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This study examines the impact of perceived human resource management practices on talent turnover intention, with work engagement mediating and self-efficacy moderating the relationship. It examines how employees' perceptions of pay satisfaction, empowerment, participation and communication are related to their turnover intentions.

A total of 283 valid questionnaires from UAE government employees were used for data analysis. Partial least squares structural equation modeling (PLS-SEM) was used to examine the proposed hypothesis.

The results reveal that employees' perceptions of pay satisfaction, empowerment, participation and communication are significant contributors to work engagement. The findings further demonstrate that work engagement significantly negatively affects talent turnover intention and acts as a mediator between employees' perceptions of individual human resource management practices and talent turnover intention. However, the results contradict the hypothesis that self-efficacy moderates the association between work engagement and talent turnover intention.

This study focuses on the impact of perceived human resource management practices on talent turnover intention, an area that has received limited attention in literature. By focusing on perceived human resource management practices, this study illuminates employees' subjective experiences and how they perceive human resource management practices intended to reduce talent turnover intention. The inclusion of the mediating effect of work engagement offers a more profound understanding of how employees' perceptions of human resource management practices influence their turnover intentions. This comprehensive approach to understanding the interplay between these variables provides valuable insights for organizations seeking to improve their human resource management practices and talent turnover intention.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

This article explores the heterogenous social mechanisms that drive responsible environmental behaviours by investigating differences in the mean effect of the psychosocial determinants of the intention to buy organic foods.

Using data for a representative sample of the Spanish population, we estimated the mean effect of the constructs represented in the responsible environmental behaviour (REB) theory that affect sustainable food consumption, and examined the social mechanisms that may explain heterogeneity in the mean effect of those constructs. Confirmatory factor analysis, linear regression, and latent class regression were used in the analysis.

We found that the effect of REB’s psychosocial constructs varied significantly, demonstrating social heterogeneity in the estimated average effect. We identified different social mechanisms that explain variations in organic food purchase intentions: environmental attitudes and social norms shape these intentions among socioeconomically privileged consumers, whereas personal norms shape these intentions among less socially advantaged consumers.

Our research contributes to the literature by highlighting the existence of differing social mechanisms explaining organic food purchase intentions. The uncovering of three social mechanisms explaining differences in the mean effect of factors driving those intentions provides valuable insights with regard to both further developing a holistic framework for responsible environmental behaviours and developing new public policies and marketing strategies aimed at improving sustainable food consumption.

This study aims to develop an in-depth understanding of how supplier selection helps social enterprises achieve their social missions while maintaining commercial viability.

The paper applies a multiple-case design to study the supplier selection processes of 15 Dutch social enterprises.

Social enterprises tend to build supply relationships through existing networks and evaluate suppliers based on value alignment, relationship commitment, resource complementarity, and cost. Depending on the possibility of social value creation in supplier selection, the importance of these criteria varies across different social enterprise models and between key and non-key suppliers. Moreover, suppliers’ long-term relationship commitment can help reconcile tensions between the social and commercial logic of a social enterprise and facilitate impact creation.

Data collection is limited to the perspectives of buyers – the social enterprises. Future research could collect supplier-side data to explore how they engage with social enterprises during the selection process.

Managers of social enterprises can use our research findings as guidance for selecting the most suitable suppliers, while organizations that want to collaborate with social enterprises should actively build network ties to be identified.

We contribute to the cross-sector collaboration literature by showing the underlying reasons for the preference for network reinforcing and indirect networking in supplier identification. We contribute to the social impact supply chain literature by revealing the critical role of supplier selection in shaping collaboration outcomes.

This study aims to develop and apply a process model for prioritizing and selecting basic research projects in developing countries.

Basic research is mainly funded by governments and since, unlike technological research, it does not have clear business goals, its prioritization is one of the complicated issues in formulating science and technology policy. Adopting a design science research methodology, the authors chose a general framework for project portfolio selection as an appropriate artifact for solving this problem. By customizing it for two specific features of this study, i.e. national scale of the problem and the basic nature of research proposals, the authors developed the proposed framework for solving the problem of priority setting.

