Job Matching Research Articles

Hyper-personalized employment in urban hubs: multimodal fusion architectures for personality-based job matching

AbstractIn the evolving landscape of smart cities, employment strategies have been steering towards a more personalized approach, aiming to enhance job satisfaction and boost economic efficiency. This paper explores an advanced solution by integrating multimodal deep learning to create a hyper-personalized job matching system based on individual personality traits. We employed the First Impressions V2 dataset, a comprehensive collection encompassing various data modalities suitable for extracting personality insights. Among various architectures tested, the fusion of XceptionResNet with BERT emerged as the most promising, delivering unparalleled results. The combined model achieved an accuracy of 92.12%, an R2 score of 54.49%, a mean squared error of 0.0098, and a root mean squared error of 0.0992. These empirical findings demonstrate the effectiveness of the XceptionResNet + BERT in mapping personality traits, paving the way for an innovative, and efficient approach to job matching in urban environments. This work has the potential to revolutionize recruitment strategies in smart cities, ensuring placements that are not only skill-aligned but also personality-congruent, optimizing both individual satisfaction and organizational productivity. A set of theoretical case studies in technology, banking, healthcare, and retail sectors within smart cities illustrate how the model could optimize both individual satisfaction and organizational productivity.

Exploring the Challenges and Opportunities of Employing Persons with Disabilities

Background To supplement the extant literature on the economics of employing persons of disability, the present study explores the challenges and opportunities companies face with the employment of people with disabilities. Despite legal frameworks and growing awareness, individuals with disabilities often face significant barriers to securing meaningful employment. Misconceptions about their capabilities and concerns over potential costs persist, overshadowing evidence of economic and social benefits. This study explores these dynamics, emphasizing the potential advantages of inclusive hiring practices for organizations. Methods Semi-structured expert interviews were conducted with HR managers from five South Tyrolean companies across diverse industries. Participants were selected based on criteria such as company size (minimum 35 employees) and HR management experience (at least 3 years). Interviews focused on exploring workplace adjustments, employment motivations, challenges, and barriers, generating qualitative insights into inclusion practices. Results The study identified several advantages of employing individuals with disabilities, including low turnover rates, increased employee motivation, and a positive workplace atmosphere. Most companies incurred minimal costs for workplace adjustments. Challenges included insufficient understanding among staff, difficulty matching job roles with capabilities, and bureaucratic obstacles. The experts also noted a lack of job applications from individuals with disabilities, potentially due to low confidence or family overprotection. Conclusions Employing people with disabilities can be economically profitable and is not limited to social aspects alone. Existing barriers can often be dismantled without significant financial or time investment, with obstacles being readily overcome. This article contributes to a better understanding of challenges and opportunities of employing staff with disabilities and demonstrates the conditions under which economic benefits emerge for companies. The findings are discussed from an economical and social/cultural perspective with practical implications for human resource practice and operation management.

Life After High School: The Employment Experiences of Autistic Young Adults

The present study examined the employment landscape for 129 autistic young adults. Data were collected over multiple waves including high school and early adulthood. Parents participated in interviews and responded to questions regarding young adult employment outcomes, on-the-job supports, job match, and overall job satisfaction. The predictive relationship between in-school variables and employment outcomes was examined. Findings indicate over 50% of the sample had at least one job working independently in the community for pay. Parents reported the need for both formal and informal support to both obtain and sustain employment. Over half of the parents reported that the young adult’s job was aligned with their interests/strengths and that the young adult was satisfied. Predictive analyses indicated a relationship between work-based learning experiences in high school and general education involvement during high school and later paid employment in adulthood. Future research, limitations, and implications for practice are discussed.

