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This study aims to illuminate the currently poorly understood inflow of knowledge originating from project managers across the value chain of construction projects. The primary purpose is to identify the domains of knowledge that project managers’ need to share in their management activities, the skills they need to develop in their sharing practices and how these relate to each other across different phases of a construction project.

Knowledge domains, skills and the relationships between them were identified following an inductive methodology, a combination of grounded theory and case study, and through the analysis of semi-structured interviews with 21 project managers and participants within a single construction project.

The outcome is a novel framework that theorizes the dynamic interplay between knowledge domains and the skills that facilitate knowledge sharing (KS) for successful project work throughout the construction project.

The combined effects of task heterogeneity, knowledge interdependencies and temporariness require paying increased attention to how knowledge domains and KS skills impact project performance. This paper addresses gaps in developing an integrative understanding of the nature of the domains of knowledge that need to be shared in a project context, the key skills contributing to KS and more importantly, how they evolve and are interpreted and reinterpreted throughout the project and assist KS practice in projects.

The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.

This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.

The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.

The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

This study proposes low-carbon technology (LCT) solutions from the perspective of incremental cost-effectiveness and public satisfaction based on calculating carbon emissions and economic costs.

According to the citation frequency, 11 indicators of low-carbon neighborhood (LCN) were selected so as to construct the low-carbon renewal potential evaluation model. Five neighborhoods were selected to evaluate low-carbon renewal potential based on the driving-pressure-state-impact-response (DPSIR). Moreover, the neighborhoods with the highest renewal potential were selected for further analysis. Then, the feasibility decision was carried out among seven typical LCTs based on the value engineering (VE) method. Finally, the TOPSIS method was applied to calculate the public satisfaction and demand so as to get the priorities of these LCTs. Through comprehensive analysis, the final LCT solutions could be carried out.

Our practice proves that the evaluation model combined with the decision-making methods can provide scientific decision-making support for the LCT solutions. Some LCTs perform consistently across different neighborhoods by comparing VE results and TOPSIS rankings. The solar photovoltaic (PV) (T3) has high value and significant attention which gives it a top priority for development, while the energy-efficient windows and doors (T2) have relatively low value.

There is a lack of research that considers the economic cost, low-carbon efficiency and public satisfaction when proposing LCT solutions for neighborhood renewal projects. Faced with the problem, we practice the decision-making from two dimensions, that is, the “feasibility decision with VE” and the “priorities decision with TOPSIS.” In this way, a balance between incremental cost-effectiveness and public satisfaction is achieved, and LCT solutions are proposed.

Improvement of the environment quality and human development has become the main focus of modern urban development. Micro-renewal is a relatively people-oriented model of urban transformation compared with traditional renewal modes. To improve the theoretical system of neighborhood micro-renewal from a microcosmic perspective, a comprehensive analysis of neighborhood residents' cognition is needed. The purpose of the study is to explore the possibilities and methods of applying gene theory into the study of neighborhood micro-renewal.

According to the meme theory, the research explores the genetic analysis of neighborhood micro-renewal. The cross-over studies with “gene theory” from natural science to social science are analyzed and the neighborhood micro-renewal system was constructed from the perspective of micro-participants and micro-objects. Moreover, the concept of neighborhood “micro-renewal gene” was put forward. Finally, the authors show three application scenarios of public participation with a specific neighborhood micro-renewal project.

The cross research on urban studies with gene theory could be divided into three scales and four research fields. The characteristics of carrying out neighborhood micro-renewal in China could be summarized as micro-participants and micro-objects. Residents' cognition could be considered as “micro-renewal gene” in refer to meme theory. The application scenarios of introducing “micro-renewal gene” into the study of neighborhood renewal are of great potentialities.

Neighborhood micro-renewal system was constructed from the perspective of micro-participants and micro-objects. Moreover, neighborhood “micro-renewal gene” was proposed and applied into the study of this system in refer to meme theory.

This study aims to understand how product attributes (object stimuli), social influences (social-psychological stimuli) and internal factors (internal stimuli) contribute to Chinese Generation Z’s purchase intentions (responses) for new Chinese style apparel (NCSA) through NCSA attitudes (cognitive state) and cultural pride (affective state) based on the stimulus-organism-response (S-O-R) model.

Data were collected from 989 respondents aged between 18 and 29 years through self-administrated questionnaires via a professional survey panel, Credamo, in China. Data were analyzed using structural equation modeling.

Results showed that among the seven stimuli, NCSA’s design, online social networking communities, cultural identity and personal norms significantly influenced Generation Z’s purchase intentions through both NCSA attitudes and cultural pride. Further, NCSA’s cultural connotations and celebrity influences elicited purchase intentions merely through the affective state – cultural pride. In contrast, NCSA’s functionality did not have a significant influence on NCSA attitudes and adversely affected cultural pride.

This study fills the research gap and extends the application of the S-O-R model within the NCSA context. The findings of this study shed light on the practical implications for marketers, brands and policymakers with regard to a better understanding of Chinese Generation Z’s NCSA consumption.

This study aims to explore the spillover effects of supply chain corruption practices (SCCPs) on stock returns along the supply chain and within the industry. Specifically, it investigates how SCCPs affect the stock returns of corrupt firms' bystander supply chain partners and industry peers, both of which are not involved in the SCCPs.

The authors employ the event study methodology to quantify SCCPs' spillover effects in terms of abnormal stock returns. The analysis is based on 117 SCCPs occurring in China between 2014 and 2021.

The event study results show that SCCPs have negative effects on the stock returns of corrupt firms' bystander supply chain partners. Such negative effects are more pronounced for bystander buyers than bystander suppliers. However, SCCPs do not have a significant impact on the stock returns of corrupt firms' industry peers. Additional analysis further suggests that SCCPs are more likely to affect the stock returns of domestic rather than overseas bystander supply chain partners.

This study is the first attempt to thoroughly examine the spillover effects of SCCPs along the supply chain and within the industry, advancing the understanding of the financial consequences of SCCPs and providing important implications for future research and practices related to supply chain corruption.

Using Generation Z consumers from China as an example and focusing on the nostalgia-driven design of brand spokes-character, this study sought to update research on the causal relationship between nostalgia and brand attitude for younger consumers. Two types of nostalgic brand spokes-characters (i.e., eliciting personal nostalgia and historical nostalgia) were examined separately and compared to verify their contributions to more positive brand attitude, as well as related mechanisms, that is, whether consumer trust in the spokes-character mediated the relationship between nostalgic spokes-characters and brand attitude.

An experiment was first conducted to test the causal effects of brand spokes-characters designed to elicit two types of nostalgic feelings (i.e., personal nostalgia and historical nostalgia). Then, the authors investigated the influencing mechanism of nostalgic brand spokes-characters based on bootstrap mediation models.

The results revealed that for less familiar brand spokes-characters, either type of nostalgia-driven design would enhance consumers' brand attitude. Moreover, consumer trust in the spokes-character mediated the relationship between personal-nostalgic brand spokes-characters and brand attitude.

This study was the first to examine personal nostalgia and historical nostalgia separately regarding the effects of nostalgic spokes-characters and related mechanisms. By combining methods of experimental design and bootstrap mediation modeling, it provided a more robust evaluation of nostalgia-driven design, and supported using certain nostalgic styles for designing brand spokes-characters, which can help modern brands draw more interest from young consumers and promote more positive brand attitude.