Empirical Analysis of AI Adoption for Talent Acquisition: A Study on Technological, Organisational and Demographic Determinants

With the fast development of science and technology in production and processing, food is being supplied with diversified taste, craft, nutrition, etc. Nevertheless, food safety is growing to be a global concern among consumers simultaneously, due to their asymmetric information on the processes, additives in the long industrial chain, and also influence of the flourishing public media. Consumers are demanding for the reinforced assurance of food safety, and even one isolated event may cause major market disruptions (Tonsor et al., 2009). In addition to the endeavors by governmental agencies and enterprises, food safety, especially from the perspective of promoting consumers’ confidence, has received considerable attention of scholars, including Knight et al. (2009), Jonge et al. (2010), Tonsor (2011), Lee et al. (2012), Yeung and Yee (2012), etc. Being the largest developing country, China has surpassed the US to become the world's largest food and grocery retail market by the end of 2011, according to the new research revealed by the international food and grocery expert IGD (Askew, 2012). At the same time, frequent outbreak of food safety scandals has undermined consumers’ confidence and resulted in huge losses to food industry. Thus scholars are imposed with the indispensible obligation to study consumer perceptions and determinants upon food safety. In latest years, especially after the melamine milk powder incident occurred in September of 2008 (for more details, refer to Qiao et al., 2012), many scholars have conducted empirical studies on food safety, on the basis of consumer survey. In general, the study topics include: (1) consumers’ overall perceptions on food safety. Wang et al. (2009) and Xu and Wu (2010) examine consumers’ willingness to pay (WTP) for the safe fishery products, certified & traceable food, respectively. Qiao et al. (2010) study the changes of consumers’ confidence on domestic dairy industry after melamine milk powder incident, while Zhang, X. et al. (2010) divide the sampled consumers into four groups, in respect to their perceptions and attitudes to GM food. (2) Consumers’ behaviors on choosing safe food. Zhang, C. et al. (2010) examine consumers’ identification of safe dairy products; Ortega et al. (2011) measure the heterogeneity in consumers’ preferences for selecting safe pork; Kim (2009) conducts factor analysis on consumers’ purchase of GM food. (3) Integrated study of perceptions and behaviors towards food safety. Han et al. (2012) compare the consistency of consumers’ stated and revealed preferences to certified pork. Although the existing studies have covered many essential aspects and provided instructive recommendations, there are still a variety of topics need to be researched with further depth. For instance, (1) consumers’ overall awareness towards the situation of food Consumer Perceptions upon Food Safety and Demographic Determinants in China: Empirical Analysis based on a Survey of 512 Respondents

This study attempts to apply quantitative methods investigate the macro-economic and demographic determinants on the quantity demand of life insurance using time series data in China. Analysis indicated that GNP Per capita has been consistently found to be significant predictor of total life insurance consumption. Pension fund system has no positive effect on life insurance consumption. And the individual coefficient estimates level of education, marriage rate and dependency ratios are not significant. The study intends to obtain some enlightenment of the life insurance market in China.

Efficiency in broadband service provision: A spatial analysis

The literature reiterates the need to employ indicators that reflect the multidimensional nature of food security. In a quest to capture the multidimensional nature of food security, this study uses a novel ideal that harmonizes two food security indicators (food expenditure (FOODexp) and dietary diversity score (DDS)) to categorize households into different levels of food security states in Nigeria. In addition, the study also examined factors that influence the probability of households being in different levels of food security states in the country. Our estimates show that about 66 and 60% of the households in the sample were food secure based on a single indicator such as FOODexp and DDS, respectively. However, by harmonizing the two indicators, results reveal that about 42% of the households are actually food secure. The implication of this is that about 24 and 18% of the households are transitory food insecure. Results also suggest that the use of a single indicator may wrongly classify households as food secure instead of categorizing them as transitory food insecure. Furthermore, the empirical results show that households that consume only home produced food have high probabilities of being food insecure, while households that consume only market-purchased food are less likely to be food insecure. The implication of this finding is that harmonization of food security indicators helps identify households with different nature of food (in) security problems that require different types of policy interventions most especially in Nigeria. The results of the empirical analysis also imply that market-based intervention policies that facilitate households’ access at all time to healthy foods of their choice should be given priority. This gives the household opportunity to benefit from greater varieties of food items offered by the market.

Child sexual abuse is a worldwide phenomenon, and India is not an exception. The magnitude of this grave crime is underrated because of under-reporting. The reality is that the incidence of child sexual abuse has reached epidemic proportions in India. In 2021 only there were 53,874 cases registered under Protection of Children from Sexual Offences Act of 2012. To enable the all-around protection of children, the Indian government administrated the “Protection of Children from Sexual Offences (POCSO)” Act of 2012. The act is a comprehensive piece of legislation designed to protect children from crimes including sexual assault, sexual harassment, and pornography. The degree to which this act has improved child protection is therefore an important issue for interrogation. Here, we consider the implications of the POCSO Act (2012) in enhancing children’s protection from sexual abuse and pin-point the role of quality of life together with other social, economic, and demographic determinants in foreshortening POCSO incidences. The empirical analysis of the paper is conducted based on secondary data compiled from National Crime Records Bureau. Our empirical results reveal that the POCSO Act has reduced the Growth rate of incidents of sexual offences against children in India from 4.681% to −4.611. Moreover, our empirical results also reveal that by enhancing the quality of life it is possible to restrict the POCSO incidences across Indian states. In addition, favourable sex-ratio, the increased gross enrolment ratio at the elementary level, the improvement in the judiciary and Public Safety Score of the state also enables the state to restrict the POCSO incidences. Based on our empirical result we recommend that future policies could include, for instance, aiming to improve the quality of life as well as the law and order conditions of the state, and increasing the enrolment of the girl children in higher education.

