Adoption of Smartphone Apps by Hotel Guests: The Roles of Trust and Word of Mouth

  • Norman ShawEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9752)


With the growth of smartphones, organizations are increasingly engaging with their customers through mobile applications (apps). In the context of hotels, guests are able to receive enhanced services during their stay if they provide additional personal information. They learn about these applications through word of mouth and in order to adopt the apps, they must have a degree of trust in the hotel that their data is safe. In order to guide practitioners in their app development, this research empirically tests the influence of trust and word of mouth. The research model is based on extending the Technology Acceptance Model, focusing on guests’ use of smartphone apps during their hotel stay. Analysis of the responses from US guests indicates that consumers learn about new applications by word of mouth and are influenced by perceived usefulness, which is mediated by trust.


Technology acceptance Trust Word of mouth Hotel smartphone apps PLS 


  1. 1.
    Carayannis, E.G., Clark, S.C., Valvi, D.E.: Smartphone affordance: achieving better business through innovation. J. Knowl. Econ. 4(4), 444–472 (2013)CrossRefGoogle Scholar
  2. 2.
    IDC: Despite a Strong 2013, Worldwide Smartphone Growth Expected to Slow to Single Digits by 2017, According to IDC (2014).
  3. 3.
    Berkus, D.: Ride the prevailing winds… Hotel Yearbook 2014 Special Editon on Technology (2013). 76Google Scholar
  4. 4.
    Shea, T.: Unlocking opportunities…and the door to your hotel room. Hotel Yearbook 2014 Special Edition on Technology (2013). 76Google Scholar
  5. 5.
    Kucukusta, D., et al.: Re-examining perceived usefulness and ease of use in online booking: The case of Hong Kong online users. Int. J. Contemp. Hospitality Manage. 27(2), 185–198 (2015)CrossRefGoogle Scholar
  6. 6.
    eMarketer US Mobile Travel Sales to Increase 60 % in 2014. eMarketer (2014)Google Scholar
  7. 7.
    Chen, M.-M., Knecht, S., Murphy, H.C.: An Investigation of Features and Functions of Smartphone Applications for Hotel Chains (2014)Google Scholar
  8. 8.
    Goh, D.H., et al.: Determining services for the mobile tourist. J. Comput. Inf. Syst. 51(1), 31 (2010)Google Scholar
  9. 9.
    Nancy Trejos, n., Hotels’ mobile apps act as concierge, butler for guests, in Gannett News Service McLean (2013)Google Scholar
  10. 10.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  11. 11.
    Gefen, D., Karahanna, E., Straub, D.W.: Trust and TAM in online shopping: An integrated model. MIS Q. 27(1), 51 (2003)Google Scholar
  12. 12.
    Lee, J.-H., Song, C.-H.: Effects of trust and perceived risk on user acceptance of a new technology service. Soc. Behav. Pers. Int. J. 41(4), 587–597 (2013)CrossRefGoogle Scholar
  13. 13.
    Jalilvand, M.R., Samiei, N.: The impact of electronic word of mouth on a tourism destination choice: Testing the theory of planned behavior (TPB). Internet Res. 22(5), 591–612 (2012)CrossRefGoogle Scholar
  14. 14.
    King, W.R., He, J.: A meta-analysis of the technology acceptance model. Inf. Manage. 43(6), 740–755 (2006)CrossRefGoogle Scholar
  15. 15.
    Ma, Q., Liu, L.: The technology acceptance model: A meta-analysis of empirical findings. J. Organ. End User Comput. (JOEUC) 16(1), 59–72 (2004)CrossRefGoogle Scholar
  16. 16.
    Wu, J.-H., Wang, S.-C.: What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inf. Manage. 42(5), 719 (2005)CrossRefGoogle Scholar
  17. 17.
    Kwon, M.J., Bae, J., Blum, S.C.: Mobile applications in the hospitality industry. J. Hospitality Tourism Technol. 4(1), 81–92 (2013)CrossRefGoogle Scholar
  18. 18.
    Kim, M., Qu, H.: Travelers’ behavioral intention toward hotel self-service kiosks usage. Int. J. Contemp. Hospitality Manage. 26(2), 225–245 (2014)CrossRefGoogle Scholar
  19. 19.
    Kim, J., Connolly, D.J., Blum, S.: Mobile technology: an exploratory study of hotel managers. Int. J. Hospitality Tourism Adm. 15(4), 417–446 (2014)CrossRefGoogle Scholar
  20. 20.
