In the era of the diffusion of e-commerce and its services offered to the consumers over the Internet, the Internet is commonly used by both consumers and businesses to buy and sell their goods and services worldwide. This study focuses on the factors influencing customers' decisions and attitudes toward adopting online shopping in Jordan. The study found that online shopping in Jordan is still not very common, due to challenges and barriers that affect the diffusion of online shopping: delivery barriers, such as the lack of prepared transportation and mapping infrastructure, lack of reliable delivery system for delivering bought goods to the customers caused by the lack of postcode system; and lack of knowledge and awareness about the benefits of e-commerce among retailers and consumers. A structured questionnaire was distributed among 50 participants (university students, employees/professionals etc.), and then the collected data were analyzed using the Statistical Package for Social Sciences Version 18.02 (SPSS). The results show that attitudes toward online shopping and intention to shop online were affected by lack of human resources, such as low level of experience in using the Internet and shopping websites for shopping, lack of developed IT infrastructure, trust in e-retailers, and online payment and delivery service concerns. However, Jordanian consumers are willing to adopt and recommend online shopping for others as an alternative way for shopping.
Online shopping has an increasing impact on the environment in terms of the related "last mile" processes, which lies in the CO2 emissions. Thus, this study compares transport-related CO2 emissions of online and conventional shopping in terms of supply, home delivery and travel data from consumers to a physical store branches in the capital of Jordan "Amman". Real data were collected from consumers and analyzed to highlight the different factors that affect CO2 emissions, such store supply, consumer trip distance to physical store, firstattempt failed delivery, returns. The results show that online shopping play an important role in minimizing CO2 emissions including all the related processes to such shopping mode. However, conventional hopping can be more environmentally friendly shopping mode in case the store distance to travel is short. In addition, the use of public transport mode for traditional shopping and the shopping behavior of the consumers are considered as advantages for such shopping mode.
In the era of the diffusion of e-commerce and its services offered to the consumers over the Internet, the Internet is commonly used by both consumers and businesses to buy and sell their goods and services worldwide. This study focuses on the factors influencing customers' decisions and attitudes toward adopting online shopping in Jordan. The study found that online shopping in Jordan is still not very common, due to challenges and barriers that affect the diffusion of online shopping: delivery barriers, such as the lack of prepared transportation and mapping infrastructure, lack of reliable delivery system for delivering bought goods to the customers caused by the lack of postcode system; and lack of knowledge and awareness about the benefits of e-commerce among retailers and consumers. A structured questionnaire was distributed among 50 participants (university students, employees/professionals etc.), and then the collected data were analyzed using the Statistical Package for Social Sciences Version 18.02 (SPSS). The results show that attitudes toward online shopping and intention to shop online were affected by lack of human resources, such as low level of experience in using the Internet and shopping websites for shopping, lack of developed IT infrastructure, trust in e-retailers, and online payment and delivery service concerns. However, Jordanian consumers are willing to adopt and recommend online shopping for others as an alternative way for shopping.
This study investigates smartphone users' perceptions of adopting and accepting Mobile Commerce (MC) based on users' perceived adoption under the extended Technology Acceptance Model (TAM2) and Innovation Diffusion Theory (IDT) by providing research constructs for the domain of MC. Also, testing them with reliability and validity and demonstrating their distinctiveness with hypothesis testing. The results show that consumer intention to adopt MC on a smartphone was primarily influenced by Uncertainty Avoidance (UA), User Experience (UX), Perceived Ease Of Use (PEOU), Perceived Usefulness (PU) and Compatibility (CMP) as well as other constructs that positively determine attitude toward using a smartphone. For researchers, this study shows the benefits of adapting TAM constructs into MC acceptance on a smartphone. The perceptions of MC adoption on a smartphone in this study investigated based on a survey of specific people. For more reliability, a comprehensive study is needed to show the attitudes of people from different environments.
Hepatitis C is a significant public health concern, resulting in substantial morbidity and mortality worldwide. Early diagnosis and effective treatment are essential to prevent the disease’s progression to chronic liver disease. Machine learning algorithms have been increasingly used to develop predictive models for various diseases, including hepatitis C. This study aims to evaluate the performance of several machine learning algorithms in diagnosing chronic liver disease, with a specific focus on hepatitis C, to improve the cost-effectiveness and efficiency of the diagnostic process. We collected a comprehensive dataset of 1801 patient records, each with 12 distinct features, from Jordan University Hospital. To assess the robustness and dependability of our proposed framework, we conducted two research scenarios, one with feature selection and one without. We also employed the Sequential Forward Selection (SFS) method to identify the most relevant features that can enhance the model’s accuracy. Moreover, we investigated the effect of the synthetic minority oversampling technique (SMOTE) on the accuracy of the model’s predictions. Our findings indicate that all machine learning models achieved an average accuracy of 83% when applied to the dataset. Furthermore, the use of SMOTE did not significantly affect the accuracy of the model’s predictions. Despite the increasing use of machine learning models in medical diagnosis, there is a growing concern about their interpretability. As such, we addressed this issue by utilizing the Shapley Additive Explanations (SHAP) method to explain the predictions of our machine learning model, which was specifically developed for hepatitis C prediction in Jordan. This work provides a comprehensive evaluation of various machine learning algorithms in diagnosing chronic liver disease, with a particular emphasis on hepatitis C. The results provide valuable insights into the cost-effectiveness and efficiency of the diagnostic process and highlight the importance of interpretability in medical diagnosis.
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