Random Utility Theory Research Articles

A novel choice model combining utility maximization and the disjunctive decision rules, application to two case studies

Most choice models, e.g. Multinomial Logit (MNL), rely on random utility theory, which assumes that a compensatory utility maximization decision rule explains an individual’s choice behaviour. Research has shown, however, that behaviour is sometimes better explained by non-compensatory decision rules. While some research has used Latent Class Choice Models (LCCMs) to account for multiple decision rules, many of them – such as the disjunctive rule – have yet to be explored. This paper formulates, estimates, and evaluates a LCCM that combines the MNL with a Generalised Random Disjunctive Model (GRDM), a new choice model we develop. Addressing deficiencies of existing disjunctive choice models, the GRDM allows for relative importance between attributes and is insensitive to irrelevant attributes. Unlike most non-compensatory models, it is tractable and incorporates random error terms for capturing unobserved heterogeneity across choice situations. The GRDM can be expressed as a Universal Logit (UL) model, which helps derive welfare metrics such as Marginal Rates of Substitution and elasticities and makes it possible to estimate the model with traditional software packages. The LCCM combining the GRDM and the MNL is estimated in two large-scale case studies: cyclists’ route choice and public transport route choice. Results are compared with other relevant LCCM specifications and the individual choice models, where it is found that the MNL + GRDM LCCM provides the best fit to the data. We also interpret the fitted parameters and calculate the Marginal Rates of Substitution, which align with behavioural expectations.

The interdependence between hospital choice and waiting time — with a case study in urban China

Hospital choice models often employ random utility theory and include waiting time as a choice determinant. When applied to evaluate health system improvement interventions, these models disregard that hospital choice in turn is a determinant of waiting time. We present a novel, general model capturing the endogeneous relationship between waiting time and hospital choice, including the choice to opt out, and characterize the unique equilibrium solution of the resulting convex problem. We apply the general model in a case study on the urban Chinese health system, specifying that patient choice follows a multinomial logit (MNL) model and waiting times are determined by M/M/1 queues. The results reveal that analyses which solely rely on MNL models overestimate the effectiveness of present policy interventions and that this effectiveness is limited. We explore alternative, more effective, improvement interventions.

Evaluating Willingness to Sell Vegetation in the White Volta Basin in Northern Ghana

Concepts of ecosystem services have been developed to make explicit connections between human welfare and ecological sustainability for policy, development, and conservation initiatives. Economic concepts such as the distinction between prices and values, and the acknowledgment of their values are context-specific which may change across space and time. Contingent valuation is a survey-based economic technique for valuing non-market resources, such as vegetation. This method is often used to establish the amount people are willing to be compensated for maintaining the existence of an environmental feature such as a tree, shrub, or grass. The level of importance attached to provisioning services as well as cultural services and cultural heritage differ in the rural communities hence different cash values attached. It is often perceived that rural community members do not put monetary value on vegetation, the study is therefore aimed at establishing monetary value rural communities have value for vegetation. The study was conducted in two irrigated and two unirrigated landscapes consisting of about 54 communities and comprising 240 respondents. Participatory Rural Appraisal tools were used. Random Utility Theory was applied and used for the analysis. The willingness to sell vegetation was significant at a 5% confidence level concerning native, sex, age, education, and household head. Marital status was, however, not significant in all the landscapes. The price trend is observed to be across a landscape, from the catchment to the downstream ecosystem.

Strategic Management Practices on Poverty Alleviation Among Farmers in Kakamega County, Kenya

Improving the standard of living in underdeveloped regions is a pressing issue in the modern era of globalization. More than 80% of Kenyans get their living from farming or other non- urban sources in rural areas. More than half of Kenya's population is impoverished, meaning they cannot afford to meet their most basic demands. There have been various strategic management practices that have been employed to alleviate poverty in Mumias East Sub County, Kakamega County. As a result, this study examined the effects of strategic management methods on the reduction of poverty among small-scale farmers in Mumias East Sub County, Kenya. The study's findings on how to reduce poverty was useful to policymakers at both the county and federal levels. The study was based on, resource mobilization theory, knowledge-based theory, technology acceptance model 3, and random utility theory. This study, which aimed to adopt a descriptive study design, recruited 105 participants from among the 221 upper and middle managers in Mumias East Sub County, Kakamega County Government. In order to combine primary and secondary sources, surveys were deployed. The validity and reliability of the research tools are established by pilot testing. The questionnaire's construct and content validity were ensured through careful design and testing to ensure that it accurately captures the intended data. Using a Cronbach alpha cutoff of 0.7 was ensured reliability. Tables and graphs were used to display the data. In order to comprehend the structures and dynamics of the data, descriptive and inferential statistics were calculated using the Statistical Package for the Social Sciences. Labeling different groups and themes were used in a thematic analysis of qualitative data. The inferential statistics included Pearson's correlation, ANOVA, and multiple regression analysis. The goal of the research was to identify the connections between poverty relief efforts and resource mobilization, technological innovation, and strategic education. The activity was conducted in an ethical manner. The modern technology variable was found to have a positive significant influence on poverty alleviation effort amongst small-scale farmers in Mumias East Sub County, Kakamega County. The study concludes that mobilizing resources for poverty alleviation can lead to increased access to basic necessities such as food, clean water, and healthcare. This can improve the overall well-being and quality of life for individuals living in poverty. The study recommends that the County should invest in education and skills development, individuals can gain the necessary knowledge and tools to secure better job opportunities and increase their earning potential.

