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| Sustained Participation Motivation of Quantified Self for Personal Health Information Management |
| Xu Xiaoting1, Zhu Qinghua2, Yang Mengqing3, Zhao Yuxiang4 |
1.School of Sociology and Population Studies, Nanjing University of Posts and Telecommunications, Nanjing 210023
2.School of Information Management, Nanjing University, Nanjing 210023
3.School of Journalism and Communication, Nanjing Normal University, Nanjing 210097
4.School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract With the development of smart devices and the mobile Internet, an increasing number of users have joined the quantified self (QS) movement to manage personal health information, with the aim of realizing changes in health behavior. Promoting the sustainability of QS is a necessary prerequisite for maintaining health behaviors, and is also conducive to guiding the development of QS-related practices. To this end, this study focuses on identifying users' sustained participation motivation of QS for personal health information management and conducting empirical research. According to a literature review, there are 10 factors of sustained participation motivation, which mainly include tool-driven, society-driven, and user-driven dimensions. We develop relevant theoretical models using the stimuli-organism-response (S-O-R) model. Then, we consider Keep, an application for personal health information management, as an empirical case study to perform confirmatory analysis. We use a questionnaire survey to collect data (N = 373) and the partial least square method to verify the research hypothesis. The empirical analysis reveals that sustained participation motivation for personal health information management has various significant effects. In the tool-driven dimension, self-regulation positively affects perceived usefulness; information seeking and ease of use positively affect self-efficacy; and extrinsic rewards positively affect enjoyment. In the society-driven dimension, subjective norms positively affect self-efficacy, and social contact positively affects enjoyment and the sense of belonging. In the user-driven dimension, perceived usefulness, self-efficacy, and enjoyment have a significant positive effect on the sustained participation motivation of QS. Furthermore, gender and age play a moderating role in certain relationships. This study enriches the theoretical basis of the sustained participation motivation of QS and proposes a few suggestions for QS-related health information service.
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Received: 03 June 2021
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1 Lupton D. Self-tracking modes: reflexive self-monitoring and data practices[J/OL]. SSRN Electronic Journal, (2014-08-19). http://dx.doi.org/10.2139/ssrn.2483549.
2 Swan M. The quantified self: fundamental disruption in big data science and biological discovery[J]. Big Data, 2013, 1(2): 85-99.
3 朱启贞, 胡德华, 张彦斐. 量化自我理论在健康领域的应用[J]. 图书馆论坛, 2018, 38(2): 17-21.
4 Pai A. Nielsen: 46 million people used fitness apps in January[EB/OL]. (2014-04-17) [2021-02-07]. http://www.mobihealthnews.com/32183/nielsen-46-million-people-used-fitness-apps-in-january/.
5 艾瑞咨询: 2016年中国全民运动健身行业报告[R/OL]. (2016-11-10) [2021-02-07]. http://www.199it.com/archives/535253.html.
6 Keep[EB/OL]. [2021-02-07]. https://www.gotokeep.com/.
7 Ledger D, McCaffrey D. Inside wearables: how the science of human behavior change offers the secret to long-term engagement[R]. Endeavour Partners LLC, 2014: 1-17.
8 Guo L. Quantified-self 2.0: using context-aware services for promoting gradual behaviour change[OL]. (2016-10-03). https://arxiv.org/ftp/arxiv/papers/1610/1610.00460.pdf.
9 李东进, 张宇东. 消费者为何放弃: 量化自我持续参与意愿形成的内在机制[J]. 南开管理评论, 2018, 21(1): 118-131.
10 Li J, Liu X, Ma L, et al. Users’ intention to continue using social fitness-tracking apps: expectation confirmation theory and social comparison theory perspective[J]. Informatics for Health and Social Care, 2019, 44(3): 298-312.
11 Kevin K. What is the quantified self?[EB/OL]. (2007-10-05) [2021-01-04]. https://www.webcitation.org/66TEY49wv?url=http://quantifiedself.com/2007/page/3/.
12 Gary W. Quantified self[EB/OL]. (2012-03-27) [2021-01-04]. https://www.webcitation.org/66TEHdz4d?url=http://aether.com/quantifiedself.
13 Wikipedia. Quantified self[EB/OL]. [2021-01-04]. https://en.wikipedia.org/wiki/Quantified_self.
14 Lupton D. Self-tracking cultures: towards a sociology of personal informatics[C]// Proceedings of the 26th Australian Computer-Human Interaction Conference on Designing Futures: the Future of Design. New York: ACM Press, 2014: 77-86.
15 Almalki M, Gray K, Martin-Sanchez F. Activity theory as a theoretical framework for health self-quantification: a systematic review of empirical studies[J]. Journal of Medical Internet Research, 2016, 18(5): e131.
