Job Portals Research Articles

Identifying Differences in the Application of HR Marketing Tools in Companies in the Secondary Sector – a Case Study of Mining, Construction, and Manufacturing Industries

HR marketing is a relatively new field in corporate practice. When implemented effectively and designed professionally, HR marketing can give companies a significant competitive advantage in the labour market. Increasingly, HR managers and executives focus on securing high-quality and skilled employees who are integral to building a strong brand and employer value. To date, no studies have specifically examined HR marketing within the secondary sector, which appears to be stagnating in economically developed countries. This study aims to identify differences in the application of HR marketing tools among companies operating in the secondary sector, particularly within the mining, construction, and manufacturing industries. The research was conducted in two stages: the first stage involved an analysis of HR marketing practices among a sample of 215 companies operating in the Czech Republic and Slovakia. The second stage focused on a case study of 10 companies from the secondary sector, specifically three companies operating in the mining, construction, and manufacturing industries. This study offers a comparative analysis of HR marketing tool usage among companies within the secondary sector, including those in the Czech Republic and Slovakia. The key findings address the research questions and hypotheses. The results indicate that companies in the secondary sector primarily use traditional HR marketing tools such as company websites, personnel agency services, and job portals. Mining and manufacturing companies employ a wider range of HR marketing tools to enhance employee loyalty than construction companies.

The role of facial cues in signalling cooperativeness is limited and nuanced

Humans display a remarkable tendency to cooperate with strangers; however, identifying prospective cooperation partners accurately before entering any new relationship is essential to mitigate the risk of being exploited. Visual appearance, as inferrable, for example, from facial images on job portals and dating sites, may serve as a potential signal of cooperativeness. This experimental study examines whether static images enable the correct detection of an individual’s propensity to cooperate. Participants first played the Prisoner’s Dilemma (PD) game, a standard cooperation task. Subsequently, they were asked to predict the cooperativeness of participants from a prior PD study relying solely on their static facial photographs. While our main results indicate only marginal accuracy improvements over random guessing, a more detailed analysis reveals that participants were more successful at identifying cooperative tendencies similar to their own. Despite no detectable main effect in our primary treatment variations (time pressure versus time delay), participants exhibited increased accuracy in identifying male cooperators under time pressure. These findings point towards a limited yet nuanced role of static facial images in predicting cooperativeness, advancing our understanding of non-behavioral cues in cooperative interactions.

From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.

Recommender System for Group of Users using Matrix Factorization for Tourism Domain (Case Study: Bali)

Choosing a product that suits a customer's needs requires a recommendation system to provide suggestions on a collection of items of interest to the user. Recommendations can be applied in various fields such as entertainment, shopping sites, social networking, job portals, discovery of relevant web pages, and so on. There are many circumstances where recommendations are needed for a group such as in tourism and entertainment purposes. The development of a Group Recommendation System (GRS) was carried out in response to the need to provide several recommendations to a group of users. We conducted this research to build a GRS that can provide item recommendations using the Collaborative Filtering (CF) method with Matrix Factorization Technique, as well as three approaches, namely After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). Determine the best approach for the three categories of groups formed, namely small groups (three members), medium groups (five members), and large groups (ten members). The focus of this research is the tourism destination domain in Bali. In the evaluation results of the precision calculation, the medium group obtained the highest score for the AF, BF, and WBF approaches of 0.944. Meanwhile, in calculating recall, the small group achieved the highest scores for the AF, BF, and WBF approaches of 0.294, 0.259, and 0.259, respectively. From the results of this study, it appears that small groups are suitable for using the BF approach, while the AF method is more effective for large groups, and the best approach for medium groups is the WBF.

JobViz: Skill-driven visual exploration of job advertisements

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design; a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’ swift comprehension of the pertinent skills necessitated by respective positions; a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach.

