Job Applicants Research Articles

Anticipated chilly climate for women: the development and validation of a measure

PurposeThis study develops a measure of anticipated chilly climate for women and provides initial evidence of its validity.Design/methodology/approachWe draw on three studies. Study 1 consisted of three focus groups to gain deeper insights into the meaning of the concept for prospective female jobseekers and generate scale items. In Study 2, we pre-tested job post vignettes (N = 203), refined the scale items and explored the factor structure (N = 136). Study 3 aimed to determine the convergent and discriminant validity of the new scale (N = 224) by testing its relationships with organisational attractiveness, person-organisation fit perceptions and gendered language.FindingsThe results show that the anticipated chilly climate is an important concept with implications for applicants’ career decision-making and career growth in the technology industry, where women tend to be underrepresented. Perceptions of anticipated chilly climate comprise expectations of devaluation, marginalisation and exclusion from the prospective employment. The masculine stereotypes embedded in the language of the job posts signalled a chilly climate for both genders, negatively affecting perceptions of fit and organisational attractiveness.Originality/valueMost previous studies have focussed on the actual experiences of chilly climates in organisations. We extend this body of literature to anticipatory climates and draw on social identity threat theory and signalling theory to highlight that job applicants make inferences about the climate they expect to find based on job ads. Specifically, they may anticipate a chilly climate based on cues from job ads signalling masculine stereotypes. Whilst the literature has emphasised women’s perceptions of chilly climates within organisations, our results show that both genders anticipate chilly climates with detrimental consequences for both organisations and prospective job applications.

Remote Work and Job Applicant Diversity: Evidence from Technology Startups

A significant element of managerial post-COVID job design regards remote work. In an era of renewed recognition of diversity, employers may wonder how diverse (gender and race) and experienced job applicants respond to remote job listings, especially for high-skilled technical and managerial positions. Prior work has shown that while remote work allows employee flexibility, it may limit career promotion prospects, so the net effect of designating a job as remote-eligible is not clear from an applicant interest standpoint, particularly when recruiting females and underrepresented minorities (URM). We analyze job applicant data from a leading startup job platform that spans long windows before and after the COVID-19 pandemic-induced shutdowns of March 2020. To address the empirical challenge that remote job designation may be codetermined with unobserved job and employer characteristics, we leverage a matching approach (and an alternative method which leverages the sudden shutdowns) to estimate how applicant characteristics differ for otherwise similar remote and onsite job postings. We find that offering remote work attracts more experienced and diverse (especially URM) job applicants, with larger effects in less diverse geographic areas. A discrete change in job posting to remote status (holding all else constant) is associated with an approximately 15% increase in applicants who are female, 33% increase in applicants with URM status, and 17% increase in applicant experience. Using the application data, we estimate an average estimated compensating wage differential for remote work that is about 7% of posted salaries in this labor market. This paper was accepted by Olav Sorenson, organizations. Funding: This work was supported by Mack Institute for Innovation Management at the University of Pennsylvania. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03391 .

The Impact of Formal Education, Specialized Training, and Offense Type on Perceptions of Employability: Can Bias Toward Hiring a Person With a Criminal History Be Mitigated?

Though hesitations to hire formerly incarcerated individuals are often related to assumptions about a person's dangerousness, there remain legitimate reasons, such as limited formal education and basic skills, that prevent returning residents from finding work. Previous research suggests that hiring decisions are confounded by offense type, even if an employer would otherwise consider a person with a criminal history. Thus, it is unclear whether returning residents' level of education or training can mitigate barriers related to their offense history. Using hypothetical case vignettes of a formerly incarcerated job applicant, this study examined whether hiring decisions were influenced by education and offense type. A between-subjects design with a sample of 223 individuals recruited via MTurk was conducted. Results of this study were largely consistent with prior research suggesting that criminal history presents an obstacle when trying to obtain employment. Implications and future directions are provided in the discussion.

