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Decision Support Systems And The Professional

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Decision Support Systems And The Professional

Decision Support Systems And The Professional

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  • 1. Decision Support Systems and the Professional Micheal Axelsen Research Colloquium 2009
  • 2. About the speaker
    • Micheal Axelsen is Chair of the CPA Australia ITM Centre of Excellence, and is principal consultant of Applied Insight Pty Ltd
    • Primary professional interest is in ensuring that clients have the right enterprise data governance strategies and practical
    • approaches in meeting business compliance obligations.
    • PhD investigates the effects of technology adoption (specifically, dominance by technology) upon the audit profession. If you aren’t careful he will tell you all about it.
    • He holds a Bachelor of Commerce (Hons), Masters of Information Systems, and is an FCPA.
    • Available on [email_address] at any time
  • 3. Outline
    • Decision support systems are important components of today’s businesses
    • In fact we rely on them more than ever in the name of efficiency and effectiveness
    • To deliver, these these support systems are becoming highly sophisticated tools
    • Let’s talk though about some potential unintended consequences about their use in practice
    • This is exciting, science fiction stuff
  • 4. Benefits of DSSs
    • Consistency
    • Reliability
    • Cover your backside in legal actions demanding rigour in the work – the ‘Your Honour’ effect
    • Controlling junior staff, and risk management
    • Increased efficiency
    • Enforces good documentation and ‘rationale’
    • Particularly helps us deal with the issues of an aging and shrinking workforce
  • 5. Decisions, decisions
    • However, technology helps us to make decisions.
    • It does not MAKE the decision
    • A frequent lament is that ‘nobody uses the thing’
    • But it is possible to over-rely on decision support systems, or to even be ‘deskilled’ by them
  • 6. Computer says ‘No’
    • Thank you, Carol
    • However, as a professional remember it is up to you to use your noggin.
    • A professional should be making a professional judgment call – this cannot be made by the computer
  • 7. A problem?
    • The monetising lure of the ‘junior burger’ effect
    • Who will write tomorrow’s decision support systems?
    • Set it up to ‘push a button’ – little fish in the sea
    • Too many spreadsheets?
    • ‘ It’s like we had to learn how to audit again.’
    • Over-purchase of inventory
    • What could possibly go wrong with that - VAR
    • The difference between risk and uncertainty
  • 8. People are strange
    • They’re not machines...
  • 9. Anchoring & adjustment heuristic
        • Anchoring & adjustment is a rule of thumb we tend to use to make estimates – it’s so much easier than getting all the facts!
    • Estimates are made by starting from an initial value that is adjusted to yield the final answer; the initial value may be suggested by problem’s formulation, or be the result of a partial computation
    • The adjustments made are usually insufficient (Slovic & Lichtenstein 1971), resulting in adjustment bias
    • People tend to rely upon the output of the decision support system more than there is a right to do.
    • This theory suggests that experience will be reduced if the DSS’s design encourages excessive anchoring, which cognitive load suggests decreases knowledge
  • 10. Anchoring & adjustment heuristic After Tversky & Kahnemann (1974); Epley & Gilovich (1996)
  • 11. Cognitive load theory
    • Decision aids tend to reduce knowledge acquisition relative to manual environments because aid users can substantially reduce the effort devoted to a task and let the aid "do the work" (Todd and Benbasat 1992; Todd and Benbasat 1994)
    • Auditor knowledge is a function of ability, effort and experience (Awasthi & Pratt 1990; Libby & Tan 1994)
    • A well established and tested theory of determinants of knowledge that suggests factors separate to those identified by TTD as contributing to deskilling
  • 12. Cognitive load theory
    • Experts and novices in a problem domain adopt differing problem solving strategies (Sweller 1988):
      • Novices identified end goals and worked backwards from those goals through identified sub-goals using means-end analysis
      • Experts eliminated the backward-looking phase
    • Experts have acquired schemas as a result of their experience with past problems, whereas novices are forced to use generalised problem-solving strategies as they do not yet possess these schemas (Sweller, 1988).
    • I wonder – if we remove their experience, will they ever get the skills?
  • 13. Cognitive load theory After Sweller (1988); Libby & Tan (1994)
  • 14. Who am I going to believe?
  • 15. Introducing the theory of technology dominance
    • Arnold & Sutton (1998) developed this theory in response to the limited success of Intelligent Decision Aids in the audit domain, and to see how IDAs might be more effectively designed for audits
    • Essentially two potential concerns:
      • A person’s judgment may be unduly influenced by the technology
      • A person’s professional skills may decline over time – they are ‘deskilled’
  • 16. Theory of technology dominance
    • Things that matter in whether the tool is used (‘relied upon’) or not:
        • Experience with doing the task before
        • Complexity of the task
        • The user’s familiarity with using that decision aid
        • Does the decision aid mesh with the user’s ‘world view’ or cognitive fit?
  • 17. Theory of technology dominance
    • Susceptibility to dominance by technology:
        • Mismatching the user expertise and the IDA level increases the risk of poor decision making
        • Matching user expertise level and an IDA level generally improves decision making
    • Long-term effects (deskilling):
        • Continued use of an IDA results in a deskilling of the knowledge workers’ abilities in that professional area
        • The more people within the profession that have been deskilled by long-term use of the IDA, the slower the growth in advancing that professional area
  • 18. What have we learned?
    • Don’t be too restrictive when you develop a DSS – allow for professional judgment
    • Realise a DSS is not the sum total of knowledge – the “S” stands for Support
    • Ask, should this decision support system be developed? Will anyone ever use it?
    • As computers increasingly dominate our lives, the temptation is to be drawn in by the promises of a DSS, but can it be delivered?
    • Oh yes and...
  • 19. Keep thinking!