For many, search engines are crucial gateways to (political) information. While extant research is concerned with algorithmic bias, user choices had been largely neglected. Yet, search queries are ...the key way in which searchers explicate their information need. Building on framing theory and selective exposure, we argue that queries are ingrained with (political) predispositions: issue frames in mind of searchers manifest themselves in search terms and queries. Using Dutch survey data (N = 1994), and manual coding and latent class analysis, we explore how types of people formulate search queries about immigration and climate change (RQ1). A regression analysis shows how these searcher types relate to political attitudes and socio-demographic characteristics (RQ2). Notably, searchers formulate queries in ways that are related to their political positions, but this differs for different issues. These findings imply systematic differences in user choices which future research needs to consider when auditing search engines.
•We provide an unsupervised method to derive rules for behavior based on data.•We develop a typology of charging behavior in terms of session types and user types.•Our typology can be used to improve ...realistic charging behavior in simulation models.•We found both typical (residential and commuting) as well as non-typical user types.•We see a change in EV user population composition over time due large Battery Electric Vehicle uptake and Charging point scarcity.
The understanding of charging behavior has been recognized as a crucial element in optimizing roll out of charging infrastructure. While current literature provides charging choices and categorizations of charging behavior, these seem oversimplified and limitedly based on charging data.
In this research we provide a typology of charging behavior and electric vehicle user types based on 4.9 million charging transactions from January 2017 until March 2019 and 27,000 users on 7079 Charging Points the public level 2 charging infrastructure of 4 largest cities and metropolitan areas of the Netherlands.
We overcome predefined stereotypical expectations of user behavior by using a bottom-up data driven two-step clustering approach that first clusters charging sessions and thereafter portfolios of charging sessions per user. From the first clustering (Gaussian Mixture) 13 distinct charging session types were found; 7 types of daytime charging sessions (4 short, 3 medium duration) and 6 types of overnight charging sessions. The second clustering (Partition Around Medoids) clustering result in 9 user types based on their distinct portfolio of charging session types. We found (i) 3 daytime office hours charging user types (ii) 3 overnight user types and (iii) 3 non-typical user types (mixed day and overnight chargers, visitors and car sharing). Three user types show significant peaks at larger battery sizes which affects the time between sessions. Results show that none of the user types display solely stereotypical behavior as the range of behaviors is more varied and more subtle. Analysis of population composition over time revealed that large battery users increase over time in the population. From this we expect that shifts charging portfolios will be observed in future, while the types of charging remain stable.
•We tested the reliability of the Gamification User Types Hexad scale.•Empirical evidence supports the structural validity of the scale in both English and Spanish.•Exploratory and confirmatory ...factor analysis showed that the proposed factor structure adequately fitted the data.•‘Philanthropist’, ‘Free Spirit’, and ‘Achiever’ are the prevalent user types, whereas ‘Disruptor’ is the least common user type.•Results suggest that a person's user type is correlated with their gender and age.
Gamification, the use of game elements in non-game systems, is now established as a relevant research field in human-computer interaction (HCI). Several empirical studies have shown that gameful interventions can increase engagement and generate desired behavioral outcomes in HCI applications. However, some inconclusive results indicate that we need a fuller understanding of the mechanisms and effects of gamification. The Gamification User Types Hexad scale allows us to parse different user motivations in participants’ interactions with gameful applications, which are measured using a self-report questionnaire. Each user type represents a style of interaction with gameful applications, for example, if the interactions are more focused on achievements, socialization, or rewards. Thus, by scoring an individual in each one of the user types of the Hexad model, we can establish a profile of user preferences for gameful interactions. However, we still lack a substantial empirical validation of this scale. Therefore, we set out to validate the factor structure of the scale, in both English and Spanish, by conducting three studies, which also investigated the distribution of the Hexad's user types in the sample. Our findings support the structural validity of the scale, as well as suggesting opportunities for improvement. Furthermore, we demonstrate that some user types are more common than others and that gender and age correlate with a person's user types. Our work contributes to HCI research by further validating the utility of the Gamification User Types Hexad scale, potentially affording researchers a deeper understanding of the mechanisms and effects of gameful interventions.
The concept of gamification is increasingly applied as a framework to understand extremist online subcultures and communications. Although a number of studies have been conducted, the theoretical and ...empirical basis to understand the role of gamification in extremist contexts remains weak. This article seeks to contribute to the development of a gamification of radicalization theory by exploring how Marczewski’s HEXAD, a user typology for gamified applications, may facilitate our understanding of individual variations in engagement with gamified extremist content. Five user types, named after their core motivational drivers for engagement, are discussed: Socializers, Competitors, Achievers, Meaning Seekers, and Disruptors. This typology may support future studies by providing a preliminary understanding of how different game elements may appeal to different users and increase their engagement with and susceptibility to extremist content in cyberspace.
