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BackgroundIn U.S. states that legalized and commercialized recreational cannabis, cannabis sales in illegal markets are still sizable or even larger than those in legal markets. This study aimed to assess cannabis consumers' preferences for purchasing cannabis from legal and illegal markets and estimate the trade-offs under various policy scenarios.Methods963 adults were recruited, who used cannabis in the past year and lived in a state with recreational cannabis legalization. In a discrete choice experiment, participants chose purchasing cannabis from a legal dispensary or an illegal dealer with varying levels in product attributes including quality, safety, accessibility, potency, and price. Mixed logit models were used to analyze preferences.ResultsThe likelihood of choosing legal cannabis increased with a higher quality, the presence of lab test, a shorter distance to seller, a higher tetrahydrocannabinol level, and a lower price. The likelihood of choosing illegal cannabis increased with a higher quality, a shorter distance to seller, and a lower price. Among product attributes, quality and accessibility were perceived to be the most important for legal cannabis and price was perceived to be the most important for illegal cannabis. Policy simulations predicted that improving quality, ensuring safety, allowing delivery services, increasing dispensary density, and lowering prices/taxes of legal cannabis may reduce illegal cannabis market share.ConclusionsIn the U.S., cannabis consumers' preferences for illegal cannabis were associated with both legal and illegal cannabis product attributes. Policies regulating legal cannabis markets should consider potential spillover effects to illegal markets.

Microgreens are young plants used as ingredients and flavoring in various dishes. Their production time is short, and their production methods can be altered to enhance the nutritional content. To date, consumers’ preferences for microgreens that display different esthetic and nonesthetic traits have not been addressed. Additionally, consumers’ perceived risk of production methods used to enhance nutrition has not been investigated. An online survey and choice experiment involving a sample of 821 consumers in Tennessee were performed to investigate these topics. A mixed logit model was used to analyze the data. Both esthetic and nonesthetic traits influenced the consumers’ preferences. Green microgreens were preferred and valued more than light or dark purple microgreens. When fertilizer was used during production to enhance nutrients, consumers perceived the microgreens as riskier to the environment and for personal consumption relative to microgreens with light-induced nutrient enhancement. Using lighting during production to enhance nutrients generated a $0.47 to $0.79 premium relative to no nutrient enhancement. In general, if participants’ perceived risk aligned with the nutrient enhancement attribute (i.e., light, fertilizer), then their willingness to pay for the microgreens decreased. This was amplified for the fertilizer nutrient enhancement attribute more than it was for the light enhancement attribute. In-state production and microgreen height also impacted consumer preferences for microgreens. [ABSTRACT FROM AUTHOR]

Studies on fatal crashes involving heavy vehicle crashes at unsignalized intersections typically emphasize the examination of injury severity at the individual level rather than analyzing the crash itself. The objective of this study is to bridge the existing gap in the literature regarding the injuries caused by heavy vehicles at unsignalized intersections. Not only that but this study also seeks to find sound answers to the research questions: Firstly, how does the absence of a traffic signal or any other form of traffic control affect the injury severity of heavy vehicle drivers? Secondly, what are the factors that contribute to injury severity sustained by drivers of heavy vehicles at unsignalized intersections? This study utilized the data regarding heavy vehicle crashes that occurred at unsignalized intersections from 2007 to 2013 in the state of Washington. A total of 4638 crashes involving heavy vehicles at unsignalized intersections were reported between 2007 and 2013. Three main injury severity categories were considered: severe (fatal and incapacitating injuries), minor (non-incapacitating and possible injuries), and no injury. A mixed logit model is estimated to achieve the objectives of the study and also to tackle the issue of unobserved heterogeneity. The estimation results show that crashes on wet roadways, crashes involving left-turning movements, and crashes involving sober drivers produced obvious heterogeneous impacts on driver injury severity while the rest of the factors had homogeneous impacts. The findings indicate that sober drivers and the lack of restraining systems increased the odds of sustaining severe injuries by 0.0078 and 0.0022 as suggested by marginal effects. In contrast, no injury chances increased if crashes occurred in daylight by 0.0512. This study can aid in a deeper understanding of the risk factors of heavy vehicle crashes at unsignalized intersections so that safety at such locations can be improved by recommending effective countermeasures. [ABSTRACT FROM AUTHOR]

Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO2 emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT. [ABSTRACT FROM AUTHOR]

In this article, I describe the garbagejnixl command, which fits the garbage class and standard panel mixed logit models in Stata. [ABSTRACT FROM AUTHOR]

In areas where science is quite incomplete, such as in the deployment and use of new environmental water reserves, it might be reasonable to expect that managers will be learning 'on the job' or 'learning by doing' as they seek to apply best available science to decision making. Similarly, it might be expected that the public would have some empathy for the challenges facing environmental managers with incomplete data on which to base decisions. However, there is also a contrasting body of literature that shows the preferences of the public and voters for certainty in outcomes. This article reviews the status of adaptive management in the context of using water to bolster environmental outcomes. We also report the results of a discrete choice experiment where respondents were asked to assign a value to adaptive management undertaken by water managers. The article reports the difficulties of developing an attribute along these lines and the challenges of encouraging the public to engage with the concept and place a value on it. This provides a platform for considering what this means more generally for environmental interventions and the acceptance of environmental policies where ecological outcomes are frequently uncertain. [ABSTRACT FROM AUTHOR]

Despite the recent decline in child deaths caused by road traffic crashes in high income countries, low- and middle-income countries (LMIC) have yet to experience a similar trend. Children are among the most vulnerable of road users accounting for 30–40 % of all road traffic deaths in LMICs, 50 % of which are vehicle occupants. Previous research suggests that children ages 0–9 are the second most vulnerable age group in Ghana with 54 % of the children being fatally injured in injury-related crashes. However, little has been done to identify the associated factors influencing injury severity outcomes for child passengers in Ghana. This study investigates the factors that are associated with the various injury severity outcomes for child vehicle occupants less than 9 years old involved in road crashes in Ghana from 2014 to 2020. Results indicate that older child passengers (aged 5–8) were associated with lower injury severities compared to younger passengers. Additionally, crashes in which the driver sustained more severe injuries resulted in a higher likelihood of the child passengers sustaining a fatal injury. Findings from this research emphasize the issue of child passenger safety and support transportation policy and decision making to reduce risks of injury for child passengers.

