Journal articles: 'PREDICTING JOB'

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Akinnuli, B. O., S. A. Oluwadare, and Adeyemi A. Aderoba. "A Computer-Aided System for Job-Shop Flow Time and Due–Date Prediction." Advanced Materials Research 367 (October 2011): 639–45. http://dx.doi.org/10.4028/www.scientific.net/amr.367.639.

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: One of the key challenges confronting a production system is the accurate prediction of flow time (completion time) and due date of jobs coming into the job-shop. The inaccuracy in the determination of flow time and due date could lead to problems of dead line misses, high holding cost for waiting jobs and/or completed jobs, technical and cost inefficiency; and customer dissatisfaction, among others. In this paper, a Computer-Aided System which is based on an empirical model for predicting job-shop flow time and due-date was developed. The Computer-Aided System makes use of historical production data and other peculiar in-house factors of an organization. The Computer-Aided System was tested with real life job-shop data in order to test its efficiency and it was found to be efficient in predicting flow time and due date of jobs.

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Al-Sayeh, Hani, Stefan Hagedorn, and Kai-Uwe Sattler. "A gray-box modeling methodology for runtime prediction of Apache Spark jobs." Distributed and Parallel Databases 38, no. 4 (March 10, 2020): 819–39. http://dx.doi.org/10.1007/s10619-020-07286-y.

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Abstract Apache Spark jobs are often characterized by processing huge data sets and, therefore, require runtimes in the range of minutes to hours. Thus, being able to predict the runtime of such jobs would be useful not only to know when the job will finish, but also for scheduling purposes, to estimate monetary costs for cloud deployment, or to determine an appropriate cluster configuration, such as the number of nodes. However, predicting Spark job runtimes is much more challenging than for standard database queries: cluster configuration and parameters have a significant performance impact and jobs usually contain a lot of user-defined code making it difficult to estimate cardinalities and execution costs. In this paper, we present a gray-box modeling methodology for runtime prediction of Apache Spark jobs. Our approach comprises two steps: first, a white-box model for predicting the cardinalities of the input RDDs of each operator is built based on prior knowledge about the behavior and application parameters such as applied filters data, number of iterations, etc. In the second step, a black-box model for each task constructed by monitoring runtime metrics while varying allocated resources and input RDD cardinalities is used. We further show how to use this gray-box approach not only for predicting the runtime of a given job, but also as part of a decision model for reusing intermediate cached results of Spark jobs. Our methodology is validated with experimental evaluation showing a highly accurate prediction of the actual job runtime and a performance improvement if intermediate results can be reused.

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Harris, Douglas H. "Prediction of Inspection Performance with a Dynamic, Computer-Based, Multi-Aptitude Test." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 1 (October 1997): 574–78. http://dx.doi.org/10.1177/1071181397041001127.

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A dynamic, computer-based, multi-aptitude test was designed to predict success on inspection jobs that require the combined aptitudes of general cognitive ability, abstract reasoning, and spatial visualization. This approach to the prediction of job performance is in contrast to most existing methods for predicting performance on technical jobs, in which aptitudes are measured one at a time by static tests. The test was computer administered and scored, requiring 36 minutes of testing time. Validation studies showed that the test was highly reliable, correlated with other measures of these key aptitudes, and was predictive of the job performance of nondestructive testing operators. These results indicated that the test can be used with confidence for the early identification of individuals who will benefit most from training and experience on certain inspection jobs, and who will be most likely to meet operator qualification standards for these jobs.

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Orpen, Christopher. "Job Dependence as a Moderator of Effects of Job Threat on Employees' Job Insecurity and Performance." Psychological Reports 72, no. 2 (April 1993): 449–50. http://dx.doi.org/10.2466/pr0.1993.72.2.449.

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The effects of job dependence on the relationships between objective job threat and, respectively, employees' job insecurity and performance were examined among 135 employees of a small Australian manufacturer. Objective job threat was given by the combined ratings of three managers of the likelihood of each of the jobs disappearing over the next few years. Moderated multiple regression analysis showed that job dependence interacted with job threat in predicting differences in insecurity, but not in performance. Subjects dependent on their jobs felt more insecure than those who were more independent, as predicted.

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Mitchell, Terence R., and Thomas W. Lee. "Some Reservations About a “Rational Choice” Model Predicting Employee Turnover." Industrial and Organizational Psychology 6, no. 2 (June 2013): 181–87. http://dx.doi.org/10.1111/iops.12030.

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Professor Russell proposes a decision model of turnover in which the attractiveness of the current job is compared with that of an alternative. In turn, an employee chooses the option with the highest judged attractiveness. For example, “Employees make decisions to quit based on the relative attractiveness of their current job compared to alternative jobs or activities” (2013, p. 163). The attractiveness of one's current job and alternative are estimated by a regression equation assessing various attributes of the two targets (i.e., current job and alternative). Evoking March and Simon (1958) for a theoretical foundation, Professor Russell offers a subjectively “rational model” for the choice to stay or leave based on expectancy and expected value type decision models. In his empirical work, he uses a “policy capturing” simulation to identify how new hires personally weigh various job attributes when deciding whether they would quit hypothetical jobs varying in those attributes (Russell & Van Sell, 2012). When these weights are applied to employees' actual survey perceptions of the levels of job attributes, the resulting “simulated turnover intention” score predicts turnover better than a survey measure of quit intentions or job attributes alone. The inference is that turnover scholars can make substantial progress toward the prediction of actual turnover by using this model.

