Demand Resources Research Articles

The Effect of Red Tape on Employability Competences of Teachers

Red tape forms a major threat to many public organizations around the world. Building on the Job Demands–Resources model, supplemented by arguments from the psychological process theory of red tape, this study explores how and when red tape affects teachers’ employability competences (i.e., balance, anticipation and optimization, and personal flexibility). Analyses ( N = 908 teachers; 92 school leaders) revealed a negative relationship between red tape, on one hand, and balance and personal flexibility, on the other hand, through perceived impact on work outcomes. Interestingly, the analyses also revealed that when organizations provide sufficient developmental rewards to their employees, the negative consequences of red tape on perceived impact are buffered, which indirectly also benefits employees’ balance and personal flexibility.

Energy in the workplace: job demands, job resources, and employees’ inner resources as pathways to organizational outcomes

In this study, we expanded upon the job demands–resources model to assess the role of employees’ vitality as an inner resource for their work engagement and job commitment. To assess vitality and related job resources, we developed an index of vitality outside of work and adapted measures of manager autonomy support and organizational support. For job demands, we measured work stress and predicted that each of these four variables would contribute independently to work-related outcomes. Then, in a preregistered study, we collected these measures from a sample of 5,280 American workers (primarily ages 18–34, 54% female). Results from multivariate regression analyses largely confirmed our hypotheses, showing that positive work-related outcomes, such as enthusiasm, enjoyment, and job satisfaction, were positively predicted by manager autonomy supports, organizational support, and individuals’ vitality, and negatively predicted by work stress. The reverse pattern was largely observed for the negative outcome of turnover intention. Exploratory analyses also suggested that individual vitality may buffer the negative effects of stress and low manager and organizational support. The results highlight the potential role of employee vitality outside of work and managerial support in bolstering work engagement and reducing turnover intentions, offering a basis for organizational strategies aimed at improving work culture and retaining talent.

Examining the dark side of quality management: analysing work overload, exhaustion and managerial support using a moderated mediation approach

PurposeIn this study, we propose a model for analysing the association between total quality management (TQM) and exhaustion, which considers work overload as a mediation variable and investigates perceptions of managerial support as a moderating condition. Through the job demands–resources model, the study sheds light on the dilemma about the negative implications of TQM for employees.Design/methodology/approachThe proposed model of relationships is analysed using matched employee–organisation survey data from two R&D-intensive sectors in Spain. Structural equation models are employed to examine the proposed relationships.FindingsOnly perceptions of hard TQM trigger a health-impairment process which increases work overload and exhaustion, whereas soft TQM decreases exhaustion and does not have negative consequences for employee well-being. We also find that managerial support can alleviate the consequences of hard TQM on exhaustion due to work overload.Originality/valuePrevious research has mainly assumed that TQM is welcomed by employees. With the aim of warning of the potential negative consequences of TQM on employees, this paper goes beyond previous contributions by analysing how hard and soft TQM may have different consequences on employees’ exhaustion and how managerial support may mitigate the negative repercussions.

Sustainable Leadership for Employee Performance and the Moderating Roles of Family-Work Conflict and Work Pressure: The Job Demands–Resources Perspective

Sustainable Leadership for Employee Performance and the Moderating Roles of Family-Work Conflict and Work Pressure: The Job Demands–Resources Perspective

Accurate Prediction of Essential Proteins Using Ensemble Machine Learning

Abstract Essential proteins are crucial for biological processes and can be identified through both experimental and computational methods. While experimental approaches are highly accurate, they often demand extensive time and resources. To address these challenges, we present a computational ensemble learning framework designed to identify essential proteins more efficiently. Our method begins by using node2vec to transform proteins in the Protein-Protein Interaction (PPI) network into continuous, low-dimensional vectors. We also extract a range of features from protein sequences, including graph-theory-based, information-based, compositional, and physiochemical attributes. Additionally, we leverage deep learning techniques to analyze high-dimensional Position-Specific Scoring Matrices (PSSMs) and capture evolutionary information. We then combine these features for classification using various machine learning algorithms. To enhance performance, we integrate the outputs of these algorithms through ensemble methods such as voting, weighted averaging, and stacking. This approach effectively addresses data imbalances and improves both robustness and accuracy. Our ensemble learning framework achieved an AUC of 0.948 and an accuracy of 0.9252, outperforming other computational methods. These results demonstrate the effectiveness of our approach in accurately identifying essential proteins and highlight its superior feature extraction capabilities.

