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Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

The purpose of this article is to present an overview of the history and development of transaction log analysis (TLA) in library and information science research. Organizing a literature review of the first twenty‐five years of TLA poses some challenges and requires some decisions. The primary organizing principle could be a strict chronology of the published research, the research questions addressed, the automated information retrieval (IR) systems that generated the data, the results gained, or even the researchers themselves. The group of active transaction log analyzers remains fairly small in number, and researchers who use transaction logs tend to use this method more than once, so tracing the development and refinement of individuals' uses of the methodology could provide insight into the progress of the method as a whole. For example, if we examine how researchers like W. David Penniman, John Tolle, Christine Borgman, Ray Larson, and Micheline Hancock‐Beaulieu have modified their own understandings and applications of the method over time, we may get an accurate sense of the development of all applications.

This study aims to investigate and compare mobile and desktop user search behaviours of the 1300K site, a Korean shopping search engine, by using transaction log analysis.

Transaction logs of 1300K site were collected over a three months’ period, from 1 January to 31 March 2018. The data set of this study consists of 1,149,690 desktop queries, 2,346,938 mobile queries, 2,481,747 desktop browsing activities and 2,550,309 mobile browsing activities. This study quantitatively analyses transaction log of 1300K site.

The results of this study show that mobile usage is higher than desktop usage: there are more mobile sessions than desktop sessions and the number of mobile queries is more than double of desktop queries. Overall, mobile query search behaviours are more simple, targeted and focused than desktop query search behaviours. Also, mobile browsing behaviours are more simple and passive than desktop browsing behaviours. However, mobile click behaviours are more active than desktop click behaviours.

To the best of the authors’ knowledge, this study appears to be the first of its type in Korea that compared search behaviours of a large number of users on desktop computers and mobile phones. To identify various characteristics of user search behaviours, this study analyses users’ directory browsing behaviour and click behaviour as well as query search behaviour. The results of this study can be implemented to address the effective improvement and development of search services and interfaces for different devices.

Digital humanities aim to use a digital-based revolutionary new way to carry out enhanced forms of humanities research more effectively and efficiently. This study develops a character social network relationship map tool (CSNRMT) that can semi-automatically assist digital humanists through human-computer interaction to more efficiently and accurately explore the character social network relationships from Chinese ancient texts for useful research findings.

With a counterbalanced design, semi-structured in-depth interview, and lag sequential analysis, a total of 21 research subjects participated in an experiment to examine the system effectiveness and technology acceptance of adopting the ancient book digital humanities research platform with and without the CSNRMT to interpret the characters and character social network relationships.

The experimental results reveal that the experimental group with the CSNRMT support appears higher system effectiveness on the interpretation of characters and character social network relationships than the control group without the CSNRMT, but does not achieve a statistically significant difference. Encouragingly, the experimental group with the CSNRMT support presents remarkably higher technology acceptance than the control group without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could assist digital humanists in the interpretation of character social network relationships. The results of the interview present positive opinions on the integration of system interface, smoothness of operation, and external search function.

Currently, the system effectiveness of exploring the character social network relationships from texts for useful research findings by using the CSNRMT developed in this study will be significantly affected by the accuracy of recognizing character names and character social network relationships from Chinese ancient texts. The developed CSNRMT will be more practical when the offered information about character names and character social network relationships is more accurate and broad.

This study develops an ancient book digital humanities research platform with an emerging CSNRMT that provides an easy-to-use real-time interaction interface to semi-automatically support digital humanists to perform digital humanities research with the need of exploring character social network relationships.

At present, a real-time social network analysis tool to provide a friendly interaction interface and effectively assist digital humanists in the digital humanities research with character social networks analysis is still lacked. This study thus presents the CSNRMT that can semi-automatically identify character names from Chinese ancient texts and provide an easy-to-use real-time interaction interface for supporting digital humanities research so that digital humanists could more efficiently and accurately establish character social network relationships from the analyzed texts to explore complicated character social networks relationship and find out useful research findings.

Webometric techniques have been applied to many websites and online resources, especially since the launch of Google Analytics (GA). To date, though, there has been little consideration of information behaviour in relation to digitised newspaper collections. The purpose of this paper is to address a perceived gap in the literature by providing an account of user behaviour in the newly launched Welsh Newspapers Online (WNO).

The author collected webometric data for WNO using GA and web server content logs. These were analysed to identify patterns of engagement and user behaviour, which were then considered in relation to existing information behaviour.

Use of WNO, while reminiscent of archival information seeking, can be understood as centring on the web interface rather than the digitised material. In comparison to general web browsing, users are much more deeply engaged with the resource. This engagement incorporates reading online, but users’ information seeking utilises website search and browsing functionality rather than filtering in newspaper material. Information seeking in digitised newspapers resembles the model of the “user” more closely than that of the “reader”, a value-laden distinction which needs further unpacking.

