Skip to main content
Springer Nature Link
Log in

The Dark Side of Personalization Recommendation in Short-Form Video Applications: An Integrated Model from Information Perspective

  • Conference paper
  • First Online:
Diversity, Divergence, Dialogue (iConference 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12646))

Included in the following conference series:

Abstract

Based on the psychological reactance, this study tries to explore the dark side and grey role of the personalization recommendation system of short-form video application in understanding the discontinuance behavior. Specifically, two major depressing consequences of the personalization recommendation system are proposed, namely, privacy concerns and perceived information narrowing. Specifically, personalization recommendation system of short-form video App has significant positive influence on both privacy concern and perceived information narrowing. Besides, the empirical study shows perceived information narrowing is positively related to psychological reactance. However, personalization recommendation system does not lead to discontinuous usage behavior through privacy concerns or perceived information narrowing. Although personalization recommendation has not an indirect effect on discontinuous usage behavior, personalization recommendation has a potential risk to create psychological pressure on users, making personalized recommendations counterproductive. This study renders new insights on the dark side of the personalization recommendation system and provides practical suggestions for short-form video application providers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Nunes, P.F., Kambil, A.: Personalization? No thanks. Harv. Bus. Rev. 79(4), 32–33 (2001). https://doi.org/10.1111/1468-0440.00114

    Article  Google Scholar 

  2. Piggot, J.: Micro-Segmentation and Personalization in Information Systems in the Financial Service Industry. (2015). https://doi.org/10.13140/RG.2.1.3073.9366

    Article  Google Scholar 

  3. Lee, G., Lee, W.J.: Psychological reactance to online recommendation services. Inf. Manage. 46(8), 448–452 (2009). https://doi.org/10.1016/j.im.2009.07.005

    Article  Google Scholar 

  4. Fitzsimons, G., Lehmann, D.: Reactance to recommendations: when unsolicited advice yields contrary responses. Market. Sci. 23(1), 82–94 (2004). https://doi.org/10.1287/mksc.1030.0033

    Article  Google Scholar 

  5. Berk, M., Blank, J., Daniels, D., Schatsky, D.: Beyond the personalization myth: cost-effective alternatives to influence intent. Jupiter Research Site Technologies and Operations (2003). 2

    Google Scholar 

  6. Aguirre, E., Mahr, D., Grewal, D., Ruyter, K.D., Wetzels, M.: Unraveling the personalization paradox: the effect of information collection and trust-building strategies on online advertisement effectiveness. J. Retail. 91(1), 34–49 (2015). https://doi.org/10.1016/j.jretai.2014.09.005

    Article  Google Scholar 

  7. Chen, Q., Feng, Y.Q., Liu, L.N., Tian, X.: Understanding consumers’ reactance of online personalized advertising: a new scheme of rational choice from a perspective of negative effects. Int. J. Inf. Manage. 44(FEB), 53–64(2019). https://doi.org/10.1016/j.ijinfomgt.2018.09.001.

  8. Newell, S., Marabelli, M.: Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of ‘datification.’ J. Strat. Inf. Syst. 24(1), 3–14 (2015). https://doi.org/10.1016/j.jsis.2015.02.001

    Article  Google Scholar 

  9. Pariser, E.: The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin, London (2011)

    Google Scholar 

  10. Zhang, X., Wu, Y., Liu, S.: Exploring short-form video application addiction: socio-technical and attachment perspectives. Telemat. Inform. 44(SEP), 101–121 (2019). https://doi.org/10.1016/j.tele.2019.101243.

  11. O'Connell, C.: 24% of Users Abandon an App After One Use (2017). https://info.localytics.com/blog/24-of-users-abandon-an-app-after-one-use.

  12. Hossein, A., Rafsanjani, N., Salim, N., Aghdam, A.R., Fard, K.B.: Recommend. Syst. Rev. 3(5), 47–52 (2013)

    Google Scholar 

  13. Pu, P., Chen, L., Hu, R.: Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Model. User-Adap. Interact. 22(4–5), 317–355 (2012). https://doi.org/10.1007/s11257-011-9115-7

  14. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2011)

    Google Scholar 

  15. Yue, S., Larson, M., Hanjalic, A.: Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput. Surv. 47(1), 1–45 (2014). https://doi.org/10.1145/2556270

    Article  Google Scholar 

  16. Su, X., Khoshgoftaar, T.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009(12), 1–9 (2009). https://doi.org/10.1155/2009/421425