    McKnight, D.H., Choudhury, V., Kacmar, C.: Developing and validating trust measures for e-commerce: An integrative typology. Inf. Syst. Res. 13(3), 334–359 (2002)CrossRefGoogle Scholar
  21. 21.
    Wang, L., et al.: Impact of hotel website quality on online booking intentions: eTrust as a mediator. Int. J. Hospitality Manage. 47, 108–115 (2015)CrossRefGoogle Scholar
  22. 22.
    Dahlberg, T., Mallat, N., Öörni, A.: Trust enhanced technology acceptance model - consumer acceptance of mobile payment solutions tentative evidence. Stockholm Mobility Roundtable. Citeseer (2003)Google Scholar
  23. 23.
    Ulmanen, H.: Antecedents of and their effect on trust in online word-of-mouth: case Finnish discussion forums (2011)Google Scholar
  24. 24.
    Cheung, C.M., Xiao, B., Liu, I.L.: The impact of observational learning and electronic word of mouth on consumer purchase decisions: the moderating role of consumer expertise and consumer involvement. In: System Science (HICSS), 2012 45th Hawaii International Conference on System Sciences. IEEE (2012)Google Scholar
  25. 25.
    Parry, M.E., Kawakami, T., Kishiya, K.: The effect of personal and virtual word-of-mouth on technology acceptance. J. Product Innov. Manage. 29(6), 952–966 (2012)CrossRefGoogle Scholar
  26. 26.
    Litvin, S.W., Goldsmith, R.E., Pan, B.: Electronic word-of-mouth in hospitality and tourism management. Tourism Manage. 29(3), 458–468 (2008)CrossRefGoogle Scholar
  27. 27.
    Gefen, D., Straub, D.W., Boudreau, M.C.: Structural equation modeling and regression: guidelines for research practice. Commun. AIS 4(7), 1–77 (2000)Google Scholar
  28. 28.
    Hair, J.F., et al.: A Primer on Partial Least Squares Structural Equations Modeling (PLS-SEM). SAGE Publications, Thousand Oaks (2014)zbMATHGoogle Scholar
  29. 29.
    Wetzels, M., Odekerken-Schröder, G., Van Oppen, C.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33(1), 177–195 (2009)Google Scholar
  30. 30.
    Preacher, K.J., Hayes, A.F.: SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behav. Res. Methods Instr. Comput. 36(4), 717–731 (2004)CrossRefGoogle Scholar
  31. 31.
    Henseler, J., Ringle, C.M., Sinkovics, R.R.: The use of partial least squares path modeling in international marketing. Adv. Int. Mark. 20, 277–319 (2009)Google Scholar
  32. 32.
    Cronbach, L.J., Meehl, P.E.: Construct validity in psychological tests. Psychol. Bull. 52(4), 281–302 (1955)CrossRefGoogle Scholar
  33. 33.
    Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50 (1981)CrossRefGoogle Scholar
  34. 34.
    Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 19(2), 139–152 (2011)CrossRefGoogle Scholar
  35. 35.
    Shrout, P.E., Bolger, N.: Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol. Methods 7(4), 422 (2002)CrossRefGoogle Scholar
  36. 36.
    Legris, P., Ingham, J., Collerette, P.: Why do people use information technology? A critical review of the technology acceptance model. Inf. Manage. 40(3), 191–204 (2003)CrossRefGoogle Scholar
  37. 37.
    Turner, M., et al.: Does the technology acceptance model predict actual use? A systematic literature review. Inf. Softw. Technol. 52(5), 463–479 (2010)CrossRefGoogle Scholar
  38. 38.
    Duane, A., O’Reilly, P., Andreev, P.: Realising M-Payments: modelling consumers’ willingness to M-pay using Smart Phones. Behav. Inf. Technol. 33(4), 318–334 (2012)CrossRefGoogle Scholar
  39. 39.
    Zhang, L., Zhu, J., Liu, Q.: A meta-analysis of mobile commerce adoption and the moderating effect of culture. Comput. Hum. Behav. 28(5), 1902–1911 (2012)CrossRefGoogle Scholar
  40. 40.
    Kim, D.Y., Park, J., Morrison, A.M.: A model of traveller acceptance of mobile technology. Int. J. Tour. Res. 10(5), 393–407 (2008)CrossRefGoogle Scholar
  41. 41.
    Gefen, D., Straub, D.W.: The relative importance of perceived ease of use in IS adoption: a study of e-commerce adoption. JAIS 1, 1–30 (2000)Google Scholar
  42. 42.
    Null, C.: Best Hotel Apps. In: Travel & Leisure (2014)Google Scholar
  43. 43.
    NewsRX: Intelity; Intelity Releases Mobile Hotel Platform for 21st-Century Travelers. J. Eng. 737 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Ryerson UniversityTorontoCanada

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