Eric Wing Kuen See-To

Modelling the determinants of a simulated experience in a virtual retail store and users' product purchasing intentions

Abstract In the past few years, virtual worlds have become increasingly popular, often hosting, in addition to gaming and social activities, commercial activities that can potentially not just cater for in-world demand but also go beyond the virtual environment’s boundaries. The purpose of this paper is to examine the determinants of users’ simulated experience in a virtual store and to show the subsequent impact of that experience on engagement. The outcome of that engagement was examined in relation to enjoyment and satisfaction, including the role of satisfaction in purchasing the real product. An experimental quantitative approach was followed, testing three models of constructing user experience. Our empirical analysis examined confounding factors of a simulated retail experience and the critical role of that experience, along with hedonic and utilitarian values, in engagement. Engagement and enjoyment were found to influence user satisfaction positively when choosing clothing products and, in turn, user satisfaction was found to influence purchasing intention positively for these products.

Interrelationship between big data and knowledge management: an exploratory study in the oil and gas sector

Purpose The purpose of this paper is to explore the relationship between big data and knowledge management (KM). Design/methodology/approach The study adopts a qualitative research methodology and a case study approach was followed by conducting nine semi-structured interviews with open-ended and probing questions. Findings Useful predictive knowledge can be generated through big data to help companies improve their KM capability and make effective decisions. Moreover, combination of tacit knowledge of relevant staff with explicit knowledge obtained from big data improvises the decision-making ability. Research limitations/implications The focus of the study was on oil and gas sector, and, thus, the research results may lack generalizability. Originality/value This paper fulfills an identified need of exploring the relationship between big data and KM which has not been discussed much in the literature.

To immerse or not? Experimenting with two virtual retail environments.

Purpose The purpose of this paper is to examine the determinants of users’ simulated experience in a virtual store and to show the subsequent impact of that experience on engagement. The outcome of that engagement is examined in relation to enjoyment, satisfaction and purchase intentions. Design/methodology/approach The method comprised an experiment comparing users’ perceptions of a standard 2D online clothing store with an enhanced, immersive one that aimed to provide shopping value approaching that of a traditional store by using a 3D experience where participants wore special glasses and a data glove. Findings Results demonstrate the major role of telepresence components in simulated experience and the critical role of that experience, along with hedonic and utilitarian values, in engagement. Purchase intention is influenced by satisfaction, which is in turn influenced by enjoyment and engagement. Engagement in turn is influenced by utilitarian and hedonic value and the experience of product simulation or telepresence, which is composed of control, colour and graphics vividness, and 3D authenticity. In the immersive, 3D environment, experience is more associated with engagement and enjoyment, leading to greater purchase intention. The immersive, 3D environment, thus, has the potential to rival traditional shopping in terms of experience, resulting in higher sales for retailers and satisfaction for consumers. Originality/value This work has evaluated a robust model of purchase intention and demonstrated it to hold not only in a 3D environment on a conventional computer platform, but also in an immersive one, where participants wear special glasses and a data glove.

How Does a Social Network Site Fan Page Influence Purchase Intention of Online Shoppers: A Qualitative Analysis

This research revisited how fan pages in the social network site created by online merchants affects the purchase intention of online shoppers using qualitative research methods. Through fine-tuning the research model developed by Jahn and Kunz (2012) which is grounded on the Uses and Gratifications Theory, the authors conducted a qualitative study through interviewing consumers who participated in fan pages. Based on their responses in the interviews, the authors reconfirmed that content-oriented needs and social interaction value of relationship-oriented needs had positive impacts on fan page usage intensity and fan page engagement, respectively. Plus, the authors discovered that content-oriented needs had a positive impact on fan page engagement. The theoretical contributions, managerial implications of this study are also discussed.

Web Information Retrieval for Health Professionals

This paper presents a Web Information Retrieval System (WebIRS), which is designed to assist the healthcare professionals to obtain up-to-date medical knowledge and information via the World Wide Web (WWW). The system leverages the document classification and text summarization techniques to deliver the highly correlated medical information to the physicians. The system architecture of the proposed WebIRS is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and a questionnaire is administrated to collect feedback on the operation and performance of WebIRS in comparison with conventional information retrieval in the WWW. A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in classification and summarizing the medical information that the physicians needed and interested. The results of the case study show that with the use of the proposed WebIRS, significant reduction of searching time and effort, with retrieval of highly relevant materials can be attained.

