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Understanding User Trust in Different Recommenders and Smartphone Applications

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Wireless Mobile Communication and Healthcare (MobiHealth 2022)

Abstract

In recent times, we are witnessing rapid growth in smartphone applications due to various types of services ranging from bank transactions to health and well-being monitoring, that these apps are providing. However, most often these apps suffer from low user trust and that directly impacts the utility and adherence to the apps. Thereby, it is crucial to understand the user trust in different types of apps and recommenders to improve the utility and adherence of the apps. In this work, we perform a detailed investigation of user trust in four major types of apps, including health apps, payment apps, news apps, and gaming apps, and four major groups of recommenders, i.e., friends, family members, external recommenders (healthcare providers, news channels, or advertisements), and no recommender. From our detailed analysis of a study with 60 smartphone users with different backgrounds, we find a higher trust in health apps and payment apps when recommended by healthcare providers or physicians, and friends or family members. In general, we do not find any significant differences among users with different backgrounds. Thereby, we recommend considering specific groups of recommenders and their recommended features while developing relevant apps to achieve higher utility and adherence.

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References

  1. What are the different types of mobile apps? https://blog.duckma.com/en/types-of-mobile-apps/. Accessed March

  2. Compound annual growth rate (CAGR). https://bit.ly/3jHm8OT. Accessed March 2022

  3. Medical apps: Improving healthcare on a global scale. https://bit.ly/3KPSRgO. Accessed March 2022

  4. Mobile app download statistics & usage statistics (2022). https://bit.ly/37nVaJF. Accessed March 2022

  5. Mobile app marketing insights: How consumers really find and use your apps. https://bit.ly/3JNxRGv. Accessed March 2022

  6. Mobile application market size, share & trends analysis. https://bit.ly/3vlV7pR. Accessed March 2022

  7. Mobile apps have a short half life; use falls sharply after first six months. https://bit.ly/3JOnIJv. Accessed March 2022

  8. Al Amin, M.T., Barua, S., Vhaduri, S., Rahman, A.: Load aware broadcast in mobile ad hoc networks. In: IEEE International Conference on Communications (ICC) (2009)

    Google Scholar 

  9. Chang, T.R., Kaasinen, E., Kaipainen, K.: What influences users’ decisions to take apps into use? A framework for evaluating persuasive and engaging design in mobile apps for well-being. In: Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia, pp. 1–10 (2012)

    Google Scholar 

  10. Chen, C.Y., Vhaduri, S., Poellabauer, C.: Estimating sleep duration from temporal factors, daily activities, and smartphone use. In: IEEE Computer Society Computers, Software, and Applications Conference (COMPSAC) (2020)

    Google Scholar 

  11. Cheung, W., Vhaduri, S.: Context-dependent implicit authentication for wearable device users. In: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (2020)

    Google Scholar 

  12. Cheung, W., Vhaduri, S.: Continuous authentication of wearable device users from heart rate, gait, and breathing data. In: IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) (2020)

    Google Scholar 

  13. Dibbo, S.V., Cheung, W., Vhaduri, S.: On-Phone CNN Model-based Implicit Authentication to Secure IoT Wearables. In: Nayyar, A., Paul, A., Tanwar, S. (eds.) The Fifth International Conference on Safety and Security with IoT. EAI/Springer Innovations in Communication and Computing, pp. 19-34. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-94285-4_2

  14. Dibbo, S.V., Kim, Y., Vhaduri, S.: Effect of noise on generic cough models. In: IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN) (2021)

    Google Scholar 

  15. Dibbo, S.V., Kim, Y., Vhaduri, S., Poellabauer, C.: Visualizing college students’ geo-temporal context-varying significant phone call patterns. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 381–385. IEEE (2021)

    Google Scholar 

  16. Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J., Sadeh, N.: Why people hate your app: making sense of user feedback in a mobile app store. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284 (2013)

    Google Scholar 

  17. Humbani, M., Wiese, M.: An integrated framework for the adoption and continuance intention to use mobile payment apps. Int. J. Bank Mark. 37, 646–664 (2019)

    Article  Google Scholar 

  18. Kim, Y., Vhaduri, S., Poellabauer, C.: Understanding college students’ phone call behaviors towards a sustainable mobile health and wellbeing solution. In: International Conference on Systems Engineering (2020)

