Job Recruitment Research Articles

Workers’ right to the subject: The social relations of data production

The use of data to profile and make decisions about data subjects for citizenship, targeted advertising, job recruitment and other reasons, has been eminently normalised, which is an emerging threat to protected spaces for personal subjectivation and identity formation. The ‘right to the subject’; or to agency via personal subject formation outside bilateral profiling; is at stake. This is especially true for workers. Algorithmic management infused with worker control mechanisms occurs in structurally and objectively unequal conditions within subjective, and unequal, social relations. Data harms protections in European privacy and data protection law, despite being heralded as the strongest in the world, are insufficient to protect workers’ right to the subject. Indeed, structural features of inequality within the capitalist data political economy mean that workers experience different power relations to consumers and citizens. Analysing the social relations surrounding policy features of ‘consent’, and ‘risk’, with focus on the General Data Protection Regulation (GDPR) and the negotiations for the AI Act, it is not difficult to see that these policies do not protect all data subjects’ rights to the subject identically. Indeed, workers never have the capacity to truly consent at work; and the risks workers face are different from that of other data subjects, such as consumers. Data subjects do not, across categories, have equal access to equality, within, and because of, the social relations of data production. From a cross-disciplinary perspective and with contributions to sociology, critical theory, media and policy studies, this article argues that workers’ right to the subject is at stake, in datafied social relations.

Ensuring generalized fairness in batch classification

In this paper, we consider the problem of batch classification and propose a novel framework for achieving fairness in such settings. The problem of batch classification involves selection of a set of individuals, often encountered in real-world scenarios such as job recruitment, college admissions etc. This is in contrast to a typical classification problem, where each candidate in the test set is considered separately and independently. In such scenarios, achieving the same acceptance rate (i.e., probability of the classifier assigning positive class) for each group (membership determined by the value of sensitive attributes such as gender, race etc.) is often not desirable, and the regulatory body specifies a different acceptance rate for each group. The existing fairness enhancing methods do not allow for such specifications and hence are unsuited for such scenarios. In this paper, we define a configuration model whereby the acceptance rate of each group can be regulated and further introduce a novel batch-wise fairness post-processing framework using the classifier confidence-scores. We deploy our framework across four real-world datasets and two popular notions of fairness, namely demographic parity and equalized odds. In addition to consistent performance improvements over the competing baselines, the proposed framework allows flexibility and significant speed-up. It can also seamlessly incorporate multiple overlapping sensitive attributes. To further demonstrate the generalizability of our framework, we deploy it to the problem of fair gerrymandering where it achieves a better fairness-accuracy trade-off than the existing baseline method.

Registered report protocol: Perceptual effects of Arabic grammatical gender on occupational expectations in a gamified speech production task.

The default use of masculine morphology to refer to all genders in Arabic-speaking countries is largely unquestioned and widely accepted. However, research on masculine generic morphology in other gender-marked languages has shown that this can create an over-representation of men and a male-bias in perception. Given the extensive use of default masculine grammatical gender in the context of job recruitment, education, and formal communication where women are typically underrepresented and men overrepresented, this widely accepted notion needs to be investigated. The primary aim of this research is to understand how grammatical gender in Arabic mediates occupational expectations based on the language currently used in job recruitment in Arabic speaking countries. Specifically, the study explores how the use of default masculine grammatical gender can create a male-bias in perception. The secondary aim of this research is to test whether gender-inclusive language can reduce this male-bias in perception and instead increase the accessibility, activation, and retrieval of exemplars related to other gender minorities (i.e., reduce male-bias in perception). This is achieved through a novel prompted speech production experiment, based on an adaptation of the popular board game 'Taboo' where participants are asked to describe role nouns presented (e.g., doctor or nurse) in different language conditions.

