Personality in Computational Advertising: A Benchmark

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person’s buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user’s experience than generic parameters, accurate predictions reveal important aspects of user’s attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer’s buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users’ personality factors and 1,200 personal users’ pictures

E-commerce and the retail sector : implications for 7-11, Taiwan

This report analyses models of e-commerce in terms primarily of B2C, but also B2B, B4B, C2C and C2B. The various models of e-business, such as pure e-tailing, click and mortar, pure play and exchange, are described. The business, technical, financial, and legal aspects of e-commerce are discussed in the report. A general overview of the industry details usage of the Internet and e-commerce on a global issue and specifically in Taiwan. Also, the current situation of the convenience store industry in Taiwan is described. PEST analysis and Five-Force Analysis are used to discuss the situation of convenience stores. Moreover, an overview of 7-11, organisational structure, delivery, and POS systems are described. The current stages of e-commerce implementation within 7-11 are presented. Also, the concerns held regarding the opportunities and threats of e-commerce for 7-11 are identified. The short term and long term plans of 7-11 are detailed and further recommendations are presented. Gladwell’s idea of a “tipping point” with regard to market growth is used to discuss 7-11’s need to move faster to the solution of trading on-line. Amazon.com is used as an example to argue that 7-11 can save more costs by moving the business on-line. The case study of 7-11 can be used as a reference for traditional retailers planning to move their business on-line.This report analyses models of e-commerce in terms primarily of B2C, but also B2B, B4B, C2C and C2B. The various models of e-business, such as pure e-tailing, click and mortar, pure play and exchange, are described. The business, technical, financial, and legal aspects of e-commerce are discussed in the report. A general overview of the industry details usage of the Internet and e-commerce on a global issue and specifically in Taiwan. Also, the current situation of the convenience store industry in Taiwan is described. PEST analysis and Five-Force Analysis are used to discuss the situation of convenience stores. Moreover, an overview of 7-11, organisational structure, delivery, and POS systems are described. The current stages of e-commerce implementation within 7-11 are presented. Also, the concerns held regarding the opportunities and threats of e-commerce for 7-11 are identified. The short term and long term plans of 7-11 are detailed and further recommendations are presented. Gladwell’s idea of a “tipping point” with regard to market growth is used to discuss 7-11’s need to move faster to the solution of trading on-line. Amazon.com is used as an example to argue that 7-11 can save more costs by moving the business on-line. The case study of 7-11 can be used as a reference for traditional retailers planning to move their business on-line

The Effectiveness of Internet Advertising on Consumer Behaviour

Advertising is a communication medium where companies made to know the consumers about the product or it is a medium where companies tries to increase the sales and branding the product and many other definitions proposed by various researches, as days past on advertising medium was classified into 2 modes 1. Online advertising and 2. Offline advertising. In this paper, internet advertising mode was explained. The objective populace becomes the publicizing companies and their customers. The research applied a defined testing strategy to pick 60 exam respondents every day.  Content research turned into utilized to dissect subjective facts simultaneously as the quantitative facts changed into broke down utilizing clean measurements utilizing SPSS. Relapse and Correlation examination changed into applied to reveal the connections among the elements. The statistics were brought via rates, implies, fashionable deviations and frequencies. The research found that web promoting turned into a hit on attain and making of mindfulness because of diverse use, and set up that its dependability as a publicizing media was low contrasted with TV. Web publicizing has huge courting with the consumers' purchase desire and along those lines is a critical determinant in impacting purchaser behaviour

Factors affecting the adoption of online auctions by internet users in Hong Kong

This is an exploratory empirical study with the aim to identify the factors that affect the adoption of online auctions by Internet users in Hong Kong. The frameworks used were the TAM (Technology Acceptance Model), TCE (Transaction Cost Economics) and SERVQUAL (Service Quality). It was found that the dimensions that affected the customer’s perceived value of the online auction are benefits, costs, risks and service quality. Data was collected from four pilot focus groups, one online survey and a final focus group. The subjects in the focus groups were 21 undergraduates, whereas the subjects in the online survey were 152 internet users. The results of the pilot focus groups guided the design of the online survey. The results of the survey was analysed using the Kruskal-Wallis test. The final focus group was used to seek explanations to some issues arose from the online survey. It was found that the factors in the benefit dimension were liquidity, enjoyment, and price transparency. The factors in the cost dimension were time, effort, service charge and reputation of the user. The factor in the risk dimension was financial risk. The factors in the service quality dimension were efficiency and system availability. The final focus group revealed that the auctioneer’s role in policing the auction web site was important. For differences among the subjects, it was also found that the adult users consider their reputation in auction website, young adults are worried about financial risks, and female users are more concerned about financial risks than male users. The implications of these differences are discussed. The main academic contribution was the development of a questionnaire and a model which can be used in further research about other forms of auction

