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Autor: Grafiati

Data publikacji: 11 września 2023

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1

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

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2

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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4

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

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5

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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6

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

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7

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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8

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

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9

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

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10

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

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11

Schultz, Sandra Lee. Predicting intent to leave from job empowerment and job satisfaction: A comparison of male and female perspectives. 1992.

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12

Murphy, Todd. Predicting career progression amongst high potential employees: Job performance, career clarity, & developmental implications. 2004.

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13

Latham, Gary P., Mary B. Mawritz i Edwin A. Locke. Goal Setting and Control Theory: Implications for Job Search. Redaktorzy Ute-Christine Klehe i Edwin van Hooft. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764921.013.003.

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The benefit of using theories in the behavioral sciences for job search is that they facilitate predicting, explaining, and influencing behavior. This chapter compares and contrasts two such theories, namely, goal setting and control theory. Empirical research, emanating from these two theories on job search, is reviewed. The chapter closes with a checklist for job seekers and suggestions for future research.

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14

Mannahan, Carol A. PREDICTING STAFF NURSE SATISFACTION: APPLICATION OF THE NEEDS-PRESS FRAMEWORK IN A HOSPITAL SETTING (NURSING, JOB SATISFACTION). 1992.

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15

Cartwright, Nancy. Causality, Invariance, and Policy. Redaktorzy Don Ross i Harold Kincaid. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780195189254.003.0015.

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This article explains the puzzling methodology of an important econometric study of health and status. It notes the widespread use of invariance in both economic and philosophical studies of causality to guarantee that causal knowledge can be used to predict the effects of manipulations. It argues that the kind of invariance seen widely in economic methodology succeeds at this job whereas a standard kind of invariance now popular in philosophy cannot. It questions the special role of causal knowledge with respect to predictions about the effects of manipulations once the importance of adding on invariance is recognized. It also draws the despairing conclusion that both causation and invariance are poor tools for predicting the outcomes of policy and technology and to pose the challenge.

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16

Davenport, Thomas H., i Steven M. Miller. Working with AI. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/14453.001.0001.

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Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings. This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers. These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems.

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17

Wenzelburger, Philipp. Model Predictive Control Framework for Flexible Job Shop Scheduling. Logos Verlag Berlin, 2022.

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18

Beck, David Lewis. PERSON-ENVIRONMENT FIT IN A THEORETICAL PREDICTIVE MODEL FOR JOB TURNOVER IN NURSING. 1991.

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19

L, Farr James, red. Personality and the prediction of job performance: A special issue of "Human performance". L.Erlbaum,U.S., 2004.

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20

Hastings, Clare Elizabeth. FACTORS AFFECTING NURSING TURNOVER: A MULTI-LEVEL PREDICTIVE MODEL (ADMINISTRATIVE SUPPORT, JOB SATISFACTION, ORGANIZATIONAL COMMITMENT). 1995.

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21

Lucander, David. “An Economic D-Day for Negro Americans”. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038624.003.0006.

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This chapter examines the role of St. Louis March on Washington Movement (MOWM) in petitioning the Fair Employment Practice Committee (FEPC) to open a branch in the city. Making the FEPC a permanent agency within the federal government was thought to be key to keeping the precarious inroads made by black workers during the war and avoiding another round of hardship that mirrored the Great Depression. By 1949, it was clear that predictions of massive postwar job losses were tragically accurate. It seemed as if securing a federal fair employment law was the most effective way to safeguard the dwindling opportunities for gainful work, so the push for a Permanent FEPC became the centerpiece of A. Philip Randolph's program. The impact of a greater FEPC presence on the employment prospects of African American workers and job seekers is difficult to quantify, but once this office opened, MOWM redirected its energies toward helping that agency remediate racist employment patterns.

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22

Decker. Predicting Biden : an FBI Profile of Joe Biden: Biden's Psychology, Future Actions and Presidency. Independently Published, 2021.

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23

Rouse, William B. Computing Possible Futures. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198846420.001.0001.

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This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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24

Nelson, Thomas Thomas. Joe Public 2030: Five Potent Predictions Reshaping How Consumers Engage Healthcare. Nelson Incorporated, Thomas, 2022.

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25

Bevolo, Chris. Joe Public 2030: Five Potent Predictions Reshaping How Consumers Engage Healthcare. Nelson Incorporated, Thomas, 2022.

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26

Cimbura, Jacqueline Anne. An exploration of stress in police officers: A study of the predictive value of pre-employment psychological measures in the development of stress reactions in a sample of Ontario police officers. 1999.

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27

Zweig, Katharina A. Awkward Intelligence. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13915.001.0001.

