Job Search Research Articles

Homeownership, the unemployed and financial hardship

We follow 3,826 Australian unemployed persons, approximately half of whom are homeowners. We conduct a matching analysis and find that homeownership reduces both experienced and perceived financial hardship. Building on recent findings in the literature that the presence of financial hardship deteriorates job search quality (i.e., stressors like financial hardship lead to the adoption of haphazard rather than focused job search strategies), we introduce financial hardship as a novel channel through which homeownership affects labour market outcomes of the unemployed. In our matching analysis, we include historical labour market performance and personality traits linked to mobility preferences, to address endogeneity. We also confirm that homeownership reduces residential mobility and increases neighbourhood social capital but find no effect on reservation wages of the unemployed. Considering declining homeownership rates across the OECD in recent years, our findings are both timely and imperative to understand the effect of homeownership on labour market outcomes of the unemployed.

ASPIREAI: AI Chatbot for Career Guidance

This paper introduces AspireAI, an AI chatbot designed for comprehensive career guidance. With the proliferation of Artificial Intelligence (AI), integrating such technology into career counseling is increasingly vital. Leveraging Large Language Models (LLMs), AspireAI provides personalized career information, job search advice, and educational recommendations. Our methods entail training LLMs on vast datasets of career-related content to enhance conversational capabilities. Results demonstrate AspireAI's efficacy in delivering tailored guidance, aiding users in making informed career decisions. Through this research, we highlight the importance and potential of AI-driven solutions in facilitating career exploration and advancement. Keyword: AI Chatbot, Career Guidance, Large Language Models (LLMs), Personalized Guidance, Job Search Advice, Educational Recommendations

A Phenomenological Exploration of Individual and University Supports Affecting U.S. International Students’ Job Searches

The present study explores the individual and university supports of U.S. international students during their job searches. Twenty-eight international students (93% Asian, 7% African; 57% female) from a U.S. Midwest public university participated in one-on-one semi-structured interviews in Spring 2021 regarding their U.S. job search experiences. All 28 participants (64% graduate students) were within one year of their graduation date. A transcendental phenomenological approach was employed to conduct a thematic analysis. Lower-order themes that significantly contributed to international students’ successful job searches in the US were organized into individual and institutional-level supports and collated into six key themes: (a) early planning for post-graduation; (b) networking; (c) in-demand majors and degree levels; (d) university-industry connections; (e) personalized department support; and (f) campus career services. Based on these findings, several implications for career development research, theory, and practice are discussed.

Personal Branding in the Digital Era: Social Media Strategies for Graduates

Purpose: This study represented an important effort to delve into the dynamics of personal branding on social media and its impact on the career trajectories of graduates. Methodology: By adopting a quantitative approach through a questionnaire-based survey from 280 respondents, the research sought to gather empirical data that could shed light on the patterns and trends associated with personal branding practices among this demographic. By designing survey questions aligned with the research objectives, the study aimed to capture a comprehensive picture of how graduates utilized social media for personal branding and how this activity correlated with their career advancement. Findings: The results showed a noticeable and meaningful connection, with a correlation coefficient (r-value) of 0.707, indicating statistical significance (p = 0.05). This indicated a robust relationship between the variables under examination. The strong correlation underscored the importance of personal branding on social media in assisting graduates with their job search endeavors on these platforms. This capability allowed them to establish direct connections with recruiters and improve their career opportunities significantly. Additionally, the rapidly evolving nature of social media platforms may have introduced complexities in capturing the most current trends and practices related to personal branding. Unique contribution to Theory, Practice and Policy: Symbolic Interactionism theory can be used to examine the idea of personal branding and how people utilize social media to present themselves and shape perceptions. Moreover, this theory could prove beneficial for future researchers or scholars keen on delving into related themes, providing a fundamental reference for their investigations. The research proposed a variety of suggestions that could enhance understanding regarding the personal branding strategies that help graduates achieve their professional goals. These recommendations encompass recognizing the importance for recruiters, gaining insights into graduates' online activities, and grasping candidates' skills and interests, all of which are essential for recruiters to make well-informed choices and extend invitations to potential team members.

Online Employment Services for Immigrant Professionals: An Environmental Scan

This environmental scan assesses the current landscape of online employment services for immigrant professionals in Canada. The data collection method involved web scanning of twelve urban centers and two rural census areas with high per capita immigrant populations. The study analyzed 80 online services for immigrants in Canada based on geographical variation, the type of digital modality, clientele, and needs addressed. The findings reveal disparities in access to online employment programs tailored for skilled immigrants across different geographic locations. In addition, e-learning and self-paced online courses were identified as the most prevalent digital modalities. In terms of targeted clientele, findings show that the services primarily targeted immigrants as a homogenous group, with a notable emphasis on supporting skilled immigrants in high-demand fields such as IT and healthcare. Finally, access to job search resources emerged as the highest priority among the needs addressed, while areas such as financial assistance have the potential for further growth.

