Online Job Research Articles

Analyzing the labor market and salary determinants for big data talent based on job advertisements in China.

The demand for big data talent is rapidly increasing with the growth of the big data industry. However, there has been limited research on what employers seek in recruiting big data talent. This paper aims to apply labor market segmentation theories to the big data labor market and develop a theoretical framework to analyze the distribution of big data talent in different labor market segments. Furthermore, we develop a salary determination model to explain wage differentials. An empirical analysis is conducted using online job advertisements from a Chinese recruitment website to investigate the labor market for big data talent in China. Our findings show that there are significant differences in the demand for big data talent across different types of cities and industries. Different types of enterprises have different requirements for individual characteristics and offer various levels of big data job positions. Furthermore, our results reveal that individual, job-related and organizational characteristics are all significant predictors of salaries. These findings can provide particularly useful insights for organizations and managers in the big data industry.

Find Your Dream Work

Job searching is a multifaceted process that encompasses various strategies, tools, and techniques to secure employment. The modern job market demands a comprehensive understanding of digital platforms, effective networking, and the ability to tailor applications to specific roles. This abstract delves into the essential components of successful job searching, highlighting the importance of online job portals, resume optimization, networking, and interview preparation Key Components of Job Searching: Online Job Portals: The rise of technology has transformed job searching, with online job portals playing a pivotal role. These platforms offer extensive listings across various industries and locations, allowing job seekers to apply for multiple positions with ease. Advanced algorithms on these sites match candidates' skills and experiences with suitable job openings, increasing the chances of finding the right fit. Resume Optimization: Crafting a compelling resume is crucial in the job search process. An optimized resume should be tailored to the job description, highlighting relevant skills and achievements. Keywords from the job posting should be incorporated to pass through Applicant Tracking Systems (ATS) used by many employers. Networking: Building a professional network is vital for uncovering hidden job opportunities. Networking can occur both online, through platforms like LinkedIn, and offline, at industry events and job fairs. Personal connections and referrals often provide a significant advantage in the competitive job market. KeyWords: Industry-Specific,Job Titles,Skills and Competencies,Education and Training,Networking and Collaboration,Personal Branding,Buzzwords for Trending Skills

The reproduction of structural inequalities in online job search strategies and outcomes

Does digital stratification foster inequalities in access to work and employment? We address this question by examining inequalities related to online job search skills and the outcomes of the online search process. Results from a representative survey of 1103 Spanish jobseekers show that online job search skills positively affect the chances of getting an interview through employment platforms but that they are unevenly distributed. Online job search skills are more important than other digital resources, including basic digital skills, in determining positive outcomes of online job searches though there are still inequalities in getting an interview independent of either. This calls for considering domain-specific digital skills both in research and in practice alongside tackling traditional inequalities.

Personal Data Protection for Online Job Seekers in the Mode of Freelance Job Vacancy Fraud

Personal Data Protection for Online Job Seekers in the Mode of Freelance Job Vacancy Fraud

A Study on Employees Perception Towards Online Job Portals with Reference to SRM Group

This Abstract discusses the importance of employees' perceptions towards online job portals, shedding light on their preferences, usage patterns, and suggestions for improvement. Throughan analysis of 180 respondents, it is revealed that online job portals play a crucial role in the job search process, with platforms like LinkedIn and Naukri being favored for their reliabilityand relevance. Despite a high usage rate and overall positive attitudes, concerns regarding security and privacy remain significant. Additionally, the study identifies gender-based differences in perceived career opportunities and highlights the importance of continuous improvement in user interface, search functionality, and security measures to meet evolving user needs. Ultimately, this research underscores the importance of understanding employee perceptions to optimize online job portals and enhance their efficacy in facilitating successful job searches. Furthermore, the study sheds light on the prevalent usage patterns among employees, highlighting the pivotal role of online job portals in facilitating job searches and career advancement. Specific platforms like LinkedIn and Naukri emerge as preferred choices due to their reliability and relevance, while also serving as valuable resources for career development. Interestingly, gender-based differences in perceptions of career opportunities areidentified, suggesting nuanced considerations in platform design and functionality. Despite overall satisfaction, the study underscores the continuous need for improvement, particularly in user interface, search functionality, and security measures, to align with evolving employeeneeds and preferences in the dynamic job market landscape

