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The purpose of this paper is to explore and describe different viewpoints on the use of social networking sites (SNSs). It analyzes perceptional typologies of SNS users and the characteristics of each type, which has similarities and differences. It also examined possible relationships among different perceptional types, users’ self-identities, and their personality.

This study utilized Q-methodology in order to identify diverse explanations of SNS users’ perspectives. Q-methodology requires participants (referred to as P sample) to sort a series of items (Q-statements). After completing a questionnaire including demographics, self-identity, etc., each respondent performed the Q-sorting work. This task involves sorting well-composed statements about SNSs (Q-sample). In all, 46 SNS users from a university in Korea were chosen as participants. Once the P sample had finished Q-sorting, participants had in-depth interviews. When all interviews were finalized, analysis of the Q-sorts was done with the help of QUNAL program.

Consequently, four types of SNS users were identified and given the following descriptive labels: Impression Management Type, Lurker Type, SNS Enjoyer and Relationship Focus Type, and Social Value Orientation Type. Further, there was a significant difference in “Relational Identity” among user types. Regarding personality, the extraversion of Type III appeared to be the highest, and the extraversion of Type II appeared to be the lowest.

Combining aspects of both qualitative and quantitative research to study the subjectivity, the findings of this study provide insight for further research regarding an audience study or an examination of the media behaviors of SNSs. It also suggests theoretical and practical implications.

The purpose of this paper is to explore user-centered design possibilities at the intersection of gamification and persuasive technology to foster energy saving behavior.

We performed a large-scale empirical study of 480 participants and analyzed how different HEXAD gamification user types perceive selected persuasive strategies embedded in an energy saving prototype. Furthermore, we investigated the role of existing energy saving behaviors (pro-environmental behavior scale–e.g. regularly turning the lights off) and their effect on the perceived persuasiveness of the proposed persuasive strategies, which may has an impact on the overall design process. Furthermore, we applied partial least squares path modeling and conducted a one-way and repeated measure ANOVA.

Results show that user types play an important role in the design of persuasive systems for energy saving. For instance, people with a high tendency toward the Socializer user type were motivated by almost all of the employed persuasive strategies, whereas Philanthropists and Players only to a limited number of strategies. Furthermore, our study reveals that existing behaviors like the individual level of energy conservation influences the perceived persuasiveness of certain strategies and therefore should be considered in the design of such applications.

Using storyboards to obtain feedback about the perceived persuasiveness of employed strategies has limitations compared to the actual use of a functional prototype. However, to offset that limitation the mock-ups used in the storyboard reflected the actual designs for a real-world prototype.

This is the first study that explores how HEXAD gamification user types can be used to inform the design of persuasive applications for energy saving (RQ1). Furthermore, and in particular, this study draws on the challenges when using user types within gamified persuasive systems by highlighting the impact and the importance of considering existing energy saving behaviors (RQ2), which has not been addressed so far.

Aims to study human information behaviour as part of the research project on the interaction of man and the information environment (project VEGA 1/9236/02) and to analyse library users' information behaviour on both sides of the information coin – information usage and information production/publishing.

The methodology was composed of the following steps: pilot studies of students' information‐seeking behaviour; a large‐scale questionnaire survey of users of academic and research libraries; data analyses and interpretations; verification of hypotheses; multiple data sorting; and modelling of user groups.

Library users appreciate easy access and well‐organised forms of information, with an emphasis on electronic sources. In their capacity as authors of professional papers, only few subjects considered print and electronic publishing to be equal. Two user types have been derived from the data analysis. Type S manifests pragmatic ways of information seeking and appreciates the low cost and speed of electronic publishing. Type A is characterised by analytic, in‐depth information processing, stressing the prestige and review process of print publishing.

Quantitative methods can form a starting‐point for typologies of human information behaviour. Additional qualitative methods, especially interviews with students, focus groups and observations, are planned for future research into modelling of users' information behaviour.

Based on the analysis, two information‐seeking styles have been identified: strategic and analytic. Differences between the search styles suggest that systems designers, knowledge managers and libraries should be open to the creative use and representation of electronic information, taking into account different information behaviours.

