turner, D. E., Gehling, R., & Prince, B. (2010). User experience, satisfaction, and continual usage intention of IT. European Journal of Information Systems, 19(1), 60-75.
    DeVellis, R. F. (2016). Scale Development: Theory and Applications. Thousand Oaks, CA. Sage publications.
    Ding, Y. (2019). Looking forward: The role of hope in information system continuance. Computers in Human Behavior, 91, 127-137.
    Domina, T., Lee, S.-E., & MacGillivray, M. (2012). Understanding factors affecting consumer intention to shop in a virtual world. Journal of Retailing and Consumer Services, 19(6), 613-620.
    Dorsch, M. J., Grove, S. J., & Darden, W. R. (2000). Consumer intentions to use a service category. Journal of Services Marketing, 14(2), 92-117.
    Dutta, B., Peng, M. H., & Sun, S. L. (2018). Modeling the adoption of personal health record (PHR) among individual: the effect of health-care technology self-efficacy and gender concern. Libyan Journal of Medicine, 13(1), 1500349.
    East, M. L., & Havard, B. C. (2015). Mental health mobile apps: From infusion to diffusion in the mental health social system. JMIR Mental Health, 2(1), e10. Retrieved from https://mental.jmir.org/2015/1/e10
    Elmorshidy, A. (2018). Factors affecting mobile applications of remote security cameras at home and office: An empirical investigation in Southern California. The International Technology Management Review, 7(1), 93-111.
    European Commission. (2012). eHealth Action Plan 2012-2020: Innovative healthcare for the 21st century. Retrieved from https://ec.europa.eu/digital-single-market/en/news/ehealth-action-plan-2012-2020-innovative-healthcare-21st-century
    Falk, R. F., & Miller, N. B. (1992). A primer for Soft Modeling University of Akron Press Akron. Kron, OH: University of Akron Press.
    Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474.
    Federal Communications Commission. (2010). Connecting America: The National Broadband Plan. Washington, D.C.: ERIC Clearinghouse.
    Felbermayr, A., & Nanopoulos, A. (2016). The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36, 60-76.
    Festinger, L. (1962). A theory of cognitive dissonance. Stanford, CA: Stanford university press.
    Furlipa, J., & Reich, K. (2018). Behavior change theory taxonomy analysis of smartphone apps for fitness, nutrition, and weight Loss. Medicine & Science in Sports & Exercise, 50(5S), 758.
    Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
    Gefen, & Straub, (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 8.
    Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing (JOEUC), 15(3), 1-13.
    Gefen, D., & Straub, D. (2003). Managing user trust in B2C e-services. E-Service, 2(2), 7-24.
    Gefen, D., & Straub, D. W (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400.
    Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 7.
    Gkargkavouzi, A., Halkos, G., & Matsiori, S. (2019). Assessing values, attitudes and threats towards marine biodiversity in a Greek coastal port city and their interrelationships. Ocean & Coastal Management, 167, 115-126.
    Gloria, T. V., & Achyar, A. (2018). Effects of externalities and flow on perceived usefulness, satisfaction, and loyalty in mobile instant messaging promotion. ASEAN Marketing Journal, 8(2), 85-103.
    Glynn, L. G., Hayes, P. S., Casey, M., Glynn, F., Alvarez-Iglesias, A., Newell, J., ÓLaighin, G., Heaney, D., O’Donnell, M., Murphy, A. W. (2014). Effectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial. Br J Gen Pract, 64(624), e384-e391.
    Grand View Research. (2017). mHealth app market by type (fitness, lifestyle management, nutrition & diet, women's health, healthcare providers, disease management) and segment forecasts, 2018-2025. Retrieved from https://www.grandviewresearch.com/industry-analysis/mhealth-app-market
    Greenspoon, P. J., & Saklofske, D. H. (1998). Confirmatory factor analysis of the multidimensional students’ life satisfaction scale. Personality and Individual Differences, 25(5), 965-971.
    Guillén, S., Sanna, A., Ngo, J., Meneu, T., Del Hoyo, E., & Demeester, M. (2009). New technologies for promoting a healthy diet and active living. Nutrition Reviews, 67, 107-110.
    Gunzler, D. D., & Morris, N. (2015). A tutorial on structural equation modeling for analysis of overlapping symptoms in co‐occurring conditions using MPlus. Statistics in Medicine, 34(24), 3246-3280.
