Performance Management Research Articles

How Supplier Customization Influences Its Performance? A Moderated Mediation Model Based on the Contract Theory

ABSTRACT Purpose This study aims to investigate the underlying psychological mechanism and the boundary conditions of the effects supplier customization (SC) on its performance in supply chain management (SCM). Methodology/approach Based upon the contract theory, the study explores how SC influences its performance by revealing mediating role of customer entitlement (CE), and examines the boundary conditions of contractual governance (CG). The dyadic questionnaire survey data of 111 supplier and customer firms in China were used to validate a moderated mediation model. Findings It is found that SC enhances its performance through the increased CE. Furthermore, the above consequential link is moderated by both contractual completeness (CCP) rather than contractual enforcement (CEF). Specifically, CCP negatively moderates the indirect effect of SC on SP through CE. However, CEF negatively moderates the direct effect of SC on CE. Research implications This research are really limitations, including its focus on single industry; single-country system; its cross-sectional design; and the oversight of the possible impact of relational governance (RG) on the link between buyers and sellers. Practical implications Executives should always attach importance to the development of psychological contract (PC), and managers should identify the underlying psychological mechanisms of a customization strategy in B2B and use contractual design and execution with caution. Originality/value/contribution The study extends the literature of the psychological mechanism and more granular characterization of CG, and provides insights for managers to recognize reasonable governance tactics in the context of SCM.

Routine health data use for decision making and its associated factors among primary healthcare managers in dodoma region

BackgroundData demand and use culture have a tremendous impact on the proper allocation of scarce resources and evidence-based decision making. However, primary healthcare managers in the majority of Sub-Saharan African countries continue to struggle with using routine health data for decision-making.Purpose/objectiveThis study aimed to assess routine health data use for decision making among primary healthcare managers in Dodoma region.MethodsCross-sectional study design involved 188 primary healthcare managers from Dodoma City Council, Kondoa Town Council and Bahi District Council was conducted. A self-administered questionnaire adapted from the Performance of Routine Information System Management (PRISM) tools was used to collect the data. Data was analysed by using the Statistical Package for Social Science (SPSS) program. Principal Component Analysis was used to find the level of routine health data use, binary logistic regression analysis was used to determine factors associated with routine health data use for decision making among primary healthcare managers. The study was conducted from May to June, 2022.ResultsThe level of adequate routine health data use for decision making among healthcare managers was 63.30%. Factors associated with adequate routine health data use for decision making among healthcare managers were; respondents characteristics: years of working experience (OR = 1.955, 95% CI= [0.892,4.287]), district surveyed (OR = 4.760, 95%CI= [1.412,16.049]), level of health facility (OR = 3.867, 95%CI= [1.354,7.122]) and male gender (OR = 1.901, 95%CI= [1.027,3.521]). Individual factors: comparing data with strategic objectives (OR = 2.986, 95%CI= [1.233–7.229]), decision based on health needs (OR = 7.330, 95%CI= [1.968–27.295]) and decision based on detection of outbreak (OR = 3.769, 95%CI= [1.091–13.019]). Technical factors: ability to check data accuracy (OR = 3.120, 95%CI= [1.682–5.789]), ability to explain findings and its implication (OR = 2.443, 95%CI= [1.278–4.670]) and ability to use information to identity gaps and targets (OR = 2.621, 95%CI= [1.381–4.974]). Organizational factors: organizational support (OR = 3.530, CI= [1.397–8.919]), analyse data regularly (OR = 2.026, 95%CI= [1.075–3.820]) and displays information on key performance indicators (OR = 3.464, 95%CI= [1.525–7.870]).Conclusion and recommendationThe level of routine health data use for decision making among primary healthcare managers was found to be modest. The level of data demand and use culture may increase more quickly if capacity building is strengthened and issues that de-motivate primary health care managers from using data are addressed.

