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This paper presents the results of a demand forecasting study about the introduction of Urban Air Mobility (UAM) services in the metropolitan area of Milan (Italy). Demand forecasting is based on random utility mode choice models, which include factors related to the individuals' perception of vertical take-off/landing and low altitude flying over urbanized areas. The use cases of UAM services include airport shuttles, intercity air connections, and "air-taxis", i.e., UAM services for short-trips within the metropolitan area. Several scenarios have been considered based on the number of access points ("vertiports") and UAM fare levels. The results indicate that airport shuttles have a modal share of trips to/from airports (for both business and leisure) in a range of 2-5%. They resulted to be more attractive than air-taxis, which have a modal share in a range of 1-3%. Furthermore, the probability of choosing UAM services for intercity travels decreases with the distance and the time required for access/egress to/from the vertiports, whose catchment areas are dependent on the level of service provided by competing modes (notably the presence of good railway and highway connections). [ABSTRACT FROM AUTHOR]

Abstract Aims This study evaluates both financial and non-financial preferences of nursing students to choose a hospital for work in future. Background In Iran’s healthcare system, the persistent shortage and uneven distribution of nurses have been significant challenges. Addressing such issues requires attention to nurses’ preferences, which can be instrumental in designing effective interventions. Methods In this cross-sectional study, 500 nursing students from Kermanshah University of Medical Sciences (KUMS) were surveyed using a convenience sampling method to determine their preferences for selecting the type of hospital in which they wish to work in the future. The study was conducted during the second half of 2023 using a discrete choice experiment (DCE) approach. To identify the most influential components on future nurses’ preferences, conditional logistic regression analysis was employed. Results The study revealed that an ideal and preferred hospital for nursing students is one that offers a high monthly salary, is located close to their homes, provides Rasmi contracts, has adequate facilities and equipment, has a low workload, and requires less than three years for career advancement. Additionally, the results indicated that the highest willingness to pay (WTP) was associated with the type of employment in the hospital, with nursing students willing to reduce their monthly salary by 27,303,610 IRR to change their employment status from Gharardadi to Rasmi. Conclusions The study confirmed that both financial and non-financial factors significantly influence nursing students’ choices regarding their future workplace. This research can serve as a valuable reference for future studies in this area. The findings can significantly aid in designing and implementing effective interventions aimed at retaining nurses in hospitals and attracting and keeping nursing staff in underserved areas.

Following an earthquake, abnormal travel demand causes traffic congestion and poses significant problems for relief efforts. Research on post-earthquake travel demand is essential for disaster management. An effective disaster management strategy ensures achieving sustainable development goals. This study focused on this critical period and analyzed post-earthquake trip decisions. The city of Elazığ, a region not at risk of tsunami, was used as a case study. A 6.8 magnitude earthquake hit Elazığ in January 2020. After the earthquake, data from 2739 individuals were collected by a household survey conducted face-to-face. The data were segregated into two categories, depending on the earthquake's intensity. The study used a binary logit model to examine the potential factors of trip decisions after an earthquake. The results showed that 75% of participants made at least one trip within 24 h after the earthquake. It was observed that household, building-and disaster-related attributes influence earthquake survivors' trip decisions. The initial location at the time of the earthquake was the most significant factor affecting trip decisions. It was also found that individuals who experienced the earthquake outside their homes in both datasets were more likely to make a trip. Additionally, the dataset with higher earthquake intensity had more significant variables affecting the trip decision. [ABSTRACT FROM AUTHOR]

In risk-based auditing, data-driven analyses are often used to automatically detect process deficiencies. This introduces a challenge: the number of deficiencies is too large to inspect manually. Current approaches addressing this challenge neglect integrating the risk dimension or rely on auditors to manually integrate it. This study aims to increase the effectiveness of such data-driven analysis approaches by including the risk dimension when presenting process deficiencies for further inspection. We investigate how the deficiency type and the affected control activity are associated with perceived risk. We run a discrete choice experiment with 58 auditors interpreting deficiencies that occur in a procure-to-pay or an order-to-cash process and find that (1) deficiencies of type "missing" or deficiencies related to asset-decrementing activities are perceived as the riskiest, (2) the control activity contributes 75 percent of the risk perception, and (3) external and internal auditors share a similar risk perception. [ABSTRACT FROM AUTHOR]

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]