Effect of Socio-Economic and Institutional Factors on Different Choices of Food Production System Practiced in Home Gardens: A Case Study in Mahakanumulla Village Tank Cascade System

The global existence of small-scale food production at the household level has a wide range in terms of physical appearance and utilization. Household, market, and field gardens can be identified as three types of home food producti on systems in a village tank cascade system (VTCS) in the dry zone of Sri Lanka. The variation in food producti on systems adapted in VTCS serves differently for the community either by providing nutrient requirements or ensuri ng financial or food security. It is important to understand the determinants of this variation to support the comm unity to adapt the most suitable food production system for their well-being. Based on random utility theory, this study analyzes the socio-economic and institutional factors affecting the choice of food production system practiced in home gardens in Mahakanumulla VTCS. A revealed preference approach was employed to identify the current home garden choice of households. The decision to have a ho usehold garden is more driven by socio-economic and institutional factors rather than market gardens and field gard ens. The effect of these factors on the decision to have a field garden is comparatively less. Using these results policymakers can predict farm management decisions of households and plan evidence-based strategic governm ent intervention to meet the daily nutrient requirement, ensure food security, and mitigate food scarcity.

Modeling the employment decisions of young men and women in nine European countries: An application of random utility theory and revealed preference

In this paper we examine the decisions of over 15,000 young adults aged 18–35 from nine European countries to choose employment over unemployment or remaining in education. Instead of focusing only on who makes those decisions as is typically done, we attempt to answer the question of why a particular decision is made. Using data from the Cultural Pathways to Economic Self-sufficiency and Entrepreneurship (CUPESSE) project, we estimate a series of country and gender-specific conditional logit (CLGT) models adjusted for individual work values, soft skills, demographic characteristics and local labor markets. We find that young women in Czechia, Germany, Greece, Hungary and Italy and both men and women in Spain and the United Kingdom, respond to exogenous wage offers providing some evidence for job search theory and for the reservation wage hypothesis. Our uptake rate simulations show that a 10% increase in wages have the potential to increase employment by as little as 2 percentage points in the UK and by as much as 14 percentage points in Czechia and Spain. Further, if unemployment benefits are also simultaneously reduced by 10% employment increases in these latter countries by 20 and 17 percentage points. We demonstrate the ways that revealed preference (RP) analysis can aid policy makers through strength of preference, uptake rate and willingness-to-pay applications.

A Different Nested Logit Model Structure Consistent with Random Utility Theory for Various Freight Types – A Case Study for Istanbul

Freight transport significantly contributes to urban traffic but is often overlooked by decision-makers compared to passenger transport. Conventional transportation modelling studies often use aggregate approaches for freight transport, undoubtedly due to the difficulty of data collection. However, the nature of freight transportation is much more complex. For this reason, examining the determinants of freight vehicle preferences with discrete approaches is crucial for the contributions that can be put forward, especially in local studies. To address this apparent gap in the study of local disaggregated approaches to freight transport, we present a discrete modelling-based methodology to investigate the factors that determine freight vehicle preferences for shippers and senders. The estimated nested logit model is constructed with the RU2 approach, the second part of random utility theory, thus avoiding the theoretical inconsistencies that arise when generic coefficients are used. As a result, the model structures provided satisfactory results compared to the literature. It was revealed that the factors affecting freight vehicle choice preferences were influenced by packaging preferences and differed according to freight groups. This local study is the first nested logit study for freight modelling in Istanbul and it is aimed to shed light on future national studies.

