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Though enormous research studies were conducted on corporate environmental responsibility (CER), few of them could empirically justify how CER helps to improve firm’s competitive advantage and firms are still hesitant to incorporate CER with their business strategy at present. The purpose of this paper is to theoretically and empirically explore how the CER strategy could help the firm to gain competitive advantage in Chinese context, particularly in terms of achieving brand sustainability (BS).

In this study, 310 listed companies in China were chosen as research sample. First, the CER strategies were classified into developing eco-friendly products, adopting EMAS or other eco-management, enhancing the impact of CER through value chain and charitable CER. Second, BS is constructed as two dimensions, i.e. resource-acquisition and consumer impact. Accordingly, this paper analyzed the relationship between CER and BS with regression model analysis, taking account of several moderating and control variables.

The results indicate that CER strategies have positive effect on BS. Among all CER strategies, developing eco-friendly products and charitable CER undertakings are the most effective ones to promote BS performance. Also, the paper found that the length of time in adopting CER strategy moderates the effect of CER on BS. The empirical evidence proves that CER strategies could enhance the brand value in terms of BS and help the company to gain competitive advantage.

First, most of our samples are of the state-owned enterprises, so our assumption might not be applicable to other types of business. Second, corporate social responsibility (CSR) communication is an important factor in the relation between CSR and corporate performance, but it is not taken into account in this study. Third, the difference in industries and ownership in this research is out of concern.

As this paper has provided empirical evidence to reveal the effectiveness of different CER strategies, firms in China could be more motivated to undertake CER not only for the sake of environment but also for their brand value and competitive advantage. More importantly, this paper could be a valuable reference for the firms in China to choose suitable and effective CER strategies, as proved in this study, to gain competitive advantage in the market.

At first, while public environmental awareness has improved gradually, we introduce the BS concept to explain how the CER strategies affect CCA. This approach gives us another perspective to highlight the relationship between these two constructs. Second, we conducted our research from practical perspective to explore how to apply the CER undertakings as the company’s strategy. Third, we conducted our empirical research in Chinese context, which will enrich the theoretical CER and CSR literature.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Corporate social responsibility is widely seen as a benefit to companies seeking competitive advantage. For many companies now, corporate environmental responsibility is the next step towards becoming competitively sustainable.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Citation content in academic papers and academic monographs promotes the knowledge flow among different publications. However, existing citation content analysis (CCA) focuses on academic papers and monographs have not received much research attention. We want to know if monographs are appropriate objects of CCA and whether existing methods of analyzing citation in papers are suitable for citation in monographs. Therefore, this paper aims to learn more about features of cited references and citation content in monographs and compare the characteristic of citation pattern between monographs and papers.

The authors manually annotate the references and syntactic citation content in academic monographs published by Morgan & Claypool and automatically extracted the references and citation content from academic papers published by Public Library of Science. Five features in two types citation pattern, namely, pattern of cited reference (including year, source and mention frequency of reference) and pattern of citation content (including location, length of citation content) are used to examine similarities and differences between monographs and papers.

The results indicate that between monographs and papers, differences are shown in location, length of citation content and year, source of reference, whereas frequency of mention of reference is similar.

Previous studies have explored the patter of citation content in academic papers. However, none of the existing literature, as far as the authors know, has considered the citation content in academic monographs and the similarities or differences among academic documents when studying the citation pattern.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Playing as a global city, to maintain the economic dynamics and urban vitality, Hong Kong government would like to take urban regeneration in urban core as a kind of urban growth strategy. The government monopolizes land supply for urban development through the leasehold system, while the redevelopment agency is authorized to take land acquisition for urban redevelopment. The transformation of agency from Land Development Corporation (LDC) to Urban Renewal Authority (URA) reflected the formation of a coalition composed of quasi-public redevelopment agency and private developer, which facilitates land and property resumption in urban redevelopment. The URA-led projects often tend to redevelop obsolete communities into up-market neighborhoods, which possibly enables redevelopment agency and developers to gain more economic benefits from real estate appreciation. Nevertheless, evidences from some large redevelopment projects conducted by URA in Hong Kong such as Lee Tung Street, Langham Palace and Kennedy Town have presented that urban redevelopment is closely associated with gentrification triggered by displacement of original neighborhood residents. Hence gentrification in Hong Kong has raised more and more concerns about booming housing price as well as fragmentation of social networks. Through urban regime combined with growth machine approach, this paper will explain the collusion of redevelopment agency and private developers that jointly turns the URA-led redevelopment into neighborhood gentrification. And by examining Kwun Tong Town Centre Project (KTTCP), findings indicate that soaring property value will crowd low-income groups and working classes out from their original neighborhoods; and then those gentrified residential estates will be occupied by rich class. Moreover, increasing rent and operation costs will inevitably eliminate those family-operated small businesses; and then they will be superseded by high-end retailing and services. In this way, urban morphology will be reshaped perpetually through more and more gentrified neighborhoods.

