Job Search Research Articles

Implementing a stabilizing intervention for traumatized refugees in temporary accommodations in South-West Germany - a randomized controlled pilot trial.

https://drks.de/search/de/trial/DRKS00022862 Deutsches Register Klinischer Studien identifier, DRKS00022862.

Older and younger job seekers' attention towards metastereotypes in job ads.

Building on social identity theory and cognitive models on information processing, the present paper considered whether and how stereotyped information in job ads impairs older/younger job seekers' job attraction. Two eye-tracking experiments with older (Study 1) and younger job seekers (Study 2) investigated effects of negatively metastereotyped personality requirements (i.e., traits) on job attraction and whether attention to and memory for negative information mediated these effects. Within-participants analyses showed for both older and younger job seekers that job attraction was lower when ads included negative metastereotypes and that more attention was allocated towards these negative metastereotypes. Older, but not younger job seekers, also better recalled these negative metastereotypes compared to not negative metastereotypes. The effect of metastereotypes on job attraction was not mediated by attention or recall of information. Organizations should therefore avoid negative metastereotypes in job ads that may capture older/younger job seekers' attention and lower job attraction.

Artificial Intelligence and its ability to reduce recruitment bias

Artificial intelligence is transforming the landscape of Human Resource Management (HRM), altering conventional methods and elevating the recruitment process for companies. The conventional approach to hiring can be incredibly time-intensive, often stretching over several weeks to sift through all applications. This process can be daunting for recruiters, who are tasked with reviewing numerous resumes. AI steps in to streamline this process by rapidly sifting through a large number of applications, identifying the most suitable candidates, and providing concise overviews of their qualifications. This not only saves recruiters time but also allows them to concentrate on improving the candidate experience and attracting top talent. AI operates around the clock, ensuring the recruitment process remains active and effective even when recruiters are not on duty. Moreover, AI can help mitigate bias, when utilized correctly, it can facilitate more equitable hiring decisions by focusing on relevant skills and experiences rather than personal biases. This article explores the multifaceted role of AI in mitigating recruitment bias, AI algorithms use objective data and set criteria to reduce unconscious bias during initial screening. This approach helps ensure that job seekers are evaluated based on qualifications and merit, rather than personal characteristics.

Multiple Stakeholder Perspectives of Implementing Work-Related Cognitive Behavioral Therapy (WCBT) in Vocational Service Settings: A Qualitative Exploration of Intervention Acceptability and Sustainability.

Growing mental health treatment access disparities elevate the urgency for identifying and implementing innovative approaches for delivering evidence-supported interventions. Work-related cognitive behavioral therapy (WCBT), a technology-assisted CBT (t-CBT) designed to address social anxiety and employment outcomes among job seekers at vocational service centers and for delivery by vocational service professionals, offers a promising way to increase access to needed mental health care. This qualitative study, guided by the Organizational Transformation Model (OTM), explored factors related to WCBT's implementation at two Jewish Vocational Services (JVS) Human Service sites in Detroit, MI and Los Angeles County, CA. Twenty-seven staff-stakeholders across the two JVS sites completed in-depth, semi-structured interviews focused on their experiences with WCBT over the four-year study period. Thematic analysis identified five core codes that most broadly captured participants' perceptions of WCBT implementation and the factors influencing implementation, including: (1) Need, (2) Buy-in and engagement, (3) Communication, (4) Sustainability concerns, and (5) Implementation facilitators. Core codes aligned with OTM factors related to impetus to transform, improvement initiatives, integration across boundaries, alignment from top to bottom, and leadership. Findings identify implementation strategies likely to optimize uptake and sustainability of WCBT within JVS sites that can be tested in a larger, multi-site implementation trial.

Sentiment Analysis Towards the KitaLulus Application Using the Naive Bayes Method from Google Play Store Reviews

Job search apps like KitaLulus are essential in helping graduates find jobs based on their skills and interests. Sentiment analysis is needed to understand user opinions about the KitaLulus application. The Naive Bayes method is used in this analysis because of its high efficiency and accuracy. This research used 597 data and achieved an accuracy rate of 91%. The eval_uation results show positive sentiment values for precision, recall, and f1-score of 0.99, 0.94, and 0.97 respectively. On the other hand, the model performance is low for negative and neutral sentiments. The aim of this research is to increase user understanding of the KitaLulus application and provide valuable assistance to developers in their efforts to improve the quality of the application.

Resume Optimization Model Using Machine Learning Techniques

In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model's decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates' qualifications and job requirements.