16 Rooksby J, Rost M, Morrison A, et al. Personal tracking as lived informatics[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM Press, 2014: 1163-1172.
17 李东进, 张宇东. 消费领域的量化自我: 研究述评与展望[J]. 外国经济与管理, 2018, 40(1): 3-17.
18 邓胜利, 夏苏迪, 韦佳岑. 基于生活信息学模型的个人健康信息管理研究[J]. 情报资料工作, 2021, 42(2): 23-30.
19 Kim S, Huber J T. Characteristics of personal health information management groups: findings from an online survey using Amazon's mTurk[J]. Journal of the Medical Library Association, 2017, 105(4): 361-375.
20 陈雪萍, 潘莺. 运动社交应用受众使用动机研究——以咕咚应用为例[J]. 东南传播, 2016(10): 66-71.
21 Asimakopoulos S, Asimakopoulos G, Spillers F. Motivation and user engagement in fitness tracking: heuristics for mobile healthcare wearables[J]. Informatics, 2017, 4(1): 5.
22 Chuah S H W, Rauschnabel P A, Krey N, et al. Wearable technologies: the role of usefulness and visibility in smartwatch adoption[J]. Computers in Human Behavior, 2016, 65: 276-284.
23 Shin D H, Biocca F. Health experience model of personal informatics: the case of a quantified self[J]. Computers in Human Behavior, 2017, 69: 62-74.
24 Whitson J R. Gaming the quantified self[J]. Surveillance & Society, 2013, 11(1/2): 163-176.
25 Sj?klint M, Constantiou I D, Trier M. The complexities of self-tracking-an inquiry into user reactions and goal attainment[J/OL]. SSRN Electronic Journal, (2015-05-29). http://dx.doi.org/10.2139/ssrn.2611193.
26 Choe Y, Fesenmaier D R. The quantified traveler: implications for smart tourism development[M]// Analytics in Smart Tourism Design. Cham: Springer, 2017: 65-77.
27 王仲杰. 可穿戴设备用户持续使用意愿的影响因素研究[D]. 合肥: 中国科学技术大学, 2019.
28 朱一梅. 大学生健康APP持续使用意愿影响因素研究[D]. 郑州: 郑州大学, 2019.
29 Rothman A J, Baldwin A S, Hertel A W, et al. Self-regulation and behavior change: disentangling behavioral initiation and behavioral maintenance[M]// Handbook of Self-Regulation: Research, Theory, and Applications. New York: Guilford Press, 2004: 130-148.
30 Stragier J, Vanden Abeele M, Mechant P, et al. Understanding persistence in the use of Online Fitness Communities: comparing novice and experienced users[J]. Computers in Human Behavior, 2016, 64: 34-42.
31 Cho J. The impact of post-adoption beliefs on the continued use of health apps[J]. International Journal of Medical Informatics, 2016, 87: 75-83.
32 Mehrabian A, Russell J A. An approach to environmental psychology[M]. Cambridge: The MIT Press, 1974: 43.
33 Eroglu S A, Machleit K A, Davis L M. Empirical testing of a model of online store atmospherics and shopper responses[J]. Psychology and Marketing, 2003, 20(2): 139-150.
34 Lam L W, Chan K W, Fong D, et al. Does the look matter? The impact of casino servicescape on gaming customer satisfaction, intention to revisit, and desire to stay[J]. International Journal of Hospitality Management, 2011, 30(3): 558-567.
35 Hsu C L, Chang K C, Chen M C. The impact of website quality on customer satisfaction and purchase intention: perceived playfulness and perceived flow as mediators[J]. Information Systems and e-Business Management, 2012, 10(4): 549-570.
36 龚艳萍, 曹玉, 李见. 短视频应用的特性对用户参与行为的影响: 心理参与的中介作用[J]. 情报科学, 2020, 38(7): 77-84.
37 Song S J, Yao X L, Wen N N. What motivates Chinese consumers to avoid information about the COVID-19 pandemic?: The perspective of the stimulus-organism-response model[J]. Information Processing & Management, 2021, 58(1): 102407.
38 Zhu Z Y, Kim H C. Antecedents and consequences of flow experience in online movie information sharing behavior: an empirical study of young Chinese moviegoers living in Korea[J]. The International Journal of Advanced Smart Convergence, 2020, 9(1): 141-153.
39 徐孝娟. 基于S-O-R理论的社交网站用户流失研究[D]. 南京: 南京大学, 2015.
