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The roles of privacy, security, and dissatisfaction in affecting switching intention on messenger applications

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Abstract

The emergence of privacy issues in messenger applications influences people to switch to an alternative application. This research investigates the users' intention to switch from the old messenger application to the new one using push, pull, and mooring factors. This study applies a quantitative approach by distributing online questionnaires. The data of 1,022 respondents were processed and analyzed by the Covariance Based Structural Equation Model. The study found that among the three push factors, namely perceived privacy risk, dissatisfaction, and perceived security risk, only dissatisfaction significantly influences switching intentions. Dissatisfaction was also significant as a mediator between perceived privacy and security risks. Pull factors of network externalities, subjective norms, and alternatives attractiveness significantly influence switching intention. As mooring factors, inertia, affective commitment, and habit influenced switching intention. Findings from this study indicate that the switching intention variable has a strong R square of 67.5%. This research contributes to the literature regarding the impact of privacy and security on the intention to switch messenger applications. This research is expected to benefit industry players to implement appropriate features, keep current users, and attract new users based on alternative attractiveness factors.

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References

  1. Peng X, Zhao Y. (Chris), Zhu Q (2016) Investigating user switching intention for mobile instant messaging application: Taking WeChat as an example. Comput Human Behav 64:206–216. https://doi.org/10.1016/J.CHB.2016.06.054

  2. Sun Y, Liu D, Chen S, Wu X, Shen XL, Zhang X (2017) Understanding users’ switching behavior of mobile instant messaging applications: An empirical study from the perspective of push-pull-mooring framework. Comput Hum Behav 75:727–738. https://doi.org/10.1016/J.CHB.2017.06.014

    Article  Google Scholar 

  3. Nurhayati-Wolff H (2021) Number of smartphone users in Indonesia from 2015 to 2025. https://www.statista.com/statistics/266729/smartphone-users-in-indonesia/ (accessed May 30, 2021)

  4. We Are Social (2021) Digital 2021. https://wearesocial.com/digital-2021. Accessed 20 May 2021

  5. WhatsApp (2021) We’re updating our Terms of Service and Privacy Policy. https://faq.whatsapp.com/general/security-and-privacy/were-updating-our-terms-and-privacy-policy (accessed May 30, 2021)

  6. Dixit P (2021) WhatsApp Fueled A Global Misinformation Crisis. Now, It’s Stuck In One. https://www.buzzfeednews.com/article/pranavdixit/whatsapp-misinfo-privacy-policy (accessed May 30, 2021)

  7. Singh M (2020) Telegram, nearing 500 million users, to begin monetizing the app. https://techcrunch.com/2020/12/23/telegram-to-launch-an-ad-platform-as-it-approaches-500-million-users/? (accessed May 30, 2021)

  8. Zhou T, Lu Y (2011) Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Comput Hum Behav 27(2):883–889. https://doi.org/10.1016/J.CHB.2010.11.013

    Article  Google Scholar 

  9. Chang I-C, Liu C-C, Chen K (2014) The push, pull and mooring effects in virtual migration for social networking sites. Inf Syst J 24(4):323–346. https://doi.org/10.1111/isj.12030

    Article  Google Scholar 

  10. Bansal HS, Taylor SF, St. James Y (2005) Migrating’ to New Service Providers: Toward a Unifying Framework of Consumers’ Switching Behaviors. J Acad Mark Sci 33(1):96–115. https://doi.org/10.1177/0092070304267928

  11. Cheng S, Lee SJ, Choi B (2019) An empirical investigation of users’ voluntary switching intention for mobile personal cloud storage services based on the push-pull-mooring framework. Comput Hum Behav 92:198–215. https://doi.org/10.1016/J.CHB.2018.10.035

    Article  Google Scholar 

  12. Cerruto F, Cirillo S, Desiato D, Gambardella SM, Polese G (2022) Social network data analysis to highlight privacy threats in sharing data. J Big Data 9(19). https://doi.org/10.1186/s40537-022-00566-7

  13. Ajzen I (1991) The Theory of Planned Behavior. Organ Behav Hum Decis Process 50:179–211. https://doi.org/10.1016/0749-5978(91)90020-T

    Article  Google Scholar 

  14. Wang L, Luo X (Robert), Yang X, Qiao Z (2019) Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications. Inf Manag 56(7):103150. https://doi.org/10.1016/J.IM.2019.02.005

