There is nothing so practical as a good theory (Lewin, 1952). Hambrick (2007) criticizes the devotion to theory in the management field; many journals require that papers submitted for publication make a strong theoretical contribution. He notes that this practice does not help the science of organizations and can hinder its progress. The word “theory” seems misused in the management field.

A theory signifies the highest level of inquiry in science. It is a formulation of the relationships among the core elements of a system of variables that ideally is arrived at after overcoming multiple hurdles and several stages of refinement and empirical testing. Theory is used in the wrong way, which will be explained in the first part. The second part focuses on two major languages that are often used in management research: mathematics and narratives. Mathematics is a language that describes ideas in a very precise way but at times at the expense of the richness of a domain. Narrative is a research language that provides rich descriptions but often at the expense of precision. The goal of scientific research in management should be contribution to knowledge that is based on a combination of conceptual, theoretical and empirical work.

Theories, models and conceptual frameworks

A theory is an analytic structure or system that attempts to explain a particular set of empirical phenomena. Theories differ in depth and scope. A few general aspects of theory:

1. A theory is constructed to provide a coherent explanation of a set of observed phenomena;

2. Theories make assumptions and, based on them, draw logical derivations. Those derivations lead to specific predictions regarding the subject matter with which the theory deals.

3. A theory should be formulated in a way that makes it clear how it can be refuted or falsified.

4. The ultimate test of a theory is achieved by comparing its predictions to reality; a theory’s predictions are subject to a false/true test.

In natural sciences, the most famous theory is the relativity theory of Einstein. In the social sciences, an influential theory is game theory. Part of the confusion among management researchers concerning the need for and evaluation of theoretical work is caused by the use of the term “theory” in a very loose sense. What roles have theories played and what role should they play in the development of organization sciences? The field of organizations emerged with empirical and observational studies, and transformed to theoretical frameworks. The Carnegie approach pushed the field of organizations into the realm of science by making assumptions, derivations and predictions and by using mathematics and formal language to describe the relations among variables.

A model implies a formulation that (1) derives predictions based on clearly specified assumptions, and (2) is precise and falsifiable. The major differences between a theory and model are the first and fourth points described previously as the criteria for theory. A test of a model is not one of a “true/false” type, but rather a kind of a “usefulness” test. Examples about the use of models based on data rather than a theory can be found in different kind of sciences, especially in natural sciences. They are also common in economics, and researchers realize that the use of mathematics for arriving at closed-form solutions is rather restrictive in studying phenomena such as human and social behaviour, but simulation models can be used to get better insights about social and organizational phenomena. Models are precise, especially if they are formulated in mathematical terms. However, sometimes researchers approach new domains that do not allow them to use precise symbols to describe the phenomena they are studying, and in such cases researchers try to build conceptual frameworks, which may be not as specific as models, but can provide a general system of organizing the observations.

The criteria for a conceptual framework are that it (1) provides a structure to organize observations, and (2) describes the structure in a clear and precise manner. Predictions may not be directly testable as in theories and models, but coherent and meaningful frameworks can help organizing empirical observations. Organizational change is an example of a conceptual framework in organizational behaviour and theory.

A conceptual framework does not necessarily make strong assumptions the way a theory does, and it may not be as tightly structured as a mathematical or computational model. Yet a good conceptual framework may lead to new insights and may open new avenues of thinking on particular phenomena. Its ultimate test is whether it leads to a better organizing of the major issues in a particular domain of inquiry. Such organization can enhance our understanding and may eventually lead to developing models for prediction and ultimately to theories that explain the nature of the domain of inquiry.

The role of language in scientific progress and theory development

Researchers need to communicate with each other about their ideas, conjectures and findings. To communicate, they need to use a common language that they and their community understand. There are different languages that can be differentiated by the degree to which they are precise on one hand and rich on another. Usually, the richer a description, the less precise it is, and vice versa. Mathematical expressions are tighter and provide a better fit with Popper’s (1959) criteria for scientific expressions. Verbal theories are much more ambiguous than mathematically formulated theories, but in some situations richness may be a better way to describe the research context than a more precise language.

In constructing theories, models and conceptual frameworks, a researcher can use different languages such as mathematics, simulations and graphical tools, as well as verbal description and narratives. A theory has to be parsimonious; if two theories explain the same phenomenon, with a similar degree of success, the one that is more concise and shorter should dominate the other. Researchers should use a language that matches the stage of the problem they are studying; descriptive narratives should be used in the first stage of a study, while models can be developed using formal language in later stages.

Meehl (1967) subscribed to the Popperian tradition and argued that science makes progress in a cumulative manner. Theories make point predictions about parameter values. Theory development progresses by attempting to overcome hurdles that are increasingly difficult. If a theory passes a test (i.e. a point prediction has been supported), that point becomes the null hypothesis in future tests. That point prediction is contrasted against an even more difficult point prediction arrived at by developing the theory further. This approach can be contrasted with the testing of “no-difference” null hypotheses. Merely increasing the sample size can reject hypotheses of this type. Meaningfulness is often sacrificed for significance.

Mathematics: the language of precision in scientific inquiry

The process of knowledge accumulation in management research is interrupted at times when multiple theories and models within the same or related domains coexist, even though they make different predictions regarding the same behaviour. This raises the question of how we can know which theory is correct or how to test and advance either theory or both. The author suggests a way of testing the predictions of such theories and shows that the language of mathematics provide great help in this process, by using Locke’s goal-setting model and Atkinson’s theory of achievement motivation.

