User Groups Research Articles

You Are Not Alone! Care Professionals‘ Acceptance of Telemedicine in Nursing Homes Comparing Pre- and Post-Implementation Evaluations

A lack of personnel in care institutions and high proportions of older people in need of care pose central challenges for today’s aging society, often resulting in the hospitalization of geriatric patients. In many cases, these hospitalizations are not medically necessary and cause deterioration of health. Applying telemedicine in nursing homes represents one approach aimed at a reduction of unnecessary hospitalizations of geriatric patients and supporting care personnel in medically uncertain situations. For a sustainable and successful implementation of technical innovations such as telemedical consultations, the care personnel’s perspectives and acceptance are especially essential. The Optimal@NRW project implemented telemedical consultations in 24 nursing homes in Germany, investigating medical and economic efficiency and in particular also the social acceptance of digital care in nursing homes. This paper presents quantitative results comparing the acceptance evaluations before (PRE: N = 130) and after (POST: N = 87) the implementation of the telemedical consultations in the nursing homes from the perspective of care professionals. The results showed positive evaluations of the telemedical consultations in both evaluation phases: POST evaluations especially showed a lower evaluation of perceived barriers of using telemedical consultations in nursing homes. This study’s insights enable one to derive guidelines and recommendations regarding the communication and information of telemedical applications considering the needs and wishes of care personnel as a central user group.

Development of an Alokon Stock Management Information System Using a Lean Development Approach

This study explores the development of an Alokon Stock Management Information System using the Lean Development approach to enhance the efficiency and effectiveness of stock management for contraceptive devices and drugs (alokon) distributed by the Women's Empowerment, Child Protection, and Family Planning Service in Indonesia. Alokon plays a critical role in family planning and reproductive health services. The research aims to address challenges such as inaccurate stock data, distribution delays, and the inability to meet demand promptly, which can disrupt health services. The Lean Development approach, emphasizing principles such as eliminating waste, strengthening learning, and delivering results quickly, was employed to create a responsive and adaptive system tailored to user needs. The resulting system incorporates features for managing alokon data, monitoring stock levels, and generating reports to ensure adequate stock availability and timely distribution. Evaluation of the system's usability was conducted using the System Usability Scale (SUS), involving ten respondents from the primary user groups. The average SUS score was 75, indicating a good level of usability. Users reported the system as comfortable and easy to use, although feedback highlighted areas for further improvement.

Tracking Census Online Self-Completion Using Twitter Posts

From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: “Official Government Agency,” “Census Advocate,” and “Census Skeptic.” The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., “Republicans in Congress signal Census cannot take extra time to count”). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages—primarily promoting completion of the Census—seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group—but not the other two—predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.

Alcohol consumption among older adults in the United States amidst the COVID-19 pandemic: an analysis of the 2017-2021 Behavioral Risk Factor Surveillance System.

Alcohol consumption is responsible for numerous life-threatening diseases, including liver cirrhosis, heart disease, and various cancers. During the pandemic, alcohol-related deaths increased from 2019 to 2021, topping out at approximately 108,000 deaths related to alcohol. This trendalso introduced the question whether heavy alcohol consumption and binge drinking increased during the pandemic, particularly in those 65 and older. The objective of this study is to determine whether heavy alcohol consumption and binge drinking increased during the pandemic in older adults in the United States. We performed a cross-sectional analysis of the Behavioral Risk Factor Surveillance System (BRFSS) to determine whether rates of overall alcohol consumption, heavy consumption, or binge drinking deviated from 2017 through 2021. We utilized chi-square tests to determine changes in rates over the included years. Our findings show that the overall rate of alcohol use in populations 65 and older from 2017 through 2021 wasapproximately 42.1 %, which peaked in 2017 at 43.7 % and declined each year, resulting in the lowest rate (41.3 %) in 2021 (χ 2 =8.96, p<0.0001). Binge and heavy drinking rates were 5.1 % and 4.2 % overall during this time frame, respectively, and the annual changes were not statistically significant. The impact of COVID-19 on the drinking behavior of older US adults was minimal in terms of binge orheavy drinking, although the overall rates of alcohol consumption among this group declined. Reports among other US age groups showed increased consumption and deaths from alcohol use. Future research is needed to determine the causes for the overall decrease in consumption or adaptive measures that this group may have taken, which led tominimal changes in binge or heavy drinking in contrast toyounger populations.

