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Abstract
Artificial Intelligence (AI) is shaping the reality of human resource management (HRM). It is ideal for HRM to do predictive decision-making tasks like recruitment and selection. Talent acquisition includes identifying qualified candidates, aligning assessment methods with job or performance-related criteria, and managing administrative discretion in the hiring process. Other threats like high volumes of applications, biases in candidate selection, and a shortage of qualified candidates also bother recruiter teams. While AI-based recruitment supported by the natural language process (NLP) addresses such challenges by automating or augmenting various aspects of recruitment, such as sourcing, screening, and interviewing candidates. Unilever started its partnership with HireVue and Pymetrics to design a system of recruitment, selection and onboarding supported by AI in 2016. This article uses the case analysis method to discuss Unilever’s application of AI in the recruitment and selection process. The analysis will be conducted in the context of social network site (SNS), gamification, and verbal computer mediated communication (VCMC). Unilever saved bunches of time and cost, while improving its HRM performance. Such decision-making tools associated with AI raise concerns of bias and private crisis. This article is also aimed to discuss such a phenomenon in the context of Unilever. Talent acquisition by AI is an inevitable trend. This article is helpful as a benchmark.
... Thus, AI can claim to surpass human abilities in the initial identification and selection of applicants (Hu, 2023). The need to use the additional time saved for enhancing professional HR skills in the later stages of selection is, however, also emphasised in the literature. ...
Orientation: This study investigates the transformative impact of artificial intelligence (AI) technologies on traditional human resource management (HRM) practices across key industries. Research purpose: This study aims to systematically review and analyse the literature on AI’s current integration into HRM practices across industries, focusing on studies published from 2020 to 2024. Motivation for the study: The motivation for this study was to identify both key benefits and possible limitations in the current employment of AI in HRM practices with a view to making recommendations for the optimal deployment of AI tools. Research approach/design and method: This study utilises the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. Data sources include Google Scholar, Scopus and ScienceDirect. Main findings: Findings reveal that while AI tools may significantly increase the efficiency and effectiveness of the hiring process, potentially enhance the accuracy and objectivity of performance appraisals and enable the implementation of more personalised training and development initiatives, several ethical implications and challenges remain. These include potential biases within AI algorithms, concerns about data privacy and over-surveillance of employees, along with exacerbating the ‘digital divide’ between those with access to technology and those without. The research also notes the limitations of concrete, quantifiable, metrics available in the literature thus far, for the extent of the benefits claimed. Practical/managerial implications: The study offers recommendations for organisations to maximise the benefits of AI while addressing its associated challenges. Contribution/value-add: The need for robust regulatory frameworks and best practices to ensure AI’s ethical deployment is clearly indicated. The findings aim to guide HR practitioners, policymakers and researchers in developing effective strategies for integrating AI into HRM practices ethically and responsibly while noting the current uncertainties regarding its concrete benefits and dangers.
... From administrative functions to more sophisticated procedures like automation through the use of artificial intelligence. Hu Q, (2023) [4] revealed the unilever's practice on Predictive decision-making tasks, like as recruiting and selection, are best suited for HRM. Finding competent applicants, matching evaluation techniques to job or performance-related requirements, and exercising administrative discretion during the recruiting process are all parts of talent acquisition. ...
... AI is emerging as a transformative force within HRM, with its applications extending across various HR functions [21]. For example, AI-powered tools are enhancing talent acquisition by automating candidate identification and evaluation processes [36]. Advanced algorithms analyze resumes and social media profiles to match candidates with appropriate roles, while applicant tracking systems (ATS) streamline administrative tasks, boosting efficiency [37,38]. ...
As artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes remains lacking. This research employs social network analysis (SNA) to examine the co-authorship network within AI applications in HRM research, providing insights into collaboration dynamics and identifying key research directions. Through analysis of centrality measures and application of the TOPSIS method, the study identifies influential authors, institutions, and emerging research themes. Analysis of 102,296 authors and 287,799 collaborations reveals distinct communities focusing on specific aspects of AI-HRM across regions. The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. The country co-authorship network analysis uncovers three main communities: Global HR Applications, HRM in the Middle East and Asia, and Global Integration of AI in HRM, reflecting shared regional challenges. Institutional collaboration patterns indicate five distinct communities, from established Asian AI research centers to emerging research hubs in developing economies. These findings provide valuable insights for researchers exploring collaboration opportunities, practitioners implementing AI solutions, and policymakers developing strategic frameworks for AI adoption in HRM. This research contributes to understanding the evolving landscape of AI-HRM research and offers practical guidelines for leveraging AI in HR practices.
... These simulations exhibit adaptive proclivity, tailoring experiences to the requisites of individual employees. Taking Unilever as an example (Hu, 2023), in 2016, they partnered with HireVue 6 and Pymetrics 7 to create an AI-driven system for recruitment, selection, and onboarding. It utilizes a Natural Language Processing (NLP) bot named Unabot to streamline the employee orientation process and gather essential insights, effectively addressing their queries. ...
... From this approach, the organisation was able to achieve a new record of employee diversity of 16 percent within record time and at a lesser cost of hiring compared to previous times. Unilever's positive experience of using AI is a good example of how, when AI is well implemented, it can help to reduce time spent on recruitment and make it more fair [22,23] Likewise, IBM has also use AI to eliminate bias in the company through the HR department in issues like job advertising and recruitment besides performance evaluations. When IBM used AI to bring down bias, it opened up opportunities for a more diverse workforce. ...
This work reveals how AI is reshaping the future of D&I in Human Resources. AI is a tool that can be used for improving business performance and making it more fair by increasing workplace diversification. One of the examples is the use of AI in sourcing by Unilever that led to the increase of the talent diversity by 16%. Such success was made possible by using bias-free data sets in training AI systems, and human supervision in the use of the systems. The study also shows the great promise of AI for enhancing workforce diversity and inclusion. When done correctly, AI can help avoid the biases that most people have, ease the process of recruitment and selection, and create opportunities for all employees. Lastly, the research insists that with the right measures put in place, AI is a key tool in enhancing work policies and culture and notably improving work environment fairness and diversification, organization productivity, and the well-being of the workers.
... Another important element fostering the implementation of AI-assisted hiring is evidence showing that such technologies may also support D&I practices (Florentine, 2016;Jora et al., 2022), by providing more consistent evaluations (Kuncel et al., 2014) and by getting rid of the traditional biases (i.e. gender, race etc.) that often affect human-based hiring decisions (Avery et al., 2023;Houser, 2019;Hu, 2023;Polli, 2019;Walkowiak, 2023). Nonetheless, despite the common assumption of AI algorithms being impartial and fair, many researchers are skeptic, urging caution in their use, as they may end up replicating the exact same biases that affect humans (Barocas and Selbst, 2016) or even amplifying them (Ajunwa, 2020). ...
Purpose
This study aims to investigate some individual factors that may positively/negatively impact upon the willingness to use AI-assisted hiring procedures (AI-WtU). Specifically, the authors contribute to the ongoing discussion by testing the specific role of individuals’ personality traits and their attitude toward technology acceptance.
Design/methodology/approach
Data have been collected from a cohort of workers ( n = 157) to explore their individual level of AI-WtU, their personality traits and level of technology acceptance, along with a series of control variables including age, gender, education, employment status, knowledge and previous experience of AI-assisted hiring.
Findings
The results obtained show the significant role played by a specific personality trait –conscientiousness – and technology acceptance in shaping the level of AI-WtU. Importantly, technology acceptance also mediates the relationship between AI-WtU and conscientiousness, thus suggesting that conscientious people may be more willing to engage in AI-assisted practices, as they see technologies as means of improving reliability and efficiency. Further, the study also shows that previous experience with AI-assisted hiring in the role of job applicants has a negative effect on AI-WtU, suggesting a prevailing negative experience with such tools, and the consequent urge for their improvement.
