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Article
Publication date: 10 July 2019

Meihua Zuo, Hongwei Liu, Hui Zhu and Hongming Gao

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Abstract

Purpose

The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.

Design/methodology/approach

Consumer sequential online click data, collected from JD.com, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.

Findings

The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

Research limitations/implications

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

Practical implications

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.

Originality/value

There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.

Details

Industrial Management & Data Systems, vol. 119 no. 6
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 8 April 2021

Jens Mattke, Christian Maier, Lea Reis and Tim Weitzel

Individuals only click on a very small fraction of the in-app advertisements (ads) they are exposed to. Despite this fact, organizations spend generously placing in-app…

Abstract

Purpose

Individuals only click on a very small fraction of the in-app advertisements (ads) they are exposed to. Despite this fact, organizations spend generously placing in-app ads without theoretical knowledge of how the structure and the semantics of in-app ads influence individuals’ clicking behavior. This study aims to identify how the processing of structural and semantic factors leads to clicking behavior.

Design/methodology/approach

Based on the limited capacity theory, this paper proposes that the sequential processing of structural and semantic factors leads to clicking behavior. To mirror the sequential process, this paper applies a process-oriented configurational approach and performs a two-step qualitative comparative analysis (QCA) using 262 incidents of exposure to in-app ads.

Findings

The results support the proposed sequential processing and show that neither structural nor semantic factors alone lead to clicking behavior. This paper reveals four different paths of sequential processing of in-app ads that lead to clicking behavior. The results show that individuals click on non-animated in-app ads even though these are perceived as irritating or privacy-concerning. When the in-app ads are animated, individuals do only click on them when these are not irritating, privacy-concerning and personalized.

Research limitations/implications

Organizations can use these findings to improve their in-app ads and generate more clicks. This study recommends that organizations place in-app ads in a prominent location, design them similar to the design of the app and use bright colors. The advertising message needs to have new and relevant information in a credible and entertaining way. Depending on the degree of personalization, organizations should use different sizes of the in-app ad and only use animation if it is unlikely that the in-app ad caused irritation or privacy concerns.

Practical implications

Organizations can use these findings to improve their in-app ads and generate more clicks. This paper recommends that organizations place in-app ads in a prominent location, design them similar to the design of the app and with bright colors. The advertising message needs to have new and relevant information in a credible and entertaining way. Depending on the degree of personalization, organizations should use different sizes of the in-app ad and only use animation if it is unlikely that the in-app ad caused irritation or privacy concerns.

Originality/value

From the in-app ad perspective, this study is the first to theoretically develop and empirically show the sequential processing of structural and semantic factors of in-app ads. From the methodological perspective, this study applies an advanced configurational two-step QCA approach, which is capable of analyzing sequential processes and is new to marketing research.

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Article
Publication date: 15 June 2021

Soyeon Park and Kihun Cho

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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

The Electronic Library , vol. 39 no. 2
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 22 October 2019

Gillian Moran, Laurent Muzellec and Devon Johnson

This paper aims to uncover the drivers of consumer-brand engagement on Facebook, understood here as users’ behavioral responses in the form of clicks, likes, shares and…

Abstract

Purpose

This paper aims to uncover the drivers of consumer-brand engagement on Facebook, understood here as users’ behavioral responses in the form of clicks, likes, shares and comments. We highlight which content components, interactivity cues (calls to action [CTA]) and media richness (e.g. video, photo and text) are most effective at inducing consumers to exhibit clicking, liking, commenting and sharing behaviors toward branded content.

Design/methodology/approach

This study analyzes 757 Facebook-based brand posts from a media and entertainment brand over a 15-week period. It investigates the relationship between interactive cues and media richness with consumer engagement using a negative binomial model.

Findings

Results show positive relationships for both interactivity cues and media richness content components on increasing consumer-brand engagement outcomes. The findings add clarity to previous inconsistent findings in the marketing literature. CTAs enhance all four engagement behaviors. Media richness also strongly influences all engagement behaviors, with visual imagery (photos and videos) attracting the most consumer responses.

Research limitations/implications

The sampled posts pertain to one brand (a radio station) and are thus concentrated within the media/entertainment industry, which limits the generalizability of findings. In addition, the authors limit their focus to Facebook but recognize that findings may differ across more visual or textual social networking sites.

Practical implications

The authors uncover the most effective pairings of media richness and interactivity components to trigger marketer-desired, behavioral responses. For sharing, for example, the authors show that photo-based posts are more effective on average than video-based posts. The authors also show that including an interactive call to act to encourage one type of engagement behavior has a near-universal effect in increasing all engagement behaviors.

Originality/value

This study takes two widely used concepts within the communications and advertising literatures – interactivity cues and media richness – and tests their relationship with engagement using real and actual users’ data available via Facebook Insights. This method is more robust than surveys or wall scrapping, as it mitigates Facebook’s algorithm effect. The results produce more consistent relationships than previous content marketing studies to date.

