Uberization of Customer Needs With Data Analytics: How Marketing Strategy Lifts Products Innovation (Published)
An unprecedented explosion of innovation in the areas of big data, cloud computing, artificial intelligence, robotics, block-chains, self-driving cars, and mobile services has made changes permanent with higher customer expectations and growing switching loyalty to new products and services. The central question addressed by the study is how the digital fragmentation is disrupting the global business environment for new product growth, customer experiences and marketing innovation in supporting marketing decisions. This study used qualitative content analysis methodology and relied on recent marketing research articles, case studies, and digital analytics surveys. The findings of the study contribute to the evolving discussion on how the innovative digital globalization powered by free-flowing data with evolving customer experiences is giving way to the success of the new product. The final sections provide directions for future digital innovation, the customer experience research and product marketing success, with significant implications for academicians, practitioners, and policymakers
Analysis of Data With Statistical Evidence of Educational Technology Standards Implementation in Basic Schools in Ghana (Published)
At a time of educational expansion, improving the quality of education and training is a critical issue and ICT is known in enhancing the quality of education in several ways by increasing learner motivation and engagement, by facilitating the acquisition of basic skills, as well as a transformational tool which, when used appropriately, can promote the shift to a learner-centered environment. The research aimed in bringing out how technology is to be incorporated into education as a means of Transforming Learning Environments for a better achievement of educational standards Analysis of data gathered gave a statistical evidence of Educational Technology implementation in basic schools in Ghana by teachers and the aspiration to promote an environment of professional learning using ICT by Administrators.
Data clustering is a vital tool when it comes to understanding data items with similar characteristics in a data set for the sake of grouping. Clustering may be for understanding or utility. Clustering for understanding, which is the focus of this work deals with grouping items with common characteristics in order to better understand a dataset and to identify possible or pre-interest sub-groups that could be formed from such data. The HIV prevalence statistics in Nigeria is measured bi-annually across 36 states and FCT which were zoned under 6 geo-political zones happens to be a suitable data to implement this subject matter. Cluster Analysis was implemented through the general methods of Hierarchical (agglomerative nesting) and Partitioning methods (K-Means). These techniques where implemented on the platform of R (Statistical Computing Language) to cluster HIV prevalence rate in Nigeria so as to find out states that could be considered same category and to investigate the concentration of the disease in respect to geo-political zones. Relative type of validation was used for cluster validation (a mechanism for evaluating the correctness of clustering).
Sources of Anchor Data and Adjustment Amounts in the Valuation of Residential Properties (Published)
This paper ascertained how valuers generate anchor data based on past valuations experiences and how adjustments were made on the anchors to obtain capital values of residential properties. From a Total population of 260 registered firms, 164 were located. Yamane’s (1967) formula with 0.05 sampling error was adopted in determining sample size. It was found that sources of anchor and what valuers have been adjusting for varies. Additionally, generating anchor data from local experts was more common than from firms’ records; while general adjustment of anchor for differences in identified attributes has highest adoption rate (92.3%). Previous value experience of subject property is the most common of considered anchor sources. Externally generated anchor ranked higher in use than internally generated anchor; but ranked lower in terms of adjustment. Adjusting without identification of differences in attributes should be avoided to prevent misrepresentation of comparable and loss of clients’ confidence