Enhancing Online Job Shop Scheduling Efficiency: Simulation-Based Optimization Incorporating Human Productivity Factors and TOPSIS Ranking

Abstract The job shop scheduling problem is a classical optimization challenge aimed at determining the optimal processing order by assigning a set of resources to a corresponding set of operations. This article investigates various approaches to address the online job shop scheduling problem, employing a simulation-based study. Dispatching rules are applied to allocate resources to operations, with discrete event simulation used for problem assessment. The study also incorporates human productivity factors, specifically investigating the shortest processing time (SPT) dispatching rule in a separate scenario. Five distinct scenarios are simulated, including four dispatching rules (first in, first out; last in, first out; longest processing time; and SPT) and an additional scenario integrating the SPT rule and human productivity factors. The simulation results are used to compare makespan across these scenarios, revealing that the scenario involving the SPT dispatching rule and human productivity factors represents the shortest makespan. Through a TOPSIS technique-based ranking, considering makespan and cost as criteria, the study identifies the SPT rule and human productivity factors as the most efficient scenario. The findings imply that employing human productivity factors with effective dispatching rules, such as SPT, can significantly improve job shop scheduling operational efficiency and lead to more optimal results in both makespan and overall operational costs.

Digital transformation in the recruitment process: Revealing Gen Z's interest in career opportunities

This study aims to analyze the influence of social media use, technology utilization, and e-recruitment on the interest of Generation Z in career opportunities in applying for jobs. Gen Z has a high reliance on technology and social media, which has an impact on the way they search for and apply for jobs. The method used in this study is a quantitative method. The data source in this study uses primary data. The data collection technique uses a google form questionnaire. The population in this study is active students who take their TA thesis. Even 2023/2024 at the Faculty of Economics and Business, Prof. Moestopo University (Religious). The sample in this study is 30 people. The sampling method is random sampling. The data analysis in this study uses multiple linear regression using validity test, reliability test, classical assumption test and t-hypothesis test and determination coefficient test. The results of the study show that the use of social media, the use of technology, and e-recruitment have a positive influence on Generation Z's interest in career opportunities in applying for jobs. Social media plays a role in disseminating vacancy information widely. Generation Z relies heavily on social media as a source of information and a means to explore career opportunities. Technology, such as online job search platforms, social media, and digital communication tools, provides easy access and quick information, thereby increasing their interest in finding and applying for jobs. Generation Z tends to be more interested in the recruitment process that is carried out online compared to conventional methods.

Understanding and Influencing Responsibility Attribution when Experiencing Technical Issues in Video Conferencing

During video conferencing, technical issues such as network impairments can unexpectedly hinder remote collaboration for distributed teams. However, it remains unclear how such issues affect the impression formation process between unacquainted interlocutors in situations like job interviews or kick-off meetings. Having first encounters online without prior in-person interaction has become prevalent nowadays. Therefore, examining the impact of technical issues on remote unacquainted interlocutors and exploring design solutions to reduce the negative impact of technical issues on the impression is essential in informing the design of future communication systems. With three controlled experiments simulating an online job interview, we discovered that technical issues made people give low credibility ratings to remote job applicants (Study 1). We further examined two intervention approaches to reduce the negative impact of technical issues in video conferencing, including a virtual agent represented in a human-like avatar to actively take responsibility for the technical issues (Study 2), and forewarning messages to inform people about the technical issues in video conferencing (Study 3). Results demonstrated that introducing an agent that represents a communication system to take responsibility for technical issues actively could reduce the responsibility people assign to remote job applicants, but without increasing their positive credibility rating for remote job applicants. Furthermore, showing forewarning messages, which explicitly made people aware the cause of technical issues was unidentifiable and its potential negative impacts on impression formation, enabled people to rate the credibility of the remote counterpart without being influenced by technical issues. We discussed how interventions can be designed to mitigate negative attribution outcomes when encountering uncontrollable technical issues in computer-mediated communication.

