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The purpose of this paper is to study the relationship between trait self-control (TSC) and emotional exhaustion, and to examine the mediating role of effort–reward imbalance (ERI) and emotional demands.

A quantitative study was conducted using data from 441 employees working in different organizations in the information technology sector in India. PROCESS macro with a bootstrap sample size of 5,000 was used for mediation analysis.

TSC demonstrated a significant negative relationship with emotional exhaustion. Results indicated the crucial role played by ERI and emotional demands in influencing the emotional exhaustion of employees with higher TSC.

This study adds substantially to our knowledge of the role of TSC in employee experiences of emotional exhaustion. Results suggest how employees’ ERI perceptions and experiences of emotional demands determine whether higher TSC would reduce experiences of exhaustion. This adds to the knowledge of positive outcomes of self-control while throwing some light on why the use of self-control does not always incur a psychological cost, as suggested by some studies. The findings suggest that self-control is an individual resource that has the ability to alleviate emotional exhaustion through its influence on employees‘ effort–reward perceptions and experiences of emotional demands.

How does business model design play a role in enabling manufacturing firms’ services? This study aims to investigate the impact of two distinct types of business model design, namely, efficiency-centered business model design (EBMD) and novelty-centered business model design (NBMD), and their effects in balanced and imbalanced configurations, on two types of services: product- and customer-oriented services.

Using matched survey data of 390 top managers and objective performance data of 195 Chinese manufacturing firms, this study uses hierarchical regression, polynomial regression and response surface analysis to test the hypotheses.

The results show that while EBMD positively affects product-oriented services, NBMD positively affects customer-oriented services. Both types of services exert a significant influence on firm performance. Furthermore, the degree of product- and customer-oriented services increases with an increasing effort level with a balance between EBMD and NBMD. Asymmetrical, imbalanced configuration effects reveal that the degree of product-oriented services is higher when the EBMD effort exceeds the NBMD effort, and the degree of customer-oriented services is higher when the NBMD effort exceeds the EBMD effort.

This study enriches the understanding of designing business models to facilitate service growth in manufacturing firms, ultimately benefiting firm performance. In addition, exploring balanced and imbalanced configurations of EBMD and NBMD offers new insights into business model dual design research.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Firms looking to advance individuals into leadership roles who display feminine gender traits should seek to build an organizational culture based on mutual trust, collaboration and unity. In turn, this can help to strengthen their intention to become a leader by increasing the self-efficacy that can make them more confident in their ability to perform the role.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Organisational strategy becomes reality by connecting organisation’s resources and capabilities in daily operations, and physical workspace is one of the environments in which this takes place. This study aims to explore to what extent factors required for successful strategy implementation are considered when designing, using and managing workspaces of knowledge-intensive organisations.

For the study, managers in 25 large and medium-sized knowledge-intensive organisations were interviewed. The semi-structured interviews focused on organisation’s strategy, strategy implementation practices and workspace design and management. To form a comprehensive framework of strategy implementation success factors for the study, the factors of 11 frameworks were analysed, grouped and renamed.

Current workspace design, usage and management mainly support human-related strategy implementation factors. However, both organisation- and human-related factors are needed for the strategy implementation to be successful. Therefore, the organisations studied may have unused potential in their workspaces to ensure strategy-aligned operations and behaviour.

Due to the potential imbalance between organisation- and human-related strategy implementation factors, a more holistic, organisational-level approach to workspace design, usage and management is recommended to ensure the success of strategy implementation.

Workspaces have extensively been studied from individual strategy implementation factors’ as well as employees’ perspectives. Prior to this work, there are only few studies exploring workspace in the holistic, strategy implementation context.