Road freight transport has the negative externalities of traffic congestion, traffic accidents, energy consumption, and environmental pollution. Therefore, understanding the factors of road freight transport mode choice is important for urban transport policy analysis. This study aims to analyze the influence of behavior patterns on mode choices in the logistics sector in Bali. Data have been collected by interviewing road freight transport operators by using questionnaires. Models have been constructed by using discrete choice analysis. The study results show that vehicle ownership, type of goods transported, and fuel costs significantly influence all freight mode choices. The estimated elasticity of choice analysis indicates that all modes are not sensitive to changes in all of that vehicle ownership, type of goods transported, and fuel costs. These may indicate the outcome of the expected success of policy measures. This study suggests that a time limit for the movement of goods should be proposed in order to ensure that freight vehicles only run at certain times, i.e. at night time or not during peak hours. In order to support this policy, further research on Origin-to-Destination (OD) analysis of freight transport is needed to analyze the pattern of road freight traffic over a given period and its impact on freight and fuel costs. [ABSTRACT FROM AUTHOR]

Startups have become a buzzword in the last couple of years, and entrepreneurship became career path for a number of people in the world. With all the advances in education and government subsidizing all over the world, still, statistics shows only a small percentage of successful startups. Studies have shown that the one the leading reasons for startup failure is the misreading of market's needs. The aim of this paper is to determine the approach to market research, knowledge of tools and methods, and preferences towards online market research tools of entrepreneurs by using Discrete Choice Analysis. The research gathered 187 valid responses from a panel of participants working on developing new products and business, using an online survey tool. In the paper it is shown that the most important attributes for entrepreneurs are the price of the market research tool, followed by the level of details in the report generated, with more indepth analysis regarding segmentation, simulations, and Marginal Willingness to Pay in the further chapters. The results of the research imply the need for a market research business model optimized for those starting a new business, focused primarily on detailed reporting and analysis, with the pricing model adjusted to the lack of resources entrepreneurs face at the start of their ventures, which would help them better understand the market-fit at the beginning and raise the statistic of successful startups. [ABSTRACT FROM AUTHOR]

Objective: The Italian healthcare jurisdiction promotes patient mobility, which is a major determinant of practice variation, thus being related to the equity of access to health services. We aimed to explore how travel times, waiting times, and other efficiency- and quality-related hospital attributes influenced the hospital choice of women needing pelvic organ prolapse (POP) surgery in Tuscany, Italy. Methods: We obtained the study population from Hospital Discharge Records. We duplicated individual observations (n = 2533) for the number of Tuscan hospitals that provided more than 30 POP interventions from 2017 to 2019 (n = 22) and merged them with the hospitals' list. We generated the dichotomous variable "hospital choice" assuming the value one when hospitals where patients underwent surgery coincided with one of the 22 hospitals. We performed mixed logit models to explore between-hospital patient choice, gradually adding the women's features as interactions. Results: Patient choice was influenced by travel more than waiting times. A general preference for hospitals delivering higher volumes of interventions emerged. Interaction analyses showed that poorly educated women were less likely to choose distant hospitals and hospitals providing greater volumes of interventions compared to their counterpart. Women with multiple comorbidities more frequently chose hospitals with shorter average length of stay. Conclusion: Travel times were the main determinants of hospital choice. Other quality- and efficiency-related hospital attributes influenced hospital choice as well. However, the effect depended on the socioeconomic and clinical background of women. Managers and policymakers should consider these findings to understand how women behave in choosing providers and thus mitigate equity gaps. [ABSTRACT FROM AUTHOR]

Through the application of discrete choice and machine learning models, the primary objective of this study is to assess the impact of NGA networks coverage on reducing the risk of contagion during the first wave of COVID-19 across 278 municipalities in mainland Portugal, while controlling for other domains under the tutelage of the local public administration. Benchmark estimations reveal that, while holding everything else constant, each spatial unit is 2.4 p.p. more likely to become a high-risk municipality with additional 10 000 cabled houses with NGA networks. In a multinomial discrete choice model setting, the technical novelty of this study lies in providing graphical visualization and economic interpretation of coefficients and average marginal effects as a function of the number of classes used to define the dependent variable, while ensuring the satisfaction of the IIA assumption. The positive and significant coefficients of NGA indicate that additional coverage of NGA networks increases the likelihood of municipalities becoming high-risk for increasing number of spatial units not belonging to the low-risk efficiency frontier. The significant, negative, and decreasing average marginal effects of NGA suggest that the ability to remain a low-risk municipality diminishes with increasing NGA networks coverage as the number of municipalities belonging to the low-risk efficiency frontier decreases. The analysis also confirms that the effect of NGA networks coverage on COVID-19 is statistically significant in the indirect channel. This impact persists due to the mediation of population density, which is directly influenced by NGA networks coverage. All these findings can be explained by the fact that benefits related to productive activities do not outweigh costs associated with leisure time. Hence, this research emphasizes the need for a normative discussion on the intended purpose of digital technologies built on top of NGA networks to ensure a level playing field in the post-pandemic era. [ABSTRACT FROM AUTHOR]