  15. Kim D (2018) Consumer welfare of informative messages via mobile instant messenger: A case of KakaoTalk’s Info-Talk. Telemat Inform 35(6):1643–1655. https://doi.org/10.1016/J.TELE.2018.04.010

    Article  Google Scholar 

  16. Oghuma AP, Libaque-Saenz CF, Wong SF, Chang Y (2016) An expectation-confirmation model of continuance intention to use mobile instant messaging. Telemat Inform 33(1):34–47. https://doi.org/10.1016/J.TELE.2015.05.006

    Article  Google Scholar 

  17. Asosiasi Penyedia Jaringan Internet Indonesia (2020) Laporan Survei Internet APJII 2019–2020 (Q2). https://apjii.or.id/survei. Accessed 20 May 2021

  18. Moon B (1995) Paradigms in migration research: exploring ‘moorings’ as a schema. Prog Hum Geogr 19(4):504–524. https://doi.org/10.1177/030913259501900404

    Article  Google Scholar 

  19. Singh R, Rosengren S (2020) Why do online grocery shoppers switch? An empirical investigation of drivers of switching in online grocery. J Retail Consum Serv 53:101962. https://doi.org/10.1016/J.JRETCONSER.2019.101962

    Article  Google Scholar 

  20. Al-Mashraie M, Chung SH, Jeon HW (2020) Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach. Comput Ind Eng 144:106476. https://doi.org/10.1016/J.CIE.2020.106476

    Article  Google Scholar 

  21. Jung J, Han H, Oh M (2017) Travelers’ switching behavior in the airline industry from the perspective of the push-pull-mooring framework. Tour Manag 59:139–153. https://doi.org/10.1016/J.TOURMAN.2016.07.018

    Article  Google Scholar 

  22. Zhang KZK, Cheung CMK, Lee MKO, Chen H (2008) Understanding the Blog Service Switching in Hong Kong: An Empirical Investigation. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), pp. 269–269. https://doi.org/10.1109/HICSS.2008.478

  23. Zengyan C, Yinping Y, Lim J (2009) Cyber Migration: An empirical investigation on factors that affect users' switch intentions in social networking sites. In: Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS 2009), pp 1–11. https://doi.org/10.1109/HICSS.2009.140

  24. Fei L, Bo X (2014) Do I switch? Understanding users' intention to switch between social network sites. In: Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS 2014), pp 551–560. https://doi.org/10.1109/HICSS.2014.76

  25. Hou AC, Shang R-A, Huang C-C, Wu K-L (2014) The effects of push-pull mooring on the switching model for social network sites migration. In: PACIS 2014 Proceedings, p 64. https://aisel.aisnet.org/pacis2014/64

  26. Ye C, Potter R (2011) The role of habit in post-adoption switching of personal information technologies: An empirical investigation. Commun Assoc Inf Syst 28(1):585–610. https://doi.org/10.17705/1CAIS.02835

  27. Hsieh JK, Hsieh YC, Chiu HC, Feng YC (2012) Post-adoption switching behavior for online service substitutes: A perspective of the push–pull–mooring framework. Comput Hum Behav 28(5):1912–1920. https://doi.org/10.1016/J.CHB.2012.05.010

    Article  Google Scholar 

  28. Wu K, Vassileva J, Zhao Y (2017) Understanding users’ intention to switch personal cloud storage services: Evidence from the Chinese market. Comput Hum Behav 68:300–314. https://doi.org/10.1016/J.CHB.2016.11.039

    Article  Google Scholar 

  29. Lin C-N, Wang H-Y (2017) Understanding users’ switching intentions and switching behavior on social networking sites. Aslib J Inf Manag 69(2):201–214. https://doi.org/10.1108/AJIM-08-2016-0127

    Article  Google Scholar 

  30. Ye D, Liu F, Cho D, Jia Z (2022) Investigating switching intention of e-commerce live streaming users. Heliyon 8(10):e11145. https://doi.org/10.1016/J.HELIYON.2022.E11145

    Article  Google Scholar 

  31. Chen L, Wu P, Dou Y, Wu Y (2023) Investigating senders’ switching intention to smart lockers: An extension of push-pull-mooring model. J Retail Consum Serv 74:103414. https://doi.org/10.1016/J.JRETCONSER.2023.103414

    Article  Google Scholar 

  32. Hashim KF, Tan FB (2015) The mediating role of trust and commitment on members’ continuous knowledge sharing intention: A commitment-trust theory perspective. Int J Inf Manage 35(2):145–151. https://doi.org/10.1016/J.IJINFOMGT.2014.11.001

