This study examined green packaging, environmental awareness, and willingness to pay for green products in consumers’ purchase decisions. A cross-sectional survey was employed in collecting data from 218 respondents in Ghana. Purposive sampling was used to select respondents for the study. The data were analyzed using SPSS software for descriptive statistics and partial least square structural equation modeling (PLS-SEM) to test our hypotheses. The study found that consumers’ environmental awareness had a positive and significant impact on green purchasing decisions. However, the impact of green packaging was not found to be significant on consumers’ purchase decisions in Ghana. The study further revealed that consumers’ willingness to pay for green products positively and significantly predicted consumer purchase decisions. Some recommendations are suggested for theoretical and managerial considerations.

The aim of this generalization is to introduce a new modified weighted Weibull distribution called geometric weighted Weibull that was derived from the concept of the weighted distribution to provide better flexibility extension and tractability of the mathematical concepts of the weighted Weibull distribution. The major statistical and mathematical concepts the researchers adopted were parameter estimation, goodness-of-fit test, hazard function, quantile, survival function, entropy, and order statistics. The method of maximum likelihood estimation was adopted to estimate the unknown four parameters of the Geometric weighted Weibull distribution. The Monte Carlo simulation method is discussed using the quantile function to examine the performance of the parameters. The processes were performed using R-Software. A new modification of the Geometric weighted Weibull distribution is derived, and its probability characteristics are obtained. The shapes of the probability density function and hazard rate functions are investigated. The geometric weighted Weibull model assumes superior performance among the compared distributions, as evidenced by the Kolmogorov-Smirnov statistics and Akaike Information Criteria values.

Statistically, to retain the originality of data, appropriate probability distribution must be employed rather than transforming the existing dataset. Classical Distributions (CD) lack the ability to model and describe some important real-life events. In this study, weighted Weibull distribution (WWD) is modified using Exponentiated Weighted Weibull distribution (EWWD). The shapes of the probability density function and hazard rate functions are investigated together with some structural statistical properties of the distribution. The performances of the maximum likelihood estimation of the unknown parameters of the distribution were done by implementing the Monte Carlo simulation method. We provide applications to two datasets to prove the flexibility of the model. The processes were performed using R-software. The EWWD is considered to be more tractable and capable than the WWD. The Exponentiated weighted Weibull distribution assumes a superior performance among the compared distributions as evidenced by the Kolmogorov-Smirnov statistics and Akaike Information Criteria values.