The average employee spends a total of 18.6 h every two months on password-related activities, including password retries and resets. The problem is caused by the user forgetting or mistyping the password (usually because of character switching). The source of this issue is that while a password containing combinations of lowercase characters, uppercase characters, digits and special characters (LUDS) offers a reasonable level of security, it is complex to type and/or memorise, which prolongs the user authentication process. This results in much time being spent for no benefit (as perceived by users), as the user authentication process is merely a prerequisite for whatever a user intends to accomplish. This study aims to address this issue, passphrases that exclude the LUDS guidelines are proposed.

To discover constructs that create security and to investigate usability concerns relating to the memory and typing issues concerning passphrases, this study was guided by three theories as follows: Shannon’s entropy theory was used to assess security, chunking theory to analyse memory issues and the keystroke level model to assess typing issues. These three constructs were then evaluated against passwords and passphrases to determine whether passphrases better address the security and usability issues related to text-based user authentication. A content analysis was performed to identify common password compositions currently used. A login assessment experiment was used to collect data on user authentication and user – system interaction with passwords and passphrases in line with the constructs that have an impact on user authentication issues related to security, memory and typing. User–system interaction data was collected from a purposeful sample size of 112 participants, logging in at least once a day for 10 days. An expert review, which comprised usability and security experts with specific years of industry and/or academic experience, was also used to validate results and conclusions. All the experts were given questions and content to ensure sufficient context was provided and relevant feedback was obtained. A pilot study involving 10 participants (experts in security and/or usability) was performed on the login assessment website and the content was given to the experts beforehand. Both the website and the expert review content was refined after feedback was received from the pilot study.

It was concluded that, overall, passphrases better support the user during the user authentication process in terms of security, memory issues and typing issues.

This research aims at promoting the use of a specific type of passphrase instead of complex passwords. Three core aspects need to be assessed in conjunction with each other (security, memorisation and typing) to determine whether user-friendly passphrases can support user authentication better than passwords.

The purpose of this paper is to clarify the characteristics of growth users over a long time to strategically collect a large amount of specific users’ tweets. Twitter reflects events and trends in users’ real lives because many of them post tweets related to their experiences. Many studies have succeeded in detecting events along with real-life information from a large amount of tweets by assuming users as social sensors. To collect a large amount of tweets based on specific users for successful Twitter studies, the authors have to know the characteristics of users who are active over long periods of time.

The authors explore the status of users who were active in 2012, and classify users into three statuses of Dead, Lock and Alive. Based on the differences between the numbers of tweets in 2012 and 2016, the authors further classify Alive users into three types of Eraser, Slumber and Growth. The authors analyze the characteristic feature values observed in each user behavior and provide interesting findings with each status/type based on Gaussian mixture model clustering and point-wise mutual information.

From their sophisticated experimental evaluations, the authors found that active users more easily dropped out than inactive users, and users who engaged in reciprocal communications often became Growth type. Also, the authors found that active users and users who were not retweeted by other users often became Eraser type. The authors’ proposed methods effectively predicted Growth/Eraser-type users compared with the logistic regression model. From these results, the authors clarified the effectiveness of five feature values per active hour to detect intended Twitter user growth for strategically collecting a large amount of tweets.

The authors focus on user growth prediction. To appropriately estimate users who have potential for growth, they collect a large amount of users and explore their status and growth after three years. The research quantitatively clarifies the characteristics of growth users by clustering using robust feature values and provides interesting findings obtained by analysis. After that, the authors propose an effective prediction method for growth users and evaluate the effectiveness of their proposed method.

The user interface, in broad terms, is the medium through which the user and the information come together. The types of searches a public access catalog (PAC) can perform are defined by the indexing strategy and retrieval software. The way the user's interest is communicated to the retrieval software and the way the results are communicated to the user is, by a more narrow definition, the interface software. Both the kinds of searches that can be performed by a variety of CD‐ROM PACs and how their workstations are used to accomplish the searches are considered.

This paper aims to explore question and answer (Q&A) participation and behavioral patterns on academic social networking sites (ASNSs) from the perspective of multiple subjects such as academic, corporate and government institutions.

Focused on the Q&A service of ASNSs, this study chooses ResearchGate (RG) as the target ASNS and collects a large-scale data set from it, involving a sample of users and a Q&A sample about academic, corporate and government institutions. First, it studies the law of Q&A participation and the distribution of the type of user according to the sample of users. Second, it compares question-asking behavior and question-answering behavior stimulated by questions among the three types of institutions based on the Q&A sample. Finally, it discusses the Q&A participation and behavioral patterns of the three types of institutions in academic Q&A exchanges with full consideration of institutional attributes, and provides some suggestions for institutions and ASNSs.