Doskonalenie warsztatu metodycznego nauczyciela języka specjalistycznego w kontekście kształcenia nieformalnego. Wyniki badania pilotażowego

The importance of foreign language skills has increased in the labor market in recent years. A 2012 survey in Poland shows that effective communication and language skills are the most important skills sought from job applicants. It is therefore not surprising that private language schools and companies are offering more and more foreign language courses for professional purposes. In the context of thinking about informal learning of a language for specific purposes (LSP), it is worth reflecting on how a LSP teacher is prepared for work. It seems that attending workshops or practical training should be part of the repertoire of enhancing the professional competence of LSP teachers. However, discussions with experienced language teachers reveal that the reality is quite different. Therefore, given the growing demand for specialized language courses, diagnosis and discussion of the difficulties faced by LSP teachers is of academic relevance. The first part of this article presents a pilot study conducted in 2023 among LSP teachers. Following this, the difficulties of teaching professional languages will be presented. The last part refers to the presentation of methods, techniques and forms of work that can improve the methodological repertoire of LSP teachers.

Fairness, AI & recruitment

The ever-increasing adoption of AI technologies in the hiring landscape to enhance human resources efficiency raises questions about algorithmic decision-making's implications in employment, especially for job applicants, including those at higher risk of social discrimination. Among other concepts, such as transparency and accountability, fairness has become crucial in AI recruitment debates due to the potential reproduction of bias and discrimination that can disproportionately affect certain vulnerable groups. However, the ideals and ambitions of fairness may signify different meanings to various stakeholders. Conceptualizing fairness is critical because it may provide a clear benchmark for evaluating and mitigating biases, ensuring that AI systems do not perpetuate existing imbalances and promote, in this case, equitable opportunities for all candidates in the job market. To this end, in this article, we conduct a scoping literature review on fairness in AI applications for recruitment and selection purposes, with special emphasis on its definition, categorization, and practical implementation. We start by explaining how AI applications have been increasingly used in the hiring process, especially to increase the efficiency of the HR team. We then move to the limitations of this technological innovation, which is known to be at high risk of privacy violations and social discrimination. Against this backdrop, we focus on defining and operationalizing fairness in AI applications for recruitment and selection purposes through cross-disciplinary lenses. Although the applicable legal frameworks and some research currently address the issue piecemeal, we observe and welcome the emergence of some cross-disciplinary efforts aimed at tackling this multifaceted challenge. We conclude the article with some brief recommendations to guide and shape future research and action on the fairness of AI applications in the hiring process for the better.

A structured curriculum supporting biomedical trainees’ transition into independent academic positions and early career success

The United States government makes a substantial investment in biomedical training programs each year. However, for most trainees, these opportunities do not translate into career progression in academic research pathways. Only about one-fifth of postdoctoral fellows eventually secure a tenure-track faculty position, and even among these candidates, attrition is high. Although a number of factors govern career choices and career longevity, the transition from trainee to faculty is a challenging process and requires knowledge and skills that are not necessarily developed during a traditional university experience. Many postdoctoral fellows receive adequate training in research skills and scientific communication, but new faculty report not being sufficiently prepared for the job search process and for starting their labs. To address this critical training gap, the ITERT core (Interdisciplinary Translational Education and Research Training) and the Office of Postdoctoral Fellows at the University of Texas MD Anderson Cancer Center implemented a structured course for both postdoctoral fellows and senior PhD students to provide formalized training for successfully navigating academic positions in biomedical research. Here we report on the pilot Navigating Academic Careers course conducted in 2021–2022 for 30 PhD students and postdocs. The nine-module course was conducted over 13 weeks in 25.5 h instructional sessions. The key educational objectives included 1) navigating the job application and the interview/negotiation process, 2) hiring, leading, and mentoring lab personnel and program support staff, 3) project administration and financial stewardship, 4) managing time and work-life balance and 5) developing collaborations, branding, personalized niche, and networking. Survey-based analysis at the time of the course was used to capture the participants’ assessment of the course content, organization, and delivery, with a follow-up survey conducted approximately 2 years post-course (2024) to evaluate longer-term impacts of the training. Initial in-course assessment revealed that 89.9% of respondents found the scope and instructional content appropriate, and 91.1% found the course relevant and applicable to their career needs. Longer-term post-course evaluation indicated that 80% of respondents applied the learnings of the course, that 80% reported feeling more confident in navigating an academic job search, and that 66.6% continued to report agreement with the course preparing them for their current role/ongoing job search, with 46.7% already securing jobs in academic research, including as independent faculty. The outcomes of this pilot course suggest that integrating this into the broader postdoctoral training curriculum can enhance both the transition and early-career success of talented scientists-in-training into working professionals in biomedical careers, as faculty and science-trained staff.

