Information Technology (IT) adoption is an important field of study in a number of areas, which include small and medium-sized enterprises (SMEs). Due to the numerous advantages of IT, SMEs are trying to adopt IT applications to support their businesses. IT adoption by SMEs differs from larger organizations because of their specific characteristics, such as resources constraints. Therefore, this research aims to provide a better and clearer understanding of IT adoption within SMEs by reviewing and analyzing current IT literature. In this research, the review of literature includes theories, perspectives, empirical research and case studies related to IT adoption, in particular within SMEs from various databases such as Business Premier, Science Direct, JStor, Emerald Insight and Springer Link. The proposed model of effective IT adoption is believed to provide managers, vendors, consultants and governments with a practical synopsis of the IT adoption process in SMEs, which will in turn assist them to be successful with IT institutionalization within these businesses

Purpose: The purpose of this paper is to develop and test a model to analyze the relationships between three aspects of technical electronic commerce (EC)-based information system (IS) resources; the supply chain process integration; and business value. Design/methodology/approach: The paper is consistent with the perspective on IS-enabled organizational capabilities and resource based view of the firm. A questionnaire-based survey was conducted to collect data from 214 supply chain, logistics, or procurement/purchasing managers of leading manufacturing firms. Findings: The findings suggest that supply chain process integration, a key EC-enabled organizational capability, can enhance business value. We found that this capability serve as a catalyst in transforming technical EC-based IS resources (technical quality of EC applications, EC advancements, and EC alignment) into higher value for a firm. Research limitations/implications: Among other limitations, this paper does not address human IS resources as the other potential determinants of firm's supply chain capabilities. Moreover, this study relies on cross-sectional data. Practical implications: The results suggest that supply chain process integration is an important intermediate organizational capability through which value of EC-based IS resources can be materialized. The technical aspects of EC-based IS resources need to be developed to effectively form supply chain capabilities. Originality/value: The paper is perhaps one of the first to show theoretically and empirically how firms, in particular in developing countries, can generate business value from ECenabled supply chain process integration; also it broadens the scope of EC alignment in relation to process integration and business value to the entire supply chain.

This paper seeks to develop and test a model to examine the relationships between, technical aspects of IS resources (IS alignment, IS resources technical quality, IS advancement), supply chain process integration, and firm performance. A questionnaire-based survey was conducted to collect data from 227 supply chain, logistics, or procurement/purchasing managers of leading manufacturing and retail organizations. Drawing on resources-based view of the firm, and through extending the concept of process integration in supply network, as well as broadening the scope of role of IS resources in relation to process integration and performance gain from the focal firm to the entire supply chain, we found that supply chain process integration is an important multidimensional intermediate organizational capability through which the value of IS resources for supply chain management can be materialized. This capability serves as a catalyst in transforming the value of technical aspects of IS resources into higher performance gain for a firm. Thus, the importance of formation of all dimensions of this capability across supply network should be realized. Moreover, the result suggests that the technical aspects of IS resources need to be jointly developed by supply partners to effectively form supply chain capabilities.

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Background: Trauma is the third leading cause of death in the world and the first cause of death among people younger than 44 years. In traumatic patients, especially those who are injured early in the day, arterial blood gas (ABG) is considered a golden standard because it can provide physicians with important information such as detecting the extent of internal injury, especially in the lung. However, measuring these gases by laboratory methods is a time-consuming task in addition to the difficulty of sampling the patient. The equipment needed to measure these gases is also expensive, which is why most hospitals do not have this equipment. Therefore, estimating these gases without clinical trials can save the lives of traumatic patients and accelerate their recovery. Methods: In this study, a method based on artificial neural networks for the aim of estimation and prediction of arterial blood gas is presented by collecting information about 2280 traumatic patients. In the proposed method, by training a feed-forward backpropagation neural network (FBPNN), the neural network can only predict the amount of these gases from the patient's initial information. The proposed method has been implemented in MATLAB software, and the collected data have tested its accuracy, and its results are presented. Results: The results show 87.92% accuracy in predicting arterial blood gas. The predicted arterial blood gases included PH, PCO2, and HCO3, which reported accuracy of 99.06%, 80.27%, and 84.43%, respectively. Therefore, the proposed method has relatively good accuracy in predicting arterial blood gas. Conclusions: Given that this is the first study to predict arterial blood gas using initial patient information(systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), and age), and based on the results, the proposed method could be a useful tool in assisting hospital and laboratory specialists, to be used. Keywords: Arterial Blood Gases, Trauma, Neural Network, Prediction.