Post-Adoption Variations in Usage and Value of E-Business by Organizations: Cross-Country Evidence from the Retail Industry
Open accessAbstract
Grounded in the innovation diffusion literature and the resource-based theory, this paper develops an integrative research model for assessing the diffusion and consequence of e-business at the firm level. Unlike the typical focus on adoption as found in the literature, we focus on postadoption stages, that is, actual usage and value creation. The model thus moves beyond dichotomous “adoption versus nonadoption” and accounts for the “missing link”—actual usage—as a critical stage of value creation. The model links technological, organizational, and environmental factors to e-business use and value, based on which a series of hypotheses are developed. The theoretical model is tested by using structural equation modeling on a dataset of 624 firms across 10 countries in the retail industry. To probe deeper into whether e-business use and value are influenced by economic environments, two subsamples from developed and developing countries are compared. The study finds that technology competence, firm size, financial commitment, competitive pressure, and regulatory support are important antecedents of e-business use. In addition, the study finds that, while both front-end and back-end capabilities contribute to e-business value, back-end integration has a much stronger impact. While front-end functionalities are becoming commodities, e-businesses are more differentiated by back-end integration. This is consistent with the resource-based theory because back-end integration possesse…
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Smaller enterprises often lack the requisite resources to be entrepreneurial.
“Other studies (Hwang et al., 2004;Zhu & Kraemer, 2005;Zhu et al., 2003) emphasize that smaller enterprises often lack the requisite resources to be entrepreneurial.”
From Using T-O-E theoretical framework to study the adoption of ERP solution (2016) · cites this paper as Zhu & Kraemer, 2005
Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314 · see citing works
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190 · see citing works
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194 · see citing works
Firm Resources and Sustained Competitive Advantage
Barney, J. (1991) · Journal of Management · doi:10.1177/014920639101700108 · Cited by 48,334 · see citing works
Bharadwaj, A. S. (2000) · MIS Quarterly · doi:10.2307/3250983 · Cited by 5,042 · see citing works
An Integrated Model of Information Systems Adoption in Small Businesses
Thong, J. Y. L. (1999) · Journal of Management Information Systems · doi:10.1080/07421222.1999.11518227 · Cited by 1,727 · see citing works
Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective1
Teo, H. H., Wei, K. K., & Benbasat, I. (2003) · MIS Quarterly · doi:10.2307/30036518 · Cited by 1,639 · cited 3× · see citing works
“Hence, we include technology competence in our model.5 Second, e-business is also influenced by organizational factors because these variables may constrain or facilitate the implementation and usage of e-business (Teo et al. 2003).”
BackgroundThe emerging role of electronic marketplaces on the Internet
Bakos, Y. (1998) · Communications of the ACM · doi:10.1145/280324.280330 · Cited by 1,386 · cited 3× · see citing works
“The Internet is characterized by open standard (versus proprietary standard), public network (versus private network), and broad connectivity (back end and front end) (Bakos 1998, Shapiro and Varian 1999).”
BackgroundInfluentialFactors Affecting the Adoption of Open Systems: An Exploratory Study1
Chau, P. Y. K., & Tam, K. Y. (1997) · MIS Quarterly · doi:10.2307/249740 · Cited by 1,067 · see citing works
The Assimilation of Software Process Innovations: An Organizational Learning Perspective
Fichman, R. G., & Kemerer, C. F. (1997) · Management Science · doi:10.1287/mnsc.43.10.1345 · Cited by 811 · see citing works
An Empirically Derived Model for the Adoption of Customer‐based Interorganizational Systems*
Grover, V. (1993) · Decision Sciences · doi:10.1111/j.1540-5915.1993.tb01295.x · Cited by 757 · cited 1× · see citing works
“Competitive pressure has long been recognized in the innovation diffusion literature as an important driver for technology diffusion (e.g., Grover 1993, Thong 1999).”