    Guo, X., Lv, B., Zhou, H., Liu, C., Liu, J., Jiang, K., & Luo, L. (2018). Gender differences in how family income and parental education relate to reading achievement in China: The mediating role of parental expectation and parental involvement. Frontiers in Psychology, 9. Retrieved from https://doi.org/10.3389/fpsyg.2018.00783
    Hadji, B., & Degoulet, P. (2016). Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of Biomedical Informatics, 61, 185-193.
    Hair, J. F., Gabriel, M., & Patel, V. (2014). AMOS covariance-based structural equation modeling (CB-SEM): guidelines on its application as a marketing research tool. Brazilian Journal of Marketing, 13(2), 44-55.
    Hair, J. F., J. R., Black, W. C., Babin, B. J., & Andersen, R. E. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
    Hair, J. F., J. R., Black, W. C., Babin, B. J., & Andersen, R. E. (2013). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
    Hayes, A. F., Preacher, K. J., & Myers, T. A. (2011). Mediation and the estimation of indirect effects in political communication research. Sourcebook for Political Communication Research: Methods, Measures, and Analytical Techniques, 23, 434-465.
    He, Z. L., Kim, S. H., & Gong, D. H. (2017). The influence of consumer and product characteristics on intention to repurchase of smart band. International Journal of Asia Digital Art and Design Association, 21(1), 13-18.
    Higgins, J. P. (2016). Smartphone applications for patients' health and fitness. The American Journal of Medicine, 129(1), 11-19.
    Hirschman, E. C. (1980). Innovativeness, novelty seeking, and consumer creativity. Journal of Consumer Research, 7(3), 283-295.
    Hong, J. C., Lin, P. H., & Hsieh, P. C. (2017). The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch. Computers in Human Behavior, 67, 264-272.
    Hong, S.J., Thong, J., & Tam, K.-Y. (2005). Understanding continued IT usage: An extension to the expectation-confirmation model in IT domain. PACIS 2005 Proceedings, 105. Retrieved from https://aisel.aisnet.org/pacis2005/105
    Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6(1), 53-60.
    Hsiao, J. L., & Chen, R. F. (2019). Understanding determinants of health care professionals’ perspectives on mobile health continuance and performance. JMIR Medical Informatics, 7(1). Retrieved from https://medinform.jmir.org/2019/1/e12350/
    Hu, L. T., & Bentler, P. M. (1995). Evaluating model fit. In R.H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage.
    Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424.
    Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
    Huang, C. Y., & Kao, Y. S. (2015). UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Mathematical Problems in Engineering, 2015, 23.
    Huang, J. C. (2013). Innovative health care delivery system—A questionnaire survey to evaluate the influence of behavioral factors on individuals' acceptance of telecare. Computers in Biology and Medicine, 43(4), 281-286.
    Hung, M. C., Hwang, H. G., & Hsieh, T. C. (2007). An exploratory study on the continuance of mobile commerce: an extended expectation-confirmation model of information system use. International Journal of Mobile Communications, 5(4), 409-422.
    Igbaria, M., & Tan, M. (1997). The consequences of information technology acceptance on subsequent individual performance. Information & Management, 32(3), 113-121.
    Jang, S., & Lee, C. (2018). The impact of location-based service factors on usage intentions for technology acceptance: The moderating effect of innovativeness. Sustainability, 10(6), 1876.
    Jena, R. K. (2019). Understanding academic achievement emotions towards business analytics course: A case study among business management students from India. Computers in Human Behavior, 92, 716-723.
    Joreskog, K. G., & Sorbom, D. (1984). Lisrel VI: Analysis of linear structural relationships by the method of maximum likelihood–User’s guide. Mooresville, IN: Scientific Software.
    Kaba, B. (2018a). Information and communication technology use continuance behavioral intention: Differential effect based on socio-economic status. Proceedings of the 51st Hawaii International Conference on System Sciences. Retrieved from http://hdl.handle.net/10125/50372
    Kaba, B. (2018b). Modeling information and communication technology use continuance behavior: Are there differences between users on basis of their status? International Journal of Information Management, 38(1), 77-85.
    Kamboj, S., & Gupta, S. (2018). Use of smart phone apps in co-creative hotel service innovation: an evidence from India. Current Issues in Tourism, 1-22. Retrieved from ttps://doi.org/10.1080/13683500.2018.1513459
    Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183-213.
    Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509-523.
    Kay, M., Santos, J., & Takane, M. (2011). mHealth: New horizons for health through mobile technologies. World Health Organization, 64(7), 66-71.
    Keen, P. G. W. (1991). Shaping the future: business design through information technology. Boston, MA: Harvard Business School Press.
    Kemani, M. K., Grimby‐Ekman, A., Lundgren, J., Sullivan, M., & Lundberg, M. (2019). Factor structure and internal consistency of a Swedish version of the Pain Catastrophizing Scale. Acta Anaesthesiologica Scandinavica, 63(2), 259-266.
    Khalifa, M., & Liu, V. (2004). The state of research on information system satisfaction. Journal of Information Technology Theory and Application (JITTA), 5(4), 4.
    Khan K., Donthula S. (2019) Investigating motivational and usability issues of mHealth wellness apps for improved user experience. Advances in Intelligent Systems and Computing, 797. Retrieved from https://doi.org/10.1007/978-981-13-1165-9_53
    Kim, K., & Timm, N. (2006). Univariate and multivariate general linear models: theory and applications with SAS (ed., pp494-496). Chapman and Hall/CRC.
    Kim, K., Hwang, J., Zo, H., & Lee, H. (2016). Understanding users’ continuance intention toward smartphone augmented reality applications. Information Development, 32(2), 161-174.
    Kim, M. S., & Kim, S. (2018). Factors influencing willingness to provide personal information for personalized recommendations. Computers in Human Behavior, 88, 143-152.
    Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513.
    Kim, T., & Chiu, W. (2019). Consumer acceptance of sports wearable technology: the role of technology readiness. International Journal of Sports Marketing and Sponsorship, 20(1), 109-126.
    Kim, Y. G., & Woo, E. (2016). Consumer acceptance of a quick response (QR) code for the food traceability system: Application of an extended technology acceptance model (TAM). Food Research International, 85, 266-272.
    Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling, (2nd Ed., pp.59). Washington, NY: Guilford Press.
    Kolokotroni, P., Anagnostopoulos, F., & Hantzi, A. (2018). The role of optimism, social constraints, coping, and cognitive processing in psychosocial adjustment among breast cancer survivors. Journal of Clinical Psychology in Medical Settings, 25(4), 1-11.
    Krebs, P., & Duncan, D. T. (2015). Health app use among US mobile phone owners: a national survey. JMIR mHealth and uHealth, 3(4). Retrieved from https://mhealth.jmir.org/2015/4/e101/
    Krey, N., Chuah, S. H. W., Ramayah, T., Rauschnabel, P. A., & Heisenberg-Weg, W. (2018). How functional and emotional ads drive smartwatch adoption: The moderating role of consumer innovativeness and extraversion. Internet Research. Retrieved from https://doi.org/10.1108/IntR-12-2017-0534
    Kumar, A., Adlakaha, A., & Mukherjee, K. (2018). The effect of perceived security and grievance redressal on continuance intention to use M-wallets in a developing country. International Journal of Bank Marketing, 36(7), 1170-1189.
    Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110.
    Kwahk, K.Y., Ahn, H., & Ryu, Y. U. (2018). Understanding mandatory IS use behavior: How outcome expectations affect conative IS use. International Journal of Information Management, 38(1), 64-76.
    Lamb, S., & Kwok, K. C. S. (2019). The effects of motion sickness and sopite syndrome on office workers in an 18-month field study of tall buildings. Journal of Wind Engineering and Industrial Aerodynamics, 186, 105-122.
    Lau, R. S., & Cheung, G. W. (2012). Estimating and comparing specific mediation effects in complex latent variable models. Organizational Research Methods, 15(1), 3-16.
    Lee, C. Y., Tsao, C. H., & Chang, W. C. (2015). The relationship between attitude toward using and customer satisfaction with mobile application services: an empirical study from the life insurance industry. Journal of Enterprise Information Management, 28(5), 680-697.
    Lee, H. E., & Cho, J. (2017). What motivates users to continue using diet and fitness apps? Application of the uses and gratifications approach. Health Communication, 32(12), 1445-1453.
    Lee, H. H., & Chang, E. (2011). Consumer attitudes toward online mass customization: An application of extended technology acceptance model. Journal of Computer-Mediated Communication, 16(2), 171-200.
