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The use of technology to provide contextual information for decision-making has been a long-standing practice. The study aims to identify the antecedents influencing tourists’ use of context-aware applications. It employs the Unified Model of Electronic Government Adoption (UMEGA) and the Expectation Confirmation Model (ECM) to investigate tourists’ behaviors in the use of Personal Locator Beacons (PLBs) during the pre-adoption and post-adoption stages at the Mole National Park in Ghana. Data was analyzed using Smart PLS 3.0 software. The findings suggest that pre-adoption performance expectancy, motivational needs, and personal situation significantly affect tourists’ pre-adoption intentions to continue using context-aware applications. In addition, information recommendation, service quality, confirmation expectation, and satisfaction influenced continued use and recommendation. However, social influence and enjoyment experience did not significantly impact tourists’ behavioral intentions. The findings contribute to theory by integrating components of the two models (UMEGA & ECM) and delving into the antecedents of users’ decision-making processes.

To better understand the actual rating variables that affects Auto insurance Policyholders’ premium, this paper attempts to provide empirical evidence to justify which ones are significant and needed to be considered by insurers by adopting the autoregressive distributed lag (ARDL) model. In satisfying all the conditions for ARDL application, unit root, Heteroskedasticity, normality, dynamic stability and serial correlation tests were conducted. We estimate the effects of each rating variable on Premium taking into consideration whether the policy of the insured is Third-Party or Comprehensive. These rating variables in the ARDL model serves as the independent variables that establishes the short and long-run relationships between them and the Premium as the dependent variable. The results suggest that not all the classical rating variables used in the market significantly impact Premium. Whiles, to some extent, we found a varying degree of variables impact on Premium depending on the insurance type, the autos cubic capacity, which plays a cogent role on the basic Premium in Ghana, is insignificant. Also, policyholders’ age characteristics are statistically significant but are excluded in the premium calculations. Thus, this paper shows the need to consider all the other possible rating variables, including policyholders’ age into the Ghanaian insurance pricing system, whiles autos cubic capacity considering the weight it put on the basic Premium should be re-examined. This would help to obtain a financially balanced and optimal pricing system for policyholders.

This paper examines the current No-Claim Discount (NCD) system used in Ghana's auto insurance market as inefficient and outmoded and therefore, proposes alternative optimal Bonus-Malus System (BMS) intended to meet the present market conditions and demand. It appears that the existing BMS fails to acknowledge the frequency and severity of policyholders' claim in its design. We minimized the auto insurance portfolios' risk through Bayesian estimation and found that the risk is well fitted by gamma, with the claim distribution modeled by the negative binomial law with the expected number of claims (a priori) as 14%. The models presented in this paper recognize the longevity of accident-free driving and fully reward higher discounts to policyholders from the second year when the true characteristics of the hidden risks posed to the pool have been ascertained. The BMS finally constructed using the net premium principle is very optimal and has reasonable punishment and rewards for both good and bad drivers, which could also be useful in other developing economies. [ABSTRACT FROM AUTHOR]