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Emerald Group Publishing Limited
A special farewell
Article Type: Editorial From: Journal of Modelling in Management, Volume 9, Issue 2
The world is going through an incredible pace of change, a huge flow of mutation. New paradigm, new concepts and new techniques are changing the face of every walk of life. So, modelling has an increasing relevant role, by trying to, in an abstract way, replicate some of the critical facets in our society, albeit in simple representations of reality. Throughout my academic career, I have always been a modeller, sometimes in conceptual terms, but mostly in the areas of statistical, computational and mathematical applications. Therefore, I felt a long time ago that there was a need in the academic publishing market to have an academic outlet that would portray the myriad of modelling applications and useful roles for business and society at large. When I came up with the idea and presented it to Emerald to launch this journal, I insisted from day one that although modelling is mostly associated with quantitative and nomothetic research, I have always been also seeking, and welcoming, qualitative research and ideographic constructs.
It has been quite a voyage of academic publishing during these past nine years, and we have published a huge range of modelling approaches and techniques also due to the broad nature of the journal itself.
After the first five years, as required, we applied for the Thomson Reuters the Institute for Scientific Information (ISI) rating. However, we had suffered some internal delays regarding our application to the Thomson ISI rating. I was able then to recover the process and we did apply in the end, but unfortunately for a reason outside our control, which was ultimately decided by Thomson Reuters itself, we will have to wait another couple of years to apply again. This fact made me very disappointed after all the tremendous amount of work and effort we have put in over the past nine years. Therefore, I have made a decision to step down from the operational duties of running the journal, although, very kindly, the publisher is going to keep my name as the Founding Editor of Journal of Modelling in Management (JM2).
Obviously, there are so many thanks to bestow on people. First of all, I would like to thank Emerald and Juliet Harrison for the trust and confidence placed in the launch of the journal and all the support given all these years. Apart from that, the greatest thanks of all goes to Wendy, for the amount of work, support, perseverance, patience and friendship without which the journal would have not been the success it is today. I would also like to thank my three associate editors for their support and assistance, but especially to Kun-Huang Huarng, as he was the Associate Editor who was there from day one and helped with many of my tasks, as the reviewing of paper submissions has been split by areas of origin from day one. Also, I would like to thank the editorial board over these nine years that has been there providing their reviews, advice and suggestions, which obviously is a vital organ of any academic journal, and our editorial board proved that they were the lifeblood of this journal.
I cannot also forget to thank all the authors who have submitted and published within JM2 for their choice of an academic journal, to showcase their work, for all the support provided by electing JM2 as an adequate scholarly outlet for their work and their creative, rigorous and robust content.
Finally, I just need to thank the top scholars whom I will not name now but they know who they are, who, after hearing that I was planning to step down as Editor of JM2, showed at length an incredible amount of support and have submitted mostly undeserved endorsements as well as requests to change my mind. I am very grateful to all of you.
The only thing left for me to say is I wish all the best to the new Editor coming in 2015, and I hope, and I am sure, that JM2 will have a very auspicious future and huge future increase in academic credibility.
Mittal and Sangwan’s paper talks about global societies today experiencing rapid changes because of the ongoing globalization, technological improvements, new economic systems, new information systems, etc. The future challenges of this change are particularly driven by the issues like rising world population, uneven living standards, shortage of natural resources and environmental impacts (Herrmann, 2010). These issues have to be taken into account for future developments of the society.
Immense efforts are needed at all levels of society to address these challenges of the twenty-first century. The industry plays a very important role, as it is responsible for nearly one-third of the global primary energy consumption (IEA, 2007). This creates environmental impacts like greenhouse gas emissions for electricity generation and the shortages of natural resources, as more and more raw material is needed for production. Additionally, the industry is a key for improving prosperity, and it helps freeing people from the daily struggle for food and shelter (Evans et al., 2009). The rising world population and the improving living standards in developing countries will put a tremendous pressure on the industry to grow which will impact the environment. The emerging countries like India and China accelerated the industrial environmental impact through their high economic growth. Therefore, there is a strong need, particularly in emerging and developing economies, to change the production systems that take care of economy, environment and society together. One such potential system is Environmentally Conscious Manufacturing (ECM). It consists of methods and tools to achieve sustainable production through process optimizations across the supply chain (SC) with environmental costs in mind (IEA, 2007).
The successful adoption of ECM initiatives in the industry can be understood by analyzing motivations for the firms to launch ECM practices. Manufacturing firms face multiple motivations called “drivers” which are motivating and/or forcing the industry to adopt ECM. These drivers include competitiveness among firms, governing legislation of the state, availability of green and efficient technology and incentives in the form of subsidies and tax exemptions provided by the government to promote the dissemination of ECM adoption. The driving factors play an active role in adoption and diffusion of ECM in industry. Availability of comprehensive overview of the drivers would raise awareness and convince the firms to justify investments on newer systems. Few authors have found drivers of ECM but not modelled these drivers. A model of the drivers, showing hierarchy and inter-relationship, will be highly useful for the policy makers in government and industry to strategically leverage their resources in a systematic way for successful implementation adoption of ECM. In Sangwan’s paper, 13 drivers for ECM, found from literature and discussion held with experts, are modelled using Interpretive Structural Modelling (ISM) technique to establish the hierarchy and inter-relationship among these drivers for successful implementation adoption of ECM. The paper is structured as follows: next section focuses on literature review. Section 3 presents development of ISM model, followed by MICMAC analysis in Section 4. The results and discussion of the model are presented in Section 5. The conclusions are given in Section 6.
