Consumers are paying close attention to green products to reduce the environmental impact on health issues. As the scope of this research, this current study focuses on determining consumers’ purchase decisions regarding green products using a survey conducted in a fast-growing developing country. This research was descriptive and considered a conceptual framework for extending the Theory of Planned Behavior (TPB), which was selected as the primary theoretical model. The significant contributions and main objectives of this study are as follows—to explore the present scenario of green marketing in Bangladesh with previous studies, and to fill a research gap regarding green purchase decisions by applying the TPB model with adding additional constructs, such as environmental concerns, green perceived quality, and future green estimates. A range of qualitative and quantitative techniques were adopted to collect data from the target groups, where a sample of young educated Bangladeshi consumers (n = 638) was used to consider the measurement and structural models by applying a partial least squares-structural equation modelling (PLS-SEM) method. The empirical findings show that consumers’ environmental concern (EC), green perceived benefits (GPB), green awareness of price (GAP), green willingness to purchase (GWP), and future estimation of green marketing (GFE) have a strong positive influence on consumer’ green purchase decision (GPD). Still, the green perceived quality (GPQ) has a negative influence on green purchase decisions (GPD). To inform consumers about green or eco-friendly products, this study provides valuable suggestions to companies, marketers, and policymakers for designing green marketing tools such as green advertising, green branding, and eco-labels. Based on these findings, it gives some managerial insights for the promotion of green products and green marketing.

The research aims to explore the technological, environmental and organizational factors that influence digital technology adoption in the small and medium enterprises (SMEs) sector in developing countries. A conceptual framework is proposed that combines the technology-organization-environment (TOE) framework and diffusion of innovation (DOI) theory to explore the determinants of digital technology, for example, information and communication technology (ICT) applicability in SMEs. In addition, this study identifies the major barriers to implementing ICT in the SMEs sector. A purposive 535 sample was accumulated from higher- and middle-level managers of SMEs firms through a structured questionnaire. Data and hypotheses were analyzed using partial least squares-structural equation modelling. Results show that relative advantage, complexity, observability, perceived cost, top management support, innovativeness of top management, competitive pressure and government support have significant determinants of ICT adoption in SMEs. Besides, compatibility, perceived trialability and organization readiness have not significantly influenced to use of ICT in SMEs. This research has crucial implications for practising managers, policymakers and SMEs business owners. To the best of the authors’ knowledge, this study is perhaps one of the first empirical surveys that widely provides a theoretical framework regarding ICT adoption in SMEs in Bangladesh.

Biofuels have received a lot of attention as an important source of renewable energy, with number of economic impacts. This study aims to investigate the economic potential of biofuels and then extract core aspects of how biofuels relate to a sustainable economy in order to achieve a sustainable biofuel economy. This study conducts a bibliometric analysis of publications about biofuel economic research covering 2001 to 2022 experimenting with multiple bibliometric tools, such as R Studio, Biblioshiny, and VOSviewer. Findings show that research on biofuels and biofuel production growth are positively correlated. From the analyzed publications, The United States, India, China, and Europe are the largest biofuel markets, with the USA taking the lead in publishing scientific papers, engaging country collaboration on biofuel, and has the highest social impact. Findings also show that the United Kingdom, the Netherlands, Germany, France, Sweden, and Spain are more inclined to develop sustainable biofuel economies and energy than other European countries. It also indicates that sustainable biofuel economies are still far behind those of less developed and developing countries. Besides, this study finds that biofuel linked to sustainable economy with poverty reduction, agriculture development, renewable energy production, economic growth, climate change policy, environmental protection, carbon emission reduction, green-house gas emission, land use policy, technological innovations, and development. The findings of this bibliometric research are presented using different clusters, mapping, and statistics. The discussion of this study affirms the good and effective policies for a sustainable biofuel economy.

Pro-environmental behaviour (PEB) helps individuals to minimize the negative effects of their actions on the environment; for example, by recycling and reducing energy consumption. This study aimed to explore consumers’ attitudes and trust in energy-efficient household appliances and their environmental and eco-label knowledge to determine how these may affect pro-environmental behaviour (PEB). In addition, the study examined the mediating effects of attitude and green trust on the proposed frameworks of environmental and eco-label knowledge in relation to consumer pro-environmental behaviour. PLS-SEM (Partial Least Squares–Structural Equation Modelling) was applied to a study from Bangladesh to examine hypotheses proposed from the data generated by 1510 valid respondents relating to their experiences of using household energy-efficient products. The results showed that environmental knowledge, eco-label knowledge, attitude, and green trust all significantly correlated with consumer pro-environmental behaviour. In addition, attitude and green trust significantly mediated the relationship between environmental knowledge, eco-label knowledge, and pro-environmental behaviour. This study also found that green trust positively affects consumer attitudes, and that those attitudes serve as a significant mediator between green trust and pro-environmental behaviour. An IPMA (importance-performance matrix analysis) map indicated that the total effects of environmental attitude and eco-label knowledge were more important to defining the pro-environmental behaviour of consumers than those of environmental knowledge and green trust. To the best of the authors’ knowledge, this research introduces for the first time an empirical survey that provides a new theoretical framework for consumer pro-environmental behaviour in regard to energy-efficient household appliances in the context of a developing country. Suggestions are provided on ways to promote energy-saving appliances from theoretical and practical perspectives.

The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study concluded users who live in Hungary and use Facebook Marketplace. This research uses the convenience sampling approach to overcome bias. Out of 475 surveys distributed, a total of 466 respondents successfully filled out the entire survey with a response rate of 98.1%. The results showed that all dimensions of social network marketing, such as entertainment, customization, interaction, WoM and trend, had positively and significantly influenced consumer purchase behavior (CPB) in Facebook Marketplace. Furthermore, we used hierarchical clustering and K-means unsupervised algorithms to cluster consumers. The results show that respondents of this research can be clustered in nine different groups based on behavior regarding demographic attributes. It means that distinctive strategies can be used for different clusters. Meanwhile, marketing managers can provide different options, products and services for each group. This study is of high importance in that it has adopted and used plspm and Matrixpls packages in R to show the model predictive power. Meanwhile, we used unsupervised machine learning algorithms to cluster consumer behaviors.