Abstract Background In U.S. states that legalized and commercialized recreational cannabis, cannabis sales in illegal markets are still sizable or even larger than those in legal markets. This study aimed to assess cannabis consumers’ preferences for purchasing cannabis from legal and illegal markets and estimate the trade-offs under various policy scenarios. Methods 963 adults were recruited, who used cannabis in the past year and lived in a state with recreational cannabis legalization. In a discrete choice experiment, participants chose purchasing cannabis from a legal dispensary or an illegal dealer with varying levels in product attributes including quality, safety, accessibility, potency, and price. Mixed logit models were used to analyze preferences. Results The likelihood of choosing legal cannabis increased with a higher quality, the presence of lab test, a shorter distance to seller, a higher tetrahydrocannabinol level, and a lower price. The likelihood of choosing illegal cannabis increased with a higher quality, a shorter distance to seller, and a lower price. Among product attributes, quality and accessibility were perceived to be the most important for legal cannabis and price was perceived to be the most important for illegal cannabis. Policy simulations predicted that improving quality, ensuring safety, allowing delivery services, increasing dispensary density, and lowering prices/taxes of legal cannabis may reduce illegal cannabis market share. Conclusions In the U.S., cannabis consumers’ preferences for illegal cannabis were associated with both legal and illegal cannabis product attributes. Policies regulating legal cannabis markets should consider potential spillover effects to illegal markets.

Previous studies have primarily focused on off-street parking lots, leveraging parking fees to reduce search time and enhance socioeconomic benefits. However, existing research overlooks the examination of parkers' choice behavior within a single parking lot and fails to incorporate differential parking fees in analyzing the choices of individual parking spaces. To this end, this study introduces the multinomial logit and mixed logit models to explore the influence of variables on parkers' choice of parking spaces. Moreover, the marginal willingness to pay is calculated for the four variables (i.e., parking fee, mechanical space, walking time, and search time) for work and recreation purposes to determine how much parkers are willing to spend for them. The results show that all four variables have a significant negative effect on parker's choice, and mechanical parking space is core for parkers. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose an innovative approach to studying consumers’ preferences for coffee, which integrates a choice experiment with consumer sensory tests and chemical analyses (caffeine contents obtained through a High-Performance Liquid Chromatography (HPLC) method). The same choice experiment is administered on two consecutive occasions, i.e., before and after the guided tasting session, to analyze the role of tasting and awareness about coffee composition in the consumers’ preferences. To this end, a Bayesian optimal design, based on a compound design criterion, is applied in order to build the choice experiment; the compound criterion allows for addressing two main issues related to the efficient estimation of the attributes and the evaluation of the sensorial part, e.g., the HPLC effects and the scores obtained through the consumer sensory test. All these elements, e.g., the attributes involved in the choice experiment, the scores obtained for each coffee through the sensory tests, and the HPLC quantitative evaluation of caffeine, are analyzed through suitable Random Utility Models. The initial results are promising, confirming the validity of the proposed approach.

Purpose: Agriculture education promotes farmers and societal welfare through a more efficient use of resources and the adoption of sustainable farming practices. Nevertheless, farmers' educational attainment is limited across countries. This study explores farmers' intention to participate in formal agriculture education. Design/methodology/approach: A discrete choice experiment survey with 363 farmers exploring their preferences for different specifications within a diploma in agriculture was employed. We focus on Northern Ireland, a country with a relatively large agricultural sector and were farmers have a low level of formal education. Findings: Our results show that farmers exhibited a preference for short courses delivered in the evenings, with 75% of the teaching time delivered online, favouring educational topics linked to farm performance against those related to socio-environmental outcomes. Part-time younger farmers, operating in small farms located in less favoured areas, reported the highest willingness to pay for the course. However, farmers that had previously participated in a Young Farmer Payment Scheme reported the lowest interest in undertaking the course. Practical implications: The study provides useful information to education providers in relation to the design of agriculture courses to better meet farmers' preferences and therefore encourage levels of uptake. Secondly, the study can help policymakers to make informed decisions about policy instruments incentivising farmers to engage in education. Theoretical implications: By drawing from random parameter choice models, the study highlights that the heterogeneity of farmers' preferences should be considered when modelling their decision to engage in agriculture education. Originality/value: This is the first study that elicits farmers' preferences towards agriculture education via a discrete choice experiment. Secondly, the study explores preference heterogeneity based on farmers and farms' characteristics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study's conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose an innovative approach to studying consumers' preferences for coffee, which integrates a choice experiment with consumer sensory tests and chemical analyses (caffeine contents obtained through a High-Performance Liquid Chromatography (HPLC) method). The same choice experiment is administered on two consecutive occasions, i.e., before and after the guided tasting session, to analyze the role of tasting and awareness about coffee composition in the consumers' preferences. To this end, a Bayesian optimal design, based on a compound design criterion, is applied in order to build the choice experiment; the compound criterion allows for addressing two main issues related to the efficient estimation of the attributes and the evaluation of the sensorial part, e.g., the HPLC effects and the scores obtained through the consumer sensory test. All these elements, e.g., the attributes involved in the choice experiment, the scores obtained for each coffee through the sensory tests, and the HPLC quantitative evaluation of caffeine, are analyzed through suitable Random Utility Models. The initial results are promising, confirming the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Microgreens are young plants used as ingredients and flavoring in various dishes. Their production time is short, and their production methods can be altered to enhance the nutritional content. To date, consumers’ preferences for microgreens that display different esthetic and nonesthetic traits have not been addressed. Additionally, consumers’ perceived risk of production methods used to enhance nutrition has not been investigated. An online survey and choice experiment involving a sample of 821 consumers in Tennessee were performed to investigate these topics. A mixed logit model was used to analyze the data. Both esthetic and nonesthetic traits influenced the consumers’ preferences. Green microgreens were preferred and valued more than light or dark purple microgreens. When fertilizer was used during production to enhance nutrients, consumers perceived the microgreens as riskier to the environment and for personal consumption relative to microgreens with light-induced nutrient enhancement. Using lighting during production to enhance nutrients generated a $0.47 to $0.79 premium relative to no nutrient enhancement. In general, if participants’ perceived risk aligned with the nutrient enhancement attribute (i.e., light, fertilizer), then their willingness to pay for the microgreens decreased. This was amplified for the fertilizer nutrient enhancement attribute more than it was for the light enhancement attribute. In-state production and microgreen height also impacted consumer preferences for microgreens.

