Distinct User Profiles Research Articles

Who Finds Pornography Stressful? A Latent Profile Analysis.

This study explores the connection between pornography use, sexual functioning, and mental health using latent profile analysis (LPA) to identify distinct profiles among pornography users and assess variations in mental health, including anxiety, depression, and emotion regulation. The aim of this study was to identify profiles among pornography users, specifically distinguishing those with sexual distress and sexual function problems, characterize these profiles, and assess variations in mental health. Data were collected from 463 participants through an anonymous online survey. Three distinct pornography user profiles were identified based on their consumption and sexual problems: high-frequency problematic pornography use (PPU, which is defined as uncontrollable patterns of pornography use resulting in adverse consequences and significant distress) with sexual problems, high-frequency non-PPU without/with low sexual problems, and no/low-frequency non-PPU without sexual problems. Differences among the profiles emerged in anxiety and depressive symptoms as well as emotion regulation capabilities. The high-frequency PPU with sexual problems group exhibited lower emotion regulation capabilities, while the no/low-frequency non-PPU without sexual problems group reported significantly lower depressive symptoms. This study emphasizes the multifaceted nature of pornography use, revealing that certain patterns may be more problematic. The findings highlight the interplay between emotional dysregulation, anxiety, depression, problematic pornography use, and sexual problems and distress. Recognizing these distinct profiles is crucial for understanding the relationship between pornography use and sexual and mental well-being, ultimately contributing to more targeted interventions and improved outcomes for all populations.

Understanding stakeholders’ intention to use construction robots: a fuzzy-set qualitative comparative analysis

User acceptance is crucial for successfully adopting robotic technologies in the architecture, engineering, and construction (AEC) sector. Previous studies have focused on domestic, service, and industrial robots, leaving the construction domain unexplored. In this study, we set out to empirically understand how various dimensions of technology, environment, robot, and user lead to AEC stakeholders’ intention to use construction robots. We apply the fuzzy-set qualitative comparative analysis approach to capture the complexity of human behavior and the interdependencies across dimensions. For the data collection, we sampled 216 cases in Switzerland, Germany, and Austria evaluating three scenarios of human–robot interaction. Our analysis identifies three distinct user profiles—the lifelike robot user, the utilitarian robot user, and the lifelike-utilitarian robot user. The results show that human–robot peering may be a fundamental solution to increase user acceptance. By testing the effect of user characteristics, we also discover a lifelike-utilitarian type of robot that is more appealing to female AEC stakeholders. The study contributes to the construction robotics literature by providing tailored design and implementation strategies. It points to future research avenues such as user experience and social factors for exploring the impact of robotics and artificial intelligence in AEC.

Customizing ICU patient monitoring: a user-centered approach informed by nurse profiles

Intensive Care Unit (ICU) nurses are burdened by excessive number of false and irrelevant alarms generated by patient monitoring systems. Nurses rely on these patient monitoring systems for timely and relevant medical information concerning patients. However, the systems currently in place are not sensitive to the perceptual and cognitive abilities of nurses and thus fail to communicate information efficiently. An efficient communication and an effective collaboration between patient monitoring systems and ICU nurses is only possible by designing systems sensitive to the abilities and preferences of nurses. In order to design these sensitive systems, we need to gain in-depth understanding of the user group through revealing their latent individual characteristics. To this end, we conducted a survey on individual characteristics involving nurses from two IC units. Our results shed light on the personality and other characteristics of ICU nurses. Subsequently, we performed hierarchical cluster analysis to develop data-driven nurse profiles. We suggest design recommendations tailored to four distinct user profiles to address their unique needs. By optimizing the system interactions to match the natural tendencies of nurses, we aspire to alleviate the cognitive burden induced by system use to ensure that healthcare providers receive relevant information, ultimately improving patient safety.

