Data Discovery Using LLMs - A Study of Data User Behaviour

"John Carey and Martin Elton are among the most skilled and insightful researchers studying the dynamic changes in technology and the impacts on consumer attitudes and behaviors. Their comprehensive and actionable observations make this a must read for anyone interested in understanding the current (and future) media environment." ---Alan Wurtzel, President, Research and Media Development, NBC Universal "When Media Are New should be read by every media manager faced with disruptive change brought on by new technology. The book transcends the fashionable topics and themes that are here today and gone tomorrow and instead places emphasis on those areas of research and implementation where fatal mistakes are made. They capture something universal, and therefore highly useful, by stripping away the hype and focusing relentlessly on consumers and the ways they adopt or fail to adopt new media products and technologies into their lives." ---Martin Nisenholtz, Senior Vice President, Digital Operations, The New York Times Company "The burgeoning development of the Internet has deflected attention from a wider history of new media innovations that has shaped its success. John Carey and Martin Elton demonstrate that earlier initiatives to launch videophones, two-way interactive cable systems, videotext and other media innovations can teach us much about the present state and future course of information and communication technologies. This is a key reference on the new media, and must reading for students of the Internet---the platform for continuing the new media revolution." ---Professor William H. Dutton, Director, Oxford Internet Institute, University of Oxford The world of communication media has undergone massive changes since the mid-1980s. Along with the extraordinary progress in technological capability, it has experienced stunning decreases in costs; a revolutionary opening up of markets (a phenomenon exemplified by but not limited to the rise of the Internet); the advent of new business models; and a striking acceleration in the rate of change. These technological, regulatory, and economic changes have attracted the attention of a large number of researchers, from industry and academe, and given rise to a substantial body of research and data. Significantly less attention has been paid to the actual and intended users of new media. When Media Are New addresses this research and publishing gap by investigating the human side of the technological changes of the last 50 years and the implications for current and future media. It will find a broad audience ranging from media scholars to policymakers to industry professionals. John Carey is Professor of Communications and Media Management at Fordham Business School and has extensive experience in conducting research about new media for companies such as AT&T, Cablevision, NBC Universal, and the New York Times (among many others) as well as foundations and government agencies. His extensive publications have focused on user adoption of new media and how consumers actually use new technologies. Martin C. J. Elton was Director of the Communication Studies Group in the UK, which pioneered in the study of user behavior with new media technologies, and founded the Interactive Telecommunication Program at New York University. He has published widely on user research, forecasting, and public policy and has conducted extensive research for many prominent foundations, companies, and government agencies in the USA and Europe.

The report covers a two-year project to develop a set of microcomputer-based training and assistance programs to support end user access to the Energy Database on the Department of Energy information retrieval systems. A set of programs were designed and tailored to the information needs, skills and information-seeking habits of energy researchers. Discussion covers the issues in development, evaluation and study of user behavior in designing an interface tailored to a special population. Some background information is provided on information systems and database use. 32 refs.

The article presents simulation technologies as an instrumental base for research in experimental economics. The main goals and objectives of modern experimental economics are considered. Attention focused on the study of markets and market structures, as one of the relevant areas of experimentation. A generalized overview of classic simulation models of the market is given. A competitive market model presented on the example of two product variants developed using system-dynamic and agent simulation paradigms on the AnyLogic system software platform. On the basis of the model, a study of user behavior in the market of household electrical products was conducted. The obtained results are illustrated by fragments of model simulation experiments.

This paper, from the perspective of users, focuses on enhancing the service efficiency and user experience of digital museums and systematically explores the influencing mechanisms of their willingness to continue using them, aiming to provide theoretical basis and practical reference for the optimized operation of digital museums and the study of user behavior. The study, based on the unified technology acceptance and use model, introduces new variables by combining keyword extraction with grounded theory to construct a model of the influencing factors of users' willingness to use digital museums, explores the influence of different variables on users' willingness to use digital museums from multiple dimensions, and conducts empirical analysis using the structural equation model. The research results will not only expand the application boundaries of the Unified Theory of Acceptance and Use of Technology model in the field of cultural technology integration, but also provide actionable improvement suggestions for the refined operation of digital museums, thereby promoting the high-quality development of public cultural services.