    Google Scholar 

  19. Liccardi, I., Pato, J., Weitzner, D.J.: Improving user choice through better mobile apps transparency and permissions analysis. J. Priv. Confidentiality 5(2), 1–55 (2014)

    Google Scholar 

  20. Muratyan, A., Cheung, W., Dibbo, S.V., Vhaduri, S.: Opportunistic multi-modal user authentication for health-tracking IoT wearables. In: Nayyar, A., Paul, A., Tanwar, S. (eds.) The Fifth International Conference on Safety and Security with IoT. EAI/Springer Innovations in Communication and Computing. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-94285-4_1

    Chapter  Google Scholar 

  21. Natarajan, T., Balasubramanian, S.A., Kasilingam, D.L.: Understanding the intention to use mobile shopping applications and its influence on price sensitivity. J. Retail. Consum. Serv. 37, 8–22 (2017)

    Article  Google Scholar 

  22. Sharmin, M., et al.: Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 505–516 (2015)

    Google Scholar 

  23. Tao, K., Edmunds, P., et al.: Mobile apps and global markets. Theor. Econ. Lett. 8(08), 1510 (2018)

    Article  Google Scholar 

  24. Vaghefi, I., Tulu, B., et al.: The continued use of mobile health apps: insights from a longitudinal study. JMIR Mhealth Uhealth 7(8), e12983 (2019)

    Google Scholar 

  25. Vagrani, A., Kumar, N., Ilavarasan, P.V.: Decline in mobile application life cycle. Procedia Comput. Sci. 122, 957–964 (2017)

    Article  Google Scholar 

  26. Vhaduri, S.: Nocturnal cough and snore detection using smartphones in presence of multiple background-noises. In: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS) (2020)

    Google Scholar 

  27. Vhaduri, S., Ali, A., Sharmin, M., Hovsepian, K., Kumar, S.: Estimating drivers’ stress from GPS traces. In: International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) (2014)

    Google Scholar 

  28. Vhaduri, S., Brunschwiler, T.: Towards automatic cough and snore detection. In: IEEE International Conference on Healthcare Informatics (ICHI) (2019)

    Google Scholar 

  29. Vhaduri, S., Dibbo, S.V., Chen, C.Y.: Predicting a user’s demographic identity from leaked samples of health-tracking wearables and understanding associated risks. In: 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). IEEE (2022)

    Google Scholar 

  30. Vhaduri, S., Dibbo, S.V., Chen, C.Y., Poellabauer, C.: Predicting next call duration: a future direction to promote mental health in the age of lockdown. In: IEEE Computer Society Computers, Software, and Applications Conference (COMPSAC) (2021)

    Google Scholar 

  31. Vhaduri, S., Dibbo, S.V., Cheung, W.: HIAuth: a hierarchical implicit authentication system for IoT wearables using multiple biometrics. IEEE Access 9, 116395–116406 (2021)

    Article  Google Scholar 

  32. Vhaduri, S., Dibbo, S.V., Kim, Y.: Deriving college students’ phone call patterns to improve student life. IEEE Access 9, 96453–96465 (2021)

    Article  Google Scholar 

  33. Vhaduri, S., Munch, A., Poellabauer, C.: Assessing health trends of college students using smartphones. In: IEEE Healthcare Innovation Point-of-Care Technologies Conference (HI-POCT) (2016)

    Google Scholar 

  34. Vhaduri, S., Poellabauer, C.: Design and implementation of a remotely configurable and manageable well-being study. In: Leon-Garcia, A., et al. (eds.) SmartCity 360 2015–2016. LNICST, vol. 166, pp. 179–191. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33681-7_15

    Chapter  Google Scholar 

  35. Vhaduri, S., Poellabauer, C.: Cooperative discovery of personal places from location traces. In: International Conference on Computer Communication and Networks (ICCCN) (2016)

    Google Scholar 

  36. Vhaduri, S., Poellabauer, C.: Human factors in the design of longitudinal smartphone-based wellness surveys. In: IEEE International Conference on Healthcare Informatics (ICHI) (2016)

    Google Scholar 

  37. Vhaduri, S., Poellabauer, C.: Design factors of longitudinal smartphone-based health surveys. J. Healthc. Inform. Res. 1(1), 52–91 (2017)

