Review of Apple iPad news applications : new media service

The launch of the Apple iPad on January 2010 has seen considerable interest from the newspaper and publishing industry in developing content and business models for the tablet PC device that can address the limits of both the print and online news and information media products. It is early days in the iPad’s evolution, and we wait to see what competitor devices will emerge in the near future. It is apparent, however, that it has become a significant “niche” product, with considerable potential for mass market expansion over the next few years, possibly at the expense of netbook sales. The scope for the iPad and tablet PCs to become a “fourth screen” for users, alongside the TV, PC and mobile phone, is in early stages of evolution. The study used five criteria to assess iPad apps: • Content: timeliness; archive; personalisation; content depth; advertisements; the use of multimedia; and the extent to which the content was in sync with the provider brand. • Useability: degree of static content; ability to control multimedia; file size; page clutter; resolution; signposts; and customisation. • Interactivity: hyperlinks; ability to contribute content or provide feedback to news items; depth of multimedia; search function; ability to use plug-ins and linking; ability to highlight, rate and/or save items; functions that may facilitate a community of users. • Transactions capabilities: ecommerce functionality; purchase and download process; user privacy and transaction security. • Openness: degree of linking to outside sources; reader contribution processes; anonymity measures; and application code ownership.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

Doing it better? Overseas online retailers aisles ahead

All the signs are there that Australian retailers are not investing enough in their online operations.

Using conceptual tool for intuitive interaction to design intuitive website for SME in India : A case study

Statistical reports of SMEs Internet usage from various countries indicate a steady growth. However, deeper investigation of SME’s e-commerce adoption and usage reveals that a number of SMEs fail to realize the full potential of e-commerce. Factors such as lack of tools and models in Information Systems and Information Technology for SMEs, and lack of technical expertise and specialized knowledge within and outside the SME have the most effect. This study aims to address the two important factors in two steps. First, introduce the conceptual tool for intuitive interaction. Second, explain the implementation process of the conceptual tool with the help of a case study. The subject chosen for the case study is a real estate SME from India. The design and development process of the website for the real estate SME was captured in this case study and the duration of the study was four months. Results indicated specific benefits for web designers and SME business owners. Results also indicated that the conceptual tool is easy to use without the need for technical expertise and specialized knowledge.

Efficient methods for empirical tests of behavioral economics theories in laboratory and field experiments

In the quest for a descriptive theory of decision-making, the rational actor model in economics imposes rather unrealistic expectations and abilities on human decision makers. The further we move from idealized scenarios, such as perfectly competitive markets, and ambitiously extend the reach of the theory to describe everyday decision making situations, the less sense these assumptions make. Behavioural economics has instead proposed models based on assumptions that are more psychologically realistic, with the aim of gaining more precision and descriptive power. Increased psychological realism, however, comes at the cost of a greater number of parameters and model complexity. Now there are a plethora of models, based on different assumptions, applicable in differing contextual settings, and selecting the right model to use tends to be an ad-hoc process. In this thesis, we develop optimal experimental design methods and evaluate different behavioral theories against evidence from lab and field experiments.

We look at evidence from controlled laboratory experiments. Subjects are presented with choices between monetary gambles or lotteries. Different decision-making theories evaluate the choices differently and would make distinct predictions about the subjects' choices. Theories whose predictions are inconsistent with the actual choices can be systematically eliminated. Behavioural theories can have multiple parameters requiring complex experimental designs with a very large number of possible choice tests. This imposes computational and economic constraints on using classical experimental design methods. We develop a methodology of adaptive tests: Bayesian Rapid Optimal Adaptive Designs (BROAD) that sequentially chooses the "most informative" test at each stage, and based on the response updates its posterior beliefs over the theories, which informs the next most informative test to run. BROAD utilizes the Equivalent Class Edge Cutting (EC²) criteria to select tests. We prove that the EC² criteria is adaptively submodular, which allows us to prove theoretical guarantees against the Bayes-optimal testing sequence even in the presence of noisy responses. In simulated ground-truth experiments, we find that the EC² criteria recovers the true hypotheses with significantly fewer tests than more widely used criteria such as Information Gain and Generalized Binary Search. We show, theoretically as well as experimentally, that surprisingly these popular criteria can perform poorly in the presence of noise, or subject errors. Furthermore, we use the adaptive submodular property of EC² to implement an accelerated greedy version of BROAD which leads to orders of magnitude speedup over other methods.

We use BROAD to perform two experiments. First, we compare the main classes of theories for decision-making under risk, namely: expected value, prospect theory, constant relative risk aversion (CRRA) and moments models. Subjects are given an initial endowment, and sequentially presented choices between two lotteries, with the possibility of losses. The lotteries are selected using BROAD, and 57 subjects from Caltech and UCLA are incentivized by randomly realizing one of the lotteries chosen. Aggregate posterior probabilities over the theories show limited evidence in favour of CRRA and moments' models. Classifying the subjects into types showed that most subjects are described by prospect theory, followed by expected value. Adaptive experimental design raises the possibility that subjects could engage in strategic manipulation, i.e. subjects could mask their true preferences and choose differently in order to obtain more favourable tests in later rounds thereby increasing their payoffs. We pay close attention to this problem; strategic manipulation is ruled out since it is infeasible in practice, and also since we do not find any signatures of it in our data.

In the second experiment, we compare the main theories of time preference: exponential discounting, hyperbolic discounting, "present bias" models: quasi-hyperbolic (α, β) discounting and fixed cost discounting, and generalized-hyperbolic discounting. 40 subjects from UCLA were given choices between 2 options: a smaller but more immediate payoff versus a larger but later payoff. We found very limited evidence for present bias models and hyperbolic discounting, and most subjects were classified as generalized hyperbolic discounting types, followed by exponential discounting.