The effects of internet use on the cultivation of non-timber forest products

Cultivating Non-Timber Forest Products (NTFPs) serves as a crucial means to increase the income of impoverished households in remote areas, while also providing an important avenue towards achieving equitable economic development and environmental preservation. This paper explores the impact of the internet use on households' NTFPs cultivation practices. Based on the available dataset of 641 valid questionnaire responses collected through field research conducted by the research team, this study employs an in-depth analysis utilizing endogenous transformation models. Furthermore, the study recognizes the importance of considering heterogeneity among different user groups and purposes. The results indicate that households who use the internet with the proportion of NTFPs in their forest land being 8.863 % higher than others. Households who use the internet for sales having a proportion of NTFPs in their forest land that is 12.083 % higher than that of non-internet-using households, while households who use it for production only has a 6.971 % increase. Further analysis reveals that internet use increases households' social capital levels, thereby promoting NTFP cultivation, which is attributable to the rise of special e-commerce models such as WeChat business. Furthermore, the use of the internet to some extent reduces the supply of agricultural labor.

An Empirical Study for the Dynamic and Personalised Learning Experience of the AI Course Generator

In a world that is quickly evolving, the demand for continuous learning and upskilling is critical for personal and professional growth. However, many learners struggle to create personalised, efficient learning paths tailored to their unique needs due to the limitations of traditional course creation methods, which require significant human input and expertise. This project aims to address this problem by developing "modulo," an innovative platform designed to automate the creation of personalised and structured learning paths. The objectives of “modulo” are to leverage artificial intelligence and external APIs to generate customised study plans for any chosen subject, integrate curated YouTube tutorials and supplemental materials, and enhance the learning experience with adaptive quizzes tailored to user progress. The methodology follows an Iterative-Waterfall approach, combining structured phases with iterative cycles to incorporate feedback and adapt to emerging challenges. The system architecture is built on a microservices framework, with a frontend developed using React and Next.js, and a backend supported by Supabase with Prisma for database management, NextAuth for user authentication, and Stripe for payment processing. The result is a scalable and maintainable platform that empowers diverse user groups by enhancing education accessibility. “modulo” provides a dynamic and personalised learning experience, making a meaningful impact on self-directed learning

Swimming, confusion, and plenty of brews: Negotiating ambivalence within Windermere's fragile waters

Windermere is a complex and contested freshwater site which encounters fluctuating social and environmental pressures. Swimmers at Windermere regularly practice across all four seasons while negotiating social concerns such as access, conflicting user groups, public health communications, and swim safety, alongside environmental complications including extreme weather, wastewater, run-off, plastic pollution, algal blooms, biosecurity, and climate change. Simultaneously, these entangled pressures generate ongoing adaptation, ambivalence, and avoidance within the swim communities. Furthermore, they disrupt individualised and inwardly focused understandings of ‘healthy’ outdoor swimming practices. In contribution to the special issue (on outdoor swimming), this article reflects on how outdoor swimming researchers may methodologically attend to these social and environmental complexities within contested lacustrine environments through an immersive 12-month wet ethnographic approach, combining ‘lake-hangouts’ and ‘swim-along interviews’ with different swimmers at Windermere. The article discusses how these relational in-situ approaches can continue to broaden inwardly focused understandings of ‘healthy’ outdoor swimming practices towards the wider social and environmental relations for both the participants and researcher. The article also highlights senses of ambivalence and ethical tension while negotiating conflicting concerns of ill-health, in and out of Windermere's fragile waters.

