Determinants of E-Business Use in U.S. Firms

By Pei-Fang Hsu, Kenneth L. Kraemer, Debora E. Dunkle. International Journal of Electronic Commerce, 2006

338 citations recorded by the source corpus.

The factors leading to variations in e-business use among U.S. firms are identified. Building on diffusion of innovation theory, an integrated model is developed that explains the relative influence of eight known determinants. Diversity and volume of e-business use are empirically investigated using a sample of 294 firms. The analysis demonstrates that (1) considering the diversity of e-business use, pressure from trading partners is the most important driver, (2) when e-business volume is investigated, government pressure emerges as the strongest factor, (3) government promotion may not have much effect on the diversity of e-business use by private companies, but does significantly influence the volume of e-business use by firms doing business with the government, and (4) the United States has a positive regulatory environment for supporting e-business. Taken together, these findings on the multidimensionality of e-business use show that diversity and volume are not only different measures of e-business use, but also have different determinants. The integrated model provides a more comprehensive explanation of e-business use and could serve as a foundation for future research on interorganizational systems.

Subfields: Media Technology, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/43d7a7d8f52fe88b1bb93435cafda8d021f04fa5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Media Technology

1. Tiago Oliveira
2. Maria F. O. Martins

In Strategy and Management

1. Tiago Oliveira
2. Dianne J. Hall
3. Lin Jia
4. Maolin Ye
5. Ni Liu
6. Yuan Pan
7. Maria F. O. Martins
8. Shiwei Sun

Citations in the corpus

• Literature Review of Information Technology Adoption Models at Firm Level (2011, methodology): “The TOE framework makes Rogers’ innovation diffusion theory better able to explain intra-firm innovation diffusion (Hsu et al. 2006).”
• The adoption of artificial intelligence in employee recruitment: The influence of contextual factors (2021, methodology): “Some rather traditional industries, such as manufacturing sectors, are less likely to adopt new technology, because they have fewer technology demands (Hsu et al., 2006).”
• On the relationship between supplier integration and time-to-market (2013, background): “…such as enterprise software (Russel and Hoag, 2004), RFID (Ranganathan and Jha, 2005), web enabled supply chain management systems (Ranganathan et al., 2004), e-business technologies (Hsu et al., 2006), agile development (Martin, 2003; Cockburn, 2006), and open innovation (Chesbrough, 2003).”
• Understanding the Factors Affecting the Organizational Adoption of Big Data (2018, background): “…a single theory is not sufficient to explain such a complex process systematically, hence our decision to integrate three approaches, DOI theory, institutional theory and the TEO framework, to explain organizational adoption and inter-organizational innovation diffusion more effectively [17].”
• Understanding e-business adoption across industries in European countries (2010, background): “Other empirical studies also validate that positive perception of the benefits of an innovation provides an incentive for its use (Beatty et al., 2001; Gibbs and Kraemer, 2004; Grover and Teng, 1994; Hsu et al., 2006; Iacovou et al., 1995; Kuan and Chau, 2001; Lin and Lin, 2008; Premkumar et al., 19”