• Post COVID-19 Australian workers are willing to forego wages for remote work. • The average Australian worker is willing to forego 4–8% of annual wages for remote work flexibility. • 20% of workers willing to forego 16–33% of annual wages for remote work flexibility. • Concerns about impacts on human relations predict willingness to forego wages. • Firms must support remote work practices that mimic traditional onsite arrangements. We surveyed 1,113 Australian employees with jobs that have some capability of being done remotely at least some of the time. Survey respondents were presented stated preference experiments where they were offered a choice between alternative working arrangements for their present job that varied in terms of ability to work remotely and wage rates. This data was used to develop and estimate a discrete choice model of worker preferences for remote working. We found that the average worker in our sample would be willing to forego roughly 4 - 8 per cent of their annual wages to have the ability to work remotely some workdays and/or workhours. However, we found considerable heterogeneity across our sample. Roughly 55 per cent of the sample were not willing to forego wages to have the ability to work remotely, while roughly 20 per cent were willing to forego 16 - 33 per cent in annual wages for the same. We found that attitudes towards the impacts of remote working on human relations were a significant predictor of these differences. Workers who did not value the ability to work remotely were more concerned about negative impacts on their relationships with their colleagues, supervisors, and the firm as a whole, as well as opportunities for learning and career advancement. [ABSTRACT FROM AUTHOR]

Regret is often experienced for difficult, important, and accountable choices. Consequently, we hypothesize that random regret minimization (RRM) may better describe evacuation behavior than traditional random utility maximization (RUM). However, in many travel related contexts, such as evacuation departure timing, specifying choice sets can be challenging due to unknown attribute levels and near-endless alternatives, for example. This has implications especially for estimating RRM models, which calculates attribute-level regret via pairwise comparison of attributes across all alternatives in the set. While stated preference (SP) surveys solve such choice set problems, revealed preference (RP) surveys collect actual behavior and incorporate situational and personal constraints, which impact rare choice contexts (e.g., evacuations). Consequently, we designed an RP survey for RRM (and RUM) in an evacuation context, which we distributed from March to July 2018 to individuals impacted by the 2017 December Southern California Wildfires (n=226). While we hypothesized that RRM would outperform RUM for evacuation choices, this hypothesis was not supported by our data. We explain how this is partly the result of insufficient attribute-level variation across alternatives, which leads to difficulties in distinguishing non-linear regret from linear utility. We found weak regret aversion for some attributes, and we identified weak class-specific regret for route and mode choice through a mixed-decision rule latent class choice model, suggesting that RRM for evacuations may yet prove fruitful. We derive methodological implications beyond the present context toward other RP studies involving challenging choice sets and/or limited attribute variability.

To accommodate the diverse users demands for consumer products, enterprises need to design and develop different lines of products according to different groups of users. Dynamic internet data, including product reviews, user attributes, and product configurations, are utilised to model users' stochastic product choice behaviours and mine the product design requirements of features, performance levels, and quantity. First, the web crawler is applied to collect internet data, and then the data are structured and the demand information is retrieved. Second, a product choice model is employed to capture the heterogeneity and correlation of user demands on product features. In particular, users' implicit requirements in terms of product function and performance are elicited from the text mining of product reviews. Third, incorporating various user requirements mined from dynamic internet data, graph theory analysis is introduced into design generation, product improvement, and market analysis. A case study on Chinese smartphones is presented, where the results show that the proposed method is practical and suitable for product-design analysis using the large volume of dynamic internet data. [ABSTRACT FROM AUTHOR]

