Environmental sustainability importance increases, the environmental degradation problems attract the discussions around the world concerning many areas of life, consumption, and disposition of goods. Green marketing and advertisement have become a popular choice by companies to influence the consumer buying process. This study aimed to examine the moderated role of green advertisement among the influence of environmental concern, social impact, and self-image on green purchase behavior. The survey sample is 458 responses from various consumers. The methodological tools were quantitative research methods. The study applied confirmatory factor analysis and moderation analysis to evaluate the six hypotheses developed and proposed in the conceptual framework. The authors performed regression and moderation analysis after achieving the model fit indices and Cronbach’s alpha. The regression analysis reveals that independent variables environmental concern, self-image, and social impact had a significant influence on green purchase behavior. Furthermore, environmental concern and self-image have a more substantial influence on green purchase behavior. The moderation results revealed that green advertisement has a positive and significant moderated relationship among environmental concern, self-image, social influence, and green purchase behavior. The obtained results indicated that marketers could increase green purchase behavior. In turn, it will allow supporting the green environment by taking the above determinants of green purchase behavior into consideration for developing green policies and strategies which are mutually beneficial for the consumer as well as for the safe environment. The findings of this study suggest that green advertisement could increase green purchase behavior. Herewith, people become greener and environment-conscious in their routine life. The authors suggested practical implications for the strategists and marketers who are willing to go green. The study results present an overview of how marketers could devise more effective strategies and advertisement to endorse the green purchase behavior.
Keywords: green purchase behavior, green products, sustainable consumption, eco-friendly environment, social influence, self-image, and green advertisement.
Supply chain agility (SCA) has become an important concept these days and gained a great deal of attention from the research fraternity. Researchers and scholars have mainly examined the dimensions of integration and flexibility as the antecedents of SCA from conceptual lenses in the past and suggested further explanations from empirical testing. Thus, this research aimed to investigate three key drivers of supply chain agility -i.e., strategic flexibility, employees' behavioral flexibility and relational (external) integration. The sample is comprised of 147 SMEs operating in Pakistan, collected via survey and then tested using moderated mediation structural equation modelling in MPlus software. As per the findings, customer integration, employees' behavioral flexibility and strategic flexibility have direct influence on supply chain agility (SCA). In addition to that, customer integration has indirect impact on SCA via strategic flexibility. Complexity, as a moderator, has conditional positive influence on the relationship between customer integration and SCA. This research advances supply chain literature by adding relationship-centric view in developing flexible and agile supply chains. Overall, the findings suggest Pakistani SME practitioners to allocate resources to activities that build flexible supply chains and invest in activities that create strong ties among downstream supply chain partners.
Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.
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