The digital payment-financial inclusion nexus and payment system innovation within the global open economy during the COVID-19 pandemic

This paper retrospectively examines how the digital payment-financial inclusion nexus fostered demand-driven payment system innovation and diffusion within the global open economy during the COVID-19 crisis. To that end, the paper uses a cross-sectional sample of 127854 respondents from across 123 economies, extracted from the 2021 wave of the Global Findex Survey. Together with a hybrid theoretical framework that bridges the quadruple helix (QH) innovation model with random utility theory (RUT), the study characterizes individuals’ stated preferences for cashless payment across the three consumption domains of in-store shopping, online shopping, and regular bills settlement. The parameters of the resulting econometric model are estimated within the R software version 3.6.3. The findings from all nine considered demand side financial inclusion indicators reveal that globally, during the crisis, formal financial inclusion had a significantly positive impact on the marginal utility, and thus likelihood that individuals use digital payment solutions (DPS) for in-store and online shopping experiences, as well as regular bills settlement. Additionally, DPS uptake for online shopping highlighted a significant gender-based gradient in favor of males. Moreover, higher education levels, labor market participation, rising income levels, reduced COVID-19 related financial worries, mobile phone ownership, and internet access, are all found to have positively contributed to individuals’ DPS uptake likelihood during the crisis. Furthermore, a cross-country heterogeneity in individuals’ cashless payment preferences remained persistent across the three consumption domains due to macroeconomic, regulatory, cultural, and digital infrastructural readiness differences between countries. Overall, the findings provide support for evidence-based decision making by all digital payment ecosystem stakeholders.

The determinants of winery visitors for local wine and non-wine products in the Northern Appalachian states

The determinants of winery visitors for local wine and non-wine products in the Northern Appalachian states

Why do fans go to football games? A discrete choice analysis of ticket buyers' preferences

PurposeTicket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.Design/methodology/approachUsing a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.FindingsThe results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.Practical implicationsThe authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.Originality/valueTo the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

Choice modeling of grocery shopping behavior: A Sri Lankan case study

Different retail forms play an integral part in the urban landscape in Sri Lankan cities shaping its spatial form. In planning new and different grocery retail forms, the main considerations are usually location, distance to population catchments and income levels. But distance-based descriptive methods are incapable of predicting the actual shopping behavior of grocery shoppers due to unrealistic assumptions that, for instance, shoppers will select the closest facility. Therefore, this study for the first time attempts to apply a behavioral model to determine the key drivers behind the store choice decision of grocery shoppers in a Sri Lankan city. A conceptual framework of individual choice behavior is adopted based on random utility theory and involves the application of a choice experiment. The analysis is based on a sample of 350 respondents representing households within the Piliyandala town area - a suburb of Colombo, Sri Lanka. The major findings are that, rather than distance being a primary influence on where people shop, attributes of retail destination such as quality of the products, price of the products and variety of goods available are more significant when maximizing utility. Thus, even though, supermarkets are typically perceived as the grocery retail form that maximizes utility, traditional grocery stores still continue to be a still popular icon of the retail landscape. This study, therefore, provides a study of how traditional grocery stores and modern supermarkets co-exist in suburbs of a major city in a developing country such as Sri Lanka. Further it broadens the understanding of grocery shopping behavior thereby facilitating the planning and design involved in commercial land uses related to grocery shopping. This is important given planners have been shown to be slow and conventional in responding to rapid changes in suburban form and function. Our findings can assist urban policy makers to reevaluate the retail policy framework in suburbs of Sri Lankan cities and therefore contribute to the facilitation of greater social and economic vibrancy in traditional economies.

Leveraging genetic data for predicting consumer choices of alcoholic products

PurposeFood choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.Design/methodology/approachA discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.FindingsThe authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).Originality/valueThis study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models

In transport demand analysis, the calibration of a model means estimation of its (endogenous) parameters from observed data with an inference statistical estimator. Indeed, these considerations apply to any choice behaviour model, such as those derived from Random Utility Theory or any other choice modelling theory. Calibration of choice models can be carried out from disaggregate vs. aggregate data, while inference statistical estimators can be specified through Bayesian vs. Classic (or Frequentist) approaches. In this paper, the resulting Bayesian or Classic disaggregate or aggregate calibration methods are discussed, analysed in detail, and compared from the mathematical point of view. These methods are applied to calibrate Logit choice models for describing path choice behaviour at national scale on a small sample. The Logit choice model can be derived from Random Utility Theory (or be considered an instance of the Bradley–Terry model). Path choice set definition is also discussed, and specialised indicators are used for result comparison. The main contributions of this study concern the use of two different estimation approaches, Bayesian vs. Classic, adopting and introducing some indicators of goodness of estimation. The results of this work, relating to the sample of users adopted, show that the Bayesian approach provides a better estimate than the Classic approach because the calibrated parameters are more stable, the specific constants of the alternatives decrease, and the resulting models show better values of clearly right indicator.