    Lee, J.-Y., & Kang, T.-G. (2018). A study on the use intention of long term evolution mobile service. Wireless Personal Communications, 98(4), 3245-3264.
    Lee, M. H., Liang, J. C., Wu, Y. T., Chiou, G. L., Hsu, C. Y., Wang, C. Y., Lin, J.W., & Tsai, C. C. (2019). High school students’ conceptions of science laboratory learning, perceptions of the science laboratory environment, and academic self-efficacy in science learning. International Journal of Science and Mathematics Education, 1-18. Retrieved from https://doi.org/10.1007/s10763-019-09951-w
    Lee, Y. K., Park, J. H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(11), 1590-1599.
    Leung, L., & Chen, C. C. (2017). e-Health/m-Health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy. Retrieved from https://doi.org/10.1016/j.telpol.2019.01.005
    Li, H., & Liu, Y. (2014). Understanding post-adoption behaviors of e-service users in the context of online travel services. Information & Management, 51(8), 1043-1052.
    Li, J., Li, Y., Li, P., & Ye, M. (2019). Early symptom measurement of post‐stroke depression: Development and validation of a new short version. Journal of Advanced Nursing, 75(2), 482-493.
    Liao, C., Palvia, P., & Chen, J. L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4), 309-320.
    Lidynia, C., Schomakers, E. M., & Ziefle, M. (2018). What are you waiting for?–perceived barriers to the adoption of fitness-applications and wearables. International Conference on Applied Human Factors and Ergonomics, 795, 41-52.
    Lim, J. S., & Noh, G. Y. (2017). Effects of gain-versus loss-framed performance feedback on the use of fitness apps: Mediating role of exercise self-efficacy and outcome expectations of exercise. Computers in Human Behavior, 77, 249-257.
    Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737.
    Lin, C. P., & Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85-108.
    Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & management, 42(5), 683-693.
    Lin, M. L., & Yu, T. K. (2018). Patent applying or not applying: What factors motivating students’ intention to engage in patent activities. Eurasia Journal of Mathematics, Science and Technology Education, 14(5), 1843-1858.
    Liu, D. S., & Chen, W. (2009). An empirical research on the determinants of user M-commerce acceptance. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, 209, 93-104.
    López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359-364.
    Lorinc, J., Tossell, I., & el Akkad, O. (2010). The globe and mail: The age of the app. Retreived from https://www.theglobeandmail.com/report-on-business/rob-magazine/the-age-of-the-app/article1510836/?cid=art-rail-economy
    Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.
    Lu, Y. C., Xiao, Y., Sears, A., & Jacko, J. A. (2005). A review and a framework of handheld computer adoption in healthcare. International Journal of Medical Informatics, 74(5), 409-422.
    Machi, L. A., & McEvoy, B. T. (2016). The literature review: Six steps to success (3rd Ed.). Thousand Oaks, CA: Corwin Press.
    Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E., & Pounders, K. (2016). Health literacy and health information technology adoption: the potential for a new digital divide. Journal of Medical Internet Research, 18(10). Retrieved from https://www.jmir.org/2016/10/e264
    Mäntymäki, M., & Islam, A. N. (2014). Social virtual world continuance among teens: Uncovering the moderating role of perceived aggregate network exposure. Behaviour & Information Technology, 33(5), 536-547.
    Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95.
    Martínez-Caro, E., Cegarra-Navarro, J. G., García-Pérez, A., & Fait, M. (2018). Healthcare service evolution towards the Internet of Things: An end-user perspective. Technological Forecasting and Social Change, 136, 268-276.
    Melone, N. P. (1990). A theoretical assessment of the user-satisfaction construct in information systems research. Management Science, 36(1), 76-91.
    Midgley, D. F., & Dowling, G. R. (1978). Innovativeness: The concept and its measurement. Journal of Consumer Research, 4(4), 229-242.
    Millington, B. (2014). Smartphone apps and the mobile privatization of health and fitness. Critical Studies in Media Communication, 31(5), 479-493.
    Mock, T. S., Francis, D. S., Jago, M. K., Glencross, B. D., Smullen, R. P., Keast, R. S., & Turchini, G. M. (2019). The impact of dietary protein: Lipid ratio on growth performance, fatty acid metabolism, product quality and waste output in Atlantic salmon (Salmo salar). Aquaculture, 501, 191-201.
    Mohamed, N., Hussein, R., Hidayah Ahmad Zamzuri, N., & Haghshenas, H. (2014). Insights into individual's online shopping continuance intention. Industrial Management & Data systems, 114(9), 1453-1476.
