Search results

This paper aims to explore a new model to manage small and medium enterprise (SME) clustering process that examines the geographical connectivity conditions within the existing theories on agglomeration. The presented work explores the dynamics governing the decisions related to both the duration and frequency of the different forms of these new clusters.

A clustering configurator tool is developed to assist managers for the best temporary cluster model. The configurator considers aspects related to the market, industry and classical clustering requirements as well as social capital (SC). Finally, the performance of various temporary clusters under different demand scenarios and operational conditions are studied using numerical simulation.

The results examined the performance of the new clusters under various internal and external defining indicators against potential economic growth, technology spillover and the new metric of SC. The results offered interesting observations suggesting various recommendations to promote these new models to SMEs as well as how to better manage them.

The presented results are understood in the context of the suggested settings of relationships and scoring weights.

The new form of clusters will help SMEs overcome the feasibility challenge when considering re-locating to existing clusters while reaping many of these clusters benefits. Furthermore, different recommendations for management aimed at enhancing clustering decisions and the efficiency of SMEs in these new setups are presented.

This paper suggests a new clustering management approach that capitalizes on the temporal domain rather than classical space or the digital clusters domains. Also, a new management concept called dynamic matching is suggested. SC is considered among clustering objectives which was disregarded in similar studies.

This study intends to examine whether the reasons learners like or dislike a learning environment can help explain the differences in the characteristics of the learner and whether learner characteristics can influence a learner's preference for a learning environment.

This study adopted an exploratory sequential mixed methods research design. The study first conducted focus groups with university students to uncover their learner characteristics by examining the reasons they liked or disliked a learning environment. This was followed by a questionnaire survey to explore how these learner characteristics influenced learner inclination for a learning environment. The survey data were analysed using exploratory and confirmatory factor analysis (partial least squares structural equation modelling).

The findings showed that two types of learner characteristics, i.e. online learner characteristics and classroom learner characteristics, significantly affected learner inclination for a learning environment. Analyses also indicated that learner demographics had no major moderating effect between learner characteristics and learner inclination for a learning environment.

The findings can be useful for education institutions, learning designers and academics to design engaging learning activities to better support different learning needs.

This study makes a novel attempt to distinguish learner characteristics based on the reasons learners like or dislike a learning environment and establishes that individual learners' characteristics play a role in influencing their preference for a specific learning environment.

This paper is aimed at analyzing the inter-contextual relationships among the factors that led to inadequate management of electronic and electrical waste (WEEE) during COVID-19 using a subjective perspective.

Grey sets and a Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based approach has been employed to identify the causal association of intertwined WEEE management barriers.

Results reveal the lack of implementation of the legislative framework, extended producer responsibility and lesser corporate initiatives are some of the most challenging WEEE management challenges during the current pandemic.

The findings of the study would enable stakeholders of WEEE management toward building resilient policies and effective implementation plans during as well as post-crisis situations.

COVID-19 led challenges related to healthcare waste have attracted a significant amount of scholarly attention, but there has been lesser attention toward e-waste management challenges during the pandemic. Negligence toward e-waste management can pose threats to the environment as well as human well-being.