User Typology Research Articles

Social inequality and news media repertoires in seven MENA countries

Social inequality shapes peoples’ chances and strategies to keep informed about current affairs. We study citizens’ news media repertoires – individual patterns of combining selected news media – in a world region of extreme social inequality within and between countries: the Middle East and Northern Africa (MENA). We report a secondary analysis of a large-scale survey in seven MENA countries to develop news user typologies. We explore how digital forces shape news media repertoires to determine the influence of social inequality on the population’s information routines regarding current affairs, and investigate the diversity of the inhabitants’ media repertoires.

Paths to digital mobile payment platforms acceptance and usage: A topology for digital enthusiast consumers

The rapid development of digital mobile payment platforms (DMPPs) has significantlytransformed the financial services landscape, offering consumers unparalleled convenience and efficiency in payment options. This study delves into the acceptance and usability factors among digital enthusiast consumers, employing fuzzy set Qualitative Comparative Analysis (fsQCA) to uncover key determinants. Analyzing data from 325 digital enthusiasts, the research identified four distinct configural paths that significantly influence DMPP usage, revealing two primary user typologies: “systems readiness and ease of use” and “affection and secured platforms.” Performance expectancy emerged as a core condition, highlighting its critical role in driving DMPP adoption among digital enthusiasts. The study also underscored the importance of grievance redress and perceived trust as essential conditions influencing user adoption and continuous usage, emphasizing their pivotal role in enhancing user experience and fostering trust. These insights suggest that addressing users' concerns and building trust are fundamental for the sustained success of DMPPs. By tailoring DMPP features to align with the distinct preferences of the identified user typologies, service providers can significantly boost adoption rates. The fsQCA approach offers novel insights into the nuances of emerging technology acceptance and usage, underscoring the necessity of categorizing users to better understand DMPP adoption dynamics. This research provides valuable contributions to both businesses and policymakers,offering strategies to optimize DMPP design, ensure superior user experiences, and promote broader adoption. The findings highlight the dynamic interplay of user expectations and technological capabilities, essential for navigating the evolving digital financial landscape.

Uncovering the theoretical basis of user types: An empirical analysis and critical discussion of user typologies in research on tailored gameful design

Gamification has become one of the main areas in information systems and human–computer interaction research related to users’ motivations and behaviors. Within this context, a significant research gap is the lack of understanding of how users’ characteristics, especially in terms of their preferences for gameful interaction (i.e., user typologies), moderate the effects of gamification and, furthermore, how gamification could be tailored to individual needs. Despite their prominence in classifying users, current typologies and their use in research and practice have received severe criticism regarding validity and reliability, as well as the application and interpretation of their results. Therefore, it is essential to reconsider the relationships and foundations of common user typologies and establish a sound empirical basis to critically discuss their value and limits for personalized gamification. To address this research gap, this study investigated the psychometric properties of the most popular player types within tailored gamification literature (i.e., Bartle’s player types, Yee’s motivations to play, BrainHex, and HEXAD) through a survey study (n=877) using their respective measurement instruments, followed by a correlation analysis to understand their empirical relations and an exploratory factor analysis to identify the underlying factors. The results confirm that user typologies, despite their different origins, show considerable overlap, some being consistent whereas others contradicted theoretically assumed relationships. Furthermore, we show that these four user typologies overall factor into five underlying and fundamental dimensions of Socialization, Escapism, Achievement, Reward Pursuit, and Independence, which could be considered common concepts that may essentially reflect key determinants of user motivation in gamification. Our findings imply that future research and practice in tailored gamification design should shift the focus from developing and applying ever more nuanced typologies to understanding and measuring the key underlying determinants of user motivation in gameful systems. Moreover, given the considerable interrelationships between these determinants, we also argue that researchers should favor continuous representations of users’ motivations in specific situations instead of a dichotomous operationalization of user types as static manifestations of their preferences.

