E-Government portals are no new phenomenon, nonetheless, their evaluation faces remarkable challenges that go far beyond mere technical requirements. Together with and for the sake of a more peculiar consideration of citizens’ needs, the redesign of existing portals must address the interplay of local and national forces, allowing state-level solutions to achieve their potential without threatening autonomous local actors and their ability to better address local needs.

This paper features a strong empirical part which encompasses the development of a questionnaire addressed to the population living in South Tyrol, Italy, to assess myCIVIS, the public service portal of the Autonomous Province of Bolzano - South Tyrol. In addition, considering that the questionnaire’s distribution to the citizens has been conducted during the first half of 2023, the results of this survey deliver insightful details about citizens’ feedback and the derived improvement proposals. At a second stage, the data from the survey will be triangulated with other statistical data related to the system usage and the overall data will then be used to provide strong scientific results to support the introduction of the new, updated version of the e-Government portal myCIVIS.

As society is delving deeper into the digital transition, there is increasing scientific attention to responsible implementation of digital technologies such as algorithms and artificial intelligence. For public sector organizations such as the police, this has translated to a focus on public and ethical values in the design and implementation of such technologies. Less attention is paid to the underlying data. Data are not neutral or objective. Literature shows, for example, that algorithms can reproduce and reinforce existing inequalities from the datasets. One of the main sources of police data consists of the reports made by street-level police officers. Such street-level policing includes e.g. emergency relief, detective work and front-office work.

This paper questions how this street-level data comes to be, and what that could mean for (future) algorithmic processing of this data. The underlying assumption is that such data-creation takes place in a system where human actors cooperate with technical actors such as forms, hardware and software interfaces. The findings will be based on 200+ hours of observation of street-level police work in the Netherlands.

Artificial intelligence (AI) concentrates currently a lot of effort among scholars and practitioners in the public sector (Samoili et al. 2020; World Bank 2020; Galindo et al. 2021). AI proponents point to many advantages that can improve public service processes through implementing AI technology. On the other hand, a growing body of literature shows that the use of AI still involves many unknowns and challenges (Hu et al. 2021; King et al. 2021; Sienkiewicz-Małyjurek 2023). These doubts generate skepticism about AI among public managers. However, according to Jarrahi et al. (2023), organizational learning capabilities could enhance power and perception of AI among public managers.

These learning capabilities result from the organizational learning culture that promotes innovation and also generation, utilization, and flow of knowledge (Naqshbandi et al. 2018; Kortsch et al. 2023). According to Marsick and Watkins (2003) organizational learning culture consists of such factors as create continuous learning opportunities, promote inquiry and dialogue, encourage collaboration and team learning, create systems to capture and share learning, empower people toward a collective vision, connect the organization to its environment, and provide strategic leadership for learning. It creates an organizational environment focused on openness, participation, agility and networking (Foerster-Metz et al. 2018). This culture should therefore foster knowledge generation about such controversial technologies as AI. However, the interplay between public organizations' learning culture and AI perception has not been explored empirically so far.

Therefore, the aim of this study is to investigate the impact of organizational learning culture on public managers' perception of AI. Data was derived from a survey of 414 out of 964 Polish city governments in the first quarter of 2022 using the Computer-Assisted Web Interview method. One survey was carried out directly with the city government authorities (31%) or the designed department heads (69%). The data were analyzed using descriptive statistics and regression analysis.

The results enrich the theory of learning culture and research on AI threats by testing the influence of learning culture factors on AI perception. The results are also relevant for practice, as they allow an understanding of how strengthening the organizational learning culture can help in a conscious approach to AI.

There is a burgeoning literature on innovation which is increasingly focussed on maximising the perceived benefits of new digital technologies. It established a body of knowledge that points to efficiency gains and new innovative ways of delivering services. Innovation is seen as critical to the advancement of organisations and services alike. The innovation literature highlights the processes of managing change and the key components of the innovation process. Within these generic processes, there is also a general view that the commercial sector is more flexible and innovative when compared to public services, which are often perceived to be less entrepreneurial, resistant to change, and less inclined to integrate digital technologies. Against this backdrop is a long history of technological innovation in the form of eGovernment. The eGovernment literature emphasises how new digital processes, mediated by new digital technologies, shape the ways of services are delivered, creating new relationships between public service providers, businesses, citizens, and users.

Innovations studies tend to emphasise different stages in the innovation process. Such approaches immediately assume that there is a uniform generic process at play and that all innovations are similar in how these stages are navigated. Recognition of barriers to innovation suggests that this may not always be the case. An innovation process may have undesirable or unforeseen circumstances or that the innovation may fail. Some studies have investigated the drivers for innovation, critical success factors, and barriers to success. In doing so, they start to draw on other explanatory factors influencing the innovation processes, which typically are a key part of the contextual environment of the innovation.

This paper seeks to develop a more nuanced in-depth characterisation of the innovation development process in public services by highlighting a range of intertwined features of an innovation’s contextual environment, including the technological characteristics of the innovation, fit with existing organisational arrangements and the role of key actors. The research is through the investigation of critical success factors and barriers within the process by analysing and comparing eight case study technology innovations in the Indonesian public services environment. Data is primarily gathered from 35 semi-structured interviews with employees involved in the innovation process, as well as the collection of policy materials and related documents.

The paper highlights the complexity and messiness of the innovation-development process and that the conceptual stages approach to innovation, which suggests a linear process, gives an oversimplified view of the process. Rather than being sequential, stages may overlap or be non-existent, and different actors may be influential at different points in the process. Here, the paper emphasises contextual elements in the environment that shape how the innovation process is experienced and managed, simultaneously providing barriers and drivers for innovation. It is suggested that the main failing of technology focussed innovation studies is that by focusing mainly on the technology, they separate the technology from its organisational and institutional setting and, in doing so, the elements of the process that are most likely to determine the success or otherwise of the innovation.