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Craiger, J. Philip, Michael D. Coovert, and Mark S. Teachout. "Predicting Job Performance with a Fuzzy Rule-Based System." International Journal of Information Technology & Decision Making 02, no. 03 (September 2003): 425–44. http://dx.doi.org/10.1142/s0219622003000744.

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Classification problems affect all organizations. Important decisions affecting an organization's effectiveness include predicting the success of job applicants and the matching and assignment of individuals from a pool of applicants to available positions. In these situations, linear mathematical models are employed to optimize the allocation of an organization's human resources.Use of linear techniques may be problematic, however, when relationships between predictor and criterion are nonlinear. As an alternative, we developed a fuzzy associative memory (FAM: a rule-based system based on fuzzy sets and logic) and used it to derive predictive (classification) equations composed of measures of job experience and job performance. The data consisted of two job experience factors used to predict measures of job performance for four US Air Force job families. The results indicated a nonlinear relationship between experience and performance for three of the four data sets. The overall classification accuracy was similar for the two systems, although the FAM provided better classification for two of the jobs. We discuss the apparent nonlinear relationships between experience and performance, and the advantages and implications of using these systems to develop and describe behavioral models.

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Yener, Müjdelen, and Özgün Coşkun. "Using Job Resources and Job Demands in Predicting Burnout." Procedia - Social and Behavioral Sciences 99 (November 2013): 869–76. http://dx.doi.org/10.1016/j.sbspro.2013.10.559.

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Skelton, Angie R., Deborah Nattress, and Rocky J. Dwyer. "Predicting manufacturing employee turnover intentions." Journal of Economics, Finance and Administrative Science 25, no. 49 (May 16, 2019): 101–17. http://dx.doi.org/10.1108/jefas-07-2018-0069.

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Purpose Employee turnover expenses can cost businesses more than 100 per cent of a single employee’s annual wages and negatively affection an organization’s production and profits. High employee turnover also could affect community tax collections, social programs and physical and mental health issues. Therefore, understanding contributors to higher employee turnover remains essential for organizational managers from both a corporate and societal standpoint. This paper aims to provide an analysis of how job satisfaction and job embeddedness could predict employee turnover intent. Design/methodology/approach A randomly selected survey which consisted of Andrews and Withey’s (1976) job satisfaction questionnaire, a global job embeddedness scale (Crossley et al., 2007) and a three-item turnover intent questionnaire derived from a survey created by Mobley et al. (1978) using a Likert-type measurement to survey randomly selected individuals used within manufacturing plants located in the Southeastern USA. Findings The results of the multiple regression analysis showed a significant relationship between job satisfaction, job embeddedness and turnover intent; and that satisfied and committed employees are less likely to plan to leave their employment. Originality/value Limited current information is available on how job satisfaction and job embeddedness predict turnover intentions in US Southeast manufacturing. This study includes information that shows the importance of job satisfaction and job embeddedness on retaining employees in this region and industry. Given the importance of employee retention on corporate productivity, morale and profits along with the ability to improve the organization’s positive contribution to society, it is important for managers to understand these factors and their effect on employee turnover intent.

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Hartmann, Peter, Lars Larsen, and Helmuth Nyborg. "Personality as Predictor of Achievement." Journal of Individual Differences 30, no. 2 (January 2009): 65–74. http://dx.doi.org/10.1027/1614-0001.30.2.65.

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General intelligence, g, is a powerful predictor of education, job status and income, but the predictive power of personality is less clear. The objective of the present paper was to investigate the predictive power of personality (and g) with respect to education, job status, and income. We derived Eysenckian personality factors (P, E, N, L) from MMPI data; g was distilled from a large number of highly diverse cognitive variables. Linear, nonlinear, and interaction power in predicting socioeconomic achievement in 4200+ middle-aged American males was tested. In the present study, broad personality factors provided little incremental validity to g, in predicting socioeconomic achievement across type of education and job categories. This is at odds with previous studies, and does not exclude the possibility that certain personality factors (higher or lower order) have more predictive validity within certain job categories and education types.

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Khalid Mohammed, Ayaz, Abdullahi Aliyu Danlami, Dindar I. Saeed, Abdulmalik Ahmad Lawan, Adamu Hussaini, and Ramadhan Kh. Mohammed. "An Ensemble Machine Learning Approach for Classifying Job Positions." Academic Journal of Nawroz University 12, no. 3 (August 30, 2023): 547–55. http://dx.doi.org/10.25007/ajnu.v12n3a1547.

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Machine learning is one of the promising research areas in computer science, with numerous applications in automated detection of meaningful data patterns. Several data-centric studies were conducted on evaluating competencies, detecting similar jobs and predicting salaries of various job positions. However, the hazy distinction between closely related job positions requires powerful predictive algorithms. The present study proposed an ensemble approach for accurate classification of various job positions. Accordingly, different machine learning algorithms were applied on 955 instances obtained from Glassdoor using web scraping. Furthermore, the present study classify various job positions based on average salary and other correlated explanatory variables that cover many aspects of job activities on the internet. The study result revealed the superior performance of heterogeneous ensembles in terms of precision and accuracy. The proposed data-centric approach produce strong models for researchers, recruiters, and candidates to assigned job positions and its competencies.

