Research Collaboration

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1. Collaboration


Although human mind has some incredible ability to generate and execute new knowledge. But this fact should be realized that an individual cannot address all the challenges. Thus, we have to collaborate with others. Collaboration is very common in the process of knowledge diffusion. Thus, an individual gains specialization over complex subjects. Therefore, the collaboration has become necessary for every field of scientific and technical research. It is regarded that in today’s time in most scientific and technical domains about ninety percent of research studies and publications are collaborative, which often result in high-impact. Nowadays in most of the areas of science, collaboration is not a preference but a work prerequisite (Bozeman, B. and Boardman, C., 2014)


2. Research collaboration:

The very basic question that comes to everyone’s mind is how we can define research collaboration or in actual what is collaboration is all about. It is a “social process whereby human beings pool their human capital for the objective of producing knowledge”. The term “research collaboration” can be described as the relationships between individuals or organisations. However, it is difficult to distinguish the individual collaborations from organisational collaborations. After all, when the organisations collaborate, it is actually individuals who are collaborating with each other on behalf of organisations. It is very difficult to measure collaboration with traditional methods i.e. observation, interviews or questionnaire. Many of us think that co-authorship is a good indicator of measuring collaboration. Due to its several advantages like verifiability, stability over time, data availability and ease of measurement, the co-authorship is a convenient indicator of collaboration measurement. So, the possible answer to the question that who collaborates is that almost every one collaborates.

2.1 Types of collaboration:

According to the Bozeman, B., et.al. there are two types of collaboration:

a) knowledge-focused (R and D activity increments to the knowledge productivity. It can be measured in terms of scientific and technical articles produced, cited or, more rarely, demonstrably used) and

b) Property-focused (collaborations increment to wealth typically measured in terms of patents, new technology, new business start-ups, and, more rarely, profits). (Bozeman, B., Fay, D., and Slade, C. P., 2013).

2.2 Organizing Framework of Research Collaboration:

The topical review of published literature on the subject of collaboration organizes three main collaboration attributes categories. These are collaborator attributes, collaboration attributes, and organizational attributes. These three major categories have subcategories of attributes of collaboration as mentioned in figure 4.7 below. Each one of them having different roles and responsibilities.

Research Collaboration Attributes

2.3 Approaches to Study Collaboration:

As mentioned above co-authorship is now regarded as a good indicator of measuring collaboration. It has several convenient advantages already discussed. The Bozeman, B., (2014) during his study entitled “Enhancing Research Collaboration Effectiveness: A Report on a 10 — Year Programme of Study” used the following approaches to study scientific collaboration:

1. Publications based or patents based.

2. CV-based.

3. Questionnaire-based.

4. Interviews, Questionnaires, Anonymous posts.

Apart from these, bibliometrics, scientometrics and social network analysis approaches are widely used for measuring research collaboration. Trends to analyze and visualize hot topics in the study of scientific collaboration using metrics based evaluation (i.e. Bibliometrics, Scientometrics) for measurement are very high. Research domain visualization techniques are also being adopted to describe the evolution of collaboration.

2.4 Factors Motivating Collaboration:

Despite the common fact the collaboration leads to increased productivity, inter-relationship, and understanding of complex subject the fundamental questions comes to mind that: who collaborates, differences in patterns of collaboration, what affects successful collaboration, what causes and determinants research collaboration. Probable answers to these questions are that most of us are collaborating, it could be individual or organisational, the success is caused and determined by various factors, some of them are:

• To pursue common research interest;

• To provide mutual support to the researchers (in terms of man, machine, method materials to fulfil intellectual needs) and promote efficiency;

• For accumulation of research experiences,

• Language fluency and increased work productivity;

• Helpful for co-authors career;

• To gain tenure and promotions;

• To gain academic reputation and recognition among peers; (Hou, Jianhua, Chen, Chaomei and Yan, Jianxin., 2010)


2.5 Science of Research Measurement:

The basic need of any discipline is to pursue scientific research for the advancement of society. Thus, it is the fundamental need to identify the gap and level of measurement. Therefore, the outcome of this process results for the development of new scientific methods, tools, and techniques also discussed in section 3.6 of chapter three.

