Choice Framework Research Articles

Climate risks and FDI

Climate-related risks have increased in recent decades, both in terms of the frequency of extreme weather events (physical risk) and the implementation of climate-change mitigation policies (transition risk). This paper explores whether multinational firms react to such risks by altering their presence in countries that are more affected. We measure this by examining foreign direct investment (FDI) dynamics at different levels of aggregation as well as at the firm level. We propose a theoretical framework for firm production location choice that explicitly incorporates transition and physical risks. The model predicts a reduction in FDI resulting from both physical and transition risks but an ambiguous interaction effect of these risks with emission productivity. In an extensive empirical analysis, we find some support for model predictions, but overall we do not find consistent evidence for statistically significant effects of physical and transition risks on FDI. However, firm-level evidence suggests that firms that are more exposed to climate risks react more negatively to physical climate risk following Paris Climate Accord. We also find that FDI outflows following extreme weather events from affected countries are smaller for industries with higher emission productivity (greener industries). Our theory and empirical results point to the importance of accounting for heterogeneity in emission productivity when analyzing the effects of climate risks.

A high-order hidden Markov model for dynamic decision analysis of multi-homing ride-sourcing drivers

Ride-sourcing drivers enjoy flexibility in scheduling work hours and choosing platforms, which generates multi-homing behavior on multiple platforms. It is challenging to observe the labor supply of multi-homing ride-sourcing drivers due to data limitations, which motivates our research on modeling drivers' dynamic decisions on labor supply in the competitive ride-sourcing market with multiple platforms. We propose a dynamic discrete choice framework by modeling drivers' high-frequency decision sequences on platform switching as high-order hidden Markov processes and considering time-varying factors. The high-order hidden Markov framework relaxes the first-order Markovian assumption and builds interdependencies of unobserved states across multiple time periods. The estimation of the proposed model takes advantage of city-scale multiple platforms datasets in Hangzhou, China, including more than 16 million records of the order payment information and more than 46 thousand records of active ride-sourcing drivers' information. The case study results indicate that the high-order hidden Markov model (HO-HMM) has superior explanatory power in fitting multiple platforms datasets than the multinomial logit model and the first-order hidden Markov model. HO-HMM performs an advantage in modeling the extended historical dependency of drivers' decisions and individual driver behavior modeling with high interpretability. The results uncover the variations of drivers' attitudes in different hidden (unobservable) states and towards different platforms. In general, ride-sourcing drivers respond actively and positively to income and working time. The findings support the platforms' decision-making on pricing, reward, and personalized management of ride-sourcing drivers, and provide beneficial suggestions for improving income in the competitive ride-sourcing market.

A conceptual framework of the entry mode choices' determinants: the ownership-location-internalisation approach

A conceptual framework of the entry mode choices' determinants: the ownership-location-internalisation approach

Game-theoretical Approaches to Cyber-crime Monitoring

Our society's diploma of reliance on IT and our online world is developing daily. Cyberspace, the call given to the worldwide and dynamic domain, composed of the infrastructure of the statistics era consisting of the net networks and statistics and telecommunications structures has supplied extraordinary globalization that gives new opportunities, but additionally includes new challenges, risks, and threats. Knowledge of its threats, dealing with the risks, and constructing suitable prevention, defense, detection, evaluation, investigation, and recuperation is essential. Given the present-day assessment of the statistics safety and intrusion detection, there's without a doubt a want for a choice and manipulation framework to cope with problems like assault modeling, evaluation of detected threats, and choice of reaction actions. We look at the goals of designing a mathematical version for gamified cybercrime tracking in a community environment.

