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The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.

Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.

The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.

There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

This study aims to explore whether, how and when leaders' artificial intelligence (AI) symbolization (i.e. the demonstration of leaders' acceptance of and support for AI by engaging in AI-related behaviors and/or displaying objects that reflect their affinity for AI) affects employee job crafting behaviors.

The authors conducted two studies (i.e. an experiment and a multi-wave field survey) with samples from different contexts (i.e. United States and China) to test our theoretical model. The authors used ordinary least squares (OLS) and hierarchical linear modeling (HLM) to test the hypotheses.

Leaders' AI symbolization is positively related to employee change readiness and, in turn, promotes employee job crafting. Moreover, employee-attributed impression management motives moderate the positive indirect effect of leaders' AI symbolization on employee job crafting via change readiness, such that this indirect effect is stronger when employee-attributed impression management motives are low (vs high).

Leaders should engage in AI symbolization to promote employee job crafting and avoid behaviors that may lead employees to attribute their AI symbolization to impression management.

By introducing the concept of leaders' AI symbolization, this study breaks new ground by illustrating how leaders' AI symbolization positively influences employees' change readiness, as well as job crafting in the workplace. Further, integrating AI as a novel and timely context for evaluating job crafting contributes to the literature where empirical research is relatively scant, particularly regarding the factors that prompt employees to engage in job crafting.

The purpose of this study is to address the limitations of traditional methods for managing intellectual property rights (IPRs) by proposing a blockchain-based solution. By leveraging blockchain technology and smart contracts, the aim is to create a comprehensive ecosystem that offers advantages such as reduced transaction costs, improved transparency, enhanced security and increased liquidity levels for IP assets.

This paper proposes using blockchain technology to manage intellectual property rights (IPRs) through a smart contract-based ecosystem. It outlines the use of non-fungible tokens (NFTs) on the blockchain to represent IPRs, with smart contracts automating interactions and encoding rules for various processes such as applications, licensing, transfers and royalty distribution. Governance mechanisms, such as decentralized autonomous organizations (DAOs), are employed to allow stakeholders to propose and vote on contract changes, ensuring adaptability. This approach aims to streamline IPR workflows, reduce transaction costs, improve transparency and enhance security.

The findings of this study suggest that implementing a blockchain-based ecosystem for managing intellectual property rights (IPRs) can lead to various benefits. These include reduced transaction costs, improved transparency, enhanced security, increased liquidity levels for IP assets and streamlined automated processes. The use of non-fungible tokens (NFTs) on the blockchain allows for detailed management, valuation and trading of IPRs. Furthermore, simulation results demonstrate the robustness and efficiency of our proposed ecosystem, outperforming traditional IP management systems in terms of transaction speed and cost-effectiveness. These simulations highlight the practical viability of integrating blockchain technology into IP management workflows.

The practical implications of adopting this blockchain-based ecosystem for managing intellectual property rights (IPRs) are significant. By streamlining processes, reducing transaction costs and improving transparency and security, organizations can expedite the protection and commercialization of their IP assets. Additionally, the increased liquidity levels and accessibility of IP assets to investors and financiers can spur innovation and economic growth.

This paper contributes to the field by proposing a novel approach to managing intellectual property rights (IPRs) using blockchain technology and smart contracts. By leveraging non-fungible tokens (NFTs) on the blockchain, the proposed ecosystem offers a more efficient and transparent way of managing IPRs, reducing reliance on costly and opaque traditional methods. The potential benefits include improved efficiency, transparency, security and collaboration in the management and commercialization of IPRs.

Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity.

Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity.

The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks.

This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.