Schedule and location

Friday November 2nd. 

Hotel Alba, Ahlmaninkatu 4, Jyväskylä

Registration 

Registration is open until 23rd of October.

Speaker

Professor Mikko Siponen, who holds a D.Soc.Sc. in philosophy and Ph.D. in IS.

Chief of Medical Oncology Tuula Klaavuniemi, who holds M.Sc. in biology, Ph.D. in biochemistry, M.D. and Specialist Degree in Medical Oncology.

Organizer

Professor Mikko Siponen, University of Jyväskylä, Finland

Overview

The need for theory is inculcated to IS since the first ICIS by Keen’s call for reference theories and “dependent variable” (1980). Keen’s (1980) view was influenced by the intellectual atmosphere in organizational and management research (OMR): “If manuscripts contain no theory, their value is suspect” (Sutton & Staw 1995 p. 371). Keen’s (1980) solution to the lack of theory was theory borrowing from other disciplines. The rest is history. The attitude from OMR “no theory – value is suspect” transferred into IS. More than 110 reference theories have been applied in IS (Benbasat & Weber 1996; Larsen et al. 2014; Lim et al. 2009). The use of reference theories in IS has become the “norm” for undertaking research (Grover & Lyytinen 2015). Reportedly, “required element” for any excellent paper is that it “sufficiently uses or develops theory” (Straub 2009 p. vi), “all top journals in our field promote strong theory” (Grover et al. 2008).

The “no theory — value is suspect” from OMR continued to influence IS. OMR writers raised concerns on theory lending. Namely, OMR theories were loaned from, for example, psychology, while OMR lacked their own theories (Oswick et al. 2011). Similar concerns were seen in IS (Straub 2012). Grover et al. (2008) summarized the issues well: “the potential negative effect of relying too much on reference disciplines in IS theory development and call for strategies that lead to bolder and more original theory” (p. 40).

These concerns gradually raised the theory expectations in IS — the required “theoretical contribution” started to shift. It has become increasingly difficult (in IS) to just borrow theories and use them as such. Papers that sufficiently utilize a theory (Straub 2009 p. vi) are hardly enough anymore for top IS journals. It is not too much of an overstatement to say that the expectation in top IS journals today is that every single paper develops a new theory or modifies existing theory.

In this course, we explain how many influential and seminal IS accounts on scientific theory profoundly obscure what is a scientific theory in contemporary science. Moreover, the IS discussion on “theory” misses the fact that the main vehicle of scientific knowledge in the contemporary sciences are not theories. We note how many IS theory authors, like OMR authors or some influential sociologists (e.g., Robert Merton and Robert Dubin), were misled by professional philosophers. These were namely, logical empiricist philosophers and Popper, who during 1920 — 1969 focused on logical constructions of scientific theories. It is clearly documented in the philosophy of science that these logical reconstructions, which only focused on logical relationships and omitted all non-logical contexts in the sciences, were not made for scientists’ use. They were made only for philosophical purposes for professional philosophers. These logical reconstructions of scientific theories — known as the received view of scientific theory — were later rejected for philosophical purposes, even by the key advocates. Alas, some influential sociologists, as well as many IS and OMR scholars, who lacked philosophical training, believe that these logical reconstructions truly present scientific theory as used by scientists. However, even key advocates of logical reconstructions were fully aware that their logical reconstructions, if used by scientists, would have seriously hindered scientific progress.

This misunderstanding —rejected logical reconstructions made for philosophical purposes are mistaken for real scientific theories— has the potential to seriously hinder IS research by focusing on theories as logical relationships alone and omitting the actual scientific context. Furthermore, we need to educate our future IS scholars not to repeat the same mistakes made by our forefathers, which were intellectually inspired by Popper, or logical empiricists such as Hempel (up to 1969). Finally, there is the need to understand scientific research beyond the rejected 1920 – 1970 logical constructions. Accordingly, in this course, we explain:

• How the professional philosophers’ logical reconstructions 1920-1970 (purposefully) misunderstood actual scientific theories and how the same misunderstandings are visible in many IS theory accounts.
• How many important types of scientific research cannot be applied to IS theory accounts.
• How many important types of scientific research (e.g., intervention or comparative research), which do not contain a “theoretical contribution”, are highly important and necessary.
• How IS theory accounts may prevent the method of isolation and idealization.
• Why are scientific theories tested in counterfactual settings (e.g., in isolated and idealized models) and not with real phenomena?
• Why theory or study scopes in real settings are typically totally different than in models and how that has confused many IS authors in terms of generalizability.
• Why many of the best scientific theories (e.g., fundamental laws of physics) may not be generalizable to any actual settings without case by case modifications; and how IS accounts do not understand this, but require such generalizations, which even the fundamental laws of physics cannot meet.
• What is explanation and prediction accuracy, and how do the IS theory accounts miss these?
• Why does an increase in study/models/theory scope (e.g., increasing explanatory breadth) typically, if not necessarily, decrease explanation or prediction accuracy in real settings?
• Why are models claimed as being more important in contemporary (philosophy of) science than theories, despite this not being recognized in IS theory accounts?
• Why do scientific theories/accounts purposefully and strategically mispresent the actual phenomenon?
• Why do scientific theories, models and methods typically contain purposefully false assumptions (on the target phenomenon)?
• Why are causal claims in sciences made in counterfactual settings (in isolated and idealized models) and not with real phenomena?

