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The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users’ online behavior. This information enables real-time personalization of web content.

This paper models transitions between pages based upon the dwell time of the initial state and then analyzes data from a web shop, illustrating how pages that are linked “compete” against each other. Relative risks for web page transitions are estimated based on the dwell time within a clickstream and survival analysis is used to predict clickstreams.

Using survival analysis and user dwell times allows for a detailed examination of transition behavior over time for different subgroups of internet users. Differences between buyers and non-buyers are shown.

As opposed to other academic fields, survival analysis has only infrequently been used in internet-related research. This paper illustrates how a novel application of this method yields interesting insights into internet users’ online behavior.

A key goal of any online retailer is to increase their customer conversation rates. Using survival analysis, this paper shows how dwell-time information, which can be easily extracted from any server log file, can be used to predict user behavior in real time. Companies can apply this information to design websites that dynamically adjust to assumed user behavior.

The method shows novel clickstream analysis not previously demonstrated. Importantly, this can support the move from web analytics and “big data” from hype to reality.

Careless responders are respondents who lack the motivation to answer survey questions accurately. Empirical findings can be significantly distorted when some respondents devote insufficient effort to the survey task, and researchers therefore attempt to identify such respondents. Many measures of careless responding have been suggested in the literature, but researchers frequently struggle with the selection and appropriate use of the available methods. This chapter offers a classification of existing measures of careless responding along two dimensions and presents a conceptual discussion of their relative strengths and weaknesses. An empirical study demonstrates how the various measures can be used to identify careless responders and how these measures are related to each other.

There is a general dearth of trustworthy information on who is using the web and how they use it. Such information is of vital concern to web managers and their advertisers yet the systems for delivering such data, where in place, generally cannot supply accurate enough data. Nor have web managers the expertise or time to evaluate the enormous amounts of information that are generated by web sites. The article, based on the experience of evaluating The Times web server access logs, describes the methodological problems that lie at the heart of web log analysis, evaluates a range of use measures (visits, page impressions, hits) and provides some advice on what analyses are worth conducting.

Provides the first exploratory analysis of the use of NHS touch‐screen information kiosks located throughout the UK. It examines, through a one month snapshot of the transactional logs of the kiosks, their use and makes comparisons between the types of organisation in which the kiosks are housed. Details of over 120 kiosks and nearly 47,000 user sessions and 310,000 page views were used for the comparison. The kiosks are proving popular, although some use appears to be of a cursory and seemingly unproductive kind. Surprisingly significant differences between kiosk locations were found. This early research reported here is part of an ongoing study of how users obtain health information by interacting with different digital platforms: kiosks, the Internet, and digital television. Comparisons are made between the results of this study and a similar study conducted by the authors on commercial health kiosks, those of InTouch With Health.

In the digital library era, library websites are recognized as the extension of library services. The usability and findability of library websites are growing more and more important to patrons. No matter how these websites have been built, they should offer the capability that patrons can find their required information quickly and intuitively. The website logs keep tracks of users' factual behaviors of finding their required information. Based on the evidences, the author attempts to reconstruct the websites to promote their internal findability.

In the past, the card sorting method has generally been applied to reconstruct websites to improve their internal findability. Alternately, in this research, a first attempt is made to try to use website log data to implement website reconstruction. The website log data was cleaned and user sub‐sessions were extracted according to their respective critical time of session navigation. Each sub‐session's threshold time of target page was then calculated with different weights to determine its navigating parent pages. The different weighted parent pages were utilized to reconstruct various websites. A task‐oriented experiment of four tasks and 25 participants was conducted to measure the effects of findability between the constructed websites.

By analysis of the variance of time to complete the tasks, it is shown that the reconstructed websites have better findability performance in the time spent to complete the tasks than the current one, if focusing much more on the target pages. The result clearly explores that when the parent pages of a page are selected, whether it is a target page is the most important issue to improve website findability. The target page plays a critical role in website reconstruction. Furthermore, the traditional card sorting method is applied to the case website to reconstruct it. The findability experiment is then conducted and its time to complete the tasks is compared to those of websites that are reconstructed. The approach proposed here has better effects than card sorting.

