Conceptualization of smartphone usage and feature preferences among various demographics


Smartphones have seen an exponential growth in their utility during the past decade. The study of mobile phone usage patterns as well as mobile phone feature preferences has been given due focus by the research community in the past. However, a comprehensive study targeting different population demographics is required for the assessment and evaluation of smartphone features, both from the consumer point of view and the vender’s perspective. This study aims to find correlation between the smartphone usage patterns and its features. The user demographics considered in this work include age, gender, marital status, education, profession, income level and geographical location of the smartphone users. The smartphone usage data for this research is collected through user surveys. The survey sought information regarding three key aspects of this study: the usage pattern of the smartphone users, their feature preferences and their demographic details. The key statistical parameters computed in this work include the mean and standard deviation of the replies given on the Likert scale against the usage of the features mentioned in the questionnaire. A normality test is performed by measuring the skewness and kurtosis of the collected dataset. The results for both kurtosis and skewness are found to be within the acceptable range, suggesting the data to be normally distributed. One-way ANOVA (analysis of variance) test is performed to identify the significant difference within the groups in relation to the usage of smartphone features. Finally, exploratory factor analysis (EFA) is performed to identify the structure of the data and the underlying relationships. Singles, urban users, younger users, college and university graduates, professional degree holders and users with high-income level formed the group using the smartphone more frequently than their counterparts. Moreover, they were also found to be using advanced features like, social apps, Internet, camera and picture viewing on a daily basis. This work has identified smartphone features like disk space, RAM, device security camera and battery talk time as key preferences for a specific group of consumer.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.


  1. 1.

    Asongu, S.A., Odhiambo, N.M.: Mobile banking usage, quality of growth, inequality and poverty in developing countries. Inf. Dev. 35(2), 303–318 (2019)

    Article  Google Scholar 

  2. 2.

    Twenge, J.M., Martin, G.N., Spitzberg, B.H.: Trends in US Adolescents’ media use, 1976–2016: the rise of digital media, the decline of TV, and the (near) demise of print. Psychol. Pop. Media Cult. 8(4), 329 (2019)

    Article  Google Scholar 

  3. 3.

    Lynn, G.S., Morone, J.G., Paulson, A.S.: Marketing and discontinuous innovation: the probe and learn process. Calif. Manag. Rev. 38(3), 8–37 (1996)

    Article  Google Scholar 

  4. 4.

    Turner, L.D., Whitaker, R.M., Allen, S.M., Linden, D.E., Tu, K., Li, J., Towsley, D.: Evidence to support common application switching behaviour on smartphones. R. Soc. Open Sci. 6(3), 190018 (2019)

    Article  Google Scholar 

  5. 5.

    Muzellec, L., O'Raghallaigh, E.: Mobile technology and its impact on the consumer decision-making journey: how brands can capture the mobile-driven “Ubiquitous” moment of truth. J. Advert. Res. 58(1), 12–15 (2018)

    Article  Google Scholar 

  6. 6.

    Waverman, L., Meschi, M., Fuss, M.: The impact of telecoms on economic growth in developing countries. The Vodafone policy paper series 2(3), 10–24 (2005)

    Google Scholar 

  7. 7.

    Gong, X., Lee, M.K., Liu, Z., Zheng, X.: Examining the role of tie strength in users’ continuance intention of second-generation mobile instant messaging services. Inf. Syst. Front. 2018, 1–22 (2018)

    Google Scholar 

  8. 8.

    Haverila, M.: What do we want specifically from the cell phone? An age related study. Telemat. Inf. 29(1), 110–122 (2012)

    Article  Google Scholar 

  9. 9.

    Haverila, M.: Cell phone usage and broad feature preferences: a study among Finnish undergraduate students. Telemat. Inf. 30(2), 177–188 (2013)

    Article  Google Scholar 

  10. 10.

    Hong, S.J., Thong, J.Y., Moon, J.Y., Tam, K.Y.: Understanding the behavior of mobile data services consumers. Inf. Syst. Front. 10(4), 431 (2008)

    Article  Google Scholar 

  11. 11.

    Kushlev, K., Hunter, J.F., Proulx, J., Pressman, S.D., Dunn, E.: Smartphones reduce smiles between strangers. Comput. Hum. Behav. 91, 12–16 (2019)

    Article  Google Scholar 

  12. 12.

