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Modern Web browsers all provide a history function that allows users to see a list of URLs they have visited in chronological order. The history log contains rich information but is seldom used because of the tedious nature of scrolling through long lists. This paper aims to propose a new way to improve users’ Web browsing experience by analyzing, clustering and visualizing their browsing history.

The authors developed a system called Personal Web Library to help users develop awareness of and understand their Web browsing patterns, identify their topics of interest and retrieve previously visited Web pages more easily.

User testing showed that this system is usable and attractive. It found that users can easily see patterns and trends at different time granularities, recall pages from the past and understand the local context of a browsing session. Its flexibility provides users with much more information than the traditional history function in modern Web browsers. Participants in the study gained an improved awareness of their Web browsing patterns. Participants mentioned that they were willing to improve their time management after viewing their browsing patterns.

As more and more daily activities rely on the internet and Web browsers, browsing data captures a large part of users’ lives. Providing users with interactive visualizations of their browsing history can facilitate personal information management, time management and other meta-level activities.

This paper aims to help users gain insights into and improve their Web browsing experience, the authors hope that the work they conducted can spur more research contributions in this underdeveloped yet important area.

The purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.

In order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.

First, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.

This research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.

The proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.

The proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.

The key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

This paper aims to examine how multi-sensory cues, when store-congruent, influence consumer browsing behaviour and its subsequent effect on purchasing.

Two studies were used with a field experimental design in a furnishing retail store to examine browsing behaviour and purchasing in a visual, auditory, olfactory and a multi-sensory treatment group. Data were gathered over 12 weeks. This study was a set of studies comprising my dissertation thesis (Helmefalk, 2017).

Findings show that multi-sensory cues in a retail atmosphere are evidently influencing purchasing via browsing behaviour as a mediator.

The findings evidence browsing behaviour as a mediator and predictor for purchasing, which emphasizes its conceptual and empirical contribution in terms of modifying retail atmospheres. The work contributes to the field of retailing, sensory marketing and consumer behaviour, a novel view on the linkages between multi-sensory cues, browsing behaviour and purchasing.

This study aims to investigate the effects of different search and browse features in digital libraries (DLs) on task interactions, and what features would lead to poor user experience.

Three operational DLs: ACM, IEEE CS, and IEEE Xplore are used in this study. These three DLs present different features in their search and browsing designs. Two information‐seeking tasks are constructed: one search task and one browsing task. An experiment was conducted in a usability laboratory. Data from 35 participants are collected on a set of measures for user interactions.

The results demonstrate significant differences in many aspects of the user interactions between the three DLs. For both search and browse designs, the features that lead to poor user interactions are identified.

User interactions are affected by specific design features in DLs. Some of the design features may lead to poor user performance and should be improved. The study was limited mainly in the variety and the number of tasks used.

The study provided empirical evidence to the effects of interaction design features in DLs on user interactions and performance. The results contribute to our knowledge about DL designs in general and about the three operational DLs in particular.

The purpose of this study is to investigate the impulsive purchase of Generation Z of Muslim women on TikTok Shop. The primary attention in this stimulation investigation drives the impulsive buying between the Generation Z of Muslim women customers in Indonesia. This study provides valuable concept to help a company or cosmetic brand grow the marketing strategy for stimulating impulsive behaviour of Generation Z customers on TikTok Shop.

An online survey of 256 Muslim women consumers of the Generation Z was used to collect the data. Covariance-based structural equation modelling was applied to assess the five hypotheses.

The results of this study indicate that micro-celebrities post authenticity has a positive and significant relationship with utilitarian browsing. However, micro-celebrities post authenticity did not have a positive and significant relationship with hedonic browsing. This study also concludes that utilitarian browsing was found to have a positive and significant relationship with hedonic browsing. Finally, hedonic browsing was found to have a positive and significant effect on reminiscence impulse purchase intention and designed impulse purchase intention.

The primary limitation is the research sample that only consists of Indonesian TikTok Shop customers. Next, the researchers could examine the research model in several countries to expand its generalisation. The second one, this research does not consider the origin country of a cosmetic product. The future study must investigate the local and foreign cosmetics to see the preference and the differences in the impulsive purchase, especially for Z women generation customer. Third, this study quantifies the intention to buy impulsively so that the following research must investigate the ownership of the cosmetics product that has been bought before. Finally, the research only involves a quantitative research method. The future study must investigate with another approach, such as a qualitative method or mixed-method, in the impulsive purchase intention context.

A cosmetic company or marketer could maximise the authenticity, relevancy and attractive information that is posted by micro media social celebrities or media social influencers. Cosmetic companies or marketers must provide comprehensive information to satisfy customers’ browsing actions. Finally, besides the information related to the limited cosmetics product stock, discount and promotion as the effective strategy to stimulate the impulsive buying, the cosmetics marketing can use gamification, increasing the picture quality and equipping product descriptions, and making educational content.

