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This study aims to explore the emergence of the human resource (HR) analyst role. The job posts on LinkedIn display the industry demand and skills required by the organizations. This study identifies the different knowledge, skills and abilities (KSA) required for an HR analyst role in different stages of professional growth (i.e. entry-level, middle-senior level and top-level) across different industries/sectors as applicable to the crisis.

A total of 80 job posts were extracted from LinkedIn. Details such as industry, job levels, qualifications, job experience, job functions, job descriptions (JDs) and job skills (JS) were collected. Further, 30 videos were extracted from YouTube and converted into text. Text analysis was conducted using NVivo software to analyze JDs, JS and job functions. Using NVivo, word frequency, word cloud, word tree and treemap were created to visualize the data. Finally, ten in-depth interviews were conducted with senior HRA managers based in India to understand the essential competencies required for the HR analyst role and the strategies to develop them.

The findings indicate that not only technical skills are needed, but business and communication skills are particularly important for all job levels during a crisis. The JD word cloud showed words, such as data, business, support and management, and the word tree depicted HR data and change agents as important words with many related sentences as branches. General JS included analytical, communication, problem-solving and management. Technical JS were the most widely used and included structure query language, system applications & products in data processing, human capital management, TABLEAU, management information system and PYTHON. Strategies to develop these competencies included case studies, live projects, internships on HR analytics (HRAs) assignments and mentoring by senior HRA professionals.

The sample used was small, as the study included 80 job posts available on LinkedIn restricted to India. The study was restricted to qualitative approach and text analytics was used. Survey methods and a quantitative approach can be used to collect data from HR recruiters, job holders and senior leaders to understand the role of HRAs in the job market and then these variables can be tested empirically.

Based on the McCartney et al.’s (2020) competency model for the HR Analyst role, this study has explored the KSA framework using data visualization techniques and used text analytics to analyze LinkedIn job posts for different levels, videos from YouTube and in-depth interviews. It also mapped the KSA for the HR analyst role to the various stages of crisis system management given by Mitroff (2005). The use of social media analytics, such as analyzing LinkedIn data and YouTube videos, are highlighted.

Human resource analytics (HRA) is an HR activity that companies and academics increasingly pay attention to. Existing literature conceptualises HRA mostly from an objectivist perspective, which limits understanding of actual HRA activities in the complex organisational environment. This paper therefore draws on the practice-based approach, using a novel framework to conceptualise HRA-as-practice.

The authors conducted a systematic literature review of 100 academic and practitioner-oriented publications to analyse existing HRA literature in relation to practice theory, using the “HRA-as-practice” frame.

The authors identify the main practices involved in HRA, by whom and how these practices are enacted, and reveal three topics in nomological network of HRA-as-practice: HRA technology, HRA outcomes and HRA hindrances and facilitators, which the authors suggest might actualize enactment of HRA practices.

The authors offer HR function and HR professionals a basic ground to evaluate HRA as a highly contextual activity that can potentially generate business value and increase HR impact when seen as a complex interaction between HRA practices, HRA practitioners and HRA praxis. The findings also help HR practitioners understand multiple factors that influence the practice of HRA.

This systematic review differs from the previous reviews in two ways. First, it analyses both academic and practitioner-oriented publications. Second, it provides a novel theoretical contribution by conceptualising HRA-as-practice and comprehensively compiling scattered topics and themes related to HRA.

The growth of the global labor force and business analytics has significantly impacted human resource management (HRM). Human resource (HR) analytics is an emerging field that creates value for employees and organizations. By examining the existing studies on HR analytics, the paper systematically reviews the literature to identify active research areas and establish a roadmap for future studies in HR analytics.

A portfolio of 503 articles collected from the Scopus database was reviewed. The study has adopted a Latent Dirichlet allocation (LDA) topic modeling approach to identify significant themes in the literature.

The HR analytics research domain is classified into four categories: HR functions, statistical techniques, organizational outcomes and employee characteristics. The study has also developed a framework for organizations adopting HR analytics. Linking HR with blockchain technology, explainable artificial intelligence and Metaverse are the areas identified for future researchers.

The framework will assist practitioners in identifying statistical techniques for optimizing various HR functions. The paper discovers that by implementing HR analytics, HR managers and business partners can run reports, make dashboards and visualizations and make evidence-based decision-making.

The previous studies have not applied any machine learning techniques to identify the topics in the extant literature. The paper has applied machine learning tools, making the review more robust and providing an exhaustive understanding of the domain.

This study aims to identify the enablers of human resource (HR) digitalization and HR analytics. This paper also aims to build a relationship map using interpretive structural modeling.

A systematic literature review is used to identify the key enablers of HR digitalization and HR analytics. Ten expert opinions have been taken from the key officials of IT firms located in New Delhi North Central Region.