Travel behaviour differences among people with disabilities: A cluster analysis of accessible taxi users before and during the COVID-19 pandemic

IntroductionPeople with disabilities (PWD) represent the largest minority group in Canada. Many municipalities have implemented paratransit services and subsidized accessible taxi services to provide affordable and equitable transport choices for PWD. However, few scholars have examined who is using these services and how they use them, making it challenging to assess the social impacts of paratransit projects. This study helps to address this knowledge gap through its examination of the differences in travel behaviour among PWD who use accessible taxi services in Toronto, Canada. Specifically, our study engages four research questions: (1) who are accessible taxi service users? (2) how are individuals using accessible taxi services? (3) are there different groups of users based on their travel behaviour? and, (4) how has travel behaviour changed in relation to the COVID-19 pandemic? MethodsThis article employs a cluster analysis to categorize users based on their travel habits and patterns and examines the impact of the COVID-19 pandemic on their travel behaviour. Study data includes approximately one million trips made by 40,000 users between June 2018 and June 2021. ResultsThe study identifies five distinct user profiles among accessible taxi users. During the pandemic, there was a significant decrease in the number of PWD using accessible taxi services, particularly for occasional users. Conversely, the percentage of users who made health-related trips increased. Additionally, the findings suggest that PWD who continued or started using accessible taxi services during the pandemic are more likely to be among the most socially disadvantaged groups. ConclusionsThis study can help transit service providers and policymakers better understand the different types of paratransit users and improve services. Additionally, the findings contribute to the existing literature on addressing transport-related social exclusion in Toronto, which is largely focused on population groups with low-income and affordability issues, and not disability.

Identifying user profiles of healthcare, social and employment services in a working-age population: A cluster analysis with linked individual-level register data from Finland.

A thorough understanding of the use of services in the population is important in order to comprehend the varying service needs of different groups. This explorative study aimed to find distinct user profiles in a working-age population based on individuals' annual use of healthcare, social and employment services and to explore socio-demographic and morbidity-related predictors of the user groups. Administrative register data on the use of various services and individual-level covariates from year 2018 were linked for all residents aged 18-64 of the municipality of Oulu, Finland (N = 119,740). K-means cluster analysis was used to group the study subjects into clusters, based on their frequency of using 22 distinct healthcare, social and employment services during 2018. Multinomial logistic regression models were utilized to assess the associations of cluster assignment with socio-demographic and health-related covariates (sex, age, marital status, education, occupational class, income, days in employment, chronic disease and receipt of different social benefits). Five distinct clusters were identified in terms of service use, labelled low to moderate users of healthcare (82.0%), regular employment services users with moderate use of healthcare (9.6%), supported employment services users with moderate use of healthcare with an emphasis on preventive care (2.9%), frequent users of healthcare, social and employment services (2.9%), and rehabilitation, disability services and specialized healthcare users (2.6%). Each cluster not only showed different patterns of service use but were also differently associated with demographic, socio-economic and morbidity-related covariates, creating distinct service user types. Knowledge on the different user profiles and their determinants may help predict future need and use of services in a population, plan timely, coordinated and integrated services, and design early interventions and prevention measures. This is important in order to save costs and improve the effectiveness of services for groups with different care needs.

Method to Produce More Reasonable Candidate Solutions With Explanations in Intelligent Decision Support Systems

The integration of Artificial Intelligence techniques into Decision Support Systems yields effective solutions to decision problems, especially when complex scenarios are at hand. However, the use of intelligent black-box models can hinder the decision support system’s potential to be fully adopted because opaque processes raise suspicions and doubts among careful decision makers. Moreover, appropriate and comprehensible explanations may foster trustworthiness and allow for reasonable adjustments or even corrections. This work proposed an approach that incorporates three reasonability aspects into Decision Systems: feasibility, rationality, and plausibility. Thus, by providing decision makers with reasonable candidate solutions for a complex problem, they are expected to perform their tasks more effectively (i.e. decide with more efficiency as well as efficacy). The new approach is accompanied by two proofs of concept in the health and public security areas. Comparative results using random and rational approaches, including the simulation of distinct user profiles, are presented. The proposed approach achieved superior metrics with regard to feasibility and plausibility, suggesting that this proposition can be applied to real-world applications.