In the last years, Urban Air Mobility (UAM) has been receiving increasing attention and even if the first services are expected to be launched shortly, there is still uncertainty about which type of commercial services (e.g., airport shuttles or city-taxis) will be implemented at an early stage, as well as which price point will be perceived as affordable by travelers. Based on data collected through a large-scale survey campaign in the Milan metropolitan area (Italy), in this paper passengers' value of travel time savings for different UAM services are estimated using advanced discrete choice modeling. Estimated mixed logit models allowed to comparatively analyze the differences between the two potential use cases, i.e., airport shuttle and city-taxi services. Results show a willingness to pay for UAM services from/to airports that is greater (in a range of 44%–57%) than for travelling within the metropolitan area, and greater (in a range of 31%–44%) for business travels than for other purposes, indicating that the most financially sustainable UAM services will potentially be available for airport-shuttle connections from/to central business districts. • Willingness to pay for UAM airport shuttles (48–69 €/h) is greater than that for UAM city-taxis (34–44 €/h). • Business travelers are willing to pay 31%–44% more than non-business ones for UAM services. • In the early stages, UAM services connecting airports and central business districts will be the most financially sustainable. [ABSTRACT FROM AUTHOR]

The charging behavior of drivers serves as a valuable reference for planning and managing charging facilities. This study examines the influence of surrounding environments on charging decisions using real trajectory data from electric vehicles. It considers the built environment, vehicle conditions, and the nearest charging station attributes. The mixed binary logit model was applied to capture the impact of unobserved heterogeneity. The findings indicate that the number of fast chargers in the charging station, parking prices, dwell time, and shopping services significantly influence charging decisions, while leisure services, scenic spots, and mileage since the last charging exhibit opposite effects. Additionally, factors related to unobserved heterogeneity include the number of fast chargers, parking and charging prices, and residential areas. The interaction effects of random parameters further illustrate the complexity of charging choice behavior. Overall, the results offer valuable insights for the planning and management of charging facilities. [ABSTRACT FROM AUTHOR]

Park and Ride (P&R) as a demand management tool has the effect of reducing traffic congestion in urban centers, saving energy and reducing pollutant emissions. Since 2000, many cities in China have been constructing P&R facilities, which have partially alleviated urban traffic congestion and provided a time-reliable mode of travel for commuters heading to urban centers. However, in recent years, due to the pricing policy of the P&R facility, there has been an insufficient supply of P&R facilities in many places. In fact, the P&R system prefers to welcome travelers who make long-distance subway rides and does not want those who make short-distance subway rides to occupy more parking spaces. To address this, this paper proposes a tiered pricing strategy that considers charging parking fees based on the distance traveled by commuters after switching to public transportation, to improve the utilization of P&R. That is, charge less for parking for long-distance subway riders and more for short-distance subway riders. Firstly, based on questionnaire data from SP surveys, a fixed pricing mixed logit model (FP model) and a tiered pricing mixed logit model (TP model) for P&R facilities are constructed. Utilizing two models, we explored the mechanisms underpinning traveler's mode choice influenced by daily habits and travel considerations through the comparison of the two models to validate the effectiveness of the tiered pricing for P&R facilities. The study found that the implementation of a tiered pricing method for P&R facilities increases its attractiveness to long-distance subway ride travelers, resulting in a higher proportion of long-distance subway riders among P&R commuters. In the study's last section, a marginal effect analysis was conducted on the per-kilometer cost (Pkm) within the P&R model. This analysis determined the optimal Pkm for three subway travel distances within the P&R model. Consequently, it calculated the corresponding P&R parking fees for these three subway travel distances. Additionally, we have predicted the implementation effects of the tiered pricing scheme. [ABSTRACT FROM AUTHOR]

Travelling using sustainable modes, such as active transport and public transport, improves students' well-being and mental health, and bolsters sustainability. This study aims to investigate factors that encourage students to use sustainable modes which are public transport and active transport. The random parameters (mixed) logit was employed using the Victorian Integrated Survey of Travel and Activity (VISTA) data from Victoria, Australia to comprehensively explore trip attributes, socio-demographics, and built environment factors impacts on school travel mode choice. The inadequacy of model validation in previous research was addressed with the inclusion of the model accuracy test, which arguably is as equally important as behavioural inference. The result showed that travel distance and destination to primary school were the biggest contributors to mode choice probability, negatively affecting sustainable mode choice. The model produced an excellent overall accuracy (85.80%). This research elaborated on the inference of factors affecting mode choice and thus provided recommendations to help develop policies supporting sustainable transportation plans to improve students' well-being. [ABSTRACT FROM AUTHOR]

