1. MIS 650 Knowledge Generation1 MIS 650 Generating Knowledge: Some Methodological Issues

2. MIS 650 Knowledge Generation2 Background Information systems research is by necessity multidisciplinary. Our focus is on users of technology in context. This automatically implies a social-scientific ecological approach. However, because we are concerned with motives, goals, and plans, we of necessity will look at images, expressions, and strategies; these imply humanistic approaches also. Our roots, however, are in maths and computer science and imply a tendency to see the world in scientific and systems terms. This implies a scientific or systems scientific method. On the other hand, IS tends to construct, modify and attempt improvement; we sometimes adopt an engineering or medical approach. It’s a stew of methods!

3. MIS 650 Knowledge Generation3 Understanding Research  Goal of our enterprise is knowledge  Knowledge requires research [from the Latin word cicare to explore from circus, a ring from IE root *(s)ker- to turn, bend]  Research requires a phenomenon, an observation method, and an interpretive scheme(-a).  Research issues centre on the phenomena, the methods and the schemes.

4. MIS 650 Knowledge Generation4 Modeling Research  Research requires a phenomenon, an observation method, and an interpretive scheme(-a). “This says That is These”

5. MIS 650 Knowledge Generation5 Modeling Research  A Phenomenon has locale, temporal status, antecedents, consequents, etc.  The phenomena, taken as a group, are a field of study. Where temporal status is fleeting and antecedents and consequents are difficult to define or observe, research is difficult.

6. MIS 650 Knowledge Generation6 Modeling Research  An observation method has procedures, resources, use characteristics, etc.  Methods that have poorly defined procedures, require a lot of resources or special users, can’t be performed reliably, or present ethical problems make for difficult situations in research

7. MIS 650 Knowledge Generation7 Modeling Research  An interpretive scheme(-a) has procedures, content, use characteristics, input requirements, output characteristics  This enables communication of results to interested consumers of the research. Where the procedures are “slippery” and only certain individuals can understand your interpretations, where it isn’t clear what the interpretations mean, research is problematic

8. MIS 650 Knowledge Generation8 Innovation in Research Big Bang Gravitational There are two different ways in which a field innovates through its ideas. (a) Big Bang: one idea or method spawns many others; soon there is specialization and different streams of research (b) Gravitational: a series of disparate ideas is drawn together to form a new line of thought or method.

9. MIS 650 Knowledge Generation9 Research Flow and Your Paper Focus  Component It (research domain) You (researcher) Them (audience) Phenomena * Observation Methods * Interpretive Schema * Chapter 1 Chapter 2 Chapter 4 Chapter 5 Chapter 3

10. MIS 650 Knowledge Generation10 Knowledge Research Issues +What is appropriate research in IS? +Do we lead or follow business? +How to avoid bias at all phases of research +Are we just researching learning? +How do we research experience? +Is anything really new? New wine in old bottles? +How central is the technology in our research? +Fatalism, determinism, particularism +Pure vs. applied research Research

11. MIS 650 Knowledge Generation11 Methodological Issues +Qualitative methods +New or different paradigms, including interpretivistic ones, action research, evaluation research +“Subtle” vs. bold effects + Problems posed by new technology, globalization, E-Commerce, etc. +Researcher bias from a variety of sources +Holding down the phenomenon long enough to measure it. Research Methods

12. MIS 650 Knowledge Generation12 Qualitative Approaches Designing research for Qualitative methods Using qualitative data Problems of reliability, informants, recording Appropriate data analysis methods Interpreting results Mixed methods and triangulation

13. MIS 650 Knowledge Generation13 New Paradigms Interpretivistic approaches Understanding “meaning” and informants Objectivity is a problem Action research Object is to change something Researcher becomes part of the situation Evaluation research Schema is the important aspect here

14. MIS 650 Knowledge Generation14 Subtle Effects How do we select appropriate analysis techniques How big an effect are we looking for? What is the difference between significant (p<0.001) and SIGNIFICANT? How permanent an effect are we looking for? How broad an effect are we looking for? Does statistics matter? What will we do with the effect? [issue of control/prediction and their costs]

15. MIS 650 Knowledge Generation15 The New Technologies The new technologies are pervasive: how to select a level of phenomenon and to sample from what sampling frame. The new technologies are global: how to overcome cultural problems and bias The new technologies are expensive: what to learn from a trial and how much technology is employ.

16. MIS 650 Knowledge Generation16 Researcher Bias Sources of bias include the The researcher, conscious or unconscious The researcher’s milieu(x), Society at large, The economics of research and resulting social pressures

17. MIS 650 Knowledge Generation17 Slippery Phenomena How do we select appropriate analysis techniques How big an effect are we looking for? What is the difference between significant (p<0.001) and SIGNIFICANT? How permanent an effect are we looking for? How broad an effect are we looking for? Does statistics matter? What will we do with the effect? [issue of control/prediction and their costs]

18. MIS 650 Knowledge Generation18 Other Issues in Methodology, some very specific indeed 1. ICT convergence: Level of aggregation 2. Ethics: informed consent, esp. in interventions 3. Use of students or other disadvantaged participants 4. “Naïve” subjects? Get with the context 5. The availability of alternative explanations 6. Sampling frame, snowball sampling, convenience sampling 7. Appropriate proxies (experience, computer capability, poorly conceptualised variables)