Background: In the context of organizational innovation frameworks, knowledge plays a crucial role in sparking new ideas and bolstering innovation capabilities. Insights gathered from various sources can act as a catalyst for generating fresh concepts and pushing boundaries. Moreover, the effectiveness of innovation within an organization can be influenced by factors like employee retention and strategies in human resource management, which can either enhance or hinder the correlation between knowledge accumulation and innovation outcomes. The employee innovation performance involves a series of tasks carried out by individuals who not only possess knowledge and skills but also demonstrate consistency, active involvement in decision-making, intrinsic motivation, and a flair for innovation. Objective: This study endeavors to provide valuable insights into how non-standard service relationships, psychological contracts, and knowledge sharing practices can collectively impact and drive innovation in the green manufacturing sector. Arrangement: In the investigation of employee innovation performance within the development of the green manufacturing industry, the focus will be on exploring non-standard service relationships, psychological contracts, and knowledge sharing. These three specific facets play a pivotal role in shaping the innovation landscape in organizations operating within the realm of sustainable manufacturing. The arrangement of this study will begin by examining the impact of non-standard service relationships on employee innovation performance. By dissecting unconventional service models and their correlation with innovation behaviors, we aim to uncover novel insights that can fuel sustainable innovation practices in the green manufacturing sector. Method: The study adopts a quantitative methodology to collect data, concentrating on a group of employees across eight distinct outsourcing firms. This selection results in a comprehensive sample of 299 participants. For the analysis and manipulation of the data, the research utilizes Sructural Equation Modeling (SEM) based on Partial Least Squares (PLS) software. This choice facilitates a meticulous and structured analysis of the data gathered, ensuring precision in the research findings. Results: The research findings reveal a significant and positive influence of psychological contracts on the propensity for knowledge sharing among employees. This suggests that organizations that emphasize establishing strong psychological contracts are likely to nurture a work environment conducive to the free exchange of knowledge and ideas, thus promoting a culture of collaboration and continuous improvement. Additionally, the data points to a noteworthy positive correlation between the act of knowledge sharing and the ability of an organization to offer unique, non-standard services. This underscores the role of knowledge sharing as a catalyst for innovation, indicating that organizations encouraging such exchanges are in a better position to innovate and provide services that adapt to the changing demands of customers and stakeholders. Conclusion: The research underscores the critical but nuanced role of knowledge sharing in driving employee innovation, especially when contrasted with its pronounced impact on developing non-standard services. It highlights the necessity for organizations to create environments conducive to the free exchange of ideas, fostering innovation. The findings also reveal the significant influence of innovative service offerings and strong psychological contracts on boosting employee creativity and service quality, respectively. For the green manufacturing sector, these insights stress the importance of robust psychological contracts and an innovation-centric culture. Emphasizing trust, open communi

In today’s rapidly evolving world, the integration of artificial intelligence (AI) technologies has become paramount, offering unparalleled value propositions and unparalleled consumer experiences. This study delves into the transformative impact of five AI activities on brand experience and consumer-based brand equity within the retail banking landscape of Lebanon. Employing a quantitative deductive approach and a sample of 211 respondents, the research employs structural equation modeling to analyze the data. The findings underscore the significant influence of four AI marketing activities on brand experience, revealing that factors such as information, accessibility, and customization play pivotal roles, while interaction has a less pronounced effect. Importantly, the study unveils that brand experience acts as a partial mediator between AI marketing activities and consumer-based brand equity. These revelations not only illuminate pathways for retail banks in Lebanon to refine their AI strategies but also underscore the importance of leveraging AI-driven marketing initiatives to bolster customer equity, acquisition, and retention efforts in an increasingly competitive market age.

This study explores the integration of data mining, customer relationship management (CRM), and strategic management to enhance the understanding of customer behavior and drive revenue growth. The main goal is the use of application of data mining techniques in customer analytics, focusing on the Extended RFM (Recency, Frequency, Monetary Value and count day) model within the context of online retailing. The Extended RFM model enhances traditional RFM analysis by incorporating customer demographics and psychographics to segment customers more effectively based on their purchasing patterns. The study further investigates the integration of the BCG (Boston Consulting Group) matrix with the Extended RFM model to provide a strategic view of customer purchase behavior in product portfolio management. By analyzing online retail customer data, this research identifies distinct customer segments and their preferences, which can inform targeted marketing strategies and personalized customer experiences. The integration of the BCG matrix allows for a nuanced understanding of which segments are inclined to purchase from different categories such as “stars” or “cash cows,” enabling businesses to align marketing efforts with customer tendencies. The findings suggest that leveraging the Extended RFM model in conjunction with the BCG matrix can lead to increased customer satisfaction, loyalty, and informed decision-making for product development and resource allocation, thereby driving growth in the competitive online retail sector. The findings are expected to contribute to the field of Infrastructure Finance by providing actionable insights for firms to refine their strategic policies in CRM.

This study investigates the influence of perceived value and perceived risk on consumer intentions to purchase counterfeit luxury goods, drawing upon an integrated theoretical framework encompassing perceived value theory, risk perception theory, and consumer behavior models. Through a quantitative research design involving a structured survey and Structural Equation Modeling (SEM), the study examines the relationships among perceived value dimensions (functional, emotional, social, economic), perceived risk factors (financial, social, performance), consumer attitudes, and purchase intentions. The findings reveal that perceived value positively influences purchase intentions, with consumer attitudes acting as a critical mediating mechanism. Conversely, perceived risk negatively impacts purchase intentions, with this relationship also mediated by consumer attitudes. Furthermore, Bayesian Network analysis uncovers the indirect pathways through which perceived risk shapes purchase intentions via its influence on consumer attitudes. By integrating these theoretical frameworks and employing advanced analytical techniques, this study contributes to a comprehensive understanding of the complex decision-making processes underlying counterfeit luxury goods consumption. The findings provide valuable insights for policymakers, luxury brand managers, and consumer protection agencies in devising targeted strategies to address consumer perceptions of value and risk, ultimately mitigating the proliferation of counterfeit luxury goods.

The lack of attention from mining companies to the majority of areas still affected by mining activities can result in regional economic disparities and high levels of social violence. It is crucial to have policy strategies for mining contributions to rural development equity and social violence reduction through CSR assistance and other aid funds. This research employs the Multi-Criteria Decision Analysis method using the MULTIPOL analysis tool. Recommended action programs include the construction of schools, provision of scholarships, job openings, business capital, and infrastructure development, supported by strong regulations and law enforcement. Cracking down on illegal mining permits is essential to reduce environmental damage. Holistic and sustainable integration policies, alongside effective law enforcement, are necessary to achieve the goals of equitable development and social violence reduction. These steps should be reinforced with incentives for traditional/community leaders and increased police/military presence in villages within the next 2 years, particularly in zones 2 and 3 of the mining areas. Failure to implement these measures could escalate social violence, jeopardize security, and impede the operations of mining companies in Kolaka. The findings of this research support the priority of security and orderliness in development and underscore the importance of diverse research methods for mining area development policies.