We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation $$\begin{align*} p_{t}\left(a,A\right) =\dfrac{e^{\frac{u\left(a\right) }{\lambda\left(t\right) }+\alpha\left(a\right) }}{\sum_{b\in A}e^{\frac{u\left(b\right) }{\lambda\left(t\right) }+\alpha\left(b\right) }}, \end{align*}$$ where |$p_{t}\left(a,A\right)$| is the probability that alternative |$a$| is selected from the set |$A$| of feasible alternatives if |$t$| is the time available to decide, |$\lambda$| is a time-dependent noise parameter measuring the unit cost of information, |$u$| is a time-independent utility function, and |$\alpha$| is an alternative-specific bias that determines the initial choice probabilities (reflecting prior information and memory anchoring). Our axiomatic analysis provides a behavioural foundation of softmax (also known as Multinomial Logit Model when |$\alpha$| is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behaviour. Jointly, the two approaches provide a thorough understanding of softmaximization in terms of internal causes (neuro-physiological mechanisms) and external effects (testable implications). [ABSTRACT FROM AUTHOR]

Different voters behave differently at the polls, different students make different university choices, or different countries choose different health care systems. Many research questions important to social scientists concern choice behavior, which involves dealing with nominal dependent variables. Drawing on the principle of maximum random utility, we propose applying a flexible and general heterogeneous multinomial logit model to study differences in choice behavior. The model systematically accounts for heterogeneity that classical models do not capture, indicates the strength of heterogeneity, and permits examining which explanatory variables cause heterogeneity. As the proposed approach allows incorporating theoretical expectations about heterogeneity into the analysis of nominal dependent variables, it can be applied to a wide range of research problems. Our empirical example uses individual-level survey data to demonstrate the benefits of the model in studying heterogeneity in electoral decisions. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) are reshaping mobility through autonomous vehicles (AVs), which may introduce risks such as technical malfunctions, cybersecurity threats, and ethical dilemmas in decision-making. Despite these complexities, the influence of consumers' risk preferences on AV acceptance remains poorly understood. This study explores how individuals' risk preferences affect their choices among private AVs (PAVs), shared AVs (SAVs), and private conventional vehicles (PCVs). Employing a lottery experiment and a self-reported survey, we first derive four parameters to capture individuals' risk preferences. Based on a stated preference experiment and the error component logit model, we analyze reference-dependent preferences for key attributes of PAVs and SAVs, using PCVs as the reference. Our analysis reveals that risk-tolerant consumers are more inclined toward PAVs or SAVs. Further, consumers exhibit a greater sensitivity to losses, such as higher purchasing prices and running costs, than to gains, such as reduced egress time. Specifically, for buying a PAV, consumers are willing to pay 3582 CNY more for 1000 CNY saving on annual running cost, 3470 CNY for a 1-min reduction in egress time, 28,880 CNY for removing driver liability for crashes, and 30,710 CNY for the improved privacy data security. For adopting SAVs, consumers are willing to pay 0.096 CNY extra per kilometer for a 1-min reduction in access time and 0.033 CNY extra per kilometer for a 1% increase in SAV availability. Therefore, this study enhances the understanding on risk preferences in AV acceptance and offers important implications for stakeholders in the AI-empowered mobility context., (© 2024 Society for Risk Analysis.)

Destinations wishing to develop new sustainable tourism strategies have to adopt proactive measures in order to address potential adverse impacts of tourism. Using a discrete choice approach, we investigated residents' preferences and willingness to pay for municipal programs that mitigate such future tourism-related concerns in Tel Aviv. Results indicate that residents who live nearby the tourist center commonly perceive tourism impacts as more severe, but do not necessarily favor interventions that mitigate these impacts. Furthermore, both perception of impacts and preferences for mitigation measures were found to be heterogeneous and associated with socio-economic characteristics and geographical proximity to the tourism center. Therefore, the study informs researchers and policymakers to consider generating neighborhood-specific intervention strategies when considering future sustainable tourism development.

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

This paper seeks to introduce a novel course design method in academic literature. It uses advanced experimental designs to capture student preferences. It distinguishes between controllable and uncontrollable design factors and seeks to minimize the negative impact of uncontrollable design factors, ensuring a successful course introduction. We propose and test a design method that combines Taguchi experimental designs with discrete choice estimation where choice probabilities are used as input data for Taguchi's inner and outer arrays. Design efficiency is measured by calculating signal to noise (S/N) ratio, which accounts for the mean and variance of choice probabilities. Using the S/N ratio, designers were able to choose the most robust design. Test results show grading and attendance policies and experiential learning as the three most factors in students' selection of an undergraduate elective course at a business school. [ABSTRACT FROM AUTHOR]