Originality/value
This study, to the best of the authors’ knowledge, is the first to test the potential role of personality traits in shaping employees AI-WtU and to provide a comprehensive understanding of the issue by additionally testing the joint effect of technology acceptance, age, gender, education, employment status and knowledge and previous experience of AI-assisted hiring in shaping individual AI-WtU.
全球化与数字化的深度融合正在重塑全球人才管理格局，对中国企业而言，国际法律环境的复杂性与多样性带来了前所未有的挑战和机遇。 在深入剖析国际劳动法、税收法规、数据隐私保护以及跨文化法律差异后可知， 国际法律的复杂性不仅影响企业的人才吸引、 管理与留用政策，还直接关系到企业的全球竞争力和可持续发展。 为了有效应对这些挑战，中国企业需要构建全面的全球法律合规治理体系、培养跨文化领导力、优化全球薪酬与激励机制、借助数字化技术提升人才管理效率以及强化企业社会责任与可持续发展战略，以期在全球市场竞争中取得领先地位，从而塑造更具活力与包容力的国际商业生态体系。
关键词：中国企业；全球人才管理；法律合规；数字创新；跨文化领导力；可持续发展
Talent management in the AI era offers businesses looking to maximize their human capital strategy both previously unheard-of potential and challenging tasks. AI technologies have the potential to change many aspects, including hiring procedures, customized learning programs, workforce planning, and employee engagement. This chapter explores the concept of talent management and AI. Further the need of AI and in talent management has discussed and how AI-driven tools enhance these processes, improving efficiency and reducing bias. It emphasizes the balance between technical AI skills and human-centered competencies like creativity and emotional intelligence. The chapter also addresses ethical considerations, transparency, and fairness in AI decision-making, and highlights future research directions, including ethics, diversity, and AI integration with other emerging technologies. Successful AI adoption in talent management blends human acumen with AI, fostering innovation and competitiveness in the digital age.
Organizations are not exempt from the fast change that artificial intelligence is bringing about in the workplace.
Artificial intelligence is being utilized to streamline procedures, enhance decision-making, and automate
operations. However, there is a lack of research on the effect of artificial intelligence on job performance.
To address this gap, this chapter investigates the impact of artificial intelligence on job performance with
experience as a mediating variable on Tunisian employees. One hundred twenty-one online questionnaires
were distributed to Tunisian service sector employees in order to answer the main question. Explanatory
factor analysis, confirmatory factor analysis, and path analysis were carried out using IBMSPSS.26 and
IBMSPSS AMOS.24, respectively. The findings showed that experience influences positively job performance.
Nevertheless, the authors found the absence of the link between the experience and the artificial intelligence.
Furthermore, there is no relationship between job performance and artificial intelligence awareness.
Quality 4.0 is a fascinating subject that interests
both theoreticians and practitioners alike. As technology
continues to advance, this topic is only going to become more
distinguishing. This paper elucidates the concept of Quality 4.0,
its advantages, and the crucial factors required for its
implementation in companies. Importantly, it highlights the
significance of having a competent workforce. The paper
analyzes the human resource management practices that affect
Quality 4.0 and identifies the most effective ones by studying
companies that have succeeded in the process of digital
transformation. By comparing the usage cases of 9 "Lighthouses
Network" companies of digital transformation in quality
management and the HR practices they apply, the paper identifies the ways of working and human resource management
practices that lead to the effectiveness of Quality 4.0.
