Skip to main content

An espoused cultural perspective to understand continued intention to use mobile applications: a four-country study of mobile social media application usability

Abstract

As most mobile applications are tailored for worldwide consumption, it is a significant challenge to develop applications that satisfy individuals with various cultural backgrounds. To address this issue, we drew on a recently developed conceptualization and associated instrument of mobile application usability to develop a model examining the impact of mobile social media application usability on continued intention to use. Drawing on Hofstede’s five cultural values, we incorporated espoused cultural values of masculinity/femininity, individualism/collectivism, power distance, uncertainty avoidance, and long-term orientation into our model as moderators. To test the model, we collected data from 1,844 consumers in four countries – the U.S., Germany, China, and India – who use mobile social media applications on their smartphones. The results provided support for the role of espoused national cultural values in moderating the impact of mobile social media application usability on continued intention to use and the model, with espoused cultural values explaining significantly more variance in continued intention to use (i.e., 38%) than the main effects-only model (i.e., 19%). Interestingly, our results demonstrated that culture at the national level did not play a significant role in affecting the relationship between usability constructs and continued intention to use, thus underscoring the importance of espoused culture.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2

References

  1. Adipat B, Zhang D and Zhou L (2011) The effect of tree-view based presentation adaptation on mobile web-browsing. MIS Quarterly 35 (1), 99–121.

    Google Scholar 

  2. Agarwal R and Venkatesh V (2002) Assessing a firm’s web presence: a heuristic evaluation procedure for the measurement of usability. Information Systems Research 13 (2), 168–186.

    Article  Google Scholar 

  3. Aiken LS and West SG (1991) Multiple Regression: Testing and Interpreting Interactions. Sage, London.

    Google Scholar 

  4. Alvesson M and Kärreman D (2007) Constructing mystery: empirical matters in theory development. Academy of Management Review 32 (4), 1265–1281.

    Article  Google Scholar 

  5. Apple (2011) iOS developer library: user experience guidelines. [WWW document] https://developer.apple.com/library/ios/documentation/UserExperience/Conceptual/MobileHIG/ (accessed 2 January 2015).

  6. Bem S (1981) The BSRI and gender schema theory: a reply to Spence and Heimrich. Psychological Bulletin 88 (4), 369–371.

    Google Scholar 

  7. Bhattacherjee A (2001) Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly 25 (3), 351–370.

    Article  Google Scholar 

  8. Boehringer M and Barnes S (2011) Testing a model of continued usage for micro-blogging services: the case of twitter. Journal of Computer Information Systems 51 (4), 1–10.

    Google Scholar 

  9. Brislin RW (1970) Back-translation for cross-cultural research. Journal of Cross-cultural Psychology 1 (3), 185–216.

    Article  Google Scholar 

  10. Brown SA, Dennis AR and Venkatesh V (2010) Predicting collaboration technology use: integrating technology adoption and collaboration research. Journal of Management Information Systems 27 (2), 9–54.

    Article  Google Scholar 

  11. Campbell D, Wells JD and Valacich JS (2009) Understanding online customer relationships: B2C relationship stage theory. AIS Transactions on Human-Computer Interaction 1 (4), 108–132.

    Google Scholar 

  12. Chin WW, Marcolin B and Newsted P (2003) A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research 14 (2), 189–217.

    Article  Google Scholar 

  13. Churchill GA (1979) A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16 (1), 64–73.

    Article  Google Scholar 

  14. Cohen J (1992) A power primer. Psychological Bulletin 112 (1), 155–159.

    Article  Google Scholar 

  15. Culnan MJ, Mchugh PJ and Zubillaga JI (2010) How large US companies can use Twitter and other social media to gain business value. MIS Quarterly Executive 35 (1), 99–121.

    Google Scholar 

  16. Cyr D, Head M, Larios H and Pan B (2009) Exploring human images in website design: a multi-method approach. MIS Quarterly 33 (3), 539–566.

    Google Scholar 

  17. Dawson JF and Richter AW (2006) Probing three-way interactions in moderated multiple regression: development and application of a slope difference test. Journal of Applied Psychology 91 (4), 917–926.

    Article  Google Scholar 

  18. Deloitte Research (2012) So many apps: so little to download. [WWW document] http://www.deloitte.com/assets/Dcom-Global/Local%20Content/Articles/TMT/TMT%20Predictions%202012/16470A%20So%20many%20apps%20lb1.pdf (accessed 2 January 2015).

