AJIBM  Vol.10 No.2 , February 2020
In the Face of Negative Data, the Effects of Goal Type and Feedback Type on the Willingness to Continue to Participate Quantified-Self
Abstract: In order to ensure the continuous participation of consumers in quantified-self, this paper studies the influence of target type and feedback type on the intention of quantified-self continuous participation in the face of negative data. The interaction between quantified-self goals and feedback types was taken as an independent variable, and self-efficacy was introduced as an intermediary to explain the influence of independent variables on the quantified willingness to continue to participate. In this study, the experimental method was used to prove the research hypothesis through two experiments. The results showed that in the face of negative data, when the type of quantified-self goal was promotion, compared with the task feedback, the adoption of ability feedback would lead to a higher willingness of quantified-self involvement due to a higher sense of self-efficacy. When the quantized-self goal type is prevention, compared with the task feedback, the adoption of capability feedback results in a lower willingness of quantified-self involvement due to a lower sense of self-efficacy. The results of relevant studies have positive guiding significance for enterprises to improve design methods to promote consumers’ willingness to participate in quantified-self sustainability.
Cite this paper: Yang, X. and Pan, D. (2020) In the Face of Negative Data, the Effects of Goal Type and Feedback Type on the Willingness to Continue to Participate Quantified-Self. American Journal of Industrial and Business Management, 10, 327-343. doi: 10.4236/ajibm.2020.102021.

[1]   Algesheimer, R., Borle, S., & Dholakia, U. M. (2010). The Impact of Customer Community Participation on Customer Behaviors: An Empirical Investigation. Marketing Science, 29, 756-769.

[2]   Azar, B. (2014). QnAs with Davis Masten and Peter Zandan. Proceedings of the National Academy of Sciences of the United States of America, 111, 1662-1663.

[3]   Bandura, A. (1977). Self-Efficacy: Toward a Unifying Theory of Behavioral Change. Advances in Behaviour Research and Therapy, 1, 139-161.

[4]   Baumgart. R. (2016). How Does Quantified Self Run? In Proceedings of the 22th Americas Conference on Information Systems (pp. 1-9). San Diego, CA: ACM.

[5]   Bhattacherjee, A., & Perols, J. (2008). Information Technology Continuance: A Theoretic Extension and Empirical Test. Journal of Computer Information Systems, 49, 17-26.

[6]   Cheng, Y. M. (2014). What Drives Nurses ‘Blended e-Learning Continuance Intention. Journal of Educational Technology and Society, 17, 203-215.

[7]   Chi, K. Y., Chan, K. W., & Lam, S. S. K. (2012). Do Consumer and Employees Enjoy Services Participation? Synergistic Effects of Self- and Other-Efficacy. Journal of Marketing, 76, 121-140.

[8]   Dahling, J., O’Malley, A. L., & Chau, S. L. (2015). Effects of Feedback Motives on Inquiry and Performance. Journal of Managerial Psychology, 30, 199-215.

[9]   DeNisi, A. S., & Kluger, A. N. (2000). Feedback Effectiveness: Can 360-Degree Appraisals Be Improved. Academy of Management Perspectives, 14, 129-139.

[10]   Dindo, P., & Tuinstra, J. (2010). A Class of Evolutionary Models for Participation Games with Negative Feedback. Computational Economics, 37, 267-300.

[11]   Guo, L. (2016). Quantified-Self 2.0: Using Context-Aware Services for Promoting Gradual Behaviour Change (pp. 1-18). Working Papers of Computers and Society.

[12]   Hayes, A. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis. Journal of Educational Measurement, 51, 335-337.

[13]   Ilgen, D. R., Fisher, C. D., & Taylor, M. S. (1979). Consequences of Individual Feedback on Behavior in Organizations. Journal of Applied Psychology, 64, 349-371.

[14]   Lazer, D., Alex, P., & Lada, A. (2009). Life in the Network: The Coming Age of Computational Social Science. Science, 323, 721-723.

[15]   Ledger, D., & McCaffrey, D. (2014). Inside Wearables: How the Science of Human Behavior Change Offers the Secret to Long-Term Engagement (pp. 1-17). Endeavour Partners LLC.

[16]   Li, D. J., & Zhang, Y. D. (2012). Quantified Self in the Field of Consumption: Research Review and Outlook. Foreign Economy and Management, 8, 3-17.

