The positive pursuit of healthy lifestyle and the development of technology both promote consumers to use related tracking tools to collect, manage, and reflect self data to better understand their behavior, and interact with the environment. From individual physical function and state to consumption behavior and habits, data is gradually infiltrating consumers’ lives, affecting consumers’ original knowledge production modes and cognitive framework. In light of this, scholars begin to pay attention to quantified cognition and behavior of consumers, and put forward the concept of quantified self. On the basis of the definition of quantified self, existing studies have explored the types and effects of quantified self, analyzing the participation motivation and periodical process of quantified self, and paying close attention to the changes in consumers’ precise consumption styles brought by it. Through a systematic review of related literature, this paper analyzes the essence of quantified self, making it clear that quantified self is the process in which consumers collect self data and acquire their own knowledge through the quantified data. During the process, consumers can realize the transformation from cognitive mode based on practical experience to cognitive mode based on quantified data. According to different driving modes and utility orientation of objects while participating in quantified self, quantified self can be divided into four types, namely stimulating-system-driven quantified self, defensive-system-driven quantified self, stimulating-user-dominated quantified self, and defensive-user-dominated quantified self. As previously unknown aspects of life become precisely monitored, quantified self drives consumers to re-examine and think about themselves, so as to enhance the precise control of consumers on their own, as well as make consumers’ decision-making more rational, and enable consumers to optimize and change their behavior in a data-driven way. The optimization of self behavior and improvement of ability, the self control and self regulation, or the exploration of knowledge and the pursuit of self enjoyment drive consumers to participate in quantified self. However, the development of information technology and the formation of social platforms have promoted the flow of data among community members, making quantified self more collective and more social. Consumers may participate in quantified self for gaining membership in a particular community or show their personalities in the community. From the perspective of self control and self optimization motivation, related studies have divided the stages of quantified self. Most studies emphasize that quantified self is a process of tracking, reflection and action, but ignore the maintenance stage behind reflection and action. With the implementation and popularization of quantified self, new business logic is being established. Consumers are increasingly satisfying their personalized and precise needs based on the analysis of self quantified data and intervention of self behavior. Consumers have more decision-making rationality and autonomy than before; enterprises can carry out the customer segmentation and the precise marketing by consumers’ quantified data, as well as better design, develop new products and monitor product performance, managing customer relationship effectively. However, quantified self is a long-term process for consumers to form self knowledge and behavioral habits based on quantified data, and the continuous participation of consumers is a key issue that needs to be considered in the application of quantified self in the consumer field. Analysis of quantified self in academia is still at the initial stage, so future research should improve and explore the connotation of related concepts from multiple perspectives, exploring the internal and influencing mechanisms of quantified self, paying attention to consumers’ continuous participation in quantified self, analyzing consumers’ psychological effects under different feedback types and presentation modes of quantified data, and exploring the paths of enterprises’ practice and consumers’ education under the background of quantified self.
/ Journals / Foreign Economics & Management
Foreign Economics & Management
LiZengquan, Editor-in-Chief
ZhengChunrong, Vice Executive Editor-in-Chief
YinHuifang HeXiaogang LiuJianguo, Vice Editor-in-Chief
Quantified Self in the Field of Consumption: A Literature Review and Prospects
Foreign Economics & Management Vol. 40, Issue 01, pp. 3 - 17 (2018) DOI:10.16538/j.cnki.fem.2018.01.001
Summary
References
Summary
Keywords
[1] 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.
[2] Ancker J S, Witteman H O, Hafeez B, et al. “You get reminded you’re a sick person”: Personal data tracking and patients with multiple chronic conditions[J]. Journal of Medical Internet Research, 2015, 17(8): e202.
[3] Azar B. QnAs with Davis Masten and Peter Zandan[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(5): 1662-1663.
[4] Barcena M B, Wueest C, Lau H. How safe is your quantified self[M]. Mountain View, CA: Symantech, 2014.
[5] Beer D. The biopolitics of biometrics: An interview with Btihaj Ajana[J]. Theory Culture & Society, 2014, 31(7-8): 329-336.
[6] Bloch M. Truth and sight: Generalizing without universalizing[J]. Journal of the Royal Anthropological Institute, 2008, 14(S1): S22-S32.
[7] Bloem S, Stalpers J. Subjective experienced health as a driver of health care behavior[R]. Nyenrode Research Paper No. 12-01, 2012.
