A constraint programming model for making recommendations in personal process management: A design science research approach

•Developing a solution for decision support in personal process management.•Context-aware mobile application utilizing constraint programming.•Fast replanning offers process flexibility and improved value gain.•Experiments with two real world scenarios are conducted with 50 participants.•System usab...

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Vydáno v:Decision Support Systems Ročník 152; s. 113665
Hlavní autoři: Oruç, Sercan, Eren, P. Erhan, Koçyiğit, Altan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.01.2022
Elsevier Sequoia S.A
Témata:
Ambient intelligence
Applications programs
Business process management
Cognitive load
Constraint modelling
Constraint programming
Decision making
Decision support systems
Design science research
Intelligent personal assistants
Mobile computing
Personal process management
Process management
Prototypes
Recommender systems
Task complexity
Personal process management
Business process management
Intelligent personal assistants
Design science research
Constraint programming
ISSN:0167-9236, 1873-5797
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Shrnutí:•Developing a solution for decision support in personal process management.•Context-aware mobile application utilizing constraint programming.•Fast replanning offers process flexibility and improved value gain.•Experiments with two real world scenarios are conducted with 50 participants.•System usability scale results and time measurements suggest high performance. Decision-making in everyday life has an essential role in effectively completing personal tasks and processes. The complexity of these processes and the resulting cognitive load of managing them may vary significantly. To decrease the cognitive load created by such decision-making efforts and to obtain better outcomes, recommendation systems carry significant potential. In order to investigate the benefits provided by decision support systems (DSS) in personal process management (PPM), we first build a constraint programming (CP) model and a prototype context-aware-mobile application employing this CP model. Then, we evaluate the application and the model via two exemplary real-world scenarios. The scenarios form the core of the experiments conducted with 50 participants. We compare the participants’ planning performances with and without the PPM system with quantitative metrics such as planning times and scenario objective values. In addition, System Usability Scale (SUS) questionnaires and open-ended questions provide qualitative evaluation results. Throughout the study, we apply the Design Science Research methodology to rigorously conduct research activities by proof of concept, proof of use, and proof of value. The empirical results clearly show that our proposed model for PPM is effective, and the developed prototype solution generates positive participant comments as well as a high SUS score. Overall, the prototype PPM system with CP implementation leads to better planning in less time in the planning phase, and it lets the user do fast replanning in the execution phase, which is invaluable in dynamically changing situations such as daily activities.
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ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2021.113665