The results show that these three types of institutions generally have a low level of participation in the Q&A service of RG, and the numbers of questions and answers proposed by institutional users conform to the power-law distribution. There are differences in Q&A participation and Q&A behavioral patterns among academic, corporate and government institutions. Government and academic institutions have more users participating in the Q&A service and their users are more willing to ask questions, while corporate institutions have fewer users who participate in the Q&A service and their users are inclined to provide answers. Questions from corporate institutions attract much more attention than those from the other two types of institutions.

This study reveals and compares the Q&A participation and the behavioral patterns of the three types of institutions in academic Q&A, thus deepening the understanding of the attributes of institutions in the academic information exchange context. In practice, the results can help guide different institutions to use the Q&A service of ASNSs more effectively and help ASNSs to better optimize their Q&A service.

A social media user behavior model is presented as a function of different user types, i.e. light and heavy users. The users’ behaviors are analyzed in terms of knowledge creation, framing and targeting.

Data consisting of 160,000 tweets by nearly 40,000 twitter users in the city of Newark (NJ, USA) were collected during the year 2014. An analysis was conducted to examine the hypothesis that different user types exhibit distinct behaviors driven from different motivations.

There are three important findings of this study. First, light users reuse existing content more often, while heavy and automated users create original content more often. Light users also use more sentiments than the heavy and automated users. Second, automated users frame more than heavy users, who frame more than light users. Third, light users tend to target a specific audience, while heavy and automated users broadcast to a general audience.

Decision-makers can use this study to improve communication with their customers (the public) and allocate resources more effectively for better public services. For example, they can better identify subsets of users and then share and track specialized content to these subsets more effectively.

Despite the broad interest, there is insufficient research on many aspects of social media use, and very limited empirical research examining the relevance and impact of social media within the public sector. The social media user behavior model was established as a framework that can provide explanations for different social media knowledge behaviors exhibited by various subsets of users, in an e-government context.

Word‐of‐mouth is a powerful communication tool which is often beyond the control of the marketer. This study used a 3 x 2 x 2 factorial experiment in a laboratory simulation to examine the main and interaction effects of three independent variables — message, source and user‐type — on credibility and behavior intention. The experiment involved 1,440 respondents from two different demographic sample groupings — secondary school students and undergraduates. ANOVA results for the experiments showed that, generally, source and user‐type were found to be significant factors affecting the credibility of word‐of‐mouth. In terms of source, father was perceived to be more credible than close friend as a word‐of‐mouth source. Likewise, past users were found to be more credible than non‐past users. Message was, however, found to affect significantly the behavioral intention variable. Negative message was found to generate the strongest negative behavioral intention than positive message and two‐sided messages. Two‐sided message was also found to have a stronger effect than positive message in behavioral intention. In addition, t‐tests results also revealed significant differences in perceptions between the two samples.

Social commerce sites offer fertile ground for users to communicate product information. Given that such sites have the potential to transform the way of doing business, it is clearly important for academics to understand user information sharing on social commerce sites. Existing research has considered motivations as core elements of user information sharing in online communities. The purpose of this paper is to advance the theoretical understanding of user information sharing by investigating differences in motivations between the different user types of social commerce sites: sellers and buyers, and the impact on social commerce.

The authors conducted an empirical study by analyzing data from a social commerce site in China, including panel data (n=892) and survey data (n=913).

This research showed that user type (i.e. sellers and buyers) plays an important moderating role in user information sharing: sellers exert a positive moderating effect on utilitarian and social motivations, while buyers are found to have a positive moderating effect on hedonic motivation.

This study contributes to existing literature, not only by exploring the antecedents of user information sharing on social commerce sites from utilitarian, hedonic and social dimensions, but also by providing an evaluation of user types (i.e. sellers and buyers). The authors believe that the results of this study offer important and interesting insights for IS research and practice.

This study will enhance social commerce site managers’ understanding of better features for information sharing and differences in motivation between sellers and buyers. This could improve the effectiveness of encouraging strategies and help social commerce sites be more sustainable in the highly competitive contemporary environment.

Based on social exchange theory and motivation theory, this paper takes user types into account, and postulate that user type (i.e. sellers and buyers) plays a moderating role in the relationships between motivations (i.e. utilitarian, hedonic and social motivation) and user information sharing intention on social commerce sites.