Structural Equation Modelling and Logistic regression Assignment

Structural Equation Modelling and Logistic regression - Assignment Example While card payments were the main cashless mode of payment up to the beginning of the last decade, mobile money payments have also emerged as a likeable payment method, mainly in fairly underdeveloped regions (Dahlberg, Mallat & Oorni, 2008). Other non-card payment methods include virtual money carriers, including Google Wallet, Skrill, and PayPal (Ayodele, Ayo & Adeyibi, 2013). Among the factors that appear pertinent towards adoption of any of these payment methods are ease of use, importance/ usefulness, risk/ security levels, and accessibility (Ayodele, Ayo & Adeyibi, 2013; Abubakar & Ahmad, 2013). According to Abubakar and Ahmad (2013), higher levels of awareness towards cashless payments correspond to higher uptake levels. Ease of use is an important factor for adoption of cashless payments. Users (and potential users) with better knowledge of the cashless payment modes available in a particular region catalyse uptake levels. To them, usage of the methods is relatively easy (Ayodele, Ayo & Adeyibi, 2013). Similarly, knowledge of usefulness/ importance of such payment modes underscores their higher acceptability among persons with such knowledge (Ayodele, Ayo & Adeyibi, 2013). The sample comprised 320 respondents who were served with questionnaires to indicate their level of agreement with assertions made regarding ease of use of cashless payments system, trust in the cashless payments system, and usefulness of the same. The three variables used several questions for each, which were then aggregated to find each individual’s average rating of each variable. That is, the variable usefulness was obtained by averaging the ratings given per individual across the four questions underlying the variable; the four responses denoting ease of use were averaged to obtain the variable; and the ratings to the five questions underlying