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Collaborative emission reduction among supply chain members has emerged as a new trend to achieve climate neutrality goals and meet consumers’ low-carbon preferences. However, carbon information asymmetry and consumer mistrust represent significant obstacles. This paper investigates the value of blockchain technology (BCT) in solving the above issues.

A low-carbon supply chain consisting of one supplier and one manufacturer is examined. This study discusses three scenarios: non-adoption BCT, adoption BCT without sharing the supplier’s carbon emission reduction (CER) information and adoption BCT with sharing the supplier’s CER information. We analyze the optimal decisions of the supplier and the manufacturer through the Stackelberg game, identify the conditions in which the supplier and manufacturer adopt BCT and share information from the perspectives of economic and environmental performance.

The results show that adopting BCT benefits supply chain members, even if they do not share CER information through BCT. Furthermore, when the supplier’s CER efficiency is low, the manufacturer prefers that the supplier share this information. Counterintuitively, the supplier will only share CER information through BCT when the CER efficiencies of both the supplier and manufacturer are comparable. This diverges from the findings of existing studies, as the CER investments of the supplier and the manufacturer in this study are interdependent. In addition, despite the high energy consumption associated with BCT, the supplier and manufacturer embrace its adoption and share CER information for the sake of environmental benefits.

The firms in low-carbon supply chains can adopt BCT to improve consumers’ trust. Furthermore, if the CER efficiencies of the firms are low, they should share CER information through BCT. Nonetheless, a lower unit usage cost of BCT is the precondition.

This paper makes the first move to discuss BCT adoption and BCT-supported information sharing for collaborative emission reduction in supply chains while considering the transparency and high consumption of BCT.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

The study aims to explore how fashion visual symbols influence consumers' inclination for repurchasing. It attempts to investigate the intricate interplay among three essential variables (social presence, collective excitement and cultural identification) from the perspective of Interaction Ritual Chains theory. Meanwhile, an attempt is made to reveal the underlying patterns in these relationships, fully harnessing the positive impact of fashion brand visual symbols in brand marketing.

This study employs a quantitative research methodology, administering an online survey in China, from which 381 valid responses were collected by simple random sampling. The acquired data were subjected to structural equation model and hypotheses testing.

The analysis reveals that heightened visual symbol perception significantly strengthens consumers' social presence, consequently elevating the probability of collective excitement. This establishes a mediated chain model, reinforcing repurchase intention. Additionally, the moderation effect analysis indicates that cultural identification negatively moderates both direct paths in the mediated chain model, with particularly pronounced effects for low cultural identification.

This study establishes a closed-loop system in fashion brand product marketing, continuously enhancing the intimacy and interactive willingness between consumers, as well as between consumers and the brand. The objective is to increase brand repurchase rates. Additionally, the research provides valuable recommendations and strategies for fashion brands to adapt to Chinese consumer demands, strengthen emotional attachment between consumers and the brand, and achieve sustainable development in the realm of fashion consumption.

The purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.

The paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.

The study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.

The research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.

The study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

Toward sustainable development, radical green innovation (RGI) is necessary. Despite extensive research on the factors influencing green innovation, few studies have been conducted on the precursors. Based on upper echelons (UE) theory, dynamic capability (DC) theory, “stimulus-organism-response” (SOR) theory, social information processing (SIP) theory and cognitive appraisal (CA) theory of emotion, the study explores how digital leadership (DL) affects RGI and investigates the mediating effects of green organizational identity (GOI) and the moderating effects of digital threat (DT) and technology for social good (TSG), as well as the multiple concurrent causalities that trigger high RGI.

The method of combining structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fs QCA) is adopted in the study. Data from 233 questionnaires were collected at two different time points.

This study's findings indicate that the four dimensions of DL can positively influence RGI and GOI partially mediates between the four dimensions of DL and RGI. DT has a negative moderating effect between DL and GOI, while TSG is positively regulated between them, DT and TSG linkage moderates the partial mediating effect of GOI in DL and RGI. Further, fs QCA is used to analyze the causal complexity of DL dimensions and GOI to RGI and nine effective configuration paths are identified. It is found that the synergy of digital thinking ability (DTA), digital detection ability (DDA), digital social ability (DSA), digital reserve ability (DRA) and GOI is crucial to high RGI. Among them, GOI core appears the most times, indicating that GOI plays a vital role in improving enterprise RGI.

