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On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Considering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerism’s impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability.

A systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023.

The findings reveal four prominent factors – behavioural, environmental, socioeconomic and technological – in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways.

Consumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour.

Addressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends.

Although the integration of sustainability into business strategies and operations has received considerable scholarly attention, little is known about how sustainability initiatives across the extended value chain affect this integration. This study aims to analyze the impact of multinational corporations’ supply chain sustainability initiatives on their environmental, social and corporate governance (ESG) performance and the moderating role of the key country-level factors of the multinational’s headquarters.

This study analyzes data published by the top 201 multinationals among Fortune Global 500 companies over the period 2011–2021 on their attempts to integrate sustainability measures in extended supply chains and the resultant impact on their ESG scores. A fixed-effect model is used in the primary empirical study.

Results indicate that managerial interventions through a more robust supply chain policy framework, monitoring mechanisms, corrective actions and training initiatives lead to better ESG-environment pillar performance for multinationals. Additionally, the ESG-environment pillar performance is influenced by the socioeconomic model and country-level ESG risks of the nation where the multinational is headquartered.

The implications of this study are vital for understanding the criticality of sustainability initiatives in the supply chain for a firm’s overall ESG performance. To attain better levels of sustainable performance, multinationals must assume a stewardship position and deploy sustainability initiatives in their extended supply chain.

The objective of the study was to explore the factors influencing the career decisions of students doing the students' undergraduate (UG) and postgraduate (PG) programmes from urban private universities/colleges in the Indian context.

Career decision-making is determined by different factors and is contextual. The present study explores and identifies the prominent factors influencing career decision-making. A pool of 33 questions was developed, and these questions were initially administered to a sample of 233 students. Principal component analysis with Varimax Rotation identified salient factors. In the second study, confirmatory analysis was performed based on the opinion of 304 students.

The study shows that the student's career deciding factors include (1) career clarity, (2) career exploration, (3) career reward and recognition and (4) career initiative for professional and personal growth.

The study focussed on career-deciding factors for UG and PG students from urban universities/colleges in the Indian context and the findings can be used for planning career counselling interventions.

The outbreak of covid-19 has affected international migration and remittance and has also narrowed down the opportunities for internal labor migrants. The pandemic has also left internal migrants in a threatening situation due to the closure of job opportunities. Taking the migration of labor from the highland toward the sesame production belt into consideration, this study aims to examine the influencing factors of migration to the sesame belt amid covid-19 and ascertain the link between migration and translocal vulnerability.

The study followed a mixed approach that combines quantitative and qualitative methods. However, the quantitative approach tends to dominate due to the nature of the objectives of the study. The study was conducted in the central Gondar zone, using a cross-sectional survey design with a sample size of 150 households collected from January to March, 2021. Both descriptive and econometrics models such as binary logit model have been used.

Based on the study result, we came to understand that migration is part and parcel of the livelihoods of the farm household that accounts for 35% of migration status amid covid-19. Particularly, the study came to conclude that households’ decision to send family members heavily relied on their prior information and fear of transmission of the coronavirus to family members which are statistically significant. As a result, this piece of work can be a good witness for translocal vulnerability where studies are very limited in the area. For this, this study suggests that concerned bodies like social and labor affairs in consultation with the agriculture offices and bureaus at a different level and the investors in the migrant’s destination should facilitate the protection and awareness mechanisms so that the spread of covid-19 can be minimized and thereby both the migrants and the investors can be benefitted from the migrants’ work amid covid-19.

This study tries to connect the current spread of covid-19 with the translocal vulnerability context. Primarily, it empirically argued the translocal vulnerability factor is the main determinant for the farm households to send families’ labor as a livelihood diversification strategy. Very limited studies consider the translocal vulnerability implication of migration; notably to the best of the researchers’ knowledge, studies that linked covid-19 with translocal vulnerability context are scant. On top of that, many studies that link migration with covid-19 tend to be inclined to international migration with very limited attention to internal migration.

The low level of financial literacy among young adults is a pressing concern at both individual and country levels. Therefore, there is a dire need to understand the best-worst antecedents of financial literacy and how they influence each other.

A two-phased multicriteria decision-making (MCDM) technique consisting of best-worst method and interpretive structural modeling (BWM-ISM) was employed for pair-wise comparison, assigning weights, ranking and establishing the relationship among antecedents of financial literacy.