Total quality management practices, organisational culture and firm performance; service or product innovation

The study determines how total quality management practices and good organizational culture will enhance operational performance in the banking sector in Ghana. The study is explanatory research design in nature. Taking the time frame of the study and the need to bring out a finding reasonable for generalization, the researcher selected a sample of 153. The findings of the study establish that total quality management has a positive and significant effect on service product innovation. Organizational culture has a positive and significant effect on service product innovation. Service product innovation has a positive and significant effect on firm performance. Organizational culture has a positive but insignificant effect on firm performance. Total quality management has a positive and significant effect on firm performance. The study further revealed that service product innovation positively and significantly mediates the relationship between total quality management and firm performance. The study finally assessed the mediating effect of service product innovation on the relationship between organizational culture and firm performance and the findings of the study indicate that service product innovation positively and significantly mediates the relationship between organizational culture and firm performance. How Total Quality Management practices and good organizational culture influence operational performance in the banking sector in Ghana.

Hardware Testing Methodologies for Wide Bandgap High-Power Converters

Wide bandgap (WBG) power semiconductor devices are increasingly replacing silicon IGBTs in high-power and high-voltage power electronics applications. However, there is a significant gap in the literature regarding efficient testing methodologies for high-power and high-voltage converters under constrained laboratory resources. This paper addresses this gap by presenting comprehensive, hardware-focused testing methodologies for high-power and high-voltage WBG power semiconductor-based converter bring-up before the control validation phase steps in. The proposed methods enable thorough evaluation and validation of converter hardware, including device switching characteristics, driving circuit functionality, thermal management performance, insulation integrity, and sustained operation at full power. We utilized the double pulse test (DPT) to characterize switching performance in a two-level phase leg configuration, extract circuit parasitics, and validate magnetic components. The DPT was further applied to optimize gate driving circuits, validate overcurrent protection mechanisms, and measure device on-resistance. Additionally, a multicycle test was introduced to rapidly assess steady-state converter performance and estimate efficiency. Recognizing the critical role of thermal management in high-power converters, our methodologies extend to the experimental extraction of key thermal parameters—such as junction-to-ambient thermal resistance and thermal capacitance—via a heat loss injection method. A correlation method between temperature sensor measurements and junction temperature is presented to enhance the accuracy of device temperature monitoring during tests. To ensure reliability and safety, dielectric withstand tests and partial discharge measurements were conducted at both component and converter levels under conventional 60 Hz sinusoidal and high-frequency PWM waveforms. Finally, we highlight the importance of testing converters under full voltage, current, and thermal conditions through power circulating tests with minimal power consumption, applicable to both non-isolated and isolated high-power converters. Practical examples are provided to demonstrate the effectiveness and applicability of these hardware testing methodologies.

Spatio-temporal dynamic of forest cover distribution in Omo-Shasha-Oluwa Tropical Forest Complex, Nigeria, (2014 – 2022)

: Forest Reserves (FRs) constitute a significant portion of the forested lands within protected areas, and their conservation is essential for Nigeria to achieve the United Nations’ Sustainable Development Goals (SDGs). Our study conducted an analysis of the dynamics of forested lands in the study area to evaluate and rank management performance over the period of 2014-2022. The forest bloc in OSO witnessed an expansion by 1.39% (3,929.67 ha) from 27.73% to 29.12% forest cover in 2014 and 2022, respectively. Forest fragments declined from 16 to 10 units for the forest bloc (>201.1 ha) with a proportionate increase in the Largest Patch Index (LPI) from 5,815.53 ha to 39,234.15 ha, in the period 2014 -2022, respectively. Omo, Ago-Owu, Ife and Shasha FRs all witnessed negative changes in the percent forest cover representing a decline of about -0.43% (529.52 ha), -37.62% (9028.8 ha), -8.54% (1217.43 ha) and -0.29% (89.55 ha), respectively. Oluwa FR had an increase in the forest cover by 17.64% or 14,596.38 ha in the period 2014 - 2022. The forest cover retention rates in the period 2014 – 2022, ranks Oluwa FR ahead of other FRs in terms of Protected Area (PA) management efficiency. The results highlight the existence of a relatively better and effective forest management system in the reserve. Landsat data are capable of providing near real-time forest cover information that can be integrated into existing tracking tools for periodically gauging management efficiency and performances across the PAs of Nigeria.

