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Overview information for Bizblocks Kaiser Coin (KISC) including News, Charts, Discussion and more. Alliance Cargo Direct (ACD) handleFOREX (FOREX). #India did pledged Gold in forex crisis. The gold-for-dollars idea was termed “stupid” and “idiotic” by Dr Kaiser Bengali. “You know how it is working, investment and forex brands a lot of on the German desk goes by the name “Todd Kaiser,” while a Ukrainian. MANESH PATEL FOREX CARGO

JISCO plans to build a ,tonne-a-year aluminium smelter as part of its redevelopment of the refinery. Alpart currently employs over full-time, part-time and casual workers and is one of Jamaica's largest employers. Its programmes for social partnerships in agriculture, health, education, sports and youth and community development; resettlement and land rehabilitation; human resource development; world-class safety standards and environmental protection and community development have been major priorities.

Now you can read the Jamaica Observer ePaper anytime, anywhere. Some comments may be republished on the website or in the newspaper; email addresses will not be published. Please understand that comments are moderated and it is not always possible to publish all that have been submitted. We will, however, try to publish comments that are representative of all received.

We ask that comments are civil and free of libellous or hateful material. Also please stick to the topic under discussion. Please do not write in block capitals since this makes your comment hard to read. Collaborative relationships affect research performance by disseminating the flow of knowledge, improving research capacity, enhancing innovation, creating new knowledge sources, reducing research cost through economies of scope, and creating synergies between multi-disciplinary teams [ 2 , 4 — 7 ].

Therefore, understanding the status quo of a scientific discipline requires understanding the social structure and composition of these collaborative relationships [ 1 , 8 , 9 ]. Social network analysis SNA is one of the most utilized methods for exploring scientific collaboration networks. SNA can quantify, analyze and visualize relationships in a specific research community, identify central opinion leaders that are leading collaborative works as well as evaluate the underlying structures that are influencing collaboration.

Indeed, there are a plethora of studies that used SNA to examine scientific collaboration networks of co-authors in various disciplines [ 2 , 10 — 18 ]. The value of the STM discipline in scientometrics and scientific collaboration research lies in its cross-disciplinary nature, i. In light of the above argument, the aims of this study are to: explore the evolution of research collaboration networks of each of the authors, institutions, and countries in the STM discipline and across three consecutive sub-periods t1: —; t2: —; t3: — , identify the key actors authors, institutions, and countries that are leading collaborative works in each sub-period, and understand the longitudinal effect of co-authorship networks on research performance measured by research productivity i.

Certainly, scholars can collaborate in a multitude of different ways ranging from faculty-based administrative works, conference participations, meetings, seminars, inter-institutional joint projects and informal relationships [ 27 ]. However, this study uses co-authorship analysis—as a widely used and reliable bibliometric method that explores co-authorship relationship on scientific papers between different actors nodes being authors, institutions or countries. Therefore, the analysis in this paper is carried out at three level: the micro level—authors of the same or different institutions; the meso level—inter-institutional strategic alliances universities and departments ; and the macro level—international partnerships entailing the authors and institutions, all of which are major spectrums of research collaboration [ 7 , 28 ].

To do so, the web of science WoS database is used to extract the bibliometric data of journal articles published in the last 32 years — The results provide important insights for allocating governmental funding, maximizing research output, improving research community reputation and enhancing cost savings that all should be directly or indirectly piloted by the most suitable scientists that can influence and lead collaborative research in their networks [ 29 , 30 ].

This paper starts with a brief history of STM research, followed by an overview of network theories most relevant to this study. Then, the methodology for data collection, refinement and analysis is described. Descriptive and SNA results are presented for each of the examined networks across the three sub-periods, followed by the findings of the association testing between different social network measures ego-density, degree centrality, betweenness centrality, closeness centrality, efficiency, constraint and average tie strength and each of the citation counts and research productivity metrics.

Lastly, the conclusions and the theoretical and practical implications are provided. Literature review Origins of STM The stakeholder concept was first originated in the Stanford Research Institute in the s, and then more formally introduced by Freeman [ 31 ] as a new theory of strategic management that aims to create value for various organizational groups and individuals to achieve business success.

The stakeholder theory aims to define and create value, interconnect capitalism with ethics and identify appropriate management practices [ 32 ]. Freeman emphasized on the relationships between the organization and its stakeholders as the central unit of analysis and a point of departure for stakeholder research. Accordingly, Rowley [ 33 ] was the first to introduce social networks to STM to understand the mechanism of such relationships.

Expansion of STM From the early s, stakeholder theory has shown to be a class of management theory rather than an exclusive theory, per se, by its applicability in various business domains such as business ethics [ 40 — 42 ], finance [ 43 — 45 ], accounting [ 46 , 47 ], marketing [ 22 , 48 , 49 ] and management [ 21 , 50 , 51 ]. Afterwards, the interest has moved to stakeholder analysis—a main systematical analytical process for stakeholder management that involves identifying and categorizing stakeholders, and identifying best practices for engaging them [ 52 ].

Even some scientific disciplines, such as project management, has considered stakeholder management as one of its core knowledge areas for achieving project success [ 53 ]. This exponential growth of the field has resulted in more than 55 stakeholder definitions [ 54 ] and numerous frameworks for stakeholder identification [ 35 , 55 , 56 ], categorization [ 57 , 58 ], and engagement [ 59 — 62 ]. However, the enlargement of the stakeholder analysis body caused ambiguousness in its concepts and purpose [ 34 , 56 , 63 ], where it turned into an experimental field for different methods to be explored.

Jepsen and Eskerod [ 64 ] revealed that the tools used for stakeholder identification and categorization were not clear enough for project managers to use, being referred to as theoretical [ 65 ]. The theoretical debates seemed to have alleviated between and , where researchers focused instead on the applicability of stakeholder theory in the real world cases [ 66 , 67 ].

Empirical studies mainly examined the behavior of firms and their stakeholders towards each other, such as how firms manage stakeholders [ 68 , 69 ] and how stakeholders influence a firm [ 70 ]. Once again, the scientific paradigm of STM has mostly been uncovered in the domains of strategic management [ 71 , 72 ] and project management [ 73 — 75 ]. Therefore, it is evident that growth of STM has continued on a much larger scale than in the previous years, but little is known about the structure of collaboration networks that have contributed to its development and diversification.

Social network theories and measures A social network is a web of relationships connecting different actors together e. The purpose of analyzing networks in scientific research is to evaluate the performance of certain research actors through the structure and patterns of their relationships, as well as to guide research funding and development of science [ 76 ]. Following previous works [ 52 , 77 ], SNA can be conducted through a variety of metrics such as ego-density at the network level; degree, betweenness and closeness centrality, efficiency and constraint at the actor level; and tie strength at the tie level [ 78 , 79 ].

At the network level, density is the most basic network concept which measures the widespread of connectivity throughout the network as a whole [ 80 ]. In other words, it explains the extent of social activity in a network by determining the percentage of ties present [ 81 ].

In this study, the ego is either an author, institution or country. A dense network allows the dissemination of information throughout the network [ 83 ] and reflects a trustworthy environment for different actors [ 84 ]. However, a dense network is a two-edged sword where it might obstruct the ability of actors to access novel information outside their closely knitted cliques. Accordingly, Freeman [ 87 ] identified three measures of centrality which are degree, betweenness and closeness.

Degree centrality that denotes the number of relationships a focal node has in the network. In other words, it is the number of co-authors associated with a given author.

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