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| Research Training Seminars (Current) XIMB FPM Leaflet Seminar Reports 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 | | Research World, Vol. 5, 2008 Research Training Seminars at XIMB: 2007-2008 [Foreword] Research Training in Perspective: Transformations in Individual Researcher
http://www1.ximb.ac.in/RW.nsf/pages/R5.1 D. P. Dash, XIMB dpdash[at]ximb.ac.in The postgraduate curriculum in management followed in India already includes several topics related to research (e.g., descriptive statistics, inferential statistics, decision modelling, forecasting, operational research, multivariate techniques of data analysis, market research, systems analysis and design, and so forth). These topics are studied by doctoral scholars as part of their course work. The RTS series is designed to provide inputs on research not commonly provided in these postgraduate courses. The seminar series facilitates interaction with researchers from different backgrounds. These seminars focus on different forms of research thinking and research practice in a wide variety of contexts relevant to management. There is a slant towards interdisciplinary and integrative forms of research, which involve transcending disciplinary and other boundaries researchers often impose on themselves. The unusual focus of this seminar series and its contribution to research education have been documented and discussed by one of the regular participants in these seminars (Vakkayil, 2006). The fresh batch of doctoral scholars joining the XIMB community spoke about their motivations behind joining a doctoral programme in management. Some of them wanted to better understand the processes they have encountered during their professional work. Some of them wanted to develop the skills to guide others who might be interested in joining similar professions. A common thread was found in their presentations. All of them wanted to do research that would be useful for some well-defined clientele, for example specific professionals, women employees, farming communities, commercial banks, regulatory bodies, and various other organisations. Ordinarily speaking, nothing in research guarantees that the intended benefits would indeed accrue to such identified clientele. Results from well-intentioned research may even be misused to produce negative outcomes for such clientele--unless of course something in the design of research prevents that possibility. Given the common interest in conducting useful research, it becomes important to discuss how the design of research needs to be changed in order to ensure that the intended benefits indeed accrue. This is an important and perhaps fundamental challenge. It may call for significant revisions to our existing notions of research. The issue is important enough to warrant ongoing exchange of ideas with collaborators at other institutions and across disciplines. If research becomes too much focused on specific clientele, then how would it meet the requirement of generalisability or transferability? To illustrate the issues involved in this, an example from the study of languages was discussed. A community of children with hearing impairment could develop a sign language of their own (Nicaraguan Sign Language, discussed in Polich, 2005). One option for language researchers would be to study the vocabulary and syntax of this new language and identify elements that can be found in most languages--thus confirming some generalised knowledge of languages. However, this may not be very useful for children with hearing impairment--whether in Nicaragua or elsewhere. Another option for language researchers would be to study the form of interaction among the children that crystallised and developed the language. If researchers can arrive at a generalised knowledge of such interactions, then it might be possible to recreate or improve such interactions. Speaking of research thinking for management, it was noted that management is a practice which has its professionals and also its researchers. A distinction was made between the mindsets and approaches these two groups would bring to a practice. This led to the notion of "research as a paradigm of practice," i.e., adopting the basic temperament of research while engaging in some practice. It would of course mean the following: (a) focusing on what is unknown (b) doubting (any/all given facts, propositions, beliefs, assumptions) (c) being curious (seeking to generate a "certain thing"--as opposed to "a thing of a certain kind") (d) developing and adopting novel methods (e) working with conjectures and staying tentative In contrast, "profession as a paradigm of practice" would focus on expertise, time-tested methods, accumulated experience, standards of performance, and so forth. Instances of these two paradigms can be found in many practical domains--even in doctoral work. Following the paradigm of profession in one’s doctoral work, one would develop certain expertise (say, in using some software) and diligently apply time-tested methods in order to produce a thesis that can be recognised to be of a certain kind. Alternatively, following the paradigm of research in one’s doctoral work, one would doubt all propositions, methods, and expertise--although some of these must be used from time to time. The aim would be to deal with the unknown in a tentative sort of way, by producing a thesis which would hopefully be recognised as something unique--not just a thing of a certain kind. However, the polar opposition described above may have to be reconciled in practice--a point discussed elaborately in Vakkayil's (2006) article. While doctoral work is expected to produce a unique thesis, the work must nevertheless happen within the norms and conventions of the field and the host institution. References Polich, L. (2005). The emergence of the deaf community in Nicaragua. Washington, DC: Gallaudet University Press. (Book information retrieved July 3, 2007, from http://gupress.gallaudet.edu/bookpage/EDCNbookpage.html) Vakkayil, J. D. (2006). Towards new visions of doctoral research: Experiences from an innovative research training programme. Graduate Journal of Social Science, 3(1), 82-101. Retrieved June 29, 2007, from http://www.gjss.nl/vol03/nr01/a05 The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.2 Jacob D. Vakkayil, XIMB jacob[at]ximb.ac.in Vakkayil, J. D. (2007). A portrait of the researcher as a boundary crosser. Journal of Research Practice, 3(1), Article M11. Retrieved July 6, 2007, from http://jrp.icaap.org/index.php/jrp/article/view/61/78 The seminar started with an exercise aimed at helping doctoral scholars reflect on the considerations that shaped their decision to enter into the programme. Doctoral programmes call for commitment and undivided concentration for a considerable period of time. Thus the decision to embark on it is often made by evaluating its value in achieving one’s long-term objectives. Doctoral-level education often attracts mature individuals with a few years of experience in their own respective fields. Thus, an analysis of its pros and cons and a consideration of its comparative merits in terms of other abandoned opportunities are also part of the process of this initial decision making. Having made the initial decision to join a particular programme, the scholars face a further set of options associated with a particular institution and academic environment. This involves strategies to be adopted in working effectively with others within a doctoral community and dealing with the expectations of this community, the institution, and the wider society. In this connection the ideas of “role taking” and “role making” (Pareek, 1993) were presented. Roles denote sets of significant expectations and there are a number of processes associated with the formation and development of roles in social situations. The idea of role taking denotes how a person responds to expectations by adhering to an existing role specification. Thus the person takes a fully formed role. In contrast, others might seek to influence the very processes of role formation and development. Here, the person proactively makes the role, leading to the formation of a new set of expectations. By drawing from his experiences, the seminar leader illustrated how the various decisions outlined above are invariably influenced by pre-doctoral experiences of the researcher. This is also true of subsequent choices in selecting a research topic and adopting a particular research methodology. There is a wide variety of approaches possible here, and this is especially true of a multi-disciplinary field such as management. However, some concerns that lie at the root of any serious intellectual pursuit are also relevant for doctoral research. These relate to certain basic philosophical considerations regarding the nature of knowledge and the issues associated with the production of new knowledge. As a process that produces new knowledge, research is particularly concerned with the idea of justification of knowledge. This is attempted on many grounds. Often, the idea of “correspondence” is suggested as a justification device. Here, the correctness of a proposition is evaluated on the basis of its correspondence with a “real” referent. Plato’s metaphor of the cave exemplifies this approach. He illustrated knowledge and ignorance by putting forth the image of persons who were trapped in a cave all their lives. Their knowledge of the outside world was through shadows in the cave and they considered these as real. If one of them escaped and were able to observe the things outside that caused these shadows, others would be unlikely to accept this new information as true. To avoid this mistake, he suggested the existence of real entities beyond those perceived by our senses. Another commonly employed ground for justification is the idea of “coherence." This evaluates the correctness of a proposition by comparing it with a set of accepted propositions in a framework. Thus any new knowledge claim would need to demonstrate how it is in agreement with prior established knowledge. A third criterion employed for justification come from the idea of “pragmatism.” Here, a proposition’s value is evaluated on the basis of how useful it is in achieving certain objectives or producing some desirable results. It should also be emphasised that contrary to a popular idea concerning research, “method” is not sufficient ground for justification. In other words, strict adherence to certain pre-specified steps do not guarantee the quality of the resultant knowledge. This is also true of principles of logic applied in evaluating the correctness of theoretical propositions. These only ensure that certain fallacies are avoided in the reasoning process and mere conformity with these logical principles does not guarantee that the results are justified. Researchers typically formulate the grounds for justifying new knowledge based on their disciplinary affiliations and the preferred modes of research associated within those disciplines. The seminar leader pointed out that the researcher’s personal orientations are also important in this. One consequence of this affirmation is a need to be alive to the particularities of human nature in this enterprise. Here, a conceptualisation put forth by the philosopher Francis Bacon might be helpful. He cautioned of dangers to the pursuit of knowledge by drawing attention to certain “idols” that could act as hindrances in this effort. He differentiated various types of such idols. These stem from the characteristics of the individual, the society and the human race, the peculiarities of language and