Designing circular supply chains with digital technologies for competitive sustainability: An operation management perspective

The objective of this research is to investigate how digital technologies, specifically blockchain, artificial intelligence (AI), and the Internet of Things (IoT), can be integrated into circular supply chains to enhance sustainability and preserve a competitive advantage. The goals of circular supply chains, which are founded on the ideas of the circular economy, are to reduce waste, promote resource regeneration, and lessen their negative effects on the environment. The study highlights the critical role that digital technologies play in guaranteeing transparency, promoting real-time monitoring, and optimizing resource distribution. Blockchain offers traceability and trust, IoT boosts visibility and operational efficiency within supply chains, and AI improves demand forecasting and resource efficiency. Though the benefits are obvious, there are significant obstacles to overcome, including high implementation costs, complex technological issues, scalability issues, and regulatory barriers. The research demonstrates how these digital technologies can effectively promote sustainability while enhancing operational efficiency through case studies of well-known companies like HP, Walmart, and Philips. The report also highlights important future trends and makes suggestions for companies looking to develop their workforces, invest in digital technologies, and work together on circular economy platforms. In the end, digital tool adoption is essential for creating supply chains that are more sustainable and competitive in the modern global economy.

Green IT and BYOD: driving sustainability, job performance, and well-being in remote work

Purpose This study aims to explore the impact of bring your own device (BYOD) practices on teleworkers’ job performance, work–life conflict and their implications for environmental sustainability, with a particular focus on Green IT in the post-pandemic remote work context. Drawing on the job demands–resources (JD-R) model, it examines the relationships between private technology adoption, job demands, job resources, job performance and work–life conflict, while also assessing how BYOD impacts environmental outcomes such as electronic waste reduction and carbon emissions. Design/methodology/approach The study used confirmatory factor analysis and structural equation modelling (SEM) using AMOS version 22 to evaluate seven hypotheses among 424 full-time teleworkers from various occupational sectors in Mauritius, a developing country. Findings SEM results confirm that BYOD enhances job performance and work–life conflict through increased job autonomy, workload and work pressure. BYOD and teleworking also contribute to Green IT and environmental sustainability by reducing energy consumption and electronic waste, indirectly supporting broader sustainability goals through reduced corporate device demand and lower commuting-related emissions. Practical implications Organisations should leverage BYOD policies to advance Green IT practices and improve work performance in remote settings, focusing on balancing job demands with resources like work autonomy to enhance productivity and well-being. Originality/value The findings strongly support the JD-R model in the context of remote work and BYOD, particularly in developing economies with limited infrastructure, offering insights for policies that enhance both employee well-being, Green IT and environmental sustainability.

8.Q. Skills building seminar: “The Church of Fail”: Learning from Our Mistakes in Co-Creation

Abstract When delving into the co-creation research literature, it might seem that researchers consistently attain success in their projects without any exceptions. At the extremes, co-creation might even be portrayed as a quasi-moral scientific approach, where the results are inherently positive and beneficial to all participants. Rarely do researchers openly discuss failures or mistakes encountered during co-creation projects, despite the well-established understanding that co-creation presents significant challenges and demands for those involved. Therefore, we aim to use the well-established “Church of Fail” method to underscore the importance of learning from failures, particularly within the dynamic and demanding landscape of co-creation. Therefore, we invite participants to embrace the inevitability of failures, emphasizing that the key lies in deriving valuable lessons from these. Failures present the greatest potential for professional growth and innovation. Reasons for the workshop: Ensuring the professional growth of researchers and practitioners is vital to unlocking the full potential of co-creation and participatory methodologies. It may appear that success in co-creation is guaranteed by applying principles such as building ownership or fostering shared understanding. However, in reality, co-creation demands significant effort, resources, and dedication from everyone involved. One way to avoid mistakes or failures is to embrace them and learn from them, thereby fostering professional growth and innovation. Objectives This workshop will: 1) Increase awareness of co-creation as a methodology within public health, by generating an open and safe learning environment for professional growth; 2) Exchange experiences with failure, to learn and encourage professional growth. The presenters will engage the participants with cases that illustrate different known pitfalls within co-creation, such as power imbalance, participation fatigue, recruitment strategies, and aligning objectives; 3) Discuss important consideration on co-creation approaches, to achieve the best possibility for success in such research projects in public health in the future. Key messages • Failure is not a flaw, but an opportunity to learn and innovate. • Success in co-creation is not a given, even though it sometimes seems so when reading the literature.

