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Purpose – The purpose of this study is to develop a model for an agile supply chain in the pharmaceutical industry. In a continuous changing global competitive environment, an organization’s supply chain agility directly impacts its ability to produce and deliver novel products to its customers in a timely and cost-effective manner. While the beneficial effect of supply chain agility is generally appreciated, the literature addressing how a pharmaceutical company can achieve supply chain agility is limited. Design/methodology/approach – This paper analyzes the three parts of pharmaceutical supply chain including supply of active pharmaceutical ingredient, manufacturing and distribution based on the supply chain operations reference model to assess agile supply chains by using three diverse questionnaires. In addition, to prioritize critical factors, TOPSIS (technique for order preference by similarity to ideal solution) algorithm as a common technique of multiple attribute decision-making (MADM) model has been used. Findings – Achieving supply chain agility is dependent on other capabilities; including flexibility, responsibility, competency and quickness. Findings reveal several factors identified as critical factors to being agile in each part of pharmaceutical supply chain. Research limitations/implications – This research was challenged with some limitations such as novelty of the subject in this environment, and the lake of data in this area is also another constraint. Originality/value – This is an initial and pioneering study to highlight the importance of agility concept in the pharmaceutical industry. The present study also provides a new aspect of supply chain management for such industry, and would be a good topic for further research. Finally, this study contributes to highlight and prioritize factors involved in this area.

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Purpose This study aims to present a systematic review of smart tourism articles using a meta-synthesis method. Effective systematic reviews are essential for assisting stakeholders in implementing smart destinations. A systematic comprehension of studies on smart tourism is needed regarding the various components of smart tourism destinations, the metrics to map these components and their expected results. This study creates a framework for understanding how smart tourism destinations are theorized and developed. Design/methodology/approach Based on the meta-synthesis approach, this study collects, analyzes and synthesizes relevant research in smart tourism published in online databases by following a predetermined review protocol. Findings This study contributes to the discourse on smart tourism destinations by increasing the knowledge on the subject of smart destinations in regard to different categories. The selected articles were analyzed according to the proposed research questions and classified into three main categories: components, measurement and outputs. This study presents a new archetype for developing smart destinations and addresses efforts to bridge the gap in this research field. Practical implications This paper is noteworthy for stakeholders because it provides a comprehensive vision into the components that influence the growth of smart destinations. The advantage of the proposed methodology is that it creates a framework for understanding how smart destinations are theorized. Furthermore, it is helpful to use qualitative methodologies that efficiently allow the analysis of related literature and that also offer conceptual insights. Originality/value The findings provide information that can be used to help shape a fully conceptualized understanding of the smart destinations concept and can also prove important in providing a guide for policymakers and stakeholders in the tourism industry who seek to intelligently develop tourism destinations.

The main goal of all commercial banks is to collect the savings of legal and real persons and allocate them as credit to industrial, services and production companies. Non repayment of such credits cause many problems to the banks such as incapability to repay the central bank's loans, increasing the amount of credit allocations comparing to credit repayment and incapability to allocate more credits to customers. The importance of credit allocation in banking industry and it's important role in economic growth and employment creation leads the development of many models to evaluate the credit risk of applicants. But many of these models are classic and are incapable to do credit evaluation completely and efficiently. Therefore the demand to use artificial intelligence in this field has grown up. In this paper after providing appropriate credit ranking model and collecting expert's knowledge, we design a hybrid intelligent system for credit ranking using reasoning-transformational models. Expert system as symbolic module and artificial neural network as non-symbolic module are components of this hybrid system. Such models provide the unique features of each components, the reasoning and explanation of expert system and the generalization and adaptability of artificial neural networks. The results of this system demonstrate hybrid intelligence system is more accurate and powerful in credit ranking comparing to expert systems and traditional banking models.

With the recent growth and advancement in Information Technology, data has produced at a very high rate in a variety of fields, which have presented to users in a structured, semi-structured, and non-structured mode [1]. New technologies for storing and extracting useful information from this volume of data (big data) have needed because the discovery and extraction of useful information and knowledge from this data volume are difficult, hence, other traditional relational databases cannot meet the needs of users [2]. If you are dealing with data beyond the capabilities of existing software, you are, in fact, dealing with big data. Large data is commonly referred to as a set of data that exceeds the extent to which it can be extracted, refined, managed, and processed by standard management tools and databases. In other words, the term "big data" refers to data that is complex in terms of volume and variety; however, it is not possible to manage them with traditional tools, and therefore, they cannot extract their hidden knowledge and knowledge at predetermined times [3, 4]. Big data is, therefore, defined with three attributes of volume, velocity, and variety that are called Gartner's commentary; some scholars have in addition; IBM cited the

Purpose The purpose of this paper is to design a qualitative model of crowdfunding dynamics through the document model building (DMB). Design/methodology/approach Methodology in this paper is the qualitative system dynamics through DMB. In DMB, the authors identify the variables that are drivers of its growth and collapse, and the model will be developed by using the systematic review of the literature. Findings Designing of the dynamics of crowdfunding model through DMB. Identifying variables that are drivers of crowdfunding growth and collapse. Determining leverage points in crowdfunding diffusion. Originality/value This paper, for the first time, with the aim of identifying and explaining the efficient positive and negative dynamics in this method, examines crowdfunding systematically and structurally.