Relevant bibliographies by topics / PREDICTING JOB

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'PREDICTING JOB'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "PREDICTING JOB"

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Weaver, Sallie. "CHARACTERISTICS FOR SUCCESS: PREDICTING INTERVENTION EFFECTIVENESS WITH THE JOB CHARACTERISTICS MODEL." Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2697.

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The current study examines the effects of the five core job characteristics (skill variety, task significance, task identity, autonomy, and feedback) proposed by Hackman-Oldham (1974) at the team level by investigating whether the model variables are related to the effectiveness of a motivationally-based team-level productivity enhancement intervention. Previous literature has almost exclusively focused on the effects of these job characteristics at the individual level and their direct relationships with employee attitudes and subjective measures of performance. This thesis aims to further the job characteristics literature by exploring the effects of the characteristics at the team level, as well as the moderating effect of the team construct of value congruence, while simultaneously exploring boundary conditions of the Productivity Measurement and Enhancement System (ProMES) developed by Pritchard (1990). Hypotheses postulated a negative relationship between the characteristics and intervention effectiveness; such that effectiveness is negatively impacted when the characteristics already exist at high levels. Results, though non-significant, are tenatively suggestive of this counter-intuitive negative relationship between four of the characteristics and intervention effectiveness. Value congruence between team leaders and members was not a significant moderator of the relationship between the characteristics and effectiveness. Results suggest that a more powerful study to further parse out these relationships would be valuable. iii
M.S.
Department of Psychology
Sciences
Industrial Org Psychology MS
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Al, Cicek. "En psykometrisk utvärdering av det arbetspsykologiska testet Predicting Job Performance." Thesis, Stockholm University, Department of Psychology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1120.

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Ett psykologiskt test används främst inom utbildningssammanhang och kliniska sammanhang, men även inom rekrytering, urval och organisationsutveckling. Det finns idag ett flertal personlighets- och begåvningstest som mäter olika aspekter som kan vara relevanta för en organisation. Predicting Job Performance, PJP, som har sin utgångspunkt i femfaktormodellen, är utvecklat av Psykologiförlaget AB och består av två delar som mäter olika dimensioner av personlighet och begåvning. I denna studie har psykometriska beräkningar utförts för att utvärdera instrumentets reliabilitet samt validitet, främst genom test-retest-metoden. Undersökningsdeltagare var 49 psykologistudenter vid Stockholms Universitet. Majoriteten av skalorna var starkt positivt korrelerade med varandra och uppvisade en tillfredsställande reliabilitet. Resultatet av test-retest-undersökningen visade att de båda delarna av PJP hade tillförlitlig reliabilitet gällande precision och stabilitet över tid. Korrelation mellan personlighetsdelen och kapacitetsdelen undersöktes och visade på god divergent validitet mellan de två deltesterna. Även inlärningsförmågan på den tillhörande kapacitetsdelen undersöktes och visade signifikant inlärning mellan testtillfällena. PJP uppvisar sammantaget på goda psykometriska egenskaper.

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Al, Cicek. "En turkisk version av det arbetspsykologiska testet Predicting Job Performance." Thesis, Stockholm University, Department of Psychology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6937.

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Personlighetstest avser att mäta olika personlighetsdrag, olika egenskaper som formar individens personlighet. Personlighetstestet Predicting Job Performance, PJP, bygger på den så kallade ”femfaktormodellen” inom personlighetspsykologin och på studier av hur generell problemlösnings-förmåga påverkar arbetsprestation. PJP är ett arbetspsykologiskt instrument för användning i urval av arbetssökande. I föreliggande studie har personlighets-delen i PJP översatts till turkiska med syftet att utforska en turkisk version av instrumentets reliabilitet samt begrepps-validitet. Undersökningsdeltagare var 71 turkisktalande personer bosatta i Sverige och 10 turkisktalande personer bosatta i Istanbul. Resultaten har jämförts med tre andra länder, Sverige, Norge och Finland. Resultatet visar att den turkiska versionen har tillfredsställande inre konsistens (Cronbach alpha) men att de antagna oberoende skalorna är högt korrelerade med varandra. En faktoranalys visar att den antagna faktorstrukturen inte går att replikera i denna studies urval. Den turkiska versionen av instrumentet bör utvärderas ytterligare för att kunna användas vid urvalssammanhang.

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Alexander, Sandra G. Marshall Linda L. "Predicting long term job performance using a cognitive ability test." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-4010.

