Book cover International Conference on Electronic Government EGOV 2022: Electronic Government pp 336–352Cite as Adoption of Robotic Process Automation in the Public Sector: A Survey Study in Sweden Gustaf Juell-Skielse, Evrim Oya Güner & Shengnan Han Conference paper First Online: 30 August 2022 407 Accesses Part of the Lecture Notes in Computer Science book series (LNCS,volume 13391) Abstract The public sector has increased its use of robotic process automation (RPA) in administration, decision making and citizen services. Available studies mostly focused on the specific cases of using RPA in public organizations. Thus, we lack the helicopter view of the adoption of RPA in a country. In this paper, we present the results of a national survey of RPA adoption in the public sector in Sweden. The results show that the awareness of RPA is high in the Swedish public sector although the level of adoption is still modest. Also, there are notable differences in the level of adoption between central and local government. The study goes beyond the limitations of case studies, and contribute new knowledge of RRA adoption, benefits, routine capability and governance on a national level. The knowledge and insights can serve as a reference for other countries and public administrative models.

Robotic process automation (RPA) is considered as a significant aspect of modernizing and digitally transforming public administration towards a higher degree of automation. By adding cognitive artificial intelligence, the use of RPA can be extended, from rule-based, routine processes to more complex applications, involving semi- and unstructured information. However, we lack a clear understanding of what is meant by cognitive RPA and the impacts of RPA on public organizations’ dynamic IT capabilities. To fill this knowledge gap, we carried out a qualitative study by conducting 13 interviews with RPA system suppliers., An abductive approach was used in analyzing the interview data. We contribute with a definition and a conceptual system model of cognitive RPA and a set of propositions for how an extended notion of RPA affects dynamic IT capabilities in public sector organizations.

Robotic Process Automation (RPA) adoption is increasing in the public sector for improving the quality and the efficiency of public services. However, we have not yet gained a sufficient understanding of how RPA advances public service practices and process routines in public organizations. To mitigate this gap, we conducted a literature review and analyzed eight reported cases of RPA in public sector organizations through the lens of technology as routine capability (Swanson 2019). The results indicate that most of the cases are from the public sector in the Nordic countries, e.g., Sweden, Norway, and Finland. RPA creates new “machine” routines and becomes integral to humans’ new routines in public service’s processes and practices. RPA as routine capability advanced practices at individual, organizational, and social levels. The evidence also indicated that changes triggered by RPA were intertwined in the four modes of routine capability: design, execution, diffusion, and shift. The research contributed to a deeper understanding of how RPA changes and cultivates routine capability and advances public service practices. In addition, we applied and critically examined technology as routine capability as the analytical framework for understanding how RPA advanced public service practices.

Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally serve as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored. 

(a) Situation faced: The prospects of digitalization in the automotive industry are enormous with emerging technology concepts, such as electrification, autonomous driving, connected mobile services, and new business models. However, digital innovation has proven difficult for original equipment manufacturers (OEM) due to complex organizational structures, corporate cultures, and technological inertia associated with the automotive industry. In a recent rating of the 50 firms that best combine new technology with effective business models, only 2 were automotive companies. The obstacles to digital innovation are related to closed innovation processes and to deficient collaboration forms with external development firms, i.e., startups. (b) Action taken: To overcome these challenges, a coalition of incumbent automotive and telecommunication firms set up a joint incubator, the Mobility X Lab (MXL), to engage with startups to support internalizing external technologies. Since its inception, the incubator has gone through several development phases and is currently transforming into a collaboration hub. So far, MXL has admitted 5 batches including 40 startups. An important distinguishing characteristic of MXL is that it only admits startups with two or more coalition partners involved. (c) Results achieved: MXL started as an incubator with a mentoring-based accelerator program. As MXL has developed, it has transformed into a collaboration hub and a neutral partner for fostering startup collaboration and engagement in the automotive industry. Based on lessons learned from startup batches and partner discussions, MXL has advanced, from offering traditional mentoring support, to be a central node in the innovation ecosystem of future mobility in Sweden, thus becoming a matchmaker for startup collaboration, providing guidance and access for startups to incumbent automotive and telecommunication firms and at the same time providing the partners with access to external technology, supporting them to stay relevant. (d) Lessons learned: Through the development of MXL, a coalition of established automotive and telecommunication firms have learned to manage some of the tensions related to digital transformation of their industry. By examining the case of MXL, a number of lessons can be learned: (1) Ensure partner interest through the “two partners” criteria, (2) initial emphasis on engaging startups and less focus on a complete process, (3) announce partner needs without disclosing partner pain-points, (4) coach startups and corporate partners simultaneously, (5) manage expectations early for both startups and corporate partners, (6) develop and implement sound collaboration measures, and (7) joint headship requires a consensus-based governance model

