Stated Choice Data Research Articles

Assessing the substitutability of mobile and fixed internet: The impact of 5G services on consumer valuation and price elasticity

In this study, we explore the dynamics of consumer choices in the Polish telecommunications market, focusing on preferences and valuations for home fixed, home mobile, and purely mobile Internet connections. Key attributes such as speed, latency, data limits, and cost are examined. Central to our research is the investigation of how the integration of 5G technology might influence demand elasticity. Using a detailed discrete choice experiment, we apply a mixed logit model with random parameters to analyze stated choice data, enabling us to unravel the complexities of demand elasticity, especially in terms of own and cross-price elasticities. This approach facilitates an assessment of the degree of substitutability between fixed and mobile Internet services.Our findings indicate a moderate substitution effect between fixed and mobile Internet services. Results from a Small but Significant and Non-transitory Increase in Price (SSNIP) test suggest that these markets should continue to be regulated separately, mirroring the distinct regulation observed in fixed and mobile telephony. Furthermore, simulations provide insights into potential future market shifts with the advent of 5G services. This paper contributes significantly to the discourse on fixed-mobile Internet substitution and offers vital insights for defining markets in antitrust discussions, competitive agreements, and potential mergers within the telecom sector.

Ecological, financial, social and societal motives for cooperative energy prosumerism: measuring preference heterogeneity in a Belgian energy cooperative

BackgroundEnergy cooperatives in Europe have become more prominent due to the growing interest in and development of renewable energy sources. Cooperatives are often seen as an instrument in the transition to a low-carbon future. The cooperative prosumer is gaining importance, taking on the role of both consumer and producer by setting up and joining electricity cooperatives as investors, shareholders and clients. As is the case with other cooperatives and social enterprises, there are tensions and trade-offs to be made between social, ecological and financial goals. To navigate these tensions and to work towards becoming a resilient institution, it is important that cooperatives preserve their support base made up of their members, who are both clients and shareholders. This study investigates the preference heterogeneity and motivations of members of a large energy cooperative in Belgium, by using stated-choice data from a Discrete Choice Experiment in combination with self-reported membership motives.ResultsDespite the significant presence of member preferences for participation and democratic voting rights, the financial and above all the ecological motives seem to be most important for being a member of the energy cooperative. Based on the stated-choice data, we classify three member types: the financial, the ecological and the social–societal member type. We find a small discrepancy in motives between members who joined early and members who joined later. Where early members care most about ecological aspects, members who joined later have more societal and financial considerations. In terms of effect size and willingness to pay per kilowatt hour, ecological motives prove to be by far the most important factor for cooperative energy prosumers in our sample. In addition, the latent-class analysis shows that over half of the member sample belongs to the ecological motives class.ConclusionsPreference heterogeneity is present to an extent, but there is an overarching preference for sustainability. Though, cooperatives should be aware that their legal form may not be the only factor that drives membership. Rather, keeping high levels of renewable energy, competitive pricing and being an interesting investment opportunity may be key to cooperatives’ resilience and further development on the energy market.

On the impact of decision rule assumptions in experimental designs on preference recovery: An application to climate change adaptation measures

Efficient experimental designs aim to maximise the information obtained from stated choice data to estimate discrete choice models' parameters statistically efficiently. Almost without exception efficient experimental designs assume that decision-makers use a Random Utility Maximisation (RUM) decision rule. When using such designs, researchers (implicitly) assume that the decision rule used to generate the design has no impact on respondents' choice behaviour. This study investigates whether the decision rule assumption underlying an experimental design affects respondents' choice behaviour. We use four stated choice experiments on coastal adaptation to climate change: Two are based on experimental designs optimised for utility maximisation and two are based on experimental designs optimised for a mixture of RUM and Random Regret Minimisation (RRM). Generally, we find that respondents place value on adaptation measures (e.g., dykes and beach nourishments). We evaluate the models' fits and investigate whether some choice tasks particularly invoke RUM or RRM decision rules. For the latter, we develop a new sampling-based approach that avoids the confounding between preference and decision rule heterogeneity. We find no evidence that RUM-optimised designs invoke RUM-consistent choice behaviour. However, we find a relationship between some of the attributes and decision rules, and compelling evidence that some choice tasks invoke RUM consistent behaviour while others invoke RRM consistent behaviour. This implies that respondents’ choice behaviour and choice modelling outcomes are not exogenous to the choice tasks, which can be particularly critical when information on preferences is used to inform actual decision-making on a sensitive issue of common interest as climate change.