    Article  Google Scholar 

  38. Vhaduri, S., Poellabauer, C.: Towards reliable wearable-user identification. In: 2017 IEEE International Conference on Healthcare Informatics (ICHI) (2017)

    Google Scholar 

  39. Vhaduri, S., Poellabauer, C.: Wearable device user authentication using physiological and behavioral metrics. In: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (2017)

    Google Scholar 

  40. Vhaduri, S., Poellabauer, C.: Biometric-based wearable user authentication during sedentary and non-sedentary periods. International Workshop on Security and Privacy for the Internet-of-Things (IoTSec) (2018)

    Google Scholar 

  41. Vhaduri, S., Poellabauer, C.: Hierarchical cooperative discovery of personal places from location traces. IEEE Trans. Mob. Comput. 17(8), 1865–1878 (2018)

    Article  Google Scholar 

  42. Vhaduri, S., Poellabauer, C.: Impact of different pre-sleep phone use patterns on sleep quality. In: IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN) (2018)

    Google Scholar 

  43. Vhaduri, S., Poellabauer, C.: Opportunistic discovery of personal places using smartphone and fitness tracker data. In: IEEE International Conference on Healthcare Informatics (ICHI) (2018)

    Google Scholar 

  44. Vhaduri, S., Poellabauer, C.: Multi-modal biometric-based implicit authentication of wearable device users. IEEE Trans. Inf. Forensics Secur. 14(12), 3116–3125 (2019)

    Article  Google Scholar 

  45. Vhaduri, S., Poellabauer, C.: Summary: multi-modal biometric-based implicit authentication of wearable device users. arXiv preprint arXiv:1907.06563 (2019)

  46. Vhaduri, S., Poellabauer, C.: Opportunistic discovery of personal places using multi-source sensor data. IEEE Trans. Big Data 7(2), 383–396 (2021)

    Article  Google Scholar 

  47. Vhaduri, S., Poellabauer, C., Striegel, A., Lizardo, O., Hachen, D.: Discovering places of interest using sensor data from smartphones and wearables. In: IEEE Ubiquitous Intelligence & Computing (UIC) (2017)

    Google Scholar 

  48. Vhaduri, S., Prioleau, T.: Adherence to personal health devices: a case study in diabetes management. In: EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2020)

    Google Scholar 

  49. Vhaduri, S., Simhadri, S.: Understanding user concerns and choice of app architectures in designing audio-based mHealth apps. Smart Health J. 26, 100341 (2022)

    Google Scholar 

  50. Vhaduri, S., Van Kessel, T., Ko, B., Wood, D., Wang, S., Brunschwiler, T.: Nocturnal cough and snore detection in noisy environments using smartphone-microphones. In: IEEE International Conference on Healthcare Informatics (ICHI) (2019)

    Google Scholar 

  51. Williams, G., Mahmoud, A.: Modeling user concerns in the app store: a case study on the rise and fall of Yik Yak. In: 2018 IEEE 26th International Requirements Engineering Conference (RE), pp. 64–75. IEEE (2018)

    Google Scholar 

  52. Yang, Y., Liu, Y., Li, H., Yu, B.: Understanding perceived risks in mobile payment acceptance. Industr. Manage. Data Syst. 115, 253–269 (2015)

    Article  Google Scholar 

  53. Zhu, H., Xiong, H., Ge, Y., Chen, E.: Mobile app recommendations with security and privacy awareness. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 951–960 (2014)

    Google Scholar 

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Authors and Affiliations

  1. Purdue University, West Lafayette, IN, 47907, USA

    Siva Simhadri & Sudip Vhaduri

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  1. Siva Simhadri
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  2. Sudip Vhaduri
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Correspondence to Sudip Vhaduri .

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Editors and Affiliations

  1. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    António Cunha

  2. University of Beira Interior, Covilha, Portugal

    Nuno M. Garcia

  3. Ossietzky Universität Oldenburg, Oldenburg, Niedersachsen, Germany

    Jorge Marx Gómez

  4. University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Sandra Pereira

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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Simhadri, S., Vhaduri, S. (2023). Understanding User Trust in Different Recommenders and Smartphone Applications. In: Cunha, A., M. Garcia, N., Marx Gómez, J., Pereira, S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-32029-3_29

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  • DOI: https://doi.org/10.1007/978-3-031-32029-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32028-6

  • Online ISBN: 978-3-031-32029-3

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