40 贾明霞, 熊回香. 虚拟学术社区知识交流与知识共享探究——基于整合S-O-R模型与MOA理论[J]. 图书馆学研究, 2020(2): 43-54.
41 Cho W C, Lee K Y, Yang S B. What makes you feel attached to smartwatches? The stimulus-organism-response (S-O-R) perspectives[J]. Information Technology & People, 2019, 32(2): 319-343.
42 Bandura A. Social cognitive theory of self-regulation[J]. Organizational Behavior and Human Decision Processes, 1991, 50(2): 248-287.
43 Michie S, Ashford S, Sniehotta F F, et al. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy[J]. Psychology & Health, 2011, 26(11): 1479-1498.
44 Lee K, Kwon H, Lee B, et al. Effect of self-monitoring on long-term patient engagement with mobile health applications[J]. PLoS One, 2018, 13(7): e0201166.
45 Jung M E, Brawley L R. Concurrent self-regulatory efficacy as a mediator of the goal: exercise behaviour relationship[J]. Journal of Health Psychology, 2013, 18(5): 601-611.
46 Hur K, Kim T T, Karatepe O M, et al. An exploration of the factors influencing social media continuance usage and information sharing intentions among Korean travellers[J]. Tourism Management, 2017, 63: 170-178.
47 Nuss K, Moore K, Nelson T, et al. Effects of motivational interviewing and wearable fitness trackers on motivation and physical activity: a systematic review[J]. American Journal of Health Promotion, 2021, 35(2): 226-235.
48 吴江, 李姗姗, 胡仙, 等. 健康类可穿戴设备用户融入意向影响因素的实证研究[J]. 信息资源管理学报, 2017, 7(2): 22-30.
49 Medhi I, Patnaik S, Brunskill E, et al. Designing mobile interfaces for novice and low-literacy users[J]. ACM Transactions on Computer-Human Interaction, 2011, 18(1): 1-28.
50 Russell E, Lloyd-Houldey A, Memon A, et al. Factors influencing uptake and use of a new health information app for young people[J]. Journal of Technology in Human Services, 2018, 36(4): 222-240.
51 Davis F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology[J]. MIS Quarterly, 1989, 13(3): 319-340.
52 陈小燕, CliquetGerard. “量化自我”运动中隐私关注的阻碍作用——以智能手环用户采纳行为为例[J]. 企业经济, 2020, 39(4): 108-118.
53 Bock G W, Zmud R W, Kim Y G, et al. Behavioral intention formation in knowledge sharing: examining the roles of extrinsic motivators, social-psychological forces, and organizational climate[J]. MIS Quarterly, 2005, 29(1): 87-111.
54 Sun Y Q, Fang Y L, Lim K H. Understanding sustained participation in transactional virtual communities[J]. Decision Support Systems, 2012, 53(1): 12-22.
55 Glascoff M A, Baker J B, Glascoff D W. Successfully promoting volunteerism by offering extrinsic rewards in a personal health course: a pilot study[J]. Journal of Health Education, 1997, 28(4): 219-223.
56 Nuijten R C Y, van Gorp P M E, Kaymak U, et al. Evaluation of the impact of extrinsic rewards on user engagement in a health promotion context[C]// Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2019: 3600-3604.
57 Stok F M, de Vet E, de Ridder D T D, et al. The potential of peer social norms to shape food intake in adolescents and young adults: a systematic review of effects and moderators[J]. Health Psychology Review, 2016, 10(3): 326-340.
58 赵杨, 谭道勋. 用户参与可穿戴式设备创新的影响因素研究[J]. 信息资源管理学报, 2017, 7(2): 31-39, 49.
59 Pfeiffer J, von Entress-Fürsteneck M, Urbach N, et al. Quantify-me: consumer acceptance of wearable self-tracking devices[C]// Proceedings of the 24th European Conference on Information Systems, Istanbul, Turkey, 2016: Research Papers 99.
60 Mathieson K. Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior[J]. Information Systems Research, 1991, 2(3): 173-191.
61 Gui X N, Chen Y, Caldeira C, et al. When fitness meets social networks: investigating fitness tracking and social practices on WeRun[C]// Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. New York: ACM Press, 2017: 1647-1659.
62 Ryan R M, Frederick C M, Lepes D, et al. Intrinsic motivation and exercise adherence[J]. International Journal of Sport Psychology, 1997, 28(4): 335-354.
63 Lee J, Kim J, Choi J Y. The adoption of virtual reality devices: the technology acceptance model integrating enjoyment, social interaction, and strength of the social ties[J]. Telematics and Informatics, 2019, 39: 37-48.