Details

Journal of Product & Brand Management, vol. 29 no. 5
Type: Research Article
ISSN: 1061-0421

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Article
Publication date: 20 November 2017

Quan Lu, Qingjun Liu, Jing Chen and Ji Li

Since researchers have utilized text signals to develop a mass of within-document visualization analysis tools for reading aid in a long document, there is an increasing…

Abstract

Purpose

Since researchers have utilized text signals to develop a mass of within-document visualization analysis tools for reading aid in a long document, there is an increasing need to study the relationship between readers’ behavior of using text signals for navigation and their reading performance in the tools. The purpose of this paper is to combine the text signals using behavior and reading performance in two kinds of analysis tools to verify their relationship and discover whether there is any efficient reading strategy when using text signals to navigate a long document.

Design/methodology/approach

The methodology is a case study. The authors reviewed related literature first. After explaining the design ideas, interface and functions of THC-DAT and BOOKMARK, which are two reading tools utilizing two main kinds of text signals, one utilizing topics and the other utilizing headings for reading aid, a case study was presented to collect click data on the text signals of participants and their reading effectiveness (score) and efficiency (time).

Findings

The results confirm that the text signals using behavior for navigation has a significant impact on reading efficiency and no impact on reading effectiveness in both BOOKMARK and THC-DAT. The discrete degree of clicks behavior on text signals has an impact on reading efficiency. The using behavior of different types of text signals has different impacts on reading efficiency.

Research limitations/implications

Using text signals for navigation time evenly can help improve reading efficiency. And a basic strategy suggested to readers is focusing on reducing their time to find answers when using text signals for navigation in a long document. As to utilizing the two different kinds of text signals, readers can have different strategies. Accordingly, personalized recommendation based on interval of adjacent clicks will help to improve computer-aided reading tools.

Originality/value

This paper combines the text signals using behavior for navigation and reading performance in two kinds of visual analysis tools, studied the relationship between them and discovers some efficient reading strategies when using text signals for navigation to read a long document.

Details

Library Hi Tech, vol. 35 no. 4
Type: Research Article
ISSN: 0737-8831

Keywords

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Article
Publication date: 7 June 2021

Amir Hosein Keyhanipour and Farhad Oroumchian

Incorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of…

Abstract

Purpose

Incorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of users among which commonly used the triple of attractiveness, examination and satisfaction. Inspired by this fact and considering the psychological definitions of these concepts, this paper aims to propose a novel learning to rank by redefining these concepts. The attractiveness and examination factors could be calculated using a limited subset of information retrieval (IR) features by the random forest algorithm, and then they are combined with each other to predicate the satisfaction factor which is considered as the relevance level.

Design/methodology/approach

The attractiveness and examination factors of a given document are usually considered as its perceived relevance and the fast scan of its snippet, respectively. Here, attractiveness and examination factors are regarded as the click-count and the investigation rate, respectively. Also, the satisfaction of a document is supposed to be the same as its relevance level for a given query. This idea is supported by the strong correlation between attractiveness-satisfaction and the examination-satisfaction. Applying random forest algorithm, the attractiveness and examination factors are calculated using a very limited set of the primitive features of query-document pairs. Then, by using the ordered weighted averaging operator, these factors are aggregated to estimate the satisfaction.

Findings

Experimental results on MSLR-WEB10K and WCL2R data sets show the superiority of this algorithm over the state-of-the-art ranking algorithms in terms of P@n and NDCG criteria. The enhancement is more noticeable in top-ranked items which are reviewed more by the users.

Originality/value

This paper proposes a novel learning to rank based on the redefinition of major building blocks of the CMs which are the attractiveness, examination and satisfactory. It proposes a method to use a very limited number of selected IR features to estimate the attractiveness and examination factors and then combines these factors to predicate the satisfactory which is regarded as the relevance level of a document with respect to a given query.

Details

International Journal of Web Information Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1744-0084

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Article
Publication date: 30 October 2019

Tingting Jiang, Qian Guo, Shunchang Chen and Jiaqi Yang

The headlines of online news are created carefully to influence audience news selection today. The purpose of this paper is to investigate the relationships between news…

Abstract

Purpose

The headlines of online news are created carefully to influence audience news selection today. The purpose of this paper is to investigate the relationships between news headline presentation and users’ clicking behavior.

Design/methodology/approach

Two types of unobtrusive data were collected and analyzed jointly for this purpose. A two-month server log file containing 39,990,200 clickstream records was obtained from an institutional news site. A clickstream data analysis was conducted at the footprint and movement levels, which extracted 98,016 clicks received by 7,120 headlines ever displayed on the homepage. Meanwhile, the presentation of these headlines was characterized from seven dimensions, i.e. position, format, text length, use of numbers, use of punctuation marks, recency and popularity, based on the layout and content crawled from the homepage.

