Digitalization is impacting almost every aspect of people’s life and is greatly affecting supply chain processes. At the same time, supply chains are becoming progressively more aware of their impacts on people, the planet and profits (triple bottom line perspective) and the concept of sustainable supply chain has received increasing attention among scholars and practitioners. In this context, this article aims to analyze scientific research on supply chain management in the field of sustainability and digitalization using a bibliometric approach. To this end, this article analyses 484 papers from Scopus database according to the following parameters: chronology, discipline, source, center, country and citations. The dataset consisted of articles from scientific journals published over the period 2002–2018. In addition, VOSviewer software is used to map the bibliographic material using the co-occurrence of keywords and most influential journals. The analysis reveals that: sustainability and digitalization are important emerging fields within supply chains that have increased significantly during the last ten years; the USA is the most influential country; and the topic is structured around 2 clusters (1) Supply chain management, concepts and practices and (2) energy related technologies. The results may facilitate the planning, designing, running and publishing of future research on this topic.

During Covid-19, organizations, particularly hospitals, encountered difficulties in providing services. Telemedicine has shown to be an alternative in service provision during these times. Based on this, acceptance of telemedicine in Jordanian public hospitals has become a very important issue to increase the attention of the health care organization toward it. A conceptual model was constructed based on previous literature. The model includes government policy, the capacity of external suppliers, and the capacity of the project team, top management support, as independent variables where their influence on the acceptance of telemedicine in Jordanian public hospitals as a dependent variable is examined. Respondents were chosen using a Purposive sampling technique. Questionnaires were delivered to 320 respondents using Google Forms. SEM was used for statistical analysis. The findings revealed that all the proposed factors including government policy, external supplier capacity, project team capacity, and top management support have a significant influence toward accepting telemedicine. The results of this study may aid Jordanian public hospitals in making the best use of the proposed factors to increase the acceptance of telemedicine in Jordanian public hospitals.

The goal of this study was to measure the main effects of e-marketing, e-WOM, and social media influencers on increasing the intention to purchase and enhancing customers’ happiness in the Amman stock exchange. 285 samples represented the research study samples which have been collected, analyzed, and used to discuss the research hypotheses. The research study gave results which showed that e-marketing, e-WOM, and social media influencers’ effects positively on increasing customers’ intention to buy and enhancing customers’ satisfaction and happiness. This research represented each main research variable through its main keys, this research represented e-marketing through internet usage benefits received, simple use with low cost, and behavior and action. The second main variable is the e-WOM, and it is represented in this research through: satisfaction, dissatisfaction and perceived novelty. The third main variable is the social media influencers and it’s represented by: expertise, trustworthiness, and attractiveness. The main output of this study is represented that using digital marketing channels, with knowing peoples’ opinion, and following social media influencers can give customers ability to decide which product have to buy in the way which they can get the maximum benefits. The novelty of this study lies in giving more details about the effects of e-marketing, e-WOM, and social media influencers which are still new fields, and it needs more research for discovering all dimensions. Also, this research is useful and innovative based on choosing the field of this study and it is the Amman stock exchange which can help people to know useful information about the nature of stock investment.

This study aims to examine the influence of brand image as a mediator between social media marketing activity and tourist intentions to visit tourist sites in Jordan. To test the hypotheses, we utilized the partial least square method and distributed a questionnaire survey to 400 visitors, receiving 289 responses for analysis using SMART-PLS4 software. The study made significant contributions to the literature on social media marketing by developing a research model that links social media usage to brand image and shopping intention, and by using statistical techniques to quantitatively assess the effect of social media marketing activity on the brand image as a mediator. The findings exposed that word of mouth, a construct of social media, significantly positively influenced tourists' purchase intention to visit sites in Jordan. This study sheds light on the importance of social media marketing and its impact on the tourism industry in Jordan, providing useful insights for practitioners and policymakers in the field.

Job selection and scheduling are among the most important decisions for production planning in today’s manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs & apos; processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational time.

Supplier selection management has been considered as an important subject for industrial organizations. In order to remain on the market, to gain profitability and to retain competitive advantage, business units need to establish an integrated and structured supplier selection system. In addition, environmental protection problems have been big solicitudes for organizations to consider green approach in supplier selection problem. However, finding proper suppliers involves several variables and it is critically a complex process. In this paper, the main attention is focused on finding the right supplier based on fuzzy multi criteria decision making (MCDM) process. The weights of criteria are calculated by analytical hierarchical process (AHP) and the final ranking is achieved by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS). TOPSIS advantage among the other similar methods is to obtain the best solution close to ideal solution. The paper attempts to express better understanding by an example of an automobile manufacturing supply chain.

Every nation in the globe endeavours to achieve sustainable growth in its various industries. The construction industry has an enormous contribution to the economy primarily for the developed nations experiencing urgent demands to develop and expand their infrastructure. While this expansion is taking place through projects, it is essential to find the factors affecting completion of project premium. This paper investigates the main time delay and cost overrun causes in construction projects within Oman related to clients, contractors, and consultants. Through a comprehensive literature review, reasons for delays and cost overrun are determined and a survey targeting 100 project managers was conducted to indicate top causes among them. The findings may help the government develop better policy and construction firms to modify criteria procedures to avoid such drawbacks.

The main issue for portfolio managers is to find a feasible schedule that guarantees the availability of the resources required by the activities at the dates they are scheduled, which is the classical objective of the Resource-Constrained Multi-Project Scheduling Problem (RCMPSP). This paper goes one step further and presents an algorithm that solves the RCMPSP where each project has a different priority for the organization. With this information, the algorithm uses a market-based mechanism to determine what activities should receive preferential treatment during the scheduling process. Therefore, among the multiple possible schedules that comply with the resource constraints (i.e. the solution provided by classical approaches to RCMPSP), our approach allows obtaining a schedule for the portfolio in which, beyond minimizing the duration of the portfolio, the activities are scheduled considering the portfolio manager’s priorities.

This paper analyzes the integration of two combinatorial problems that frequently arise in production and distribution systems. One is the Bin Packing Problem (BPP) problem, which involves finding an ordering of some objects of different volumes to be packed into the minimal number of containers of the same or different size. An optimal solution to this NP-Hard problem can be approximated by means of meta-heuristic methods. On the other hand, we consider the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which is a variant of the Travelling Salesman Problem (again a NP-Hard problem) with extra constraints. Here we model those two problems in a single framework and use an evolutionary meta-heuristics to solve them jointly. Furthermore, we use data from a real world company as a test-bed for the method introduced here.

Two procedures to evaluate fracture resistance of notched components are proposed in this contribution: the Strain Energy Density (SED) over a control volume and the Cohesive Zone Model (CZM). With the aim to simplify the application of the two fracture criteria, the concept of the ‘equivalent local mode I’ is presented. The control volume of the SED criterion and the cohesive crack of the CZM, have been rotated along the notch edge and centered with respect to the point where the elastic principal stress is maximum. Numerical predictions are compared with experimental results from U and V shaped notches under three point bending with notch root radius ranging from 0.2 to 4.0 mm. In parallel the loading conditions vary, from pure mode I to a prevailing mode II. All specimens were made of PMMA and tested at -60°C. The good agreement between theory and experimental results adds further confidence to the proposed fracture criteria.