Human Resource Decision-makers Research Articles

A Human Resource Evaluation and Recommendation System based on Big Data Mining

This investigation presents a paradigm-shifting Human Resource (HR) Assessment and Recommendation Framework, leveraging progressed huge information mining procedures. Drawing bits of knowledge from cybersecurity, healthcare, education, and further detecting spaces, our framework utilizes a different cluster of calculations, counting Arbitrary Forests, Support Vector Machines, K-Means Clustering, and a Feedforward Neural Organize. The comparative investigation uncovers the Feedforward Neural Network as the standout entertainer, emphasizing its various levels including learning for complicated design acknowledgement inside HR measurements. Uniquely, this framework draws on methodologies and insights from varied domains such as cybersecurity, healthcare, and education, applying these rich, interdisciplinary perspectives to HR analytics. This cross-pollination of ideas enables the framework to adopt sophisticated data mining and pattern recognition techniques that are not traditionally utilized within HR, offering new avenues for detecting and interpreting complex employee data patterns. Result values illustrate the system’s adequacy, with a precision of 88%, an accuracy of 90%, a review of 87%, and an F1 score of 88%. These measurements emphasize the system’s capacity to comprehensively assess worker execution, giving exact suggestions for key HR decision-making. Ethical contemplations, innovation acknowledgement, and custom-fitted proposal frameworks, propelled by related works, are coordinates to guarantee the system’s reasonability over assorted organizational settings. This research contributes to the advancing scene of HR administration, offering a spearheading arrangement for organizations looking for data-driven, comprehensive, and morally sound approaches to workforce optimization.

Orthopedic surgeons' transition into full-time practice over the last 20 years: an analysis using Ministry of Health billing data.

Underemployment is a reality for many new graduates, who accept locum or part-time work as an alternative to unemployment because of lack of opportunities. We sought to analyze orthopedic surgeons' Ontario Health Insurance Program (OHIP) billing data over a 20-year period as a proxy of practice patterns and hypothesized that billing in the first 6 years of practice would be affected by underemployment and locum. We analyzed the annual average billing totals of orthopedic surgeons, broken down by year of graduation, year of billings, and number of surgeons billing in that year. We analyzed public census data of the Ontario population size as a proxy of orthopedic demand. A 2019 cross-sectional analysis showed that around 15 surgeons per graduating year were billing in Ontario from the 1995 to 2016 cohorts, while 2017 and 2018 saw an increase to 30 and 36 actively billing surgeons, respectively. The number returned to more historical numbers in 2019, with 20 actively billing surgeons. For those surgeons billing in Ontario, billing trends have been roughly stable, with average billings increasing each year for the first 6 years in practice (p < 0.001). Year of graduation did not have an effect on the first 6 years of billings (p > 0.5). Billings were stable after 6 years in practice (p > 0.09). The Ontario health care system has not expanded to support more orthopedic surgeons despite the aging and growing population; despite our growing population, the number of surgeons being trained and retained has not matched this growth. Further research needs to be done to guide optimal health human resource decision-making.

BIG DATA ANALYSIS IN HUMAN RESOURCES DECISION MAKING: OPTIMIZING WORKFORCE MANAGEMENT

This research aims to conduct an in-depth analysis of the application of Big Data in human resource decision-making, focusing on optimizing workforce management. By measuring four main variables, namely recruitment efficiency, accuracy of employee selection, employee development, and organizational performance based on HR decision-making—Big Data analysis is expected to provide a deeper understanding of workforce dynamics. The research results show that applying Big Data can build recruitment efficiency, increase the accuracy of employee selection, support more appropriate employee development, and ultimately improve overall organizational performance. Big Data makes organizational performance based on HR decision-making by providing a solid foundation for more effective human resource management strategies. This research uses qualitative methods with the help of the NVivo 12 Plus tool to identify research results. Implementing Big Data in various aspects of human resource management is recognized as a catalyst for positive change in workforce management.