Artificial intelligence has limited applicability in human resource management processes despite technological advancements. Talent acquisition is one of the crucial activities where the impact of AI is significantly conspicuous as compared to other HR processes. But implementing AI in this process requires effective adoption of the technology for ensuring operational efficiency. Moreover, there is a lack of conceptual clarity due to the limited scope of the study in this area. Hence, this study proposed a conceptual framework to help academicians and HR professionals adopt AI in talent acquisition and other allied areas of people management. This study reviewed three theories, the Value-based adoption model (V.A.M.), Technology-Organization-Environment (TOE), and the Task-technology fit (TTF) model, to identify the gap and propose a new framework. The traditional models were used in AI adoption, lacking a rational approach. Organizations should adopt Technology that provides maximum value to the organization compared with the costs and benefits. The study also proposed that the traditional TA methods organization and environmental-related factors and stickiness moderated between perceived value and adoption of AI in talent acquisition.

Factors influencing the rate of sorted solid waste collection: An empirical analysis towards local management in Catalonia (NE Spain)

Artificial intelligence is a technology that has transformed all the sectors which imitates human potential and intellectual potential. So, authors were curious to investigate that how AI is adopted in the domain of talent acquisition process. Main focus of this study is to contribute towards the technology adoption and the crucial role performed by the HR leader in adoption of AI. This study aims to provide the valuable insights regarding the same to the researchers, corporates, service provider and policy maker in different domains. Furthermore, it aims at understanding innovativeness and attitude of HR leader toward adopting the AI in talent acquisition. Data has been collected from 389 HR managers of SMEs and startups in Delhi NCR. Results indicated the positive orientation of HR mangers in case of innovativeness construct there is high orientation towards AI adoption in talent acquisition.

A prominent enhancement in health expenditure is a severe apprehension for the whole world because of the increasing cost of health. Consideration of various aspects that are playing role in the increase of health expenditure need to be considered. Health is one of the significant parts of the world, therefore, improvement in health status has become the main objective of the nations and a top leading goal of the Sustainable Development Goals (SDGs) and Millennium Development Goals (MDGs). The main goal of the present study is to investigate the worldwide, and income-based categorized countries (developed, developing, and transitional countries) macroeconomic determinants of health expenditure. The aim is to estimate the health expenditures, and its impact on demographic, economic, and environmental determinants. For the empirical analysis, the data was retrieved from the World Bank, covering the time span 2000-2016 for developed, developing, and transitional countries. The results of the study concluded that macroeconomic variables significantly affect health expenditures. Moreover, personal remittances have a dual effect on health expenditure, i.e. a positive impact on transitional economies, while a negative impact on developing economies. In the same way, CO2 emission has a negative impact on worldwide, developed countries.

In nearly all jurisdictions, adoption of a new form of gambling has been a controversial and contentious subject. Online gambling has been no different, though there are many aspects that affect online gambling that do not appear in the brick and mortar environment. This study seeks to identify whether demographic, economic, political, technological, and/or sociological determinants contribute to online poker gambling adoption. A theoretical discussion of these categories' importance to online poker is provided and exploratory empirical analysis is used to examine their potential validity. The analysis revealed support for all of the proposed categories of variables thought to be predictive of online gambling legality.

Purpose - The purpose of this paper is to provide insights into the effectiveness of AI in optimizing recruitment strategies and overcoming challenges related to talent acquisition. Aim - This study aims to investigate the impact of AI tools, such as predictive analytics, chatbots, and automated screening, on recruitment processes. It seeks to highlight how these technologies can reduce time-to-hire, improve decisionmaking, and enhance workforce diversity while addressing critical issues such as AI biases and data privacy. Design/Methodology/Approach - The research adopts a literature review methodology, synthesizing findings from existing studies on AI technologies in recruitment. It examines key AI applications, ethical challenges, and best practices for AI-driven talent acquisition. The study also provides recommendations based on a review of evidence-based practices in organizations that have implemented AI in their recruitment processes. Findings - The study finds that AI can significantly improve recruitment efficiency by automating repetitive tasks, streamlining the hiring process, and providing more informed decision-making. However, it also reveals ongoing challenges with AI biases, ethical concerns, and data privacy. These issues necessitate transparency, regular audits, and comprehensive training for HR professionals to ensure the responsible and effective use of AI. Limitations of the Study - The study is limited by its reliance on secondary data from existing literature, which may not fully capture emerging trends or specific organizational contexts. Additionally, the ethical and practical challenges of AI adoption in recruitment require further empirical research to validate the findings across different industries. Originality/Value - This paper highlights both the opportunities and risks associated with AI adoption in recruitment and provides organizations with practical recommendations for navigating ethical and operational challenges while leveraging AI for improved hiring outcomes.