Gender of a manager and firm performance in Africa: Does the business environment play a moderating role?

AbstractThis study explores the moderating role of the business environment in the relationship between top manager gender and firm performance, using firm‐level data from 29 African countries. Controlling for endogeneity and country fixed effects, we find that female‐managed firms generally underperform male‐managed firms, except among large firms where female managers excel. The performance gap is exacerbated by a weak business environment, highlighting the need for reforms to close the managerial gender gap in Africa. Strengthening the business environment is essential for improving female‐led firm performance and by extension promoting gender equality in African business leadership.

Influence of soft skills on career development: Exploring performance appraisal as a mediating mechanism

Performance appraisal plays a crucial mediating role in the complex interaction between soft skills and career development. This practice provides a comprehensive view of workers’ competencies and directly influences their professional trajectories. This investigation checked the significance of these competencies in career development, employing performance appraisal as a mediating mechanism. The sample comprised 262 employees in Portugal. Data were collected from a convenience sample using the snowball sampling method, in which participants were asked to invite their contacts to participate in the study. The questionnaire included the short skills inventory, the career growth scale, and the performance management scale. The more developed the soft skills, the greater the tendency for employees to progress in their careers. Performance appraisal has a significant influence on employees’ career trajectories, as the possibility of career advancement largely depends on their performance. The data further revealed that by mediating the relationship between soft skills and career progression, performance appraisal helps guide and support the organization’s strategic decisions. However, when the mediating variable is introduced into the model, the influence of soft skills on career development is no longer significant, indicating a full mediation effect. The results suggest that 26.8% of the possibility of progressing in the career is explained by the performance appraisal results. These results act as a critical link between soft skills and career development. Soft skills exert a significant influence on career development, and performance appraisal emerges as a valuable means to recognize and promote these competencies.

Performance Management, Metric Quality, and Trust: Survey Evidence from Healthcare Organizations

We examine the impact of performance management (PM) and metric quality on trust and performance in healthcare organizations. Prior research found that PM can elicit feelings of distrust, as healthcare professionals can perceive control as curbing their autonomy. We examine whether the quality of metrics (i.e., their accuracy, sensitivity, and verifiability) can help PM to enhance interpersonal trust in healthcare settings. We mobilize self-determination theory (SDT) and argue that PM using high-quality metrics can address managers' needs for competence and autonomy, which in turn can impact interpersonal trust and unit performance. Using survey data from 152 middle managers in healthcare settings, we find that metric quality moderates the relationship between PM and interpersonal trust, which is subsequently positively linked with unit performance. Also, we find that action control and cultural control are positively associated with interpersonal trust. We complement literature on the control-trust nexus by highlighting the importance of metric quality, and by showing which forms of control can be instrumental in creating a high-trust work environment.

STUDI KAUSALITAS MOTIVASI DAN DISIPLIN KERJA TERHADAP KINERJA KARYAWAN BANK MUAMALAT KCU FATMAWATI

The aim of this research is to determine the influence of work motivation and discipline on employee performance at PT Bank Muamalat KCU Fatmawati partially and simultaneously. The method used is descriptive quantitative. The sampling technique uses a saturated sampling technique from a population of 60 as samples. The method used to test and analyze the influence partially and simultaneously uses hypothesis testing (t test and f test). This study investigates the significant impact of work discipline and work motivation on employee performance. Data analysis reveals that work discipline has a substantial influence on performance, with a contribution value of 0.933. This suggests that adherence to rules and punctuality play a crucial role in enhancing employee output, as supported by reinforcement theory and time management principles. Additionally, work motivation shows a significant effect on performance, with a contribution value of 0.672, highlighting the importance of intrinsic motivation factors, such as achievement and recognition, in driving employees to excel. The findings underscore the need for a comprehensive approach in performance management, emphasizing both the establishment of strong disciplinary practices and the cultivation of high motivation levels to optimize employee performance.