Angry leaders and coworkers: Cross-cultural evaluation of anger expression through high-status and equal-status roles

Anger expressed in organizations conveys potent social information that influences social perceptions and determines subsequent relationships among employees. The present research examined how cultural contexts and hierarchical structure of a given relationship interact to shape perceptions of anger expression. Conducting a survey on subsidiary employees of a multinational high-tech company, Study 1 showed that employees from the Confucian Asian culture (specifically Korea) evaluate anger expressed by high-status counterparts (i.e., managers) more positively in terms of appropriateness and effectiveness compared to those from the Anglo culture (specifically the United States). Study 2, which was based on different samples from the same company, showed that employees from another Confucian Asian country (Japan), compared to those from the Anglo culture (the United States), provide more positive evaluations of the anger expressed by high-status counterparts. In contrast, Study 2 further showed that this cultural difference is reversed when anger is expressed by equal-status counterparts (coworkers). Finally, Study 3 replicated the findings of Study 2 using online-recruited samples and further demonstrated that Confucian Asian sample (Koreans) is more likely to decide to hire a job applicant who expressed anger as a leader than Anglo sample (Americans) is. In contrast, this pattern is reversed when considering a job applicant who expressed anger as a coworker. Theoretical and practical implications for social emotions and cross-cultural management are discussed.

Blockchain Based Placement Management System And Job Recommendation Using Content-Based Filtering

The integration of blockchain technology with placement management systems and job recommendation mechanisms presents a robust solution for enhancing the efficiency, transparency, and security of the recruitment process. This paper proposes a Blockchain-based Placement Management System (BPMS) coupled with Content-Based Filtering (CBF) techniques to streamline the job matching process for both job seekers and recruiters. The BPMS leverages the decentralized nature of blockchain to ensure immutable records of job listings, candidate profiles, and transactional data, thereby reducing the risk of fraudulent activities and ensuring data integrity. Smart contracts are utilized to automate various aspects of the recruitment process, such as job application submissions, candidate evaluations, and offer acceptance, facilitating trustless interactions between parties.In parallel, the CBF approach is employed to provide personalized job recommendations to candidates based on the analysis of their skills, qualifications, and preferences. By analyzing the content of job descriptions and candidate profiles, the system generates tailored recommendations that match the expertise and interests of the candidates, improving the likelihood of successful placements.The proposed system offers several advantages over traditional placement management systems, including increased transparency, reduced operational costs, minimized bias in job recommendations, and enhanced security of sensitive data. Through the synergy of blockchain technology and content-based filtering techniques, the BPMS aims to revolutionize the recruitment industry by fostering trust, efficiency, and fairness in the job market.

Transforming the experiential learning of sport management graduates into transferable employability signals

ABSTRACT Graduate employability plays a critical role during job recruitment and selection. Practical experience provides a strong foundation from which sport management graduates can signal their employability. Our Australian-based mixed-method study explored signals that managers seek from sport management graduate-entry applicants during job recruitment and selection. Semi-structured interviews were conducted with 10 sport managers. Interview transcripts were deductively coded using the Experiential Learning Cycle and thematically analysed. An online employability survey, guided by Employability Dimensions, was distributed and gained responses from 166 sport managers. Analysis was conducted using non-parametric statistics. Findings revealed 10 observed signals: to assist sport management graduates indicate their employability during job recruitment and selection and for sport managers to recognise employable graduates. The observed signals comprise multiple signal indicators, developed to guide theoretical knowledge and practical application of the employability signals for graduate job applicants.

