Success Factors in Mobile Viral Marketing: A Multi-Case Study Approach

A prior study showed that mobile viral marketing is an important issue of mobile marketing. Using a multicase study research approach, we introduce a typology of four standard types of mobile viral marketing and extract eight success factors for this new form of marketing. As a final step, we structure the relationship between both, showing success factors’significance in different standard types and deriving a success factor framework. We conclude with a consideration of research implications.

Success Factors in Mobile Viral Marketing: A Multi-Case Study Approach

A prior study showed that mobile viral marketing is an important issue of mobile marketing. Using a multicase study research approach, we introduce a typology of four standard types of mobile viral marketing and extract eight success factors for this new form of marketing. As a final step, we structure the relationship between both, showing success factors’significance in different standard types and deriving a success factor framework. We conclude with a consideration of research implications

Mobile Learning Business Model Framework

M-learning has become more and more important but still is a young educational and economical (Edu-Eco) technology. M-learning strategies are aimed at economic, academic and technological objectives, however they lack in modeling ensured economic measurements in the sense of profitable products. Throughout this paper, we discuss the prime categories of elements that participate in the m-learning value net and give an overview of their business models. In addition to considering the mobile and e-learning business, business models, we deconstruct the m-learning value net, and also use a literature review in order to identify different actors in a business model for M-learning

Educational Technology and Education Conferences, June to December 2012

The conference list contains events such as "Learning and Teaching","Innovation in e-Learning", "Online Teaching", "Distance Learning Administration", "The World Open Educational Resources Congress", "Mobile Health", and "Realizing Dreams"

Context-Adaptive Coordination of Pervasive Services by Interpreting Models during Runtime

[EN] One of the most important goals of pervasive systems is to help users in their daily life by automating their behaviour patterns. To achieve this, pervasive services must be dynamically coordinated, executed and adapted to context according to user behaviour patterns. In this work, we propose a model-driven solution to meet this challenge. We propose a task model and a context ontology to design context-adaptive coordination of services at a high level of abstraction. This design facilitates the coordination analysis at design time and is also reused at runtime. We propose a software architecture that interprets the models at runtime in order to coordinate the service execution that is required to support user behaviour patterns. This coordination is done in a context-adaptive way and decoupled from service implementation. This approach makes the models the only representation of service coordination, which facilitates the maintenance and evolution of the executed service coordination after deployment.This work has been developed with the support of (a) MICINN under the project EVERYWARE TIN2010-18011 and (b) MITYC under the project LIFEWEAR TSI-020400-2010-100 co-funded with ERDF.Serral Asensio, E.; Valderas Aranda, PJ.; Pelechano Ferragud, V. (2013). Context-Adaptive Coordination of Pervasive Services by Interpreting Models during Runtime. Computer Journal. 56(1):87-114. https://doi.org/10.1093/comjnl/bxs019S8711456

IFIP TC 13 Seminar: trends in HCI proceedings, March 26, 2007, Salamanca (Spain)

Actas del 13o. Seminario de la International Federation for Information Processing (IFIP), celebrado en Salamanca el 26 de marzo de 2007, sobre las nuevas líneas de investigación en la interacción hombre-máquina, gestión del conocimiento y enseñanza por la Web

Recommending privacy preferences in location-sharing services

Location-sharing services have become increasingly popular with the proliferation of smartphones and online social networks. People share their locations with each other to record their daily lives or satisfy their social needs. At the same time, inappropriate disclosure of location information poses threats to people's privacy. One of the reasons why people fail to protect their location privacy is the difficulty of using the current mechanisms to manually configure location-privacy settings. Since people's location-privacy preferences are context-aware, manual configuration is cumbersome. People's incapability and unwillingness to do so lead to unexpected location disclosures that violate their location privacy. In this thesis, we investigate the feasibility of using recommender systems to help people protect their location privacy. We examine the performance of location-privacy recommender systems and compare it with the state-of-the-art. We also conduct online user studies to understand people's acceptance of such recommender systems and their concerns. We revise our design of the systems according to the results of the user studies. We find that user-based collaborative filtering can accurately recommend location-privacy preferences and outperform the state-of-the-art when training data are insufficient. From users' perspective, their acceptance of location-privacy recommender systems is affected by the openness and the context of recommendations and their privacy concerns about the systems. It is feasible to use data obfuscation or decentralisation to alleviate people's concerns and meanwhile keep the systems robust against malicious data attacks

Smartphone traffic characteristics and context dependencies

Smartphone traffic contributes a considerable amount to Internet traffic. The increasing popularity of smartphones in recent reports suggests that smartphone traffic has been growing 10 times faster than traffic generated from fixed networks. However, little is known about the characteristics of smartphone traffic. A few recent studies have analyzed smartphone traffic and given some insight into its characteristics. However, many questions remain inadequately answered. This thesis analyzes traffic characteristics and explores some important issues related to smartphone traffic. An application on the Android platform was developed to capture network traffic. A user study was then conducted where 39 participants were given HTC Magic phones with data collection applications installed for 37 days. The collected data was analyzed to understand the workload characteristics of smartphone traffic and study the relationship between participant contexts and smartphone usage. The collected dataset suggests that even in a small group of participants a variety of very different smartphone usage patterns occur. Participants accessed different types of Internet content at different times and under different circumstances. Differences between the usage of Wi-Fi and cellular networks for individual participants are observed. Download-intensive activities occurred more frequently over Wi-Fi networks. Dependencies between smartphone usage and context (where they are, who they are with, at what time, and over which physical interface) are investigated in this work. Strong location dependencies on an aggregate and individual user level are found. Potential relationships between times of the day and access patterns are investigated. A time-of-day dependent access pattern is observed for some participants. Potential relationships between movement and proximity to other users and smartphone usage are also investigated. The collected data suggests that moving participants used map applications more. Participants generated more traffic and primarily downloaded apps when they were alone. The analyses performed in this thesis improve basic understanding and knowledge of smartphone use in different scenarios