WHAT SKILLS DO EMPLOYERS WANT FROM RECENT COLLEGE GRADUATES?

Currently, it is not enough for university graduates to acquire only occupation-specific academic knowledge to find a job. Graduates are also expected to possess an additional set of skills called employability skills. In this study, job vacancies were analyzed to determine the skills employers want from new graduates. In this study, 178 job posts for new graduates on an online job search platform (Kariyer. net) were analyzed using content analysis. According to the findings of the study, fewer hard skills and more soft skills were demanded by recent university graduates. The most demanded skills for both associate degree and bachelor's degree graduates were skills in social and communication groups. When examined in detail, the highest-demanded soft skills include "strong communication skills,” "predisposition to teamwork,” "planning and organization skills,” "effective written and oral communication skills.” In addition, "mastery of MS Office applications, knowledge of foreign languages and "knowledge of computer programs related to the field" are among the technical skills that graduates require. This research is expected to guide new graduates in terms of which skills they should develop and educational institutions in terms of updating their curricula in line with the demand for skills.

Labor Market of Regular Workers in Japan: A Perspective from Job Advertisement Data

We present an analysis of wages and labor market tightness for regular workers (full-time permanent workers) in Japan, utilizing a novel dataset of online job advertisements spanning the period from 2015 to 2022. Despite the importance of online job advertisements for the labor market, little has been studied for the case of Japan. Our study documents labor market conditions reflected in online job advertisement data and investigates how posted wages are related to the labor market tightness, and how they affect actual wages of existing workers. The results uncover several aspects of the labor market which are not captured by official statistics. First, we find that posted wages for regular workers increased at a pace faster than wages for existing regular workers in the sample period, with some heterogeneity across industries and skill requirements. Second, we find that the estimated job-filling rate observed from the dataset declined in recent years, which suggests difficulties that firms faced in hiring workers, and that this was associated with an increase in posted wages. Third, we find that an increase in posted wages positively affects the wages of existing regular workers after some time lag. Our empirical findings suggest that spillovers are driven by underlying mechanisms such as retention of existing workers, and fairness norms to maintain a balance in wages between newly hired workers and existing workers.

The Rise of AI Pricing: Trends, Driving Forces, and Implications for Firm Performance

We document key stylized facts about the time-series trends and cross-sectional distributions of AI pricing and study its implications for firm performance, both on average and conditional on monetary policy shocks. We use the universe of online job posting data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job opening that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI-pricing jobs in all pricing jobs has increased by more than tenfold since 2010. The increase in AI-pricing jobs has been broad-based, spreading to more industries than other types of AI jobs. At the firm level, larger and more productive firms are more likely to adopt AI pricing. Moreover, firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more sensitive to high-frequency monetary policy surprises than non-adopters. We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about the demand function invests in AI pricing to acquire information.

Jobs During the Pandemic: Re-Evaluating the Importance of Teleworkability, Contact-Intensity, and Being 'Essential'

This paper investigates the shock to labour demand from the government’s large-scale emergency policy response to COVID-19. A primary goal being to identify which occupational characteristics elicit significant responses to COVID-19 policy. A secondary goal was to evaluate the allocation efficiency of federal business assistance programs during COVID-19. This paper utilizes high-frequency Canadian online job postings data provided by the Labour Market Information Council (LMIC) in a Random Effects regression framework. This paper finds that occupations classified as teleworkable or essential experienced a decrease in labour demand during the pandemic. This non-intuitive result could be attributable to the assumption that COVID-19 and its policies only influence labour demand changes from a business feasibility perspective.

