Behaviour Monitoring in Context: a Methodology for Measuring the Impact of the Human Factor on Smart Cities Big Data and Technologies

Francesca Spagnoli, Shenja van der Graaf, Annelien Smets

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In this paper, a methodology is presented, that is designed to enable smart cities to create large scale experiments with citizens in real-life contexts, so as to modify their behaviour to a desired goal. This model affords a linkage between technologies and the ‘human factor’, by facilitating behavioural change processes through the use of ICT, Internet of Things (IoT) and wearables in the interventions, enabling the collection of large amounts of data. This methodology has been developed to support city governments and public institutions to effectively use large data sets generated in urban environments. This integrated framework represents the need of considering both the personal determinants and the contextual ones, when designing behavioural change interventions within the complex environment of a smart city, in combination with the use of ICT. Indeed, technology is seen as a facilitator for behaviour change, even if often there is a de-synchronisation between tech development and context of use.
It is estimated that by 2050, 70% of the world population will live in cities [1]. Urban big data can develop relevant benefits for the smart cities [2]. Indeed, ICT has always played a primary role in mobilising urban innovation and supporting the creation of the “Smart Citizenship” concept [3]. Traditionally, smart cities use the IoT technologies and big data to improve its services and products, by taking advantage of co-creation processes, methodologies and tools [4]. It has been demonstrated that the use of urban big data is a fair tool for creating new business opportunities and markets in smart cities, especially in terms of service innovation and creation of new business models [5, 6].
What and why are the so-called smart cities then? The concept has been variously defined within the literature, on the one hand as urban places composed of “Everyware”, a context pervaded from ubiquitous computing to monitor urban data flows with the final scope of engaging with its citizens [7]; on the other hand, the term has been related to the ability to enact the development of knowledge economies, where the governance of an urban environment is triggered by the innovation and creativity of its smart citizens [8]. It is clear that smart cities can play an active role for enabling socio-economic progress for increasing the sustainability of urban contexts [8, 9]. Indeed, they have produced a relevant impact in different sectors, especially in mobility, health, energy and education. These social and economic transformations are even more enacted by the implementation of big data applications to improve the living standards of citizens [10]. According to Batty, big data in a smart city context have the potential to activate a change in every sector of the society and to generate a positive impact on the economy of all nations [11]. The fact that the current volume of big data is approximately 2.5 quintillion bytes [12] demonstrates that the potential is enormous. In cities, data analysis (and integration) is increasingly seen as the main source of promoting growth and wellbeing, thus improving the quality of life of citizens and the sustainability of cities.
In this view, smart cities are likely to be able to achieve a full urban sustainability, but only if they succeed to trigger or support a sustainable behaviour of their citizens, as well as drafting a sustainability plan, but only if they work collectively, involving all actors and citizens, who ultimately are also needed to be incentivised to change their current behaviour to reach smart city goals. This includes the human factor as a required element for changing behaviour. Urban sustainability means that both social, economic and environmental impacts are exploited, and this is only possible by considering the whole complex system of a smart city, as well as applying a participatory governance model including the “human factor” within this process.
The proposed methodology was drafted by taking into account these projections in the near future and by collecting directly the challenges arising in smart cities. The methodology is built on an extensive literature review from psychology, health and behavioural economics, and provides a comprehensive approach, enabling a reduction in weaknesses apparent in current available theories used in research within the smart cities context [13]. As behaviour is intrinsic context-dependent, the main scope of this methodology is to map therefore behaviours into specific profiles, determine the value of behaviour, and collect a set of data derived from behaviour change interventions, which can be used for predicting the effects of behaviour. The final expected results to implement such methodology are to:
●Steer tailored interventions.
●Provide requirements for technological development stages.
●Make informed decisions for several points of interventions (value trade off).
●Deploy data-streams and real-time randomized trials in a cost-effective way through an enriched city dashboard
● Identify path dependencies influencing business modeling.
● Provide policy recommendations to municipalities, service and utility providers.
● Increase technological take-up and minimize adoption risks.
● Improve the sustainability of technological solutions.
The first version of the methodology has been validated through a specific use case in the domain of health. In this paper, an extension of the methodology is presented, including contextual analysis, to best address the needs of smart cities to consider the contextual and human factors in changing behaviour. The model will be first applied to the “City of Things” within the city of Antwerp in Belgium, to be further refined and potentially implemented in smart cities worldwide.
Original languageEnglish
Title of host publicationMeasuring Behavior 2018
Subtitle of host publicationConference Proceedings
EditorsR.A. Grant
PublisherMeasuring Behavior
ISBN (Print)978-1-910029-39-8
Publication statusPublished - 18 Jun 2018
Externally publishedYes
EventMeasuring Behavior 2018: 11th International Conference on Methods and Techniques in Behavioral Research - Kraków, Poland
Duration: 6 Jun 20188 Jun 2018


ConferenceMeasuring Behavior 2018


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