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Objective : This study addresses the critical gap in understanding how Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and Extended Reality (XR)—synergistically transform customer-centric supply chains in creative industries (e.g., music, fashion, film). It moves beyond fragmented analysis to develop a unified framework for customer-integrated value delivery. Methodology: A systematic meta-synthesis was conducted following established seven-stage protocols. A targeted search across Scopus and Web of Science (2016–2025) identified relevant literature. After stringent screening and quality appraisal (the Critical Appraisal Skills Programme (CASP) checklist), 59 high-quality publications were analyzed through iterative coding and thematic analysis. Results : The analysis produced a novel framework of five interdependent components: (1) Customer-Integrated Value Creation (e.g., AI co-design), (2) Omnichannel & Immersive Fulfillment (e.g., XR commerce), (3) Dynamic Value Capture Models (e.g., fan-driven financing), (4) Algorithmic & Gamified Community Engagement , and (5) Networked Co-Creation Ecosystems . The framework demonstrates how AI, IoT, blockchain, and XR interconnect to transform linear supply chains into agile, experiential, community-integrated value networks. Conclusion : This study presents the first synthesized framework for Industry 4.0-enabled, customer-centric supply chains in creative industries. It bridges technology, culture, and operations management, offering researchers a structured model for future inquiry and providing practitioners with a strategic roadmap for building responsive, community-driven operations. The research fills a significant literature gap and offers a blueprint for competitive advantage in the digital creative economy.

Objectives: This study aims to examine the effect of intellectual capital efficiency and its components—human capital, structural capital, and employed capital—on the future stock returns of companies listed on the Tehran Stock Exchange. The Value-Added Intellectual Coefficient (VAIC) model is used to measure intellectual capital efficiency. Methodology/Design/Approach: This applied study employs a descriptive-correlational and causal research design. The statistical population consists of all companies listed on the Tehran Stock Exchange from 2018 to 2023. A sample of 104 companies was selected using the systematic elimination method. Panel data analysis and multiple regression models were used to test the hypotheses, with EViews software employed for data analysis. Findings: The results indicate that overall intellectual capital efficiency, human capital, and structural capital positively and significantly affect future stock returns, while employed capital shows no statistically significant relationship. Furthermore, control variables such as growth opportunities and sales growth also have significant impacts on market returns. Innovation: The findings highlight the critical role of intangible assets—particularly human capital and organizational infrastructure—in value creation for shareholders. This underscores the importance of investing in knowledge-based resources to improve financial performance and stock market returns.

The food packaging industry has been investigated as a case study in this study.The present study was conducted based on interpretive philosophy and with an inductive approach. Also, the present study is an applied-developmental study in terms of objective, and it is a non-experimental (descriptive) research in terms of data collection method. A qualitative research design was used to achieve the research objective. In this study, population includes professors of management and food industry managers. Purposeful sampling (snowball method) was used and theoretical saturation was achieved with 20 participants. The participants of this research include theoretical experts (university professors) and experimental experts (food industry managers). A semi-structured interview and a decision matrix-based questionnaire were used to collect data. The results of qualitative coding were validated with the Holsti method (0.717) and Cohen's Kappa (κ) (0.659). The validity of the questionnaire was confirmed by the formal method and the reliability of the questionnaire was also confirmed by estimating the internal correlation coefficient (0.815). Analysis and coding of the text of the interviews was done using the theme analysis method in MaxQDA 20. Also, structural-interpretive modeling method and MicMac can be used to determine relationships between constructs and model design in the second part. According to the results, the investigated factors and components were grouped in seven levels. These components are: Level 7 components including business resources, digital transformation leadership and digital/sustainable foresight, Level 6 components, including digital/sustainable innovation strategies, the components of level 5, including the use of new technologies in processes and operations, core digital/sustainable capabilities, and employees' digital experience, level 4 components of the model, including a new approach to food packaging industry processes, improving sustainable food packaging functions, and designing innovative food packaging, the level 3 components, including the alignment of the digital/sustainable innovation strategy with Digital/Sustainable Capability and Business Sustainability in the Digital Age, the level 2 components, including the component of improving customer experience (digital/sustainable), and finally, the “consequences of digital transformation” component with an sustainability-driven innovation approach in the food industry.