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.

The exponential speed of advancement of innovation has expanded the needs of all users to avail all their information on the Internet 24/7. The Internet of things (IoT) enables smart objects to develop a significant building block in the development of the pervasive framework. The messaging between objects with one another means the least work and least expense for the enterprise. The industry that intends to implement the Internet of medical things (IoMT) in its organizations is still facing difficulties. Recognition and solving of these challenges are a time-consuming task and also need significant expenses if not adequately evaluated and prioritized. The application of the Internet of things is covered in almost every area, including medical/healthcare. In this research, the authors investigated the factors dealing with the Internet of medical things. The outcome of this study is to prioritize the level of significance of the elements causing these challenges, evaluated through fuzzy logic and multicriteria decision-making (MCDM) techniques like Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP). It would be beneficial for enterprises to save time and revenue. The main criteria, as well as subcriteria, were determined after due consultation with the Internet of medical things experts. In this study, our goals are to figure out which criteria/factors create hurdles in the adoption of the Internet of medical things. Through the investigation, we figured out 20 criteria ought to be given more importance/preference by the industry that is in the transition phase of the Internet of medical things adoption. The enterprise, with the help of this study, will be enabled to accelerate that adoption by limiting time and fiscal misfortune.

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Existing studies on business process improvement (BPI) have widely focused on development of separate techniques for resource management and structural redesign of processes. In this study, we contend that the relationship of resource with task is not sufficiently understood. Particularly, business intelligence based approaches to BPI have not adequately explored this relationship. To understand this, we classify resources into human and non-human resources, because the two types of resources behave differently leaving different impact on process performance. Subsequently, a set of relationships between human resources, non-human resources and tasks (named suitability, preference and competence) are presented. However, only human resource's relationship with non-human resource/s (named preference) is presented in detail, as a resource preference model. The model bundled with the presented preference evaluation method guides users for evaluation of resources' preference. The applicability of the method is illustrated through a healthcare case study.

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