Identifying the role of stated process strategies in business location decisions

Camila Balbontin and David Hensher

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 141, issue C

Abstract: Discrete choice studies are increasingly used in urban planning to understand preferences and to make informed decisions based on its outcomes. Traditional discrete choice modelling approaches have evolved in a setting in which some very specific behavioural assumptions are made in specifying decision-making. These assumptions have given rise to the study of alternative process strategies in decision-making, such as majority of confirming dimensions (MCD), attribute non-attendance (ANA), or value learning (VL). In this paper, a stated choice experiment was designed to understand business location decisions, where a location specialist had to compare their current location with two alternative locations. After each choice task, respondents were asked whether they used ANA in processing the choice tasks, and at the end of the experiment a number of questions were asked to identify whether specific process heuristics were used such as MCD and VL. Choice models were estimated to compare the influence of including different stated heuristics responses as conditioning effects. The results show that the model which included the stated heuristics responses is superior in terms of the goodness of fit and in the estimates’ significance levels than the model that assumes everyone might be using the heuristics. The willingness to pay estimates derived from a traditional model were statistically equivalent to the ones derived from the stated multiple heuristics model. However, the median WTP derived from the stated multiple heuristics model was slightly lower and the confidence intervals higher than in the traditional model.

Keywords: Business location decisions; Process strategies; Stated heuristics; Discrete choice models; Stated choice experiment (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.tre.2020.102028

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