This study is focused on the enablers of HR analytics. It is found that change management (CM) in the organization is the key enabler of implementing HR digitalization and analytics in an organization. However, other elements like learning culture, training and development, E-learning management and HR transformation (HRT) play a vital role in implementing HR analytics. It is also found that implementing artificial intelligence for HR practices is the ultimate goal for every organization.

Management teams in IT firms should focus on the continuous learning process in the organization. The CM should be expedited for digitalization and adoption of HR analytics. Managers must go through the ramification of HRT, which possesses diligence in HR analytics and artificial intelligence.

This study explicitly talks about the enablers of HR digitalization and HR analytics. It also explores the relationship between the enablers. This study also describes the driving and dependence power of all the enablers.

The firms use training evaluation practices (TEPs) to determine the return of billions of dollars spent on employee training and development activities. The firms need to modernize the set of TEPs for evidence-based workforce management decisions. This study aims to examine a mediation mechanism to explain how human resource (HR) professionals’ design thinking (DT) mindset strengthens the set of TEPs using predictive workforce analytics (PWAs).

The authors used SPSS computational named MLMED to test the proposed relationships by collecting data from 180 management professionals serving in subsidiaries of multinational corporations in Pakistan.

The statistical results demonstrated that DT is not directly related to firms’ TEPs. However, the statistical results supported the mediating role of firms’ use of PWAs between DT and TEPs.

The findings offer a new perspective for firms to use HR professionals’ DT mindset for modernizing the set of existing HR practices.

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.

The aim of this research is to determine the extent to which the human resource (HR) function can screen and potentially predict suicidal employees and offer preventative mental health assistance.

Drawing from the 2019 National Survey of Drug Use and Health (N = 56,136), this paper employs multivariate binary logistic regression to model the work-related predictors of suicidal ideation, planning and attempts.

The results indicate that known periods of joblessness, the total number of sick days and absenteeism over the last 12 months are significantly associated with various suicidal outcomes while controlling for key psychosocial correlates. The results also indicate that employee assistance programs are associated with a significantly reduced likelihood of suicidal ideation. These findings are consistent with conservation of resources theory.

This research demonstrates preliminarily that the HR function can unobtrusively detect employee mental health crises by collecting data on key predictors.

In the era of COVID-19, employers have a duty of care to safeguard employee mental health. To this end, the authors offer an innovative way through which the HR function can employ predictive analytics to address mental health crises before they result in tragedy.

Agile methods are increasingly being applied in the contexts of innovation beyond traditional information technology (IT) and physical product development projects, such as when process improvements are being implemented. Nevertheless, this phenomenon is still recent and little addressed in the literature, with few descriptions of empirical cases. This study aims to address this gap.

This multiple case study aims to present and discuss the application of Agile practices embedded in large companies’ innovation value chains, focusing on improvements of business processes. The following research question is pursued: How are large companies applying elements of Agile methods to their innovation processes when implementing incremental improvements in their operational processes? Based on the idea that the Agile-Stage-Gate model is an alternative to this challenge, this study investigates the application of this hybrid model in two large Brazilian companies by presenting their idiosyncrasies, lessons learned, adaptations, challenges and benefits.

Overall, it was observed that the experience with the application of the Agile-Stage-Gate model is positive for these companies, with better customer engagement, easier project control and increased productivity of the project team.

For those aiming to implement the Agile-Stage-Gate model, this paper identifies the main adaptations made in order to combine the purist approaches and critical success factors for its implementation.

This study aims to contribute knowledge about different professional identities represented among HR practitioners from Weber's “ideal types” framework.

The paper is based on semi-structured interviews with 34 Swedish HR practitioners working in large public and private organisations.

The findings reveal that HR practitioners' identity is perceived as indistinct, unclear and shattered, which leaves lots of room for interpreting HR identity. Based on a thematic content analysis, three different ideal-type identities are presented, each representing the characteristic traits of an HR identity type. These are the Defender who always supports the managers, the Disturber who questions the managers in favour of the employees and the Driver who focuses on the economic expansion of the organisation.

One of the potential constraints of this study is the authors’ reliance on interview data. This finding implies that future research can employ mixed methods or observational techniques to bridge the gap between narrated responsibilities and real-time actions. The data source, predominantly from larger organisations, presents another limitation. This raises a significant research implication: there is a need to study identity formation among HR practitioners in smaller organisations. The theoretical framework this study contributes can aid in comprehending HR practitioners' identities and their corresponding actions. Continued research might explore the significance of these ideal-type identities.

The model presented provides a new way of understanding HR practitioners' complex and shattered professional identity and the various stakeholders that direct different expectations towards them. This knowledge can be used both in HR education and in HR work as a basis for discussing the social work environment of HR practitioners and negotiating their work and identity.

The study contributes knowledge of the professional identities of HR managers, an under-researched area, especially when it comes to empirical research about the HR practitioners' own experiences of their everyday work and view of the HR profession.

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.