Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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PurposeThe transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences.Design/methodology/approachThe authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment (n = 252), providing both qualitative and quantitative data.FindingsResults indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact.Research limitations/implicationsThis study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications.Practical implicationsThis paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey.Originality/valueThis paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.

-With the increasing interest towards the concept of Smart Cities from the city governments world-wide there is a need for useful and Information Systems oriented approach to understand Smart City propositions. In this paper we review a Smart City from an Enterprise Architecture (EA) perspective. We adapt TOGAF Architecture Development Method (ADM) to derive the concept of Enterprise Concerns. These concerns will subsequently be used to review the Smart City literature. Finally, we summarize our findings and propose the concept of the Urban Enterprise composed of Urban Enterprise Components.

Abstract. There is a significant challenge in the smart cities implementation because it is not straightforward to align the smart city strategy with the impact on the life of quality. Stakeholders' concerns are multiple and diverse, and there are a high interdependency and heterogeneity of technologies and solutions. To tackle this challenge, cities can be understood as enterprises. Enterprise Architecture (EA) approach can be applied to support its development and transformation. This approach specifies core requirements on business, information, and technology domains, which are essential to model architecture components and to establish relations between these domains. Existing smart cities frameworks describe different components and domains. However, the main domain requirements and the relations between them are still missing. This paper identifies essential requirements of enterprise architecture in smart cities. These requirements will be used to review and compare current smart city frameworks.