Description

Recent times have been characterized by accelerated developments in the information and communication technologies as noted by several scholars (Black et. al 2001; Devlin and Yeung, 2003; Remenyi and Cinnamond, 1996). Prendergast and Marr (1994) have noted how the aforementioned has seen a paradigm shift in the way banking in done around the world. Gone are the times when banking was confined to brick and mortar branches as digitalization has now seen the wide spread of branchless banking giving people access to banking services from anywhere and at any time. Within South Africa, the central bank has licensed the first fully digital bank called TYME Bank which is owned by the Commonwealth Bank and is to offer banking services via digital platforms such as mobile phones and kiosks. The digital platform is being set up by BPC Banking Technologies who have been recognized by industry analysts after FIS and ACI as the third biggest electronic payments company in the world and the fastest growing one within that industry. With a significant presence around the world represented by 19 offices globally, the company has sort to disrupt traditional banking.
In recent times mobile based payments have characterized the payment landscape. The absence of traditional payments within rural communities has seen the successful launch and continued growth of mobile money services use in those areas. Traditionally rural areas have always been characterized by poor infrastructure noticeable through the absence of brick and mortar bank branch networks. The Post Office which is found in many places, both rural and urban was seen as the provider of banking services. Various scholars have used TAM as the theoretical basis upon which mobile money services have been adopted in various communities across the world. The use of mobile money services for banking is characteristic of third world countries which are characterized by huge unbanked or under-banked populations. This study used a quantitative approach which sort to understand the causative explanation of mobile money services in rural communities in Zimbabwe. Having gone through existing literature, a conceptual model based on extending TAM was proposed. A total sample size of 2,000 participants was selected and data was analyzed using SmartPLS (Structured Equation Modeling) and SPSS statistical tools. Data was collected using a questionnaire.
Many scholars in the USA and other parts of Europe have come up with models and theories that seek to explain the adoption behavior of new technologies within their countries. The applicability of such models and theories within the African context has always been subject to debate as prevailing conditions between the countries in which the modeling was originally done is not comparable in whatsoever way to the African countries. USA and Europe are generally characterized as developed areas whenever Africa is seen as being developing nations. The respective models and theories have been crafted with different sets of determinants which could be largely related to their respective environments. This study sort to validate such determinates within the African context and come up with newer ones in the process. Scholars such as myself have identified the said disparity and hence embarked on coming up with theories or models that are more applicable to the African context.
The current study reviewed the existing literature paying particular attentions to models and theories explaining adoption behavior of new technologies. The study proposed a new model which is to be used as the basis for explaining the adoption of mobile money services within rural communities in Zimbabwe and potentially applicable to other rural communities in Africa. Data was gathered from a sample population which was used as the basis for validating the proposed model. The proposed model saw the addition of the following constructs to TAM (Davis, 1989); relative benefits, convenience, social norms/influence, perceived risk and cost. From the findings perceived usefulness H3 (t=1.067), perceived risks H4 (t=1001) and costs H7 (t=1.738) are all supported but are insignificant since the t statistics are less than 1.96. Finally, behavioral intention H8 (t=7.519) is strong and supported since both the t statistics are above 1.
A path coefficient of 0.211 was realized after testing H1. This means that relative benefits have has a positive influence on Behavioral Intention. Furthermore, the results indicate that the relationship of Relative Benefits (RB) and Behavioral Intention (BI) is significant (t=3.236).
The results obtained following the test of H2 confirmed that there is an association between Perceived ease of use (PEOU) and Behavioral Intention (BI). A path coefficient of 0.222 was realized after testing H2. This means that perceived ease of use has a strong relationship with Behavioral Intention. Furthermore, the results indicate that the relationship between perceived ease of use and Behavioral Intention.
The results obtained following the test of H3 confirmed that there is an association between Perceived Usefulness (PU) and Behavioral Intention (BI). A path coefficient of 0.058 was realized after testing H3. This means that perceived usefulness has a negative influence on behavioral intention. Moreover, the results indicate that the relationship between Perceived Usefulness (PU) and Behavioral Intention is insignificant way (t= 1.067).
The results obtained following the test of H4 confirmed that there is an association between Perceived Risks (PR) and Behavioral Intention (BI). A path coefficient of -0.033 was realized after testing H4. This means that Perceived Risks have a negative influence on Behavioral Intention. Furthermore, the results indicate that the relationship of Perceived Risks (PR) and Behavioral Intention (BI) is insignificant way (t=1.001).
The results obtained following the test of H5 confirmed the existence of an association between Convenience (CE) and Behavioral Intention (BI). A path coefficient of 0.178 was realized after testing H5. This means that Convenience has a strong relationship with Behavioral Intention. Furthermore, the results indicate that the relationship between Convenience and Behavioral Intention is positive and significant (t= 2.479).
The results obtained following the test of H6 confirmed that an association between Social Norms (SN) and Behavioral Intention (BI) exists. A path coefficient of 0.104 was realized after testing H6. This mean that the relationship between Social Norms and Behavioral Intention is in a significant way (t= 2.176).
The results obtained following the test of H7 confirmed that there is an association between Cost (CO) and Behavioral Intention (BI). A path coefficient of 0.072 was realized after testing H7. This means that cost has a negative relationship with Behavioral Intention. Furthermore, the results indicate that the relationship between Cost and Behavioral Intention is in an insignificant way (t= 1.738).
The results obtained following the test of H8 confirmed that there is an association between Behavioral Intention (BI) and Mobile Money Service Use (MMSU). A path coefficient of 0.532 was realized after testing H8. This means that Behavioral Intention (BI) is significantly related to Mobile money Service Use. Moreover, the results indicate that Behavioral Intention (BI) is positively related to Mobile Money Service Use (MMSU) in a significant way (t= 7.519).
An understanding of the proposed model will help respective stakeholders in coming up with mobile money services which are fit for purpose as they will be conforming to targeted market characteristics.