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The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

The purpose of this paper is to examine the association between consumers’ emotions towards emerging e-banking technology, perceived risk and subsequent intention to adopt emerging e-banking technology.

An online questionnaire was used to collect data, which were analysed in a quantitative study. The final sample of 224 educated young consumers, familiar with emerging e-banking technology, allowed testing of the research hypotheses by applying confirmatory factor analysis and structural equation modelling (SEM).

The empirical results indicate that deterrence emotions and hedonic motivation are associated with consumers’ perceived risk and, subsequently, their intention to adopt emerging e-banking technology. Additionally, analysing the moderating role of hedonic motivation in the association between consumers’ deterrence emotions towards emerging e-banking technology and their perceived risk highlights the significant association of deterrence emotions with perceived risk, regardless of the presence of hedonic motivation.

This study demonstrates the association between consumers’ emotions, perceived risk and subsequent intention to adopt emerging e-banking technology whilst underscoring the importance of distinguishing between different types of emotions and their corresponding appraisals.

Intrinsic motivation affects job satisfaction and turnover intention. Still, previous motivational studies among real estate brokers (brokers) have primarily focused on extrinsic rewards, leaving intrinsic rewards/motivation practically unexplored. The purpose of this study is therefore to evaluate the role of both satisfaction with intrinsic rewards (SIR) and satisfaction with extrinsic rewards (SER) on job satisfaction and turnover intention among Swedish brokers.

This article is a replication, more precisely an empirical generalization and extension, of Mosquera et al.’s (2020) study conducted among brokers in Portugal. Using a sample of 910 Swedish brokers, the study analyzes a conceptual framework and tests hypotheses by using partial least squares (PLS).

Results indicate that SIR has a very strong impact on job satisfaction, which is not the case in the Portuguese sample. On the other hand, SER does not have an impact on job satisfaction, which is the case in the Portuguese sample. SIR does not have an impact on turnover intention in the Swedish sample, whereas SER does. Job satisfaction has twice the positive impact on turnover intention in the Swedish sample compared to the Portuguese. Furthermore, job satisfaction mediates the relationship between SIR/SER and turnover intention.

Findings of this study extend the existing literature of satisfaction with extrinsic and in particular intrinsic rewards on job satisfaction and turnover intention in the context of the brokerage industry. The most interesting difference between the samples is that Swedish brokers display much higher levels of satisfaction with intrinsic rewards. On the other hand, Swedish brokers appear to be less driven by extrinsic rewards, which is not in line with prior studies within brokerage.

Both managers and students planning to become brokers should consider that SIR has a stronger impact on job satisfaction than SER. What are perceived as intrinsic rewards, however, is highly subjective, which is troublesome from a managerial perspective, even more so as SIR is much harder to influence than SER. Given that intrinsic motivation is primarily a consequence of needs fulfillment, screening of applicants for person-job fit ought to increase job satisfaction and reduce turnover given its focus on the congruence between job demands and worker’s needs, respectively, what a job provides and the worker’s needs.

This study contributes to the brokerage research field by indicating that being a broker differs substantially between countries and that intrinsic rewards matter for Swedish brokers.