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Borgogni, Laura, Silvia Dello Russo, Laura Petitta, and Michele Vecchione. "Predicting Job Satisfaction and Job Performance in a Privatized Organization." International Public Management Journal 13, no. 3 (September 2, 2010): 275–96. http://dx.doi.org/10.1080/10967494.2010.504114.

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Laughery, Kenneth R., Andrew S. Jackson, and Gail A. Fontenelle. "Isometric Strength Tests: Predicting Performance in Physically Demanding Transport Tasks." Proceedings of the Human Factors Society Annual Meeting 32, no. 11 (October 1988): 695–99. http://dx.doi.org/10.1518/107118188786762496.

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A study explored the applicability of a battery of four isometric strength tests to steward, utility and warehouse jobs in a company that services offshore drilling and production facilities in the petroleum industry. The jobs involve frequently transporting materials up stairs, a category of tasks not prominent in situations where these tests have previously been applied. A job analysis established critical task requirements such as procedures, weights, distances, sizes of containers, etc. An experiment was then carried out with 25 male and 25 female subjects. The subjects performed two self-paced job-related tasks: transporting a 15.9 kg box up and down stairs and similarly transporting a 22.7 kg box. Measures included heart rate and amount of work performed, which, along with known task parameters, was used to calculate work power. Subjects also performed four standard isometric strength tests: grip, arm lift, back lift and arm press. Correlations between job task and strength performance indicated these tests are applicable to jobs with such requirements, thus extending the generalizability of the strength test battery.

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Mirjafari, Shayan, Hessam Bagherinezhad, Subigya Nepal, Gonzalo J. Martinez, Koustuv Saha, Mikio Obuchi, Pino G. Audia, et al. "Predicting Job Performance Using Mobile Sensing." IEEE Pervasive Computing 20, no. 4 (October 1, 2021): 43–51. http://dx.doi.org/10.1109/mprv.2021.3118570.

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Feeley, Thomas Hugh, Katherine Hart LaVail, and George A. Barnett. "Predicting faculty job centrality in communication." Scientometrics 87, no. 2 (November 27, 2010): 303–14. http://dx.doi.org/10.1007/s11192-010-0324-3.

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Martin, Cody, Lance Anderson, Brian Cronin, Beth Heinen, and Sukanya Swetharanyan. "predicting job decisions in tomorrow's workforce." Journal of Employment Counseling 47, no. 4 (December 2010): 167–79. http://dx.doi.org/10.1002/j.2161-1920.2010.tb00101.x.

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Uyiomendo, Efosa E., and Markeset Tore. "Subsea maintenance service delivery." Journal of Quality in Maintenance Engineering 21, no. 1 (March 9, 2015): 34–54. http://dx.doi.org/10.1108/jqme-11-2013-0071.

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Purpose – The purpose of this paper is to propose a multi-variable analysis (MVA) model for predicting potential delays in the delivery of subsea inspection, maintenance and repair (IMR) services. Design/methodology/approach – Based on data from 351 subsea IMR service jobs executed between 2006 and 2008, a MVA model is proposed for predicting the potential delays in the delivery of IMR services in different plausible scenarios. Findings – A model for predicting the delays in IMR service delivery, based on four practical variables that are readily available during the planning phase, was developed and tested. The factors contributing to delays in petroleum subsea IMR services based on importance are: water depth, weather, job complexity, job uncertainty as well as job complexity mix. Research limitations/implications – The MVA model is developed based on analyzing subsea IMR service jobs performed in the petroleum industry from 2006-2008. The model can be used in the planning stage to predict potential delays in service delivery based on practical variables available. Originality/value – The research proposes a MVA model for predicting delays in service delivery. The model is useful for predicting potential delays in service delivery and for improving the plan based on model analysis results.

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Winner Walecha and Dr. Bhoomi Gupta. "Salary Estimator using Data Science." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 15, 2020): 319–22. http://dx.doi.org/10.46501/ijmtst061259.

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This paper presents a salary prediction system using the job listings from an employment website, in this case Glassdoor.com. A data mining technique is used to generate a model which will scrape number of jobs from the employment website, clean it on the basis of number of factors including the rival companies, revenue and skill required thereby predicting the salary to be expected when applying for a data science job. Techniques like linear regression, lasso regression, random forest regressors are optimised using GridsearchCV to reach the best model. The model can be further extended to build a flask API thus can be deployed on the internet for public usage.

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Blood, Gordon W., Jenna Swavely Ridenour, Emily A. Thomas, Constance Dean Qualls, and Carol Scheffner Hammer. "Predicting Job Satisfaction Among Speech-Language Pathologists Working in Public Schools." Language, Speech, and Hearing Services in Schools 33, no. 4 (October 2002): 282–90. http://dx.doi.org/10.1044/0161-1461(2002/023).