We investigate the migration‐inward foreign direct investment (FDI) nexus in narrow geographies. A novel two‐stage empirical strategy allows us to investigate the role of migration as a determinant of multinational enterprises (MNEs) location choices and unpick heterogeneity in foreign investors' preferences towards the presence of migrants in the host location. This allows us to shed light on the relative importance of the underlying mechanisms linking migration and inward FDI. Relying on 1113 greenfield investments by 895 MNEs in Italian NUTS3 regions over 2003–2015, we find that immigrants from the country of origin of the investor exert a positive but highly heterogeneous effect on MNE location choices. Investors are more sensitive to the presence of migrants from their country of origin when they lack experience in the destination country (first‐time investors) and when their investments concern market‐access or business‐services (downstream) activities. This is consistent with the view that migrants act as information brokers that bridge the fixed costs of international business activities. [ABSTRACT FROM AUTHOR]

This paper introduces a new shifted negative log‐normal distribution for the price parameter in mixed multinomial logit models. The new distribution, labeled as the μ‐shifted negative log‐normal distribution, has desirable properties for welfare analysis and in particular a point mass that is further away from zero than the negative log‐normal distribution. This contributes to mitigating the "exploding" implicit prices issue commonly found when the price parameter is specified as negative log‐normal and the model is in preference space. The new distribution is tested on five stated preference datasets. Comparisons are made with standard alternative approaches such as the willingness‐to‐pay (WTP) space approach. It is found that the μ‐shifted distribution yields substantially lower mean marginal WTP estimates compared to the negative log‐normal specification and similar to the values derived from models estimated in WTP‐space with flexible distributions, while at the same time fitting the data as well as the negative log‐normal specification. [ABSTRACT FROM AUTHOR]

Amman, the capital of Jordan, has experienced significant traffic congestion due to the rise in private vehicle ownership and limited public transportation services. A Stated Preference (SP) survey was conducted to determine public transportation users' willingness to use the Bus Rapid Transit (BRT) service. Another survey assessed the demand for an on-demand transit bus service with flexible and moderate costs, particularly for individuals far from the main BRT stations who need to reach them. Two models, Multinomial Logit (MNL) and Mixed Logit (ML), were utilized to understand user preferences for work-related trips. The study findings indicate that the cost of the trip and the waiting time are the two primary factors influencing public transport users' choices. Furthermore, sociodemographic factors, such as age, income, household size, and current status, were found to have a significant impact. The results reveal that approximately 71% of participants would utilize an integrated public transportation system comprising BRT and on-demand services. The findings underscore the potential benefits of an integrated public transport system in addressing Amman's traffic congestion. By combining BRT and on-demand services, the city can offer residents comfortable, affordable, and efficient transportation options, thus effectively mitigating congestion. [ABSTRACT FROM AUTHOR]

Malaysia needs to fully utilize its renewable energy resources to meet its goal of installed capacity of 31% of renewable energy in 2025 and 40% in 2035. In order to empower renewable energy sources, the government has established a fund known as the renewable energy fund (RE FUND). In Malaysia, most manufacturing sectors contribute to the RE FUND through their monthly electricity bills due to their electricity consumption exceeding 300kwh per month. As Malaysia's highest electricity consumer, the manufacturing sector needs government investment incentives to switch to renewable energy sources to generate electricity. Therefore, this study was conducted to identify attribute preferences of the manufacturing sector due to investing in renewable energy sources. The Choice Experiment method was employed where the Mixed Logit model was chosen to identify the willingness to pay for the manufacturing sectors based on their preferences among the four attributes: types of renewable energy, project location, annual reduction in GHG emissions, and RE FUND. The study results found that the manufacturing sector places the highest value on the project location, where they prefer to improve the project location from current condition to far location. This study can also help to achieve the Goal 7 in the Sustainable Development Goal (SDG), where investment in renewable energy sources can guarantee that all individuals obtain affordable, reliable, sustainable, and modern electricity in 2030.

Electric vehicles (EVs) offer a viable technological solution for mitigating greenhouse gas emissions in the transportation industry, addressing pressing societal concerns regarding climate change, air pollution, and sustainable energy consumption. To effectively promote widespread adoption of EVs, it is crucial to understand consumer preferences and evaluate market dynamics. In Nepal, where proven fossil fuel reserves are absent, the government is actively working towards accelerating EV adoption, leveraging the nation's significant hydroelectric power generation potential to fulfill EVs' charging demands. To gain insight into consumer preferences and evaluate market dynamics regarding EVs in Nepal, this study employs a comprehensive approach. Stated preference data are collected through a meticulously designed survey, and sophisticated analytical techniques, namely, the mixed logit model and latent class model, are applied for estimation purposes. The results of this study show that potential EV consumers with small family sizes, lower monthly travel distances, heightened environmental awareness, and substantial knowledge about electric vehicles are more inclined to embrace EV technology. Notably, the study highlights that a reduction in the purchase price exerts the most significant influence on increasing consumers' likelihood of adopting battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Market simulation results suggest that a policy mix scenario, encompassing a combination of supportive measures, proves more effective in promoting EV adoption compared to relying on single policy initiatives. Furthermore, through latent class estimation, the study identifies three distinct classes of consumers within Nepal, each exhibiting significant variations in preferences. Recognizing and addressing these variations within policy frameworks is crucial for the successful promotion and widespread acceptance of EVs in Nepal. [ABSTRACT FROM AUTHOR]

Choice models are used by applied economists for many purposes, such as non‐market valuation or estimating willingness to pay for novel food and product attributes. Mixed‐logit models allow researchers to account for preference heterogeneity and complex decision‐making processes when modelling choices. In mixed‐logit models, parameters of monetary attributes such as prices typically are assumed to follow a negative lognormal random distribution to ensure that the marginal utility of a price increase is strictly negative. However, this practice can cause means and standard deviations of welfare estimates to 'explode' to unfeasibly large levels, as the model assumes there are some marginal utilities of cost approaching zero. This paper examines whether cost non‐attendance, which occurs when respondents ignore costs in stated‐preference studies, could be a cause of inflated welfare estimates when a lognormal cost parameter is used. A two‐class equality‐constrained latent‐class model is proposed, in which the cost parameter is fixed at zero for a cost non‐attender class and is specified as a random lognormal parameter for cost attenders. This proposed model produces mean welfare estimates that are 17 times lower than a mixed‐logit model with a lognormal cost parameter, and 10% lower than a model with a non‐random cost parameter. These results suggest that cost non‐attendance can result in inflated welfare estimates when employing a lognormal cost parameter, and that accounting for cost non‐attendance could be a simple, parsimonious solution to this problem. [ABSTRACT FROM AUTHOR]