Analysing Part-of-Speech Errors in Job Interviews Among Malaysian Fresh Graduates in Malaysia: Implications for Communication Competence

This study examined the utilisation of parts of speech in job interviews to assess their accuracy due to a lack of English language proficiency among job applicant candidates. Data was gathered from 26 fresh graduates applying for customer service executive and officer positions across three distinct recruitment organisations. Utilizing a qualitative method, the analysis focused on identifying instances of parts of speech using Bachman and Palmer's (1996) framework, emphasizing the crucial role of grammar competence in effective communication. The findings highlighted prevalent errors, particularly in verbs and conjunctions, while adverbs and adjectives exhibited fewer mistakes. Additionally, the study revealed a lack of awareness among candidates regarding the importance of parts of speech in both spoken and written communication. These results underscore the urgent need for collaborative efforts among involved parties in job interviews to enhance communication skills, specifically targeting proficiency in using parts of speech. The implications stress the vital significance of mastering parts of speech in job interviews, as adept linguistic abilities significantly influence candidates' capacity to convey qualifications and skills effectively, thus impacting their suitability for employment opportunities. Implementing measures to refine part-of-speech competence can substantially enhance candidates' abilities to articulate ideas accurately and professionally, thereby positively affecting their success in job interviews.

You Want Me to Do What in This Job? An Experiential Exercise in Understanding the Usefulness of Realistic Job Previews for Recruitment and Retention

A key management function for any organization is recruitment. An organization needs to attract qualified applicants for open positions and retain effective employees. To enhance the recruitment and retention of employees, job applicants should be given Realistic Job Previews (RJP’s) which includes both the positive and negative aspects of the position. Research shows that RJP’s provides applicants a clearer view of the tasks of a job, which results in reduced turnover and other benefits. This paper summarizes the research on the effectiveness of RJP’s, then presents an experiential exercise in which students write RJP’s for various jobs, so they can learn how RJP’s are created and gain a better understanding of why RJP’s are useful to both job seekers and organizations.

FP‐JSC: Job failure prediction on supercomputers through job application sequence correlation

SummarySupercomputers are advanced computing systems interconnected through high‐speed communication networks, consisting of independent computational nodes. During the unfolding of the big data era, the potent computational capabilities of these supercomputers play a pivotal role in scientific computing. Despite executing numerous advanced computational science and engineering tasks on supercomputers, many submitted jobs fail due to various factors, resulting in user inefficiencies. These failures not only consume system resources but also reduce the overall efficiency of the system. Previous research often couples job performance features with a single machine learning method for predicting job failure. However, a primary hurdle emerges from the high cost of gathering these features, complicating their real‐world applicability. To address this challenge, our study establishes correlations among job applications through extensive job log analysis. Leveraging correlations, we propose a predictive framework based on job application sequence correlation (called FP‐JSC). This innovative framework employs multiple machine learning models to offer holistic predictions, selecting the most suitable model based on its learning effectiveness. Moreover, the framework optimizes feature collection expenses without adversely affecting job execution. We determine job applications using both job paths and job names, with the former emerging as a novel feature derived from supplementary monitoring data. Empirical results underscore FP‐JSC's effectiveness, accurately identifying over 89% of jobs with 95% specificity and 89% sensitivity—outperforming single prediction methods employed in related works.

Artificial intelligence capital and employment prospects

Abstract There is limited research assessing how AI knowledge affects employment prospects. The present study defines the term ‘AI capital’ as a vector of knowledge, skills, and capabilities related to AI technologies, which could boost individuals’ productivity, employment, and earnings. Subsequently, the study reports the outcomes of a genuine correspondence test in England. It was found that university graduates with AI capital, obtained through an AI business module, experienced more invitations for job interviews than graduates without AI capital. Moreover, graduates with AI capital were invited to interviews for jobs that offered higher wages than those without AI capital. Furthermore, it was found that large firms exhibited a preference for job applicants with AI capital, resulting in increased interview invitations and opportunities for higher-paying positions. The outcomes hold for both men and women. The study concludes that AI capital might be rewarded in terms of employment prospects, especially in large firms.

