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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.

This study aims to contribute to the literature on board gender diversity by soliciting university students' views on several perceptions raised by academics concerning the suitability of women to serve on corporate boards. In particular, if the opinions of male students differ from those of female students, this showing any gender bias.

The study is part of a much more comprehensive investigation into board gender diversity. It adopts a questionnaire approach, with this paper focussing on twelve research statements. Two hundred and ninety-six university students completed the questionnaires at a public university in the Czech Republic during March–April 2023. A pilot questionnaire was conducted in February 2023, resulting in minor changes being made. The data is analysed using SPSS and MedCalc® statistical software.

Whilst, in some respects, it supports the literature in relation to the observations highlighted in the research statements concerning female traits/characteristics, there is unmistakable evidence of gender bias in the respondents' opinions regarding the qualities women can bring to corporate boards. Overall, this research shows a negative bias by male respondents towards the positive attributes females can bring to the boardroom. This bias may influence the selection of female directors in the future. This research suggests that the apparent discrimination against women is not just because they are female but from a perceived mismatch between inferred female characteristics and male stereotype leadership requirements. There is, however, no gender bias with respect to students' leadership aspirations.

The findings of this research should help with policy-making decisions concerning the selection of future corporate board directors and help break down any negative gender selection bias. The paper adds to the discussion and debate about ethical issues related to business and broader society concerning gender diversity in senior management roles. It also adds to the political debate on the issue of legislative gender initiatives.

The research respondents' perceptions may well influence the decision-making process for the selection of future corporate directors. Whilst these current perceptions may, and invariably will, change over time, it is important to identify them at an early stage in the respondents' careers. This research gives a better understanding of the perceived qualities that women bring to corporate boards from an inexperienced perspective.

This study investigates the relationship between child gender and a CEO’s top management hiring decisions.

Hypotheses were tested using secondary data on 121 S&P 500 male CEOs, their children, and their top management teams.

Results indicate that child gender is associated with a male CEO’s TMT hiring decisions. Specifically, we find that male CEOs with only daughters were significantly more likely to hire women to their TMTs than male CEOs with only sons and those with both sons and daughters.

This study provides evidence for the roles of familiarity, learning, and empathy in reducing gender biases in selection decisions. Top management hiring decisions have wide implications for organizational settings in general and for the breaking of the glass ceiling in particular.

Reducing gender bias in top manager hiring decisions directly relates to the United Nations’ Sustainable Development Goal 5 of achieving gender equality as women are consistently under-represented at the top of organizations across the world.

By focusing on the hiring of top managers, this study includes hiring decisions that directly impact firm operations. To our knowledge, this is the first study to examine the relationship between child gender and executive hiring decisions with a US S&P 500 sample.

We investigate whether women are more likely than men to choose to pursue a competency-based labour market integration programme, rather than the time-based labour market integration programme. We further investigate whether women with existing but uncertified skills are even more likely to pursue a competency-based labour market integration programme.

We test our hypotheses using ordinary least squares applied to linear probability models. We discuss the relative advantages of this methodology. We show the robustness of our results through multiple specifications and estimation methods. Finally, we discuss the reasons preventing us from granting our results a causal interpretation and discuss how they are surmountable in future research.

Women are significantly more likely to enrol into competency-based programmes, relative to time-based. Women with existing but uncertified skills are significantly more likely to enrol into competency-based programmes, whereas women without skills or with college degrees are not significantly different from the baseline. Our findings are robust to various specifications, and we include a comprehensive set of fixed-effect vectors, addressing industrial, occupational and time-varying state specificities.

First, our empirical test of hypothesis H2 is hindered by the construction of the “some college or associate’s degree” variable in RAPIDS data. “Some college” is very different from an associate’s degree. Second we had to choose between omitted variable bias and selection bias. Because of the demonstrated importance of the occupation and industry variables in existing literature, we included those variables at the risk of selection bias. Occupation and industry fixed effects reduce, but do not eliminate, omitted variable bias. Finally, the third limitation of this paper is external validity. Registered Apprenticeship programmes are quite idiosyncratic to the United States.

The rollout and expansion of CBRA may thus be an avenue through policymakers may reduce the gender training gap. This may in turn give more women access to the labour market and allow more women to benefit from the “wage premia” of Registered Apprenticeship completion on the labour market (Lou and Hawley, 2019).

This article is the first that applies econometric methods to investigate women’s choices of labour market integration programmes, using Registered Apprenticeship as a case study. We discuss the implications of our findings, highlighting how competency-based programmes may be an approach to better serving more diverse populations in Registered Apprenticeship.

The Million Farmers School (MFS) program—also known as Kisan Pathshala was launched to impart training to the farmers by the government of the state of Uttar Pradesh (India) in December 2017. This study estimates the impact of training on agricultural knowledge of the farmers.

The study is based on household survey conducted in Uttar Pradesh (UP), India, during March–May 2019. The authors employed matching methods, the two-stage least square (2SLS)-residual and endogenous switching regression approaches to control for selection bias and endogeneity.

The results suggest that knowledge outcomes are significantly better among participants vis-à-vis non-participants. The results are robust to different model specifications. Further, the benefits are observed across different regions and social groups.

The MFS program can go a long way in enhancing agricultural know-how and the farmers' economic well-being, bringing a transformative change in the agricultural landscape of UP.

This study is based on a field survey data and analyzes various aspects of the program's impact, design and implementation, and offers implementation advice for greater efficacy in future.