A recent surge of users migrating from Twitter to alternative platforms, such as Mastodon, raised questions regarding what migration patterns are, how different platforms impact user behaviors, and how migrated users settle in the migration process. In this study, we elaborate on how we investigate these questions by collecting data over 10,000 users who migrated from Twitter to Mastodon within the first ten weeks following the ownership change of Twitter. Our research is structured in three primary steps. First, we develop algorithms to extract and analyze migration patterns. Second, by leveraging behavioral analysis, we examine the distinct architectures of Twitter and Mastodon to learn how user behaviors correspond with the characteristics of each platform. Last, we determine how particular behavioral factors influence users to stay on Mastodon. We share our findings of user migration, insights, and lessons learned from the user behavior study.

PurposeThis paper aims to describe a method for combining perceived community support, relationship quality and the extended technology acceptance model in the same empirically derived associative network. The research also examines the moderating role of accumulation of knowledge (based on beliefs and opinions) derived from social interactions.Design/methodology/approachThe Pathfinder algorithm is a valid approach for determining network structures from relatedness data. Such a graphical representation provides managers with a comprehensible picture of how social behaviours relate to loyalty-based dimensions.FindingsAs the benefits of community participation and integration might be differently evaluated by new and long-term users, the research examines the associative network by levels of user familiarity. This study indeed contributes to the analysis of enduring social bonds with respect to individuals’ decision-making processes, as it provides details representing specific relationships between diverse concepts based on true-loyalty.Practical implicationsThe application of Pathfinder to the study of online social services and user behaviour appears to have potential for unveiling the structures of social network sites members and designing successful strategies for prospective community managers.Originality/valueThis is the first study to the author’s knowledge that empirically tests a theory-grounded framework for integrating individual characteristics and relational driver and focuses on associative structures evidenced as a representation of the most salient loyalty-based concepts by also studying the moderating effects of familiarity.

In recent years, multi-player online games (MOGs) have gained enormous popularity and become a major trend in the entertainment industry. Much research has been focusing on improving the performance and scalability of MOG systems. However, relatively little attention has been paid to the study of user behavior. As with other complex interactive applications, a good understanding of user behavior is important to the design of MOG systems. In this paper, we discuss the advantages of a user behavior workload model to the software industry and the research community, and describe a method to develop such a model. For illustrative purposes, we study user behavior by using the measurement data collected from an existing MOG system. This includes the characterization of the interarrival time of logon users, the transition probability of avatars within the virtual environment, the residence time at a room, and the session length. Our results not only provide an insight into user behavior in MOGs, but they are also useful in the development of workload models in performance studies of MOG systems.

Collaborative management of water resources requires a broad suite of “water data” that extends beyond basic information about water quantity and quality to other related topics such as water infrastructure, aquatic ecosystem health, socioeconomic factors, and power generation. Water data are disparate in nature because they are collected and provided by many entities, and in some cases, remain challenging to access and use. The U.S. Department of Energy’s Water Power Technologies Office initiated a project to characterize relevant categories of water data; describe the current state of accessing, using, and visualizing water data; and outline investigatory pathways for future efforts aimed at improving the discovery, sharing, and use of water data. Input on these topics was solicited from a small but diverse cross section of members of the water resources community. Fourteen broad categories of water data were identified: dams; ecology; flood control; hydroclimatology; hydrography; hydrology; hydropower; management landscape; migratory barriers; recreation and aesthetic importance; socioeconomic; water quality; water availability and use; and weather. Stakeholder perspectives on the accessibility and usability of water data indicate these aspects are affected by a complex set of technical and social factors. However, stakeholders generally agreed that better access to water data can provide a range of benefits to water management, and they stressed the need to generate broad support from water data users and producers. Two investigatory pathways were outlined that, taken together, provide a logical progression toward the goals of the project. The first pathway emphasizes further investigation to better define target audiences and data needs, identify opportunities for collaboration between related efforts, and conduct value demonstration activities to generate further support for improving discovery and access of water data. The second pathway focuses on creating a comprehensive vision for potential solutions that improve the discovery of water data. Several activities that align with the first pathway are suggested for the next phase of the project.

