Developing a Usage Space Dimension Model to Investigate Influence of Intention to Use on Actual Usage of Mobile Phones

  • Geeta KumarEmail author
  • P. K. Kapur
Part of the Asset Analytics book series (ASAN)


India is one of the largest mobile markets. This study aims to validate a usage space dimension model for investigating the influence of intention to use on actual usage of mobile phones and the effect of gender and age on intention to use and actual usage across the usage space dimensions. This paper used a systematic usage space analysis across age and gender to extend the intention to use from the original UTAUT model. Structural equation modeling (SEM) was used to develop a usage space dimension model. The empirical finding suggests that intention to use as a variable is not one-dimensional as initially proposed in the UTAUT model. Two distinct factors have emerged specific to mobile intention to use: inter-factors pertaining to coordinating and using mobile with the world outside; intra-factors pertaining to coordinating and using mobile for self. The study concluded that both gender and age influence the actual mobile phone usage, age having a stronger influence on intra-intention. The study also validates usage dimensions as a viable and practical approach to understand the adoption and integration of mobile phone in everyday life. It also helps give a picture on the potential adoption of new mobile features and applications. Using the usage space dimension, the study brings out the different ways mobile users are using and applying the mobile features in their daily lives.


Mobile usage space dimensions Intention to use mobile phones Actual usage Gender and age differences Integration of mobile phones 


