Discrete Choice Experiments in Management Research

Abstract

Discrete choice experiments (DCEs) are a promising yet underutilized research method that can provide rigorous empirical evidence about the microfoundations of choice. This method has potential applications in many areas of management research. While DCE methods are similar to conjoint analysis and policy capturing, they are conceptually and methodologically distinct and deliver unique and valuable results that can—in the right contexts—improve external validity. This paper disambiguates DCE from related methods and provides detailed guidance on best practices for conducting DCE research, with emphasis on the elements of experimental design and analysis that are unique to DCEs. Based on a systematic literature review, we identify several emergent and canonical research domains within management where DCE methods could be used to generate novel theoretical and empirical insights. We supplement this review and best-practice guidance with a demonstration experiment and provide all the code and documentation needed for researchers to conduct DCEs with skill and confidence.

Keywords

discrete choice experiment conjoint analysis policy capturing best practices experiment

Get full access to this article

View all access options for this article.

References

Aguinis

Bradley

K. J.

(2014). Best practice recommendations for designing and implementing experimental vignette methodology studies. Organizational Research Methods, 17(4), 351–371. https://doi.org/10.1177/1094428114547952

Aiman-Smith

Scullen

S. E.

Barr

S. H.

(2002). Conducting studies of decision making in organizational contexts: A tutorial for policy-capturing and other regression-based techniques. Organizational Research Methods, 5(4), 388–414. https://doi.org/10.1177/109442802237117

Akinc

Street

D. J.

Vandebroek

(2024). Varying choice set sizes in discrete choice experiments. Journal of Choice Modelling, 52, Article 100493. https://doi.org/10.1016/j.jocm.2024.100493

Araña

J. E.

León

C. J.

(2013). Dynamic hypothetical bias in discrete choice experiments: Evidence from measuring the impact of corporate social responsibility on consumers demand. Ecological Economics, 87, 53–61. https://doi.org/10.1016/j.ecolecon.2012.12.005

Assele

S. Y.

Meulders

Vandebroek

(2023). Sample size selection for discrete choice experiments using design features. Journal of Choice Modelling, 49, 100436. https://doi.org/10.1016/j.jocm.2023.100436

Atzmüller

Steiner

P. M.

(2010). Experimental vignette studies in survey research. Methodology, 6(3), 128–138. https://doi.org/10.1027/1614-2241/a000014

Audia

P. G.

Greve

H. R.

(2021). Organizational learning from performance feedback: A behavioral perspective on multiple goals: A multiple goals perspective. Cambridge University Press.

Balcombe

Fraser

Williams

& McSorley

. (2017). Examining the relationship between visual attention and stated preferences: A discrete choice experiment using eye-tracking. Journal of Economic Behavior & Organization, 144, 238–257. https://doi.org/10.1016/j.jebo.2017.09.023

Bansak

Hainmueller

Hopkins

D. J.

& Yamamoto

. (2018). The number of choice tasks and survey satisficing in conjoint experiments. Political Analysis, 26(1), 112–119. https://doi.org/10.1017/pan.2017.40

10.

Bansak

Hainmueller

Hopkins

D. J.

& Yamamoto

. (2021). Conjoint survey experiments. In Druckman

J. N.

Green

D. P.

(Eds.), Advances in experimental political science (pp. 19–41). https://doi.org/10.1017/9781108777919.004 .

11.

Bansal

Kim

E.-J.

Ozdemir

(2024). Discrete choice experiments with eye-tracking: How far we have come and ways forward. Journal of Choice Modelling, 51, 100478. https://doi.org/10.1016/j.jocm.2024.100478

12.

Ben-Akiva

McFadden

Train

(2019). Foundations of stated preference elicitation: Consumer behavior and choice-based conjoint analysis. Foundations and Trends in Econometrics, 10(1–2), 1–144. https://doi.org/10.1561/0800000036

13.

Boxebeld

(2024). Ordering effects in discrete choice experiments: A systematic literature review across domains. Journal of Choice Modelling, 51, Article 100489. https://doi.org/10.1016/j.jocm.2024.100489

14.