Developing Digital Classical Concert Stream Offerings – A Typology of Audience Preferences

To explore the issues around the production and design of digital concert streams, we conducted an online survey of n = 1,619 classical concert stream audience members (56,5% females, mean age 49 years). Participants were asked about their preference of various characteristics of digital concert productions, their use of and experience with various stream platforms and their socio-demographics. Considering various media user typologies previously identified in the literature, we were further interested in segmenting the audience via Latent Profile Analysis based on their preference for many different concert stream production features. We identified three distinct profiles, representing different target groups for digital concert stream productions: Digital Concert Purists (34.3% of participants), Undecided and Less Engaged Concert Users (15.2% of participants), and Digital Concert Enthusiasts (50.2% of participants). Different socio-demographic characteristics and music preferences were significantly, albeit only weakly associated with a person’s probability of being a member of a certain user group. These results imply that producers of classical concert streams should consider that there are different audience groups that should be addressed differently, especially with regards to conventional and more innovative concert features.

Transferability analysis of user groups in travel behaviour surveys using a random forest classification model

There are many different travel behavior surveys with a wide range of sample sizes and contents, whose data sets are often difficult to merge. This contribution aims to evaluate the possibilities of transferring user typologies from one travel survey to another using a random forest classification model. By incorporating the parameters of usage frequencies, socio-demographics and mobility resources, the classification achieved a mean F1 score of 0.93. In conclusion, it can be shown that the proposed method is suitable for transferring mobility types developed on the basis of a specialized data set to other surveys using mobility behavior parameters.

Farmer Insights Regarding Zero Tillage Systems Against Weeds in the Eastern Gangetic Plains of South Asia

A large amount of literature has now proven that Zero Tillage (ZT) as a part of Conservation Agriculture-based Sustainable Intensification (CASI) practices has the potential to help smallholder farmers in the Eastern Gangetic Plains (EGP) of South Asia transition to more productive, profitable, and sustainable production systems. Despite this, changes in weed management under ZT are commonly identified as a constraint to wider adoption, based primarily on quantitative investigations. Yet the contribution of this to farmers’ evaluation and adoption behaviour remains underexplored. To address this issue, we explore farmers’ perceptions of CASI-based herbicide weed management systems using semi-structured interviews from six locations across the EGP. This study identifies a divergence in experiences with herbicides, both geographically (with Sunsari and Bihar more negative than other locations) and in terms of user typologies (where users are overwhelmingly positive, and non-users are overwhelmingly negative). This divergence suggests that an information void exists that has the potential to contribute strongly to the negative evaluation of CASI, as well as potentially negative changes in household labour dynamics. To overcome this, promotional efforts should target education and training programs that address how to effectively spray, potentially with increased emphasis on use of weather forecasting. This would ensure equitable outcomes for household members, and increased interest and use of CASI could be promoted. South Asian J. Agric., Vol. 9, No.1&2, 2022-‘23: 52-63

Functional view and technological progress of a sociotechnical system: illustrated with an interventionist state

PurposeOrganizations use innovations and respond to external pressures, creating a transition to the sociotechnical system. In their transitions, they interact with the environment and undergo adaptation-selection. The extant literature used a multilevel perspective (MLP) with a structural view and examined dynamics and transitions (phenomena) in a noninterventionistic setting. This study aims to examine the dynamics and phenomena with a microstructural or functional view and expand the MLP; this paper uses neo-institutionalism and human values as part of the functional view. Moreover, when the authors examine the phenomena in an interventionistic setting, they can obtain certain unique dynamics and their influence on the phenomena.Design/methodology/approachThe authors need to examine the phenomena in its setting, so this paper selected a case study, Indian electricity generation. For diverse heuristic and analytic views, it selected two Indian states.FindingsThe findings from the functional view showed that organizations exhibit certain traits of neo-institutionalism and human values, which mediate their responses (behavior) to external pressures. Additionally, due to the interventionist state, their dynamics use shaping instead of selection logic for innovations, which decides the transition pathway selection (technology adoption). It further decided the extent to which innovations cumulate as stable designs. As a result, the responses and the transition provide benefits in the short term while invariably failing in the long term.Research limitations/implicationsBy selecting cases with higher investments in renewable energies and combustible fuels, the authors can expand the functional view to include user typologies such as producers, intermediaries and citizen groups and obtain further insights into transitions.Practical implicationsThe study highlights the generation dynamics specific to Indian electricity generation and its transition pathways. The study’s outcome provides insights to researchers and practitioners in formulating policy changes and transforming electricity generation.Originality/valueThe study uses a functional view comprising neo-institutionalism and human values and expands the sociotechnical transition theory. In addition, selecting an interventionist setting provided insights into dynamics specific to organizational behavior and associated services. Finally, the obtained insights offer suggestions for technology development to better manage transitions with adaptation-selection.