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Purpose:The aims of the present study were (a) to compare the job satisfaction ratings of speech-language pathologists (SLPs) working in schools with other workers on a standardized index and (b) to examine whether geographic setting (i.e., rural, suburban, and urban), specific demographic variables (i.e., gender, ethnicity, age, and education), and practice-related variables (i.e., years in current position and caseload size) explain/predict job satisfaction among SLPs working in public schools.Method:A mail survey methodology was employed. Two thousand practicing SLPs, members of the American Speech-Language-Hearing Association (ASHA), residing in the United States were randomly selected from the national membership list. Usable responses were obtained from 1,207 SLPs, representing a 60.4% response rate. Participants completed the Job Satisfaction Survey (Spector, 1996), demographic and practice-related questions, and information regarding their geographic work setting.Results:These results suggest that the majority of SLPs are generally satisfied (42.2%) or highly satisfied (34.1%) with their jobs. Results of the regression analyses revealed that the age of participants (i.e., older were more satisfied), years at current job (i.e., SLPs with greater number of years were more satisfied), and caseload size (i.e., SLPs with smaller caseloads were more satisfied) were predictive of job satisfaction in SLPs working in the schools.Clinical Implications:The findings provide additional assurance for SLPs concerning overall group satisfaction. The information could also assist educational training programs with data concerning SLPs' satisfaction. Smaller caseload size is predictive of job satisfaction. There were no differences among rural, suburban, and urban SLPs in their overall job satisfaction. This information could be useful for personnel directors in recruiting and retaining SLPs.

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Shi, Lei, Jing Xu, Lunfei Wang, Jie Chen, Zhifeng Jin, Tao Ouyang, Juan Xu, and Yuqi Fan. "Multijob Associated Task Scheduling for Cloud Computing Based on Task Duplication and Insertion." Wireless Communications and Mobile Computing 2021 (April 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6631752.

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With the emergence and development of various computer technologies, many jobs processed in cloud computing systems consist of multiple associated tasks which follow the constraint of execution order. The task of each job can be assigned to different nodes for execution, and the relevant data are transmitted between nodes to complete the job processing. The computing or communication capabilities of each node may be different due to processor heterogeneity, and hence, a task scheduling algorithm is of great significance for job processing performance. An efficient task scheduling algorithm can make full use of resources and improve the performance of job processing. The performance of existing research on associated task scheduling for multiple jobs needs to be improved. Therefore, this paper studies the problem of multijob associated task scheduling with the goal of minimizing the jobs’ makespan. This paper proposes a task Duplication and Insertion algorithm based on List Scheduling (DILS) which incorporates dynamic finish time prediction, task replication, and task insertion. The algorithm dynamically schedules tasks by predicting the completion time of tasks according to the scheduling of previously scheduled tasks, replicates tasks on different nodes, reduces transmission time, and inserts tasks into idle time slots to speed up task execution. Experimental results demonstrate that our algorithm can effectively reduce the jobs’ makespan.

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Alijanzadeh, Ebrahim, Ali Asghar Razavi, and Safiyeh T. Ahmasebi Limuni. "Predicting Job Performance Based on Knowledge Management." Journal of Management and Accounting Studies 8, no. 4 (September 29, 2020): 34–38. http://dx.doi.org/10.24200/jmas.vol8iss4pp34-38.

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The objective of this research is to predict job performance based on knowledge management. The methodology of this research was applied according to its objective and descriptive-correlational based on the execution method. The statistical population of this research is all the librarians from public libraries of Mazandaran province with 265 members by full-census manner. 179 questionnaires were turned back. The research tool was Hosseinzadeh (2019) personal knowledge management questionnaire and Hosseini job performance questionnaire (2013). Cronbach’s alpha coefficient was used to estimate the face and content validity, and reliability of the questionnaire was estimated according to professors and specialists’ ideas which were obtained higher than 0.7 in all questionnaires. Data was analyzed using SPSS 18 software. The results of this research showed that the components of knowledge management have a positive and significant effect on job performance (P<0.01). Moreover, 37.6% of changes caused by job performance are predicted by the components of knowledge management. According to the obtained results, some suggestions are offered to improve the research variables.

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Sultan, Sarwat. "Equity Sensitivity Predicting Job Satisfaction and Commitment." Journal of Independent Studies and Research-Management, Social Sciences and Economics 9, no. 1 (June 30, 2011): 1–10. http://dx.doi.org/10.31384/jisrmsse/2011.09.1.1.

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Sackett, Paul R., Melissa L. Gruys, and Jill E. Ellingson. "Ability-personality interactions when predicting job performance." Journal of Applied Psychology 83, no. 4 (1998): 545–56. http://dx.doi.org/10.1037/0021-9010.83.4.545.

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Wilford, J., A. D. McMahon, J. Peters, S. Pickvance, A. Jackson, L. Blank, D. Craig, A. O'Rourke, and E. B. Macdonald. "Predicting job loss in those off sick." Occupational Medicine 58, no. 2 (January 22, 2008): 99–106. http://dx.doi.org/10.1093/occmed/kqm141.

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Mol, Stefan T., Marise Ph Born, Madde E. Willemsen, and Henk T. Van Der Molen. "Predicting Expatriate Job Performance for Selection Purposes." Journal of Cross-Cultural Psychology 36, no. 5 (September 2005): 590–620. http://dx.doi.org/10.1177/0022022105278544.

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Hayes, Read. "Predicting the Job Performance of Store Detectives." Security Journal 17, no. 4 (October 2004): 7–20. http://dx.doi.org/10.1057/palgrave.sj.8340180.

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Reisel, William D. "Predicting Job Insecurity via Moderating Influence of Individual Powerlessness." Psychological Reports 92, no. 3 (June 2003): 820–22. http://dx.doi.org/10.2466/pr0.2003.92.3.820.