Bridges

J. F.

Hauber

A. B.

Marshall

Lloyd

Prosser

Regier

Johnson

F. R.

& Mauskopf

. (2011). Conjoint analysis applications in health—A checklist: A report of the ISPOR good research practices for conjoint analysis task force. Value in Health, 14(4), 403–413. https://doi.org/10.1016/j.jval.2010.11.013

15.

Brykman

K. M.

O'Neill

T. A.

(2023). How conflict expressions affect recipients’ conflict management behaviors. Organizational Behavior and Human Decision Processes, 174, Article 104208. https://doi.org/10.1016/j.obhdp.2022.104208

16.

Burgess

Street

D. J.

(2006). The optimal size of choice sets in choice experiments. Statistics, 40(6), 507–515. https://doi.org/10.1080/02331880601013841

17.

Carson

R. T.

Louviere

J. J.

(2011). A common nomenclature for stated preference elicitation approaches. Environmental and Resource Economics, 49(4), 539–559. https://doi.org/10.1007/s10640-010-9450-x

18.

Cheyne

Dalgleish

Tucker

Kane

Shetty

McLeod

& Niven

. (2012). Risk assessment and decision making about in-labour transfer from rural maternity care: A social judgment and signal detection analysis. BMC Medical Informatics and Decision Making, 12(1), 1–13. https://doi.org/10.1186/1472-6947-12-122

19.

Chrzan

Orme

(2000). An overview and comparison of design strategies for choice-based conjoint analysis. Sawtooth Software Research Paper Series, 98382, 161–178. https://d1wqtxts1xzle7.cloudfront.net/41021413/desgncbc-libre.pdf?1452363089=&response-content-disposition=inline%3B+filename%3DAn_Overview_and_Comparison_of_Design_Str.pdf&Expires=1774376192&Signature=XvrDgWv2QGStR5pomFh9ILg-ga1u5nlfz1~4G9DmN6X9dZKtL3IWPtmwPybN7JhY2Sm1uF8wx9mfgPpqHqgqAmiwby8NWGO9p2haRwkHX6Q4IWGN~SARGu99oU-jGIFGfeCE5GtX67CjmnGT9KpDNwYdF07C29LbxlFpHPN~y8yLLb081xtTOsMRNHPAeDPBHKYl9CaZsltZvWxFAjpQjhq4kFzOKta073zKHRNa1sJqpMvcQepWR6ewbt3UzLSG6jv4HbdnFPnO0YeeIPVpns1ytS7krC8Nh61MsmPW9TEDMv8-~dKEpFzZesLG2UxrmJBUKvZ0AFvQj9meLL1D4g__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA

20.

Chung

Boyer

Han

(2011). How many choice sets and alternatives are optimal? Consistency in choice experiments. Agribusiness, 27(1), 114–125. https://doi.org/10.1002/agr.20252

21.

Colombo

Budziński

Czajkowski

& Glenk

. (2022). The relative performance of ex-ante and ex-post measures to mitigate hypothetical and strategic bias in a stated preference study. Journal of Agricultural Economics, 73(3), 845–873. https://doi.org/10.1111/1477-9552.12484

22.

Dalal

R. S.

Bonaccio

(2010). What types of advice do decision-makers prefer? Organizational Behavior and Human Decision Processes, 112(1), 11–23. https://doi.org/10.1016/j.obhdp.2009.11.007

23.

Davis

K. J.

Burton

Kragt

M. E.

(2019). Scale heterogeneity and its implications for discrete choice analysis. Land Economics, 95(3), 353–368. https://doi.org/10.3368/le.95.3.353

24.

de Bekker-Grob

E. W.

Donkers

Jonker

M. F.

& Stolk

A. (2015). Sample size requirements for discrete-choice experiments in healthcare: A practical guide. The Patient-Patient-Centered Outcomes Research, 8(5), 373–384. https://doi.org/10.1007/s40271-015-0118-z

25.

DeShazo

Fermo

(2002). Designing choice sets for stated preference methods: The effects of complexity on choice consistency. Journal of Environmental Economics and Management, 44(1), 123–143. https://doi.org/10.1006/jeem.2001.1199

26.