The reputational and ethical consequences of deceptive chatbot use

The use of chatbots is becoming widespread as they offer significant economic opportunities. At the same time, however, customers seem to prefer interacting with human operators when making inquiries and as a result are not as cooperative with chatbots when their use is known. This specific situation creates an incentive for organizations to use chatbots without disclosing this to customers. Will this deceptive practice harm the reputation of the organization, and the employees who work for them? Across four experimental studies, we demonstrate that prospective customers, who interact with an organization using chatbots, perceive the organization to be less ethical if the organization does not disclose the information about the chatbot to their customers (Study 1). Moreover, employees that work for an organization which requires them to facilitate the deceptive use of a chatbot exhibit greater turnover intentions (Study 2) and receive worse job opportunities from recruiters in both a hypothetical experimental setting (Study 3) and from professional job recruiters in the field (Study 4). These results highlight that using chatbots deceptively has far reaching negative effects, which begin with the organization and ultimately impact their customers and the employees that work for them.

Do unique names fit people for creative work? Implications for job recruitment, name change, and product evaluation

AbstractUnique names have commonly been viewed as undesirable. However, our studies (total N = 6049) suggest the opposite: unique names are perceived as favourable to creativity in occupational contexts. Generally, people held a perception that unique‐named individuals are more creative (albeit less likable) and therefore more suitable for jobs requiring greater creativity (Studies 1–2). Accordingly, participants tended to select candidates with more unique names for creative positions (Study 3) and recommend more unique names to workers in creative jobs for a name change (Study 4). Furthermore, real‐world archival data revealed that artists (typical creative professions) tended to adopt more unique new names, which was replicated cross‐culturally in American, British, and Chinese samples (Study 5), and that Chinese movie directors with more unique names received higher evaluations for the movies they directed (Study 6). Our findings demonstrate a novel form of name stereotype and its behavioural manifestations and real‐world consequences.

Placement Wizard (Recruitment Made Easier using Statistics)

The recruitment process for jobs is often challenging and time consuming. It entails multiple stages, including screening resumes, conducting interviews, and assessing candidates through tests. Recently, machine learning algorithms have gained popularity in the recruitment process as they can predict the likelihood of a candidate's placement based on various factors. In this study, we aimed to develop and compare three machine learning algorithms, including LR, DT, NB, KNN & PCA for predicting the placement status of students in a job recruitment process

A STUDY ON THE DIFFERENT TECHNIQUES FOR FACIAL EMOTION IDENTIFICATION AND CLASSIFICATION

Facial expression plays a significant role in our everyday lives. These expressions help in identifying the frame of mind of people. Henceforth, it is used in various fields like medical psychology, criminal investigations, job recruitment, etc., to study human behaviour and analyze people. In the emerging field of artificial intelligence, various categories and analyses are developed to identify and classify facial emotions. Various methods have been implemented, from image analysis to live facial emotion recognition. In our proposed work, we have implemented micro-expression based real-time facial emotion recognition (FER) using a convolution neural network in conjunction with the artificial bee colony algorithm (ABC). Datasets like FER2013, Google Data Set, CK+ and in the wild dataset were used for the training process. We achieved a 95% to 99% accuracy in real- time video-based facial recognition. Key Words: Facial Emotion Recognition (FER), Artificial Bee Colony (ABC), Multi-channel Convolution Neural Network (MCNN), Optical Flow Analysis, Empirical Wavelet Transform (EWT).

A Job Recommendation Method Based on Attention Layer Scoring Characteristics and Tensor Decomposition

In the field of job recruitment, a classic recommendation system consists of users, positions, and user ratings on positions. Its key task is to predict the unknown rating data of users on positions and then recommend positions that users are interested in. However, traditional recommendation methods only rely on user rating data for jobs and provide recommendation services for recruiters and candidates through simple information matching. This simple recommendation strategy not only causes a lot of information waste but also cannot effectively utilize the multi-source heterogeneous data information in the field of job recruitment. Therefore, this paper proposes a job recommendation model based on users’ attention levels and tensor decomposition for specific recruitment positions. This model puts forward assumptions based on browsing time for the special behaviors and habits of users in the field of job recruitment, defines corresponding label values for different interactive behaviors, and establishes a grading method based on the attention of job seekers, thus constructing a three-dimensional tensor of “job seeker user-position-attention layered”. Then, a recommendation model is constructed by decomposing the three-dimensional tensor. The effectiveness of the model is verified by comparative experiments with other recommendation algorithms.