The Effect of Job Demands and Job Resources on Turnover Intention Mediated by Work Engagement at PT Prinal

This research aims to identify the influence of job demands and job resources on turnover intention, with work engagement as a mediating variable at PT Prinal. The retail industry faces high turnover rates, posing a significant challenge at PT Prinal despite the industry's enormous potential. The study involved 250 employees of PT Prinal, using a non-probability sampling technique. Analysis was conducted with SmartPLS v4.1.0.5, utilizing Partial Least Square (PLS) and Structural Equation Model (SEM) techniques to test the hypotheses. The findings show that greater job expectations dramatically reduce employee engagement and increase the likelihood of leaving the position. The job demands at PT Prinal are generally counterproductive, as demonstrated by how they diminish employee engagement. On the other hand, more excellent job resources considerably raise employee engagement at work and lower the intention to leave. Since job demands and job resources directly affect turnover intention, work engagement plays a role in mediating the interaction between these three variables. These results highlight how crucial it is to manage job expectations and resources to boost work engagement and increase employee retention.

Does work overload of odd-job platform workers lead to turnover intention? An empirical study on platform workers

PurposeAgainst the background of the digital economy, odd-job platforms rely on artificial intelligence algorithms to efficiently allocate tasks and monitor platform workers’ performance, putting these workers under enormous pressure. This paper explores the relationship between work overload and turnover intention of platform workers on odd-job platforms and the factors that lead to platform workers’ turnover.Design/methodology/approachBased on the job demands–resources model (JD-R), we construct a theoretical model to explain the relationship between work overload and turnover intention of platform workers. We test job burnout as a mediator variable and perceived algorithmic fairness and job autonomy as moderating variables. We conducted a study at food delivery platforms and ride-hailing platforms in China.FindingsThe empirical results show that: (1) work overload increases the turnover intention of platform workers by increasing job burnout and (2) perceived algorithmic fairness and job autonomy moderate the positive relationship between work overload and job burnout.Originality/valueWe provide a theoretical basis to explain the influence of work overload on turnover intention of odd-job platform workers and provide practical recommendations for management of platform workers.

Stochastic measurement-based multi-cloud consolidation for efficient resource distribution

In distributed cloud systems, due to high computational complexity, existing methods cannot infinitely reduce the measurement interval to obtain the mean demand. However, considering both the unpredictability of demand and the diversity of pricing adds significant complexity to the problem. In response, we introduce an efficient algorithm called Stochastic Demand-oriented Resource consolidation based on Meta-heuristics (SDRM). The algorithm introduces the stochastic demand model and formulates a cross-cloud demand consolidation and resource scheduling problem, and tries to solve it efficiently by combining differential evolution algorithm with dynamic programming optimization. The results are provided for experiments utilizing both simulated and real-world data. It demonstrates that compared with traditional algorithms, increasing gain by up to 53%, SDRM just incurs an affordable time expense (i.e., 1.10-2.51 times that of existing algorithms). Therefore, SDRM stands as a compelling solution for resource distribution in heterogeneous cloud environments.