The moderating role of income on consumers' preferences and usage for online and offline payment methods

In this paper we examine consumer attitudes towards a payment method, which is a key factor affecting the probability of completing a transaction offline and online. More specifically, we constructed a model that surveyed the offline and online usage of prepaid e-cash, debit cards, credit cards and cash. User perceptions of the attractiveness of e-cash and various traditional payment means were also empirically assessed. Consumer attitudes towards a payment technology were found to be influential on users’ perceptions in both online and offline environments. User perceptions of offline purchases with a payment technology had significant and positive effects on the corresponding online usage perceptions. The effects of our research model are contingent on the income level of users. Our findings have significant implications, as they could help shed light on why consumers abandon their shopping carts and do not complete their transactions, which could potentially play a significant role when it comes to designing applications targeting sspecific consumer segments.

Knowledge retention and aging workforce in the oil and gas industry: a multi perspective study

Purpose The purpose of this paper is to investigate how companies are handling the issue of knowledge retention from old age retiring workers in the oil and gas sector. This is achieved by providing a detailed insight on the challenges and strategies related to knowledge retention through study of companies from different geographical locations across the globe. Design/methodology/approach The study adopts a qualitative research methodology and 20 semi-structured interviews, with open-ended and probing questions, were conducted to gain an in-depth insight into the knowledge retention phenomena. Findings Knowledge retention activities tend to be inconsistent in majority of the oil and gas companies, with not much work being done regarding knowledge loss from old employees, partly because of the fall in oil prices and layoffs. Oil prices turn out to be a decisive factor in oil and gas industry regarding workforce and knowledge retention activities. The political situation and geographical locations of the companies also affect the knowledge retention activities. Moreover, the aging workforce and retirement issue is more acute in the upstream sector. Research limitations/implications The focus of the study was on the oil and gas sector, and thus the research results may lack generalizability. Originality/value This paper fulfills an identified need for investigating the issues and challenges of knowledge retention regarding old age retiring employees by taking into account a global perspective and providing a comparison among different companies in different geographical locations.

Market sentiment dispersion and its effects on stock return and volatility

Behavioral economics has revealed that investor sentiment can profoundly affect individual behavior and decision-making. Recently, the question is no longer whether investor sentiment affects stock market valuation, but how to directly measure investor sentiment and quantify its effects. Before the era of big data, research uses proxies as a mediator to indirectly measure investor sentiment, which has proved elusive due to insufficient data points. In addition, most of extant sentiment analysis studies focus on institutional investors instead of individual investors. This is despite the fact that United States individual investors have been holding around 50% of the stock market in direct stock investments. In order to overcome difficulties in measuring sentiment and endorse the importance of individual investors, we examine the role of individual sentiment dispersion in stock market. In particular, we investigate whether sentiment dispersion contains information about future stock returns and realized volatility. Leveraging on development of big data and recent advances in data and text mining techniques, we capture 1,170,414 data points from Twitter and used a text mining method to extract sentiment and applied both linear regression and Support Vector Regression; found that individual sentiment dispersion contains information about stock realized volatility, and can be used to increase the prediction accuracy. We expect our results contribute to extant theories of electronic market financial behavior by directly measuring the individual sentiment dispersion; raising a new perspective to assess the impact of investor opinion on stock market; and recommending a supplementary investing approach using user-generated content.

Customer Relationship Management and Interface Redesign: A Study on the Website Design on the eBay Websites on Cultural Perspectives

This study is focused on the cross-cultural issues in the post-adoption phases of customer relationship management CRM for an international electronic marketplace, which operates in more than 30 countries. In particular, the authors focus on how the electronic marketplace modifies its interface redesign for addressing the different tastes of users from different cultural backgrounds. The authors hope this study can address to how cultural and language differences affect the interface redesign of CRM, which is part of the enterprise system, in the multinational and global context through a qualitative study.

Identifying industrial clusters with a novel big-data methodology: are SIC codes (not) fit for purpose in the Internet age?

Abstract In this paper we propose using a novel big-data-mining methodology and the Internet as a new source of useful meta-data for industry classification. The proposed methodology can be utilised as a decision support system for identifying industrial clusters in almost real time in a specific geographic region, contributing to strategic co-operation and policy development for operations and supply chain management across organisational boundaries through big data analytics. Our theoretical discussion on discerning industrial activity of firms in geographical regions starts by highlighting the limitations of the Standard Industrial Classification (SIC) codes. This discussion is followed by the proposed methodology, which has three main steps revolving around web-based data collection, pre-processing and analysis, and reporting of clusters. We discuss each step in detail, presenting the experimental approaches tested. We apply our methodology to a regional case, in the North East of England, in order to demonstrate how such a big data decision support system/analytics can work in practice. Implications for theory, policy and practice are discussed, as well as potential avenues for further research.