Large-scale analysis of query logs to profile users for dataset search

PurposeWith an explosion of datasets available on the Web, dataset search has gained attention as an emerging research domain. Understanding users' dataset behaviour is imperative for providing effective data discovery services. In this paper, the authors present a study on users' dataset search behaviour through the analysis of search logs from a research data discovery portal.Design/methodology/approachUsing query and session based features, the authors apply cluster analysis to discover distinct user profiles with different search behaviours. One particular behavioural construct of our interest is users' expertise that the authors generate via computing semantic similarity between users' search queries and the title of metadata records in the displayed search results.FindingsThe findings revealed that there are six distinct classes of user behaviours for dataset search, namely; Expert Research, Expert Search, Expert Explore, Novice Research, Novice Search and Novice Explore.Research limitations/implicationsThe user profiles are derived based on analysis of the search log of the research data catalogue in this study. Further research is needed to generalise the user profiles to other dataset search settings. Future research can take on a confirmatory approach to verify these user groups and establish a deeper understanding of their information needs.Practical implicationsThe findings in this paper have implications for designing search systems that tailor search results matching the diverse information needs of different user groups.Originality/valueWe propose for the first time a taxonomy of users for dataset search based on their domain expertise and search behaviour.

Price Discrimination in the Online Airline Market: An Empirical Study

Big Data technologies have significantly increased the possibility for sellers to adopt personalisation strategies, especially in digital markets. Among such strategies, price discrimination, a practice where the same commodity is sold at different prices, either to the same customer or to different customers, stands out. Particularly, the online airline ticket market has risen the attention of economists in recent studies, both because of its specificity and of the high data availability. This manuscript enters the debate and analyses the airline ticket market in an original way. Indeed, the aim of this work is to empirically understand whether some airline companies discriminate in prices by using the customers’ data that they collect from their websites, and, if so, which is the impact on social welfare. For performing this assessment, a software that is able to automatically collect pricing data is developed and freely released. By executing the software in two time periods, tickets prices of three airlines, three itineraries, and four different user profiles are acquired. A double analysis is performed to check if customers’ information are used to discriminate (intra-user), and if different prices are offered to distinct user profiles (inter-user). Moreover, the analyses consider control data collected from the API of the Global Distribution System Amadeus, the main flight booking platform dedicated to travel agents. Upon inspection, no evidence is found in this study to support the hypothesis that airlines use price discrimination techniques.

Young Adults' Vaping, Readiness to Quit, and Recent Quit Attempts: The Role of Co-use With Cigarettes and Marijuana.

E-cigarette cessation intervention research is limited. Young adult e-cigarette use and cessation is particularly nuanced, given various user profiles (ie, polytobacco use, co-use with marijuana) warranting different intervention approaches. The current study is an analysis of baseline survey data (collected September-December 2018) among 1133 young adult (aged 18-34) e-cigarette users in a 2-year longitudinal study. We examined (1) e-cigarette user profiles (ie, e-cigarette only; e-cigarette/other tobacco; e-cigarette/marijuana; e-cigarette/other tobacco/marijuana) and (2) correlates of readiness to quit e-cigarette use in the next 6 months and past-year e-cigarette quit attempts. In this sample (Mage = 23.91, 47.3% male, 35.5% sexual minority, 75.2% White, 13.7% Hispanic), e-cigarette user profiles were as follows: 16.8% e-cigarettes-only, 23.4% e-cigarette/other tobacco, 18.0% e-cigarette/marijuana, and 41.8% e-cigarette/other tobacco/marijuana. Multinomial logistic regression (referent: e-cigarette-only use) indicated that all polyuse groups were more likely to use high-nicotine e-liquids (containing ≥9 mg of nicotine). Other predictors included e-cigarettes/other tobacco users being older and male; e-cigarettes/marijuana users using closed systems; and e-cigarettes/other tobacco/marijuana users being sexual minority (p's < .01). Readiness to quit e-cigarettes and past-year quit attempts were reported by 20.8% and 32.3%, respectively. Per multilevel regression, readiness to quit and quit attempts correlated with using fewer days, high-nicotine e-liquids, and closed systems, but not marijuana, as well as being heterosexual and Black (vs White); readiness to quit also correlated with being single; past-year quit attempts correlated with other tobacco use and being Hispanic. Young adult e-cigarette users demonstrate distinct user profiles and cessation-related experiences that should be considered in developing cessation interventions. The vast majority of young adult e-cigarette users use other tobacco products and marijuana. Unfortunately, few reported readiness to quit or attempting quit. Moreover, certain subgroups (eg, sexual/racial/ethnic minorities) are more likely to be ready or attempt to quit, but may not be successful. Vaping cessation interventions must attend to these nuances.