Navigating the New Normal: Psychological Capital's Mediating Role in the Work-From-Home Paradigm

Working from home (WFH) has become the expected trend as a result of the COVID-19 epidemic. The impact of family conflict, social isolation, distracting environment, job autonomy, and self-leadership on employees' productivity, work engagement, and stress levels have been examined in this study with a mediating role of Psychological capital. There has been little study on the impact of Psychological capital as a mediator and its influence on remote work productivity, work engagement, and stress. The data was collected from employees of IT sector organizations. Through online and print surveys, a sample of 429 respondents was gathered using the convenience sampling method. The dependent variables were workers’ productivity, work engagement, and Stress, the mediating variable was Psychological Capital and the independent variables were family-work conflict, social isolation, distracting environment, job autonomy, and self-leadership under the framework of the Job Demand and Resource model. The PLS-SEM tool was used to investigate the association between research variables. The SPSS tool was used to run the descriptive statistics of respondents. Through this study, it was able to conclude that Job Demand and Job Resources significantly impact productivity, work engagement, and stress through psychological capital. Job Demand negatively impacts productivity, and work engagement and positively impacts stress while Job Resource positively impacts productivity, and work engagement and negatively impact stress.

What Matters in PhD Students' Creativity? The Roles of Academic Psychological Capital and Academic Engagement

ABSTRACTDrawing on the job demands–resources theory, this study pioneered the investigation of the influence of academic psychological capital (including self‐efficacy, hope, resilience, and optimism) on PhD students' creativity (including novelty and usefulness). It further explored the mediating role of academic engagement (including vigour, dedication, and absorption) in this relationship. A sample of 376 PhD students at two comprehensive universities in Jiangsu province, mainland China, responded to an online survey. Results showed that: (1) PhD students' self‐efficacy, resilience, and optimism positively predicted novelty, and all four dimensions of academic psychological capital positively predicted the usefulness dimension of creativity; and (2) dedication mediated the effects of hope and optimism on both novelty and usefulness; however, vigour and absorption did not mediate the association between academic psychological capital and creativity. The findings highlight the significance of academic psychological capital and academic engagement, especially dedication, in optimising PhD students' creativity.

A Liquid Well Barrier Element for Temporary Plug and Abandonment Operations: A Breakthrough Approach

Plug and abandonment (P&A) operations demand valuable time and resources for operational procedures and materials to establish the well barrier element. This study aims to investigate the application of a water-based fluid as a liquid well barrier element for temporary abandonment, based on estimates of its lifespan and the survival probabilities of downhole temperatures acquired through accelerated life tests. To achieve this, the water-based formulation was tested and exposed to 95, 110, 140, and 150 °C temperatures for time intervals ranging from 1 to 10 days. After the temperature exposure, the fluid properties were verified, and failure was detected by accounting for any deterioration in rheological parameters and/or a substantial increase in filtrate volume. A statistical analysis of the failure data was performed in RStudio 4.1.3 software using the Weibull Model, and the fluid average lifespans and survival probabilities were estimated for the P&A temperatures. The results obtained demonstrate that the degradation of the fluid was only observed for 140 and 150 °C temperatures. According to the results, the fluid is a promising alternative for temporary abandonment until 80 °C, with no need for monitoring once its lifetime expectation exceeds three years at this temperature. For downhole temperatures above 80 °C, the fluid is a possible alternative, however, the operation’s maximum time and monitoring requirements should consider reliability metrics for each temperature.

Text Mining Strategies: RoBERTa Optimization for Efficient Pain Assessment in Hospice Care

The hospice unit in medical care offers comprehensive, personalized care to patients, yet the recent epidemic and associated illnesses have strained medical resources, leading to a shortage in capacity. The necessity for frequent physiological documentation and patient assessments places a considerable burden on the nursing staff, particularly in the context of limited personnel. This study addresses this challenge by leveraging natural language processing to aid in the evaluation of pain indices, aiming to enhance implementation quality and reduce associated costs. Three BERT models— BERT, MacBERT, and RoBERTa were employed for training purposes. Among these models, RoBERTa demonstrated exceptional performance, achieving an impressive accuracy rate of 99%. This research highlights the potential of natural language processing tools, specifically the RoBERTa model, in alleviating the workload of nursing staff and improving the efficiency of pain assessment in hospice care during times of heightened demand and limited resources.

