MIS 650 Generating Knowledge Some Methodological Issues MIS

MIS 650 Generating Knowledge: Some Methodological Issues MIS 650 Knowledge Generation

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! MIS 650 Knowledge Generation

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

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

Modeling Research NA Phenomenon has locale, temporal status, antecedents, consequents, etc. NThe 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. MIS 650 Knowledge Generation

Modeling Research NAn observation method has procedures, resources, use characteristics, etc. NMethods 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 MIS 650 Knowledge Generation

Modeling Research NAn interpretive scheme(-a) has procedures, content, use characteristics, input requirements, output characteristics NThis 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 MIS 650 Knowledge Generation

Innovation in Research Big Bang 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. MIS 650 Knowledge Generation Gravitational

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

Research Issues Research +What is appropriate research in IS? +Do we lead or follow business? +How to avoid bias at all phases of research Knowledge +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 MIS 650 Knowledge Generation

Methodological Issues Methods +Qualitative methods +New or different paradigms, including interpretivistic ones, action research, evaluation research 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. MIS 650 Knowledge Generation

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 MIS 650 Knowledge Generation

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 MIS 650 Knowledge Generation

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] MIS 650 Knowledge Generation

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. MIS 650 Knowledge Generation

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 MIS 650 Knowledge Generation

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] MIS 650 Knowledge Generation

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) MIS 650 Knowledge Generation