COIN th JRC Annual Training on Composite Indicators and MCDA 22-26/09/2014, Ispra IT. Dorota Weziak-Bialowolska. Weighting.

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1 Equal weights Weighting Conjoint analysis Weights assigned on purely statistical fashion Principal component/factor analysis Regression approach Unobserved components models Data envelopment analysis 12 th JRC Annual Training on Composite Indicators & Multicriteria Decision Analysis (COIN 2014) European Commission Joint Research Centre Econometrics and Applied Statistics Unit Composite Indicators Research Group (JRC-COIN) Weights based on opinions: participatory methods Budget allocation Public opinion Analytic hierarchy process Conjoint analysis Weighting Conjoint Analysis 1 Weighting Conjoint Analysis 2 Merely asking respondents how much importance they attach to an individual indicator is unlikely to yield effective willingness to pay valuations. These can be inferred by using conjoint analysis from respondents rankings of alternative scenarios (Hair et al., 1995). Conjoint analysis Source the theory of consumer preference Belongs to the larger family of preference-based methods where preferences for a product/situation/service are assessed Frequently applied in marketing research, consumer research and environmental economics Weighting Conjoint Analysis 3 Weighting Conjoint Analysis 4

2 Correspondence analysis is based on the utility maximization: consumer utility (or satisfaction/well-being) stems from the characteristics of the goods rather than from the goods themselves goods are valued for their attributes the overall utility of a product/situation/service is decomposed into separate utilities of its attributes Conjoint analysis in the field of CI is used to: 1) Explore experts preferences over various dimensions/subdimensions/variables and to estimate their relative importance on the aggregate measures 2) Identify the hidden rules experts employ to make trade-offs between different dimensions/sub-dimensions/variables and between the values they place on different levels of dimensions/sub-dimensions/variables Weighting Conjoint Analysis 5 Weighting Conjoint Analysis 6 Steps in conjoint analysis Marketing research Composite indicators 1. Determine attributes 2. Assign levels of attributes 1. Determine the framework (dimensions, sub-dimensions, indicators) 2. Assign levels to each dimension/sub-dimension/ indicator EXAMPLE To establish the importance of the variables in the Economic Component Based on Sydorovych, O., & Wossink, A. (2008). The meaning of agricultural sustainability: Evidence from a conjoint choice survey. Agricultural Systems, 98, doi: /j.agsy Weighting Conjoint Analysis 7 Weighting Conjoint Analysis 8

3 Steps in conjoint analysis EXAMPLE Marketing research Composite indicators 1. Determine attributes 2. Assign related attributes levels 1. Determine the framework (dimensions, sub-dimensions, indicators) 2. Assign levels to each dimension/sub-dimension/ indicator Based on Sydorovych, O., & Wossink, A. (2008). The meaning of agricultural sustainability: Evidence from a conjoint choice survey. Agricultural Systems, 98, doi: /j.agsy Compile the profiles of the levels of attributes 3. Compile the profiles of the levels of dimensions/subdimensions/indicators Weighting Conjoint Analysis 9 Weighting Conjoint Analysis 10 We ask experts to give an overall evaluation of product/situation bundles that vary systematically on a number of attributes evaluation = ranking Example: To understand stakeholders preferences for agricultural sustainability in the economic dimension we select We choose realistic levels of each variable/attribute Based on the selected attributes and levels, the as the most important variables/attributes. Weighting Conjoint Analysis 11 Weighting Conjoint Analysis 12

4 SOLUTION Fractional factorial design (reduced selection of profiles) Based on the selected attributes and levels, the Based on the selected variables/attributes and their levels, we create profiles of possible combinations of levels of attributes for experts to evaluate (rank) in terms of their knowledge, preference, acceptability, soundness, etc. In our case it is 2 6 =64 possibile economic agricultural sustainability profiles TOO MANY!!! A reduced selection of profiles should represent the full number of stimuli as close as possible. This is usually done using an orthogonal design In an orthogonal design the proportional frequency of occurrence of any variable/attribute level in the full design is maintained in the reduced design. That is, for any combination of two variables/attributes, the levels of one variable/attribute must occur with equal frequency with the levels of the other variable/attribute Weighting Conjoint Analysis 13 Weighting Conjoint Analysis 14 In SPSS orthoplan command Using orthoplan command in SPSS we establish that is it to give experts 8 different economic agricultural sustainability profiles to calculate weights (i.e., part-worths/utilities) of all 64 economic agricultural sustainability Variable/Attribute Profile 1 Prospects for long-run profit against long-run Income stability/predictability in the short comparison with other opportunities Reliance on purchased inputs and borrowed capital Sufficiency of cash flow to cover operational expenses on time Reliance on governmental subsidies or payments Extent of governmental regulations Highly relevant than Not Weighting Conjoint Analysis 15 Weighting Conjoint Analysis 16