Supply (chain) disruptions present considerable managerial challenges with potentially severe consequences. To protect their firms, managers often must decide whether or not to take proactive measures. Protection motivation theory suggests that individuals' intention to respond to a threat proactively results from their cognitive appraisal (situational interpretation) processes. These processes evaluate the characteristics of potential coping responses (e.g., its effectiveness in averting the threat) and the threat itself (e.g., its severity). Building on this framework, this study presents an analysis of what drives managers to, or deters them from, proactively responding to the threat of a disruption. The results from a discrete choice experiment suggest that decision makers have a strong subconscious focus on cost‐related aspects of a specific proactive action, all the while consciously prioritizing the efficacy (effectiveness) of the action over its costs. Moreover, decision makers' perceptions of the relative importance of proactive action attributes deviate considerably from their actual choice behavior. This study investigates additional behavioral aspects of supply chain risk management such as a proactive personality, risk attitude, control appraisal, and experience, many of which have significant effects on the relative importance of certain proactive action attributes. The improved understanding has three relevant messages for managerial practice, which are related to the perception–action gap, the importance of self‐assessment and self‐awareness, and training. [ABSTRACT FROM AUTHOR]

Exclaves are characterized by several advantages and disadvantages related to physical, economic, historical, social and cultural attributes; these depend on the owner- and the host-state as well as on relationship between the two. Using the case of Ceuta, a Spanish pene-exclave located in north-western Africa bordering Morocco, air travel mobility and accessibility issues faced by residents are identified and discussed primarily in the context of outbound tourism. The empirical research uses a mixed method approach of data collection based on questionnaires distributed to Ceuta residents and participant observation of the local community. Findings suggest that while enhancing accessibility is generally considered to be positive for travel, the reality is more complex in the case of exclaves and pene-exclaves, where geographical discontinuity and lack of land transport connections with the mainland and hinterland of the owner state may co-exist with geo-political conflicts between the owner and the host countries, i.e. Spain and Morocco respectively in the present context. Based on the concept of tourism for peace and its implications for border tourism and the scaling-up of spatial interaction between neighboring regions belonging to different countries, simpler border-crossing procedures along with innovative destination management policies at local level are proposed to improve exclave accessibility and travel mobility in practice to the benefit of both local residents and inbound tourists. [ABSTRACT FROM AUTHOR]

The importance of new foreign direct investment as a vehicle for the formation of productive capital has stimulated interest in understanding why multinational enterprises (MNEs) choose particular locations for their investments. Using data from greenfield investments reported on the Bureau of Economic Analysis Survey of new foreign direct investment in the United States, I analyze how locational characteristics influence the choice of where foreign MNEs establish their new investments in the United States. Identifying location choice at the metropolitan statistical area level allows me to analyze both traditional determinants of location choice such industrial agglomeration as well as measures of international connectivity that have been studied in research on global cities. My results support the finding that global cities are attractive to foreign direct investors in the United States not just by virtue of their population or industrial agglomeration, which serve to attract investment, but by virtue of a much wider set of attributes, related to their demography and connections to the wider world. [ABSTRACT FROM AUTHOR]

Hotels are vulnerable to health-related crises due to their communal nature of living spaces and the unavoidable contact with strangers. Consequently, many hotels have initiated safety measures against COVID-19. However, different influences of such measures on customers' hotel choice behavior are yet to be understood. Therefore, this research conducted a discrete choice analysis to reveal the relative importance of eleven safety measures including price per night and further estimate customers' willingness to pay more. This study contributes to the safety literature in the hotel context and to the practical knowledge of hotel resiliency planning after a health crisis. [ABSTRACT FROM AUTHOR]

The article analyzes the public's response to a hypothetical room tax applied to overnight stays in the Canary Islands. Topics include the use of room taxes in Spain, main findings from the opinion survey about a room tax in Gran Canaria, Spain, and results of stated choice survey and accompanying discrete choice model estimation.