    Molinillo, S., Liébana-Cabanillas, F., Anaya-Sánchez, R., & Buhalis, D. (2018). DMO online platforms: Image and intention to visit. Tourism Management, 65, 116-130.
    Moslehpour, M., Amri, K., & Promprasorn, P. (2017). Factors influencing intention to use of smartphone applications in Thailand. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 1108-1112.
    Mousavi, S., Najafi Kalyani, M., Karimi Sh, Kokabi, R., & Piriaee, S. (2015). The relationship between social support and mental health in infertile women: The mediating role of problem-focused coping. Scholars Journal of Applied Medical Sciences (SJAMS), 3(1), 244-248.
    Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363.
    Najmabadi, K. M., Karimi, F. Z., Roudsari, R. L., Abdollahi, M., & Zarifnejad, G. (2019). Evaluation of validity and reliability of persian version of the gender-equitable men-scale (GEM-Scale) in male students, Iran. Journal of Clinical and Diagnostic Research, 13(1), 21-24.
    Nakhasi, A., Shen, A. X., Passarella, R. J., Appel, L. J., & Anderson, C. A. (2014). Online social networks that connect users to physical activity partners: a review and descriptive analysis. Journal of Medical Internet Research, 16(6). Retrieved from https://www.jmir.org/2014/6/e153/?new Design
    Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable technology: What explains continuance intention in smartwatches? Journal of Retailing and Consumer Services, 43, 157-169.
    Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8-22.
    Neff, G. (2013). Why big data won't cure us. Big Data, 1(3), 117-123.
    Neneh, B. N. (2019). From entrepreneurial alertness to entrepreneurial behavior: The role of trait competitiveness and proactive personality. Personality and Individual Differences, 138, 273-279.
    Newaz, M. T., Davis, P. R., Jefferies, M., & Pillay, M. (2019). Validation of an agent-specific safety climate model for construction. Engineering, Construction and Architectural Management, 26(3), 462-478.
    Ngangi, S. C. W., & Santoso, A. J. (2019). Customer acceptance analysis of customer relationship management (CRM) systems in automotive company using Technology Acceptance Model (TAM) 2. Indonesian Journal of Information Systems, 1(2), 133-146.
    Nkohkwo, Q. N. A., & Islam, M. S. (2013). Challenges to the Successful implementation of e-Government Initiatives in Sub-Saharan Africa: A literature review. Electronic Journal of E-government, 11(1), 253-267.
    Nusair, K. K., Bilgihan, A., & Okumus, F. (2013). The role of online social network travel websites in creating social interaction for Gen Y travelers. International Journal of Tourism Research, 15(5), 458-472.
    Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34-47.
    Oh, H., Rizo, C., Enkin, M., & Jadad, A. (2005). What is eHealth (3): A systematic review of published definitions. Journal of Medical Internet Research, 7(1). Retrieved from https://www.jmir.org/2005/1/e1/
    Okumus, B., Ali, F., Bilgihan, A., & Ozturk, A. B. (2018). Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, 72, 67-77.
    Oliver, R. L. (1977). Effect of expectation and disconfirmation on postexposure product evaluations: An alternative interpretation. Journal of Applied Psychology, 62(4), 480.
    Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 460-469.
    Oliver, R. L. (1997). Satisfaction: A behavioral perspective on the consumer (2nd. Ed.). Washington, NY: Irwin/McGraw-Hill.
    Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. Journal of Consumer Research, 14(4), 495-507.
    Oliver, R. L., & Westbrook, R. (1993). Profiles of consumer emotions and satisfaction in ownership and usage. Emotion, 6(13), 12-27.
    Olson, J. C., & Dover, P. A. (1979). Disconfirmation of consumer expectations through product trial. Journal of Applied Psychology, 64(2), 179.
    Ou, Z. P., & Liu, X. (2018). An empirical study on the influence factors of mobile phone dependence of college students based on SPSS and AMOS. International Conference of Pioneering Computer Scientists, Engineers and Educators, 902, 581-593.
    Ozturk, A. B., Nusair, K., Okumus, F., & Hua, N. (2016). The role of utilitarian and hedonic values on users’ continued usage intention in a mobile hotel booking environment. International Journal of Hospitality Management, 57, 106-115.