    Article  Google Scholar 

  17. Pimenidis, E., Polatidis, N.,Mouratidis, H.: Mobile recommender systems: identifying the major concepts. J. Inf. Sci. 165–176 (2018). https://doi.org/10.13140/RG.2.2.24011.08488

  18. Portilla, I.: Privacy concerns about information sharing as trade-off for personalized news. El profesional de la información 27(1), 19–26 (2018). https://doi.org/10.3145/epi.2018.ene.02

    Article  Google Scholar 

  19. Taddei, S., Contena, B.: Privacy, trust and control: Which relationships with online self-disclosure? Comput. Hum. Behav. 29(3), 821–826 (2013). https://doi.org/10.1016/j.chb.2012.11.022

    Article  Google Scholar 

  20. Posner, R.: The economics of privacy. Am. Econ. Rev. 71(2), 405–409 (19 81). https://doi.org/10.2139/ssrn.2580411.

  21. Mai, J.: Three models of privacy: new perspectives on informational privacy. Nordicom Rev. 37, 171–175 (2016). https://doi.org/10.1515/nor-2016-0031

    Article  Google Scholar 

  22. Youn, S.: Determinants of online privacy concern and its influence on privacy protection behaviors among young adolescents. J. Consum. Aff. 43(3), 389–418 (2009). https://doi.org/10.1111/j.1745-6606.2009.01146.x

    Article  Google Scholar 

  23. Mohamed, N., Ahmad, I.: Information privacy concerns, antecedents and privacy measure use in social networking sites: evidence from Malaysia. Comput. Hum. Behav. 28(6), 2366–2375 (2012). https://doi.org/10.1016/j.chb.2012.07.008

    Article  Google Scholar 

  24. Li, Y.: Theories in online information privacy research: a critical review and an integrated framework. Decis. Support Syst. 54(1), 471–481 (2012). https://doi.org/10.1016/j.dss.2012.06.010

    Article  Google Scholar 

  25. Sunstein, C.R.: Republic. com. Princeton University Press. Princeton (2002)

    Google Scholar 

  26. Chen, T.: Opportunities, anomalies and governance of new media under capital logic: case study of wechat subscription “Mi Meng”. In: 2nd International Symposium on Social Science and Management In-novation (SSMI 2019). Atlantis Press. https://doi.org/10.26914/c.cnkihy.2019.048893

  27. Bennett, W.L., Lyengar, S.: A new era of minimal effects? The changing foundations of political communication. J. Commun. 58(4), 707–731 (2010). https://doi.org/10.1111/j.1460-2466.2008.00410.x

    Article  Google Scholar 

  28. Sude, D.J., Pearson, G.D.H., Knobloch-Westerwick, S.: Journal pre-proof Self-expression just a click away source interactivity impacts on confirmation bias and political attitudes. Comput. Hum. Behav. 114, 2020. https://doi.org/10.1016/j.chb.2020.106571.

  29. Hundt, M., Schneider, B., Mennatallah, E.F., Daniel, A.K., Alexandra, D.: Visual analysis of geolocated echo chambers in social media. In: EuroVis 2017 Eurographics/IEEE VGTC Conference on Visualization 2017, pp. 125–128 (2017). https://doi.org/10.2312/eurp.20171185.

  30. Negroponte, N.: Being Digital. Alfred A. Knopf, New York(1995)

    Google Scholar 

  31. Chandler, T.: Why discipline strategies are bound to fail. Clear. House J. Educ. Strat. Issues Ideas 64(2), 124–126 (1990). https://doi.org/10.1080/00098655.1990.9955826

    Article  Google Scholar 

  32. Brehm, J.W.: A Theory of Psychological Reactance. Academic Press, New York (1966)

    Google Scholar 

  33. Kwon, S., Chung, N.: The moderating effects of psychological reactance and product involvement on online shopping recommendation mechanisms based on a causal map. Electron. Commerce Res. Appl. 9(6), 522–536 (2010). https://doi.org/10.1016/j.elerap.2010.04.004

    Article  MathSciNet  Google Scholar 

  34. Edwards, S., Li, H., Lee, J.: Forced exposure and psychological reactance: antecedents and consequences of the perceived intrusiveness of pop-up ads. J. Advert. 31(3), 83–95 (2002). https://doi.org/10.1080/00913367.2002.10673678