This study analyzes the formative factors of the spatial image of two urban neighborhoods in Tokyo, Japan. Cognitive maps were collected from both residents and visitors, depicting spatial domains as imagined by the names of the neighborhoods. Using street segments as spatial units of analysis, this study analyzed the factors affecting individuals' perceptions of streets. As a result, as well as boundaries such as rivers and avenues, visual landscape features that were extracted from street images had non-trivial effects on street perception. In particular, the presence of visible streetscape greenery is positively associated with the probability of streets being perceived within the neighborhoods. However, the effect sizes differ between the two neighborhoods, suggesting the need for streetscape designs according to the existing city images. The results also revealed inter-individual heterogeneity in neighborhood cognition by gender, age, and familiarity with the neighborhood. While the original hypothesis was that the spatial extent of the perceived neighborhood would widen according to familiarity, the opposite result was obtained. This might explain the process of people obtaining certainty of neighborhood cognition and optimizing/fixing daily activity places according to experiences. • Neighborhood cognition is analyzed based on street segments as the spatial unit. • Individual-level perception model evaluates the extent to which attributes affect the perception probability of a street. • Streetscape greenery enhances the spatial image of the target neighborhoods. • Network distance sensitivity in neighborhood cognition is heterogeneous by directions and individuals. • Familiarity with the neighborhood is negatively associated with the extent of cognitive area. [ABSTRACT FROM AUTHOR]

The unique and at the same time very complex nature of the wine requires a more thorough selection process compared to other consumer products. Wine consumers are faced with a number of brands, grape varieties, and regions of origin, diverse tastes and prices. Using discrete choice experiment this study explores the importance that consumers in Serbia attach to five attributes of the local wine brands. Respondents were asked to evaluate 10 choice tasks, each with three different wines, and to choose the one that they would like to have with friends or family. The sample results indicate the high importance of the wine brand, while the price proved to be the least important. However, preference-based segmentation identified three clusters that differ primarily in the type of wine they favor, but also whether they like or not sweet and sparkling wines. It turns out that the price is a moderately important attribute in all clusters.

El objetivo de este artículo es analizar el grado de los efectos de red en el mercado móvil de telecomunicaciones en Colombia, e identificar otros determinantes de la elección del consumidor basándose en una encuesta de usos y hábitos realizada por el Centro Nacional de Consultoría para la Comisión de Regulación de Comunicaciones en el año 2016. A partir de esa encuesta se realizó este estudio que muestra que no existen diferencias regionales en la adopción de operadores en Colombia, y que el atractivo de los diferentes operadores depende del tamaño total del operador al cual se quiera suscribir el usuario. Este hallazgo significa que el mercado relevante se debe definir a nivel nacional y que las estrategias de precios de los operadores deben ser de carácter nacional y no regional. También se encuentra que en la elección de operador son determinantes claves el sexo de las personas, la edad, el estrato, el gasto mensual en servicios móviles, el tipo de plan y el número de llamadas que se realizan. Por último, los usuarios prefieren al operador que les brinde la mayor experiencia por lo cual es un determinante clave a trabajar por parte de los operadores de servicios móviles en Colombia. || The objective of this article is to analyze the degree of network effects in the mobile telecommunications market in Colombia, and identify other determinants of consumer choice based on a survey of uses and habits carried out by the National Consulting Center for the Commission of Regulation of Communications in 2016. Based on this survey, this study was conducted which shows that there are no regional differences in the adoption of operators in Colombia, and that the attractiveness of the different operators depends on the total size of the operator to which the user wishes to subscribe. This finding means that consumers are not likely to be affected by other people's choices within their local area but rather by the overall size of the network. The relevant market must be defined at the national level and the pricing strategies of the operators must be national and not regional. It is also found that in the choice of operator key factors are gender, the age, the stratum, the monthly expenditure on mobile services, the type of plan and the number of calls made. Finally, users prefer the operator that offers them the greatest experience, which is why it is a key determinant to work for operators of mobile services in Colombia.

The article briefly presents discrete choice analysis as a method to investigate human judgment from an econometric perspective. It focuses on an analysis of the results of a survey on passengers in Slovakia that took place in 2014. The passenger survey addressed 811 respondents who made decisions regarding the choice of transport mode between car, train and bus in stated preference design. In addition, the results of the survey included the values of time in public transportation and for car users. The value of time for car users (3.86 EUR/h) is greater than value of time for public transport passengers (2.98 EUR/h for bus and 2.46 EUR/h for rail passengers). The value of time for leisure trips is greater than value of time for work commuting or other purposes.