Goal Orientation for Fair Machine Learning Algorithms

A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases.

The double-edged sword of envy: effects of envy type and regulatory focus on consumer decision-making

Purpose This study aims to investigate the fit of a promotion (prevention) focus with malicious (benign) envy and how this fit influences positive and negative behaviours, depending on the context. Design/methodology/approach Four empirical studies (two laboratory and two online experiments) were used to test key hypotheses. Study 1 manipulated regulatory focus and envy in a job application setting with university students. Study 2 engaged similar manipulations in a social media setting. Studies 3 and 4 extended the regulatory focus and envy manipulations to the general population in pay-what-you-want (PWYW) and pay-it-forward (PIF) restaurant contexts. Findings The findings showed that a promotion (prevention) focus fits with the emotion of malicious (benign) envy. In the social media context, promotion and prevention foci demonstrated negative behaviour, including unfollowing the envied person, when combined with malicious and benign envy. In the PWYW and PIF contexts, combining envy with a specific type of regulatory focus encouraged both positive and negative behaviours through influencing payments. Research limitations/implications Future research could validate and extend this study’s findings with different product/service categories, cross-cultural samples and research methods such as field experiments. Practical implications The four studies’ findings will assist managers in formulating marketing strategies to enhance their positioning of target products/services, possibly leading to higher prices for PWYW and PIF businesses. Originality/value The conceptual model is novel as, to the best of the authors’ knowledge, no prior research has proposed and tested the fit between envy type and regulatory foci.

Discrimination against lesbian and gay job seekers: An artefactual field experiment in urban Ecuador

This paper presents the findings of an artefactual field experiment conducted in urban Ecuador to investigate discrimination against individuals self-identifying as lesbian or gay (LG) job seekers in the labor market. Focusing on occupations and sectors that attract the employment interest of both LG and non-LG individuals, the study employed fictitious job applications evaluated by 394 human resource analysts. The results reveal gender-based differential discrimination. Female LG candidates experienced positive discrimination and were more likely to be selected and offered higher wages compared to their heterosexual counterparts. In contrast, male LG candidates experienced negative discrimination, saw no wage differences, and were less likely to be selected. The study finds that female recruiters are especially influential in driving these discriminatory behaviors. These findings contribute to the understanding of the complex dynamics of discrimination towards LG workers in the labor market and of the interaction of that form of discrimination with gender.

Detection of arousal and valence from facial expressions and physiological responses evoked by different types of stressors.

Automatically detecting mental state such as stress from video images of the face could support evaluating stress responses in applicants for high risk jobs or contribute to timely stress detection in challenging operational settings (e.g., aircrew, command center operators). Challenges in automatically estimating mental state include the generalization of models across contexts and across participants. We here aim to create robust models by training them using data from different contexts and including physiological features. Fifty-one participants were exposed to different types of stressors (cognitive, social evaluative and startle) and baseline variants of the stressors. Video, electrocardiogram (ECG), electrodermal activity (EDA) and self-reports (arousal and valence) were recorded. Logistic regression models aimed to classify between high and low arousal and valence across participants, where "high" and "low" were defined relative to the center of the rating scale. Accuracy scores of different models were evaluated: models trained and tested within a specific context (either a baseline or stressor variant of a task), intermediate context (baseline and stressor variant of a task), or general context (all conditions together). Furthermore, for these different model variants, only the video data was included, only the physiological data, or both video and physiological data. We found that all (video, physiological and video-physio) models could successfully distinguish between high- and low-rated arousal and valence, though performance tended to be better for (1) arousal than valence, (2) specific context than intermediate and general contexts, (3) video-physio data than video or physiological data alone. Automatic feature selection resulted in inclusion of 3-20 features, where the models based on video-physio data usually included features from video, ECG and EDA. Still, performance of video-only models approached the performance of video-physio models. Arousal and valence ratings by three experienced human observers scores based on part of the video data did not match with self-reports. In sum, we showed that it is possible to automatically monitor arousal and valence even in relatively general contexts and better than humans can (in the given circumstances), and that non-contact video images of faces capture an important part of the information, which has practical advantages.