5 Variable/Attribute Profile 2 Prospects for long-run profit Income stability/predictability in the short comparison with other opportunities Reliance on purchased inputs and borrowed capital Sufficiency of cash flow to cover operational expenses on time Reliance on governmental subsidies or payments Extent of governmental regulations Not Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile 8 Highly than Highly than Not Not than than Not Highly Highly Not Weighting Conjoint Analysis 17 Weighting Conjoint Analysis 18 Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile COIN Odds 12th are JRC Annual Odds Training are on run run run against long- against long- against long- Highly than Highly than Not Not RANKS than than Not Highly Highly Not Stakeholder Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile 8 Stakeholder Stakeholder Stakeholder n Weighting Conjoint Analysis 19 Model of CA U ijp utility of an expert i associated with jp profile β utilities/part-worth utilities to be estimated (for of each level of each variable/attribute) x ijp set of p dummy (or other) variables corresponding to attributes of profile jp presented to expert i ε ijp stochastic portion of the utility function Estimation OLS (the most common) Weighting Conjoint Analysis 20

6 Model of CA Model of CA Rating of a given profile β utilities/part-worth utilities to be estimated (for of each level of each attribute) x ijp vector of attributes of profile jp presented to expert i ε ijp stochastic portion of the utility function Stakeholder Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile 8 Stakeholder Stakeholder Stakeholder n Weighting Conjoint Analysis 21 Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile Weighting Conjoint Analysis 22 Variable/Attribute Prospects for long-run profit Income stability/predictability in the short comparison with other opportunities Reliance on purchased inputs and borrowed capital Sufficiency of cash flow to cover operational expenses on time Reliance on governmental subsidies or payments Extent of governmental regulations JRC-COIN Profile 1 Profile 2 Profile 3 Profile 4 Profile 5 Profile 6 Profile 7 Profile 8 Highly than Highly than Not Not than than Not Highly Highly Not Weighting Conjoint Analysis 23 Significant estimates of part-worth: RELATIVE IMPORTANCE non-significant estimate of part-worth weight =0 significant estimate of partworth Weighting Conjoint Analysis 24

7 RELATIVE IMPORTANCE RELATIVE IMPORTANCE non-significant estimate of part-worth weight =0 significant estimate of partworth all estimates of part-worth utilities are considered Sum of significant estimates of part-worth = 3 Weight of each significant attribute 1.85/3 = prospect for long-run profit 0.49/3 = reliance on purchased inputs 0.66/3 = extent of govermental regulations Weighting Conjoint Analysis 25 Weighting Conjoint Analysis 26 Numerator Weight 12th JRC Annual Training on Composite Indicators and MCDA 1.85 (-1.85) = /6.98 = (-0.08) = /6.98 = (0.49) = /6.98 = (-0.16) = /6.98 = (-0.25) = /6.98 = (-0.66) = /6.98 = Sum 6.98 Sum References Acosta, L. a., Eugenio, E. a., Enano, N.H., Magcale-Macandog, D.B., Vega, B. a., Macandog, P.B.M., Eugenio, J.M. a., Lopez, M. a., Salvacion, A.R., Lucht, W.: Sustainability trade-offs in bioenergy development in the Philippines: An application of conjoint analysis. Biomass and Bioenergy. 64, (2014). Claret, A., Guerrero, L., Aguirre, E., Rincón, L., Hernández, M.D., Martínez, I., Benito Peleteiro, J., Grau, A., Rodríguez-Rodríguez, C.: Consumer preferences for sea fish using conjoint analysis: Exploratory study of the importance of country of origin, obtaining method, storage conditions and purchasing price. Food Qual. Prefer. 26, (2012). Hanisch, D.N., Rau, S.B.: Application of metric conjoint analysis in family business research. J. Fam. Bus. Strateg. 5, (2014). Kucukusta, D., Denizci Guillet, B.: Measuring spa-goers preferences: A conjoint analysis approach. Int. J. Hosp. Manag. 41, (2014). Sydorovych, O., Wossink, A.: The meaning of agricultural sustainability: Evidence from a conjoint choice survey. Agric. Syst. 98, (2008). Wu, W.Y., Liao, Y.K., Chatwuthikrai, A.: Applying conjoint analysis to evaluate consumer preferences toward subcompact cars. Expert Syst. Appl. 41, (2014). Weighting Conjoint Analysis 27 Weighting Conjoint Analysis 28