Using Generative AI for Strategic HR Planning: How AI Can Elevate Your People Strategy

This article explores how generative artificial intelligence (AI) technologies such as machine learning and deep learning can be applied to enhance strategic human resource (HR) planning. Drawing from academic literature and industry case studies, the article outlines key techniques of gen AI and principles for responsible implementation. It then discusses specific opportunities for gen AI to elevate HR processes like recruiting, training, performance management, compensation planning, and cultural transformation from descriptive to prescriptive levels through data-driven decision making and insights. Aligning AI goals with organizational values around diversity, ethics and worker well-being is emphasized. The essay aims to translate technical concepts into practical recommendations while arguing that when guided properly, gen AI shows strong potential to empower HR leaders and elevate their people strategy through personalization, prediction, continuous improvement and robust measurement of ROI.

Strategic Planning In The Public Sector: Analyzing Its Effects On Local Government Performance

This study investigates the impacts of strategic planning, strategic types, and stakeholder involvement on the performance of Brazilian local governments. Using a sample of 286 municipalities, the research employs multiple regression analysis to evaluate how these variables influence various dimensions of organizational performance, including the management of objectives, operations, people, external relations, and overall performance. The primary goal of the study is to empirically validate the relationship between strategic planning and the performance of local governments, as well as to identify which strategic types are most effective in different contexts. Additionally, it seeks to understand the role of stakeholder involvement in the strategic planning process. The methodology included the collection of primary data through electronic surveys sent to municipal managers, as well as secondary data from the Municipal Management Index (IGM-CFA). The variables analyzed were the Strategic Planning Perception Index (IPE), strategic types (defender, prospector, reactor), and the Stakeholder Involvement Index (IEST). The results indicate that a positive perception of strategic planning is significantly associated with improvements in all areas of performance analyzed. Proactive and innovative strategies (prospectors) showed positive correlations with performance, while reactive strategies showed negative correlations. Stakeholder involvement also contributed positively to the management of objectives, operations, and people, although it showed a negative correlation with the management of externalrelations.

Enhancing Performance of Construction Waste Management: Factor Analysis from the Building Contractors’ Perspectives

Enhancing Performance of Construction Waste Management: Factor Analysis from the Building Contractors’ Perspectives

All-day superhydrophobic photo-thermal and electro-thermal icephobic surfaces based on ZrN/MoSe2 composite

Ice formation on equipment and infrastructure surfaces can lead to severe damage and operational disruptions, necessitating advancements in passive anti-icing technologies to enhance de-icing efficiency. Superhydrophobic materials offer notable improvements, such as minimal water affinity and reduced ice adhesion. However, their practical application is hindered by limited durability and vulnerability to cold and humid conditions. This study presents the development of a micro-nanostructured ZrN/MoSe2 photo-electrothermal superhydrophobic composite coating, which exhibits physical and chemical self-cleaning capabilities both day and night. This innovative coating rapidly heats due to the combined effects of solar absorption and Joule heating. It reaches 89.4 °C in 600 s under simulated sunlight and 81 °C with a 15 V electrical input in 300 s. With a contact angle exceeding 155°, it demonstrates superior self-cleaning abilities. Moreover, the coating retains its exceptional anti-icing and de-icing properties even after exposure to mechanical stress, abrasion, and harsh chemical environments. It can rapidly melt ice from surfaces in just 66 s at −20 °C under one sun, achieving a 76 % efficiency in photo-thermal de-icing. Electro-thermal de-icing tests reveal that the coating can melt ice in 55 s with an 87 % efficiency. These promising features, attributed to the unique properties of ZrN nanoparticles and MoSe2 nanosheets, significantly provide substantial advancements in superhydrophobic coating technologies. They offer the potential for large-scale production and enhanced performance in ice management.

