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Natural gas makes up about 50 per cent of Pakistan's energy consumption, which is decreasing at an alarming pace, making Karachi – the largest city of Pakistan – one of the major victims of natural gas shortfall. The purpose of this paper is to model natural gas demand and supply using geographic information systems.

Sui Southern Gas Company datasets were used to derive annual per capita gas usages and to analyze the capacity of gas supplies. Union Council is taken as a smallest and basic analytical administrative spatial unit for appraisal of badly affected, with insufficient, low and better gas supply regions.

Results show that the per capita demand for natural gas is higher in high class residential areas while supply is lowest for upper middle and lower middle class residential area populations. Only 19 per cent of the total residential areas are expected to be free from gas shortfalls. About 53 per cent of residential areas are facing the problem of low gas availability; and 3 per cent and 11 per cent areas are under stress of badly affected and insufficient gas supply, respectively.

This research shows an example of constructing a Geographic Information System (GIS)‐based gas demand and supply model that can be used for building strategic guidelines for top‐level planners, engineers and decision makers at the micro‐level. This is not a “deep” paper, but it works in identifying a problem and presenting a quantitative way forward. This paper will have significant impact both on society and academia, as this innovative technology and its implementation on gas utility has happened for the first time in Pakistan. Furthermore, in the international literature this has also been in infancy and will open new avenues of research, especially in developing countries. The work would certainly be applicable for other consumer‐based utilities such as electricity, telecommunication, sewerage and municipal water and they would all benefit from it substantially.

The purpose of this paper is to examine the diversification effect of the Korean Ship Investment Fund (KSF) under Markowitz portfolio theory by analyzing short-term and long-term relationships with stocks and bonds.

For this purpose, unit root, correlation and cointegration tests are performed. Monthly data from 2004 to 2015 for stocks, bonds and KSFs are obtained for this study.

The correlation coefficients indicate that KSFs are uncorrelated with stocks and negatively correlated with bonds, and no long-term equilibrium relationships exist with all three variables by the Johansen and Engle-Granger cointegration tests.

This paper makes contribution to the literature as follows: first, whereas the previous literature investigated diversification effect of ship investment using freight indices or freight rates which are not able to represent returns from ship investment, this study is the first study to use actual stock prices of the KSFs to the authors’ best knowledge; and second, diversification effect of ship investment represented by KSFs is empirically verified in the both short term and long term.

Policy-makers and managers of shipping companies can have sound ground that the KSFs are alternative and attractive assets to investors. It is also shown that the KSFs have potential to improve risk and return structure of investors on their own regardless of existence of incentives. Therefore, decisions of policy-makers can be made free from expectations for stronger incentives provided by the government. In addition, those countries that do not have such a ship investment platform may consider introducing a similar ship investment fund in order to revitalize the capital markets of the country.

This study holds its significance in investigating diversification properties of the KSFs for the first time in Korea since the KSFs were introduced.

To foster environmental and sustainable practices, foodservice enterprises should exhibit a high degree of corporate social responsibility (CSR), ecological consciousness and innovation in green initiatives. This study examines the influences of CSR practices (i.e. economic, legal, ethical and philanthropic), environmental marketing activities (i.e. strategic and tactical) and innovation (i.e.technological and organizational) as drivers of market and eco-performance in the foodservice industry.

Based on an established framework of CSR, environmental marketing and innovation, an exploratory conceptual model was formulated and empirically assessed. Survey data were collected from representatives of the Korean foodservice franchise industry. Data analysis consisted of frequency analysis, reliability analysis, confirmatory factor analysis, correlation analysis and path analysis.

Market performance was influenced by tactical environmental marketing, as well as technological and organizational innovation. Eco-performance was affected by tactical environmental marketing and technological innovation. However, technological innovation and organizational innovation were influenced by tactical environmental marketing, but were not significantly affected by strategic environmental marketing. Additionally, strategic environmental marketing was influenced by economic, legal, ethical and philanthropic CSR. Last, tactical environmental marketing was affected by economic, ethical and philanthropic CSR.

CSR, environmental marketing activities and innovation provide numerous benefits to businesses. Such benefits include enhanced market and eco-performance, both of which create competitive advantages.

There is a paucity of research on the effects of environmental marketing and innovation on corporate performance in the foodservice industry. The findings provide greater insights into the impacts of CSR, environmental marketing and innovation on corporations’ desired outcomes.

The main streams of the supply chain are defined as material, information and financial flow. There have been many studies and practical cases regarding the flow of material and information including information sharing. However, financial flow related studies have not been widely examined relatively, compared with their importance.

