Author(s):

  • Xiaowei Fan
  • Jun Fan
  • Jianglu Li

Abstract:

Purpose: The COVID-19 pandemic has greatly influenced the health and lifestyles of individuals. Increasing numbers of consumers now participate in quantified self (QS) process to learn more about their health-related behaviors. Understanding how to increase consumers’ QS continuance participation intention is critical. Drawing on Social Cognitive Theory and Self-Construal Theory, this study investigates how the presentation characteristics of QS data and consumers’ self-construal can influence their continuance participation intention during QS process.
Methods: Three between-subjects scenario simulation experiments were conducted to examine the influence mechanisms of the presentation mode and type of QS data and self-construal on consumers’ continuance participation intention.
Results: The study found: (1) the presentation mode (horizontal comparison vs vertical comparison) and type (descriptive vs analytic) of QS data had significant interaction effects on consumers’ continuance participation intention; (2) consumers’ self-construal (interdependent vs independent) and the presentation mode of QS data had obvious interaction effects on their continuance participation intention; and (3) consumers’ self-construal and the presentation type of QS data had interaction influences on their continuance participation intention.
Conclusion: This research combined Social Cognitive Theory and Self-Construal Theory to analyze the influence mechanisms of the presentation characteristics of QS data and consumers’ self-construal on their continuance participation intention. These findings not only expand the research field and the scope of application of Social Cognitive Theory, but also provide new insights for the study of consumers’ QS problems. They have reference value for the optimization of the presentation features of QS data, and for improving the match between QS data presentation and consumers’ self-construal types, to motivate continued participation in QS process

Documentation:

https://doi.org/10.2147/PRBM.S381705

References:

