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This paper aims to examine the technology usage for talent management and its effect on organizational performance.

The grounded theory approach was used for this research. Semi-structured interviews with 122 senior HR officers of national and multinational companies in India were conducted after extensive literature review. NVivo 8.0 software was used for the analysis of the interview data.

Technology usage for talent management contributes to talent analytics and strategic HR management (SHRM). It was found that talent analytics and SHRM lead to developing a high-performing talent pool, which in turn contributes to organizational performance.

This study used the grounded theory approach to develop the proposed conceptual model for organizational performance using talent management technology. This study delivers important insights for talent managers, HR technology marketers and developers of technology.

The era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework.

The methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems.

This research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized.

Big data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management.

This research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.

Previous research assumes that technologies 4.0, particularly big data, may be highly relevant for organizations to increase human resources (HR) communication strategies, but the research provides little or no evidence on whether and how these tools are applied in employees and labor market relations. This study intends to offer a first insight on the adoption of data-driven HR/talent management approach, contributing to the ongoing debate on the Industry 4.0. This study aims to investigate the use of 4.0 technologies in HR and talent management functions, focusing also on the adoption of big data analytics for internal and recruitment communication.

The analysis of the literature enables to define the research questions and an exploratory web survey was carried out through a structured questionnaire. The analysis unit of the empirical survey includes the communication and marketing managers of 90 organizations in Italy, examined in the Mediobanca Report on the “Main Italian Companies.”

Findings highlight a lack of the use of 4.0 technologies and big data analytics in employee and labor market relations and reveal some sectoral differences in the adoption of 4.0 technologies. Moreover, the study points out that the development of HR analytics is hampered by short-term perspective, data quality problems and the lack of analytics skills.

Due to the exploratory research design and the circumscribed sample from a single country (Italy), further cross-national evidence is needed. This study provides digital communication managers with useful insights to improve the data-driven HR/talent management approach, which is a strategic asset for ensuring a sustainable competitive advantage and optimizing business performance.

The study offers an overview about the use of big data analytics in internal and recruitment communications. Considering the alignment between Italian and European trends in the use of big data and in the adoption of HR analytics, the study can provide insights also for other European organization.

This paper aims to study the opportunities that have been created through technological advancement in the talent acquisition industry and how this links to strategic HR management (SHRM) and business strategy. It focuses on how an organisation can embrace the world’s leading technology and compose a unique technology stack to overcome its challenges in talent acquisition.

This paper opted for a blend of qualitative and quantitative data gathered through interviews, online questionnaires and the literature review of relevant secondary data. The justification of the primary research methodology was to gather valid and reliable data to inform decisions and address the research question.

The analysis of the data shows a significant improvement in all the key metrics related to the talent acquisition process after the implementation of “Talent Rising” model. The role of a recruiter and the talent acquisition team has radically changed and has now become an internal strategic partner with aligned interest, an advisor to the organisation. There is an increase in the use of new media/technology to attract candidates and with the increase in millennial candidates entering the workforce, social media will be increasingly important in talent branding and attraction; the digital by default generation is already here.

The “Talent Rising” model has been specifically created and designed to be used by others: organisations, academics and policy makers. It is a “plug and play” tech stack model which can be used like an a la carte menu dependent on the actual requirements (and investment appetite) for each organisation.

This paper includes implications for the creation of a people analytics and talent technology framework to reduce cost per hire, reduce time to hire and increase quality of hire.

This paper fulfils an identified need to study how people analytics and technology can drive talent acquisition strategy.

This paper aims to propose a research model with eight constructs, i.e. BDA leadership, BDA talent quality, BDA security quality, BDA privacy quality, innovation, financial performance, market performance and customer satisfaction.

The research model focuses on whether (1) Big Data Analytics (BDA) leadership influences BDA talent quality, (2) BDA talent quality influences BDA security quality, (3) BDA talent quality influences BDA privacy quality, (4) BDA talent quality influences Innovation and (5) innovation influences a firm's performance (financial, market and customer satisfaction). An instrument was designed and administered electronically to a diverse set of employees (N = 188) in various organizations in the USA. Collected data were analyzed through a partial least square structural equation modeling.