In these models the passage of time is linear. We instead consider a psychological model where the perception of time is subjective. We prove that when the biological (subjective) time is positively dependent, it gives rise to hyperbolic discounting and temporal choice inconsistency.

We also test the predictions of behavioral theories in the "wild". We pay attention to prospect theory, which emerged as the dominant theory in our lab experiments of risky choice. Loss aversion and reference dependence predicts that consumers will behave in a uniquely distinct way than the standard rational model predicts. Specifically, loss aversion predicts that when an item is being offered at a discount, the demand for it will be greater than that explained by its price elasticity. Even more importantly, when the item is no longer discounted, demand for its close substitute would increase excessively. We tested this prediction using a discrete choice model with loss-averse utility function on data from a large eCommerce retailer. Not only did we identify loss aversion, but we also found that the effect decreased with consumers' experience. We outline the policy implications that consumer loss aversion entails, and strategies for competitive pricing.

In future work, BROAD can be widely applicable for testing different behavioural models, e.g. in social preference and game theory, and in different contextual settings. Additional measurements beyond choice data, including biological measurements such as skin conductance, can be used to more rapidly eliminate hypothesis and speed up model comparison. Discrete choice models also provide a framework for testing behavioural models with field data, and encourage combined lab-field experiments.

Generación dinámica de parrillas de productos en Magento en base al análisis de datos de fuentes abiertas

En este Trabajo de Fin de Grado desarrollado en la empresa On4U, se ha implementado un módulo para Magento, cuya función principal es la generación dinámica de parrillas de productos en base al análisis del tiempo meteorológico, teniendo en cuenta la localización del cliente. Además, el módulo guarda automáticamente las compras efectuadas, junto con la información externa, para un posible análisis posterior que relacione los hábitos de compra con el tiempo meteorológico. Aunque se haya centrado en este caso de uso, se ha desarrollado con un enfoque modular, de tal manera que fuese fácil de integrar en el módulo el uso de otra fuente abierta de información. Para poder realizar el proyecto, se ha tenido que profundizar en varios conceptos relacionados con la plataforma de eCommerce Magento, entre ellos, el patrón Modelo-Vista-Controlador y el ciclo de vida de una petición.

Internet y Redes Sociales

Sin apenas darnos cuenta, las Tecnologías de la Información y Comunicación (TIC) han pasado a formar parte de nuestra vida, revolucionando todos los ámbitos de nuestra economía y de nuestra sociedad. Ya nada es igual a como lo era ayer. La tecnología ha cambiado tanto el modo en el que se hacen los negocios, la forma en la que nos relacionamos con los demás, como nuestros hábitos de ocio y de consumo. A lo largo del presente trabajo, analizaremos con detenimiento el fenómeno de Internet y las redes sociales, así como el impacto que están teniendo tanto en la sociedad como en el tejido empresarial español. En este marco, describiremos y haremos un análisis comparativo de las redes sociales más comunes en España, y contemplaremos las posibilidades que ofrecen éstas para su utilización en el marco de las estrategias de marketing.

E-Commerce Innovations in the Personal Tax System of the United Kingdom

This study finds evidence that attempts to reduce costs and error rates in the Inland Revenue through the use of e-commerce technology are flawed. While it is technically possible to write software that will record tax data, and then transmit it to the Inland Revenue, there is little demand for this service. The key finding is that the tax system is so complex that many people are unable to complete their own tax returns. This complexity cannot be overcome by well-designed software. The recommendation is to encourage the use of agents to assist taxpayers or simplify the tax system. The Inland Revenue is interested in saving administrative costs and errors by encouraging electronic submission of tax returns. To achieve these objectives, given the raw data it would seem clear that the focus should be on facilitating the work of agents.

Factores determinantes del uso de Internet en la comercialización del aceite de oliva. Análisis del sector en Cataluña

Las cooperativas son entidades con una gran presencia económica y social en España, y tienen una gran influencia en la economía rural de las zonas donde están ubicadas. El principal objetivo del presente trabajo es el análisis del uso de las nuevas tecnologías por parte de las cooperativas agroalimentarias, centrándose en las productoras de aceite de oliva para determinar los principales factores que condicionan su comportamiento en la Red. En el presente estudio se analizan sus sitios web y se determina qué tipo de información aporta, tanto datos generales como datos de comercialización. A partir de los resultados obtenidos, se busca la relación que pueda existir entre el tamaño de la cooperativa, su actividad exportadora o la actividad de comercio electrónico con la presencia online, mediante una regresión logística. De esta manera podremos conocer si realmente la implantación de nuevas tecnologías en las cooperativas permite desarrollar una óptima actividad económica.

Risk assessment of E-Commerce projects using evidential reasoning

The purpose of this study is to develop a decision making system to evaluate the risks in E-Commerce (EC) projects. Competitive software businesses have the critical task of assessing the risk in the software system development life cycle. This can be conducted on the basis of conventional probabilities, but limited appropriate information is available and so a complete set of probabilities is not available. In such problems, where the analysis is highly subjective and related to vague, incomplete, uncertain or inexact information, the Dempster-Shafer (DS) theory of evidence offers a potential advantage. We use a direct way of reasoning in a single step (i.e., extended DS theory) to develop a decision making system to evaluate the risk in EC projects. This consists of five stages 1) establishing knowledge base and setting rule strengths, 2) collecting evidence and data, 3) determining evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process, 4) combining prior mass and different rules; i.e., the second half of the single step reasoning process, 5) finally, evaluating the belief interval for the best support decision of EC project. We test the system by using potential risk factors associated with EC development and the results indicate that the system is promising way of assisting an EC project manager in identifying potential risk factors and the corresponding project risks.