Information use in public administration and policy decision‐making: A research synthesis

AbstractThis article presents a research synthesis of 162 studies focusing on information use for decision‐making in public administration, management, and policy. The findings reveal that a significant proportion of work is centered around performance management and policy implementation. Notably, around one third of the reviewed studies adopt a behavioral science perspective. The analysis predominantly includes civil servants and citizens as the subjects, with quantitative studies outnumbering qualitative investigations by more than twofold. We identify three distinct components in understanding information use: the objective features of information architecture; the subjective mechanisms involving cognitive biases (i.e., over‐/under‐reaction to irrelevant information features) and decision noise (i.e., heterogeneity); and the moderating role of information user typology. Context should also be taken into account. The article explores how these findings relate to current societal challenges and emphasizes the potential of mixed‐methods, multisample, and/or multisite research in advancing knowledge in this area.

Predicting user types with symbolic images: An empirical validation based on two card-sorting studies

In gamification, personalization is considered an important field of research since it can improve user engagement and motivation. Particularly, effective gamified learning needs to strike a balance between gameful design that promotes engagement and does not negatively affect content comprehension and absorption. The existing traditional user-type questionnaires approaches are time-consuming and intrusive, hampering the user’s focus. Previous research in gamification demonstrated how to automate player profile creation through log file analysis of gameful systems instead of traditional approaches, however, psychological archetypes and motivational groups can simplify the process of collecting data to achieve a balanced level of personalization for gamified systems, which are grounded on the user’s profile and preferences which can help with their immersion. To attend to the needs of a wide range of fields that introduce gamification to systems that are not classified as games, a new ludic approach was created to define user types based on user interaction using symbolic images. In this exploratory research, we tested this approach in two card-sorting studies (N=35 and N=19). Our results show that the images can be used to predict the users’ archetypes and motivational groups accurately. Thus, we contribute with validating an image-based user type classification method that is easier to deploy in systems that do not aim to behave as a game but are looking to reap the benefits of personalizing a user’s content and gameful experience where relevant. Our second contribution is delivering guidelines to replicate this validation method to other existing approaches. We expect that with this guideline, we facilitate personalization with other user typologies without disrupting the gameful experience.

Proposed Typology for Ridesourcing Using Survey Data from Tennessee

Although ridesourcing users have been studied in literature, it is unlikely that everyone uses ridesourcing homogenously. Identifying a ridesourcing user typology could help to further understand how ridesourcing is used, to better plan and manage these services. This study employed survey data collected in 2019 from residents of three heavily auto-oriented metro areas in Tennessee to generate a ridesourcing user typology based on demographic, socioeconomic, and preference variables. We identified four ridesourcing user and nonuser types: “young urban local users,”“wealthy travelers,”“tagalong users,” and “nonusers.” The young urban local users made up about 20% of the sample and included those who used ridesourcing locally. They tended to use ridesourcing for social purposes, were younger and had higher incomes. These findings aligned with prior research. The wealthy traveler type was comprised of those who used ridesourcing primarily when traveling. Wealthy travelers were older and had higher incomes than other user types. Tagalong users typically rode with friends/family; they tended to be younger, female, and/or Black. Prior research has largely excluded the tagalong user type. The nonuser was the fourth and largest (53%) type. Nonusers were usually older, had lower incomes, and were located in rural areas. Their most common reasons for not using ridesourcing included car ownership, safety concerns, and cost. Understanding the differences between these user types could help practitioners and policy makers better plan for ridesourcing services and integrate them into the operations of local transportation systems, particularly in more autocentric metropolitan areas.

User types, psycho-social effects and societal trends related to the use of consumer health technologies.

