An m- shopping adoption model for developing economies. A study of Kisii town retail shops

Abstract

Mobile shopping has been growing at a very fast pace in the developed world, but the trend has not quite picked up in the developing nations, including Kenya. It is still a relatively new trend. This study examined factors influencing m-shopping adoption and critical success factors that influence the adoption of m-shopping using extended Technology Acceptance Model (TAM). The study used descriptive survey research design as it was helpful in indicating trends in attitudes and behaviors and enabled generalization of the findings of the research study. This design was considered appropriate for this study because the quality information yielded was valid, while interviewer bias was reduced because participants completed identically worded self-reported measures. The population that was selected for the research was from Kisii Town, and a minimum sample of 384 respondents was selected to take part. The data was collected using questionnaires. An intercept personal interview approach was used to collect the data for the research. The data was analyzed using Statistical Package for the Social Science (SPSS) and Microsoft Excel. The advancement in this research area is as a result of increased use of mobile computing technology to support new data analysis techniques and remote discovery of knowledge. The quantitative data about perceived risks, convenience, commodity prices and product variety were analyzed using descriptive statistics. The data presentation was done using frequency distribution tables. The findings of the study revealed that m-shopping was a new trend in the Kisii Town and was taking root. Some of the reasons cited for adoption of m-shopping included; time saving, easy comparison of alternative products, fairer prices of online goods, expert/user review of products and access to a market without borders. Some challenges of m-shopping adoption that needs to be addressed were perceived risks, negatively influences consumers’ intentions and actual use of m-shopping. Online stores ought to introduce security mechanisms to reduce associated risks. The findings further indicated that this model can predict consumer intention to use m-shopping. Specifically, perceived usefulness, perceived credibility and awareness about m-shopping have significant effect on user’s attitude thus influence the intention toward m-shopping. The study provides relevant business advantage in terms of providing insights on how m-shopping is being embraced the challenges, and how to improve it.