Park and Ride (P&R) as a demand management tool has the effect of reducing traffic congestion in urban centers, saving energy and reducing pollutant emissions. Since 2000, many cities in China have been constructing P&R facilities, which have partially alleviated urban traffic congestion and provided a time-reliable mode of travel for commuters heading to urban centers. However, in recent years, due to the pricing policy of the P&R facility, there has been an insufficient supply of P&R facilities in many places. In fact, the P&R system prefers to welcome travelers who make long-distance subway rides and does not want those who make short-distance subway rides to occupy more parking spaces. To address this, this paper proposes a tiered pricing strategy that considers charging parking fees based on the distance traveled by commuters after switching to public transportation, to improve the utilization of P&R. That is, charge less for parking for long-distance subway riders and more for short-distance subway riders. Firstly, based on questionnaire data from SP surveys, a fixed pricing mixed logit model (FP model) and a tiered pricing mixed logit model (TP model) for P&R facilities are constructed. Utilizing two models, we explored the mechanisms underpinning traveler’s mode choice influenced by daily habits and travel considerations through the comparison of the two models to validate the effectiveness of the tiered pricing for P&R facilities. The study found that the implementation of a tiered pricing method for P&R facilities increases its attractiveness to long-distance subway ride travelers, resulting in a higher proportion of long-distance subway riders among P&R commuters. In the study’s last section, a marginal effect analysis was conducted on the per-kilometer cost (Pkm) within the P&R model. This analysis determined the optimal Pkm for three subway travel distances within the P&R model. Consequently, it calculated the corresponding P&R parking fees for these three subway travel distances. Additionally, we have predicted the implementation effects of the tiered pricing scheme.

In a recent study, it was demonstrated that the maximum simulated likelihood (MSL) estimator produces significant biases when applied to the bivariate normal and bivariate Poisson-lognormal models. The study’s conclusion suggests that similar biases could be present in other models generated by correlated bivariate normal structures, which include several commonly used specifications of the mixed logit (MIXL) models. This paper conducts a simulation study analyzing the MSL estimation of the error components (EC) MIXL. We find that the MSL estimator produces significant biases in the estimated parameters. The problem becomes worse when the true value of the variance parameter is small and the correlation parameter is large in magnitude. In some cases, the biases in the estimated marginal effects are as large as 12% of the true values. These biases are largely invariant to increases in the number of Halton draws.

Copyright of Journal of International Business Studies is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a data-based strategy for prioritizing sewerage services and developing consumer-centric asset management systems. Sewerage systems are essential infrastructure, but resource-intensive, and governments have faced challenges due to limited budgets and aging facilities. Most countries are still in the early stages of adopting consumer-centric approaches. By using the mixed logit model, this study identified heterogeneous service preferences among consumers based on their priority for physical and social services. Groups who attributed their importance to physical services were more sensitive to sewerage bills; however, those who emphasized social services were more interested in water activity environment improvement and odor intensity reduction. This study also showed that people are willing to pay USD 10 for odor intensity reduction and USD 6 for water activities improvement on average, but those who prioritized sinkhole reduction and flood reduction answered less willingness to pay for each service improvement. Applying these heterogeneous service preferences to service improvement strategies can lead not only to higher consumer satisfaction and lower economic burden, but also to a deeper understanding of residents' willingness to pay for service improvement in certain service preference areas. Therefore, the results contribute to the development of sustainable asset management strategies. [ABSTRACT FROM AUTHOR]

Health issues and the production of differentiated foods influence the consumers' decisions and lead them to opt for products with particular organoleptic characteristics. The objective of this research was to estimate the preferences and willingness to pay for antibiotic-free pork in the municipality of Texcoco, State of Mexico, in order to determine the existence or absence of an incentive for the commercialization of antibiotic-free pork. The Discrete Choice Method of Experiments was used. The attributes considered in the design were: color, fat content, and presence of antibiotics. A survey of 196 consumers was carried out for this purpose. The results were analyzed with the NLOGIT 4.0 software, using a mixed logit model. The most valued attribute was antibiotic content, which means that respondents would be willing to pay an additional premium of $30.65 MXN/kg for antibiotic-free pork. It was found that the higher the income and the higher the frequency of pork consumption, the greater the willingness to pay an additional premium for antibiotic-free meat. A WTP of $5.78 MXN was determined for meat with normal fat, and of $3.73 MXN for red meat. Pork consumers in the eastern part of the State of Mexico would be willing to pay an additional premium for antibiotic-free pork and for such quality aspects as fat content and color. [ABSTRACT FROM AUTHOR]

Mobility as a Service (MaaS) is often seen as a promising solution to address societal and environmental challenges. Despite the importance of quantifying its potential benefits, few previous works have focused on the impacts on the environment, and all of them considered large cities. This study aims to forecast the diffusion of MaaS in a medium-sized city and quantify the consequent reduction in pollutant emissions for commuting trips. Answers from a mobility survey administered to employees of the Municipality of Padua (Italy) were used to calibrate a model predicting MaaS adoption, which was applied to real working trips to estimate daily vehicle emissions savings in future scenarios with different MaaS bundles. The results indicated that the opportunity to have multimodal mobility options providing door-to-door travel is a fundamental element to ensure wide MaaS diffusion. Furthermore, public transport was confirmed to be the backbone of such a system. Compared to the current scenario, we observed up to a 41% reduction in pollutant emissions. The analysis pointed out that MaaS adoption is highly dependent on the characteristics of the proposed bundles, thus highlighting the importance of a proper design of the service and ex ante evaluation of emission savings. [ABSTRACT FROM AUTHOR]

