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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.

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.

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.

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 study was undertaken in order to show how talent management (TM) was performed in practice in a multinational organization as well as how the TM practices affected both different groups of workers and the perception of talent within the organization.

Performing talent management was reassessed in the relationship between TM practices, view and identification of talent, attributed positioning and self-positioning of older and younger workers; retrieved from an exploratory single case study in a multinational organization, based on interviews.

The findings illustrate that despite the struggling to fill key positions with skilled workers, the studied organization adopted approaches to TM that excluded older workers' talent. First, central to performing TM was how talent was viewed and identified, and second, two types of positioning acts were important: the organizations (re)producing of talent management through attributive positioning acts on older/younger workers and older workers' self-positioning of their own talent. The two sides of performing talent management were complex and intertwined resulting in an age-based devaluation of talent at work.

The study points to important issues in designing and performing TM that may be useful to HR and managers as a point of departure in the development of more inclusive approaches to TM.

The concept “performing talent management” was developed as an intertwined relationship between on-going positioning acts and (re)production of status, talent and age at work; recognizing preferences of what was viewed and identified as valued talent as main drivers made it possible to develop an understanding of exclusion and inclusion mechanisms in performing TM.

This paper describes the entrepreneurial ecosystems of three public research universities involved in the National Science Foundation (NSF) Midwest I-Corps TM (trademark symbol) Node. It presents a synthesis of programming, functional structure, commonly referenced university metrics and their limitations in measuring impact on commercialization and regional development.

Based on current literature, university data and discussions with entrepreneurship leaders at the University of Michigan/Ann Arbor, University of Illinois/Urbana Champaign and Purdue University, this paper provides an overview and analysis of entrepreneurial resources and education initiatives.

University contributions to entrepreneurial ecosystems can be described with respect to infrastructure and leadership, technology and talent and culture of innovation. Four main university entities are responsible for driving entrepreneurship initiatives. Identification of these entities, their respective activities and their outcomes allows us to propose a framework for analyzing and measuring university entrepreneurial ecosystem impact.

The paper describes the variety of university-based entrepreneurial initiatives believed to contribute to university entrepreneurial vibrancy and ultimately regional development. It identifies ecosystem stakeholders and provides a framework for examining their role and impact for continuous development.

The research complements prior reviews and empirical studies of university-wide entrepreneurial ecosystems by focusing on programming within and across institutions according to four dimensions (academic, research administration, technology transfer and community engagement) with respect to technology and talent development. It describes similarities across institutions and limitations associated with measuring impact. It provides a foundation for future empirical research related to the impact of NSF I-Corps and entrepreneurial programming in academic settings.

The creation and distribution of human capital, often termed talent, has been recognised in economic geography as an important factor in the locational decisions of firms (Florida, 2002), and at a more general level as a key driver of economic growth (Romer, 1990). The purpose of this paper is to consider how talent is created and distributed across the cities of the UK and the key factors which are driving this spatial distribution. They also consider what the economic outcomes of these disparities are for cities.

The multivariate models can estimate the dynamic inter-relationships between human capital (talent), innovative capacity, and economic value added. These can be estimated, using talent as an example, in the form: human capital measure_i =α_0i+α_1i innovative capacity +α_2i quality of life + α_3i labour market indicators + α_4i economic indicators + α_5i HEI indicators + β_6i population demographics + β_7i population + υ_i.

The first finding is that talent is unequally distributed across cities, with some having three times more highly educated workers than others. Talent concentration at the city level is associated with entrepreneurial activity, culture, the presence of a university, and to a lesser degree the housing market. This feeds into more knowledge-based industry, which is associated with higher gross value added.

The research is limited in a practical sense by the fact that UK data at this level have only become available quite recently. Thus, it is only possible to capture talent flows and city growth in a relatively small window. But the prospects going forward will allow more detailed analysis at the city level of the relationship between talent flows and local economic growth. And additional insights could be considered relating to the on-going changes in the UK university system.

The question of whether universities are simply producers of talent or play a much broader and deeper role in the socio-economic landscape and outcomes of cities is an open one. This research has identified what the key drivers of city level economic growth and knowledge creation are, and sought to explain why some cities are capable of attracting and harnessing three times more talent than other cities. This has significant implications for the future development of UK cities and for those seeking to address these imbalances.

Universities are a major economic agent in their own right, but they are increasingly being asked to play a wider role in local economic development. The authors’ evidence suggests that universities do play a wider role in the growth and development of cities, but that there are large discrepancies in the subsequent spatial distribution of the talent they create. And this has significant implications for those seeking to address these imbalances and promote a broader and less unequal economic landscape.

The authors explore how cities create economic value via a process whereby talent is attracted and then this stimulates knowledge-based industry activity. The originality relates to several key aspects of the work. First, the authors look at the stock of talent, and then the authors explore how “new” talent from universities is attracted by looking at graduate flows around the cities of the UK, differentiating between top-level graduates and less talented graduates. The authors then allow a wide variety of economic, cultural, and population factors to influence the locational decision of talented people. The results highlight the complexity of this decision.

It may be a cliché of modern business for a company to say that “people are our greatest asset,” but this is one truism that should be taken seriously. Up to 70 percent of a company's value is tied up in the skills and experience of its employees. All too often, however, business executives and Human Resource (HR) departments have very little insight into how to use this asset for better business outcomes. This paper aims to look at the importance of effective talent metrics and to examine the problems organizations face when trying to develop talent intelligence.

The paper discusses the findings of Talent Intelligence: Key to Business Success, an independent research report examining business and HR attitudes to talent metrics and analytics.

Despite the business value that accurate, accessible talent intelligence can provide, the research finds that there are significant differences between those talent metrics that organizations consider important and the data to which they have access. A legacy of disparate technology systems and a focus on measuring efficiency rather than effectiveness are the primary reasons for the lack of talent intelligence among many businesses.

The paper examines the findings of a comprehensive international survey of HR and business managers, identifying the barriers to collecting and analyzing useful talent metrics and laying out the key steps towards generating talent intelligence.