  19. Dinev T and Hart P (2006) An extended privacy calculus model for e-commerce transactions. Information Systems Research 17 (1), 61–80.

    Article  Google Scholar 

  20. Dou W, Lim KH, Su C, Zhou N and Cui N (2010) Brand positioning strategy using search engine marketing. MIS Quarterly 34 (2), 261–279.

    Google Scholar 

  21. Dudezert A and Leidner DE (2011) Illusions of control and social domination strategies in knowledge mapping system use. European Journal of Information Systems 20 (5), 574–588.

    Article  Google Scholar 

  22. Earley PC and Stubblebine P (1989) Intercultural assessment of performance feedback. Group and Organization Studies 14 (2), 161–181.

    Article  Google Scholar 

  23. Facebook (2013) Facebook: user statistics. [WWW document] http://www.facebook.com/press/info.php?statistics (accessed 10 July 2013).

  24. Fang X, Benamati J and Lederer AL (2011) Coping with rapid information technology change in different national cultures. European Journal of Information Systems 20 (5), 560–573.

    Article  Google Scholar 

  25. Flavian C, Guinaliu M and Gurrea R (2006) The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information and Management 43 (1), 1–14.

    Article  Google Scholar 

  26. Fornell C and Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research 18 (1), 39–50.

    Article  Google Scholar 

  27. Forrester Research (2010) Social mobile technographics: how consumers socialize on mobile phones. [WWW document] http://www.forrester.com/Social+Mobile+Technographics174+How+Consumers+Socialize+On+Mobile+Phones/fulltext/-/E-RES57636 (accessed 5 June 2013).

  28. Forrester Research (2011) Mobile commerce, there’s an app for that. [WWW document] http://blogs.forrester.com/peter_sheldon/11-06-17-mobile_commerce_theres_an_app_for_that (accessed 28 May 2012).

  29. Gerow JE, Galy E, Thatcher JB and Srite M (2010) Within-culture variation and information technology. In Technological Advancement in Developed and Developing Countries: Discoveries in Global Information Management (Hunter G and Tan F, Eds), pp 337–364, IGI Global, Hershey, Pennsylvania.

    Chapter  Google Scholar 

  30. Goodhue D, Lewis W and Thompson R (2007) Statistical power in analyzing interaction effects: questioning the advantage of PLS with product indicators. Information Systems Research 18 (2), 211–227.

    Article  Google Scholar 

  31. Hair JF, Anderson RE, Tatham RL and Black WC (1998) Multivariate Data Analysis with Readings, 5th edn, Prentice-Hall, Englewood Cliffs, New York.

    Google Scholar 

  32. Hampton KN, Goulet LS, Rainie L and Purcell K (2011) Social networking sites and our lives – PEW research center. [WWW document] http://www.pewinternet.org/Reports/2011/Technology-and-social-networks.aspx (accessed 1 December 2011).

  33. Hess TJ, Fuller MA and Campbell D (2009) Designing interfaces with social presence: using vividness and extraversion to create social recommendation agents. Journal of the Association for Information Systems 10 (12), 889–919.

    Google Scholar 

  34. Hess TJ, Fuller MA and Mathew J (2005) Involvement and decision-making performance with a decision aid: the influence of social multimedia, gender, and playfulness. Journal of Management Information Systems 22 (3), 15–54.

    Article  Google Scholar 

  35. Hoehle H and Venkatesh V (2015) Development and validation of a mobile usability conceptualization and instrument. MIS Quarterly, (in press).

  36. Hoffman DL and Fodor M (2010) Can you measure the ROI of your social media marketing? MIT Sloan Management Review 52 (1), 41–49.

    Google Scholar 

  37. Hofstede G (1980) Culture’s Consequences: International Differences in Work Related Values. Sage, Beverly Hills, California.

    Google Scholar 

  38. Hofstede G (1983) The dimension of national cultures in fifty countries and three regions. In Explications in Cross-Cultural Psychology (Deregowski JB, Daiurawiec S and Annis RC, Eds), pp 335–355, Swets and Zeitlinger, Amsterdam-Lisse, Netherlands.

    Google Scholar 

  39. Hofstede G (1984) Culture’s Consequences. Sage, Newbury Park, California.

    Google Scholar 

  40. Hofstede G (1993) Cultural constraints in management theories. The Academy of Management Executive 7 (1), 81–94.

    Google Scholar 

  41. Hofstede G (2007) Geert Hofstede™ cultural dimensions. [WWW document] http://www.geert-hofstede.com (accessed 23 March 2007).