Parking choice models proposed over recent years have been aimed at choosing the relevant variables and pricing policies affecting said choice. In our analyses, we have encountered some gaps to cover; for this reason, we introduce a methodology for addressing user parking choices, including the availability of parking alternatives in each of the studied zones presenting pivoted scenarios. This methodology has been developed based on a stated preferences survey design to define user behaviour when making parking choices through discrete-choice models. Furthermore, the estimated models have considered systematic and random variations in user tastes and the correlation between similar or related parking alternatives. This methodology has made it possible to provide greater realism in generating fictitious parking scenarios. The results show the importance of the fee attribute and the differences in the users' preferences depending on whether they live inside or outside the study area and their arrival time at the destination. Several scenarios have been simulated, and in these scenarios, it is found that removing free parking areas or restricting free parking in favour of parking fee regulation in congested zones can be an efficient policy measure to promote other parking alternatives and reduce the number of vehicles searching for a parking space. [ABSTRACT FROM AUTHOR]

Many studies have analyzed the road characteristics that affect the severity of truck crashes. However, most of these studies have only examined road alignment or grade separately, without considering their combined effects. The purpose of this article is to address this gap in the literature. Our study uses truck crash data from 2015 to 2019 on freeways in the Yunnan Province of China, where the severity levels of the crashes were determined by taking into account economic loss and the number of injuries and fatalities. Our study develops three models to examine the severity of truck crashes: a multinomial logit model, a mixed logit model, and a generalized ordered logit model. The findings suggest that the mixed logit model, which can suffer from unobserved heterogeneity, is more suitable because of the higher pseudo-R-squared (ρ2) value and lower Akaike Information Criterion and Bayesian Information Criterion. The estimation results show that the combination of curve and slope significantly increases the severity of truck crashes compared to curves and slopes alone. In addition, risk factors such as crash type, vehicle type, surface condition, time of day, pavement structure, and guardrails have a significant impact on the severity of truck crashes on mountainous freeways. Based on these findings, we developed policy recommendations for reducing the severity of multi-truck collisions on mountainous highways and improving transport sustainability. For example, if possible, the combination of curve and slope should be avoided. Additionally, it is recommended that trucks use tires with good heat resistance. [ABSTRACT FROM AUTHOR]

• Discrete choice models with multiplicative stochasticity in choice environment variables that do not vary across alternatives. • Additive stochasticity on such variables is saddled with parameter identification problems. Multiplicative stochasticity overcomes identification problems. • Proposed model is employed to accommodate errors in drivers' perception of traffic environment variables in driver behaviour models, using vehicle trajectory data from a heterogeneous traffic stream in Chennai, India. • Stochasticity due to drivers' errors in perceiving traffic environment variables is found to be more prevalent than unobserved heterogeneity in drivers' response to those variables. This paper presents a mixed multinomial logit-based discrete choice modelling framework to accommodate decision-makers' errors in perceiving choice environment variables that do not vary across choice alternatives. An analysis is undertaken to evaluate two different ways of specifying errors in the choice environment variables in discrete choice models – (a) the additive specification and (b) the multiplicative specification. Between these two approaches, the multiplicative error specification is consistent with psychophysical theories of human perception of physical quantities in that the variability in perception tends to be greater for quantities of greater magnitude. Further, it is shown that models with an additive error specification run into parameter (un)identifiability problems if the analyst attempts to accommodate errors in several variables. In contrast, models with multiplicative errors in variables allow separate identification of stochasticity in as many variables as needed, as long as those variables have a significant influence on the choice outcome. The usefulness of the proposed framework with multiplicative errors is demonstrated through simulation experiments and an empirical application for analysing driver behaviour while considering drivers' errors in perceiving traffic environment variables. The empirical analysis is carried out using space-time trajectories of vehicles from a heterogeneous, disorderly (HD) traffic stream in Chennai, India. Results suggest that the proposed model, with power lognormal distributed multiplicative errors in traffic environment variables, outperformed the typically used mixed logit models with random coefficients (uncorrelated and correlated) or error components. Further, allowing for perception errors in traffic environment variables was found to be more important than allowing unobserved heterogeneity in the drivers' sensitivity to those variables. In addition, the empirical model offers interesting insights into the extent of variability due to perception errors in different traffic environment variables. [ABSTRACT FROM AUTHOR]

This study was carried out to examine the factors associated with motorcycle crash severity in Alabama, under different manner of crash and causal scenarios using mixed logit modeling. Three crash mechanisms were considered in this study: single-vehicle motorcycle crash with motorcyclist at fault, multi-vehicle collision between a motorcycle and another vehicle with motorcyclist being at fault, and motorcyclist not at fault in a collision between a motorcycle and another vehicle. The model estimation results showed that crashes that happened in rural areas were more likely to be severe, irrespective of the causal unit or manner of collision. The results also show that fatigue among motorcyclists was associated with severe injury, whereas driver fatigue was linked to no injury outcome. Further, it was found that risky behaviors such as speeding, driving/riding under the influence of alcohol or drugs, driving/riding with invalid license were significantly associated with severe injury outcome. Developing the injuryseverity models based on the segmented crash data has helped to reveal some similarities and differences in crash outcomes based on the crash mechanism and the at-fault road user. It is expected that these findings would provide a datadriven evidence to improve motorcycle safety in the state. [ABSTRACT FROM AUTHOR]

Eco-labels are crucial in helping consumers make sustainable food choices. However, previous literature has shown that eco-labels lack visibility and, frequently, are not easy for consumers to see. The main goal of the present study was to analyse the influence of aquaculture eco-labels' visual elements—size and saliency—on consumers' visual attention and choice. The study uses an eye-tracking methodology, together with a choice experiment and a semiotic analysis. A word association (WA) task was used to explore how each eco-label's graphic design influenced consumers' perceptions. Sixty-one consumers' eye movements were tracked while choosing smoked salmon and seabass products carrying different eco-labels. The results showed that size and saliency largely influence visual attention. The choice of aquaculture products was influenced only by the size of the eco-labels. According to the WA task, the shape, the symbols and the language in which the claim was written influenced consumers' preferences. The findings contribute to marketing and food research, suggesting which visual elements should be considered to increase consumers' interest in eco-labels. [ABSTRACT FROM AUTHOR]