2.5.1 Librametry/Librametrics:

The term Librametrics has two roots, namely, Libra, and metrics. The term Libra connotes library, and metrics means measurement. In 1948 at the Aslib’s Annual Conference, held in Leamington, S.R. Ranganathan first proposed the term Librametry. He emphasized the need to develop the mathematical and statistical method for measurement at par with sociometric or psychometric. According to him, the statistical and mathematical analysis is the key technology for understanding the structural analysis and forecasting studies. He knew that library work and services involve a large number of statistics. Sengupta (1985) defined librametrics as: “Quantitative analysis of various facets of library activities and library documents by application of mathematical and statistical calculus with a view to seeking the solution to library problems” (Jeyasekar, J. John. and Saravanan, P., 2015).

2.5.2 Bibliometrics:

Alan Pritchard proposed the term Bibliometrics in 1969. It was proposed as an alternative to the scientometrics and librametry. Pritchard defined bibliometrics as the application of mathematical methods to books, and other media of communications. The bibliometrics is used to provide a quantitative analysis of bibliographic literature (Pritchard, Alan., 1969). Fairthorne in 1969 defined bibliometrics as the “quantitative treatment of properties of recorded discourse, and behavior appertaining it”. While, Potter (1981) defined Bibliometics as “the study and measurement of the publication patterns of all forms of written communications and their authorship” (Potter, 1981).

2.5.3 Informetrics:

Leo Egghe has defined informetrics in a broader sense. The term `informetrics is comprising all-metrics based studies related to information science which includes bibliometrics and scientometrics. Hood, W.W. and Wilson, C. S., further defined informetrics as “the quantitative study of collections of moderate sized units of potentially informative text, directed to the scientific understanding of information processes at the social level” (Wood, and Wilson, 2001, Wolfgang, and Sonja, 2006).

2.5.4 Scientometrics:

It was introduced by Nalimov and Mulchenko in 1969. Scientometrics is widely exercised by organisations and countries for measurement of the performance of an individual, organisation, in a specific field to map its effectiveness.

2.5.5 Cybermetrics:

The term cyber comes from the Greek, it was coined by the Norbert Weiner in 1948. Cybernetics was concerned with the study of communication and control systems in living beings and machines. The word cybermetrics consists of two distinct Greek words “cyber” meaning skilled in steering or governing and “metrics” measure. Thus, it is the application of quantitative techniques to study the cyber objects (B K Sen, 2004).

2.5.6 Webometrics:

The metrics analysis of world wide web (hyperlinks, in links, out links, usage pattern) is known as webometrics. Michael Thelwall (2009) explained “webometrics concerned with measuring aspects of the www”. According to Bjorneborn, Lennart and Ingwersen, Peter “webometrics consists of four main research areas, (1) content analysis of the webpage; (2) structural analysis of weblink; (3) usage analysis of web; (4) Web technology analysis” (Bjorneborn and Ingwersen, 2001). Content analysis of web pages is a special kind of subject analysis, the study of the structure of web links has its roots in citation analysis.

2.5.7 Altmetrics:

In 2010 Jason Priem propounded the term #altmetrics. The Altmetrics is also known as `Alternative Metrics’ (ALM) or ‘Alt-metrics’ enhancing and complementing the traditional citation-based impact assessment. It supports multidimensional measurements beyond citation analysis. The altmetrics provides the online measurement of scholars or scholarly content based on the web 2.0 social media platforms. Altmetrics is diversified in nature and categorised into five categories i.e. (i) recommended (ii) cited (iii) saved (iv) discussed and (v) viewed (Dhiinan, A.K., 2015).


2.6 Metrics Laws:

Various empirical laws of scientometrics are discussed below.

2.6.1 Bradford’s Law:S.C. Bradford gave the “Law of Scattering or Law of Frequency Distribution”. The law describes the degree of distribution of literature in journals in the ratio of 1: n: n2. He also proposed the term ‘core journal’. This law is discussed in detail in the section 3.16.2 of chapter three (Bradford, S.C., 1934 and Sudhier, K.G. 2010)

2.6.2 Zip’s Law: The George Kingsley Zipf proposed the Law of “Word Frequency Distributions” (r x f = k). Zipfs Law is often used to predict the frequency of words within a text (Potter, W. G., 1988).