  42. Hofstede G (2012) National culture: countries. [WWW document] http://geert-hofstede.com/ (accessed 1 December 2012).

  43. Hofstede G and Bond MH (1988) The Confucius connection: from cultural roots to economic growth. Organizational Dynamics 16 (1), 4–21.

    Google Scholar 

  44. Hong W, Thong JYL and Tam KY (2004) The effects of information format and shopping task on consumers’ online shopping behavior: a cognitive fit perspective. Journal of Management Information Systems 21 (3), 151–188.

    Google Scholar 

  45. Hong W, Thong JYL and Tam KY (2007) How do web users respond to non-banner-ads animation? The effects of task type and user experience. Journal of the American Society for Information Science and Technology 58 (10), 1467–1482.

    Article  Google Scholar 

  46. Hu PJ, Chen H, Hu H, Larson C and Butierez C (2011) Law enforcement officers’ acceptance of advanced e-government technology: a survey study of COPLINK mobile. Electronic Commerce Research and Applications 10 (1), 6–16.

    Article  Google Scholar 

  47. Hu PJ, Ma PC and Chau PY (1999) Evaluation of user interface designs for information retrieval systems: a computer-based experiment. Decision Support Systems 27 (1-2), 125–143.

    Article  Google Scholar 

  48. Huy Q and Shipilov A (2012) The key to social media success within organizations. MIT Sloan Management Review 54 (1), 73–81.

    Google Scholar 

  49. Johns G (2006) The essential impact of context on organizational behavior. Academy of Management Review 31 (2), 386–408.

    Article  Google Scholar 

  50. Jokela T, Koivumaa J, Pirkola J, Salminen P and Kantola N (2006) Methods for quantitative usability requirements: a case study on the development of the user interface of a mobile phone. Personal and Ubiquitous Computing 6 (10), 345–355.

    Article  Google Scholar 

  51. Jordan JV and Surrey JL (1986) The self-in-relation: empathy and the mother-daughter relationship. In The Psychology of Today’s Woman: New Psychoanalytic Visions (Bernay T and Cantor DW, Eds), pp 81–104, Harvard University Press, Cambridge, MA.

    Google Scholar 

  52. Kang SH (2007) The impact of digital iconic realism on anonymous interactants’ mobile phone communication. In Proceedings of Computer and Human Interaction ACM Student Research Competition (28 May 2007), pp 2207–2212.

  53. Kim H, Chan H and Gupta S (2007) Value-based adoption of mobile internet: an empirical investigation. Decision Support Systems 43 (1), 111–126.

    Article  Google Scholar 

  54. Kim S and Stoel L (2004) Apparel retailers: website quality dimensions and satisfaction. Journal of Retailing and Consumer Services 2 (11), 109–117.

    Article  Google Scholar 

  55. Kim C, TAO W, SHIN N and KIM KS (2010) An empirical study of customers' perceptions of security and trust in e-payment systems. Electronic Commerce Research and Applications 9 (1), 84–95.

    Article  Google Scholar 

  56. Kirkman BL, Lowe KB and Gibson CB (2006) A quarter century of culture’s consequences: a review of empirical research incorporating Hofstede’s cultural value framework. Journal of International Business Studies 37 (3), 285–320.

    Article  Google Scholar 

  57. Kirkman BL and Shapiro DL (2005) The impact of cultural value diversity on multicultural team performance. In Advances in International Management: Managing Multinational Teams (Shapiro DL, Von Glinow MA and Cheng JL, Eds), 33–67, Elsevier, London.

    Google Scholar 

  58. Kurniawan S (2008) Older people and mobile phones: a multi-method investigation. International Journal of Human-Computer Studies 66 (12), 889–901.

    Article  Google Scholar 

  59. Larson K and Watson R (2011) Tying social media strategy to firm performance: a social media analytics framework. In Proceedings of International Conference on Information Systems, pp 1–18. [WWW document] http://aisel.aisnet.org/icis2011/proceedings/onlinecommunity/10 (accessed 7 December 2011).

  60. Lee AS and Baskerville RL (2003) Generalizing generalizability in information systems research. Information Systems Research 14 (3), 221–243.

    Article  Google Scholar 

  61. Leidner DE and Kayworth T (2006) A review of culture in information systems research: toward a theory of information technology culture conflict. MIS Quarterly 30 (2), 357–399.

