Theme 3:1

Although widely known of as a concept, and always required within scientific studies, theory has always been a struggle to define, especially as various scientific institutions chose different perspectives to understand and explain their idea of theory. However, in this week’s post I aim to explain a general understanding of theory and put it in a relatable context, applicable for people of different backgrounds and faculties.

In her essay The Nature of Theory in Information Systems, Shirley Gregor breaks down the concept of theory stating that a common, central understanding of theory is the goal of being able to explain and predict a phenomenon. In order to be able to explain a phenomenon, a connection between the cause and an event (causation) has to be presented. Further on, to predict a phenomenon it is necessary to apply some level of generalization, nonetheless there are different opinions on how comprehensive this generalization should be. To Gregor, generalization and causation is the core of theory, however, according to philosophers it should also be added that these explanations and predictions are supposed to be testable (able to verify of falsify). Essentially, theory is institutionalized ideas of understanding, used as supporting logic in scientific studies.

It is easier to get a better understanding of theory by simply determining what it is not. Robert I. Sutton and Barry M. Staw lists five common occurrences that frequently substitute and present itself as theory in scientific essays and articles.

These occurrences consist of references (previous presented arguments), data (empirical evidence), list of variables or constructs (concepts), diagrams (categories for data) and hypothesis or predictions (assumptions without logical explanations about empirical relationships).

In order to put theory, and the use of it, in a context, I have read the article “Influential IT management trends: an international study” by Jerry Luftman et al. (2015). This is an empirical study, based on a survey of senior executives that has been conducted since 1980.  The aim of the study is to detect, present and predict the main management concerns and most influential technologies of the time. For the most part in the essay, we are shown diagrams and variables with the writers describing what the numbers represent. It shows the growth and/or decrease of phenomena over the past few years, but while it is possible to conclude some generalized flows, the variables do not explain the reason, why and/or how the flows occur.

In addition to explaining theory, Gregor also determined five types of theories in Infomastions Systems (IS). These types consist of (1.) theory for analysing, (2.) theory for explaining, (3.) theory for predicting, (4.) theory for explaining & predicting and (5.) theory for design & action.

Luftman et al. presents the study of IT management trends as a theory for predicting in their introduction and add predictive remarks in the data analysis e.g.:

“we expect that [business productivity] will continue to remain among the top 10 for the foreseeable future” (p.296)

However, in the conclusion these statements are not specifically discussed apart from a few vague comments about the future, and as these never reach a conclusion about the future the predictive aspect of the analysis lose its value.

Although somewhat using predictive generalizations, I would define this essay mainly as a theory for analysing. It describes the currently occurring IT phenomena, summarizing the changes and flows without explicitly referring to any causality. The data is recorded and classified according to a framework based on geography and year, which gives a broad idea of the conditions over time. Gregor mentions that this kind of schematic approach is useful in cases where little is known about a phenomenon, making it meaningful by putting it into categorical terms. It is important that the phenomenon’s logic and characteristics defining each category are clear in order to mediate the facts/knowledge correctly. In addition, it is also vital for the classifications system to be extensive enough to present all features of the phenomenon. In failing to use the right classifications and categories, valuable information can be excluded from the analysis, which would in turn invalidate the study’s results. 

Luftman et al. have had multiple, extensive choices in the survey, but have chosen to only present the most prominent results in order to present credible results. I do believe they have organized the schema in the best possible way, but on the other side, there is a lack of theory throughout the essay. As substitute they use a few references to Luftman’s earlier studies and rely on the repeatability of the data in the study.

Although their only ambition is to show the current state, with Sutton and Staw’s article in mind, I would say Luftman et al. could improve their study by providing the reader with the perspective of a theory - considering that without a clear theoretic background an essay lose its value.

Luftman Jerry, Derksen Barry, Dwivedi Rajeev, et al. ”Influential IT Management Trends: an international Study”, Journal of Information Technology (2015) 30

http://link.springer.com.focus.lib.kth.se/article/10.1057%2Fjit.2015.18 accessed 14.09.2016