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Alexander, Sandra G. "Predicting long term job performance using a cognitive ability test." Thesis, University of North Texas, 2007. https://digital.library.unt.edu/ark:/67531/metadc4010/.

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This study focuses on the relationship of one cognitive ability test on long-term job performance as measured by personnel data. Archival data from over 3,000 employees at an international technology company were used to assess how aptitude test scores relate to both objective and subjective job performance measures. Supervisory performance ratings, level of promotion, and salary increase significantly contributed to variance in test scores; however, these results were inconsistent. Number of training courses did not have a significant relationship with test scores. Additionally, type of turnover did not moderate the relationship between aptitude test scores and job performance. These results indicate that although aptitude test score is related to long term job performance factors, other factors account for the majority of the variance. The implication is that aptitude should not be the sole consideration when predicting long term job success.
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Weaver, Sallie J. "Characteristics for success predicting intervention effectiveness with the job characteristics model /." Orlando, Fla. : University of Central Florida, 2008. http://purl.fcla.edu/fcla/etd/CFE0002040.

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Hyland, Shelley S. "Predicting job performance in correctional officers with pre-employment psychological screening." Thesis, State University of New York at Albany, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3701053.

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There is substantial cost in the hiring and training of a correctional officer, with a high rate of turnover compounding these costs. While pre-employment psychological screening is suggested as one method to prevent these losses, mandates to screen are not as common in corrections as they are in law enforcement. Further, minimal research has examined the validity of psychological testing in correctional officers. This dissertation examined pre-employment psychological screening for 421 correctional officers hired by one of three upstate New York sheriff's departments. Assessments were conducted by Public Safety Psychology, PLLC from March, 1997 to June, 2012. T scores and risk estimates from the California Psychological Inventory (CPI) and Personality Assessment Inventory (PAI), DQ admission and problem points from the Personal History Questionnaire (PHQ) and Psychological History Questionnaire (PsyQ) and the psychologist's recommendation were used as predictors of supervisor rating and job status. Utilizing logistic regression and controlling for agency of hire, high ratings by the psychologist, high scores on PAR-H and low scores on BOR-S from the PAI were associated with satisfactory supervisor ratings. Multinomial logistic regression revealed that being non-White, having a lower rating by the psychologist, higher To and Ai scores and lower So scores from the CPI, and more General problem points on the PsyQ were predictive of officers who were fired compared to being currently employed. Furthermore, previous law enforcement experience, being younger, lower Gi, So and Wo scores on the CPI, higher To and Sc scores on the CPI, and lower probability of substance abuse issues as based on the PAI and PHQ were predictive of officers quitting rather than staying on the job. Limitations and future directions are discussed.

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Jefferson, Rachara. "Intrinsic and Extrinsic Job Motivators Predicting Likelihood of Employee Intent to Leave." Thesis, Walden University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10932923.

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An employee’s intent to leave an organization is the most common predictor of employee turnover. Employee turnover can cost an organization 150% to 250% of a worker’s annual compensation to replace and train an employee. Understanding employee intent to leave is vital for federal agency leaders to help reduce turnover. Grounded in Herzberg’s 2-factor model, the purpose of this correlational study was to examine the likelihood of employee perceptions regarding work experience, leadership practices, and supervisor relationships with employees predicting employee intent to leave. Archival data were analyzed for 297 employees who completed the 2015 Federal Employee Viewpoint Survey. The results of the binary logistic regression analysis indicated the full model, containing the 3 predictor variables (employee perceptions regarding work experience, leadership practices, and supervisor relationships with employees), was useful in distinguishing between respondents who reported and did not report they intended to take another job outside the federal government within the next year, with X2 (3, N = 297) = 111.27 and p < .001. Two of the predictor variables--employee perceptions of work experience and leadership practices--made a statistically significant contribution to the model. Employee perceptions of supervisor relationships with employees were not significant. The implications of this study for positive social change include the opportunity for human resources professionals and organizational leaders to gain an understanding of employee intent to leave, its impact on the workplace, and the potential to contribute to higher employment levels.

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Roberts, Heather Elise. "The role of self-leadership and employment characteristics in predicting job satisfaction and performance." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-09192009-040538/.

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Thissen-Roe, Anne. "Adaptive selection of personality items to inform a neural network predicting job performance /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/9138.

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Books on the topic "PREDICTING JOB"

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D, Johnson Cecil, ed. The economic benefits of predicting job performance. New York: Praeger, 1991.

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Baehr, Melany E. Predicting success in higher-level positions: A guide to the System for testing and evaluation of potential. New York: Quorum, 1992.

