Search engine advertising in web retailing : an efficiency analysis

This study examines the efficiency of search engine advertising strategies employed by firms. The research setting is the online retailing industry, which is characterized by extensive use of Web technologies and high competition for market share and profitability. For Internet retailers, search engines are increasingly serving as an information gateway for many decision-making tasks. In particular, Search engine advertising (SEA) has opened a new marketing channel for retailers to attract new customers and improve their performance. In addition to natural (organic) search marketing strategies, search engine advertisers compete for top advertisement slots provided by search brokers such as Google and Yahoo! through keyword auctions. The rationale being that greater visibility on a search engine during a keyword search will capture customers' interest in a business and its product or service offerings. Search engines account for most online activities today. Compared with the slow growth of traditional marketing channels, online search volumes continue to grow at a steady rate. According to the Search Engine Marketing Professional Organization, spending on search engine marketing by North American firms in 2008 was estimated at $13.5 billion. Despite the significant role SEA plays in Web retailing, scholarly research on the topic is limited. Prior studies in SEA have focused on search engine auction mechanism design. In contrast, research on the business value of SEA has been limited by the lack of empirical data on search advertising practices. Recent advances in search and retail technologies have created datarich environments that enable new research opportunities at the interface of marketing and information technology. This research uses extensive data from Web retailing and Google-based search advertising and evaluates Web retailers' use of resources, search advertising techniques, and other relevant factors that contribute to business performance across different metrics. The methods used include Data Envelopment Analysis (DEA), data mining, and multivariate statistics. This research contributes to empirical research by analyzing several Web retail firms in different industry sectors and product categories. One of the key findings is that the dynamics of sponsored search advertising vary between multi-channel and Web-only retailers. While the key performance metrics for multi-channel retailers include measures such as online sales, conversion rate (CR), c1ick-through-rate (CTR), and impressions, the key performance metrics for Web-only retailers focus on organic and sponsored ad ranks. These results provide a useful contribution to our organizational level understanding of search engine advertising strategies, both for multi-channel and Web-only retailers. These results also contribute to current knowledge in technology-driven marketing strategies and provide managers with a better understanding of sponsored search advertising and its impact on various performance metrics in Web retailing.

Is more information always good? : investigating the impact of website interface features on e-retailer's sales performance

A number of frameworks have been suggested for online retailing, but still there exists little consensus among researchers and practitioners regarding the appropriate amount of information critical and essential to the improvement of customers' satisfaction and their purchase intention. Against this backdrop, this study contributes to the current practical and theoretical discussions and conversations about how information search and perceived risk theories can be applied to the management of online retailer website features. This paper examines the moderating role of website personalization in studying the relationship between information content provided on the top US retailers' websites, and customer satisfaction and purchase intention. The study also explores the role played by customer satisfaction and purchase intention in studying the relationship between information that is personalized to the needs of individual customers and online retailers' sales performance. Results indicate that the extent of information content features presented to online customers alone is not enough for companies looking to satisfy and motivate customers to purchase. However, information that is targeted to an individual customer influences customer satisfaction and purchase intention, and customer satisfaction in tum serves as a driver to the retailer's online sales performance.

The von Neumann Minimax Theorem and its relatives and a study of externality in on-line auctions

This work consists of a theoretical part and an experimental one. The first part provides a simple treatment of the celebrated von Neumann minimax theorem as formulated by Nikaid6 and Sion. It also discusses its relationships with fundamental theorems of convex analysis. The second part is about externality in sponsored search auctions. It shows that in these auctions, advertisers have externality effects on each other which influence their bidding behavior. It proposes Hal R.Varian model and shows how adding externality to this model will affect its properties. In order to have a better understanding of the interaction among advertisers in on-line auctions, it studies the structure of the Google advertisements networ.k and shows that it is a small-world scale-free network.