A novel affordable user interface for robotic surgery training: design, development and usability study.

The use of robotic systems in the surgical domain has become groundbreaking for patients and surgeons in the last decades. While the annual number of robotic surgical procedures continues to increase rapidly, it is essential to provide the surgeon with innovative training courses along with the standard specialization path. To this end, simulators play a fundamental role. Currently, the high cost of the leading VR simulators limits their accessibility to educational institutions. The challenge lies in balancing high-fidelity simulation with cost-effectiveness; however, few cost-effective options exist for robotic surgery training. This paper proposes the design, development and user-centered usability study of an affordable user interface to control a surgical robot simulator. It consists of a cart equipped with two haptic interfaces, a VR visor and two pedals. The simulations were created using Unity, which offers versatility for expanding the simulator to more complex scenes. An intuitive teleoperation control of the simulated robotic instruments is achieved through a high-level control strategy. Its affordability and resemblance to real surgeon consoles make it ideal for implementing robotic surgery training programs in medical schools, enhancing accessibility to a broader audience. This is demonstrated by the results of an usability study involving expert surgeons who use surgical robots regularly, expert surgeons without robotic surgery experience, and a control group. The results of the study, which was based on a traditional Peg-board exercise and Camera Control task, demonstrate the simulator's high usability and intuitive control across diverse user groups, including those with limited experience. This offers evidence that this affordable system is a promising solution for expanding robotic surgery training.

Accessibility in Human Computer Interaction and Human Robot Interaction: A brief review on definitions, developers guidelines, laws and regulations

This paper presents an exploratory study of accessibility in both Human Computer Interaction and Human Robot Interaction focusing on its intersection with interrelated concepts such as usability and inclusive design. It underscores the overreaching goal of these concepts in improving the user experience and provide equal access to all user interfaces, albeit with different targeted user groups. Furthermore, the study presents a short review on main accessibility laws and developers guidelines for both Human Computer Interaction and Human Robot Interaction. The review reveals a remarkable gap in accessibility laws and guidelines for Human Robot Interaction, hence, there is an urgent need to broader initiatives and efforts from authorities, researchers community, and industry stakeholders to formulate and create accessibility laws and developers guidelines pertaining to Human Robot Interaction products, where no such laws or guidelines were found in this study, except an individual initiative by the author, which includes a proposal dedicated to help developer in creating accessible interfaces for Socially Assistive Robots. This paper also discusses some practical challenges in implementing accessibility guidelines in HCI and HRI interfaces.

Mechanism to effectively manage the interaction of a two-sided platform with buyers and sellers

Aim. To formalize and investigate a mechanism for effectively managing the interaction of a two-sided platform with buyers and sellers.Objectives. Determination of the purpose and tasks of interaction management; allocation of the core of tools for managing the interaction, formation of criteria of the mechanism functioning efficiency.Methods. The authors used logical, structural, comparative analysis, as well as applied meta-analysis of previous studies to form the theoretical basis of the mechanism of management of the interaction of bilateral platform with buyers and sellers. Consistent with the subject area, the study is based on the standard model of bilateral platforms and extends it with the basic methodological approaches of entrepreneurship theory.Results. The article contains the formalization of the mechanism of effective management of interaction of bilateral platform with buyers and sellers. Aim and objectives of interaction management are defined, the core of tools for managing the interaction is identified, and criteria for the effectiveness of the mechanism are proposed and substantiated. The study identifies key factors affecting the effectiveness of bilateral platforms, including the importance of market saturation and the need to provide flexible conditions for different groups of users. The correlation between the improvement of working conditions on the platform and the growth of its financial performance was proved.Conclusions. The formalized mechanism allows to effectively manage the interaction with parties on a two-sided platform, ensuring the growth and sustainable development of the marketplace. Further adaptation of management tools is recommended, taking into account the specifics of markets and user behavior.

Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender Systems

Despite the benefits of personalizing items and information tailored to users’ needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various accountability issues, such as stereotypes, biases, and miscalibration. We propose a unified framework that distinguishes the sources of prediction errors into a set of key measures that quantify the various types of system-induced effects, at both the individual and collective levels. Based on our measuring framework, we examine the most widely adopted algorithms in the context of movie recommendation. Our research reveals three important findings: (1) Differences between algorithms: recommendations generated by simpler algorithms tend to be more stereotypical but less biased than those generated by more complex algorithms. (2) Disparate impact on groups and individuals: system-induced biases and stereotypes have a disproportionate effect on atypical users and minority groups (e.g., women and older users). (3) Mitigation opportunity: using structural equation modeling, we identify the interactions between user characteristics (typicality and diversity), system-induced effects, and miscalibration. We further investigate the possibility of mitigating system-induced effects by oversampling underrepresented groups and individuals, which was found to be effective in reducing stereotypes and improving recommendation quality. Our research is the first systematic examination of not only system-induced effects and miscalibration but also the stereotyping issue in recommender systems.

Testing the Quality of the Mobile Application Interface Using Various Methods—A Case Study of the T1DCoach Application

The T1DCoach mobile application is designed to educate patients—children with type 1 diabetes (T1D) and their caregivers and diabetes educators. The idea behind the mobile application is that its users perform actions that the patient needs to perform in real life. These include measuring blood glucose levels, operating the insulin pump, meal calculation, bolus administration, etc. These in-application activities are performed on the patient’s digital twin. To increase user engagement, gamification elements have been implemented in the application. An important element of the T1DCoach mobile application is its interface, which should be adapted to very different groups of users: children, their caregivers and educators. In addition to presenting the T1DCoach application, the paper presents the stage examining the quality of the interface using three research groups: children, their caregivers and educators. The research was conducted using the scenario method, using eye-tracking, recording activities and thinking aloud. After the application testing sessions, surveys were carried out using the System Usability Scale method and focus group interviews were conducted. The research results are presented in the article along with the most important recommendations for improving the application interface.

The Management and Guidance of Network Behaviors of Students in Vocational Colleges in the Internet + Era

With the wide spread and rapid development of the Internet, the "Internet +" era has fully arrived. As a key group of Internet users, students in vocational colleges show significant diversity and increasing complexity in their network behaviors. This article focuses on this, aiming to deeply explore the specific current situation, prominent problems and effective management and guidance strategies of network behaviors of students in vocational colleges in the Internet + era. First-hand data is obtained through questionnaires and detailed analysis is conducted by combining typical cases to comprehensively and deeply explore the network behaviors of students in vocational colleges. On this basis, highly targeted and feasible management and guidance suggestions are put forward, committed to creating a healthy network environment for students in vocational colleges, promoting their healthy growth and all-round development, and enabling them to better adapt to the needs of the Internet + era.

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation.

Although the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults. Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC. Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area. The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs. The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.

The Effects of Nicotine Pouches and E-Cigarettes on Oral Microbes: A Pilot Study.

It remains uncertain whether nicotine pouches and electronic cigarettes alter the oral environment and result in a high presence of periodontopathogenic bacteria in saliva, compared to that among cigarette users or non-tobacco users. In this study, saliva samples were collected from respondents using nicotine pouches, electronic cigarettes, and conventional cigarettes, alongside a control group of non-tobacco users. Polymerase chain reaction was used to identify clinical isolates of the following periodontal bacteria: Porphyromonas gingivalis, Tannerella forsythia, Prevotella intermedia, Fusobacterium nucleatum, Fusobacterium periodonticum, Porphyromonas endodontalis, and Rothia mucilaginosa. The presence of some periodontal pathogens was detected in the saliva samples from users of nicotine pouches, electronic cigarettes, and conventional cigarettes but not in samples taken from the control group. Therefore, the initial results of this pilot study suggest that the presence of periodontopathogenic bacteria in the saliva of nicotine pouch and electronic cigarette users could alter the oral microbiome, leading to periodontal diseases. However, further quantitative investigation is needed.

AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering mental health support. Despite their potential benefits, research on public attitudes toward health care chatbots is still limited. To effectively support individuals with long-term health conditions like long COVID (or post-COVID-19 condition), it is crucial to understand their perspectives and preferences regarding the use of AI chatbots. This study has two main objectives: (1) provide insights into AI chatbot acceptance among people with chronic health conditions, particularly adults older than 55 years and (2) explore the perceptions of using AI chatbots for health self-management and long COVID support. A web-based survey study was conducted between January and March 2023, specifically targeting individuals with diabetes and other chronic conditions. This particular population was chosen due to their potential awareness and ability to self-manage their condition. The survey aimed to capture data at multiple intervals, taking into consideration the public launch of ChatGPT, which could have potentially impacted public opinions during the project timeline. The survey received 1310 clicks and garnered 900 responses, resulting in a total of 888 usable data points. Although past experience with chatbots (P<.001, 95% CI .110-.302) and online information seeking (P<.001, 95% CI .039-.084) are strong indicators of respondents' future adoption of health chatbots, they are in general skeptical or unsure about the use of AI chatbots for health care purposes. Less than one-third of the respondents (n=203, 30.1%) indicated that they were likely to use a health chatbot in the next 12 months if available. Most were uncertain about a chatbot's capability to provide accurate medical advice. However, people seemed more receptive to using voice-based chatbots for mental well-being, health data collection, and analysis. Half of the respondents with long COVID showed interest in using emotionally intelligent chatbots. AI hesitancy is not uniform across all health domains and user groups. Despite persistent AI hesitancy, there are promising opportunities for chatbots to offer support for chronic conditions in areas of lifestyle enhancement and mental well-being, potentially through voice-based user interfaces.

Cryptocurrency’s emergence in North Cyprus: a mixed-methods exploration of attitudes and usage trends of an emerging payment method

PurposeThe purpose of this study is to investigate offline retailers’ cryptocurrency adoption through the unified theory of acceptance and use of technology (UTAUT/2), technology acceptance model (TAM) and other related variables.Design/methodology/approachA mixed-method (QUANT-Qual) was employed. The study employed a quantitative approach (partial least squares-structural equation modeling) for Study 1 and a qualitative focus group discussion for Study 2. The qualitative segment enhances the comprehension and interpretation of outcomes derived from the quantitative analysis.FindingsStudy 1 identified six crucial factors in cryptocurrency adoption: trust, personal innovation, hedonic motivation, social influence, safety and a positive attitude. Perceived ease of use, price value and satisfaction were non-significant. Study 2 elaborates on the findings, and highlights the reasons for the insignificant factors.Research limitations/implicationsThis research contributes to theoretical knowledge by empirically testing established technology acceptance theories like UTAUT/2 and TAM in the context of retail cryptocurrency adoption. It introduces previously unexplored factors, such as personal innovativeness, hedonic motivation and safety/reliability, enriching the theoretical landscape.Practical implicationsThis study provides practical insights for cryptocurrency stakeholders, including government, regulators, consumers, businesses and the financial sector, regarding retailers' intentions to adopt cryptocurrencies in North Cyprus.Social implicationsThis study can enhance understanding of factors facilitating cryptocurrency integration into mainstream retail, potentially reshaping traditional finance. Economically, adoption can boost financial inclusion, offering efficient transaction methods. This transition may disrupt existing economic models, drive payment technology innovation and change consumer behavior toward digital assets.Originality/valueThis study stands out by using a mixed-method approach to comprehensively examine cryptocurrency adoption by offline retailers, considering various factors and providing a holistic perspective, unlike previous research that often focused on specific user groups.