The research study explored the role of Artificial Intelligence in HR Analytics and its impact on talent acquisition, employee engagement, performance management, and learning and development. We have used the mixed-methods research approach by studying the extensive literature review and a quantitative survey of 146 HR professionals to examine how AI-driven tools are reshaping the Human Resource management dimension and its functions. Key findings highlight that AI has significantly reduced time-to-hire by 51.1%, improved appraisal accuracy by 50.8%, increased employee satisfaction by 51.3%, and enhanced training relevance by 26.1%. AI applications such as resume screening, sentiment analysis, predictive analytics, and adaptive learning systems have resulted in more data-driven, efficient, and personalized HR practices. Apart from the benefits, several challenges and issues are raised, such as ethical concerns, data privacy, and organizational resistance to AI adoption. Our research recommends integrating transparency, fairness, and ethical frameworks in AI-driven HR practices and improving the HR analytics so that HR professionals can leverage AI technologies responsibly, enhancing both workforce experience and organizational effectiveness. Future research is suggested to explore AI's role in diversity, hybrid work management, and the long-term cultural impact of AI adoption in Human Resource Management functions.

This study explores the transformative impact of AI on talent acquisition, highlighting its evolution within HRM and redefining recruitment in the digital era. It examines AI's integration across key recruitment functions, including resume screening, chatbots, interview scheduling, and predictive analytics for candidate suitability. The research reviews core technologies such as Natural Language Processing (NLP), Machine Learning (ML), Robotic Process Automation (RPA), and facial recognition. While AI enhances efficiency, reduces bias, and supports data-driven decisions, it also introduces ethical and regulatory challenges related to fairness, privacy, and transparency. The study draws upon theoretical models like the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Diffusion of Innovation (DOI) to contextualize AI adoption. Finally, it presents a comparative analysis of global trends and Sri Lanka's local context, offering insights into the factors influencing AI adoption in recruitment and the future of digital HR practices.

<span id="docs-internal-guid-43377b33-7fff-4342-8e71-b985af862527"><span>Amidst the ongoing competitive challenges in the business environment, AI plays a fundamental role. Despite the pace of AI adoption has shown slow advancement in emerging markets, particularly in Pakistan. Forthcoming years will become essential for organisations to integrate AI into different facets of task management like recruitment and selection (R&S). The core purpose of the research is to address how numerous factors influence the adoption of AI under technology-organisation-environment (TOE) model. The study introduces a basis to examine the importance and interrelationship of key success factors in the adoption of AI. Six key factors influencing AI adoption were derived from a comprehensive review of the literature. The structural model was empirically validated using data collected via a Google-based mail survey conducted in Pakistan. The data is analysed through Structural Equation Modelling, revealing that factors such as relative advantage under Technology factor, technological competency, and support from top-management for Organisational factor, while competitive pressure and vendor support for Environment factor have a significant association with AI adoption regarding R&S in selected firms in Pakistan. The study’s findings offer valuable insights for organisations in Pakistan to refine their AI adoption strategies, particularly in R&S, helping them gain a competitive edge. It also highlights key factors specific to the Pakistani context, enhancing understanding of how local dynamics shape AI adoption in HR. These insights can guide organisations in overcoming challenges and optimising AI’s potential for organisation success.</span></span>

Purpose: This paper explores the multifaceted impact of AI on the recruitment process, highlighting its role in automating routine tasks, analyzing vast amounts of data for better decision-making, and providing predictive analytics to anticipate hiring needs. AI-driven tools, such as chatbots and applicant tracking systems, streamline candidate sourcing and screening, significantly reducing time-to-hire and improving the quality of hires. Methodology: To assess the impact of AI in recruitment and talent acquisition, this study employs a mixed-methods approach, combining both qualitative and quantitative research methods to provide a comprehensive understanding of the topic. The methodology involves the following Literature Review, Data Collection, Case Studies, Data Analysis, Integration of Findings, Validation and Reliability and Ethical Considerations. By employing this rigorous methodology, the study aims to provide a comprehensive and reliable assessment of the impact of AI in recruitment and talent acquisition, offering valuable insights for HR professionals, policymakers, and researchers in the field. Findings: This paper highlights the key benefits AI brings to recruitment processes, such as efficiency improvements, better decision-making, and enhanced candidate experience, as well as the challenges like data privacy concerns and algorithmic bias. Unique Contribution to Theory, Policy, and Practice: This study advances the theoretical understanding of AI in recruitment by integrating technology adoption and HR management theories. It provides significant policy implications, emphasizing robust data privacy measures, ethical AI use, and inclusive hiring practices. For practitioners, it offers actionable insights on streamlining recruitment processes, enhancing candidate experience, and managing AI adoption challenges. It also shares best practices for bias mitigation in AI algorithms, ensuring fair and equitable recruitment practices.