The purpose of this research was to examine the effects of digital transformation, digital leadership, and entrepreneurial motivation on business decision making and business process performance in the Greater Amman Municipality. The study's hypotheses were put to the test and proven through a variety of quantitative analysis and data processing techniques. The research hypotheses were evaluated using a Structural Equation Model. Participants in this research are managers from the middle and high echelons of the Greater Amman Municipality, who were responsible for making decisions in their respective divisions. One hundred and eighty middle and upper-level managers with at least eight years' experience in public service were recruited for this study from the Greater Amman Municipality. Distributed questionnaires and the deliberate sampling technique were used to compile this data. Decision making had a positive effect on the business process performance, and the results of hypothesis testing data processing using Structural Equation Model indicated that digital transformation, digital leadership, and the motivation of the business environment all had positive and significant effects on business decision making and business process performance in the Greater Amman Municipality. To conduct their research, the authors opted to focus on digital transformation, which includes four primary components: process transformation, business model change, domain transformation, and cultural transformation. The following dimensions commander, communicator, collaborator, and co-creator were all indicative of digital leadership. Entrepreneurial motivation could be seen in the word’s communication, hassle-free work environment, mastering the art of constructive criticism, and trust among others. The novel aspect of this study is the model developed to explain the interplay between digital transformation, digital leadership, and entrepreneurial motivation, and how these factors influence business decision making and business process performance at Greater Amman Municipality.
Due to the increased availability of measurements of various geophysical processes, a need has arisen for statistical methods suitable for the analysis of very large nonstationary spatial data sets. The nearest‐neighbor Gaussian process (NNGP) models are one of the latest and most popular Gaussian process‐based models, which reduce computational complexity and memory storage. The Bayesian inference is based on the assumption of a parametric covariance function that is often assumed stationary or known. Given that NNGP models are sensitive in the stationary assumption in comparison to other reduction methods, there is a need to build nonstationary covariance functions within the NNGP models. However, the construction of a nonstationary covariance function and/or matrix may be computationally expensive by itself in the presence of big data. In this paper, we develop an efficient two‐stage approach that deals with nonstationarity and the computational complexity in the presence of a big spatial data set. We propose a new low‐cost data‐driven tree‐structured partitioning technique to divide the spatial region into distinct subregions. Given the partitions, we construct computationally efficient nonstationary covariance functions for NNGP models. We demonstrate the performance of our approach through simulation experiments and an application to the global Total Ozone Matrix Spectrometer (TOMS) data set, in which the proposed approach performs well in terms of both prediction accuracy and computational complexity.
Building efficient sampling procedures to provide more accurate results with small sample sizes is one of the main goals in sampling surveys. The ranked set sampling (RSS) is a well‐known procedure for selecting representative samples and improving parametric estimation by employing ranking on observations. In order to have a more efficient sampling procedure, new sampling procedures are proposed and investigated in this article. The main focus of this article is to enhance the mean estimation of the study population using the proposed RSS procedures. The performance of the proposed sampling procedures is compared with their competitors in RSS, double RSS (DRSS), extreme RSS (ERSS), and double extreme RSS (DERSS) by conducting simulation studies for numerous (symmetric and asymmetric) distributions. An application to a real dataset is also considered to exemplify the achievement of the proposals. Numerical simulations show that the new modified estimators are unbiased for the population mean for symmetric distributions and they outperform their competitors in most of the cases investigated in this article.
The goal of this study is to develop and verify a model for successful e-Learning based on the experiences of students in the "new normal". From Jordanian universities, 550 students who have taken any e-Learning course were randomly selected. Data were collected via a survey questionnaire, and Structural Equation Modeling (SEM) was employed to test the proposed study model. The findings indicate that contactless learning and high-quality e-learning systems have a beneficial impact on student satisfaction. In addition, e-Learning cognitive involvement was found to solidify e-Learning satisfaction. Furthermore, the results show a positive and significant impact of e-Learning cognitive involvement and e-Learning satisfaction on e-Learning achievement. Also, e-Learning system quality positively affects e-Learning cognitive involvement, besides a direct impact of contactless learning quality on e-Learning cognitive involvement.
The purpose of this paper is to estimate the parameters of Downton’s<br />bivariate exponential distribution using moving extreme ranked set sampling<br />(MERSS). The estimators obtained are compared via their biases and<br />mean square errors to their counterparts using simple random sampling (SRS).<br />Monte Carlo simulations are used whenever analytical comparisons are difficult.<br />It is shown that these estimators based on MERSS with a concomitant<br />variable are more efficient than the corresponding ones using SRS. Also,<br />MERSS with a concomitant variable is easier to use in practice than RSS with<br />a concomitant variable. Furthermore, the best unbiased estimators among all<br />unbiased linear combinations of the MERSS elements are derived for some<br />parameters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.