Exploring data collection techniques for continuous improvement on IoT products and services.
With the Internet of Things (IoT), there is a fundamental shift in how software products are developed, and in how the life cycles of these products are perceived. Up until recently, product development has typically been initiated and conducted within the boundaries of a firm, and with feedback from users collected in the early phases of the process. As the final step, release and deployment to users represents the closure of the development effort, and the time at which the firm instead adopts a ‘maintenance mode’ of the finished product. While the characteristics of the process described might still be true for certain contexts, it has stopped being a truth in the software industry. Instead, and as a result of “things” in our surrounding environment becoming connected to the Internet, companies are facing a paradigm shift with new and exciting opportunities arising.
As one of these opportunities, the IoT paradigm allows for completely new engagement models with users, and increasing opportunities to learn about user behaviour. As products go on-line, companies can monitor them, collect data on how they perform, predict when they break, know where they are located, learn about when/how they are used or not used etc. This opportunity implies that (1) user feedback collected in the early phases of product development is now complemented with product data revealing real-time product use, and (2) user feedback is no longer collected only in the early phases of product development, but continuously after product release and deployment. This means that the post-deployment phase that has been considered to mark the end of the development process, now literally becomes the start. Instead of ‘develop–deliver’ we now ‘deliver–develop’ in the sense that IoT products allow for continuous development and improvement of products also after deployment to users. To understand this shift, we need research that improves our understanding for IoT products and how they can be developed to successfully meet user needs now and in the future. The goal of the project is to help companies advance their current techniques for collection of user feedback, and successfully combine different techniques to allow for continuous improvement of IoT products and services.
This project brings together interaction designers and software engineers to provide a holistic understanding of how companies can learn from users throughout the development process of IoT products and services. Research questions include:
- How can existing techniques for collection of user feedback be adapted to better address the complexity of the IoT context?
- How can a diverse set of techniques for collection of user feedback be combined to improve the development of IoT products and services?
- How can a diverse set of techniques for collection of user feedback help us identify and predict emerging user behaviours and needs in an IoT context?
- Exploring IoT User Dimensions: A multi-case study on user interactions in ‘Internet of Things’ Systems. H.H. Olsson, J. Bosch, and B. Katumba. International Conference on Product-Focused Software Process Improvement. 2016. [open access]
- Self-Learning, Self-Actuation and Decentralized Control: How Emergent System Capabilities Change Software Development. H.H. Olsson and J. Bosch. Swedish Workshop on the Engineering of Systems of Systems. 2016. [open access]
Application areas: Smart Energy, Smart Living
Partners: Malmö University, Data Ductus and Eon
Project period: 2014-12-01 – 2016-11-30
Funding: The project is funded by the Knowledge Foundation within the Internet of Things and People Research Profile, Malmö University, and the business partners.
Contact: The project is coordinated by Helena Holmström Olsson, IOTAP, Malmö University.