DeTienne

D. R.

Shepherd

D. A.

De Castro

J. O.

(2008). The fallacy of “only the strong survive”: The effects of extrinsic motivation on the persistence decisions for under-performing firms. Journal of Business Venturing, 23(5), 528–546. https://doi.org/10.1016/j.jbusvent.2007.09.004

27.

Dhami

M. K.

Mumpower

J. L.

(2018). Kenneth R. Hammond’s contributions to the study of judgment and decision making. Judgment and Decision Making, 13(1), 1–22. https://doi.org/10.1017/S1930297500008780

28.

Dorobantu

Lindner

Müllner

(2020). Political risk and alliance diversity: A two-stage model of partner selection in multipartner alliances. Academy of Management Journal, 63(6), 1775–1806. https://doi.org/10.5465/amj.2017.0265

29.

Durand

Hawn

Ioannou

(2019). Willing and able: A general model of organizational responses to normative pressures. Academy of Management Review, 44(2), 299–320. https://doi.org/10.5465/amr.2016.0107

30.

Dushnitsky

Piva

Rossi-Lamastra

(2022). Investigating the mix of strategic choices and performance of transaction platforms: Evidence from the crowdfunding setting. Strategic Management Journal, 43(3), 563–598. https://doi.org/10.1002/smj.3163

31.

Eggers

Sattler

Teichert

& Völckner

. (2021). Choice-based conjoint analysis. In Homburg

Klarmann

Vomberg

(Eds.), Handbook of market research (pp. 781–819). Springer.

32.

Ethiraj

S. K.

Levinthal

(2009). Hoping for A to Z while rewarding only A: Complex organizations and multiple goals. Organization Science, 20(1), 4–21. https://doi.org/10.1287/orsc.1080.0358

33.

Fairchild

A. O.

Reed

S. D.

Gonzalez

J. M.

(2023). Method for calculating the simultaneous maximum acceptable risk threshold (SMART) from discrete-choice experiment benefit-risk studies. Medical Decision Making, 43(2), 227–238. https://doi.org/10.1177/0272989X221132266

34.

Felin

Foss

N. J.

Ployhart

R. E.

(2015). The microfoundations movement in strategy and organization theory. Academy of Management Annals, 9(1), 575–632. https://doi.org/10.5465/19416520.2015.1007651

35.

Finkelstein

E. A.

Mansfield

Wood

Rowe

Chay

& Ozdemir

. (2017). Trade-offs between civil liberties and national security: A discrete choice experiment. Contemporary Economic Policy, 35(2), 292–311. https://doi.org/10.1111/coep.12188

36.

Fosfuri

Giarratana

M. S.

Roca

(2015). Walking a slippery line: Investments in social values and product longevity. Strategic Management Journal, 36(11), 1750–1760. https://doi.org/10.1002/smj.2311

37.

Franke

Gruber

Harhoff

& Henkel

. (2008). Venture capitalists’ evaluations of start–up teams: Trade–offs, knock–out criteria, and the impact of VC experience. Entrepreneurship Theory and Practice, 32(3), 459–483. https://doi.org/10.1111/j.1540-6520.2008.00236.x

38.

Fraser

Balcombe

Williams

& McSorley

. (2021). Preference stability in discrete choice experiments. Some evidence using eye-tracking. Journal of Behavioral and Experimental Economics, 94, 101753. https://doi.org/10.1016/j.socec.2021.101753

39.

Friedel

J. E.

Foreman

A. M.

Wirth

(2022). An introduction to “discrete choice experiments” for behavior analysts. Behavioural Processes, 198, Article 104628. https://doi.org/10.1016/j.beproc.2022.104628

40.

Gary

M. S.

Wood

R. E.

(2011). Mental models, decision rules, and performance heterogeneity. Strategic Management Journal, 32(6), 569–594. https://doi.org/10.1002/smj.899

41.

Gibson

C. J.

Hobson

F. W.

(1983). Policy capturing as an approach to understanding and improving performance appraisal: A review of the literature. Academy of Management Review, 8(4), 640–649. https://doi.org/10.2307/258265

42.

Goeken

Kurz

Steiner

W. J.

(2023). Multimodal preference heterogeneity in choice-based conjoint analysis: A simulation study. Journal of Business Economics, 94(1), 137–185. https://doi.org/10.1007/s11573-023-01156-6

43.

