Nexus between economic sanctions and inflation: a case study in Iran

Conventional studies have applied dummy variables to analyse the relationship between economic sanctions and inflation while we construct an index which is called Trade-Financial Sanctions (TF index). TF Index is a liner combination of indices which includes trade openness and foreign investment by applying the principal component model. Through the TF index and market exchange rate the impact of economic sanctions on inflation is analysed in the three phases of sanctions; free sanctions, heavy sanctions, and light sanctions. The results illustrate that the TF index decreases inflation when the Iran’s economy experiences free sanctions or light sanctions relative to when the economy is in heavy sanctions. Heavy sanctions create instability in the market exchange rates and widening the gap between the market and the official exchange rates. Furthermore, economic sanctions increase expected inflation among the people and drive higher inflation. Therefore, these results suggest that the government should work more seriously to solve the main obstacles of trade and investment inflows imposed by the economic sanctions.     

A New Decision Making Model for Subcontractor Selection and Its Order Allocation

All experts agree on the importance of subcontracting. The high impact of subcontractors on the construction process means that the selection of subcontractors is a sensitive activity. Previous investigations documented the selection of subcontractors based on criteria but did not consider the number of subcontracted tasks. This paper explores allocating the best portion of tasks to subcontractors while optimizing the risk and cost in the fixed project schedule. The study's main finding demonstrates that subcontractor selection without attention to the order allocation is not a realistic approach; therefore, a hybrid model that applies continuous ant colony and fuzzy set theory is proposed.     

Facilitating knowledge management strategies through IT and HRM

In an increasingly competitive environment, where new business practices are regularly introduced, organizations have to be innovative to survive. In the present competitive climate, knowledge is considered as the main distinguishing factor of business success, and it is seen as the foundation of organization＇s innovation. The emergence of knowledge-intensive society has changed the nature of business competition. Hence knowledge needs to be appropriately managed. Knowledge Management （KM） focuses on managing different knowledge processes such as acquiring, creating, storing, sharing, transferring and applying implicit and explicit knowledge with objective of product and process innovation, performance development and sustainable competitive advantage. This paper tries to demonstrate KM lead to promotion of innovation and performance when it is correctly supported by human resource management （HRM） and information technology （IT）. The questions we will try to investigate in this paper are： How knowledge in organizations can be managed？ What is the connection between HRM, IT and effective implementation knowledge management strategies and how these relationships affect on organizational goals？ For this means, this study ftrst states the importance of knowledge, KM process and introduces two strategies for managing knowledge （exploitative and explorative strategy）. Next, it demonstrates each strategy requires to desired facilitator to support them in action. Finally, with presenting of model, this study concludes that each one of strategy can cover some KM process. Hence for the effective implementation of knowledge management process, organizations have to utilize both strategies.     

A New Linear Programming Method for Weights Generation and Group Decision Making in the Analytic Hierarchy Process

This paper proposes a new linear programming method entitled by LP-GW-AHP for weights generation in analytic hierarchy process (AHP) which employs concepts from data envelopment analysis. We propose four specially constructed linear programming (LP) models which are used to derive weight vector from a pair-wise comparison matrix or a group of them. We can use both interval and relative importance weights for each decision maker in LP-GW-AHP. In this method, solving only one LP model is enough for local weights derivation from pair-wise comparison matrices. Five numerical examples are examined to illustrate the potential applications of the LP-GW-AHP method. We show that not only derived weights of the new method have slight differences than Saaty’s eigenvector weights but sometimes they are better than eigenvector method weights in the fitting performance index as well. LP-GW-AHP is compared with a method which has been recently proposed for the weights derivation in the group AHP and it becomes obvious that LP-GW-AHP provides better weights. The simple additive weighting method is utilized to aggregate local weights without the need to normalize them.