Mandatory Notice of Layoff, Job Search, and Efficiency

Abstract In all OECD countries, mandatory notice (MN) policies require firms to inform workers in advance of a layoff. In our theoretical framework, MN helps workers avoid unemployment and find better jobs by encouraging them to search for a new job while still employed, thereby increasing future production. The magnitude of this production gain depends on the relative effectiveness of search while employed versus unemployed. But on-the-job search and diminished work incentives reduce current production. If future gains outweigh current production losses, longer advance notice improves production efficiency. If not, Coasian bargaining predicts that firms offer a larger severance instead of longer notice. With bargaining, the sole efficiency loss of MN is due to delayed separations of unproductive job matches. We test these predictions using novel Swedish administrative data on layoff notifications. Workers eligible for extended MN receive longer notice and larger severance, resulting in less exposure to nonemployment spells and higher-paying jobs. These favorable labor market outcomes are solely due to longer notice; in contrast, larger severance delays job finding and has no impact on wages. We also show that advance notice replaces job search while unemployed with more effective search while employed. On the production side, we document a productivity drop among notified workers and estimate a production loss due to delayed separations. Using our estimates of production gains and losses to evaluate the overall production efficiency, we conclude that the gains of MN seem to outweigh the losses.

Standortnachteil Arbeitskräftemangel: Österreich braucht eine umfassende Fachkräfteoffensive

Abstract Shortages of skilled workers are now widespread on the Austrian labor market. This results in serious locational disadvantages such as rising labor costs and a lack of personnel to develop and make use of innovations. Austria should therefore launch a comprehensive skilled labor campaign soon. This should correct the framework conditions that currently hinder satisfactory labor market integration of women, older people and migrants, as well as improve employee mobility, since productivity reserves can be leveraged through better job matches.

Analysis of Instruments for Implementing Intelligent Job Matching Models

Vocational high schools in Indonesia face challenges in producing graduates who meet the needs of the job market. The link and match policy along with the Job Matching system are proposed to enhance curriculum relevance to the workforce. The intelligent job matching model, which integrates machine learning technology, is expected to provide job recommendations aligned with graduates' competencies. Data analysis techniques used include qualitative and quantitative statistical analysis, encompassing Validity Analysis, Practicality Analysis, and Effectiveness Analysis. The intelligent job matching model has proven effective in assisting in matching vocational high school students' competencies with workforce demands in the industry. In the 2023 job fair, the success ratio of students invited for interviews until the employment process reached 53.13%, exceeding the Ministry of education and culture's target of 40%. This indicates that the model can provide job recommendations accurately enough, aiding in improving the direct employment rate of vocational high school students. Research indicates that the intelligent job matching model is effective in facilitating students' transition to the workforce. The model has been tested for validity, practicality, and effectiveness in enhancing students' employability and strengthening the relationship between education and industry.

Improving job matching with deep learning-based hyper-personalization

This study introduces a novel approach to streamline the recruitment process, benefiting both employers and job seekers. It leverages real-time personality-based classification to match candidates with the most suitable roles in a scalable and precise manner. This is achieved through machine learning-driven hyper-personalization, employing deep learning models to create a predictive language model. The study encompasses two key tasks: binary classification, distinguishing sentences containing soft skills (1) from those that do not (0), and multi-class classification, categorizing positive sentences into five classes based on Big Five personality traits. The research involved a series of experiments. Initially, multiple machine learning algorithms were employed to establish baseline models. Subsequently, the study investigated the impact of deep learning versus these baseline models. The results demonstrated an accuracy of 0.79% and 0.68% for binary classification tasks, and 0.79% and 0.60% for multi-class classification tasks, using Support Vector Machines in the machine learning task, and Bidirectional Long Short-Term Memory in the deep learning task, respectively. This approach showcases promise in revolutionizing the job matching process, offering a more efficient and accurate means of connecting individuals with their ideal employment opportunities based on their unique soft skills and personality traits.