Gonzalez

J. M.

(2019). A guide to measuring and interpreting attribute importance. The Patient-Patient-Centered Outcomes Research, 12(3), 287–295. https://doi.org/10.1007/s40271-019-00360-3

44.

Gonzalez

J. M.

Johnson

F. R.

Finkelstein

(2024). To pool or not to pool: Accounting for task non-attendance in subgroup analysis. Journal of Choice Modelling, 51, Article 100487. https://doi.org/10.1016/j.jocm.2024.100487

45.

Graham

M. E.

Cable

D. M.

(2001). Consideration of the incomplete block design for policy-capturing research. Organizational Research Methods, 4(1), 26–45. https://doi.org/10.1177/109442810141002

46.

Gray

S. M.

Howell

Sackett

(2024a). Talking past each other: Construal level, utilitarian motives, and entrepreneurial team formation. Organization Science, 35(5), 1745–1769. https://doi.org/10.1287/orsc.2022.16693

47.

Gray

S. M.

Howell

Strassman

& Yamamoto

. (2024b). Credentials or Chemistry? Entrepreneur Gender and Cofounder Selection. Academy of Management Journal, 67(5), 1302–1330. https://doi.org/10.5465/amj.2022.0640

48.

Grebitus

Lusk

J. L.

Nayga

R. J.,

Jr . (2013). Explaining differences in real and hypothetical experimental auctions and choice experiments with personality. Journal of Economic Psychology, 36, 11–26. https://doi.org/10.1016/j.joep.2013.02.004

49.

Green

P. E.

Krieger

A. M.

Wind

(2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31(3_supplement), S56–S73. https://doi.org/10.1287/inte.31.3s.56.9676

50.

Green

P. E.

Rao

V. R.

(1971). Conjoint measurement-for quantifying judgmental data. Journal of Marketing Research, 8(3), 355–363. https://www.jstor.org/stable/3149575

51.

Green

P. E.

Srinivasan

(1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54(4), 3–19. https://doi.org/10.1177/002224299005400402

52.

Gronau

Q. F.

Bennett

M. S.

Brown

S. D.

& Hawkins

G. E.

& Eidels

. (2023). Do choice tasks and rating scales elicit the same judgments? Journal of Choice Modelling, 49, 100437. https://doi.org/10.1016/j.jocm.2023.100437

53.

Gustafsson

Schmidt-Gallas

& Huber

. (2000). Adaptive conjoint analysis: Understanding the methodology and assessing reliability and validity. In Gustafsson

Herrmann

Huber

(Eds.), Conjoint measurement: Methods and applications (pp. 305–329).

54.

Haghani

Bliemer

M. C.

Rose

J. M.

Oppewal

Lancsar

. (2021a). Hypothetical bias in stated choice experiments: Part I. Macro-scale analysis of literature and integrative synthesis of empirical evidence from applied economics, experimental psychology and neuroimaging. Journal of Choice Modelling, 41, 100309. https://doi.org/10.1016/j.jocm.2021.100309

55.

Haghani

Bliemer

M. C.

Rose

J. M.

Oppewal

& Lancsar

. (2021b). Hypothetical bias in stated choice experiments: Part II. Conceptualisation of external validity, sources and explanations of bias and effectiveness of mitigation methods. Journal of Choice Modelling, 41, 100322. https://doi.org/10.1016/j.jocm.2021.100322

56.

Hainmueller

Hangartner

Yamamoto

(2015). Validating vignette and conjoint survey experiments against real-world behavior. Proceedings of the National Academy of Sciences, 112(8), 2395–2400. https://doi.org/10.1073/pnas.1416587112

57.

Hainmueller

Hopkins

D. J.

Yamamoto

(2014). Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments. Political Analysis, 22(1), 1–30. https://doi.org/10.1093/pan/mpt024

58.

Hammond

K. R.

(1955). Probabilistic functioning and the clinical method. Psychological Review, 62(4), 255. https://doi.org/10.1037/h0046845

59.

Hammond

K. R.

(1966). The Psychology of Econ Brunswik. Holt, Rinehard & Winston.

60.

