Technology acceptance and critical mass: Development of a consolidated model to explain the actual use of mobile health care communication tools

Thomas F. Byrd*, Jane S. Kim, Chen Yeh, Jungwha Lee, Kevin J. O'Leary

*Corresponding author for this work

Research output: Contribution to journalComment/debatepeer-review

1 Scopus citations

Abstract

Objective: Secure mobile communication technologies are being implemented at an increasing rate across health care organizations, though providers’ use of these tools can remain limited by a perceived lack of other users to communicate with. Enabling acceptance and driving provider utilization of these tools throughout an organization requires attention to the interplay between perceived peer usage (i.e. perceived critical mass) and local user needs within the social context of the care team (e.g. inpatient nursing access to the mobile app). To explain these influences, we developed and tested a consolidated model that shows how mobile health care communication technology acceptance and utilization are influenced by the moderating effects of social context on perceptions about the technology. Methods: The theoretical model and questionnaire were derived from selected technology acceptance models and frameworks. Survey respondents (n = 1254) completed items measuring perceived critical mass, perceived usefulness, perceived ease of use, personal innovativeness in information technology, behavioral intent, and actual use of a recently implemented secure mobile communication tool. Actual use was additionally measured by logged usage data. Use group was defined as whether a hospital's nurses had access to the tool (expanded use group) or not (limited use group). Results: The model accounted for 61% and 72% of the variance in intent to use the communication tool in the limited and expanded use groups, respectively, which in turn accounted for 53% and 33% of actual use. The total effects coefficient of perceived critical mass on behavioral intent was 0.57 in the limited use group (95% CI 0.51–0.63) and 0.70 in the expanded use group (95% CI 0.61–0.80). Conclusion: Our model fit the data well and explained the majority of variance in acceptance of the tool amongst participants. The overall influence of perceived critical mass on intent to use the tool was similarly large in both groups. However, the strength of multiple model pathways varied unexpectedly by use group, suggesting that combining sociotechnical moderators with traditional technology acceptance models may produce greater insights than traditional technology acceptance models alone. Practically, our results suggest that healthcare institutions can drive acceptance by promoting the recruitment of early adopters though liberal access policies and making these users and the technology highly visible to others.

Original languageEnglish (US)
Article number103749
JournalJournal of Biomedical Informatics
Volume117
DOIs
StatePublished - May 2021

Keywords

  • Mobile communication
  • Moderation
  • Sociotechnical
  • Structural equation modeling
  • Technology acceptance
  • Vocera

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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