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While job insecurity has been extensively studied since Greenhalgh and Rosenblatt called attention to it in 1984, this paper examined why some individuals respond to the antecedents of job insecurity with increased job insecurity while others do not. This research sought to assess whether a moderator variable, individual powerlessness, might offer explanatory information on why people experience job insecurity. Moderated multiple regression analysis on a sample of 276 managers using Caplan, Cobb, French, Van Harrison, and Pinneau's 1975 Job Insecurity scale and Ashford, Lee, and Bobko's 1989 Powerlessness scale, suggested that an individual's powerlessness moderates the effect of antecedents on measured job insecurity. An interaction was found for subjects scoring high on powerlessness and not for those scoring low as hypothesized.

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Jen, Sern Tham, Amirul Aizat Mohd Ali, and Hongjie Zhang Thomas. "Predicting The Role of Organisational Listening and Job Resources in Job Engagement." Jurnal Komunikasi: Malaysian Journal of Communication 38, no. 2 (June 29, 2022): 107–26. http://dx.doi.org/10.17576/jkmjc-2022-3802-07.

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Job engagement is defined as one’s enthusiasm and involvement in his or her job. Individuals who are profoundly engaged with their job are motivated by the work itself. They will, in general, work harder and more productively than others and are bound to create the outcomes that their clients and organisation need. Although existing empirical studies have provided evidence that perceived organisational support (POS) and supportive leader behaviour can significantly predict job engagement, the effect of organisational listening on employee job engagement is still under researched. Guided by the Job Demand-Resources (JD-R) model, this study proposed that organisational listening is a vital predictor of job engagement. We analysed whether organisational listening holds a more substantial statistical power than perceived organisational support and supportive leader behaviour as job resources. We conducted an online survey that involved 207 employees from a Malaysian IT management consulting company. After analysing the survey data through multiple regression analysis, the results showed that organisational listening was a significant predictor of job engagement. Besides, organisational listening showed an incremental validity above and beyond perceived organisational support and supportive leader behaviour in predicting job engagement. These findings suggest that employees become engaged in their job when they believe that the leader incorporates values and action to listen accurately and is supportive. Implications in theoretical and practical perspectives were discussed. Keywords: Organisational listening, job engagement, job resources, perceived organisational support, supportive leader behaviour.

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Shaik, Subhani, P. Santhosh Kumar, S. Vikram Reddy, K. Sai Srinivas Reddy, and Sunil Bhutada. "Machine Learning based Employee Attrition Predicting." Asian Journal of Research in Computer Science 15, no. 3 (April 4, 2023): 34–39. http://dx.doi.org/10.9734/ajrcos/2023/v15i3323.

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Now a day’s variety of reasons for job resignations due to this, we have to take different types of measurements for prediction of job seekers. They have different reasons for not doing jobs well and fell like pressure. Many employees suddenly come to an end of their service without any reason. Techniques of machine learning have full-grown in fame in the middle of researchers in current years. It is accomplished of propose answer to a broad range of problems. Help of machine learning, you may produce prediction concerning staff abrasion. So machine learning model we will be using TCS employee attrition a genuine time dataset to train our model. The aim of this study is to at hand a comparison of different machine learning algorithms for predict which employees are probable to go away their society. We propose two methods to crack the dataset into train and test data: the 75 percent train 25 percent test split and the K Fold methods. Three techniques are three methods that we employ to train our model for correctness comparison, and we will compare the exactness of the models generate using these three Boosting Algorithms.

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Villanueva, Ernesto. "Estimating Compensating Wage Differentials Using Voluntary Job Changes: Evidence from Germany." ILR Review 60, no. 4 (July 2007): 544–61. http://dx.doi.org/10.1177/001979390706000405.

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The author develops a model predicting that in a labor market that attaches a wage premium to jobs with a disamenity (a compensating wage differential), the premium's upper bound will be defined by the average wage change of voluntary job movers whose consumption of the disamenity rises as a result of their move; its lower bound, by the wage change of those whose consumption of the disamenity falls. These predictions will not hold if, as predicted by a “segmented” labor market model, the labor market attaches a wage penalty to workplace disamenities. Using longitudinal data on job characteristics and wages in Germany in 1984–2001, the author estimates the market returns to four workplace disamenities: heavy workload, job insecurity, poor hours regulation, and a mismatch between skills possessed and skills required. The results broadly support the existence of compensating differentials in the German labor market.

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Hintsa, Taina, Mirka Hintsanen, Markus Jokela, and Liisa Keltikangas-Järvinen. "Temporal and Energetic Characteristics of Behavior Predicting Long-term Job Strain, Job Demands, and Job Control." Journal of Occupational and Environmental Medicine 55, no. 3 (March 2013): 331–36. http://dx.doi.org/10.1097/jom.0b013e31827b736a.

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Xu, Hui, and Huaying Li. "Operationalize Interest Congruence: A Comparative Examination of Four Approaches." Journal of Career Assessment 28, no. 4 (March 9, 2020): 571–88. http://dx.doi.org/10.1177/1069072720909825.

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Although interest congruence is a cornerstone of career counseling, little is known about the relative importance of different operationalization approaches to interest congruence (i.e., how to calculate interest congruence). Using a sample of U.S. employees ( n = 303), the current study comparatively examined four profile-based conceptual congruence approaches, namely Euclidean distance, angular agreement, profile deviance, and profile correlation, in terms of their predictions for job and life satisfaction, turnover intention, and perceived person–job fit. The results found that profile correlation demonstrated complete dominance (i.e., ubiquitously stronger predictive utility) over the other three congruence indices in predicting all four career outcomes. Therefore, the current study portrays profile correlation as a preferred operationalization approach to interest congruence and offers rich implications for congruence research and practice.