The information sharing is recognized as the method that can reduce the Bullwhip effect in supply chain management. The author intends to analyze the impact of financial information sharing on the results of the supply chain.

In the point of supply chain risk management view, the author examined the impact of financial flow among the various factors that can impede the stability of the supply chain.

In this study, the author embodied the simulation regarding the impact of financial information flow on supply chain performance and stability based on the system dynamics methodology and analyzed the performance.

Assuming the supply chain, composed of supplying company, manufacturing company and sales company , the author embodied the simulation model and assumed that working capital and cash information sharing were achieved. The author embodied the model to affect the settlement conditions according to the results of financial information sharing.

The maritime freight transportation industry has played an important role in the Korean economy. The Korean maritime freight transportation industry is faced with a period of transforming it competitively and efficiently in this global age. This paper, therefore, aims to identify the impact of the maritime freight transportation industry in the Korean national economy. Hence, this paper provides policy-makers with accessible and reliable information regarding the role of the Korean maritime freight transportation industry. This study employs input-output (I-O) analysis to examine the role of the maritime freight transportation industry in the national economy for the period 1995-2003, with specific application to Korea. This study pays particular attention to the maritime freight transportation industry by taking the industry as exogenous variable and then investigates its economic impacts. We identify inter-industry linkage effects in 20 sectors, production-inducing effects, added value-inducing effects, and supply-shortage effects of the maritime freight transportation industry.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

Selective laser melting (SLM) is a major additive manufacturing (AM) process in which laser beams are used as the heat source to melt and deposit metals in a layerwise fashion to enable the construction of components of arbitrary complexity. The purpose of this paper is to develop a framework for accurate and fast prediction of the temperature distribution during the SLM process.

A fast computation tool is proposed for thermal analysis of the SLM process. It is based on the finite volume method (FVM) and the quiet element method to allow the development of customized functionalities at the source level. The results obtained from the proposed FVM approach are compared against those obtained from the finite element method (FEM) using a well-established commercial software, in terms of accuracy and efficiency.

The results show that for simulating the SLM deposition of a cubic block with 81,000, 189,000 and 297,000 cells, the computation takes about 767, 3,041 and 7,054 min, respectively, with the FEM approach; while 174, 679 and 1,630 min with the FVM code. This represents a speedup of around 4.4x. Meanwhile, the average temperature difference between the two is below 6%, indicating good agreement between them.

The thermal field for the multi-track and multi-layer SLM process is for the first time computed by the FVM approach. This pioneering work on comparing FVM and FEM for SLM applications implies that a fast and simple computing tool for thermal analysis of the SLM process is within the reach, and it delivers comparable accuracy with significantly higher computational efficiency. The research results lay the foundation for a potentially cost-effective tool for investigating the fundamental microstructure evolution, and also optimizing the process parameters in the SLM process.

China is currently developing and promoting an industrial cluster policy at the government level. By enacting the ‘Opinion on promoting industrial cluster development’, China is supporting the development of industrial clusters. Building an industrial cluster is done by using a single factor but requires many additional factors like regional characteristics, competitiveness factors are also diversified. To evaluate the competitiveness of the Chinese automobile industry cluster, a competitiveness element index should be developed and a competitiveness evaluation method is needed to evaluate the importance of each element. To accomplish this objective, this research applied the analytic hierarchy process (AHP) and focused on the importance of the competitiveness elements.

This research investigated the character is tics regarding cases of clusters and also analyzed the competitiveness of the Changchun automobile cluster located in northeastern China. The purpose of this research is to help Korean enterprises who enter China in the hopes that Korea will emerge as a top automobile production country.

The purpose of this paper is to examine the effects that reference prices and associated information sources (websites that consumers use to explore and their friends who have similar perspectives on value) have on deal evaluation and intention to disseminate electronic word of mouth (eWOM).

A stratified survey is conducted to empirically test the relations between reference prices, associated information sources (the top five Consumer-to-consumer (C2C) websites and top five Facebook friends with similar perspectives and values on consumption), deal evaluation, and eWOM intention. The study uses a Facebook API to help participants pick five Facebook friends to act as their favorite sources for advice on shopping.

The results suggest that consumers’ deal evaluations (as shaped by the recency effects of previous exposure to prices and the influence of Facebook friends and C2C websites) have carry-over effects on their eWOM intentions. The influence of Facebook friends and C2C websites on deal evaluation is as powerful as that of reference price, especially concerning the mean and the lowest prices.

The findings encourage marketers to invest their resources in targeting online groups, and suggest that C2C website marketers should set their offer prices between the mean and the lowest prices.

This study extends prior research on the motives for eWOM dissemination and elaborates an approach to initiate eWOM intention through deal evaluation.