This paper aims to test the impact of remittances receipt on agricultural productivity. The paper empirically assesses whether heterogeneity in economic activity of farming households affects the effects of remittances on productivity of tradable and nontradable crop farming households in Ghana.

The authors employ propensity score matching (PSM) methods to address potential endogeneity issues that could arise from the estimation due to selection bias. This paper uses the seventh round of Ghana living standard survey dataset for Ghana.

The authors find that, the involvement of farming households in other economic activities alters the impact of remittances on crop yield. This differential impact also varies according whether the crop is tradeable or not.

Policy can reduce the cost of sending remittances and include financial literacy modules in the farmer training modules to increase farmers' knowledge on investment of remittance in agricultural production.

The authors distinguish the paper from others by controlling for crop types (particularly tradeable or otherwise and gestation period), farming of a second or more crops and engagement of smallholder farmers in nonfarm economic activities.

This study aims to investigate the impact of Internet use on household financial market participation and portfolio choice.

Based on the Chinese General Social Survey 2017 (CGSS2017), this study empirically explores whether Internet use affects household financial market participation in China with an Endogenous Switching Probit model.

The results show that households using the Internet are more likely to invest in financial markets. Further research shows that households with high Internet use are significantly more likely to participate in financial markets than households with low Internet use. From the perspective of household portfolio choice, Internet use has a certain role in increasing the probability of portfolio diversification. However, among households that have invested in financial markets, those with a high-frequency use of the Internet do not show an impact on portfolio diversification.

This study complements existing research about the impact of Internet use or not on household financial market decisions and portfolio choice, expands the knowledge on the household financial market choice from the respective of the degree of Internet use.

Given the importance of growth-oriented entrepreneurship in the context of economic development and the need to understand how rural communities can be developed, the purpose of this research paper is to determine how the drivers of growth expectations differ between urban and rural settings.

The methodology is threefold: firstly, a descriptive analysis with non-parametric testing is conducted; then pooled regression model is used to analyse the predictors of growth expectations in both contexts, and finally, coarsened exact matching is used to identify possible self-selection bias.

In contrast to mainstream entrepreneurship theory, it is found that entrepreneurs’ intrinsic knowledge, skills and abilities are not significant in the rural-specific model. The only exception is entrepreneurs’ educational level, the importance of which is emphasised as a pivotal factor in increasing high-growth ventures in rural communities. Additionally, when self-selection is eliminated, rurality worsens growth intentions.

There is evidence that some growth-oriented entrepreneurs self-select into rural communities. Because the high-growth entrepreneurial dynamics in rural areas are unique, public policies should target purpose-driven entrepreneurial education. This includes encouraging “lifestyle entrepreneurship” (e.g. retirees returning to rural areas to become entrepreneurs), preventing entrepreneurial brain drain in rural areas and attracting highly educated urban entrepreneurs to exploit opportunities in rural areas.

This research attempts to contribute to the ongoing debate regarding the factors that drive high-growth entrepreneurs in rural areas by analysing rural entrepreneurs in the high-growth context of a developing economy. The focus is on Chile – a country that is rarely investigated compared to the USA or Europe – to extend the literature on high-growth ventures and entrepreneurial ecosystems.

This article analyzes the hypothesis that analysts issue higher long-term earnings growth (LTG) forecasts following a market-wide investor sentiment.

This study analyzed 193 publicly traded Brazilian firms listed on B3 (Brasil, Bolsa, Balcão), totaling 2,291 observations. To address the potential selection bias resulting from analysts' preference for more liquid firms, this study used the Heckman model in the analysis with samples with only one analyst and the entire sample. The study also applied other robustness tests to ensure the reliability of the findings.

The results suggest that market-wide investor sentiment influences LTG when the firm's stocks are difficult to value. Market optimism did not reflect five-year profit growth after the forecast issue, suggesting lower forecast accuracy during high investor sentiment values.

Volatile-earnings firms have relevant implications in LTG forecasts during bullish moments. According to the study’s evidence, investors' decisions and policymakers' and regulators' rules should consider analysts' expertise as independent information when considering LTG as input for valuation models, even under market optimism.

This paper contributes to the literature on the influence of investor sentiment on analysts' forecasts by incorporating two crucial elements in the discussion: the scenario free from herding behavior, as usually only one analyst issues LGT forecast for Brazilian firms, and the analysis of research hypotheses incorporates the difficulty of pricing a firm given the uncertainty of its earnings as an explanation to bullish forecast.

Stock investing choices of individual investors are predominantly influenced by heuristic biases, leading to sub-optimal choices. Accordingly, this study aims to identify, categorize, validate, prioritize, and find causality among the heuristic biases shaping stock investment decisions of individual investors.

This research offers original contribution by employing a hybrid approach combining fuzzy DELPHI method (FDM), fuzzy analytical hierarchy process (FAHP), and fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) techniques to validate, prioritize, and find causality among the heuristic biases.

Twenty sub-heuristic biases were identified under five main heuristic bias categories. Out of which, 17 were validated using FDM. Further, availability and representativeness within main heuristic categories, and availability cascade and retrievability within sub-heuristic biases were prioritized using FAHP. Overconfidence and availability were identified as the causes among the five main biases by F-DEMATEL.

This study offers the stock investors a deeper understanding of heuristic biases and empowers them to make rational investment decisions.

This paper is the inaugural effort to identify, categorize, validate, prioritize and examine the cause-and-effect relationship among the heuristic biases.