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The purpose of this paper is to demonstrate how the use of data mining (DM) analysis can be used to evaluate how well cameras that monitor red‐light‐signal controlled intersections improve traffic safety by reducing fatalities.

The paper demonstrates several different data modeling techniques – decision trees, neural networks, market‐basket analysis and K‐means models. Decision trees build rule sets that can abet future decision making. Neural networks try to predict future outcomes by looking at the effects of historical inputs. Market‐basket analysis shows the strength of the relationships between variables. K‐means models weigh the impact of homogenous clusters on target variables. All of these models are demonstrated using real data gathered by the Department of Transportation from fatal accidents at red‐light‐signal controlled intersections in Maryland and Washington, DC from the year 2000 through 2003.

The results of the DM analysis will show predictable relationships between the demographic data of drivers and fatal accidents; the type of collision and fatal accidents and between the time of day and fatal accidents.

The limitations of missing or incomplete data sets are addressed in this paper.

This paper can act as a guide to follow for red light camera program managers or local municipalities to conduct their own analysis.

This paper builds upon prior research in DM and also extends the body of research that examines the effectiveness of red camera programs as they mature.

This research applies data mining techniques to discover the relationship between driver inattention and motor vehicle accidents.

The data used in this research is obtained from the Fatality Analysis Reporting System of the National Highway Traffic Safety Administration, focused on the Maryland and Washington, DC area from years 2000 to 2003. The data are first clustered using the Kohonen networks. Then, the patterns and rules of the data are explored by decision tree and neural network models.

Results suggests that when inattention and physical/mental conditions take place at the same time, the driver has a higher tendency of being involved in a crash that collides into static objects. Furthermore, with regards to the manner of collision, the relative importance of colliding into a moving vehicle as the first harmful event is two times higher relative to that of colliding into a fixed object as the first harmful event in a crash.

The data used in this research are limited to fatal crashes that happened in Maryland and Washington, DC from years 2000 to 2003.

This is one of the first research papers utilizing data mining techniques to explore the possible relationships between driver inattention and motor vehicle crashes.

This paper aims to identify a possible mismatch between the theory found in academic research and the practices of investment managers in Brazil.

The chosen approach is a field survey. This paper considers 78 survey responses from 274 asset management companies. Data obtained are analyzed using independence tests between two variables and multiple regressions.

The results show that most Brazilian investment managers have not adopted current best practices recommended by the financial academic literature and that there is a significant gap between academic recommendations and asset management practices. The modern portfolio theory is still more widely used than the post-modern portfolio theory, and quantitative portfolio optimization is less often used than the simple rule of defining a maximum concentration limit for any single asset. Moreover, the results show that the normal distribution is used more than parametrical distributions with asymmetry and kurtosis to estimate value at risk, among other findings.

This study may be considered a pioneering work in portfolio construction, risk management and performance evaluation in Brazil. Although academia in Brazil and abroad has thoroughly researched portfolio construction, risk management and performance evaluation, little is known about the actual implementation and utilization of this research by Brazilian practitioners.

The study investigates the impact of various factors on the number of active investors in digital health startups. Through nine hypotheses, we examine the influence of metrics such as patents, online presence, financial aspects and company valuation on investor interest. The results reveal positive associations between these metrics and investor numbers, highlighting their role in signaling strength and attracting investment. This research enhances the understanding of investor valuation in digital health startups, emphasizing the importance of credible signals for building trust and securing funding. However, we acknowledge limitations in data analysis methods and suggest future research to explore industry signals, longitudinal trends and failed startups for comprehensive insights.

This study delves into the design methodology and approach, aiming to fill gaps in understanding investor roles in valuing digital health ventures. We focus on deciphering factors driving valuations for these startups to secure growth financing. Using signaling theory, we investigate how entrepreneurs communicate their latent strengths to bridge information gaps, aiding investment decisions. We analyze a sample of 482 healthcare startups from the Pitchbook database using Poisson regression in SPSS.

This research sheds light on the factors driving investor interest in digital health startups. Despite the critical role of entrepreneurs in patient care innovations, the relationship between investor characteristics and funding for digital health technologies still needs exploration. We examine factors influencing investor valuation in healthcare startups and identify patents, social followers and financial disclosures as pivotal elements shaping investor interest. The findings show that all factors for active investors are significant for all variables except similar unique visitors.

These results significantly enhance our understanding of investor decision-making in digital health startups. They confirm the importance of various signals, like patent activity, online presence and financial performance, in attracting investor attention. We utilize unique data sources, offering insights into investors' behavior across different funding stages. In conclusion, these findings underscore investors' crucial role in the growth and funding of healthcare tech startups, emphasizing the need for robust signals to attract investment.

This paper investigates the impact of some of the underlying dynamics of volunteering choices in organizational contexts, focusing on individual, group, and organizational level causes. Three scenario‐based experiments manipulate individuals' standing within their organization (i.e., whether they are doing well or poorly) in combination with variables such as the expected efficacy of one's team and positive or negative organizational performance. In comparison to other recent volunteering studies, all three current experiments focused on an explicit organizational context and found much stronger intentions to volunteer, particularly when a person's standing was good. The combination of poor standing with expectations of poor performance by one's group or one's organization led to reductions in these otherwise strong intentions to volunteer. The results also show that feelings of obligation, expectations of extrinsic rewards, and identifying with one's organization are all significantly related to volunteering choices.