    Article  Google Scholar 

  33. Polites GL, Karahanna E (2012) Shackled to the Status Quo: The Inhibiting Effects of Incumbent System Habit, Switching Costs, and Inertia on New System Acceptance. MIS Q 36(1):21–42. https://doi.org/10.2307/41410404

    Article  Google Scholar 

  34. Westin A (1968) Privacy and freedom. Wash Lee Law Rev 25(1):166

    Google Scholar 

  35. Hung M-C, Hwang H-G, Hsieh T-C (2007) An exploratory study on the continuance of mobile commerce: an extended expectation-confirmation model of information system use. Int J Mob Commun 5(4):409–422. https://doi.org/10.1504/IJMC.2007.012788PDF

    Article  Google Scholar 

  36. Li J et al (2020) A comparative study of international and Chinese public health emergency management from the perspective of knowledge domains mapping. Environ Health Prev Med 25(1). https://doi.org/10.1186/s12199-020-00896-z

  37. Kuo RZ (2020) Why do people switch mobile payment service platforms? An empirical study in Taiwan. Technol Soc 62:101312. https://doi.org/10.1016/J.TECHSOC.2020.101312

    Article  Google Scholar 

  38. Belanger F, Hiller JS, Smith WJ (2002) Trustworthiness in electronic commerce: the role of privacy, security, and site attributes. J Strateg Inf Syst 11(3–4):245–270. https://doi.org/10.1016/S0963-8687(02)00018-5

    Article  Google Scholar 

  39. Li CY (2018) Consumer behavior in switching between membership cards and mobile applications: The case of Starbucks. Comput Hum Behav 84:171–184. https://doi.org/10.1016/J.CHB.2017.12.042

    Article  Google Scholar 

  40. Jin X-L, Lee MKO, Cheung CMK (2010) Predicting continuance in online communities: model development and empirical test. Behav Inf Technol 29(4):383–394. https://doi.org/10.1080/01449290903398190

    Article  Google Scholar 

  41. Bateman PJ, Gray PH, Butler BS (2010) Research Note—The Impact of Community Commitment on Participation in Online Communities. Inf Syst Res 22(4). https://doi.org/10.1287/isre.1090.0265

  42. Jones MA, Mothersbaugh DL, Beatty SE (2000) Switching barriers and repurchase intentions in services. J Retail 76(2):259–274. https://doi.org/10.1016/S0022-4359(00)00024-5

    Article  Google Scholar 

  43. Samuelson W, Zeckhauser R (1988) Status quo bias in decision making. J Risk Uncertain 1:7–59. https://doi.org/10.1007/BF00055564

    Article  Google Scholar 

  44. Verplanken B, Aarts H (1999) Habit, Attitude, and Planned Behaviour: Is Habit an Empty Construct or an Interesting Case of Goal-directed Automaticity? Eur Rev Soc Psychol 10(1):101–134. https://doi.org/10.1080/14792779943000035

    Article  Google Scholar 

  45. Hsu CL, Lu HP (2004) Why do people play on-line games? An extended TAM with social influences and flow experience. Inf Manag 41(7):853–868. https://doi.org/10.1016/J.IM.2003.08.014

    Article  Google Scholar 

  46. Kothari CR (2004) Research Methodology: Methods and Techniques, 2nd Editio. New Age International Publishers, New Delhi

    Google Scholar 

  47. Santoso S (2018) Konsep Dasar dan Aplikasi SEM dengan Amos 24. Elex Media Komputindo

  48. Hair JF, Black WC, Babin BJ, Anderson RE (2014) Multivariate data analysis, 7th Edition, Pearson Education, Upper Saddle River, United States

  49. Gaskin J (2021) Causal Models. http://statwiki.gaskination.com/index.php?title=Causal_Models#Mediation (accessed May 04, 2021)

  50. Hair JJF, Black WC, Babin BJ, Anderson RE (2018) Multivariate Data Analysis, 8th Editio. Cengage, Hampshire, United Kingdom

  51. Zengyan C, Yinping Y, Lim J (2009) Cyber migration: An empirical investigation on factors that affect users? Switch intentions in social networking sites. In: Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS, pp. 1–11. https://doi.org/10.1109/HICSS.2009.140

