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The choice behaviour of the lower-level player is simulated by a random utility model.
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We performed an overall analysis of the data utilizing a conditional logit model by specifying a random utility model in which the utility function U ki, when respondent k chooses option i, is expressed by {U}_{ki}={V}_{ki}+{varepsilon}_{ki} where V ki specifies an observable discrete element among the utilities and ε ki specifies a non-observable stochastic element.
We assume a random utility model for the analysis.
A random utility model is estimated for landowners and for land trust professionals.
The producers' market outlet choice can be conceptualized using a random utility model (RUM).
Specifically, we adopted a choice model derived in a random utility maximization model (RUM) framework, in which decision makers are assumed to be utility maximizers.
The results were analysed using a random utility model incorporating random intercepts to account for the repeated testing of the same animals.
A conjoint analysis examined individuals' willingness to invest in wood-based energies following a random utility model.
Where U ik represents a random utility associated with the market channel j = k, V ij represents an index function denoting the decision-makers' average utility associated with this alternative, and ε ij represents the random error.
The DCE data were analysed within a random utility maximisation framework (McFadden, 1973).
Choice data were modelled using a random utility maximisation framework [ 50].
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