Consumers’ preference for urban last-mile delivery: effects of value perception and long-term COVID-initiated contextual shifts

ABSTRACT The economic and environmental challenges associated with Last Mile Delivery (LMD) give rise to the self-collection concept, yet the practical adoption is often below expectations. In this consumer-centric segment, adequate understandings of consumers are vital but are often lacking. To address this gap, this study examines consumers’ LMD preference with the Integrated Choice and Latent Variable framework. A stated choice experiment was commissioned in Singapore during the transition of COVID pandemic toward endemic. Our results showed that consumers accept unattended delivery methods and are no longer concerned with the delivery time window, a traditionally critical delivery attribute. Regarding latent perceptions, this study confirmed the significant effects of value perception on self-collection preferences. Nonetheless, individuals’ environmental consciousness was found to be insignificant. The relative effects of specific value statements were quantified using the marginal rate of substitution and the mediation roles of value perception on the sociodemographic-preferences relationships were validated.

The Role of Catch Portfolios in Characterizing Species' Economic Linkages and Fishers' Responses to Climate Change Impacts

The collective harvest behavior of fishers, as revealed by their catch portfolios, gives rise to an economic linkage among targeted species. Mapping the economic connectivity among species helps identify species that may play a role in fishers’ adaptive capacity to climate change impacts. In this study, we used survey data and network analysis to map the connectivity among species harvested by small-scale Galician fishers holding the same set of fishing permits. We characterized species by describing their roles in the system of connected species via four network centrality measures: degree, closeness, betweenness, and eigenvector. We used connectivity metrics in a discrete choice framework to evaluate the relation between species roles and fishers' species choices under a hypothetical climate change impact. In particular, we asked the following question: Which species are likely to be added to a catch portfolio in response to a climate-driven negative impact on fishing returns? Galician fishers responded to hypothetical scenarios by adding species not central to the network and species that are likely to be located within clusters in the network and by diversifying their portfolios of permits. Our work suggests that species connectivity due to fishers' behavior deserves serious consideration for practitioners who seek to improve institutional arrangements that promote the resilience of small-scale fisheries.

Conceptions of Legacy Among People Making Treatment Choices for Serious Illness: Protocol for a Scoping Review.

Legacy-what one leaves behind and how one hopes to be remembered after death-is an unexplored and important dimension of decision-making for people facing serious illnesses. A preliminary literature review suggests that patients facing serious illness consider legacy when making medical decisions, for example, forgoing expensive treatment with limited or unknown clinical benefit to preserve one's inheritance for their children. To date, very little is known about the conceptual foundations of legacy. No conceptual frameworks exist that provide a comprehensive understanding of how legacy considerations relate to patient choices about their medical care. The objective of this scoping review is to understand the extent and type of research addressing the concept of legacy by people facing serious illness to inform a conceptual framework of legacy and patient treatment choices. This protocol follows the guidelines put forth by Levac et al, which expands the framework introduced by Arksey and O'Malley, as well as the Joanna Briggs Institute Reviewer's manual. This scoping review will explore several electronic databases including PubMed, Medline, CINAHL, Cochrane Library, PsycINFO, and others and will include legacy-specific gray literature, including dissertation research available via ProQuest. An initial search will be conducted in English-language literature from 1990 to the present with selected keywords to identify relevant articles and refine the search strategy. After the search strategy has been finalized, 2 independent reviewers will undertake a 2-part study selection process. In the first step, reviewers will screen article titles and abstracts to identify the eligibility of each article based on predetermined exclusion or inclusion criteria. A third senior reviewer will arbitrate discrepancies regarding inclusions or exclusions. During the second step, the full texts will be screened by 2 reviewers, and only relevant articles will be kept. Relevant study data will be extracted, collated, and charted to summarize the key findings related to the construct of legacy. This study will identify how people facing serious illness define legacy, and how their thinking about legacy impacts the choices they make about their medical treatments. We will note gaps in the literature base. The findings of this study will inform a conceptual model that outlines how ideas about legacy impact the patient's treatment choices. The results of this study will be submitted to an indexed journal. Very little is known about the role of legacy in the treatment decisions of patients across the continuum of serious illness. In particular, no comprehensive conceptual model exists that would provide an understanding of how legacy is considered by people making decisions about their care during serious illness. This study will be among the first to construct a conceptual model detailing how considerations of legacy impact medical decision-making for people facing or living with serious illnesses. DERR1-10.2196/40791.