[8] Buchanan K, Lockton D. Understanding human connectivity and the Quantified Self[R]. Working Paper of the Sustainable Society Network+, 2015: 1-7.
[9] Choe E K, Lee N B, Lee B, et al. Understanding quantified-selfers’ practices in collecting and exploring personal data[A]. Proceedings of the SIGCHI conference on human factors in computing systems[C]. Toronto, Ontario, Canada: ACM, 2014: 1143-1152.
[10] Choe Y, Fesenmaier D R. The quantified traveler: Implications for smart tourism development[A]. Xiang Z, Fesenmaier D R. Analytics in smart tourism design: Concepts and methods[C]. Cham: Springer, 2016: 65-71.
[11] Clawson J, Pater J A, Miller A D, et al. No longer wearing: Investigating the abandonment of personal health-tracking technologies on craigslist[A]. Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing[C]. Osaka: ACM, 2015: 647-658.
[12] 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.
[13] De Maeyer C, Jacobs A. Sleeping with technology-designing for personal health[A]. Proceedings of the 2013 Medicine 2.0 Conference[C]. Toronto: JMIR Publications, 2013: 11-16.
[14] Elsden C, Kirk D, Selby M, et al. Beyond personal informatics: Designing for experiences with data[A]. Proceedings of the 33rd annual ACM conference extended abstracts on human factors in computing systems[C]. Seoul: ACM, 2015: 2341-2344.
[15] Etkin J. The hidden cost of personal quantification[J]. Journal of Consumer Research, 2016, 42(6): 967-984.
[16] Fawcett T. Mining the quantified self: Personal knowledge discovery as a challenge for data science[J]. Big Data, 2015, 3(4): 249-266.
[17] Fogg B J. A behavior model for persuasive design[A]. Proceedings of the 4th international conference on persuasive technology[C]. Claremont, California: ACM, 2009: 1-7.
[18] Guo L. Quantified-self 2.0: Using context-aware services for promoting gradual behaviour change[R]. Working Papers of Computers and Society, 2016: 1-18.
[19] Haddadi H, Ofli F, Mejova Y, et al. 360-degree quantified self[A]. Proceedings of the 2015 international conference on healthcare informatics[C]. Texas: ACM, 2015: 587-592.
[20] Hammarfelt B, de Rijcke S, Rushforth A D. Quantified academic selves: The gamification of research through social networking services[J]. Information Research, 2016, 21(2): 1-9.
[21] Henning K J. Overview of syndromic surveillance: What is syndromic surveillance?[J]. MMWR Morbidity & Mortality Weekly Report, 2004, 53(S1): 5-11.
[22] Ledger D, McCaffrey D. Inside wearables: How the science of human behavior change offers the secret to long-term engagement[M]. Endeavour Partners LLC, 2014: 1-17.
[23] Li I, Dey A, Forlizzi J. A stage-based model of personal informatics systems[A]. Proceedings of the 28th annual ACM conference on human factors in computing systems[C]. Atlanta, Georgia: ACM, 2010: 557-566.
[24] Li I. Personal informatics and context: Using context to reveal factors that affect behavior[J]. Journal of Ambient Intelligence and Smart Environments, 2012, 4(1): 71-72.
[25] Lupton D. Quantifying the body: Monitoring and measuring health in the age of mHealth technologies[J]. Critical Public Health, 2013, 23(4): 393-403.
[26] Lupton D. Self-tracking modes: Reflexive self-monitoring and data practices[J]. SSRN Electronic Journal, 2014a, 391(1): 547-551.
[27] Lupton D. Self-tracking cultures: Towards a sociology of personal informatics[A]. Proceedings of the 26th Australian computer-human interaction conference on designing futures[C]. Sydney: ACM, 2014b: 77-86.
[28] Marcengo A, Rapp A. Visualization of human behavior data: The Quantified Self[A]. Huang M L, Huang W D. Innovative approaches of data visualization and visual analytics[C]. Hershey: IGI Global, 2014, 1: 236-265.
[29] Miltenberger R G. Behavior modification: Principles and procedures[M]. 5th ed. Belmont, CA: Wadsworth Cengage Learning, 2011.
[30] Moore P, Robinson A. The quantified self: What counts in the neoliberal workplace[J]. New Media & Society, 2016, 18(11): 2774-2792.
[31] Morschheuser B S, Rivera-Pelayo V, Mazarakis A, et al. Interaction and reflection with quantified self and gamification: An experimental study[J]. Journal of Literacy and Technology, 2014, 15(2): 136-156.
