Unfortunately, if we turn to the philosophy of science for views on theory we still find disagreement on many important issues. Godfrey-Smith (2003) notes that there has been a state of fermentation in recent years concerning many problems: causality, the distinction between experimental laws and theories, induction, and the cognitive status of theories, to name just a few. Some of the views of prominent philosophers that appear especially relevant to at least some types of information systems theory are discussed here. Note, however, that the term ‘post-positivist’ is not appropriate for describing these views since some are pre-positivist (Hume, Locke, Kant) and some are anti-positivist (Popper).
Sir Karl Popper is a philosopher of science whose views appeal to many working scientists and who is regarded as a hero by many (Godfrey-Smith, 2003). Popper (1980, p. 59) gives this view of theory:
Scientific theories are universal statements. Like all linguistic representations they are systems of signs or symbols. Theories are nets cast to catch what we call ‘the world’; to rationalise, to explain and to master it. We endeavour to make the mesh ever finer and finer.
Popper sees theories as uncertain and as approximate representations of reality. His ontological position recognises theory as having an existence separate from the subjective understanding of individuals. Theory is an inhabitant of World 3, the objectively existing but abstract world of man-made entities – language, mathematics, knowledge, science, art, ethics, and institutions, for example. Other worlds are World 1, the objective world of material things, and World 2, the subjective world of mental states (Popper, 1986). Popper saw the work of science as being to take a theory that is proposed, to deduce an observational prediction from it, and then to test the prediction. If the prediction fails, then we have refuted or falsified the theory. If the prediction is supported, then all we can say is that the theory has not been falsified – yet. This position is referred to as the ‘hypothetico-deductive’ model and is reasonably common among philosophers of science and practising scientists.
Popper was not much concerned about where theories come from in the first place, and was strongly opposed to the use of inductive methods in science; that is, in building or supporting a theory on the basis of a large number of observations of a certain kind. Popper has been criticised on these grounds and others have included, in the hypothetico-deductive model, a first stage in which observations are collected and a conjecture (a theory) is generated from these observations (Godfrey-Smith, 2003).
Space precludes a detailed treatment of many of the compelling issues that are discussed under the heading of the philosophy of science. In summary, views that appear useful in discussion of information system theory, which in synthesis can be referred to as a ‘scientific perspective’ of theory and theorising follow:
Theories, as systematic and responsibly supported explanations, are the aim of science. Such explanations may be offered for individual occurrences, for recurring processes or for invariable as well as statistical regularities. The explanations offered can rely on different ideas of causality and what constitutes an explanation (Nagel, 1979, p. 15).
Theorising, in part, involves the specification of universal statements in a form that enables them to be tested against observations of what occurs in the real world (Popper, 1980).
Some propose a distinction between experimental laws and theories, though the distinction is not clear-cut (Nagel, 1979). Experimental laws, such as the gas laws, which relate pressure, temperature and volume in invariant relationships, refer to ‘observable’ entities in at least a loose sense of the word. Theories, on the other hand, tend to offer a more comprehensive interrelated set of explanations and include terms like ‘molecule’ or ‘gene’ which are less readily directly observable, relying on assumptions for their definition. This point is interesting because the experimental laws, which may result from close observation and description of nature, and not necessarily impute causality, may give rise to a broader scientific theory. For example, an experimental law arising from observation, such as ‘All platypuses suckle their young’, can be eventually fitted into a theory about the nature of mammals.
It is expected that theories and laws in the social sciences, for a number of reasons, will be pervasively generalised in statistical terms (e.g. ‘most rural Americans belong to some religious organisation’). Compared with the natural sciences, theories in the social sciences will have narrower scope, or lower-order generality (Nagel, 1979). This observation is not intended pejoratively as social scientists can still manage to advance explanations for a large variety of social phenomena.
Dubin (1978) gives a very detailed treatment of how theories can be specified in the social sciences, which is in accord with the scientific perspective described here. He describes how theory can be used for both understanding and prediction, and how ideally it should deal with both process and outcomes.
The development of theory or conjectures in the first place can occur in many ways: as a result of observations of what occurs in the real world (Nagel, 1979; Godfrey-Smith, 2003) or from insights, imagination, problems or feelings (Popper, 1980).
Scientific theory often, but not always, involves the use of mathematical tools and logic, both for specifying and testing theory (Godfrey-Smith, 2003).
Epistemologically, knowledge for the building and testing of theories, can be gained both empirically (the ‘empiricist’ tradition of Locke and Hume) and from thinking (the ‘rationalist’ view of Descartes and Leibniz). Kant (1781) developed this intermediate position: that thinking involves a subtle interaction between experiences and pre-existing mental structures that we use to make sense of experience, and others, including Schopenhauer and Popper, have followed in this tradition.
Naïve realism is not necessarily a part of a scientific perspective, and neither is a theory-neutral view of observations of the real world (see Godfrey-Smith, 2003 for a ‘scientific realist’ view).
This scientific view of theorising has been little recognised in information systems research, usually because writers in the field confuse scientific views with positivism. Researchers who use Dubin’s principles for the formulation of theory are implicitly following a scientific-like prescription (e.g. Weber, 1997).
An exception in information systems is Lee (1989), who explicitly describes a scientific methodology for case studies and provides a description of the scientific method that is largely congruent with the perspective given above. A second exception is Cushing (1990), who describes the role of frameworks, paradigms and scientific research in management information systems in similar terms, and suggests that frameworks are a precursor to the development of theory with generalisations and laws. Otherwise, the richness of the discussions in the philosophy of science on the nature of theory has been little recognised in information systems as a source for our perspective on theory.
From this discussion of scientific views of theory, we can draw several useful ideas for information systems. Observation of phenomena can precede analysis and description (Type I and Type II theory) and description of regularities (predictive Type III theory). Scientific-type laws that allow both prediction and understanding can also be searched for, but as they will have aspects of human social behaviour included, they are likely to be cast in a probabilistic form (Type IV theory below). Insights for a new theory can come from almost anywhere.