Hauber

A. B.

Fairchild

A. O.

Johnson

F. R.

(2013). Quantifying benefit–risk preferences for medical interventions: An overview of a growing empirical literature. Applied Health Economics and Health Policy, 11(4), 319–329. https://doi.org/10.1007/s40258-013-0028-y

61.

Hauber

A. B.

González

J. M.

Groothuis-Oudshoorn

C. G.

Prior

Marshall

D. A.

Cunningham

Ijzerman

& Bridges

J. F. P

. (2016). Statistical methods for the analysis of discrete choice experiments: A report of the ISPOR conjoint analysis good research practices task force. Value in Health, 19(4), 300–315. https://doi.org/10.1016/j.jval.2016.04.004

62.

Hensher

D. A.

(2010). Hypothetical bias, choice experiments and willingness to pay. transportation Research Part B: Methodological, 44(6), 735–752. https://doi.org/10.1016/j.trb.2009.12.012

63.

Hess

(2024). Latent class structures: Taste heterogeneity and beyond. In Hess

Daly

(Eds.), Handbook of choice modelling (pp. 372–391). Edward Elgar Publishing.

64.

Highhouse

(2009). Designing experiments that generalize. Organizational Research Methods, 12(3), 554–566. https://doi.org/10.1177/1094428107300396

65.

Horiuchi

Markovich

Yamamoto

(2022). Does conjoint analysis mitigate social desirability bias? Political Analysis, 30(4), 535–549. https://doi.org/10.1017/pan.2021.30

66.

Hsu

D. K.

Simmons

S. A.

Wieland

A. M.

(2017). Designing entrepreneurship experiments: A review, typology, and research agenda. Organizational Research Methods, 20(3), 379–412. https://doi.org/10.1177/1094428116685613

67.

Huertas-Garcia

Gázquez-Abad

J. C.

Forgas-Coll

(2016). A design strategy for improving adaptive conjoint analysis. Journal of Business & Industrial Marketing, 31(3), 328–338. https://doi.org/10.1108/JBIM-02-2013-0043

68.

Janssen

Bridges

(2016). Nine tests for assessing validity of A discrete-choice experiment. Value in Health, 19(7), Article A352. https://doi.org/10.1016/j.jval.2016.09.034

69.

Janssen

E. M.

Marshall

D. A.

Hauber

A. B.

& Bridges

J. F. P

. (2017). Improving the quality of discrete-choice experiments in health: How can we assess validity and reliability? Expert Review of Pharmacoeconomics & Outcomes Research, 17(6), 531–542. https://doi.org/10.1080/14737167.2017.1389648

70.

Johnson

F. R.

(2023). Comment on: Taking the shortcut: Simplifying heuristics in discrete choice experiments. The Patient-Patient-Centered Outcomes Research, 16(4), 289–292. https://doi.org/10.1007/s40271-023-00629-8

71.

Johnson

F. R.

Lancsar

Marshall

Kilambi

Mühlbacher

Regier

D. A.

Bresnahan

B. W.

Kanninen

& Bridges

J. F. P

. (2013). Constructing experimental designs for discrete-choice experiments: Report of the ISPOR conjoint analysis experimental design good research practices task force. Value in Health, 16(1), 3–13. https://doi.org/10.1016/j.jval.2012.08.2223

72.

Johnson

F. R.

Yang

J.-C.

Reed

S. D.

(2019). The internal validity of discrete choice experiment data: A testing tool for quantitative assessments. Value in Health, 22(2), 157–160. https://doi.org/10.1016/j.jval.2018.07.876

73.

Johnston

R. J.

Boyle

K. J.,

Adamowicz

Bennett

Brouwer

Cameron

T.A.

Hanemann

W.M.

Hanley

Ryan

Scarpa

Tourangeau

& Vossler

C. A

. (2017). Contemporary guidance for stated preference studies. Journal of the Association of Environmental and Resource Economists, 4(2), 319–405. https://doi.org/10.1086/691697

74.

Jonker

M. F.

(2022). The garbage class mixed logit model: Accounting for low-quality response patterns in discrete choice experiments. Value in Health, 25(11), 1871–1877. https://doi.org/10.1016/j.jval.2022.07.013

75.

