The Influence of Information Types and Search Intention in Using Branded Or Generic Search Query

This study aims to analyze the effect of each type of information and search intention on the use of branded or generic search queries on search engines. This is done because of the emergence of the phenomenon of information overload or explosion of information. The types of tourism information studied are transportation, accommodation, restaurants, shopping places, and tourist attractions with two types of intention, namely purchase intention and intention to search. This study used a quantitative approach by distributing online questionnaires to users who had searched using a search engine and managed to collect 1,256 respondents. Data were analyzed using binary logistic regression. We found that intention influences the use of branded or generic search queries and several types of information influence the use of search queries. Transportation and shopping information could influence users in choosing to use generic or branded search queries

Improving customer generation by analysing website visitor behaviour

This dissertation describes the creation of a new integrated Information Technology (IT) system that assisted in the collection of data about the behaviour of website visitors as well as sales and marketing data for those visitors who turned into customers. A key contribution to knowledge was the creation of a method to predict the outcome of visits to a website from visitors’ browsing behaviour. A new Online Tracking Module (OTM) was created that monitored visitors’ behaviour while they browsed websites. When a visitor converted into a customer, then customer and marketing data as well as sales activity was saved in a new Customer Relationship Management (CRM) system that was implemented in this research. The research focused on service websites. The goal of these websites was to promote products and services online and turn enquiries into offline sales. The challenge faced by these websites was to convince as many visitors as possible to enquire. Most websites relied on Search Engine Optimisation (SEO) and Pay Per Click (PPC) advertising for traffic generation. This research used PPC advertising to generate traffic. An important aspect of PPC advertising was landing page optimisation. The aim of landing page optimisation was to increase the number of visitors to a website who completed a specific action on the website. In the case of the websites investigated in this research the action consisted of completing and sending an enquiry form from the websites. The research looked for meaningful commonalities in the data collected by MS CRM and the OTM and combined this with feedback from the collaborating company’s sales team to create two personas for website visitors who had enquired. Techniques for improving landing pages were identified and these led to changes to landing pages. Some of these changes were targeted at a particular visitor persona. The effect of changes made to a landing page was measured by comparing its conversion rate and bounce rate before and after the changes. Behavioural data collected by the OTM was then analysed using a data mining engine to find models that could predict whether a user would convert based on their browsing behaviour. Models were found that could predict the outcome of a visit to a service website.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Statistical modelling of clickstream behaviour to inform real-time advertising decisions

Online user browsing generates vast quantities of typically unexploited data. Investigating this data and uncovering the valuable information it contains can be of substantial value to online businesses, and statistics plays a key role in this process. The data takes the form of an anonymous digital footprint associated with each unique visitor, resulting in $10^{6}$ unique profiles across $10^{7}$ individual page visits on a daily basis. Exploring, cleaning and transforming data of this scale and high dimensionality (2TB+ of memory) is particularly challenging, and requires cluster computing. We outline a variable selection method to summarise clickstream behaviour with a single value, and make comparisons to other dimension reduction techniques. We illustrate how to apply generalised linear models and zero-inflated models to predict sponsored search advert clicks based on keywords. We consider the problem of predicting customer purchases (known as conversions), from the customer’s journey or clickstream, which is the sequence of pages seen during a single visit to a website. We consider each page as a discrete state with probabilities of transitions between the pages, providing the basis for a simple Markov model. Further, Hidden Markov models (HMMs) are applied to relate the observed clickstream to a sequence of hidden states, uncovering meta-states of user activity. We can also apply conventional logistic regression to model conversions in terms of summaries of the profile’s browsing behaviour and incorporate both into a set of tools to solve a wide range of conversion types where we can directly compare the predictive capability of each model. In real-time, predicting profiles that are likely to follow similar behaviour patterns to known conversions, will have a critical impact on targeted advertising. We illustrate these analyses with results from real data collected by an Audience Management Platform (AMP) - Carbon

