Social media influencer marketing has emerged as an essential marketing strategy in the online interactive environment. This study investigates the impact of influencer-consumer fit (ICF) on behavioral intentions; intention to co-create brand value (ICC) and purchase intention (PI), with the serial mediation of influencer authenticity (IA) and attitude toward brand (ATB). A self-administered questionnaire was distributed to followers of social media influencers in Pakistan. The data were collected from 421 female followers of social media influencers through survey and partial least squares—structural equation modeling was used for data analysis. The findings reveal that ICF impacts IA, while the latter impacts ATB. ATB in turn impacts behavioral intentions. The direct effects suggest that ICF impacts consumers’ PI but not the ICC. However, with the serial mediation of IA and ATB, the relationship becomes significant. The findings of this study may assist managers in building brand strategies to achieve excellence in a highly dynamic and competitive market by leveraging the power of influencer marketing.

Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R² = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R² = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.

As a key factor in the macroeconomic process, the interaction between public confidence and the commodity market, especially its impact on commodity facilitation returns and macroeconomic linkages, is worth exploring in depth. This study adopts the TVP-SV-VAR model to analyze the causal linkages, dynamic characteristics, and mechanisms of the interaction, and reveals the following core findings: (1) The economic background and information shocks contribute to the variations in the effects and orientations of the economic variables, which highlight the time-varying nature of the economic interactions. (2) Consumer and investor confidence exert heterogeneous influence on the macroeconomy, and their different responses to the negative effect of interest rates and convenience gains are particularly significant in the post-crisis recovery period. (3) In the short-term perspective, the influence of public confidence on monetary policy and inflation exceeds that in the medium and long term, highlighting the immediate sensitivity of individual economic behavior. (4) Since 2015, accommodative monetary policy has accelerated market capital flows, delaying the interaction between confidence indices and inflation, revealing policy time lag effects. (5) Convenience gains exhibit complex time-varying interactions with key economic parameters (interest rates, commodity prices, and inflation), with 2011 and 2014 displaying particular patterns, mapping differences between short- and long-term mechanisms, respectively. The study highlights the central role of consumer and investor confidence in the precise tailoring of macroeconomic policies, providing a scientific basis for policy forecasting and economic regulation, and contributing to economic stability. Meanwhile, the dynamic evolution of consumer confidence deepens market trend foresight, enhances the precision of market participants’ decision-making, and reinforces the resilience and predictability of economic operations.

Noise pollution in construction sites is a significant concern, impacting worker health, safety, communication, and productivity. The current study aims to assess the paramount consequences of ambient noise pollution on construction activities and workers’ productivity in Peshawar, Pakistan. Noise measurements have been recorded at four different construction sites in Peshawar at different times of the day. Statistical analysis and Relative Importance Index (RII) are employed to evaluate the data Risk variables, such as equipment maintenance, noise control, increased workload, material handling challenges, quality control issues, and client satisfaction. The results indicated that noise levels often exceeded permissible limits, particularly in the afternoon, posing significant worker risks. In addition, RII analysis identified communication difficulties, safety hazards, and decreased productivity as significant issues. The results show that noise pollution is directly linked with safety risks, decreased performance, and client dissatisfaction and needs immediate attention by authorities. This paper proposes a strategic policy framework, recommending uniform hand signals and visual communication methods without noise for workers, worker training about safety, and using wearable devices in noisy settings. Communication training for teams and crane operators, proactive quality control, and customer-oriented project schedules are also proposed. These recommendations aim to mitigate the adverse effects of noise pollution, enhance construction industry resilience, and improve overall operational efficiency, worker safety, and client satisfaction in the construction sector of Peshawar, aligning with policy and sustainable development objectives.