ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846
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Academics or Technicians?

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The demand for higher education is growing in India, matching the growth of the economy and population. Along with this, the demand for PhD programmes is also rising. The two main drivers are the University Grants Commission’s (UGC) requirement for having PhD degrees in order to become faculty in colleges and universities, and the global university ranking system that favours research over teaching. Participation in the global ranking system is also increasing among universities. As a result, the production and publication of PhDs has become a numbers game. The number of PhD candidates enrolled in India in 2016–17 was 1,23,712. The corresponding figures for 2015–16 and 2014–15 were 1,09,552 and 1,00,792,respectively. This indicates a 22% growth in PhD enrolment over three years. The Government of India has set a target of graduating 20,000 PhDs per year by 2020.

This is indeed, a positive development if the PhDs produced are of high quality. But, on several occasions, questions regarding the “quality” of research have been raised. In order to improve quality, the UGC has made several changes. One important change is the restriction of enrolment under each research supervisor to a maximum of eight PhD students. Of course, the question of quality research is not country specific; it is a global issue. In an article on 8 February 2014, the Economist commented: “Oceans of papers with little genuine insight are published in obscure periodicals that no manager would ever dream of reading.”

The ways in which modern, sophisticated, and easily-accessible statistical software packages are used have created a different kind of problem in the quality of PhDs, especially in the field of management studies. Such softwares have “deskilled” statisticians’ jobs from the application point of view. They have immensely helped in learning the necessary skills for application of statistical analysis in data analytics within a short period of time, hence increasing the productivity of analysts. However, this is only one side of the development. On another side, research scholars in academia have become no different from analysts. Of course, this cannot be generalised; but neither must the phenomenon be ignored. The question is whether such outcomes are desirable. Further, this does not dilute the contribution of the statistical packages. The contributions of such softwares—both in industry and in academic research—are immense. However, the question pertains to how such statistical tools are being used.

Researchers—both PhD scholars and faculty members—are applying high-end statistical tools in the analysis of data, and achieving impressive outputs, thereby building the “scholarly” standard of the research which gets published in peer-reviewed journals. The use of such tools also convince and reassure thesis defence committee members, who are oftentimes starved for time and are impatient. Limited experience in business research suggests that, in many cases, PhD scholars do not go into the details of the methodology, or fully understand the concepts behind these statistical tools or the assumptions behind them. Many software packages test the assumptions while processing the data itself, without the knowledge of users. Many a time, scholars lack basic conceptual understanding of statistical techniques, particularly those who do not have exposure to statistical techniques at the undergraduate level. This does not mean that, irrespective of disciplines, scholars should be experts in statistics. But, it is reasonable to expect that, when scholars apply a tool, they should be aware of the theory behind it, and its assumptions and limitations.

With the help of these impressive statistical presentations/models, scholars skilfully take charge of viva presentations. The discussion is often limited to the model applied, while other aspects—the intellectual argument on the principal subject of inquiry, the rationality of selecting a particular methodology/tool from among competing tools or methods, questions on whether the data appropriately measures what the researcher intends to measure and is in line with the theoretical concept that the researcher is enquiring into—all take a back seat.

What is expected from PhD scholars is that they attain mastery of a discipline and training of the mind. As examiners, are we doing justice to this objective? I believe that there is great scope for improvement in this regard. No government or UGC regulation will help. The onus lies on the examiners.

Most of the scholars emerging from such a system will go on to become faculty members. By virtue of holding a PhD degree, many will teach subjects of research methodology as well as statistical tools and techniques. It is anybody’s guess as to what the quality of teaching will be.

The Carnegie Foundation and Ford Foundation sponsored two inquires in 1959, which argued that business schools are a little better than trade schools and they should give more emphasis to academics. With the high priority given, both by students and parents, to placements, the situation is no different today. Many still feel that a Master of Business Administration (MBA) is vocational education. Now, with low-quality teachers, there is little hope that academics will take the front seat in MBA institutes.

Therefore, the responsibility of reviewers and examiners is vital. Until they become sensitive to these issues, the situation will not change. In the name of “PhDs,” we will be producing statistical technicians. This may be good for industry, but not for academics, which invariably requires rigour.

Prabir Kumar Bandyopadhyay

Pune

Updated On : 8th Jun, 2018

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