Designing an Effective Job Recommender System based on Embedded Machine Learning Models

The need to automate employment offers to qualify job searchers has gain attentions. For an automated recommendation systems to be used more frequently, a better user-friendly filtering techniques are required. This paper designs an automated process, referred to as “the job recommender”, which focuses on user-centric design and personalization for recommending and matching applicants with appropriate jobs. We use the bottom-up approach that uses dataset based on filtering algorithms to predict and make recommendations for job seekers. The algorithm helps the recruiters to produce the list of the résumé that best meets the job descriptions. In this context, the random forest (RF) and support vector machines (SVM) are adopted to train the data. They are supplied personalized information (qualifications, result of aptitude test, age, and work experience) reported on the résumés of individual candidates from the pool of submissions, and the system train data to learn the evolution of job selection by candidates based on these machine learning tools. The algorithm used would help the recruiters to produce the list of the résumé that best meets the job descriptions. The algorithms are designed to recommend personalized items tailored to each user's interests. Under the minimum hardware and software requirements, the job recommender system was implemented in streamlit - a python template, for designing the frontend.

Reservation Wages and the Wage Flexibility Puzzle

Abstract Using micro data for the UK and Germany, we provide novel evidence on the cyclical properties of reservation wages and estimate that wages and reservation wages are characterised by moderate and very similar degrees of cyclicality. Several job search models that quantitatively match the cyclicality of wages tend to overpredict the cyclicality in reservation wages. We show that this puzzle can be addressed when reservation wages display backward-looking reference dependence. Model calibrations that allow for reference dependence match the empirically observed cyclicality of wages and reservation wages for plausible value of all other model parameters.

Working for Jessica or Michael? Implications of gender stereotypes for job application intentions at technology startups

AbstractResearch SummaryWe examine a critical yet underexplored aspect of human resource management in nascent technology ventures: employee recruitment. Applying theories of gender stereotyping, we contend that female‐led technology startups face greater obstacles in attracting job applicants than their male counterparts. Evidence from a randomized online experiment conducted in 2020/2021 with 777 US job seekers substantiates this barrier, indicating that the disparities are partly rooted in gender‐stereotypical perceptions of female technology entrepreneurs as less competent, agentic, and warm, which contribute to less favorable assessments of their ventures' economic potential and employee empowerment potential. Startups with gender‐diverse leadership teams appear to overcome these biases. Confirmatory evidence comes from a 2024 replication study with 455 US job seekers, underscoring the need to address gender biases in the technological ecosystem.Managerial SummaryIn the competitive landscape of technology startups, attracting talent is key. Our study reveals that startups with female leaders face gender biases during recruitment, with job candidates perceiving female technology entrepreneurs as less competent, agentic, and warm—and their startup ventures as less likely to have what it takes to grow and to empower employees. Analysis from a randomized online experiment involving 777 US job seekers in 2020/2021 and a follow‐up study with 455 US job seekers in 2024 confirm such biases. Crucially, a gender‐balanced leadership team significantly counters such biases, enhancing the venture's appeal to potential hires. These insights highlight the need for technology startups to promote gender diversity within their leadership to dismantle stereotypes and attract a broader talent pool.

Perceptions of nursing graduates regarding artificial intelligence interviews: A Q-methodology study

Background & Aim: The integration of Artificial Intelligence (AI) in recruitment processes is increasingly prevalent, particularly within medical institutions. AI interviews are becoming a common practice, and their impact on the perceptions and experiences of candidates is a subject of growing interest. Nursing graduates, who are often at the forefront of medical practice, frequently encounter these AI-driven evaluations during their job searches. This study attempted to examine the perception of AI interviews among nursing graduates who have experienced AI interviews in medical institutions. Methods & Materials: The Q-methodology was applied by selecting 34 Q samples from the 102 concourses extracted through a literature review and in-depth clinical interviews with nursing graduates. The P sample consisted of 35 nursing graduates who had experienced AI interviews. Data were collected using the PQ Method's Q-methodology program, measured on a 9-point scale frame, and Q samples were normally distributed. Results: The study identified four factors of perception: Proactive AI Interview Preparation, Negative Perception of AI Interviews, Positive Perception of AI Interviews, and Critical Acceptance of AI Interviews. Conclusion: In conclusion, perceptions of AI interviews can be categorized into four main types, highlighting both the positive and negative aspects of this technology. The positive aspects include efficiency, fairness, and convenience, while the negative aspects involve concerns about privacy, bias, and the lack of human elements. To design AI interview programs tailored to specific job roles, it is crucial to balance these pros and cons. Additionally, reducing the burden of AI interviews through informative resources and pre-training programs is essential. For successful implementation, ongoing improvements, transparency, and a balanced integration of human judgment are necessary.