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Tomas, Jasmina, Darja Maslić Seršić, and Hans De Witte. "Psychological climate predicting job insecurity through occupational self-efficacy." Personnel Review 48, no. 2 (March 4, 2019): 360–80. http://dx.doi.org/10.1108/pr-05-2017-0163.

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Purpose The purpose of this paper is to test the hypothesized mediation model that specifies psychological climate dimensions as antecedents of job insecurity, while accounting for occupational self-efficacy. Stemming from the conservation of resources theory, the authors hypothesize that job challenge, role harmony, leader support and co-worker cooperation negatively relate to job insecurity due to its positive correlation with occupational self-efficacy. Design/methodology/approach Data were collected with a sample of 329 white-collar employees from the ICT sector who were employed full-time and for a period of at least six months in their current organization. All hypotheses were tested via structural equation modeling using the bootstrap method to test the significance of indirect effects. Findings Among the four work environment domains, only job challenge had a significant contribution in explaining job insecurity variance. This relationship was fully mediated by occupational self-efficacy. Research limitations/implications The cross-sectional research design limits the ability to make causality inferences, while the convenience sampling method limits the generalizability of findings. Practical implications The study results indicate that well-designed (i.e. challenging, autonomous and important) job tasks may be advantageous in organizational interventions aimed at reducing job insecurity due to their potential to strengthen employees’ efficacy beliefs. Originality/value The study results contribute to current knowledge regarding the relative importance of work environment antecedents of job insecurity, as well as the prominent role played by occupational self-efficacy in explaining some of these relationships.

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Postelnicu, Maria Daniela, and Oana Maria Grigore. "Comparative Evaluations of Performance and Self-esteem as Determinants of Job Insecurity." Studia Doctoralia 9, no. 2 (December 29, 2018): 117–26. http://dx.doi.org/10.47040/sd/sdpsych.v9i2.83.

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Job insecurity is a toxic stressor brought forward by the world’s financial crisis. The purpose of this study is to demonstrate that comparative judgements predict a unique percentage of job insecurity’s variance, above self esteem. Subsequently, questionnaires were applied via social media or via paper-pencil to 108 participants. Hierarchical regression results show that comparative judgements do not have any incremental effect in predicting job insecurity above self esteem. Self esteem predicts 21% variance in job insecurity. In conclusion, job insecurity is predicted by how individuals globally evaluate themselves, with comparative judgements regarding one’s own performance or the company’s performance having no incremental predictive power. These two variables can be regarded as undifferentiated from self esteem.

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Postelnicu, Maria Daniela, and Oana Maria Grigore. "Comparative Evaluations of Performance and Self-esteem as Determinants of Job Insecurity." Studia Doctoralia 9, no. 2 (December 29, 2018): 117–26. http://dx.doi.org/10.47040/sd0000064.

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Job insecurity is a toxic stressor brought forward by the world’s financial crisis. The purpose of this study is to demonstrate that comparative judgements predict a unique percentage of job insecurity’s variance, above self esteem. Subsequently, questionnaires were applied via social media or via paper-pencil to 108 participants. Hierarchical regression results show that comparative judgements do not have any incremental effect in predicting job insecurity above self esteem. Self esteem predicts 21% variance in job insecurity. In conclusion, job insecurity is predicted by how individuals globally evaluate themselves, with comparative judgements regarding one’s own performance or the company’s performance having no incremental predictive power. These two variables can be regarded as undifferentiated from self esteem.

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Park, Jisung, and Chiho Ok. "The Differentiating Effects of Job Seekers’ Spec on Actual Employment: Focusing on Screening Hypothesis and Dual Labor Market Theory." Institute of Management and Economy Research 13, no. 4 (December 31, 2022): 11–24. http://dx.doi.org/10.32599/apjb.13.4.202212.11.

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Purpose - The purpose of this study was to examine how job seekers’ spec influence their actual employment especially focusing on the differentiating effects of applicants’ specs depending on whether general or decent job employment. Design/methodology/approach - This study conducted analyses on 54,443 samples that incorporated data from the Graduates Occupational Mobility Survey for three years (2017-2019) collected by the Korea Employment Information Service. The linear probability model and logit model were used to examine the research questions. Findings - The results analyzed with the hierarchical regression model showed that most job seekers’ specs were statistically significant in predicting employment status. Interestingly, there is a difference between the factors predicting employment for a general job and a decent job. This study suggests academic and practical implications for future research in the selection/ recruitment field by clarifying the critical factors to influence applicants’ employment. Research implications or Originality The results of this study follow the screening hypothesis which explains that the applicants’ specs have significant impacts on actual employment. Also, the dual labor market theory, which explains that applicants’ specs differently affect actual employment between general and decent jobs, was reaffirmed.

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Reilly, Eithne, Katie Dhingra, and Daniel Boduszek. "Teachers’ self-efficacy beliefs, self-esteem, and job stress as determinants of job satisfaction." International Journal of Educational Management 28, no. 4 (May 6, 2014): 365–78. http://dx.doi.org/10.1108/ijem-04-2013-0053.

