Browsing by Author "Hernandez, Ivan"

Now showing 1 - 5 of 5
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    The AI-IP: Minimizing the guesswork of personality scale item development through artificial intelligence
    Hernandez, Ivan; Nie, Weiwen (Wiley, 2022)
    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-ofthe- 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 intelligencebased 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 scales developed and optimized by traditional methods. By leveraging neural networks’ ability to generate text relevant to a given topic and infer semantic similarity, this project demonstrates how to support creative and open-ended elements of the scale development process to increase the likelihood of one’s initial scale being valid, and minimize the need to modify and revalidate the scale.
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    BCC’ing AI: Using Modern Natural Language Processing to Detect Micro and Macro E-ggressions in Workplace Emails
    Cornett, Kelsi E. (Virginia Tech, 2024-05-24)
    Subtle offensive statements in workplace emails, which I term "Micro E-ggressions," can significantly impact the psychological safety and subsequent productivity of work environments despite their often-ambiguous intent. This thesis investigates the prevalence and nature of both micro and macro e-ggressions within workplace email communications, utilizing state-of-the-art natural language processing (NLP) techniques. Leveraging a large dataset of workplace emails, the study aims to detect and analyze these subtle offenses, exploring their themes and the contextual factors that facilitate their occurrence. The research identifies common types of micro e-ggressions, such as questioning competence and work ethic, and examines the responses to these offenses. Results indicate a high prevalence of offensive content in workplace emails and reveal distinct thematic elements that contribute to the perpetuation of workplace incivility. The findings underscore the potential for NLP tools to bridge gaps in awareness and sensitivity, ultimately contributing to more inclusive and respectful workplace cultures.
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    Examining Social Support Seeking Online
    Minton, Brandon (Virginia Tech, 2021)
    Research across healthcare and organizational settings demonstrates the importance of social support to increase physical and mental well-being. However, the process of seeking social support is less well-understood than its outcomes. Specifically, research examining how people seek social support in natural settings is scarce. One natural setting increasingly used by people to seek support is the internet. In this online setting, people seek and provide social support verbally via social media platforms and messages. The present project seeks to further examine the nature of social support seeking in these online contexts by examining people’s language. This analysis includes discovering the common language features of social support seeking. By applying a data-driven content analysis approach, this research can examine the underlying themes present when seeking social support and build upon that insight to classify new instances of support seeking. These results would have important practical implications for occupational health. By identifying individuals who are seeking social support, future interventions will be able to take a more targeted approach in lending additional support to those individuals who have the greatest need. Subsequently, this application potentially provides the mental and physical health benefits of social support. Therefore, this research extends our knowledge of both the nature of support seeking and how to develop effective interventions.
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    Teleoperator-Robot-Human Interaction in Manufacturing: Perspectives from Industry, Robot Manufacturers, and Researchers
    Kim, Sunwook; Hernandez, Ivan; Nussbaum, Maury A.; Lim, Sol (Informa, 2024-02-08)
    OCCUPATIONAL APPLICATIONS: Industrial robots have become an important aspect in modern industry. In the context of human-robot collaboration, enabling teleoperated robots to work in close proximity to local/onsite humans can provide new opportunities to improve human engagement in a distributed workplace. Interviews with industry stakeholders highlighted several potential benefits of such teleoperator-robot-human collaboration (tRHC), including the application of tRHC to tasks requiring both expertise and manual dexterity (e.g., maintenance and highly skilled tasks in sectors including construction, manufacturing, and healthcare), as well as opportunities to expand job accessibility for individuals with disabilities and older individuals. However, interviewees also indicated potential challenges of tRHC, particularly related to human perception (e.g., perceiving remote environments), safety, and trust. Given these challenges, and the current limited information on the practical value and implementation of tRHC, we propose several future research directions, with a focus on human factors and ergonomics, to help realize the potential benefits of tRHC.
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    Thankful or Thank You? Exploring the Impact of Intrapersonal and Interpersonal Gratitude
    Wardale, Jack (Virginia Tech, 2023-08)
    Gratitude has been found to have many positive benefits, whether it is introspective or interpersonal in nature. This research explored the differential effects of an interpersonal and intrapersonal gratitude intervention on subjective well-being (SWB). Participants were assigned to one of three intervention conditions that were characterized by a weekly writing task—an interpersonal gratitude letter (n = 73), an intrapersonal gratitude journal (n = 65), or a learning journal (n = 67), which served as the control. A four-week, repeated gratitude intervention design was conducted, wherein participants' SWB was assessed across 12-time points, including a pre- and post-intervention SWB battery to assess the intervention’s overall impact. Participants in both gratitude conditions reported an overall increase in positive affect, supported by text analysis. However, participants who wrote gratitude letters had significantly less negative affect compared to the gratitude journal participants. Further analysis revealed a significant difference in SBW between the two gratitude conditions. Specifically, participants who experienced the intrapersonal gratitude journal-writing task reported a significant improvement in life satisfaction, while participants in the interpersonal gratitude letter-writing task evidenced a significant improvement in perceived social support. The control condition unexpectedly exhibited an increase in SWB that was likely due to the salience of the participants’ scholastic accomplishments. Finally, individual differences, including The Big Five, predicted gratitude and positive affect, consistent with prior research.