THE DIGITAL GATEAWAY TO EMPLOYMENT: INSIGHTS INTO ONLINE JOB PORTAL USAGE

Abstract – The feelings of WEB users have a great benefits for students , colleges and companies. It is necessary to structure well the unstructured data from various placement platforms for proper and meaningful analyses. For the classification of multilingual data, the analysis of feelings has recognized significant information od student and companies. This is called the Job Portal that may be used to maintain the visible connection between company and colleges and companies and student through the mediator Admin. To solve the problem of old job portal this introduced that it has minimum manual work for minimum error and other problems. These architectures may be used Java based languages. In this research variuos data, we had started this project to give more and opportunities for student placement. Key Words: project, job, management, system, application, online software, deployment, security.

Online Job Portal

ABSTRACT___ The Online Job Portal is a web application developed using the Python Django framework. This project aims to provide a platform for job seekers and companies to interact and fulfill their employment needs. The application allows job seekers to create profiles, search and apply for jobs, and receive updates on their application status. Companies can post job vacancies, search for suitable candidates, and manage the hiring process. The evolution of technology has revolutionized the job search and recruitment process, giving rise to online job portals as essential tools for job seekers and employers. This abstract presents the concept and development of a comprehensive online job portal designed to bridge the gap between job seekers and employers efficiently. INDEX TEARMS – HTML, CSS , JavaScript, Python, Django Framework, SQLlite.

Analysis of Acceptance Factors of Job Portal Applications for Job Seekers in Indonesia Using TAM

This research is based on the problem of the high number of job portal application users which is supported by the large number of job seekers in Indonesia. This research aims to determine the acceptance of job portal applications for job seekers in Indonesia with external factors, information quality and system quality. This research uses quantitative methods with the data analysis technique used is PLS-SEM using smartPLS 3.2.9 software. Data collection was carried out by distributing online questionnaires to job portal application users in Indonesia and involving 610 respondents. Based on the results of the data analysis that has been carried out, it can be seen that information quality and system quality have a positive and significant influence on perceived usefulness and the magnitude of the influence is 58.8%, information quality and system quality have a positive and significant influence on perceived ease of use and magnitude. The influence is 56.4%, perceived usefulness and perceived ease of use have a positive and significant influence on continuance intention to use and the magnitude of the influence is 63.8% and continuance intention to use has a significant positive influence on actual system use. Overall, information quality and system quality play a role in influencing actual system use indirectly.

Cultural values and the P-O fit: comparative NLP analysis of German online job advertisements

PurposeWithin the Person-Organization fit framework and Signalling Theory, this study investigates the performance of word dictionaries detecting cultural values in online job advertisements as one form of external communication of an organization. Based upon a merge of the dictionaries, a corporate value analysis of Germany is conducted.Design/methodology/approachThe study builds on a dataset (n > 151 k) of online job advertisements which were scraped from a German job portal. It was pre-processed according to natural language processing standards. For analysing the values of an organization a dictionary based word count was applied. Therefore, the current state-of-the-art dictionaries were tested, and an enhanced dictionary was developed and translated from English to German. Finally, a cluster analysis was conducted.FindingsThis study supports the possibility of measuring cultural values in texts where the enhanced dictionary based on Ponitzovskiy shows the best results. It thereby supports the use of the Universal Value Structure model (Schwartz, 1992) as well as the Signalling Theory (Guest et al., 2021), that values spread across 10 core or 4 aggregated dimensions are communicated via online job advertisements. Finally, the study offers a profile of the German corporate culture average as well as 4 cultural clusters and separate organizations, all with different profiles.Originality/valueThis study develops an enhanced dictionary based on a large dataset of online job advertisements for analysing the external communication of values or culture of an organization for improving the Person-Organization fit.