The existence of TikTok Shop is increasing the number of cosmetics products and competitors because of product’s excessive availability in the market. In the social benefit context, the Generation Z Muslim women can purchase and use various cosmetics product available on TikTok Shop, but customers must be on guard by knowing the cosmetics material to prevent the healthiness. In addition, the existence of TikTok Shop and the impulsive purchases by the Generation Z Muslim women are also increasing the retailer or local cosmetics producer income.

This study is a contribution to consumer behaviour literature by raising the Stimulus–Organism–Response framework used in determining the factors influencing browsing and impulsive consumption of cosmetic products on TikTok Shop.

Online reviews have shown important information that affects consumers’ online shopping behavior. However, little research has examined how they may influence consumers’ online impulse buying behavior. The purpose of this paper is to bring theoretical and empirical connections between them.

The framework of this study was tested on three popular online group shopping websites in China (ju.taobao.com, dianping.com, and meituan.com). An online survey with 315 participants who had experience using these websites was recruited to verify the effects of consumers’ perceived value from reading online reviews on urge to buy impulsively and impulse buying behavior.

The empirical findings show that consumers’ perceived utilitarian and hedonic value from reading online reviews enhance their browsing behavior. Browsing positively affects consumers’ urge to buy impulsively and finally affects their impulse buying behavior. Further, this study finds that consumers with high impulsiveness focus more on hedonic value of online reviews, whereas consumers with low impulsiveness put more emphasis on utilitarian value. Browsing demonstrates a stronger effect on urge to buy impulsively for consumers with high impulsiveness.

This study is one of the early studies to investigate the relationship between social influence (e.g. influence of online reviews) and impulse buying. It draws upon the perspectives of browsing and consumer’s perceived value from the literature. This research also considers consumer differences regarding the level of impulsiveness.

Regional accreditation standards for library services reveal a clear shift away from resources per se towards actual student use to meet their information needs. They also recognize increasing reliance on electronic access to remote information sources. However, unobtrusive data of actual student borrowing/browsing behaviour suggest a wide discrepancy between meeting needs via the conventional on‐site library collection and the significant efforts to maintain and increase collections regardless of the behaviour. Proposes a shift in evaluation of library services to bring resource planning into closer harmony with accreditation standards, students’ library use patterns and information age realities.

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.

The purpose of this paper is to study what extent readers’ socio-demographic characteristics, literary preferences and search behavior predict success in fiction search in library catalogs.

In total, 80 readers searched for interesting novels in four differing search tasks. Their search actions were recorded with a Morae Recorder. Pre- and post-questionnaires elicited information about their background, literary preferences and search experience. Readers’ literary preferences were grouped into four orientations by a factor analysis. Linear regression analysis was applied for predicting search success as measured by books’ interest scores.

Most literary orientations contributed to search success, but in differing search tasks. The role of result examination was greater compared to querying in contributing search success almost in each task. The proportion of variance explained in books’ interest scores varied between 5 (open-ended browsing) and 50 percent (analogy search).

The distribution of participants was biased toward females, and the results are aggregated within search session, both reducing the variation of the phenomenon observed.

This study is one of the first to explore how readers’ literary preferences and searching are associated with finding interesting novels, i.e. search success, in library catalogs. The results expand and support the findings in Mikkonen and Vakkari (2017) concerning associations between reader characteristics and fiction search success.

This paper aims to propose a method for summarizing the topics of tweets using the Wikipedia category structure as common knowledge for supplementing the understanding of the Twitter user’s interests. There are many topics in the tweets, and the topics can be treated as a tree structure. However, when the topic hierarchy is constructed using existing hierarchal clustering approach, the granularity of tweet groups differs for each user. For summarizing the topics, identification of the topics which are heterogeneous and which are not is necessary because it is easy to understand if several groups are categorized into parent groups. However, if the group units are different for each user, a number of users’ interests cannot be summarized. If some tweets are grouped into the presidential election, and the others are into Donald Trump, there cannot be a count of how many users are interested in Donald Trump.

One solution of this issue is to construct topic structures by mapping one common tree structure. In this paper, a method is proposed for constructing the topic structure using the Wikipedia category tree similar to a common tree structure. The tweets are categorized, mapped to titles of articles in the Wikipedia category tree and then visualized as the hierarchal structure to the users.

The effectiveness of the proposed hierarchal topic structure is confirmed. In theme “politics”, the proposed method works well. The main reason is that there are many technical terms about politics in the Wikipedia categories and articles. It was found that a number of the terms of politics do not have multiple meanings, multiple semantics. However, in theme “sports”, the proposed method does not perform well. The main reason for this case is that there are a number of names of people present as topic names.

One important feature of the proposed method is that it is easy to grasp not only about the topics which are heterogeneous or homogeneous with each other but also consider the missing time when extracting topics. Another feature is that the topic structures for multiple users are easy to compare with each other.