Mobile Commerce Environment Research Articles

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Mobile commerce is defined as any direct or indirect transaction conducted and facilitated through a wireless telecommunication network such as payment, ticketing, auction and mobile banking. Despite the importance of mobile commerce, there have not been so many academic studies on the unique characteristics of mobile commerce environments. In this study, our research model is developed based on TAM(Technology Acceptance Model) to investigate the user's technology acceptance process in mobile commerce environment. The factors, such as ubiquity, compatibility, perceived usefulness, perceived ease of use, cost, security, attitude and behavior intention for mobile commerce were selected from pier study in information systems area. The data for empirical analysis of the research model is collected online-questionnaire of 167 mobile commerce users in South Korea. The hypotheses were analyzed using SPSS 15.0 and AMOS 7.0. The results of our study show that: (1) compatibility, perceived usefulness and security had influences on the attitude whereas perceived ease of use and cost did not have any significant impact on the attitude, (2) compatibility influenced perceived usefulness whereas perceived ease of use did not, and finally (3) ubiquity, perceived usefulness and attitude had considerable influences on intention for mobile commerce. Therefore, practitioners should focus on enhancing security, quality of contents and services as well as offering what mobile commerce users want. The results of this study may useful for academicians and practitioners alike.

An empirical study on effectiveness of temporal information as implicit ratings

Collaborative filtering is a widely used and proven method of building recommender systems, which provide personalized recommendations on products or services based on explicit ratings from users. Recommendation accuracy becomes an especially important factor in some e-commerce environments (such as a mobile environment, due to limited connection time and device size). As user preferences change over time, temporal information can improve recommendation accuracy.This paper presents a variety of temporal information including item launch time, user buying time, the time difference between the two, as well as several combinations of these three. We conducted an empirical study on how temporal information affects the accuracy of a collaborative filtering system for recommending character images (wallpapers) in a mobile e-commerce environment. Empirical results show the degree of effectiveness of a variety of temporal information. The empirical results give insight on how to incorporate temporal information to maximize the effectiveness of collaborative filtering in various e-commerce environments.

A time-based approach to effective recommender systems using implicit feedback

Recommender systems provide personalized recommendations on products or services to customers. Collaborative filtering is a widely used method of providing recommendations using explicit ratings on items from users. In some e-commerce environments, however, it is difficult to collect explicit feedback data; only implicit feedback is available. In this paper, we present a method of building an effective collaborative filtering-based recommender system for an e-commerce environment without explicit feedback data. Our method constructs pseudo rating data from the implicit feedback data. When building the pseudo rating matrix, we incorporate temporal information such as the user’s purchase time and the item’s launch time in order to increase recommendation accuracy. Based on this method, we built both user-based and item-based collaborative filtering-based recommender systems for character images (wallpaper) in a mobile e-commerce environment and conducted a variety of experiments. Empirical results show our time-incorporated recommender system is significantly more accurate than a pure collaborative filtering system.

Mining Mobile Sequential Patterns in a Mobile Commerce Environment

In this paper, we explore a new data mining capability for a mobile commerce environment. To better reflect the customer usage patterns in the mobile commerce environment, we propose an innovative mining model, called mining mobile sequential patterns, which takes both the moving patterns and purchase patterns of customers into consideration. How to strike a compromise among the use of various knowledge to solve the mining on mobile sequential patterns is a challenging issue. We devise three algorithms (algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LS</sub> , algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PT</sub> , and algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PF</sub> ) for determining the frequent sequential patterns, which are termed large sequential patterns in this paper, from the mobile transaction sequences. Algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LS</sub> is devised in light of the concept of association rules and is used as the basic scheme. Algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PT</sub> is devised by taking both the concepts of association rules and path traversal patterns into consideration and gains performance improvement by path trimming. Algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PF</sub> is devised by utilizing the pattern family technique which is developed to exploit the relationship between moving and purchase behaviors, and thus is able to generate the large sequential patterns very efficiently. A simulation model for the mobile commerce environment is developed, and a synthetic workload is generated for performance studies. In mining mobile sequential patterns, it is shown by our experimental results that algorithm TJ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PF</sub> significantly outperforms others in both execution efficiency and memory saving, indicating the usefulness of the pattern family technique devised in this paper. It is shown by our results that by taking both moving and purchase patterns into consideration, one can have a better model for a mobile commerce system and is thus able to exploit the intrinsic relationship between these two important factors for the efficient mining of mobile sequential patterns

Developing Trust in M-commerce: A Vendor and Certificate Authority Model

AbstractThe goal of creating a secure wireless network in which to conduct commerce is an elusive one. Mobile commerce, (m-commerce) therefore, relies on the wired network to deliver services to its consumers. Trust of the service delivery is a security concern that has prevented a greater acceptance of m-commerce by consumers. This research proposes a new network model that can ensure trust in a mobile commerce environment. A goal of the trusted relationship is to guarantee data and relational integrity in the wireless environment. Research in progress provides a diverse array of solutions from mobile agent systems (Critchlow, 2004, Wang, 2005, Zhang, 2004), the use of a unique asymmetric authentication protocol (He, 2003), to the use of XML to enable Web service efficiencies in architecture (Chang, 2005). This work recognizes and provides plausible solutions to the four elements of challenges and threats to wireless networks outlined by Claessens (2003) and the model is proposed to ensure trust in the m...

Issues in Mobile E-Commerce

Though many companies are still just beginning to grasp the potential uses and impacts of the Web and e-commerce, advances in technologies and their application continue. These advances often present various managerial and technological issues for individuals, companies, governments, and other entities. One significant area of technological advancement is the development of mobile e-commerce, which encompasses interactive business activities and processes related to a (potential) commercial transaction conducted through communications networks that interface with wireless devices. These systems provide the potential for organizations and users to perform various commerce-related tasks without regard to time and location (anytime from anywhere). This emerging mobile e-commerce environment presents a new set of issues. This paper identifies and categorizes some of these issues so that researchers, developers, and managers have a starting point for focusing their activities within the emerging m-commerce domain. Our examination finds categories that include technological (both client and infrastructure) issues, application issues, and areas for future research.

A Prediction-Based Query Processing Strategy in Mobile Commerce Systems

The rapid advances and popularity of the Internet encourages the wide dissemination of electronic commerce. Various applications of electronic commerce, e.g., querying supermarket catalogs, purchasing products, booking a hotel room of a trip, etc., are rapidly increasing. Moreover, the emergence of mobile computing makes it possible for users to access information anywhere and at anytime. With the merging of these two technologies, millions of users in the near future could carry a mobile device requesting services through electronic commerce applications. While serving such a mobile user, the server should consider how to minimize the response time of the request. However, conventional strategies for query processing are no longer adequate in the mobile environment. This is mainly because the user may not be at the same site as where he/she issued the request when the result of the request is ready to be delivered to the user. The issue becomes more complicated when the user’s request involves data residing in multiple data sources. This paper proposes some strategies for query processing in a mobile commerce environment. All the strategies are presented, along with a cost analysis as the theoretical basis for the support of our design. Through the analysis of each strategy, we find the weakness of the strategy and improve it by a new design. Finally, the performances of all strategies are studied and compared to find the performance difference of the strategies.