Heterogeneity in agents' preferences is generally analysed through mixed logit models, which assume taste parameters are distributed in the population according to a certain mixing distribution. As a result, if the utility function is linear in attributes, the willingness to pay is the ratio of two random parameters and is itself random. This paper proposes a technique built on the Delta method, partly analytical and partly based on simulations, to obtain the sampling distribution of the willingness to pay, accounting for both heterogeneity and sampling error. The paper contributes to the literature by: (i) redressing some imprecisions in Bliemer and Rose (2013) that produce biased results; (ii) proposing a faster estimation process, compared to the Krinsky and Robb (1986, 1990) method that, relying on simulation only, proves computationally more demanding; (iii) comparing the performance of different methods using both synthetic and real data sets. The paper shows, via a Monte Carlo study, that the method we develop and the Krinsky and Robb one produce similar results, while outperforming that proposed by Bliemer and Rose. • Uncertainty in estimating Willingness To Pay under Mixed Logit Model arises from both heterogeneity and sampling error. • Delta method can be used, in combination with normal mixtures, to estimate both sources of uncertainty. • Both confidence and prediction intervals can be computed for Willingness To Pay by means of Delta method. • Delta method, being partly analytical, proves time-saving when compared to Krinsky and Robb method. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have a number of potential advantages, although some research indicates that this technology may increase dependence on private cars. An alternative approach to bringing such technology to market is through autonomous taxis (ATs) and buses, which can assist in making transportation more sustainable. This paper aims at examining the role of attitudinal, travel-related, and individual factors in preferences for a modal shift from conventional cars toward ATs and exclusive-lane autonomous buses (ELABs), exploring the existence of heterogeneity and its possible sources. The proposed mixed logit model with a decomposition of random coefficients uses 1251 valid responses from a stated preference survey distributed in Tehran, in 2019. Results show that there is significant taste variation among individuals with respect to ATs' travel costs, ELABs' travel times, and walking distances to ELAB stations. Furthermore, exploring the sources of heterogeneity indicates that women are more sensitive to ATs' travel costs and walking distances to ELAB stations while they are less sensitive to ELABs' travel times. Moreover, travel time in discretionary activities reduces the utility of ELABs more than it does in mandatory activities. Transportation authorities can use these findings to establish more effective policies for the successful implementation of AVs. [ABSTRACT FROM AUTHOR]

Crash rates from national and state sources conclusively show that vehicles of all types are prone to crashes on Wyoming downgrades. Crashes on steep downgrades are exacerbated by difficult terrain and an increase in the driving task required to safely navigate such landscape. An important step to evaluate safety on downgrades is to analyze the effects of driver action due to its influence on vehicle operation and crash outcomes. This analysis is critical due to the difference in vehicle dynamics on downgrades compared to level sections. However, most studies on driver action have been disparate and not focused on downgrades. This has led do a dearth of literature on the subject. This paper developed mixed (random parameter) logit models to evaluate factors impacting driver action on downgrades for single- and multiple-vehicle crashes. The approach accounts for unobserved heterogeneity potentially related to crash characteristics, driver factors, and road surface condition. The results were mostly consistent with previous studies, but some unexpected results were highlighted and explained in the light of published literature and engineering intuition. [ABSTRACT FROM AUTHOR]

We compare responses from an online survey among 700 customers of transportation network companies (TNC) in Boston and Philadelphia to investigate TNC's impact on vehicle ownership, trip making, and mode choice. We first use a qualitative comparative analysis to examine changes in respondents' travel behavior and vehicle ownership after adopting TNC. We then use a random parameter logit regression analysis to investigate customers' preferences between transit and TNC based on a choice experiment. We find that in both cities, TNC allows customers, including those who currently do not own a car, to either delay purchasing a car or forgo a car altogether. TNC enables customers across income levels to take trips that they otherwise would not have taken. Meanwhile, TNC substitutes for more than complementing transit. The random parameter logit analysis indicates that when choosing between TNC and transit, individuals in both cities consider waiting time and overall travel time for transit to be more burdensome than those for TNC. Bostonians perceive the time spent walking to and from transit to be less burdensome, and the time spent traveling in vehicle to be more burdensome than do Philadelphians. Differences in built environment, mode share within transit systems, and income likely contribute to respondents' different values of time between the two cities. Our paper is the first to compare individual trade-off between transit and TNC in two cities with different urban settings and transit services. The findings have implications on transit service planning, station area improvements, parking regulations, and traffic management. [ABSTRACT FROM AUTHOR]