Assessing the Substitutability of Mobile and Fixed Internet: The Impact of 5G Services on Consumer Valuation and Price Elasticity

In this study, we explore the dynamics of consumer choices in the Polish telecommunications market, focusing on preferences and valuations for home fixed, home mobile, and purely mobile internet connections. Key attributes such as speed, latency, data limits, and cost are examined. Central to our research is the investigation of how the integration of 5G technology might influence demand elasticity. Using a detailed discrete choice experiment, we apply a mixed logit model with random parameters to analyze stated choice data, enabling us to unravel the complexities of demand elasticity, especially in terms of own- and cross-price elasticities. This approach facilitates an assessment of the degree of substitutability between fixed and mobile internet services. Our findings indicate a moderate substitution effect between fixed and mobile internet services. Results from a Small but Significant and Non-transitory Increase in Price (SSNIP) test suggest that these markets should continue to be regulated separately, mirroring the distinct regulation observed in fixed and mobile telephony. Furthermore, simulations provide insights into potential future market shifts with the advent of 5G services. This paper contributes significantly to the discourse on fixed-mobile internet substitution and offers vital insights for defining markets in antitrust discussions, competitive agreements, and potential mergers within the telecom sector.

A Bayesian hierarchical approach to the joint modelling of Revealed and stated choices

Revealed and stated choice data are fundamental inputs to understanding individuals’ preferences. Owning to the distinctive characteristics and complementary nature of these two types of data, making joint inference based on their combined information content represents an attractive approach to preference studies. However, complications may arise from the different decision protocols under the two distinct choice contexts. In this study, a Bayesian hierarchical model is proposed to make joint inference from combined RP and SP data, with special attention paid to capturing the behavioural differences between the two choice contexts. In addition to the well-recognised issues of decision inertia and scale differences, the proposed model also takes into account other behavioural characteristics such as a decision-maker ignoring situation constraints, non-attending attributes, and misinterpreting attributes. An empirical analysis of a combined RP and SP dataset of travel mode choices is used to demonstrate the advantageous features of the model. Upon examining the empirical evidence, two main advantages emerge: the model provides direct measures of the effect of behavioural issues arising from ignoring situation constraints and non-attending attributes, as well as evidence for the misinterpretation of attributes.

Choice Heuristics in Processed Food Purchases: The Case of Hotdog Purchase in the U.S.

The classical discrete choice model assumes that decision-makers consider all attributes in choice situations. However, decision-making often relies on some heuristic process instead of full attribute assessment. Unlike previous literature based on stated choice data, this study attempted to accommodate attribute inattention into a discrete choice model using market data. Our test reports getting better models fit in where they include both choice heuristic and full attribute preservation rules than in the conventional assumption. Accordingly, the heterogeneous decision rules need to be considered in choice modeling as an alternative to the traditional assumption. It is expected that this approach will help to derive food marketing implications for the consumer segments corresponding to each decision rule, reflecting multiple decision-making rules.

The built environment impacts on route choice from home to school for rural students: A stated preference experiment.

