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 rapid evolution of technology and a large number of smartphone users are transforming the way the masses access financial services. Fintech companies consistently innovate in developing customized products and services for users and SMEs to increase financial access and inclusiveness to achieve the Indonesian national financial inclusion target of 90 percent by 2024. Access to digital financial products via Fintech contributes to greater financial inclusion for SMEs, particularly during the COVID-19 pandemic, which restricted economic activities. Using an extended TAM model, this study explores the driving factors of Fintech adoption for Indonesian SMEs during the COVID-19 outbreak. Data analysis of 415 respondents was conducted utilizing Smart-PLS 3.0 software. The findings confirm perceived usefulness, perceived ease of use, government support, trust, and user innovativeness to have a direct positive effect on the intention of SMEs to adopt Fintech. The result also reveals that financial literacy indirectly correlates with Fintech adoption mediated by user innovativeness. This indicates that Fintech could contribute to bridging financial inclusion where SMEs with lower financial literacy can utilize financial products and services via Fintech. This is a positive contribution of Fintech for SMEs in developing economies. The present study suggests that policymakers could foster the expansion of Fintech business infrastructure to improve access to SME financial services.

Corporate social responsibility (CSR) has become an innovative strategic management tool of socially and environmentally conscious business organizations in the 21st century. Although external CSR activities are better researched, firms’ internal CSR activities such as workplace health promotion and its impact on employee wellbeing are less understood, especially during a pandemic where job security is relatively lower in many sectors of employment. Additionally, wellbeing and good health have been recognized as important targets to achieve as part of the United Nation’s Sustainable Development Goal 3. Therefore, this study investigates the relationship between health-related work benefits and employee wellbeing, satisfaction and loyalty to their workplace. Large scale survey research was performed with responses from 537 employees in Hungary and 16 hypotheses were tested. Data analysis and path modelling using PLS-SEM (Partial Least Squares Structural Equation Modelling) reveal two-layers of factors that impact employee wellbeing, satisfaction and loyalty. We term this as ‘internal locus of control’ and ‘external locus of control’ variables. Internal locus of control variables such as mental and emotional health leads to wellbeing at the workplace but do not directly impact employee satisfaction and loyalty. In contrast, external locus of control factors such as healthcare support leads to wellbeing, satisfaction and loyalty. Employer commitment to healthcare support system is found pertinent especially during the pandemic. We discover wellbeing as a unique standalone construct in this study, which is vital as is it formed by mental and emotional wellbeing of employees, albeit not a determinant of employee workplace satisfaction and loyalty. We theorize workers’ self-reliance and preservation as possible explanations to the disassociation between employee wellbeing and loyalty to workplace during times of crisis and the pandemic.

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 rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation.

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.