The significance and possibilities of idea generation and evaluation are increasing due to the increasing demands for digital innovation and the abundance of textual data. Textual data such as social media, publications, patents, documents, etc. are used to generate ideas, yet manual analysis is affected by bias and subjectivity. Machine learning and visual analytics tools could be used to support idea generation and evaluation, referred to as idea mining, to unlock the potential of voluminous textual data. Idea mining is applied to support the extraction of useful information from textual data. However, existing literature merely focuses on the outcome and overlooks structuring and standardizing the process itself. In this paper, to support idea mining, we designed a model following design science research, which overlaps with the Cross-Industry-Standard-Process for Data Mining (CRISP-DM) process and adapts well-established models for technology scouting. The first layer presents and business layer, where tasks performed by technology scouts, incubators, accelerators, consultants, and contest managers are detailed. The second layer presents the technical layer where tasks performed by data scientists, data engineers, and similar experts are detailed overlapping with CRISP-DM. For future research, we suggest an ex-post evaluation and customization of the model to other techniques of idea mining.

Self-driving technology is part of smart city ecosystems, and it touches a broader research domain. There are advantages associated with using this technology, such as improved quality of life, reduced pollution, and reduced fuel cost to name a few. However, there are emerging concerns, such as the impact of this technology on transportation systems, safety, trust, affordability, control, etc. Furthermore, self-driving cars depend on highly complex algorithms. The purpose of this research is to identify research agendas and innovative ideas using unsupervised machine learning, dynamic topic modeling, and to identify the evolution of topics and emerging trends. The identified trends can be used to guide academia, innovation intermediaries, R&D centers, and the auto industry in eliciting and evaluating ideas. The research agendas and innovative ideas identified are related to intelligent transportation, computer vision, control and safety, sensor design and use, machine learning and algorithms, navigation, and human-driver interaction. The result of this study shows that trending terms are safety, trust, transportation system (traffic, modeling traffic, parking, roads, power utilization, the buzzword smart, shared resources), design for the disabled, steering and control, requirement handling, machine learning, LIDAR (Light Detection And Ranging) sensor, real-time 3D image processing, navigation, and others. 

Organisations have started to implement Robotic Process Automation (RPA) to improve process efficiency and to advance business process management (BPM) practise. However, literature tends to study RPA from a limited technical view and thus, we have not yet gained a sufficient understanding of how RPA advances BPM practises. To mitigate this gap, we conduct a literature review and analyse 13 reported cases of RPA through the lens of technology as routine capability (Swanson 2019). The results indicate that a few early adopters have implemented RPA. RPA creates new “machine” routines and become integral of humans’ new routines. RPA as routine capability does advance practises in individual, organisational and social levels though we only have evidence from the 13 cases found in literature. The evidence also indicates that changes of RPA are intertwined in the four modes: design, execution, diffusion and shift. The research contributes to a deeper understanding of how RPA changes and cultivate routine capability and advanced BPM practises. In addition, we apply and critically examine technology as routine capability as the analytical framework for understanding how RPA advance BPM practise.

As non-corporate (herewith referred to as “independent”) incubators gain in popularity for propelling digital innovation, traditional automotive firms have set up in-house incubators (herewith referred to as “corporate”) to accelerate innovation without disrupt-ing too much the inherent organizational structures and corporate cultures. The overarching objective is to establish the expected benefits for automotive firms from independent incubators when organizing corporate incubators. Using a comparative interview study, ten successful independent incubators in North America are discussed in terms of their ability to provide support in the digital domains. Our work has resulted in novel operating models for categorizing incubators to describe variations in focus areas and support for digital innovation. The results sheds light on how corporate incubators (internal to automotive firms) have the potential to shield digital ventures from the complexities of large and traditional establishments, and to promote interactions with other business units within the firm when performing digital innovation.

The growing open data market opens possibilities for the development of viable digital artifacts that facilitate the creation of social and business values. Contests are becoming popular means to facilitate the development of digital artifacts utilizing open data. The increasing popularity of contests gives rise to a need for measuring contest performance. However, the available measurement model for digital innovation contests, the DICM-model, was designed based on a single case study and there is a need for a methodological approach that can accommodate for contests’ variations in scope. Therefore, we use design science to construct a nine-step method, the DRD method, to design and refine DICM-models. The DRD-method is designed using goal- and quality oriented approaches. It extends innovation measurement to the application domain of digital innovation contests and provides an improvement of innovation measurement as it offers a new solution for a known problem. The DRD-method provides comprehensive support to practice for designing and refining DICM-models and supports reflection and organizational learning across several contests. For future study, we suggest an ex-post evaluation of the method inconjunction with real contests and systematic efforts to generalize the method within as well as beyond the context of the contest. Finally, we propose to further investigate the potential of topdown and goal oriented approaches to measure open and iterative forms of innovation.