Rural roads and built environment in China have been developed enormously, but it is not clear whether these roads fulfill the needs of school children as they need to travel long to school every day. It is crucial to understand the influencing factors of their travel mode choices to better design future country roads and built environment, aiming to promote physical activities of school children in a safe built environment. This study thus attempts to explore the impacts of rural built environment attributes on children's school travel mode preferences. Eight rural built environment attributes are considered: distance from home to school; the number of intersections passed on the way to school; whether there are sidewalks/bicycle lanes; the traffic speed of school access routes; whether there are separation facilities between motor vehicles and non-motor vehicles; whether there are traffic lights and zebra crossings; availability of greenery such as lawns, flower ponds and street trees and whether there are shops on the way to school and at the school gate. Six hundred and thirty eight valid questionnaires were obtained through face-to-face interviews with school-age children in villages. A multinomial logit model was estimated to unravel the preferences and choices of rural school-age children in different models of school travel using the stated choice data. All the eight attributes have significant impacts on rural children's school travel choices on foot, bicycle, electric bicycle or motorbike. And four rural road design attributes have significant effects on rural children's school travel by private cars. A travel path with pavements or bike lanes, few intersections, low traffic speeds, greenery and shops can facilitate children's school travels on foot or by bike. The conclusions can provide reference for the further upgrading planning, designing and construction of rural roads, as well as enriching the theory and practice of child-friendly villages construction.

Modeling preference heterogeneity using model-based decision trees

This article investigates the usage of a general model-based recursive partitioning algorithm to model preference heterogeneity. We use the algorithm to grow a decision tree based on statistical tests of the stability of individuals’ preference parameters. In particular, we used a Mixed Logit (MIXL) model with alternative-specific attributes at the end leaves of the tree while using individual characteristics as partition variables. This configuration allows us to search for instabilities of the taste parameters across individuals’ characteristics. We conduct a simulation study to investigate the algorithm’s ability to recover different data generating processes with structural breaks in the taste parameters. The results show that the algorithm can correctly recover diverse tree-like data generating processes. Additionally, we applied the algorithm to stated choice data of the preferences for the environmental impact of (hypothetical) energy generation plans in Chile. The results show that the model-based decision tree fits the data better than MIXL in terms of information criteria. Moreover, we show that the derived tree structure depends on the assumptions on the parameters’ distributions. Additionally, we compare the model-based decision tree model with Latent Class (LC) models with and without within-class heterogeneity. Finally, we show that the recursive partitioning algorithm can inform the selection of variables to be included in the LC allocation models.

Modeling Commuter Preferences for the Proposed Bus Rapid Transit in Dar-es-Salaam

The paper analyzes individual commuter preferences towards the proposed bus rapid transit (BRT) system in Dar-es-Salaam, Tanzania. The objective of the survey was to identify how commuters perceive and value the proposed BRT service quality attributes. A stated preference survey of potential users of the proposed BRT was administered to 684 commuters who traveled to the central business district (CBD) on a regular basis. To this end, a special pictorial score card was developed that was suited for the local context and needed to capture the preferences of the commuter respondents. The BRT attributes considered for study are travel time, travel fare, and comfort. The stated choice data were analyzed using a binary logit model. The findings reveal, in order of importance, that comfort is the most valued attribute compared to travel time and travel fare, respectively.

Modeling Generalized Cost of Travel for Rural Bus Users: A Case Study

In order to formulate rational improvement proposal for rural bus services, it is necessary to understand how people value different attributes of travel. In this article, the disutilities of travel have been modeled based on stated choice data collected from trip-makers traveling along a rural bus route in Midnapur district, West Bengal, India. Multinomial Logit Model (MNL) is used to develop utility equations and the total disutility of travel is estimated in the form of generalized cost. The perceived values associated with in-vehicle travel time, service headway, and comfort level for the study route are estimated and found to be significant.

Preference estimation under bounded rationality: Identification of attribute non-attendance in stated-choice data using a support vector machines approach

