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The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

The rise of evidence‐based medicine and more recently evidence‐based policy reflect the increasing importance of evidence as a basis for the organisation and delivery of health care. Evidence‐based practice is central to the “modernisation” of health care in current UK policy. The latest manifestation of this process is the emergence of evidence‐based management in health care. This paper examines the development of evidence‐based approaches in health care and questions the appropriateness of such an approach to management. The problems inherent in applying the principles of EBP to management are explored and alternative apporoach based on the notion of craft is suggesteed as more practical and realistic.

The purpose of this study is to examine the rostering practices and work experiences of medical scientists at four health services in the Australian public healthcare sector. There are over 16,000 medical scientists (AIHW, 2019) in Australia responsible for carrying out pathology testing to help save the lives of thousands of patients every day. However, there are systemic shortages of medical scientists largely due to erratic rostering practices and workload issues. The purpose of this paper is to integrate evidence-based human resource management (EBHRM), the LAMP model and HR analytics to enhance line manager decision-making on rostering to support the wellbeing of medical scientists.

Using a qualitative methodological approach, the authors conducted 21 semi-structured interviews with managers/directors and nine focus groups with 53 medical scientists, making a total 74 participants from four large public hospitals in Australia.

Across four health services, manual systems of rostering and management decisions do not meet the requirements of the enterprise agreement (EA) and impact negatively on the wellbeing of medical scientists in pathology services. The authors found no evidence of the systematic approach of the organisations and line managers to implement the LAMP model to understand the root causes of rostering challenges and negative impact on employees. Moreover, there was no evidence of sophisticated use of HR analytics or EBHRM to support line managers' decision-making regarding mitigation of rostering related challenges such as absenteeism and employee turnover.

The authors contribute to HRM theory by integrating EBHRM, the LAMP model (Boudreau and Ramstad, 2007) and HR analytics to inform line management decision-making. The authors advance understandings of how EBHRM incorporating the LAMP model and HR analytics can provide a systematic and robust process for line managers to make informed decisions underpinned by data.

Despite the growth and adoption of human resource (HR) analytics, it remains unknown whether HR analytics can impact organizational performance. As such, this study aims to address this important issue by understanding why, how and when HR analytics leads to increased organizational performance and uncover the mechanisms through which this increased performance occurs.

Using data collected from 155 Irish organizations, structural equation modeling was performed to test the chain mediation model linking HR technology, HR analytics, evidence-based management (EBM) and organizational performance.

The study's findings support the proposed chain model, suggesting that access to HR technology enables HR analytics which facilitates EBM, which in turn enhances organizational performance.

This research contributes significantly to the HR analytics and EBM literature. First, the study extends our understanding of why and how HR analytics leads to higher organizational performance. Second, the authors identify that access to HR technology enables and is an antecedent of HR analytics. Finally, empirical evidence is offered to support EBM and its impact on organizational performance.

Evidence‐based information practice is an important paradigm that is now emerging in mainstream information work from within healthcare information. This paper aims to provide an introduction to the concept before considering the imperative for practitioners to use insights from research within their professional practice and day‐to‐day decision making. The importance of a focused question and a systematic approach to critical appraisal are rehearsed and similarities with the domain of information systems are briefly considered. The paper concludes with state‐of‐the‐art observations from a recent conference in Canada and recommendations for further development of the paradigm. The objective is to achieve the eventual extinction of the concept through complete integration as simply another tool for reflective practice.

The purpose of this paper is threefold. First, to explore the importance of specific work environment facets for the overall job satisfaction (JS) of primary full-time permanent teachers (PTs) and substitute/temporary teachers (STs). Second, to highlight the similarity or difference in JS patterns among PTs and STs. Third, to provide guidelines for effective evidence-based human resource management (HRM) interventions targeting to boost PTs and STs JS levels by considering: (1) the perceived importance of individual work facets for them and (2) the school's performance in providing a satisfactory work environment.

Data on overall and important JS facets (i.e. satisfaction with opportunities for self-fulfillment, work intensity/load, salary/income, leadership and collegial relations) were collected from a sample of 438 PTs and STs in Greece. Moreover, MUSA, a method that combines Multi-Criteria Decision (MCDA) and Importance-Performance Analysis (IPA), was applied to uncover the critical work environment facets of PTs and STs overall JS that call for interventions.

The findings suggest that PTs seem to value, more than STs, the transactional and economic aspects of the school environment. More precisely, on the part of PTs, self-fulfillment and salary/income are the main contributors to their JS, whilst leadership is the least important facet of JS. For STs self-fulfillment and collegial relationships are the aspects of work that contribute the most to their overall JS, whilst salary/income is the least important contributor. The study results further indicate that self-fulfillment is the strong attribute of Greek schools' work environment in boosting TJS regardless teachers' status, whilst salary/income and workload are potential threats.