Perceived antimicrobial dispensing practices in medicine outlets in Ghana: A maximum difference experiment design.

Antimicrobials are consumed among patients globally, but in developing and middle-income countries, these drugs can be obtained without a prescription from pharmacies and licensed drug stores due to inadequate regulation in the pharmaceutical sector. This study aimed to assess antimicrobial dispensing practices in medicine sales outlets (i.e., pharmacies and licensed drug stores) to provide quantitative evidence for policy discussions to enhance patient safety and care quality in Ghana's pharmaceutical industry. The data for this study were obtained from a cross-sectional survey conducted in the Greater Accra region between July and August 2022. The survey was conducted through interviewer-administered questionnaires, and 200 staff members from medicine sales outlets were randomly selected using a two-stage cluster and random sampling technique. The maximum difference experiment model, rooted in random utility theory, was used to analyze their antimicrobial dispensing practices. We found that medicine sales outlet staffs were highly concerned about following the drug act and not dispensing antimicrobials without a prescription, and usually refer a patient to get a prescription from a doctor if the patient has complications (like high fever, generalized malaise, fatigue as symptoms, sinusitis). Stronger concerns were also observed for medicine outlet staff not dispensing antimicrobials without a prescription if the patient is pediatric or geriatric with a severe infection. They also evaluated patients and dispensed antimicrobials based on symptoms, not their age or gender. However, they tended not to dispense antibiotics if the patient had a mild fever and requested it without a prescription. Our results provide insight into the need for a national surveillance system for monitoring antimicrobial prescribing and dispensing practices at medicine sales outlets. Therefore, the selection of antimicrobials for treating infectious diseases may be informed by evidence-based antimicrobial prescribing and dispensing surveillance data and will help policymakers to know the pattern of commonly consumed antimicrobials in the medicine sales outlets.

Taking the Shortcut: Simplifying Heuristics in Discrete Choice Experiments.

Health-related discrete choice experiments (DCEs) information can be used to inform decision-making on the development, authorisation, reimbursement and marketing of drugs and devices as well as treatments in clinical practice. Discrete choice experiment is a stated preference method based on random utility theory (RUT), which imposes strong assumptions on respondent choice behaviour. However, respondents may use choice processes that do not adhere to the normative rationality assumptions implied by RUT, applying simplifying decision rules that are more selective in the amount and type of processed information (i.e., simplifying heuristics). An overview of commonly detected simplifying heuristics in health-related DCEs is lacking, making it unclear how to identify and deal with these heuristics; more specifically, how researchers might alter DCE design and modelling strategies to accommodate for the effects of heuristics. Therefore, the aim of this paper is three-fold: (1) provide an overview of common simplifying heuristics in health-related DCEs, (2) describe how choice task design and context as well as target population selection might impact the use of heuristics, (3) outline DCE design strategies that recognise the use of simplifying heuristics and develop modelling strategies to demonstrate the detection and impact of simplifying heuristics in DCE study outcomes.

Configuring the agricultural platforms: farmers' preferences for design attributes

PurposeAlthough growing Internet penetration in the hinterlands has attracted agribusinesses to promote digital platforms, farmers are sceptical about using them. The literature discusses agricultural platforms from the theoretical perspective of technological determinism, where the platforms are developed and promoted by firms in a top-down manner to be accepted by farmers. However, this approach results in poorly configured platforms with limited utility for farmers. It is evident from the existing literature that the mere creation of a platform business is not sufficient to guarantee adoption by users. Hence, this study explores how to make the agricultural platform more attractive for farmers.Design/methodology/approachThe present study is based on a discrete choice experiment performed on 126 Indian farmers using agricultural platforms. The data were analysed using the conditional logistic regression method.FindingsThe study suggests that farmers expect government and cooperative entities to be also embedded with the platforms. Complementary features such as prompt service, competitive pricing and farm credit were identified as essential attributes. Further, the platforms should enable smallholders to trade farm produce by providing a mechanism for real-time online nudging and bargaining with buyers.Research limitations/implicationsThe study is based on the applications of random utility theory. The research has utility for Agtech managers, cooperative institutions and agricultural policymakers.Originality/valueThis is one of the first studies focussing on agricultural platform design from the farmers' perspective. The study implies that incorporating preferred attributes can help practitioners configure platforms to benefit farmers with prospects concerning farm management decisions.

