From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques

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

Business Process Management Education in Academia: Status, challenges, and Recommendations

In response to the growing proliferation of Business Process Management (BPM) in industry and the demand this creates for BPM expertise, universities across the globe are at various stages of incorporating knowledge and skills in their teaching offerings. However, there are still only a handful of institutions that offer specialized education in BPM in a systematic and in-depth manner. This article is based on a global educators’ panel discussion held at the 2009 European Conference on Information Systems in Verona, Italy. The article presents the BPM programs of five universities from Australia, Europe, Africa, and North America, describing the BPM content covered, program and course structures, and challenges and lessons learned. The article also provides a comparative content analysis of BPM education programs illustrating a heterogeneous view of BPM. The examples presented demonstrate how different courses and programs can be developed to meet the educational goals of a university department, program, or school. This article contributes insights on how best to continuously sustain and reshape BPM education to ensure it remains dynamic, responsive, and sustainable in light of the evolving and ever-changing marketplace demands for BPM expertise

Strategies for E-Commerce Platform Adoption in the Manufacturing Sector in Western India

While 95% of Indian SME leaders have not adopted an e-commerce platform, the few SME leaders having adopted such platforms reported 64% higher sales and 65% higher profits. The purpose of this multi-case study, guided by the diffusion of innovation (DOI) theory, was to explore the strategies that Indian SME leaders used to adopt e-commerce platforms to expand their businesses. Data for this study emerged from conducting face-to-face, semistructured interviews with 3 SME leaders who operated in the manufacturing industry in western India. The data analysis process included validating, coding, interpreting, and summarizing data and generating themes. Methodological triangulation of data obtained from interviews, observations, and document review resulted in 4 major themes: leveraging the marketplace model, dealing with tedious governmental requirements, finding well-trained employees, and handling fraudulent product returns. The study results may contribute to positive social change in western India by generating greater employment opportunities and increasing e-commerce literacy among online shoppers. Wider e-commerce adoption by SME leaders can generate a large number of employment opportunities for people living in western India resulting in a better quality of life. Increased use of e-commerce activities among online shoppers can result in higher awareness about online frauds, identity theft, malware threats, and overall online security

Credit Scoring with AHP and Fuzzy Comprehensive Evaluation Based on Behavioural Data from Weibo Platform

It is increasingly necessary to evaluate the customers\u27 credit. In the era of big data, Information on the Internet is commonly used to judge the credit worthiness of customers. Some users\u27 credit information is incomplete or unavailable, so credit managers cannot judge the true credit situation of these users. However, with the support of social data especially behavioural data and credit evaluation system, this problem can be effectively solved. This study used Weibo to obtain the behavioural data of Chinese users for credit evaluation. Two methods are used to calculate the credit scores of Weibo users, which are the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation methods. By analysing social processes and inviting experts to make decisions, we constructed a credit evaluation system to expose users\u27 behavioural characteristics. We found that the three key indexes determining the user’s social credit are personal identification, behavioural characteristics and interaction among friends. Then, AHP was used to determine the weight of each index. Finally, a static algorithm was proposed to compute the credit evaluation system of Weibo users using fuzzy comprehensive evaluation methods

A New Big Data and Logistic Regression-Based Approach for Small and Medium-Sized Enterprises

Businesses are being asked to assess an expanding volume of actual semi-structured and unstructured statistics to address the obstacles of internationalization and deal more effectively with the uncertainties of international integration. Big Data (BD) analytics can therefore play a strategic role in promoting the international expansion of Small and Medium-Sized Enterprises (SMEs). The exact connection between BD Analytics and globalization has, however, only been sporadically examined in the existing literature. In this study, a quantitative analysis using a Logistic Regression (LR) concept revealed that the interaction effects between BD Analytics architecture and BD Analytics functionality are both helpful and significant but the connection between the management of BD Analytics architecture and the Degree of Internationalization (DI) is not required for internationalization development. This shows that increasing internationalization in SMEs requires more than BD Analytics governance alone. Instead, this study emphasizes the importance of building particular BD Analytics abilities and the availability of a beneficial interaction between management of BD Analytics architecture and BD Analytics abilities that could take advantage of the new information gained via BD Analytics in SME global expansion