A Model for Decision-Makers’ Adoption of Big Data in the Education Sector

Factors for the implementation of the circular economy in Big Data environments in service companies in post pandemic times of COVID-19: The case of Colombia

In emerging economies, Big Data (BD) analytics has become increasingly popular, particularly regarding the opportunities and expected benefits. Such analyzes have identified that the production and consumption of goods and services, while unavoidable, have proven to be unsustainable and inefficient. For this reason, the concept of the circular economy (CE) has emerged strongly as a sustainable approach that contributes to the eco-efficient use of resources. However, to develop a circular economy in DB environments, it is necessary to understand what factors influence the intention to accept its implementation. The main objective of this research was to assess the influence of attitudes, subjective norms, and perceived behavioral norms on the intention to adopt CE in BD-mediated environments. The methodology is quantitative, cross-sectional with a descriptive correlational approach, based on the theory of planned behavior and a Partial Least Squares Structural Equation Model (PLS-SEM). A total of 413 Colombian service SMEs participated in the study. The results show that managers' attitudes, subjective norms, and perceived norms of behavior positively influence the intentions of organizations to implement CB best practices. Furthermore, most organizations have positive intentions toward CE and that these intentions positively influence the adoption of DB; however, the lack of government support and cultural barriers are perceived as the main limitation for its adoption. The research leads to the conclusion that BD helps business and government develop strategies to move toward CE, and that there is a clear positive will and intent toward a more restorative and sustainable corporate strategy.

Ecological aspects of online learning in higher education: a qualitative multi-level exploration in a developing country

Using 22 undergraduate business students' online learning experiences during the COVID-19 lockdown in Pakistan (as the pandemic was the only time these students were enrolled as online students in Pakistan), this study demonstrates that online learning is a multi-level phenomenon and a practice situated within the environment. Despite online learning being a heavily researched area, research has under-examined the interaction of the Community of Inquiry (CoI) framework with the context. By adopting an ecological perspective, and by examining the interaction of micro, meso, and macro levels, this qualitative research provides useful insights into the interaction of the individual (micro-level) captured through the CoI framework, with the broader environment in which learners are located (meso and macro levels). It contributes to research on online learning broadly, and the CoI framework specifically, by revealing that each element of the CoI framework (micro-level), is influenced by macro (developing country), as well as meso (institutional policies and institutional preparedness) levels. It also spotlights the negotiated relationship between the individual and the systemic forces. The findings of this study are particularly relevant given that online education has the potential to become a norm in higher education in developing countries.

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.