Personnel Psychology最新文献_第10页

What You Say Matters: Moving Beyond Gossiping Extent to Explore the Positive Effects of Gossip Truthfulness and Gossip Interestingness 你说的很重要：超越流言蜚语的范围，探索流言蜚语的真实性和趣味性的积极影响

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-11-06 DOI: 10.1111/peps.12552

Ryan Outlaw, Michael D. Baer

引用次数: 1

Recruitment in personnel psychology and beyond: Where we've been working, and where we might work next 人事心理学及其他方面的招聘:我们一直在哪里工作，下一步可能在哪里工作

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-11-05 DOI: 10.1111/peps.12548

Brian R. Dineen, K. Yu, Julia Stevenson‐Street

引用次数: 1

Serving the cause when my organization does not: A self‐affirmation model of employees’ compensatory responses to ideological contract breach 当我的组织没有为事业服务时：员工对意识形态契约违约的补偿反应的自我肯定模型

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-10-29 DOI: 10.1111/peps.12546

Hong Deng, J. Coyle-Shapiro, Yanting Zhu, Chia-huei Wu

引用次数: 2

Winter 2022 ad hoc reviewers 2022年冬季特别评审员

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-10-25 DOI: 10.1111/peps.12549

B. Erdogan

引用次数: 0

Personnel psychology 2022 awards 人事心理学2022年获奖

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-10-25 DOI: 10.1111/peps.12550

B. Erdogan

引用次数: 0

After the Break‐Up: How Divorce Affects Individuals at Work 分手后：离婚如何影响工作中的个人

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-10-17 DOI: 10.1111/peps.12547

Connie R. Wanberg, Borbala Csillag, M. Duffy

引用次数: 1

What's age got to do with it? A primer and review of the workplace aging literature 这跟年龄有什么关系?职场老龄化文献的入门与回顾

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-09-24 DOI: 10.1111/peps.12544

M. Beier, R. Kanfer, D. Kooij, D. Truxillo

引用次数: 7

The AI‐IP: Minimizing the guesswork of personality scale item development through artificial intelligence AI‐IP：通过人工智能最大限度地减少对个性量表项目开发的猜测

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-09-15 DOI: 10.1111/peps.12543

Ivan Hernandez, Weiwen Nie

{"title":"The AI‐IP: Minimizing the guesswork of personality scale item development through artificial intelligence","authors":"Ivan Hernandez, Weiwen Nie","doi":"10.1111/peps.12543","DOIUrl":"https://doi.org/10.1111/peps.12543","url":null,"abstract":"We propose a framework for integrating various modern natural language processing (NLP) models to assist researchers with developing valid psychological scales. Transformer-based deep neural networks offer state-of-the-art performance on various natural language tasks. This project adapts the transformer model GPT-2 to learn the structure of personality items, and generate the largest openly available pool of personality items, consisting of one million new items. We then use that artificial intelligence-based item pool (AI-IP) to provide a subset of potential scale items for measuring a desired construct. To better recommend construct-related items, we train a paired neural network-based classification BERT model to predict the observed correlation between personality items using only their text. We also demonstrate how zero-shot models can help balance desired content domains within the scale. In combination with the AI-IP, these models narrow the large item pool to items most correlated with a set of initial items. We demonstrate the ability of this multimodel framework to develop longer cohesive scales from a small set of construct-relevant items. We found reliability, validity, and fit equivalent for AI-assisted scales compared to","PeriodicalId":48408,"journal":{"name":"Personnel Psychology","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43347686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

The Social Process of Coping with Work‐Related Stressors Online: A Machine Learning and Interpretive Data Science Approach 在线应对工作相关压力源的社会过程:机器学习和解释数据科学方法

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-08-10 DOI: 10.1111/peps.12538

S. Sajjadiani, Michael A. Daniels, H. Huang

{"title":"The Social Process of Coping with Work‐Related Stressors Online: A Machine Learning and Interpretive Data Science Approach","authors":"S. Sajjadiani, Michael A. Daniels, H. Huang","doi":"10.1111/peps.12538","DOIUrl":"https://doi.org/10.1111/peps.12538","url":null,"abstract":"People are increasingly turning to social media and online forums like Reddit to cope with work-related concerns. Previous research suggests that how others respond can be an important determinant of the sharer's affective and well-being outcomes. However, less is known about whether and how cues embedded in the content of what is shared can shape the type of responses that one receives from others, obscuring the joint and interactive role that both the sharer and listener may play in influencing the sharer's outcomes. In this study, we develop theory to advance our understanding of online coping with an explicitly social focus using computational grounded theorizing and machine learning (ML) techniques applied to a large corpus of work-related conversations on Reddit. Specifically, our theoretical model sheds light on the dynamics of the online social coping process related to the domain of work. We show that how sharers and listeners interact and react to one another depends on the content of stressors shared, the social coping behaviors used when sharing, and whether the sharer and listener belong to the same occupational context. We contribute to the social coping literature in three ways. First, we clarify how social actors respond to cues embedded in the social coping attempt. Second, we examine the moderating role that such responses play in shaping sharer outcomes. Finally, we extend theory on social coping with work-related stressors to the online domain. Taken together, this research highlights the importance of the dynamic interplay between sharer and listener in the context of online social coping.","PeriodicalId":48408,"journal":{"name":"Personnel Psychology","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49008557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A History of Data Visualization & Graphic Communication by Michael Friendly and Howard Wainer, Harvard University Press, Cambridge MA, 308 pages including references and index. 2021. Reviewed by Malcolm James Ree, Our Lady of the Lake University Michael Friendly和Howard Wainer的《数据可视化和图形通信的历史》，哈佛大学出版社，剑桥，308页，包括参考文献和索引。2021. 书评:马尔科姆·詹姆斯·里，圣母湖大学

IF 5.5 2区心理学

Personnel Psychology Pub Date : 2022-08-09 DOI: 10.1111/peps.12540

Malcolm James Ree

引用次数: 1