Why continue sharing: determinants of behavior in collaborative economy services

La economía colaborativa está revolucionando la forma en que los consumidores utilizan los bienes y servicios. En nuestro estudio modificamos y ampliamos el modelo de confirmación de expectativas para determinar los factores motivacionales que impulsan la satisfacción y la intención de continuar usando los servicios de viajes colaborativos. Más importante aún, agregamos el valor social como un factor adicional. En este estudio fueron encuestados usuarios españoles experimentados de BlaBlaCar. La calidad del servicio, la utilidad percibida, la confianza y el valor social son determinantes de la satisfacción de los usuarios experimentados y, a través de ella, de la intención de continuar usando; mientras que no es el caso para el impacto ambiental ni para los beneficios económicos. Además, la confianza afecta directamente a la intención de continuar. Estos resultados tienen implicaciones gerenciales relevantes, mostrando que los usuarios de algunos servicios colaborativos están motivados por otros factores además de los económicos.The sharing economy is revolutionizing the way consumers use goods and services. In our study we modify and extend the expectation confirmation model to determine the motivational factors which drive the satisfaction and continue intention to use ridesharing services. Most importantly, we add social value as an additional factor to those previously studied in the literature. We apply our model in a survey among experienced Spanish users of BlaBlaCar. Service quality, perceived usefulness, trust and social value are determinants of satisfaction of experienced users and through it, of intention to continuance; while it is the case neither for environmental impact nor for economic benefits. Additionally, trust affects directly continuance. These results have relevant managerial implications, showing that users of some sharing services are motivated by other factors than purely economic

Recommender Systems Research

We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science, such as artificial intelligence and human-computer interaction, and has progressed to an important research area of its own. It is important to note that recommendations are not delivered within a vacuum, but rather cast within an informal community of users and social context. Ultimately all recommender systems make connections among people. This observation is under-emphasized in the recommender systems literature. Thus, we pay particular attention to the inherently social aspect of recommender systems and the connections among users that they foster. This approach represents a departure from the traditional content-based filtering versus collaborative design perspective. As we show, recommender systems connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, we characterize recommender systems by how they model users to bring people together: explicitly or implicitly. Such user modeling as well as a connection-centric viewpoint raise broadening and social issues such as evaluation, targeting, and privacy and trust which we also briefly address. Lastly, we introduce shilling, the newest issue facing recommender system researchers. A shilling attack on a recommender system involves inundating the system with data intended to coerce it to artificially recommend the perpetrator’s products more often than those of a competitor

Collaborative model development increases trust in and use of scientific information in environmental decision-making

While science matters for environmental management, creating science that is credible, salient to decision-makers, and deemed legitimate by stakeholders is challenging. Collaborative modeling is an increasingly-used approach to enable effective science-based decision-making. This work evaluates the modeling process conducted for two hydropower dam licensing negotiations, to explore how differences in the collaborative development of hydrological models affected differences in their use in subsequent decision-making. In one case, the model was developed iteratively through deliberation with stakeholders. Consequently, stakeholders understood the model and its limitations and trusted the model and modelers; the model itself was also better designed to evaluate resource managers’ questions. The collaboratively-developed model became the focal point for subsequent negotiations and enabled creative group problem-solving. Conversely, in the case with less engagement during model development, the model was not used subsequently by decision-makers. These differences are argued to result from trust built during the modeling process, applicability of the model to test real management scenarios, and the broader social context in which the models were used

Security in online learning assessment towards an effective trustworthiness approach to support e-learning teams

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper proposes a trustworthiness model for the design of secure learning assessment in on-line collaborative learning groups. Although computer supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks which limit their potential in collaborative learning activities. Among these limitations, we investigate information security requirements in on-line assessment, (e-assessment), which can be developed in collaborative learning contexts. Despite information security enhancements have been developed in recent years, to the best of our knowledge, integrated and holistic security models have not been completely carried out yet. Even when security advanced methodologies and technologies are deployed in Learning Management Systems, too many types of vulnerabilities still remain opened and unsolved. Therefore, new models such as trustworthiness approaches can overcome these lacks and support e-assessment requirements for e-Learning. To this end, a trustworthiness model is designed in order to conduct the guidelines of a holistic security model for on-line collaborative learning through effective trustworthiness approaches. In addition, since users' trustworthiness analysis involves large amounts of ill-structured data, a parallel processing paradigm is proposed to build relevant information modeling trustworthiness levels for e-Learning.Peer ReviewedPostprint (author's final draft