1. Faraci P, Bottaro R, Valenti GD, Craparo G. Psychological well-being during the second wave of COVID-19 pandemic: the mediation role of
generalized anxiety. Psychol Res Behav Manag. 2022;15:695–709. doi:10.2147/PRBM.S354083
2. Almalki M, Gray K, Martin-Sanchez F. Activity theory as a theoretical framework for health self-quantification: a systematic review of empirical
studies. J Med Internet Res. 2016;18(5):131–148. doi:10.2196/jmir.5000
3. Shin DH, Biocca F. Health experience model of personal informatics: the case of a Quantified Self. Comput Human Behav. 2017;69:62–74.
doi:10.1016/j.chb.2016.12.019
4. Crawford K, Lingel J, Karppi T. Our metrics, ourselves: a hundred years of self-tracking from the weight scale to the wrist wearable device. Eur
J Cult Stud. 2015;18(4–5):479–496. doi:10.1177/1367549415584857
5. Zhang YD, Li DJ. Research on obstructive factors and the influencing mechanism of consumers’ involvement in Quantified-Self. Chin J Manage.
2018;15(1):74–83.
6. Van Berkel N, Luo C, Ferreira D, Goncalves J, Kostakos V, editors. The curse of Quantified-Self: an endless quest for answers. Adjunct
Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM
International Symposium on Wearable Computers; 2015 Sep 07–11; Osaka, Japan. 2015; New York: Association for Computing Machinery.
7. Shen XL, Li YJ, Sun YQ. Wearable health information systems intermittent discontinuance: a revised expectation disconfirmation model. Ind
Manage Data Syst. 2018;118(3):506–523. doi:10.1108/IMDS-05-2017-0222
8. Lazar A, Koehler C, Tanenbaum J, Nguyen DH, editors. Why we use and abandon smart Devices. Proceedings of the 2015 ACM International
Joint Conference on Pervasive and Ubiquitous Computing; 2015 Sep 07–11; Osaka, Japan. 2015; New York: Association for Computing
Machinery.
9. Zhang J, Lowry PB, editors. Designing Quantified-Self 2.0 running platform to ensure physical activity maintenance: the role of achievement goals
and achievement motivational affordance. Proceedings of the 20th Pacific Asia Conference on Information Systems; 2016 Jun 27-Jul 1; 2018;
Chiayi, Taiwan.
10. Li DJ, Zhang YD. Why consumers give up: the mechanism underlying the formation of willingness to continue participating in Quantified Self.
Nankai Bus Rev. 2018;21(1):118–131.
11. Jin H, Yan JY, Zhang YD, Zhang HL. Research on the influence mechanism of users’ quantified-self immersive experience: on the convergence of
mobile intelligence and wearable computing. Pers Ubiquitous Comput. 2020;3:1–12.
12. Shi HJ, Chen R. Goal specificity or ambiguity? Effects of self-quantification on persistence intentions. J Res Interact Mark. 2021. doi:10.1108/
JRIM-07-2021-0181
13. Bandura A. Human agency in social cognitive theory. Am Psychol. 1989;44(9):1175–1184. doi:10.1037/0003-066X.44.9.1175
14. Zhu Z, Zhao J. The decision-making behavior of e-business adoption in organizational level: an empirical study from social cognitive theory.
Nankai Bus Rev. 2011;14(3):151–160.
15. Bandura A. The explanatory and predictive scope of self-efficacy theory. J Soc Clin Psychol. 1986;4(3):359–373. doi:10.1521/jscp.1986.4.3.359
16. Locke EA. Social foundations of thought and action: a social-cognitive view. Acad Manage Rev. 1987;12(1):169–171.
Psychology Research and Behavior Management 2022:15 https://doi.org/10.2147/PRBM.S381705
DovePress 2875
Dovepress Fan et alPowered by TCPDF (www.tcpdf.org)17. Lin HC, Chang CM. What motivates health information exchange in social media? The roles of the social cognitive theory and perceived
interactivity. Inform Manage. 2018;55(6):771–780. doi:10.1016/j.im.2018.03.006
18. Lim JS, Choe MJ, Zhang J, Noh GY. The role of wishful identification, emotional engagement, and parasocial relationships in repeated viewing of
live-streaming games: a social cognitive theory perspective. Comput Human Behav. 2020;108:1–10. doi:10.1016/j.chb.2020.106327
19. Wu D, Gu H, Gu SY, You H. Individual motivation and social influence: a study of telemedicine adoption in China based on social cognitive theory.
Health Policy Technol. 2021;10(3):1–10. doi:10.1016/j.hlpt.2021.100525
20. Zhou JJ, Fan TT. Understanding the factors influencing patient e-health literacy in online health communities (OHCs): a social cognitive theory
perspective. Int J Environ Res Public Health. 2019;16(14) :1–12.
21. Voskuil VR, Robbins LB. Youth physical activity self-efficacy: a concept analysis. J Adv Nurs. 2015;71(9):2002–2019. doi:10.1111/jan.12658
22. Zhao Y, Ni Q, Zhou RX. What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. Int J Inf
Manage. 2018;43:342–350. doi:10.1016/j.ijinfomgt.2017.08.006
23. Stiglbauer B, Weber S, Batinic B. Does your health really benefit from using a self-tracking device? Evidence from a longitudinal randomized
control trial. Comput Human Behav. 2019;94:131–139. doi:10.1016/j.chb.2019.01.018
24. Brinson NH. Fit or Fail? Examining the Impact of Quantified Self Health and Fitness Tracking Technologies and Data Collection on College Youth
[dissertation]. Asutin: The University of Texas; 2017.
25. Singelis TM. The measurement of independent and interdependent self-construals. Pers Soc Psychol Bull. 1994;20(5):580–591. doi:10.1177/
0146167294205014
26. Markus HR, Kitayama S. Culture and the self: implications for cognition, emotion, and motivation. Psychol Rev. 1991;98(2):224–253. doi:10.1037/
