Exploring drivers of overnight stays and same-day visits in the tourism sector

: We employ mobile network data referred to the area of Lombardy in Italy to investigate alternative touristic behaviours, such as same-day visits and overnight stays in Italy. We show that larger availability of tourism accommodations, cultural and natural endowments are relevant factors explaining overnight stays. Conversely, temporary entertainment and transportation facilities increase municipalities attractiveness for same-day visits. The results also highlight a trade-off in the capability of municipalities of being attractive in connection to both the tourism behaviours. For instance, higher tourists arrivals are observed in areas receiving limited visitors, coming from municipalities with low same-day visits outflows. We highlight mobile data offer an adequate level of spatial and temporal granularity and can be thus employed to support policy makers in the design of effective tourist management strategies

B2c e-commerce adoption in inner cities: An evolutionary perspective

Internet makes it possible for consumers to shop without visiting a physical store. As online shopping is becoming more popular, this could have significant impact on in-store shopping. The extent to which consumers, producers and retailers make use of the Internet as a complementary channel or as a substitute for in-store shopping is fundamental for the way traditional retailing will be affected. It is only recently that geographers are becoming interested in the spatial consequences of this new form of commerce. From a traditional geographical perspective, one could expect that business-to-consumer (b2c) e-commerce could make physical shopping redundant, leading to a ‘death of distance’. There are, however, several factors that may limit this new form of commerce, such as logistical constraints (e.g., personal delivery of goods may be quite expensive), habits of people, and the need for social contact. The main goal of the paper is to draw some expectations concerning the relationship between b2c e-commerce and inner city retailing. Using new insights based on evolutionary economics, hypotheses will be developed concerning the impact of b2c e-commerce on consumers’ shopping behaviour, retailers’ store strategy, and the inner city retailing environment as a whole. We claim that habits may act as a constraint to change consumers’ shopping behaviour. In addition, routines can explain why retailers may be rather reluctant in exploiting this new channel of commerce, and why they are most likely to adopt rather conservative e-commerce strategies. We also explain how and why inner cities, as important retailing and consumption places, may affect the way actors deal with this new form of commerce. One may expect that especially in these localities, both stimulating and limiting factors of b2c e-commerce adoption are predominant, depending on the quality or the attractiveness of the inner cities, among other things.evolutionary economics, e-commerce, urban economics

Defining the "figure of merit" for places in the age of digital nomadism

The aim of this work is to find analogy between the “figure of merit” of electronic devices and place formulas that could be useful to explain in synthesis what could be the characteristics of a place to be considered by digital nomads to live a work experience. Digital nomads are people with a particular life style: they live in a sustainable, cooperative, and social network-oriented life; for a limited period of time, they choose places in line with their particular work needs and with their life style. Digital nomads could be a particular target for places, because they represent a form of knowledge and culture openness and could become a relevant driver for the evolution of the place. Places must organize themselves to attract this segment with the efforts on a specific interpretation matrix that could start from the “figure of merit” concept. Each digital nomad chooses his or her device using this approach, and that could be the key that provides the place value to them. This work provides a “figure of merit” formula for places toward digital nomads. In the following research project, it will be possible to test the formula and to measure the results in a consumer behaviour approach

Performance of Public Transport Appraisal using Machine Learning

Public passenger transport holds immense significance in the overall transportation system. Forecasting the movement of public transport has emerged as a crucial problem in transport planning due to its practical implications. Recently, there has been a lot of significant attention in Intelligent Transportation Systems (ITS), introducing various advancements and innovative applications to develop conditions for public transit that are safer, more effective, and fun. To fully leverage the potential of ITS applications and deal with road situations proactively, it becomes crucial to have a reliable method for predicting traffic flow. This opens up opportunities for ITS applications to anticipate and address potential challenges in advance. Enhancing the efficient functioning of Public Transport (PT) networks is a primary objective for urban area authorities, and the proliferation of location and communication devices has led to an abundance of operational data. Applying appropriate Machine Learning (ML) methods can help identify patterns in the data to improve the Schedule Plan. This research focuses on heterogeneous information that influences the prediction value, aiming to predict the required transport demand for specific routes and the arrival time of public transport. Utilizing DBSCAN clustering with SARIMA Algorithm, real-time passenger demand forecasting is extensively promoted to enhance dynamic bus scheduling and management. Furthermore, this paper compares the accuracy of the proposed Prophet Model with traditional time series models like ARIMA and SARIMA. The aim is to provide precise and robust passenger demand predictions, enabling more effective planning and management of PT services

Exploring doctoral students’ expectations of work-based skills training

Purpose - Doctoral students are expected to undertake work-based skills training within their doctoral studies in areas such as problem solving, leadership and team working. This study explores student expectations of doctoral training within a UK Higher Education context. Design - The data for the study was gathered via two focus groups conducted among doctoral students from different faculties in a post-92 UK University. Participants were selected using a snowball sampling approach. Findings - The findings suggest that the expectations of doctoral students are contingent upon their year of study, study mode, perceived fit between training goals and available training, peer recommendations, Word-of-Mouth (WoM) and the scholarly support they received from their supervisors. Practical Implications - The study suggests a better understanding of students’ segmentation can help Higher Education Institutions deliver training that meets the expectations of doctoral students in a way that result in zero or a positive disconfirmation. Originality/Value – This paper develops and deepens the understanding of the doctoral students’ expectations of work-based skills training and highlights the need for universities to adapt their doctoral training according to the expectations of different student segments