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In the last few decades, tourism has become one of the fastest growing economic sectors in the world, with an increasing economic, social and environmental role. It has been recognised as a strategic driver, able not only to heighten economic growth, employment and enhancement of cultural values, diversity and heritage, but also to help countries transition towards more inclusive and resilient economies. In this framework, slow tourism has been playing an important role, compliant with the universal 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs). Its different forms – such as eco-tourism, rural and village tourism, as well as religious routes – can improve social inclusiveness, poverty reduction and environmental protection while empowering host communities, generating trade opportunities and fostering peace and intercultural understanding.

The pilgrimage on religious routes in particular has been showing a renewed potential. This ancient practice, largely rooted in many confessions as an expression of a mainly religious experience has been gaining new values for both people and territories hosting destinations: its target groups of travellers have enlarged to those looking for spiritual holidays (individuals and groups) as well as well-being and integrated experiences combining religious sites, cultural heritage, landscape and nature, traditions and crafts, food, wine and local events (shared with local people to feel part of the local community). This form of tourism responds to the sustainability challenge as an opportunity for local development in depopulated areas, but still rich in history, nature, art and traditions.

On this basis, this chapter deals with eco-sustainable and religious tourist routes in Sicily (South Italy), focusing on: (1) their relevance in relation to emerging strategies and policies (i.e. cultural ecclesial parks, regional development plans, etc.) (2) and their aptitude to generate sustainable and innovative local development. In particular, it addresses the recent experiences in progress on the Itinerarium Rosaliae in Sicily as opportunities for sustainable and local development.

The lesser-known tourist destinations thanks to new technologies are experiencing a period of growth and development. Sharing economy has given new opportunities to smaller places, in particular the villages that are living a difficult period due to depopulation. The revival of small villages has been supported by the spread of good practices which, with the help of the Internet and the sharing economy, has led to a greater social, economic and tourist growth. The opportunities to emerge on the international market have increased thanks to the introduction of web and social networks. Information and communications technologies (ICTs) help overcome accessibility and isolation problems in some places. Sharing resources and increasingly democratic communication channels have been the basis for the creation of a new economy based on authenticity, unique experiences and consequently a slow tourism linked to rural villages. The aim of this research is to show how the sharing economy is important for the development of little villages; technologies in particular help the less-known destinations to grow up economically and socially. This research analyzes the concept of authenticity, very important for the experiential tourism and the sustainability considered the key for a good development of places. Then it considered the idea of technologies related to the development of little villages, with some example of good practice from Italy.

Offers a critical commentary on the Evers and Lakomski research programme as summarized in their article, “Theory in educational administration: naturalistic directions”. The origins of their naturalistic epistemology are reviewed and some of its limitations for understanding social action considered, especially the limitations it imposes on understanding social behaviour at the level of meaning. Brief attention is given to their coherence criteria, particularly to the manner in which these should be appropriately employed in theory choice. Concludes with a short consideration of the general problem of developing theory in educational administration and the appropriate role for modern science.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.

To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.

The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.

Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.

The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.

The purpose of this paper is to provide a boundary-focused analysis of career patterns in Greek public education.

A descriptive, interpretive design from the naturalistic paradigm was chosen for the study, based on narratives of 27 public education employees who used decision-making models for outlining their career plans.

This study depicted career-related boundaries and intentions of employees to develop their career within their domain or crossing particular boundaries, namely, the public-private boundary, the public education to higher education boundary, the occupational and other less salient boundaries.

The delimitation of the study on public education employees studying for a postgraduate diploma might limit the scope of inter-occupational mobility.

This study highlights the subject of the first degree as the most critical determinant of career development, and identifies the role of structural constraints, especially of promotion systems, in “bounding” graduate careers.

This study developed a typology of career paths of public education employees and associated them with the identification of two main employee profiles, related to the subject of their first degree.

The purpose of this article is to explore the ability of a novel psychological theory of how people make decisions, narrative‐based decision theory, to help explain people's decisions about whether to continue investment in a research and development (R&D) project (R&D progress decisions).

The paper applies the new theory to an empirical finding of existing research on R&D progress decisions; the finding that instruction in the sunk cost principle seems to mitigate the sunk cost effect in R&D progress decision‐making.

By interpreting the empirical finding in terms of narrative‐based decision theory, the paper is able to clarify and extend an earlier explanation for the empirical finding. More specifically, by drawing on narrative‐based decision theory the paper is able to provide a more detailed explanation of how the predictor variable (sunk cost) and the moderator variable (instruction in the sunk cost principle) may exert an influence.

Based on the result of the exploration, the authors call for further investigations into narrative‐based decision theory's value in explaining R&D progress decisions, and other management decisions.

Furthermore, the authors call for investigations into how narrative‐based decision theory may help decision‐makers in improving the quality of R&D progress decisions.

Narrative‐based decision theory is a recent theory from the field of naturalistic decision‐making. To the authors' knowledge, this is the first article that, by using an example, illustrates how the theory may help in explaining the findings of empirical research on management decisions.

This paper aims to identify computational thinking (CT) in 4th to 6th grade students in the context of project-based problem-solving while engaged in an after-school program.

This case study approach was selected due to its suitability for answering “how” or “why” questions about real-world phenomena within a set context (Creswell and Poth, 2018; Yin, 2018). This was an appropriate fit given the context of an after-school program and the research question asked how to identify learners’ demonstrated CT through project-based learning hands-on activities and problem-solving in a naturalistic environment.

Results show that heuristics, algorithms and conditional logic were observed more than other components of CT such as data collection, simulations and modeling. Descriptions of common activities in a naturalistic learning environment are presented to illustrate how the students practiced CT over time, which could help readers develop an understanding of CT in conjunction with hands-on problem-solving activities in elementary students. Identifying and classifying CT in this study focused on students’ learning process.

This study contributes to the challenging field of evaluating CT while focusing on observable behaviors and problem-solving activities with various degrees of teacher’s facilitation instead of final artifacts. Implications for researchers and educators interested in integrating CT in K-12 learning and its assessment are discussed.

The purpose of this paper is to examine the dominant consumer decision theory models and understand why that theory has received little empirical validation. A “decision waves” theory is proposed – an alternative, multi‐phase approach to decision making using image theory. An approach to validating empirically the multi‐phase theory is outlined.

This conceptual paper examines the foundations of modern consumer decision theory and argues for a more representative model of actual consumer decisions.

Decision waves provide a theoretical approach to represent more accurately consumer decision making and improve understanding in this foundational component of marketing. Decision waves do not change detailed empirical findings: however, they do change the macro perspective of how those findings are assembled for marketing.

An empirical test of decision waves theory is ongoing.

The concepts outlined in this paper will change segmentation, positioning and how tactical plans are developed within the marketing mix, particularly for promotional strategies.

A theoretical approach that represents decision making more accurately will bring us closer to understanding this foundational component of marketing. It provides a basis for differentiation in congested markets.