Assessing evaluative measures for Internet-based courseware

Although there is a consensus in th~ literature on the many uses of the Internet in education, as well as the unique features of the Internet for presenting facts and information, there is no consensus on a standardized method for evaluating Internetbased courseware. Educators rarely have the opportunity to participate in the development of Internet-based courseware, yet they are encouraged to use the technology in their learning environments. This creates a need for summative evaluation methods for Internet-based health courseware. The purpose ofthis study was to assess evaluative measures for Internet-based courseware. Specifically, two entities were evaluated within the study: a) the outcome of the Internet-based courseware, and b) the Internet-based courseware itself. To this end, the Web site www.bodymatters.com was evaluated using two different approaches by two different cohorts. The first approach was a performance appraisal by a group of endusers. A positive, statistically significant change in the students performance was observed due to the intervention ofthe Web site. The second approach was a productoriented evaluation ofthe Web site with the use of a criterion-based checklist and an open-ended comments section. The findings indicate that a summative, criterion-based evaluation is best completed by a multidisciplinary team. The findi~gs also indicated that the two different cohorts reported different product-oriented appraisals of the Web site. The current research confirmed previous research that found that experts returning a poor evaluation of a Web site did not have a relationship to whether or not the end-users performance improved due to the intervention of the Web site.

Elementary computer-mediated online distance education : identification of criteria for successful implementation and practice

This study had three purposes related to the effective implem,entation and practice of computer-mediated online distance education (C-MODE) at the elementary level: (a) To identify a preliminary framework of criteria 'or guidelines for effective implementation and practice, (b) to identify areas ofC-MODE for which criteria or guidelines of effectiveness have not yet been developed, and (c) to develop an implementation and practice criteria questionnaire based on a review of the distance education literature, and to use the questionnaire in an exploratory survey of elementary C-MODE practitioners. Using the survey instrument, the beliefs and attitudes of 16 elementary C'- MODE practitioners about what constitutes effective implementation and practice principles were investigated. Respondents, who included both administrators and instructors, provided information about themselves and the program in which they worked. They rated 101 individual criteria statenlents on a 5 point Likert scale with a \. point range that included the values: 1 (Strongly Disagree), 2 (Disagree), 3 (Neutral or Undecided), 4 (Agree), 5 (Strongly Agree). Respondents also provided qualitative data by commenting on the individual statements, or suggesting other statements they considered important. Eighty-two different statements or guidelines related to the successful implementation and practice of computer-mediated online education at the elementary level were endorsed. Response to a small number of statements differed significantly by gender and years of experience. A new area for investigation, namely, the role ofparents, which has received little attention in the online distance education literature, emerged from the findings. The study also identified a number of other areas within an elementary context where additional research is necessary. These included: (a) differences in the factors that determine learning in a distance education setting and traditional settings, (b) elementary students' ability to function in an online setting, (c) the role and workload of instructors, (d) the importance of effective, timely communication with students and parents, and (e) the use of a variety of media.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Novices' learning from the Internet : an exploration of navigation behaviours, learner-related factors, and mental effort

The current study was an exploration of why some novices are more successful than their peers when learning from the Internet by examining the relations among time spent with relevant information and changes in invested mental effort during Internet navigations as well as achievement. Navigation behaviours and learner characteristics were investigated as predictors of time spent with relevant information and changes in mental effort. Undergraduates (N = 85, Mage = 20 years, 5 months) searched the Internet for information corresponding to a low knowledge topic for 20 min while their eye gaze and pupil size were recorded. Pupil diameter was used as an objective, continuous measure of mental effort. Participants also completed questionnaires or computer tasks pertaining to s e l f-regulated learning characteristics (general intrinsic goal orientation and effort regulation) and cognitive factors (working memory control, distractibility and cognitive style). All analyses controlled for general mental ability, reading comprehension, topic and Internet knowledge, and overall motivation. A greater proportion of time spent with relevant information predicted higher scores on an achievement test. Interestingly, time spent with relevant information partially mediated the positive relation between the frequency of increases in invested mental effort and achievement. Surprisingly, intrinsic goal orientation was negatively related to time spent with relevant information and effort regulation was negatively related to the frequency of increases in invested mental effort. These findings have implications for supports when novices guide their own learning, especially when using the Internet.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.