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Business leaders and policymakers within service economies are placing greater emphasis on well-being, given the role of workers in such settings. Whilst people’s well-being can lead to economic growth, it can also have the opposite effect if overlooked. Therefore, enhancing subjective well-being (SWB) is pertinent for all organisations for the sustainable development of an economy. While health conditions were previously deemed the most reliable predictors, the availability of data on people’s personal lifestyles now offers a new dimension into well-being for organisations. Using open data available from the national Annual Population Survey in the UK, which measures SWB, this research uncovered that among several independent variables to predict varying levels of people's perceived well-being, long-term health conditions, one's marital status, and age played a key role in SWB. The proposed model provides the key indicators of measuring SWB for organisations using big data.

PurposeThe purpose of this paper is to offer a consolidative approach in exploring the potential contribution of digital technologies in sustainable supply chain management (SSCM) for the sustainable performance of food supply chain business, through the circular economy concepts.Design/methodology/approachAs a single case study, this qualitative, interpretivist research was based on one of the largest food producers in the United Kingdom. The research utilises semi-structured interviews and applies thematic analysis to offer rich insights into SSCM challenges and their relationship with the business performance, through ten in-depth interviews.FindingsFindings derived from thematic analysis of the interview transcripts suggest four main critical success factors underpinning SSCM practices and businesses performance – i.e. business continuity, waste reduction, performance measurement approach, and organisational learning, which could use the help of digital technologies to improve. This led to seven propositions to be addressed in the future research.Originality/valueThis research offers real, practical insights into SSCM challenges, within the context of food supply chain and explores the potential of digital technologies in overcoming them. Accordingly, the primary contribution of this work is grounded in the identification of critical success factors in SSCM for food supply chains (FSC). Hence, this work contributes further to the literature on SSCM, as well as circular economy, by providing a study of a business in the context of the highly pertinent and valuable food industry.

This study explores how British South Asian parents perceive their children's technology consumption through their collectivist lenses and interdependent values. The findings for this qualitative study indicate that second and third generation South Asian parents acknowledge the benefits of children's technology use; but largely perceive technology as a disruptive agent, whereby children are becoming isolated and increasingly independent within the household. The analysis aims to understand how parents view their children's relationship with others as a result of technology consumption. Accordingly, this paper proposes an extension of the Construal of self conceptualisation and contributes a Techno-construal matrix that establishes a dyadic connection between technology consumption and cultural values. Overall, the study reveals that children display less inter-reliance and conformance typically associated with collectivist cultures, resulting from their technology use. Consequently, parents interpret their children's shift from interdependence to more independence as a disruptive and unsettling phenomenon within the household.

Every contact centre engages in some form of Call Quality Monitoring in order to improve agent performance and customer satisfaction. Call centres have traditionally used a manual process to sort, select, and analyse a representative sample of interactions for evaluation purposes. Unfortunately, such a process is marked by subjectivity, which in turn results in a distorted picture of agent performance. To address the challenge of identifying and removing subjectivity, empirical research is required. In this paper, we introduce an evidence-based, machine learning-driven framework for the automatic detection of subjective calls. We analyse a corpus of seven hours of recorded calls from a real-estate call centre using Deep Neural Network (DNN) for a multi-classification problem. The study establishes the first baseline for subjectivity detection, with an accuracy of 75%, which is comparable to relevant speech studies in emotional recognition and performance classification. We conclude, among other things, that in order to achieve the best performance evaluation, subjective calls should be removed from the evaluation process or subjective scores deducted from the overall results.