Dr. Antonio Borriello is a lecturer at the Business Intelligence & Data Analytics (BIDA) Research Centre, Business School. Dr. Borriello joined UTS in June 2018 after obtaining his Ph.D in Economics from the Università della Svizzera italiana (Lugano, Switzerland).
He collaborated with Professor John Rose [Business Intelligence & Data Analytics Research Centre (BIDA) at UTS] from June 2016 to November 2017. Together, they worked on several projects, employing discrete choice modelling techniques in the context of transportation.
Teaming up with Professor Rose improved Dr. Borriello’s ability in dealing with stated preference experiments data, working especially on experimental design generation (using the software Ngene), survey programming (employing Qualtrics platform) and data analysis.
From 2013 until 2017, Dr. Borriello worked as a researcher at the Institute for Economic Research, Università della Svizzera italiana. The multidisciplinary research project “Post-Car-World” he was engaged in (https://archiveweb.epfl.ch/postcarworld.epfl.ch/page-102193.html), was sponsored by the Swiss National Science Foundation and aimed at exploring the future of mobility and questioning the need for or the role of the car.
In the framework of this project, Dr. Borriello developed an RP (Revealed Preferences)/SP (Stated Preferences) strategy to inquire into the attitudes and choice behaviour regarding the eventual substitution of the car as an individual transport mode. More importantly, he analysed aspects of moving versus being moved and generally, the “pleasure of driving”, using advanced discrete choice modelling techniques.
Doc.Mobility, 2016. (http://www.snf.ch/en/funding/careers/doc-mobility/Pages/default.aspx)
The grant was funded by the Swiss National Science Foundation in 2016. It allowed to enhance my scientific profile by working at a research institution abroad. Specifically, I visited Prof. John M. Rose for twelve months, when he worked at the Institute for Choice (I4C – University of South Australia) in Sydney.
- International Choice Model Conference (ICMC), August 19-21 2019 (Kobe, Japan): "Consumers' preferences for different energy mixes in Australia";
- International Conference on Traffic and Transport Psychology (ICTTP), August 2-5 2016 (Brisbane, Queensland): “Components behind the pleasure of driving: a Partial Least Squares – Path Modelling approach;
- International Conference on Traffic and Transport Psychology (ICTTP), August 2-5, 2016 (Brisbane, Queesnland): “Detecting positive, negative, indifferent and ambivalent attitudes towards driving and commuting by car: an application with evaluative space grids”;
- World conference on Transport Research Society (WCTRS), July 10-16, 2016 (Shanghai, China): “The pleasure of driving as a constraint for leaving the car: evidence from a hybrid choice model”;
- Swiss Transport Research Conference (STRC), May 18-20, 2016 (Monte Verità, Ascona, Switzerland): “Pleasure of driving components: a partial least square – path modelling approach);
- Swiss Transport Research Conference (STRC), April 15-17, 2015 (Monte Verità, Ascona Switzerland): Are commuters in Lugano ready to leave the car? Evaluating conventional and innovative solutions to facilitate the switch”.
Consumers’ preferences for different energy mixes in Australia
The topic of discrete choice models has become my main focus during my doctoral studies, when I mainly explored this methodology in the context of transportation. My primary interest lies in the role of psychological aspects in determining individual choices, such as attitudes and perceptions. The methodological innovations in hybrid choice models could bring further useful insights on how people choose as well as on what affects their decisions. Furthermore, I am also interested in empirical research in other fields, such as environmental economics and health economics.