    Paglialonga, A., Lugo, A., & Santoro, E. (2018). An overview on the emerging area of identification, characterization, and assessment of health apps. Journal of biomedical informatics, 83, 97-102.
    Pagliari, C., Sloan, D., Gregor, P., Sullivan, F., Detmer, D., Kahan, J. P., Oortwijn, W., MacGillivray, S. (2005). What is eHealth (4): A scoping exercise to map the field. Journal of Medical Internet Research, 7(1). Retrieved from: https://www.jmir.org/2005/1/e9/
    Pai, F. Y., & Huang, K. I. (2011). Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change, 78(4), 650-660.
    Pal, D., Funilkul, S., & Vanijja, V. (2018). The future of smartwatches: assessing the end-users’ continuous usage using an extended expectation-confirmation model. Universal Access in the Information Society, 1-21.
    Pal, D., Funilkul, S., Vanijja, V., & Papasratorn, B. (2018). Analyzing the elderly users’ adoption of smart-home services. IEEE Access, 6, 51238-51252.
    Pan, A., & Zhao, F. (2018). User acceptance factors for mHealth. International Conference on Human-Computer Interaction, 10902, 173-184
    Papa, A., Mital, M., Pisano, P., & Del Giudice, M. (2018). E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation. Technological Forecasting and Social Change. Retrieved from: https://doi.org/10.1016/j.techfore.2018.02.018
    Papalia, Z., Wilson, O., Bopp, M., & Duffey, M. (2018). Technology-based physical activity self-monitoring among college students. International Journal of Exercise Science, 11(7), 1096.
    Parasuraman, A., & Colby, C. L. (2007). Techno-ready marketing: How and why your customers adopt technology. Washington, NY: The Free Press.
    Paré, G., Leaver, C., & Bourget, C. (2018). Diffusion of the digital health self- tracking movement in Canada: Results of a national survey. Journal of Medical Internet Research, 20(5). Retrieved from: https://www.jmir.org/2018/5/e177/
    Park, D. Y., Goering, E. M., Head, K. J., & Ellis, R. J. B. (2017). Implications for training on smartphone medication reminder app use by adults with chronic conditions: Pilot study applying the technology acceptance model. JMIR Formative Research, 1(1). Retrieved from: https://formative.jmir.org/2017/1/e5/
    Phan, K., & Daim, T. U. (2011). Exploring technology acceptance for mobile services. Journal of Industrial Engineering and Management, 4(2), 339-360.
    Pössel, P., Burton, S. M., Cauley, B., Sawyer, M. G., Spence, S. H., & Sheffield, J. (2018). Associations between social support from family, friends, and teachers and depressive symptoms in adolescents. Journal of Youth and Adolescence, 47(2), 398-412.
    Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.
    Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36(1), 64-75.
    Pua, P. K., Lee, M. F., & Lai, C. S. (2019). Construct validity and internal consistency reliability of mental health monitoring instrument for technical university students. Journal of Technical Education and Training, 11(1), 87-92.
    Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696.
    Rogers, E. M. (2010). Diffusion of innovations (4d ed). Washington, NY: Simon and Schuster.
    Rogers, E. M., & Shoemaker, F. F. (1971). Communication of innovations: A cross-cultural approach (2d ed). Washington, NY: The Free Press.
    Rubio, N., Villaseñor, N., & Yague, M. J. (2019). Does use of different platforms influence the relationship between cocreation value-in-use and participants’ cocreation behaviors? An application in third-party managed virtual communities. Complexity, 2019, 1-15.
    Salisbury, W. D., Chin, W. W., Gopal, A., & Newsted, P. R. (2002). Better theory through measurement—developing a scale to capture consensus on appropriation. Information Systems Research, 13(1), 91-103.
    San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350.
    Santoro, E., Mosconi, P., Radrezza, S., Sgarbossa, C., Lettieri, E., & Balliero, N., (2017). App salute e tecnologie indossabili: Ecco quanto piacciono (e sono utili) ai pazienti. Il Sole 24 Ore Sanità, 10, 8.
    Saufi, R. A., ABU, Z. K., Mohidin, R., Mahmud, R., Idrus, D., & Shah, U. S. A. (2019). Academia capabilities, knowledge transfer programme mechanism and performance. Management, 7(2), 96-105.
    Serenko, A. (2008). A model of user adoption of interface agents for email notification. Interacting with Computers, 20(4-5), 461-472.
