@article{KowalskiEsteveJankin, author = {Kowalski, Radoslaw and Esteve, Marc and Jankin, Slava}, title = {Improving Public Services by Mining Citizen Feedback: An Application of Natural Language Processing}, series = {Public Administration}, journal = {Public Administration}, doi = {10.1111/padm.12656}, abstract = {Research on user satisfaction has increased substantially in recent years. To date, most studies have tested the significance of pre-defined factors thought to influence user satisfaction, with no scalable means of verifying the validity of their assumptions. Digital technology has created new methods of collecting user feedback where service users post comments. As topic models can analyze large volumes of feedback, they have been proposed as a feasible approach to aggregating user opinions. This novel approach has been applied to process reviews of primary-care practices in England. Findings from an analysis of more than 200,000 reviews show that the quality of interactions with staff and bureaucratic exigencies are the key drivers of user satisfaction. In addition, patient satisfaction is strongly influenced by factors that are not measured by state-of-the-art patient surveys. These results highlight the potential benefits of text mining and machine learning for public administration.}, language = {en} } @article{CampionGascoJankinetal., author = {Campion, Averill and Gasco, Mila and Jankin, Slava and Esteve, Marc}, title = {Managing Artificial Intelligence Deployment in the Public Sector}, series = {Computer/IEEE, 2020}, volume = {53}, journal = {Computer/IEEE, 2020}, number = {10}, doi = {10.1109/MC.2020.2995644}, pages = {28 -- 37}, abstract = {The scarcity of empirical evidence surrounding the organizational challenges and successful approaches to artificial intelligence (AI) deployment has resulted in mostly theoretical conceptualizations. By analyzing policy labs and offices of data analytics across the US to understand organizational challenges of AI adoption and implementation in the public sector as well as to identify successful management strategies to address such challenges, our study moves from speculation to gathering evidence. Our findings show that most challenges are found during the implementation stage and include challenges related to skills, culture, and resistance to share the data driven by data challenges. Further, our results indicate that long term strategies and short term actions need to be put in place to address these challenges. Among the first ones, leadership and executive support and stakeholder management seem to play an important role. Data standardization, training, and data-sharing agreements also seem to be successful specific short-term actions.}, language = {en} }