IntelliView: An AI Based Mock Interview Platform

The IntelliView initiative represents a pioneering endeavor designed to empower novice job seekers through the integration of state-of-the-art Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies. This platform employs a sophisticated blend of HTML, CSS, JavaScript, and the Deep Face method, introducing a comprehensive framework to redefine interview assessment and augment job application preparation. The primary module facilitates text-based analysis, enabling users to engage with real-time interview questions by entering responses directly into the web interface. Subsequently, advanced algorithms compare users' responses to expected answers, yielding a percentage similarity score and presenting the correct answer. Beyond affording essential interview practice, this feature furnishes constructive feedback, enhancing users' responses and ultimately refining their interview performance. In the second module, IntelliView introduces a dynamic interview environment with video-based analysis. Users respond to inquiries utilizing webcams, enabling the system to meticulously record both verbal responses and facial expressions. Leveraging the Deep Face method, the platform conducts real-time emotion and sentiment analysis, offering users insights into their emotional states throughout interviews. This feedback facilitates the refinement of non-verbal communication skills, empowering candidates to recognize emotional tendencies and adapt interview strategies accordingly. The third module functions as a comprehensive resume builder, employing HTML, CSS, and JavaScript to provide diverse templates tailored to individual needs. In summation, IntelliView heralds a transformative paradigm in job application preparation, seamlessly amalgamating technological advancements and human interaction to equip first-time job seekers with the requisite tools for navigating the competitive job market successfully. Key Words: Interview Assessment, DeepFace, NLP, Text- based analysis, Video-based analysis, Resume Builder

Impact of Interest value, Social, and Economic Factors on the Application Intentions towards Gojek Startup among Job Seekers in the Jabodetabek Region

The attractiveness of employer branding strategies, particularly in startups such as the Indonesian fintech company Gojek, plays a pivotal role in drawing Generation Z's and Millennials' attention. This research aimed to empirically examine the influence of interest, social, and economic values on the intention to apply to Gojek among job seekers in the Jabodetabek area. Utilizing a quantitative research design, the study adopted a non-probability judgment sampling technique to select participants, surveying 150 Generation Z and Millennial job seekers. The data collected were analyzed through SPSS version 25. The outcomes indicated that the decision to apply is influenced concurrently by interest values, social values, and economic values. Specifically, the analysis revealed that interest value significantly enhances application intentions (t-count > t-table: 4,340 > 1,655), as do social values (t-count > t-table: 2,290 > 1,655) and economic values (t-count > t-table: 2,488 > 1,655). The study underscores the necessity for Gojek to bolster its value propositions, especially by fostering promising career paths aligned with the valued attributes and economic benefits, which are crucial factors in attracting job applicants. These insights advocate for a strategic emphasis on nurturing an employer brand that harmonizes with the aspirational and pragmatic expectations of contemporary job seekers, thereby enhancing their inclination to pursue employment opportunities within Gojek.

Job-seeking narratives: in search of graduate capitals acquired during student placements

Being a graduate is no longer sufficient to secure a fulfilling and rewarding graduate role. This paper drew on Tomlinson’s Graduate Capital Model to analyse the job-seeking narratives of recent computing graduates searching for a graduate job. Participants (n = 38), drawn from a national placement programme, were interviewed up to 12 months after they had graduated. Narrative interviews were analysed using the five graduate capitals of the Model, providing empirical evidence of the acquisition and application of graduate capitals, in particular through experiences of student work placements. The study found strong claims of human and cultural capital, arising from placement experience, while social and identity capital acquisition was found, but to a lesser extent. Fragile psychological and identity capitals were eroded by multiple unsuccessful job applications. This work empirically tested the Graduate Capital Model, contributing to understanding of capitals and their interplay.