Success Measures Evaluation for Mobile Commerce Using Text Mining based on Customer Tweets

The customer acceptance of mobile commerce application systems is one of the driving factors of a successful mobile commerce implementation. However, there is an increasing number of companies that fail to implement. This shows the need to measure the success in implementing mobile commerce. One of the approach to determine success measures of mobile commerce can use customer perspective through user feedback regarding the application performance. The development of technology allows customers to share their feedback about mobile commerce services through online media without the limitation of time and place, for example using Twitter. Any tweets that contain comments or questions about a service is called a customer tweet. Customer tweets, processed using text mining, facilitate the company to get information about the success measures of a particular service based on customer point of view. Based on previous literature study, research on success measures of mobile commerce from the customer perspective is still in the early stages. Therefore, there are several reasons why this research is important. First, every mobile service has different characteristics, especially in the measurement of success. This shows that many explorations can be done regarding the success measures of mobile commerce. Second, the availability of customer tweets that contain comments about mobile commerce has allowed the data to be collected and processed into information to analyse the success measures of mobile commerce systems. Third, there is very little or no studies that use text mining method to model the success measures for mobile commerce based on customer tweets. Thus, the study reported in this paper aims at developing the success measures for mobile commerce based on customer tweets using text mining approach. The results from data processing indicate that some constructs that represent the success measures for mobile commerce include System Quality, Information Quality, Process Quality, Service Quality, Use, User Satisfaction, Individual Benefit and IT Infrastructure.