Gamification studies in the educational domain usually focus on motivating students to increase their learning performance by enhancing their motivation. Classifications of behavioural profiles are ...often used for this (referred to as “gamer” or “user types”), which support the personalization of students’ experiences. These classifications consider these profiles from gamers’ or non-gamers’ points of view. However, within education research, it is necessary to broadly inspect these behavioural profiles to create an instructional design based on learners’ intrinsic drivers and motivations. The relationship between these concepts is subjective, complex, and difficult to categorize, demanding research to bridge this gap. Therefore, in this article we present the design and evaluation of an application ontology that seeks to represent relationships between Jung’s archetypes (e.g., the Hero, the Outlaw and others) adapted for educational purposes, creating a new approach for modelling user profiles, a taxonomy of game elements specific for use in educational contexts, and Bloom’s revised taxonomy to classify learning activities types. This ontology enables personalized and instructional designs directly related to the learning activity type for students. We demonstrate that the proposed ontology can help create better gamification designs to support learning, and we envision it to be used both to create unplugged gamification strategies and personalized gamified educational systems.
User engagement is widely recognised as beneficial for the innovation efforts of firms scarce with internal resources but our knowledge about the relevance of user types and engagement strategies is ...scarce and mostly limited to the front-end phases of the innovation process. To fill this gap, we use data from over 10,000 companies operating in four emerging innovation ecosystems in Central Europe and examine the contribution of users to product development, distribution, market positioning and the commercialisation of innovations. The results show that user engagement through customisation and co-creation promotes the success of the innovation process, with the impact of co-creation being much stronger. Consumers, intermediary users (businesses) and public sector users play different roles at different stages of innovation. These findings provide important insights for firms seeking to optimise their user engagement strategies in product innovation management.
•We explore the impact of user types and engagement strategies on product innovation.•Customisation and co-creation facilitate product innovation development.•Users matter for execution, marketing and commercialisation of innovations.•Households, firms and public entities play distinct roles in the innovation process.
A digital divide continues to be reported across and within nations, first as an access issue and second as an issue of effective usage. To address the latter issue, we argue that digital equity ...should not be conceptualized solely as a technical or resource related issue. We developed a conceptual framework based on cultural capital and parental mediation to investigate the complexity of digital inequity. Our in-depth case study of 22 Hong Kong students revealed that although information and communication technology (ICT) is thoroughly integrated with students’ everyday lives, some students lack the cultural or parenting resources required to build their capacity to effectively and meaningfully use ICT. Three salient clusters of users emerged: “celebrating” users, “coping” users, and “struggling” users. The results reveal the significance of parental mediation and cultural capital for students’ ICT use and thus digital equity.
The rapid adoption of social networking sites (SNSs) raises important questions about the social implications of such usage. Drawing on unique longitudinal data, surveying a representative sample of ...Norwegian online users (N = 2,000, age 15–75 years) in 3 annual waves (2008, 2009, and 2010), this study found a significantly higher score among SNS users in comparison to nonusers in 3 out of 4 social capital dimensions: face‐to‐face interactions, number of acquaintances, and bridging capital. However, SNS‐users, and in particular males, reported more loneliness than nonusers. Furthermore, cluster analyses identified 5 distinct types of SNS users: Sporadics, Lurkers, Socializers, Debaters, and Advanced. Results indicate that Socializers report higher levels of social capital compared to other user types.
The purpose of this study is to better understand the digital divide by identifying the variety of ways in which people in Europe use the Internet. First, by using cluster analysis on survey data (
...N=12,666/age: 16–74 years) from Eurostat on Internet usage in Norway, Sweden, Austria, the UK, and Spain, we identified five user types: Non-Users (42%), Sporadic Users (18%), Instrumental Users (18%), Entertainment Users (10%), and Advanced Users (12%). These user types differ in their distributions over country, age, access, household members, and gender. An alarming finding is that 60% of the population was found to be either Non-Users or Sporadic Users, which reflects a large digital divide in Europe. Second, we conducted a logistic regression to identify the predictors for different user types. We found on a cross-national level that age and Internet access are the most salient predictors, whereas gender and household seems to be less relevant. However, the amount of variance explained differs between countries. We also suggested a future increase in the digital divide between the identified user types—a user type divide. The user typology and the identified predictors might help researchers, practitioners, and decision makers to better understand Internet users and the multi-complex variations among individuals and countries. This knowledge will also serve as a means to understand the digital divide by providing a more nuanced perspective on Europeans' unequal usage of the Internet and participation in an increasingly digital society.
►This research identify five distinct Internet user types in Europe. ►Non-Users, Sporadics, Instrumentals, Entertainment Users and Advanced Users. ►60% are either Non-Users or Sporadics, which reflects a large digital divide in Europe. ►User types show differences with regard to country, age, access, and gender. ►We suggest a future increase in the digital divide between the identified user types.
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