    Article  Google Scholar 

  35. Youn, S., Kim, S.: Understanding ad avoidance on Facebook: antecedents and outcomes of psychological reactance. Comput. Hum. Behav. 98, 232–244 (2019). https://doi.org/10.1016/j.chb.2019.04.025

    Article  Google Scholar 

  36. Dillard, J., Shen, L.: On the nature of reactance and its role in persuasive health communication. Commun. Monogr. 72(2), 144–168 (2005). https://doi.org/10.1080/03637750500111815

    Article  Google Scholar 

  37. Moyer-Gusé, E.: Toward a theory of entertainment persuasion: explaining the persuasive effects of entertainment-education messages. Commun. Theory 18(3), 407–425 (2008). https://doi.org/10.1111/j.1468-2885.2008.00328.x

    Article  Google Scholar 

  38. Han, K., Kim, S.: Toward more persuasive diabetes messages: effects of personal value orientation and freedom threat on psychological reactance and behavioral intention. J. Health Commun. 24(2), 95–110 (2019). https://doi.org/10.1080/10810730.2019.1581304

    Article  Google Scholar 

  39. Lee, G., Lee, J., Sanford, C.: The roles of self-concept clarity and psychological reactance in compliance with product and service recommendations. Comput. Hum. Behav. 26(6), 1481–1487 (2010). https://doi.org/10.1016/j.chb.2010.05.001

    Article  Google Scholar 

  40. Samah, N., Ali, M.: Individual differences in online personalized learning environment. Educ. Res. Rev. 6(7), 516–521 (2011). https://doi.org/10.5897/ERR.9000199

    Article  Google Scholar 

  41. Lee, S., Lee, Y., Lee, J., Park, J.: Personalized e-services: consumer privacy concern and information sharing. Soc. Behav. Pers. Int. J. 43(5), 729–740 (2015). https://doi.org/10.2224/sbp.2015.43.5.729

    Article  Google Scholar 

  42. Guo, X., Gan, X.Y.: Burst your bubbles: reflection on the formation and resolution of filter bubbles in an era of recommendation algorithm. Global Media J. 5(2), 76–90 (2018)

    Google Scholar 

  43. Rajat, K.B., Pradip, K.B., Rashmi, J.: A rule-based automated machine learning approach in the evaluation of recommender engine. Benchmark. Int. J. 27(10), 2721–2757(2020). https://doi.org/10.1108/BIJ-01-2020-0051.

  44. Nguyen, C.T.: Echo Chambers and Epistemic Bubbles. Cambridge Unniversity Press 17(2), 141–161 (2020). https://doi.org/10.1017/epi.2018.32

    Article  Google Scholar 

  45. Beheshti, A., Yakhchi, S., Mousaeirad, S., Ghafari, S.M., Goluguri, S.R., Edrisi, M.A.: Towards cognitive recommender systems. Algorithms 13(8), 12–13 (2020). https://doi.org/10.3390/a13080176

    Article  Google Scholar 

  46. Belknap, J.K., Ondrusek, G., Berg, J., Waddingham, S.: Barbiturate dependence in mice: effects of continuous vs. discontinuous drug administration. Psychopharmacology 51(2), 195–198 (1977). https://doi.org/10.1007/BF00431740.

  47. Furneaux, B., Wade, M.R.: An exploration of organizational level information systems discontinuance intentions. MIS Q. 35(3), 573–598 (2011). https://doi.org/10.1016/j.biopsycho.2011.11.004

    Article  Google Scholar 

  48. Lakshmanan, A., Krishnan, H.S.: The aha! experience: Insight and discontinuous learning in product usage. J. Market. 75(6), 105–123 (2011). https://doi.org/10.2307/41406862

    Article  Google Scholar 

  49. Zhao, L., Lu, X., Hu, Y.: a proposed theoretical model of discontinuous usage of voice-activated intelligent personal assistants (IPAs). In: PACIS (2018)

    Google Scholar 

  50. Zhao, L., Lu, Y.B., Yang, J., Zhang, S.W.: Get tired of socializing as social animal? An empirical explanation on discontinuous usage behavior in social network services. In PACIS (2015)

    Google Scholar 

  51. Luqman, A., Cao, X., Ali, A., Masood, A., Yu, L.: Empirical investigation of Facebook discontinues usage intentions based on SOR paradigm. Comput. Hum. Behav. 70(5), 544–555 (2017). https://doi.org/10.1016/j.chb.2017.01.020