A Descriptive Study of Managers' Perceptions of the Use of Financial Statements in the HR Management Decision-Making Process

The purpose of this study is to explore human resource (HR) managers' perceptions of using financial statements in HR decision-making, particularly in compensation, workforce planning, and budgeting. The research design employed a qualitative approach involving interviews with HR managers to understand how financial data is interpreted and applied in strategic decisions. The methodology focused on identifying the barriers HR managers face, including financial literacy limitations and organizational silos, that hinder the effective integration of financial data into HR practices. The findings reveal significant variability in financial literacy among HR managers, directly influencing their ability to leverage financial data. While some managers demonstrate a strong understanding of financial statements, others rely heavily on operational metrics. The study highlights the need for organizational improvements, particularly in fostering collaboration between HR and finance departments, to ensure more informed and aligned decision-making. In terms of implications, the study offers practical recommendations for improving HR decision-making by increasing financial literacy through targeted training programs and promoting interdepartmental collaboration. This research contributes to both theory and practice by emphasizing the importance of financial data in HR strategies, with implications for more integrated HR and financial management approaches.

Is Psychometrics So Useful for Academic Psychology?

Psychological theories regarding ability and personality traits often rely on the results of psychometric modelling. The latter is assumed to link responses to test items to an unobserved 'construct' (trait, ability), which is 'modelled' from the test data. However, does the agreement between the data and the model indicate that the model represents a psychological construct? To what extent is ‘psychometric modelling’ modelling in the general scientific sense of the term? The validity of using modelling data to understand psychological phenomena depends on the answer to these questions. The article analyses the logic of psychometric modelling in comparison with modelling in other sciences and argues that psychological phenomena as a subject of modelling are not involved neither in the construction nor in the correction of models. The problem of unjustified interpretations of modelling results in psychology and their undesirable consequences for psychological theory is raised. At the same time, the use of psychometric modelling for human resource decision-making is still waiting for its evaluation.

Department chairs’ perceptions of union membership on academic human resource decision-making

PurposeThe purpose of this study is to explore unionization’s impact on university department chairs for academic human resource decision-making in terms of faculty hiring; re-employment, promotion and tenure; other faculty evaluation decisions; and discipline and discharge.Design/methodology/approachThe subjects were department chairs with more than one year of experience in institutions with faculty bargaining units in the USA. Half of the department chairs were members of the bargaining unit and half were not members of the bargaining unit. T-test and Chi square statistics were used to examine and compare 136 chair responses.FindingsWhile there were significant differences between the groups when making all academic human resource decisions based on role affiliation, there was a weak identification as a bargaining unit member. There was no difference between groups when determining how the participants perceived the process rules, and the majority of the responses indicated membership in the union does not complicate making academic human resource decisions.Originality/valueBased on these findings, this study provides the first empirical evidence against a long-held administrative belief department chairs should not be unionized as they will side with union colleagues in difficult academic human resource cases. The findings of this study also produce three key recommendations for practice and faculty development.

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in‐depth analysis of HR strategic decision‐making and an HR strategic decision model is constructed for simulation. The four evaluation dimensions of index screening are extracted, the generalized gray correlation analysis is applied to screen the initial selection indexes of HR strategic decision‐making, and then the HR strategic decision‐making index system of power supply enterprises is constructed. On this basis, the applicability of the BP neural network in HR strategic decision‐making is analysed and demonstrated, a BP neural network‐based HR strategic decision‐making model for power supply enterprises is constructed, and the rationality of the model is illustrated through the model training and testing. Finally, an empirical study is conducted with S power supply company as an example to illustrate the operability of the BP network model for HR strategic decision‐making. The results of this paper provide a scientific basis for human resource decision‐making in power supply enterprises and provide theoretical support for promoting the healthy development of power supply enterprises. Improving the level of human resource management can make fuller use of human resources and realize the strategic goals of the enterprise. A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross‐entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. The optimization of the model training process is finally completed, and the recommendation quality is improved. The Bayesian probability distribution table is learned to be filled in the expert data set; finally, the accuracy and effectiveness of the model are tested on the experimental platform. The results of the experiments show that the established Bayesian model beats the platform’s built‐in intelligent method with a 78.2% win rate. That is, the model can make intelligent recommendations for strategies in staffed and unmanned platform decision‐making and command‐and‐control combat units to execute tactical actions to achieve the best operational effectiveness.