The rise of virtual reality (VR) has opened up more possibilities for human-computer interaction. In recent years, many VR interventions have gone beyond pure entertainment purposes and derived the definition of "Serious Games'', which means applying VR design to focus on practicality in serious fields, including rehabilitation, training, education, etc. As the technology thrived, the study of user experience and behavior became the cornerstone of all VR design. In intervention designs of education and training applications, it is common for users to complete tasks in situational-stressful scenarios such as interview simulations, classroom tests, and so on. This project aims to conduct an in-depth study of the relationship between situational stressor designs in the VR environments and player engagement and cognitive efficiency. The study looked at people aged 18 to 44, the primary audience for VR (Katatikarn, 2024). This game-based experiment designed two immersive VR scenes with different stressor designs, which combined internal and external stress factors, namely the nature of the scene and architectural elements. Participants were randomly assigned to two immersive VR scenarios and completed the same basic cognitive tasks. Player engagement in this experiment was defined as four aspects: sensory involvement, emotional engagement, cognitive engagement, and flow. Surveys and an in-game task reaction time collection mechanism comprise a comprehensive data collection approach to measuring and analyzing player engagement and cognitive performance. Results showed that participants in the experimental group (scenario with stressor designs) were identified with significantly higher stress levels and higher cognitive engagement. However, no significant differences were observed in the other three aspects of engagement. Participants in the control group performed better in cognitive efficiency. In addition, no relationships were found between engagement levels and cognitive efficiencies, or between individuals' neuroticism score and reported stress level. Keywords: Virtual Reality (VR), Player Engagement, Cognitive Efficiency, Situational Stressor design and Response, Neuroticism, Game-based Experiment.--Author's abstract

Purpose This study is designed to investigate the adoption of AI-generated health information in video platforms, utilizing an integrated theoretical framework that incorporates the SOR, PET and SCM theories. Design/methodology/approach The study conducted a mixed method combining SEM and fsQCA to analyze data gathered from 468 participants. Findings The findings revealed that tailored response, human-like cues and digital culture exposure influenced perceived warmth and competence of AIGC, while bottom-line mentality did not exhibit a significant effect. These two factors further drove the adoption of AI-generated health information. Familiarity with health information technology exerted a negative moderating effect. Additionally, the fsQCA results identified six paths of adoption, highlighting human-like cues and perceived warmth of AIGC as the most critical factors among all determinants. Research limitations/implications This study contributes theoretically to the study of AIGC user behavior. Additionally, it offers practical insights into the deployment of AIGC by platforms and highlights potential directions for the future development of AIGC. Originality/value While existing studies have explored factors influencing standalone AIGC (e.g. ChatGPT), limited research has examined adoption drivers for platform-based AIGC implementations, particularly regarding AI-generated health information. To fill this gap, this study employs an integrated framework that combines the SOR, PET and SCM theories to elucidate the mechanisms underlying the adoption of AI-generated health information in video platforms.

Greenway is a linear green open space located along natural or man-made landscapes such as rivers, ridgelines, abandoned railways and scenic roads, with multiple functions of ecology, recreation and social culture. Greenway in the modern sense originated in the United States and has rapidly become a global movement since the 1990s. Greenway construction in China began in 2008, and most of it focuses on the function, benefit and planning of greenway space. There are few studies on users, and scholars engaged in this field come from multiple disciplines, so the theoretical basis is weaker than overseas studies. This study first elaborated the concept and type of greenway space, the definition of greenway space user, summarized the development process of greenway space overseas and China. By systematically summarizing the research progress of greenway space users in China and overseas, it is found that more and more scholars in the field of greenway research begin to pay attention to user issues, and the research area is increasingly globalized. Most of them are empirical studies, and the research methods tend to be diverse. Most of them are American scholars. At present, greenway user research is mostly based on the research of developed countries. However, the purpose of greenway construction in China is different, and the current public awareness of greenway is still little. Therefore, this study can provide theoretical case reference for the study of greenway user behavior in developing countries.

