• Why is it so hard to get organizations to use DA methods? • Why do some managers accept DA with open arms, but others resist? • Why do some firms adopt DA methods, but others do not? • Are we doing something wrong? 1 Who uses Decision Analysis, and Why? Bob Clemen Duke University With Kelly E. See and Guy McCumber Paper: See, K. E., & Clemen, R. T. (2006). Psychological and organizational factors influencing decision process innovation: The role of perceived threat to managerial power. Draft. Download from: http://faculty.fuqua.duke.edu/~clemen/bio/work.htm 1 Outline • A survey of senior managers – Whom did we survey? – What did we ask? • Results – What did we learn? • Wait until the end – bonus “short subject” 3 Remember back to 2000 • “Value of DA” – informal survey of 18 DAAG members • We learned a little about – How many, what kind of decisions DA used for – Little or no tracking of DA value added – Ways DA adds non-monetary value – Detractors of DA? (Yes) 4 2 Survey of Senior Managers • What factors influence the use of decision analysis (DA) in organizations? • Individual factors – – – – Attitude toward change? Aversion to technology? Threat to managerial control and value? Perceived usefulness of DA? • Organizational factors – – – – Organizational culture of change? Centralized decision making? Lots of formal procedures? Need to justify? • Industry environment 5 Whom did we ask? • 160 senior managers (average experience = 17 years) • All students or alumni of Duke’s Global Executive MBA • All exposed to decision analysis • Responded to survey online • Around 100 questions 6 3 Two Categories of DA 1. Qualitative problem-structuring methods (“soft DA”) • Understanding objectives, preferences (e.g., “Value-focused thinking”) • Creating new alternatives (e.g., Strategy tables) • Strategy tables • Decision hierarchy • Influence diagrams • Including stakeholders (e.g., Dialogue decision process) • Other group decision making techniques 2. Quantitative and technical tools (“hard DA”) • Scenario analysis • Sensitivity analysis (e.g., Tornado diagrams) • Identifying ranges (best case/worst case) of outcomes • Probability distributions for outcomes (risk profiles) • Expert judgment • Monte Carlo simulation • • • • • • • • Decision trees Value of information, control Real options Optimization methods Risk tolerance Cost-benefit analysis Multiattribute utility Decision support systems, “expert systems” • … and others 7 Basic Results • High incidence of DA use: 70% • Top qualitative methods: – Create alternatives – Understand objectives – Org process (DDP) 61% 59% 53% • Top quantitative methods – Cost-benefit analysis – Ranges of outcomes – Scenario analysis 68% 63% 59% 8 4 Va lu eFo c Cr use ea d te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc er O e D e D arc th ia hy er eci gr G si o ro n am s up Pr Sc Te oce en ch ss ni Se ar qu ns io An e s it al Id ivit ys en y tif An is a yi ng lys Ra is Pr ng ob es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e o n rl o Re of al In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul ef tia Tr a i t tt d rib eut Of fs O e th U til er ity Co m pu DS S te r Ai ds Basic Results • DA used in 30% of decisions (median) • 55% “outsource” (use consultants) • 41% offer training resources • 36% have standard operating procedures for decision making 9 Tool Use By Industry 5.00 4.50 Soft DA Manufacturing Hard DA 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Finance, Insurance, Banking, Real Estate Services 10 5 lu e -F oc Cr use ea d te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc e r O e D e D arc th ia hy er eci gr G si o ro n am s up Pr Sc Te oce ss en ch ni S e ar qu ns io An es it al Id ivit ys en y tif An is a yi ng lys Ra i s Pr ng ob es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e on rlo Re o al f In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul ti a Tr ef a i t tt d rib eut Of fs O e th U til er ity Co m pu DS S te r Ai ds Va lu e Cr -F oc us ea ed te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc e r O e D e D arc th ia hy er eci gr G si o ro n am s up Pr Sc Te oce ss en ch ni S e ar qu ns io An es it al Id ivit ys en y tif An is a yi ng lys Ra i s Pr ng ob es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e on rlo Re o al f In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul ti a Tr ef a i t tt d rib eut Of fs O e th U til er ity Co m pu DS S te r Ai ds Va Company Size & Average Tool Use 4.50 4.00 4.00 Soft DA Hard DA 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 0-99 (N=23) 100-999 (N=31) Soft DA <$10M (N=35) 1000-9999 (N=26) >10000 (N=23) 11 R&D Budget and Average Tool Use 4.50 Hard DA 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 >$10M (N=29) 12 6 lu e Cr -F oc us ea ed te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc e r O e D e D arc th ia hy er eci gr G si o ro n am s up Pr Sc Te oce ss en ch ni S e ar qu ns io An es it al Id ivit ys en y tif An is a yi ng lys Ra i s Pr ng ob es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e on rlo Re o al f In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul ti a Tr ef a i t tt d rib eut Of fs O e th U til er ity Co m pu DS S te r Ai ds Va Uncertainty (Mean Split of envunc2) and Average Tool Use 4.00 3.50 Soft DA Hard DA 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Low Uncertainty High Uncertainty 13 Results from regression analysis: Individual-level variables Relationship • Technical aversion • Ease of use of DA tool Demonstrate positive results • Diminishes managerial value, discretion, control negative positive negative 14 7 lu e -F oc Cr use ea d te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc e r O e D e D arc th ia hy er eci gr G si o ro n am s up Pr Sc Te oce ss en ch ni S e ar qu ns io An es it al Id ivit ys en y tif An is a yi ng lys Ra i s Pr ng ob es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e on rlo Re o al f In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul ti a Tr ef a i t tt d rib eut Of fs O e th U til er ity Co m pu DS S te r Ai ds Va c Cr use d ea te Th Al ink St ter ing na D rat tiv e ec es i s gy In ion Ta D ia f bl lu H lo es ie gu en r c O e D e D arc th h ia y er eci gr G si o ro n am s up Pr Sc Te oce en ch ss ni Se ar qu ns io An es it al Id ivit ys en y tif An is a yi ng lys Ra is Pr ng o b es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e o n rl o Re o al f In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul t i a Tr e tt ade fit rib ut Of fs O e th U til er ity Co m pu DS S te r Ai ds Va lu eFo Organization-level variables Organizational Culture of Change (Mean-Split of "org chang") and Average Tool Use 4.0000 3.5000 4.50 Soft DA Hard DA 3.0000 2.5000 2.0000 1.5000 1.0000 0.5000 0.0000 Little Change Soft DA Low Centralization Much Change 15 Centralization & Average Tool Use 5.00 Hard DA 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 High Centralization 16 8 Va lu eFo c Cr use ea d te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc e r O e D e D a rc th ia hy er eci gr G sio ro n am s u p Pr Sc Te oce ss en ch ni Se ar qu ns io es A it Id ivit nal ys en y is A tif yi nal ng ys Ra is Pr n g ob es Ex Ris abi li pe k Pr t y rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e on rlo Re of al I T O nfo ree pt / io Co s nt ns An rol O pt aly U til imi sis ity za Fu tio n n Co c t st ion s B M e ul tia Tr nef tt ade it rib ut Of fs O e th U til er ity Co m pu DS S te r Ai ds lu e Cr -F oc us ea ed te Th Al ink St ter ing na r a D ec teg tiv es is y In ion Ta D ia lo flue Hi ble gu s nc e r O e D e D arc th ia hy er eci gr G si o ro n am s up Pr Sc Te oce ss en ch ni S e ar qu ns io An es it al Id ivit ys en y tif An is a yi ng lys Ra i s Pr ng ob es Ex Ris abi li pe k Pr ty rt Ju ofi dg les M em on e Va De te nt Ca lu ci si e on rlo Re o al f In Tr f e O pt o/C es io ns ont An rol O pt aly U si i m til s ity iza Fu tio nc n Co t st ion -B s M en ul ti a Tr ef a i t tt d rib eut Of fs O e th U til er ity Co m pu DS S te r Ai ds Va Need for Legitimacy (Mean-Split of legit) and Average Tool Use 4.5000 4.0000 3.50 Soft DA Hard DA 3.5000 3.0000 2.5000 2.0000 1.5000 1.0000 0.5000 0.0000 Low Need for Legitimacy Soft DA Low Formalization High Need for Legitimacy 17 Formalization & Average Tool Use 4.00 Hard DA 3.00 2.50 2.00 1.50 1.00 0.50 0.00 High Formalization 18 9 But … • Low formalization – Perceived threat matters. More perceived threat, less DA use • High formalization – Perceived threat doesn’t matter • Why? 19 Implications • Some variables make sense – Individual: Tech aversion, ease of use, perceived threat – Organizational: Legitimacy, Org change culture • Others may be less obvious – Low centralization – Formalization × perceived mgr threat • Consider doing an organizational “audit” – Highlight strengths and weaknesses of the organization – Develop implementation plan guided by audit results 20 10 Bonus “Short Subject” • Rex Brown’s recent Interfaces article, “The Operation was a Success but the Patient Died.” – Argues that outside consultant’s priorities may differ from client’s Æ Failure of DA project. • We asked questions that can be used to address this: – Does your organization outsource DA (use consultants)? – In your experience, has DA been poorly executed? • Some research questions: CT E F EF – Outside consultants vs. in-house DA experts? – What about extent of DA use, or how many different tools used? NO Stay tuned! … 21 11
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