2.6.3 Lotkas Law: Alfred J. Lotka propounded the “Inverse Square Law of Scientific Productivity”. This law described the “frequency of publication by authors in any given field”.  (Potter, W.G., 1988 and Lotka, A.J., 1926).

2.6.4 Garfield’s Law: Eugene Garfield proposed “Garfield’s Law of Concentration”. His law is an extension of Bradford’s law”. (Bensman, S. J., 2001).


2.7 Science Indicators:

Lord Kelvin, (2017) had said: “when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind”. In the light of above quote, it is emphasized that measurements have provided opportunities for future improvements otherwise no improvement. The modern day science has evolved many standard measurements techniques and procedures for this purpose. These measurements indicate the positive and negative effects of the development of the society.

2.7.1 Indicators: It is general feeling that the indicators are the data of specific field but the indicators are somewhat different from the data. Data is raw and does not have any significant value attached to it until and unless it is processed whereas, an indicator is the abstract form of the processed information, which incorporates information in it and reveals the impact of the processed information. Very common examples are GDP of the country revealing economic condition; literacy rate reveals literate people, BMI Index revealing the biological growth of humans, etc. Based on their speciality there could be various types of indicators, some of them can be categorized as:

• Input Vs Output

• Quantitative Vs Qualitative

• Activity, Productivity and Progress

• Quality, Importance and Impact

• Functional Vs Instrumental Indicators.

2.7.2 Literature based Indicators:

Research evaluation is not a rocket science it is an art. Researchers, Policy Makers, Academicians are using many evaluation methods for analysing and comparing the research output. Each one has its own strengths and weaknesses. Thus, one should select the evaluation method with utmost care. Evaluation of literature based data is one of the core areas of research in bibliometrics and scientometrics. Literature based indicators are derived from the published knowledge in the form of journal articles, publication types other than the article, letter, note and review are often omitted. The popular publication Indicators are:

• Publication Counts

• Production Index (PI)

• Activity Index (Al)

• Relative Specialisation Index (RS 1)

2.7.3 International Collaboration Indicators:

Well established indicators are discussed below.

• Co-authorship (COA): set of co-authored articles indicates output.

• Cooperative effort (COP): indicator of cooperative efforts of a country through co-authored publication (to know the degree of collaboration between two countries).

• International Cooperation Index (ICI): the number of joint publications divided by the square root of the product of the number of total publications.

• Affinity Index (AFI): is the amount of collaboration between two countries in comparison to the to the total collaboration of the given country with world.

• Internationalisation Index (INI): it is a percentage of a total number of coauthored articles of a given country with the total number of articles published in the same given country during the same period.

2.7.4 Citation-based Indicators:

Citations based indicators used globally are discussed in this section.

• Observed citation rate: actual number of times a paper is cited. The mean value is the number of citations per publication.

• Expected citation rate: the average of the number of citations of all papers published in the same journal in the same year.

• Relative citation rate: the ratio of observed citation rate divided by the expected citation rate.

2.8 Mapping of Science:

The maps are used by geographers to provide a two-dimensional representation to scale the world. Similarly, scientometrics maps provide two dimensional representation of scientific knowledge, by depicting spatial relationships. Scientometrics is used for the mapping of science. Scientometrics analyses are very efficient in future forecasting and prediction of trends in information diffusion. Analyst and planner execute metrics studies for policy formulation. The metaphorical use of the word mapping consists of cognitive mapping and descriptive mapping of science. Common mapping activities are -journal inter-citation mapping, document co-citation mapping, author co-citation mapping, journal co-citation mapping, co-word mapping and co-classification mapping. Domestic and international mapping using author addresses in multi-authored collaborated papers.


Reference Article:

  • Kumar, A. (2018). Global Knowledge Diffusion Vocational Education and Training.

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