The digital divide: Risk and protective factors and the differences in problematic use of digital devices among Hungarian youth

A number of psychological factors have been found to be relevant in terms of problematic use of digital devices. Some of them may serve as risk factors, while others mean protection. The main goal of present study was to determine user profiles and to examine differences among them based on several psychological variables using cluster analysis. Data were collected from high school and university students via an online questionnaire from November 2017 to January 2018 (N = 249; females: 62.2%, Mean age = 22.5 years, SD = 3.5). Based on bivariate correlations, sensation seeking, flow and boredom proneness acted as risk factors for developing problematic technological use. However, self-esteem, self-regulation and resilience were significant protective factors against problematic technological use. In an effort to develop a set of profiles that would capture this student population and their usage relative to risk, we identified four distinct user profiles through the use of cluster analysis. The first cluster was labeled as strongly protected sensation-seekers who were more prone to problematic use (18.5%) with moderately high levels of protection, but with the highest scores on the boredom proneness and sensation seeking scales. The second cluster consisted of more balanced and non-vulnerable users (26.1%), who achieved average scores on both risk and protective factors scales. The third cluster was labeled protected, conscious users who were slightly prone to problematic use (39.8%) with lower levels of problematic use and risk factors, and with higher levels of protective factors. The fourth cluster was labeled as strongly problematic, unprotected users (15.7%), who achieved the highest scores of problematic use, and were the less protected having the lowest levels of protective factors scores. We conclude that cluster analysis was suitable for detecting differences in vulnerability to problematic Internet and smartphone use. Psychological factors, such as sensation seeking, flow and boredom, self-esteem, self-regulation and resilience serve as useful tools for providing information to better understanding the role of risk and protective factors in the prevention of youth’s problematic use of digital devices.

A comparative analysis of Twitter users who Tweeted on psychology and political science journal articles

Purpose The purpose of this paper is to understand the similarities and differences between the Twitter users who tweeted on journal articles in psychology and political science disciplines. Design/methodology/approach The data were collected from Web of Science, Altmetric.com, and Twitter. A total of 91,826 tweets with 22,541 distinct Twitter user profiles for psychology discipline and 29,958 tweets with 10,478 distinct Twitter user profiles for political science discipline were used for analysis. The demographics analysis includes gender, geographic location, individual or organization user, academic or non-academic background, and psychology/political science domain knowledge background. A machine learning approach using support vector machine (SVM) was used for user classification based on the Twitter user profile information. Latent Dirichlet allocation (LDA) topic modeling was used to discover the topics that the users discussed from the tweets. Findings Results showed that the demographics of Twitter users who tweeted on psychology and political science are significantly different. Tweets on journal articles in psychology reflected more the impact of scientific research finding on the general public and attracted more attention from the general public than the ones in political science. Disciplinary difference in term of user demographics exists, and thus it is important to take the discipline into consideration for future altmetrics studies. Originality/value From this study, researchers or research organizations may have a better idea on who their audiences are, and hence more effective strategies can be taken by researchers or organizations to reach a wider audience and enhance their influence.

Lessons learned from longitudinal modeling of mobile-equipped visitors in a complex museum

Cultural sites are evolving toward smart environments, including the notion of hyper-connected museums. In this context, stakeholders of cultural assets need more advanced and comprehensive ICT systems for monitoring and modeling visitors behaviors. In this paper, we discuss the results of a longitudinal research study embracing multiple seasons, in a complex cultural structure including outdoor and indoor attractions, multiple floors and multiple routes for visitors. Here, interactive mobile devices were used for both offering multimedial context-aware assistance to visitors and monitoring services to museum stakeholders. We deeply analyzed the data using an unsupervised classification approach, obtaining a model where the number of distinct user profiles and the number of features were considered not trivial as well as not too complex for museum stakeholders. We observed that some optimistic expectations about visitor performances were only partially met, devising possible explanations in terms of the different user profiles and features of the model. Finally, we also compared some outcomes from our interactive system with those obtained in another complex cultural structure using a noninvasive monitoring system.

Children, Extracurricular Activities, and Digital Media: The Process of Displacement and School Performance

This work, situated in the Greek sociocultural context, attempts to analyze the time spent by primary school students on digital and traditional extracurricular activities and the impact of these activities on the educational outcome. The first objective was to determine, by means of a quantitative survey, if the children as “digital natives” replace traditional entertainment activities with new digital technologies or if, on the contrary, they supplement them with the novel forms of entertainment. A second objective was to examine the impact of the frequency of extracurricular activities with and without screens on the school performance. Our final objective was to explore the existence of distinct user profiles based on the extracurricular activities with and without screens and to identify the relationship between different types of extracurricular activities.

Adolescent digital profiles: A process-based typology of highly engaged internet users

High online engagement is common among adolescents. Besides concerns, not all highly involved adolescents online develop maladaptive patterns of use. The focus of the present qualitative study was to explore the experiences of highly engaged adolescents with signs of internet addictive behaviors. We aimed to uncover the processes differentiating high online engagement, and formulate a typology of users. Semi-structured individual interviews were conducted with 72 adolescents (Mage = 15.7 years; SD = 0.6) living in Greece, Spain, Iceland and Poland. Interviews were analyzed using grounded theory. A typology of highly engaged adolescents emerged based on three processes: satisfying needs across contexts (online-offline), experiencing personal gains and losses and self-regulating use. Four distinct user profiles emerged, ranging from adaptive (Juggling it All, Coming Full Cycle) to maladaptive (Stuck Online, Killing Boredom). The developed typology can help parents, teachers and professionals better understand the ways high engagement is experienced within a developmental context. Such knowledge can inform the development of prevention and supportive services. Implications for assessment and intervention are discussed for each profile.

Children and the internet in Portugal: usage profiles

On the basis of a questionnaire survey carried out among Portuguese children, this article aims to characterise their possession and usage of the internet. The survey ascertained that the children can be grouped under four distinct user profiles — “confirmed cybernauts”, “applied students”, “inveterate gamers” and “beginners” — and demonstrates that these profiles are highly conditioned by variables such as a child’s sex and age and his or her parents’ educational levels and socio-professional categories. These profiles also correspond to different forms of parental mediation and different representations regarding the internet.

Is Online Health Activity Alive and Well or Flatlining? Findings From 10 Years of the Health Information National Trends Survey

The Internet increasingly enables diverse health communication activities, from information seeking to social media interaction. Up-to-date reporting is needed to document the national prevalence, trends, and user profiles of online health activities so that these technologies can be best used in health communication efforts. This study identifies prevalence, trend, and factors associated with seeking health information, e-mailing health care providers, and using social media for health purposes. Four iterations of HINTS survey data, collected in 2003, 2005, 2008, and 2012, were analyzed to assess population-level trends over the last decade, and current prevalence of Internet-based health communication activities. Sociodemographic and health correlates were explored through weighted logistic regression modeling. Findings demonstrated that Internet use has steadily increased, with 78% of U.S. adults online in 2012; however several digital divide factors—among them education, age, and race/ethnicity—still predict access. Once online, 70% of adults use the Internet as their first source for health information, and while 19% have e-mailed health care providers, engagement in health communication on social media is still relatively low. Distinct user profiles characterize each type of communication, with age, population density, and gender emerging as important predictors across online health activities. These findings have important implications for health communication research and practice.