  1. 1.
    Taipale S, Fortunati L (2014) Capturing methodological trends in mobile communication studies. Inf Commun Soc 17(5):627–642CrossRefGoogle Scholar
  2. 2.
    Jacobson RB, Mortensen RP, Cialdini CR (2011) Bodies obliged and unbound: differentiated response tendencies for injunctive and descriptive social norms. J Pers Soc Psychol 100(3):433–448Google Scholar
  3. 3.
    Campbell SW, Ling R, Bayer JB (2014) The structural transformation of mobile communication. In: Oliver MB, Raney AA (eds) Media and social life. Routledge, New YorkGoogle Scholar
  4. 4.
    Ling R (2004) The mobile connection: the cell phone’s impact on society. Morgan Kaufmann, San FranciscoGoogle Scholar
  5. 5.
    Ling R (2012) Taken for grantedness: the embedding of mobile communication into society. MIT Press, CambridgeCrossRefGoogle Scholar
  6. 6.
    Ling R (2010) New tech, new ties: how mobile communication is reshaping social cohesion. MIT Press, CambridgeGoogle Scholar
  7. 7.
    Farman J (2012) Mobile interface theory. Routledge, New YorkGoogle Scholar
  8. 8.
    Ito M, Okabe D, Anderson K (2010) Portable objects in three global cities: the personalization of urban places. In: Ling R, Campbell SW (eds) The reconstruction of space and time: mobile communication practices. Transaction Publishers, New Brunswick, pp 67–87Google Scholar
  9. 9.
    Humphreys L (2005) Cellphones in public: social interactions in a wireless era. New Media Soc 7(6):810–833CrossRefGoogle Scholar
  10. 10.
    Barkhuus L, Polichar VE (2011) Empowerment through seamfulness: smart phones in everyday life. Pers Ubiquitous Comput 15(6):629–639CrossRefGoogle Scholar
  11. 11.
    Hislop D, Axtell C (2011) Mobile phones during work and non-work time: a case study of mobile, non-managerial workers. Inf Organ 21:41–56CrossRefGoogle Scholar
  12. 12.
    Townsend K, Batchelor L (2005) Managing mobile phones: a work/non-work collision in small business. New Technol Work Employ 20(3):259–267Google Scholar
  13. 13.
    Sarker S, Sarker S, Xiao X, Ahuja M (2012) Managing employees’ use of mobile technologies to minimize work-life balance impacts. MIS Q Exec 11(4):143–157Google Scholar
  14. 14.
    Lanaj K, Johnson RE, Barnes CM (2014) Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organ Behav Hum Decis Process 124(1):11–23CrossRefGoogle Scholar
  15. 15.
    Kumar G, Prakash N (2016) A cross sectional study on mobile phone perceptions, usage and impact among urban women and men. Int J Interdiscip Multidiscip Stud 3(4):80–92Google Scholar
  16. 16.
    Palackal A, Nyaga Mbatia P, Dzorgbo D-B, Duque RB, Ynalvez MA, Shrum WM (2011) Are mobile phones changing social networks? A longitudinal study of core networks in Kerala. New Media Soc 13(3):391–410Google Scholar
  17. 17.
    Grover S, Basu D, Chakraborty K (2010) Pattern of Internet use among professionals in India: critical look at a surprising survey result. Ind Psychiatry J 19(2):94CrossRefGoogle Scholar
  18. 18.
    Chen JV, Yen DC, Chen K (2009) The acceptance and diffusion of the innovative smart phone use: a case study of a delivery service company in logistics. Inf Manag 46(4):241–248Google Scholar
  19. 19.
    Ahn H, Wijaya ME, Esmero BC (2014) A systemic smartphone usage pattern analysis: focusing on smartphone addiction issue. Int J Multimed Ubiquitous Eng 9(6):9–14CrossRefGoogle Scholar
  20. 20.
    Falaki H, Mahajan R, Kandula S, Lymberopoulos D, Govindan R, Estrin D (2010) Diversity in smartphone usage. In: Proceedings of the 8th international conference on mobile systems, applications, and services, MobiSys’10, pp 179–194Google Scholar
  21. 21.
    Kang J-M, Seo S-S, Hong JW-K (2011) Usage pattern analysis of smartphones. In: Network operations and management symposium (APNOMS), pp 1–8Google Scholar
  22. 22.
    Froehlich J (2007) My experience: a system for in situ tracing and capturing of user feedback on mobile phones. In: 5th international conference on mobile systems, applications and services. ACMGoogle Scholar
  23. 23.
    Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478Google Scholar
  24. 24.
    Singh T, Sharma A, Singh N (2015) Digital library acceptance model and its social construction: conceptualization and development. J Web Librariansh 9(4):162–181CrossRefGoogle Scholar
  25. 25.
    Zhan Y, Wang P, Xia S (2011) Exploring the drivers for ICT adoption in government organization in China. In: 2011 fourth international conference on business intelligence and financial engineering (BIFE), pp 220–223Google Scholar
  26. 26.
    Anderson JE, Schwager PH (2004) SME adoption of wireless LAN technology: applying the UTAUT model. In: Proceedings of the 7th annual conference of the southern association for information systems, vol 7, pp 39–43Google Scholar
  27. 27.
    Birch A, Irvine V (2009) Preservice teachers’ acceptance of ICT integration in the classroom: applying the UTAUT model. EMI Educ Media Int 46(4):295–315Google Scholar
  28. 28.
    Venkatesh V, Sykes TA, Zhang X (2011) Just what the doctor ordered: a revised UTAUT for EMR system adoption and use by doctors. In: 44th Hawaii international conference on system sciences, pp 1–10Google Scholar
  29. 29.
    Ifinedo P (2012) Technology acceptance by health professionals in Canada: an analysis with a modified UTAUT model. In: 45th Hawaii international conference on system science (HICSS), pp 2937–2946Google Scholar
  30. 30.
    Ahmad MI (2014) Unified theory of acceptance and use of technology. In: Fourth international conference on ICT in our lives 2014, pp 1–4Google Scholar
  31. 31.
    Ling R, Yttri B (1999) Nobody sits at home and waits for the telephone to ring: micro and hyper coordination through the use of the mobile phone. KjellerGoogle Scholar
  32. 32.