Academic social (question and answer) Q&A sites are now utilised by millions of scholars and researchers for seeking and sharing discipline-specific information. However, little is known about the factors that can affect their votes on the quality of an answer, nor how the discipline might influence these factors. The paper aims to discuss this issue.

Using 1,021 answers collected over three disciplines (library and information services, history of art, and astrophysics) in ResearchGate, statistical analysis is performed to identify the characteristics of high-quality academic answers, and comparisons were made across the three disciplines. In particular, two major categories of characteristics of the answer provider and answer content were extracted and examined.

The results reveal that high-quality answers on academic social Q&A sites tend to possess two characteristics: first, they are provided by scholars with higher academic reputations (e.g. more followers, etc.); and second, they provide objective information (e.g. longer answer with fewer subjective opinions). However, the impact of these factors varies across disciplines, e.g., objectivity is more favourable in physics than in other disciplines.

The study is envisioned to help academic Q&A sites to select and recommend high-quality answers across different disciplines, especially in a cold-start scenario where the answer has not received enough judgements from peers.

With the development of the internet, huge numbers of reviews are generated, disseminated, and shared on e-commerce and social media websites by internet users. These reviews usually indicate users’ opinions about products or services directly, and are thus valuable for efficient marketing. The purpose of this paper is to mine online users’ attitudes from a huge pool of reviews via automatic question answering.

The authors make use of online reviews to complete an online investigation via automatic question answering (AQA). In the process of AQA, question generation and extraction of corresponding answers are conducted via sentiment computing. In order to verify the performance of AQA for online investigation, online reviews from a well-known travel website, namely Tuniu.com, are used as the experimental data set. Finally, the experimental results from AQA vs a traditional questionnaire are compared.

The experimental results show that results between the AQA-based automatic questionnaire and the traditional questionnaire are consistent. Hence, the AQA method is reliable in identifying users’ attitudes. Although this paper takes Chinese tourism reviews as the experimental data, the method is domain and language independent.

To the best of the authors’ knowledge, this is the first study to use the AQA method to mine users’ attitudes towards tourism services. Using online reviews may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

The purpose of this paper is to explore which structures of academic articles referees would pay more attention to, what specific content referees focus on, and whether the distribution of PRC is related to the citations.

Firstly, utilizing the feature words of section title and hierarchical attention network model (HAN) to identify the academic article structures. Secondly, analyzing the distribution of PRC in different structures according to the position information extracted by rules in PRC. Thirdly, analyzing the distribution of feature words of PRC extracted by the Chi-square test and TF-IDF in different structures. Finally, four correlation analysis methods are used to analyze whether the distribution of PRC in different structures is correlated to the citations.

The count of PRC distributed in Materials and Methods and Results section is significantly more than that in the structure of Introduction and Discussion, indicating that referees pay more attention to the Material and Methods and Results. The distribution of feature words of PRC in different structures is obviously different, which can reflect the content of referees' concern. There is no correlation between the distribution of PRC in different structures and the citations.

Due to the differences in the way referees write peer review reports, the rules used to extract position information cannot cover all PRC.

The paper finds a pattern in the distribution of PRC in different academic article structures proving the long-term empirical understanding. It also provides insight into academic article writing: researchers should ensure the scientificity of methods and the reliability of results when writing academic article to obtain a high degree of recognition from referees.

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

Aluminum alloy is considered an ideal material in aerospace, automobile and other fields because of its lightweight, high specific strength and easy processing. However, low hardness and strength of the surface of aluminum alloys are the main factors that limit their applications. The purpose of this study is to obtain a composite coating with high hardness and lubricating properties by applying GO–PVA over MAO coating.

A pulsed bipolar power supply was used as power supply to prepare the micro-arc oxidation (MAO) coating on 6061 aluminum sample. Then a graphene oxide-polyvinyl alcohol (GO–PVA) composite coating was prepared on MAO coating for subsequent experiments. Samples were characterized by Fourier infrared spectroscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. The friction test is carried out by the relative movement of the copper ball and the aluminum disk on the friction tester.

Results showed that the friction coefficient of MAO samples was reduced by 80% after treated with GO–PVA composite film.

This research has made a certain contribution to the surface hardness and tribological issues involved in the lightweight design of aluminum alloys.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0427/