Systematic Measurement of Centralized Online Reputation Systems

Background: Centralized online reputation systems, which collect users' opinions on products, transactions and events as reputation information then aggregate and publish it, have been widely adopted by Internet companies. These systems can help users build trust, reduce information asymmetry and lter information. Aim: Much research in the area has focused on analyzing single type systems and the cross-type evaluation usually concentrates on one aspect of the system. This research proposes a systematic evaluation model (SERS) that can measure different types of reputation system. Method: From system perspective, all reputation systems can be divided into five underlying components. Input refers to the collection of ratings and reviews; Processing is the aggregation of ratings. Output publishes the information. Feedback Loop is the collection of the feedback of the review, which can be seen as the `review of the review'; Finally, Storage stores all the information. Therefore, based on each component's characteristics, a series of benchmark criteria can be dened and incorporated into the model. Results: The SERS has dened 29 criteria, which can compare and measure different aspects of reputation systems. The model was theoretically assessed on its coverage of the successful factors of reputation systems and the technical dimensions of information systems. The model has also been empirically assessed by applying it to 15 commercial sites. Conclusion: The results obtained indicated that the SERS model has identified most important characteristics that have been proposed by reputation systems literature. In addition the SERS has covered most dimensions of the two basic technical information system measurements: information quality and system quality. The empirical assessment has shown that the SERS can evaluate dierent types of reputation systems and is capable of identifying the weakness of current systems

Development of an intelligent e-commerce assurance model to promote trust in online shopping environment

Electronic commerce (e-commerce) markets provide benefits for both buyers and sellers; however, because of cyber security risks consumers are reluctant to transact online. Trust in e-commerce is paramount for adoption. Trust as a subject for research has been a term considered in depth by numerous researchers in various fields of study, including psychology and information technology. Various models have been developed in e-commerce to alleviate consumer fears, thus promoting trust in online environments. Third-party web seals and online scanning tools are some of the existing models used in e-commerce environments, but they have some deficiencies, e.g. failure to incorporate compliance, which need to be addressed. This research proposes an e-commerce assurance model for safe online shopping. The machine learning model is called the Page ranking analytical hierarchy process (PRAHP). PRAHP builds complementary strengths of the analytical hierarchy process (AHP) and Page ranking (PR) techniques to evaluate the trustworthiness of web attributes. The attributes that are assessed are Adaptive legislation, Adaptive International Organisation for Standardisation Standards, Availability, Policy and Advanced Security login. The attributes were selected based on the literature reviewed from accredited journals and some of the reputable e-commerce websites. PRAHP’s paradigms were evaluated extensively through detailed experiments on business-to-business, business-to-consumer, cloud-based and general e-commerce websites. The results of the assessments were validated by customer inputs regarding the website. The reliability and robustness of PRAHP was tested by varying the damping factor and the inbound links. In all the experiments, the results revealed that the model provides reliable results to guide customers in making informed purchasing decisions. The research also reveals hidden e-commerce topics that have not received attention, which generates knowledge and opens research questions for future researchers. These ultimately made significant contributions in e-commerce assurance, in areas such as security and compliance through the fusing of AHP and PR, integrated into a decision table for alleviating trustworthiness anxiety in various e-commerce transacting partners, e-commerce platforms and markets.College of Engineering, Science and TechnologyD. Phil. Information System

Conversion rate prediction based on text readability analysis of landing pages

Digital marketing has been extensively researched and developed remarkably rapidly over the last decade. Within this field, hundreds of scientific publications and patents have been produced, but the accuracy of prediction technologies leaves much to be desired. Conversion prediction remains a problem for most marketing professionals. In this article, the authors, using a dataset containing landing pages content and their conversions, show that a detailed analysis of text readability is capable of predicting conversion rates. They identify specific features that directly affect conversion and show how marketing professionals can use the results of this work. In their experiments, the authors show that the applied machine learning approach can predict landing page conversion. They built five machine learning models. The accuracy of the built machine learning model using the SVM algorithm is promising for its implementation. Additionally, the interpretation of the results of this model was conducted using the SHAP package. Approximately 60% of purchases are made by nonmembers, and this paper may be suitable for the cold-start problem