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The purpose of this paper is to analyze merchandise trade patterns among the GCC states with the backdrop of economic diversification within these economies.

This empirical research quantitatively analyses patterns of merchandise trade among the GCC states during 1995-2015 with specific focus on concentration, diversification and similarity of (export and import) trade indices as well as diversification within GCC economies.

The paper concludes that while Bahrain merchandise export structure shows dissimilarity when compared with other GCC states during 1995 and 2015, its imports appear to be very similar with those of the rest. The other five GCC states show more similarity among themselves in both merchandise exports and imports than that of Bahrain. Only UAE has shown an increase in both concentration and diversification indices though the increased numbers are still lower than those of the other GCC states and low in absolute terms.

The GCC has embarked on economic diversification; however, there is relatively less trade within the GCC as compared with other regional trading blocks. The paper considers trade within the GCC to explore the degree of similarity, diversification and concentration of traded products of each country. Further study should analyze the impact of diversification on intra-GCC trade. The results of this paper will be of value to GCC policymakers for providing a clear rationale for boosting trade and diversification with the long-term goal of a single currency economic union.

This paper examines the nature of regional trade integration between the United States and Canada by using a Similarity Index that summarizes the behavior of exports of states along the U.S./Canadian border relative to U.S. states that are not on the Canadian border. An export Similarity Index is used to show the considerable importance of industry mix relative to distance. Similarity Index changes suggest that increased export sales between the U.S. and Canada between 1996 and 2001 were not primarily driven by proximity factors that underlie a regional phenomenon. Industry factors independent of location and distance were important contributors to changes in U.S. exports to Canada. The upshot is that global, not regional, factors may underlie increasing trade between the U.S. and Canada. That is, an apparent global phenomenon may have been mistaken for a regional one.

A similarity index of maximum residue level (MRL) regulations is introduced into a variable elasticity of substitution (VES) model to analyze the impacts of MRL regulation similarity on trade flows and social welfare. We specially consider the situation where the requirements set by the importing country are stricter than those of the exporting country. We find that the more similar the MRL regulation between trading partners is, the more substitutable their goods are, and for the consumers that have home preferences for domestic goods, they prefer the imported goods that are more similar to the domestic goods. Our results also show that if the developing countries upward harmonized their MRL standards to developed countries, their exports would expand.

The purpose of this paper is to investigate a novel generalised grey target decision method (GGTDM) with index and weight involving mixed attribute values.

The mixed attribute values are transformed into binary connection numbers and also comprised of two-tuple (determinacy, uncertainty) numbers to fulfil the decision-making task. The proposed method constructs the weight function to convert the mixed attribute-based weights into the certain number-based weights and determines the alternatives ranking by the comprehensive weighted Gini–Simpson indices (CWGSIs).

The result of decision making regarding the numerical example by the proposed approach is somewhat different from that obtained by the reported vector-based method. The reasons for this are threefold: the decision-making bases are different, the target centre indices are determined by different mechanisms and certain number-based weights are calculated in different ways.

The proposed method ranks an alternative based on the Gini–Simpson index, as derived from the viewpoint of measuring the uncertainty (heterogeneity): however, the vector-based GGTDM makes a decision based on proximity, as is the case when measuring the similarities between index vectors.

The proposed approach is admissible to solving mixed attribute-based decision making especially for alternative indices and attribute weights containing both uncertain numbers.

The proposed method provides a new perspective on measuring the difference of alternatives to the target centre via the CWGSI: the CWGSI is obtained by relying on the pseudo-probabilities achieved by the ratios of the alternative indices to the target centre indices. It also builds a weight function converting the mixed attribute-based weights into certain number-based weights. This method provides a framework that should be tested in terms of its effective decision making using real data and an actual problem.

This paper aims to assess knowledge relatedness as a possible determinant of business innovation performance. Knowledge relatedness is understood as the degree of similarity between a firm’s knowledge and that of its parent, i.e. the company that the entrepreneur leaves to establish his or her own firm. Innovation performance results from the competitive position that the company achieves through its management of new products and services on the market.

For the empirical work, the authors used a database composed of 356 entrepreneurs who established recently their own business in Costa Rica: people who stopped working in multinational companies in Costa Rica and created their own businesses, and people who created their own businesses simultaneously as the former employees of multinationals.

This paper reports a positive and significant correlation between knowledge relatedness and innovation performance for a number of young firms.

This paper presents the fact of including knowledge relatedness as a research topic linked to business innovation.