Machine Learning for Treatment Management Prediction in Laryngeal Fractures

ObjectivesLaryngeal fractures are rare but potentially life-threatening traumas. Complications, such as airway obstruction and disrupted laryngeal anatomy, associate with significant morbidity. Early identification of at-risk patients and optimal management remain crucial for improved outcomes. Recently, machine learning (ML) has attained great attention as a unique and novel technique for evaluating complex non-linear relationships between multiple observations to create a predictive model with greater accuracy. This study aimed to demonstrate the potential of ML in predicting airway and surgical management of laryngeal fracture patients and identify key contributing parameters for the predictive performance of the ML models. MethodsThe ML models were developed using a patient series managed at the Helsinki University Hospital during 2005–2019. The developed models were further evaluated independently using a different cohort collected from the same institution between 1995–2004. ResultsThe ML showed a weighted area under curve (AUC) of 0.93 and accuracy of 0.86 following training for airway management. Likewise, for treatment approach, weighted AUC was 0.85 and accuracy 0.78. Injury type, Schaefer-Fuhrman grade (SF gr), age at incident, cause of injury, and fracture of the cricoid, in decreasing order of significance, were the most prominent features for the model's predictive performance for airway management. Similarly, our model identified SF gr, fracture of the cricoid, injury type, age at incident, and cause of injury as the most significant predictors for surgical treatment approach. ConclusionsThe proposed prediction of management approach by an ML technique can provide accurate predictions and thus aid clinicians in administering early and personalized interventions. The model may serve as a supporting tool in recognizing at-risk patients and in timely decision making. Further independent external validation is warranted for model generalizability.

Associations Between Cognitive Performance and Self-Efficacy for Pain Management in Older Adults With Chronic Pain

BackgroundSelf-efficacy for pain management is the key to successful pain management, yet little is known about the effect of cognitive performance on self-efficacy for pain management. This study aimed to examine to what extent cognitive performance is related to self-efficacy for pain management in older adults with chronic pain. MethodsThe analyses utilized data from the baseline assessment of the MOBILIZE Boston Study. Five neuropsychological tests—the Clock in the Box Test, Letter Fluency Test, Trail-making Test, Hopkins Verbal Learning Test, and WORLD Test—were performed to measure the cognitive performance domains of 458 participants, aged 70 years and reporting chronic pain and self-efficacy for pain management. We measured self-efficacy for pain management using the Chronic Pain Self-Efficacy Scale. Statistical analyses were done using multiple linear regression analysis. ResultsAfter adjusting for sociodemographic factors, general cognitive performance, executive function, as measured by the Clock in the Box Test, and attention, as measured by the Trail-Making Test Part A, were significantly associated with self-efficacy for pain management in older adults with chronic pain. However, after controlling for sociodemographic factors, chronic conditions, pain interference, and physical performance, the associations between cognitive performance measures and self-efficacy for pain management weakened. ConclusionGreater cognitive performance in attention and executive function might be associated with better self-efficacy for pain management. Future longitudinal research is required to investigate the long-term implications of cognitive performance changes on the progress of self-efficacy for pain management in community-dwelling older adults.

DeepEMS: Multimodal optimal energy management of microgrid systems based on a hybrid multi‐stage machine learning model

AbstractThe effective management of microgrids is important towards transition to sustainable energy paradigm. By optimizing the utilization of different energy sources, such as solar photovoltaic panels and energy storages, it improves the reliability of the grid and develops resiliency in dealing with of challenges of unexpected variations in demand. To this end, the proposed paper presents DeepEMS, a system developed to manage the energy of microgrids through the incorporation of diverse intelligent algorithms. DeepEMS provides dynamic microgrid management through the utilization of Bidirectional Long Short‐Term Memory (BiLSTM) networks, Sliding Linear Programming (SLP), and Random Forest (RF). By implementing these methodologies, DeepEMS can optimize energy consumption throughout the microgrid by dynamically identifying and coordinating the needs of various energy sources. DeepEMS achieves precise multimodal optimization and facilitates integration of storage systems, grid interactions, and renewable energy sources (RES), as demonstrated by simulations and data analytics. DeepEMS presented performance in control, resource allocation, management, and grid utilization. Furthermore, in a comparative analysis with alternative intelligent models including XGBoost, Light GBM, RF, and Decision Trees, DeepEMS consistently demonstrated higher performance as measured by several key performance indicators (KPIs).