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems. [ABSTRACT FROM AUTHOR]

This paper draws on exchange and utility theories to propose and test a model of sponsorship decision making using discrete choice analysis. Grounded in prior sponsorship research and current management practice, the model describes sponsorship selection as a sequence of choices about sponsorship packages of differing value. The core thesis is that managers attempt to maximise their utility when selecting sponsorships by evaluating the various pay-offs from the alternatives available. This research applies a simulated choice-based experiment with 196 sponsorship managers to assess their preferences for a set of criteria likely to shape their future decisions about sponsorship selection. While perceptions of brand-image congruence and relationship quality significantly affect sponsor preferences, contrary to some evidence, short-term business arrangements appear more desirable than longer-term sponsorships. [ABSTRACT FROM AUTHOR]

Food safety remains a major issue to many consumers. Previous studies examining the economic impact of food safety recalls have focused on Class I recalls. Antibiotic residue in meat products, a Class II recall, has increased in consumer importance yet little is known about how much research and development expenditure should be allocated to reduce antibiotic residue pre- and post-harvest. This study compares demand elasticities and the decrease in willingness to pay in response to either an E. coli (Class I) or antibiotic residue (Class II) recall. We compare and contrast two competing behavioral frameworks, Random Utility and Regret Minimizing. Modeling behavior using the random regret framework is found to be more powerful for assessing consumer responses. In addition, we explore if different groups of consumers exist that either maximize utility or minimize regret. Consumer devaluations of E. coli (Class I) are 40–65% larger than antibiotic residue (Class II). Approximately 60% of consumers are identified as regret minimizers and 40% were identified as utility maximizers. While industry response and government policy recommendations differed conditional on modeling framework, the regret minimizing framework required smaller price discounts than regret minimizing to maintain the same level of market share.

The unique and at the same time very complex nature of the wine requires a more thorough selection process compared to other consumer products. Wine consumers are faced with a number of brands, grape varieties, and regions of origin, diverse tastes and prices. Using discrete choice experiment this study explores the importance that consumers in Serbia attach to five attributes of the local wine brands. Respondents were asked to evaluate 10 choice tasks, each with three different wines, and to choose the one that they would like to have with friends or family. The sample results indicate the high importance of the wine brand, while the price proved to be the least important. However, preference-based segmentation identified three clusters that differ primarily in the type of wine they favor, but also whether they like or not sweet and sparkling wines. It turns out that the price is a moderately important attribute in all clusters.

Abstract Background The 20–70% participation of diabetes patients in lifestyle interventions (LSI) worldwide seems to be rather sub-optimal, in spite of all intents of such interventions to delay further progress of the disease. Positive effects through LSI are expected in particular for patients who suffer less from diabetes-related limitations or other chronic diseases. Seeing that diabetes prevalence and with it mortality are increasing, LSI have become an inherent part of diabetes treatment standards. Various qualitative studies have been carried out to identify participation barriers for LSI. However, these have not resulted in more detailed knowledge about the relative importance of factors with an inhibiting impact on participation. Since it cannot be assumed that all of the influencing factors have equivalent values, it is necessary to investigate their individual importance with regard to a positive or negative decision about participating. There are no systematic reviews on patient preferences for LSI programs in diabetes prevention. As a result, the main objectives of this systematic review are to (i) identify existing patient preference elicitation studies related to LSI for diabetic patients, (ii) summarize the methods applied and findings, and (iii) appraise the reporting and methodological quality of such studies. Methods We will perform systematic literature searches to identify suitable studies from 14 electronic databases. Retrieved study records will be included based on predefined eligibility criteria as defined in this protocol. We will run abstract and full-text screenings and then extract data from all selected studies by filling in a predefined data extraction spreadsheet. We will undertake a descriptive, narrative synthesis of findings to address the study objectives, since no pooling for quantified preferences is for methodological reasons implementable. We will pay special attention to aspects of methodological quality of preference elicitation by applying established evaluation criteria of the ISPOR and some own developed criteria for different elicitation techniques. All critical stages within the screening, data extraction, and synthesis processes will be conducted by two pairs of authors. This protocol adheres to PRISMA and PRISMA-P standards. Discussion The proposed systematic review will provide an overview of the methods used and current practice in the elicitation and quantification of patients’ preferences for diabetes prevention lifestyle interventions. Furthermore, the methodological quality of the identified studies will be appraised as well. Systematic review registration PROSPERO CRD42018086988

