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How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.

This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.

Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.

Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.

To propound a broadened perspective on emotional labor management by exploring mitigatory approaches that could be pre-emptively deployed prior to actual episodic experiences of emotional dissonance and their associated negative consequences (e.g. burnout). At present, the management of emotional labor appears to skew toward reactive measures, such as deploying employee assistance programs (EAPs) to assist overwhelmed employees in coping better with their emotional demands, reducing job-related emotional demands or a combination of both.

Intricate processes of emotion emergence and established literature on emotion regulation are considered. By conceptualizing emotion emergence as a process entailing situation, attention, appraisal and response, current efforts can be seen as primarily acting upon the late stages of this process. General emotion regulation strategies that act upon more upstream processes are then considered and applied to the specific context of emotional labor.

Pre-emptive steps could be taken from the early stages of job selection as well as personnel selection and assessment through systematic and concerted efforts in identifying job-related emotional demands (e.g. specific display rules, frequency and intensity). Formal job descriptions could then reflect these demands to better facilitate self-selection processes. Additionally, considering these identified parameters as personnel selection and assessment criteria could further enhance person-job fit in terms of emotional congruency. For current hires, pre-emptive steps could also be taken to subliminally modulate their emotional emergence trajectory toward more job-congruent emotions. Collectively, these steps may facilitate the pre-emptive reduction of emotionally dissonant work episodes and bear substantive potential to be deployed synergistically with current, more reactive measures.

This paper offers a broadened perspective on emotional labor management. Through considering intricate processes of emotion emergence and established literature on emotion regulation, a pre-emptive perspective toward managing work emotions and emotional labor is propounded. It is believed that the synergistic incorporation of these pre-emptive management approaches with current strategies (e.g. reducing emotional demands, EAPs, etc.) would holistically allow for greater amelioration of this debilitating issue. Finally, it is hoped that this paper could serve as a primer for future research and discourses to be conducted, such that our arsenal available for combating emotional labor could be substantively expanded to holistically target all stages of the emotion emergence process.

This paper aims to examine the effects on firms' outcomes of a new government regulation on the private security industry that aimed to enhance the selection and training processes for armed-private security officers.

By using human capital theory and using a data set built from various public sources, this study analyzes the effects of a new regulation implemented in 2013–2014 in Brazil mandating psychological assessments for hiring private security armed officers. Firm-level data and a Difference-in-Differences (DiD) identification strategy are used to investigate the effects on turnover and human capital outcomes.

The study identifies substantial changes resulting from the new government regulation in private security firms. While it has led to increased turnover rates, the regulation has also facilitated firms in enhancing the human capital composition of their workforce by enabling the recruitment of more experienced personnel.

This research informs to current debates on the effects of policy interventions on firm's outcomes by showing how regulations aimed to improve the configuration of human capital can generate win-win situations for both firms and citizens, despite the short-term trade-offs between higher turnover rates and improved human capital outcomes.

Refining selection and training processes can enhance the workforce in private security firms by replacing less capable professionals with more experienced ones. Insights from this study offer guidance to policymakers and industry practitioners in shaping effective business and public policies.

This study underscores the role of training and psychological assessments in enhancing the composition of human capital in the private security industry.

By highlighting the role of policy interventions in establishing barriers to unskilled workers engaging in hazardous activities, this study contributes to the burgeoning literature in strategic management on the interaction between policy interventions and firm outcomes.

Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making.

In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method.

The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market.

In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness.

The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

This research paper aims to integrate the employee-related factors that empirical literature considers antecedents of performance (skills, work motivation, personal characteristics) into a multiple linear regression model, and to test such a model in order to measure the level of each individual factor on the performance.

Quantitative, multisource research approach. After testing the validity of the model with a Confirmatory Factor Analysis, this research applies the multiple linear regression model Work performance = a(Skills) + b(Work Motivation) + c(Personal Characteristics) + e(constant) to two different samples of workers: chemical technicians (N = 63) and salespeople (N = 61).

This study confirms the factorial structure of the antecedents of work performance, showing that skills, motivation, and personal characteristics are three general employee-related factors underlying work performance. The statistical analysis highlights a variance in performance between 40 and 65% explained by employee-related factors, hence leaving 35–60% as due to factors outside the model (firm/environment-related and/or job-related factors, or other skills and personal characteristics not considered in the model). The study also highlights that employee-related factors sometimes affect performance differently than job designers' expectations, and sometimes even negatively.

The equation was tested on two case studies, so further explorations are needed. Furthermore, the approach adopted is inductive thus describing performance as it is, not as it should be. Therefore, it explains the best actual performance of workers, not the ideal performance.

The equation tested here represents a simple and valid tool to guide many Human Resource Management practices, such as; selection, training, development, and career orientation.

Findings provide a valid indication for designing and managing human resource management systems more even-handedly, from an organizational and employee point of view. In doing so, it drives organizations towards a better Person/Job fit.

The study represents one of the first attempts to take into consideration multiple factors simultaneously in explaining work performance.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

The a priori identification and development of army personnel competencies are necessary to enable effective and efficient responses to rapidly changing climate conditions. Accordingly, this study aims to identify the performance requirements of a military flood responder and the competencies (knowledge, skills and abilities) required to perform it.

