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The objective of this paper is to investigate how webmasters within government bodies explain quality of websites. Despite the central position for advancing the communication, bridging usability tests and design, there are surprisingly few studies on how webmasters perceive, experience and explain website quality or design issues.

The authors' unit of analysis is webmasters from Norwegian web‐award‐winning organizations. Eight webmasters from four types of websites were interviewed. The websites were purposefully sampled, using the strategy of maximal variation sampling to maximize difference between the four types of websites.

The findings reveal that issues concerning usability are found to be an important dimension of website quality. The authors' analysis of how webmasters explain website quality reveals substantial variance in explanation of website quality. Repeated keywords of website quality are mainly related to user‐friendliness, effective website usage, content‐related issues and accessibility (WAI‐principles).

This study includes webmasters from award‐winning websites. In upcoming research contributions, it would add to the richness of the study if webmasters from non‐award‐winning websites were included. Measurement of website quality and success is widely addressed within the research literature. This paper offers the opportunity to understand how practitioners (i.e. webmasters) facilitate for website quality, grounded in their perception and explanations of which quality aspects they found to be of importance.

The website quality aspects identified in this paper can be used as insights for how to develop and improve the quality of websites with the public sector.

The overall digital enabled transformation of government appears to be guided by a rather heterogeneous set of quality standards. While a variance of quality standards might stimulate innovation in websites, it can also lead to a substantial difference in digital services provided to citizens. Thus, the authors' research stimulates the awareness of diversity of quality parameters and could have as an implication that national and international standards beyond accessibility standards are more explicitly shared and debated.

The aim of this paper is to provide insights into website practitioners' (i.e. webmasters') perception and explanation of quality aspects in websites. Webmasters are important contributors to the quality of available websites, and it is of particular benefit to learn about their suggestions. Most studies tackle perception of website quality from a user's point of view, while the added knowledge in this paper is the webmaster's explanation.

The study aimed to examine how different communities concerned with dementia engage and interact on Twitter.

A dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.

Classification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.

The study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.

The advent of technology has enabled frequent innovations in the digital modes of doing business. While some users are reluctant to adopt one, others have made it their way of life. The purpose of this study is to find out the approximate time frame of a digital user in an emerging economy like India to adopt their favorite digital application. The importance of an individual’s level of internet skills is explored in the aspect of adopting digital innovations.

The study follows a quantitative approach where data collected from both primary and secondary sources are analyzed to arrive at the results. In total, 837 responses collected with the help of a structured questionnaire are entered and analyzed using SPSS. Multinomial logistic regression is the statistical tool used in the research.

Five sets of adopter categories based on Rogers (1983) are created using an adoption score and are represented in a Time Horizon to understand better. The Unaffected adopter category is used as the reference point to compare the set of internet skills against each of the other five categories. The five sets of categories comprising the internet skills depict interesting results in the case of all the four comparisons made. Social skills are not seen to have any influence on a user being an Innovator or Early Adopter as compared to an Unaffected user. It is also established that Operational and Creative skills play important role in users’ probability of being an innovator, early adopter and early majority compared to being unaffected.

The study analyzes the role of internet skills in the process of adopting digital innovations. The role of internet skill although is associated with every aspect of the digital revolution taking place all over, its application including the direct score of a digital user’s level of internet skills is not found in the literature.

The purpose of this paper is to clarify the correlations between amount of individual’s knowledge of a specific area and his/her visit pattern to point of interest (POI, interested places) located in the area.

This paper proposes a visit-frequency-based familiarity estimation method that estimates individuals’ knowledge of areas in a quantitative manner. Based on the familiarity degree, individuals’ visit logs to POIs are divided into a set of groups followed by analyzing the differences among the groups from various points of view, such as user preference, POI categories/popularity, visit time/date and subsequent visits.

Existence of statistically significant correlations between individuals’ familiarity to areas and their visit patterns is observed by our analysis using 1.4-million POI visit logs collected from a popular location-based social network (LBSN), Foursquare. There exist different skewness of the visit time and visited POI distribution/popularity with regard to the familiarity. For instance, users go to unfamiliar areas on weekends and visit POIs for cultural experiences, such as museums. A notable point is that the correlations can be detected even in the areas in home city, which have not been known so far.

This is the first in-depth work that studies both estimation of individuals’ familiarity and correlations between the familiarity and individuals’ mobility patterns by analyzing massive LBSN data. The methodologies used and the findings of this work can be applicable not only to human mobility analysis for sociology, but also to POI recommendation system design.

To fully grasp the potential of using social media, one must know what governments and citizens are communicating in these media. Despite much statistics published, there is a dearth of research analyzing information content in detail. The purpose of this paper is to identify the kind of information exchange that occurs between a local government and its constituent citizens using social media.