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Ree, Malcolm James. Relationships of general ability, specific ability, and job category for predicting training performance. Brooks Air Force Base, Tex: Air Force Human Resources Laboratory, Air Force Systems Command, 1990.

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Choose a career that likes you: How to predict career success. Moraga, CA: Effective Learning Systems Press, 1993.

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Townsley, Jacqueline A. The predictive power of selection interview and biographical data on job performance criteria. [s.l: The Author], 1994.

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Ruokanen, Tapani. Entä jos--: Suomi ja mahdolliset maailmat. 2nd ed. Porvoo: W. Söderström, 1995.

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Bentz, V. Jon. Explorations of scope and scale: The critical determinant of high-level executive effectiveness. Greensboro, N.C: Center for Creative Leadership, 1987.

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First hired, last fired: How to become irreplaceable in any job market. Abilene, Texas: Leafwood, 2013.

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Organisation for economic co-operation and development. The OECD jobs study: Facts, analysis, strategies. Paris: OECD, 1994.

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Henwood, Nadya. Predicting job success in information technology. 1996.

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Book chapters on the topic "PREDICTING JOB"

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Smith, Warren, Ian Foster, and Valerie Taylor. "Predicting application run times using historical information." In Job Scheduling Strategies for Parallel Processing, 122–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0053984.

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Mouri, Ishrat Jahan, Biman Barua, M. Mesbahuddin Sarker, Alistair Barros, and Md Whaiduzzaman. "Predicting Online Job Recruitment Fraudulent Using Machine Learning." In Lecture Notes in Electrical Engineering, 719–33. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7753-4_55.

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Yasin, Nurul Hafizah Mohd, Razli Che Razak, Fadhilahanim Aryani Abdullah, Nurhaiza Nordin, Nur Naddia Nordin, and Mohd Safwan Ghazali. "Investigating the Role of Job Demands and Job Resources in Predicting Burnout Among Nursing Home Nurses." In Contributions to Management Science, 663–73. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27296-7_60.

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Goffin, Richard D., Mitchell G. Rothstein, and Norman G. Johnston. "Predicting Job Performance Using Personality Constructs: Are Personality Tests Created Equal?" In Problems and Solutions in Human Assessment, 249–64. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4397-8_11.

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Krasniqi, Valdrin, Yulita, Mohd Awang Idris, and Maureen F. Dollard. "Psychosocial Safety Climate and Job Demands–Resources: A Multilevel Study Predicting Boredom." In Psychosocial Safety Climate, 129–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20319-1_5.

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Saillant, Théo, Jean-Christophe Weill, and Mathilde Mougeot. "Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems." In Lecture Notes in Computer Science, 63–82. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50743-5_4.

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Bekelaar, Jorg W. R., Jolanda J. Luime, and Renata M. de Carvalho. "Predicting Patient Care Acuity: An LSTM Approach for Days-to-day Prediction." In Lecture Notes in Business Information Processing, 378–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_28.

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AbstractIn recent years, hospitals and other care providers in the Netherlands are coping with a widespread nursing shortage and a directly related increase in nursing workload. This nursing shortage combined with the high nursing workload is associated with higher levels of burnout and reduced job satisfaction among nurses. However, not only the nurses, but also the patients are affected as an increasing nursing workload adversely affects patient safety and satisfaction. Therefore, the aim of this research is to predict the care acuity corresponding to an individual patient for the next admission day, by using the available structured hospital data of the previous admission days. For this purpose, we make use of an LSTM model that is able to predict the care acuity of the next day, based on the hospital data of all previous days of an admission. In this paper, we elaborate on the architecture of the LSTM model and we show that the prediction accuracy of the LSTM model increases with the increase of the available amount of historical event data. We also show that the model is able to identify care acuity differences in terms of the amount of support needed by the patient. Moreover, we discuss how the predictions can be used to identify which patient care related characteristics and different types of nursing activities potentially contribute to the care acuity of a patient.
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Hong, Wei-Chiang, Ping-Feng Pai, Yu-Ying Huang, and Shun-Lin Yang. "Application of Support Vector Machines in Predicting Employee Turnover Based on Job Performance." In Lecture Notes in Computer Science, 668–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_85.

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Kuhn, Max, and Kjell Johnson. "Case Study: Job Scheduling." In Applied Predictive Modeling, 445–60. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6849-3_17.