associated communication, and the rigidity of established frameworks and systems. An awareness of these might help doctoral scholars avoid fallacies that arise from an unreflective pursuit of knowledge. Reference Pareek, U. (1993). Making organizational roles effective. New Delhi: Tata McGraw-Hill. The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.3 S. S. Ganesh, XIMB ssganesh[at]ximb.ac.in Ganesh, S. S. (2007). An exploratory study of executive alienation and their experiential perceptions of performance review systems in organizations. Doctoral dissertation, Dharamsinh Desai University, Nadiad, India. The seminar was based on the seminar leader’s experience as a doctoral researcher. It focused on the doctoral research process and the difficulties encountered at various stages, from choosing a topic for research to the final defence before the examination committee. The seminar revolved around the researcher’s journey through these stages: (a) Identification of Domain/Topic: Reviewing the research literature facilitates the identification of a topic. Of course, the topic should interest both the researcher as well as the research guide. An assessment needs to be made as to the amount of work involved and whether it can be accomplished within the timeframe. Usually, one goes through a process of narrowing down the focus to make the topic precise and researchable. (b) Research Design: This requires the identification of a research perspective, which would guide the researcher’s engagement with the chosen domain/topic. Any research perspective presents a set of methodological notions and standards associated with it. For example, a particular research perspective provides notions such as population, sample, measurement, hypothesis, and so forth. It sets out standards such as representativeness, validity, reliability, and so forth. Following the relevant notions and standards, one spells out a design for the overall research process such that following the process would ensure that sufficient conditions are created for knowledge generation. (c) Data Collection, Analysis, and Interpretation: This stage involves an implementation of the research design within the practical setting of the chosen domain/topic. As it often turns out, no matter how specific and how detailed the research design, the practical setting always throws up surprises to which the research process and the researcher must adapt. (d) Documentation or Thesis Writing: Documenting the process, results, and interpretations requires several skills. Usually one goes through a process of writing and rewriting the thesis document several times before reaching a final version. (e) Defence: This is the final stage in a doctoral research project. The researcher is expected to defend the thesis and answer any queries or objections raised by the examination committee. The session leader’s choice of his doctoral research topic was influenced by the context of his employment as an Academic Associate. His performance review was coming up and the continuation of the contract depended on the results of the review. By that time, he was familiar with the literature in human resource management, which suggested that, despite the intended purpose of employee development, performance appraisal systems can be used as yet another mechanism for managerial control. In his personal situation, he realised that the continuation of his employment contract (as an academic associate at IIM, Ahmedabad) depends on his performance review in which progress in his doctoral work was an important element. This triggered in him the interest to study performance management as a topic, especially from the viewpoint of the appraisee, i.e., one whose performance is being appraised. The research focused on the following questions: What happens to the persons whose performance is being appraised? Do they experience alienation? A review of the literature revealed multiple dimensions of alienation, namely powerlessness, meaninglessness, normlessness, social isolation, and self-estrangement. Similarly, the literature also revealed multiple dimensions of performance appraisal systems, namely design, process, sources, formats, and outcome. On one hand, the research guide was appreciative of the topic since performance appraisal and employee alienation constitute two different disciplines (human resource management and sociology). On the other hand, the guide was also apprehensive about the ability of the researcher to examine the sociological concept of alienation from the functional human resource management perspective. However, the research scholar’s persistence and conviction in the topic won his guide’s approval. Reaching this decisive stage took nearly two years. Each dimension of performance appraisal system and alienation needed to be operationalised through a number of variables. However, the more the number of variables, the more complicated the research design becomes. Therefore, as is common in such studies, a pilot study was conducted, followed by exploratory factor analysis. This reduced the number of variables. One methodological issue in this study was the possibility of common method bias. This bias can occur when the data-collection instruments affect the scores or measures being gathered. Under its influence, some correspondence between the independent and dependent variables can arise due to the fact that both are being provided by the same respondent. Quoting from the literature on research methods in behavioural science: “. . . some sources of common method biases result from the fact that the predictor and criterion variables are obtained from the same source or rater, whereas others are produced by the measurement items themselves, the context of the items within the measurement instrument, and/or the context in which the measures are obtained.” (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003, p. 881) This issue was addressed by selecting the technique of snowball sampling to find potential respondents and thereby reducing the probability of common method/source bias. In snowball sampling, one respondent suggests names of other respondents who in turn suggest names of still other respondents and so on. It is a useful method for getting respondents from a hard-to-reach population. Some researchers tend to focus too much on data analysis and pay less attention to the interpretation of results. One needs to allocate time judiciously to the various stages of the research process. The session leader emphasised the need to ensure that the thesis should defend itself. It should contain answers to questions which can be anticipated beforehand. Reference Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. Retrieved August 3, 2007, from http://www.usq.edu.au/users/patrick/PAPERS/Common%20Method%20Variance.pdf The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.4 Sunil K. Mohanty, University of St. Thomas, USA Skmohanty[at]stthomas.edu Cochrane, J. H. (2005). Writing tips for Ph. D. students. Retrieved July 20, 2007, from http://faculty.chicagogsb.edu/john.cochrane/research/Papers/phd_paper_writing.pdf The seminar focused on the challenges faced by a researcher while communicating one’s research results to the external world. Communication per se is complex and has been a subject of research interest. The complexity further increases when it comes to communicating research. A researcher is involved in activities such as creating new concepts, developing new models, formulating mathematical equations, and so forth; communicating all these to the reader in an effective way is a challenge. Writing a research article, speaking at a conference, and conducting a seminar are some of the modes of communication used by researchers. The beneficiaries of the information include professionals from academics and industry. While academicians may use this information to carry out further research and or enhance teaching contents, professionals working in industry may use this information to improve their processes and systems. The most common mode of communicating research is through publication of research articles. In the academic world, publication of high-quality research articles is crucial to advancing one’s career. The quality of a research article is usually gauged by the number of times it has been cited by others. A research article is used by fellow researchers, academicians, experts, and professionals from diverse backgrounds. The writing style ought to match the expectations of the intended readers. One’s writing style usually develops with experience, as one learns the style predominant in one’s field and gradually tries to goes beyond it to establish a unique style of one’s own. However, at a more basic level, one needs to develop the skill to write clear and error-free sentences. Too many grammatical errors, incoherent sentences, and typographical errors can annoy the reader, thus becoming a barrier to effective communication. Different writing standards have been developed to suit the requirements of different fields. The writing standard developed by the American Psychological Association (APA) is the widely recognised standard for research writing in psychology, education, and social sciences. It is also used in many areas of management studies. Over the years, something like a general structure has evolved for research articles. It is often expected that a writer of a research article should adhere to this structure. It is a surprising development, since, in research, it is common to look beyond norms and standards. Insisting on the general structure for a research article does overlook the fact that research can be a messy process, not always following a clear and logical sequence of steps. Following the general structure blindly would mask this reality and may also act as a barrier to authentic self-expression. In certain fields that give importance to individual experiences and identities, there have been attempts to invent new genres of research writing. The new genres include dialogues, analytical narratives, auto-ethnographies, and interviews with embedded analysis. “These innovative genres have created a new intellectual and emotional writing space that has enabled writers to better connect their academic work with their personal lives” (Suchan, 2004, p. 301). “[W]riting well within these genres requires skill, craft, discipline, and creativity. Moreover, reading these writings well as an editor, reviewer, student, or field expert requires an open mind, discipline, and careful thought” (Suchan, 2004, p. 314). Presenting one’s research in a seminar or conference is quite different from writing a research article. In oral presentations, one must consider the audience, who may come from diverse backgrounds, having varying levels of expectation and understanding of the subject. Moreover there is a limited time to communicate one’s message. Researchers spend a long time studying and contemplating on specific subjects. During this process, the thinking as well as the language of the researcher can get influenced. The concepts generally used in one’s field of study become a part of the researcher’s language. This can create a difficulty in communicating with persons not familiar with those concepts. It is a challenge to speak to an audience who do not share the same background as the researcher. The objective of communicating research is to share the knowledge gained in the research process. A researcher may have to use different modes of communication. Each mode has its own requirements. Although constrained by these requirements, a research can still try to be innovative in communicating effectively. Reference Suchan, J. (2004). Writing, authenticity, and knowledge creation: Why I write and you should too. Journal of Business Communication, 41(3), 302-315. The seminar was attended by http://www1.ximb.ac.in/RW.nsf/pages/R5.5 Greg Demirchyan, University of California, Berkeley, USA gdemi[at]berkeley.edu The field of development studies currently projects a notion of development that is more than solely economic development (for more information on this field, see Sumner, 2006, p. 644). It is a young field of inquiry that is characterised by a great diversity of approaches put forward, disciplines involved, and problems to be solved. However, in development studies, there exists little consensus on any core theory of development. The “theories” that exists can be better described as sets of ideas rather than well-defined and testable scientific theories. What can be done to make the field more rigorous? How can we think more systematically about development? According to Greg Demirchyan, methodological reflections might point us towards possible answers. Thinking about development requires us to think about the objectives of development. Even though identifying objectives remains essentially a normative issue, methodological thinking can help in two ways: (a) it can help in strengthening the conceptual basis of these objectives, and (b) it can help in relating the objectives to their practical consequences. These two possibilities are discussed below. (a) Referring to the theory of justice by philosopher John Rawls, Demirchyan describes various methods that enable us to better define the objectives of development by systematically examining them. One such method is called reflective equilibrium, which holds that people have certain strong judgments as opposed to weak judgments. Whereas a theory of development must conform with all our strong judgments, i.e., ideas that are usually thought through and that we are not willing to give up, it need not to conform with all our weak judgments, which are preferences that we can give up if a good theory rejects them. Another method is to ensure internal consistency among the objectives. Next to this, objectives must in principle also be achievable within a certain amount of time. Utopian goals such as “everyone will be a millionaire” must be rejected. Assuming a continuous scarcity of resources, the goals must also be prioritised, depending on the urgency experienced in the context. In addition to this, the objectives must also be externally consistent. It is important because an internally consistent set of objectives may in fact achieve results which are inconsistent or in conflict with the realities obtained in the context. (b) Thinking about relating the objectives to their practical consequences, one could think of demanding that the objectives be amenable to some type of measurement. Also negative externalities of the objectives must be taken into account. Next, to ensure the achievability of the objectives, one needs to get feedback from the field on their experiences in achieving the objectives. Demirchyan stresses that the explanation of failed projects is one of the most important issues for development studies, in order to prevent repeating the mistakes. Usually no explanation is given at all and, even if reasons for failure are given, these are usually not subject to peer review that could weed out the wrong explanations. Certain rules of thumb can be devised to find potential explanations. One can think of paying close attention to social and cultural norms, looking for special vulnerabilities, and the workings of the institutions carrying out the project. In the end, of course, explanations of why certain projects were successful are equally important, providing information that will allow replicating the success. Even though methods like the ones mentioned above can help in defining objectives, this essentially remains a normative issue. In the end, science cannot entirely determine the objectives of development. The theory of development is not a description of the world but a statement on how we want the world to be, a perspective on what we ought to do. Reaching a global consensus on the objectives of development is problematic. Take for example the objective of making people literate. At first glance, this may seem to be a safe choice. However, as was promptly countered during the seminar, achieving literacy may simultaneously mean a loss of indigenous and non-literate forms of communication, knowledge, and culture. In certain cases this leads to uprooting persons from their communities and alienating them from their cultures. Although Demirchyan emphasised universal values such as “human dignity” and appeared rather optimistic about building a development theory upon such foundations, the discussions brought back echoes of scepticism. Can the notion of development be generalised enough to accept a set of basic universal values as its building blocks? The discussions seemed to suggest otherwise. No matter which generalisations one adopts, there will always be fieldworkers who can counter-argue: “This is all very well, but it doesn’t apply to the Bongo-Bongo” (Douglas, 1970, p. xxxvii). This problem will be encountered as long as one insists on global objectives for development action. There will always be local exceptions to the global rule. Normative issues, methodologically slippery as they are, may even backfire on the task of formulating objectives itself. If one is not able to define clear normative objectives, how could one start development at all? Should we drop the demand for rigorous general objectives at all? Luckily it does not seem likely that development studies, or development work itself for that matter, would stop if there are no globally shared normative objectives. But, of course, this neither means that we should give up the effort and stop asking ethical and methodological questions about this discord between universalism and particularism vis-à-vis the development of development studies. References Douglas, M. (1970). Natural symbols: Explorations in cosmology. London & New York: Routledge. Sumner, A. (2006). What is development studies? Development in Practice, 16(6), 644-650. The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.6 Tirthankar Nag, PricewaterhouseCoopers tirthankar.nag[at]gmail.com Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550. Nag’s doctoral study looked into the electrical power industry, in the context of the recent regulatory reforms of the industry in India. The study focused on issues such as generating efficiency, technology choice, and environmental impact. The characteristics of this industry call for some form of government regulation: the processes of power generation can degrade the natural environment, transmission lines can pose a safety threat, consumers cannot switch to a different supplier easily, and the price of electricity can go beyond the reach of many consumers. The form and extent of government regulation may have a bearing on the type of plant, technology, and fuel chosen. Nag’s study involved a detailed comparison among some power plants in two Indian states and some power plants in China. Although the forms of government in China and India have been so different at the national level, both the countries have devolved some autonomy to the sub-national level (i.e., states in India and provinces in China) to govern the power industry within their territories. This feature made the Indian and the Chinese cases comparable. The research process followed a multiple-case design (Yin, 1994). This study provides a backdrop for deliberating on assessing theoretical contributions of case-study research. Whetten’s (1989) suggestions provide a lens to examine the theoretical contributions of any research project. To appreciate Whetten’s suggestions, one would need to appreciate the basic ingredients of theory, which, according to Whetten, are variables, constructs, concepts, and their interrelationship. This led to some discussion on the key distinction between construct and concept, namely measurability. Different examples were considered. In the domain of financial management, profitability is an important concept. One way to measure profitability would be return-on-investment (RoI). So, RoI would be a construct, related to the concept, profitability. However, there can be different measures of RoI, with different combinations of numerators and denominators. These different measures could be considered as different constructs of RoI. This leads to an ambiguity regarding the status of RoI: Is it a construct or a concept? It appears the notions of construct and concept may be relative in nature--relative to the specific context of research and the level of abstraction relevant to the specific research project. Some theoretical terms, for example, group behaviour, are clearly not meant to be measurable; these are concepts, which may have other concepts and constructs associated with them. In order to assess the contribution of a specific research project, we also need to consider the objective of the project. There can be different types of objective: to identify or formulate a problem, to solve a known problem using a new method, to interpret a known result, and so forth. Therefore, no common yardstick can be used to assess research projects in general. In case of Nag’s doctoral research, the objective was to explore the interconnections among generating efficiency, technology choice, and environmental impact of power plants. What the cases revealed necessitated additional quantitative analysis to establish specific patterns in specific power plants. The research shows that generation efficiency is influenced by ownership, technology, and unit sizes. Also, that fuel choices are driven by fuel linkages and, with constrained primary energy markets, the least-cost fuel choices are not always feasible. Overall, reforms appear to have had a positive impact on generation efficiencies. One feature of the doctoral work was the comparison between the emission-intensity-baselines of two Indian states, Andhra Pradesh and Gujarat, and those of three Chinese provinces where access to primary data was obtained from studies using identical methodology. Results suggest that reforms influence energy efficiency and emissions in an identical manner across both the countries. The contributions from Nag’s study include preparation of detailed generation-unit-level data and analysis, setting up a model for benchmarking generation efficiency, setting emissions-baselines at the state level, linking reforms and environment, application of modelling and spatial analysis for identifying emission sources which require attention, and making subnational cross-country comparisons which are of relevance to policy-makers. This seminar highlighted the difficulties in assessing contributions of any research. There are different considerations and the conclusions can be different depending upon the perspective adopted. The objective of one’s research work needs to be defined: whether it is primarily to identify a particular problem in a difficult subject-area and find a solution, or to solve a known problem using a new methodology, or to give a unique interpretation to a known result. The methodology followed in research and the methods deployed have a bearing on the type and quality of results attained. Whetten (1989) lists seven specific questions to determine legitimate, value-adding contributions research can make. A researcher may use these to assess one’s contribution to new knowledge or theory. References Whetten, D. A. (1989). What constitutes a theoretical contribution. Academy of Management Review, 14(4), 490-495. Yin, R. (1994). Case-study research: Design and methods (2nd ed.). Thousand Oaks, CA: Sage. The seminar was attended by http://www1.ximb.ac.in/RW.nsf/pages/R5.7 Anand Agrawal, ICFAI Business School, Hyderabad prof.anand[at]gmail.com Agrawal, A. (2007). Understanding research and theory development. Unpublished manuscript. The seminar focused on theory development in research and the various widely held myths concerning the activity of research. The seminar started with a discussion in which the participants had a chance to put forth their ideas on research and research methodology. The discussion focused on the concept of research as understood by doctoral students. This brought out certain popular notions concerning research. The seminar leader then proceeded to list certain commonly held myths about research. Myth 1: Formulating a research problem (or a research question) is always the first step in research. Myth 2: Data collection always starts after finalizing research questions and research design. According to Eisenhardt (2001), the research problem may get changed many times during the process of research. Defining a concrete research problem at the beginning of the research may not always be possible. Mintzberg’s experiments show that the researcher should make sense out of the observable world by ordering the relationships among elements that engage our focus of attention in the real world. According to Jenkins (2000), in case of absence of clarity on the issue, narrative based techniques are used to find research problems and research questions. Many researchers have converted theory testing research into theory building research. Sometimes the identification of research problem begins after data collection. During the process of data collection, some important research questions may arise leading to the adjustments in research design. Myth 3: All deductive research is quantitative and all inductive research is qualitative. Myth 4: Nearly all qualitative data are subjective, while quantitative data are more objective. It is important to recognise that systematic observation and testing can be accomplished using a wide variety of methods. The selection of research approach in a given study should be based on the problem of interest, resources available, the skills and training of the researcher, and the audience for the research. The qualitative approach investigates the why and how of a social phenomenon, whereas quantitative methods focus on what, where, and when. Mintzberg (2000) argues that theories can be assessed without numbers (even judgementally) and numbers can be used to induce theories. According to Yin (1981), case-study research can involve qualitative data only, quantitative data only, or both. The so-called qualitative methodology recognises that the researcher's subjectivity is intimately involved in the research process. The search for objectivity renders the researcher a passive recipient of external information. Myth 5: Researchers should never start with a priori constructs in mind (to avoid bias). According to Eisenhardt (2001) and Mintzberg (2005), while starting research, a priori specification of constructs (or an outline of the theory) can be useful to shape the initial design of research. However, these constructs (and outline) are tentative and no construct is guaranteed a place in the resultant theory. Myth 6: Anomalies should be discarded whenever found during research. Mintzberg (2005) suggests that anomalies provide the opportunities to improve upon the existing theories. Mintzberg (2005) states that falsification should not be an end but the means to the creation of new theories or, at least, the significant adaptation of old ones. Anomalies should be cherished; reflecting on the reasons for the anomalies can provide new insights. Carlile and Christensen (2005) suggest that discovery of an anomaly gives researchers the opportunity to revisit the categorisation scheme--to cut the data in a different way, so that the anomaly and the prior associations of attributes and outcomes can all be explained. Myth 7: Use of complicated research methods and statistical tests will provide rigour to the research. This is a widely held myth especially by many doctoral scholars. Research methods should be simple and straightforward. This helps in formulating theory. Myth 8: Theories are discovered. Theories are not discovered but created. Explanations are conceived, not found. Researchers create (or develop) theories by combining observations from previous literature, data, experience, and common sense. Theory Development An important part of research is theory development. The American Heritage Dictionary defines theory as “a system of assumptions, accepted principles, and procedures devised to analyze, predict, or otherwise explain the nature or behaviour of a specified set of phenomenon." Literature suggests that theory development is a continuum--from lists (categories) to typologies (comprehensive lists) leading to impressions of relationships among factors to causations between and pattern among these relationships, and finally to fully explanatory models. According to Carlile and Christensen (2005), theory building occurs in two major stages--the descriptive stage and the normative stage. The essence of the literature survey in theory building is to go beyond deductive research and venture into inductive research. Therefore, even if deductive research is undertaken initially, the research effort must progress towards inductive in the normative theory building stage. The seminar called upon researchers to be aware of their own mental constructs about research, and also to recognise that different mental constructs of research can be relevant in different contexts. A researcher needs to think clearly about how the methods of research contribute to theory development. References Carlile, P. R., & Christensen, C. M. (2005). The cycles of theory building in management research. Unpublished manuscript (Version 6). Retrieved November 14, 2007, from http://www.innosight.com/documents/Theory%20Building.pdf Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550. Jenkins, S. R. (2000). Introduction to the special issue: Defining gender, relationships, and power. Sex Roles, 42(7-8), 467-493. Mintzberg, H. (2000). View from the top: Henry Mintzberg on strategy and management. Academy of Management Executive, 14(3), 31- 45. Mintzberg, H. (2005). Developing theory: About the development of theory. In K. G. Smith & M. A. Hitt (Eds.), Great minds in management: The process of theory development (pp. 355-372). Oxford, UK: Oxford University Press. Yin, R. K. (1981). The case study crisis: Some answers. Administrative Science Quarterly, 26(1), 58-65. The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.8 D. P. Dash, XIMB dpdash[at]ximb.ac.in The session was an attempt to review the Research Training Seminars (RTS) at XIMB. Reflecting on the RTS series of seminars held at XIMB over the last 4 years, the participants recalled their experiences and identified areas and initiatives for improving the RTS. The process of brain writing was used to gather ideas and opinions. Some trigger questions were shared a few days in advance. This allowed the participants to write their response freely, independently, and at their own convenience. The responses were compiled and discussed in this session. The trigger questions were the following: * Which factors triggered or facilitated your learning in the RTS? * Which factors inhibited or prevented your learning in the RTS? Facilitators of Learning Figuring prominently among the factors that facilitated learning was the diversity of the seminar leaders invited and the range of topics discussed. The reading guides for the seminars helped broaden the knowledge base of the participants. As new topics were broached, awareness of and familiarity with research topics increased. This also facilitated easier understanding of some of the subjects in the coursework. The interaction with seminar leaders who had successfully completed their doctoral studies was a source of inspiration for doctoral students at various stages of their programme. More importantly, it gave the doctoral students an opportunity to learn from the experiences of those who have gone before them. There was a consensus that the informal nature of the seminars and lack of the usual faculty-scholar hierarchy had enabled free exchange of ideas. The duration of the RTS being 3 hours helped to explore a topic at some length and aided thought processes to develop. The seminars helped to develop critical thinking abilities in some participants. Participants opined that the practice of writing the seminar reports had invariably improved and honed their academic writing skills. The report writing exercise becomes a collaborative effort to understand the seminar topic. The writer takes up issues discussed at the seminar and presents his/her image of the topic. This helps to look at the seminar topic through a different lens. Doubts were raised regarding the relevance of adhering to the APA guidelines even in draft reports used for private circulation. It was emphasised by the seminar leader that APA writing was a skill and it helps to make a habit of following the APA guidelines. The refreshment breaks, a regular feature in the RTS seminars, has assisted informal interactions between the speakers and the participants. It was also expressed that the presence of supportive research community plays a major role in facilitating learning. Inhibitors of Learning Participants expressed that, at times, the pace of the seminar influenced their learning. A slow pace which might be necessary for elaborating a point sometimes disinterested some participants. The seminar leader might be assuming something about the participants' background knowledge. Giving the seminar leader an idea about the backgrounds and expectations of the audience could make the seminars more effective. Similarly, the selection of seminar leaders and seminar topics should also take into account the needs of the participants. It was suggested that some seminars should focus on the current issues in management research. Sometimes, the inhibitors originated from the participants’ side such as not reading the requisite guide for the seminar. Unfamiliarity with the basic vocabulary of research in the early stages of one's doctoral study was identified as one of the key obstacles. The demands of the coursework and its pedagogical emphasis being different from that of research training accentuates the difficulty. To make research vocabulary more familiar it was suggested that a compendium of terms could be prepared by the participants working together. Mobilising the rich internal resources of the Institute was suggested as a means to ease the new student into the world of research and its vocabulary. There should be more discussions among colleagues before and after the seminars in order to facilitate understanding of the seminar topic. Suggested Initiatives (a) Choose topics and seminar leaders to address participants' learning needs (b) Prepare a document containing the FPM students' backgrounds and expectations from RTS (c) Arrange for structured inputs on basic research concepts and skills (d) Create a compendium of research terms (starting with a set of terms used in the issues of Research World) (e) Read and discuss the important management writers (Ackoff, Mintzberg, and others) (f) Learn and use the APA guidelines with respect to writing style, citation formats, and so forth The seminar was attended by: A. G. Menon, Doctoral Scholar, XIMB, u505001[at]stu.ximb.ac.in C. D. Kuruvilla, Doctoral Scholar, XIMB, u505002[at]stu.ximb.ac.in D. P. Dash, XIMB Faculty, dpdash[at]ximb.ac.in Jacob D. Vakkayil, XIMB Faculty, jacob[at]ximb.ac.in Jogendra Behera, Doctoral Scholar, XIMB, u507003[at]stu.ximb.ac.in Madhavi Latha Nandi, Doctoral Scholar, XIMB, u506001[at]stu.ximb.ac.in Mousumi Padhi, Doctoral Scholar, XIMB, u507004[at]stu.ximb.ac.in Sanjay Varma, Doctoral Scholar, XIMB, u507005[at]stu.ximb.ac.in Reported by Mousumi Padhi, with inputs from D. P. Dash. [September 28, 2007] http://www1.ximb.ac.in/RW.nsf/pages/R5.9 Rajakishore Nath, Department of Humanities and Social Sciences, IIT Mumbai nath[at]hss.iitb.ac.in Nath, R. (2007). Machine intelligence (MI), competence and creativity. AI & Society, Online First. Nath enquires into the possibility of emulating a human brain by a physical symbol system. In particular, he examines the claims made by the researchers in the domain of artificial intelligence (AI). AI deals with the design and development of intelligent systems--systems having the computational ability to solve problems, take decisions, and so forth. The ultimate objective of AI researchers is to develop a physical symbol system which can function like a human mind. In fact, many researchers believe that a lot of advancement has already been made in this regard and soon it is going to be a reality. AI researchers have put forward various models to explain the human mind. One of the most widely accepted models is the functionalist model of mind ("Philosophy of mind," 2007). The functionalist model of mind suggests that different mental states such as beliefs, desires, and pain are constituted solely by their functional role, that is, their causal relations to other mental states, sensory inputs, and behavioural outputs. One of the important accounts of functionalism is the idea of multiple-realizability. Multiple-realizability suggests that mental states are not dependent upon the underlying physical medium, rather it is an outcome of higher level functions in the cognitive system. Since mental states are not limited to any medium, they can be realized in multiple ways, including non-biological systems such as computer systems. Similarly, another model, namely the multi-draft model, suggests that there is nothing such as a central command center in the brain where some sort of director controls the self. On the contrary, all activity is developed by distributed subprocesses concurrently created in the brain. The multi-draft model rejects any kind of conscious content in the brain. These models and explanations are widely accepted in the domain of AI and reinforce the belief that machines can emulate the human mind. However, Nath presents a philosophical critique of this view. According to Nath, consciousness and creativity are central to mind and cannot be emulated by any machine. Consciousness is essentially a first person, subjective phenomenon, and conscious states cannot be reduced to a third-person perspective. A machine can show some intelligence by performing certain assigned functionalities, but it will not be able to show intentionality, free will, or creativity, which are quite intrinsic to consciousness. Nath points out that there is an explanatory gap existing between physical processes and mental processes, and the reason behind this gap is consciousness or subjectivity. According to Nath, the models presented by the AI researchers leave aside the subjective experiences associated with consciousness and hence are only concerned with the third-person perspective. The intrinsic quality of conscious experience or qualia is lost while trying to understand it through the third-person perspective. Researchers in AI have a different perspective towards qualia. According to them, qualia are ineffable, intrinsic, and quite private to an individual (Nath, 2007, p. 14). These are nothing but functional states of the brain. It is possible that qualia are actually the aberrations and may not have much importance. According to AI researchers, mind should be considered as nothing but a machine which can be functionally represented by another machine. It seems that the third-person functionalist view and the first-person subjective view of mind have inspired some interdependent streams of enquiry. Nath has predominantly used the so-called critical method of research to conduct his study. He has raised questions at different levels of abstractions and tried to answer them through critical reasoning. While doing so, he has also drawn heavily from previous works to support and justify his point of view. If we look into the history of enquiry, different methods of philosophical enquiry have been used in different contexts. Some of the methods of philosophical enquiry are as follows: (a) Dialectical Method: The dialectical method is essentially a method of conversation or debate. It has a long history and owes its origin to the Socratic method of philosophical enquiry through questions and answers. The objective is to resolve the disagreement through rational discussion. (b) Critical Method: The critical method encourages one to be critical of the theories formulated so far. In the critical method, a researcher usually subjects the theories, general concepts to critical review, and measure their validity according to how well they withstand the criticism. (c) Speculative Method: Speculative thinking expresses human curiosity about the world while striving to understand the reality in natural terms. The method encourages the researcher to reflect upon different aspects of human experience such as rational, social, ethical, aesthetic, and religious experience and then theorise based upon that. (d) Scientific Method: In the scientific method one proposes a hypothesis as a possible explanation of some phenomena and then designs experimental studies to test the hypothesis. If the experiments do not bear out the hypothesis, it must be rejected or modified. The scientific method attempts to minimise errors while carrying out experiments. (e) Phenomenological Method: Phenomenology studies conscious experience as experienced from the subjective or the first person point of view. It studies the structure of various types of experience ranging from perception, thought, memory, imagination, emotion, and desire--striving to extract from it the essential features of experiences. (f) Existential Method: The central proposition of existentialism is that existence precedes essence. A human being’s existence is more fundamental and real than any other meaning that can be ascribed to human life. Existentialism suggests that the existential experience or personal experience is superior to any other experience. (g) Positivistic Method: Positivism (or logical empiricism) states that the only authentic knowledge can come from empirical observations, against which statements can be verified. Central to positivism is empiricism and verificationism. Positivists seek to systemise the acquisition of knowledge through empirical methods. (h) Hermeneutics Method: Hermeneutics may be described as the development and study of theories of interpretation and understanding of texts or any other object of interpretation. Hermeneutics also involves cultivating the ability to understand things from somebody else's point of view, and to appreciate the cultural and social forces that may have influenced their outlook. All the above methods of philosophical enquiry have come into existence at different points of time to cater to specific needs. For example, the earlier methods such as dialectical, critical, or speculative methods were developed to assist in theological studies. Later the scientific and phenomenological methods were developed when the individual's capacity to observe and experience gained importance. Similarly the existential method gained ground after the Second World War. Clarification of thought is the essence of philosophical inquiry. All the enquiry methods start with questioning or doubting common beliefs and end with either obtaining an answer to the question raised or changing the question asked. The methods differ in their focus and scope. For example, while the speculative approach is purely based on reflection, the positivist approach is primarily based on empirical verification. In management research, the positivist method is relevant for studying the operating principles of an existing system whereas the critical method is perhaps more relevant for studying whether the operating principles are appropriate to the circumstances in which the system operates. A researcher should have an appreciation of different methods. Reference Philosophy of mind. (2007). In Wikipedia. Retrieved October 30, 2007 from http://en.wikipedia.org/wiki/Philosophy_of_mind The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.10 Jaydeep Mukherjee, XIMB Jaydeep[at]ximb.ac.in Bhattacharya, B., & Mukherjee, J. (2006). Indian stock price movements and the macroeconomic context: A time-series analysis. Journal of International Business and Economics, 5(1), 88-93. Part I: Introduction to Econometrics Econometrics literally means measurement in the field of economics. Economic theory suggests statements or hypotheses that are qualitative in nature. Econometrics is the application of statistical and mathematical methods to empirically test economic theory and suggest solutions to economic problems. Despite the common assumption, econometrics is not statistics. It differs in one important aspect--the study of statistics is performed in controlled experiments, whereas econometrics often has to deal with observational data. In some sense, an econometrician is similar to an astronomer, who gathers data but cannot conduct experiments. Therefore econometricians often seek natural experiments in the absence of evidence from controlled experiments. In the absence of experimental data, it is difficult for the researcher to pin down the exact cause or causes affecting a particular situation. It is not possible for a researcher to establish the exact relationship between economic variables and this is where the difference lies between a mathematical model and an econometric model. Unlike the deterministic nature of a mathematical model, an econometric model captures the inexact relationships between economic variables through the introduction of an error term, which is a random variable and has well defined probabilistic properties. An important tool used in econometrics is regression analysis that is concerned with the study of the dependence of one variable, the dependent variable, on one or more other variables, the independent variables, with a view to estimating and/or predicting the population mean or average value of the former in terms of the known or fixed values of the later. The dependent variable is assumed to be statistical, random, or stochastic in nature while the independent variables are assumed to be fixed (non-random) in nature. The general form of a linear regression model is: n = number of observations k = number of explanatory variables The error term acts as a surrogate or proxy for all the omitted or neglected variables that may affect The objective of regression analysis is to establish the relationship between the regressor(s) and regressand by estimating the best possible values of parameters. The two commonly used methods of estimation are ordinary least squares (OLS) method and maximum likelihood (ML) method. While developing the econometric model, certain assumptions are made to facilitate the process of parameter estimation and hypothesis testing. For example, in classical normal linear regression model (CNLRM), the error term is assumed to be normally, identically, and independently distributed with zero mean, constant variance, and absence of autocorrelation. Only then can one draw statistical inferences about the population parameters and conduct hypothesis testing. These assumptions may appear pedantic and often unrealistic in nature, however, these are necessary in building models. A researcher is required to treat these assumptions with skepticism as these are only theoretical and may not always hold true. The quality of estimation is based on the assumptions and, if assumptions are not met, the results may not be trustworthy. For example when there is heteroskedasticity (unequal variances), the variance of the coefficients tends to be underestimated, inflating t-ratios and, sometimes, making insignificant variables appear statistically insignificant. There are several statistical tests and procedures available for the detection and removal of such errors arising out of wrong assumptions. A researcher needs to be aware of these tests and their conditions of applicability to be able to choose the appropriate ones. A regression analysis involves three distinct stages: the specification of a model, the estimation of the parameters of this model, and the interpretation of these parameters. The first stage of model specification is the most critical of these stages. Some of the questions a researcher has to ask while specifying the model are: (a) What variables should be included in the model? (b) What is the functional form of the model? Is it linear in parameters or variables? (c) What is the probabilistic assumption made about A model is wrongly specified by omitting important variables from the model, or by including irrelevant variables in the model, or by choosing the wrong functional form, or by making wrong stochastic assumptions about the variables of the model resulting in model specification error. The researcher may often has to apply own judgment while choosing the number of variables and the functional form of the econometric model. One has also to make some assumptions about the stochastic nature of the variables included in the model. While choosing an appropriate model, a great deal of skill and experience is required on the part of a researcher. Part II: Time-Series Data There are generally three types of data available for empirical analysis: time-series data, cross-section data, and panel data. Time-series data are a set of observations on the values that a variable takes at different time intervals such as daily, weekly, monthly, and so forth. Cross-section data are data on one or more variables collected at the same point of time. Panel data are two-dimensional data, which is a combination of time-series and cross-section data. The two main objectives of time-series analysis are identifying the nature of the phenomenon represented by the sequence of observations, and forecasting. Both of the above goals require that the pattern of observed time-series data is identified and formally described. As in most other econometric analyses, time-series data exhibit a random noise which usually makes the pattern difficult to identify. Most time-series analysis techniques involve some form of filtering out noise in order to make the pattern more prominent. Empirical work based on time series assumes that the underlying time series is stationary in nature. A time series is said to be stationary if its mean and variance are constant over time and the value of covariance between any two periods depends only on the time lag. It is a necessary assumption, as in the case of nonstationary process, the observations in the time series are time-dependent and it is not possible to generalise the pattern for the purpose of forecasting. The classic example of a nonstationary stochastic process is the random walk model (RWM). Two types of random walk models are: (a) Random walk without drift: (b) Random walk with drift: In the random walk model without drift, the means remain constant whereas the variance increases indefinitely. Similarly in the random walk model with drift the mean as well as variances increase indefinitely over time thus violating the condition of stationarity. Many statistical tests are available to examine whether a time series is stationary or not. At an informal level, the plotting of time-series data or sample correlogram can give the clue of the likely nature of time series. At a more formal level, the unit root test is the most commonly used method to identify the stationarity of a time series. Since the usual t and F tests can be applied in the presence of unit root (unit root does not follow normal distribution), the Dickey Fuller test is preferred. The Dickey Fuller test is based upon the assumption that the error term is uncorrelated, but in case the error term is correlated the augmented Dickey Fuller test is the more appropriate test. The nonstationary time series can be transformed into stationary time series to avoid the problem of spurious regression. The transformation can happen through difference stationary process (DSP) or trend stationary process (TSP). In the difference stationary process, the first differences of the time series are taken to obtain a stationary time series. In the trend stationary process, the series is stationary around a trend line, which can be linear or nonlinear in nature. The TSP time series has to be regressed on time to obtain the stationary process (i.e., the de-trended series). A researcher needs to be careful while identifying the nature of the time series, because the choice of transformation method would depend upon it. A misidentification of the time series (such as DSP instead of TSP or vice versa) will result in specification errors. A researcher also needs to be vigilant where a combination of two or more time series is involved. For example, a linear combination of two nonstationary processes may produce a stationary time series with unexpected results. This phenomenon is known as cointegration. In time series, the two popularly used forecasting methodologies involve autoregressive integrated moving average (ARIMA) and vector auto regression (VAR). Unlike the regression models, ARIMA models allow There are special types of models to cater to the specific requirements of certain data sets. For instance, the financial time-series data such as stock prices, exchange rates, and inflation rates often show wide swings in their values for an extended period of time followed by periods of relative calm. This phenomenon is termed as volatility clustering. A special class of models, namely autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) are used for analysing and forecasting financial time series. Econometrics offers researchers a wide variety of choices at each stage of model building. The challenge before a researcher is to choose the appropriate model. The results obtained are conditional upon the chosen model which implies that a researcher needs to be very careful while formulating the econometric model, especially when there may be several competing theories to explain an economic phenomenon. Initially a researcher may have to do a large number of hit and trial experiments to arrive at the appropriate model. The number of attempts is expected to decrease as one develops the necessary skill and intuition. Thus, econometric model building is sometimes considered an art rather than a science. References Gujarati, D. N. (2004). Basic econometrics (4th ed.). New Delhi, India: Tata McGraw-Hill. The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.11 Koen Beumer, University of Maastricht, Netherlands koen.beumer[at]gmail.com "If you want to make an apple pie from scratch, you must first create the universe." -- Carl Sagan (1934 - 1996) Carl Sagan’s journey of research on the entire cosmos and Koen Beumer’s journey of research on culture and technology on this lonely planet appear to have brought both to the same conclusion, quoted above. Beumer, is interning at the University of Maastricht in the Netherlands and at XIMB, in pursuit of his Research Masters (MPhil). While introducing himself and his work, he recounted the diverse topics he has selected to study during his tenure at Maastricht for his Bachelors and Masters in Cultures of Arts, Science, and Technology. One topic was user innovation, which refers to innovations developed by consumers and end-users rather than by manufacturers. Eric von Hippel of MIT had shown how user innovation could be used in new product development. Another topic that Buemer studied was scarification of human bodies and its significance in the West (scarification is scratching, etching, or some sort of superficial incision or cut inflicted on the body). Yet another topic was panopticon in traffic systems (panopticon is a type of prison building that is designed in a way that all prisoners have a constant sense of being under observation, even when the guards are not watching them--the design became famous, or notorious, after Michel Foucault’s analysis of the design as a metaphor for modern “disciplinary societies" with perverse inclination to observe and normalise). It was Beumer's diverse interests and desire to visit India that made him pursue the suggestion of his guide at Maastricht to explore working with Dr C. Shambu Prasad of XIMB. He chose to work on a topic with which he had no prior familiarity--agricultural innovation and the System of Rice Intensification (SRI). He had no background in agriculture and had never seen a rice field before he came to India. So, in a new country, with a new guide and a new subject, surrounded by new languages and cultures, Beumer was in totally unfamiliar territory. This was partly by design, as Beumer explained, as his aim was to develop skills in adaptation. For the community of doctoral scholars and research guides at XIMB, Beumer’s experiences offered a chance to discuss the process of choosing the path of research. One normally selects a topic with which one has some familiarity. Beumer’s approach was just the opposite. One would normally like to work in an area that would build up to later work. Beumer was consciously choosing areas to work in that he may never touch again. By the time he had finished with a topic, however, he would have assimilated it in a way that it becomes integral to him. His focus was more on developing different types of skills of assimilation. For instance, SRI poses the challenge of acceptance. SRI or System of Rice Intensification is a set of six principles of rice-growing practices, first distinctly assembled in Madagascar by the French Jesuit priest, Father Laulanie, in 1983. A number of countries have adopted it or are in the process of adopting it, including major rice-producing countries such as China, India, Indonesia, and Cambodia. Scientists or science-oriented establishments have been hesitant to bestow SRI with the status of a true innovation, as it has come not from the laboratory but from efforts of civil society. Even where it has been accepted, it is seen in the “green revolution” mould with researchers believing that they could transfer scientific agricultural knowledge to farmers through extension systems. The learning alliance now developed around SRI has been emphasising that taking a narrow view of SRI is unfortunate, because SRI works best when accepted as a set of ideas to be locally adopted. There is something to be said for the more rigorous scrutiny that potential innovations are subject to under the scientific approach. However, considering that in farming many innovations have traditionally come from farmers and civil society, there is need to let go of the linear view of innovation. Further, accepting SRI requires breaking out of the mould set by prior investments in infrastructure such as fertiliser plants and a subsidy regime, which discourage any diversion from the accepted path. Farmers’ approach to innovation is less rigid. Instead of trying to purify some "real" SRI as the scientist would do, the farmer would look at what SRI can be, as operationalised in different settings. But this “economics of evolution” could turn favourable in future, when SRI turns from being “nobody’s business” at present, to becoming “hot property” as commercial organisations and lobbies develop products and services around SRI. Through his work on SRI, Beumer has become part of the community that are pushing for wider acceptance and practice of SRI. While working on SRI, Beumer could learn the terminology related to rice and its cultivation, and experienced that the farmers had different priorities and could not relate easily to the queries and concerns of the researchers. The field visits helped in the research design. Actually putting one’s feet in the muddy field offered the researcher an insight into the farmer’s world. Presenting his work at the national symposium on SRI in Tripura helped in sharpening Beumer’s perspectives on SRI and its progress. The symposium was also a source of empirical knowledge. Hence, being open to learning can lead to interesting results for a researcher. For Beumer, his ability to be part of an emerging community in SRI enabled him to use his editing skills to document and even edit a book with his colleagues. He has come up with ideas for papers and a possible PhD in the field. The seminar was attended by: C. Shambu Prasad, XIMB Faculty, shambu[at]ximb.ac.in Krishnapriya, Doctoral Scholar, Utkal University, kpriya_sep05[at]yahoo.co.in D. P. Dash, XIMB Faculty, dpdash[at]ximb.ac.in Jacob D. Vakkayil, XIMB Faculty, jacob[at]ximb.ac.in Sanjay Varma, Doctoral Scholar, XIMB, u507005[at]stu.ximb.ac.in Esha Agarwal, Student, XIMB, eesshhaa[at]gmail.com Madhavi Latha Nandi, Doctoral Scholar, XIMB, u506001[at]stu.ximb.ac.in A..G.Menon, Doctoral Scholar, XIMB, u505001[at]stu.ximb.ac.in Amit Kumar, Student, XIMB, amitkumar.ag[at]gmail.com C.D.Kuruvilla, Doctoral Scholar, XIMB, u505002[at]stu.ximb.ac.in Sumita Sindhi, Doctoral Scholar, XIMB, u507006[at]stu.ximb.ac.in Mousumi Padhi, Doctoral Scholar, XIMB, u507004[at]stu.ximb.ac.in Jogendra Behera, Doctoral Scholar, XIMB, u507003[at]stu.ximb.ac.in Ranjan Kumar Mishra, Student, XIMB, ranjanmishra[at]gmail.com Reported by Sanjay Varma, with inputs from Koen Beumer, C. Shambu Prasad, and D. P. Dash. [November 30, 2007] http://www1.ximb.ac.in/RW.nsf/pages/R5.12 Manish Singhal, XLRI Jamshedpur manishs[at]xlri.ac.in Singhal, M., & Chatterjee, L. (2006). A person-organization fit-based approach for spirituality at work: Development of a conceptual framework. Journal of Human Values, 12(2), 161-178. Singhal’s doctoral research was on spirituality at work (SAW)--a relatively novel and somewhat atypical topic. This triggered a discussion on why some researchers take up atypical topics for research. What might be the influencing factors in deciding upon a research topic? The researcher’s personal orientation, disciplinary training, and work experience influence the choice of research topic. The inclination towards a particular topic and method is probably influenced by the researcher’s own swabhaava--the overall make-up of an individual, distinguishing it from other individuals. The very inclination towards academics could be an aspect of swabhaava. The topic should be such that it can hold the researcher's interest for 3-4 years and elicit continuous zeal. However, not everything in research should be determined by personal preferences alone. The research design, for example, should be well suited to the research topic--not determined by the researcher’s bias. Any bias of the researcher towards a particular research design and methodology can create a road block. At times, a compromise may have to be made between the researcher's preferences and the practicality of the situation under study, in order to complete the doctoral research in time. The methodological dualisms in research, such as objectivism-subjectivism, reductionism-holism, realism-constructivism, and so forth, should be carefully studied and dealt with. The choice of guide is equally important to the choice of research topic and methodology. The guide should also have similar interests as the doctoral student so that a coherent relationship is established between the two. A struggle for data collection can at times lead to despairing moments. While collecting data one should keep in mind the unit of analysis. Although data collection commonly follows the choice of research topic, the order may sometimes be reversed--the data collection process can also lead to change in the research topic and the choice of methodology. Serendipity plays a big role and can actually lead the research forward. Mostly researchers follow the usual academic style of thesis writing. However, depending upon the researcher's subject and skills, different writing styles can be adopted. Singhal (the seminar leader) chose the writing style of scholarly personal narrative (SPN). According to Nash (2004), scholarly personal narratives are pieces of scholarship that use the author’s personal beliefs and experiences as a springboard for scholarly inquiry. In an SPN, the author explores deeply some aspect of her/his personal life and uses this exploration, in conjunction with the insights available from others’ scholarship, to examine larger theoretical and practical questions in an academic field. It helps to portraying only one coherent story in the thesis. The thesis should not appear to be an incoherent compilation but rather a story with a continuous flow. Academic research is also a test of the integrity and internal strength of the researcher. Authenticity is important in the research process. Integration of one’s thoughts, expressions, and behaviour facilitates intellectual work. The lack of such integration creates cognitive dissonance. Through the trials and travails faced by doctoral scholars, some new lessons are learnt. Doctoral research is a like a learning laboratory, where academic and personal lessons go hand in hand producing continuous learning. Some lessons from Singhal’s doctoral research journey: (a) Document every article read. (b) Prepare field notes everyday. (c) Reflect on what you have done. (d) Qualitative research is not as easy as it may seem initially. (e) Do not assume access to data. (f) Be very particular about the unit of analysis. (g) Establish a support network. (h) Help but do not keep score and ask for help unabashedly. Reference Nash, R. (2004). Liberating scholarly writing: The power of personal narrative. New York: Teachers College Press. The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.13 Paromita Goswami, XIMB paromita[at]ximb.ac.in In July 2007, the subject of RTS 5.4 was communicating research, where the discussion was on writing and presenting one's research work. Continuing with the theme, the subject of RTS 5.13 was publishing doctoral work, where the session leader Goswami recounted her publishing experience, highlighting the potential lessons. Journal publications are an important medium for communicating research. Publishing in scholarly journals serves many purposes: peer review (which may bring some useful course-correction to one’s research), recording of work, recognition, networking with researchers, and so forth. Even when a submission is not accepted, the review comments can help one identify shortcomings in the work. The comments may also be educative and serve to expand the author’s knowledge of the field. However, sometimes there may be a difficulty when the reviewers differ from each other in their observations and suggestions. This can lead to dilemmas for the author. One way to deal with such dilemmas is to improve the focus of the article, so as to reduce the potential of multiple readings of the work. A lot of effort goes towards finding the right journal and getting one's article accepted for publication. Before establishing oneself as a scholarly author, a suggested path for a doctoral scholar could be to do a book summary first, then a book review, and then attempt a somewhat less scholarly magazine article. This could be followed by participating in academic conferences. In the second or third conference, one could present a research paper. Such experiences would prepare one for writing in a peer-reviewed academic journal. Sometimes, it may be relatively easier to publish in the journal brought out by one’s own institution. However, eventually, one should target journals which are known to publish articles related to one's research area. There can be many surprises in the publishing process. Some journals complete the peer-review in a matter of weeks, say 3-4 weeks, while others can take a long time to decide and publish. The Web site of a well-known journal in India states, “It can take upto four months for a final decision on whether the paper is accepted for publication.” Further, “Articles accepted for publication can take up to six to eight months from [the] date of acceptance to appear . . .” Often, journals do not specify their rejection rate (i.e., the proportion of submissions usually rejected). Although all useful information may not always be available through formal channels, sometimes such information can be obtained informally from one’s colleagues, seniors, and others in one’s research network who have first-hand experience in academic publishing. While some editorial offices are very quick and responsive, some can be quite casual and insensitive. Not all journals promptly acknowledge a submission. There are cases where editorial offices misplace a submission and realise the fact only after repeated reminders from the author. However, authors should be careful not to annoy editors with too many reminders; some editors are used to a relatively slow pace of work and they may not like being pressurised to respond fast. Sometimes journals reject submissions without assigning any reason, or without sharing the reviewers' comments with the author. Therefore, while submitting one's work for publication, journals should be chosen with care. One option these days is to publish in electronic journals, some of which are “open access” journals--providing free full-text access to readers. The Directory of Open Access Journal (DOAJ) provides a selected list of such journals. The Social Science Research Network (SSRN) provides a mechanism through which one could post one’s work, if only to establish authorship of a new idea or method. Journal articles should be edited as per the journal's specified style. Many journals relating to management follow the APA style. Top-rated journals tend to have a clear editorial focus that reflects their scholarly vision. It may help to read any articles written by the editorial members in order to get a clearer idea about the journal’s focus and scope. Since many scholarly journals are associated with research communities, it helps to read and cite the work of some of the important members of the associated research community. Some reviewers expect the authors to be familiar with the most recent work in the field. Therefore, it helps to cite recent publications in one’s article. At the same time, some reviewers expect the authors to be familiar with the older but more seminal work in the field. So, it also helps to cite such older literature. Publishing parts of one’s doctoral research before one’s thesis defence does establish one's scholarly credentials. It certainly helps in the thesis examination process. The seminar was attended by: C. D. Kuruvilla, Doctoral Scholar, XIMB, u505002[at]stu.ximb.ac.in Madhavi Latha Nandi, Doctoral Scholar, XIMB, u506001[at]stu.ximb.ac.in Talat Yasmin, Independent Research Scholar, talat2_yasmin[at]yahoo.co.in A. G. Menon, Doctoral Scholar, XIMB, u505001[at]stu.ximb.ac.in Sanjay Varma, Doctoral Scholar, XIMB, u507005[at]stu.ximb.ac.in Sumita Sindhi, Doctoral Scholar, XIMB, u507006[at]stu.ximb.ac.in Jogendra Behera, Doctoral Scholar, XIMB, u507003[at]stu.ximb.ac.in D. P. Dash, XIMB Faculty, dpdash[at]ximb.ac.in C. Shambu Prasad, XIMB Faculty, shambu[at]ximb.ac.in Niraj Kumar, XIMB Faculty, niraj[at]ximb.ac.in Reported by Sanjay Varma, with inputs from Madhavi Latha Nandi, C. D. Kuruvilla, Adwaita Govind Menon, D. P. Dash, and Paromita Goswami. [January 10, 2008] http://www1.ximb.ac.in/RW.nsf/pages/R5.14 Prajit K. Basu, University of Hyderabad pkbsh[at]uohyd.ernet.in Doctoral education can be viewed as a process of social reproduction within academic research communities. Social reproduction refers to the process by which groups of people, notably social classes, reproduce their social structure and patterns over time. As the research student undergoes training under a supervisor and becomes trained to become an independent researcher he/she often gets persuaded to adopt the practices considered “appropriate.” Often, this is not learnt explicitly but through tacit understanding. The process of research begins with asking appropriate questions. The appropriateness of the question is defined by ontology and finding out details of the specific kinds of things that exist. Ontology is a description of the concepts and relationships that can exist for an agent or a community of agents (Gruber, 1992). For example, the current ontology in physics would consider appropriate questions such as what causes state of matter. Questions such as what causes motion would not be appropriate. The cause of motion has now long been established in physics. No research is free from such ontological commitments. For example, in Marxism, class is firmly embedded in the ontology. The concept of mass is similarly embedded within the physical sciences, but it changes as the framework changes from physics to chemistry. While we consider mass to be constant in chemistry, the Einsteinian concept of mass in physics assumes that the mass of a body increases when the velocity of the body tends towards the velocity of light. Students work for a fixed ontology which gives them a well-worked fixed guideline. Despite the boundaries set by ontology some researchers are breaking away from the ontologies established in their fields of research. It is interesting to note that about 40 Nobel laureates have broken the ontological barriers in their fields to pursue curiosity-driven research. Path-breaking research where a new ontology replaces an old one is not therefore a research under supervisory guidance as this can be done only when there is absence of social reproduction. Further, ontologies cannot always be mixed because some may be contrary to each other. Once in use, an ontology tends to perpetuate, although theories based on the ontology may change. Subjects such as management do not have a single ontology. There is a certain degree of relativity in the social and behavioural sciences which allows the construction of competing and overlapping ontologies. According to philosophy, one cannot do research without an ontology. Research does not start on a blank slate, so we have to resort to previous theories or previous work done in some area to understand the issues involved. This indoctrinates us into the use of the established ontology and thus completes the process of social. Ontology refers to the subject of existence while epistemology is about knowledge and knowing (Gruber, 1992). While ontology would refer to the nature of reality and the filters through which we see and experience the world. Epistemology would be questioning the sources and assumptions of knowledge and therefore questioning what we “do know” and we “can know” (Allison & Pomeroy, 2000, p. 13). The relationship between philosophy and science and the evolution of that relationship were then discussed. It was in the nineteenth century that the demarcation of science became an important concern in philosophy. The question whether parapsychology was a science or not led to the need for a distinction between science and nonscience. Science came to be seen as a particular way of gathering knowledge. Gradually the domain of science has become broader while that of philosophy has become narrower. In the twentyfirst century, philosophy seems to have reached its last bastion where it is limited to the study of mind or consciousness. The kind of help that philosophy can extend while pursuing research in natural and social sciences indeed can be varied. The idea of a doctoral programme is to get initiated into a certain way of exploring issues that interest us. It involves getting used to asking uncomfortable questions to ourselves. These questions may relate to some objects of investigation. Are those questions appropriate? How do we establish that the objects of investigation are out there? Philosophy helps us pose such questions in ways that make them discussable in scholarly communities. References Allison, P., & Pomeroy, E. (2000). How shall we "know?" Epistemological concerns in research in experiential education. The Journal of Experiential Education, 23(2), 91-98. Gruber, T. (1992). What is an ontology? Retrieved January 15, 2007, from Stanford University, Knowledge Systems, AI Laboratory Web site: http://www-ksl.stanford.edu/kst/what-is-an-ontology.html The seminar was attended by: http://www1.ximb.ac.in/RW.nsf/pages/R5.15 Somendra Pant, Clarkson University School of Business, USA pants[at]clarkson.edu Suddaby, R. (2006). From the editors: What grounded theory is not. Academy of Management Journal, 49(4), 633–642. One of the key aims of the Research Training Seminar (RTS) series is to initiate doctoral scholars to different forms of research. This helps the scholar develop an appreciation for differences within research. The last seminar of the academic year drew our attention to the “grounded theory” perspective in management research. Pant’s recent work on the process of information management in supply-chains adopts the grounded theory perspective. Since his work is still in process and the results are only provisional, Pant shared his concern regarding making his work available in the public domain now. Therefore, it was agreed to restrict this report to the generic issues of doing research from the grounded theory perspective. Grounded theory refers to the process (and outcome) of developing patterns of conceptualisation from field-data, refining the results through continuous comparison with any new data being collected. In this perspective on social research, the theoretical concepts are supposed to be thoroughly grounded in the field context--rather than, say, any preexisting concept (or model) (Glaser, 2002). The main feature of grounded theory is the development of new conceptualisation through the collection and analysis of data around a phenomenon. Various data-collection techniques can be used, particularly interview and observation. A key feature of grounded theory is the simultaneous collection and analysis of data using a process known as “constant comparative analysis” (Hancock, 1998). Broadly speaking, in any research perspective, theory development takes into account empirical data. How then is grounded theory different from other research perspectives? A key difference may be in the attitude towards preexisting concepts (or models) that the grounded perspective brings to research. Due to the mistrust towards any prior conceptualisation, research based on grounded theory proceeds from the raw data and the fresh conceptualisations that emerge from the data. In other words, the grounded perspective seeks to prevent any potential domination of ideas not grounded in the experienced realities of the actors in specific social settings. Grounded theory is based on two key research processes: "constant comparison," in which data are collected and analysed simultaneously, and "theoretical sampling," in which the decision about which data should be collected next is determined by the emergent concept/theory. Grounded theory provides a research approach that is more appropriate for some questions than others. It is most suited to efforts to understand the process by which actors construct meaning out of intersubjective experience. It is important for a researcher to be aware of the common misconceptions surrounding grounded theory. Suddaby (2006) identifies six common misconceptions that should be avoided. (a) Grounded theory is not an excuse to ignore the literature. It is desirable and usually necessary to do a literature survey of existing theories so as to get the initial idea and direction. (b) Grounded theory is not a presentation of raw data. Grounded theory emphasises on theoretical abstractions arising from subjective experiences. (c) Grounded theory is not theory testing, content analysis, or word counts. The purpose of grounded theory is to elicit fresh understandings about patterned relationships between social actors and how these relationships and interactions actively construct reality. The key variables of interest are internal and subjective in nature. (d) Grounded theory is not simply routine application of formulaic technique to data. Grounded theory is an interpretive process, in which the researcher is an active element and the act of research has a creative component that cannot be delegated to any formulaic technique. The researcher has to focus on where to collect the next iteration of data and, more importantly, what meaning ought to be ascribed to the data collected. (e) Grounded theory is not perfect. Grounded theory is inherently messy in nature and requires a researcher to develop a tacit understanding of the findings. The research usually has a large number of iterations with no clear discrete boundary between data collection and analysis. (f) Grounded theory is not easy. Grounded research depends upon the sensitivity of a researcher to tacit elements of the data or meanings and connotations that may not be apparent from a mere superficial reading. A researcher requires a lot of intuitive skills and pattern perceiving abilities to develop a theory from observations. Pant’s current work on information management in supply-chains is designed to answer questions relating to the subjective experience of the members of a supply chain, while linking it to their collective performance as an interorganisational system. According to him, the research literature in this area is not adequate to answers the questions relevant to his research. The situation calls for fresh conceptualisations and, thus, lends itself to grounded research. The research is expected to offer new theoretical perspectives, which may have to be tested subsequently for their general applicability. Reference Glaser, B. (2002). Conceptualization: On theory and theorizing using grounded theory. International Journal of Qualitative Methods, 1(2). Article 3. Retrieved March 10, 2008, from http://www.ualberta.ca/~iiqm/backissues/1_2Final/pdf/glaser.pdf Hancock, B. (1998, updated 2002). Trent focus for research and development in primary health care: An introduction to qualitative research. Trent Focus Group, UK. The seminar was attended by | ||||||||||||||||||||||||||||||||||||||||||||||||
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