BackgroundAn Empirical Examination of the Concern for Information Privacy Instrument
Stewart, K. A., & Segars, A. H. (2002) · Information Systems Research · doi:10.1287/isre.13.1.36.97 · Cited by 734 · see citing works
Information Technology and Firm Boundaries: Evidence from Panel Data
Hitt, L. M. (1999) · Information Systems Research · doi:10.1287/isre.10.2.134 · Cited by 341 · see citing works
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Zhu, K., Kraemer, K. L., & Xu, S. (2006) · Management Science · doi:10.1287/mnsc.1050.0487 · Cited by 1,440 · see citing works
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Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350 · see citing works
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Chen, D. Q., Preston, D. S., & Swink, M. (2015) · Journal of Management Information Systems · doi:10.1080/07421222.2015.1138364 · Cited by 829 · see citing works
Understanding e-business adoption across industries in European countries
Oliveira, T., & Martins, M. F. (2010) · Industrial Management & Data Systems · doi:10.1108/02635571011087428 · Cited by 641 · see citing works
Assessing business value of big data analytics in European firms
Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.011 · Cited by 538 · see citing works
Zhu, K., Kraemer, K. L., Gurbaxani, V., & Xu, S. X. (2006) · MIS Quarterly · doi:10.2307/25148771 · Cited by 500 · see citing works
Self-citationAlbayati, H., Kim, S. K., & Rho, J. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101320 · Cited by 464 · see citing works
Determinants of e-business diffusion: A test of the technology diffusion perspective
Lin, H.-F., & Lin, S.-M. (2008) · Technovation · doi:10.1016/j.technovation.2007.10.003 · Cited by 447 · see citing works
Schryen, G. (2013) · European Journal of Information Systems · doi:10.1057/ejis.2012.45 · Cited by 420 · see citing works
Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020) · Industrial Marketing Management · doi:10.1016/j.indmarman.2019.11.009 · Cited by 347 · see citing works
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Awa, H. O., Ukoha, O., & Emecheta, B. C. (2016) · Cogent Business & Management · doi:10.1080/23311975.2016.1196571 · Cited by 272 · see citing works
“Other studies (Hwang, Ku, Yen, & Cheng, 2004; Zhu & Kraemer, 2005; Zhu et al., 2003) emphasize that adoption is slower among smaller enterprises because they rarely possess economy of scale advantage and the facilitating slacks as well as the resilience to bear the associated risks and to encourage…”
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Zhang, T. (., Agarwal, R., & Lucas, H. C. (2011) · MIS Quarterly · doi:10.2307/41409964 · Cited by 233 · see citing works
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Intan Salwani, M., Marthandan, G., Daud Norzaidi, M., & Choy Chong, S. (2009) · Information Management & Computer Security · doi:10.1108/09685220910964027 · Cited by 202 · see citing works
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Hazen, B. T., Overstreet, R. E., & Cegielski, C. G. (2012) · The International Journal of Logistics Management · doi:10.1108/09574091211226957 · Cited by 117 · see citing works
The assimilation of RFID technology by Chinese companies: A technology diffusion perspective
Wei, J., Lowry, P. B., & Seedorf, S. (2015) · Information & Management · doi:10.1016/j.im.2015.05.001 · Cited by 112 · see citing works
Yang, J., Luo, B., Zhao, C., & Zhang, H. (2022) · DIGITAL HEALTH · doi:10.1177/20552076221126034 · Cited by 81 · see citing works
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Schaefer, C., Lemmer, K., Samy Kret, K., Ylinen, M., Mikalef, P., & Niehaves, B. (2021) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.286 · Cited by 63 · see citing works
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Pumplun, L., Fecho, M., Wahl, N., Peters, F., & Buxmann, P. (2021) · Journal of Medical Internet Research · doi:10.2196/29301 · Cited by 57 · see citing works
The relationship between ERP capabilities, use, and value
Ruivo, P., Johansson, B., Sarker, S., & Oliveira, T. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103209 · Cited by 58 · see citing works
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Awa, H. O., Uko, J. P., & Ukoha, O. (2017) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2016.1265828 · Cited by 48 · see citing works
Ahmadi, H., Nilashi, M., Shahmoradi, L., Ibrahim, O., Sadoughi, F., Alizadeh, M., & Alizadeh, A. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.05.021 · Cited by 47 · see citing works
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Kassim, E. S., & Hussin, H. (2013) · International Journal of Electronic Government Research · doi:10.4018/jegr.2013010101 · Cited by 34 · see citing works
Weinert, L., Müller, J., Svensson, L., & Heinze, O. (2022) · JMIR Medical Informatics · doi:10.2196/34678 · Cited by 33 · see citing works
- Authors
- Zhu, K., & Kraemer, K. L.
- Affiliations
- University of California, Irvine (US)
- ORCID
- Zhu, Kevin
- Venue
- Information Systems Research
- Published
- 2005-03
- Volume
- 16
- Issue
- 1
- Pages
- 61-84
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2103371636
- Cited by
- 1,723
- References
- 13
- Citations by year
- 2026: 80 · 2025: 86 · 2024: 87 · 2023: 72 · 2022: 101 · 2021: 94 · 2020: 100 · 2019: 84 · 2018: 92 · 2017: 86 · 2016: 87 · 2015: 137 · 2014: 98 · 2013: 106 · 2012: 82
- FWCI
- 69.39
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Licence
- cc-by
- Locations
- Information Systems Research (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Keywords
- e-business adoption, retail trade, diffusion of innovations, resource-based view, technological competence, firm size, financial commitments, competitive pressure, regulatory support, international technology diffusion, developing countries
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