Results showed that leadership significantly and positively affects BDA talent quality, which, in turn, significantly and positively impacts security quality, privacy quality and innovation. Moreover, innovation significantly and positively impacts firm performance. The theoretical and practical implications of the findings are discussed. Recommendations for future research are provided.

The study provides empirical evidence that leadership significantly and positively impacts BDA talent quality. BDA talent quality, in turn, positively impacts security quality, privacy quality and innovation. This is important, as these are all critical factors for organizations that collect and use big data. Finally, the study demonstrates that innovation significantly and positively impacts financial performance, market performance and customer satisfaction. The originality of the research results makes them a valuable addition to the literature on big data analytics. They provide new insights into the factors that drive organizational success in this rapidly evolving field.

More and more, the leaders of business functions are turning for competitive insights to the massive data they can now capture. But to date, human resources departments have lagged behind the efforts of marketing, IT, CRM and other functions. The purpose of this article is to show how executives can start using data to measure and improve HR's contributions to business performance.

The article identifies six analytical tools that HR can use to connect HR efforts to business performance. Survey results underscore the value of an analytical approach while revealing that many HR departments are heavily focused on internal measures rather than business outcomes. Each analytical tool is exemplified through case studies. A model is presented to suggest how executives can get started by focusing on five key areas.

Leading companies are using six analytical tools to improve the connection between HR investments and business returns: employee databases; segmentation of talent; targeted investments; customization of the employee value proposition; long‐term workforce planning; and talent supply chains.

As the case studies reveal, the tools identified here can help HR leaders actively shape their organization's future – managing talent and directing programs toward the long‐term needs of the business. Survey data shows that most companies increasingly seek to use analytics for long‐term advantage, and the model presented here can help HR executives take the first critical steps.

In the context of a corporate case study, this paper aims to propose and test a talent analytics model that enables organisations to optimise their employer value propositions and branding strategies in a comparative manner.

After the conceptual framework was translated into a quantitative model, data were collected via online self-completed questionnaires and analysed with statistical techniques.

Revealing misalignments with the employment preferences of Gen Z, the model’s capability to optimise employer value propositions and branding strategies is effectively demonstrated.

Providing an actionable framework for corporate and academic readers, this paper has scope to serve as a guide for scholarly or practitioner talent analytics projects focused on attraction.

Focused exclusively on attraction, to the best of the author’s knowledge, this is the first paper to apply an in-depth talent analytics model which prescribes employer branding as a mediator variable.

The purpose of this paper is to summarise the different areas of talent management and how HR metrics and analytics can be harnessed to make those areas more effective.

The paper first discusses the different definitions of “talent”. It then takes three areas for the application of metrics and analytics – data about individuals, the effectiveness and efficiency of talent processes and the extent of the supporting culture.

The definition of talent should not be confined to senior leadership only, nor be fully inclusive of every employee, but organisations need to define those individuals and groups where some specific attention will benefit the organisation; it is as important to understand the potential of all employees as it is to assess their performance; metrics should be chosen for all talent processes and related to business KPIs where possible.

This is a practical paper giving guidance to talent managers in organisations on how to apply and utilise people analytics.

This paper is based mostly on the writing, models and experience of the author.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.

This paper aims to contribute to the literature on human resources (HR) digitalization, specifically on HR analytics, disentangling the concept of analytics applied to HR and explaining the factors that hinder companies from moving to analytics. Therefore, the central research questions addressed in this study are: what does HR analytics encompass? What impedes the adoption of analytics in HR within organizations?

The authors performed a comprehensive literature review on analytics as applied in HR. The authors relied on two of the major multidisciplinary publication databases (i.e. Scopus and WoS). A total of 64 manuscripts from 2010 to 2019 were content analyzed.

The results reveal that there is an ongoing confusion on HR analytics conceptualization. Yet, it seems that there is an emerging consensus on what HR analytics encompasses. The authors have identified 14 different barriers for HR analytics adoption grouped into four categories, namely, data and models, software and technology, people and management. Grounding on them the authors propose a set of 14 key factors to help to successfully adopt HR Analytics in companies.

This paper brings clarity over the conceptualization of HR analytics by offering a comprehensive definition. Additionally, it facilitates business and HR leaders in making informed decisions on adopting and implementing HR analytics. Moreover, it assists HR researchers in positioning their paper more explicitly in current debates and encouraging them to develop some future avenues of research departing from some questions posed.