Características do trabalho e desempenho adaptativo: O papel da satisfação com o trabalho e do suporte organizacional percebido

O presente estudo tem o propósito de aprofundar o conhecimento científico acerca do desempenho adaptativo e os seus preditores. A globalização, a inovação tecnológica e a volatilidade dos mercados representam alguns dos maiores desafios sentidos pelas organizações, tornando-se imprescindível compreender o modo como os colaboradores evidenciam a sua capacidade para desempenhar comportamentos adaptativos, à luz das características de trabalho (especificamente, complexidade, feedback da chefia e dos colegas e workload) e da satisfação com o trabalho. Igualmente pertinente foi averiguar o papel do suporte organizacional percebido na relação das características do trabalho com o desempenho adaptativo. Os dados foram recolhidos através de um inquérito online, no qual participaram 125 indivíduos. Os resultados revelaram-se coerentes com as hipóteses formuladas, constatando-se que o desempenho adaptativo é influenciado positivamente pelas características do trabalho averiguadas. Verificou-se ainda que a satisfação com o trabalho medeia a relação entre o feedback da chefia e colegas e o desempenho adaptativo. Finalmente, confirmou-se que o suporte organizacional percebido modera a relação entre o workload e o desempenho adaptativo no sentido em que quando a perceção de suporte organizacional é elevada, o workload elevado está associado uma menor frequência de desempenho adaptativo dos colaboradores. Já em níveis de suporte organizacional inferiores, o efeito do workload sobre o desempenho adaptativo aumenta. Em termos teóricos e práticos esta investigação pode contribuir para que instituições de ensino e organizações criem estratégias que lhes permitam melhorar o desenvolvimento da satisfação com o trabalho e das características do trabalho essenciais para o desempenho adaptativo.This study aims to deepen scientific knowledge about adaptive performance and its predictors. Globalization, technological innovation and market volatility represent some of the biggest challenges faced by organizations, making it essential to understand how employees show their ability to perform adaptive behaviors, in light of work characteristics (specifically, complexity, feedback from management and colleagues and workload) and job satisfaction. Equally important was to investigate the role of perceived organizational support in the relationship between work characteristics and adaptive performance. The data were collected through an online survey, in which 125 individuals participated. The results proved to be consistent with the hypotheses formulated, showing that the adaptive performance is positively influenced by the work characteristics investigated. It was also found that job satisfaction mediates the relationship between feedback from management and colleagues and adaptive performance. Finally, it was confirmed that the perceived organizational support moderates the relationship between the workload and adaptive performance in the sense that when the perception of organizational support is high, the high workload is associated with a lower frequency of adaptive performance of employees; At lower organizational support levels, the effect of workload on adaptive performance increases. In theoretical and practical terms, this research can contribute for educational institutions and organizations to create strategies that allow them to improve the development of job satisfaction and work characteristics essential for adaptive performance

Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models

Recently, work in NLP has shifted to few-shot (in-context) learning, with large language models (LLMs) performing well across a range of tasks. However, while fairness evaluations have become a standard for supervised methods, little is known about the fairness of LLMs as prediction systems. Further, common standard methods for fairness involve access to models weights or are applied during finetuning, which are not applicable in few-shot learning. Do LLMs exhibit prediction biases when used for standard NLP tasks? In this work, we explore the effect of shots, which directly affect the performance of models, on the fairness of LLMs as NLP classification systems. We consider how different shot selection strategies, both existing and new demographically sensitive methods, affect model fairness across three standard fairness datasets. We discuss how future work can include LLM fairness evaluations

The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces

Scholarly publications are key to the transfer of knowledge from scholars to others. However, research papers are information-dense, and as the volume of the scientific literature grows, the need for new technology to support the reading process grows. In contrast to the process of finding papers, which has been transformed by Internet technology, the experience of reading research papers has changed little in decades. The PDF format for sharing research papers is widely used due to its portability, but it has significant downsides including: static content, poor accessibility for low-vision readers, and difficulty reading on mobile devices. This paper explores the question "Can recent advances in AI and HCI power intelligent, interactive, and accessible reading interfaces -- even for legacy PDFs?" We describe the Semantic Reader Project, a collaborative effort across multiple institutions to explore automatic creation of dynamic reading interfaces for research papers. Through this project, we've developed ten research prototype interfaces and conducted usability studies with more than 300 participants and real-world users showing improved reading experiences for scholars. We've also released a production reading interface for research papers that will incorporate the best features as they mature. We structure this paper around challenges scholars and the public face when reading research papers -- Discovery, Efficiency, Comprehension, Synthesis, and Accessibility -- and present an overview of our progress and remaining open challenges