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SubmittedOpen Access (License Unspecified), Open
Journal article
Controlling for Lexical Closeness in Survey Research: A Demonstration on the Technology Acceptance Model
Journal of the Association for Information Systems, v 18(10), pp 727-757
01 Oct 2017
Abstract
Word co-occurrences in text carry lexical information that can be harvested by data-mining tools such as latent semantic analysis (LSA). In this research perspective paper, we demonstrate the potency of using such embedded information by demonstrating that the technology acceptance model (TAM) can be reconstructed significantly by analyzing unrelated newspaper articles. We suggest that part of the reason for the phenomenal statistical validity of TAM across contexts may be related to the lexical closeness among the keywords in its measurement items. We do so not to critique TAM but to praise the quality of its methodology. Next, putting that LSA reconstruction of TAM into perspective, we show that empirical data can provide a significantly better fitting model than LSA data can. Combined, the results raise the possibility that a significant portion of variance in survey based research results from word co-occurrences in the language itself regardless of the theory or context of the study. Addressing this possibility, we suggest a method to statistically control for lexical closeness.
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Details
- Title
- Controlling for Lexical Closeness in Survey Research: A Demonstration on the Technology Acceptance Model
- Creators
- David Gefen - Drexel UniversityKai Larsen - Univ Colorado, Informat Syst, Div Org Leadership & Informat Analyt, Leeds Sch Business, Boulder, CO 80309 USAUniversity of Colorado
- Publication Details
- Journal of the Association for Information Systems, v 18(10), pp 727-757
- Publisher
- Assoc Information Systems
- Number of pages
- 31
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000416356000003
- Scopus ID
- 2-s2.0-85032887586
- Other Identifier
- 991019169706604721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Information Science & Library Science