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Article

Yanwu Yang, Xin Li, Daniel Zeng and Bernard J. Jansen

The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are…

Abstract

Purpose

The purpose of this paper is to model group advertising decisions, which are the collective decisions of every single advertiser within the set of advertisers who are competing in the same auction or vertical industry, and examine resulting market outcomes, via a proposed simulation framework named Experimental Platform for Search Engine Advertising (EXP-SEA) supporting experimental studies of collective behaviors in the context of search engine advertising.

Design/methodology/approach

The authors implement the EXP-SEA to validate the proposed simulation framework, also conduct three experimental studies on the aggregate impact of electronic word-of-mouth (eWOM), the competition level and strategic bidding behaviors. EXP-SEA supports heterogeneous participants, various auction mechanisms and also ranking and pricing algorithms.

Findings

Findings from the three experiments show that both the market profit and advertising indexes such as number of impressions and number of clicks are larger when the eWOM effect is present, meaning social media certainly has some effect on search engine advertising outcomes, the competition level has a monotonic increasing effect on the market performance, thus search engines have an incentive to encourage both the eWOM among search users and competition among advertisers, and given the market-level effect of the percentage of advertisers employing a dynamic greedy bidding strategy, there is a cut-off point for strategic bidding behaviors.

Originality/value

This is one of the first research works to explore collective group decisions and resulting phenomena in the complex context of search engine advertising via developing and validating a simulation framework that supports assessments of various advertising strategies and estimations of the impact of mechanisms on the search market.

Details

Internet Research, vol. 28 no. 4
Type: Research Article
ISSN: 1066-2243

Keywords

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Article

Partha Mukherjee and Bernard J. Jansen

It is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth…

Abstract

Purpose

It is important to measure the interaction between conversing in social media and searching on the web in order to understand the impact on electronic word-of-mouth marketing. The paper aims to discuss this issue.

Design/methodology/approach

The authors research the relationship between social media conversing and web searching concerning brands on three major social soundtrack platforms (Instagram, Twitter, and Tumblr) and on a major web search engine (Google). The authors examine the effects of changes in both volume and attitude of conversing and volume of searching for two phases (Pre and Post) concerning brands in commercials aired during Super Bowl XLIX. The authors perform Granger causality testing and panel data regression analysis to determine the causal relationship between social media conversing and web searching.

Findings

Results show that volume and attitude of social media conversing has a significant causality relationship to the volume of web searching. Each unit increase of volume on Twitter, Instagram, and Tumblr significantly increases Google search volume for the same brands by 4.7 times, 11.9 times, and 8.7 times, respectively. Each unit increase of attitude score on Twitter significantly increases web search volume 3.96 times, while for Tumblr, search volume significantly increases 0.95 times with each unit. Interestingly, search volume also has a significant causality relationship on the volume of social media postings.

Originality/value

This research seeks to understand the commercial impacts of the interaction among broadcast advertising, social media conversing, and web searching for which there is limited prior work, especially in the context of a major media event.

Details

Internet Research, vol. 27 no. 5
Type: Research Article
ISSN: 1066-2243

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Article

Amanda Spink, Judy Bateman and Bernard J. Jansen

Web search services are now a major source of information for a growing number of people. We need to know more about how users search Web search engines to improve the…

Abstract

Web search services are now a major source of information for a growing number of people. We need to know more about how users search Web search engines to improve the effectiveness of their information retrieval. This paper reports results from a major study exploring users’ information searching behavior on the EXCITE Web search engine. The study is the first to investigate Web users’ successive searching behavior as they conduct related searches

Details

Internet Research, vol. 9 no. 2
Type: Research Article
ISSN: 1066-2243

Keywords

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Article

Ashish Kathuria, Bernard J. Jansen, Carolyn Hafernik and Amanda Spink

Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some…

Abstract

Purpose

Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries.

Design/methodology/approach

For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k‐means clustering approach based on a variety of query traits.

Findings

The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational.

Research limitations/implications

This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs.