UNIFIDE EMPLOYEE DATA TEAM TRCKER

Abstract : A Team Track: “Unified Employee Data” provides a thorough overview of important metrics, tasks, and insights pertinent to each individual’s function inside a business, acting as a centralized center for them. With the use of this digital technology, staff members can easily access critical information, improving productivity, efficiency, and transparency all around. Fundamentally, an employee dashboard compiles information from multiple sources—such as project management software, HR systems, and performance metrics—and presents it in an intuitive manner. Employees can monitor their progress, evaluate critical updates, and remain up to date on company-wide efforts thanks to this consolidation. With its many features and functionalities, the employment dashboard acts as a focal point for effective job matching and recruitment. The dashboard uses data analytics and state-of-the-art technology to give users real-time insights into market trends, demand signals, and talent assessments. This allows users to make well-informed decisions regarding their hiring strategies and career routes. The dashboard gives staff members the ability to efficiently manage their obligations by giving them access to training materials and project timetable tracking.Additionally, the employee dashboard encourages teamwork and communication. Through the provision of real-time project progress updates Yes, the following are some important details you should provide in an abstract regarding an employee dashboard: Overview: Give a succinct explanation of the purpose of an employee dashboard in contemporary work environments. Mention how it provides a consolidated platform where staff members may access a range of tools, services, and data pertaining to their jobs and companies. Features: Emphasize the key elements that are commonly present in an employee dashboard, such as customized employee profiles, task management tools, collaboration features, access to corporate announcements, HR papers, and payroll data. Benefits: Talk about how having an employee dashboard can lead to better decision-making through real-time data analytics, increased productivity through streamlined resource access, improved communication and transparency within the company, and enhanced employee engagement. Index Terms – Chatgpt, AI tool, Java script

Inclusive Human Capital—Employment Transition Programme for Adults with Autism at Enabling Academy, Yayasan Gamuda

The case focuses on the Enabling Academy (EA) team within Yayasan Gamuda, the philanthropic arm of Malaysia-based Gamuda Berhad. Gamuda is recognized as one of the largest Malaysian infrastructure companies and has been acknowledged for its pioneering efforts in neurodiversity employment for white-collar positions in Malaysia. EA provided employment training, job matching and job coaching for adults with autism, aiming for sustainable employment. While the programme initially saw success, its outreach remained limited. Gamuda had a goal to disseminate its best practices and encourage other entities to integrate more individuals with autism into their workforce. The team needed to devise strategies for senior management. This case addresses significant issues related to responsible organizational practices, untapped human potential, neurodiversity employment frameworks, workplace diversity and inclusion, and sustainable human resource management. It illustrates the decision-making process of the EA team under the guidance of the company’s leadership. The framing of the case reflects EA’s non-profit status and its preference for a collectivist management approach.

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.

The Influence of Parents’ Social Origin on Occupational Achievements of University Graduates: An Analysis of Education–Job Match

Education–job mismatch is an indicator that describes the quality of an individual’s occupational achievements, with socio-familial background being one of the most influential factors in attaining these achievements. In this study, we aim to identify the extent to which the educational level attained by parents influences the education–job mismatch of university graduates. The analysis, deploying binary logistic regression models, is based on the Spanish Survey on the Labor Insertion of University Graduates 2019, with more than 30,000 cases. Main results are that parents’ educational level largely determines the education–job mismatch of university graduates, acting through the intergenerational transmission of opportunities. In this sense, variables such as the field of study or those related to educational investment while at university are highly influential. When we analyze the persistence of mismatch, the influence of these variables is more decisive, so it is considered that there is an indirect influence of social background on occupational attainment through the variables linked to it. Recommendations are made for educational administrations that should favor equal opportunity measures and enhance the effectiveness of educational guidance services.

Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model

Vocational high schools are one of the educational stages impacted by Indonesia's low quality of education. Vocational High Schools play a crucial role in improving human resources. Graduates of Vocational High Schools can continue their education at universities or enter the workforce directly. Many students are found to have not yet considered their career path after graduation. At the same time, graduates are still expected to find mismatched employment with their expertise and skills. This research uses CRISP-DM, or Cross Industry Standard Process for Data Mining, to build machine learning models. The approach used is content-based filtering. This model recommends items similar to previously liked or selected items by the user. Item similarity can be calculated based on the features of the items being compared. After students receive job recommendations through intelligent job matching, they can use these recommendations as references when applying for jobs that align with their results. This process helps students direct their steps toward finding jobs that match their profiles, ultimately increasing their chances of success in the job market. These recommendations are crucial in guiding students toward career paths that align with their abilities and interests. The Intelligent Job Matching Model developed in this research provides recommendations for the job-matching process. This model benefits graduates by providing job recommendations aligned with their profiles and offers advantages to the job market. By implementing the Model of Intelligent Job Matching in the recruitment process, applicants with job qualifications can be matched effectively.

