Job Search Research Articles

A market driven identification of current and emerging skills requiring an MBA degree: a topic modelling approach

ABSTRACT The emerging field of Labour Market Intelligence focuses on extracting real-time information for professional skills. Online job posting websites provide unique opportunities to gather such intelligence, useful for job seekers and academic administrators in charge of preparing the future labour force. We propose a probabilistic topic modelling approach, incorporating occupational ontologies, to extract skillsets required for jobs needing a Master of Business Administration (MBA) degree. Analysing LinkedIn job postings in Pennsylvania, USA, we identified 28 job functions and associated skillsets confirming known demands and revealing new ones not yet identified in government and private labour market reports. By aggregating these skills, we can align MBA curricula with market needs, offering a superior alternative to conventional surveys. Our results are comparable with other popular neural models. Our approach provides improved computational efficiency with simpler methods Additionally, this methodology can be applied globally in other fields with existing occupational ontologies.

‘I'm a resourceful person and I ask questions everywhere I go:’ adult job seekers’ adaptive literacy practices in the platformized workforce development system

ABSTRACT This article focuses on the experiences of adult job seekers in a community technology center, primarily exploring three cases. In adult education, the term digital literacy has been used to refer to technical skills, reflecting a hierarchical framing of learning that positions technologies as neutral tools. Drawing from a sociomaterial perspective, I explore the broader range of adaptive literacy practices emerging for adult job seekers as they moved across entangled platforms and workforce agencies during a six-month ethnographic case study. Findings explore three aspects of the adults’ practices across the platforms: maneuvering resumes across platforms as boundary objects; collaboratively navigating platforms’ rules; and drawing from formal and informal knowledge.

Online vs Traditional Recruitment Approaches: Preferences of Saudi Employers and Employees

Purpose: This study analyses and compares traditional and e-recruitment methods for job applications from the perspectives of employees and employers. Research Design: The study collected data from employees and employers in business organizations located in Yanbu Industrial City, Kingdom of Saudi Arabia. An internet-mediated approach was used, and a survey questionnaire was designed. The sample size was determined to be 328 using an unknown population sample size formula. Data were gathered from 103 employees and 53 employers. Descriptive statistical analysis was conducted, and Chi-square was used to analyze the approaches of Traditional and e-recruitment for the job application process. Findings: The findings indicate that a high percentage of employees and employers’ preference for online methods in job applications. However, the chi-square test reveals a discrepancy between the perspectives of employees and employers. Employees support the use of online methods, while employers reject the hypothesis that online application methods are effective. Implications: One of the implications of online recruitment systems is that managers who use e-recruitment systems may have concerns about the accuracy and accountability of applicant data. Additionally, the lack of in-person interactions during the online application process can lead to frustration for job seekers and missed opportunities for employers to obtain or share more information. Originality: This study is arguably one of the leading studies that investigates the perceptions of employers and employees regarding the e-recruitment process in Saudi Arabia. Its findings would be of great help to employers in improving the e-recruitment system.

Analysis of Employment Competitiveness of College Students Based on Binary Association Rule Extraction Algorithm

 Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.

AN IMPACT OF TALENT ACQUISITION AND RECRUITMENT THROUGH SOCIAL MEDIA

The SAIL industry can utilize social media to attract new employees. Recruiters can engage job seekers and develop two-way contact through social networking sites. Corporations find social media an appealing recruiting strategy due to the decreased costs. Nevertheless, data regarding job-seekers' perspectives on social media and their capacity to research available positions is lacking. In this prospective research, we will poll job-seekers to find out how they feel about a company's employment brand, website, and overall appeal. To discover, acquire, evaluate, and hire candidates to fill positions needed to achieve company objectives and complete projects, it will also research job applicants' anticipated dedication to the organization after employment. Keywords:- Impact, Talent, Acquisition, Recruitment, Social Media

The impact of left-behind experience on urban identity of new-generation migrant workers.

We investigate the impact of left-behind experiences on the urban identity of new-generation migrant workers using data from the 2017 China Migrants Dynamic Survey. The results show the following: (1) The left-behind experience is an important factor undermining the urban identity of new-generation migrant workers, and the conclusion remains consistent after robustness checks, such as propensity score matching. (2) Left-behind experiences of both parents away from home had the most significant negative impact on urban identity. (3) The results of the mechanism tests indicate that the left-behind experience exerts an adverse impact on urban identity through the pathways of poorer physical health, more frequent migration, more challenging job search, and stronger dependence on preexisting social networks. The findings of this study also offer policy suggestions for promoting the urban identity of new-generation migrant workers.

