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Prior work underscores the important role of customer advocacy for brands. The purpose of this study is to explore the critical role customers can play as brand heroes. The authors developed and validated a measurement scale composed of properties that are derived from distinct brand hero motivational mechanisms.

The authors conducted one exploratory pilot, using semi-structured interviews, with industry and academic experts, and employed three main studies across varying brands and market settings.

This study explores and empirically demonstrates how the brand hero scale (BHS) is related to, yet distinct from, existing scales of opinion leaders, market mavens, attachment and customer advocacy. The six-item BHS demonstrates convergent, discriminant, nomological and predictive validity across several different brand contexts.

This research extends the extant body of work by identifying and defining brand heroes, developing and validating a parsimonious BHS, and demonstrating how its predictive validity extends both to a range of key advocacy and loyalty customer behaviors.

The study provides provocative insights for marketing researchers and brand managers and ascertains the important role heroes may play for brands in terms of strong customer advocacy and loyalty behaviors.

Building on the theory of meaning, this study shows that identifying and working with brand heroes is of great managerial importance and offers critical avenues for future research.

With rapid urbanization, cities are facing various ecological and environmental problems. Living in harmony with nature is more important than ever. This paper aims to evaluate the ecosystem and ecological features of Azheke village, a key component of the Hani Rice Terraces World Cultural Heritage in China. The focus is on exploring effective ways to improve the relationship between humans and the natural environment through urban design in order to create a livable and sustainable city that can promote the development of sustainable smart urban ecology design.

This study conducted a systematic literature review to answer the following research questions: (1) How does Azheke design achieve harmony between humans and nature? (2) What are the effective approaches to improve the relationship between humans and nature within urban ecosystems? (3) How can urban design learn and integrate from Azheke’s ecological features to improve the relationship between humans and nature?

Azheke sustains long-term human-nature harmony through traditional ecological knowledge (TEK) and efficient natural resource use. By incorporating biophilic design and nature-based solutions from Azheke, along with biodiversity-friendly urban planning, we can boost urban ecosystem health and create unique Azheke-inspired urban designs.

This research primarily focuses on the human-nature relationship, exploring design strategies based on biodiversity without delving into the interactions between other components of urban ecosystems, such as social-cultural and economic components.

This paper provides a new perspective and strategies for developing sustainable and smart urban ecology design. These findings can provide theoretical references for urban planners, designers and decision-makers.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

This study aims to empirically examine the relationships among perceived environmental uncertainty (EV), the level of knowledge distance (KD) and the impact of value network on firm performance.

The quantitative analysis is based on data from 243 Chinese companies with engineering, procurement and construction (EPC) business in the context of the COVID-19 pandemic.

The two dimensions of value network [network centrality (NC) and network openness (NO)] have a different impact on firm performance [financial performance (FP) and market performance (MP)]. NC has a positive impact on FP, but not on MP. NO has a positive effect on MP, but not on FP. A reduced KD mediates the relationship between value network and firm performance. Moreover, it fully mediates the relationship between NC and MP, NO and FP. Finally, during the COVID-19 pandemic, only EV has a moderating effect on KD and MP.

This study is limited in terms of data set because it relies on a limited amount of cross-sectional data from one specific country. Therefore, researchers are encouraged to test the proposed propositions further.

The present findings suggest that EPC professionals should pay more attention to the EV, which may be impacted by policy, technology and the economy. This research has actionable implications for the reform of EPC in the construction industry, and practical recommendations for EPC firms to improve their corporate performance.

The results measure the complementary effects of both dimensions of value network (NC and NO) on two distinct aspects of firm performance (MP and FP) and assess the moderating effect of EV and KD in the context of the COVID-19 pandemics.

The purpose of this study is to explore the economic impact of financial restatements by major customers on the audit opinion of their suppliers, showing that non-financial information disclosure potentially helps auditors make better assessments.

Using a sample of China’s listed firms from 2007 to 2021, the authors aim to find the relationship between customers’ financial restatements and their suppliers’ audit opinions. Heckman selection model, placebo tests and other robustness checks are used as well.

The findings reveal that customers’ financial restatements have a significant effect on the likelihood of suppliers receiving modified audit opinions. This relationship is pronounced when suppliers face a higher level of financial constraints, exhibit poorer accounting conservatism or receive more negative media coverage. Additionally, this effect occurs through increased business risk and information risk, which heightens auditors’ perceived audit risk. Moreover, the study highlights the influence of switching costs, auditor expertise and restatement severity on this relationship.

Risks originating from customers can spread along the supply chain, emphasizing the necessity for auditors to give heightened attention to both the audited firms and their customer information. Moreover, regulators should carefully consider the important impact of customer information disclosures to maximize the protection of the interests of external information users.

This study not only confirms the crucial role of customer information disclosures in annual reports for stakeholders and auditors but also contributes to the existing literature on customer–supplier relationships.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.