Regardless of where the business stood at the beginning of 2020, COVID-19 afflicted the corporate world as well as most of the workforce worked remotely due to government-mandated isolation. The range of personal and professional problems experienced due to remote working by employees was explored at one of the Non-Financial Banking companies and how the company responded to their well-being by interviewing 124 employees and a senior HR personnel. Remote work induced by COVID-19 was not a cakewalk as 56.5% of employees desired physical connectivity with colleagues, for 50.8% work done from home was not interesting and more than 50% of the respondents agreed to work more hours than usual. The outcome was anxiety for some of them. The imbalance between personal and professional life was dissipated with the support of the management as 94.4% of them got sufficient support from their respective managers and colleagues, as they were able to communicate and collaborate with them. The managerial side validated the responses in a way that a flexible and target-oriented approach was adopted for employees. They organized stress management activities and ensured connections through regular employee connect and communication by Learning and Development department. Additionally, COVID-19 advisories and guidelines were followed while engaging with employees. The work–life balance shaken due to remote work was managed with responsive humanistic management practices that had the enduring effect of garnering employee engagement levels with the organization.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

This chapter investigates the experiences of doctoral students and supervisors in the doctoral process, focusing on the potential impact of imbalances in the distribution of power. In this respect, there are troublesome manifestations of excessive faculty entitlement that appear to be a source of inequality and injustice. These phenomena call into question the crucial relationship of support expected of doctoral students, as thesis supervisors have a fundamental role to play in guiding them towards the doctorate and ensuring their successful entry into the research community. Looking at the issue from the angle of the theory of social fields, I examine instances of dysfunction in supervisory experiences. Such problematic practices tend to conform to the relationships and traditions that sustain and (re)produce the practices of the academy, constraining the establishment of what Bakhtin describes as a dialogical relationship, between doctoral students and supervisors. I examine this problem from my own experience, both as a doctoral student and as a supervisor. I approach the question by combining self-study and narrative inquiry to make use of the data from my experience to analyse the issues raised during the supervision of doctoral programmes. I connect accounts drawn from literature, real-life testimonies and a corpus of discussions and notes to explore the manifestations of excessive faculty entitlement in the form of asymmetries and difficulties that can negatively impact the quality of supervision.

This study aims to inform prospective listing firms, investors and regulators of the unique drivers of Chinese initial public offering (IPO) pricing on the Hong Kong Exchange.

Using a hand-collected IPO dataset, we investigate whether information uncertainty or investor exuberance drives underpricing and Chinese IPOs’ performance from 2002 to 2015, including 114 state-owned enterprises (SOEs).

Contrasting with the “listing bubble” in the China domestic stock market, generated by the overoptimism of retail investors, we highlight a “placing bubble” among Chinese firms listed in Hong Kong. This is driven by institutional investors’ buoyant demand for Chinese IPO shares, particularly those of SOEs. Chinese listing firms employ discreet earnings management strategies with their working capital accounts to smooth pre-IPO earnings, which becomes apparent to the market only in the long term.

This study is the first to examine the pricing of sought-after Chinese IPOs among international investors, who face various restrictions when investing in the Chinese domestic stock market. Additionally, it is the first study to measure earnings management using hand-collected pre-IPO data in IPO underpricing studies.

This study develops and tests a comprehensive model that examines whether dimensions of supervisors’ job demands and resources influence their work motivation through their job strain levels while managing disability accommodation (DA).

The proposed model leverages the assumptions of established job demand and resources theories, including demand-ability fit, job demand-control, job demand-control-support, and effort-reward balance models. Then, we tested with the quantitative data from 335 British, Canadian, American, Australian, Dutch, and German supervisors with recent DA experience.

This study found support for the proposed model. Job control and social support directly affected work motivation, while job strain did not mediate the relationship between job control and social support and work motivation. The results suggest that employers looking to improve the likelihood of DA success should focus on providing adequate job control, social support, and rewards to supervisors responsible for accommodating employees with disabilities.

This research enhances our understanding of how additional DA responsibilities impact supervisors and aids in the development of effective DA management policies and interventions, providing robust support for practitioners.

This study contributes to extending the DA literature by testing the applicability of different theoretical models to explain the effect of the additional DA responsibility on supervisors’ job demand, strain, and motivation levels and identify the resources to mitigate them.

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.