Key Words: Quality 4.0, HR practices, digital transformation,
lighthouses of digital transformation
The transformation of the intelligence ecosystem associated with the digital transformation represents a critical juncture for diversity and inclusion (D&I). We present a multidisciplinary perspective on digital transformation and D&I that demonstrates that, in the context of automated decision making, where algorithmic biases and the standardisation of thought represent new risks, neurodiversity initiatives become a cornerstone for advancing D&I. Based on interviews with neurodiversity experts, we identify innovative ways to efficiently configure an inclusive organisational design targeting neurodiversity by leveraging technologies. We identify several properties of technologies that support D&I in neurodiversity initiatives: the neutralisation of biases during interviews, the development of digital support for physical and mental well‐being and the facilitation of different cognition modes. Finally, we critically discuss the risks and opportunities offered by various technologies in terms of performance evaluation, new forms of dominance, and design of a digital ecosystem for mental well‐being.
Adoption of new technology to support selection interviews may distort the validity of source data in HR analytics, with implications for Artificial Intelligence (AI) algorithms used to assess candidates' personality traits. Using a field experiment with real selection interviews, we compare two common selection interview modes—Face‐to‐Face and videoconference, to evaluate their impact on personality trait assessments. Our findings indicate that candidates scored more highly on agreeableness, openness, extroversion, and conscientiousness, but lower on neuroticism, during a Face‐to‐Face interview compared with videoconference. There was also greater variation in personality ratings when interview sequence commenced with videoconference followed by Face‐to‐Face, compared with the reverse order. Our results suggest that Face‐to‐Face followed by videoconference provide a less distorted assessment of personality traits than videoconference followed by Face‐to‐Face. This study also contributes to practical and academic debates centred on human and AI selection practices and the use of data analytics in HR processes.
Recent developments in text mining and natural language processing (NLP) have paved a new way for analysing text data. These techniques are particularly useful for human resource management (HRM) due to the large amount of text information in the field. This paper adds to the literature by introducing and demonstrating steps of using NLP. Two demonstrations are presented: Demonstration One illustrates how simple and straightforward Bag‐of‐Word models applied on textual comments can be used to predict numerical ratings of companies; Demonstration Two shows how personality (self‐reported scores on the Big Five) can be predicted from situational interview questions through more complex Doc2Vec algorithms. Together, these demonstrations show that both simple and complex techniques are effective tools in predicting organizational outcomes. Accessible syntax and guides for beginners are also provided.
Purpose
This study aims to explore the consequences of inconsistent diversity-related signals for job seekers. Information sources include strategically crafted corporate signals and independent sources. The authors seek to understand the effect of inconsistent diversity signals on job seekers attitudes and behavior during recruitment.
Design/methodology/approach
An experiment was conducted wherein two samples from job-seeking populations were first exposed to a fictitious corporate website and then to LinkedIn profiles of that organization’s employees, with systematically varied diversity signals.
Findings
Results demonstrated that conflicting diversity signals had negative effects on perceived organizational attractiveness in the student sample ( N = 427) and on organizational agreeableness in the working sample ( N = 243). Negative organizational attraction was related to a lower likelihood of participants applying.
Practical implications
This work provides a stark but an important message to practitioners: signaling diversity-related values on corporate websites may backfire for organizations that actually lack diversity.
Originality/value
Few studies have combined communication theories with recruitment to examine the link between diversity signals and inconsistent information gathered via social media.
In search of greater resource savings and efficiencies, companies are turning to new technologies in the interview process, such as artificial intelligence evaluation (AIE). However, little is known about candidate reactions to this new tool. We identified outstanding questions regarding reactions to AIE arising from justice and signaling theories and conducted interviews with 33 professionals to understand their perceptions of AIE use in selection. Participants raised issues related to all four types of justice and indicated that there is a signaling effect of AIE use. Despite acknowledging the superior objectivity of AIE, participants expressed a desire for the maintenance of human elements in the evaluation process, seemingly preferring ‘the devil they know’ (human biases and intuition) rather than the one they do not (AIE algorithm). This result is explored through the lens of uncertainty reduction, discussing theoretical implications for justice and signaling theories, and providing implications for the implementation of AIE in the selection process.