A Spatio-Temporal Cognitive Framework for Individual Route Choice in Outdoor Evacuation Scenarios

Route choice is a complex issue in simulating individual behaviors and reproducing collective phenomena during evacuations. A growing concern has been given to the individual cognitive mechanism to investigate how routing decisions are made in specific situations. However, the essential role of multiple spatio-temporal scales has not been completely considered in the current cognitive frameworks, which leads to the inaccuracy of cognition representation in evacuation decisions. This study proposes a novel spatio-temporal cognitive framework integrated with multiple spatio-temporal scales for individual route choice. First, a complete spatio-temporal cognitive mechanism is constructed to depict the individual evacuation cognition process. Second, a spatio-temporal route choice strategy that emerges from agent-based simulation and extends into the spatio-temporal potential field is designed to represent the overall time-varying cost along routes in individual subjective estimation. Finally, a spatio-temporal A* algorithm is developed for individual optimal route planning in complex outdoor evacuation scenarios. The experimental results show that the proposed framework outperformed the conventional potential field model in evacuation performance, in both objective crowd evacuation evaluation metrics and individual subjectively estimated evacuation cost in cognition, and may provide more insights on crowd evacuation management and guidance.

POSTER ABSTRACTS: P001Telehealth or in-clinic medication abortion care: How patients choose between care options

<h3>Objectives</h3> To understand how patients decide whether to choose direct-to-patient telehealth or in-clinic services for medication abortion care. <h3>Methods</h3> This qualitative study used in-depth, semi-structured interviews collected from September 2021 to January 2022 and analyzed using inductive-deductive constant comparative analysis. We conducted 30 one-on-one, one-hour remote interviews with participants who received a medication abortion from a high-volume reproductive healthcare clinic in Washington State (20 telehealth, 10 in-clinic). Outcomes included clinic and patient sociodemographic and psychological factors from the Patient Choice and Empowerment Framework. Interviews were recorded, transcribed, and quality checked; major themes were identified and summarized. <h3>Results</h3> The majority of those interviewed were aged 30–35 (56.7%) and half were in the first six weeks of their pregnancy at time of care (50.0%). Six individuals (20%) self-identified as Hispanic/Latino, five (17%) as Non-Hispanic White, and 10 (33%) as Non-Hispanic, other race. How soon a clinic appointment was available was the most important consideration for choosing modality of care, with high value placed on same-day or next-day appointments. Participants preferred telehealth services to in-person services due to (1) convenience, (2) less disruption from other responsibilities at home or work, (3) familiarity of telehealth due to increased use during the COVID-19 pandemic, and (4) less stigmatization of being in-person. Participants who chose in-clinic services over telehealth options did so to ensure their care was legitimate. <h3>Conclusions</h3> Both telehealth and in-clinic medication abortion services meet different patient needs. Providing patients with a choice about modality for care meets these diverse needs and allows for more accessible and satisfactory services.

From the exotic to the everyday: The Avocado crossing borders via cyberspace

With the globalization of food sales and consumption, exotic foods are now regularly crossing geographical and cultural borders and moving into local areas. This process is attracting ever-increasing attention from academics. Taking avocado consumption presented on Sina Weibo as an example, this research analyzes avocado related user-generated content on Sina Weibo over three years– 2013, 2015, and 2017– and employs topic modeling and semantic network methods to obtain the mechanism by which exotic food cross borders to appear in local consumers' daily food choices. Two specific links are explored: online information dissemination and offline daily consumption. The result indicates that a selective geographical narrative and framework for avocado information influence local consumers' choice of exotic foods according to three aspects: edibility, accessibility, and acceptability. For local consumers, the avocado is now connected with local objects and spaces, gradually transforming from a novelty to functional daily food and from low to high-frequency consumption to high-frequency consumption, escaping the marginal and penetrating into the core cultural context and completing the process of embedment into the everyday. This study refutes the assertion that “globalized diets bring about homogenized diets,” explores the mechanism of influence by which information dissemination in cyberspace affects cultural borders, complements the study of food consumption in Southern countries, and provides new thoughts on the theoretical and practical exploration of food globalization from the perspective of food geography.