Empowering sustainable workplaces: A perspective on employee well‐being in the light of the job demand resource model

AbstractAccentuated by unprecedented challenges, burnout has become a prominent concern affecting employee well‐being. The job demands resource (JD‐R) model suggests that job demands has the capacity to mitigate employee burnout. Nevertheless, there has been limited attention on the integration of the psychological and organisational processes contributing to such effect. This research aims to analyse the integrative role of psychological (i.e., employee self‐regulation) and organisational processes (such as the Human Resource Management system) in the JD‐R model to enhance well‐being in workplaces. Consistent with Sustainable Development Goal (SDG) 3 of the United Nations (‘healthy lives and well‐being’), the results of this research conducted in public sector hospitals suggest that job demands affect employee burnout through the mediation of employee coping (self‐regulation) strategies. Furthermore, the research enriches the existing literature by showing how employee self‐regulation and organisational Human Resource Management practices may alter the effect of job demands on burnout.

How and when artificial intelligence usage facilitates task performance

This study investigated how the increasing usage of artificial intelligence (AI) in diverse business applications has affected task performance. Building on the job demands–resources model, we employed moderated mediation modeling to investigate the association between AI usage and the task performance of delivery drivers. Participants comprised 251 delivery drivers who completed online surveys over a 1-month period. Hierarchical regression analysis revealed that work engagement played a mediating role in the association between AI usage and the task performance of delivery drivers. Moreover, the mediating role of work engagement was moderated by an individual's concept of their future work self. When an individual had a stronger sense of their future work self, the mediating role of work engagement became more pronounced. This study has introduced a new mediating mechanism linking artificial intelligence with employee performance and has identified boundary conditions that increase its beneficial impact on employee performance, thus contributing theoretically to the extant literatures. The study findings can help companies improve employee engagement and performance.

Exploring the Impact of Model Complexity on Laryngeal Cancer Detection.

Background: Laryngeal cancer accounts for a third of all head and neck malignancies, necessitating timely detection for effective treatment and enhanced patient outcomes. Machine learning shows promise in medical diagnostics, but the impact of model complexity on diagnostic efficacy in laryngeal cancer detection can be ambiguous. Methods: In this study, we examine the relationship between model sophistication and diagnostic efficacy by evaluating three approaches: Logistic Regression, a small neural network with 4 layers of neurons and a more complex convolutional neural network with 50 layers and examine their efficacy on laryngeal cancer detection on computed tomography images. Results: Logistic regression achieved 82.5% accuracy. The 4-Layer NN reached 87.2% accuracy, while ResNet-50, a deep learning architecture, achieved the highest accuracy at 92.6%. Its deep learning capabilities excelled in discerning fine-grained CT image features. Conclusion: Our study highlights the choices involved in selecting a laryngeal cancer detection model. Logistic regression is interpretable but may struggle with complex patterns. The 4-Layer NN balances complexity and accuracy. ResNet-50 excels in image classification but demands resources. This research advances understanding affect machine learning model complexity could have on learning features of laryngeal tumor features in contrast CT images for purposes of disease prediction.

Will Fun and Care Prevent Gig Workers' Withdrawal? A Moderated Mediation Model

ABSTRACTThis study investigates how gig work challenges influence job withdrawal, mediated by stressors and perceived job insecurity, while considering the moderating effects of playful work design and a caring organizational ethical climate. Drawing on Cognitive–Emotional Systems Theory and the job demands–resources model, data were gathered from 516 Chinese gig workers. Results show that gig work challenges positively impact job withdrawal by triggering stressors and job insecurity. Notably, playful work design moderates this relationship, mitigating the impact of gig work challenges as hedonic job crafting levels increase. However, the caring ethical climate does not significantly influence the relationship between stressors and perceived job insecurity in the gig work context. The findings suggest that promoting playful work design can be an effective strategy to alleviate the negative effects of gig work challenges, thereby enhancing job satisfaction and retention in the gig economy. This study also provides a new perspective on understanding the mechanisms behind gig workers' workplace withdrawal behaviors, deepening the application of the Cognitive–Emotional Systems Theory and the Job Demands–Resources Model in the gig economy.

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.