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Investigation of moderator factors in e-business adoption: A quantitative meta-analysis of moderating effects on the drivers of intention and behavior

Boštjan Šumak¹, Marjan Heričko¹, Zoran Budimac² and Maja Pušnik¹

1. Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000 Maribor
   University of Maribor, Slomškov trg 15, 2000 Maribor, Slovenia
   {bostjan.sumak, marjan.hericko, maja.pusnik}@um.si
2. Faculty of Sciences, University of Novi Sad, Trg D. Obradovića 4
   21000 Novi Sad, Serbia
   zjb@dmi.uns.ac.rs

Abstract

E-business technology is becoming one of the most important global markets where e-business solutions will have to adapt to new technologies. The main objective in this study was to synthesize existing knowledge in the field of e-business technology acceptance and to understand differences in Technology Acceptance Model (TAM) related causal effect sizes for different e-business contexts. A quantitative meta-analysis of existing empirical research about factors affecting e-business adoption was conducted using 89 published papers that provided empirical data about causal relationships. A moderator analysis was carried out to investigate the moderating effect of four factors: consumer type, device type, continent and respondent type. The results of the study showed a moderating effect for all four proposed factors in almost all TAM-related causal paths. The study also showed that TAM is the most common theory being applied in e-business adoption research.

Key words

e-business acceptance; meta-analysis; moderator factors analysis; TAM; UTAUT; B2C; B2B

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS160902033S

Publication information

Volume 14, Issue 1 (January 2017)
Year of Publication: 2017
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Šumak, B., Heričko, M., Budimac, Z., Pušnik, M.: Investigation of moderator factors in e-business adoption: A quantitative meta-analysis of moderating effects on the drivers of intention and behavior. Computer Science and Information Systems, Vol. 14, No. 1, 75–102. (2017), https://doi.org/10.2298/CSIS160902033S