    Shang, D., & Wu, W. (2017). Understanding mobile shopping consumers’ continuance intention. Industrial Management & Data Systems, 117(1), 213-227.
    Sharif, A., & Raza, S. A. (2017). The influence of hedonic motivation, self-efficacy, trust and habit on adoption of internet banking: a case of developing country. International Journal of Electronic Customer Relationship Management, 11(1), 1-22.
    Sheth, J. N. (Ed.). (2011). Models of buyer behavior: conceptual, quantitative, and empirical (pp. 151). Decatur, GA:Marketing Classics Press.
    Shi, Y. (2018). The impact of consumer innovativeness on the intention of clicking on SNS advertising. Modern Economy, 9(2), 278-285.
    Silva, B. M., Rodrigues, J. J., de la Torre Díez, I., López-Coronado, M., & Saleem, K. (2015). Mobile-health: A review of current state in 2015. Journal of Biomedical Informatics, 56, 265-272.
    Smith, J. J., Beauchamp, M. R., Faulkner, G., Morgan, P. J., Kennedy, S. G., & Lubans, D. R. (2018). Intervention effects and mediators of well-being in a school-based physical activity program for adolescents: The ‘Resistance Training for Teens’ cluster RCT. Mental Health and Physical Activity, 15, 88-94.
    Sobaih, A. E. E., Ibrahim, Y., & Gabry, G. (2019). Unlocking the black box: Psychological contract fulfillment as a mediator between HRM practices and job performance. Tourism Management Perspectives, 30, 171-181.
    Statista. (2018a). mHealth (mobile health) industry market size projection from 2012 to 2020 (in billion U.S. dollars). Retrieved from https://www.statista.com/statistics/295771/mhealth-global-market-size/
    Statista. (2018b). Number of connected wearable devices worldwide from 2016 to 2021 (in millions). Retrieved from https://www.statista.com/statistics/487291/global-connected-wearable-devices/
    Stenlund, T., Eklöf, H., & Lyrén, P. E. (2017). Group differences in test-taking behaviour: An example from a high-stakes testing program. Assessment in Education: Principles, Policy & Practice, 24(1), 4-20.
    Streiner, D. L. (2003). Starting at the beginning: an introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99-103.
    Sulistiyani, E. (2017, November). Beberapa determinan perilaku kerja inovatif pada karyawan industri batik skala ekspor surakarta. Prosiding Sentrinov (Seminar Nasional Terapan Riset Inovatif), 3(1), 308-319.
    Swan, M. (2013). The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), 85-99.
    Tabachnick, B. G., & Fidell, L. S. (2007). Multivariate analysis of variance and covariance. Using Multivariate Statistics, 3, 402-407.
    Taherdoost, H. (2018). Development of an adoption model to assess user acceptance of e-service technology: E-service technology acceptance model. Behaviour & Information Technology, 37(2), 173-197.
    Taimalu, M., & Luik, P. (2019). The impact of beliefs and knowledge on the integration of technology among teacher educators: A path analysis. Teaching and Teacher Education, 79, 101-110.
    Talukder, M. S., Chiong, R., Bao, Y., & Hayat Malik, B. (2018). Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119(1), 170-188.
    Thatcher, J. B., Loughry, M. L., Lim, J., & McKnight, D. H. (2007). Internet anxiety: An empirical study of the effects of personality, beliefs, and social support. Information & Management, 44(4), 353-363.
    Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
    Tomlinson, M., Rotheram-Borus, M. J., Swartz, L., & Tsai, A. C. (2013). Scaling up mHealth: where is the evidence? PLOS Medicine, 10(2). Retrieved from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001382
    Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of Marketing Research, 25(2), 204-212.
    Turner-McGrievy, G. M., Beets, M. W., Moore, J. B., Kaczynski, A. T., Barr-Anderson, D. J., & Tate, D. F. (2013). Comparison of traditional versus mobile app self-monitoring of physical acality and dietary intake among overweight adults participating in an mHealth weight loss program. Journal of the American Medical Informatics Association, 20(3), 513-518.
    van Velsen, L., Beaujean, D. J., & van Gemert-Pijnen, J. E. (2013). Why mobile health app overload drives us crazy, and how to restore the sanity. BMC Medical Informatics and Decision Making, 13(1), 23.
