The workload in healthcare is increasing and the quality of healthcare is deteriorating as a result. By having social assistive robots to help nurses with their tasks or by taking them over, socially assistive robots can ease the workload of healthcare staff and it would give nurses more time to focus on individualised patient care and devising treatment plans.
Regarding the topic delineation, the focus will be on socially assistive robots that can help the nurses in hospitals by assisting them with tasks or by taking over their tasks. For example, by helping patients get into and out of bed, reminding patients when to take medication to provide emotional support and interaction for those lacking regular human contact, and assisting nurses with the multitude of tasks that they perform, such as taking blood, recording temperature, or improving patient hygiene. I will exclude surgical robots, cleaning robots, laboratory specimens, or transportation robots for sensitive material within a hospital environment.
Despite the advantages, care robots do not yet live up to the high expectations in care and robotization in long-term care is still in its infancy, according to research (AD, 2020). Several studies have already been performed on the diffusion of socially assistive robots. These studies discuss enablers such as relieving pressure on staff in healthcare, providing and receiving better care, and long-term cost savings (Papadopoulos et al., 2020). Moreover, barriers such as unemployment, lack of human contact, technical development, and high investment costs are known (Papadopoulos et al., 2020). However, a gap can be found in the literature, as no studies were found focusing on the innovation system regarding the technology. Moreover, performing a patent landscape analysis could be helpful to reveal business, scientific and technological trends. Based on these insights, recommendations can be provided to developers of socially assistive robots to promote the diffusion of the technology. Therefore, this thesis researches the diffusion of socially assistive robots with the aim to provide policy recommendations and suggestions for companies who are developing these types of robots.
The research question of this thesis is as follows: “How can the diffusion of socially assistive robots in hospitals be stimulated?” To answer the research question, two-subquestions have been formulated. The first sub-question is:
I: “What are the enablers and barriers that influence system innovation initiatives?”
Analyzing the technological innovation system can be helpful, as social assistive robots are an emerging technology, and the TIS analysis can identify possible bottlenecks for anchoring initiatives for system innovation on emerging technologies.
While the TIS analysis gives the possibility to formulate policy recommendations based on the innovation system, it is also relevant to analyze the patents of companies who are developing socially assistive robots, as these companies play a significant role in the development and thus diffusion of the technology. Performing a patent landscape analysis can be used to predict commercialization of new technologies (Pargaonkar, 2016) and support informed decision-making within firms by giving an overview of patent activities for a certain technology (WIPO, 2015). Moreover, performing a patent landscape analysis could be helpful to reveal business, scientific and technological trends. Based on these insights, recommendations can be provided to developers of socially assistive robots to promote the diffusion of the technology. Therefore, the second sub-question has been formulated as:
II: “What are implications for companies who are developing socially assistive robots based on the patent landscape?”
My thesis deals with both technology and people, as I will focus on socially assistive robots that are socially in contact with human patients. Moreover, the robots are in contact with the nurses by helping them with their tasks. My project fits the Innovation Sciences domain, as I will gain a deeper understanding of the enablers and barriers of the technology I focus on and how that development can be improved. Moreover, I will use analysis I learned during my studies and my track, such as the patent landscape analysis (Innovation Strategy and Policy).
To answer my research questions, I will perform a TIS analysis and patent landscape analysis. To collect data for the TIS analysis, I will perform a literature review using scientific data from e.g. Google Scholar, news articles and policy letters. I will base the enablers and barriers on the TIS system functions and the corresponding indicators and rate them with ++, +, +/-, -, –.
If possible, I will reach out to 3-4 managers from the field to discuss my preliminary results and the potential enablers and barriers to validate my research.
For the patent landscape analysis, I will use the Derwent Innovation Index, Microsoft Excel, and VOSviewer. I will analyze trends in patenting intensity, the inventors and assignees, geographic features, technical focus, citations, networks, Co-IPC codes, and co-citations. Based on the results of the analysis, I will formulate recommendations for companies that are developing social assistive robots.