Jonker

M. F.

(2024). Level overlap and level color coding revisited: Improved attribute attendance and higher choice consistency in discrete choice experiments. Journal of Choice Modelling, 52, Article 100494. https://doi.org/10.1016/j.jocm.2024.100494

76.

Jonker

M. F.

Roudijk

Maas

(2022). The sensitivity and specificity of repeated and dominant choice tasks in discrete choice experiments. Value in Health, 25(8), 1381–1389. https://doi.org/10.1016/j.jval.2022.01.015

77.

Kang

Zaheer

(2018). Determinants of alliance partner choice: Network distance, managerial incentives, and board monitoring. Strategic Management Journal, 39(10), 2745–2769. https://doi.org/10.1002/smj.2930

78.

Kapoor

Furr

N. R.

(2015). Complementarities and competition: Unpacking the drivers of entrants’ technology choices in the solar photovoltaic industry. Strategic Management Journal, 36(3), 416–436. https://doi.org/10.1002/smj.2223

79.

Karren

R. J.

Barringer

M. W.

(2002). A review and analysis of the policy-capturing methodology in organizational research: Guidelines for research and practice. Organizational Research Methods, 5(4), 337–361. https://doi.org/10.1177/109442802237115

80.

Keller

Dlugos

(2023). Advance’Em to Attract’Em: How promotions influence applications in internal talent markets. Academy of Management Journal, 66(6), 1831–1859. https://doi.org/10.5465/amj.2021.1174

81.

Kleinert

Hildebrand

(2025). Venture capitalists’ decision-making in hot and cold markets: The effect of signals and cheap talk. Entrepreneurship Theory and Practice, 49(2), 571–598. https://doi.org/10.1177/10422587241268311

82.

Krantz

D. H.

Tversky

(1971). Conjoint-measurement analysis of composition rules in psychology. Psychological Review, 78(2), 151.

83.

Kuhfeld

W. F.

(2005). Experimental design, efficiency, coding, and choice designs. Marketing Research Methods in sas: Experimental Design, Choice, Conjoint, and Graphical Techniques, 47–97. https://support.sas.com/techsup/technote/mr2010c.pdf

84.

Kuhfeld

W. F.

Tobias

R. D.

Garratt

(1994). Efficient experimental design with marketing research applications. Journal of Marketing Research, 31(4), 545–557. https://doi.org/10.1177/002224379403100408

85.

Lancsar

Fiebig

D. G.

Hole

A. R.

(2017). Discrete choice experiments: A guide to model specification, estimation and software. Pharmacoeconomics, 35(7), 697–716. https://doi.org/10.1007/s40273-017-0506-4

86.

Lancsar

Louviere

Flynn

(2007). Several methods to investigate relative attribute impact in stated preference experiments. Social Science & Medicine, 64(8), 1738–1753. https://doi.org/10.1016/j.socscimed.2006.12.007

87.

Lange

Bundy

Park

(2022). The social nature of stakeholder utility. Academy of Management Review, 47(1), 9–30. https://doi.org/10.5465/amr.2018.0456

88.

Levine

S. S.

Schilke

O.,

Kacperczyk

& Zucker

L. G

. (2023). Primer for experimental methods in organization theory. Organization Science, 34(6), 1997–2025. https://doi.org/10.1287/orsc.2023.18093

89.

Lohrke

F. T.

Holloway

B. B.

Woolley

T. W.

(2010). Conjoint analysis in entrepreneurship research: A review and research agenda. Organizational Research Methods, 13(1), 16–30. https://doi.org/10.1177/1094428109341992

90.

Louviere

J. J.

Eagle

(2006). Confound it! That pesky little scale constant meses up our convenient assumptions. In Proceedings of the Sawtooth Software Conference. Delray Beach, Florida, USAMarch 29-31, https://opus.lib.uts.edu.au/bitstream/10453/7515/1/2006005536.pdf .

91.

Louviere

J. J.

Flynn

T. N.

Carson

R. T.

(2010). Discrete choice experiments are not conjoint analysis. Journal of Choice Modelling, 3(3), 57–72. https://doi.org/10.1016/S1755-5345(13)70014-9

92.

Louviere

J. J.

Flynn

T. N.

Marley

A. A. J.

(2015). Best-worst scaling: Theory, methods and applications. Cambridge University Press.