    Varma Citrin, A., Sprott, D. E., Silverman, S. N., & Stem Jr, D. E. (2000). Adoption of Internet shopping: the role of consumer innovativeness. Industrial Management & Data Systems, 100(7), 294-300.
    Vélez, O., Okyere, P. B., Kanter, A. S., & Bakken, S. (2014). A usability study of a mobile health application for rural Ghanaian midwives. Journal of Midwifery & Women's Health, 59(2), 184-191.
    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., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
    Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1),157-178.
    Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two‐stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527-555.
    Waegemann, C. P. (2016). mHealth: History, analysis, and implementation: M-Health Innovations for Patient-Centered Care, 1-19. Retrieved from https://www.igi-global.com/chapter/mhealth/145002
    Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
    Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51(2), 249-259.
    Wang, Y., Wang, S., Wang, J., Wei, J., & Wang, C. (2018). An empirical study of consumers’ intention to use ride-sharing services: Using an extended technology acceptance model. Transportation, 1-19. Retrieved from https://link.springer.com/article/10.1007/s11116-018-9893-4
    Whitson, J. R. (2013). Gaming the quantified self. Surveillance & Society, 11(1/2), 163-176.
    Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102.
    Woldeyohannes, H. O., & Ngwenyama, O. K. (2017). Factors influencing acceptance and continued use of mHealth apps. International Conference on HCI in Business, Government, and Organizations, 239-256. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-58481-2_19
    Wu, B. (2018). Patient continued use of online health care communities: Web mining of patient-doctor communication. Journal of Medical Internet Research, 20(4). Retrieved from https://www.jmir.org/2018/4/e126/
    Wu, L., Li, J. Y., & Fu, C. Y. (2011). The adoption of mobile healthcare by hospital's professionals: An integrative perspective. Decision Support Systems, 51(3), 587-596.
    Xie, J., Burstein, F., Garad, R., Teede, H. J., & Boyle, J. A. (2018). Personalized mobile tool AskPCOS delivering evidence-based quality information about polycystic ovary syndrome. Seminars in Reproductive Medicine, 36(1), 66-72.
    Xin, Z., Liang, M., Zhanyou, W., & Hua, X. (2019). Psychosocial factors influencing shared bicycle travel choices among Chinese: An application of theory planned behavior. PLOS One, 14(1). Retrieved from https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210964
    Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42-52.
    Xu, H., Teo, H.-H., Tan, B. C., & Agarwal, R. (2009). The role of push-pull technology in privacy calculus: The case of location-based services. Journal of Management Information Systems, 26(3), 135-174.
    Xu, W., & Liu, Y. (2015). mHealthApps: A repository and database of mobile health apps. JMIR MHealth and UHealth, 3(1). Retrieved from https://mhealth.jmir.org/2015/1/e28/
    Yang, K. C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and informatics, 22(3), 257-277.
    Yassierli, Y., Vinsensius, V., & Mohamed, M. S. (2019). The importance of usability aspect in M-commerce application for satisfaction and continuance intention. Makara Journal of Technology, 22(3), 149-158.
    Yi, Y. (1990). A critical review of consumer satisfaction. Review of Marketing, 4(1), 68-123.
    Yoganathan, D., & Kajanan, S. (2014). What drives fitness apps usage? An empirical evaluation. International Working Conference on Transfer and Diffusion of IT, 179-196. Retrieved from https://hal.inria.fr/hal-01381187/document
    Zeithaml, V. A., Parasuraman, A., Berry, L. L., & Berry, L. L. (1990). Delivering quality service: Balancing customer perceptions and expectations. Washington, NY: Simon and Schuster.
    Zhang, J., Brown, C., Qiao, G., & Zhang, B. (2019). Effect of Eco-compensation schemes on household income structures and herder satisfaction: Lessons from the grassland ecosystem subsidy and award scheme in inner Mongolia. Ecological Economics, 159, 46-53.
    Zhao, L., Lu, Y., Zhang, L., & Chau, P. Y. (2012). Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems, 52(3), 645-656.
    Zheng, Y. L., Ding, X. R., Poon, C. C. Y., Lo, B. P. L., Zhang, H., Zhou, X.L., Yang, G. Z., Zhao, Ni, Zhang, Y. T. (2014). Unobtrusive sensing and wearable devices for health informatics. IEEE Transactions on Biomedical Engineering, 61(5), 1538-1554.
    Zhou, T., & Lu, Y. (2011). Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, 27(2), 883-889.

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