The Double Empathy Problem and Person‐Environment Fit: Mutual Understanding and Bidirectional Adjustment in Autistic Talent Acquisition

ABSTRACTOrganizations are starting to actively invest in hiring initiatives aimed at recruiting autistic talent. However, given many of these initiatives are in their infancy, knowledge of autism and how best to assess and support autistic employees is low. Building on person‐environment fit (P‐E fit) theory and the double empathy problem, we examine how neurodiversity employment programs build mutual understanding and construct fit between autistic employees and their hiring organizations. We further explore the role of third‐party neurodivergent talent consultants (NTCs) in aiding these efforts during the birth stage of the cycle of talent. In Study 1, we use action research involving in‐depth structured interviews of participants (managers, coaches, and autistic employees) in a neurodiversity employment program at a major corporation in the financial services industry, supplemented with additional interviews of hiring managers and coaches with similar roles in other organizational settings. In Study 2, we collect qualitative survey data from autistic adults participating in other neurodiversity employment programs. Using these data, we explicate the practices that remove barriers to autistic employment and processes of ongoing coaching and support from NTCs that build and manage the double empathy problem by enhancing mutual understanding and increasing the perceptions of fit. We find that NTCs facilitate the acquisition of autistic talent through revised job search and recruitment (e.g., tailored job descriptions), alternative screening procedures (e.g., hands‐on assessments), and supported socialization and onboarding (e.g., communication, networking).

Downplaying difference: how ethnoracial minorities strive for labour market access in an egalitarian context

ABSTRACT This article explores the type of destigmatisation strategies minorities use to access employment in Norway, a country characterised by ambiguous egalitarianism, a rapidly changing demographic, and a public “silence about race”. Analysing 52 interviews with minority Norwegians about their job search behaviour, we found that they hide or downplay minority-specific cues in their resumes to avoid immigrant stereotypes and to signal Norwegianness. Non-white informants also engage in detailed management of their phenotypical appearance through photographs but seldom frame their strategies as responses to racial stereotypes. These findings suggest that Norwegian minorities respond to a clearly defined hierarchy of national belonging related to immigrant status, which includes an unarticulated racial component. Drawing on comparative scholarship on destigmatisation, we argue that minorities’ job search strategies are shaped by broader sociocultural factors and highlight the importance of conducting studies of this kind in national contexts that lack a language for addressing racial differences.

Re-examining ‘personalised conditionality’: full-time obligations, partial adjustments and power asymmetries in the UK’s approach to work-related conditionality

Abstract Work-related conditionality policy in the UK is built around the problematic assumption that people should commit to ‘full-time’ work and job search efforts as a condition of receiving benefits. This is potentially in conflict with the idea that what is required of people should be tailored to their circumstances in some way – ‘personalised conditionality’ – and implies a failure to recognise that conditionality is being applied to a diverse group of people and in a context where the paid work that is available is often temporary and insecure. Drawing on thirty-three qualitative interviews with people subject to intensive work-related conditionality whilst receiving Universal Credit or Jobseeker’s Allowance in Manchester, the paper explores the work-related time demands that people were facing and argues that these provide a lens for examining the rigidities and contradictions of conditionality policy. The findings indicate that expectations are often set in relation to an ideal of full-time hours and in a highly asymmetric context that is far from conducive to being able to negotiate a reasonable set of work-related expectations. Work search requirements affect people differently depending on their personal circumstances and demand-side factors, and can act to weaken the position of people entering, or already in, work.

Empirical Study on the Influence of Internet Use on the Employability of Local College Students

Based on the data of China Family Panel Studies (CFPS) in 2020, this paper analyzes the impact of Internet use on the employability of local college students through OLS and logit regression models. The study found that the Internet has become an important tool for local college students to improve their employability. Students who actively participate in online learning, use career recruitment platforms and social media to expand career networks in local colleges and universities show stronger competitiveness in terms of professional skills, comprehensive quality and job search skills. The importance of Internet use to work, learning and information channels has a significant impact on the employability of local college students, and has a significant moderating effect. Through heterogeneity analysis, it is found that the impact of Internet use on the employability of local college students in eastern China is significantly higher than that in central and western China : the employability of college students with urban household registration is higher than that of local college students with rural household registration, and the employability of female local college students is lower than that of male local college students. Therefore, this study suggests that local colleges and universities should further guide students to make rational use of Internet resources and strengthen network literacy education, so as to better adapt to the employment market demand and promote high-quality employment of graduates.