Practical implications

The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research.

Originality/value

This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay‐off for web search engines can be quite beneficial.

Details

Internet Research, vol. 20 no. 5
Type: Research Article
ISSN: 1066-2243

Keywords

Content available
Article

Anette Rantanen, Joni Salminen, Filip Ginter and Bernard J. Jansen

User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these…

Abstract

Purpose

User-generated social media comments can be a useful source of information for understanding online corporate reputation. However, the manual classification of these comments is challenging due to their high volume and unstructured nature. The purpose of this paper is to develop a classification framework and machine learning model to overcome these limitations.

Design/methodology/approach

The authors create a multi-dimensional classification framework for the online corporate reputation that includes six main dimensions synthesized from prior literature: quality, reliability, responsibility, successfulness, pleasantness and innovativeness. To evaluate the classification framework’s performance on real data, the authors retrieve 19,991 social media comments about two Finnish banks and use a convolutional neural network (CNN) to classify automatically the comments based on manually annotated training data.

Findings

After parameter optimization, the neural network achieves an accuracy between 52.7 and 65.2 percent on real-world data, which is reasonable given the high number of classes. The findings also indicate that prior work has not captured all the facets of online corporate reputation.

Practical implications

For practical purposes, the authors provide a comprehensive classification framework for online corporate reputation, which companies and organizations operating in various domains can use. Moreover, the authors demonstrate that using a limited amount of training data can yield a satisfactory multiclass classifier when using CNN.

Originality/value

This is the first attempt at automatically classifying online corporate reputation using an online-specific classification framework.

Details

Internet Research, vol. 30 no. 1
Type: Research Article
ISSN: 1066-2243

Keywords

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Article

Amanda Spink, Bernard J. Jansen and H. Cenk Ozmultu

Examines the use of query reformulation, and particularly the use of relevance feedback by users of the Excite Web search engine. A total of 985 user search sessions from…

Abstract

Examines the use of query reformulation, and particularly the use of relevance feedback by users of the Excite Web search engine. A total of 985 user search sessions from a data set of 18,113 user search sessions containing 51,473 queries were examined. Includes a qualitative and quantitative analysis of 191 user sessions including more than one query, to examine patterns of user query reformulation; and second, all 804 user sessions including relevance feedback were examined. Results show limited use of query reformulation and relevance feedback by Excite users – only one in five users reformulated queries. Most relevance feedback sessions were successful. Identifies the most common pattern of searching and discusses implications for Web search system design.

Details

Internet Research, vol. 10 no. 4
Type: Research Article
ISSN: 1066-2243

Keywords

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Article

Bernard J. Jansen and Theresa B. Clarke

This research is based on the premise that current metrics for search engine advertising (SEA) are misleading and do not sufficiently allow managers to evaluate traffic…

Abstract

Purpose

This research is based on the premise that current metrics for search engine advertising (SEA) are misleading and do not sufficiently allow managers to evaluate traffic and conversions simultaneously. This study aimed to conceptually develop and assess conversion potential (CvP) as a unifying construct for both measuring and evaluating the performance of SEA campaigns.

Design/methodology/approach

A data set of nearly seven million records covering almost three years of a multi-million-dollar keyword marketing campaign from a major US retailer was used to validate the construct of CvP.

Findings

Results empirically validate how CvP measures both campaign traffic and sales in SEA, using the optimization factor of ad rank, which is one of many possible factors.

Research limitations/implications

Although the data set is large and covers a lengthy period of time, it is limited to one company in the retail sector.

Practical implications

The research instantiates CvP as a metric for overall SEA account performance while demonstrating that it is a practical tool for future campaign planning. The metric simultaneously incorporates a sales ratio and a traffic ratio.

Originality/value

This is the first study to formalize and provide a working definition of CvP in the academic literature. The contribution is a theoretical and practical managerial framework to mutually evaluate, measure and make decisions about SEA efforts.