The term consumer health technologies we use in this paper refers to fitness and health apps, wearables and other self-tracking devices that collect health-related data. Our paper aims to bridge the gap between the growing literature base of sociological research and ethical reflection on the (non-intended) effects of consumer health technology use on the psycho-social level, such as stress, responsibilization or a loss of intuitive sense for signs of health or illness. Special consideration should be given to vulnerable individuals, as the positive and negative effects of consumer health technology use may be unequally distributed. This perspective may help to guide policymaking and the responsible development of consumer health technologies. Using a narrative review approach, we refer to empirical and theoretical studies dealing with user types and effects related to the use of consumer health technologies. We provide an overview of consumer health technology user typologies and evidence of the unintended psycho-social effects of consumer health technology use. On this basis, we propose a user typology that may serve as a future tool for ethical reflection on negative side effects. Evidence of the potential negative side effects of consumer health technology use, as presented in the literature, is inconclusive due to the high diversity of consumer health technology users and the way they use consumer health technologies. Our proposed user typology aims to more comprehensively document the diversity of users by incorporating the way in which users identify with and use their self-tracked data, attitudes towards the new technology and social interactions via consumer health technologies, and the purpose and self-determinedness of consumer health technology use. More systematic and quantitative empirical research on the effects of consumer health technology use in diverse settings and with diverse user types is necessary to inform public health policy. In addition to evidence-based certification of medical consumer health technologies, more practical and flexible ways to protect users from side effects may have to be developed and adopted, especially regarding the increasing number of non-medical consumer health technologies.

Implicit Error, Uncertainty and Confidence in Visualization: An Archaeological Case Study.

While we know that the visualization of quantifiable uncertainty impacts the confidence in insights, little is known about whether the same is true for uncertainty that originates from aspects so inherent to the data that they can only be accounted for qualitatively. Being embedded within an archaeological project, we realized how assessing such qualitative uncertainty is crucial in gaining a holistic and accurate understanding of regional spatio-temporal patterns of human settlements over millennia. We therefore investigated the impact of visualizing qualitative implicit errors on the sense-making process via a probe that deliberately represented three distinct implicit errors, i.e., differing collection methods, subjectivity of data interpretations and assumptions on temporal continuity. By analyzing the interactions of 14 archaeologists with different levels of domain expertise, we discovered that novices became more actively aware of typically overlooked data issues and domain experts became more confident of the visualization itself. We observed how participants quoted social factors to alleviate some uncertainty, while in order to minimize it they requested additional contextual breadth or depth of the data. While our visualization did not alleviate all uncertainty, we recognized how it sparked reflective meta-insights regarding methodological directions of the data. We believe our findings inform future visualizations on how to handle the complexity of implicit errors for a range of user typologies and for highly data-critical application domains such as the digital humanities.

Typology of Canadian adult cannabis users following legalization

ABSTRACT Background Typologies are useful to make sense of the heterogeneity in substance users, but most cannabis user typologies to date have focused on youth or were conducted in the broader context of illegal cannabis use. The purpose of the current study is to examine if subgroups of Canadian adult cannabis users can be defined based on cannabis use patterns as well as mental health variables, and if they differ in negative cannabis-related outcomes. Methods A total of 361 past year cannabis users (M = 27.55 years, SD = 10.60) completed measures assessing substance use, mental health symptoms, and cannabis-related negative outcomes. Results Three clusters of cannabis users emerged, characterized by increasing intensity of cannabis use, which were associated with differences in cannabis-related outcomes. Conclusions Like the Risk Need Responsivity Model of criminal behavior, cannabis users differ in their needs and the intensity of treatment should be matched accordingly.

What Do Twitter Users Think about Climate Change? Characterization of Twitter Interactions Considering Geographical, Gender, and Account Typologies Perspectives

Abstract Climate change (CC) is a topical issue of profound social interest. This paper aims to analyze the sentiments expressed in Twitter interactions in relation to CC. The study is performed considering the geographical and gender perspectives as well as different user typologies (individual users or companies). A total of 92 474 Twitter messages were utilized for the study. These are characterized by analyzing sentiment polarity and identifying the underlying topics related to climate change. Polarity is examined utilizing different commercial algorithms such as Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob, in conjunction with a procedure that uses word embedding and clustering techniques in an unsupervised machine learning approach. In addition, hypothesis testing is applied to inspect whether a gender independence exists or not. The topics are identified using latent Dirichlet allocation (LDA) and the usage of n-grams is explored. The topics identified are (in descending order of importance) CC activism, biodiversity, CC evidence, sustainability, CC awareness, pandemic, net zero, CC policies and finances, government action, and climate emergency. Moreover, globally speaking, it is found that the interactions on all topics are predominantly negative, and they are maintained as such for both men and women. If the polarity by topic and country is considered, it is also negative in most countries, although there are several notable exceptions. Finally, the presence of organizations and their perspective is studied, and results suggest that organizations post with more frequency when addressing topics such as sustainability, CC awareness, and net zero topics. Significance Statement The purpose of this research is to gain a better understanding of the perception of Twitter users in relation to climate change. To do so, Twitter interactions are characterized by analyzing polarity (positive or negative sentiment) and identifying underlying topics that, with greater or lesser intensity, were discussed during the period analyzed. Then, to contextualize the information retrieved, several classifications are performed: by gender, location, and account typology (individual users and companies). Interesting differences and commonalities are found both by geographic dimension and by gender. Similarly, some dissimilarities exist between interactions from individuals and companies. The findings of this work are significant because they can help institutions and governments to properly target public awareness efforts on climate change.

