Availability of Infrastructure and Adoption of Point of Sales of Selected Small and Medium Enterprises (SMEs) in Lagos State, Nigeria

Abstract

Nigerian payment systems are cash-driven and it is the main mode of payments transactions. However, the Point of Sales (POS), which is meant to encourage cashless economy as against the cashcentered operations, is challenged with issues of availability of infrastructure and security. This study examined the availability of infrastructure and adoption of POS of selected SMEs in Lagos state, Nigeria. The study adopted a cross-sectional survey research design. The population of the study consisted of selected SMEs who are users of POS in Lagos State estimated at 11,663 and sample size of 2,059 was adopted for the study. The respondents were selected from among the SMEs used for this study. A validated questionnaire from the sectors surveyed was used for the study. The sectors include: oil & gas, banking and insurance, and manufacturing. A total of 2,059 copies of the questionnaire were administered, with a response rate of 77.1%. The Cronbach's alpha coefficients for the constructs are 0.727 for adoption of POS, 0.799 for availability infrastructure and 0.758 for POS security. The data were analysed using descriptive and inferential (Pearson Product Moment correlation) statistics. The findings revealed that there was a significant relationship between availability of infrastructure and Adoption of POS (r=0.407; p < 0.01) and similar relationship also existed between POS security and Adoption of POS (r = 0.437; p < 0.01). The study concluded that availability of infrastructure had significant and positive relationship with adoption of POS of selected business organisations who are SMEs in Lagos State, Nigeria. Thus recommended that there is need to ensure the adequate infrastructural availability by various stakeholders to adopt POS and assure of security of data transactions and reports generated using the device by the selected organizations in Lagos state, Nigeria.

The gains and pains of small and medium-scale enterprises (SMEs): the way forward for entrepreneurship development in Nigeria

“…Successive governments in Nigeria often amend policies and laws in line with their political party's agenda on SMEs and entrepreneurship which mostly leads to policies neglect and new policies, making a substantial impact on the status, survival and competitiveness of SMEs (Dandago and Usman, 2011). The study done on available infrastructures and the nature of enabling policies to support SME's by Akerejola et al (2019) gave hints on lack of proper guidelines and enabling policies to enable SME's thrive and survive the harsh business environment; this prompted the formulation of H3:…”

“…There has been a recurrent feeling that SMEs in Nigeria could only be assisted for social reasons and not for their potential impact on the economy (Oramah et al, 2015). The opinion, held by some citizens and government with little foresight began to change over time as job creation orientation sunk into many, triggering many start-ups that make valuable contributions to the development of the Nigerian economy (Fowosire et al, 2017;Akerejola et al, 2019).…”

“…Successive Nigerian government across all tiers have shown interest in financing SMEs by establishing specialized banks, credit agencies and schemes to provide tailored funding for SMEs. Despite the renewed attention and contributions given to SME's by the Nigerian government, SMEs experience numerous problems labelled the Nigerian factor (Akerejola et al, 2019) and surprisingly, financing (single-digit loans) is the apex of them all (Victor et al, 2019). Others include infrastructural deficiency and the absence of a workable policy framework to drive SME establishment and growth (Victor et al, 2019).…”

Application of Neural Networks to Explore Manufacturing Sales Prediction

“…These inferred potential variables were called factors [11,12]. Previous factor studies have covered commercial, e.g., [13,14]; sales, e.g., [15][16][17]; industry, e.g., [18][19][20]; and manufacturing, e.g., [21][22][23] applications amongst many others, and has been shown to effectively extract important factors that can then be employed for prediction using different methods.…”

“…Predictive metrics compare predicted and actual values to confirm model feasibility. This study used the root mean squared error (RMSE) [16]:…”