Are they telling the truth? Revealing hidden traits of satisfaction with a public bike-sharing service

ABSTRACT

Public bike-sharing systems (BSSs) are an emerging mode of transportation introduced by municipalities to solve congestion problems in metropolitan areas, especially when integrated with other types of transportation. In the last years, the number of public bike-sharing services has been constantly on the rise all over the world, and generally the overall satisfaction with them is high. However, satisfaction with public services is driven by mechanisms that can differ from those in the private sector. It is important to establish to what extent a high satisfaction is genuine or simply ephemeral. Even “old” public services (like public transportation) become “gold” when accompanied by the introduction of new technologies. In this paper we analyze this phenomenon using data from a satisfaction web-survey conducted among customers of the public BSS “BikeMi” in Milan, Italy, in a period when mobile technologies have been introduced to speed up the service. On analyzing the responses to satisfaction questions using simple summary statistics, the level of satisfaction resulted very high. However, our aim was to look for potential “darker” sides of the service by detecting possible hidden satisfaction components. For this purpose, we used the Nonlinear Principal Components Analysis, which is particularly powerful in this context. A simple textual analysis was also performed as a validating test. Results from our analysis indicated that satisfaction is flawed by a set of factors like the mechanics of the bikes, the picking and dropping system, and the apps used to organize the service. Less concern was detected for more general aspects of the service.

KEYWORDS:

• Bike sharing
• nonlinear principal component analysis
• public transport
• quality perception
• satisfaction indicators

Acknowledgements

The authors would like to thank Sergio Verrecchia, Contracts, Development and Bike Sharing Director, Clear Channel Italia, for giving permission to use the survey dataset, Fabio Cannetti, Bike Sharing Customer Care Manager, Clear Channel Italia, and Giuseppe Mistretta, Bike Sharing Operations Manager, for their precious suggestions in developing and delivering the survey.

The authors also would like to thank the Editor and three anonymous referees for valuable suggestions and comments on earlier drafts of the paper.

Notes

¹ A new US National Household Travel Survey has been planned for 2016–17, but results are not available until early 2018 (http://nhts.ornl.gov, accessed on June 28th, 2017).

² These are 2015 data taken from the ATM website (http://www.atm.it/it/IlGruppo/ChiSiamo/Pagine/Numeri.aspx, accessed on April 29th, 2017, in Italian). ATM is a public company operating the underground and bus transport services in Milan.

³ Milan's main railway station, ‘Milano Centrale’ counts about 320,000 passengers per day (http://www.grandistazioni.it/cms/v/index.jsp?vgnextoid=aaf5d92b909ea110VgnVCM1000003f16f90aRCRD, accessed on April 29th, 2017, in Italian). No recent data about passengers’ flow are available for the other two major railway stations, ‘Milano Porta Garibaldi’, and ‘Milano Cadorna’, but Milano Porta Garibaldi is classified as a ‘platinum station’ (i.e. a station with more than 25,000 passengers per day) by RFI, the company operating the railway infrastructure service in Italy (http://www.rfi.it/rfi/LINEE-STAZIONI-TERRITORIO/Le-stazioni/Vivibilità-e-fruibilità/La-classificazione-delle-stazioni-ferroviarie, accessed on April 29th, 2017, in Italian), whereas for Milano Cadorna (operated by the LeNord company) it could be inferred a similar or even larger flow since for only one among the many train lines ending in Milano Cadorna (the line ‘Varese-Milano Cadorna’) it is estimated a flow of 39,000 passengers per working day (http://www.varesenews.it/2017/03/trenord-numeri-ritardi-passeggeri-orari/607330/, accessed on April 29th, 2017, in Italian).

⁴ These data were presented by Urbi, a company producing a mobile application for real-time access and information about the car-sharing system in Milan, at the Second Workshop “Smart Mobility in Smart City”, March 14th, 2017, University of Milan (http://milano.repubblica.it/cronaca/2017/03/14/news/milano_car_sharing-160537165/, in Italian, accessed on April 29th, 2017).

⁵ Subscription fees presented here are those of 2017.

⁶ Data on BikeMi traffic are provided by Clear Channel Italy, the company in charge of the service.

⁷ These low percentages for students and pensioners can be explained from the fact that youngsters and elders in Italy do not hugely use credit cards. Pulina (2011) examined 320,000 Italian data on credit cards collected from recent bankcard account archives for an Italy-based issuer. Only 3.2 and 6.7% of credit cards were issued to customers aged 18–25 and 66–75 years, respectively.

⁸ This analysis was performed using CATPCA in IBM SPSS 23.0.

⁹ Cronbach's α is in general a measure of reliability and internal consistency, that is how much a group of items can be considered closely related; in NLPCA it has a direct relationship with the corresponding eigenvalue: (Heiser & Meulman, 1994).

¹⁰ To have a better understanding of the components, a varimax rotation of the components is normally suggested (see, for example, Kaiser, 1958). Therefore, in Table 5 the rotated component loadings are reported.