Research on Enterprise Human Resource Decision-Making Technology based on Machine Learning Big Data Analysis Model

This article has been retracted.

Exploring the implementation gap: organizational autonomy and line managers’ participation in human resource decision–making

ABSTRACT This article explores the assumption that the level of human resource (HR) autonomy of an organisation influences line managers’ participation in HR decision-making, and it seeks to understand to what extent such a participation affects the effective implementation of HR practices. The results of an empirical study of HR policies and practices in three public hospitals in Pakistan reveal that greater level of HR autonomy of an organisation, with less pressure to comply with public personnel policies, leads to increased participation of middle managers in HR decision-making. This, in turn, helps to reduce the gap between the intended and implemented HR practices, which is important for human resource management (HRM) – organisational performance linkage. These findings contribute to ongoing discussions related to HRM in the public sector context and line management enactment of HR practices by highlighting the profound implications of the institutional context on the HR management of public hospitals. Additionally, this research proposes a bottom-up approach to HR practices, through its focus on the participation of managers in HR decision-making as a possible solution to better implementation of HR practices.

Measure what matters: descriptive and predictive metrics of HRM-pathway toward organizational performance

PurposeTo understand human resource (HR) practices outcomes on HR decision making, strategic human resource management (HRM) and organizational performance by exploring the HR data quality along with descriptive and predictive financial and non-financial metrics.Design/methodology/approachThis work utilizes the grounded theory method. After the literature was reviewed, 113 HR managers of multinational and national companies in India were interviewed with a semi-structured questionnaire. The collected interview data was analyzed with NVivo 8.0 software.FindingsIt is interesting to uncover the descriptive and predictive non-financial and financial metrics of HR practices and their influence on organizational performance. It was found that HR data quality moderates the relationship between the HR practices outcome and HR metrics. This study found that HR metrics help in HR decision-making for strategic HRM and subsequently affect organizational performance.Originality/valueThis study has uniquely provided the descriptive and predictive non-financial and financial metrics of HR practices and their impact on HR decision making, strategic HRM and organizational performance. This study highlights the importance of data quality. This research offers insights to the HR managers, HR analysts, chief HR officers and HR practitioners to achieve organizational performance considering the various metrics of HRM. It provides key insights to the top management to understand the HR metrics' effect on strategic HRM and organizational performance.

Research on Human Resource Management for Telework Informatization under the Background of Epidemic Prevention

The Corona Virus Disease in 2019 will have a serious impact on the normal operation of various industries. Under the environment of resuming work and production and ensuring safety, remote online office has become a core option for the operation of various industries. At present, remote office has met a lot of troubles, such as information asymmetry, inefficiency, regulatory difficulties, communication barriers, weakened confidentiality, and so on. This article focuses on exploring the mechanism of human resource management in remote office informatization in the context of epidemic prevention and control, and proposes distance education innovation for employee training, big data transformation performance evaluation, and cloud computing. This article also focuses on solving the problems existed in the remote office. It suggests some useful and innovative human resource management paths. For example, improving data sharing, network optimization and remote collaborative office and others, all of them offer a good idea which provides human resource decision-making reference for the orderly resumption of work and production in various industries.

Beyond Human Judgment: Exploring the Impact of Artificial Intelligence on HR Decision-Making Efficiency and Fairness

This study aims to evaluate the impact of artificial intelligence (AI) on the efficiency and fairness of human resources (HR) decision-making. The key goals are to determine how artificial intelligence improves decision-making efficiency, investigate the fairness issues involved in AI-driven human resource practices, and make policy suggestions for engaging in ethical HR practices. The approach utilized is known as secondary data analysis. It is used to synthesize insights and patterns by pulling upon previously published literature and empirical investigations; even though artificial intelligence technologies present an opportunity to optimize human resource operations and improve organizational performance, significant findings demonstrate that these technologies also create ethical problems connected to algorithmic biases and an absence of transparency. Regulatory oversight, ethical standards, data governance, diversity and inclusion programs, and constant monitoring and assessment are some of the policy implications that should be considered to guarantee responsible deployment of artificial intelligence in human resource contexts. When it comes to human resource decision-making, companies can embrace the revolutionary potential of artificial intelligence (AI) while maintaining ethical standards if they prioritize justice, openness, and accountability.

