The increasing global emphasis on sustainability necessitates the integration of environmentally responsible practices within supply chains. This study explores the impact of green supply chain management practices (GSCMPs) on firm sustainable performance in Pakistan’s manufacturing and service industries. Unlike prior research, which primarily focuses on the direct impact of GSCMPs, this study advances knowledge by incorporating green technological innovation (GTI) and green managerial innovation (GMI) as mediators and green organizational culture (GOC) as a moderator. The study looks at survey data from 480 industry professionals and uses partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA). It discovers that GSCMPs greatly enhance sustainability outcomes, especially when green innovations are used. Furthermore, the impact of GSCMPs is more pronounced in the manufacturing sector, emphasizing the role of regulatory pressures and technological advancements. This study makes a significant contribution to the literature by integrating post-pandemic sustainability challenges, highlighting industry-specific dynamics, and providing actionable strategies to enhance green supply chain adoption in emerging markets. The study provides applicable strategies for managers and policymakers to embed sustainability deeper into corporate strategies, ensuring resilience and competitive advantages in evolving global markets.

The purpose of this study is to assess the ecological efficiency of manufacturing small and medium-sized enterprises (SMEs) by analyzing their adoption of green human resource management practices (GHRMPs), green intellectual capital (GIC), and green innovation (GIN). The study was carried out on a representative sample of 367 manufacturing SMEs in Pakistan, and data were gathered using a particular survey questionnaire. The results were analyzed using the partial least squares–structural equation modeling (PLS-SEM) technique in SmartPLS4. This research indicates that the adoption of GHRMPs significantly impacts environmental performance (EP), GIC, and GIN. Additionally, the study found that the correlation among GHRMPs is positively mediated by GIC and GIN. The study contributes to the body of knowledge by investigating EP based on the Ability-Motivation-Opportunity Theory (AMO) through empirical evidence on hypothesized relationships. The paper provides a valuable understanding and novel approach for managers of SMEs in developing countries to improve their EP through the adoption of GHRMP, GIC, and GIN.

Digital retailing is increasingly transitioning toward hyper-personalized and AI-enabled consumer experiences, necessitating theoretical frameworks that integrate neurobiological data processing, advanced computational efficiency, and consumer psychological responses. This study develops and empirically validates a neuroadaptive engagement framework examining how multimodal biometric integration and quantum-enhanced processing efficiency influence real-time marketing optimization and neuroadaptive consumer engagement through the mediating mechanisms of cognitive load optimization and emotional congruence alignment. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study further investigates the moderating roles of privacy calculus acceptance and technology readiness. Using data collected from 500 Chinese e-commerce consumers and analyzed through partial least squares structural equation modeling (PLS-SEM), the findings reveal that neuroadaptive system responsiveness primarily enhances consumer engagement through emotional congruence alignment, whereas quantum-enhanced processing efficiency operates through both cognitive and emotional pathways. The results further indicate that privacy calculus acceptance and technology readiness significantly strengthen these relationships under conditions of higher consumer acceptance. The proposed model explains substantial variance in neuroadaptive consumer engagement and personalization effectiveness. This study contributes to the literature by extending UTAUT into neuroadaptive digital retail environments, introducing the constructs of quantum-enhanced processing efficiency and consumer digital twin fidelity, and identifying the boundary conditions under which biometric personalization enhances engagement rather than consumer resistance. The findings also provide strategic implications for retailers seeking to optimize neuroadaptive personalization investments based on consumer privacy perceptions and technological readiness.