Використання великих даних для інформаційного забезпечення ринку праці

The challenges of collecting current labor market data amidst global changes and geopolitical fluctuations increasingly necessitate new approaches and alternative methods of data collection and analysis. This highlights the relevance of research aimed at developing and adapting labor market information provision methods to contemporary challenges. A promising approach in this context is the use of Big Data for labor market assessment, which involves collecting information from sources such as online job search and vacancy portals. This method allows for a deeper analysis of market trends and provides a more accurate and timely assessment of labor market needs and opportunities. The aim of this article is to discuss approaches to developing labor market information systems using Big Data, particularly online data from job vacancy websites. It examines the use of Big Data in labor market analysis based on a database containing over four million job vacancies posted on Ukrainian job search portals over the last five years, provided by the European Training Foundation (ETF). The effectiveness of these approaches in facilitating job search for all interested parties is evaluated, particularly through providing insights into the dynamics of supply and demand in the labor market based on data from these portals. The opportunities and limitations of using Big Data in this context are analyzed, including their impact on employment policy development and labor market planning. The potential benefits of Big Data in providing deeper and more accurate market condition analyses are outlined, along with technical aspects and challenges associated with their processing and interpretation. The article examines methodological approaches to data collection and analytical processing in the context of accelerated transformations, volatility, and limited access to traditional information resources. The scientific novelty of the article lies in the substantiation of the feasibility and appropriateness of using open data from online job portals for labor market information provision under current conditions. In conducting the research, methods of analysis, synthesis, and generalization were applied to identify the main contemporary issues of labor market information provision in Ukraine. The effectiveness of data collection methods based on web scraping and parsing techniques was evaluated, as well as the use of the integrated Snowflake platform to identify key trends and patterns in the labor market. The conclusions summarize the main points and substantiate directions for further research, highlighting the significance of Big Data in developing employment strategies and optimizing the labor market.

Small work pleasures and two types of well‐being

AbstractDoes wage labor contribute to well‐being beyond providing an income? Well‐being can be understood in eudaimonic terms as the happiness derived from a socially valued life or in hedonic terms as the experience of pleasure. The eudaimonic–hedonic divide is replicated in competing progressive visions of the place of work in a good life. Laborist theories stress the centrality of paid work for a meaningful life. By contrast, for post‐work theories, pleasure is important for well‐being, and work is generally not expected to be pleasurable. Surprisingly, many of the participants in my study of diverse US job seekers described one or more of the jobs they had held as “fun.” Fun connotes enjoyment without deeper meaning, a hedonic rather than eudaimonic account of nonfinancial work rewards. What made a job fun were small work pleasures: enjoyment of the tasks and feeling competent at them, enjoyment of the physical work environment, or enjoyment of social relations on the job. These small pleasures could be found in both standard and nonstandard, precarious jobs. This study indicates the need for a labor politics that improves hedonic well‐being on the job. It also expands an “anthropology of the good” to include ordinary enjoyment.

Real And Fake Job Post Detection Using Machine Learning

The transition to online platforms has streamlined processes, reducing manual efforts. Job postings, now predominantly online, offer companies a broader reach for talent acquisition. However, amidst legitimate postings, fraudulent ones exist. This study aims to distinguish between real and fake job postings using machine learning techniques with optimal accuracy. Employing various data mining methods and classification algorithms—Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron—we predict job authenticity. Supervised machine learning techniques guide dataset analysis, including variable identification and univariate, bivariate, and multivariate analyses, alongside handling missing values. Comprehensive data validation, cleaning, preparation, and visualization are conducted. Our experimentation, utilizing the Employment Scam Aegean Dataset (EMSCAD) with 17,881 samples, informs our approach. Ultimately, this research aims to enhance job seekers' security by effectively identifying fraudulent job postings. remove the writing issues. Key Words: Logistic Regression, KNN, Decision Tree, XGBoost, Support Vector, Random Forest, and Multilayer Perceptron.