Spatial perceptions mediate human–environment interaction, and understanding spatial perceptions of humans can play a key role in the planning of activities. This study aims to analyze spatial multivariate binary choice data representing if an individual perceives a spatial unit to belong to a certain category (e.g. , her neighborhood or set of potential activity places). To reasonably analyze such data, we present a spatial autoregressive mixed logit (SAR-MXL) model that accounts for both inter-individual heterogeneity and spatial dependence. We rely on the Bayesian approach for posterior inference of model parameters, where Pólya-Gamma data augmentation (PG-DA) is adopted to address the non-conjugacy of the logit kernel. The PG-DA technique eliminates the need for the Metropolis–Hastings step during the Markov Chain Monte Carlo process and allows for fast and efficient posterior inference. The high efficiency of the Bayesian SAR-MXL model is demonstrated through a numerical experiment. The proposed framework is applied to street-based neighborhood perception data, and we empirically analyzed the factors associated with the street perception probability of individuals. The result suggests a clear improvement of the model fit by incorporating spatial dependence and random parameters. • We study spatial multivariate binary choice data representing spatial perception. • We explicitly consider spatial dependence and individual taste heterogeneity. • We develop a hierarchical Bayesian spatial autoregressive mixed logit model. • We apply Pólya-Gamma data augmentation to address the non-conjugacy issue. • We analyze neighborhood perception and reveal strong spatial dependence based on street network topology. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) offer a viable technological solution for mitigating greenhouse gas emissions in the transportation industry, addressing pressing societal concerns regarding climate change, air pollution, and sustainable energy consumption. To effectively promote widespread adoption of EVs, it is crucial to understand consumer preferences and evaluate market dynamics. In Nepal, where proven fossil fuel reserves are absent, the government is actively working towards accelerating EV adoption, leveraging the nation’s significant hydroelectric power generation potential to fulfill EVs’ charging demands. To gain insight into consumer preferences and evaluate market dynamics regarding EVs in Nepal, this study employs a comprehensive approach. Stated preference data are collected through a meticulously designed survey, and sophisticated analytical techniques, namely, the mixed logit model and latent class model, are applied for estimation purposes. The results of this study show that potential EV consumers with small family sizes, lower monthly travel distances, heightened environmental awareness, and substantial knowledge about electric vehicles are more inclined to embrace EV technology. Notably, the study highlights that a reduction in the purchase price exerts the most significant influence on increasing consumers’ likelihood of adopting battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). Market simulation results suggest that a policy mix scenario, encompassing a combination of supportive measures, proves more effective in promoting EV adoption compared to relying on single policy initiatives. Furthermore, through latent class estimation, the study identifies three distinct classes of consumers within Nepal, each exhibiting significant variations in preferences. Recognizing and addressing these variations within policy frameworks is crucial for the successful promotion and widespread acceptance of EVs in Nepal.

The main competitor of air transportation is High-Speed Railway (HSR). However, in an oil-exporting country with low fuel prices and strong car dependence, HSR can face fierce competition with private cars and even buses. There is little previous research that forecast modal share in this situation. The case study of this research is the Tehran-Hamedan route in Iran that has high travel demand due to several historical and economic reasons and in the absence of air transportation, building the HSR in this route attracted foreign investment. To analyse the travel behaviour of passengers after the introduction of HSR, 409 stated and revealed preferences were collected in a self-designed questionnaire. Multinomial logit (MNL) model and mixed logit (ML) model were developed and modal share of each mode of transportation were forecasted up to 2045. HSR modal share is compared with other routes of the world to see the impact of air competition. The overall modal share of railway in this route is estimated to reach 64%, which is close to the average of major HSR routes globally (around 60%). Therefore, private cars can be a fierce competitor for HSR when there is no air link on the route and fuel is rather cheap.

Low-density areas like suburbs can be challenging to serve with fixed-route transit. Demand responsive transit has been used to service these areas with mixed results, but suburban travellers may respond to ridesourcing integrated with transit more positively because of the improved booking and dispatching interface offered by ridesourcing. This paper explores how suburban residents perceive transit-integrated ridesourcing, both for cost and time attributes of the system and in comparison to other travel modes in the area (cycling, auto, taxi or Uber, and transit). A Hierarchical Bayes mixed logit model was estimated using the results of a revealed-preference/stated-preference survey, revealing that transit-integrated ridesourcing had similar preferences to fixed-route transit. Across time and cost variables, respondents were more sensitive to walk time, transfer time, the number of transfers, parking cost, and taxi or Uber fare, and were minimally impacted by changes in the IVTT deviation of their trip times. The change in utility for the length of transit or transit-integrated ridesourcing in-vehicle travel time versus auto in-vehicle travel time was found to have a strongly linear relationship, while other attributes exhibited non-linearity to different degrees, particularly walk time, transfer time, transit-integrated ridesourcing fare, and taxi or Uber fare. Respondents who were female, younger, taking home-based school trips, or had lower household incomes were more open to transit-integrated ridesourcing and transit than other demographic groups. The likelihood of taking each of the five travel modes was assessed after COVID-19, finding generally minimal to negative impact for transit-integrated ridesourcing. Insights into vehicle and transit pass ownership, existing travel alternatives, and a prior transit-integrated ridesourcing pilot in the area are presented.

This study aims to analyze farmers’ willingness to contribute labor for the ongoing natural resource rehabilitation interventions in watershed development in East and West Belesa Districts, Amhara region. The aim of the study to assesses farmers’ intervention in natural resource rehabilitation activities and examines the mean willingness to pay family labor required for public watershed development activities from the two districts; a total of 501 households were selected by using a multistage sampling technique. The collected data were analyzed using a descriptive and econometrics model. In the econometric part, the mixed logit model is used to analyze the mean willingness to contribute to family labor. The descriptive result showed that farmers have to improve the watershed ecosystem service through soil and water conservation, area enclosure, forestation, and reforestation natural resource management intervention methods. The mean willingness to pay contribution results from the mixed logit, revealing that the sampled households are willing to contribute for livestock fodder availability 0.79 labors per month means Farmer’s Contribute 8 daily labors per months for watershed management activity for stabilizing spring water flow 0.80 labors per month, for reduced soil erosion 0.14 labors per month, and increase crop productivity 0.09 labors per month, respectively. The results important for policymakers and extension workers not to expect all farmers’ have homogeneous daily labor contribute to public watershed development activity.

We carried out a discrete choice experiment to test users' readiness to co-finance a ride-hailing service using their personal data. We designed an experiment in which respondents were asked to choose between a regular service and hypothetical app-based taxi rides offering discounts in exchange for respondents consenting to their personal data being shared. Our analysis compared how awareness of rights stemming from the GDPR affects respondents' privacy preferences. Cross-group analysis indicated that reminding users about their rights stemming from the GDPR significantly increases the value they ascribe to protection of personal data. The results of WTA analysis suggest that there is a market for 'pay with your data' business models. [ABSTRACT FROM AUTHOR]