Results suggest that use of Internet (SF1), role of financial advisors (SF3) and education level of individuals (DS7) are top ranked antecedents, whereas masculinity/feminity, language and power distance in society are the least ranked antecedents of financial literacy. Findings will help both academicians and practitioners focus on the key factors and make efforts to increase financial literacy by minimizing resource usage.

The current study provides clarity among antecedents of financial literacy by following BWM-ISM approach for the first time in the financial literacy context.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0746

The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques.

Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots.

Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist.

Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.

The purpose of this article is to examine synergies between a eudaimonic model of psychological well-being (Ryff, 1989) and mental health practice. The model grew out of clinical, developmental, existential and humanistic perspectives that emphasized psychological strengths and capacities, in contrast to the focus on emotional distress and dysfunction in clinical psychology.

Conceptual foundations of the eudaimonic approach are described, along with the six components positive functioning that are used to measure well-being. These qualities may be important in facilitating the recovery experiences, which are of interest in Mental Health and Social Inclusion.

Four categories of empirical evidence about eudaimonia are reviewed: how it changes with aging, how it matters for health, what are its biological and neurological underpinnings and whether it can be promoted. Major contemporary forces against eudaimonia are also considered, including ever-widening inequality, the enduring pandemic and world-wide strife. In contrast, encounters with the arts and nature are put forth as forces for eudaimonia. The relevance of these ideas for mental health research and practice is considered.

Enormous suffering defines our contemporary world. Such realities call for greater attention to factors that undermine as well as nurture the realization of human potential, the core of eudaimonic well-being.

Mental health is often defined as the absence of mental illness. The novelty of the eudaimonic approach is to define mental health as the presence of well-being, assessed with different components of positive functioning.

Different age groups display different shopping patterns in terms of how and where consumers buy products. During times of crisis, such behavioural differences become even more striking yet remain under-researched with respect to elderly consumers. This paper investigates the impact of age on retail-related behavioural changes and behavioural stability of elderly shoppers (in comparison to younger consumers) during a crisis.

The authors surveyed 643 Austrian consumers to assess the impact of perceived threat on behavioural change and the moderating effect of age groups. Based on findings from this survey, they subsequently conducted 51 semi-structured interviews to understand the causes of behavioural change and behavioural stability during a crisis.

Elderly shoppers display more stable shopping behaviour during a crisis compared to younger consumers, which is influenced by perceived threat related to the crisis. Such findings indicate that elderly shoppers reinforce their learnt and embedded shopping patterns. The causes of change and stability in behaviour include environmental and inter-personal factors.

Through the lens of social cognitive theory, protection motivation theory and dual process theory, this research contributes to an improved understanding of changes in shopping behaviour of elderly consumers, its antecedents and consequences during a time of crisis. The authors reveal reasons that lead to behavioural stability, hence the absence of change, in terms of shopping during a crisis. They further outline implications for retailers that might wish to better respond to shopping behaviours of the elderly.

The quality of higher education has become a topic of increasing interest to researchers in recent decades. This study, therefore, aims to investigate the comparative effectiveness of instructors at higher education institutions in Kuwait and the USA, while also investigating the parallel differences in student culture and gender.

The researchers employed a quantitative research paradigm, using a questionnaire survey method to examine four dimensions of effective instructorship (teaching skills, human relations, ethics, and assessment). Descriptive statistics were used to analyze data from 254 college students (N = 254), comprising 132 students at Bemidji State University in the USA and 122 students at Kuwait University in Kuwait.

The findings showed that students ranked “human relations” as the most significant attribute of an effective university instructor. Study results also indicated that culture is an important influencer of student perceptions regarding effective instructor characteristics. Gender also played a role in student perceptions of teacher effectiveness. Cross-culturally, female participants ranked teacher effectiveness dimensions such as human relations, ethics, and assessment, significantly higher than did their male colleagues, while within each culture, male students at the American university showed significantly greater concern for ethics in comparison to their counterparts in Kuwait.

This study offers findings from a cross-cultural comparative perspective. It provides value to administrators, deans, and department chairs at higher educational institutions who are evaluating their current rank, tenure, and promotions criteria and processes for teaching faculty. Additionally, while K-12 education has received significant attention over the past few decades regarding the qualities and practices of effective teachers in that realm, this study extends such research significantly into higher education.