Social media for job recruitment: A review study

The use of social media for job recruiting is examined in this review research along with its effects on the hiring procedure, candidate selection, and organizational results. In order to give a thorough knowledge of the contributions and consequences of social media in the field of job recruiting, it synthesizes the body of current literature. By highlighting areas for more study and knowledge gaps, the findings add to the corpus of knowledge. From a practical standpoint, the study provides organizations, and recruitment specialists with the information that helps them create efficient recruiting strategies and encourage equity and inclusion in applicant selection using social media. The study also provides the organizations and candidates (job seekers) how to use social media in the job recruitment process. The success of various social media recruiting techniques, the impact of applicant traits on online presence, and the part played by privacy concerns in influencing candidate behavior are all areas that require more investigation.

Design and implementation of an android base system for job recruitment

The benefit of computerisation in the organisation, either public or private, is essential to make the organisation's activities run effectively and efficiently. lt simplifies the complexities in getting data, analysing it and producing the output in a specified format based on the result of the program analysis of the data used. Despite the complexities of both entries and analysis of vast data of the applicant records, this project is aimed at simplifying it by designing a computer-based program that will manage part of the activities taking place at the Nigerian Institute Of Leather And Science Technology and also will perform all necessary tasks of analysing those that will yield the correct and meaningful result (output) format in the required by the Institute recruitment. The program produces a room for using an entry form as an interface between the database and the data, which will then be analysed and the desired result produced. The development of the analysed data is then presented in a specific format using the report designed to suit the usual request by the Nigerian police recruitment on such report or data. This includes the applicant's Name, qualifications and other things the applicant records. Therefore the computer program developed in this project work. Serve as a considerable improvement over this existing system and as a tool for technology development for Nigerian police recruitment.

The Role of Negative Perfectionism and the Relationship between Critical Thinking and the Halo Effect: Insights from Corporate Managers in Human Resources.

This research aims to explore the relationship between critical thinking and the halo effect among managers working in the Human Resources (HR) departments of corporations. By utilizing a sample of over 301 corporate HR managers as participants, this study provides valuable insight into the dynamics between critical thinking, the halo effect, and the mediating role of negative perfectionism. The findings of this study suggest a significant negative relationship between critical thinking and the halo effect, as well as a significant positive relationship between negative perfectionism and the halo effect. Notably, negative perfectionism acts as a mediator between critical thinking and the halo effect. Our research also reveals that compensation level moderates this relationship, with lower-income HR managers exhibiting a stronger association between negative perfectionism and the halo effect compared to higher-income HR managers. These findings significantly contribute to our understanding of the interplay between critical thinking and the halo effect among HR managers in corporate settings. Identifying negative perfectionism as a mediating factor clarifies the underlying mechanisms between critical thinking and the halo effect, while the moderating effect of compensation level highlights the importance of considering contextual factors. The practical implications of this research include the significance of promoting critical thinking skills among HR managers to mitigate the halo effect in job recruitment and performance evaluation. Additionally, organizations should prioritize fairness and consistency in compensation levels to minimize the influence of negative perfectionism and its impact on the halo effect.

Explicit and tacit knowledge have diverging urban growth patterns

This article utilizes an online job recruitment dataset of more than 4.6 million jobs in China to examine the urban scaling patterns of explicit and tacit knowledge. Knowledge complexity is considered essential for economic development and innovation, and recent studies find complex economic activities of many fields concentrate more in large cities. However, it remains unclear whether the urban concentration tendency would differ by explicit and tacit knowledge, given the latter is often argued as the hard core knowledge more difficult to transfer. We measure explicit/tacit knowledge in job descriptions regarding education/experience requirements. Our analysis reveals that knowledge of different natures differs to a great extent in their property of urban concentration. Specifically, jobs requiring greater explicit knowledge show higher urban scaling rates. This, however, is not true for tacit knowledge, as it demonstrates the exact opposite pattern. Our findings suggest that while cities are centers of knowledge and innovation, the engines of continued growth tend to become more biased towards explicit rather than know-how knowledge.