Stated-choice experiments have been useful in helping to make a number of operations management decisions. Many recent advances in this area have raised questions about estimating consumers’ preferences when they partially ignore the information provided in discrete choice experiments, a problem introduced as attribute non-attendance (ANA). This line of research explores the consequences of assuming that consumers consider all available information concerning attributes to evaluate product alternatives, when in fact, they might ignore some attributes completely. Diverse choice models, such as latent class models, have been developed to accommodate ANA using choice data. Due to the combinatorial nature of such an approach, researchers typically explore a limited number of specifications. Furthermore, although diverse modeling approaches have been proposed to accommodate ANA, no research has investigated the capability of these approaches to correctly identify ANA at the individual level. In this work, we propose the use of a machine learning approach based on support vector machines to identify ANA at the individual level and to predict consumer choices in conjoint experiments. We conduct an extensive simulation study varying the degree of non-attendance and the noise in the choice data to investigate the performance of the proposed approach. Our results with simulated data show good performance in terms of the identification of attended and non-attended attributes. We test our approach in two empirical applications and compare it to state-of-the-art benchmarks in the field. We demonstrate the usefulness and the alternative insights derived from our method.

Heterogeneity in Personal Attitudes and Preferences for High-Speed Metropolitan Railway Service by Travel Contexts in South Korea: A Hybrid Choice Modeling Approach

High-speed metropolitan railway services have been introduced in several mega cities to solve traffic problems stemming from urban sprawl. The success of such huge public transportation projects depends on how to design them more attractively based on profound insight into people’s preferences in the context of travel mode choice. Although many previous studies have investigated factors affecting the preferences for high-speed metropolitan railway services in general, possible heterogeneity in the preferences by different travel contexts tends to be largely ignored. In addition, the effects of personal latent attitudes toward travel mode choice have not been investigated thoroughly in the context of using high-speed metropolitan railway services. Incorporating such latent attitudes in mode choice analysis is helpful for better understanding the motivation of choosing a travel mode. The present study investigates people’s preferences for high-speed metropolitan railway service and their heterogeneity by different travel contexts using stated choice data. In addition, the hybrid choice modeling approach is employed to identify simultaneously the relevant latent attitudes and their effects on the preference for high-speed metropolitan railway service. The data collection was conducted in 2021, with respect to the great train express (GTX) project of the Seoul metropolitan area. The estimation results suggest that the latent attitudes about risk-minimizing and comfort-seeking have different effects on people’s intention to use GTX according to travel contexts. Moreover, the difference in sensitivity to the attributes of GTX exists depending on the travel contexts.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

Can repeated surveys reveal the variation of the value of travel time over time?

This paper studies intertemporal changes in the value of travel time (VTT) and investigates whether the change of VTT over time can be studied based on national VTT data, collected at two points in time. We use repeated national VTT data from the Netherlands and Sweden, collected 13 and 14 years apart. The results show mostly a declining VTT for a given income level. The results show also a large within-country heterogeneity across modes and purposes, in the cross-sectional income elasticity of the VTT, and in its development over time. The explanation most consistent with our results and those of others is that the VTT has in fact increased due to income increases, but that the repeated stated choice data cannot detect this given the data, methodology and population changes. In particular, it seems that the response rate has dropped considerably in the later surveys partly due to a higher share of (busy) respondents declining to be recruited. The main contribution of this paper is to document the differences between the studies carried out in different years, indicating the reasons why it is difficult to identify temporal changes in the VTT.

Multiple Herbicide Use in Cropland: A Discrete-Continuous Model for Stated Choice Data

Andrea Pellegrini, Visiting Research Fellow Centre for Business Intelligence and Data Analytics, Business School, University of Technology, Sydney; Andrea.Pellegrini{at}uts.edu.au

Differences in street-scale built environment preferences towards biking: a latent class analysis of stated choice data

ABSTRACT Biking contributes to a better environment by reducing short car trips and improving public health. The street-scale built environment affects the appeal of streets to cyclists, and thus influences route choice behaviour. This study develops a stated choice experiment that systematically varies eight attributes of the built environment to examine preference structure differences pertaining to the street-scale built environment among cyclists in the context of access/egress trip to a metro station. The environment is systematically varied in terms of road length, average number of building floors, retail frontage, cycling facilities at intersections, bike lane width, greenery, lamp density, and crowdedness. Eight hundred and three respondents completed the choice task using face to face interviews in Tianjin, China. Results suggest the existence of two latent classes of cyclists that differ in their preference for the street-scale built environment.

