Conducting business nowadays has become much more challenging, and the importance of collective behavioral capacity is paramount regarding the behavior of employees along with internal information systems that form the backbone of many organizations in terms of competitiveness and survivability. This study, therefore, examined the effect of commitment, communication, competency, community, connection, consideration, and coordination on information system artifacts and the effect of information system artifacts on organizational resilience among Malaysian SMEs. To verify these statements and positioning, a sample of employees from registered organizations of the Malaysian Digital Economic Corporation Sdn Bhd (MDEC), participated in this study to examine a number of aspects. Quantitative data were collected from a total of 252 respondents through a questionnaire survey and data were analyzed using PLS-SEM. We performed the endogeniety assessment for the all the seven input variables and confirmed lack of endogeniety issues. The findings of this study constitute essential results that the commitment, communication, competency, community, connection, consideration, and coordination positively influenced the ISA. Furthermore, the mediating effect of ISA on the relationships of organizational resilience with commitment, communication, competency, community, connection, consideration, and coordination, had been revealed. The study concluded that a positive set of behavioral capital, if it exists inside organizations, enables the organizations to prosper and survive. Similarly, policymakers need to address the issue surrounding resilience in SMEs by establishing the correct benchmarking mechanism by introducing it as a formal activity to be undertaken regularly within the SMEs. Given the vast majority of employment in Malaysia and globally is provided through SMEs, if they become more resilient to change and to unforeseen events, then the employment of the vast majority of the working class can be secured across different nations.Sustainability 2019, 11, 3177 2 of 23 important. Resilience can be described as the capacity to utilize internal resources, such as people and processes within an organization in dealing with uncertainty and misfortune and accomplishment. Mallak and Yildiz [1] revealed that organizational resilience is the most important attribute of modern organizations to remain competitive and likewise, organizations with poor resilience may not survive into the next decade. Therefore, organizational resilience is the capacity that can be developed and maintained at the organizational level through utilizing resources at the disposal of the organization [2]. From a developmental viewpoint, it is a means to manage, develop, and maintain the much-needed capacities to ensure the organization is resilient [3]. This also facilitates the progression of the organization at the national level by helping to created job opportunities. In this study, the capacity viewpoint concerning organizational resilience is premise...

E-commerce system has become more popular and implemented in almost all business areas. E-commerce system is a platform for marketing and promoting the products to customer through online. Customer segmentation is known as a process of dividing the customers into groups which shares similar characteristics. The purpose of customer segmentation is to determine how to deal with customers in each category in order to increase the profit of each customer to the business. Segmenting the customers assist business to identify their profitable customer to satisfy their needs by optimizing the services and products. Therefore, customer segmentation helps E-commerce system to promote the right product to the right customer with the intention to increase profits. There are few types of customer segmentation factors which are demographic psychographic, behavioral, and geographic. In this study , customer behavioral factor has been focused. Therefore users will be analyzed using clustering algorithm in determining the purchase behavior of E-commerce system. The aim of clustering is to optimize the experimental similarity within the cluster and to maximize the dissimilarity in between clusters. In this study there are relationship between three clusters: event type, products, and categories. In this research, the proposed approach analyzed the groups that share similar criteria to help vendors to identify and focus on the high profitable segment to the least profitable segment. This type of analysis can play important role in improving the business. Grouping their customer according to their similar behavioral factor to sustain their customer for long-term and increase their business profit. It also enables high exposure of the e-offer to gain attention of potential customers. In order to process the collected data and segment the customers, an learning algorithm is used which is known as K-Means clustering. K-Means clustering is implemented to solve the clustering problems.

