Assessing contextual mood in public transport: a pilot study

In recent years, the technological developments in mobile and communication networks have paved the way for smart environments, whose final goal is to provide users with enhanced experiences. The measure of user experience satisfaction, or quality of experience, may be defined as an affective state in response to a service. Thus, an experiment was devised to explore the relationship between users' affective state and their context, for assessing quality of experience in urban public transport services. A pilot study, conducted to evaluate the feasibility and requirements of such an experiment is presented, leading to a large scale field study.

[1]  João G. Vieira.  Smart mobile sensing for measuring quality of experience (QoE) in urban public transports , 2012 .

[2]  Mahadev Satyanarayanan,et al.  Pervasive computing: vision and challenges , 2001, IEEE Wirel. Commun..

[3]  R. Oliver,et al.  Assessing the Dimensionality and Structure of the Consumption Experience: Evaluation, Feeling, and Satisfaction , 1993 .

[4]  G. MacKerron.  Happiness Economics from 35,000 Feet , 2012 .

[5]  Jeremy V. Pitt,et al.  Investigating mobile quality of experience in public transport , 2012, Mobile HCI.

[6]  Alex Pentland,et al.  Social sensing for epidemiological behavior change , 2010, UbiComp.

[7]  Rosalind W. Picard.  Affective computing: challenges , 2003, Int. J. Hum. Comput. Stud..

[8]  Jeremy Pitt,et al.  Cloud2Bubble: enhancing quality of experience in mobile cloud computing settings , 2012, MCS '12.

[9]  Russell Beauregard.  Assessing the Quality of User Experience , 2007 .

[10]  Fariba Sadri,et al.  Ambient intelligence: A survey , 2011, CSUR.

[11]  Bilge Mutlu,et al.  Pay attention!: designing adaptive agents that monitor and improve user engagement , 2012, CHI.

[12]  Gabi Dreo Rodosek,et al.  TOPICS IN NETWORK AND SERVICE MANAGEMENT , 2009 .

[13]  E. Molin,et al.  Use and Effects of Advanced Traveller Information Services (ATIS): A Review of the Literature , 2006 .

[14]  Karl Kottenhoff,et al.  Dynamic at-stop real-time information displays for public transport: effects on customers , 2007 .

[15]  Ashish Kapoor,et al.  Automatic prediction of frustration , 2007, Int. J. Hum. Comput. Stud..

[16]  T. Gärling,et al.  FREQUENCY OF NEGATIVE CRITICAL INCIDENTS AND SATISFACTION WITH PUBLIC TRANSPORT SERVICES , 2001 .

[17]  Amitava Mukherjee,et al.  Pervasive Computing: A Paradigm for the 21st Century , 2003, Computer.

[18]  Diane J. Cook,et al.  How smart are our environments? An updated look at the state of the art , 2007, Pervasive Mob. Comput..

[19]  Yvonne Rogers,et al.  From spaces to places: emerging contexts in mobile privacy , 2009, UbiComp.

[20]  Sunny Consolvo,et al.  Using the Experience Sampling Method to Evaluate Ubicomp Applications , 2003, IEEE Pervasive Comput..

[21]  J. Russell.  A circumplex model of affect. , 1980 .

[22]  Richard A. Spreng,et al.  A Reexamination of the Determinants of Consumer Satisfaction , 1996 .