Breakthroughs and Emerging Insights from Ongoing Design Science Projects: DESRIST 2016 Research-in-Progress Proceedings

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https://cora.ucc.ie/handle/10468/5226

Breakthroughs and Emerging Insights from Ongoing Design Science Projects: Research-in-progress papers and poster presentations from the 11th International Conference on Design Science Research in Information Systems and Technology (DESRIST) 2016. St. John, Canada, 23-25 May.

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AdBo: A mobile application to boost adherence of physical exercises for elderly suffering from cognitive decline
(DESRIST 2016, 2016-05) Alsaqer, Mohammed; Chatterjee, Samir; Parsons, Jeffrey; Tuunanen, Tuure; Venable, John R.; Helfert, Markus; Donnellan, Brian; Kenneally, Jim; National University of Ireland, Maynooth; Claremont Graduate University, United States; Memorial University of Newfoundland, Canada
According to the UN, the number of elderly people over the age of 60 will reach 2 billion by 2050. Aging is accompanied with functional and cognitive decline that impact elderly independence and quality of life. This often results in issues such as forgetting, fall, and depression. Physical exercises can help. However, only 16% of elderly above the age 65 years do enough exercise to meet HHS (Department of Health and Human Services) physical activity guidelines for Americans. Several barriers impact the elderly's adherence to physical exercises. In this paper, we discuss the barriers and proven strategies that can be used to overcome them. Then, we discuss the development of the AdBo smartphone application, which aims to increase the elderly adherence to physical exercises. The application will guide the elderly though appropriate exercises, measure cognition ability before and after the exercises regimen, and track cognitive improvement over time.
Check the temperature. Rapid assessment of common ground in startup teams
(DESRIST 2016, 2016-05) Bonazzi, Riccardo; Cimmino, Francesco Maria; Parsons, Jeffrey; Tuunanen, Tuure; Venable, John R.; Helfert, Markus; Donnellan, Brian; Kenneally, Jim; National University of Ireland, Maynooth; Claremont Graduate University, United States; Memorial University of Newfoundland, Canada
This research in progress aims at identifying a set of design guide-lines to perform rapid diagnostic of common ground among participants of a startup team and their coach. Previous studies have shown that teams with high common ground are more efficient. Nonetheless, no existing tool can rapidly monitor its progression and visualize it in a simple way to allow the coach to perform team diagnostic. In this paper we present a prototype, which monitors the evolution of joint objectives and joint resources among team members and that represents the updated path of a startup team in less than five minutes. Empirical data collected at a startup weekend shows that it is possible (a) to rapidly monitor the evolution of common ground within the team, (b) to intervene whenever the joint commitment of participants gets too low and (c) positively affect the performance of a startup team.
Mining social media data from sparse text: an application to diplomacy
(DESRIST 2016, 2016-05) Chua, Cecil; Li, Xiaolin (David); Kaul, Mala; Storey, Veda C.; Parsons, Jeffrey; Tuunanen, Tuure; Venable, John R.; Helfert, Markus; Donnellan, Brian; Kenneally, Jim; National University of Ireland, Maynooth; Claremont Graduate University, United States; Memorial University of Newfoundland, Canada
Publicly available data in social media provides a wealth of unstructured data for applications, such as sentiment analysis and location-based services. This research analyzes a specific application of diplomats, who seek to understand the people with whom they must negotiate. Social media data about a negotiating partner can, potentially, be used to build a profile of that partner. However, such data is difficult to mine effectively because it has sparse text with high dimensionality. This research uses a design science approach to develop a method for extracting critical information from sparse text. The method mines sparse text from publically available Facebook data to extract patterns from individual communications. The method is applied to Facebook posts of a political figure to identify meaningful categories of information for insightful inferences. Preliminary evaluation shows support for the method.
Architecture of in-app ad recommender system
(DESRIST 2016, 2016-05) Mukherjee, Anik; Sundarraj, R. P.; Dutta, Kaushik; Parsons, Jeffrey; Tuunanen, Tuure; Venable, John R.; Helfert, Markus; Donnellan, Brian; Kenneally, Jim; National University of Ireland, Maynooth; Claremont Graduate University, United States; Memorial University of Newfoundland, Canada
Increased adoption of smartphones has caused mobile advertising to be the secondmost revenue-generating medium among all forms of existing online advertising. Application (henceforth called app) developers try to monetize their apps by selling in-app ad-spaces to the advertisers (or ad-agencies) through various intermediaries such as ad-networks. Surveys, however, indicate that mobile ad campaigns are not as successful as they can be, in part due to inappropriate audience targeting, and in turn, user-apathy toward such ads. This motivates the need for a system, where both advertisers and mobile-app developers gain from the in-app advertising eco-system. In this paper, we propose an architecture of design-science artifacts for an ad-network, to meet the objectives of both these stakeholders.
On semantics-contingent syntax for conceptual modelling
(DESRIST 2016, 2016-05) Lukyanenko, Roman; Samuel, Binny M.; Castellanos, Arturo; Maddah, Mahed; Parsons, Jeffrey; Tuunanen, Tuure; Venable, John R.; Helfert, Markus; Donnellan, Brian; Kenneally, Jim; National University of Ireland, Maynooth; Claremont Graduate University, United States; Memorial University of Newfoundland, Canada

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