PurposeThis study proposes to identify potential liver patients based on the results of a liver function test performed during a health screening to search for signs of liver disease. It is critical to detect a liver patient at an early stage in order to treat them effectively. A liver function test's level of specific enzymes and proteins in the blood is evaluated to determine if a patient has liver disease. Methods According to a review of the literature, general practitioners (GPs) rarely investigate any anomalies in liver function tests to the level indicated by national standards. The authors have used data pre-processing in this work. The collection has 30691 records with 11 attributes. The classification model is utilized to construct an effective prediction system to aid general practitioners in identifying a liver patient using data mining. ResultsThe collected results indicate that both the Naïve Bayes and C4.5 Decision Tree models give accurate predictions. However, given the C4.5 model offers more accurate predictions than the Naïve Bayes model, it can be assumed that the C4.5 model is superior for this research. Consequently, the liver patient prediction system will be developed using the rules given by the C4.5 Decision Tree model in order to predict the patient class. The training set, suggested data mining with a classification model achieved 99.36% accuracy and on the testing set, 98.40% accuracy. On the training set, the enhanced accuracy relative to the current system was 29.5, while on the test set, it was 28.73. In compared to state-of-the-art models, the proposed approach yields satisfactory outcomes. ConclusionThe proposed technique offers a variety of data visualization and user interface options, and this type of platform can be used as an early diagnosis tool for liver-related disorders in the healthcare sector. This study suggests a machine learning-based technique for predicting liver disease. The framework includes a user interface via which healthcare providers can enter patient information.

The goal of the work is to enhance existing financial market forecasting frameworks by including an additional factor–in this example, a collection of carefully chosen tweets—into a long-short repetitive neural channel. In order to produce attributes for such a forecast, this research used a unique attitude analysis approach that combined psychological labelling and a valence rating that represented the strength of the sentiment. Both lexicons produced extra properties such 2-level polarization, 3-level polarization, gross reactivity, as well as total valence. The emotional polarity explicitly marked into the database contrasted well with outcomes of the innovative lexicon approach. Plotting the outcomes of each of these concepts against actual market rates of the equities examined has been the concluding step in this analysis. Root Mean Square Error (RMSE), preciseness, as well as Mean Absolute Percentage Error (MAPE) were used to evaluate the results. Across most instances of market forecasting, attaching an additional factor has been proven to reduce the RMSE and increase the precision of forecasts over lengthy sequences.

Introduction: The spread of the COVID-19 virus and the supremacy of digital technologies have amplified global market volatility in all industries. This circumstance will have a lasting impact on students’ employability, so the education sector, particularly universities, should refocus its learning objectives. Design thinking (DT) is a collaborative and resourceful approach to problem-solving in which the demands of end-users and content creators take precedence. Objectives: In this study, the author seeks to comprehend how design thinking procedures in higher learning institutions inspire innovative behavior among undergraduate students. In light of the extensive literature regarding the adoption of Information and Communication Technology (ICT) in terms of innovative actions, this study integrates two theoretical foundations (i.e., activity theory to mediate the nature of human activity and how its internalization affects mental development) and constructive learning theory to enhance students’ innovative action. Methods: The data for this quantitative investigation were acquired using an online survey. A total of 300 questionnaires were delivered to undergraduate university students in the eastern part of Saudi Arabia, of whom 208 responded. SmartPLS was utilized to analyze the data. The methodology proposed in this study aims to cultivate in university undergraduate students the sensibility and techniques of designers that are compatible with technological feasible innovative action. Results: This study addresses technology-assisted education in the context of Saudi Arabia. Students’ innovative learning experiences are characterized by autonomy and are supported by design thinking processes mediated by information and communication technology (ICT). On the basis of the findings of this study, the role of empathy and prototype in the DT process appears to be crucial to innovativeness, whereas the roles of define and ideate are detrimental to innovativeness. It has also been determined that ICT indirectly promotes innovative student behavior. Conclusions: Students valued the incorporation of design thinking and ICT in the creation of inventive action to foster creativity in problem-solving skills throughout the digital acceleration. To evaluate the transferability of these findings, future study might be undertaken in other education sectors, such as schools, vocational institutes, and the industry itself. In addition, future data should be analyzed through in-depth interviews or root cause analysis from the perspective of educators and instructional designers.