The recent advances in machine learning and deep learning algorithms, along with the advent of generative AI, have led AI to become the \"new normal\" in organizations. This trend has extended to CRM, resulting in the development of AI-enabled CRM systems, or AI-CRM. Despite the growing adoption of AI as part of competitive strategies, many firms report minimal or no positive effect of AI on performance. This study addresses the research questions: \"What are the critical features of AI-CRM systems?\" and \"How do these features impact organizational competitive advantage?\" To explore this, we aim to identify key characteristics of AI-CRM and assess their impact on organizational performance. In Study 1, we utilize BERTopic topic modeling to extract critical features of AI-CRM from user reviews. Study 2 employs PLS-SEM to examine how these features influence organizational competitive advantage. Study 1 reveals four main characteristics of AI-CRM (general, marketing, sales, and service/support), each comprising distinct features. Study 2 shows that these characteristics differentially impact CRM capability, significantly affecting performance and competitive advantage. The findings offer valuable insights for both theory and practice regarding the effective use of AI in organizations.

UNASSIGNED: Nowadays, many studies discuss scholarly publishing and associated challenges, but the problem of hijacked journals has been neglected. Hijacked journals are cloned websites that mimic original journals but are managed by cybercriminals. The present study uses a topic modeling approach to analyze published papers in hijacked versions of medical journals.
UNASSIGNED: A total of 3384 papers were downloaded from 21 hijacked journals in the medical domain and analyzed by topic modeling algorithm.
UNASSIGNED: Results indicate that hijacked versions of medical journals are published in most fields of the medical domain and typically respect the primary domain of the original journal.
UNASSIGNED: The academic world is faced with the third-generation of hijacked journals, and their detection may be more complex than common ones. The usage of artificial intelligence (AI) can be a powerful tool to deal with the phenomenon.

The microbiome, a complex micro-ecosystem, helps the host with various vital physiological processes. Alterations of the microbiome (dysbiosis) have been linked with several diseases, and generally, differential abundance testing between the healthy and patient groups is performed to identify important bacteria. However, providing a singular species of bacteria to an individual as treatment has not been as successful as fecal microbiota transplant therapy, where the entire microbiome of a healthy individual is transferred. These observations suggest that a combination of bacteria might be crucial for the beneficial effects. Here we provide the framework to utilize topic modeling, an unsupervised machine learning approach, to identify a community of bacteria related to health or disease. Specifically, we used our previously published gut microbiome data of patients with multiple sclerosis (MS), a neurodegenerative disease linked to a dysbiotic gut microbiome. We identified communities of bacteria associated with MS, including genera previously discovered, but also others that would have been overlooked by differential abundance testing. This method can be a useful tool for analyzing the microbiome, and it should be considered along with the commonly utilized differential abundance tests to better understand the role of the gut microbiome in health and disease.

To address the persistent challenges in healthcare, it is crucial to incorporate firsthand experiences and perspectives from stakeholders such as patients and healthcare professionals. However, the current process of collecting, analyzing and interpreting qualitative data, such as interviews, is slow and labor-intensive. To expedite this process and enhance efficiency, automated approaches aim to extract meaningful themes and accelerate interpretation, but current approaches such as topic modeling reduce the richness of the raw data. Here, we evaluate whether Large Language Models can be used to support the semi-automated interpretation of qualitative interview data. We compare a novel approach based on LLMs to topic modeling approaches and to manually identified themes across two different qualitative interview datasets. This exploratory study finds that LLMs have the potential to support incorporating human perspectives more widely in the advancement of sustainable healthcare systems.

This paper explores the prevalent themes across multiple threads on the popular Swedish discussion forum Flashback. Among its diverse array of topics, the forum actively engages users in addressing and debating questions pertaining to COVID-19 vaccines and vaccination. Through distinguishing between positive and negative perspectives within posts across 14 relevant thread discussions, we employ BERTopic, a modular topic modeling framework, which utilizes pre-trained language models and applies clustering techniques to identify prevailing topics. This enables us to conduct a nuanced exploration of overarching themes, offering valuable insights into the multifaceted nature of the discussions regarding COVID-19 vaccines and vaccination in Sweden.

The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters\' and parties\' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.