A type 1 hybrid multi-site randomized controlled trial protocol for evaluating virtual interview training among autistic transition-age youth

A number of policies mandate that autistic transition-age youth receive employment services to prepare for the workforce before high school graduation. A key limitation to these services is the job interview component, which relies on non-standardized, resource-intensive, staff-led role-plays to help autistic transition-age youth improve their interview skills. The autism community has called for better job interview preparation. To address this gap in services, our team, collaborated with the autism community to adapt the intervention, Virtual Reality Job Interview Training (VR-JIT; effective among adults with serious mental illness), into Virtual Interview Training for Transition Age Youth (VIT-TAY). This adapted intervention was tailored to meet the needs of autistic transition age youth while maintaining the core components of VR-JIT (i.e., an online job interview simulator with four levels of automated feedback and e-learning content). A pilot randomized controlled trial (RCT) demonstrated VIT-TAY's feasibility and initial effectiveness at improving job interview skills, reducing anxiety, and increasing employment rates within six months when added to transition services or pre-employment transition services (Pre-ETS). Thus, the overarching goal of this Hybrid Type 1 effectiveness-implementation study protocol is to conduct a fully-powered RCT of VIT-TAY across 16 schools in various geographical locations. Our specific aims are to 1) Evaluate whether Pre-ETS (or transition services) with VIT-TAY, as compared to Pre-ETS (or transition services) with an active control intervention (i.e., job interview didactics/e-learning with a series of 3–5 min videos of employed autistic adults talking about their career pathways) enhances employment outcomes; 2) Evaluate mechanisms of employment by nine months post-randomization; and 3) Conduct a multilevel, mixed-method process evaluation of the initial implementation of VIT-TAY across settings (e.g., acceptability, feasibility, and barriers and facilitators of implementation).

Auditor Skill Demands and Audit Quality: Evidence from Job Postings

This study empirically examines the relation between audit quality and auditors’ cognitive and social skills. Using a novel data set of online job postings by accounting firms, we document substantial variation in the stated demand for auditors’ cognitive and social skills, suggesting that audit offices are not homogeneous in their preferences for such skills. We find a positive relation between audit quality and the prevalence of cognitive and social skills within an audit office’s job postings. This association is stronger for audit engagements that are more complex or require greater coordination, suggesting that cognitive and social skills are particularly important in engagements where effective communication and knowledge transfer, as well as sound professional judgment and skepticism, are needed. The association is also stronger for audit offices with greater investments in new technology, consistent with the complementary relation between cognitive and social skills and the use of technology. Overall, our study offers empirical evidence linking specific auditor skills to audit quality. This paper was accepted by Suraj Srinivasan, accounting. Funding: C. Ham acknowledges financial support from the Blanche “Peg” Philpott Faculty Fellowship. R. N. Hann acknowledges financial support from KPMG. W. Wang acknowledges the financial support from the Hong Kong Research Grant Council [Grant 11503720]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01640 .

Embracing artificial intelligence in the labour market: the case of statistics

In an era marked by rapid advancements in artificial intelligence (AI), the dynamics of the labour market are undergoing significant transformation. A common concern amidst these changes is the potential obsolescence of traditional disciplines due to AI-driven productivity enhancements. This study delves into the evolving role and resilience of these disciplines within the AI-influenced labour market. Focusing on statistics as a representative field, we investigate its integration with AI and its interplay with other disciplines. Analyzing 279.87 million online job postings in the United States from 2010 to 2022, we observed a remarkable 31-fold increase in the demand for AI-specialized statistical talent, diversifying into 932 distinct AI-related job roles. Additionally, our research identified four major interdisciplinary clusters, encompassing 190 disciplines with a statistical focus. The findings also highlight a growing emphasis on specific hard skills within these AI roles and the differences in demand for AI talent in statistics across economic sectors and regions. Contrary to the pessimistic view of traditional disciplines’ survival in the AI age, our study suggests a more optimistic outlook. We recommend that professionals and organizations proactively adapt to AI advancements. Governments and academic institutions should collaborate to foster interdisciplinary skill development and evaluation for AI talents, thereby enhancing the employability of individuals from traditional disciplines and contributing to broader economic growth.