It is one of the few studies that provide insights into the differing JS patterns of STs and PTs through the application of a MCDA/IPA method. Therefore, it offers evidence-based guidelines that take into consideration both the school's performance (overall and facet JS) and importance of core aspects of the work experience for STs and PTs.

Decision-making competence (DMC) of entrepreneurs and managers is a longstanding topic in this increasingly globalized world. These figures operate in conditions not within their own control, and good levels of DMC are often considered to be desirable for the flourishing of business and society. This paper reports an empirical investigation on the DMC of entrepreneurs and managers, in an attempt to inform about their tendencies to incur in risky and costly choices.

Three cognitive biases associated with operational strategies and individual characteristics of entrepreneurs and managers, namely under/overconfidence (UOC, i.e. self-confidence in taking decisions), resistance to sunk costs (RSC, i.e. propensity to take cost investments) and consistency in risk perception (CRP, i.e. how well individuals understand probability rules) were considered . Cognitive biases measures were used in a cross-sectional study on a sample of n = 639 entrepreneurs and n = 512 managers. Data collected via online survey were analyzed using descriptive statistics and inferential statistics to determine differences among entrepreneurs and managers DMC.

Analyses reveal that entrepreneurs exhibit higher levels of UOC compared to managers with a marked presence of UOC among entrepreneurs at younger ages. Conversely, performance regarding RSC improves with higher education levels while age and RSC are positively correlated only for managers, regardless of education. Lastly, entrepreneurs and managers resulted as not being affected by CRP. This study discusses these results to provide initial insights for further avenues of research and practice.

The study offers an innovative, evidence-based viewpoint on how entrepreneurs and managers deal with risky and costly decisions. It offers an initial understanding of the role of UOC, RSC and CRP, that is specific cognitive biases associated with operational strategies and individual characteristics, in the DMC of these working figures. The study forwards avenues of scrutiny of quick-witted entrepreneurs and systematic managers.

This viewpoint article is concerned with an attempt to advance organisational plasticity (OP) modelling concepts by using a novel community modelling framework (PhiloLab) from the social simulation community to drive the process of idea generation. In addition, the authors want to feed back their experience with PhiloLab as they believe that this way of idea generation could also be of interest to the wider evidence-based human resource management (EBHRM) community.

The authors used some workshop sessions to brainstorm new conceptual ideas in a structured and efficient way with a multidisciplinary group of 14 (mainly academic) participants using PhiloLab. This is a tool from the social simulation community, which stimulates and formally supports discussions about philosophical questions of future societal models by means of developing conceptual agent-based simulation models. This was followed by an analysis of the qualitative data gathered during the PhiloLab sessions, feeding into the definition of a set of primary axioms of a plastic organisation.

The PhiloLab experiment helped with defining a set of primary axioms of a plastic organisation, which are presented in this viewpoint article. The results indicated that the problem was rather complex, but it also showed good potential for an agent-based simulation model to tackle some of the key issues related to OP. The experiment also showed that PhiloLab was very useful in terms of knowledge and idea gathering.

Through information gathering and open debates on how to create an agent-based simulation model of a plastic organisation, the authors could identify some of the characteristics of OP and start structuring some of the parameters for a computational simulation. With the outcome of the PhiloLab experiment, the authors are paving the way towards future exploratory computational simulation studies of OP.

With a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?

This study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.

The analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.

This literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

Drawing upon the career construction theory, the present study aims to propose and test a mediation model exploring the association among technology readiness, adoption of human resource (HR) analytics by HR professionals, and organizational career growth.

A survey has been conducted to collect data from HR professionals (N = 347) working in various industrial sectors in India. The data collected is analyzed for mediation using SPSS PROCESS Macro (Model 4).

The study provided evidence about the mediating role of individual adoption of HR analytics between technology readiness (motivators, inhibitors) and organization career growth (career goal progress, professional ability development, promotion speed and remuneration growth).

Firstly, the results provide evidence in relation to the career construction theory with respect to adoption of human resource analytics by HR professionals. Secondly, the study findings validate the technology readiness model in the context of adopting HR analytics. Thirdly and most significantly, the study proposes a novel theoretical framework for adoption of HR analytics by HR professionals in organizations.

The findings imply that HR professionals' technology readiness (motivators and inhibitors) can facilitate better adoption of HR analytics in organizations. Moreover, the adoption of HR analytics shall promote better career growth for HR professionals.

The present study builds and tests a theoretical framework based on technology readiness, individual adoption of HR analytics, and organization career growth. The study is the first of its kind to the best of the authors' knowledge.