We present Cicero – a middleware solution to support developers design and implement persuasive mobile apps. Based on the Action-Behavior Model (ABM), Cicero provides developers with powerful class libraries and collaboration methodology to streamline the development of mobile persuasive apps without requiring a steep knowledge of behavior science theory or venturing into domain-specific knowledge and artifacts. Cicero guides the developers in following the ABM steps, provides APIs for cyber sense and cyber influence, and embodies the necessary model computations including measuring end-user compliance and response to influence and persuasion. Cicero also facilitates the engagement of domain experts in a clearly defined collaborative role. Here we also originally detail the design and implementation of an Android version of the Cicero middleware and we present a use case to practically exemplify how Cicero can facilitate the application developers’ work.

Cicero: Middleware for developing persuasive mobile applications

BELLAVISTA, PAOLO
2016

Abstract

We present Cicero – a middleware solution to support developers design and implement persuasive mobile apps. Based on the Action-Behavior Model (ABM), Cicero provides developers with powerful class libraries and collaboration methodology to streamline the development of mobile persuasive apps without requiring a steep knowledge of behavior science theory or venturing into domain-specific knowledge and artifacts. Cicero guides the developers in following the ABM steps, provides APIs for cyber sense and cyber influence, and embodies the necessary model computations including measuring end-user compliance and response to influence and persuasion. Cicero also facilitates the engagement of domain experts in a clearly defined collaborative role. Here we also originally detail the design and implementation of an Android version of the Cicero middleware and we present a use case to practically exemplify how Cicero can facilitate the application developers’ work.
2016
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
137
149
D’Aloia, Antonello; Lelli, Matteo; Lee, Duckki; Helal, Sumi; Bellavista, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/553681
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