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This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

Accelerating digital transformation and the basic needs of businesses to adapt to environmental transformations and complexities have doubled the necessity for extensive stakeholder interactions. Gamification is a powerful tool that facilitates stakeholder interaction and a common understanding of the vision and contributes to the sustainability of businesses. Businesses' sustainability in the digital transformation age depends on the continuous interaction of stakeholders and a comprehensive understanding of all the business pillars, which will be possible through the flow of ideas inside and outside the workplace and by providing innovative processes. In this regard, this study explores the effect of gamification on corporate sustainability by explaining the role of digital transformation, open innovation, and value co-creation. The statistical population of this study comprises top managers and experts in e-businesses who use gamification in their processes. The statistical sample included 117 managers and experts active in this field, and they were selected through convenience sampling. The data collection tool was a questionnaire reliability of which was 0.763 using Cronbach's alpha. Using Smart PLS software, the gathered data were analyzed by structural equation modeling (SEM). Gamification, with a factor of 74%, has a positive effect on open innovation, while a factor of 85% has a positive effect on digital transformation. Gamification, both directly and indirectly, creates value co-creation in businesses, and finally, value co-creation at 78% affects corporate sustainability.

Despite all blockchain's advantages, it has experienced some challenges and disadvantages. Identifying these challenges and minimizing them are important steps in advancing the goals of this technology. The purpose of this study is to investigate and identify the challenges of using blockchain technology in international markets with regard to the risks of such newfound technologies. This is an applied research in terms of purpose and a mixed research in terms of methodology. The effective factors in using blockchain technology have been discovered by the meta-synthesis method at first; then the ranking of these factors has been done by the fuzzy pairwise comparison. A systematic search was performed in the database of Science Direct, Emerald, and Google Scholar. Also, the seven-step method of Sandlowski and Barroso (2007) was applied. All the related articles and the challenges of using blockchain in international markets were identified and coded. The comparison method between the two coders was used to adjust the reliability and the kappa coefficient was used to calculate the correlation coefficient between the two codes. The findings of the extracted sources were finally proposed and 41 challenges were classified into 6 categories of technical, educational, structural, market, infrastructure and legal which divided in both internal and external areas.

Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.  One of the most growing attacks is on Android systems due to its use in many devices worldwide.  De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance.  There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families.  Hence, understand the distinct ob-jective of malware after identifying their family and category.  This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CIC-AndMal2020 on dynamic layers to detect Android malware categories and families.  The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work.  The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods.  The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware.