Most of the sustainability management models proposed in the literature are based on conceptual designs, which limits their potential to be applied in practice. In order to develop a holistic tool for organizational sustainability management, it is imperative to account for, and integrate, the sustainability best practices in the conceptual models. The primary objective of this work is to explore the organizational sustainability themes, functional areas, and the corresponding best practices of the most sustainable organizations. Based on the results, we aim to propose a framework which can support the theoretical models. The starting set of 100 most sustainable organizations is obtained through a well-defined sustainability ranking, Global 100. A systematic method is developed and applied to screen the organizational reports between 2012 and 2016. As a result, 61 reports of 20 organizations are selected for review. We used grounded mechanism to conduct the review. The results of the review indicate that the most sustainable organizations rely on the following nine themes to advance their sustainability performance: (i) resource optimization and minimization of waste and emissions; (ii) business and operational excellence; (iii) corporate citizenship and social development; (iv) research and innovation; (v) procurement, supply chain, and logistics; (vi) governance; (vii) sustainability management tools; (viii) employee relations; and (ix) health, wellness, safety, and security. In addition, there are around 38 functional areas which are of great significance to sustainability managers from an applied perspective and to researchers for constructing sustainability management models.

The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world’s population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.

Developing means of secure and trustful communications for Vehicular Social Networks (VSN) is essential to enable active information sharing among vehicles. Reputation-based trust management system is a popular security mechanism used in vehicular networks and proven to be robust in many other applications. In this paper, we propose a similarity-based bootstrapping method using analytic hierarchy process (AHP) for trust management in VSN. Then we introduce a reputation mechanism that takes into account, the user behaviors as well as historic features such as the total distance driven. Our simulation results show that the proposed system is robust against high density of malicious nodes (up to 60%). Furthermore, we also prove the accuracy of the proposed system in different conditions.