How Employee Use of Generative Artificial Intelligence Affects Self-Evaluation: Investigating Implications for Job Insecurity and Career Commitment

This research delves into the transformative influence of Generative Artificial Intelligence (GenAI) on the self-evaluation processes of employees within contemporary organizations. In an era marked by rapid technological advancements, the integration of AI into various facets of the workplace is reshaping traditional paradigms. We use Self-Evaluation Maintenance (SEM) Model, which is our theoretical lens, and combining with the concept of Core Self-Evaluation (CSE) to conduct our research model. This study seeks to elucidate whether the usage of GenAI, specifically in the context of performance compares to GenAI, and then the impact on CSE, which we plan to use in this research, has discernible effects on how employees perceive and evaluate their own contributions. In addition, we adapt various reliable scales to assess the constructs in our research model. This research employs surveys and content analysis of questionnaire data to investigate the perceptions of employees in organizations that have introduced GenAI-driven tools for performance appraisal. The objective is to determine whether these tools, by providing real-time feedback, personalized recommendations, and novel evaluation metrics, result in changed self-perceptions and attitudes towards one\u27s work

The Impact of Initial Trust on Usage Intention of Generative Artificial Intelligence: A Social Exchange Perspective on Human-Automation Interaction

Social exchange theory (SET) is a theory that is widely implemented to analyze or explain human behaviors and relationships. Nevertheless, as humans tend to behave socially toward technologies and computers, SET is applied in the field of human-automation interaction (HAI). Thus, this study adopts SET to investigate the interactions between humans and generative artificial intelligence (GAI). Because of the increasing popularity of GAI and the rising importance of technology integration in the workplace nowadays, this study values the research context of using GAI at work. Despite GAI could assist individuals while working, there are drawbacks of GAI, leading initial trust to an important role for individuals to assume the drawbacks in GAI adoption. Considering the utilization of GAI as a form of social exchange behavior, this work aims to examine the impact of initial trust on the usage intention of GAI while incorporating three moderating factors related to social exchange dynamics. The research methodology employs stratified random sampling to gather survey data from individuals with working experience. Research subjects are chosen from different industries. Subsequently, analysis will be conducted to evaluate the findings. Finally, this work can make a valuable contribution to the literature on HAI and can provide practical insights for organizations seeking to employ GAI technologies

The Log Product Formula in quantum KK-theory

We prove a formula expressing the $K$-theoretic log Gromov-Witten invariants of a product of log smooth varieties $V \times W$ in terms of the invariants of $V$ and $W$. The proof requires introducing log virtual fundamental classes in $K$-theory and verifying their various functorial properties. We introduce a log version of $K$-theory and prove the formula there as well.Comment: 21 pages, comments welcome! v2: 27 pages, example added distinguishing log Gysin map from ordinary on

A Novel Robust Algorithm for Information Security Risk Evaluation

Abstract As computer becomes popular and internet advances rapidly, informatio

Therapeutic Strategies for RB1-Deficient Cancers: Intersecting Gene Regulation and Targeted Therapy

The retinoblastoma (RB) transcriptional corepressor 1 (RB1) is a critical tumor suppressor gene, governing diverse cellular processes implicated in cancer biology. Dysregulation or deletion in RB1 contributes to the development and progression of various cancers, making it a prime target for therapeutic intervention. RB1\u27s canonical function in cell cycle control and DNA repair mechanisms underscores its significance in restraining aberrant cell growth and maintaining genomic stability. Understanding the complex interplay between RB1 and cellular pathways is beneficial to fully elucidate its tumor-suppressive role across different cancer types and for therapeutic development. As a result, investigating vulnerabilities arising from RB1 deletion-associated mechanisms offers promising avenues for targeted therapy. Recently, several findings highlighted multiple methods as a promising strategy for combating tumor growth driven by RB1 loss, offering potential clinical benefits in various cancer types. This review summarizes the multifaceted role of RB1 in cancer biology and its implications for targeted therapy

Understanding Mobile Apps Continuance Usage Behavior and Habit: An Expectance-Confirmation Theory

With the growing development of information technology and the wireless telecommunication network nowadays, mobile devices have been expanding rapidly and have been emerging as important tools for consumers. Using m-services and applications (apps) on mobile devices becomes custom in people’s daily lives. This study proposes a theoretical model to explore the continued usage behavior for smartphone. The objective of this study is to explore how perceived usefulness, perceived enjoyment, and confirmation influencing satisfaction and habit of consumers, and in turn influencing continued usage behavior, as well as the moderating effect of three characteristics of m-commerce. The proposed model will empirically be tested using survey method and collecting data from smartphone users in longitudinal setting. The structural equation modeling technique will be used to evaluate the causal model and confirmatory factor analysis will be performed to examine the reliability and validity of the measurement model. The findings of this study are expected to illustrate how factors influence individuals to use m-services and mobile apps and become a habit, as well as how these habits influence continued smartphone usage

Metastatic Gallbladder Cancer Presenting as a Gingival Tumor and Deep Neck Infection

Gallbladder cancer has an extremely poor prognosis because it is often diagnosed at an advanced stage. We describe a 63-year-old woman who was treated 4 years previously for gallbladder cancer, with laparoscopic cholecystectomy and secondary hepatectomy after presenting with acute cholecystitis and gallbladder rupture. At her second presentation, she had a left lower gingival tumor and deep neck infection. Incision and drainage and tumor biopsies were performed, and pathology at both sites revealed adenocarcinoma. Positron emission tomography revealed other tumors in the left breast and left lower lung field, which were both proven to be adenocarcinoma by biopsy. The patient's presentation with a metastatic oral tumor was rare. Although the incidence is very low, physicians should consider the possibility of metastatic cancer in a patient with a history of cancer, who presents with new oral tumor or deep neck infection

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003)