JobEdKG: An uncertain knowledge graph-based approach for recommending online courses and predicting in-demand skills based on career choices

Modern job markets often require an intricate combination of multi-disciplinary skills or specialist and technical knowledge, even for entry-level positions. Such requirements pose increased pressure on students, new graduates, and job seekers to find suitable sources of information to enhance their employability. Unlike standard competence management systems, this paper presents JobEdKG, which helps job seekers from various backgrounds choose online courses based on their prospective careers in addition to predicting different skills that may be required in their chosen career path. While existing solutions focus on internal institutional data, such as previous student experiences and a fixed set of skills provided by curated datasets, JobEdKG considers external data, recommending online courses that best cover the knowledge and skills required by selected job roles, in addition to extracting and predicting skills of that particular job roles. To achieve this, we first extract skills from job postings and online courses. These skills are linked to job titles, online courses, and other concepts in order to create a knowledge graph (KG). We assign an uncertainty score to each fact in the KG based on the prevalence of the fact in the source data (i.e. job listings and online courses), which results in an uncertain KG (UKG). Finally, we model the constructed UKG in order to infer different relations between the different concepts. The code and the data are available on our GitHub repository (https://github.com/team611/JobEd) and a user interface to browse the KG is available at (http://jobed.datanets.org/).

Smart Chatbot to Assist Users of Employment Websites in Job Search, Skill Development, and Networking Opportunities

Abstract: This paper presents the development and implementation of a smart chatbot designed to assist users of employment websites in various aspects of their job search, skill development, and networking endeavors built using bert model, spacy model and large language model. With the increasing reliance on online platforms for job hunting and career advancement, there is a growing need for intelligent tools that can streamline the process and provide personalized support to users. This article examines a chatbot that provides many functionalities to improve job search outcomes and user experience. These functionalities include personalized job recommendations, skill development suggestions, and networking opportunities tailored to the user's queries. The paper outlines the methodology used in the development of the chatbot, including the integration of natural language processing techniques and machine learning models. The paper presents findings from comprehensive evaluation, shedding light on the efficacy of the chatbot's functionalities. The implications of this research extend to both academia and industry, underscoring the potential of AI-driven chatbots to revolutionize the way individuals navigate the job market and pursue career advancement opportunities.

Resume Application Tracking System with Google Gemini Pro

Abstract: Resume Application Tracking System with Google Gemini Pro provides an innovative solution to the problems in the recent hiring. The main aim of this system is to help the job seekers in order to grab an oppurtunity in the competitive job market and ensure that the process of resume screening goes smoothly. The technology used in this system is Google Gemini Pro, the most capable and large language model created by Google DeepMind. This technology is capable of interpreting code, video, audio, image, text and helps to increase the accuracy and speed of resume screening. The system also makes use of Streamlit for the user interface and PyPDF2 for direct text extraction from resumes. Users can provide the appropriate job description and upload their resumes, which will then be analyzed and the system will display the approximate matching percentage, missing keywords and a brief profile summary. The system will automatically evaluate and classify resumes in real-time applications, delivering recruiters an easy way to find eligible applicants. This approach is used in wide range of sectors and can be beneficial as the job market is down due to recession. Therefore, the goal of this system is to tranform the resume processing industry, saving recruiters time and money while also raising the standard of candidate selection.

EVALUATING THE EFFECTIVENESS OF TALENT ACQUISITION THROUGH E-RECRUITMENT IN NIIT

Online or E-Recruitment has become nearly ubiquitous in medium and large businesses and is growing in popularity for small business owners, too. According to industry experts, from 1998 to 2001, the number of Fortune 500 companies that had job recruitment websites increased from 29% to 88%. By 2008, over 98% of them were using some sort of online recruitment process. As may be expected, this sharp increase means that traditional avenues for recruitment, such as newspaper advertisements, no longer command the share of use they once enjoyed. In 2000, employment newspaper classified advertising in the U.S. was worth $8.7 billion. By 2002, however, the Newspaper Association of America reported only $4.3 billion, a more than 50% loss. Borrell Associates predicts that newspapers will suffer a further 12% revenue decline by 2012 as recruitment budgets continue to move online. By contrast, online advertising for recruitment purposes is growing. Jupiter Research has predicted that online advertising overall will exceed $16 billion by the end of 2008, adding more than $10 billion from the industry’s total revenues in 2005. Recruitment advertising will likely be the single largest segment of this sector. Job seekers have come to expect online access to job data and the application process. Research by Mintel reveals that 42% of respondents with Internet access look for job information via online recruitment sites, a number that is projected to increase substantially, with the format rapidly gaining acceptance. Employers and job seekers note a number of benefits of e-recruitment over traditional methods. The Human Resources Management Guide (www.hrmguide.net) lists some of the more important ones.

