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This study examines whether and how government fiscal pressure influences corporate charitable giving (CCG).

The authors exploit sub-national tax revenue sharing changes as exogenous variations to government’s fiscal pressure at the city level and then construct a quasi difference-in-differences (DiD) model to conduct the analysis based on a sample that consists of 14,168 firm-year observations in China during the period of 2003 to 2012.

The authors found that firms increase charitable donations when local governments face higher fiscal pressure. Such effects are more pronounced for firms that have stronger demand for political connectedness in the sample period. Furthermore, this study’s findings suggest that the timing strategy of donating helps firms to lower the effective tax rate and to build stronger political connections. In addition, donating firms outperform non-donating firms in terms of bank loan access and market reputation.

The authors contribute to at least three lines of literature: first, extend the understanding of timing strategies of corporate charitable behaviors; second, contribute to the literature studying the “crowd out” effect between government-provided charitable funds and private donations; finally, contribute to the emerging literature exploring the financial interests associated with corporate donation strategy (Claessens et al., 2008; Cull et al., 2015).

With the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers’ economic models to help drive online sales. Initially, the purpose of this paper is to undertake an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this paper reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps and eventually propose new research solutions to benefit apparel retailing market.

This study utilizes empirical research drawing on 130 academic publications indexed from online databases. The authors introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications and keywords.

This study classified high-tech integrated apparel systems into 3D CAD systems, personalised design systems and recommendation systems. The authors’ research interest is focussed on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms tested in system modelling, existing research is lacking in terms of apparel and users profiles research. Thus, from the review, the authors have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system.

Based on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Originating from a theoretical stance that situates knowledge organization in its social context, the study applies a multifaceted framework pertaining to five categories of textual data: the Seven Epitomes; biographical information about the classificationist Liu Xin; and the relevant intellectual, political, and technological history.

The study discovers seven principles contributing to the epistemic foundation of the catalogue's classification: the Han imperial library collection imposed as the literary warrant; government functions considered for structuring texts; classicist morality determining the main classificatory structure; knowledge perceived and organized as a unity; objects, rather than subjects, of concern affecting categories at the main class level; correlative thinking connecting all text categories to a supreme knowledge embodied by the Six Classics; and classicist moral values resulting in both vertical and horizontal hierarchies among categories as well as texts.

A major limitation of the study is its focus on the main classes, with limited attention to subclasses. Future research can extend the analysis to examine subclasses of the same scheme. Findings from these studies may lead to a comparison between the epistemic approach in the target classification and the analytic one common in today's bibliographic classification.

The study is the first to examine in depth the epistemic foundation of traditional Chinese bibliographic classification, anchoring the classification in its appropriate social and historical context.

The big challenge in apparel recommendation system research is not the exploration of machine learning technologies in fashion, but to really understand clothes, fashion and people, and know what to learn. The purpose of this paper is to explore an advanced apparel style learning and recommendation system that can recognise deep design-associated features of clothes and learn the connotative meanings conveyed by these features relating to style and the body so that it can make recommendations as a skilled human expert.

This study first proposes a type of new clothes style training data. Second, it designs three intelligent apparel-learning models based on newly proposed training data including ATTRIBUTE, MEANING and the raw image data, and compares the models’ performances in order to identify the best learning model. For deep learning, two models are introduced to train the prediction model, one is a convolutional neural network joint with the baseline classifier support vector machine and the other is with a newly proposed classifier later kernel fusion.

The results show that the most accurate model (with average prediction rate of 88.1 per cent) is the third model that is designed with two steps, one is to predict apparel ATTRIBUTEs through the apparel images, and the other is to further predict apparel MEANINGs based on predicted ATTRIBUTEs. The results indicate that adding the proposed ATTRIBUTE data that captures the deep features of clothes design does improve the model performances (e.g. from 73.5 per cent, Model B to 86 per cent, Model C), and the new concept of apparel recommendation based on style meanings is technically applicable.

The apparel data and the design of three training models are originally introduced in this study. The proposed methodology can evaluate the pros and cons of different clothes feature extraction approaches through either images or design attributes and balance different machine learning technologies between the latest CNN and traditional SVM.

Shanghai ANE Logistics Co., Ltd., established on June 1, 2010, is a business of road part-load logistics for goods from 5 to 300 kilograms. Mr. Wang Yongjun and his management team have spent five consecutive years building ANE into the biggest part-load franchising network in China, and set up a brand new business model, through integration of traditional transport lines, part-load express network and information technology platform.

The purpose of this study was to investigate the relationship between gross domestic product (GDP) growth and renewable and non-renewable energy consumption in 82 developing countries categorized by region.

To achieve the goal of this study, the panel model was used taking the period 1990-2009.

The Kao co-integration test results showed that both renewable and non-renewable energy consumption had a long-running relationship with all the economic sectors in all regions. Moreover, the FMOLS revealed that the renewable and non-renewable energy consumption had a long-run positive relationship with the economic sectors. However, the results also revealed that non-renewable energy consumption has a more significant effect on the economic sectors than the renewable energy consumption. In addition, the Granger causality showed the same results, that the causal relationship between the economic sectors and non-renewable energy consumption is more significant than the causal relationship between the economic sectors and renewable energy.

The reason behind these results is that these regions still depend on fossil fuels to promote their economic growth. Fossil fuels basically contribute more than 80 per cent of their total energy consumption. Thus, the study recommends the developing countries to increase their investment on renewable energy projects to increase the share of the renewable energy of total energy consumption.

This study is considered different from all the previous studies because it will investigate the disaggregate relationship between GDP and energy consumption (renewable and non-renewable) in East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, South Asia and the Sub-Saharan African developing countries.

Little systematic work has been completed on the incidence of employee ownership in a Chinese context. Similar to the situation in Eastern Europe, this type of ownership was quite widespread in China, particularly during the 1990s. Based on the existing literature and available statistical data, the purpose of this paper is to identify drivers of, and barriers to, the development of employee ownership in China.

The scattered evidence from the literature and official statistical sources are collected and structured in a systematic analysis where the drivers and barriers for employee ownership in the transition process from plan to market are identified at three levels: society, the company and the individual.

Employee ownership developed as a transitory stage between state and private ownership; employees acquired ownership stakes as part of the privatisation of small- and medium-sized state-owned enterprises as well as collectively owned enterprises. However, in most cases the dynamics of ownership resulted in dominant ownership by managers. This trend became more noticeable at later stages of the privatisation process.

The paper shows how policies and institutional settings at the society level are determining for the development of employee ownership.

The contribution of the paper is to give a general and systematic analysis of the development of employee ownership in China both based on a comprehensive literature review and by utilising existing statistical sources.