This paper presents a qualitative single case study of the Facebook presence of the municipality and city of Karlstad, Sweden, one of the most prominent local governments on Facebook in the country. Facebook page data were collected between May 2015 and July 2015. A content analysis was performed on the data to explore new and existing categories that drive the analysis.

The paper identifies 11 content categories for municipality posts and 13 content categories for user posts (citizen or organization). The frequency for each content category reveals that the page owner is first of all using its Facebook page to promote different happenings in the municipality while the page user is asking questions to the municipality or other users.

The study is limited to one municipality, but achieves analytical generalizability in its findings. In other words, the categories and mode of communication are a finding in and of itself that in future research may be validated in terms of commonality on a national scale.

Management concerns about opening up a municipality Facebook page for user posting may be exaggerated: positive posts are as common as complaints. If an organization wants to use the Facebook page for increased user participation and collaboration, it seems that users, both citizens and organizations, welcome such opportunities. However, such posts are not likely to receive many comments or shares from other users.

The paper is one of the first that uses content analysis to categorize both page owner posts and user posts on a local government Facebook page. The content categories identified in the paper provide novel and detailed insights on what types of information exchange occur on social media between a local government and citizens but also highlight the need to distinguish organizations from citizens among the visitors to the government Facebook page. Different types of user will post different types of content. The identified content categories can serve as a basis for future empirical research within e-government research.

The problem of privacy protection in digital libraries is causing people to have increasingly extensive concerns. This study aims to design an approach to protect the preference privacy behind users’ book browsing behaviors in a digital library.

This paper proposes a client-based approach, whose basic idea is to construct a group of plausible book browsing dummy behaviors, and submit them together with users’ true behaviors to the untrusted server, to cover up users’ sensitive preferences.

Both security analysis and evaluation experiment demonstrate the effectiveness of the approach, which can ensure the privacy security of users’ book browsing preferences on the untrusted digital library server, without compromising the usability, accuracy and efficiency of book services.

To the best of the authors’ knowledge, this paper provides the first attempt to the protection of users’ behavior privacy in digital libraries, which will have a positive influence on the development of privacy-preserving libraries in the new network era.

The purpose of this paper is to explain the “good-to-good” app switching phenomenon that has not been specifically addressed in the prior switching literature. Drawing on the consumer learning theory, this study explores how external social word of mouth (WOM) and internal satisfaction influence app users’ switching intention through social learning route and analogical learning route. This study also examines the moderating effect of app heterogeneity.

An online survey was used to collect data. Two categories of mobile apps with different levels of within-category heterogeneity were targeted in survey questions. A total of 525 valid survey responses were collected.

Social WOM about a competing app increases users’ switching intention through both social norm influence and social information influence, resulting in a direct effect on switching intention and an indirect effect through the perceived attractiveness of a competing app. Users’ satisfaction with an adopted app positively influences the perceived attractiveness of an unadopted competing app, offering evidence for analogical learning in user switching. Meanwhile, users’ satisfaction imposes a direct negative effect on switching intention. A higher level of within-category heterogeneity strengthens (weakens) the positive effect of social WOM (satisfaction) on users’ perceived attractiveness of a competing app.

This study complements the existing switching literature by disentangling the “good-to-good” switching phenomenon in the mobile app market from the consumer learning perspective. This study extends the understanding of cross-category user switching by considering different levels of product heterogeneity.

This study seeks to investigate users' virtual reference queries in concert with their search histories to better understand user needs and motivations.

The paper initially examines one semester of data, including both search histories and user questions, from a database platform virtual reference service. These data were used to develop categories of frequently occurring search problems. One year of further data (n=90) is then coded and analyzed with these categories for problems exhibited in search histories and user questions.

It was found that one‐third of users display a misunderstanding of what content they could expect to find in library databases. Users treated the databases examined as a one‐stop shop for their daily information needs. There was a positive correlation between number of databases simultaneously searched and number of searches users performed before seeking help. Additionally, it was found that users recognize when they have problems with search strategy, but they are disproportionally concerned with term selection as the cause of their trouble rather than other problems with search mechanics (i.e. Boolean operators.)

The results of this study illustrate commonalities in users' expectations of what they may search for, find, or do in a database. It shows what tends to lead users to seek help with their searching, exposes how the systems can lead users astray, and illustrates basic misunderstandings of what content can be found in library databases.

This study is the first virtual reference study that examines users' search histories in concert with their reference queries.

This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems.

A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user‐centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system.

The results show that among the proposed UPSC formulas in this paper, the (query‐document)‐graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems.

This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information.

The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.