Profiling information technology users: En route to dynamic personalization

Colin G. DeYoung, Ian Spence

Research output: Contribution to journalArticlepeer-review

35 Scopus citations

Abstract

This study describes the initial stages in the development of a Technology Profile Inventory (TPI) and its potential use in a variety of contexts, including dynamic personalization. The initial version of the TPI was constructed based on the responses of 318 participants. Factor analysis was used to establish the major components of individuals' attitudes toward information technology. The factors extracted demonstrate the utility of updating measures of computer attitudes to include items related to the Internet and to a broader range of attitudes. The factors of the TPI were correlated with a variety of demographic and usage variables. Gender differences were found for most TPI factors, though not for approval of information technology, and possible explanations of these differences are discussed. Correlations with usage variables provided both convergent and divergent validation, as both past experience and present use of computers and the Internet were associated with more positive TPI attitudes, but cellular phone use was unrelated to all TPI factors.

Original languageEnglish (US)
Pages (from-to)55-65
Number of pages11
JournalComputers in Human Behavior
Volume20
Issue number1
DOIs
StatePublished - Jan 2004
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by grants to Ian Spence from the Bell University Laboratories (BUL), the Communications and Information Technology Ontario (CITO), and a Collaborative Research and Development (CRD) grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada. We thank Colleen Ray for generating many items in the initial pool of items. We also thank Dilara Arkin and Patrick Wong for their help with the execution of this study.

Keywords

  • Attitudes
  • Factor analysis
  • Information technology
  • Personality measures
  • Personalization
  • Technology profiling

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