Requirements to the position of a financial manager in a modern commercial organization

The article is devoted to the study and identification of new competencies for the position of a financial manager, which an employee in a modern commercial organization must have, as well as the analysis of the requirements that companies impose on financial managers. It considers the role and features of the position of a financial manager in a commercial organization, determines his plays in the job hierarchy of the company. Particular attention is paid to the influence of digitalization and technologies based on artificial intelligence on the work of financial managers in a commercial organization. The article uses such research methods as analysis, synthesis, modeling, observation and comparison, as well as methods of data processing and interpretation. The relevance of the work lies in increasing the interest of financial managers in digital technologies and ways to increase the company’s profits thanks to modern tools. Scientific novelty of the study consists of identifying the relationship between the degree of use of mechanisms based on artificial intelligence in the work of a financial manager with the effectiveness of his activities. The work provides recommendations for changing the professional competencies for a candidate for the position of financial manager in a modern commercial organization. The scientific study was conducted in relation to large companies in various industries and based on analysis of the labor market and job search services.

"Without IPS I Think I Would Really Fall Apart": Individual Placement and Support as Experienced by People With Mental Illness-Phenomenological Peer Research Study.

Having a job is an important component of recovery from mental illness and a source of economic, social, and health benefits. Most people experiencing severe mental illness (SMI) want to work but are excluded from employment opportunities. Employment specialists (ESs) working in individual placement and support (IPS) teams help persons struggling with SMI obtain competitive employment. This study is a qualitative phenomenological study of 10 IPS participants in the Norwegian context, serving to develop a deeper understanding of the IPS phenomenon as it is experienced in the everyday life of IPS participants. The study was designed as a peer research project including four members of a competence group with experience in IPS and SMI. The results, analyzed using the reflective lifeworld research approach, revealed four constituents: "Having a safety net along the way toward employment," "Feeling more like a person, not just a patient," "Brighter future," and "Going above and beyond employment support." IPS functions as an anchor in participants' journey toward employment. Strong and meaningful relationships with an ES seem crucial for IPS participants to gain the strength and confidence essential to engage in the job search. IPS participants experience various challenges in everyday life, resulting in ESs exceeding their vocational role to cover the unmet needs that health services seem unable to fully address. Closer coordination between vocational and health services, as well as a deeper focus on everyday life issues, will positively affect IPS participants' vocational outcomes and quality of life.

A More Effective Social Protection System for Older Workers: A Case Study of the Older Adult Employment Project in Thailand

The phenomenon of old-age pensioners reentering the workforce is neither new nor unique and has been well-reported in several published literature. However, there are remaining challenges to this group of job seekers whose needs have yet to be well-documented from employer’s perspectives, especially regarding decent work, legal landscape, and organizational culture. Our study seeks to provide a better understanding of the underlying causes of these and to help policymakers introduce social protection policies to create a more welcoming and supportive environment for older workers. The study used a case study from service companies in Thailand that participated in the Thai government’s campaign on promoting older employment. Data was collected from interviews with top managers of the human resources department, and that information was analyzed by thematic analysis. Key findings highlighted that the supportive mechanisms for matching decent work, a room for integrating legal framework, and a culture that values inclusivity and fair treatment of all workers, regardless of age, can enhance the social protection system for older workers. The establishment of a modern database of older workers’ skills, which ensures easy accessibility to both employers and older workers, the establishment of a task force specific for monitoring laws and regulations concerning older employment, and the development of effective measures against workplace discrimination are recommended as effective interventions for social protection.

“Sorry, We Rejected Your Application”: A Study on the Age Limit of Job Seekers in Indonesia from a Human Rights Perspective

“Sorry, We Rejected Your Application”: A Study on the Age Limit of Job Seekers in Indonesia from a Human Rights Perspective

Positioning LIS Students for Career Success

There is an assumption that students enter graduate programs with a certain level of knowledge and awareness about the field they have chosen to study and their career plans post-graduation. However, the literature shows that even graduate students benefit from services and programming related to career exploration and preparation. This is especially true in the library and information science (LIS) field, which offers myriad career paths and settings for graduates. Despite resources such as the ALISE Statistical Report, San José State University iSchool’s Career Trends report, and research on the core knowledge and skills desired of information professionals, extant literature examining career services provided to and desired by LIS graduate students is lacking. This project examined career planning services and programming in LIS graduate programs using a descriptive research approach. Data was collected via surveys of current LIS program administrators and enrolled graduate students. As LIS job seekers report such long and frustrating job search experiences that they consider leaving the field entirely, insights from this research will inform the creation or improvement of career services for LIS students, advance discussion of career services for graduate students, and identify exemplars that can be used to guide programming efforts. This paper will share preliminary findings from surveys completed by 25 program administrators and 400 current students.

Skill Signals in a Digital Job Search Market and Duration in Employment Gaps

Skill Signals in a Digital Job Search Market and Duration in Employment Gaps