Findings

This study identified a series of presentation characteristics that prompted users to click on the headlines, including placing them in the central T-shaped zones, using images, increasing text length properly for greater clarity, using visually distinctive punctuation marks, and providing recency and popularity indicators.

Originality/value

The findings have valuable implications for news providers in attracting clicks to their headlines. Also, the successful application of nonreactive methods has significant implications for future user studies in both information science and journalism.

Details

Aslib Journal of Information Management, vol. 72 no. 1
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 12 March 2019

Artha Sejati Ananda, Ángel Hernández-García, Emiliano Acquila-Natale and Lucio Lamberti

The purpose of this study is to investigate the perceived exposure of fashion consumers to different types of fashion brands’ social media marketing (SMM) actions in…

Abstract

Purpose

The purpose of this study is to investigate the perceived exposure of fashion consumers to different types of fashion brands’ social media marketing (SMM) actions in social media, and its relationship with the intention to engage in electronic word-of-mouth (eWoM) behaviors.

Design/methodology/approach

The empirical study uses a survey conducted on a stratified random sample of 241 Indonesian members of fashion social media brand communities (SMBCs). The research design includes 19 types of SMM actions and 3 types of eWoM engagement behaviors, and investigates their relationship using point-biserial correlation.

Findings

Generation of intention to engage in “pass-on” and “endorsement” eWoM has different drivers and serves different purposes. The findings suggest that endorsement engagement is contingent on the consumer’s perceived exposure to marketing action stimuli, while pass-on engagement is driven by cognitive-inducing actions.

Research limitations/implications

This study extends current theory on SMM strategy and its relationship with eWoM engagement with a theoretically grounded conceptualization of eWoM engagement behaviors through the use of one-click social plug-ins.

Practical implications

The study offers guidelines for fashion brands to effectively design their SMM strategies by identifying specific drivers of consumers’ intention to engage in eWoM.

Originality/value

This study identifies sources of generation of eWoM engagement behavioral intention from a fine-grained analysis of marketing actions across various fashion SMBCs. Besides, it extends the applicability of the “mere exposure” effect to the SMM context. The research pioneers the study on fashion consumers’ eWoM engagement behaviors in Indonesia, a country with one of the largest social media populations.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 31 no. 2
Type: Research Article
ISSN: 1355-5855

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Article
Publication date: 17 May 2018

Kristin Stewart, Matt Kammer-Kerwick, Hyeseung Elizabeth Koh and Isabella Cunningham

This paper aims to develop a framework for understanding consumers’ response to digital advertising using the affect transfer hypotheses and incorporating search behaviors

Abstract

Purpose

This paper aims to develop a framework for understanding consumers’ response to digital advertising using the affect transfer hypotheses and incorporating search behaviors. The paper also offers future research suggestions.

Design/methodology/approach

A quantitative approach is used in this paper by conducting survey research on a research panel. Structural equation model with multi-group comparisons is conducted. The research is conducted using a general US population sample.

Findings

Findings demonstrate that the affect transfer hypothesis is sufficient to enhance extant understanding of consumers’ response to digital advertising, but the incorporation of search intentions into the model improves the explanatory power.

Originality/value

To date, little research in digital marketing has studied search intentions and less has done so in the context of digital video advertising. Interestingly, theory from a more traditional domain can lends support for the authors hypotheses.

Details

Journal of Research in Interactive Marketing, vol. 12 no. 2
Type: Research Article
ISSN: 2040-7122

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Article
Publication date: 8 February 2008

Pongsak Hoontrakul and Sunil Sahadev

To describe the process of customer segmentation by data mining and expert judgment in a real‐world setting.

Abstract

Purpose

To describe the process of customer segmentation by data mining and expert judgment in a real‐world setting.

Design/methodology/approach

Data collected in four case studies of on‐line enquiries via one web‐based intermediary and customer profiling were used as the input to K‐means clustering calculations relating to four tourist destinations in Thailand, two already familiar internationally and two less so.

Findings

The case study illustrates the use of data mining techniques to unravel the basic pattern of customer enquiries across various attributes, as an input to actionable strategies.

Research limitations/implications

The methodology limits inferences to the single organization studied across the four destinations.

Practical implications

The findings suggest a practical planning strategy for customer segmentation in similar on‐line situations. The methodology incorporates both qualitative and quantitative phases, and can be easily be applied in practice.

Originality/value

The paper, focusing on Thailand, presents an application of data mining techniques in the on‐line travel industry.

Details

Marketing Intelligence & Planning, vol. 26 no. 1
Type: Research Article
ISSN: 0263-4503

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