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Purpose – The purpose of this paper is to examine the role of teaching self-efficacy, perceived stress, self-esteem, and demographic characteristics (age, gender, education, and years of teaching experience) in predicting job satisfaction within a sample of 121 Irish primary school teachers. Design/methodology/approach – Survey data were collected from teachers from eight primary schools. Hypotheses were tested using a comparison of means, correlations, and multiple regression. Findings – Results indicated that the predictor variables accounted for 22 per cent of variance in teachers’ job satisfaction. However, only perceived stress was found to explain unique predictive variance, with high levels of occupations stress related to low levels of job satisfaction. Practical implications – Perceived stress should be targeted in efforts to improve teachers’ job satisfaction. Originality/value – The results make an additional contribution to the literature by providing important information on the factors contributing to teachers’ job satisfaction in Ireland.

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BAL TASTAN, Seçil. "Predicting Psychological Strain with Job Demands and Organizational Injustice through the Implications of Job Demand-Control Model and Fairness Theory." Postmodern Openings 5, no. 4 (December 31, 2014): 111–43. http://dx.doi.org/10.18662/po/2014.0504.09.

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Maric, Zorica. "Predicting job-seeking intensity and job-seeking intention in the sample of unemployed." Psihologija 38, no. 2 (2005): 181–95. http://dx.doi.org/10.2298/psi0502181m.

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In this study Ajzen' theory (1991) of planned behavior was used to predict job - seek intention and behavior among unemployed people (N = 650). In addition to theory of planned behavior variables (job - seek attitude, subjective norm, self - efficacy and controllability of job seek process) we used several other psychological (financial pressure, self - mastery, self - esteem and depression) and demographic (gender, age, education, marriage and lent of unemployment) variables to build a model of predictors for both criterion variables. Financial pressure, intention to seek employment, job seek - self - efficacy, job - seek controllability, marriage and job - seek attitude predicted job - seeking behavior, while attitude toward job - seeking, subjective norm, job - seek self - efficacy and financial pressure predicted job seek - intention. Results are discussed in light of theory of planned behavior, current research of job - seeking behavior and recommendations are made for practice.

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Aramburu-Zabala Higuera, Luis, and Marina Casals Riera. "Validation of a Trainability Test for Young Apprentices." European Psychologist 9, no. 1 (January 2004): 56–63. http://dx.doi.org/10.1027/1016-9040.9.1.56.

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Predictive test validation data were collected to analyze the usefulness of a content-valid trainability test of carpentry for predicting success at a full-scale course. Results supported the effectiveness of the standardized test CAR-1 to predict the individual job-learning potential in a sample of 109 young unemployed persons. Several advantages of structured trainability tests as predictors of job learning are indicated. These include greater standardization, the content and predictive validity, and the possibility of assessing the potential of candidates with no prior experience. The high and significant coefficients obtained in this study suggest that the trainability testing methodology may provide useful guidelines for developing job learning predictors in other areas of vocational training.

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Abu Samah, Khyrina Airin Fariza, Nurqueen Sayang Dinnie Wirakarnain, Raseeda Hamzah, Nor Aiza Moketar, Lala Septem Riza, and Zainab Othman. "A linear regression approach to predicting salaries with visualizations of job vacancies: a case study of Jobstreet Malaysia." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (September 1, 2022): 1130. http://dx.doi.org/10.11591/ijai.v11.i3.pp1130-1142.

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This study explicitly discusses helping job seekers predict salaries and visualize job vacancies related to their future careers. Jobstreet Malaysia is an ideal platform for discovering jobs across the country. However, it is challenging to identify these jobs, which are organized according to their respective and specific courses. Therefore, the linear regression approach and visualization techniques were applied to overcome the problem. This approach can provide predicted salaries, which is useful as this enables job seekers to choose jobs more easily based on their salary expectations. The extracted Jobstreet data runs the pre-processing, develops the model, and runs on real-world data. A web-based dashboard presents the visualization of the extracted data. This helps job seekers to gain a thorough overview of their desired employment field and compare the salaries offered. The system’s reliability as tested using mean absolute error, the functionality test was performed according to the use case description, and the usability test was performed using the system usability scale. The reliability results indicate a positive correlation with the actual values. The functionality test produced a successful result, and a score of 96.58% was achieved for the system usability scale result, proving the system grade is ‘A’ and usable.

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Knapp, Joshua R., Brett R. Smith, and Therese A. Sprinkle. "Is It the Job or the Support? Examining Structural and Relational Predictors of Job Satisfaction and Turnover Intention for Nonprofit Employees." Nonprofit and Voluntary Sector Quarterly 46, no. 3 (January 1, 2017): 652–71. http://dx.doi.org/10.1177/0899764016685859.

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We examine the relative efficacy of two theoretically distinct variables for predicting job satisfaction and turnover intentions for workers in nonprofit organizations. The first, perceived job characteristics, reflects the structure of jobs in terms of autonomy, skill variety, task identity, task significance, and feedback. The second, perceived organizational support, reflects the quality of the employee–organization relationship. We collected data from 196 full-time, nonprofit employees across two time periods, and we tested hypotheses using hierarchical regression and relative importance analysis. Results emphasize the significance of managing employees in a supportive manner and structuring jobs so that employees can work autonomously.

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Mussel, Patrick. "Introducing the construct curiosity for predicting job performance." Journal of Organizational Behavior 34, no. 4 (June 27, 2012): 453–72. http://dx.doi.org/10.1002/job.1809.