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An expert offers a guide to where we should use artificial intelligence—and where we should not. Before we know it, artificial intelligence (AI) will work its way into every corner of our lives, making decisions about, with, and for us. Is this a good thing? There's a tendency to think that machines can be more “objective” than humans—can make better decisions about job applicants, for example, or risk assessments. In Awkward Intelligence, AI expert Katharina Zweig offers readers the inside story, explaining how many levers computer and data scientists must pull for AI's supposedly objective decision making. She presents the good and the bad: AI is good at processing vast quantities of data that humans cannot—but it's bad at making judgments about people. AI is accurate at sifting through billions of websites to offer up the best results for our search queries and it has beaten reigning champions in games of chess and Go. But, drawing on her own research, Zweig shows how inaccurate AI is, for example, at predicting whether someone with a previous conviction will become a repeat offender. It's no better than simple guesswork, and yet it's used to determine people's futures. Zweig introduces readers to the basics of AI and presents a toolkit for designing AI systems. She explains algorithms, big data, and computer intelligence, and how they relate to one another. Finally, she explores the ethics of AI and how we can shape the process. With Awkward Intelligence, Zweig equips us to confront the biggest question concerning AI: where we should use it—and where we should not.

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28

Higgins, Daniel Michael. The roles of psychometric intelligence and prefrontal cognitive ability in the prediction of academic and job performance. 2009.

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29

A study of the relationship between self-concept and job performance of counselors in organized camps in New Brunswick, Canada. 1986.

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30

Borman, Walter C. Personality and the Prediction of Job Performance : More Than the Big Five: A Special Issue of Human Performance. Taylor & Francis Group, 2021.

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31

Borman, Walter C. Personality and the Prediction of Job Performance : More Than the Big Five: A Special Issue of Human Performance. Taylor & Francis Group, 2021.

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Personality and the Prediction of Job Performance : More Than the Big Five: A Special Issue of Human Performance. Taylor & Francis Group, 2017.

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33

Borman, Walter C. Personality and the Prediction of Job Performance : More Than the Big Five: A Special Issue of Human Performance. Taylor & Francis Group, 2021.

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34

Borman, Walter C. Personality and the Prediction of Job Performance : More Than the Big Five: A Special Issue of Human Performance. Taylor & Francis Group, 2021.

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35

Batson, C. Daniel. The Pleasure of Empathic Joy. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190651374.003.0008.

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Kyle Smith, Jack Keating, and Ezra Stotland proposed the empathic–joy hypothesis, which claims that people feeling empathic concern help to get the pleasure of sharing vicariously in the joy that the target of empathy feels when his or her need is removed. They predicted that if those feeling high empathy for a person in need don’t anticipate feeling empathic joy, they will help no more than those feeling low empathy. Smith and colleagues conducted an experiment to test this prediction and claimed support. However, this claim relied on turning away from their experimental manipulation of empathy to a (probably inappropriate) assessment of self-reported empathic concern minus distress. Three additional experiments conducted to test the empathic–joy hypothesis found no support. Results instead consistently patterned as predicted by the empathy–altruism hypothesis. Again, it seemed time to accept the altruism hypothesis, but again acceptance was premature. Others researchers suggested three new possibilities.

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36

Blackman, Melinda. The Effective Interview. Redaktorzy Susan Cartwright i Cary L. Cooper. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199234738.003.0009.

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The face of the employment interview has been gradually transforming over the past fifty years with the culmination of new research, theory, and practices. Now more than ever, researchers and human resource professionals are demanding interview formats that accurately and reliably predict a plethora of criteria in addition to the job candidate's skill set. No longer is the implementation of the traditional structured interview format sufficient for screening applicants. The effective interview is on its way to being transformed into a multifaceted instrument that aims to surpass the predictive precision of standardized selection tests. This article outlines the impetus of research, theory, and practice that spurred on the interview's transformation and the long-term payoff which employers are receiving from the changes. It gives a glimpse of where the employment interview is headed in the upcoming decade and the factors that make today's employment interview so effective.

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37

Borman, Walter C. Personality and the Prediction of Job Performance: More Than the Big Five: A Special Issue of Human Performance (Human Performance, Volume 17, Number 3, 2004). Lawrence Erlbaum, 2004.

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38

Graves, Laura M., i Gary N. Powell. Sex and Race Discrimination in Personnel Decisions. Redaktorzy Susan Cartwright i Cary L. Cooper. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199234738.003.0019.

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This article focuses on the role of personnel decision-making processes within organizations in perpetuating the disadvantaged status of women and people of color. Personnel decisions, which include judgments about who to hire, promote, and develop, and what to pay them, determine whether women and people of color have access to jobs, financial rewards, and advancement opportunities. Social scientists have offered numerous theoretical explanations for sex and race discrimination. This article reviews the key explanations and discusses how they apply to organizational personnel decisions, citing relevant research findings. It then attempts to make sense of the multiplicity of theories, identifying similarities and contradictions in their arguments and the predictions that follow from them. The article also considers the role of organizational factors in the occurrence of sex and race discrimination. Finally, it concludes by offering implications for research and practice.

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39

Staikova, Ivelina, Dimitrinka Staikova i Stoyanka Staikova. Biden's Government. Democrats Against Trump. Clairvoyant/Psychic Predictions for USA and the World Year 2021 - 2024 Through the Eyes of: Joe Biden, Donald Trump, Janet Yellen, Lloyd Austin, Christopher Wray , Antony Blinken, Avril Haines , Libya... Independently Published, 2021.

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40

Ace the Corporate Personality Test. McGraw-Hill, 2000.

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41

Ace the Corporate Personality Test. New York: McGraw-Hill, 2003.

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