Essays in Modeling the Consumer Choice Process

In this dissertation, I utilize and develop empirical tools to help academics and practitioners model the consumer\u27s choice process. This collection of three essays strives to answer three main research questions in this theme. In the first paper, I ask: how is the consumer\u27s purchase decision impacted by the search for general product-category information prior to search for their match with a retailer or manufacturer ( sellers )? This paper studies the impact of informational organic keyword search results on the performance of sponsored search advertising. We show that, even though advertisers can target consumers who have specific needs and preferences, for many consumers this is not a sufficient condition for search advertising to work. By allowing consumers to access content that satisfies their information requirements, informational organic results can allow consumers to learn about the product category prior to making their purchase decision. We develop a model characterize the situation in which consumers can search for general information about the product category as well as for information about the individual sellers\u27 offerings. We estimate this model using a unique dataset of search advertising in which commercial websites are restricted in the organic listing, allowing us to identify consumer clicks as informational (from organic links) or purchase oriented (from sponsored links). With the estimation results, we show that consumer welfare is improved by 29%, while advertisers generate 19% more sales, and search engines obtain 18% more paid clicks, as compared to the scenario without informational links. We conduct counterfactuals and find that consumers, advertisers, and the search engine are significantly better off when the search engine provides free general information about the product. When the search engine provides information about the advertisers\u27 specific offerings, however, there are fewer paid clicks and advertisers at high ad positions will obtain lower sales. We further investigate the implications on the equilibrium advertiser bidding strategy. Results show that advertiser bids will remain constant in the former scenario. When the search engine provides advertiser information, advertisers will increase their bids because of the increased conversion rate; however, the search engine still loses revenue due to the decreased paid clicks. The findings shed important managerial insights on how to improve the effectiveness of search advertising. In the second paper, I ask: how is the consumer\u27s search for information, during their choice process and in an advertising context, influenced by the signaling theory of advertising? Using a dataset of travel-related keywords obtained from a search engine, we test to what extent consumers are searching and advertisers are bidding in accordance with the signaling theory of advertising in literature. We find significant evidence that consumers are more likely to click on advertisers at higher positions because they infer that such advertisers are more likely to match with their needs. Further, consumers are more likely to find a match with advertisers who have paid more for higher positions. We also find strong evidence that advertisers increase their bids when there is an improvement in the likelihood that their offerings match with consumers\u27 needs, and the improvement cannot be readily observed by consumers prior to searching advertisers\u27 websites. These results are consistent with the predictions from the signaling theory. We test several alternative explanations and show that they cannot fully explain the results. Furthermore, through an extension we find that advertisers can generate more clicks when competing against advertisers with higher match value. We offer an explanation for this finding based on the signaling theory. In the third paper, I ask: can we model the consumer\u27s choice of brand as a sequential elimination of alternatives based on shared or unique aspects while incorporating continuous variables, such as price? With aggregate scanner data, marketing researchers typically estimate the mixed logit model, which accounts for non-IIA substitution patterns among brands, which arise due to similarity and dominance effects in demand. Using numerical examples and analytical illustrations, this research shows that the mixed logit model, which is widely believed to be a highly flexible characterization of brand switching behavior, is not well designed to handle non-IIA substitution patterns. The probit allows only for pair-wise inter-brand similarities, and ignores third-order or higher dependencies. In the presence of similarity and dominance effects, the mixed logit model and the probit model yield systematically distorted marketing mix elasticities. This limits the usefulness of mixed logit and probit for marketing decision-making. We propose a more flexible demand model that is an extension of the elimination-by-aspects (EBA) model (Tversky 1972a, 1972b) to handle marketing variables. The model vastly expands the domain of applicability of the EBA model to aggregate scanner data. Using an analytical closed-form that nests the traditional logit model as a special case, the EBA demand model is estimated with marketing variables from aggregate scanner data in 9 different product categories. It is compared to the mixed logit and probit models on the same datasets. In terms of multiple fit and predictive metrics (LL, BIC, MSE, MAD), the EBA model outperforms the mixed logit and the probit in a majority of categories in terms of both in-sample fit and holdout predictions. The results show significant differences in the estimated price elasticity matrices between the EBA model and the comparison models. In addition, a simulation shows that the retailer can improve gross profits up to 34.4% from pricing based on the EBA model rather than the mixed logit model. Finally, the results suggest that empirical IO researchers, who routinely use mixed logit models as inputs to oligopolistic pricing models, should consider the EBA demand model as the appropriate model of demand for differentiated products

