The impact of organizational factors on-business adoption: An empirical analysis

Abstract

Purpose: Provide empirical validation of the model developed by García Moreno et al. (2016) on the factors influencing the adoption of e-business in firms.Design/methodology/approach: Consideration is given to the method for measuring each one of the variables included in the model. Use has been made of the e-Business Watch database, which contains measures for the theoretical model’s three categories: firm, technology, and environment. Multinomial logistic regression models have been provided.Findings: The variables included have revealed significant statistical relationships for the model in question, although the intensity of the relationships differs. the variables related to the environment also reveal statistically significant relationships, whereby the attitude of trading partners appears to have a relevant and growing impact on e-business adoption.Research limitations/implications: Data come from just one database: the e-Business Watch database/enriched data from alternative databases could be included. Practical implications: The infrastructure of information and communications technologies (ICTs) is confirmed to be a determining factor in e-business development. Nevertheless, the effect of competitor rivalry has a more erratic influence that is encapsulated in a significant relationship in intermediate models, with a sharper increase in the likelihood of being in the category of customer-focused firms, and less internally focused.Social implications: The human capital linked to ICTs is a driving force behind the adoption of these practices. Albeit with a more moderate effect, note should also be taken of the capacity for entering into relationships with third parties within the scope of ICTs, with significant effects that become more robust as they are tested in models that seek to explain the probability of recording higher levels of e-business adoption. Originality/value: The article presents a first empirical analysis to apply the previous developed model published in this journal in 2016.