Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

A grave concern to an organization’s information security is employees’ behavior when they do not value information security policy compliance (ISPC). Most ISPC studies evaluate compliance and noncompliance behaviors separately. However, the literature lacks a comprehensive understanding of the factors that transform the employees’ behavior from noncompliance to compliance. Therefore, we conducted a systematic literature review (SLR), highlighting the studies done concerning information security behavior (ISB) towards ISPC in multiple settings: research frameworks, research designs, and research methodologies over the last decade. We found that ISPC research focused more on compliance behaviors than noncompliance behaviors. Value conflicts, security-related stress, and neutralization, among many other factors, provided significant evidence towards noncompliance. At the same time, internal/external and protection motivations proved positively significant towards compliance behaviors. Employees perceive internal and external motivations from their social circle, management behaviors, and organizational culture to adopt security-aware behaviors. Deterrence techniques, management behaviors, culture, and information security awareness play a vital role in transforming employees’ noncompliance into compliance behaviors. This SLR’s motivation is to synthesize the literature on ISPC and ISB, identifying the behavioral transformation process from noncompliance to compliance. This SLR contributes to information system security literature by providing a behavior transformation process model based on the existing ISPC literature.

This paper provides a systematic literature review in the information security policies’ compliance (ISPC) field, with respect to information security culture, information security awareness, and information security management exploring in various settings the research designs, methodologies, and frameworks that have evolved over the last decade. Studies conducted from 2006 to 2016 reporting results from data collected through diverse means have been explored; however, only a few studies have focused primarily on a sensitive infrastructure under risk, as is the case with higher education institutions (HEIs). This study reports that ISPC in HEIs remains scarce, as is the realization of security threats and dissemination of information security policies to end users (employees). This research makes a novel contribution to the body of knowledge as a unique study that has reviewed the influence of institutional governance in HEIs on protection motivation leading towards ISPC.

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou’s Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D Dominic Communicated by Ramaswamy H. Sarma

Blockchain is a cutting-edge technology that is transforming and reshaping many industries. Hence, the adoption of Blockchain is becoming an increasingly significant topic. The number of publications discussing the potential of Blockchain adoption has been expanding significantly. In addition, not enough attention has been given to Blockchain adoption in the software development industry. As a result, a systematic overview to investigate the research trends in this area is needed. This study uses a Scientometric analysis and critical review to examine the evolution of Blockchain adoption research on the Web of Science Principal Collection. In addition, a systematic literature review (SLR) was conducted to identify gaps in Blockchain adoption research and the top reasons for adopting Blockchain with the intention of proposing a sustainable adoption framework. This study extends the body of knowledge by discussing the most influential countries, authors, organizations, publication themes, and most cited publications on Blockchain adoption research. Additionally, this study identifies the 30 relevant studies from the Web of Science and Scopus, including their industries, countries, methods, and respondent sample size, and the top 18 adoption factors among them. Consequently, this study proposes a suitable Blockchain adoption framework based on these top 18 factors. Finally, this study’s aim and unique contribution is to serve as an initial launching point for upcoming Blockchain adoption in software development industry research.