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Therasa, C., R. Gayathri, C. Vijayabanu, S. Karthikeyan, and V. Badrinath. "The incremental effect of demographic variables personality and positive psychological capital in predicting job satisfaction of bank employees." In Interdisciplinary Research in Technology and Management, 335–44. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003358589-44.

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Conference papers on the topic "PREDICTING JOB"

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Zhou, Longfang, Xiaorong Zhang, Wenxiang Yang, Yongguo Han, Fang Wang, Yadong Wu, and Jie Yu. "PREP: Predicting Job Runtime with Job Running Path on Supercomputers." In ICPP 2021: 50th International Conference on Parallel Processing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472456.3473521.

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Zhou, Longfang, Xiaorong Zhang, Wenxiang Yang, Yongguo Han, Fang Wang, Yadong Wu, and Jie Yu. "PREP: Predicting Job Runtime with Job Running Path on Supercomputers." In ICPP 2021: 50th International Conference on Parallel Processing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3472456.3473521.

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Tran, Hieu Trung, Hanh Hong Phuc Vo, and Son T. Luu. "Predicting Job Titles from Job Descriptions with Multi-label Text Classification." In 2021 8th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2021. http://dx.doi.org/10.1109/nics54270.2021.9701541.

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Teh, Pei-Lee, and Laosirihongthong Tritos. "Job involvement, job satisfaction, and organizational commitment: Predicting propensity to leave a job among skilled employee." In 2011 IEEE International Conference on Quality and Reliability (ICQR 2011). IEEE, 2011. http://dx.doi.org/10.1109/icqr.2011.6031670.

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Gupta, Manish. "Predicting click through rate for job listings." In the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526852.

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Singhal, Rekha, and Abhishek Verma. "Predicting Job Completion Time in Heterogeneous MapReduce Environments." In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2016. http://dx.doi.org/10.1109/ipdpsw.2016.10.

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Ranasinghe, H. R. I. E., K. S. Ranasinghe, and R. A. H. M. Rupasingha. "Ensemble Learning Approach for Predicting Job Satisfaction on Freelancing Jobs in Sri Lanka." In 2022 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON). IEEE, 2022. http://dx.doi.org/10.1109/centcon56610.2022.10051528.

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Dutta, Sananda, Airiddha Halder, and Kousik Dasgupta. "Design of a novel Prediction Engine for predicting suitable salary for a job." In 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2018. http://dx.doi.org/10.1109/icrcicn.2018.8718711.

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Ullah, N., R. K. Acree, M. J. Gonzalez, and M. L. Weems. "Predicting the limits of multiple processor performance using job profiles." In Eleventh Annual International Phoenix Conference on Computers and Communication [1992 Conference Proceedings]. IEEE, 1992. http://dx.doi.org/10.1109/pccc.1992.200535.

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Chen, Xin, Charng-Da Lu, and Karthik Pattabiraman. "Predicting job completion times using system logs in supercomputing clusters." In 2013 43rd Annual IEEE/IFIP Conference on Dependable Systems and Networks Workshop (DSN-W). IEEE, 2013. http://dx.doi.org/10.1109/dsnw.2013.6615513.

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Reports on the topic "PREDICTING JOB"

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Zeidner, Joseph, and Cecil D. Johnson. The Economic Benefits of Predicting Job Performance. Fort Belvoir, VA: Defense Technical Information Center, September 1989. http://dx.doi.org/10.21236/ada216744.

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Bakaç, Cafer, Jetmir Zyberaj, and James C. Barela. Predicting telecommuting preferences and job outcomes amid COVID-19 pandemic : A latent profile analysis. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49214.

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Telecommuting is defined as “a work practice that involves members of an organization substituting a portion of their typical work hours (ranging from a few hours per week to nearly full-time) to work away from a central workplace—typically principally from home —using technology to interact with others as needed to conduct work tasks”(Allen, Golden, & Shockley, 2015: 44). This kind of practice substantially differs from the regular and ordinary modes of work because employees perform their usual work in different settings, usually from home (Allen et al., 2015). Although research has been conducted on telecommuting since the 1970s, it has recently become critical when life incidents, like the COVID-19 pandemic has forced many to work from home. Such events offer rare opportunities, for a wide range of researchers and from various fields, to study important questions that would not typically be able to be asked, such as about telecommuting experiences. We took this opportunity and conducted two studies regarding telecommuting, basing our rationale on the fact that many on-site employees were forced to work from home, across a wide range of occupations as a direct result of the pandemic(Kramer & Kramer, 2020). The aim of our study, thus, was to investigate the preferences of employees who were forced to work from home. Specifically, bycreating latent profiles from important work and personality related constructs, we aimed at predicting employees’ preference for working from home or working on-site based on these profiles, and further investigate the relationship of these latent profiles to perceived productivity, job satisfaction, and job engagement.
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Bolton, Laura. The Economic Impact of COVID-19 in Colombia. Institute of Development Studies (IDS), February 2021. http://dx.doi.org/10.19088/k4d.2021.073.