    Han Sze Tjong S, Weber I, Sternberg J (2003) Mobile youth culture: shaping telephone use in Australia and Singapore. In: ANZCA03 conferenceGoogle Scholar
  33. 33.
    Tamminen S, Oulasvirta A, Toiskallio K, Kankainen A (2004) Understanding mobile contexts. Pers Ubiquitous Comput 8(2):135–143Google Scholar
  34. 34.
    Katz JE, Sugiyama S (2005) Mobile phones as fashion statements: the co-creation of mobile communication’s public meaning. In: Ling R, Pedersen P (eds) Mobile communications: re-negotiation of the social sphere. Springer, Surrey, pp 63–81Google Scholar
  35. 35.
    Campbell S, Russo T (2003) The social construction of mobile telephony: an application of the social influence model to perceptions and uses of mobile phones within personal communication networks. Commun Monogr 70(4):317–34Google Scholar
  36. 36.
    Marcus A, Chen E (2002) Designing the PDA of the future. Interactions 9(1):34–44Google Scholar
  37. 37.
    Marcus A, Chen E (2002) Development of a future wireless information device. In: Re: wireless, mobile device design seminar, pp 1–7Google Scholar
  38. 38.
    Marcus A (2005) Tutorial: mobile user-interface design for work, home, play and on the way. In: CHI-SAGoogle Scholar
  39. 39.
    Marcus A (2005) Tutorial: cross-cultural user-interface design for work, home, play and on the way. Paper presented at the CHI-SAGoogle Scholar
  40. 40.
    van Biljon JA (2006) A model for representing the motivational and cultural factors that influence mobile phone usage varietyGoogle Scholar
  41. 41.
    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340CrossRefGoogle Scholar
  42. 42.
    Institute of Human Development (2013) Delhi human development reportGoogle Scholar
  43. 43.
    Ericsson Consumer Lab (2014) Performance shapes smartphone behavior. Understanding mobile broadband user expectations in IndiaGoogle Scholar
  44. 44.
    Singh P (2012) Smartphone: the emerging gadget of choice for the urban Indian: delivering consumer clarity. Nielsen Featured InsightsGoogle Scholar
  45. 45.
    Campbell SW, Park YJ (2008) Social implications of mobile telephony: the rise of personal communication society. Sociol Compass J Br Sociol Assoc 2(2):371–387CrossRefGoogle Scholar
  46. 46.
    Green N (2003) Outwardly mobile: young people and mobile technologies. In: Katz JE (ed) Machines that become us: the social context of personal communication technology. Transaction Publishers, New Brunswick, pp 201–218Google Scholar
  47. 47.
    Skog B (2002) 16 mobiles and the Norwegian teen: identity, gender and class. In: Katz JE, Aakhus M (eds) Perpetual contact: mobile communication, private talk, public performance. Cambridge University Press, Cambridge, pp 255–273Google Scholar
  48. 48.
    Hair JF, Anderson RE, Tatham RL, Black WC (1998) Multivariate data analysis, 5th edn. Prentice-Hall, Englewood CliffsGoogle Scholar
  49. 49.
    Tabachnick BG, Fidell LS (2007) Using multivariate statistics, 5th edn. Allyn and Bacon, BostonGoogle Scholar
  50. 50.
    Kline RB (2004) Beyond significance testing: reforming data analysis methods in behavioral research. American Psychological AssociationGoogle Scholar
  51. 51.
    Joreskog KG, Sorbom D (2002) Lisrel VIII: structural equation modeling with the SIMPLIS command language, 5th print. Scientific Software International, LincolnwoodGoogle Scholar
  52. 52.
    Hu L-T, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 6(1):1–55Google Scholar
  53. 53.
    Bentler PM, Bonett DG (1980) Significance tests and goodness-of-fit in the analysis of covariance structures. Psychol Bull 88:588–600Google Scholar
  54. 54.
    Bollen K (1986) Sample size and Bentler and Bonett’s nonnormed fit index. Psychom Soc 51(3):375–377CrossRefGoogle Scholar
  55. 55.
    Browne MW, Cudeck R (1993) Alternative ways of assessing model fit. In: Bollen KA, Long JS (eds) Testing structural equation models. Sage, Newbury Park, pp 136–162Google Scholar
  56. 56.
    Steiger JH (2007) Understanding the limitations of global fit assessment in structural equation modeling. Pers Individ Differ 42(5):893–898CrossRefGoogle Scholar
  57. 57.
    Busch T (1995) Gender differences in self-efficacy and attitudes toward computers. J Educ Comput 12(12):147–163CrossRefGoogle Scholar
  58. 58.
    Aronsson G, Dallner M, Aborg C (1994) Winners and losers from computerization: a study of the psychosocial work conditions and health of Swedish state employees. J Hum Comput Interact 6(1):17–36Google Scholar
  59. 59.
    Henwood F (1993) Establishing gender perspectives in information technology: problems, issues, and opportunities. In: Green E, Owen J, Pain D (eds) Gendered by design? Information technology and office systems. Taylor and Francis, LondonGoogle Scholar
  60. 60.
    Faulkner W (2001) The technology question in feminism: a view from feminist technology studies. Womens Stud Int Forum 24(1):79–95CrossRefGoogle Scholar
  61. 61.
    Jin R, Punpanich W (2011) Influence of gender difference in reference group on smartphone users’ purchasing decision-making process. In: Seminar 1st June, 2011, course: BUSM08 degree project in international marketing and brand managementGoogle Scholar
  62. 62.
    Borges AP, Joia LA (2015) Paradoxes perception and smartphone use by Brazilian executives: is this genderless? J High Technol Manag Res 26(2):205–218CrossRefGoogle Scholar
  63. 63.
    Kumar G, Prakash N (2018) Investigating the role of gender as a moderator in influencing the intention to use and actual use of mobile telephony. Eur J Soc Sci (EJSS) 1(1)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Amity International Business School, Amity UniversityNoidaIndia
  2. 2.Amity Center of Interdisciplinary Research, Amity UniversityNoidaIndia

Personalised recommendations