Economic policy uncertainty and the demand for labor: Evidence from the online job postings of Chinese firms

This paper examines the impact of economic policy uncertainty (EPU) on labor demand. Using data from a Chinese online job posting site, this study empirically investigates how EPU affects firms' hiring behavior. The results suggest that EPU has overall negative effects. Firms tend to reduce job postings in times of increasing policy uncertainty. More particularly, the demand for skills such as teamwork, personnel management, project management, and customer service declines, while individual qualities become more important to enterprises. The study also reveals that there is a positive relationship between EPU and the educational background required in firms' job ads. Further analysis indicates that non-state-owned enterprises, those with poor internal control quality, smaller scale, greater industry competitiveness, and those in non-high-tech industries are more significantly affected by EPU in hiring. This research provides empirical support and policy recommendations for the government to maintain employment stability and stimulate enterprise labor demand.

Enhancing taxonomy-based extraction: Leveraging information from online community platforms for digital skills demand identification in job ads

The rapid technological changes have revolutionised how we function, including how we search for work and what skills we need to be equipped with to perform the tasks at the workplace. As employers more often recruit using online job advertisements, their content becomes a natural source of information for analytical purposes on the skills demanded in the labour market, especially for analysing emerging skills like digital. There are still some challenges with the extraction of information from online content. However, the extraction improvements go hand in hand with new technological developments like natural language processing techniques. This article presents the experimental method of updating the classification of digital skills to keep it up to date for information extraction applied to online job advertisements. The evaluation proved this method successfully identified terms related to programming skills but failed to identify terms associated with artificial intelligence sufficiently. The latter is related to the fact that the AI field is among the fastest developing areas of technology advancement, and new terms (e.g. Chatgpt) always appear.

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.

The response of labour demand to different COVID-19 containment measures: evidence from online job postings in Austria

This paper analyses changes in the speed of labour demand for new hires in response to the lockdowns that were repeatedly put in place to contain the spread of the COVID-19 pandemic. It tests whether the uncertainty-reducing effect of similar lockdowns occurring in quick succession increased the responsiveness of the labour market, thereby allowing for more rapid adjustment, both at the beginning and at the end of subsequent lockdowns. It uses high-frequency online job-posting data and applies an event study approach to the beginning of three national lockdowns and the subsequent reopening in Austria between 2020 and 2022. In view of the importance of progress in vaccination for labour market recovery, it also looks at vaccine roll-out as an additional COVID-19 containment measure, with 2021 as the main roll-out period. The results indicate very different responses to the three lockdowns, with a decline in job-posting activity of between 47 and 50% during the first lockdown and of between 29 and 31% during the second; but an increase of 23% to 28% during the last lockdown. Moreover, responses to the first lockdown were sluggish, with a slow decline at the beginning and a very slow recovery after it was lifted; but over subsequent lockdowns the responses were more rapid and more symmetrical. Responses to the various events differed by occupation and industry: the strongest responses were to be observed in the highly skilled and more-teleworkable occupations of technicians, and managers and professionals, who were badly affected during the first lockdown; the leisure and hospitality industry, which was the hardest hit on account of the mandatory closures and the widespread travel restrictions and bans, and which recovered only very slowly; and the IT, internet and telecommunications industry, where posting activity developed in a direction opposite to that seen in the other industries. Finally, there is little robust evidence of a differentiated effect of vaccinations during lockdowns, suggesting that vaccination roll-out did not have an additional demand-generating effect, over and above the lockdowns.

The Influence of Labor Market Power in the Audit Profession

ABSTRACT This paper examines the influence of labor market power in the audit profession. Using a dataset of online job postings, we confirm that audit offices in more concentrated labor markets have greater labor market power and exercise it in the form of higher skill requirements and greater required effort from their auditors, at similar or slightly lower wages. We then show that client firms of audit offices in more concentrated labor markets are less likely to restate their earnings and have lower absolute discretionary accruals. These findings are only present when employees have lower mobility across professions and geographies, consistent with audit offices’ power in the local labor market explaining the results. Collectively, our findings highlight the importance of labor market power in understanding audit quality. Data Availability: Data are available from the sources cited in the text. JEL Classifications: J31; J40; J42; M42; M55.