93.

Luce

R. D.

Tukey

J. W.

(1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1(1), 1–27. https://doi.org/10.1016/0022-2496(64)90015-X

94.

Mangham

L. J.

Hanson

McPake

(2009). How to do (or not to do)… designing a discrete choice experiment for application in a low-income country. Health Policy and Planning, 24(2), 151–158. https://doi.org/10.1093/heapol/czn047

95.

Marcus

MacDonald

H. A.

Sulsky

L. M.

(2015). Do personal values influence the propensity for sustainability actions? A policy-capturing study. Journal of Business Ethics, 127(2), 459–478. https://doi.org/10.1007/s10551-013-2032-4

96.

Mattmann

Logar

Brouwer

(2019). Choice certainty, consistency, and monotonicity in discrete choice experiments. Journal of Environmental Economics and Policy, 8(2), 109–127. https://doi.org/10.1080/21606544.2018.1515118

97.

McKelvie

Haynie

J. M.

Gustavsson

(2011). Unpacking the uncertainty construct: Implications for entrepreneurial action. Journal of Business Venturing, 26(3), 273–292. https://doi.org/10.1016/j.jbusvent.2009.10.004

98.

Mindruta

Moeen

Agarwal

(2016). A two-sided matching approach for partner selection and assessing complementarities in partners’ attributes in inter-firm alliances. Strategic Management Journal, 37(1), 206–231. https://doi.org/10.1002/smj.2448

99.

Montano-Campos

J. F.

Gonzalez

J. M.

Rickert

Fairchild

A.O.

Levitan

& Reed

S. D

. (2023). Use of patient preferences data regarding multiple risks to inform regulatory decisions. MDM Policy & Practice, 8(1), 23814683221148715. https://doi.org/10.1177/23814683221148715

100.

Norman

Craig

B. M.

Hansen

Jonker

M. F.

Rose

Street

D. J.

& Mulhern

. (2019). Issues in the design of discrete choice experiments. The Patient-Patient-Centered Outcomes Research, 12(3), 281–285. https://doi.org/10.1007/s40271-018-0346-0

101.

Orme

(2021). Estimating willingness to pay (WTP) given competition in conjoint analysis. In Proceedings of the Sawtooth software Conference. San Antonio, Texas, USA, https://content.sawtoothsoftware.com/assets/6a67030b-8548-429f-8f20-14afd5247af2#page=141 .

102.

Orme

Johnson

(2006). External effect adjustments in conjoint anlaysis. In Proceedings of the Sawtooth Software Conference. Delray Beach, Florida.

103.

Rakotonarivo

O. S.

Schaafsma

Hockley

(2016). A systematic review of the reliability and validity of discrete choice experiments in valuing non-market environmental goods. Journal of Environmental Management, 183(1), 98–109. https://doi.org/10.1016/j.jenvman.2016.08.032

104.

Robert Mitchell

Shepherd

D. A.

Sharfman

M. P.

(2011). Erratic strategic decisions: When and why managers are inconsistent in strategic decision making. Strategic Management Journal, 32(7), 683–704. https://doi.org/10.1002/smj.905

105.

Sajjadiani

Kammeyer-Mueller

Benson

(2023). Who is leaving and why? The dynamics of high-quality human capital outflows. Academy of Management Journal, 66(6), 1929–1953. https://doi.org/10.5465/amj.2021.1327

106.

Schillebeeckx

S. J.

Chaturvedi

George

& King

. (2016). What do I want? The effects of individual aspiration and relational capability on collaboration preferences. Strategic Management Journal, 37(7), 1493–1506. https://doi.org/10.1002/smj.2396

107.

Schüler

Anderson

B. S.

Y. Murnieks

Baum

Küsshauer

. (2024). Test-retest reliability in metric conjoint experiments: A new workflow to evaluate confidence in model results. Entrepreneurship Theory and Practice, 48(2), 742–757. https://doi.org/10.1177/10422587231184071

108.

Shang

Chandra

(2023). Discrete choice experiments using R: A how-to guide for social and managerial sciences. Springer Nature.

109.

