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This study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.

We conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.

We discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.

Scientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.

The purpose of this paper is to investigate the mediating effect of organizational trust on the relationship between perceived corporate social responsibility (CSR) practices and firm performance.

A total of 674 questionnaires were sent randomly to Chinese firms to obtain a total of 168 reliable responses. A confirmatory factor analysis was conducted for a validity test, and structural equation modeling was employed to test the mediating effect of organizational trust.

The empirical results show that perceived CSR practices of firms had significant direct effects on employee well-being and organizational performance and that organizational trust partially mediated the relationships of CSR practices to employee well-being as well as to organizational performance.

The data may not fully represent a generalized survey of all industries with CSR management. In this regard, future research should focus on a specific Chinese industry. The results suggest that firms should more actively promote the role of employees in CSR strategies to better build organizational trust.

Previous CSR studies have generally focused on customers’ perceptions, paying little attention to employees’ viewpoints. This study provides the first empirical analysis of the relationship between CSR and firm performance from the perspective of employees in Chinese firms. In addition, the study examines the mediating role of trust in CSR, which has been rarely considered in the context of Chinese firms.

Trade fairs have crucial socio-economic, cultural and political impacts. This paper aims to explore these multi-faceted effects at the local level from a citizens' perspective.

The economic, social, environmental and socio-cultural effects of trade fair activity are studied by employing the case study of Thessaloniki International Fair. These impacts are examined based on the views of people in the local community of the host area, conducting a fieldwork survey with questionnaires.

The analysis, based on descriptive statistics, factor analysis and induction statistics, indicates that the economic, environmental and socio-cultural effects of trade fair activity on the city are largely viewed positively by residents of Thessaloniki. Differences in representations of fair's impacts are evident in what specific groups, according to age, educational level and occupation, expect from trade fair activity.

While fairs' effects have been examined by studies in economics, sociology and politics, providing useful insights about the inter-linkages between trade fairs and host cities, they tend to pay little attention to citizens' perceptions on fairs' socio-economic implications. This paper enriches the literature on trade fairs' effects by adopting a citizens' perspective, being among the first studies to focus on representations, perceptions and views of residents of the host city to examine fairs' socio-economic implications.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.

Existing studies have found that most merger and acquisition (M&A) activities do not create the intended synergy. These studies have mainly investigated how firms' internal factors contribute to M&A successes or failures. The current study differs from the earlier ones by exploring how governments' activities can contribute to the creation of acquisition synergy.

A novel technique based on multi-objective optimization by ratio analysis and complex proportional assessment method under an interval-valued intuitionistic fuzzy (IVIF) environment is proposed to prioritize these government roles needed during the M&A process focusing on the Chinese M&A market.

Enactments of regulations and loan guarantees are the most important strategies to help Chinese acquirers overcome acquisition failures. While tax relief ranks third, government training support ranks fourth. Finally, the result shows that government institutional support is the least to help acquirers overcome acquisition failures.

The government has a role to play in the acquisition success. Although this study has prioritized governments' role in relative importance order, the authors recommend that governments capable of providing all these strategies should do so without any specific order. However, if otherwise, governments should not neglect the strategies with less weight completely but rather consider reducing capital allocations to such strategies. Moreover, this study shows how firms with stronger business ties with government officials may enjoy success during acquisition activities. The authors recommend that firms intending to make acquisitions develop stronger ties with governments in order to benefits from governments.

This is the first study to develop a theoretical framework showing how government can contribute to M&A success. The study achieves this by extending Keynesian's arguments and identifies five (5) ways in which governments can ensure acquisition success. Second, within fuzzy multi-criteria decision-making (F-MCDM) research, this study is the first to show the applicability of integrated multi-objective optimization by ratio analysis (MULTIMOORA) and complex proportional assessment (COPRAS) techniques in an IVIF environment. The novel methodology proposed in this study offers an insightful research method to future studies focusing on group decision problems.

