Abstract Owing to the growing popularity of electronic marketplaces, individual online retailers must evaluate a large volume of customer text reviews to gather feedback about consumer preferences and support sustainable product evaluation. Product evaluation involves multiple product attributes simultaneously, making it a multi-criteria decision-making problem. While traditional sentiment analysis captures isolated customer insights, this approach is subject to three key limitations. These are reliance on context-blind extraction methods for attribute identification, subjective weighting schemes that cannot handle correlated criteria, and insufficient treatment of linguistic uncertainty arising from inter-reviewer variability in sentiment intensity. To address this, this study introduces BSLMM (BERT-based Sentiment analysis with Language Models and Multi-Criteria Decision Making), a systematic framework that integrates BERT for contextual attribute extraction and aspect-level sentiment scoring, Principal Component Analysis (PCA) for transforming correlated attributes into independent criteria with objectively derived weights, and fuzzy COPRAS for ranking product alternatives. This study contributes a data analytics framework that blends sentiment analysis, pre-trained language models and MCDM for product benchmarking and development. Furthermore, it offers practitioners a low-cost data-driven approach to competitive product development towards sustainable innovation.

ABSTRACT The wider impact of blockchain is considered one of the core technologies for the next generation of intelligent web that adopts information technologies to enhance food safety and security, thereby enhancing sustainable food trading. This technology can be adopted in a wide range of applications such as healthcare, logistics, food trading, etc. Among these, coffee trading is one of the sectors that can benefit from blockchain due to its traceability and security features. Traditional coffee trading faces challenges due to geographical and cultural differences, leading to unfair practices and trust issues. Currently, there is no reliable and secure platform in the coffee trading business. This article introduces “Cof3Lib,” a coffee trading platform integrating decentralization and blockchain to provide food safety and trading security. To our knowledge, it's one of the first blockchain‐based platforms addressing traditional coffee trading challenges. It ensures transparency, traceability, visibility, and accuracy in coffee trading operations and logistics management. By leveraging blockchain technology, it aims to revolutionize the food industry, creating a more equitable and efficient trading ecosystem for food technology.

This study contributes to assessing sharing economy concepts such as sustainability, organizational efficiency, and stakeholder engagement, and develops business-to-business sharing drivers in a hierarchical structure. Job shop production systems are challenged by demand fluctuation and low production capacity. This study adopted the fuzzy Delphi method to arrive at a valid set of attributes and a fuzzy decision-making trial and evaluation laboratory to address the proposed attributes’ relationships. The results show that stakeholder engagement and technological performance are important causal aspects, and economic benefits act as a mediating aspect that drives the perceived organizational utility in JPS. The top causal criteria were identified as outsourced labor services, information sharing, digital platforms, workplace vitality, new technologies, and cost efficiency. These criteria provide a practical solution for managers with which to improve B2B sharing practices in JPS.