Artificial intelligence algorithms govern in subtle, yet fundamental ways, the way we live and are transforming our societies. The promise of efficient, low‐cost or ‘neutral’ solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services. As AI algorithms have permeated high‐stakes aspects of our public existence – from hiring and education decisions, to the governmental use of enforcement powers (policing) or liberty‐restricting decisions (bail and sentencing), this necessarily raises important accountability questions: What accountability challenges do AI algorithmic systems bring with them, and how can we safeguard accountability in algorithmic decision‐making? Drawing on a decidedly public administration perspective, and given the current challenges that have thus far become manifest in the field, we critically reflect on and map out in a conceptually‐guided manner, the implications of these systems, and the limitations they pose, for public accountability.
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Discussions about the potential for artificial intelligence (AI) in modern society have ranged across virtually every aspect of human endeavors. At least at present, general-purpose artificial intelligence remains quite distant. The best prospects lie in developing evidence-based algorithms to aid decisions especially in those situations where human judgment is now dominant. Peter Cappelli, Prasand Tambe, and Valery Yakubovich consider the possibilities for data science-based algorithms in one area of organizational and business life, and that is human resource decisions. The need for better and more objective decisions here is evident. It is also clear that the realities and context of human resources is often lost on data scientists racing to introduce tools developed in other contexts. They outline the opportunities and limitations for using these algorithms in human resources and the management of employees.
HRM has embraced video interviewing through verbal computer‐mediated communication (VCMC) technology. However, empirical research in recruitment remains scant. Drawing on communication theories to analyse data from three studies, we present a conceptual framework explaining VCMC adoption and practice. We argue that VCMC technology has a larger effect on recruitment and selection outcomes than presumed. We broaden signalling theory to video recruitment and posit that interaction effects due to characteristics of the technology and a candidate's personality may affect recruitment outcomes. We also broaden media richness theory by uncovering memory effects arising from multiple interview modes. HR managers should be mindful of these and others limitations highlighted in the study before fully embracing this technology.
To attract a gender diverse workforce, many employers use diversity statements to publicly signal that they value gender diversity. However, this often represents a misalignment between words and actions (i.e., a diversity mixed message) because most organizations are male dominated, especially in board positions. We conducted 3 studies to investigate the potentially indirect effect of such diversity mixed messages through perceived behavioral integrity on employer attractiveness. In Study 1, following a 2 × 2 design, participants (N = 225) were either shown a pro gender diversity statement or a neutral statement, in combination with a gender diverse board (4 men and 4 women) or a uniform all-male board (8 men). Participants' perceived behavioral integrity of the organization was assessed. In Study 2, participants (N = 251) either read positive or negative reviews of the organization's behavioral integrity. Employer attractiveness was then assessed. Study 3 (N = 427) investigated the impact of board gender composition on perceived behavioral integrity and employer attractiveness using a bootstrapping procedure. Both the causal-chain design of Study 1 and 2, as well as the significance test of the proposed indirect relationship in Study 3, revealed that a diversity mixed message negatively affected an organization's perceived behavioral integrity, and low behavioral integrity in turn negatively impacted employer attractiveness. In Study 3, there was also evidence for a tipping point (more than 1 woman on the board was needed) with regard to participants' perceptions of the organization's behavioral integrity. (PsycINFO Database Record
Advances in big data and artificial intelligence (AI), including machine learning (ML) and other cognitive computing technologies (CCT), have facilitated the development of human resource management (HRM) applications promising greater efficiency, economy, and effectiveness for public administration (Maciejewski, 2017) and better alignment with the modern, constantly evolving employment landscape. It is not surprising then that these advanced technologies are featured in proposals to elevate the government’s human capital. This article discusses current and emerging AI applications that stand to impact most (if not all) HRM functions and their prospects for elevating public human capital. In particular, this article (a) reviews the current state of the field with regards to AI and HRM, (b) discusses AI’s current and potential impact upon the core functional areas of HRM, (c) identifies the main challenges AI poses to such concerns as public values, equity, and traditional merit system principles, and (d) concludes by identifying research needs for public HRM scholarship and practice that highlight the growing role and influence of AI applications in the workplace.
