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This study explores the influence of heritage brand extension on consumer purchase intention and analyses the effects of pop culture involvement. The extension of heritage brands is becoming increasingly difficult because such an extension needs to be consistent with the unique characteristics of brands and resonate with consumers. However, few scholars discuss the influence of consumers' level of pop culture involvement on brand extension and purchasing behaviour.

Taking time-honoured brands as an example, this study established a conceptual model based on a comprehensive review of the literature, and then tested the model using a sample of 255 respondents who were familiar with one of the selected Chinese time-honoured brands. Structural equation modelling was used to analyse the relationships amongst brand extension fit, pop culture involvement, perceived value and purchase intention.

Time-honoured brand extension fit has a positive impact on consumer purchase intention, and this path is significantly influenced by the mediation mechanisms of perceived value. Situational pop culture involvement can significantly strengthen the relationship between time-honoured brand extension fit and perceived value, whereas enduring pop culture involvement does not.

The results clarify and expand on the different roles of cultural involvement in time-honoured brands and broaden research on the influence of cultural involvement in this regard. This study has significant theoretical value for the inheritance and revival of heritage brands and provides a reference for the practice of time-honoured brands.

The paper aims to propose a novel method based on deep sparse convolutional neural network (CNN) for clothing recognition. A CNN based on inception module is applied to bridge pixel-level features and high-level category labels. In order to improve the robustness accuracy of the network, six transformation methods are used to preprocess images. To avoid representational bottlenecks, small-sized convolution kernels are adopted in the network. This method first pretrains the network on ImageNet and then fine-tune the model in clothing data set.

The paper opts for an exploratory study by using the control variable comparison method. To verify the rationality of the network structure, lateral contrast experiments with common network structures such as VGG, GoogLeNet and AlexNet, and longitudinal contrast tests with different structures from one another are performed on the created clothing image data sets. The indicators of comparison include accuracy, average recall, average precise and F-1 score.

Compared with common methods, the experimental results show that the proposed network has better performance on clothing recognition. It is also can be found that larger input size can effectively improve accuracy. By analyzing the output structure of the model, the model learns a certain “rules” of human recognition clothing.

Clothing analysis and recognition is a meaningful issue, due to its potential values in many areas, including fashion design, e-commerce and retrieval system. Meanwhile, it is challenging because of the diversity of clothing appearance and background. Thus, this paper raises a network based on deep sparse CNN to realize clothing recognition.

From the perspective of local government tax administration, the impact of geographic dispersion on the corporate tax burden is investigated in this paper.

Using unbalanced panel data with a sample of listed companies from 2003 to 2020 in China, this paper focuses on the effect of geographic dispersion on corporate tax burden and the mechanisms.

It is found that corporate tax burden is positively related to geographic dispersion. It is also found that geographic dispersion affects the corporate tax burden by increasing the effort of local government tax administration. In addition, the relation between geographic dispersion and corporate tax burden is more pronounced for local SOEs prior to the implementation of Golden Tax Project III and in cases where local governments face stronger financial pressure to obtain revenue.

This study has important implications for the promotion of the coordinated development of the regional economy, as well as the legalization, modernization and informatization of tax administration.

The purpose of this study is to investigate how book group recommendations can be used as a meaningful way to suggest suitable books to users, given the increasing number of individuals engaging in sharing and discussing books on the web.

The authors propose reviews fine-grained classification (CFGC) and its related models such as CFGC1 for book group recommendation. These models can categorize reviews successively by function and role. Constructing the BERT-BiLSTM model to classify the reviews by function. The frequency characteristics of the reviews are mined by word frequency analysis, and the relationship between reviews and total book score is mined by correlation analysis. Then, the reviews are classified into three roles: celebrity, general and passerby. Finally, the authors can form user groups, mine group features and combine group features with book fine-grained ratings to make book group recommendations.

Overall, the best recommendations are made by Synopsis comments, with the accuracy, recall, F-value and Hellinger distance of 52.9%, 60.0%, 56.3% and 0.163, respectively. The F1 index of the recommendations based on the author and the writing comments is improved by 2.5% and 0.4%, respectively, compared to the Synopsis comments.