Optimizing User Retention for a Digital Recruitment App with CLM and the Hooked Model

As the digital recruitment market in Indonesia becomes increasingly competitive, it is crucial for Chatterhire, a job portal tech startup, to retain a strong user base for sustainability and growth. Retention is vital for demonstrating product-market fit, monetization potential, and attracting future investments. To achieve this, Chatterhire aims to shift its focus from aggressive user acquisition to retention, by optimizing its platform for regular use. This research combined quantitative data on user in-app activities with qualitative feedback from in-depth interviews. The quantitative analysis utilized Customer Lifecycle Management (CLM) techniques, including cohort analysis, behavioral cohort analysis, and funnel analysis, to identify critical experiences that encourage users to return, reasons for user churn, and features that need enhancement for better retention. The study found that retaining users is challenging for an episodic-use app like a job portal, where engagement is sporadic and infrequent. Once users secure a job, their need for the app diminishes, leading to significant drops in engagement and even app uninstalls. Therefore, the company needs to expand the platform’s features to provide additional reasons for users to return. Additionally, ensuring users perceive the app’s value through repeated interactions is essential. The research revealed that users who apply for at least four jobs within the first seven days are likely to have higher retention. This research highlights the importance of integrating intrinsic value into the product to foster a sustained user journey. The Hooked Model’s core elements—Trigger, Action, Variable Reward, and Investment—can encourage habitual use and sustained engagement, crucial for maintaining a competitive edge in the digital recruitment industry.

DIGITAL RECRUITMENT REVOLUTION: EVALUATING THE EFFECTIVENESS OF ONLINE JOB PORTALS

Abstract – The feelings of WEB users have a great benefits for students , colleges and companies. It is necessary to structure well the unstructured data from various placement platforms for proper and meaningful analyses. For the classification of multilingual data, the analysis of feelings has recognized significant information od student and companies. This is called the Job Portal that may be used to maintain the visible connection between company and colleges and companies and student through the mediator Admin. To solve the problem of old job portal this introduced that it has minimum manual work for minimum error and other problems. These architectures may be used Java based languages. In this research variuos data, we had started this project to give more and opportunities for student placement. Key Words: project, job, management, system, application, online software, deployment, security.

CareerConnector

Abstract: CareerConnector is an innovative online platform designed to facilitate efficient job searching and recruitment processes, connecting job seekers with potential employers. Powered by a robust technology stack, with .Net serving as the backend language, CareerConnector offers a seamless user experience for both job seekers and employers. Key functionalities encompass user registration and profile creation, advanced job search and filtering, streamlined job posting and application processes, user notifications, and an array of administrative tools for platform management. Additionally, the system incorporates advanced features such as resume upload and parsing, applicant tracking, machine learning-driven job recommendations, and user feedback and review systems.CareerConnector places paramount importance on data privacy and security, adhering to pertinent data protection laws while implementing stringent encryption measures. Scalability and performance optimization are integral to ensuring a smooth and responsive user experience, even as the platform expands. By embracing.Net as the backend language, CareerConnector benefits from its reputation for reliability, scalability, and a wealth of community support. .Net , including React.js, provide the essential infrastructure for handling HTTP requests and database interactions. The ultimate aim of CareerConnector is to create a dynamic, user-centric job portal that effectively bridges the gap between job seekers and employers. By streamlining the job search and recruitment process, it contributes to a more efficient and vibrant

A Study on Role of Social Media in Modern Recruitment – A Review on Benchmark one

Organizations ability to attract and retain top talent is directly impacted by their recruitment efforts, making it a crucial function. The evolution of recruitment practices is examined in this dissertation, with particular attention paid to the incorporation of digital technologies and the move toward data-driven decision-making. Modern approaches that make use of social media, job portals, and sophisticated applicant tracking systems are either supplementing or replacing traditional methods in increasing numbers. The study looks at how effective these contemporary methods are, evaluating how they affect candidate quality, candidate efficiency, and overall organizational performance. Important issues like preserving data privacy, guaranteeing diversity and inclusivity, and handling the large number of applications are also covered. This research offers recommendations for improving the hiring process in a competitive job market, highlights best practices, and offers insights into current trends and future directions in recruitment through a thorough literature review and empirical analysis.