  52. Wu CH, Kao SC, Chiu HY (2019) Determinants of discontinuous intention of attention to mobile instant message services. J Retail Consum Serv 49:219–230. https://doi.org/10.1016/J.JRETCONSER.2019.03.024

    Article  Google Scholar 

  53. Calvo-Porral C, Faíña-Medín A, Nieto-Mengotti M (2017) Satisfaction and switching intention in mobile services: Comparing lock-in and free contracts in the Spanish market. Telemat Inform 34(5):717–729. https://doi.org/10.1016/J.TELE.2016.08.022

    Article  Google Scholar 

  54. Bölen MC (2020) From traditional wristwatch to smartwatch: Understanding the relationship between innovation attributes, switching costs and consumers’ switching intention. Technol Soc 63:101439. https://doi.org/10.1016/J.TECHSOC.2020.101439

    Article  Google Scholar 

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Acknowledgements

This work is supported by Penelitian Dasar Unggulan Perguruan Tinggi (PDUPT) Grant from Ministry of Education, Culture, Research, and Technology, Republic of Indonesia with contract number: NKB-869/UN2.RST/HKP.05.00/2023.

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Authors and Affiliations

  1. Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia

    Janitra Ariena Sekarputri, Widia Resti Fitriani & Achmad Nizar Hidayanto

  2. School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia

    Sherah Kurnia

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  1. Janitra Ariena Sekarputri
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Correspondence to Widia Resti Fitriani.

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Appendix 1

Appendix 1

Variable

Items

Instruments

U: The most current used primary messenger app

Q: The alternative messenger app target

  Perceived Privacy Risk

PR1

I'm worried that other people can get my data from U

PR2

I am concerned that U may collect my activities without my permission

PR3

I am worried that U will use my data without my permission

PR4

I am worried that U will monitor my activity without my permission

  Dissatisfaction

DS1

I am not satisfied with my overall experience using U

DS2

I feel displeased if I receive messages less relevant to my needs at U

DS3

I feel some features are less relevant to my needs at U

DS4

I get a slow application response when using U due to extensive memory usage

  Perceived Security Risk

SR1

I'm worried that U does not implement security procedures to protect my stored data

SR2

I'm afraid that U does not have an effective mechanism to ensure that my data is protected against accidental/intentional changes without my permission during message transmission

SR3

I'm worried that unauthorized third parties, such as hackers, could manipulate my data stored on U

  Inertia

IN1

I will continue using U because switching to P is very difficult

IN2

I will continue to use U because I feel comfortable using it

IN3

I will continue to use U as I'm used to using U before

IN4

I will continue using U even though I know U is not the best choice

  Affective Commitment

AC1

I feel emotionally connected to U

AC2

I feel powerful ownership of U's valuable data and information (Examples: chat history, documents, and media)

AC3

I will accept most changes to the terms (including the privacy policy) to continue using U

  Habit

HB1

When I need to communicate with other people, I automatically choose U

HB2

When I need to communicate with other people, I get used to using U

HB3

When I need to communicate with others, using U is the clear/definite choice

  Switching Cost

SC1

I will spend a lot of effort and time trying to find an appropriate P

SC2

It will take me quite a while to finally decide on P

SC3

I'd have a hard time getting used to how P works

SC4

It would take a lot of time and effort for me to move the contacts from U to P

  Network Externalities

NE1

Most of my friends and coworkers use P

NE2

I feel a lot of people are already using P

NE3

I feel a lot of people will join to use P

  Subjective Norm

SN1

People who affect my behavior (Boss, professor, etc.) feel that I should switch from U to P to interact with them

SN2

People who are valuable to me (For example, family, relatives, etc.) will think I should use P to interact with them

SN3

People I communicate with frequently (For example, friends, acquaintances, etc.) hope me to use P to interact with them

  Alternative Attractiveness

AA1

If I want to switch from U, there are other exciting messenger apps to choose

AA2

I would be delighted with P's features and services

AA3

I might like the P more than the U

  Switching Intention

SI1

I'm considering starting to increase P uptime and reduce U uptime

SI2

I would consider moving to P soon

SI3

I have a high probability of moving to P

SI4

I plan to move to P

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Sekarputri, J.A., Fitriani, W.R., Hidayanto, A.N. et al. The roles of privacy, security, and dissatisfaction in affecting switching intention on messenger applications. Multimed Tools Appl 83, 45625–45651 (2024). https://doi.org/10.1007/s11042-023-17466-4

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