Business education is undergoing paradigmatic changes, and business schools are feeling the brunt of these changes. This article proposes that “business as usual” is over for traditional business schools. Using Ohmae’s 3Cs—customers, competitors, and
company—as an analytical framework, I examine important changes from different vantage points. From the perspective of customers, the focus lies on technological and value changes. In terms of competitors, the analysis centers on the growing number of alternative suppliers of business education and the geographic shifts in the business school landscape. As to the company dimension, I comment on the vast number and heterogeneity of business schools and suggest that they are heading toward a business model competition. In considering potential development paths for business schools, the article concludes that they require radical innovations to stay relevant.
The Fourth Industrial Revolution is changing everything - from the way we relate to each other, to the work we do, the way our economies work, and what it means to be human. We cannot let the brave new world that technology is currently creating simply emerge. All of us need to help shape the future we want to live in. But what do we need to know and do to achieve this?
In Shaping the Fourth Industrial Revolution, Klaus Schwab and Nicholas Davis explore how people from all backgrounds and sectors can influence the way that technology transforms our world. Drawing on contributions by more than 200 of the world's leading technology, economic and sociological experts to present a practical guide for citizens, business leaders, social influencers and policy-makers this book outlines the most important dynamics of the technology revolution, highlights important stakeholders that are often overlooked in our discussion of the latest scientific breakthroughs, and explores 12 different technology areas central to the future of humanity.
Emerging technologies are not predetermined forces out of our control, nor are they simple tools with known impacts and consequences. The exciting capabilities provided by artificial intelligence, distributed ledger systems and cryptocurrencies, advanced materials and biotechnologies are already transforming society. The actions we take today - and those we don't - will quickly become embedded in ever-more powerful technologies that surround us and will, very soon, become an integral part of us.
By connecting the dots across a range of often-misunderstood technologies, and by exploring the practical steps that individuals, businesses and governments can take, Shaping the Fourth Industrial Revolution helps equip readers to shape a truly desirable future at a time of great uncertainty and change.
Performance appraisal research has focused almost entirely on traditional numerical ratings despite narrative text comments regularly being collected within appraisals. The current study investigated the theory and utility of leveraging narrative comments to better understand employee performance. Narrative sentiment scores were derived using text mining on a large sample of narrative comments. These scores were then applied to an independent set of two years of performance data. It was assumed that narrative comments would reflect true performance variance that overlaps with traditional ratings, but also that they would capture incremental variance due to increases in total information and a reduction in rater-motivated biases in contexts in which narrative data was not explicitly linked to administrative outcomes. The derived narrative scores were reliable across years, converged with traditional numerical ratings, and explained incremental variance in future performance outcomes (performance ratings, involuntary turnover, promotions, pay increases). Collectively, this study highlights how narratives can enhance performance measurement and demonstrates how these data can be economically scored in applied settings.
Corporate recruitment efforts have evolved from traditional newspaper want ads to highly sophisticated, rhetorically powerful recruiting Web sites or “career sites.” This e-cruiting phenomenon offers a unique opportunity not only to examine organizations’ persuasive attempts to recruit potential applicants online, but also to uncover contemporary corporate representations of the meaning(s) of work. Using a random sample of recruitment Web sites of Fortune 500 companies, we employ content analysis and rhetorical criticism to catalogue content types, identify persuasive structure, and analyze rhetorical themes in representations of work. The investigation reveals that career sites are not merely places to post job openings, but reflect corporations’ attempt to sell a glorified image of work, one which positions workers as powerful actors and employers as kind benefactors. In view of current reports on working conditions, we argue these glorified representations reflect a rhetoric of idealization and discuss potential consequences of such a strategy.