    Palen L, Salzman M (2002) Voice-mail diary studies for naturalistic data capture under mobile conditions. In Proceedings of the 2002 ACM conference on Computer supported cooperative work. p 87–95.

  13. 13.

    Swallow D, Blythe M, Wright P (2005) Grounding experience: relating theory and method to evaluate the user experience of smartphones. In Proceedings of the 2005 annual conference on European association of cognitive ergonomics (pp. 91–98). University of Athens.

  14. 14.

    Rogers, E.M.: A prospective and retrospective look at the diffusion model. J. Health Commun. 9(S1), 13–19 (2004)

    Article  Google Scholar 

  15. 15.

    Işıklar, G., Büyüközkan, G.: Using a multi-criteria decision making approach to evaluate mobile phone alternatives. Comput. Stand. Interfaces 29(2), 265–274 (2007)

    Article  Google Scholar 

  16. 16.

    Auter, P.J.: Portable social groups: willingness to communicate, interpersonal communication gratifications, and cell phone use among young adults. Int. J. Mobile Commun. 5(2), 139–156 (2006)

    Article  Google Scholar 

  17. 17.

    Page, T.: Touchscreen mobile devices and older adults: a usability study. Int. J. Hum. Factors Ergon. 3(1), 65–85 (2014)

    Article  Google Scholar 

  18. 18.

    Economides, A.A., Grousopoulou, A.: Use of mobile phones by male and female Greek students. Int. J. Mobile Commun. 6(6), 729–749 (2008)

    Article  Google Scholar 

  19. 19.

    Aman, T., Shah, N., Hussain, A., Khan, A., Asif, S., Qazi, A.: Effects of mobile phone use on the social and academic performance of students of a public sector medical college in khyber pakhtunkhwa pakistan. KJMS 8(1), 99–103 (2015)

    Google Scholar 

  20. 20.

    Wahla, R.S., Awan, A.G.: Mobile phones usage and employees’ performance: a perspective from Pakistan. Int. J. Acad. Res. Acc. Financ. Manag. Sci. 4(4), 153–165 (2014)

    Google Scholar 

  21. 21.

    Vainikka, E., Herkman, J.: Generation of content-producers? The reading and media production practices of young adults. Participations 10(2), 118–138 (2013)

    Google Scholar 

  22. 22.

    Oksman, V., Turtiainen, J.: Mobile communication as a social stage: meanings of mobile communication in everyday life among teenagers in Finland. New Media Soc. 6(3), 319–339 (2004)

    Article  Google Scholar 

  23. 23.

    Alexander, E., Ward, C. B., & Braun, C. K. Cell phone attachment: A measure and its benefits. In IABE-2007 Annual Conference pp. 407 (2007).

  24. 24.

    McVicker, D. (2001). Teen angels? Young Americans hold the keys to the kingdom, as far as wireless providers are concerned. Teledotcom.

  25. 25.

    Duggan, M., & Rainie, L. Cell phone activities 2012. Pew Research Center’s Internet & American Life Project, (2012).

  26. 26.

    Taylor, A. S., & Harper, R. Age-old practices in the'new world': a study of gift-giving between teenage mobile phone users. In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 439–446 (2002).

  27. 27.

    Shade, L.R.: Feminizing the mobile: gender scripting of mobiles in North America. Continuum 21(2), 179–189 (2007)

    Article  Google Scholar 

  28. 28.

    Comunello, F., Fernández Ardèvol, M., Mulargia, S., Belotti, F.: Women, youth and everything else: age-based and gendered stereotypes in relation to digital technology among elderly Italian mobile phone users. Media Cult. Soc. 39(6), 798–815 (2017)

    Article  Google Scholar 

  29. 29.

    Noguti, V., Singh, S., Waller, D.S.: Gender differences in motivations to use social networking sites. Social media marketing: breakthroughs in research and practice, pp. 680–695. IGI Global, Pennsylvania (2018)

    Google Scholar 

  30. 30.

    Singla, A., Ahuja, I.S., Sethi, A.P.S.: Technology push and demand pull practices for achieving sustainable development in manufacturing industries. J. Manuf. Technol. Manag. (2018).

    Article  Google Scholar 

  31. 31.

    Rogers, J., Renoir, T., Hannan, A.J.: Gene-environment interactions informing therapeutic approaches to cognitive and affective disorders. Neuropharmacology 145, 37–48 (2019)

    Article  Google Scholar 

  32. 32.

