INFS 838: Decision Support and Knowledge Management Research — Dr. Amit Deokar

Syllabus (at Box.Net)

  • Assignments: 42%
  • Research Paper: 58%
    • Title/Abstract: 8% — DUE 2/4! Think of a topic fast!
    • Intermediate Draft: 15% — DUE 3/11
    • Final Paper: 25% — DUE 5/7
    • Presentation: 10% — 4/22 & 5/6
  • Research Paper Expectations
    • DS, KM, or both
    • Good enough for national-level conference (AMCIS, ICIS, HICSS,…)
    • IEEE style
    • EndNote required: buy it now!
    • 10 pages max
    • Deokar is co-author; close collaboration expected and scheduled
    • welcome to involve additional faculty
    • we’re building portfolio here

O.K., nuts and bolts:

Decision Support Systems:

  • application domains: business, military, health care
  • Deokar prefers term “decision-makers” to “managers”

Early DSS research cranked out really big reports, but who can read all that stuff? That research didn’t help.

Neural networks nice, but act like black boxes; user can’t see why certain things are happening (but advocates say you can’t understand your own thinking, either).

Chat/forum doesn’t feel like DSS: no structure! Lack of acceptance by industry, says Deokar.

Knowledge Management-based DSS: just what it sounds like… possible overlap with HULK?

We learn from Arnott & Pervan (2005) that DSS research has been publishing fewer papers in the past few years. A & P suggest increasing rigor may be decreasing relevance. They find about an even split between articles of no/low relevance and med/hi/veryhi relevance. That’s bad, and it makes sense that that split would lead to less interest in DSS. We’re supposed to be supporting decisions. This is inherently an applied field. I would suspect that most of our clients will be vaguely impressed by rigor but really just want to know how what to do.

DSS research does have a high percentage of empirical research, since we do lots of (essentially) design research.

Very few papers make clear who the decision maker (purchaser, end user…); that means the researchers aren’t thinking relevance, in that they aren’t thinking about the actual people who will get their hands on the system. (Poor identification of clients and users is a reason my ePB research might get a leg up. Managers picking up a paper want to know if they are your target audience.)

DSS research contribuions to IS:

  1. evolutionary systems development
  2. dimensional modeling
  3. critical success factors

DSS research really focuses on the IT artifact

DSS research does lots of theory-building, almost no theory refinement. Environments keep changing quickly on us, throwing out the old theories before we get a chance to really solidify them.

Big needs: improve professional relevance and improve theoretical foundation of studies (yeah, I can do that)

Academia is disconnected from what industry wants (go figure!). But Deokar says all this research could still be relevant; it’s just being presented really poorly. We need to communicate to industry how this research is relevant.

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