956
Views
19
CrossRef citations to date
0
Altmetric
Original Articles

The intranet's role in newcomer socialization in the hotel industry in Taiwan – technology acceptance model analysis

&
Pages 1163-1179 | Published online: 24 Mar 2011
 

Abstract

This study examined a research model for explaining the relationships of intranet adoption and newcomers' organizational socialization in the hotel industry. Data were gathered for a structural equation model (SEM) analysis, from 298 individual participants, who had only worked in a hotel for 6 months to 1 year. The research results demonstrated the mediation role of the perceived usefulness (PU) and perceived ease of use of the intranet. The SEM results also revealed that usage of the intranet did increase the extent of an employee's socialization into the organization. Moreover, PU has direct and indirect effects on socialization. Tests on gender and age differences were conducted. The invariance analysis of the theory model showed that both males and females demonstrated the same patterns in performing technology acceptance factors and socialization, whereas different age groups demonstrated significantly different paths. The direct effect of PU was not significant for the newcomers aged over 35.

Acknowledgements

This research work was supported by National Science Council, Taiwan, under grant number NSC96-2511-S-011-002-MY3, NSC90-2416-H-011-013, and NSC90-2413-H-031-002.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 352.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.