Both Atkinson and Locke make predictions about where an individual’s performance reaches its maximum. Atkinson drew on expectancy theory and focused on task difficulty, whereas Locke’s model describes motivation to perform based on intentions and goals. A main difference between the two is their prediction of the point where performance reaches its maximum. Atkinson provides a point prediction of 0.50 for the probability of success on a task that high-need achievers would choose. Locke’s model suggests that the harder the goal, the better the performance.

Comparing the theories’ predictions

One way to compare these theories’ predictions is by testing the following hypotheses:

H₀: Performance on achievement tasks reaches its maximum at P_s = 0.5

H₁: Performance on achievement tasks reaches its maximum at P_s

Where P_s is the probability of success on the task. Although this approach is familiar, in comparing two theories whose predicted distributions have means of M₁ and M₂, the following problems arise:

1. Point null hypothesis (e.g., H₀: M₁ = M₂ or H₀: M₁ – M₂ = 0) are always (quasi) false as significant differences can be detected only due to a large sample size.

2. A directional null hypothesis (e.g., H₀: M₁ 2) does not generate a theoretically expected distribution because it is not precise. Because H₀ is always (quasi) false, properly run experiments are going to provide data that would support H₁ in about half of the cases, regardless of whether it has any merit.

An alternative approach to comparing the theories’ predictions is:

H₀: Performance on achievement tasks reaches its maximum at P_s = 0.5

H₁: Performance on achievement tasks reaches its maximum at P_s = 0.3

This is superior to testing the theories with a directional null hypothesis as described earlier. However, there are people who argue that a hypothesis of the type H₁: P_s = 0.3 cannot be accepted, and therefore a third theory will be described. This approach enables a researcher to generate point predictions that are consistent with but not generated by the original theory.

Comparing the two theories using point predictions

Locke’s model can be interpreted as implying a particular value of k, which can be derived from the following equation: F_j = P_j x c (1 – P_j)^k, where P_j is the probability of success on the specific task j; F_j is a family of functions that depends on P_j and k, and it gets its maximum at P_j = 1 / (k + 1) (see figure 1 at p. 1317). The family of functions described in this equation all rise to (different) maximum levels and then decline as a function of the probability of success.

Mathematics as a language can help in cumulative development of a scientific domain by sorting out the better theories and providing them with increasingly difficult hurdles to surmount. However, several areas in management are not developed to the degree that they can be subject to such a process.

Narratives: the rich language of scientific inquiry

Efforts need to be made to ensure that the precision of mathematics does not become the standard by which all empirical work is judged. The richness of narrative-based investigations plays an important role in knowledge accumulation in situations where the relevant data can be expressed only in a verbal format. Narrative analysis means a very orderly and precise analysis of the use of words, a process whose intent is to extract meaning from text. An advantage of narratives in ethnographic research is the ability to richly convey the study context. Formal modelling and narrative approaches can be combined when an attempt is made to study a phenomenon like managerial work in the field. See for an extensive example p. 1318.

A tale of a research project on managerial work

The author of the article developed a model to describe the phenomenon observed in a textile factory and to lay down the basis for further developments in the study of how interactions among tasks and interruptions affect the attention of managers and their work. The model would have been sterile if he had not spent time observing, interviewing and learning from the foremen narratives.

Discussion

This paper raised two thoughts: (1) research formulation can take on the forms of conceptual frameworks, models and theories and (2) there are different languages of research. The author presented two of them, mathematics and narratives, which very in their precision and richness. These ideas can help resolve part of the problems Hambrick (2007) identified. A discussion of a few issues that underlie these ideas will follow.

1. Organization science is seen as a field where knowledge is progressing in a cumulative manner. In that respect, the different modes of research formulation can describe sequential progress in research in a new domain.

2. Scientific theories improve by being subjected to falsification and disproof. In this respect, using mathematics is beneficial, because embedded in it is proof (or disproof) by negation.

3. It is not possible to prove theory with empirical findings. Proofs are only possible in a tightly structured system of rules, such as in logic or mathematics. More corroboration is not proof and human observers have a “confirmation bias”, where they look for more positive evidence in support of their claims, ideas or conjectures. However, this is neither efficient nor a logical process. So, we should be critical of studies that we conduct, and ask ourselves if collecting more data is warranted and evaluate the data against the logical mechanism of disproof.

4. Theory construction should not be confused with quantitative methods for data analysis. There is a fundamental difference between theory construction using mathematical tools and data analysis that also uses such tools. The important goal of theory development is finding meaningfulness, not merely significance. It is the former that leads to scientific progress, with the help of the latter.

5. Even though this paper emphasizes the use of mathematical tools in theory construction, the importance of other languages is emphasized as well. Not every research idea or finding can be expressed in mathematical tools, and insisting on this can lead to an increased loss of meaning.

This author sees theory construction as the highest level of scientific inquiry. Organization science needs more rigorous empirical research, newer conceptual frameworks, models and better theories. The practice of requiring contribution to theory from every submitted paper hinders rather than facilitates progress. That part of the problem arises from a misunderstanding of the term theory. We need to look for research that uses appropriate language and provides high-quality empirical studies, conceptual frameworks, models and theories, as long as it contributes to our knowledge in the organization field.