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Keyword Competition and Determinants of Ad Position in Sponsored Search Advertising

Given the significant growth of the Internet in recent years, marketers have been striving for new techniques and strategies to prosper in the online world. Statistically, search engines have been the most dominant channels of Internet marketing in recent years. However, the mechanics of advertising in such a market place has created a challenging environment for marketers to position their ads among their competitors. This study uses a unique cross-sectional dataset of the top 500 Internet retailers in North America and hierarchical multiple regression analysis to empirically investigate the effect of keyword competition on the relationship between ad position and its determinants in the sponsored search market. To this end, the study utilizes the literature in consumer search behavior, keyword auction mechanism design, and search advertising performance as the theoretical foundation. This study is the first of its kind to examine the sponsored search market characteristics in a cross-sectional setting where the level of keyword competition is explicitly captured in terms of the number of Internet retailers competing for similar keywords. Internet retailing provides an appropriate setting for this study given the high-stake battle for market share and intense competition for keywords in the sponsored search market place. The findings of this study indicate that bid values and ad relevancy metrics as well as their interaction affect the position of ads on the search engine result pages (SERPs). These results confirm some of the findings from previous studies that examined sponsored search advertising performance at a keyword level. Furthermore, the study finds that the position of ads for web-only retailers is dependent on bid values and ad relevancy metrics, whereas, multi-channel retailers are more reliant on their bid values. This difference between web-only and multi-channel retailers is also observed in the moderating effect of keyword competition on the relationships between ad position and its key determinants. Specifically, this study finds that keyword competition has significant moderating effects only for multi-channel retailers

Matching Possible Mitigations to Cyber Threats: A Document-Driven Decision Support Systems Approach

Cyber systems are ubiquitous in all aspects of society. At the same time, breaches to cyber systems continue to be front-page news (Calfas, 2018; Equifax, 2017) and, despite more than a decade of heightened focus on cybersecurity, the threat continues to evolve and grow, costing globally up to $575 billion annually (Center for Strategic and International Studies, 2014; Gosler & Von Thaer, 2013; Microsoft, 2016; Verizon, 2017). To address possible impacts due to cyber threats, information system (IS) stakeholders must assess the risks they face. Following a risk assessment, the next step is to determine mitigations to counter the threats that pose unacceptably high risks. The literature contains a robust collection of studies on optimizing mitigation selections, but they universally assume that the starting list of appropriate mitigations for specific threats exists from which to down-select. In current practice, producing this starting list is largely a manual process and it is challenging because it requires detailed cybersecurity knowledge from highly decentralized sources, is often deeply technical in nature, and is primarily described in textual form, leading to dependence on human experts to interpret the knowledge for each specific context. At the same time cybersecurity experts remain in short supply relative to the demand, while the delta between supply and demand continues to grow (Center for Cyber Safety and Education, 2017; Kauflin, 2017; Libicki, Senty, & Pollak, 2014). Thus, an approach is needed to help cybersecurity experts (CSE) cut through the volume of available mitigations to select those which are potentially viable to offset specific threats. This dissertation explores the application of machine learning and text retrieval techniques to automate matching of relevant mitigations to cyber threats, where both are expressed as unstructured or semi-structured English language text. Using the Design Science Research Methodology (Hevner & March, 2004; Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007), we consider a number of possible designs for the matcher, ultimately selecting a supervised machine learning approach that combines two techniques: support vector machine classification and latent semantic analysis. The selected approach demonstrates high recall for mitigation documents in the relevant class, bolstering confidence that potentially viable mitigations will not be overlooked. It also has a strong ability to discern documents in the non-relevant class, allowing approximately 97% of non-relevant mitigations to be excluded automatically, greatly reducing the CSE’s workload over purely manual matching. A false v positive rate of up to 3% prevents totally automated mitigation selection and requires the CSE to reject a few false positives. This research contributes to theory a method for automatically mapping mitigations to threats when both are expressed as English language text documents. This artifact represents a novel machine learning approach to threat-mitigation mapping. The research also contributes an instantiation of the artifact for demonstration and evaluation. From a practical perspective the artifact benefits all threat-informed cyber risk assessment approaches, whether formal or ad hoc, by aiding decision-making for cybersecurity experts whose job it is to mitigate the identified cyber threats. In addition, an automated approach makes mitigation selection more repeatable, facilitates knowledge reuse, extends the reach of cybersecurity experts, and is extensible to accommodate the continued evolution of both cyber threats and mitigations. Moreover, the selection of mitigations applicable to each threat can serve as inputs into multifactor analyses of alternatives, both automated and manual, thereby bridging the gap between cyber risk assessment and final mitigation selection