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Available data provide a picture for the macro-economy of Colombia, agriculture, and infrastructure. Recent data on trends on public procurement were difficult to find within the scope of this rapid review. In 2020, macro-level employment figures show a large drop between February and April when COVID-19 lockdown measures were first introduced, followed by a gradual upward trend. In December 2020, the employment rate was 4.09 percentage points lower than the employment rate in December 2019. Macro-level figures from the National Administrative Department of Statistics (DANE) show that a higher percentage of men experienced job losses than women in November 2020. However, the evidence presented by the Universidad Nacional de Colombia based on the DANE great integrated house survey shows that a higher proportion of all jobs lost were lost by women in the second quarter. It may be that the imbalance shifted over time, but it is not possible to directly compare the data. Evidence suggests that women were disproportionately more burdened by home activities due to the closure of schools and childcare. There is also a suggestion that women who have lost out where jobs able to function during lockdowns with technology are more likely to be held by men. Literature also shows that women have lower levels of technology literacy. There is a lack of reliable data for understanding the economic impacts of COVID-19 for people living with disabilities. A report on the COVID-19 response and disability for the Latin America region recommends improving collaboration between policymakers and non-governmental organisations. Younger people experienced greater job losses. Data for November 2020 show 3.3 percent of the population aged under 25 lost their job compared to 1.8 percent of those employed between 24 and 54. Agriculture, livestock, and fishing increased by 2.8% in 2020 compared to 2019. And the sector as a whole grew 3.4% between the third and fourth quarters of 2020. In terms of sector differences, construction was harder hit by the initial mobility restrictions than agriculture. Construction contracted by 30.5% in the second quarter of 2020. It is making a relatively healthy recovery with reports that 84% of projects being reactivated following return to work. The President of the Colombian Chamber of Construction predicting an 8.4% growth in the construction of housing and other buildings in 2021.
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Birur, Dileep, Thomas Hertel, and Wally Tyner. Impact of Biofuel Production on World Agricultural Markets: A Computable General Equilibrium Analysis. GTAP Working Paper, April 2007. http://dx.doi.org/10.21642/gtap.wp53.

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This paper introduces biofuels sectors as energy inputs into the GTAP data base and to the production and consumption structures of the GTAP-Energy model developed by Burniaux and Truong (2002), and further modified by McDougall and Golub (2008). We also incorporate Agro-ecological Zones (AEZs) for each of the land using sectors in line with Lee et al. (2005). The GTAP-E model with biofuels and AEZs offers a useful framework for analyzing the growing importance of biofuels for global changes in crop production, utilization, commodity prices, factor use, trade, land use change etc. We begin by validating the model over the 2001-2006 period. We focus on six main drivers of the biofuel boom: the hike in crude oil prices, replacement of MTBE by ethanol as a gasoline additive in the US, and subsidies for ethanol and biodiesel in the US and EU. Using this historical simulation, we calibrate the key elasticities of energy substitution between biofuels and petroleum products in each region. With these parameter settings in place, the model does a reasonably good job of predicting the share of feedstock in biofuels and related sectors in accordance with the historical evidence between 2001 and 2006 in the three major biofuel producing regions: US, EU, and Brazil. The results from the historical simulation reveal an increased production of feedstock with the replacement of acreage under other agricultural crops. As expected, the trade balance in oil sector improves for all the oil exporting regions, but it deteriorates at the aggregate for the agricultural sectors.
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Flórez, Luz Adriana, Didier Hermida, and Leonardo Fabio Morales. The Heterogeneous Effect of Minimum Wage on Labor Market Flows in Colombia. Banco de la República Colombia, October 2022. http://dx.doi.org/10.32468/be.1213.