Ethics of care and selective organisational caregiving by private employers for employees with chronic illness in a middle-income country.

For people with chronic illnesses in low-and-middle-income countries, access to enabling resources that contribute to health, economic and social resilience such as continued employment, often fall outside the health sector's remit or delivery of national structural protection. In the absence of sufficient laws and policies that mitigate discrimination and enhance reasonable work modifications, private employers have a high degree of agency and discretion in how they hire, manage, or terminate employees with chronic illnesses (ECI). There is a scarcity of research on how employers make decisions under these conditions. Using a constructivist grounded theory approach, we interviewed and analysed data from 30 human resource (HR) professionals and decision-makers within private organisations in Klang Valley, Malaysia (June 2015-September 2016). In this paper, we use 'ethics of care' as an analytic, and moral lens to present HR's decision-making rationales in caring for and managing ECI. Respondents described the positive influence of international practices, including through parent company policies, as a reference for best practice. While overt bias and discriminatory perceptions were predictably described, participants also discussed care as relational organisational culture, and strategy, albeit selectively. Apart from illness factors such as duration and severity, descriptions of 'selective caregiving' included considerations of an employee's duration in organisations, the perceived value of the employee to employers, organisation size, ethos, resources and capabilities, and how organisations managed the uncertainty of illness futures as a potential risk to organisation outcomes. Selective caregiving can contribute to social, economic and health inequalities in populations with chronic illness. Nevertheless, global health actors can use the problems identified by participants, as entry points to engage more closely with employers and the broader private and commercial sectors in LMICs, to facilitate more inclusive care, and care-based intersectoral work to address the social and economic determinants of health.

Improve HR Decision-Making Based On Data Mart and OLAP

The organizations always need to manage their operations, process the data electronically, and find a platform that help them to support their strategic decisions. The success of the human resource department can reflect overall organizational success. The human resource department professionals try to ensure finding the right person at the right time for the job that fits the person according to skills and qualifications. This task needs a platform that supports making the right decision based on historical managerial information. The data mart is a departmental based decision support system that used departmental data to help decision-makers to support short term decisions. Human resource (HR) data mart is the base stone for building an enterprise data warehouse. The paper presents the implementation process of HR data mart starting from implementing data mart schema to online analytical processing (OLAP) reports. The data mart is implemented on retired employees’ data of Basra Oil Company for over 15 years. A human resource data mart can provide a base platform to perform a different analysis operation to support the right decisions. Different OLAP reports are implemented to help analysts and decision-makers to get the answers for their questions as OLAP queries. Two categories of reports are implemented offline reports using Microsoft Excel Pivot Table 2010 and web OLAP reports using SQL Server Reporting Service 2014 (SSRS). The tools used to implement data mart vary from SQL Server Management Services (SSMS) 2014, SQL Server Integration Service 2014 (SSIS), SQL Server Analytical Service 2014 (SSAS), SQL Server Reporting Service 2014 (SSRS), SQL Server Data Tools 2013 (SSDT), and Microsoft Excel Pivot Table 2010.

