Exploring Boundary Conditions of the Impact of Accessibility to Mobile Networks on Employees’ Perceptions of Presenteeism: from Both Individual and Social Perspectives

  • Jinbi YangEmail author
  • Chunxiao Yin


Mobile networks, such as Wi-Fi and networks provided by mobile operators, are present everywhere nowadays and help employees to deal with business-related issues. With accessibility to mobile networks, employees perceive that they are constantly reachable to others, which is defined as “presenteeism”. With the importance of presenteeism in mind, this study aims to explore under what conditions employees’ perceptions of presenteeism based on accessibility to mobile networks can be increased or reduced. Based on self-determination theory and normative social influence, both individual-level (i.e., need for autonomy and need for relatedness) and social-level (i.e., norm of responsiveness) boundary conditions are indicated. Data was collected from 223 employees who use mobile technology at work. Our empirical results show that need for relatedness positively moderates the relationships between accessibility to mobile networks on employees’ perceptions of presenteeism. We also found that norm of responsiveness negatively moderates the relationships between accessibility to mobile networks on employees’ perceptions of presenteeism. This study contributes to the literature on presenteeism as well offers guidelines for practitioners.


Mobile technology Accessibility Presenteeism Psychological needs Normative social influence 



  1. Ahuja, M. K., Chudoba, K. M., Kacmar, C. J., McKnight, D. H., & George, J. F. (2007). IT road warriors: Balancing work-family conflict, job autonomy, and work overload to mitigate turnover intentions. MIS Quarterly, 31(1), 1–17.Google Scholar
  2. Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858.Google Scholar
  3. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.Google Scholar
  4. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.Google Scholar
  5. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory: Prentice-hall, Inc.Google Scholar
  6. Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a source and symbol of stress. Organization Science, 22(4), 887–906.Google Scholar
  7. Bittman, M., Brown, J. E., & Wajcman, J. (2009). The mobile phone, perpetual contact and time pressure. Work, Employment and Society, 23(4), 673–691.Google Scholar
  8. Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge.Google Scholar
  9. Carpenter, D., McLeod, A., Hicks, C., & Maasberg, M. (2018). Privacy and biometrics: An empirical examination of employee concerns. Information Systems Frontiers, 20(1), 91–110.Google Scholar
  10. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (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.Google Scholar
  11. Chou, C.-H., Wang, Y.-S., & Tang, T.-I. (2015). Exploring the determinants of knowledge adoption in virtual communities: A social influence perspective. International Journal of Information Management, 35(3), 364–376.Google Scholar
  12. Day, A., Scott, N., & Kelloway, E. K. (2010). Information and communication technology: Implications for job stress and employee well-being. Research in Occupational Stress and Well Being, 8, 317–350.Google Scholar
  13. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer.Google Scholar
  14. Deci, E. L., & Ryan, R. M. (2004). Handbook of self-determination research. Univ of Rochester Pr.Google Scholar
  15. Deci, E. L., Ryan, R. M., Gagne, M., Leone, D. R., Usunov, J., & Kornazheva, B. P. (2001). Need satisfaction, motivation, and well-being in the work organizations of a former eastern bloc country: A cross-cultural study of self-determination. Personality and Social Psychology Bulletin, 27(8), 930–942.Google Scholar
  16. Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629–636.Google Scholar
  17. Donmeza, B., Matsonb, Z., Savanb, B., Farahanib, E., Photiadisb, D., & Dafoeb, J. (2014). Interruption management and office norms: Technology adoption lessons from a product commercialization study. International Journal of Information Management, 34(6), 741–750.Google Scholar
  18. Edwards, J. R. (1996). An examination of competing versions of the person-environment fit approach to stress. Academy of Management Journal, 39(2), 292–339.Google Scholar
  19. Erskine, M. A., Gregg, D. G., Karimi, J., & Scott, J. E. (2018). Individual decision-performance using spatial decision support systems: A geospatial reasoning ability and perceived task-technology fit perspective. Information Systems Frontiers.
  20. Folkman, S., Lazarus, R. S., Dunkel-Schetter, C., DeLongis, A., & Gruen, R. J. (1986). Dynamics of a stressful encounter: Cognitive appraisal, coping, and encounter outcomes. Journal of Personality and Social Psychology, 50(5), 992–1003.Google Scholar
  21. Gebauer, J., & Shaw, M. J. (2004). Success factors and impacts of Mobile business applications: Results from a Mobile e-procurement study. International Journal of Electronic Commerce, 8(3), 19–41.Google Scholar
  22. Gebauer, J., Shaw, M. J., & Gribbins, M. L. (2010). Task-technology fit for mobile information systems. Journal of Information Technology, 25, 259–272.Google Scholar
  23. Gefen, D., Straub, D. W., & Rigdon, E. E. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), iii–xiv.Google Scholar
  24. Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285–292.Google Scholar
  25. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.Google Scholar
  26. Goodhue, D., Lewis, W., & Thompson, R. (2007). Statistical power in analyzing interaction effects: Questioning the advantage of PLS with product indicators. Information Systems Research, 18(2), 211–227.Google Scholar
  27. Hong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information appliances: The case of Mobile data services. Information Systems Research, 17(2), 162–179.Google Scholar
