The influence of m-commerce service and system quality dimensions on overall perceived service quality

Information Technology (IT) has an increasing importance and development in business life. Nowadays, businesses have been seeking to reach their customers through m-services especially m-commerce. The concern is to what extent this m-commerce system can satisfy the consumers’ needs and contributes to the overall online purchasing development.This study aims to examine the effect of m-commerce service quality dimensions (website design, responsiveness, and trust), and system quality dimension (accessibility) on overall perceived service quality in m-commerce by customers.The data were collected from the Arab Open University in Jordan through a self administered questionnaire, in order to test the hypotheses of the proposed model.618 of questionnaires were used for analysis data, out of 870 distributed. The result of this study revealed that there are a significant effect of responsiveness and accessibility on overall perceived service quality. This study has some important implications for business practice and research

Measuring service quality in m-commerce context: The case of Arab Open University, Jordan

This study explores the impact of service quality dimensions (website design, reliability, responsiveness, trust, personalization, perceived risk and perceived cognitive control), information quality dimensions (content usefulness and content adequacy) and system - quality dimensions (ease of use, accessibility, interactivity and perceived website innovativeness) on overall perceived service quality, customer satisfaction and behavioral intention. Based on existing literature, a conceptual model was developed. The SERVQUAL model and the Information system theories were used to explicate the relationship among the variables in the conceptual model. Using a survey research design, a sample of 618 university students and staff was drawn through simple random sampling. Combinations of inferential and descriptive statistics were performed assisted by the Statistical Package for Social Science (SPSS) and Partial Least Square (PLS). The outcomes of this study show that responsiveness, content usefulness, content adequacy, ease of use, interactivity, and perceived website innovativeness have significant positive relationships with overall service quality. However, website design, reliability, trust, personalization and perceived risk do not have significant relationships with overall service quality. Similarly, and as expected, overall service quality significantly influences satisfaction while satisfaction positively influences the behavioral intention of mobile commerce customers in Jordan. As for policy and managerial recommendations, it is important that managers lay more emphasis on those factors that can make customers perceive the website of m-commerce to be of high quality as this will eventually affect their satisfaction and future behavioral intentions. Similarly, m-commerce service policy-makers should come up with policies that will enhance the nature of services being rendered, and that will bring greater benefits to the customers. Additionally, the policymakers should endeavour to position m-commerce in the minds of customers in such a way that it will bring about the intention to repeat patronage in the future. Finally, directions for future research are discussed

Relationships between system quality, service quality, and customer satisfaction

Purpose: This study aims to explore the impact of system quality dimensions, namely, ease of use, accessibility, interactivity and website innovativeness on service quality (SQ) and customer satisfaction.Design/methodology/approach: The study used a quantitative approach, by using a survey method.The unit of analysis was the individual. A total of 618 questionnaires were randomly distributed to university students and staff in Jordan.The partial least square path-modeling method was used in the estimation of causal relationships of the constructs examined in the study.Findings: The outcomes of this study showed that ease of use, interactivity and website innovativeness have significant positive relationships with the SQ. Consequently, SQ significantly influences customer satisfaction. Research limitations/implications: Limitations of this research were related to the unit of analysis, as it was conducted within the geographical region of Jordan and the university context, where the culture and level of the technological advancement may be different than other countries. Practical implications: This research can assist mobile commerce (m-commerce) service policymakers to formulate significant policies that could enhance the nature of services being rendered and thus bring greater benefits to the customers. Originality/value: This research has extended the body of knowledge on emerging trends in m-commerce innovation adoption, more specifically in the university context. Furthermore, it offers insight on the importance of m-commerce in the minds of customers, in such a way that it will bring about the intention to repeat patronage in the future

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems