Scsientific Research Methods

Development of Scientific Research Methods

Introduction: In business of all kinds, whether small or big, running in any part of the globe, face problems and issues. Such as the business may be facing short production, less than the expected sales, cope with the competitors, poor commitment of workers, social and financial problems of the personnel working in the company, etc. In order to see the problems we need a high degree of conceptualization to understand the gravity, consequences including its financial effects currently and in the long run.  Such problems can be solved using scientific research methods. My topic consists of two parts – ‘Development of Scientific Research Methods’ and ‘Conceptualization in Business Administration’. We shall discuss them one after the other. Let us see first how ‘Scientific Research Methods’ are developed in business research.

Scientific Research: Before we may discuss the topic in detail let us first discuss the phrase ‘scientific research’.  Scientific research focuses on solving problems and pursues a step-by-step logical, organized, and rigorous method to identify the problems, gather data, analyze them and draw valid conclusions there from. Thus scientific research is not based on hunches, experience and intuition (though these may play a part in final decision making), but is purposive and rigorous. Because of the rigorous way in which it is done scientific research enables all those who are interested in searching and knowing about the same or similar issues to come up with comparable findings when the data are analyzed. Scientific research also helps researchers to state their findings with accuracy and confidence. This helps various other organizations to apply those solutions when they encounter similar problems. Furthermore scientific investigation tends to be more objective than subjective and helps managers to highlight the most critical factors at the workplace that need specific attention so as to avoid, minimize or solve problems. Scientific research and managerial decision making are integral aspects of effective problem solving.

The term scientific research applies to both basic and applied research. Applied research may or may not be generalizable to other organizations, depending on the extent to which differences exist in such factors as size nature of work characteristics of the employees and structure of the organization. Never-the-less applied research also has to be an organized and systematic process where problems are carefully identified data scientifically gathered and analyzed and conclusions drawn in an objective manner for effective problem solving.

 However, sometimes the problem may be simple that it does not require the elaborated and rigorous type of scientific research. Just using the past experience the problem may be solved. And some other times, decision may be required on urgent and immediate bases. Similarly we may not be willing to spend extra money on research or due to some other factors, lodging of proper scientific research may not be desirable and the solution to the problem may be based on simply the hunches. It is experienced that there is high probability of failure of such decisions. Running business is highly sensitive. Even a single decision can convert a successful business into ruins. Therefore each and every decision should be based on logical thinking based on real representative data.

The Hallmarks of Scientific Research

The hallmarks or main distinguishing characteristics of scientific research may be listed as follows:

1.      Purposiveness

2.      Rigor

3.      Testability

4.      Replicability

5.      Precision and Confidence

6.      Objectivity

7.      Generalizability

8.      Parsimony

All these characteristics can be explained using some example. Let us see a case of a manager who is interested in investigating how employees’ commitment to the organization can be increased.

Purposiveness: The manager has started the research with a definite aim or purpose. The focus is on increasing the commitment of employees to the organization, as this will be beneficial in many ways. An increase, in commitment, will translate into lesser turnover, lesser absenteeism and probably increased performance levels, all of which would definitely benefit the organization. The research thus has a purposive focus.

Rigor: A good theoretical base and a sound methodological design would add rigor to a purposive study. Rigor connotes carefulness, scrupulousness and the degree of exactitude in research investigations. In the case of our example if the manager asks 10 to 12 of the employees what would increase their level of commitment to the organization, it may not be solely representative of the whole workforce and the research will be unscientific.

Testability: If after talking to a random selection of employees of the organization and study of the previous research done in the area of organization commitment, the manager or the researcher develops certain hypotheses on how employee commitment can be enhanced then these can be tested by applying certain statistical tests to the data collected for the purpose. For instance, the researcher might hypothesize that those employees who perceive greater opportunities for participation in decision making would have a higher level of commitment. This is a hypothesis that can be tested when the data are collected. A correlation analysis would indicate whether the hypothesis is substantiated or not.

Scientific research thus lends itself to testing logically developed hypotheses to see whether or not the data support the educated conjectures or hypotheses that are developed after a careful study of the problem situation. Testability thus becomes another hallmark of the scientific research.

Replicability: Let us suppose the manager or the researcher, based on the results of the study, concludes that participation, in decision making, is one of the most important factor that influences the commitment of employees to the organization, we will place more faith and credence in these findings and conclusion if similar findings emerge on the basis of data collected by other organizations employing the same methods. To put it differently the results of the tests of hypotheses should be supported again and yet again when the same type of research is repeated in other similar circumstances. To the extent that this does happen (i.e., the results are replicated or repeated), our hypotheses would not have been supported merely by chance but are reflective of the true state of affairs in the population. Replicability is thus another hallmark of scientific research.

Precision and Confidence: In management research we seldom have the luxury of being able to draw ‘definitive’ conclusions on the basis of the results of data analysis. This is because we are unable to study the universe of items, events or population we are interested in and have to bases our findings on a sample that we draw from the universe. In all probability the sample in question may not reflect the exact characteristics of the phenomenon we try to study. Measurement of errors and other problems are also bound to introduce an element of bias or error in our findings. However, we would like to design the research in a manner that ensures that our findings are close to reality (i.e., the true state of affairs in the universe) as possible so that we can place reliance or confidence in the results.

Precision: Precision refers to the closeness of the findings to ‘reality’ based on a sample. In other words, precision reflects the degree of accuracy or exactitude of the results on the basis of the sample to what really exists in the universe. For example,  if I estimated the number of production days lost during the year due to absenteeism at between 30 to 40 as against the actual of 35, the precision of my estimation compares more favourably than if I had indicated that the loss of production days was somewhere between 20 and 50. This is equivalent to the statistical term ‘confidence interval’.

Confidence:  It refers to the probability that our estimations are correct. That is, it is not merely enough to be precise but it is also important that we can confidently claim that 95% of the time our results would be true and there is only a 5% chance of our being wrong. This is also called as ‘confidence level.’ The narrower the limits within which we can estimate the range of our predictions (i.e. the more precise our findings) and the greater the confidence we have in our research results the more useful and scientific the findings become.

Objectivity: The conclusions drawn through the interpretation of the results of data analysis should be objective that is they should be based on the facts of the findings derived from actual data and not on our own subjective or emotional values. For instance, if we had a hypothesis that stated that greater participation in decision making will increase organizational commitment and this was not supported by results it makes no sense if the researcher continues to argue that increased opportunities for employee  participation would still help! Such an argument would be based not on the factual data-based research findings, but on the subjective opinion of the researcher. If this was the researcher’s conviction all along then there was no need to do the research in the first place!

Much damage can be sustained by organizations that implement non-data-based or misleading conclusions drawn from research. For example, if the hypothesis relating to organizational commitment in our previous example was not supported, considerable time and effort would be wasted in finding ways to create opportunities for employee participation in decision making. We would only find later that employees still keep quitting, remain absent and do not develop any sense of commitment to the organization. Likewise, if research shows that increased pay is not going to increase the job satisfaction of employees then implementing a revised increased pay system will only drag down the company financially without attaining the desired objective. Such a futile exercise, then, is based on nonscientific interpretation and implementation of the research results.

The more objective the interpretation of the data the more scientific the research investigation becomes. Though managers or researchers might start with some initial subjective values and beliefs, their interpretation of the data should be stripped of personal values and bias. If managers attempt to do their own research they should be particularly sensitive to this aspect. Objectivity is thus another hallmark of scientific research.

Generalizability: Generalizability refers to the scope of applicability of the research findings in one organizational setting to other settings. Obviously the wider the range of applicability of the solutions generated by research, the more useful the research is to the users. For instance if a researcher’s findings that participation in decision making enhances organizational commitment are found to be true in a variety of manufacturing, industrial and service organizations and not merely in the particular organization studied by the researcher, then the generalizability of the findings to other organizational settings is enhanced. The more generalizable the research, the greater is its usefulness and value. However, not many research findings can be generalized to all other settings, situations or organizations. For wider generalizability, the research sampling design has to be logically developed and a number of other details in the data-collection methods need to be meticulously followed. However, a more elaborate sampling design, which would doubtless increase the generalizability of the results, would also increase the costs of research. Most applied research is generally confined to research within the particular organization where problem arises, and the results, at best, are generalizable only to other identical situations and settings. Though such limited applicability does not necessarily decrease its scientific value (subject to proper research), its generalizability is restricted.

Parsimony: Simplicity in explaining the phenomena or problems that occur, and in generating solutions for the problems, is always preferred to complex research frame-work that considers an unmanageable number of factors. For instance if two or three specific variables in the work situation are identified, which when changed would raise the organizational commitment of the employees by 45%, that would be more useful and valuable to the manager than if it were recommended that he should change 10 different variables to increase organizational commitment by 48%. Such an unmanageable number of variables might well be totally beyond the manger’s control to change. Therefore, the achievement of a meaningful and parsimonious, rather than an elaborate and cumbersome, model for problem solution becomes a critical issue in research.

Economy in research models is achieved when we can build into our research framework a lesser number of variables that would explain the variance far more efficiently than a complex set of variables that would only marginally add to the variance explained. Parsimony can be introduced with a good understanding of the problem and the important factors that influence it. Such a good conceptual theoretical model can be realized through unstructured and structured interviews with the concerned people and a thorough literature review of the previous research work in the particular problem area.

In sum, scientific research encompasses the eight criteria just discussed above. Here a question may arise as to why a scientific approach is necessary for investigations when systematic research by simply collecting and analyzing data would produce results that can be applied to solve the problem. The reason for following a scientific method is that the results will be less prone to errors and more confidence can be placed in the findings because of the greater rigor in application of the design details. This also increased the replicability and generalizability of the findings.

Building Blocks of Science in Research

One of the primary methods of scientific investigation is the hypothetico-deductive method. The deductive and inductive processes in research are described below.

Deduction and Induction: Answers to issues can be found either by the process of deduction or the process of induction, or by a combination of the two. Deduction is the process by which we arrive at a reasoned conclusion by logical generalization of a known fact. For example, we know that all high performers are highly proficient in their jobs. If Aamir is a high performer, we then conclude that he is highly proficient in his job. Induction, on the other hand, is a process where we observe certain phenomena and on this basis arrive at conclusions. In other words, in induction we logically establish a general proposition based on observed facts. For instance, we see that the production processes are the prime features of factories or manufacturing plants. We therefore conclude that factories exist for production purposes. Both the deductive and the inductive processes are applied in scientific investigations. The building blocks of scientific inquiry are depicted in the figure shown on the next page. The blocks in the figure include the processes of initially observing phenomena, identifying the problem, constructing a theory as to what might be happening, developing hypotheses determining aspects of the research design collecting data, analyzing the data and interpreting the results.

The Hypothetico-Deductive Method

The following are the Seven-Step Process in the Hypothetico Deductive Method;

1. Observation
2. Preliminary information gathering
3. Hypothesizing
4. Further scientific data collection
5. Data analysis
6. Deduction

Observation: It is the first stage in which one senses that certain changes are occurring or that some new behaviors, attitudes, and feelings are surfacing in one’s environment (i.e. the workplace). When the observed phenomena are seen to have potentially important consequences, one would proceed to the next step. How does one observe phenomena and changes in the environment? The people-oriented manager is always sensitive to and aware of what is happening in and around the workplace. Changes in attitudes, behaviors, communication patterns and analysis, and a score of other verbal and nonverbal cues be readily picked up by managers who are sensitive to the various nuances. Irrespective of whether we dealing with finance, accounting, management, marketing or administrative matters and regardless of the sophistication of the machines and the Internet, in the ultimate analysis, it is the people who achieve the goals and make things happen. When there is indeed a problem in the situation, the manager may not understand what exactly is happening but can definitely sense that things are not what they should be.

Preliminary Information Gathering: It involves the seeking of information in depth, of what is observed. This could be done by talking informally to several people in the work setting or to clients, or to other relevant sources, thereby gathering information on what is happening and why. Through these unstructured interviews, one gets an idea or a ‘feel’ for what is transpiring in the situation. Once the researcher increases the level of awareness as to what is happening, the person could then focus on the problem and the associated factors through further structured formal interviews with the relevant groups. Additionally, by doing library research or obtaining information through other sources, the investigator would identify how such issues have been tackled in other situations. This information would give additional insights of possible factors that could be operating in the particular situation – over and above those that had not surfaced in the previous interviews.

Theory Formulation:  Theory formulation is the next step to the gathering of preliminary information. It is an attempt to integrate all the information in a logical manner, so that the factors responsible for the problem can be conceptualized and tested. The theoretical framework formulated is often guided by experience and intuition. In this step the critical variables are examined as to their contribution or influence in explaining why the problem occurs and how it can be solved. The network of associations identified among the variables would then be theoretically woven together with justification as to why they might influence the problem.

Instead of using the previous information, gathered for such similar purposes, a separate theory has to be formulated each time a problem is investigated. It is because the different studies might have identified different variables some of which may not be relevant to the situation on hand. Further in the previous studies, some of the hypotheses might have been substantiated and some others not, presenting a perplexing situation. Hence problem solving in every complex problem situation is facilitated by formulating and testing theories relevant to that particular situation.

Hypothesizing: It is next logical step after theory formulation. From the theorized network of associations among the variables, certain testable hypotheses or educated conjectures can be generated. For instance, at this point, one might hypothesize that if a sufficient number of items are stocked on shelves, customer dissatisfaction will be considerably reduced. This is hypothesis that can be tested to determine if the statement would be supported.

Hypothesis testing is called deductive research. Sometimes, hypothesis that were not originally formulated do get generated through the process of induction. That is, after the data are obtained, some creative insights occur and based on these, new hypotheses could get generated to be tested later. Generally, in research, hypotheses testing through deductive research and hypotheses generation through induction are both common. The Hawthorne experiments are good example of this. In the relay assembly line, many experiments were conducted that increased lighting and the like, based on the original hypothesis that these would account for increases in productivity. But later, when these hypotheses were not substantiated, a new hypothesis was generated based on observed data. The mere fact that people were chosen for the study gave them a feeling of importance that increased their productivity whether or not lighting, heating, or other effects were improved, thus the coining of the term the Hawthorne effect!

Further Scientific Data Collection: After the development of the hypotheses, data with respect to each variable in the hypotheses need to be obtained. In other words, further scientific data collection is needed to test the hypotheses that are generated in the study. For instance, to test the hypothesis that stocking sufficient items will reduce customer dissatisfaction, one needs to measure the current level of customer satisfaction and collect further data on customer satisfaction levels whenever sufficient number of items are stocked and made readily available to the customers. Data on every variable in the theoretical framework from which hypotheses are generated should also be collected. These data then form the basis for further data analysis.

Data Analysis: In the data analysis step, the data gathered are statistically analyzed to see if the hypotheses that were generated have been supported. For instance, to see if stock levels influence customer satisfaction, one might want to do a correlational analysis and determine the relationship between the two factors. Similarly, other hypotheses could be tested through appropriate statistically analysis. Analyses of both quantitative and qualitative data can be done to determine if certain conjectures are substantiated. Qualitative data refer to information gathered in a narrative form through interviews might be conducted with managers after budget restrictions are imposed. The responses from the managers who verbalize their reactions in different ways might be then organized to see the different categories under which they fall and the extent to which the same kinds of responses are articulated by the managers.

Deduction: Deduction is the process of arriving at conclusions by interpreting the meaning of the results of the data analysis. For instance, if it was found from the data analysis that increasing the stocks was positively correlated to (increased) customer satisfaction (say, .5), then one can deduce that if customer satisfaction is to be increased, the shelves have to be better stocked. Another inference from this data analysis is that stocking of shelves accounts for (or explains) 25% of the variance in customer satisfaction (.5²). Based on these deductions, the researcher would make recommendations on how the ‘customer dissatisfaction’ problem could be solved.

In summary, there are seven steps involved in identifying and resolving a problematic issue. To make sure that the seven steps of the hypothetico-deductive method are properly understood, a following example is explained briefly.

Example:

Observation: The Chief Information Officer (CIO) of a firm observes that the newly installed Management Information System (MIS) is not being used by middle managers as much as was originally expected. The managers often approach the CIO or some other ‘computer expert’ for help, or worse still, make decisions without facts. ‘There is sure a problem here,’ the CIO exclaims.

Information Gathering Through Informal Interviews: Talking to some of the middle-level managers, the CIO finds that many of them have very little idea as to what MIS is all about, what kinds of information it could provide, and how to access it and utilize the information.

Obtaining More Information Through Literature Survey: The CIO,  immediately uses the Internet to explore further information on the lack of use of MIS in organizations. The search indicates that many middle-level managers – especially the old-timers – are not familiar with operating personal computers and experience ‘computer anxiety.’ Lack of knowledge about what MIS offers is also found to be another main reason why some managers do not use it.

Formulating a Theory: Based on all this information, the CIO develops a theory, incorporating all the relevant factors contributing to the lack of access to the MIS by managers in the organization.

Hypothesizing: From such a theory, the CIO generates various hypotheses for testing, one among them being: Knowledge of the usefulness of MIS would help managers to put it to greater us.

Data Collection: The CIO then develops a short questionnaire on the various factors theorized to influence the use of the MIS by managers, such as the extent of knowledge of what MIS is, what kinds of information MIS provides, how to gain access to the information, and the level of comfort felt by managers in using computers in general and finally, how often managers have used the MIS in the preceding 3 months.

Data Analysis: The CIO ten analyze the data obtained through the questionnaire to see what factors prevent the managers from using the system.

Deduction: Based on the results, the CIO deduces or concludes that managers do not use MIS owing to certain factors. These deductions help the CIO to take necessary action to rectify the situation, which might include among other things, organizing seminars for training managers on the use of computers, and MIS and its usefulness.