The Double Lanes Cell Transmission Model of Mixed Traffic Flow in Urban Intelligent Network

The connected and autonomous vehicle (CAV) is promised to ease congestion in the future with the rapid development of related technologies in recent years. To explore the characteristics of mixed-traffic flow and the dynamic transmission mechanism, this paper firstly detailed the car-following model of different vehicle types, establishing the fundamental diagram of the mixed-traffic flow through considering the different penetration rates and fleet size of CAV. Secondly, this paper constructed the lane-changing judgment mechanism based on the random utility theory. Finally, the paper proposed a lane-level dynamic cell transmission process, combined with a lane-changing strategy and cell transmission model. The effectiveness and feasibility of the model are verified using simulation analysis. This model makes a systematic, theoretical analysis from the perspective of the internal operation mechanism of traffic flow, and the lane-level traffic strategy provides a theoretical basis for balancing urban lane distribution and intelligent traffic management and control.

Residents' Choice of Transportation Mode Based on Multiple Logit Model

In recent years, with the promotion of urbanization, China's urban structure has become larger and larger, and most cities have only one urban center. Commercial and residential areas are mainly concentrated in the city center, with high population density, which leads to great traffic pressure. Therefore, the factors affecting the residents' travel mode can be analyzed. The multiple logit model based on the random utility theory is used to analyze the travel mode of residents, and then the suggestions to improve the urban transportation facilities and land use mode are put forward, which is helpful to solve some urban congestion problems and reduce the urban traffic pressure. This paper takes Hangzhou, which is located in Zhejiang Province in East part of China as an example to analyze the travel mode of residents. Based on the investigation of local residents' choice of travel mode, three general travel modes, namely private cars, public transportation and non-motor vehicles, were selected as logarithmic model parameters for coefficient calibration, and the influence of gender, age, travel purpose, income and other factors on the choice of travel mode were analyzed. The study found that men are more likely to use private cars. The higher the monthly income, the higher the probability of private car travel. If the public transportation is more convenient in the place of departure and destination, the probability of choosing public transportation is also higher.

Consumer willingness to pay for production attributes of cotton apparel

AbstractWe investigate US consumers' willingness to pay for cotton apparel production and country of origin attributes. Using a choice‐based conjoint experiment and information treatments, we examine the preferences of 727 US shoppers for the attributes: cotton fiber production systems and country of manufacture of the cotton fiber. Random utility theory is the basis for the survey's responses analysis to estimate willingness to pay (WTP) values for the attributes. Choices made by consumers are modeled using a mixed logit model in WTP space estimated using simulated maximum likelihood procedures. Results show that consumers are willing to pay more for cotton apparel from the United States than apparel from other countries, and more for apparel made from fiber produced in organic systems than in conventional systems. Only some subgroups of consumers were found to be affected by exposure to an information treatment regarding potential labor exploitation in cotton farms and textile mills. [EconLit Citations: D12].

Modeling of intra-city transport choice behaviour in Budapest, Hungary

The discrete choice modeling and decisions-tree technique are used to understand the travel behavior of people in Budapest. The discrete choice modeling is applied to develop transport mode choice that mimics the travel behavior of people using their personal and travel characteristics. A revealed preference (RP) survey was conducted by the Hungarian Census Bureau which contains information about the households in Budapest in 2014, is used. Understanding the daily main trips of people is firstly analyzed using decisions-tree technique, where the impact of each variable is presented based on its importance in affecting the travel choice mode. In random utility theory, travelers choose one option out of certain available options to them to maximize their utility. The Multinomial Logit (MNL) model is used to examine the relationship between various variables connected to travelers in order to understand the travel behavior pattern. The result of the analysis shows a clear pattern between car ownership and each of family size, and age, and trip cost variables. The result of decision tree analysis demonstrates that travelers’ trip duration is the most important factor that has impact on their transport mode choice, which is mainly distributed within private cars, public transportation, and walking. The developed multinomial transport mode choice model includes sociodemographic, economic, and travel characteristics. The trip time, trip cost, age, car ownership index, trip purpose, gender, employment, and income are the main determinants the impact the transport choice mode. The developed models (i.e., decision tree and the multinomial transport logit choice) are beneficial for the decision-makers who can used it in predicting the travel demand.