A Study on Perception of Job Seeker on Recruitment through Job Portals

This study investigates the perceptions of job seekers regarding the effectiveness and reliability of job portals as a recruitment platform. With the proliferation of online job search platforms, understanding job seekers' perspectives is crucial for both employers and job portal operators. Through a mixed-method approach involving surveys and interviews, this research delves into the preferences, concerns, and experiences of job seekers utilizing job portals. Initial findings suggest a mixed perception among job seekers, highlighting factors such as platform usability, trustworthiness of job listings, and the relevance of job matches. The study aims to provide insights for improving job portal functionalities and enhancing the overall recruitment experience for both job seekers and employers.

Just reallocated? Robots displacement, and job quality

AbstractConcerns over widespread technological unemployment are often dismissed with the argument that human labour is not destroyed by automation but rather reallocated to other tasks, occupations or sectors. When focusing on pure employment levels, the idea that workers are not permanently excluded but ‘just’ reallocated might be reassuring. However, while attention has been devoted to the impact of automation on employment levels, little has been said about the quality of new job matches for displaced workers. Using an administrative longitudinal panel covering a large sample of Spanish workers from 2001 to 2017, we investigate the short‐ and medium‐term re‐employment prospects of workers displaced from sectors with an increasing density of industrial robots. Furthermore, we examine the role of reallocation to other sectors or local labour markets as adjustment mechanisms. Our analysis suggests that exposed middle‐ and low‐skilled workers are more likely than non‐exposed workers to remain unemployed 6 months after displacement. Among those who find a new occupation, an additional robot per 1000 workers increases the probability of being re‐employed in a lower paying job by about 1.9 percentage points for middle‐ and low‐skilled workers, with significantly higher penalties for those who relocate to a different sector. Moreover, these workers tend to face a qualification downgrading in the new job and are more likely to be re‐employed through temporary employment agencies. High‐skilled workers are less negatively affected by exposure, although they can also incur a penalty when changing sectors.

Changes and Challenges of Recruitment Models in the Era of Big Data Intelligence

With the rapid development of information technology, we have ushered in the era of big data intelligence. In this era, there is an explosive growth of data, and the application of intelligent technology is becoming more and more widespread. The arrival of the Big Data Intelligence Era has brought a far-reaching impact on the recruitment model. This paper first introduces the development of big data and artificial intelligence, and then analyzes the application of big data and artificial intelligence technology in enterprise recruitment management, and then analyzes the changes it brings to the recruitment mode, including effectively improving the efficiency and accuracy of recruitment, providing data support and scientific guidance for the development of recruitment plans, making network recruitment more efficient and cost advantageous, recruiting groups tend to be more diversified, solving the pain point of accurate matching of people and jobs, etc., and then analyzes the changes it brings to the recruitment mode. Finally, it analyzes the challenges to the recruitment management process of enterprises in the era of big data intelligence.

Tobit 모형을 활용한 취업 만족도 결정요인 분석

In this paper, the factors affecting job satisfaction of college graduates were analyzed. Job satisfaction consists of various factors, but job satisfaction is limited to job match, job satisfaction, and wages in this paper. Job match, job satisfaction, and wages each have information on employment status and compound information on the magnitude of job satisfaction. However, many previous studies have used only information on the size of job satisfaction to analyze it, overlooking the problem of sample selection bias. In order to overcome this problem, the Tobit model was used in the analysis. The main results are summarized as follows. First, Tobit model was statistically significant. Second, it can be seen that as male compared to female, age, and average GPA increase, job satisfaction increases significantly. Third, it can be seen that natural science compared to arts is higher, and the higher the major satisfaction and parents' income, the higher the job satisfaction. Finally, as a result of homogeneous test of the regression coefficients for each category level of independent variables, it can be seen that there is a statistically significant difference in most of the independent variables.

