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Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and information systems spread and become more and more diverse, the transportation community is getting more and more interested in the potential of data fusion processes to help gather more complete datasets and help give additional utility to available data sources. Research is looking for ways to enhance the available information by using both various data collection methods and data from various sources, surveys or observation systems. Survey response rates are decreasing over the world, and combining survey modes appears to be an interesting way to address this problem. Letting interviewees choose their survey mode allows increasing response rates, but survey mode could impact the data collected. This paper first discusses issues rising when combining survey modes within the same survey and presents a method to merge the data coming from different survey modes, in order to consolidate the database. Then, it defines and describes the data fusion process and discusses how it can be relevant for transportation analysis and modelling purposes. Benefiting from the availability of various datasets from the Greater Montréal Area and the Greater Lyon Area, some applications of data fusion are constructed and/or reproduced to illustrate and test some of the methods described in the literature.

Purpose – The author examines the implementation and characteristics of teacher evaluation and explores their associations with improvement in teachers’ practice of constructivist instruction.Methodology – This quantitative study uses statewide longitudinal Teachers’ Opportunity to Learn survey data collected in 2009 and 2010 from middle school mathematics teachers in Missouri and estimates a series of value-added models with two-level Hierarchical Linear Modeling.Findings – Teachers in this study were mainly evaluated by principals who conducted classroom observations and held face-to-face meetings to evaluate teaching practice and professional development activities. The study provides empirical evidence and support for the use of multiple evaluators with multiple evaluation data and outcomes in teacher evaluation. Additionally, it highlights the potential benefits of focusing on teachers’ instructional data instead of student achievement in teacher evaluation in order to improve their teaching practice.Research limitations – This study focused on middle school mathematics teachers in a single state in the United States. Whether these findings can be generalized to teachers of other subject areas or grades, or to states with different policy contexts, or to countries with country-specific structural, cultural, and social differences is unknown.Value – This study is the first effort to systematically examine teacher evaluation practices across a single state and provide empirical evidence on the relationships between the implementation characteristics of teacher evaluation and improvement in teachers’ instructional practice. Findings of this study provide school, district, state, federal, and international policymakers and administrators with important, up-to-date information on teacher evaluation at the middle-school level in the United States.

The management of consumer privacy has become a critical concern for organizations in the age of artificial intelligence–powered marketing. The impact of data on the market environment has brought both benefits and challenges, with marketers gaining valuable insights but also raising privacy concerns. As artificial intelligence–powered marketing advances, consumer vulnerability increases due to the sensitivity of collected data. This vulnerability leads some consumers to resort to falsifying information, posing a significant threat to the digital economy. Privacy empowerment and customer control play a vital role in addressing these challenges. This chapter explores the influencing factors and ethical considerations surrounding data falsification. It also discusses strategies to mitigate perceived vulnerability through privacy controls and explores the consequences of data breaches and customer vulnerability. The chapter further emphasizes the need for organizations to balance benefits, risks, and customer trust while harnessing the value of customer data. An ethical framework for data privacy marketing audits is proposed to help organizations assess their data practices responsibly and competitively. By integrating personal data protection strategies within an ethical framework, organizations can protect consumer privacy, enhance customer trust, and maintain their competitive edge in the market.

We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.

Technical solutions can be important when key communicators take on the task of making sense of social media flows during crises. However, to provide situation awareness during high-stress assignments, usability problems must be identified and corrected. In usability studies, where researchers investigate the user-friendliness of a product, several types of data gathering methods can be combined. Methods may include subjective (surveys and observations) and psychophysiological (e.g. skin conductance and eye tracking) data collection. This chapter mainly focuses on how the latter type can provide detailed clues about user-friendliness. Results from two studies are summarised. The tool tested is intended to help communicators and journalists with monitoring and handling social media content during times of crises.

Sustainable Development Goal (SDG) Indicator 4.2 calls for all girls and boys to have access to high-quality early childhood education by 2030. This global mandate establishes a new framework of accountability to increase access to preprimary education in low- and middle-income countries through measurement and reporting. As with other global indicators, however, the measurement of preprimary education access is more complex and nuanced than may be supposed. This data-oriented chapter delves deeply into the measurement of SDG 4.2 and explores the accuracy of the indicator being used: the adjusted net enrollment ratio, one year before the official age of primary entry. The chapter analyzes data from both education management information systems (EMIS) and household surveys to triangulate information about children’s access to preprimary education before they begin primary school. The analysis concludes that the indicator used to measure SDG 4.2 is overestimating access to preprimary education, because it includes large numbers of children who enroll in primary school before the official age of entry. This suggests that parents “vote for preschool” by sending their under-age children to primary school when access to affordable preprimary is limited. Implications for SDG measurement and preprimary policy are discussed.

Introduction: Big data in the insurance industry can be defined as structured or unstructured data that can affect the rating, marketing, pricing, or underwriting. The five Vs of big data provide insurers with a valuable framework for converting their raw data into actionable information. These five Vs are specifically: (1) Volume: The need to look at the type of data and the internal systems; (2) Velocity: The speed at which big data is generated, collected, and refreshed; (3) Variety: Refers to both the structured and unstructured data; (4) Veracity: Refers to trustworthiness and confidence in data; and (5) Value: Refers to whether the data collected are good or bad.

Purpose: Insurance companies face many data challenges. However, the administration of big data has allowed insurers to acknowledge the demand of their customers and develop more personalised products. In addition, it can be used to make correct decisions about insurance operations such as risk selection and pricing.

Methodology: We do this by conducting a systematic literature review on big data. Our emphasis is on gathering information on the five Vs of the big data and the insurance market. Specifically, how big data can help in data-driven decisions.

Findings: Big data technology has created an endless series of opportunities, which have ensured a surge in its usage. It has helped businesses make the process more systematic, cost-effective, and helped in the reduction in fraud and risk prediction.

Ethnography is a qualitative, naturalistic research method derived from the anthropological tradition. Ethnography uses participant observation supplemented by other research methods to gain holistic understandings of cultural groups’ beliefs and behaviors. Ethnography contributes to bioethics by: (1) locating bioethical dilemmas in their social, political, economic, and ideological contexts; (2) explicating the beliefs and behaviors of involved individuals; (3) making tacit knowledge explicit; (4) highlighting differences between ideal norms and actual behaviors; (5) identifying previously unrecognized phenomena; and (6) generating new questions for research. More comparative and longitudinal ethnographic research can contribute to better understanding of and responses to bioethical dilemmas.