Theory or Not Theory

I must admit that it is a tad confusing to explain what theory is, which is why I will begin by describing what theory is not because it’s a little easier to understand. Robert Sutton and Barry Stow in their paper, “What theory is Not”, single out five keys areas, which are constituents of research papers which fall prey of being confused as theory or tend to replace theory, its development or explanation.

References or citations, which are favourites of researches to include in papers in abundance are not theory. They don’t show the relationship between what is cited and the theory that the reference is based on. Another misconception is that data is theory. Data is good for proving a theory, disproving an already existing theory or creating a completely new theory. The difference between data and theory is that data seeks to explain which empirical patterns are observed, while theory seeks to explain why those patterns are observed. List of variables are also not theory. They only give expectations of results. Diagrams, although very useful in clearly outline the flow of logic from one component or idea to another about a theory, they still do not explain the logic behind the theory and can therefore not be used on their own. Finally, hypotheses, which can be described as concise statements of what is expected to occur, are not theory, for the simple reason that they do not explain the why those results are expected to occur.

From the description above, one can therefore deduce that theory seeks to answer the question “WHY”. It generates associations between occurrences; explaining why acts, events and thoughts take place. The emphasis of theories lies in expounding the causal relationships between phenomena, but only within certain bounds.

In informations systems, there are five main types of theory. Theory for 1) Analyzing, 2)Explaining, 3) Predicting, 4) Explaining and Predicting, 5) Design and Action. Within those four theories, are the main components of each: means of representation, constructs, statements of  relations and scope.

Analysis simply states what the theory is. The what, where, how, when and where of the theory ought to be covered in the explanation. Predictions observe the theory in the current state and attempt to foresee what it will result in the future. Prescription gives instructions of how to apply the theory.

The research paper, “Everything is plentiful - Except attention” by Kortelainen & Katvala, studied the types of social media tools that are used by scientific and scholarly journals websites, the kind of attention data that can be found and its focus and the topics in the postings of different social media tools about the scientific or scholarly journals.

The paper proposed a theory: Social media tools as a source of attention data. Attention data in this paper is informally defined as liking, commenting on, sharing, linking, following articles on Facebook, blogs, Twitter. After adequate research was carried out, it is found that the liking or commenting on a paper or journal may increase its visibility or use. Blogs, were found to contribute to professional and scientific knowledge. The author concludes that although attention data is difficult to measure, scientific journals in partnership with their users, create on social media fascinating information resources that is[are] relevant, authoritative but nevertheless also entertaining.

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It is observed that most researchers, are either good theorist or empiricists, but never both. Most writers, feign from develop the theories that they state in their papers and instead compensate with the five components discussed earlier which are not theories. A good balance is therefore required when forming research groups so as to ensure that both skills are included in order to come up with a good research study. Most important, as observed by Sutton & Staw, is that more knowledge is required about how to write better theory and more energy devoted to explaining and elaborating it in research papers.