The concept of sport team identification has been widely used as a theoretical framework in explaining sport fan behavior. However, limited attention has been devoted to the consequences of distant (i.e., foreign) team identification. The purpose of the current research was to examine the way in which fans (local and distant) can increase their levels of collective and personal self-esteem due to their team identification.

Data were accumulated from three Greek websites (N = 742). Among them, 623 subjects were grouped as local and 119 as distant football fans. A structural invariance analysis was followed.

The results revealed how team identification, enduring team-related social connections, and basking in reflected glory are interrelated to affect collective and finally personal self-esteem. Moreover, no differences were found between local and distant fans regarding the paths from eam identification to collective self-esteem and from collective self-esteem to personal self-esteem.

This is one of the first endeavors to examine the psychological consequences of distant team identification and to test the invariance across local and distant fans concerning the mechanisms that their personal self-esteem can be enhanced because their psychological connection to their favorite sport team.

China adopted its new Accounting Standards for Business Enterprises No. 6 in 2007, which substantially converges with the International Financial Reporting Standards. It stipulates that firms operating in China shall capitalize development costs provided specific criteria have been met. This paper aims to examine the effects of the new accounting policies of R&D on the value-relevance and stock performance of 36,299 Chinese firms-years from 2007 to 2020.

A comprehensive multi-stage analysis was conducted. Multiple linear regressions were performed on the pooled cross-sectional time-series total R&D, capitalized expenditures, expensed costs and other key financial factors to test for the effects of R&D on the stock prices, contemporaneous stock returns and subsequent stock returns for the full sample, capitalizer sample and expenser sample, respectively.

First, majority of Chinese firms (about 80% of those reported) elect to adopt expensing R&D approach, while about 20% deploys capitalization treatment. Second, key attributes such as size, profitability, leverage and R&D intensity are highly associated with capitalization propensity. Third, current capitalization affects the contemporaneous stock prices and stock returns (priced-in) with yearly volatility. Finally, intertemporal association exists between firms’ expensing costs and subsequent returns due to a delayed reaction.

As the world largest emerging economy, the results show that research and development information adds value, and capitalizers outperforms expensers in the area of stock performance. This strategy works irrespectively of economic development stage or capital market maturity. The findings call for more capitalization.

The purpose of this paper is to explore employment perception of students as a relevant indicator of higher education quality, using blended multi-criteria decision-making methods.

The differential impact of these variables was analyzed in this paper taking a sample of 641 students and six higher education lecturers identified as experts on young vocational careers. The traditional study of student behavior and perceptions of employability does not incorporate the uncertainty associated with multi-criteria decision processes and is therefore less adapted to the human reasoning process. This research applies traditional techniques together with fuzzy techniques capable of managing more effectively the uncertainty associated with student actions and behaviors.

This research shows that it is important to consider previous work experience, academic achievement and soft skills developed during education experiences. In this way, this research shows the lecturers how to adapt their pedagogical practices according to students' perceptions of employability and assess their students' perceptions of employability. In addition, lecturers will be able to incorporate the uncertainty associated with decision-making processes to optimize employability perception.

Higher education-related research on uncertainty environments as multi-criteria decision problems is still in early stages. The incorporation of the uncertainty associated with decision-making processes to this field allows to optimize employability perception thanks to its adaptation to real human behavior in the adoption of decisions.

This study explores the impact mechanism of corporate digital transformation (CDT) on the quality of accounting information.

Samples of A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2007 to 2020 are used as a research sample. The empirical analysis is based on the ordinary least squares regression model, and mediation and moderation effect models were used in further analysis.

This study finds that CDT enhances accounting information quality by alleviating the agency problem. This positive effect is more significant among firms that exhibit less media coverage, have low industry competition and are not subject to cyber-attack.

This study extends the economic consequences of CDT and enriches the literature on the factors that affect accounting information quality. Further, this study's findings guide the government to actively promote CDT, facilitate the digital upgrading of industries and improve accounting information quality and efficiency in capital markets.