Due to the coronavirus disease 2019 (COVID-19) pandemic, many employees have been strongly encouraged or mandated to work from home. The present study sought to understand the attitudes and experiences of the general public toward remote work by analyzing Twitter data from March 30 to July 5 of 2020. We web scraped over 1 million tweets using keywords such as “telework,” “work from home,” “remote work,” and so forth, and analyzed the content using natural language processing (NLP) techniques. Sentiment analysis results show generally positive attitudes expressed by remote work-related tweets, with minor dips during the weekend. Topic modeling results uncovered themes among tweets including home office, cybersecurity, mental health, work–life balance, teamwork, and leadership, with minor changes in topics revealed over the 14-week period. Findings point to topics of particular concern regarding working from home and can help guide hypothesis generation for future research.
Despite the centrality of technology to understanding how humans in organizations think, feel, and behave, researchers in organizational psychology and organizational behavior even now often avoid theorizing about it. In our review, we identify four major paradigmatic approaches in theoretical approaches to technology, which typically occur in sequence: technology-as-context, technology-as-causal, technology-as-instrumental, and technology-as-designed. Each paradigm describes a typically implicit philosophical orientation toward technology as demonstrated through choices about theory development and research design. Of these approaches, one is unnecessarily limited and two are harmful oversimplifications that we contend have systematically weakened the quality of theory across our discipline. As such, we argue that to avoid creating impractical and even inaccurate theory, researchers must explicitly model technology design. To facilitate this shift, we define technology, present our paradigmatic framework, explain the framework's importance, and provide recommendations across five key domains: personnel selection, training and development, performance management and motivation, groups and teams, and leadership. Expected final online publication date for the Annual Review of Organizational Psychology and Organizational Behavior, Volume 8 is January 21, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
In the context of game-based assessments (GBAs), we examined the potential of trace data modeling to supplement or replace an existing GBA's scoring approach.We used data science tools and "big data" practices, such as feature engineering and a series of machine learning algorithms, to predict traditionally measured cognitive ability and conscientiousness scores from the copious trace data generated by a theory-driven GBA designed to measure cognitive ability. Several types of predictors were developed from the raw trace data from 621 participants, including counts of game objects that the player interacted with, the amount of time spent doing so, and mouse movement data across a variety of meaningful intervals. Broadly, we found promising evidence for trace data modeling of cognitive ability, including incremental contribution to the prediction of a criterion grade point average (GPA), but less promising evidence for trace data modeling of conscientiousness, suggesting that trace data modeling like this may be more valuable for assessing traits in games that were developed to target those traits.
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