The paper introduces bibliometrics to the research area of knowledge organization – more precisely in relation to construction and maintenance of thesauri. As such, the paper reviews related work that has been of inspiration for the assembly of a semi‐automatic, bibliometric‐based, approach for construction and maintenance. Similarly, the paper discusses the methodical considerations behind the approach. Eventually, the semi‐automatic approach is used to verify the applicability of bibliometric methods as a supplement to construction and maintenance of thesauri. In the context of knowledge organization, the paper outlines two fundamental approaches to knowledge organization, that is, the manual intellectual approach and the automatic algorithmic approach. Bibliometric methods belong to the automatic algorithmic approach, though bibliometrics do have special characteristics that are substantially different from other methods within this approach.

The purpose of this paper is to develop the methodology which can facilitate the concept of reconfiguration in the manufacturing system.

Design methodology includes the calculation of similarity matrix, formation of part family, and selection of part family. ALC algorithm has been used for part family formation and three criteria have been considered for the selection of part family. These criteria are reconfiguration effort, under-utilization cost, and floor space cost. AHP has been used to calculate the weights of criteria and reference ideal method has been used for the selection of alternatives.

In the manufacturing system, machines should be grouped on the basis of reconfiguration cost. When the time period is less, light machines and Group 1 machines are added and removed. In the case study, the concept of reconfiguration is useful for families (A, B, C, D). Machines can be reused by adding/removing some modules of machines. The concept of reconfiguration becomes more useful when it is implemented with lean manufacturing. Lean manufacturing techniques Jidoka and Poka-yoke are used to increase the diagnosability of the system.

Industrial case study has been considered.

Market competition is increasing rapidly and it increases the demand and variety of products, due to which manufacturing enterprises are forced to adapt a manufacturing system which can adjust its capacity and functionality quickly at low cost. To reconfigure manufacturing system from one product/product family to another product/product family, changes can be done in hardware and/or software components in response to sudden changes in the market or in regulatory requirements.

An integrated approach for reconfiguration has been proposed considering the industrial application. It includes weighted Jaccard function, ALCA, AHP, RIM. The methodology for calculation of reconfiguration effort, under-utilization cost, and floor space cost has been presented for industrial case.

The purpose of this study is twofold; first, it aims to understand the underlying dynamics of the organizations behind the terrorist attacks, and second, to investigate the dynamics of terrorist organizations in relation to one another to detect whether there exist shared patterns of terror between different organizations.

To achieve this purpose, the researcher proposes a computational algorithm that extracts data from global terrorism database (GTD); calculates similarity indices between different terrorist groups; generates a network data file from the calculated indices; and apply network analysis techniques to the extracted data. The proposed algorithm includes applying SQL database codes for data extraction, building a tailored C# computer software to calculate similarity indices and generate similarity networks and using GEPHI software to visualize the generated network and calculate network metrics and measures.

Applying the proposed algorithm to Egypt, the results reveal different shared patterns of terror among different terrorist groups. This helps us in creating a terror landscape for terrorist groups playing in Egypt.

The importance of the study lies in that it proposes a new algorithm that combines network analysis with other data-manipulation techniques to generate a network of similar terror groups.

This study aims to provide a way to derive inter-brand similarities from user-generated content on online brand forums, which enables the authors to analyze the market structures based on consumers' actual information searching and sharing behavior online. This study further presents a method for deriving inter-brand similarities from data on how the sales of competing brands covary over time. The results obtained by the above two methods are compared to each other.

In drawing similarities between brands, the authors utilized a newly proposed measure that modified the lift measure. The derived similarity information was applied to multidimensional scaling (MDS) to analyze the perceived market structure. The authors applied the proposed methodology to the imported car market in South Korea.

In light of some clear information such as the country of origin, the market structure derived from the presented methodology was seen to accurately reflect the consumer's perception of the market. A significant relevance has been found between the results derived from user-generated online content and sales data.

The presented method allows marketers to track changes in competitive market structures and identify their major competitors quickly and cost-effectively. This study can contribute to improving the utilization of the overflowing information in the big data era by proposing methods of linking new types of online data with existing market research methods.

The optimization of quality‐of‐service (QoS) aware service selection problems is a crucial issue in both grids and distributed service‐oriented systems. When several implementations per service exist, one has to be selected for each workflow step. This paper aims to address these issues.

The authors proposed several heuristics with specific focus on blackboard and genetic algorithms. Their applicability and performance has already been assessed for static systems. In order to cover real‐world scenarios, the approaches are required to deal with dynamics of distributed systems.

The proposed algorithms prove their feasibility in terms of scalability and runtime performance, taking into account their adaptability to system changes.

In this paper, the authors propose a representation of the dynamic aspects of distributed systems and enhance their algorithms to efficiently capture them.

By combining both algorithms, the authors envision a global approach to QoS‐aware service selection applicable to static and dynamic systems.

The authors prove the feasibility of their hybrid approach by deploying the algorithms in a cloud environment (Google App Engine), that allows simulating and evaluating different system configurations.