Query-Time Data Integration

Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to traditional up-front integration. It aims at enabling users to issue ad-hoc queries on their own data as if all potential other data sources were already integrated, without declaring specific sources and mappings to use. Automated data search and integration methods are then coupled directly with query processing on the available data. The ambiguity and uncertainty introduced through fully automated retrieval and mapping methods is compensated by answering those queries with ranked lists of alternative results. Each result is then based on different data sources or query interpretations, allowing users to pick the result most suitable to their information need. To this end, this thesis makes three main contributions. Firstly, we introduce a novel method for Top-k Entity Augmentation, which is able to construct a top-k list of consistent integration results from a large corpus of heterogeneous data sources. It improves on the state-of-the-art by producing a set of individually consistent, but mutually diverse, set of alternative solutions, while minimizing the number of data sources used. Secondly, based on this novel augmentation method, we introduce the DrillBeyond system, which is able to process Open World SQL queries, i.e., queries referencing arbitrary attributes not defined in the queried database. The original database is then augmented at query time with Web data sources providing those attributes. Its hybrid augmentation/relational query processing enables the use of ad-hoc data search and integration in data analysis queries, and improves both performance and quality when compared to using separate systems for the two tasks. Finally, we studied the management of large-scale dataset corpora such as data lakes or Open Data platforms, which are used as data sources for our augmentation methods. We introduce Publish-time Data Integration as a new technique for data curation systems managing such corpora, which aims at improving the individual reusability of datasets without requiring up-front global integration. This is achieved by automatically generating metadata and format recommendations, allowing publishers to enhance their datasets with minimal effort. Collectively, these three contributions are the foundation of a Query-time Data Integration architecture, that enables ad-hoc data search and integration queries over large heterogeneous dataset collections

Local inquiry and alternative knowledge: cognitive dimensions of the sustainable agriculture movement

This study analyzes the experiential and cognitive practices of a group of alternative agriculturists in the North Central region as a concrete expression of a grassroots social movement, which is critical of conventional agriculture, and seeks a new knowledge base for social change in the U.S. agricultural landscape. The study inventories inquiries that alternative agriculturists in the North Central region make to ATTRA (Appropriate Technology Transfer to Rural Areas), a USDA funded alternative agricultural information center based in Fayetteville, Arkansas. The purpose is to determine whether the ideational and value dimensions of the inquiries are congruent with the knowledge interests reflected in alternative agriculturists experimental and cognitive activities. Two theoretical perspectives, social movement theory and sociology of knowledge, ground the study;Aided by NUD * IST, a qualitative research software, I employ an interpretive, hermeneutically informed process of reading and interpreting symbolic meanings embedded in textual discourse to analyze the experiential and cognitive practices of alternative agriculturists. I also use a keyword search strategy to determine the ideational and value dimensions of inquiries to ATTRA;Alternative agriculturists\u27 cognitive practices show ideological precepts about stewardship, social justice, and human relationships with nature, are not necessarily irreconcilable with reason and creativity. Farmers are creating and disseminating critical new knowledge. However, that creative process is firmly guided and shaped by underlying ideological world-views about their ecological and social environments. The processes are integrally linked. They also neither reject the possibility of conventional scientific agricultural knowledge nor reify local knowledge. Rather, they continue to learn through their local and situation specific experimentation, the most effective approaches to selectively combine diverse kinds of agricultural knowledge in ways that work for them both philosophically and pragmatically;While the imperatives of financial and material sustainability underpin initial shifts towards alternative management practices, the interpretive analysis illuminate a concern for the quality and perpetuity of our environmental capitals---water quality, soil quality and ecological diversity, and social capital;This analysis also crystallizes the functional importance of resources in risky shifts. As farmers transition to alternative agricultural systems, institutional support structures are pivotal in sustaining farmer behavior