Evaluation of online job portals for HR recruitment selection using AHP in two wheeler automotive industry: a case study

Evaluation of online job portals for HR recruitment selection using AHP in two wheeler automotive industry: a case study

Job Portal

In the current scenario, there is a rat race in each and every professional field. It is also true for job market. A job portal is a website dedicated for online information about recruiters as well as job seekers. A job portal helps both the job seekers and recruiters finding the right organization for the employees. In the case of job seekers, according to their educational qualification, experience and their preferences, the job portal shows the list of companies to the job seeker. And, to the recruiters, provides the suitable candidates from a pool of lacks. So, a job portal is the perfect online arena, where both the job seekers and the employers find their goal in the pursuit of getting a top notch company for the suitable candidates. The web application “Job Portal” provides an easy and convenient search application for the job seekers to find their desired jobs and for the recruiters to find the right candidate. Job seekers from any background can search for the current job openings. Job seekers can register with the application and update their details and skill set. They can search for available jobs and apply to their desired positions.

Shocking Offers: Gender, Wage Inequality, and Recessions in Online Labor Markets

Using data from the largest online job portal in Nigeria, we document (i) gender differences in salary offers for jobs and (ii) the response of (i) to recessions. Jobs in industries where the number of job applicants skews female offer lower starting salaries than jobs in industries where applicants skews male. During Nigeria's 2016 recession, applications to jobs in industries that skew female fell, and salary offers rose. In contrast, applications to jobs in industries that skew male rose, and salary offers fell. In accordance with this relative shift in applications, in 2016 the salary-offer gap almost disappears.

A Comprehensive Review of the Three Main Topic Modeling Algorithms and Challenges in Albanian Employability Skills

Today’s jobseekers face many obstacles while trying to find a career that aligns with their interests, employability soft skills, and professional experience. In Albania, jobseekers frequently initiate their job search by actively exploring job vacancies listed on various online job portals. The analysis of job vacancies posted online provides an added advantage to the labour market actors compared to traditional survey-based analyses. This is because it enables a faster analytical process, promotes decision-making based on accurate data, and should be carefully considered by every country when formulating their Labor Market Policies. Since the data posted online are unlabelled, it has been proven that the potential of unsupervised learning techniques, more precisely the Topic Modelling algorithms, is outstanding when applied to analysing job vacancies, mainly with regard to assessing employability soft skills. Algorithms in topic modelling are essential for uncovering hidden patterns in texts, facilitating the extraction of important data, generating document summaries, and enhancing content comprehension. This paper analyses and compares the three primary methodologies and algorithms used in topic modelling, which can be applied to analyse employability soft-skills: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and BERTopic. At the end of the paper, conclusions are drawn regarding superior performance and optimal algorithm applicability, challenges, and limitations through a review of studies conducted in the Albanian job market.

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.

The Impact of a Skill-Driven Model on Scrum Teams in Software Projects: A Catalyst for Digital Transformation

Human skills are a critical factor in the success or failure of a digital project. Limited studies have been conducted to identify the industry demand for skills of scrum roles (product owner, scrum master, web developer) and levels (entry, associate, mid-senior). The evaluation of skills over time benefits both decision-makers and associated team members, which leads to successful project completions. The aim of this research is to improve decision making concerning the level-specific skills of selected scrum roles for digital projects. The study identifies major and minor skills, patterns, and relationships between levels, and formulates the mathematical equations as the most important inputs to the skill-driven model’s implementation and evaluation. Both qualitative and quantitative research methods were used to analyse 900 surveyed job advertisements published on LinkedIn in Europe. Descriptive analysis was used to analyse quantitative data while the deductive approach was followed with thematic analysis. There are required skill sets for each level of roles, level-specific skills, industry-demanded skills, and formulas related to the initial and individual skill ratings that are investigated. A new mechanism for evaluation is introduced based on “the time spent with skills”. As a result, the proposed model is implemented by feeding research findings into the Mendix programming platform. The skill-driven model is a decision-support solution in software project management to evaluate skills which assist in assigning the right person to the right digital project. Further investigation on different job portals can help to improve the accuracy of industry standards and reduce the lack of progression skills by overcoming limitations identified in this paper.

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.