This research examines the impact of green marketing and its factors on purchase intention through green brand trust and image. It examines the correlation between six concepts: green knowledge, environmental attitude and knowledge, green image, green trust, and purchase intention. An empirical survey was used to test the hypotheses. Data were collected from a total of 300 respondents from Pakistan, and structural equation modeling (SPSS-AMOS) is proposed to assess the relationships of the research model. The findings show a significant positive association between practical green marketing approaches (green knowledge, attitude, environmental knowledge, and purchase intention for green products); additionally, green products with high ecological qualities create a positive intention towards consumer choice. Furthermore, green marketing approaches (green knowledge, attitude, and environmental knowledge) directly and significantly influence green image and trust. This study suggests that green approaches can be used as a brand marketing strategy to enhance customer purchase intention. The study also found that green marketing can help brands achieve and maintain brand equity. This research explores the relationship between the multidimensional green marketing approaches (green knowledge, attitude, and environmental knowledge) to enhance the purchase intention of green products; the creation of an overpowering market brand was one of its primary objectives. This study offers managerial implications for the development of green firms.

There has been a rapid rise in utilization of artificial intelligence (AI) in many different sectors in the last several years. However, business-to-business (B2B) marketing is one of the more notable examples. The initial assessments emphasize the significant advantages of AI in B2B marketing, including its knack for yielding unique understandings into consumer behaviors, recognizing crucial market trends, and improving operational efficiency. However, there seems to be a limited grasp of the optimal way to develop artificial intelligence competencies (AIC) for B2B marketing and how these attributes inevitably affect customer lifetime value (CLV). Equipped with AIC and B2B marketing literary fiction, this research unveils a theoretical research framework for evaluating the repercussions of AIC on B2B marketing capabilities and, subsequently, on CLV. We analyze the suggested research model using partial least squares structural equation modeling (PLS-SEM), leveraging 367 survey replies from Pakistani companies. The outcomes show a significant relationship that describe the ability to leverage AIC to enhance CLV, and also signifies the mediating role of B2B marketing capabilities to enhance CLV by integrating AIC in internet marketing. The findings of this study provide practical implications for marketers to monetize their marketing skills to enhance CLV and researchers with theoretical underpinnings of integration of AIC into marketing.

The use of social media marketing (SMM) use in the garment sector is increasing daily, and well-known brands are shifting their marketing campaign to social media. This study aims to assert the relationship between SMM customer- brand engagement (CBE) and brand knowledge (BK). For this purpose, data were collected from online garment buyers through a questionnaire. Results of the study show that SMM significantly enhances the CBE and BK. Results further elaborate that the CBE substantially mediates the relationship between SMM and BK. This study will bridge the gap in the literature between SMM and CBE. Moreover, this study will help the management of garment brands to shift their marketing campaign on social media to meet the requirements of modern marketing tools. Although this study only focuses on garments brands, future researchers can apply the model of this study to other brands and also enhance the sample size.

The rapid rise of livestream social commerce has reshaped online shopping by combining algorithmic personalization, entertainment, and real-time persuasion. However, how algorithmic quality, algorithmic governance, and hedonic experience jointly influence impulse-buying intentions across different digital shopping contexts remains insufficiently understood. Drawing on dual-process perspectives from persuasion and stimulus-organism-response theories, this study develops a dual-route framework explaining how algorithmic and psychological factors drive impulse buying through cue-driven and confidence-based mechanisms. Survey data were collected from 564 Chinese consumers, evenly split between livestream commerce and conventional online marketplaces. The results suggest selective contextual differences. In livestream settings, algorithmic quality and governance function as salient cues that directly stimulate impulse-buying intentions. In contrast, in marketplace environments, hedonic motivation and algorithmic governance primarily influence impulse buying indirectly by strengthening consumers’ decision confidence. Configurational analysis further identifies multiple sufficient combinations of technological and motivational conditions that lead to high impulse-buying intentions, demonstrating equifinality across contexts. By integrating algorithmic quality and governance into a dual-route impulse-buying model, this study advances understanding of how transparency and system design actively shape spontaneous consumer behavior. The findings also offer practical guidance for designing transparent, confidence-enhancing, and ethically responsible recommendation systems in digital commerce.