[17]   Li, D. J., & Zhang, Y. D. (2018a). Effect of Quantified Self and Its Influence Mechanism on Consumer Participation. Science and Technology, 31, 112-124.

[18]   Li, D. J., & Zhang, Y. D. (2018b). Why Consumers Give up: The Internal Mechanism of Quantifying the Formation of Self-Sustaining Participation Intention. Nankai Management Review, 21, 118-131.

[19]   Li, I. (2010). Personal Informatics and Context: Using Context to Reveal Factors that Affect Behavior. Journal of Ambient Intelligence and Smart Environments, 4, 71-72.

[20]   Li, L. (2012). Leadership Feedback Titer, Feedback Style and Subordinate Regulation Focus: Discussion on Interactive Influence on Subordinate Creativity. Science of Science and Technology Management, No. 5, 150-159.

[21]   Ling, Y. L., Abdullah, A. G. K., & Ismail, A. (2015). Feedback Environment and Coaching Communication in Malaysia Education Organizations. Asian Journal of Social Sciences & Humanities, 4, 66-73.

[22]   London, M. (2015). The Power of Feedback: Giving, Seeking, and Using Feedback for Performance Improvement. New York: Routledge.

[23]   Luo, X. L., & Li, M. (2017). A Preliminary Study on the Sharing and Use of Free Online Academic Resources—A Case Study of Baiduwenku. Modern Intelligence, 37, 100-105.

[24]   Lupton, D. (2014). Self-Tracking Modes: Reflexive Self-Monitoring and Data Practices. SSRN Electronic Journal, 391, 547-551.

[25]   Meng, H., Liang, Q. F., & Shi, Y. Y. (2010). Goal Orientation and the Relationship between Self-Efficacy and Subjective Well-Being. Journal of Psychology, 33, 96-99.

[26]   Miltenberger, R. G. (2011). Behavior Modification: Principles and Procedures (5th ed.). Belmont, CA: Wadsworth Cengage Learning.

[27]   Oliver, R. L. (1980). A Cognitive Model for the Antecedents and Consequences of Satisfaction. Journal of Marketing Research, 17, 460-469.

[28]   Ruckenstein, M. (2014). Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles. Societies, 4, 68-84.

[29]   Steelman, L. A., Levy, P. E., & Snell, A. F. (2004). The Feedback Environment Scale: Construct Definition, Measurement, and Validation. Educational and Psychological Measurement, 64, 165-184.

[30]   Swan, M. (2009). Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking. International Journal of Environmental Research and Public Health, 6, 492-525.

[31]   Topol, E. J. (2013). The Creative Destruction of Medicine. New York: Basic Books.

[32]   Van Berkel, N., Luo, C., Ferreira, D. et al (2015). The Curse of Quantified-Self: An Endless Quest for Answers. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 973-978). Osaka: ACM.

[33]   Woltin, K. A., & Yzerbyt, V. (2015). Regulatory Focus in Predictions about Others. Personality and Social Psychology Bulletin, 41, 379-39.

[34]   Yao, K. (2008). Review of Self-Efficacy: A New Trend in Organizational Behavior. Journal of Management, 5, 463.

[35]   Yin, M., & Li, Q. (2017). Research on the Willingness to Use Mobile APP Continuously Integrating ECT and IS Success Theory—A Case Study of Health APP. Journal of Dalian University of Technology (Social Science Edition), 38, 81-87.

[36]   Zhang, Y. D., & Li, D. J. (2018). Obstacles to Quantified Self in the Field of Consumption and Their Influencing Mechanism. Progress in Psychological Science, 15, 74-83.

[37]   Zhang, Z. T., & Li, Q. (2015). Are People Who Feel Good about Themselves More Willing to Work Hard?—The Influence of Self-Efficacy on Individuals’ Willingness to Work. Economic Management, 37, 134-144.

[38]   Zhang, Z., Wang, Y. N., Chen, X. H., & Gao, Y. (2016). Empirical Study on Factors Influencing MOOC Continuous Learning Intention—Based on Improved Expectation Confirmation Model. Research on Audio-Visual Education, 37, 30-36.

[39]   Zhu, G., Ma, L., Sunanda, S. W. et al. (2010). Consumer Adoption Model and Empirical Research Based on Social Cognition Theory. Nankai Management Review, 13, 12-21.