The Aim2Be mHealth Intervention for Children With Overweight or Obesity and Their Parents: Person-Centered Analyses to Uncover Digital Phenotypes.

BackgroundDespite the growing number of mobile health (mHealth) interventions targeting childhood obesity, few studies have characterized user typologies derived from individuals’ patterns of interactions with specific app features (digital phenotypes).ObjectiveThis study aims to identify digital phenotypes among 214 parent-child dyads who used the Aim2Be mHealth app as part of a randomized controlled trial conducted between 2019 and 2020, and explores whether participants’ characteristics and health outcomes differed across phenotypes.MethodsLatent class analysis was used to identify distinct parent and child phenotypes based on their use of the app’s behavioral, gamified, and social features over 3 months. Multinomial logistic regression models were used to assess whether the phenotypes differed by demographic characteristics. Covariate-adjusted mixed-effect models evaluated changes in BMI z scores (zBMI), diet, physical activity, and screen time across phenotypes.ResultsAmong parents, 5 digital phenotypes were identified: socially engaged (35/214, 16.3%), independently engaged (18/214, 8.4%) (socially and independently engaged parents are those who used mainly the social or the behavioral features of the app, respectively), fully engaged (26/214, 12.1%), partially engaged (32/214, 15%), and unengaged (103/214, 48.1%) users. Married parents were more likely to be fully engaged than independently engaged (P=.02) or unengaged (P=.01) users. Socially engaged parents were older than fully engaged (P=.02) and unengaged (P=.01) parents. The latent class analysis revealed 4 phenotypes among children: fully engaged (32/214, 15%), partially engaged (61/214, 28.5%), dabblers (42/214, 19.6%), and unengaged (79/214, 36.9%) users. Fully engaged children were younger than dabblers (P=.04) and unengaged (P=.003) children. Dabblers lived in higher-income households than fully and partially engaged children (P=.03 and P=.047, respectively). Fully engaged children were more likely to have fully engaged (P<.001) and partially engaged (P<.001) parents than unengaged children. Compared with unengaged children, fully and partially engaged children had decreased total sugar (P=.006 and P=.004, respectively) and energy intake (P=.03 and P=.04, respectively) after 3 months of app use. Partially engaged children also had decreased sugary beverage intake compared with unengaged children (P=.03). Similarly, children with fully engaged parents had decreased zBMI, whereas children with unengaged parents had increased zBMI over time (P=.005). Finally, children with independently engaged parents had decreased caloric intake, whereas children with unengaged parents had increased caloric intake over time (P=.02).ConclusionsFull parent-child engagement is critical for the success of mHealth interventions. Further research is needed to understand program design elements that can affect participants’ engagement in supporting behavior change.Trial RegistrationClinicalTrials.gov NCT03651284; https://clinicaltrials.gov/ct2/show/NCT03651284International Registered Report Identifier (IRRID)RR2-10.1186/s13063-020-4080-2

How to encourage farmers to digitize? A study on user typologies and motivations of farm management information systems

Farm Management Information Systems (FMIS) have undergone tremendous development in the recent past. With the sharp increase in documentation requirements for agricultural businesses, the potential of FMIS to become widely used comprehensive management tools is high but not yet fully exploited in practice. Consequently, there is a need to improve and adjust these systems. However, there is little research about how to optimize FMIS in order to meet the needs of the users. To answer this question, a deeper understanding of how users differ from other groups regarding their intensity of use and their perceptions of FMIS is needed but has hardly been explored so far. By means of a standardized online questionnaire in the summer of 2019, the skills and attitudes of 280 German farmers regarding the use of FMIS were surveyed. By means of a cluster analysis, different user segments were analyzed to gain deeper insights into which characteristics FMIS users and non-users have. Four clusters were identified including two non-user groups and two user groups with varying potential for FMIS providers and marketers. The results show that the lack of persuasiveness of the existing systems by not (yet) offering solutions to farmers’ problems and needs is a major barrier. One main driver for the use of FMIS is to manage the increasing political and social requirements with the help of a professional documentation of farm activities. On the one hand, the results accompany the ongoing digital transformation in agribusiness and contribute on the other hand to the digitization process on farms by increasing new knowledge about farmers’ requirements regarding FMIS. The results also provide relevant insights for software development companies to adjust their products according to the farmers’ needs.