Buildings emerge as drivers of health and profits

A confluence of factors has moved buildings to the forefront of efforts to promote the health and wellness of employees. This paper addresses why this has occurred, and how it will influence corporate real estate (CRE) and human resource decisionmaking in the future. The paper continues by describing and documenting a methodology for assessing the financial performance of health and wellness investments that can be used to analyse property and portfolio decisions (Property Health and Wellness ROI Model [The ROI Model]). The ROI Model is then applied to a hypothetical investment in the WELL Building StandardTM for a 200,000 sq.ft office building to demonstrate the sensitivity and substantial profit potential of building level investments in health and wellness. Proper financial assessment of health and wellness investments can enable dramatic improvements in occupant health and productivity, and provide a strong financial foundation for other energy and sustainability investments.3

Construction and Analysis of the Decision Model based on the Transfer of Human Resources

The transfer of human resources refers to the transfer of human resources across different regions, industries and organizations depending on the development trends in the respective areas, economic environment and personal development needs. It is a process where the revenue of human resources improves continuously, and regional economy, industries and organizations are growing. Reasonable transfers across regions, industries and organizations can further realize the potential of individuals and enhance their advantageous position in the job market. Moreover, the transfers can optimize human resource allocation and the overall regional industrial development, improving the utilization of human resources. According to the law of diminishing returns, when there is perfect competition across regions, industries and organizations, the marginal utility of the side that has human resources shortage is higher than the side that has surplus. The human resources will flow to the side with shortage till the marginal revenue of the two sides reaches equilibrium. This is the ideal condition for human resources transfer. This paper is aimed to build models for individual human resource decisionmaking, organizational decision-making and dynamic balance respectively, in order to facilitate the equilibrium transfer of human resources across different regions, industries and organizations, and finally to achieve the optimal allocation of human resources.

SINGLE‐ATTRIBUTE UTILITY ANALYSIS MAY BE FUTILE, BUT THIS CAN’T BE THE END OF THE STORY: CAUSAL CHAIN ANALYSIS AS AN ALTERNATIVE

Research on providing single‐attribute utility analysis has shown moderate or even negative effects on the acceptance of selection and training tests by human resource decision makers. In this study, we contrasted the perceived utility of single‐attribute utility analysis with causal chain analysis as an alternative way of conducting utility analysis. Causal chain analysis focuses on measuring the linkages between HRM interventions and organizational outcomes mediated by employee attitudes and customer perceptions. We compared 144 managers' reactions to both methods of utility analysis concerning the variables understandability, information quality, perceived usefulness, user information satisfaction, and intention to use. Causal chain analysis yielded higher results than single‐attribute analysis for these variables, and a compound measure of these constructs supported this finding. This indicates that causal chain analysis is a valuable alternative method of communicating the utility of HRM interventions.

Managers' Reactions to Utility Analysis and Perceptions of What Influences Their Decisions

Recent research on providing utility analysis (UA) information has shown small positive effects on acceptance of selection tests by human resource decision-makers (Carson, Becker, & Henderson, Journal of Applied Psychology, 83 84–96 (1998). In the present study, 218 managers provided reactions to UA information. Not only do our findings replicate the positive effects of UA, we found managers also responded favorably to UA information when asked independently of the decision making process. Specifically, when asked to rank order information in terms of what they perceived to be most influential in helping them make their final decisions, those who received UA information ranked it among the highest.

Reasonable Notice for Wrongful Dismissal

This study identifies and compares the severance compensation determinants of legal and human resource (HR) professionals, using court cases and a policy-capturing HR survey. Results confirmed that HR practitioners not only considered the factors critical in court decisions, which were length of service, age, employment status, and labour market conditions, but also had economic and social justice concerns as revealed by significant factors such as a company’s financial situation and reason for dismissal. Certain HR decision-makers’ characteristics were also found to influence the decisions. Overall, HR notices were shorter than court notices in similar situations. Implications of such findings are discussed.

STEP-UP: A Decision-Support System for Transforming the Dislocated US Defense Workforce

We developed STEP-UP, a database-intensive, decision-support system for dislocated workers. We modeled the work positions at a micro- and macro-level and created tailored profiles for individuals to reflect prior positions, training, certifications, and interests. With the profile, career counselors matched employees to labor-market positions or identified training programs in growth industries. Also, giving the prospective employers an indication of the wealth of skilled employees, STEP-UP could provide client matches for job openings. We have continued to extend the capabilities of STEP-UP and offer it as a Web-based system. We expect that more decision support systems like STEP-UP will be developed for use in human resource decision-making situations.