An instrumentation guide to measuring thermal conductivity using frequency domain thermoreflectance (FDTR).

Frequency Domain Thermoreflectance (FDTR) is a versatile technique used to measure the thermal properties of thin films, multilayer stacks, and interfaces that govern the performance and thermal management in semiconductor microelectronics. Reliable thermal property measurements at these length scales (≈10nm to ≈10μm), where the physics of thermal transport and phonon scattering at interfaces both grow in complexity, are increasingly relevant as electronic components continue to shrink. While FDTR is a promising technique, FDTR instruments are generally home-built; they can be difficult to construct, align, and maintain, especially for the novice. Our goal here is to provide a practical resource beyond theory that increases the accessibility, replicability, and widespread adoption of FDTR instrumentation. We provide a detailed account of unpublished insights and institutional knowledge that are critical for obtaining accurate and repeatable measurements of thermal properties using FDTR. We discuss component selection and placement, alignment procedures, data collection parameters, common challenges, and our efforts to increase measurement automation. In FDTR, the unknown thermal properties are fit by minimizing the error between the phase lag at each frequency and the multilayer diffusive thermal model solution. For data fitting and uncertainty analysis, we compare common numerical integration methods, and we compare multiple approaches for fitting and uncertainty analysis, including Monte Carlo simulation, to demonstrate their reliability and relative speed. The instrument is validated with substrates of known thermal properties over a wide range of isotropic thermal conductivities, including Borofloat silica, quartz, sapphire, and silicon.

Subjects With MS Improve Physical Performance, Bone Health, And Lipid Management Following High-intensity Interval Training

Subjects With MS Improve Physical Performance, Bone Health, And Lipid Management Following High-intensity Interval Training

A Current View on Strawberry Production Practices and Trends in the Northeastern United States and Canada

Strawberries (Fragaria ×ananassa) are an important crop for diversified fruit and vegetable farms in the northeastern United States as well as in Ontario and Québec, Canada. The industry in this cold climate region differs greatly from that in the major strawberry-producing states of Florida and California because strawberries typically represent a smaller share of total farm revenue and are sold primarily through retail markets. In recent years, strawberry production and marketing methods in the northeastern United States and Canada have diversified to meet the unique challenges of the region, including regional demand, extended seasons, and management of an increasingly challenging pest and disease complex. In 2020, we distributed an online survey to 163 commercial producers to obtain a snapshot of marketing and production practices used on strawberry-producing farms in this region and to assess research and outreach needs to better serve the industry. We conducted in-depth case study interviews with a small number of producers after the survey. We found that traditional June-bearing strawberry production continues to represent >50% of production, and that most fruit are grown conventionally. Day-neutral strawberry production was reported by an increased number of producers in New York as well as in Canada, and a higher percentage of day-neutral growers, compared to that of June-bearing producers, reported using protected culture. Botrytis (gray mold), weeds, and leaf spots were the top overall pest pressures indicated by growers. Survey respondents preferred written outreach materials and on-farm meetings to obtain information. Integrated pest management, organic production, and cultivar performance were the top priorities for subjects. The survey and interviews also provided information about the most common strategies for winter protection, frequency of pesticide applications, lifespan of plantings, and other current strawberry production practices in the region. Our survey results may be used by researchers and extension professionals to better serve this diversifying industry.

The Influence of Talent Management Development, Recruitment, and Competence on Employee Performance Through Job Training

This research focuses on the employee performance of the Sungai Penuh City Government. The study adopts a quantitative causality approach, using data collected through questionnaires. The analysis was conducted using multiple linear regression techniques with the SmartPLS program, and a saturated sample of 50 respondents was used for the study. The results indicate that talent management has a positive and significant effect on job training, and recruitment also shows a positive and significant impact on job training. However, competency does not significantly affect job training. Furthermore, talent management has a positive and significant effect on employee performance, while recruitment and competency do not significantly influence employee performance. Additionally, job training itself does not significantly impact employee performance. It is also concluded that job training does not mediate the relationship between talent management and employee performance. However, job training mediates the effects of recruitment and competency on employee performance.