This study expands the literature on leadership and innovation by constructing a framework of “DL-GOI-RGI” and exploring the transmission of GOI and the boundary effect of DT and TSG. The study used fs QCA and SEM to better understand the statistical associations and the set relations between the conjunctions and conditions.

The purpose of this study is to analyse and discuss the influencing factors of user experience in university mobile libraries and the improvement path of user experience in the context of mobile learning.

The study adopted the grounded theory research method, and the sample included 28 students from five universities, with mobile libraries as the research objects and semi-structured interview as data acquisition method. A step-by-step coding analysis of the original interview materials was conducted, which comprehensively identified the main concerns and problems encountered by users of the university mobile library apps especially in the mobile learning behaviour mode, and then a theoretical model of the influencing factors of the app user experience of the university mobile library was constructed.

A theoretical model of influencing factors was constructed, which determined that system quality, interaction quality, content quality, interface quality and function quality were the key elements of mobile library user experiences. Furthermore, based on the research results and user feedback obtained in the research process, the content and key points relating to the user experience can be elaborated in detail. In addition, this study was able to determine users' perspectives and their behavioural characteristics when engaging in mobile learning.

This study establishes a theoretical model of the factors influencing of the user experience of university mobile libraries based on mobile learning, which could provide a valuable reference for the design of other programs and strategies to promote user learning experiences of mobile library app in colleges and universities.

The existence of the advertising delay effect and its impact on supply chain operations have been demonstrated in the current study. Therefore, this study develops a timely inventory control strategy for the fresh produce supply chain to address the advertising delay effect in the fresh produce supply chain.

This study proposes a game model based on the Nerlove-Arrow time delay differential equation and Pontryagin's maximum principle. Through comparative analyses of the optimal equilibrium strategies, the authors compare the optimal equilibrium strategies, product goodwill and optimal inventory trajectories for suppliers and retailers under secondary replenishment decisions and decentralized decisions.

The authors find that (1) Only when the sales cycle meets certain conditions can the overall profit of the supply chain under the secondary replenishment decision be greater than that under the decentralized decision. As the price markup coefficient increases, the total profit of the supply chain first increases and then decreases. (2) With the increase in the delay time, the replenishment quantity during the initial period gradually decreases. After the delay time elapses, the inventory depletion rate under secondary replenishment decisions is faster than that under decentralized decision-making. (3) Although there is a continuously increasing maximum value of product goodwill with the increase in delay time, it becomes difficult to achieve this value for longer delays.

The authors’ findings provide a theoretical basis for supply chain members of fresh agricultural products to select replenishment and inventory control strategies when adopting different levels of delay in advertising marketing.

Firstly, this paper explains the impact of advertising delay effect on fresh produce supply chain from a dynamic perspective, and secondly, it provides guidance on advertising formulation and inventory replenishment for fresh produce retailers under the influence of advertising delay effect.

The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.

People usually try to avoid uncertainty. Recently, however, uncertainty has become an emerging marketing tool in the hedonic product industry. In the case of blind box consumption, for example, the consumers become addicted to the uncertainty created by businesses, leading to repeat purchases and even indulgences. Previous research has, yet, to focus on the impact of uncertainty on indulgence and the role of emotions.

This paper constructs and validates a chain mediation model of uncertainty triggering indulgent consumption based on the information gap theory, positive emotion theory and uncertainty resolution theory and examines the difference between resolved and unresolved uncertainty. This study also explores differences in the impact of whether uncertainty is resolved on emotions. The uncertainty-resolved group elicited a more positive emotional response than the uncertainty-unresolved group, leading to a more indulgent consumption.

The results of three studies show that uncertainty influences indulgent consumption through curiosity and positive emotion, and that curiosity and positive emotion play separate and chain mediating roles between uncertainty and indulgent consumption, respectively. We validate our central hypothesis with questionnaires among blind box consumer groups, examining the moderating role of perceived luck and risk preferences.

The findings shed new light on firms' use of uncertainty to promote consumer purchases.