• People keen on digital mobility tend to try free-floating bikesharing. • People in areas with low coverage of stations tend to try free-floating bikesharing. • People satisfied with usability of app remain users of free-floating scheme. • Flexibility of the system is a reason to keep using free-floating bikesharing. • Identified user groups and motivators similar to results on station-based bikesharing. Free-floating bikesharing (FFBS) is a fairly new mobility service. It spread rapidly throughout Europe's major urban areas in 2017; a development accompanied by a variety of problems that soon culminated in a retreat of providers from most cities. The main characteristic of FFBS is the absence of fixed docking stations; instead, users can borrow and leave the bikes wherever they want as long as they adhere to traffic rules and the operators' regulations. Its market entry has caused controversial public debates, although – or even because – little is known about this new mobility service, its users, their motivators as well as usage patterns. One of the FFBS pioneer cities in Europe was Vienna with two FFBS operators providing their services from summer 2017 onwards. Although both withdrew from the city within a year, it was possible to collect and analyse user data in order to gain an understanding of the factors supporting FFBS usage. For this purpose, the research uses a series of discrete choice models explaining why some people (i) share bikes (including FFBS and the established Viennese station-based scheme), (ii) try out the new FFBS scheme and (iii) remain with the new FFBS scheme or quit the membership. Reasons for users to try FFBS are very similar to those of station-based bikesharing as reported in literature. Subjective factors including attitudes and degree of satisfaction with system features are gaining in importance within the series of models. They are particularly decisive for remaining with the FFBS scheme. [ABSTRACT FROM AUTHOR]

In an effort to meet the diverse needs of today's highly competitive global marketplace better, many companies are utilizing product families and platform-based product development to increase variety, shorten lead-times, and reduce costs. Current research in the area of product family design mostly focuses on the cost-savings benefits of the platform-based approach and does not sufficiently examine broader enterprise considerations such as profit and market share. Furthermore, very few existing design methods integrate market considerations (e.g., customer preferences, competition) with product development efforts in their formulation. In this work, in addition to integrating market considerations with traditional product family concerns (e.g., modular design, decisions regarding shared parts and processes), the scope of the product family design problem is expanded to include the product line positioning problem, i.e., the problem of determining the appropriate market niche for each product variant in the family. The novel market-driven product family design (MPFD) methodology proposed here is introduced to systematically examine the impact of increasing the variety in the product offerings across different market segments and explore the cost-savings associated with commonality decisions. A unique representation scheme is also introduced to enable us to integrate the qualitative market segmentation grid with mathematically rigorous demand models, and the demand modelling approach employed in this paper models the dissimilar impacts of competition in different market segments and plays a significant role in determining the appropriate platform leveraging strategy. The design of a family of universal motors is used to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

• The experienced mode choice set is identified based on historic observations. • This set is smaller than currently assumed, often includes only one mode. • More determinants are relevant to identify the mode choice set than currently assumed. • Reimbursement for using a mode and accessibility to a mode are most important. Active modes take up an increasingly important place on the global policy-making agenda. In the Netherlands, a country that is well-known for its high shares of walking and cycling, the government aims at achieving a modal shift among 200,000 commuting car drivers towards using the bicycle. To this end, policy measures need to be introduced. When the aim is to achieve a modal switch over an enduring period of time, it is more relevant to know the likelihood of including or excluding a mode in the mode choice set, compared to choosing a mode for a single trip. Therefore, we investigate the formation of the experienced choice set (set of modes used over a long period of time), where the aim is to identify determinants that influence the inclusion or exclusion of a mode in this set. We estimate discrete choice models, based on survey data from the Netherlands Mobility Panel (MPN) and a complementary survey, where individuals were asked to report the frequency of using certain modes of transport for commuting trips over the course of half a year. This study shows that the experienced choice set for commuting is unimodal for the majority of the individuals, and remains constant over time for most individuals. Reimbursement by the employer for using a certain mode is the most important determinant influencing the experienced choice set, followed by ownership characteristics and urban density. We show that the mode choice set formation depends on more determinants than previously assumed. [ABSTRACT FROM AUTHOR]

A sound understanding of determinants of travel mode choice is an efficient way to design proactive transportation policy and urban planning to encourage non-motorized mode trips in the metropolitan area. The thrust of this study is to develop and apply travel mode choice models for basic education school students in Metro Manila. The study used two different discrete choice models, i.e., multinomial logit (MNL) and multinomial probit (MNP), by taking account of socioeconomic characteristics and urban form attributes. The Person product-moment correlation coefficient function was applied to control co-linearity among explanatory variables. The correlation coefficients implied that household income, travel distance from home to school, and the presence of commuters were the potential factors for the development of travel mode choice models. The findings highlighted that school students with high income and the presence of commuters are most likely to travel by private mode. Respondents located close to schools have the highest likelihood to travel by non-motorized mode. Also, the MNL-based travel mode choice model was applied to simulate the percentage changes of the travel mode choice in response to changes in household income and travel distance. [ABSTRACT FROM AUTHOR]