Road user behavior: Describing, inferring, predicting and beyond

статья посвящена исследованию поведения пользователей цифровых экосистем и предложению стратегий их развития в контексте расширения спектра предоставляемых услуг и повышения качества взаимодействия с аудиторией. Цель эмпирического количественного исследования в формате онлайн-опроса пользователей – представителей поколений Y и Z – заключалась в определении основных факторов и направлений развития дополнительных услуг цифровых экосистем. Полученные результаты обрабатывались методами описательной статистики и кластерного анализа. Маркетинговое исследование позволило выявить факторы выбора респондентом конкретной экосистемы и источники получения информации о новых сервисах в рамках цифровой экосистемы. Показано, что наличие лучших услуг и функций в другой экосистеме может стать основной причиной для смены пользователем его цифровой платформы, хотя в целом лояльность респондентов к используемым ими цифровым экосистемам велика. Это обуславливает важность постоянного внимания компании-организатора экосистемы к широте ассортимента предоставляемых услуг и уровню их интегрированности. Кластеризация респондентов привела к выявлению двух основных сегментов, названных «Восторженными фанатами» и «Осторожными критиками». Практическая значимость исследования заключается в том, что для каждого их этих кластеров предложены соответствующие их покупательскому поведению стратегии развития дополнительных услуг цифровых экосистем. the article is devoted to the study of the digital ecosystems user behavior and the proposal of strategies for their development in the context of expanding the range of services provided and improving the quality of interaction with the audience. The purpose of the empirical quantitative study in the format of users’ online survey – representatives of generations Y and Z – was to identify the main factors and directions for the development of additional services of digital ecosystems. The collected data were processed using descriptive statistics and cluster analysis methods. The marketing research allowed identifying the factors of choice of a specific ecosystem and sources of information about new services within the digital ecosystem. It is shown that the availability of superior services and functions in another ecosystem can become the main reason for a user to change his digital platform, although in general the loyalty of respondents to the digital ecosystems they use is high. This determines the importance of the constant attention of the ecosystem orchestrator to the breadth of service range and their integration level. Clustering of respondents resulted in the identification of two main segments, labeled as "Enthusiastic Fans" and "Cautious Critics". The practical significance of the study lies in the fact that for each of these clusters, strategies for the development of digital ecosystems additional services are proposed corresponding to their purchasing behavior.

When humans share space in road traffic, as drivers or as vulnerable road users, they draw on their full range of communicative and interactive capabilities. Much remains unknown about these behaviors, but they need to be captured in models if automated vehicles are to coexist successfully with human road users. Empirical studies of human road user behavior implicate a large number of underlying cognitive mechanisms, which taken together are well beyond the scope of existing computational models. Here, we note that for all of these putative mechanisms, computational theories exist in different subdisciplines of psychology, for more constrained tasks. We demonstrate how these separate theories can be generalized from abstract laboratory paradigms and integrated into a computational framework for modeling human road user interaction, combining Bayesian perception, a theory of mind regarding others' intentions, behavioral game theory, long-term valuation of action alternatives, and evidence accumulation decision-making. We show that a model with these assumptions-but not simpler versions of the same model-can account for a number of previously unexplained phenomena in naturalistic driver-pedestrian road-crossing interactions, and successfully predicts interaction outcomes in an unseen data set. Our modeling results contribute to demonstrating the real-world value of the theories from which we draw, and address calls in psychology for cumulative theory-building, presenting human road use as a suitable setting for work of this nature. Our findings also underscore the formidable complexity of human interaction in road traffic, with strong implications for the requirements to set on development and testing of vehicle automation.

The rapid adoption of AI chatbots for emotional support and quasi-therapeutic interaction raises questions that existing clinical and regulatory frameworks are not equipped to address. This Perspective applies the psychic arbitrage framework—which reconceptualizes defense mechanisms as energy-conversion operations on internal psychic markets—to analyze the specific transactional distortions produced by chatbot interaction. The framework identifies four dysfunctions: liquidity illusion (apparent emotional processing without genuine containment), market-making blockage (narcissistic reflection replacing transformative engagement), closure of arbitrage circuits (path-dependent externalization displacing autonomous elaboration), and repertoire degradation (progressive intolerance of costly but productive transactions). The article proposes that chatbots trained via Reinforcement Learning from Human Feedback (RLHF) produce a functional analogue of the Dark Triad profile—narcissistic mirroring, Machiavellian retention, and psychopathic detachment—as systematic architectural output regularities, not personality attribution. Preliminary converging evidence from mechanistic interpretability, formal reward-learning analysis, clinical reports, and user behavior studies is broadly consistent with these predictions but does not yet demonstrate direct long-term causal effects. The framework offers clinicians a transactional vocabulary for assessing AI-related risk and generates falsifiable predictions for future research.