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As an effective measure for reducing energy consumption and achieving carbon neutrality, prefabricated building projects (PBPs) have attracted considerable attention in China. Although the Chinese Government has vigorously promoted PBPs, neither developers nor consumers have high recognition of PBPs. This study aimed to explore the decision-making behaviour of governments, developers and consumers in promoting the development of prefabricated buildings in China and to better optimise the incentive strategies for prefabricated buildings in China.

Based on prospect and evolutionary game theories, an evolutionary game model of three stakeholders in the development of PBPs – government, developers and consumers – was constructed. Combined with the system dynamics theory, the incentive policy behaviour and influencing factors of the three parties in the evolutionary game model were analysed.

The results showed that the initial probability of the three parties affects the decision-making behaviour of each party and that of other stakeholders. Government subsidies to developers are more sensitive than developers themselves. There is a certain threshold for the scope of government subsidies to consumers, and exceeding this threshold does not promote the development of PBPs. Based on the results, policy recommendations to the government, developers and consumers were proposed to enhance PBP development.

This study provides suggestions for governments to formulate reasonable incentive policies for prefabricated buildings and a specific theoretical basis for the sound development of prefabricated buildings.

This paper aims to assess the links among these demand chain constructs by conducting a full-scale systematic review of all demand chain management (DCM) literature reviews published in marketing and operations management journals from 2013 to 2020. Marketing and supply chain management are central to DCM; thus, this study briefly describes the contributions to knowledge provided by the papers contained in this issue. In addition, some additional areas of research in which the DCM can be gainfully deployed are outlined.

This paper makes a systematic literature review of 70 literature samples by means of content analysis and comprehensive analysis. These approaches guarantee a replicable, rigorous and transparent research process and minimize researcher bias. The analytical categories required for the content analysis are defined along the constructs of marketing and supply chain management.

As can be expected, this paper highlights the key role of the two constructs in the strategy of DCM. In this light, the paper claims to provide evidence of a link between the constructs of marketing and supply chain management. This paper reviews the connotation of DCM through literature review, distinguishes the relationship between DCM and supply chain management from a strategic management perspective and discusses the future research direction.

This study assesses the link between the strategic constructs of marketing and supply chain management through research embedded in literature reviews, pinpointing research gaps and potential future research directions in the field. Contributing to DCM theory building, a thorough review provides qualitative comparison of the link between marketing and supply chain management.

Although some literature reviews have been conducted in the past on the constructs of DCM, no full review of literature reviews aiming to test a strategic theoretical link in the demand chain related to supply chain and marketing.

Live-streaming platforms emphasize dynamic social interaction and fan engagement. Users integrate into the live-streaming community through continuous social learning activities, such as sending bullet comments, reviewing comments and interacting with celebrities. However, comprehensive research on the transactional intricacies of live-streaming e-commerce from the perspectives of community and learning is still lacking.

Focusing on the behavior characteristics of the reference group represented by online celebrities and fans in the live-streaming environment, this study utilized social learning as the theoretical basis to examine how reference groups affect consumer purchase intention through a series of intermediary effects. An empirical investigation and machine learning algorithms were utilized to explore and verify the hypothesized model.

The results show that: (1) reference groups’ behavior positively stimulated social presence and enhanced consumer purchase intention through the chain-mediating effect of social presence and trust in online celebrities; (2) celebrity characteristics (professionalism, attractiveness and interactivity) positively impacted consumer trust; (3) in addition, machine learning algorithms substantiated that reference groups’ behavior, social presence, trust and celebrity characteristics had a remarkably robust predictive effect on purchase intention.

These findings hold theoretical implications for understanding how the social community affects consumers’ purchase intention in the live-streaming context and practical significance for marketing strategies toward live-streaming e-commerce.

Leveraging theory from the dynamic capability literature, this study aims to explore how information technology (IT) capability influences firm agility and subsequently translates into firm performance.

This study examines the proposed relationships by using survey data from a sample of 296 Chinese retail firms. Structural equation modeling is used to test this study’s hypotheses.

