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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.

Despite the vast talk about digitalization and its ability to transform how organizations manage their workforce and their talent, our understanding of how the implementation and use of Talent Management Information Technology (TM IT) changes various organizational processes is limited. In this chapter, we use a qualitative case study of a multi-business unit professional services firm to highlight the complexity of the human – technology interface. Contributing to our understanding of the role of human resource management in talent management, we show how perceptions and attitudes toward information technology, in combination with existing social systems influence the role HR managers play in high potential talent identification. The chapter provides a more nuanced and context-based account of how eHRM, HRM and talent management materialize in organizations as HR professionals struggle to remain relevant in an increasingly digital world.

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.

Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.

This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.

This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.

This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.

This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.

Fundamental literature exists on the employment of diverse information technology (IT) innovations relied on utility computing, a little experimental literature exists on cloud computing adoption by medium enterprises. So, this essay aims to analyze the determinants of the acceptance of cloud computing in medium-sized organizations. It also targets the impact of cloud-depended services on employers' talent management in the analysis areas of research reports, hypotheses and frameworks used by them.

Cloud computing is a novel and notable development in the network application field with a novel IT viewpoint. The present investigation aims to assist and enrich the scientific argument regarding the phenomenon nominated cloud computing from a managerial perspective. A quantitative study design was utilized to gather and analyze the data. In total, 345 employees from different medium enterprises reported their perspectives via an online survey. Partial least squares was used to evaluate the model of the study. The investigation was performed from April 2020 to November 2020.

As expected, the empirical analysis results revealed that cloud computing adaption drivers positively and significantly associated with talent management. The results showed that organizational level is the most important factor in the effectiveness of talent management. Further, this study established a cloud strategy that moderates the relationships between cloud computing adaption drivers and talent management. As a general result, organization managers must implement cloud computing to improve organizational performance, reduce costs, enhance organizational innovation and reduce repetitive work. So, international trade facilitates and helps the country's economy.

There are still some drawbacks of this analysis that require addressing the findings. Initially, the authors gathered a country's data. Second, by considering multiple significant structures from four aspects, they established the study model. Third, utilizing one-time cross-sectional data, the suggested model was evaluated. As a proposal, future researchers could investigate cloud computing adoption drivers' outcomes within other organizations.

This research presents a straightforward and easy model for understanding the cloud's effect on employee talent management. By systematizing the unrelated studies on the subject and organizing those contributions into strong managerial literature paradigms, such an aim has been accomplished.

The purpose of our study was two-fold: (1) to examine the effect of perceived value derived from perceived benefits and sacrifices in the adoption of artificial intelligence (AI) in talent acquisition and (2) to investigate the moderating role of human resource (HR) readiness in the association between perceived value and AI adoption intention.

A structured questionnaire was administered to 198 talent acquisition executives and HR professionals of Indian IT companies based on a purposive sampling technique. Partial least squares structural equation modeling (PLS-SEM) was used on the Smart PLS 2.0 platform to analyse the data and test the model.

Results revealed that perceived benefits and sacrifices significantly predict perceived value which significantly affects the HR professional’s AI adoption intention. The study further found that HR readiness moderates the link between perceived value and the intention of HR professionals to adopt AI in the talent acquisition process in the Indian IT industry.

IT companies are advised to continuously monitor and evaluate the performance of AI tools to ensure that they are meeting the recruitment process needs to leverage AI’s benefits in talent acquisition. This study seeks to provide the impetus for a planned AI adoption in talent acquisition.

This research provides ample evidence for the existing technology adoption theories. It explored the predictors of adoption by validating the value-based adoption model in the Indian context. It provides valuable insights into the practice of acquiring talents in the IT sector using artificial intelligence.

This study aims to examine the spatial distribution and movement patterns of creative talent within the Yangtze River Delta Bay Area (YRDBA) and the factors that contribute to such trends.

The study examines data for the period 2006 to 2018 from the regions that constitute the YRDBA: Shanghai, Jiangsu, Zhejiang and Anhui. Spatial distribution pattern analysis is adopted to interpret the flow tendency both spatially and chronologically and a Lasso regression model is used to investigate variables that influence this tendency.

It is found that creative talents in YRDBA are accumulating steadily in provincial capitals and financially advanced cities. Technology infrastructure, women’s rights, medical care amenities and housing affordability are major determinants of such spatial distribution. The talent spillover effect raises attention in talent saturated areas, while the surrounding regions should prepare to receive and retain the overflow.

Creative talents geography in China and the dynamism of creative talent in YRDBA are rarely discussed. Determinants of creative talents lack systematic pectination, literature that filters multiple determinants of creative talents migration is limited and discussion specific to the Chinese context is scarce. This case can, thus, provide insights into creative talents in developing countries and add to the current literature, bridge the gap of the current understanding of creative talents in YRDBA – the innovation and development center in China and provide a reference for policymakers when making macro decisions.

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

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.