Content of the course and some highlights

• The history of the Received View (RV) of the scientific theories:
  • Reichenbach’s logical analysis and resulting logical reconstructions as the aim of “scientific philosophy” by professional philosophers
  •  Carnap’s “method of explication”.
• Science-based philosophers’ critique on the RV
• Key theses of the RV and how they are visible in influential IS theory accounts:
  • The main vehicle of scientific knowledge is theory
  • Scientific theories describe real phenomena and they are evaluated (accepted/rejected) against real world observations
  • Carnap’s theoretical/observational
  • Theories are true laws
  • RV as the statement view, axiomatic systems

• Rejection of key RV theses:
  • Hempel’s wholesale rejection of RV in 1969
  • Scientific theories DO NOT describe a real phenomenon and they are NOT evaluated (accepted/rejected) against real world observations
  • Carnap’s theoretical/observational: “the problem for which this dichotomy [theoretical/ observational] was invented…does not exist [in actual science]” (Putnam 1962 p. 241, italics original)
  • Even theories in physics are NOT true laws: Even the “fundamental laws [of physics] are not true, nor nearly true, nor true for the most part” (Cartwright 1983 p. 175). Or “the most interesting fact about laws of nature is that they are virtually all known to be in error.” (Scrivens 1961 p. 91).
  • Scientific theories are not presented as statements or axiomatic systems.

• Implications of the RV for IS — case examples:
  • A critique on IS views on scope: why increasing practical relevance tends to require narrowing the scope of the study. Cancer biology/oncology and IS examples.
  • Merton philosophy of middle-range theories revised
  • Generalizability: Humean-Popperian problem of induction is a logical riddle and not a scientific problem
  • Generalizability: why local findings do not really generalize in real settings (outside of counterfactual laboratory settings or models)?

• Model view of science since 1970s:
  • Models as the main vehicle of scientific knowledge: I opened Structure of Scientific Theories asserting that the "most central or important" problem in philosophy of science is "the nature and structure of theories…For theories are the vehicle of scientific knowledge and one way or another become involved in most aspects of the scientific enterprise" (Suppe 1974a). Don't believe it for a moment! Today much of science is atheoretical, as it was then. For example, theory development is incidental to most of today's chemistry…Today, models are the main vehicle of scientific knowledge.  (Suppe 2000 p. 109)
  • “Explanations in physics generally begin with a model.” (Cartwright 1983 p. 103)
  • Models are fictional systems, which purposefully mispresent the actual phenomenon.
  • How and why replications are used in science?
  • Case examples: fundamental laws of physics, cancer treatments and possible IS examples.

• The method of isolation, abstraction, idealizations: “the methods of abstraction and idealization that are at the heart of modern science…” (Cartwright 1989 p. 211-212).

• How any scientific study (using idealization, isolation or abstraction) necessary decreases realism and adds purposeful falsehoods: “explanation in physics relies essentially on idealizations (idealized models) of physical systems, and the explanations themselves contain false statements about the both the explanatory relevant features of the physical system and the phenomenon to be explained” (Wayne 2011 p. 831).
• Isolation: philosophy of isolation and case examples
• Abstraction: philosophy of abstraction and case examples
• Idealizations: philosophy of idealizations and case examples
• A case example of a complex reticulated phenomenon examined by isolation and abstraction.
• Determinism, probabilistic causality and random causality.
  • Why do many natural sciences, medical research, or social sciences, are neither truly deterministic nor probabilistic, but possess random causality?

• The theory of intervention research
• Intervention research example (cancer treatments) and possible IS examples.

Credit points

Doctoral students participating in the seminar can obtain 1 credit point. This requires participating in all the lectures of the course on “Reconsidering Theories in IS”.

Registration fee

This seminar is free-of-charge for Inforte.fi member organization's staff and their PhD students. For others the participation fee is 250 €. If you take part to Theorizing in Design and Design Theories on both days, this seminar is free-of-charge. The participation fee includes access to the event and the event materials. Lunch and dinner are not included.