Hsu et al.’s study was to explore the impact of the formation of industrial clusters on the obtainment of professional human resources, to verify the impact of human resources on clustering relationships and firm’s performance and to understand whether the formation of clusters can contribute to the obtainment of professional human resources and the improvement of competitiveness of enterprises. It was expected that solutions could be found to make new contributions through the verification of special economic zones.
Using manufacturers in Taiwan’s special economic zones as the subjects, this study explored the impact on the obtainment of professional human resources after the formation of industrial clusters in special economic zones, through conducting and empirical study with a questionnaire survey.
The professional human resources are the essential factor for the formation of industrial clusters and the improvement of competitiveness. This study also confirmed that industries can have professional human resources by industrial clustering, and that this will produce a positive impact on the enterprise clustering relationships, which can also have a positive impact on firm’s performance and can enhance the enterprise’s competitive advantage.
Industrial clustering is the key factor to attract professional human resources. Industrial clusters can enhance firm’s performance. Professional human resources affect firm’s performance of enterprises.
No study has discussed the topic of clusters from the perspective of Special Economic Zones also including six Export Processing Zone (EPZ) parks in Taiwan. Lai’s study discussed the topic using theories relating to clustering and human resources. The formation of industrial clusters can result in higher competitiveness in the face of the global market. The EPZ industrial cluster provides an excellent investment environment. Coupled with one-stop express services and geographic advantage, the land use rate is up to 97 per cent, and the per hectare output value amounts to NTD 3.2 billion, setting a successful example of an industrial cluster.
The purpose of Patel and Kant’s article is to identify the Critical Success Factors (CSFs) of Knowledge Management (KM) adoption in the SC using the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method through an empirical case study.
Patel and Kant’s paper examines the influencing factors of KM adoption in SC which have been identified through the literature survey and expert opinion. The fuzzy DEMATEL method has been used to evaluate identified influencing factors. Considering the interdependence among factors, the fuzzy DEMATEL method forms a structural model and then visualizes the causal relationships among factors through a cause-effect relationship diagram. On the basis of cause-effect relationship diagram, CSFs that are extraordinarily essential for KM adoption in SC are identified. Empirical case study of an Indian automobile organization is presented to illustrate the fuzzy DEMATEL method and demonstrates its usefulness.
The results gathered from the implementation of the fuzzy DEMATEL method to identify CSFs of KM adoption in SC to the chosen case illustrate that the factors such as top management support, employee training and education, integration of knowledge and information flow, communication among the SC members and trustworthy teamwork to exchange knowledge within SC are needed to highlight as critical factors for successful adoption of KM in SC. The finding not only offers a meaningful base to deepen the understanding with regard to the KM adoption in SC, but also provides a clue to develop an effective adoption of KM in SC in a stepwise manner.
The empirical case study contributes to the literature on KM adoption in SC, suggesting how an organization can identify CSFs of KM adoption in SC and implement them progressively to greatly improve the efficiency of the whole SC performance.
The purpose of Glock and Jaber’s paper is to develop a mathematical model that describes group learning processes with and without worker turnover. Based on an extensive literature review, fundamental characteristics of group learning processes are first identified and then incorporated into a group learning curve. The developed group learning curve is then validated by fitting to empirical data.
The results show that the behaviour of the developed model is in conformance with the characteristics identified in the literature. A comparison with two other learning curves that have frequently been discussed in the literature shows that the group learning curve developed in this paper is a good mathematical representation of group learning processes.
The model developed in this paper enables practitioners to predict performance improvement in groups. Glock and Jaber’s paper is one of the first to propose a mathematical formulation of a group learning curve.
Today’s business environment is characterized as highly competitive, dynamic and volatile market. Sharma and Bhat’s agile SC is seen as the winning strategy to be adopted by manufacturers bracing themselves for dramatic performance enhancements to become national and international leaders. The purpose of this paper is to present an approach to effective SC management by understanding the dynamics between various enablers of agile SC.
Today’s business environment is characterized as highly competitive, dynamic and volatile market. Agile SC is seen as the winning strategy to be adopted by manufacturers bracing themselves for dramatic performance enhancements to become national and international leaders. The purpose of this paper is to present an approach to effective SC management by understanding the dynamics between various enablers of agile SC.
Using ISM, the research presents a hierarchy-based model and the mutual relationships among the enablers of agile SC. The research shows that there exists a group of enablers having a high driving power and low dependence requiring maximum attention and of strategic importance, while another group consists of those variables which have high dependence and are the resultant actions.
This classification provides a useful tool to SC managers to differentiate between independent and dependent variables and their mutual relationships which would help them to focus on those key variables that are most important for building cost-effective and agile SCs.
Presentation of enablers in a hierarchy and the classification into driver and dependent categories is unique effort in the area of agile SC management.
The purpose of Politis et al.’s paper is to implement a multi-criteria preference disaggregation approach to measure logistics service quality of manufacturing companies’ SCs.
A total of 216 Greek manufacturing companies took part in a survey with the use of a dedicated questionnaire. They were asked to assess the logistics service quality of their primary supplier regarding a predefined set of criteria and sub-criteria. The data were analysed with the Multi-criteria Satisfaction Analysis method, which represents an ordinal regression-based approach used for customer satisfaction measurement.
Weak points of the suppliers as well as dimensions that drive satisfaction were identified. Furthermore, the competitive advantages of the suppliers as well as their priorities for improvement were spotted.
The sampling framework, including only the manufacturing companies operating in a specific area of Greece, does not ensure the full generalization of the results. A larger sample of manufacturing companies from all over Greece would be useful to obtain more reliable results and would enable the comparison of logistics service quality for different manufacturing sectors.
Politis et al.’s paper proposes a method to explore the relationships between logistics service quality and industrial customers’ satisfaction to prioritize strategic plans of companies in the SCs.