TJTJ7720 Theory in Information Systems (3 op)

Avaa opintojakson esite Sisussa

Opinnon taso:

Jatko-opinnot

Arviointiasteikko:

Hyväksytty - hylätty

Suorituskieli:

englanti

Vastuuorganisaatio:

Informaatioteknologian tiedekunta

Opetussuunnitelmakaudet:

2020-2021, 2021-2022, 2022-2023

Avainteksti

This course describes scientific theory in information systems, and how it is discovered

Kuvaus

0) Introduction

1) Philosophers' logical reconstructions for philosophical purposes and how they were confused with "real" theories in IS theory

2) The (once) received view of scientific theory and its impact on scientific theory in IS

2.1. Theories as laws in IS

2.2. Theories as inferential relationships in IS

2.3. Truth versus idealized accounts

3. Theoretical versus Empirical

3.1. Theoretical versus Empirical in Philosophy of Science

3.2. Theoretical versus Empirical in IS

3.3. Problems of Requiring "Theoretical contribution" and the value of "Empirical" contribution in IS

4. The deductive-nomological (D-N) model of explanations

4.1. Basics idea, including explanation versus prediction, and Critique

4.2. D-N confusions in IS and why IS model of explanation is not D-N

4.3. IS and Covering Theory Model of explanations (CTM)

4.3.1. inductive-qualitative CTM of explanations

4.3.2 probabilistic-statistical CTM of explanations

5. Mechanism-based explanations (MBEs)

5.1. History of mechanisms-based explanations

5.2. Modern mechanism accounts as alternative to laws-based explanations

5.3. Common misunderstandings of mechanisms in IS.

5.3.1 why and how explanations and mechanisms

5.3.2 laws and causal confusions regarding MBEs

5.4 the ontic conception of explanations

5.5 Deliberate misrepresentations in MBEs

5.6 Naturalism versus critical realism in MBEs

6. On theory characteristics preferences in IS

6.1. Should there be a premium on IS specific theories?

6.2. Theory scope: why a wider theory scope is not necessarily better than a narrow scope?

7. Merton's middle range theories reconsidered and implications for IS

8. Mohr's variance and process theories

8.1. basic ideas and critique

8.2. How "process" or "variance" is different from MBEs

9. Stage Theorizing

9.1 what makes a theory a stage theory?

9.2. Is stage an empirical or theoretical?

9.3. stage as idealization

9.4. Discovering and testing stage theories

10. Hypothetico-deductive method

10.1 "H-D" in IS

10.2. H-D in the philosophy of science

10.3. why H-D in IS is not a H-D method?

10.4. hypothetico-inductive-qualitative method

10.5. hypothetico-inductive-statistical method

11. Inductive methods

Osaamistavoitteet

A basic understanding of different fundamental types of scientific theories and how they are discovered

Esitietojen kuvaus

A basic course in the philosophy of science

Suoritustavat

Tapa 1

Arviointiperusteet:

1) Exercise tasks, 2) home exam based on materials discussed in the lecture. Both of these are required. Online lectures by professor are not mandatory, but highly recommended for passing the home exam, because the course covers complex philosophical concepts that can difficult to understand without the lectures.

Valitaan kaikki merkityt osat

Suoritustapojen osat

Osallistuminen opetukseen (3 op)

Tyyppi:

Osallistuminen opetukseen

Arviointiasteikko:

Hyväksytty - hylätty

Suorituskieli:

englanti

Opetus

8.1.–28.2.2021 Luento-opetus

Ilmoittautumisaika:

30.11.2020 0.00–5.1.2021 23.59

Tyyppi:

Luento-opetus

Suorituskieli:

englanti

Vastuuorganisaatio:

Informaatioteknologian tiedekunta

Lisätietoja:

Broadly speaking, this course focuses on characteristics of scientific theory, especially in information systems (IS). We also describe some strategies on how theories are discovered. The latter is often termed “theorizing” or “theory development” in the information systems literature.

