USERS’ PERCEPTION TOWARDS SMARTPHONE APPLICATIONS IN DELHI NCR: DEMOGRAPHIC PERSPECTIVE

Authors

  • Dr. Neelam Jain Prof., IMSAR, MDU, Rohtak
  • Vaibhav Jindal Research Scholar, IMSAR, MDU, Rohtak

DOI:

https://doi.org/10.29121/shodhkosh.v4.i2.2023.5025

Keywords:

Smartphone Perception, Communication Applications, User Attitude, Demographic Influence

Abstract [English]

This study investigates the perception of smartphone users in Delhi NCR towards communication applications, focusing on their attitudes and usage patterns. The research employed a survey method with a sample of 200 respondents aged 18-65. Data were analyzed using Chi-square tests to examine associations between demographic variables (age, gender, income, education) and perceptions. Results indicate that users in Delhi NCR generally hold a positive and receptive attitude towards smartphone applications. Demographic factors such as age, gender, income, and education play a significant role in shaping user perceptions. Younger individuals and those with higher educational backgrounds tend to demonstrate greater openness and comfort with these digital tools, indicating a higher level of digital engagement. Similarly, users with higher income levels are more likely to appreciate the benefits of smartphone apps, possibly due to better access and exposure. Gender differences are also observed, with females showing more favorable attitudes towards communication applications, suggesting a growing alignment of smartphone technology with academic and professional aspirations. Overall, the findings emphasize that user perception is deeply influenced by personal and socioeconomic factors, offering valuable insights for developers, educators, and policymakers aiming to expand the reach and impact of mobile applications in urban India.

References

Arora, A., Malik, A., & Chawla, R. (2020). Understanding mobile app adoption in India: A study of users in the National Capital Region. Journal of Mobile Technology & Communication Research, 8(4), 330–340.

Busch, P. A., & McCarthy, S. (2019). Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area. Computers in Human Behavior, 114, 106414. https://doi.org/10.1016/j.chb.2020.106414

Cha, S.-S., & Seo, B.-K. (2019). Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use. Health Psychology Open, 6(1), 1–15. https://doi.org/10.1177/2055102919843359

Gupta, R., & Singh, A. (2022). Smartphone use among Indian youth: Patterns, perceptions, and problems. Journal of Youth and Media Studies, 4(2), 55–68.

Keusch, F., Antoun, C., Couper, M. P., & Kreuter, F. (2020). Perceptions of data collection through mobile devices: A comparison of smartphone users’ attitudes toward privacy and research participation. PLOS ONE, 15(4), e0232520. https://doi.org/10.1371/journal.pone.0232520

Kemp, S. (2021). Digital 2021: India. DataReportal. https://datareportal.com/reports/digital-2021-india

Kushlev, K., Proulx, J. D., & Dunn, E. W. (2021). Smartphones reduce smiles between strangers. Computers in Human Behavior, 114, 106561. https://doi.org/10.1016/j.chb.2020.106561

Montag, C., Wegmann, E., Sariyska, R., Demetrovics, Z., & Brand, M. (2020). How to overcome taxonomical problems in the study of Internet use disorders and what to do with “smartphone addiction”? Journal of Behavioral Addictions, 9(4), 908–914. https://doi.org/10.1556/2006.8.2019.59

Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The media and technology usage and attitudes scale: An empirical investigation. Computers in Human Behavior, 29(6), 2501–2511. https://doi.org/10.1016/j.chb.2013.06.006

Triplett, R. E. (1994). Consumer perception and product performance. Journal of Consumer Research, 21(3), 543–547.

Van Deursen, A. J. A. M., Bolle, C. L., Hegner, S. M., & Kommers, P. A. M. (2015). Modeling habitual and addictive smartphone behavior: The role of smartphone usage types, emotional intelligence, social stress, self-regulation, age, and gender. Computers in Human Behavior, 45, 411–420. https://doi.org/10.1016/j.chb.2014.12.039

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

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Published

2023-07-19

How to Cite

Jain, N., & Jindal, V. (2023). USERS’ PERCEPTION TOWARDS SMARTPHONE APPLICATIONS IN DELHI NCR: DEMOGRAPHIC PERSPECTIVE. ShodhKosh: Journal of Visual and Performing Arts, 4(2), 4326–4332. https://doi.org/10.29121/shodhkosh.v4.i2.2023.5025