An unstructured CD-grid variational formulation for sea ice dynamics

We present a new unstructured CD-grid formulation of the elastic-viscous-plastic rheology, where the velocity unknowns are co-located at the edges. Our framework of choice is the Model for Prediction Across Scales (MPAS) within E3SM, the climate model of the U.S. Department of Energy, although our approach is general and could be applied to other models as well. The mesh cells in our analysis have n sides, with n greater than or equal to three. Given an initial mesh, we compute the divergence of the stress using a sub-grid, which introduces two separate sets of cells that may differ in shape or be equal depending on the initial topology of the initial mesh. The proposed approach is of higher order compared to the existing B-grid discretization of MPAS-Seaice, and does not need stabilization as other CD-grid formulations on unstructured grids. Our new methodology will contribute to transitioning the dynamics of MPAS-Seaice to a CD-grid mesh, facilitating improved coupling with MPAS-Ocean and reducing numerical errors associated with interpolation.

A two-phased decision-making based grey theory framework for the best choice of payment methods in international trade

This study aims to propose a two-staged Analytic Hierarchy Process and COmplex PRoportional ASsessment model-based Grey Theory (AHP-G and COPRAS-G) to explore critical elements affecting international payment method selection in the case of import-export Small and Medium-Sized Enterprises (SMEs) from emerging countries. First, the AHP-G model is applied to determine the relative weights of essential factors. Second, the COPRAS- G approach prioritizes international payment method alternatives. From the literature review and experts' opinions, five main criteria were established, including: “relationship between the two parties” (RE), “type of goods” (GO), “political-economic framework factor” (MA), “characteristics of payment method” (PA); “opinions and requirements of external parties” (EX) and four international payment methods are considered as frequent payment alternatives. A real-world case study from Vietnamese SMEs in the import-export industry is presented to exemplify the applicability and robustness of the proposed approach. The weighting results indicated that (PA) had the most significant impact on international payment method selection, followed by (MA) and (RE). Letter of credit (L/C) is considered the most reliable payment method for SMEs. Furthermore, a comparative analysis was performed to examine the validity of the proposed model. The proposed approach would assist stakeholders in import-export SMEs and other industries in effectively selecting suitable payment methods in international trade.

Mode Split Equilibrium Microsimulation Approach for Early-Stage On-Demand Shared Automated Mobility.

The initial hype around Automated Vehicle (AV) technologies has subsided, and it is now being realized that near-term deployment of AV technologies will be in the form of low-speed shared automated shuttles in geofenced districts with a high density of trip demand. A concept labeled ‘Automated Mobility Districts’ (AMD) has been coined to define such deployments. A modeling and simulation toolkit that can act as a decision support tool for early-stage AMD deployments is desired for answering the questions such as (i) for a series of given conditions, such as the amount of travel demand and automated shuttle fleet configuration, what is the expected mode split for shared automated vehicle (SAV) services? (ii) for that mode share of SAVs, what level-of-service and network performance can be anticipated? To answer these research questions, an innovative and integrated framework of multi-mode choice and microscopic traffic simulation model is presented to obtain the equilibrium of mode split for various modes in AMDs, based on real-time traffic simulation data. The proposed framework was tested using travel demand and road network data from Greenville, South Carolina, considering a car, walk, and two SAV on-demand ridesharing modes in a proposed AMD. Results from the study demonstrated the efficacy of the proposed framework for solving the mode split equilibrium in an AMD. In addition, sensitivity analyses were conducted to understand the impact of factors such as waiting times and fleet resources on mode share equilibrium for SAVs.

Modeling activity-travel behavior under a dynamic discrete choice framework with unobserved heterogeneity

The major challenges in dynamic activity-based models include predicting activity-related choices and understanding inherent heterogeneous preferences. The dynamic discrete choice model (DDCM) has been used for daily activity-travel planning. However, ignoring unobservable heterogeneity can bias the estimation and prediction results. To solve this problem, we propose a DDCM that accounts for unobserved heterogeneity to capture useful hidden information on travelers’ characteristics. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. The algorithm iteration depends mainly on the mapping relationship between posterior distributions and conditional choice probabilities. Meanwhile, a less complex log-likelihood function is proposed in the maximization step to estimate two types of parameters simultaneously. The proposed techniques are verified using household travel survey data from Chongqing (China). Two unobserved types of travelers, time and cost sensitivity, are identified based on the Bayesian information criterion (BIC) value. Time- and segment-varying sensitivity analyses are conducted to present choice probability differences of mode and activity scheduling under a one-unit increase in the number of schoolchildren and cars. Different impacts on activity-travel patterns, such as trip frequency, mode share, and activity schedule, generated by changes in auto ownership, are analyzed. Finally, the adjusted rho-squared value, BIC values compared with other single-effect models, and aggregate validation results demonstrate the exceptional performance of the proposed model.

Long-term impacts of COVID-19 pandemic on travel behaviour

The need to understand the influence of the COVID-19 pandemic on the long-term travel behaviour of people has never been higher as a consequence of the second wave of pandemic. In this context, the current study aims to understand the willingness of people to use sustainable modes of transportation including shared modes of transport, and non-motorized transport, against non– shared modes of transport such as personal 2-wheelers and 4-wheelers in a post-vaccinated scenario. The study further models the willingness to choose public transportation under various COVID-19 preventive measures representing the perception of safety among people. An Integrated Choice and Latent Variable (ICLV) framework a employed in the modelling. The fear of contracting COVID-19 and the belief in remedial measures significantly influenced the mode choice of individuals. This highlighted a significant long-term impact of the pandemic on the travel behaviour of individuals. The study concludes by presenting different strategies that could be adopted to make the existing sustainable modes safer, and hence, more attractive.

Enabling direct ink write edible 3D printing of food purees with cellulose nanocrystals

Additive manufacturing using edible feedstock – known as “edible three-dimensional (3D) printing” – offers a unique method of producing visually appealing meals with customizable nutrition profiles. Direct ink write (DIW) 3D printing is a popular choice for edible 3D printing due to its low cost and open framework for broad material choices. However, the breadth of food suitable for DIW 3D printing is hindered due to the unsuitably low viscosity of many potential edible feedstocks such as food purees. In this paper, we present cellulose nanocrystals (CNCs) as a safe and renewable rheological modifier capable of enabling DIW 3D printing of a variety of foodstuffs, specifically spinach puree, tomato puree, and applesauce, and using freeze-dry to obtain final solid structures. We first analyzed the rheological characterization of foodstuffs combined with varying volume fractions of CNCs to produce shear-thinning, printable inks. The print quality of different inks was then analyzed via image processing techniques. Finally, we demonstrated the capability of CNC-laden inks by printing a variety of structures, including multi-material structures with integrated packaging. This study found that CNCs are an effective rheological additive which promoted shear-thinning, viscous behavior in the studied edible feedstocks necessary for DIW 3D printing of self-supporting edible structures.