    Google Scholar 

  62. Leidner DE, Koch H and Gonzalez E (2010) Assimilating generation Y IT new hires into USAA’s workforce: the role of an enterprise 2.0 system. MIS Quarterly Executive 9 (4), 229–242.

    Google Scholar 

  63. Li X, Hess T, Mcnab A and YU Y (2009) Culture and acceptance of global web sites: a cross-country study of the effects of national cultural values on acceptance of a personal web portal. DATA BASE for Advances in Information Systems 40 (4), 62–87.

    Article  Google Scholar 

  64. Lin C, Hu PJ and Chen H (2004) Technology implementation management in law enforcement: COPLINK systems usability and user acceptance evaluations. Social Science Computer Review 22 (1), 24–36.

    Article  Google Scholar 

  65. Luo X and Warkentin M (2007) Consumers’ preferences and attitudes toward mobile office use: a technology trade-off research agenda. In E-Business Process Management: Technologies and Solutions (Sounderpandian J and Sinha T, Eds), pp 176–185, Idea Group Publishing, Hershey, Pennsylvania.

    Google Scholar 

  66. Mackenzie SB, Podsakoff PM and Podsakoff NP (2011) Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Quarterly 35 (2), 293–334.

    Google Scholar 

  67. Martinsons MG and Davison RM (2007) Strategic decision making and support systems: comparing American, Chinese and Japanese management. Decision Support Systems 43 (1), 284–300.

    Article  Google Scholar 

  68. Martonik A (2013) Larry page: 1.5 million android devices activated every day. [WWW document] http://www.androidcentral.com/larry-page-15-million-android-devices-activated-every-day (accessed 14 July 2013).

  69. McCoy S, Galletta DF and King WR (2007) Applying TAM across cultures: the need for caution. European Journal of Information Systems 16 (1), 81–90.

    Article  Google Scholar 

  70. Middleton CA and Cukier W (2006) Is mobile email functional or dysfunctional? Two perspectives on mobile email usage. European Journal of Information Systems 15 (3), 252–260.

    Article  Google Scholar 

  71. Molinsky A (2007) Cross-cultural code-switching: the psychological challenges of adapting behavior in foreign cultural integrations. Academy of Management Review 32 (2), 622–640.

    Article  Google Scholar 

  72. Neyem A, Ochoa SF and Pino JA (2008) Coordination patterns to support mobile collaboration. Lecture Notes in Computer Science 5411 (1), 248–265.

    Article  Google Scholar 

  73. Newman KL and Nollen SD (1996) Culture and congruence: the fit between management practices and national culture. Journal of International Business Studies 27 (4), 753–779.

    Article  Google Scholar 

  74. Nielsen J (2012) Mobile usability update. [WWW document] http://www.useit.com/alertbox/mobile-usability.html (accessed 13 May 2012).

  75. Nielsen Norman Group (2012) Usability of mobile websites & applications. [WWW document] http://www.nngroup.com/reports/mobile/ (accessed 13 May 2012).

  76. Nunnally JC (1978) Psychometric Theory, 2nd edn, McGraw-Hill, New York.

    Google Scholar 

  77. Ou CXJ and Davison RM (2011) Interactive or interruptive: instant messaging at work. Decision Support Systems 52 (1), 61–72.

    Article  Google Scholar 

  78. Parboteeah D, Valacich JS and Wells JD (2009) The influence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research 1 (20), 60–78.

    Article  Google Scholar 

  79. Piskorski MJ (2011) Social strategies that work. Harvard Business Review 89 (11), 116–122.

    Google Scholar 

  80. Porter CE, Donthu N, Macelroy WH and Wydra D (2011) How to foster and sustain engagement in virtual communities. California Management Review 53 (4), 80–110.

    Article  Google Scholar 

  81. Rai A, Lang S and Walker R (2002) Assessing the validity of IS success models: an empirical test and theoretical analysis. Information Systems Research 13 (1), 50–69.

    Article  Google Scholar 

  82. Rai A, Maruping L and Venkatesh V (2009) Offshore information systems project success: the role of social embeddedness and cultural characteristics. MIS Quarterly 33 (3), 617–641.

    Google Scholar 

  83. Ringle CM, Wende S and Becker JM (2005) SmartPLS 2.0. Hamburg. [WWW document] http://www.smartpls.de (accessed 14 July 2011).

  84. Santos AC, Cardoso JM, Ferreira DR, Diniz PC and Chaínho P (2010) Providing user context for mobile and social networking applications. Pervasive and Mobile Computing 6 (3), 324–341.

