Building trustworthy e-Commerce wesite

The process of building consumer trust in E-Commerce is based on the presence of trust features or trust attributes in the websites. Consumer may examine e-Commerce websites for the existence of trust attributes. However, to date, which trust attributes contribute to the website’s trustworthiness and which trust attributes give more value to consumer has not been adequately explored. Therefore, the purpose of the paper is to look for the relevant trust attributes for e-Commerce websites and to identify the importance ranking of trust attributes that contribute significantly to the trustworthiness of e-Commerce website. Various journal papers and articles related to e-Commerce field have been referred in order to identify the trust attributes. An online survey that received 1230 respondents was carried out to investigate the importance ranking of ten trust attributes. The paper contributes to the discussion on how to build trust in e-Commerc

The Importance Ranking of Trust Attributes in e-Commerce Website

The process of building consumer trust in e-Commerce is based on the presence of trust features or trust attributes in the websites. Trust attributes are usually presented to the consumer by some clues on the homepage. For example, the clue ‘contact us’ will be linked to the trust attribute ‘company address’. Consumers may examine e-Commerce websites for the existence of trust attributes. However, to date, which trust attributes contribute to the website’s trustworthiness and which trust attributes give more value to consumers has not been adequately explored. Therefore, the purpose of the paper is: (1) to look for relevant trust attributes that should be placed in e-Commerce websites and (2) to identify the importance ranking of trust attributes that contribute to the trustworthiness of e-Commerce website. Five e-Commerce trust models were used for deriving the trust attributes. An online survey that received 1230 respondents was carried out to investigate the importance ranking of important trust attributes. This paper contributes to the discussion on how to build trust in e-Commerce for various stakeholders that include consumers, business organizations, system developers, and also to the researchers

Please, talk about it! When hotel popularity boosts preferences

Many consumers post on-line reviews, affecting the average evaluation of products and services. Yet, little is known about the importance of the number of reviews for consumer decision making. We conducted an on-line experiment (n= 168) to assess the joint impact of the average evaluation, a measure of quality, and the number of reviews, a measure of popularity, on hotel preference. The results show that consumers' preference increases with the number of reviews, independently of the average evaluation being high or low. This is not what one would expect from an informational point of view, and review websites fail to take this pattern into account. This novel result is mediated by demographics: young people, and in particular young males, are less affected by popularity, relying more on quality. We suggest the adoption of appropriate ranking mechanisms to fit consumer preferences. © 2014 Elsevier Ltd

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

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

UNDERSTANDING ONLINE GROUP PURCHASE DECISION MAKING: A MEANS-END CHAIN APPROACH

Given the enormous growth and significant impacts of group buying on Internet business marketplaces, this study aims to understand consumer decision making process in an online group buying context from a Means-end Chain (MEC) theory perspective. The laddering interview technique was used to interview 58 online group buying users and to capture their reasons behind the online shopping behaviour, with grounded theory used to determine categories. The study found 35 factors in relation to consumer decision making process, which were classified into attributes, consequences, and values. The hierarchical relationships among 35 factors were developed, in which consumer decision making paths were identified. This study has the potential to make significant contributions to both IS research and e-business regarding consumer online group buying decision making process by identifying not only the major consequences/ benefits consumers emphasising, but also the concrete attributes which directly correspond with these benefits as well as the goals/values consumers aim to achieve