Using Global Positioning System Big Data to Define a Bikeshare User Typology and Model On-/Off-Road Bicycle Facility Preferences

Bikeshare programs have grown exponentially over the last decade and have been found to be influential in increasing the nonmotorized mode share. With shared microbility programs proving to be a key component of modern urban environments and in first- and last-mile connections to transit, there is a need to better understand users and their facility preferences. This study utilizes 3 years’ worth of data from a campus bikeshare program in Auburn, AL, that includes over 100,000 trips and almost 9,000 users to develop a bikeshare user typology. Whereas past research developing cyclist groupings has been based on self-reported cyclist comfort and confidence levels, this study utilized actual ridership data from the bikeshare program to create new typologies specific to bikeshare. Using a K-means cluster analysis, this study proposes four bikeshare user types: unsupported users, aspirational users, flexible users, and purposeful users. These users have varying travel motivations, needs, and patterns. Their choice between an on-road and off-road facility option was analyzed at nine specific locations displaying different roadway and physical conditions. A binary logistic regression analysis was performed to determine which variables are influential in a bikeshare user’s choice between riding on an on-road or off-road facility. The modeling results found two typology memberships were a significant predictor of bicycle facility preference, keeping all other factors constant. Specifically, flexible and purposeful users had stronger preferences for off-road bicycle facilities. Additionally, the physical conditions were found to be significant with users choosing off-road options away from motor vehicle traffic as the roadway environment becomes more intimidating.

What Can User Typologies Tell Us about Carsickness Criticality in Future Mobility Systems

What Can User Typologies Tell Us about Carsickness Criticality in Future Mobility Systems

Agricultural information systems acceptance and continuance in rural communities: A consumption values perspective

This study unearths the different configurations of user motivations that foster success in Agricultural Information Systems (AIS) acceptance and continuance among small-holder farmers in rural communities in a developing economy. Anchored on the fact that significant proportions of the world's poorest live in sub-Saharan countries characterized by small-holder agriculture, the study explores the multiple pathways that result in AIS acceptance and continuance while revealing the necessary and sufficient conditions that are required to achieve a given outcome. Using the consumption values theory and fuzzy set qualitative comparative analysis (FsQCA), the findings reveal three AIS user typologies namely extrinsic, intrinsic, and hybrid motivated users. The results further reveal that AIS usability, farmer group and family support, together with the inspiration of joy, hope and trust are motivating factors for the attainment of success in AIS acceptance and continuance. The study provides foundational knowledge on how development actors can use AIS as interventions to improve Agri-food outcomes among rural dwellers in the global south.

Addiction to Social Networking Sites and User Responses

Prior studies have primarily used "variable-centered" perspectives to identify factors underlying user responses to social networking site (SNS) addiction, their predictors and outcomes. This paper extends this perspective by taking a person-centered approach to examine (1) the prototypical subpopulations (profiles) of users' extent of SNS addiction and responses to it, (2) how affiliations with these profiles can explain user behaviors toward SNS use, and (3) how personality traits can predict affiliations with these profiles. To this end, we propose a typological theory of SNS addiction and user responses to it via two empirical, personcentered studies. Study 1 draws on survey data from 188 SNS users to develop a typology of users based on the extent of their SNS addiction and their responses to it. It further examines the relations between affiliation with these profiles and users' SNS discontinuance intention, as a typical behavioral response to SNS addiction. Study 2 uses survey data from 284 SNS users to validate the user typology developed in Study 1 and investigate its relations to users' Big Five personality traits. Our findings shed light on a typology of five prototypical profiles of SNS users-cautious, regular, consonant, dissonant, and hooked-who differ in their extent of SNS addiction and their cognitive, emotional, and behavioral responses to it. Our findings also demonstrate how Big Five personality traits can predict user affiliations with these prototypical profiles.