The process for selecting basic research proposals consists of several steps, which can be categorized into eight steps including strategic decisions, preparation, pre-screening, evaluating individual proposals, screening, portfolio selection and monitoring. This study emphasizes the necessity of defining goals that can be evaluated for the national basic research portfolio, as a key strategic decision. Evaluating individual proposals is a peer-review-based process. In contrast, portfolio selection is done through a zero-one linear programming model. The validity of the proposed framework has been confirmed based on the data obtained from the Iran National Science Foundation.

To the best of the authors’ knowledge, in this research, for the first time, a mathematical model for prioritizing basic research at the national level has been presented, which effectively contributes to policymaking regarding the development of an optimum national research portfolio.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

Traditional purchasing best practices primarily follow a commercial logic and may not necessarily be applicable for social enterprises (SEs) supplier selection. This study examines how SEs focused on poverty alleviation select suppliers amidst competing institutional logics to achieve both social impact and economic performance.

A grounded theory methodology is applied to guide semi-structured interviews with 18 fair trade verified SEs. Constant comparison methods aided in determining the point of data saturation was reached.

The results of this study indicate that SEs select marginalized suppliers based on implicit criteria that is initially based on social-welfare logic and then through a blend of commercial and social-welfare logic based on company structure.

This study is the first to reveal that SEs addressing social issues do not follow the traditional criteria for supplier selection but have their own unique selection criteria when selecting suppliers.

This paper aims to mitigate the subjective nature of wine rating by introducing statistical and optimization tools for analysis, providing a unique approach not found in existing literature.

The research uses an unsupervised machine learning algorithm, k-means, to cluster wines based on their chemical characteristics, followed by the application of the PROMETHEE II multicriteria decision-making model to rank the wines based on their sensorial characteristics and selling price. Lastly, a linear programming model is used to optimize the selection of wines under different scenarios and constraints.

The study presents a method to rank wines based on both chemical and sensorial characteristics, providing a more comprehensive assessment than traditional subjective ratings. Clustering wines based on their characteristics and ranking them according to sensorial characteristics provides the user/consumer with meaningful information to be used in an optimization model for wine selection.

The proposed framework has practical implications for wine enthusiasts, makers, tasters and retailers, offering a systematic approach to ranking and selecting/recommending wines based on both objective and subjective criteria. This approach can influence pricing, consumption and marketing strategies within the wine industry, leading to more informed and precise decision-making.

The research introduces a novel framework that combines machine learning, decision-making models and linear programming for wine ranking and selection, addressing the limitations of subjective ratings and providing a more objective approach.

This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development.

The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact.

The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs.

While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness.

This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

This study aims to develop and test a model of the antecedents, mediators and consequences of the glass cliff phenomenon through public sector service organizations in Iran to explore more insights on gender inequality in managerial positions.

The current research was conducted based on a mixed-method approach, using both qualitative and quantitative research designs. First, the qualitative method includes content analysis by conducting semi-structured interviews with 20 university professors and expert managers working in public sector service organizations in Iran. The outcomes of the qualitative phase lead to designing the conceptual framework and research hypothesis. Then, through a quantitative phase, 384 female managers working in public sector service organizations in Iran are selected using stratified random sampling and fill out the research questionnaire. The exploratory factor analysis was used to verify the model. Moreover, structural equation modeling, using AMOS 24, was used to test the research hypothesis.

The findings of the qualitative phase were represented in three categories including antecedents (e.g. the characteristics of women’s leadership, the selection of women based on meritocracy criteria, women’s preferences and organizational factors), mediation effect (e.g. succession planning, personal development planning and support networks) and consequences of the glass cliff phenomenon (e.g. positive and negative consequences). The results of the exploratory factor analysis show there are ten components, explaining 88.5% of variances. Moreover, the test of the structural model supports the direct effect of antecedents on the glass cliff phenomenon. The results also show the effect of the glass cliff phenomenon on consequences through mediation effects.

There are some limitations that can be addressed by other researchers. Accordingly, the limited number of female managers in Iran prevented larger quantitative research. Moreover, the current research only found casual and mediation consequences of the glass cliff phenomenon, and potential moderators were not considered in this study.

The present study’s innovations may include using a mixed-method approach to investigate the antecedents, mediators and consequences of the glass cliff phenomenon in this study and examining the model constructs in some public sector service organizations. This research may provide a deep understanding of the antecedents, mediators and consequences of the glass cliff phenomenon by finding new factors using a mixed-method approach.