Shepherd

D. A.

(1999). Venture capitalists’ assessment of new venture survival. Management Science, 45(5), 621–632. https://doi.org/10.1287/mnsc.45.5.621

110.

Shepherd

D. A.

Zacharakis

(1999). Conjoint analysis: A new methodological approach for researching the decision policies of venture capitalists. Venture Capital, 1(3), 197–217. https://doi.org/10.1080/136910699295866

111.

Suarez

F. F.

Grodal

Gotsopoulos

(2015). Perfect timing? Dominant category, dominant design, and the window of opportunity for firm entry. Strategic Management Journal, 36(3), 437–448. https://doi.org/10.1002/smj.2225

112.

Svenningsen

L. S.

Jacobsen

J. B.

(2018). Testing the effect of changes in elicitation format, payment vehicle and bid range on the hypothetical bias for moral goods. Journal of Choice Modelling, 29, 17–32. https://doi.org/10.1016/j.jocm.2018.08.001

113.

Tomassetti

A. J.

Dalal

R. S.

Kaplan

S. A.

(2016). Is policy capturing really more resistant than traditional self-report techniques to socially desirable responding? Organizational Research Methods, 19(2), 255–285. https://doi.org/10.1177/1094428115627497

114.

Train

Weeks

(2005). Discrete choice models in preference space and willingness-to-pay space. Springer.

115.

Train

K. E.

(2009). Discrete choice methods with simulation. Cambridge university press.

116.

Van Houtven

Johnson

F. R.

Kilambi

& Hauber

A. B

. (2011). Eliciting benefit–risk preferences and probability-weighted utility using choice-format conjoint analysis. Medical Decision Making, 31(3), 469–480. https://doi.org/10.1177/0272989X10386116

117.

Vass

Boeri

Karim

Marshall

Craig

Mott

Ngorsuraches

Badawy

S.M.

Mühlbacher

Gonzalez

J. M.

& Heidenreich

. (2022). Accounting for preference heterogeneity in discrete-choice experiments: An ISPOR special interest group report. Value in Health, 25(5), 685–694. https://doi.org/10.1016/j.jval.2022.01.012

118.

Veldwijk

Marceta

S. M.

Swait

J. D.

Lipman

S. A.

& De Bekker-Grob

E. W

. (2023). Taking the shortcut: Simplifying heuristics in discrete choice experiments. The Patient-Patient-Centered Outcomes Research, 16(4), 1–15. https://doi.org/10.1007/s40271-023-00625-y

119.

Wittink

D. R.

Krishnamurthi

Reibstein

D. J.

(1990). The effect of differences in the number of attribute levels on conjoint results. Marketing Letters, 1(2), 113–123. https://doi.org/10.1007/BF00435295

120.

Yang

J.-C.

Johnson

F. R.

Kilambi

& Mohamed

A. F

. (2015). Sample size and utility-difference precision in discrete-choice experiments: A meta-simulation approach. Journal of Choice Modelling, 16, 50–57. https://doi.org/10.1016/j.jocm.2015.09.001

121.

Yang

J.-C.

Reed

S. D.

Hass

Skeen

M. B.

& Johnson

F. R

. (2021). Is easier better than harder? An experiment on choice experiments for benefit-risk tradeoff preferences. Medical Decision Making, 41(2), 222–232. https://doi.org/10.1177/0272989X20979833

122.

Goos

Vandebroek

(2009). Efficient conjoint choice designs in the presence of respondent heterogeneity. Marketing Science, 28(1), 122–135. https://doi.org/10.1287/mksc.1080.0386

123.

Zacharakis

A. L.

Meyer

G. D.

(1998). A lack of insight: Do venture capitalists really understand their own decision process? Journal of Business Venturing, 13(1), 57–76. https://doi.org/10.1016/S0883-9026(97)00004-9

124.

Zhu

Tomassetti

A. J.

Dalal

R. S.

Schrader

S. W.

Loo

Sabat

I. E.

Alaybek

Zhou

Jones

& Fyffe

. (2022). A test-retest reliability generalization meta-analysis of judgments via the policy-capturing technique. Organizational Research Methods, 25(3), 541–574. https://doi.org/10.1177/10944281211011529

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.26 MB

0.00 MB