From collections to connections in libraries, archives and museums: Do theories meet practice?

Libraries, archives and museums are primarily seen as institutions that preserve and promote collections. However, ongoing social changes are causing them to increasingly focus on their social partners and the relationships that can be established with them. The evolving nature of these institutions’ priorities raises questions about how they are adapting to this shift and its impact on their operations. The objective of this research is to contribute to the conceptualization of the paradigm shift through verification of how selected social and cultural theories of social relations are reflected in the activities of libraries, archives and museums, as well as their connections with various partners. A content analysis of articles from three journals indicates that the relation-centric theories studied, and the concepts presented therein, are taken into account by their authors, albeit to varying degrees. The relationships of libraries, archives and museums primarily concern local communities and special user groups, but also involve other legal or individual entities, as well as the professional community.

Examining the Use of DanMu for Crowdsourcing Control in Virtual Gatherings

The advent of web-based interactive technologies has opened up new possibilities for virtual gatherings in 3D environments. Live-streaming, in particular, has gained increasing attention due to its effectiveness in engaging a large number of users in collective online activities. With an emphasis on audience participation, live-streaming shares common characteristics of the outlook of the metaverse and is driving new waves of interaction in virtual gatherings, such as engaging users through crowdsourcing control. However, this type of social interaction has not been examined in the Asian context, and it lacks systematic investigation of user experience with different crowdsourcing control methods. In this paper, we present a novel crowdsourcing control method based on DanMu, the subtitle system of Bilibili, one of the most successful and prevalent live-streaming platforms in Asia. We organized virtual gatherings by live-streaming a Minecraft virtual campus and examined the use of DanMu for crowdsourcing control. Our first study investigated the influence of three crowdsourcing control methods (First Come First Served, Vote, and Super Command) on collective navigation task efficiency and user experience. These influences were further discussed with user activeness and group sizes in a follow-up study. The results showed that Super Command, a representative mode on top of the democratic voting mechanism, offers better user experiences and social richness in large groups. Participants also rated its usability higher in small groups. Besides, virtual gathering in small groups allows greater pragmatic quality, usability, and a sense of agency than in large groups. Our work provides design guidelines for developers and HCI practitioners to develop crowdsourcing control methods and improve novel virtual gathering experiences in virtual worlds and the future metaverse.

Revealing biases in insect observations: A comparative analysis between academic and citizen science data.

Citizen Science is a powerful tool for biodiversity research, as it facilitates data recording at large scales that would otherwise be impossible to cover by standard academic research. Despite its benefits, the accuracy of citizen science data remains a subject of concern among scientists, with varying results reported so far. Neither citizen science data nor academic records are immune to biases, which can significantly impact the quality and reliability of observations. Here, using insects in the Iberian Peninsula as a case study, we compare data collected by participatory platforms to those obtained through academic research projects, and assess their taxonomic, spatial, temporal, and environmental biases. Results show a prominent taxonomic bias in both academic and citizen science data, with certain insect orders receiving more attention than others. These taxonomic biases are conserved between different participatory platforms, as well as between groups of users with different levels of contribution performance. The biases captured by leading contributors in participatory platforms mirrored those of sporadic users and academic data. Citizen science data had higher spatial coverage and less spatial clustering than academic data, showing also clearer trends in temporal seasonality. Environmental coverage over time was more stable in citizen science than in academic records. User behaviour, preference, taxonomical expertise, data collection methodologies and external factors may contribute to these biases. This study shows the multifaceted nature of biases present in academic records and citizen science platforms. The insights gained from this analysis emphasize the need for careful consideration of these biases when making use of biodiversity data from different sources. Combining academic and citizen science data enhances our understanding of biodiversity, as their integration offers a more comprehensive perspective than relying solely on either dataset alone, especially since biases in these two types of data are not always the same.