    Article  Google Scholar 

  85. Schein EH (1985) Organizational Culture and Leadership. Jossey-Bass, San Francisco, California.

    Google Scholar 

  86. Schoknecht P (2012) 5 factors of mobile application success. [WWW document] http://www.clickz.com/clickz/column/2144961/factors-mobile-application-success (accessed 1 December 2011).

  87. Schwartz SH (1992) Universals in the content and structure of values: theoretical advances and empirical tests in 20 countries. In Advances in Experimental Social Psychology (Zanna MP, Ed), pp 1–65, Academic Press, New York.

    Google Scholar 

  88. Sia CL, Lim KH, Leung K, Lee MKO, Huang WW and Benbasat I (2009) Web strategies to promote internet shopping: is cultural-customization needed? MIS Quarterly 33 (3), 491–521.

    Google Scholar 

  89. Sorensen C and Altaitoon A (2008) Organisational usability of mobile computing-volatility and control in mobile foreign exchange trading. International Journal of Human-Computer Studies 12 (66), 916–929.

    Article  Google Scholar 

  90. Spector PE et al (2002) Locus of control and well-being at work: how generalizable are western findings? Academy of Management Journal 45 (2), 453–466.

    Article  Google Scholar 

  91. Srite M and Karahanna E (2006) The role of espoused national cultural values in technology acceptance. MIS Quarterly 30 (3), 679–704.

    Google Scholar 

  92. Tan FB, Tung LL and Xu Y (2009) A study of web-designers’ criteria for effective business-to-consumer (B2C) website using the repertory grid technique. Journal of Electronic Commerce Research 10 (3), 155–177.

    Google Scholar 

  93. Thatcher JB, Srite M and Stepina LL (2003) Culture, overload, and personal innovativeness with information technology: extending the nomological net. Journal of Computer Information Systems 44 (1), 74–82.

    Google Scholar 

  94. Thong JYL, Hong W and Tam KY (2002) Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human-Computer Studies 57 (3), 215–242.

    Article  Google Scholar 

  95. Trompenaars F and Hampden-Turner C (1998) Riding the Waves of Culture: Understanding Cultural Diversity in Global Business, 2nd edn, McGraw-Hill, New York.

    Google Scholar 

  96. Valacich JS, Parboteeah D and Wells JD (2007) Not all interface characteristics are created equal: the online consumer’s hierarchy of needs. Communications of the ACM 50 (9), 84–90.

    Article  Google Scholar 

  97. Vance A, Elie-Dit-Cosaque C and Straub D (2008) Examining trust in information technology artifacts: the effects of system quality and culture. Journal of Management Information Systems 24 (4), 73–100.

    Article  Google Scholar 

  98. Venkatesh V and Agarwal R (2006) Turning visitors into customers: a usability-centric perspective on purchase behavior in electronic channels. Management Science 52 (3), 367–382.

    Article  Google Scholar 

  99. Venkatesh V, Davis F and Morris M (2007) Dead or alive? the development, trajectory and future of technology adoption research. Journal of the Association for Information Systems 8 (4), 267–286.

    Google Scholar 

  100. Venkatesh V and Goyal S (2010) Expectation disconfirmation and technology adoption: polynomial modeling and response surface analysis. MIS Quarterly 34 (2), 281–303.

    Google Scholar 

  101. Venkatesh V, Morris M, Davis F and Davis G (2003) User acceptance of information technology: toward a unified view. MIS Quarterly 27 (3), 425–478.

    Google Scholar 

  102. Venkatesh V, Morris MG, Sykes TA and Ackerman PL (2004) Individual reactions to new technologies in the workplace: the role of gender as a psychological construct. Journal of Applied Social Psychology 34 (3), 445–467.

    Article  Google Scholar 

  103. Venkatesh V and Ramesh V (2006) Web and wireless site usability: understanding differences and modeling use. MIS Quarterly 30 (1), 181–206.

    Google Scholar 

  104. Venkatesh V, Thong JYL and Xu X (2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly 36 (1), 157–178.

    Google Scholar 

  105. Venkatesh V and Zhang X (2010) Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management 13 (1), 5–27.

    Article  Google Scholar 

  106. Wagner JA (1995) Studies of individualism-collectivism: effects on cooperation in groups. Academy of Management Journal 38 (1), 152–172.

