Journal of Business Economics and Management

Could e-commerce activities drive to climate change mitigation? Novel evidence from panel quantile regression model

2025-04-15T16:23:34+03:00

This study aims at shedding light on how e-commerce contributes to climate change in European Union countries. To explore this relationship, we introduced a novel index designed to measure the evolution of climate change as the dependent variable, while considering e-commerce activities as the independent variable within 2011 to 2022 period. Our investigation of the correlation between these variables employed a panel quantile regression model. The outcomes revealed a statistically significant increasing trend for higher quantiles. This finding indicates that, as we traverse the climate change index from lower to higher percentiles, the impact of e-commerce becomes more conspicuous and is statistically significant. This underscores that the influence of e-commerce on climate change intensifies as we move towards the upper percentiles of the climate change index. To address how e-commerce affects climate change, it becomes imperative to introduce and enforce the concept of environmental responsibility in the management of e-commerce activities. This could involve adopting sustainable practices, optimizing packaging materials, and encouraging eco-friendly delivery methods. Policymakers and businesses alike should consider these findings in their efforts to strike an equilibrium between the convenience of e-commerce and the need to curb its environmental impact.

Determinants of AI adoption intention in SMEs. Romanian case study

2025-04-18T18:31:40+03:00

The paper investigates the drivers and barriers that encourage or hinder the adoption of artificial intelligence (AI) technologies within Romanian SMEs. By using the Technology-Organisation-Environment (TOE) framework, we examined the role of several factors from each TOE dimension in predicting the AI adoption behaviour. The factors were constructed through factor analysis followed by the estimation of a linear regression model. Partial least squares structural equation modelling was then used in order to further explore the relationships and to check the robustness of the linear regression model. Our findings highlight the significant role played by leadership, organizational readiness, as well as the “push-and-pull” effect of competitors and customers in encouraging SMEs to adopt AI technologies. However, in the case of Romania, specific challenges related to a lack of digital skills among employees, a limited understanding of the relative advantage that digitalisation can offer, as well as a lack of marketing efforts from the side of vendors make it difficult for SMEs to consider the implementation of AI technologies. This exploratory study seeks to understand the underlying trends of the phenomenon and serves as a stepping stone for vendors, managers, as well as researchers to better understand the market for AI tools and solutions among Romanian SMEs.