Previous studies on book recommendation often recommend relevant books for users by mining the similarity between books, so the set of book recommendations recommended to users, especially to groups, always focuses on the few types. The proposed method can effectively ensure the diversity of recommendations by mining users’ tendency to different review attributes of books and recommending books for the groups. In addition, this study also investigates which types of reviews should be used to make book recommendations when targeting groups with specific tendencies.

In light of growing concerns related to the psychological vulnerability during the pandemic, this study aims to examine the impact of fear or trauma of COVID-19 on stress, anxiety and depression among management students. Additionally, the study also explores the possible strategies adopted by professional students to cope with the pandemic situation.

With an approach to establish a probable concrete relationship between fear with the level of stress, anxiety and depression, the data for the study was collected from 1,408 management students through a structured questionnaire designed in Google Form and administered through WhatsApp. The survey was carried out in the month of July and August 2020 during the lockdown period. Correlation and structural equation modeling have been used to examine the relationship among the test attributes.

The results from the study discovered that “fear of COVID-19” has a significant and considerable impact on the increased level of anxiety and stress among the professional students, but the observations did not demonstrate a significant influence of the “fear” on “depression.” The responses reveal that students have developed anxiety and felt stressed mostly due to uncertainty in the upcoming academic plans, disturbances in their regular academic routines and concerns about their future careers. Further, the findings have portrayed that students have adopted both protective and avoidance coping strategies to overcome the adverse consequences of the pandemic.

The study gives an insight on the psychological vulnerability of the management students and their capability to overcome such sudden disruptions due to pandemics. This research could thus, serve as a reference to the policymakers, universities and institutions while planning out programs and schemes, which would encourage the aspiring managers to overcome the crisis and prepare themselves to befit the vibrant corporate world.

Several studies exist on the impact of the pandemic on undergraduate students in different universities. However, there are a dearth of literature, which reflects the psychological vulnerability of professional graduates especially management students who are on the verge of starting their professional career.

The connection between digital manufacturing technologies (Industry 4.0) and the environment has sparked discussions on firms' disclosure of negative information on pollutant emissions and the pursuit of positive environmental outcomes. However, very few studies explore how it relates to a firm's robot usage and its mechanism. The purpose of this paper is to investigate the impacts of robot penetration on firms' environmental governance in China.

The ordered probit model (and probit model) are employed and empirically tested with a sample of 1,579 Chinese listed firms from 2010 to 2019.

The study reveals a negative relationship between robot usage and the disclosure of negative indicators and a U-shaped relationship between robot usage and positive environmental outcomes. Among the sample, nonstate-owned enterprises (SOEs) display unsatisfactory performance, while heavily polluting industries disclose more information on pollutant emissions. The robot–environmental governance nexus is conditional on firm size, capital intensity and local economic development.

The study proposes a fresh view of corporate environmental governance to assess the environmental implications of robot adoption. It also contributes to identifying the curvilinear, moderating and heterogenous effects in the robot–environment nexus. The results provide rich policy implications for the development of industrial intelligence and corporate environmental governance in the circular economy (CE) context.

Few efforts have considered political embeddedness heterogeneity and examined whether different types of political embeddedness can pose different valuation effect on green innovation. Address to this concern, this paper aims to provide a more nuanced conceptualization of different types of political embeddedness and their effects on green innovation.

This paper conducts negative binomial method to test our predicts and adopts propensity score match (PSM) and placebo test to mitigate endogeneity issues.

The interpersonal political embeddedness (IPPE) has a stronger positive effect on green innovation than the interorganizational political embeddedness (IOPE) and that such effect depends on multiple factors at an individual (i.e. Cheif executive officer (CEO) duality), firm (i.e. firm growth) and environment (i.e. industrial competition) level. Figure 1 is the research model. The relationship is more pronounced when the firm has a dual leadership structure and a high level of firm growth and is less pronounced when a firm is engaged in intensive industrial competition.

The authors extend political embeddedness literature by introducing and distinguishing the concept of IPPE and IOPE. The authors enrich green innovation research by revealing how corporate green innovation is effected by the IPPE and the IOPE.