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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.

Purpose – This chapter gives an overview of methods for defining and analysing crash severity.

Methodology – Commonly used methods for defining crash severity are surveyed and reviewed. Factors commonly found to be associated with crash severity are discussed. Approaches for formulating and estimating models for predicting crash severity are presented and critiqued. Two examples of crash severity modelling exercises are presented and findings are discussed. Suggestions are offered for future research in crash severity modelling.

Findings – Crash severity is usually defined according to the outcomes for the persons involved. The definition of severity levels used by law enforcement or crash investigation professionals is less detailed and consistent than what is used by medical professionals. Defining crash severity by vehicle damage can be more consistent, as vehicle response to crash forces is more consistent than that of humans. Factors associated with crash severity fall into three categories – human, vehicle/equipment and environmental/road – and can apply before, during or after the crash event. Crash severity can be modelled using ordered, nominal or several different types of mixed models designed to overcome limitations of the ordered and nominal approaches. Two mixed modelling examples demonstrate better prediction accuracy than ordered or nominal modelling.

Research Implications – Linkage of crash, roadway and healthcare data sets could create a more accurate picture of crash severity. Emerging statistical analysis methods could address remaining limitations of the current best methods for crash severity modelling.

Practical Implications – Medical definitions of injury severity require observation by trained medical professionals and access to private medical records, limiting their use in routine crash data collection. Crash severity is more sensitive to human and vehicle factors than environmental or road factors. Unfortunately, human and vehicle factor data are generally not available for aggregate forecasting.

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.

This article examines the relationships between objective overqualification, volunteering as an extra-work activity and job satisfaction.

The study draws on a vast secondary sample of 20,686 British employees across four waves covering the period 2009–2017. The bivariate ordered probit estimate was used to test the study hypotheses in the bioprobit procedure in STATA.

Our study unravels compelling insights. Overqualified employees experience lower job satisfaction and engage more in volunteering activities. The results emphasised that voluntary work allows the utilisation of skills and fulfils basic psychological needs, leading to enhanced general well-being and higher job satisfaction.

Overqualified employees, by actively engaging in volunteering, not only make valuable contributions to society but also experience positive spillover effects that significantly influence their workplace attitudes and behaviours. This underscores the potential for promoting volunteering as an effective means to mitigate the private and social overqualification.

This study provides valuable insights into the role of overqualification as well as resulting job dissatisfaction, in shaping volunteering decisions. This insight contributes to the overqualification literature and strengthens our understanding of volunteering as an important mechanism in the relationship between overqualification and job satisfaction.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

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.