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R, Valanarasu. "Comparative Analysis for Personality Prediction by Digital Footprints in Social Media." June 2021 3, no. 2 (May 31, 2021): 77–91. http://dx.doi.org/10.36548/jitdw.2021.2.002.

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The use of social media and leaving a digital footprint has recently increased all around the world. It is being used as a platform for people to communicate their sentiments, emotions, and expectations with their data. The data available in social media are publicly viewable and accessible. Any social media network user's personality is predicted based on their posts and status in order to deliver a better accuracy. In this perspective, the proposed research article proposes novel machine learning methods for predicting the personality of humans based on their social media digital footprints. The proposed model may be reviewed for any job applicant during the times of COVID'19 through online enrolment for any organisation. Previously, the personality prediction methods are failed due to the differing perspectives of recruiters on job applicants. Also, this estimation is modernized and the prediction time is also reduced due to the implementation of the proposed hybrid approach on machine learning prediction. The artificial intelligence based calculation is used for predicting the personality of job applicants or any person. The proposed algorithm is organized with dynamic multi-context information and it also contains the account information of multiple platforms such as Facebook, Twitter, and YouTube. The collection of the various dataset from different social media sites constitute to the increase in the prediction rate of any machine learning algorithm. Therefore, the accuracy of personality prediction is higher than any other existing methods. Despite the fact that a person's logic varies from season to season, the proposed algorithm consistently outperforms other existing and traditional approaches in predicting a person's mentality.

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Page, Diana. "Predicting the job performance of scientists and engineers." Academy of Management Perspectives 12, no. 2 (May 1998): 98–99. http://dx.doi.org/10.5465/ame.1998.650522.

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Ree, Malcolm James, James A. Earles, and Mark S. Teachout. "Predicting job performance: Not much more than g." Journal of Applied Psychology 79, no. 4 (1994): 518–24. http://dx.doi.org/10.1037/0021-9010.79.4.518.

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Hausdorf, Peter A., and Stephen D. Risavy. "Predicting Training and Job Performance for Transit Operators." International Journal of Selection and Assessment 23, no. 2 (April 28, 2015): 191–95. http://dx.doi.org/10.1111/ijsa.12107.

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Kinicki, Angelo J. "Predicting occupational role choices after involuntary job loss." Journal of Vocational Behavior 35, no. 2 (October 1989): 204–18. http://dx.doi.org/10.1016/0001-8791(89)90041-9.

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Sharma, Prachi, and Urmila Rani Srivastava. "Emotion Regulation and Job Satisfaction as Predictors of Subjective Well-being in Doctors." Management and Labour Studies 45, no. 1 (December 25, 2019): 7–14. http://dx.doi.org/10.1177/0258042x19894084.

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This study examined the role of emotion regulation and job satisfaction in predicting affective (positive and negative affect) and cognitive (life satisfaction) components of subjective well-being (SWB) in doctors. The predictors used were the dimensions of job satisfaction—intrinsic, extrinsic job satisfaction as well as the total score of job satisfaction and the following dimensions of intra-personal emotion regulation—cognitive reappraisal and expressive suppression. The participants included in the study were doctors from multi-specialty hospitals in Gurgaon district of Haryana. A total of 102 doctors were included in the study using convenience sampling. Correlational and step-wise multiple regression analyses were conducted to test the predictions. The results of the analysis confirmed the predictions as intrinsic job satisfaction and cognitive reappraisal significantly and positively predicted life satisfaction. The findings were discussed in the light of available research along with implications of the study and possible avenues for future research.

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Kuehn, Eileen, Max Fischer, Sven Lange, Andreas Petzold, and Andreas Heiss. "Predicting resource usage for enhanced job scheduling for opportunistic resources in HEP." EPJ Web of Conferences 245 (2020): 07039. http://dx.doi.org/10.1051/epjconf/202024507039.

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To overcome the computing challenge in High Energy Physics available resources must be utilized as efficiently as possible. This targets algorithmic challenges in the workflows itself but also the scheduling of jobs to compute resources. To enable the best possible scheduling, job schedulers require accurate information about resource consumption of a job before it is even executed. It is the responsibility of the user to provide an accurate resource estimate required for jobs. However, this is quite a challenge for users as they (i) want to ensure their jobs to run correctly, (ii) must manage to deal with heterogeneous compute resources and (iii) face intransparent library dependencies and frequent updates. Users therefore tend to specify resource requests with an ample buffer. This inaccuracy results in inefficient utilisation by either blocking unused resources or exceeding reserved resources. Especially in the context of opportunistic resource provisioning the inaccuracies have an even broader impact that does not even target utilisation of resources but also composition of the most suitable resources. The contribution of this paper is an analysis of production and end-user workflows in HEP with regards to optimizing the various resources types. We further propose a method to improve user estimates.

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Sharma, Deepak. "Factors Predicting Job Satisfaction Among Managers in Voluntary Organization." BSSS Journal of Social Work 14, no. 1 (July 13, 2022): 49–63. http://dx.doi.org/10.51767/jsw1405.

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This study examines the factors associated with job satisfaction among managers in voluntary organizations. 100 samples were collected across India were surveyed. Data on job satisfaction were analyzed with using binary logistic regression. Findings suggest that job satisfied managers had more transparency in the organization, had delegation of authority, working environment, roles and responsibilities, had enough manpower which devote time in giving performance for the organization, get compensation that too adequate and better on performance appraisals. The implications of the study are discussed.

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Journal articles on the topic 'PREDICTING JOB'

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