Strengthening the State through Crisis Management: Battles against the SARS and COVID-19 Pandemics in China

How do state leaders use crisis management to strengthen state infrastructural power? What explains the strategic choices of a state’s selective institutionalization of crisis measures? Crises offer unique opportunities for state-building, yet the role of crisis management in consolidating state power is underexamined. This paper explores these important issues by examining how the Chinese government has deployed wartime-like measures in battling the spread of Severe Acute Respiratory Syndrome (SARS) and COVID-19. While authorities in China have adopted unconventional measures in managing the crises, they have selectively normalized ad hoc practices and institutionalized certain measures to strengthen state infrastructural power once they have ended or been temporarily contained. Drawing on the frameworks of rational choice and historical institutionalism, our analysis suggests that the central government normalizes or institutionalizes measures that help to consolidate its control of the bureaucracy and enhance regime legitimacy.

A hybrid additive manufacturing process for production of functional fiber-reinforced polymer composite structures

Fiber reinforced polymer composites (FRPCs) are valued for their high strength and lightweight and have found applications from aerospace to renewable energy. Additive manufacturing (AM, or 3D printing) of FRPCs is of great interest in recent years due to the manufacturing flexibility offered by AM. Direct ink write (DIW) 3D printing is a popular choice for FRPC 3D printing due to its low cost and open framework for broad material choices. However, previously explored techniques for AM of FRPCs are hindered by heavy reliance on fiber orientation dictated by extrusion toolpath and often require specialized hardware to print FRPCs with low viscosity and long cure time thermoset epoxy matrices. In this paper, we introduce a single-stream hybrid DIW AM technique for the creation of mechanically robust FRPC functional structures where the matrix is DIW 3D printed, and pre-epoxy impregnated (prepreg) woven carbon fiber (CF) fabrics are robotically placed. Additionally, functional components, such as conductive elements, can be readily integrated. We investigated the impact of prepreg woven CF reinforcement on a two-stage photo-thermal thermoset resin matrix on the mechanical characteristics of manufactured FRPCs. We also combined these efforts to fabricate functional structures, including strain sensors for in-situ deformation monitoring, heating elements, and embedded light sensors. This study found that the proposed single-stream hybrid DIW AM process could be a facile approach to fabricate high-strength FRPC functional structures.

Endogeneity and Measurement Bias of the Indicator Variables in Hybrid Choice Models: A Monte Carlo Investigation

We investigate the problem of endogeneity and measurement bias arising from incorporating indicator variables (e.g., measures of attitudes) into discrete choice models. We demonstrate that although a hybrid choice framework can resolve both endogeneity and measurement problems, the former requires explicit accounting for in the model, which has not typically been done in applied studies to date. By conducting a Monte Carlo experiment, we demonstrate the extent of the bias resulting from measurement and endogeneity problems. We propose two novel solutions to address the endogeneity problem: explicitly accounting for correlation between structural and discrete choice component error terms (or with random parameters in a utility function), or introducing additional latent variables. Using simulated data, we demonstrate that these approaches work as expected, i.e. they successfully recover the true values of all model parameters.

Modelling online job search and choices of dentists in the Australian job market: Staged sequential DCEs and FIML econometric methods

Workforce participation decisions involve multiple stages: search, screening and offer evaluation. Standard econometric practice focusses on these stages in isolation. We conceptualize the focal behaviours as separate sequential decision stages, and provide a stated preference measurement framework for online job search and choice with a behaviourally consistent modelling approach. We demonstrate this approach in an empirical application of 275 dentists who completed an online survey including two Discrete Choice Experiments: the first mimicked an online job search site in which dentists decided which jobs they would apply to and the second presented dentists with a job offer which they accepted or rejected. Modelling these tasks requires a two-stage econometric model that incorporates the likelihood of application (first stage) into the job offer choice (second stage). The model detects differences in preferences (hence behaviours) across stages, facilitating the differentiation of policy aimed at search and job choice behaviours. Job screening occurs during search and the marginal propensity to apply for a job-type differs from the offer stage. We suggest that the approach presented provides a valuable way to investigate how dentists particularly, and perhaps the health workforce more generally, respond at different stages of workforce participation decisions and discuss practical implications.