PERAN KONSELOR DALAM PASAR KERJA DI PROVINSI SULAWESI BARAT

In West Sulawesi, young people face difficulties in finding employment because of employment opportunities are limited. By looking at the portrait of the labor market in West Sulawesi Province, counselors can help them through career counseling by working together with various institutions, such as schools, vocational training centers (BLK), even schools. with various institutions, such as schools, vocational training centers (BLK), and even the West Sulawesi Provincial Regional Government of West Sulawesi Province. The approach in this research approach in this research is descriptive qualitative with the data source of labor market portrait in West Sulawesi Province in 2022. West Sulawesi Province in 2022. The purpose of this research is to explain what can be done by counselors in the labor market in West Sulawesi Province. The result of this research The results of this study describe 1) a portrait of the labor market in West Sulawesi Province in 2022 which presents statistical data that includes statistical data on the labor market in West Sulawesi Province. 2022 which presents statistical data that includes information on job seekers, job vacancies, to the salary offered; 2) Counselors can use portrait of the job market in West Sulawesi Province as occupational knowledge in career counseling.

ROLE OF AI IN HUMAN RESOURCE MANAGEMENT :

AI can help pick up the pace by helping managers nurture each potential hire automatically, and it allows them to receive notifications when a candidate applies for an open position. Artificial intelligence (AI) can help usher in a new era of human resource management, where data analytics, machine learning and automation can work together to save people time and support higher-quality outcomes. As AI technology moves beyond automation to augmentation, companies may be looking at how AI tools can make the work of human resources (HR) better for employees and job seekers. It’s not just about saving time; it’s also about providing information, insights and recommendations in near real-time. And that’s just the start of AI in human resources. Keywords: Professional learning and development, Candidate sourcing and hiring, Procurement of short-term workers, Automating HR service, Enhanced employee support, Increased Efficiency, Enhanced candidate experiences, Reskilling talent and restructuring job role.

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

Fake Job Detection Using Machine Learning

The research suggests using machine learning classification methods to identify and prevent fake job postings online. Nowadays, many companies prefer to advertise their job vacancies on the internet for easy access by job seekers. However, there are scammers who trick people into paying for non-existent jobs. Through data analysis and machine learning, we can distinguish between legitimate and fake job postings. Various algorithms are utilized to detect fraudulent posts and protect job seekers from falling victim to scams. The system is designed to teach the model how to distinguish between real and fake job listings using past data on fraudulent and legitimate job postings. Initially, supervised learning algorithms like classification techniques can be used to tackle the problem of identifying scammers in job ads. It will utilize multiple machine learning algorithms and choose the one with the best accuracy in predicting if a job posting is legitimate or not. Key Words: Fraud Job, Job Seeker, Machine Learning, Internet Recruitment, Classification

Significance of Artificial Intelligence in Recruitment Process

HR is the division of a business that is charged with finding, screening, recruitment, training job applicants. It typically finds, trains, hires and fires employees. Organization are increasingly recognizing the critical role of HRD in nurturing their most valuable asset- their people, it starts from the very sourcing, screening, recruiting, induction, performance management and their development. With the changing scenario, technological advancement has impacted HR process significantly. The innovation of artificial intelligence and machine learning are now significantly entered the field of human resource development. AI system can analyze vast amount of HR data to identify potential candidate and predict their chances of getting shortlisted foe a particular job. AI recruitment reduces by automating all the mundane tasks. Recruitment has become a hotbed for AI applications in HR. The platform has led to an improvement in candidate experience by making it easier for job seeker to apply for open position and communicate directly with the company. With the changing scenario and advancement in technology, AI has incorporated in every disciple. It helps I increasing the human efficiency of doing task for quick and better achievement of organizational objectives. HR department in itself is in a pivot position as it starts before any function comes into action. It plays key role in streamlining all the departments and processes that are working towards business operations. AI isn’t here to replace human recruitment but it’s here to make their lives infinitely easier. Though AI finds no comparison to the human brain, still it’s a game changer for improving the quality of work done.

Systematic Adaptation of the JOBS Program for Use with Adults with Visual Impairments

Abstract Introduction The purpose of this study was to use a systematic process to adapt the JOBS program, an evidence-based job search intervention, for use with unemployed adults with visual impairments and implementation via videoconferencing. Method The Planned Adaptation approach, an established framework for adapting evidence-based practices, guided the adaptations. The four-step Planned Adaptation process consists of examining the program’s theory of change, identifying population differences, adapting the program content, and adapting the evaluation strategy. Results The original JOBS program has five key components, two critical causal mechanisms, and several potential moderators. Differences between the original and new target populations include needs, experiences, and employment barriers. Adaptations include activity changes, new processes to enhance outcomes, and changes to the implementation procedures. The evaluation plan for the adapted program includes a pilot study to test the updated activities, processes, and procedures, and it is followed by an efficacy study to assess outcomes. Discussion The JOBS program was a strong candidate for systematic adaptation due to its theoretical foundation, thorough documentation, and established active ingredients. Stakeholder input increased the relevance and feasibility of the adaptations. Future research is needed to evaluate the adapted program and its outcomes. Application for Practitioners After the completion of the evaluation plan, the new JOBS adaptation will be available for practitioners to use with adults with visual impairments. Practitioners may find the Planned Adaptation approach useful for adapting other programs to better meet the needs of the populations they serve.