    Article  Google Scholar 

  107. Wang S and Barnes SJ (2009) An analysis of the potential for mobile auctions in China. International Journal of Mobile Communications 7 (1), 36–49.

    Article  Google Scholar 

  108. Wells JD, Fuerst WL and Palmer JW (2005) Designing consumer interfaces for experiential tasks: an empirical investigation. European Journal of Information Systems 14 (3), 273–287.

    Article  Google Scholar 

  109. Wells JD, Valacich JS and Hess TJ (2011) What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly 35 (2), 373–396.

    Google Scholar 

  110. Wobbrock JO, Myers BA and Aung HH (2008) The performance of hand postures in front-and back-of-device interaction for mobile computing. International Journal of Human-Computer Studies 66 (12), 857–875.

    Article  Google Scholar 

  111. Xiao Z and Tsui AS (2007) When brokers may not work: the cultural contingency of social capital. Administrative Science Quarterly 52 (1), 1–31.

    Google Scholar 

  112. Xu Y, Tung LL and Tan FB (2009) Attributes of website usability: a study of web users with the repertory grid technique. International Journal of Electronic Commerce 4 (13), 99–128.

    Google Scholar 

  113. Yen B, Hu PJ and Wang M (2007) Towards analytical approach to effective website designs: a framework for modeling, evaluation and enhancement. Electronic Commerce Research and Applications 6 (2), 159–170.

    Article  Google Scholar 

  114. Youens R (2011) 7 habits of highly effective apps. [WWW document] http://gigaom.com/2011/07/16/7-habits-of-highly-effective-apps/ (accessed 28 May 2011).

  115. Zhang D, Lowry PB, Zhou L and Fu X (2007) The impact of individualism-collectivism, social presence, and group diversity on group decision making under majority influence. Journal of Management Information Systems 23 (4), 53–80.

    Article  Google Scholar 

  116. Zhang X and Maruping LM (2008) Household technology adoption in a global marketplace: incorporating the role of espoused cultural values. Information Systems Frontiers 10 (4), 403–413.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hartmut Hoehle.

Appendices

Appendix A

Table A1

Table A1 Items used to measure each construct

Appendix B

Table B1

Table B1 Hofstede’s (2012) Original country-specific scores

Appendix C

In order to inform future research, we conducted a post-hoc exploratory analysis and tested all possible interaction effects in one model. The results are shown in Table C1. There were a few noteworthy results. First, one theorized interaction changed when running the full model – that is the interaction effect between application utility and masculinity on continued intention to use became non-significant. In the research model, the results showed that this interaction effect was significant. Second, the full model also included interaction effects that were not theorized in our work. For instance, the full model included interaction effects of application design and uncertainty avoidance as well as application design and long-term orientation on continued intention to use. The results from the full model test showed that these interaction effects were found to be non-significant. This was expected given that we were unable to find any prior work suggesting such interactions. Likewise, we found little support in the existing literature for interaction effects of application utility and uncertainty avoidance as well as application utility and long-term orientation on continued intention to use. The results from the full model showed that both interaction effects were tested non-significant. We also did not find any literature suggesting interaction effects between interface graphics and individualism/collectivism on continued intention to use. The results from the full model test supported this and the interaction effects were non-significant. In contrast, the results of the full model test suggested that there was a significant interaction between interface graphics and masculinity/femininity on continued intention to use. This was unexpected because we could not find any prior work on the basis of which such an interaction could be predicted. A reasonable explanation for this might be that individuals high on feminism appreciate well-designed interface graphics because they allow them to interact with others effectively and build relationships successfully through mobile social media applications. The results from the full model test confirmed that the interaction effects between interface graphics and power distance on continued intention to use were found to be non-significant. We also found little prior work suggesting interaction effects between interface structure and individualism/collectivism, masculinity/femininity and power distance on continued intention to use. The results from the full model test confirmed that these interactions were non-significant. The existing literature also did not suggest that there are interaction effects between interface output and individualism/collectivism, masculinity/femininity and power distance on continued intention to use. The results from the full model test were in line with this and these interaction effects were found to be non-significant.

Table C1 Full research model including all interaction effects

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hoehle, H., Zhang, X. & Venkatesh, V. An espoused cultural perspective to understand continued intention to use mobile applications: a four-country study of mobile social media application usability. Eur J Inf Syst 24, 337–359 (2015). https://doi.org/10.1057/ejis.2014.43

Download citation

Keywords

  • culture
  • espoused national culture
  • mobile social media applications
  • continued intention to use