Job Recommendation System Using NLP

Abstract: Recommender systems is one of the most successful machine learning applications. We are exposed to the products/information recommended by these systems everywhere in our daily life. This project will introduce several recommender systems in NLP, specifically in the domain of online recruitment. I will explain the important ideas behind the recommender systems in our web application, it might be also similar with those in recruiting platforms like LinkedIn, Xing. One of my main jobs is to design and develop recommender systems on the skill-based matching app. Before getting into this specific topic, I would like to go over the general concepts and methods of current recommender system in different domains. We have provided a brief overview of the approaches, techniques, and applications of recommendation systems in this paper. One significant application of recommendation systems is in the field of job recruitment, where candidates are chosen using online job recruitment portals based on their profiles, employment histories, and behavioral tendencies. According to a recent survey, this field has not been thoroughly investigated up to this point, and the current job recommender system has many flaws. They analyze resumes, profiles, and job descriptions, but because of the "cold start" problem, new job postings and candidate profiles are not properlymatched. In some cases, potential candidates lose their jobs as a result of inadequate job descriptions and education information in the ontology.

Smart Resume Analyzer

Smart Resume Analyzer is a project aimed at simplifying the job recruitment process by automating the initial screening of job applications. The system utilizes natural language processing and machine learning techniques to analyze and extract relevant information from a candidate's resume. The extracted information is then used to evaluate the candidate's qualifications against the job requirements, providing a score that reflects their suitability for the position. The system also offers personalized feedback to candidates to help them improve their resumes and increase their chances of landing a job. Smart Resume Analyzer aims to streamline the recruitment process for employers and candidates alike, saving time and increasing the likelihood of finding the best-fit candidates for open positions. The software will be able to extract relevant information such as work experience, education, skills, and achievements, and match them against job descriptions to identify the best fit for the job

Emotional intelligence and fraud tendency: a survey of future accountants in Nigeria

PurposeThis study examines the association between emotional intelligence (EI) and the tendency of future accountants to rationalize and engage in occupational fraud.Design/methodology/approachThe study adopts a survey methodology and uses a questionnaire containing a fraud scenario and EI construct to gather data from 225 participants. It performed a Cronbach alpha to assess the measurement parameters consistency of EI and fraud tendency and employed Pearson correlation and regression analysis to test its hypothesis.FindingsThe study found that future accountants in Nigeria are emotionally intelligent and have a high fraud tendency. Also, it found a significant and positive association between EI and fraud tendency, suggesting that future accountants that are emotionally intelligent have a higher tendency to rationalize and engage in occupational fraud. In addition, the study found that academic intelligence, a control variable, positively associates with fraud tendency.Practical implicationsThe study offers rare insights into the fraud tendency of future accountants, which would benefit the counter fraud community in Nigeria and other developing countries. Recruiters and employers will find the study beneficial in decision-making on job recruitment, placements and moral orientation for prospective accountant employees.Originality/valueThe study is the first to directly associate EI with the fraud tendency of future accountants from a developing country with high fraud profile and underdeveloped counter fraud strategy. Thus, it provides a benchmark for future studies in other developing countries.

Pengetahuan pengguna tentang kematangan karir ditinjau dari kualitas informasi pada Instagram @glintsid

Glints Indonesia is one of the media that is commonly used as an ecosystem for career maturity, career development, and online-based job recruitment. Not only active through the website, but Glints Indonesia is also active through the Instagram platform with the username @glintsid. The information contained in @glintsid's post suggests new knowledge to its users. This study aims to determine user knowledge about career maturity in terms of the quality of Instagram @glintsid on career tips content. The research method used in this research is qualitative.descriptive Data collection techniques used is observation and interviews with 5 (five) respondents who are final year students of Universitas Padjadjaran who are also Instagram followers @glintsid. The data analysis method used in this research includes data reduction, data presentation, and conclusion. The results of this study indicate that @glintsid's Instagram posts have fairly information quality with accurate, relevant, timely, and completeness indicator. Respondents found that the information content of @glintsid's posts is easy to understand, relevant to needs, timeliness, and clear enough to avoid ambiguity in seeking career tips. In terms of user knowledge indicators, respondents considered that the presence of career tips content on Instagram @glintsid had increased their knowledge through fulfilling indicators such as career planning, career exploration, knowledge about decision making, a world of work information, knowledge of preferred occupation group, and knowledge about the realization of career decisions. The study results show that the quality of information on Instagram @glintsid is quite good and can add new knowledge to respondents.