    Article  Google Scholar 

  52. Wu, K.W., Huang, S.Y., Yen, D.C., Popova, I.: The effect of online privacy policy on consumer privacy concern and trust. Comput. Hum. Behav. 28(3), 889–897 (2012). https://doi.org/10.1016/j.chb.2011.12.008

    Article  Google Scholar 

  53. Naveen, F.A., Krishnan, M.S.: The personalization privacy paradox: an empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Q. 30(1), 13–28 (2006). https://doi.org/10.2307/25148715

    Article  Google Scholar 

  54. Pappas, I.O., Kourouthanassis, P.E., Giannakos, M.N., Chrissikopoulos, V.: Sense and sensibility in personalized e-commerce: how emotions rebalance the purchase intentions of persuaded customers. Psychol. Market. 34(10), 972–986 (2017). https://doi.org/10.1002/mar.21036

    Article  Google Scholar 

  55. Huang, C.D., Goo, J., Nam, K., Yoo, C.W.: Smart tourism technologies in travel planning: the role of exploration and exploitation. Inf. Manage. 54(6), 757–770 (2017). https://doi.org/10.1016/j.im.2016.11.010

    Article  Google Scholar 

  56. Maier, C., Laumer, S., Weinert, C., Weitzel, T.: The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use. Inf. Syst. J. 25(3), 275–308 (2015). https://doi.org/10.1111/isj.12068

    Article  Google Scholar 

  57. Du, J., You, J.: Consumers’ willingness to adopt personalized push under the effect of “information cocoon house”: the perspective of psychological resistance. Enterpr. Econ., 103–110(2019).

    Google Scholar 

  58. Ringle, C.M., Wende, S., Becker, J.M.: SmartPLS 3. SmartPLS GmbH, Boenningstedt (2015)

    Google Scholar 

  59. Teo, H.H., Wei, K.K., Benbasat, I.: Predicting intention to adopt interorganizational linkages: an institutional perspective. Society for Information Management and The Management Information Systems Research Center (2003). https://doi.org/10.2307/30036518.

  60. Fornell, C., Larcker, D.F.: Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Market. Res., 382–388 (1981). dhttps://doi.org/10.2307/3150980.

  61. Straub, D., Gefen, D.: Validation guidelines for IS positivist research. Commun. Assoc. Inf. Syst. 24 (2004). 10.17705/1CAIS.01324.

  62. Wang, Y., Wang, P., Zhang, L., Zhang, W.: Research on the “Information Cocoons” of content intelligent distribution platform from the perspective of network information ecological chain. Res. Library Sci. 2 (2018)

    Google Scholar 

  63. Alyson, L.Y., Anabel, Q.H.: Information revelation and internet privacy concerns on social network sites: a case study of Facebook. Communities Technol. (2009). https://doi.org/10.1145/1556460.1556499

    Article  Google Scholar 

  64. Chen, J.V., Tran, A., Nguyen, T.: Understanding the discontinuance behavior of mobile shoppers as a consequence of technostress: an application of the stress-coping theory. Comput. Human Behav. 83–93 (2019). https://doi.org/10.1016/j.chb.2019.01.022.

  65. Huang, C.K., Chen, S.H., Tang, C.P., Huang, H.Y.: A trade-off dual-factor model to investigate discontinuous intention of health app users: fFrom the perspective of information disclosure. J. Biomed. Inform. 2–10 (2019). https://doi.org/10.1016/j.jbi.2019.103302

Download references

Author information

Authors and Affiliations

  1. Sun Yat-Sen University, Guangzhou, Guangdong, China

    Jing Li, Shah Hussain & Jie Wu

  2. Jilin Agricultural Science and Technology University, Jilin, Jilin, China

    He Zhao

  3. Wuhan Institute of Technology, Wuhan, Hubei, China

    Junren Ming

Authors
  1. Jing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. He Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shah Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junren Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Wu .

Editor information

Editors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Katharina Toeppe

  2. Renmin University of China, Beijing, China

    Hui Yan

  3. The University of Hong Kong, Hong Kong, Hong Kong

    Samuel Kai Wah Chu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, J., Zhao, H., Hussain, S., Ming, J., Wu, J. (2021). The Dark Side of Personalization Recommendation in Short-Form Video Applications: An Integrated Model from Information Perspective. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12646. Springer, Cham. https://doi.org/10.1007/978-3-030-71305-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71305-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71304-1

  • Online ISBN: 978-3-030-71305-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics