The Preferences of Choosing Different Levels of Taxi-Hailing Attributes Through the Best–Worst Scaling Method Case 2 (Case Study: Qazvin)

Direct and indirect hedonic scaling methods: A comparison of the labeled affective magnitude (LAM) scale and best–worst scaling

IntroductionBest–worst scaling (BWS) is becoming increasingly popular to elicit preferences in health care. However, little is known about current practice and trends in the use of BWS in health care. This study aimed to identify, review and critically appraise BWS in health care, and to identify trends over time in key aspects of BWS.MethodsA systematic review was conducted, using Medline (via Pubmed) and EMBASE to identify all English-language BWS studies published up until April 2016. Using a predefined extraction form, two reviewers independently selected articles and critically appraised the study quality, using the Purpose, Respondents, Explanation, Findings, Significance (PREFS) checklist. Trends over time periods (≤2010, 2011, 2012, 2013, 2014 and 2015) were assessed further.ResultsA total of 62 BWS studies were identified, of which 26 were BWS object case studies, 29 were BWS profile case studies and seven were BWS multi-profile case studies. About two thirds of the studies were performed in the last 2 years. Decreasing sample sizes and decreasing numbers of factors in BWS object case studies, as well as use of less complicated analytical methods, were observed in recent studies. The quality of the BWS studies was generally acceptable according to the PREFS checklist, except that most studies did not indicate whether the responders were similar to the non-responders.ConclusionUse of BWS object case and BWS profile case has drastically increased in health care, especially in the last 2 years. In contrast with previous discrete-choice experiment reviews, there is increasing use of less sophisticated analytical methods.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-016-0429-5) contains supplementary material, which is available to authorized users.

Anchored scaling in best–worst experiments: A process for facilitating comparison of conceptual profiles

Solar cooking in Senegalese villages: An application of best–worst scaling

Aims: Different methods have been used to analyze “object case” best–worst scaling (BWS). This study aims to compare the most common statistical analysis methods for object case BWS (i.e. the count analysis, multinomial logit, mixed logit, latent class analysis, and hierarchical Bayes estimation) and to analyze their potential advantages and limitations based on an applied example.Methods: Data were analyzed using the five analysis methods. Ranking results were compared among the methods, and methods that take respondent heterogeneity into account were presented specifically. A BWS object case survey with 22 factors was used as a case study, tested among 136 policy-makers and HTA experts from the Netherlands, Germany, France, and the UK to assess the most important barriers to HTA usage.Results: Overall, the five statistical methods yielded similar rankings, particularly in the extreme ends. Latent class analysis identified five clusters and the mixed logit model revealed significant preference heterogeneity for all, with the exception of three factors.Limitations: The variety of software used to analyze BWS data may affect the results. Moreover, this study focuses solely on the comparison of different analysis methods for the BWS object case.Conclusions: The most common statistical methods provide similar rankings of the factors. Therefore, for main preference elicitation, count analysis may be considered as a valid and simple first-choice approach. However, the latent class and mixed logit models reveal additional information: identifying latent segments and/or recognizing respondent heterogeneity.

Scale changes and model linking methods for integrated assessment of agri-environmental systems

Summary A better understanding of the complexity of groundwater-surface water interactions is highly required for managing the existing water resources efficiently in the agricultural landscapes. As a case study, the seepage rate is estimated using the reach gain/loss method via selected canal reaches of different sizes and lithology in the Treasure Valley (TV), USA. Then, the total gain/loss across the TV is estimated using 3 scaling methods. The resulting net water losses of the TV’s canals are 3.23 × 10, 1.18 × 10, and 1.11 × 10acre ft/yr. To better constrain the canal seepage variability and uncertainty, electrical resistivity tomography (ERT) technique is deployed applying the advanced time-lapse ERT inversion method. The subsurface resistivity variations are monitored over two months, which are attributed to the saturation and water content rates as a result of the lateral water flow movement from the adjacent water-filled surface canal. The inversion results of 2D-ERT profile are useful for getting a quantitative seepage estimate based on the dimensions of the saturated zone over time. This research provides a better understanding of groundwater-surface water interactions in such heavily managed systems for evaluating alternative management options of the existing water resources.

-The spectra of maize field are of great uncertainty duo to the difference in planting date, irrigation condition, fertilizing and soil etc. The spectral library is a quick means acquiring the crop spectrum in a growth stage. The variance of image and in-situ maize spectra is analysized for extracting pure pixels of maize crop. It presents a good result for removing disturbed greensward, residential area, and nursery garden from variance based pure maize pixel classification. Keywords- Image endmember, spectral library, variance, maize, pure pixel, recognition Ⅰ. INTRODUCTION Remote sensing of the extent and distribution of individual crop types has proven useful to a wide range of end-users, including governments, farmers, and scientists (Qi-Jing Liu et al,2005). Maps of cropland distributions are usually generated by supervised classification of multiple Landsat images throughout the growing season. These approaches require amounts of manual interpretation and cloud-free high spatial resolution imagery that are prohibitive for operational implementation over large areas and in multiple years (David B. Lobell,2004). The NASA Moderate Resolution Imaging Spectroradiometer MODIS, including the daily global coverage, moderate spatial resolution (0.25 to 1 km), has rapid availability of various products, and cost-free status may allow for operational mapping of croplands. However, the large size of even the 250-m MODIS data relative to most fields results in MODIS pixels containing mixtures of different fields, crop types, and non-crop surfaces. As a result, approaches that assign a single hard classification to each pixel may be prone to significant errors when mapping crop types ( Fisher, 1997). If the pure spectral pixels of different crops can be gained in finer resolution, a big problem will be solved for mixture pixel unmixing and scaling reversion in quantitative remote sensing application by coarser resolution satellite , for example Landsat TM to Terra MODIS sensor. Maize is one of the most important crops in China. During maize growth with rain season of May to September month every year in North China Plain, it is low available for Landsat TM images. During maize growth of June to September, 2003, there is only a scene of TM image captured while the maize spectrum experiment of ground all-growth period is performing. It is necessary to research the maize spectrum characteristic of each month all over the growth period. Another, the every-day gained MODIS image is becoming an important data source of crop monitoring. The ground orchard, greensward and woodland (using in virescence of urban and road) usually disturb the maize classification by TM image. Therefore, it is of great meaning to extract pure maize pixels for improving the classification of the pure maize pixel recognition for maize condition monitoring and yield estimation by scaling method. In this study, we investigated the impact of greensward

Explaining farmers’ reluctance to adopt green manure cover crops planting for sustainable agriculture in Northwest China

Risk Analysis: An International Journal has long emphasized articles that advance the state-of-the-art in the theory and practical application of health, safety, and environmental (HS&E) risk analysis, and, more recently, adversarial risk analysis. Papers more narrowly focused on specific risk-related topics—such as financial portfolio risk management, project risk management, insurance premium pricing, inventory control, toxicology, or the mathematics of expected utility theory and its generalizations—are more likely to be published in other journals. However, including more papers that address risks to other (non-HS&E, nonadversarial) aspects of human well-being using scientific analytic methods may increase the value of Risk Analysis. Two papers in this issue consider risks to the well-being of children and young adults. Camasso and Jagannathan examine how risk analysis concepts and technical methods, such as outrage and ROC curves, can be used to improve the difficult risk management decisions taken by Child Protective Services, such as whether to separate children from possibly abusive families. Both type 1 and type 2 errors (mistakenly concluding that the (uncertain) risk of abuse justifies removing a child from the family setting, and mistakenly concluding that it does not) may have grave consequences for the children and families involved. Camasso and Jagannathan point out how better use of risk analysis methods might reduce both. Keeney and Palley address the question of how decision and risk analysis concepts and techniques can be used to reduce mortality risks for children and young adults during the high-risk ages of 15–24. They point out that most of the 20,000 deaths per year in this age group in the United States due to causes, such as fatal car accidents (many arising from poor personal decisions, such as texting while driving), murder, suicide, and alcohol and drug abuse, could be avoided through better decision making. They suggest a practical, constructive decision framework to help parents and children identify and avoid needlessly high mortality risks during these years by using better decision strategies. Several papers in this issue advance technical aspects and important applications of dose-response modeling, suggesting constructive ways to improve our ability to quantify the probable adverse health impacts of given exposures, or, inversely, to estimate the “benchmark dose” (BMD) that would cause a specified elevation in risk of response. Hoelzer et al. summarize the results of a 2011 workshop coorganized by the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) to advance dose-response modeling for Listeria monocytogenes, the bacterium that causes listeriosis food poisoning. They review and compare dose-response relations estimated for various subpopulations from data on outbreaks, FoodNet surveillance data, and results of animal feeding experiments. Different subtypes of L. monocytogenes may differ in virulence by more than four orders of magnitude. In addition to reviewing available statistical and mechanistic (exponential and beta-Poisson) dose-response models, the authors recommend short- and longer-run priorities for collecting additional information to improve future dose-response models for L. monocytogenes. Marshall et al. propose a novel quantitative approach (based on distances between BMD estimates) to give concrete operational meaning to the concept of two mixtures of chemicals being “sufficiently similar,” from a toxicological perspective, so that toxicity data for one can be used as a useful surrogate for toxicity data for the other. Phung et al. estimate the dose-response relation between exposure to the organic insecticide chlorpyrifos (estimated from analysis of urine samples) and potential neurological effects (e.g., fatigue, emotional states, weakness, memory problems) among rice farmers in Vietnam, estimated from epidemiological data. Monte Carlo uncertainty analysis is used to quantify the fraction of the population that might receive enough exposure from spraying to experience adverse effects. Guha et al. provide statistical methods for estimating a BMD (i.e., the dose that creates a specified increase in the probability of an adverse response) from quantal dose-response data without imposing any specific parametric modeling assumptions. A Bayesian prior specifies the desired amount of smoothness in the estimated dose-response relation (as well as allowing other prior knowledge about its shape to constrain the estimated relation). Applied to eight cancer bioassay data sets and an in vitro genetic toxicity data set, the nonparametric methods generally produce results similar to those from parametric models in EPA's BMD risk assessment software (BMDS), but also provide useful fits for data sets for which none of the parametric models in BMDS provides an adequate fit. Finally, Schmidt et al. advance the theory and practice of microbial dose-response estimation and risk assessment by applying readily available computational Bayesian methods (using the OpenBUGS software for automating Markov chain Monte Carlo (MCMC) uncertainty analysis) to better estimate and characterize uncertainty and variability in the widely used exponential and beta-Poisson dose-response models. A striking finding is that the beta distribution in the beta-Poisson model cannot adequately characterize variability across individual pathogens. Moreover, as demonstrated in a case study, the risks estimated in the low-dose region of a beta-Poisson model (often the area of greatest practical interest for microbial risk assessment) may be highly uncertain. This is due to second-order uncertainty, that is, uncertainty about the parameters of beta-Poisson distribution, arising from the lack of adequate data to constrain the shape of the posterior distribution of the beta-Poisson model in this region. Together, these papers present substantial advances in methods and applications of dose-response modeling for BMDs, mixtures, epidemiological exposure-response relations, and microbial risk assessment. Two papers illustrate the importance of multicriteria decision making in risk management. Santos et al. apply an input–output model to assess the economic and productivity impacts of workforce absenteeism during the 2009 H1N1 pandemic in the National Capitol Region. They conclude that prioritizing sectors for recovery based on two different metrics—economic loss and inoperability—yields quite different rankings of the sectors, so that risk management requires making multiobjective trade-offs. Yemshanov et al. show how to develop integrated risk maps for invasive pest risks even when individual criteria scores are uncertain. To do so, they apply and compare the concepts of multiattribute efficient frontiers (and iterated versions, e.g., based on identifying successive new efficient frontiers as previous ones are removed) and multicriteria (linear-weighted average) techniques. They study the robustness of risk rankings to uncertainties in scores for the multiple components of a risk (e.g., potential for introduction of a pest via ports of entry, geographic distribution of hosts, and potential for the pests to become established and to spread among host populations). A case study of the risk of invasion by oak splendor beetles, a significant invasive pest responsible for the decline of oaks outside North America, shows that the different multiattribute and multicriteria techniques considered identify similar risk maps for North America. Results of different techniques largely agree on where available information suffices to confidently rank different areas in terms of risk of invasion and spread, despite remaining uncertainties. Three papers shed new light on aspects of risk belief and perception elicitation, combination, and uncertainty characterization. Roelofs and Roelofs explain how probability boxes (p-boxes) can be used to combine elicited intervals for multiple uncertain inputs to a risk analysis (including intervals for quantiles and shapes of distributions), yielding bounds (or intervals) for output quantities of interest, without assuming or requiring any specific dependency structure for the inputs. They illustrate the methodology via a case study of the costs of animal disease outbreaks in the United Kingdom, where different disease parameters may have unknown dependencies. Erdem and Rigby investigate levels of perceived control and levels of concern for 20 food and nonfood risks. A careful consideration of how to elicit perceived degrees of control over risks (using the “best–worst scaling” technique, in which respondents identify two items in a choice set that are maximally different on an underlying comparative scale) leads to the discovery that individuals vary widely in their perceptions of control, with women tending to perceive themselves as having less control over risks than men. Becker et al. examine the relation between sources of information (including public education measures), beliefs about earthquake risk, and adoption of household preparation measures in New Zealand. The results confirm and extend previous findings that salient beliefs (e.g., about inevitability and imminence, optimistic bias, and personal responsibility and responsibility for others) affect preparedness. This research also identifies additional salient beliefs that drive preparedness behaviors, such as beliefs about the importance of safety in everyday life. In this issue, Tony Cox looks across several recent books to create a coherent narrative on Decision and Risk Psychology explaining “… how real people do make decisions under risk, uncertainty, time pressure, and peer pressure, and about how they can make such decisions better.” Cox identifies several common elements, integrating across disciplines and experimental results from behavioral economics and brain imaging studies. Interestingly, the authors include both scientists and journalists, reflecting the appeal of this literature to the general public. Cox's review is not only invaluable for risk analysts for its synthesis of important topics, but also provides foundational understanding for students, lay people, and young professionals. We encourage feedback about the content of the review and will publish brief notes to encourage dialogue. In the next two issues, we will continue to review books on risk management, moving into the realm of prediction. The Signal and the Noise will be reviewed followed by another “mega review” of several books that help to extend and enrich the theme. The “mega review” model for reviewing books began in Risk Analysis under the aegis of Mike Greenberg. We are continuing to experiment, striving to include reviews by students, to compare perspectives across cultures, and to hear from new voices internationally.

Landowner attitudes and willingness to accept compensation from forest carbon offsets: Application of best–worst choice modeling in Florida USA

This study examines consumer preferences for the potential benefits of CRISPR technology using a best–worst scaling (BWS) approach within an online survey of a representative Spanish sample. The BWS discrete choice experiment focuses on seven key environmental and health-related benefits of CRISPR, using tomatoes as a case study. The selected benefits are derived from science-based information and align with the EU regulatory context, following the European Commission’s 2023 proposal on gene-editing technologies. Estimates from a random parameter logit (RPL) model indicate that pesticide reduction is the most highly valued benefit, followed by water saving and health improvement, thereby highlighting the combined influence of environmental and personal benefits on consumer acceptance of genetically engineered food. The significant standard deviations in the RPL estimates reveal substantial heterogeneity in preferences, which is further examined by identifying two distinct consumer segments. While both segments strongly prioritise pesticide reduction, one is primarily motivated by environmental sustainability outcomes, whereas the other places greater emphasis on health and sensory quality improvements. These findings underscore the need for targeted communication strategies to address distinct consumer concerns, rather than a uniform approach.

Severe accidents in light water reactors are characterized by an occurrence of multiphase flow with complicated phase changes, chemical reaction and various bifurcation phenomena. Because of the inherent difficulties associated with full-scale testing, scaled down and simulation experiments are essential part of the severe accident analyses. However, one of the most significant shortcomings in the area is the lack of well-established and reliable scaling method and scaling criteria. In view of this, the stepwise integral scaling method is developed for severe accident analyses. This new scaling method is quite different from the conventional approach. However, its focus on dominant transport mechanisms and use of the integral response of the system make this method relatively simple to apply to very complicated multi-phase flow problems. In order to demonstrate its applicability and usefulness, three case studies have been made. The phenomena considered are (1) corium dispersion in DCH, (2) corium spreading in BWR MARK-I containment, and (3) incore boil-off and heating process. The results of these studies clearly indicate the effectiveness of their stepwise integral scaling method. Such a simple and systematic scaling method has not been previously available to severe accident analyses.

PurposeInnovation ecosystems face many environmental challenges. The literature review shows that innovation ecosystems accelerate innovation activity, but empirical studies have not provided enough case studies focusing on the minimum-waste business strategy as one aspect of the circular economy. Various forms of interaction between members occur in the innovation ecosystems, which determines the level of cooperation. This paper aims to show the structure and forms of cooperation in an innovation ecosystem using the Czech Hemp Cluster (CHC) and its surroundings and suggest research directions in the field of interaction between members in an innovation ecosystem. Although hemp is associated with the production and distribution of narcotics, it is a versatile plant supporting the minimum-waste business strategy.Design/methodology/approachThe research is based on a theoretical part of a literature review of major scientific articles on innovation ecosystems from 2016 to 2021. The case study of the CHC and the hemp ecosystem is based on qualitative research in the form of a content analysis of the mission of the cluster members. In addition to content analysis, the classic multidimensional scaling method and hierarchical cluster analysis were used to reveal ecological guilds.FindingsThe case study highlighted the specific relationship between the cluster and the ecosystem. The cluster does not determine the ecosystem boundaries, but the ecosystem is a much broader system of cooperation and interaction between organisations. Clusters emerge after an ecosystem has existed for a particular time to coordinate collaboration and information between organisations and stakeholders. The analysis of the CHC revealed the specific role of non-profit organisations (NPOs) in the innovation ecosystem. NPOs are not engaged in primary functions in the value chain, but they provide supporting activities through coordinated networking, disseminating information on innovation, awareness-raising and stakeholder education. Compared to natural ecosystems, innovation ecosystems are typically characterised by higher forms of collaboration between members.Research limitations/implicationsAn exciting opportunity for research on innovation ecosystems is the ecological guilds taken from natural ecosystems and whose identification can help define the boundaries of innovation ecosystems. An opportunity for further research is the comparison of NPO-based and government-based clusters playing a central role in developing innovation ecosystems. Regarding the problematic generalisability of the case study to the entire agricultural production, a challenge is a search for minimum-waste business models in agriculture characterised by the biological nature of production.Originality/valueTheoretical and empirical studies have not yet considered innovation ecosystems in the minimum-waste context to a sufficient extent. The paper builds on previous scholarly studies focusing on innovation ecosystems and, for the first time, discusses the role of NPOs in the innovation ecosystem. The CHC case study adds a suitable minimum-waste business model to the still very scarce literature on sustainable innovation ecosystems. The article discusses the purpose and forms of cooperation in an innovation ecosystem, identifies a complementarity of roles in the innovation cluster and describes the interrelationship between the cluster and the ecosystem. Discussion of the ecosystem leader in the cluster-based innovation ecosystem shows the differences between Czech, Polish and German life science ecosystems.

Best–worst scaling: An introduction and initial comparison with monadic rating for preference elicitation with food products