Higher technology education and industry interface: how the theory of planned behavior applies in student work-integrated learning and job search intention link

Higher technology education and industry interface: how the theory of planned behavior applies in student work-integrated learning and job search intention link

A challenge-based survey of e-recruitment recommendation systems

E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of job seekers for positions and on job seekers’ and recruiters’ preferences. Therefore, e-recruitment recommendation systems may greatly impact people’s careers. Moreover, by affecting the hiring processes of the companies, e-recruitment recommendation systems play an important role in shaping the competitive edge of companies. Hence, it seems prudent to consider what (unique) challenges there are for recommendation systems in e-recruitment. Existing surveys on this topic discuss past studies from the algorithmic perspective, e.g., by categorizing them into collaborative filtering, content-based, and hybrid methods. This survey, instead, takes a complementary, challenge-based approach. We believe this is more practical for developers facing a concrete e-recruitment design task with a specific set of challenges, and also for researchers that look for impactful research projects in this domain. In this survey, we first identify the main challenges in the e-recruitment recommendation research. Next, we discuss how those challenges have been studied in the literature. Finally, we provide future research directions that we consider most promising in the e-recruitment recommendation domain.

Job Recruitment Analysis based on Xgboost Decision Tree

With the increasing number of college students, "the most difficult graduation season" is a topic of anxiety for many parents and children. Therefore, before looking for a job, Xgboost decision tree analysis of the position is of great significance to job seekers and work units. In view of the diversity of region, position, salary and education, Xgboost decision tree algorithm can visually show information to job seekers in the way of word cloud for job seekers, so as to facilitate the corresponding job recommendation for job seekers.

The Impact of Job Referral from Different Social Connections on Initial Wages: Evidence from Chinese Labor Market

Job referrals are a popular approach to finding a job through social ties. In the Chinese labor market, it is popular to locate jobs through social contacts, which is thought to be a means to earn a higher starting salary. The purpose of this paper was to investigate the disparities in starting salaries across employment found through various social relationships. Based on social network theory, this research added variables to the Mincer-type equation for regression analysis to measure the strength of social relations. The findings of this study, which used data from the China Household Income Project (CHIP 2018), showed that, unlike previous studies that focused on strong ties in the Chinese labor market, jobs found through weak ties have significantly higher starting salaries than jobs found through strong ties, and the difference in starting salaries for jobs found by migrant workers through various social ties is more pronounced. As a result, under the bleak backdrop of the labor market and decreasing economic growth, it was recommended that job seekers, particularly migrant workers, broaden their social circle in order to seek better initial pay.

Navigating the Job-Seeking Journey: Challenges and Opportunities for Digital Employment Support in Kashmir

The issue of youth unemployment is a global concern, with active initiatives, policies, and technologies in place to support employment seekers. Despite these efforts, youth unemployment in various regions is attributed to the complex challenges that impede access to resources, particularly in contexts characterized by socio-political unrest. To understand the use of job-seeking resources in such a context, we situate our work in the region of Jammu & Kashmir, which has a long-standing history of socio-political conflict ongoing for more than three decades. For this work, we conducted 21 semi-structured interviews with participants from Kashmir to understand their current job-seeking practices and the challenges they encounter during the entire process. Our findings present the systemic infrastructural challenges the youths face due to the ongoing crisis. Analyzing the youths' workarounds to mitigate these challenges, we discuss how we might leverage these workarounds into designing systems while navigating through the contextual complexities. We discuss the challenges of designing in the context and provide design recommendations to support job seekers.

The impact of digital economy on emerging employment trends: Insights from the China Family Panel Survey (CFPS)

In the context of rapid Internet technology advancement, the digital economy is reshaping employment trends, transitioning from traditional structures to innovative work paradigms. This investigation, leveraging macro-level digital economy indices and micro-level data from the China Family Panel Survey (CFPS) spanning 2016 to 2020, unveils that the digital economy not only bolsters employment quality but also fosters the growth of new employment models. It highlights how the digital economy's facilitation of efficient information exchange on online platforms aids in aligning job seekers with suitable opportunities, thereby nurturing the sustainable evolution of these emerging employment paradigms.