Mining web log data to discover user behaviors on the library website, this research applies a heuristic method to analyze the data collected to reconstruct websites. Focusing on the target pages, the reconstructed websites will have better findability. Besides traditional card sorting techniques, this paper provides an alternative way to reconstruct websites such that users can find what they need more conveniently and intuitively.

Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors.

This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website.

Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits.

This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

This article presents the early findings of an exploratory deep log analysis of journal usage on OhioLINK, conducted as part of the MaxData project funded by the US Institute of Museum and Library Services. OhioLINK, the original “big deal”, provides a single digital platform of nearly 6,000 full‐text journal for more than 600,000 people in the state of Ohio. The purpose of the paper is not only to present findings from the deep log analysis of journal usage on OhioLINK, but, arguably more importantly, to try test a new method of analysing online information user behaviour – deep log analysis.

The raw server logs were obtained for the period June 2004 to December 2004. For this exploratory study one month (October) of the on‐campus usage logs and seven months of the off‐campus transaction logs were analysed.

During this period approximately 1,215,000 items were viewed on campus in October 2004 and 1,894,000 items viewed off campus between June and December 2004. The paper presents a number of usage analyses including: number of journals used, titles of journals used, use over time, a returnee analysis and a special analysis of subject, date and method of access.

The research findings help libraries evaluate the efficiency of big deal and one‐stop shopping for scholarly journals and also investigate their users' information seeking behaviours.

The research is a part of efforts to test the applications of a new methodology, deep log analysis, for use and user studies. It also represents the most substantial independent analysis of, possibly, the most important and significant of the journal big deals ever conducted.

The purpose of this study is to propose a real-time lecture supporting system. The target of this study is on-site classrooms where teachers give lectures and a lot of students listen to teachers’ explanations, conduct exercises, etc.

The proposed system uses an e-learning system and an e-book system to collect teaching and learning activities from a teacher and students in real time. The collected data are immediately analyzed to provide feedback to the teacher just before the lecture starts and during the lecture. For example, the teacher can check which pages were well previewed and which pages were not previewed by students using the preview achievement graph. During the lecture, real-time analytics graphs are shown on the teacher’s PC. The teacher can easily grasp students’ status and whether or not students are following the teacher’s explanation.

Through the case study, the authors first confirmed the effectiveness of each tool developed in this study. Then, the authors conducted a large-scale experiment using a real-time analytics graph and investigated whether the proposed system could improve the teaching and learning in on-site classrooms. The results indicated that teachers could adjust the speed of their lecture based on the real-time feedback system, which also resulted in encouraging students to put bookmarks and highlights on keywords and sentences.

Real-time learning analytics enables teachers and students to enhance their teaching and learning during lectures. Teachers should start considering this new strategy to improve their lectures immediately.

Online search effort is routinely measured by the duration of visit at the website as obtained from clicksream data or surveys. Measuring search effort by the time spent at a website assumes that all consumers who search for the same duration obtain the same amount of information. This would be acceptable if all consumers possessed the same navigational ability. In practice, different consumers have different levels of ability to navigate a website. The purpose of this study is to find whether an individual’s navigational ability has an influence on visit duration and purchase likelihood.

The authors use visit duration data from a real website which makes it possible to partition the visit duration into the times spent on relevant and irrelevant pages. The data were collected through an experimental study. The authors develop an empirical model, comprising hazard and choice models, to assess the relationship between navigational ability and elements of website usage.

A consumer with poor navigational ability spends more time searching on the Web and has lower purchase probability compared to a consumer with superior ability.

The study is limited to one data.

This research has managerial implications for website design, such as link-structure, appearance, size and the number of graphics.

This is the first study to research navigational ability’s influence on online consumer behavior.

Evaluates, through deep log analysis, the impact of “Big Deal” agreements on the online searching behaviour of users of the Emerald digital library Web site, which provides access to more than 150 journals in the fields of business and information science. The purpose of the evaluation was to map the online information seeking behaviour of the digital library user and to see whether those signed‐up to a Big Deal arrangement behaved any differently from the others. In general they did. The real surprise proved to be the strong consumer traits of the library’s users. Research reported here refers to the first stage of a three‐stage research project.