Minimum wages, wage dispersion and financial constraints in firms

This paper studies how minimum wages affect the wage distribution if firms face financial constraints. Using German employer-employee data and firm balance sheets, we document that the within-firm wage dispersion decreases more with higher minimum wages when firms are financially constrained. We introduce financial frictions into a search and matching labor market model with stochastic job matching, imperfect information, and endogenous effort. In line with the empirical literature, the model predicts that a higher minimum wage reduces hirings and separations. Firms become more selective such that their employment and wage dispersion fall. If effort increases strongly, firms may increase employment at the expense of higher wage dispersion. Financially constrained firms are more selective and reward effort less. As a result, within-firm wage dispersion and employment in these firms fall more with the minimum wage.

Enhancing employability through training programmes in Chile: A qualitative study of the challenges identified by local actors involved in a subnational experience

AbstractActive labour policies are globally adopted to tackle unemployment and enhance job matching. In Chile, a predominantly supply‐focused approach involves training workers, especially in vulnerable groups, as it is believed that investing in human capital will align job demand and available skills. This subnational Chilean case study in the Coquimbo region delves into the factors influencing worker training and placement programme implementation, with a specific focus on stakeholders' perceptions. Through qualitative data from interviews and focus groups, the research seeks to explore subjective challenges, including individual perspectives, governance and structural factors. Various variables affect policy implementation at different levels. On the micro level, actor motivations like personal goals and financial incentives are crucial for policy design. Regional governance levels grapple with coordination difficulties that impact programme implementation. Collaboration among stakeholders like government agencies, educational institutions and employers is essential. Structural issues within the regional economy and the target population also pose challenges to policy implementation. The study suggests that a comprehensive policy approach that considers the interconnectedness of institutions, labour markets, skills and the various stakeholders involved in the process is vital to address the complexity of active labour policies at the local level in Chile.

Systematic development of entrepreneurship skills framework for students of electrical technology in Nigeria

The rising rate of youth unemployment and the attendant consequences on the general population in Nigeria called for concern. The academia and other relevant stakeholders have gradually come to realize that the possession of academic qualifications alone cannot guarantee a good quality job. Thus, many of Nigerian higher institutions are now introducing entrepreneurship studies into their school curriculum without a clear framework. This study intends to create a topology of skills that will help Electrical Technology students in Nigeria find jobs. To gather information about the different skills needed, the Google Scholar search engine was adopted. The search was between 2018 and 2023. The inclusion criteria involve entrepreneurship studies with a focus on Technical Education, Vocational education, and Electrical Technology. A total of 80 Journal articles were extracted for the study. Data were directly collected from the critical stakeholders. The study was guided by 3 objectives. The participants for the study involved 12 experts from the industry and academics. The sample size was purposive and obtained based on Theoretical saturation. The topology of skills was achieved with the application of the Nvivo12 software and the thematic analysis. A Broad skill suitable for the 21st-century job matching for electrical technology students was generated. The study is recommended for in-depth analysis in order the isolate all the in fits.

How Close Collaboration With Unions During the COVID-19 Pandemic Helped the SIA Group to Save Jobs and Emerge Stronger

When airports around the world were brought to a standstill by the pandemic, Singapore Airlines (SIA) Group was faced with its biggest challenge—management of its almost 30,000-strong staff in trying times which called for resource cuts. Nevertheless, the SIA Group successfully surmounted these challenges and emerged as one of the most resilient airlines in the post-pandemic landscape. The collaboration between the SIA Group and unions was pivotal in skilfully navigating a balance between a compassionate approach towards its workers and strategic measures aimed at ensuring organisational survival. Through the collaboration, schemes rooted in the SMART Strategy model—comprising Job Support, Job Matching, Job Advocacy, and Job Re-creation and Training—were developed to safeguard the livelihoods and bolster resilience within the workforce. This paper offers valuable lessons for other organisations at risk of technological disruption or industry transitions, and as layoffs become more commonplace.