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We provide evidence of the negative effect of the minimum wage on labor market flows, such as job creation, job destruction, hiring, and separations in Colombia. Depicting firms' minimum wage (MW) compliance cost, we find evidence of an adverse effect of increases on MW compliance cost on employment. This negative effect is explained mainly by a reduction in job creation and hiring rate and the rise in job destruction and separations. In contrast to the evidence for developed economies, our results are in line with the predictions of the standard search model. We also explore this differential effect by firm size and age. We found that an increase in the MW compliance cost has relatively critical negative impacts on small and medium-sized firms (with less than 250 employees); and new and young firms (lower than six years old).
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Narayanan, Badri, Thomas Hertel, and Mark Horridge. Disaggregated Data and Trade Policy Analysis: The Value of Linking Partial and General Equilibrium Models. GTAP Working Paper, July 2009. http://dx.doi.org/10.21642/gtap.wp56.

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Computable General Equilibrium (CGE) models are now routinely utilized for the evaluation of trade policy reforms, yet they are typically quite highly aggregated, which limits their usefulness to trade negotiators who are often interested in impacts at the tariff line. On the other hand, Partial Equilibrium (PE) models, which are typically used for analysis at disaggregate levels, deprive the researcher of the benefits of an economy-wide analysis, which is required to examine the overall impact of broad-based trade policy reforms. Therefore, a PE-GE, nested modeling framework has the prospect of offering an ideal tool for trade policy analysis. In this paper, we develop a PE model that captures international trade, domestic consumption and output, using Constant Elasticity of Transformation (CET) and Constant Elasticity of Substitution (CES) structures, market clearing conditions and price linkages, nested within the standard GTAP Model. In particular, we extend the welfare decomposition of Huff and Hertel (2001) to this PE-GE model in order to contrast the sources of welfare gain in PE and GE analyses. To illustrate the usefulness of this model, we examine the contentious issue of tariff liberalization in the Indian auto sector, using PE, GE and PE-GE models. Both the PE and PE-GE models show that the imports of Motorcycles and Automobiles change drastically with both unilateral and bilateral tariff liberalization by India, but the PE model does a poor job predicting the overall size and price level in the industry, post-liberalization. On the other hand, the GE model overestimates substitution between regional suppliers due to “false competition” and underestimates the welfare gain, due to the problem of tariff averaging in the aggregated model. These findings are shown to be robust to wide variation in model parameters. We conclude that the linked model is superior to both the GE and PE counterparts.
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Fabio Morales, Leonardo, Eleonora Dávalos, and Raquel Zapata. Estimating Vacancy Stocks from Aggregated Data on Hires: A Methodology to Study Frictions in the Labor Market. Banco de la República de Colombia, April 2023. http://dx.doi.org/10.32468/be.1228.

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We develop a methodology that recovers an estimate of the average stock of vacancies using the information on aggregated hires. We show that our prediction of the vacancy stock is unbiased, and it captures well the level and the dynamics of the United States job opening positions reported in the Job Openings and Labor Turnover Survey. We use the methodology to predict vacancies in Colombia for formal and informal salaried workers; together with unemployment, we estimate Beveridge curves and matching functions by occupations, which allows us to study the nature of the efficiency, frictions, and mismatches for different occupations. We find that the formal labor market of technicians is the most inefficient of them all; this inefficiency comes from the mismatch between the abilities of the workers and the requirement of the vacancies. Reducing friction in this occupation will require education and job-oriented training policies. In contrast, the frictions in the market for unskilled workers come from informational lacks. The reductions of friction, in this case, will come from better intermediation and active search policies.
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Salter, R., Natàlia Garcia-Reyero, Alicia Ruvinsky, Maria Seale, and Edward Perkins. Adverse outcome pathways for engineered systems. Engineer Research and Development Center (U.S.), July 2023. http://dx.doi.org/10.21079/11681/47336.

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Companies and organizations around the world spend massive amounts of money each year to discover, predict, and remediate failures within engineered systems. These tasks require individuals with specialized knowledge in a variety of topics related to failure. This knowledge is often acquired through years of academic and on-the-job training centered around the review of scientific documentation such as books, reports, manuals, and peer-reviewed publications. The loss of this knowledge through employee attrition can be detrimental to a group as knowledge is often difficult to reacquire. The aggregation and representation of known failure mechanisms for engineered materials could aid in the sharing of knowledge, the acquisition of knowledge, and the discovery of failure causes, reducing the risk of failure. Thus, the current work proposes the Adverse Outcome Pathway for Engineered Systems (AOP-ES) framework, an extension of the Adverse Outcome Pathway used in toxicology. The AOP-ES is designed to document failure knowledge, enabling knowledge transfer and the prediction of failures of novel engineered materials based on the performance of similar materials. This paper introduces the AOP-ES framework and its key elements alongside the principles that govern the framework. An application of the framework is presented, and additional benefits are explored.
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