The ideal prerequisite is a basic course in the philosophy of science. However, first lecture re-introduces some basic concepts in philosophy of science. The first lecture also presents some typical misunderstanding of some philosophical concepts in information systems literature.

Lecture 1: some basic philosophical concepts and their common misunderstandings in Information Systems literature, Friday 8.1.2021, 2 pm – 4 pm.
- Includes: Positivism, anti-positivism, Popper’s falsification, theory-ladennes, underdetermination versus objective tests
- Introduction to other topics.

Lecture 2: the Once Received View of Scientific Theory vs Modern view of theory, Friday 15.2.2021, 2 pm – 4 pm.
1) Philosophers' logical reconstructions for philosophical purposes and how they were confused with "real" theories in IS theory
2) The (once) received view of scientific theory and its impact on scientific theory in IS
2.1. Theories as laws in IS
2.2. Theories as inferential relationships in IS
2.3. Truth versus idealized accounts
Exercise 1: Monday 11.1.2021: exercise on recognizing potential idealizations on scientific theories and models, 2 pm – 4 pm.

Lecture 3: Questioning the Requirement of Theoretical Contribution in IS, Friday 22.1, 2 pm – 4 pm.
3. Theoretical versus Empirical
3.1. Theoretical versus Empirical in Philosophy of Science
3.2. Theoretical versus Empirical in IS
3.3. Problems of Requiring "Theoretical contribution" and the value of "Empirical" contribution in IS
Exercise 2: Monday 18.1.2021: exercise on empirical contributions, 2 pm – 4 pm.

Lecture 4 Deductive nomological versus inductive-statistical explanations, Friday 29.1.2021, 2 pm – 4 pm.
4. The deductive-nomological (D-N) model of explanations
4.1. Basics idea, including explanation versus prediction, and Critique
4.2. D-N confusions in IS and why IS model of explanation is not D-N
4.3. IS and Covering Theory Model of explanations (CTM)
4.3.1. inductive-qualitative CTM of explanations
4.3.2 probabilistic-statistical CTM of explanations

Lecture 5: Mechanism-based Explanations, Friday 29.1.2021, 2 pm – 4 pm.
5. Mechanism-based explanations (MBEs)
5.1. History of mechanisms-based explanations
5.2. Modern mechanism accounts as alternative to laws-based explanations
5.3. Common misunderstandings of mechanisms in IS.
5.3.1 why and how explanations and mechanisms
5.3.2 laws and causal confusions regarding MBEs
5.4 the ontic conception of explanations
5.5 Deliberate misrepresentations in MBEs
5.6 Naturalism versus critical realism in MBEs
Exercise 3: Monday 1.2.2021: mechanisms-based explanations, 2 pm – 4 pm.

Lecture 6: On theory Preference Characteristics in IS, Friday 5.2.2021, 2 pm – 4 pm.
6. On theory characteristics preferences in IS
6.1. Should there be a premium on IS specific theories?
6.2. Theory scope: why a wider theory scope is not necessarily better than a narrow scope?
7. Merton's middle range theories reconsidered and implications for IS
8. IT artefact as a part of theory

Lecture 7: Variance, process, and stage, Friday 12.2.2021, 2 pm – 4 pm
8. Mohr's variance and process theories
8.1. basic ideas and critique
8.2. How "process" or "variance" is different from MBEs
9. Stage Theorizing
9.1 what makes a theory a stage theory?
9.2. Is stage an empirical or theoretical?
9.3. stage as idealization

Lecture 8. On developing Theories, Friday 19.2.2021, 2 pm – 4 pm
9.4. Discovering and testing stage theories
10. Hypothetico-deductive method
10.1 "H-D" in IS
10.2. H-D in the philosophy of science
10.3. why H-D in IS is not a H-D method?
10.4. hypothetico-inductive-qualitative method
10.5. hypothetico-inductive-statistical method
11. ”Inductive” methods
Exercise 4: Monday: exercise on inductive versus nomological, Monday 22.2.2021, 2 pm – 4 pm.

Lecture 9, if needed, Friday 26.2.2021, 2 pm – 4 pm.