While the behaviour discussed in this paper is likely to be more widely representative, a larger longitudinal data set would increase the study’s significance. Additionally, the methodology of this paper can only tell us what users are doing, and further research is needed to identify the drivers for this behaviour.

This study provides important insights into the underinvestigated area of digitised newspaper collections, and shows the importance of webometric methods in analysing online user behaviour.

To provide a review of the log analysis studies of use and users of scholarly electronic journals.

The advantages and limitations of log analysis are described and then past studies of e‐journals' use and users that applied this methodology are critiqued. The results of these studies will be very briefly compared with some survey studies. Those aspects of online journals' use and users studies that log analysis can investigate well and those aspects that log analysis can not disclose enough information about are highlighted.

The review indicates that although there is a debate about reliability of the results of log analysis, this methodology has great potential for studying online journals' use and their users' information seeking behaviour.

This paper highlights the strengths and weaknesses of log analysis for studying digital journals and raises a couple of questions to be investigated by further studies.

The aim of this paper is to show that online learning behaviors are dictated by both personal characteristics and regional differences.

Data were collected from 16,133 users in 25 regions of Taiwan. The paper examined usage behaviors by looking at 11 items of categorical variables about online learning. This study implemented a multi‐level latent class model to investigate online learning behavior patterns that exhibit regional differences.

The results showed that online learning patterns do exhibit regional differences, as the regional segments are dictated by the individual segments of different use patterns. For instance, the urban area segment comprised a higher proportion of members who are good at using the internet. The rural area segment made up a higher proportion of members who occasionally use the internet. Interestingly, rural users went online more often than urban users when in search of e‐learning or entertainment. On the other hand, the individual segments are dictated by users' personal characteristics. For instance, younger people are good at employing online learning and entertainment services. Moreover, those who use many types of online applications pay less respect to intellectual property rights than those who only use a few types of applications.

By using a massive amount of survey data to show regional differences in online learning behavior patterns, the findings herein will help internet service providers form an applicable guideline for developing service strategies of higher service satisfaction between products and users' needs.

Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions.

Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps.

The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low.

The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency.

Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems.

This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.

The purpose of this paper is to present a new human dynamics model to explain the process of verified users' (VUs) posting on Sina micro-blog.

A common human dynamics research method with three steps is applied. Firstly, a large-scale behavioral dataset is collected involving 495 VUs and five topics on Sina micro-blog. Second, five important indicators that reveal the characteristics of posting behavior are analyzed. Then, a quantitative model is constructed to describe the process of posting behavior, and its validity is verified by simulations.

Three important characteristics of VUs' micro-blog posting behavior are observed: fat-tailed distribution, fluctuation and periodicity. These characteristics do not fit the assumption of interest-driven models proposed by previous literature. An optimized task-driven model is introduced to describe this complex phenomenon mathematically. The model is verified on empirical data, confirming that task-driven models can be optimized to explore information behavior on social media.

Being different from previous studies that mainly describe common users' posting behavior on social media by applying interest-driven models, this paper customizes an optimized task-driven model for VUs, who mainly treat social media as a platform for work and play a crucial role in information creation on social media.

This paper aims to presents Real-time Usage Mining (RUM), an approach that exploits the rich information provided by client logs to support the construction of adaptive Web applications. The main goal of RUM is to provide useful information about the behavior of users that are currently browsing a Web application. By consuming this information, the application is able to adapt its user interface in real-time to enhance the user experience. RUM provides two types of services as follows: support for the detection of struggling users; and user profiling based on the detection of behavior patterns.

RUM leverages the previous study on usability evaluation to provide a service that evaluates the usability of tasks performed by users while they browse applications. This evaluation is based on a metric that allows the detection of struggling users, making it possible to identify these users as soon as few logs from their interaction are processed. RUM also exploits log mining techniques to detect usage patterns, which are then associated with user profiles previously defined by the application specialist. After associating usage patterns to user profiles, RUM is able to classify users as they browse applications, allowing the application developer to tailor the user interface according to the users’ needs and preferences.

The proposed approach was exploited to improve user experience in real-world Web applications. Experiments showed that RUM was effective to provide support for struggling users to complete tasks. Moreover, it was also effective to detect usage patterns and associate them with user profiles.

Although the literature reports studies that explore client logs to support both the detection of struggling users and the user profiling based on usage patterns, no existing solutions provide support for detecting users from specific profiles or struggling users, in real-time, while they are browsing Web applications. RUM also provides a toolkit that allows the approach to be easily deployed in any Web application.