64 Sniehotta F F, Scholz U, Schwarzer R. Bridging the intention-behaviour gap: planning, self-efficacy, and action control in the adoption and maintenance of physical exercise[J]. Psychology & Health, 2005, 20(2): 143-160.
65 Rapp A. Drawing inspiration from world of warcraft: gamification design elements for behavior change technologies[J]. Interacting with Computers, 2017, 29(5): 648-678.
66 Rapp A, Cena F, Marcengo A. Editorial of the special issue on quantified self and personal informatics[J]. Computers, 2018, 7(1): 14.
67 Atkinson M, Kydd C. Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation[J]. ACM SIGMIS Database, 1997, 28(2): 53-62.
68 Lee H E, Cho J. What motivates users to continue using diet and fitness apps? Application of the uses and gratifications approach[J]. Health Communication, 2017, 32(12): 1445-1453.
69 Suh A. Sustaining the use of quantified-self technology: a theoretical extension and empirical test[J]. Asia Pacific Journal of Information Systems, 2018, 28(2): 114-132.
70 Byun H, Chiu W, Bae J S. Exploring the adoption of sports brand apps: an application of the modified technology acceptance model[J]. International Journal of Asian Business and Information Management, 2018, 9(1): 52-65.
71 Teo H H, Chan H C, Wei K K, et al. Evaluating information accessibility and community adaptivity features for sustaining virtual learning communities[J]. International Journal of Human-Computer Studies, 2003, 59(5): 671-697.
72 Zhao L, Lu Y B, Wang B, et al. Cultivating the sense of belonging and motivating user participation in virtual communities: a social capital perspective[J]. International Journal of Information Management, 2012, 32(6): 574-588.
73 Crawford K, Lingel J, Karppi T. Our metrics, ourselves: a hundred years of self-tracking from the weight scale to the wrist wearable device[J]. European Journal of Cultural Studies, 2015, 18(4/5): 479-496.
74 Hystad P, Carpiano R M. Sense of community-belonging and health-behaviour change in Canada[J]. Journal of Epidemiology and Community Health, 2012, 66(3): 277-283.
75 Khalaf S. Health and fitness apps finally take off, fueled by fitness fanatics[EB/OL]. (2014-06-19) [2020-11-23]. https://www.flurry.com/blog/health-and-fitness-apps-finally-take-off-fueled/#.VNgmAfmUf_3.
76 Fox S, Duggan M. Tracking for health[R/OL]. Pew Research Center’s Internet & American Life Project, 2013. [2021-02-07]. https://www.pewinternet.org/2013/01/28/tracking-for-health/.
77 Chin W W. The partial least squares approach to structural equation modeling[M]// Modern Methods for Business Research. Mahwah: Lawrence Erlbaum Associates, 1998: 295-336.
78 Venkatesh V, Morris M G. Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior[J]. MIS Quarterly, 2000, 24(1): 115-139.
79 百度百科. Keep[EB/OL]. (2020-05-14) [2020-09-07]. https://baike.baidu.com/item/keep/17618239.
80 Barclay D, Higgins C, Thompson R. The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration[J]. Technology Studies, 1995, 2(2): 285-309.
81 武松, 潘发明. SPSS统计分析大全[M]. 北京: 清华大学出版社, 2014.
82 Hair J F, Ringle C M, Sarstedt M. PLS-SEM: indeed a silver bullet[J]. Journal of Marketing Theory and Practice, 2011, 19(2): 139-152.
83 Bagozzi R P, Yi Y. On the evaluation of structural equation models[J]. Journal of the Academy of Marketing Science, 1988, 16(1): 74-94.
84 Fornell C, Larcker D F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of Marketing Research, 1981, 18(1): 39-50.
85 萧文龙. 统计分析入门应用: SPSS中文版+SmartPLS 3 (PLS-SEM)[M]. 3版. 台北: 碁峰信息股份有限公司, 2018.
86 Podsakoff P M, MacKenzie S B, Lee J Y, et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies[J]. Journal of Applied Psychology, 2003, 88(5): 879-903.
87 Podsakoff P M, Organ D W. Self-reports in organizational research: problems and prospects[J]. Journal of Management, 1986, 12(4): 531-544.
88 Gimpel H, Ni?en M, G?rlitz R A. Quantifying the quantified self: a study on the motivations of patients to track their own health[C]// Proceedings of the 34th International Conference on Information Systems. Association for Information Systems, 2013: 1-16.
89 Nielsen. Hacking health: how consumers use smartphones and wearable tech to track their health[EB/OL]. (2014-04-16) [2020-12-30]. https://www.nielsen.com/us/en/insights/article/2014/hacking-health-how-consumers-use-smartphones-and-wearable-tech-to-track-their-health/. |
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