[32] Orenga-Roglá S, Chalmeta R. Social customer relationship management: Taking advantage of Web 2.0 and big data technologies[J]. SpringerPlus, 2016, 5: 1462.
[33] Pantzar M, Ruckenstein M. The heart of everyday analytics: Emotional, material and practical extensions in self-tracking market[J]. Consumption Markets & Culture, 2015, 18(1): 92-109.
[34] Petersen R R, Lukas A, Wiil U K. QS Mapper: A transparent data aggregator for the quantified self: Freedom from particularity using two-way mappings[A]. Proceedings of the 10th international joint conference on software technologies[C]. Colmar: IEEE, 2015: 1-8.
[35] Prochaska J O, Velicer W F. The transtheoretical model of health behavior change[J]. American Journal of Health Promotion, 1997, 12(1): 38-48.
[36] Robertsson L. Quantified self: An overview & the development of a universal tracking application[D]. Sweden: Department of Computing Science, Umea University, 2014.
[37] Rooksby J, Rost M, Morrison A, et al. Personal tracking as lived informatics[A]. Proceedings of the SIGCHI conference on human factors in computing systems[C]. Toronto: ACM, 2014: 1163-1172.
[38] Ruckenstein M. Visualized and interacted life: Personal analytics and engagements with data doubles[J]. Societies, 2014, 4(1): 68-84.
[39] Ruckenstein M, Pantzar M. Beyond the quantified self: Thematic exploration of a dataistic paradigm[J]. New Media & Society, 2017, 19(3): 401-418.
[40] Sharon T, Zandbergen D. From data fetishism to quantifying selves: Self-tracking practices and the other values of data[J]. New Media & Society, 2017, 19(11): 1695-1709.
[41] 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.
[42] Shull P B, Jirattigalachote W, Hunt M A, et al. Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention[J]. Gait & Posture, 2014, 40(1): 11-19.
[43] Sjöklint M, Constantiou I D, Trier M. The complexities of self-tracking: An inquiry into user reactions and goal attainment[J]. Social Science Electronic Publishing, 2015, 13(6): 603-611.
[44] Swan M. Emerging patient-driven health care models: An examination of health social networks, consumer personalized medicine and quantified self-tracking[J]. International Journal of Environmental Research and Public Health, 2009, 6(2): 492-525.
[45] Swan M. Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen[J]. Journal of Personalized Medicine, 2012, 2(3): 93-118.
[46] Swan M. The quantified self: Fundamental disruption in big data science and biological discovery[J]. Big Data, 2013, 1(2): 85-99.
[47] Swan M. Connected car: Quantified self becomes quantified car[J]. Journal of Sensor and Actuator Networks, 2015, 4(1): 2-29.
[48] Taylor S E. Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis[J]. Psychological Bulletin, 1991, 110(1): 67-85.
[49] Thaler R H, Sunstein C R. Nudge: Improving decisions about health, wealth, and happiness[M]. New Haven: Yale University Press, 2008.
[50] Till C. Exercise as labour: Quantified self and the transformation of exercise into labour[J]. Societies, 2014, 4(3): 446-462.
[51] Trusov M, Ma L Y, Jamal Z. Crumbs of the cookie: User profiling in customer-base analysis and behavioral targeting[J]. Marketing Science, 2016, 35(3): 405-426.
[52] Van Berkel N, Luo C, Ferreira D, et al. The curse of quantified-self: An endless quest for answers[A]. Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing[C]. Osaka: ACM, 2015: 973-978.
[53] Verhoef P C, Venkatesan R, McAlister L, et al. CRM in data-rich multichannel retailing environments: A review and future research directions[J]. Journal of Interactive Marketing, 2010, 24(2): 121-137.
[54] Whitson J. Gaming the quantified self[J]. Surveillance & Society, 2013, 11(1-2): 163-176.
[55] Wolf G. Know thyself: Tracking every facet of life, from sleep to mood to pain, 24/7/365[R]. Wired Magazine, 2009.
[56] Yang Y, Lee H, Gurrin C. Visualizing lifelog data for different interaction platforms[A]. Proceedings of the CHI '13 extended abstracts on human factors in computing systems[C]. Paris: ACM, 2013: 1785-1790.
Cite this article
Li Dongjin, Zhang Yudong. Quantified Self in the Field of Consumption: A Literature Review and Prospects[J]. Foreign Economics & Management, 2018, 40(1): 3–17.
Export Citations as:
For
ISSUE COVER
RELATED ARTICLES