Android Based Job Search Application “Megawe” for The Segment of Workers with an Education Level Below a University Degree

Indonesia is a vast market for talents recruiting, with around 203 million people that count into work age definition (above 15 years) and around 138 million of them are workforce, while 7 percent of the workforce are open for hire. There are several players of job-seeking apps in Indonesia. But most of them are targeting talents with undergraduate degrees and above. Mid to high-level jobs have become such an attractive market for these players. We can see it on several job seeker hub websites, where job seekers proudly post their personal profiles to attract recruiters from top employers. It is not possible for lower-level job seekers to post their profiles on these websites. They will just be under the recruiter's radar. Whereas from the report data released by the Indonesian statistical center bureau regarding the percentage of open unemployment based on education level, the total unemployment rate from diploma education and below is very large compared to the unemployment rate with university education (undergraduate and above). The Covid-19 pandemic also gave a negative impact on the lower job segment, with a decrease in demand and sales resulting in a higher layoff rate which also means an increased percentage of the open unemployment rate. We need a solution to this problem. A solution that can erode a large chunk hidden under an iceberg. This paper offers “Megawe”, a mobile application-based solution to assist both job seekers and low-level job recruiters. This segment may have a smaller contract value than upper segments but has a much larger quantity of labor force. Because we are focusing on development speed and accuracy of requirements, we used the prototyping methodology in the implementation of the solution. Moreover, before it is launched publicly, we conducted usability testing to gain a deeper understanding of the user experience directly from users of the targeted segment.

When Job Candidates Experience Social Media Privacy Violations

This study uses a cross-cultural sample from the U.S. and China to compare information privacy-protective responses to a breach in privacy during a job interview. Using a job recruitment scenario, the relationships among individuals' concern for information privacy, disposition to trust, judgment of moral issues, and their information privacy-protective responses were examined. Based on the multiple group analysis results, this paper find that the privacy-protective responses significantly vary between the American and Chinese cultures. The findings shed light on individuals' responses to privacy issues in the United States and China.

Perceptions of candidate strength in job recruitment: Does candidate race moderate the attractiveness bias in White women?

AbstractThe attractiveness bias suggests that people who are more attractive will be positively favored across life outcomes. This study sought to test whether candidate attractiveness, sex, and race, affect perceptions of candidate strength in a job recruitment task. In total, 338 White women (Mage = 20.94 ± 5.65) were asked to make judgements of a potential candidate for an administrative job (resume with candidate photograph). The vignettes differed in terms of candidate ability (strong/weak), sex (male/female), race (Black/White), and attractiveness (attractive/less attractive). Participants rated perceived candidate strength and likelihood to invite for interview. Results showed no significant main effects for attractiveness. However, there was a significant interaction for target attractiveness and race, such that attractive/White candidates were more likely to be invited for interview than less attractive/White candidates. There was also a significant main effect for race such that Black candidates were rated as stronger and more likely to be interviewed. Sensitivity analyses (with nonheterosexual women removed from the sample) also showed a main effect for target sex such that female candidates were favored over male candidates. Overall, these findings provide evidence that attractiveness, sex, and race have important, albeit complex, effects on hiring decisions in the workplace.

(Under)valuing lived experience in the disability workforce: A snapshot of Australian job recruitment

AbstractThe Australian disability workforce has grown, and there is compelling evidence that peer work has a number of valuable impacts. Despite these apparent opportunities, there continue to be high levels of unemployment among people with disability and failure to draw on the assets of people with disability in Australian workplaces. To understand these ostensible missed opportunities, this study examined the ways in which lived experience was described as an asset in disability workforce recruitment. Advertisements for positions in New South Wales, Australia, that referred to lived experience or peer work and disability were collected over a 3‐month period. Through a process of content analysis, we found that there were more employment opportunities for people with lived experience of mental illness than for other types of disability. Community engagement was a key function, and peer workers were described as complementary to professionals. Recruitment processes for lived experience jobs were not necessarily accessible. We recommend systemic reforms to facilitate recognition of lived experience as a valuable criterion for disability sector employment and to harness the benefits of a disability peer workforce.