Are all alcohol and energy drink users the same? Examining individual variation in relation to alcohol mixed with energy drink use, risky drinking, and consequences.

Individuals who consume alcohol mixed with energy drinks (AmEDs) have been identified as higher-risk drinkers, as they are more prone to drink increased amounts of alcohol and experience more consequences compared with non-AmED users. The present study examined differential AmED use and alcohol consumption simultaneously as multidimensional risk behaviors among AmED users. Students who identified as drinkers and current AmED users (n = 195) completed a Web-based survey related to their AmED consumption and typical drinking patterns. Latent profile analysis was used to classify participants into distinct AmED user profiles. Profiles were then compared on AmED-based cognitive factors (e.g., expectancies, norms) and alcohol-related consequences. Four AmED user profiles emerged: moderate drinker, low proportion AmED users (ML); heavy drinker, low proportion AmED users (HL); moderate drinker, high proportion AmED users (MH); and heavy drinker, high proportion AmED users (HH). Membership in higher-proportion AmED groups was associated with more positive AmED expectancies and perceived norms. No significant differences were observed in the amount of consequences endorsed by HL and HHs; however, MHs experienced significantly more alcohol-related physical consequences than MLs. This suggests that increased use of AmEDs is associated with increased risk of experiencing alcohol-related consequences for moderate drinkers. Screening students for AmED use could be used as a novel, inexpensive tool to identify high-risk drinkers for targeted interventions aimed at reducing alcohol consumption and related problems.

Tool-use in a blended undergraduate course: In Search of user profiles

The popularity of today's blended courses in higher education is driven by the assumption that students are provided with a rich toolset that supports them in their learning process. However, little is known on how students actually use these tools and how this affects their performance for the course. The current study investigates how students use the entire toolset at their disposal, whether tool-use patterns can be found and if these patterns affect performance for the course. Logging students (n = 156) actions throughout the content management system and registering students' use of the face-to-face support in an undergraduate course, the study reveals large student differences and an underuse for some tool-types. Further to this, K-means cluster analysis reveals three distinct tool-use patterns or user profiles: the no-users, the intensive users and the incoherent users. These patterns are characterized by different tool-choices and even different use intensity among students. Evidence is retrieved that these tool-use differences are problematic since multivariate analysis of variance reveals significant performance effects. Hence, these results imply that not all students seem to profit from the learning affordances that are provided. Similar as evidence in controlled settings, the results suggest that learner control in using tools cannot be taken for granted. Consequently, this study legitimates more research into the influencing (student and context) variables that can explain these differences.

Social Connectedness on Facebook - An Explorative Study on Status Message Usage

With over 400 million active users Facebook is undeniably a large social phenomenon and one of the largest social networks on the Internet. Together with Facebook a variety of novel communication styles have developed, dramatically influencing social interaction. The underlying paper reports the results of a survey (N=109) analyzing Facebook’s micro-blogging function available through users’ status updates. Our results suggest that the use of status update messaging generates a feeling of connectedness between users. Furthermore, non-parametric analyses distinguishing between low and high connected groups have been performed and experimentally confirmed the existence of distinct user profiles as a function of the variable “feeling connected.” The analyses revealed that the more individuals use their status message function to actively reveal information about themselves, the more connected they feel. Connectedness seems the result of active information sharing modulated by the amount of information shared rather than by the type of information an individual is sharing.

Increasing the Usability of High‐Tech Products Through DESIGN RESEARCH

FASTER! BETTER! EASIER! These are terms often associated with new technologies. But what do users actually think? Addressing this question, Laurence Feldman defines criteria for measuring the usability of technological products and identifies three distinct user profiles, each hallmarked by unique attitudes regarding high‐tech products. Feldman's conclusion, based on survey results, is that the market for new technologies is segmented—a reality companies should respond to more effectively in their design strategies.