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Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

The purpose of this paper is to understand the association between the capacity to use enterprise architecture tools and the effectiveness of business model digitalization in companies.

The authors used two research strategies – survey and focus group – to analyze the relationship between maturity in using enterprise architecture (EA) and digital maturity, under the perspective of sociomateriality.

The use of EA is not a strategic competence that contributes to building sustainable competitive advantage, in the process of business model digitalization. On the other hand, top management’s determination and clarity, expressed by its sponsorship to communicating the strategy, contribute to the integration, engagement and adaptability of those involved and are responsible for higher maturity in the digitalization of business models.

The statistical treatment used does not allow understanding the causality between the variables.

It provides executives with important elements for clarifying and operationalizing digital business models.

The study operationalizes a theoretical and measurement model, through a strategy that used simultaneously a survey and a focus group, which allowed to know associations between technological capacities and maturity in digital business models.

Current literature argues that citizen engagement platforms must be used to gather citizens’ feedback to provide improved quality of services to citizens. However, limited studies consider the challenges faced by practitioners at the local level during the incorporation of those feedback for continuous service improvement. As a result, these services fail to fulfil the need of citizens. The purpose of this study is to structure the relationship between citizens’ feedback and continuous service improvement to meet the need of citizens.

Design science research methodology has been adapted under which a case study approach has been followed to investigate one of the citizens’ engagement platforms in Ireland.

The results from this study highlighted that practitioners faced challenges (e.g. capacity, risk and constraints) in terms of fulfilling the needs of citizens and there is a lack of structured approach to continuously provide improved services to them.

This study provides a structured approach in the form of a process model to showcase how citizens’ feedback can be incorporated for continuously providing improved services to the citizens.

This research provides a prescriptive view to assist municipalities during the incorporation of citizens’ feedback for continuous service improvement while addressing the challenges they face during this process.

This paper proposes a process model based on the guidelines of the open group architecture framework enterprise architecture and the collaboration with practitioners that would assist local authorities in continuously providing improved services to the citizens.