The following results are produced: the direct effect of IT capability on firm agility is confirmed; firm agility has a direct impact on firm performance; and the indirect effect of IT capability on firm performance via firm agility is demonstrated (i.e. partial mediation).

The catastrophic outbreak of the COVID-19 pandemic has heightened the importance of firm agility more than ever. Although the traumatic event is painful, however, there is nothing like a crisis to offer a tremendous business opportunity. In response to the pandemic circumstance, firms are required to operate their business by reacting to unpredictable and dynamic market changes quickly and efficiently. This study sheds light on why firms should develop their IT capability and how it affects firm performance via firm agility during the COVID-19 outbreak.

This study aims to (1) investigate the influence of firms’ social media utilization on performance through supply chain agility, (2) examine the mediating role of supply chain agility and (3) explore the indirect effect of social media utilization on operational performance via supply chain agility as knowledge transfer increases.

A survey of 298 Chinese manufacturing firms was conducted to assess the proposed relationships, employing moderated mediation analysis with Andrew Hayes (2017) PROCESS macro.

Social media utilization indirectly enhances operational performance through supply chain agility, supporting our mediation hypothesis (H1). Additionally, knowledge transfer moderates the positive impact of social media utilization on supply chain agility (H2). The moderated mediation analysis reveals that the mediating effect of supply chain agility on operational performance is stronger at higher levels of knowledge transfer (H3), shedding light on the intricate relationships between these variables and providing insights for businesses seeking to leverage social media and knowledge transfer to enhance supply chain resilience and operational performance.

This study empirically investigates the role of social media utilization in supply chains within the digital age. We explore how social media enhances supply chain agility and knowledge transfer, highlighting its transformative potential for real-time communication, responsiveness and collaboration across networks. By integrating dynamic capability theory with contemporary digital practices, we demonstrate how leveraging digital platforms alongside traditional supply chain processes can significantly improve manufacturing efficiency. This research bridges existing gaps in the literature and provides valuable insights for businesses navigating complex, rapidly changing environments in the era of digital transformation.

This paper aims to make a systematic study on the factors that hinder the development of China’s intelligent automobile manufacturing industry; based on comprehensive understanding of these obstacles and by optimization means, ultimately, the healthy and sustainable development of intelligent automobile manufacturing industry in China can be promoted.

Based on a questionnaire survey of intelligent automobile manufacturing listed companies in China, first, fuzzy semantic scale was adopted to collect respondents’ choices, the fuzzy score function is used to calculate the fuzzy score value and these data are used as the basis for subsequent model analysis. Then, structural equation modeling (SEM) was adopted to analyze the causal relationship between influencing factors to explore the main hinder factors.

It is found that, in the short term, the backwardness of technological industrialization is the main reason leading to low permeability of intelligent automobile; in the medium term, the imperfect industrial R&D ability and the insufficiency of infrastructure are major causes for high manufacturing cost and low competitiveness of intelligent automobile manufacturing industry; in the long term, the lack of national policy and industrial strategic planning is the main factors affect intelligent automobile manufacturing cost and the industry competitiveness.

The research conclusion has important policy implications for promoting intelligent automobile manufacturing sustainable development. In recent years, China’s intelligent automobile manufacturing industry has gradually stepped out of breeding period; therefore, the role of government should be gradually transformed from participants to managers and regulators. Considering the fact that intelligent automobile cost is very high, and still higher than the cost of fuel vehicle, government should focus on the issues such as improving R&D capabilities, infrastructure construction, policy framework system, legal system and technological industrialization. Specifically, in short-term planning, improving technological industrialization level is the key to development; in medium-term planning, policymakers should focus on the improvement of R&D capabilities and infrastructure; considering the long-term development, establishing appropriate national policies and dealing with the adverse impact of imperfect strategic planning are the most sensible choice.

This paper analyzes the factors that hinder the development of China’s intelligent automobile manufacturing industry for the first time, and provides the basic logic of integration factors at different levels with the development of intelligent automobile to reveal the uniqueness and facts of China’s economic development.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.