0033-295X.98.2.224
27. Liu J. Internet-Based Knowledge Sharing Services: Investigations of Self-Construal and Sharing Behavior [dissertation]. Beijing: Tsinghua
University; 2014.
28. Kim JH, Kim MS, Nam Y. An analysis of self-construals, motivations, facebook use, and user Satisfaction. Int J Hum-Comput Int. 2010;26(11–
12):1077–1099. doi:10.1080/10447318.2010.516726
29. Rooksby J, Rost M, Morrison A, Chalmers M, editors. Personal tracking as lived informatics. Proceedings of the SIGCHI Conference on Human
Factors in Computing Systems; 2014 Apr 26-May 1; Toronto, Canada. 2014; New York: Association for Computing Machinery.
30. Martinko MJ, Gundlach MJ, Douglas SC. Toward an integrative theory of counterproductive workplace behavior: a causal reasoning perspective.
Int J Select Assess. 2002;10(1/2):36–50. doi:10.1111/1468-2389.00192
31. Li Y, Guo YK. Wiki-health: from quantified self to self-understanding. Future Gener Comp Sy. 2016;56:333–359. doi:10.1016/j.future.2015.08.008
32. Franque FB, Oliveira T, Tam C, Santini FD. A meta-analysis of the quantitative studies in continuance intention to use an information system.
Internet Res. 2020;31(1):123–158. doi:10.1108/INTR-03-2019-0103
33. Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 2013;1(2):85–99. doi:10.1089/
big.2012.0002
34. Choe EK, Lee NB, Lee B, Pratt W, Kientz JA, editors. Understanding quantified-selfers’ practices in collecting and exploring personal data.
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 2014 Apr 26-May 1; Toronto, Canada. 2014; New York:
Association for Computing Machinery.
35. Pirzadeh A, He L, Stolterman E, editors. Personal informatics and reflection: a critical examination of the nature of reflection. CHI ‘13 Extended
Abstracts on Human Factors in Computing Systems; 2013 Apr 27-May 2; Paris, France. 2013; New York: Association for Computing
Machinery.
36. Cho H, Yoon H, Kim KJ, Shin DH, editors. Wearable health information: effects of comparative feedback and presentation model. Proceedings of
the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems; 2015 Apr 18–23; Seoul, Korea. 2015; New York:
Association for Computing Machinery.
37. Petkov P, Köbler F, Foth M, Krcmar H, editors. Motivating domestic energy conservation through comparative, community-based feedback in
mobile and social media. Proceedings of the 5th International Conference on Communities and Technologies; 2011 Jun 29-Jul 2; Brisbane,
Australia. 2011; New York: Association for Computing Machinery.
38. Fawcett T. Mining the quantified self: personal knowledge discovery as a challenge for data science. Big Data. 2015;3(4):249–266. doi:10.1089/
big.2015.0049
39. Consolvo S, Everitt K, Smith I, Landay JA, editors. Design requirements for technologies that encourage physical activity. Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems; 2006 Apr 22–27; Montréal, Canada. 2006; New York: Association for Computing
Machinery.
40. Cross SE, Hardin EE, GercekSwing B. The what, how, why, and where of self-construal. Pers Soc Psychol Rev. 2011;15(2):142–179. doi:10.1177/
1088868310373752
41. Yao Q, Chen R, Zhao P. The influence of self-construals on the imagery advertising strategy. Acta Psychol Sin. 2011;43(6):674–683.
42. Afsar B, Badir YF, Saeed BB. Transformational leadership and innovative work behavior. Ind Manage Data Syst. 2014;114(8):1270–1300.
doi:10.1108/IMDS-05-2014-0152
43. Liu Y. Self-construal: review and prospect. Adv Psychol Sci. 2011;19(3):427–439.
44. Konrath S, Bushman BJ, Grove T. Seeing my world in a million little pieces: narcissism, self-construal, and cognitive-perceptual style. J Pers.
2009;77(4):1197–1228. doi:10.1111/j.1467-6494.2009.00579.x
45. Kang C, Zhou AB. The effect of the representational mode of information and learners’ cognitive style on learning in the multimedia environment.
Psychol Sci. 2010;33(6):1397–1400.
46. Kühnen U, Hannover B, Schubert B. The semantic-procedural interface model of the self: the role of self-knowledge for context-dependent versus
context-independent modes of thinking. J Pers Soc Psychol. 2001;80(3):397–409. doi:10.1037/0022-3514.80.3.397
47. Kühnen U, Oyserman D. Thinking about the self influences thinking in general: cognitive consequences of salient self-concept. J Exp Soc Psychol.
2002;38(5):492–499. doi:10.1016/S0022-1031(02)00011-2
48. Krishna A, Zhou RR, Zhang S. The effect of self-construal on spatial judgments. J Consum Res. 2008;35(2):337–348. doi:10.1086/588686
49. Xiong SH. A Impulse Buying Study Based on Personality Trait: The Roles of Regulatory Focus & Self-Contrual [dissertation]. Wuhan: Huazhong
University of Science & Technology; 2009 .
https://doi.org/10.2147/PRBM.S381705
DovePress
Psychology Research and Behavior Management 2022:152876
Fan et al DovepressPowered by TCPDF (www.tcpdf.org)50. Kim Y, Sundar SS. Visualizing ideal self vs. actual self through avatars: impact on preventive health outcomes. Comput Human Behav. 2012;28
(4):1356–1364. doi:10.1016/j.chb.2012.02.021
51. Guo L. Quantified-self 2.0: using context-aware services for promoting gradual behaviour change. Working Papers of Computers Society;
2016:1–18. Available from: https://arxiv.org/abs/1610.00460. Accessed September 22, 2021.

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