To Elevate the Preparation of Students for Placement by Developing a Website

Abstract: Comprehensive platform designed to assist students and job seekers in achieving success in placement processes. Our website offers a diverse array of resources, tools, and guidance necessary to excel in various stages of placement, including aptitude tests, technical interviews, group discussions, and personal interviews. Users can access a wealth of study materials tailored to different industries and job roles, including aptitude test questions, programming exercises, case studies, and interview tips. Our extensive library of mock tests provides realistic simulations of placement exams, accompanied by detailed feedback and performance analysis to help users identify strengths and areas for improvement. Additionally, our expert-curated resources on interview preparation cover both technical and HR aspects, offering insight into commonly asked questions, sample answers, and strategies for handling challenging scenarios. The platform also features interactive learning modules and video tutorials on essential topics such as communication skills, problem-solving techniques, and career development strategies.

A Study on an Impact of Talent Acquisition and Recruitment through Social Media

Social media is essential to modern recruiting, particularly when it comes to corporate branding. 76 percent of businesses, according to Employer Branding International, use social media to develop and market their employer brands. In an effort to attract candidates who will fit in as employees, businesses strategically use social media to advertise their cultures, values, and beliefs. However, for effective employer brand promotion, the business must have an identity that is real, credible, relevant, distinctive, and aspirational in order to draw in both present and potential employees. Recruiters can engage job seekers and establish two-way conversation via social networking platforms. Social media is a desirable recruiting tool due to the appeal of decreased expenses for businesses. But little is known about how job prospects use social media during the application process or their capacity to research available positions. In the proposed study, job seekers will be surveyed in order to gauge their perceptions of employer brands, corporate websites, and applicant appeal. Additionally, it will research how dedicated job applicants anticipate being to the business once hired in order to discover, acquire, evaluate, and hire prospects to fill positions necessary to achieve project objectives and company goals.

Organisational practices and social inclusion: Inclusionary place‐making in the library

AbstractSocial psychology has long been concerned with social exclusion. Much of this interest has focused on people's negative intergroup attitudes and how these may be changed through individual‐level interventions. In this paper we explore a different level of intervention – one that targets the routine organisational practices that communicate who is welcome and able to draw on the organisation's services. Specifically, we investigate how a public‐facing organisation—a library service—engaged in a process of self‐reflection on its routine social practices with the aim of making people experiencing various forms of exclusion (e.g., job seekers, benefit claimants, ethnic minorities) more welcome. Our data arise from interviews (N = 19) with staff concerning their attempts to transform the practices that constitute the library as a distinctive public place. Throughout, we explore how they reflected on their everyday organisational practices, how these may unintentionally exclude, and how they could be modified to facilitate social inclusion. Moreover, we pay particular attention to our participants' understanding of the challenges involved in changing organisational culture and creating a social space in which diversity is accepted.

Assessing the Impact of a Job Interview Skills Training Course: Participants’ Perceptions and Outcomes

Job interview skills are essential for job seekers to secure employment. Effective training programmes can benefit individuals develop these skills and confidence required to succeed in job interviews. This study aimed to evaluate the efficacy of a job interview skills training course conducted by the facilitator. The course was evaluated using a course evaluation form which assessed the facilitator's knowledge, preparation, and delivery of the course content, as well as the relevance and practicality of the course materials and activities. The results showed that the majority of participants found the course to be helpful, relevant, and easy to follow. The facilitator was also rated highly in terms of his knowledge, preparation, and delivery of the course content. On the contrary, some participants felt that there was not enough opportunity for interactive participation among the students. In summary, the findings suggest that the job interview skills training course was useful in improving participants' skills and knowledge related to job interviews.

Job Assistant: Job Preparation and Community Engagement Application with AI

This paper outlines the offerings of our application tailored for job seekers, emphasizing the extensive range of job listings provided alongside an interactive community platform and AI Assistant for personalized guidance and support, streamlining the job preparation journey. The research project delves into the pivotal role of job search websites and underscores the importance of efficient user logins on these platforms. Given the growing significance of job placements in sustaining livelihoods, both job seekers and recruiters increasingly rely on job portals to find the ideal match for their requirements. Moreover, job portals facilitate engagement within communities, fostering connections between individuals and companies. This study aims to shed light on the multifaceted benefits of integrating AI-driven features, interactive platforms, and comprehensive job listings within a single application, ultimately enhancing the job search experience and facilitating meaningful engagement within professional communities. Key Words: Employment, AI Job Assistant, Interview preparation, Job hunting, AI-powered job support, Career development.

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.