Background: This study investigates the discourse on anxiety management strategies within the TikTok platform, analyzing a substantial dataset of 45,639 comments collected over a year.
Aims: The primary aim is to explore the various strategies users employ to manage anxiety, focusing on how these strategies are discussed and shared on TikTok.
Methods: A mixed-method approach was utilized, combining Latent Dirichlet Allocation (LDA) for topic modeling with qualitative analysis. This methodology allowed for the identification of nine distinct topics, which were further grouped into three main categories: Self-Empowerment and Coping Strategies, Community Support and Social Connectivity, and Recognizing and Navigating Triggers.
Results: The analysis revealed a diverse range of strategies users employ to manage anxiety, spanning personal coping mechanisms, social support networks, and the recognition and mitigation of triggers. These findings underscore TikTok\'s role as a dynamic space for sharing, exploring, and validating experiences related to anxiety management.
Conclusions: TikTok offers unique opportunities for identity construction and community support amidst the challenges of anxiety. However, the study acknowledges limitations, such as potential biases in keyword-based data collection and the complexity of capturing multimodal discourse on the platform. The conclusion emphasizes the need for further refinement of digital mental health platforms, calling for sophisticated algorithmic solutions to enhance user support and content relevance.

UNASSIGNED: This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care.
UNASSIGNED: Through qualitative research, we sought expert insights to understand the factors contributing to this complexity. The study involved semi-structured interviews with 28 professionals from medical device companies, specializing in various aspects of regulatory affairs. These interviews were analyzed using a mix of qualitative coding and natural language processing (NLP) techniques.
UNASSIGNED: The findings reveal key sources of complexity within the regulatory landscape, divided into five domains: (1) regulatory language complexity, (2) intricacies within the regulatory process, (3) global-level complexities, (4) database-related considerations, and (5) product-level issues.
UNASSIGNED: The participants highlighted the need for strategies to streamline regulatory compliance, enhance interactions between regulatory bodies and industry players, and develop adaptable frameworks for rapid technological advancements. Emphasizing interdisciplinary collaboration and increased transparency, the study concludes that these elements are vital for establishing coherent and effective regulatory procedures in the medical device sector.

BACKGROUND: Heart failure (HF) is a significant global clinical and public health challenge, impacting 64.3 million individuals worldwide. To address the scarcity of donor organs, left ventricular assist device (LVAD) implantation has become a crucial intervention for managing end-stage HF, serving as a bridge to heart transplantation or as a destination therapy. Web-based health forums, such as MyLVAD.com, play a vital role as trusted sources of information for individuals with HF symptoms and their caregivers.
OBJECTIVE: We aim to uncover the latent topics within the posts shared by users on the MyLVAD.com website.
METHODS: Using the latent Dirichlet allocation algorithm and a visualization tool, our objective was to uncover latent topics within the posts shared on the MyLVAD.com website. Through the application of topic modeling techniques, we analyzed 459 posts authored by recipients of LVAD and their family members from 2015 to 2023.
RESULTS: This study unveiled 5 prominent themes of concern among patients with LVAD and their family members. These themes included family support (39.5% weight value), encompassing subthemes such as family caregiving roles and emotional or practical support; clothing (23.9% weight value), with subthemes related to comfort, normalcy, and functionality; infection (18.2% weight value), covering driveline infections, prevention, and care; power (12% weight value), involving challenges associated with power dependency; and self-care maintenance, monitoring, and management (6.3% weight value), which included subthemes such as blood tests, monitoring, alarms, and device management.
CONCLUSIONS: These findings contribute to a better understanding of the experiences and needs of patients implanted with LVAD, providing valuable insights for health care professionals to offer tailored support and care. By using latent Dirichlet allocation to analyze posts from the MyLVAD.com forum, this study sheds light on key topics discussed by users, facilitating improved patient care and enhanced patient-provider communication.

As the online market grows rapidly, people are relying more on product review when they purchase the product. Hence, many companies and researchers are interested in analyzing product review which essentially a text data. In the current literature, it is common to use only text analysis tools to analyze text dataset. But in our work, we propose a method that utilizes both text analysis method such as topic modeling and statistical network model to build network among individuals and find interesting communities. We introduce a promising framework that incorporates topic modeling technique to define the edges among the individuals and form a network and uses stochastic blockmodels (SBM) to find the communities. The power of our proposed method is demonstrated in real-world application to Amazon product review dataset.