2019: Business Statistics Tutorials (Undergraduate)
Borriello, A, Scagnolari, S & Rose, JM 2019, 'Reducing the randomness of latent variables using the evaluative space grid: Implementation in a hybrid choice model', Transportation Research Part F: Traffic Psychology and Behaviour, vol. 62, pp. 192-211.View/Download from: Publisher's site
© 2019 Elsevier Ltd The study of latent variables, and in particular of attitudes, contributes to a better understanding of individual preferences and behavior and it is now common practice within transportation literature. However, the procedure of attitude measurement is still not optimal. Two major issues are the misspecification of the attitude itself and the number of suitable items used for defining the psychological factor. The incorrect measurement entails a poor representation of individuals on the latent continuum and a less precise definition of the latent variable itself. These issues become even more relevant when a Likert scale is used. Indeed, the neutral point of this scale is selected by both individuals having an ambivalent and an indifferent attitude, and the poor representation makes impossible to distinguish these categories. Nevertheless, such a distinction can be very profitable for policy reasons. To overcome this issue and to suggest more effective policies, we propose using the Evaluative Space Grid (ESG), which is a single-item measure of positivity and negativity, to collect attitudinal variables. This tool can distinguish between individuals with indifferent and ambivalent attitudes, as well as those with positive and negative inclinations. This paper models the ESG using a pair of ordered logit regressions and suggests a procedure to include this approach in the framework of hybrid choice models. Furthermore, it endeavors to shed light on the preferences of individuals having indifferent and ambivalent inclinations in a transportation context, showing the hypothesis that their preferences are different for commuting trips.
Policy makers worldwide face several challenges in addressing climate change, including an understanding of how to successfully introduce initiatives reliant on renewable energy sources (RES). A key component in this is understanding citizen preferences in terms of willingness to pay (WTP). This research focuses on utilising a discrete choice experiment and associated hybrid choice model to model individual WTP for four different RES types (biomass, hydro, solar and wind) against four current and potential non-RES types (gas, oil, nuclear and coal). The model accounts for latent segments in relation to WTP based on pro-environmental attitudes and various socio-demographics. The research examines the case of Australia, but reports on WTP at each state and territory level rather than at the national level. The findings indicate that respondents from different states and territories have heterogeneous preferences in terms of energy mix composition, which led to different WTP values. A large dissonance emerges also comparing preferences at national and state/territory level, which may potentially act as hindrance to the achievement of the goal set for the Paris agreement.
Borriello, A 2016, 'Pleasure of driving components: a partial leastsquare – path modeling approach.', Swiss Transport Research Conference, Monte Verita', Ascona - Switzerland.
Borriello, A, Scagnolari, S & Maggi, R 2016, 'The pleasure of driving as a constraint for leaving the car: Evidence from a hybrid choice model', http://wctrs.conference-services.net/reports/template/onetextabstract.x…, World conference on transport research, Shanghai.
Borriello, A, Scagnolari, S & Maggi, R 2015, 'Are commuters in Lugano ready to leave the car? Evaluating conventional and innovative solutions to facilitate the switch', Swiss Transport Research Conference, Monte Verita', Ascona - Switzerland.
Borriello, A 2018, 'The role of attitudes in determining individual behavior in transportation : From psychology to discrete choice modeling'.
This dissertation focuses on the role of psychological factors, particularly attitudes, in determining individual behavior in transportation and proposes procedures and methods to improve the measurement of psychological variables to be used in choice modeling. The thesis is divided into three chapters containing a methodological section, showing the innovation of the econometrics steps, and an empirical work, based on datasets collected in the context of transportation. The first chapter describes the drawbacks of using common instruments for attitude measurement, such as Likert scale or semantic differentia scale, and proposes to analyze attitudinal data using the Evaluative Space Grid in order to distinguish individuals having indifferent and ambivalent attitudes, as well as positive and negative inclinations. The second chapter endeavors to integrate the Evaluative Space Grid in the framework of discrete choice modelling in order to avoid the aggregation of the individuals lying on the neutral part of the latent continuum of the attitude object of the study. In addition, it empirically tests the hypothesis that individuals revealing indifferent and ambivalent attitudes behave differently in the context of transport mode choice for commuting purposes. Finally, the last chapter proves that both long-term stable constructs which are “memory-based”, namely generalized attitudes, and short-term situational specific constructs which are built at the time a specific situation occurs, namely localized attitudes, contribute in shaping individual preferences.