[u' @incollection{collazos_effectiveness_2013, series = {Lecture {Notes} in {Computer} {Science}}, title = {Effectiveness {Measurement} {Framework} for {Field}-{Based} {Experiments} {Focused} on {Android} {Devices}}, volume = {8278}, isbn = {978-3-319-03067-8 978-3-319-03068-5}, url = {http://dx.doi.org/10.1007/978-3-319-03068-5_21}, abstract = {Most of the mobile phones have turned into full-connected devices. This provides companies with a perfect channel to interact with their potential clients and employees. The quality of the experience with these applications can directly affect the profits of the company it represents. Focusing on the mobile field and its extremely dynamic context, the quality of the experience can highly fluctuate. Inside this field, several methods and tools have been developed by defining a context of use. However, current methods can only capture it through adding external capture tools (added cameras, human observers...) that can change the experience. The main contribution in this article is a new approach to automatically measure effectiveness through a tiny but powerful mobile tool that can capture interaction metrics and the surrounding context without biasing the measured experience.}, booktitle = {Human {Computer} {Interaction}}, publisher = {Springer International Publishing}, author = {Pretel, Ivan and Lago, Ana B.}, editor = {Collazos, C\xe9sar and Liborio, Andr\xe9ia and Rusu, Cristian}, year = {2013}, keywords = {Mobile HCI, context, effectiveness, evaluation, framework, mobile services, quality, usability}, pages = {123--130} }']