  28. Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65–74.Google Scholar
  29. Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853–868.Google Scholar
  30. Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.Google Scholar
  31. Hung, W. H., Chang, L. M., & Lin, C. H. (2011). Managing the risk of overusing Mobile phones in the working environment: A study of ubiquitous technostress. Paper presented at the Pacific Asia Conference on Information Systems.Google Scholar
  32. Isaac, H., & Leclercq, A. (2006). Assessing the value of Mobile technologies in organizations: An exploratory research. Paper presented at the International Conference on Mobile Business.Google Scholar
  33. Jarvenpaa, S. L., & Lang, K. R. (2005). Managing the paradoxes of Mobile technology. Information Systems Management, 22(4), 7–23.Google Scholar
  34. Junglas, I., & Watson, R. (2003). U-commerce: An experimental investigation of ubiquity and uniqueness. Paper presented at the International Conference on Information Systems.Google Scholar
  35. Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061–1072.Google Scholar
  36. Ke, W., & Zhang, P. (2010). The effects of extrinsic motivations and satisfaction in open source software development. Journal of the Association for Information Systems, 11(12), 5.Google Scholar
  37. Kelman, H. C. (1958). Compliance, identification, and internalization: Three processes of attitude change. The Journal of Conflict Resolution, 2(1), 51–60.Google Scholar
  38. Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333.Google Scholar
  39. Kim, D., Cavusgil, S. T., & Calantone, R. J. (2006). Information system innovations and supply chain management: Channel relationships and firm performance. Journal of the Academy of Marketing Science, 34(1), 40–54.Google Scholar
  40. Lee, D. J., Sirgy, M. J., Brown, J. R., & Bird, M. M. (2004). Importers’ benevolence toward their foreign export suppliers. Journal of the Academy of Marketing Science, 32(1), 32–48.Google Scholar
  41. Lien, C.-H., Cao, Y., & Zhou, X. (2017). Service quality, satisfaction, stickiness, and usage intentions: An exploratory evaluation in the context of WeChat services. Computers in Human Behavior, 68, 403–410.Google Scholar
  42. Mazmanian, M., Yates, J., & Orlikowski, W. (2006). Ubiquitous email: Individual experiences and organizational consequences of BlackBerry use. Paper presented at the Academy of Management Best Conference.Google Scholar
  43. Middleton, C. A., & Cukier, W. (2006). Is Mobile email functional or dysfunctional? Two perspectives on Mobile email usage. European Journal of Information Systems, 15(3), 252–260.Google Scholar
  44. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.Google Scholar
  45. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.Google Scholar
  46. Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417–433.Google Scholar
  47. Roca, J. C., & Gagné, M. (2008). Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. Computers in Human Behavior, 24(4), 1585–1604.Google Scholar
  48. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78.Google Scholar
  49. Sharma, S. K. (2017). Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling. Information Systems Frontiers.
  50. Sheldon, K. M., & Bettencourt, B. (2002). Psychological need satisfaction and subjective well being within social groups. British Journal of Social Psychology, 41(1), 25–38.Google Scholar
  51. Shen, A. X., Cheung, C. M., Lee, M. K., & Chen, H. (2011). How social influence affects we-intention to use instant messaging: The moderating effect of usage experience. Information Systems Frontiers, 13(2), 157–169.Google Scholar
  52. Shih, H. P., & Huang, E. (2014). Influences of web interactivity and social identity and bonds on the quality of online discussion in a virtual community. Information Systems Frontiers, 16(4), 627–641.Google Scholar
  53. Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328.Google Scholar
  54. Tarafdar, M., Tu, Q., & Ragu-Nathan, T. (2010). Impact of technostress on end-user satisfaction and performance. Journal of Management Information Systems, 27(3), 303–334.Google Scholar
  55. Tsai, H.-T., & Bagozzi, R. P. (2014). Contribution behavior in virtual communities: Cognitive, emotional and social influences. MIS Quarterly, 38(1), 143–163.Google Scholar
  56. Tsai, H.-T., & Pai, P. (2014). Why do newcomers participate in virtual communities? An integration of self-determination and relationship management theories. Decision Support Systems, 57, 178–187.Google Scholar
  57. Van den Broeck, A., Vansteenkiste, M., De Witte, H., & Lens, W. (2008). Explaining the relationships between job characteristics, burnout, and engagement: The role of basic psychological need satisfaction. Work and Stress, 22(3), 277–294.Google Scholar
  58. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.Google Scholar
  59. Wang, Y., Meister, D. B., & Gray, P. H. (2013). Social influence and knowledge management systems use: Evidence from panel data. MIS Quarterly, 37(1), 299–313.Google Scholar
  60. Williams, L. J., Cote, J. A., & Buckley, M. R. (1989). Lack of method variance in self-reported affect and perceptions at work: Reality or artifact? Journal of Applied Psychology, 74(3), 462–468.Google Scholar
  61. Zhang, M., Guo, L., Huc, M., & Liud, W. (2016). Influence of customer engagement with company social networks on stickiness: Mediating effect of customer value creation. International Journal of Information Management, 37(3), 229–240.Google Scholar
  62. Zhou, T., & Li, H. (2014). Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Computers in Human Behavior, 37, 283–289.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of BusinessJiangnan UniversityWuxiChina
  2. 2.Computer and Information ScienceSouthwest UniversityChongqingChina

Personalised recommendations