    Mohammadyari, S., Singh, H.: Understanding the effect of e-learning on individual performance: the role of digital literacy. Comput. Educ. 82, 11–25 (2015)

    Article  Google Scholar 

  33. 33.

    Cronbach, L.J.: Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297–334 (1951)

    MATH  Article  Google Scholar 

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Rotated component matrixa,b Rotated component matrixc,d Rotated component matrixe,f
  Component   Component   Component
1 2 1 2 1 2 3
RAM 0.82   Tech_Support 0.72   Material_Quality 0.82   
Battery_Talktime 0.81   Data_Backup 0.72   Ease_of_Use 0.76   
Ease_of_Use 0.79   Material_Quality 0.66   Tech_Support 0.73   
Asthetics 0.73   Sensors 0.66   Resistance_Impact 0.71   
Price 0.68   Business_Services 0.65   Stanadrd_parts 0.66   
Device_Security 0.65   Stanadrd_parts_used 0.62   Mobile_Brand 0.62   
Resistance_Impact 0.65   Mobile_Brand 0.52   Battery_Talktime 0.55   
Display_Size 0.63   Water_Resistance 0.5   Water_Resistance    
Disk_Space 0.62   Pre_Installed_Apps 0.47   Price    
Camera_Imp 0.57   Device_Security 0.47   Device_Security   0.87  
Material_Quality 0.52 0.47 Local_Langauge    Display_Size   0.73  
Business_Services   0.74 Resistance_Impact   0.67 Disk_Space   0.72  
Pre_Installed_Apps   0.73 Battery_Talktime   0.63 RAM   0.69  
Local_Langauge   0.7 RAM 0.45 0.62 Sensors   0.6 0.48
Games_Imp   0.68 Asthetics   0.6 Data_Backup_feature   0.51  
Data_Backup_feature   0.67 Disk_Space 0.55 0.6 Business_Services    
Tech_Support   0.67 Ease_of_Use   0.59 Games_Imp    0.7
Stanadrd_parts_used   0.65 Display_Size 0.46 0.59 Local_Langauge    0.66
Sensors   0.64 Price   0.56 Pre_Installed_Apps    0.63
Mobile_Brand   0.58 Camera_Imp 0.47 0.54 Asthetics    0.5
Water_Resistance   0.56 Games_Imp    Camera_Imp    0.49
Rotated component matrixg,h Rotated component matrixi,j
  Component   Component
  1 2 3   1 2 3
Material_Quality 0.82    Pre_Installed_Apps 0.82   
Ease_of_Use 0.76    Business_Services 0.8   
Tech_Support 0.73    Stanadrd_parts_used 0.78   
Resistance_Impact 0.71    Sensors 0.75   
Stanadrd_parts_used 0.66    Data_Backup_feature 0.73   
Mobile_Brand 0.62    Local_Langauge 0.72   
Battery_Talktime 0.55    Camera_Imp 0.72   0.5
Water_Resistance     Material_Quality 0.6   
Price     Games_Imp 0.58   
Device_Security   0.87   Tech_Support 0.56   
Display_Size   0.73   Device_Security   0.77  
Disk_Space   0.72   Water_Resistance   0.74  
RAM   0.69   Resistance_Impact   0.71  
Sensors   0.6 0.48 Display_Size   0.69  
Data_Backup_feature   0.51   Disk_Space   0.6  
Business_Services     Mobile_Brand 0.5 0.58  
Games_Imp    0.7 Ease_of_Use    0.8
Local_Langauge    0.66 Price    0.68
Pre_Installed_Apps    0.63 Battery_Talktime    0.67
Asthetics    0.5 Asthetics    0.63
Camera_Imp    0.49 RAM 0.48   0.61
  1. Extraction method: maximum Likelihood
  2. Rotation method: direct oblimin
  3. aPROF_1 = Engineering
  4. bRotation converged in 3 iterations
  5. cPROF_1 = Student
  6. dRotation converged in 3 iterations
  7. ePROF_1 = Service sector
  8. fRotation converged in 6 iterations
  9. gPROF_1 = Service sector
  10. hRotation converged in 6 iterations
  11. iPROF_1 = Teaching/education
  12. jRotation converged in 6 iterations

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Rashid, A., Zeb, M.A., Rashid, A. et al. Conceptualization of smartphone usage and feature preferences among various demographics. Cluster Comput 23, 1855–1873 (2020).

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  • Smartphone usage
  • Mobile ecosystems
  • Mobile applications
  • Feature preferences