Randregret: A command for fitting random regret minimization models using Stata

In this article, we describe the randregret command, which implements a variety of random regret minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181–196), the generalized RRM model introduced in Chorus (2014, Transportation Research, Part B 68: 224–238), and also the µRRM and pure RRM models, both introduced in van Cranenburgh, Guevara, and Chorus (2015, Transportation Research, Part A 74: 91–109). We illustrate the use of the randregret command by using stated choice data on route preferences. The command offers robust and cluster standarderror correction using analytical expressions of the score functions. It also offers likelihood-ratio tests that can be used to assess the relevance of a given model specification. Finally, users can obtain the predicted probabilities from each model by using the randregretpred command.

Preferences of urban rail users for first- and last-mile autonomous vehicles: Price and service elasticities of demand in a multimodal environment

Integrating autonomous vehicles (AVs) into transit networks is a critical problem for metropolitan areas worldwide. This study investigates the preferences of urban rail/rapid transit users for first- and last-mile AV services, which are not well understood. First, it develops an access mode choice model incorporating on-demand transit access enabled by AVs. Second, the model is estimated with stated choice data obtained from a survey of 2,300 residents living within 1–5 km of their nearest rail stations in the Tokyo metropolitan area, in which choice tasks are based on recent access to home-end stations. Finally, estimated models are used to compute elasticities of demand for access modes with respect to AV cost and wait time. The results show that price sensitivities of demand for AV services lie within a reasonable range of transit fare elasticities. AVs have diverse substitution patterns with existing modes. Riding in AV services is more likely to be a substitute for riding on buses/in slower modes of transit for all travel purposes, and for driving cars, particularly for leisure trips, but less likely for cycling and walking, particularly for work trips. The AV wait time elasticities might vary according to the waiting environment and conditions. The results also suggest that AVs may particularly benefit those who currently have restrictions in accessing transit.

Active mobility in an Italian city: Mode choice determinants and attitudes before and during the Covid-19 emergency

The paper investigates the choice of the transport mode to access the city center of Trieste, Italy. We have collected revealed and stated choice data and attitudinal data in order to investigate the mode of transport used before and during the Covid-19 pandemic. We studied the main choice determinants jointly with respondents’ psychological attitudes using an integrated choice/latent variable modelling framework. The model incorporates choice data, generalized cost variables, random parameters, and latent variables. The latter capture the concern for the environment, both at global and local level, the attitudes towards physical exercise and the Covid-19-related risks. The model is applied to assess the impact of extending the density of the cycling lanes on active mobility. Our estimates indicate a potential increase in cycling which, however, would not translate into an increase in active mobility, since the bike would substitute some trips currently made by foot. We also detect a high substitutability between the bike and the bus, while the motorcycle and the car are much less affected. It is confirmed that the Covid-19 pandemic altered significantly the transport mode choices, having a strong negative impact on bus and shifting bus users towards private modes, both motorized and non-motorized.

Investigating the effect of security and privacy on IoT device purchase behaviour

Given the significant privacy and security risks of Internet-of-Things (IoT) devices, it seems desirable to nudge consumers towards buying more secure devices and taking privacy into account in the purchase decision. In order to support this goal, this study examines the effect of security and privacy on IoT device purchase behaviour and assesses whether these effects are sensitive to framing, using a mixed methods approach. The first part of the study focuses on quantifying the effect of security and privacy compared to the effect of other device attributes such as price or functionality, by testing a causal model with choice models that have been developed from stated choice data. The second part aims to reveal the underlying mechanisms that determine the effect of privacy and security on purchase behaviour by means of a qualitative survey. The results suggest that security and privacy can strongly affect purchase behaviour, under the circumstances that privacy- and security-related information is available and communicated in an understandable manner, allowing consumers to compare devices. Moreover, the results show that a description of security that focuses on gains is more effective in nudging consumers towards buying secure devices. Future efforts could build upon this study by comparing the effect of security and privacy to more device attributes, such as ease of use or cost reduction. The results can serve as a basis for interventions that nudge consumers towards buying more secure and privacy-friendly devices.