Using an abductive approach, the authors conducted both secondary and primary research to generate a validated framework of performance criteria and competencies for army personnel responding to floods. This literature review integrated both the peer-reviewed academic literature and public sector grey literature. Using the critical incident technique, the authors then conducted semi-structured interviews with 15 members of the Canadian Armed Forces (CAF) who had previously been tasked with flood response operations. Participants were asked about the tasks required while conducting flood response operations. Interview transcripts were then content analysed to identify themes regarding those tasks, and the competencies needed to perform those tasks were then extracted and contrasted with the literature review findings. Inter-rater reliability for the analysis was established via iterative discussion between the two co-authors.

The primary data reinforced and expanded the list of performance expectations that the authors deductively identified from the integrated literature review, adding granularity to each. It also identified competencies (including both hard and soft skills) and highlighted previously neglected contextual antecedents of military flood response effectiveness.

though knowledge saturation was achieved from the 15 interviews conducted, further research with larger samples could more deeply ground the evidence discovered in this study. Nevertheless, the competencies identified in this paper could serve as a starting guide to staffing and/or training interventions targeted at improving these competencies for personnel responding to flood scenarios.

The theoretical findings also have immediate practical relevance to training for flood response operations. In particular, the subtle challenges in competency crossover from military operations to flood response operations may facilitate not only more efficient, targeted training (that could improve the effectiveness of army personnel involved in humanitarian roles), but could be applied to the selection of army personnel as well. This study may also help provincial/municipal operators and emergency planners by better communicating the strengths and limitations of army personnel in addressing civilian military cooperation for humanitarian operations. Thus, the findings of this research study represent an important first step in prompting attention to the strategic human resource planning studies required to make all responders more efficient and effective in their respective division of labour within the humanitarian domain.

Peering a little beyond these research findings, human-induced climate change is expected to continue increasing the frequency of such events (IPCC, 2021), and a timely, national force is likely to be increasingly required for Canadians impacted by major disasters stemming from natural hazards when local resources become overwhelmed. Yet, there is some concern from the CAF that increasing responsiveness to disaster operations will affect their military readiness (Leuprecht and Kasurak, 2020). One can indeed envision a paradox whereby the CAF is both a “force of last resort” while increasingly becoming a “first choice for domestic disaster and emergency assistance”. The practical implications from this research also suggest that military personnel, while fully capable of successfully conducting flood response operations, may become overburdened and less able to adopt yet greater capacity and training for other additional humanitarian work. Nevertheless, the competencies highlighted by participants can help inform the next flood response operation in Canada.

Most literature in the field of emergency response focuses on cooperation between civilian and military resources and other strategic-level themes. The findings address critical granularity missing at the operational and tactical levels of humanitarian assistance and disaster relief research. The authors also draw implications beyond the military context, including for local/regional governmental players (operators and emergency planners) as well as for volunteers in flood response roles.

The purpose of this study is to explore the diffusion of digital innovation for talent management in Italian learning organizations.

It has been implemented a qualitative methodology to collect data, interviewing 16 experts; a mix method analysis was applied to explore thematic categories and to analyze co-occurrences by a quantitative approach analysis using T-Lab software.

There are some relevant points to underline: digital technologies are meant as a support to human resource management (HRM), and there is often the reference to digital gamification or gamified processes implemented for talent management procedures. Learning is a central element both for employees’ point of view and for HR specialists who feel the need for a major and more specific training on digital technologies.

The limited size and composition of the sample put restrictions on the generalizability of results. The explorative nature of the study provides an in-depth consideration of digital innovation in learning organization, representing a first starting point for future quantitative investigations. From a practical point of view, this study emphasizes a learning organization culture as an essential attitude set to attract, select and retain top talents.

From a practical point of view, this study emphasizes a learning organization culture as an essential attitude set to attract, select and retain top talents.

Giving space and voice to HR and information and communication technologies experts has provided insights regarding the digitalization process in HRM in Italy, in particular, digital learning has been told as a necessary element for the competitiveness of the workforce.

Business schools play a significant role in providing individuals with the ability to adapt to constantly changing environments. Such agile organizations require deans who, as leaders, possess the knowledge and attributes of astute and responsible executives. In this regard, the measurement of the attributes of leadership paves the way for evaluating a leader’s options process. In this study, we measure the attributes of leadership to pave the way for evaluating a leader’s decision-making process.

The rich data included the opinions of 93 university professors from seven countries: Iran, India, China, France, the UK, Canada and the USA. In appraising the responses, the authors considered the nationality and the development level of each participant’s country and continent. In this study, the authors developed an online questionnaire based on the best-worst method (BWM). By performing a one-way analysis of variance (ANOVA), the authors also determined the significant statistical differences of the scientific communities through the lenses of authentic leadership, leader-member exchange and social identity and leadership.

The results provide evidence of transparency, measured as the most important criterion for leading a business school, i.e. knowledgeable deanship. Furthermore, the findings reveal a meaningful difference between developed and developing countries in the context of an authentic leadership pillar.

This paper contributed to the literature in five major ways as follows: The authors investigated the attitudes of scientific communities from different countries, business schools, BWM, dean selection and leadership evaluation.By means of the BWM, the authors measured the criteria culminating in the selection of a knowledgeable leader for a business school.The authors compared and contrasted the attitudes of scientific communities in developing countries vis-à-vis those in developed ones.The authors addressed the differences and similarities among countries in relation to the selection of a knowledgeable business school leader.The authors provided beneficial insights by addressing the different perspectives of researchers on the weights of the criteria involved in the selection procedure for a business school dean.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.