Managing sustainable practices has received increasing attention in the field of supply chain management to tackle environmental, financial and social issues in an integrated way. However, extant research still lacks a systemic framework explaining how sustainable practices can support individual companies to promote orchestration, collaboration, and coordination in the entire supply chain. This paper aims to provide a systemic framework including a taxonomy of sustainable development practices supporting individual firms in managing supply chain relationships. The proposed framework seeks to connect the supply chain practices and the technological systems that support them. Specifically, the findings highlight the relationship between technology capabilities and supply chain practices, focusing on the benefits their interaction brings to the complex dyadic relationships of supply networks. Finally, this study outlines promising future research directions for scholars and practitioners operating in the field of sustainable supply chain design and supply chain relationship management.

Purpose: Nowadays, a prominent research area is the development of competitive advantages in companies, due to their environmental commitment and orientation. Based on resource-based view (RBV) and institutional theory (InT), this paper aims to investigate the influence of internal and external orientation on businesses' sustainable performance while considering the effect of sustainable supply chain management (SSCM) practices.Design/methodology/approach: Data from 351 manufacturing companies in the Kingdom of Saudi Arabia have been collected and analysed through structural equation modelling (SEM) using the partial least squares (PLS) method.Findings: The results indicated that both internal and external environmental orientation have important effects on SSCM practices, which in turn have a considerable beneficial effect on environmental, social and economic performance.Originality/value: Although SSCM is constantly gaining ground in the literature, most SSCM research and models examine its effects, antecedents or motivation, mainly adopting a qualitative approach. Research on the topic adopting a large-scale empirical approach is still limited. In this context, this study contributes to the SSCM management literature by exploring the role of environmental orientation in facilitating the adoption of SSCM practices and improving companies' performance.

In the last few years, the use of educational technology, particularly the concept of Learning Management System (LMS), has increased rapidly. With this fast development, the question arises as how to manage the LMS to obtain success and efficiency in online courses. One of the important factors that have received many citations in literature studies (and has a special position in information system research) is the user satisfaction. It is a crucial factor that can predict the success or failure of any LMS. In relation, this research examined the success factors that affect the user satisfaction and outcomes of LMS. This paper discusses the conceptual User Satisfaction Evaluation Model (USEM) employed to measures LMS success. In particular, it seeks to examine “the relationship between: Service quality, system quality, ease of use, perceived usefulness, information quality and students satisfaction, as well as to measure the outcomes of the LMS.” Results from the data analysis indicate that all proposed factors have a positive effect on student satisfaction. The result also concludes that a higher rate of user satisfaction will lead to greater benefits for the students.

The conventional Hotelling’s charts are evidently inefficient as it has resulted in disorganized data with outliers, and therefore, this study proposed the application of a novel alternative robust Hotelling’s charts approach. For the robust scale estimator , this approach encompasses the use of the Hodges-Lehmann vector and the covariance matrix in place of the arithmetic mean vector and the covariance matrix, respectively. The proposed chart was examined performance wise. For the purpose, simulated bivariate bootstrap datasets were used in two conditions, namely independent variables and dependent variables. Then, assessment was made to the modified chart in terms of its robustness. For the purpose, the likelihood of outliers’ detection and false alarms were computed. From the outcomes from the computations made, the proposed charts demonstrated superiority over the conventional ones for all the cases tested.