Details

Journal of Research in Interactive Marketing, vol. 11 no. 2
Type: Research Article
ISSN: 2040-7122

Keywords

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Article

Amanda Spink and Bernard J. Jansen

The purpose of this research is to show that federated content collections are important for providing access to multiple content repositories, including image, video…

Abstract

Purpose

The purpose of this research is to show that federated content collections are important for providing access to multiple content repositories, including image, video, audio and Web sites.

Design/methodology/approach

This paper presents findings from an analysis of differences in users' Web searching patterns as they access various federated content collections. A dataset of 4,056,374 records submitted to the Dogpile.com Web meta‐search engine were analysed. An analysis was conducted of search session length, query length, number of results pages viewed, use of systems' assistance and the frequency of repeat queries.

Findings

Overall, users entered two to three terms per query and examined only the first pages of results. However, findings include differences in users' access patterns to various content collections. Web, news and audio queries were longer sessions but shorter queries. More users seeking images and videos sought systems assistance.

Originality/value

This is a large‐scale original study using data from a commercial Web search engine. The paper provides a valuable comparison of different types of search – text v. audio, image, etc.

Details

Online Information Review, vol. 30 no. 5
Type: Research Article
ISSN: 1468-4527

Keywords

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Article

Bernard J. Jansen, Karen J. Jansen and Amanda Spink

The web is now a significant component of the recruitment and job search process. However, very little is known about how companies and job seekers use the web, and the…

Abstract

Purpose

The web is now a significant component of the recruitment and job search process. However, very little is known about how companies and job seekers use the web, and the ultimate effectiveness of this process. The specific research questions guiding this study are: how do people search for job‐related information on the web? How effective are these searches? And how likely are job seekers to find an appropriate job posting or application?

Design/methodology/approach

The data used to examine these questions come from job seekers submitting job‐related queries to a major web search engine at three points in time over a five‐year period.

Findings

Results indicate that individuals seeking job information generally submit only one query with several terms and over 45 percent of job‐seeking queries contain a specific location reference. Of the documents retrieved, findings suggest that only 52 percent are relevant and only 40 percent of job‐specific searches retrieve job postings.

Research limitations/implications

This study provides an important contribution to web research and online recruiting literature. The data come from actual web searches, providing a realistic glimpse into how job seekers are actually using the web.

Practical implications

The results of this research can assist organizations in seeking to use the web as part of their recruiting efforts, in designing corporate recruiting web sites, and in developing web systems to support job seeking and recruiting.

Originality/value

This research is one of the first studies to investigate job searching on the web using longitudinal real world data.

Details

Internet Research, vol. 15 no. 1
Type: Research Article
ISSN: 1066-2243

Keywords

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Article

Bernard J. Jansen

The purpose of this paper is to examine the way in which end user searching on the web has become the primary method of locating digital images for many people. This paper…

Abstract

Purpose

The purpose of this paper is to examine the way in which end user searching on the web has become the primary method of locating digital images for many people. This paper seeks to investigate how users structure these image queries.

Design/methodology/approach

This study investigates the structure and formation of image queries on the web by mapping a sample of web queries to three known query classification schemes for image searching (i.e. Enser and McGregor, Jörgensen, and Chen).

Findings

The results indicate that the features and attributes of web image queries differ relative to image queries utilized on other information retrieval systems and by other user populations. This research points to the need for five additional attributes (i.e. collections, pornography, presentation, URL, and cost) in order to classify web image queries, which were not present in any of the three prior classification schemes.

Research limitations/implications

Patterns in web searching for image content do emerge that inform the design of web‐based multimedia systems, namely, that there is a high interest in locating image collections by web searchers. Objects and people images are the predominant interest for web searchers. Cost is a factor for web searching. This knowledge of the structure of web image queries has implications for the design of image information retrieval systems and repositories, especially in the area of automatic tagging of images with metadata.

Originality/value

This is the first research that examines whether or not one can apply image query classifications schemes to web image queries.

Details

Journal of Documentation, vol. 64 no. 1
Type: Research Article
ISSN: 0022-0418

Keywords

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