This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the composite marginal likelihood (CML) approach in multivariate ordered-response situations. The ability of the two approaches to recover model parameters in simulated data sets is examined, as is the efficiency of estimated parameters and computational cost. Overall, the simulation results demonstrate the ability of the CML approach to recover the parameters very well in a 5–6 dimensional ordered-response choice model context. In addition, the CML recovers parameters as well as the MSL estimation approach in the simulation contexts used in this study, while also doing so at a substantially reduced computational cost. Further, any reduction in the efficiency of the CML approach relative to the MSL approach is in the range of nonexistent to small. When taken together with its conceptual and implementation simplicity, the CML approach appears to be a promising approach for the estimation of not only the multivariate ordered-response model considered here, but also for other analytically intractable econometric models.

In this paper, we consider the analysis of models for univariate and multivariate ordinal outcomes in the context of the latent variable inferential framework of Albert and Chib (1993). We review several alternative modeling and identification schemes and evaluate how each aids or hampers estimation by Markov chain Monte Carlo simulation methods. For each identification scheme we also discuss the question of model comparison by marginal likelihoods and Bayes factors. In addition, we develop a simulation-based framework for analyzing covariate effects that can provide interpretability of the results despite the nonlinearities in the model and the different identification restrictions that can be implemented. The methods are employed to analyze problems in labor economics (educational attainment), political economy (voter opinions), and health economics (consumers’ reliance on alternative sources of medical information).

This paper explores the historical roots of accounting for biodiversity and extinction accounting by analysing the 18th-century Naturalist's Journals of Gilbert White and interpreting them as biodiversity accounts produced by an interested party. The authors aim to contribute to the accounting history literature by extending the form of accounting studied to include nature diaries as well as by exploring historical ecological accounts, as well as contributing to the burgeoning literature on accounting for biodiversity and extinction accounting.

The authors’ method involves analysing the content of Gilbert White's Naturalist's Journals by producing an 18th-century biodiversity account of species of flora and fauna and then interpretively drawing out themes from the Journals. The authors then provide a Whitean extinction account by comparing current species' status with White's biodiversity account from 250 years ago.

This paper uses Gilbert White's Naturalist's Journals as a basis for comparing biodiversity and natural capital 250 years ago with current species' status according to extinction threat and conservation status. Further the paper shows how early nature diary recording represents early (and probably the only) forms of accounting for biodiversity and extinction. The authors also highlight themes within White's accounts including social emancipation, problematisation, aesthetic elements and an example of an early audit of biodiversity accounting.

There are limitations to analysing Gilbert White's Naturalist's Journals given that the only available source is an edited version. The authors therefore interpret their data as accounts which are indicative of biodiversity and species abundance rather than an exactly accurate account.

From the authors’ analysis and reflections, the authors suggest that contemporary biodiversity accounting needs to incorporate a combination of narrative, data accounting and pictorial/aesthetic representation if it is to provide a rich and accurate report of biodiversity and nature. The authors also suggest that extinction accounting should draw on historical data in order to demonstrate change in natural capital over time.

Social implications include the understanding gleaned from the authors’ analysis of the role of Gilbert White as a nature diarist in society and the contribution made over time by his Journals and other writings to the development of nature accounting and recording, as well as to one’s understanding and knowledge of species of flora and fauna.

To the authors’ knowledge this is the first attempt to analyse and interpret nature diaries as accounts of biodiversity and extinction.

In the current decade, client requirements appear to play an increasingly important role in designing not‐for‐profit organisations, in particular in the domains of services and healthcare. Quality function deployment (QFD) is a well‐known design method. This method has a vested reputation in industrial production as a means of systematically incorporating customer requirements in product design. However, in the domain of the services, and especially the professional services, there is little experience in applying QFD. Application in this domain probably causes problems, for instance with respect to the customer concept, which is more ambiguous in this domain, and with respect to the interrelated nature of the product (service) and the process. In this paper we present some limitations of conventional QFD outside physical industrial production and we present a refinement and an extension of QFD for healthcare applications, based on research methods in the social sciences. Illustrations are given from two cases in Dutch healthcare organisations.

The purpose of this paper is to present the contents and the didactic approach that characterize, respectively, the “Introductory Statistics with R” and “Statistics and Foresight” courses of the Master in Social Foresight.

The two courses “Introductory Statistics with R” and “Statistics and Foresight” are designed to provide an introduction to quantitative methods in the social sciences with specific applications to social foresight. In particular, the first course introduces students to data analysis providing the necessary tools to study and represent socio-economic phenomena through graphical summaries and numerical measures. During the course, example applications based on the use of the open-source software R are shown. At the end, the students should be able to perform data management, conduct descriptive analysis of categorical and quantitative variables and analyze bivariate distributions. The subsequent course “Statistics and Foresight” presents the most efficient methods to make decisions in a context of uncertainty while visualizing the potential errors of wrong decisions and computing the probability of their occurrence.

This paper is a description of an interesting and promising way of teaching applied statistics in social sciences.

With the main aim of learning the correct use of statistics, specific attention is devoted to the use and interpretation of the aforementioned methods rather than to their theoretical aspects. Even in the second course, an important role is played by the treatment of real data by the use of the R software.

This paper attempts to systematize a method of teaching statistics based on the practical use of open-source software.