Search results

Purpose — To provide an overview of recent research that examined how search engine users evaluate and select Web search results and how alternative search engine interfaces can support Web users' credibility assessment of Web search results.

Design/methodology/approach — As theoretical background, Information Foraging Theory (Pirolli, 2007; Pirolli & Card, 1999) from cognitive science and Prominence-Interpretation-Theory (Fogg, 2003) from communication and persuasion research are presented. Furthermore, a range of recent empirical research that investigated the effects of alternative SERP layouts on searchers' information quality or credibility assessments of search results are reviewed and approaches that aim at automatically classifying search results according to specific genre categories are reported.

Findings — The chapter reports on findings that Web users often rely heavily on the ranking provided by the search engines without paying much attention to the reliability or trustworthiness of the Web pages. Furthermore, the chapter outlines how alternative search engine interfaces that display search results in a format different from a list and/or provide prominent quality-related cues in the SERPs can foster searchers' credibility evaluations.

Research limitations/implications — The reported empirical studies, search engine interfaces, and Web page classification systems are not an exhaustive list.

Originality/value — The chapter provides insights for researchers, search engine developers, educators, and students on how the development and use of alternative search engine interfaces might affect Web users' search and evaluation strategies during Web search as well as their search outcomes in terms of retrieving high-quality, credible information.

Numerous research questions in e-HRM research are directly related to the usage of diverse information systems by HR professionals, line managers, employees, and/or applicants. Since they are regularly based on Internet technologies, information systems in e-HRM automatically store detailed usage data in log files of web servers. Subsumed as “web mining,” such data are frequently used as inputs for innovative data analysis in e-commerce practice. Though also promising in empirical e-HRM research, web mining is neither discussed nor applied in this area at present. Our chapter therefore aims at a methodological evaluation of web mining as an e-HRM research approach. After introducing web mining as a possible approach in e-HRM research, we examine its applicability by discussing available data, feasible methods, coverable topics, and confirmable privacy. Subsequently, we classify the approach methodologically by examining major issues. Our evaluation reveals that “web mining” constitutes a promising additional research approach that enables research to answer numerous relevant questions related to the actual usage of information systems in e-HRM.

This chapter aims to discuss the issues associated with social indexing as a solution to the challenges of current information organization systems by investigating the quality and efficacy of social indexing.

The chapter focuses on the study which compared indexing similarity between two professional groups and also compared social tagging and professional indexing. The study employed the method of the modified vector-based Indexing Consistency Density (ICD) with three different similarity measures: cosine similarity, dot product similarity, and Euclidean distance metric.

The investigation of social indexing in comparison of professional indexing demonstrates that social tags are more accurate descriptions of resources and reflection of more current terminology than controlled vocabulary. Through the characteristics of social tagging discussed in this chapter, we have a clearer understanding of the extent to which social indexing can be used to replace and improve upon professional indexing.

As investment in professionally developed web directories diminishes, it becomes even more critical to understand the characteristics of social tagging and to obtain benefit from it. In future research, the examination of subjective tags needs to be conducted. A survey or user study on tagging behavior also would help to extend understanding of social indexing practices.

This study develops a typology of web site structure and then makes a cross-national comparison between Korea and Australia. Using a content analysis method, the study classifies 383 corporate web sites based on the typology. The study identifies two general types of web site structure: the hypermedia type and static image type and uses cluster analysis and discriminant analysis to verify the results. The study then tests predictions as to which type of web site will be more prominent in Australia versus Korea based on cultural factors. Results show that Korean firms employ the hypermedia type more frequently, whereas Australian companies are more prone to use the static image type. Cultural factors and industry-based factors are used to explain the results.

Purpose – This chapter examines how sentiment analysis and web-crawling technology can be used to conduct large-scale data analyses of extremist content online.

Methods/approach – The authors describe a customized web-crawler that was developed for the purpose of collecting, classifying, and interpreting extremist content online and on a large scale, followed by an overview of a relatively novel machine learning tool, sentiment analysis, which has sparked the interest of some researchers in the field of terrorism and extremism studies. The authors conclude with a discussion of what they believe is the future applicability of sentiment analysis within the online political violence research domain.

Findings – In order to gain a broader understanding of online extremism, or to improve the means by which researchers and practitioners “search for a needle in a haystack,” the authors recommend that social scientists continue to collaborate with computer scientists, combining sentiment analysis software with other classification tools and research methods, as well as validate sentiment analysis programs and adapt sentiment analysis software to new and evolving radical online spaces.

Originality/value – This chapter provides researchers and practitioners who are faced with new challenges in detecting extremist content online with insights regarding the applicability of a specific set of machine learning techniques and research methods to conduct large-scale data analyses in the field of terrorism and extremism studies.

This chapter demonstrates how the University of Waikato in New Zealand adapted a global standard (the Library of Congress Classification) for local use by inscribing topics related to and about Māori history and people.

The findings are the result of using library catalogs and classifications as primary historical documents.

The University of Waikato’s classification simultaneously uses and implicitly critiques a universal system written from a U.S. vantage point. It seems to acknowledge the benefits and necessities of using a globally recognized standard, as well as a need to inscribe local, anticolonial perspectives into that system.

The research relies on historical documents, and some aspects related to purpose and attribution are difficult to ascertain.

The local adaptation of the Library of Congress Classification may serve as a model for other local adaptations.

This may bring new dimensions to thinking about colonialism and anticolonialism in knowledge organization systems. It contributes to ongoing conversations regarding indigenous knowledge organization practices.

Although scholars have examined Māori subject headings, research on local shelf classifications in New Zealand have not been objects of study in the context of global and local knowledge organization. This chapter brings an important classification to light.

Purpose — To develop methodologies to evaluate search engines according to an individual's preference in an easy and reliable manner, and to formulate user-oriented metrics to compare freshness and duplication in search results.

Design/methodology/approach — A personalised evaluation model for comparing search engines is designed as a hierarchy of weighted parameters. These commonly found search engine features and performance measures are given quantitative and qualitative ratings by an individual user. Furthermore, three performance measurement metrics are formulated and presented as histograms for visual inspection. A methodology is introduced to quantitatively compare and recognise the different histogram patterns within the context of search engine performance.

Findings — Precision and recall are the fundamental measures used in many search engine evaluations due to their simplicity, fairness and reliability. Most recent evaluation models are user oriented and focus on relevance issues. Identifiable statistical patterns are found in performance measures of search engines.

Research limitations/implications — The specific parameters used in the evaluation model could be further refined. A larger scale user study would confirm the validity and usefulness of the model. The three performance measures presented give a reasonably informative overview of the characteristics of a search engine. However, additional performance parameters and their resulting statistical patterns would make the methodology more valuable to the users.

Practical implications — The easy-to-use personalised search engine evaluation model can be tailored to an individual's preference and needs simply by changing the weights and modifying the features considered. A user is able to get an idea of the characteristics of a search engine quickly using the quantitative measure of histogram patterns that represent the search performance metrics introduced.

Originality/value — The presented work is considered original as one of the first search engine evaluation models that can be personalised. This enables a Web searcher to choose an appropriate search engine for his/her needs and hence finding the right information in the shortest time with the least effort.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).