1,720,959 research outputs found
Can Pushing Employees to Recover from Work Backfire? The Joint Effect of Perceived Pressure from the Supervisor to Perform and to Recover on Daily Employee Outcomes
No full textHigh performance demands made to employees by supervisors can be perceived as motivating or abusive depending on the eye of the beholder (Bies et al., 2016). One of the ways in which supervisors make high performance demands is by putting pressure on their employees to successfully complete their job tasks. However, the extant literature is inconsistent in terms of the outcomes of experiencing general performance pressure. Some studies show that it leads to functional outcomes (e.g., Eisenberger & Aselage, 2009), while others show that it leads to dysfunctional ones (e.g., Mitchell et al., 2018). Recent work integrates these findings, explaining that performance pressure is a dynamic phenomenon, fluctuating within-person on a daily level, leading to both positive outcomes as well as negative ones (Mitchell et al., 2019). Drawing on the Job Demands and Resources Model (Demerouti et al., 2001), supplemented by Basic Psychological Needs Theory (Deci & Ryan, 2000), I conduct an empirical study with an experience sampling methodology to assess the daily, within-person process of interpreting performance pressure from the supervisor and the impact of the process on individual wellbeing and workplace deviance behavior. I also investigate how recovery pressure from the supervisor interacts with daily performance pressure to play a moderating role. I discuss theoretical contributions and practical implications
Does AI help or harm? Why and how AI use influences workplace outcomes and employee well-being
Get PDFAlthough the recent surge in excitement surrounding artificial intelligence (AI) might suggest it is a new development, AI has been applied in organizations for over forty years (Fanti et al., 2022; Markelius et al., 2024). Indeed, the term artificial intelligence itself emerged in the 1950s during a research project at Dartmouth College, where it was used to describe machines able to simulate human intelligence (Haenlein & Kaplan, 2019, p. 3). Most recently, in 2022, the landscape of AI use for laypeople greatly changed when OpenAI introduced ChatGPT, a generative AI tool, to the public; ChatGPT is an advanced AI language model designed to understand and generate human-like text based on user input (Kalla et al., 2023). Within two months of its launch, ChatGPT achieved the fastest user adoption in history, reaching 100 million active users (Hu, 2023).
Alongside this rapid consumer uptake, investments in AI by businesses have been steadily increasing and are expected to double next year alone (Annual Global Corporate Investment in Artificial Intelligence, by Type, 2023; McWilliams, 2024). While research is showing that the adoption of generative AI in organizations is beginning to show value for the business, such as, greater stock returns when AI is used in business to business marketing (Zhan et al., 2024), meaningful cost reductions in human resources, and revenue increases in supply chain management (Singla et al., 2024), its impact on employees remains under-explored.
Despite the excitement around AI and particularly generative AI among individual consumers and the growing investments by companies, research on AI in the workplace is limited. Some general advice exists on using AI effectively, such as a practical paper explaining how to create valuable prompts so that HRM assistants can use ChatGPT effectively (Aguinis et al., 2024). Another paper sheds light onto employee preferences, demonstrating that in work situations requiring high empathy, employees prefer human managers as opposed to AI ones; in fact, employees perceive AI management to be less benevolent and this leads to a negative impact on trust in AI management (Li & Bitterly, 2024).
Some studies have begun exploring the impact of AI in the workplace on employees, but the general trend is that effects are not straightforwardly positive or negative. However, predictive studies showing why and under what conditions generative AI adoption is influencing individuals’ workplace outcomes and well-being are rare. One study looks at how employee wellbeing and performance can be helped or harmed as a function of AI use at work; the research finds that employees’ wellbeing suffers when they interact with AI a lot, because they feel increased loneliness, which leads to greater insomnia and alcohol use. This effect is greater for those with attachment anxiety (Tang et al., 2023). Yet, employees can also interact with AI a lot, feel a need for affiliation, and thus be more helpful to coworkers, thereby increasing their own contextual performance (Tang et al., 2023). Another study explores the use of AI for repetitive tasks at a telemarketing company; on one hand, it finds that employees can solve more difficult tasks creatively when they use AI for basic tasks, but this is usually for skilled employees (Jia et al., 2024). On the other hand, lower skilled employees experience negative reactions to the AI help (Jia et al., 2024). Another study finds that frequent use of AI leads to greater knowledge gain and improved task performance, but also to information overload, lowered performance and detachment from work (Shao et al., 2024). These relationships depend on employee levels of openness and positive affect (Shao et al., 2024). Overall, the few studies that explore the impact of AI at work show mixed impacts
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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