Explainable Artificial Intelligence (XAI) is a growing area of research that aims to improve the interpretability of the not-so-informative black-box models. However, it is currently difficult to categorize an existing method in terms of its intrinsic characteristics and explainability. We provide a new unified yet simple taxonomy for the categorization of XAI methods and present the explainability methods currently being applied in finance applications. For both purposes, we present two separate systematic literature reviews: an anthological search for surveys on XAI methods in order to present a unified taxonomy, followed by an exposition of the XAI methods currently in use that have been found. We also concisely define the existing explainability methods using the proposed categories based on the ones most commonly addressed in the reviewed literature and pinpoint specific XAI methods being used in practical applications in Finance.

Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions. However, many Higher Education (HE) institutions do not have accessible and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation is becoming ever more acute as HE operates today in a complex and dynamic environment brought forward by globalization and the rapid development of information technologies. This paper proposes a domain-specific BIA maturity model (MM) for HE–the HE-BIA Maturity Model. Following a design science approach, this paper details the design, development, and evaluation of two artifacts: the MM and the maturity assessment method. The evaluation phase comprised three case studies with universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment with the HE-BIA model was (i) useful and adequate for their needs; (ii) and contributed to a better understanding of the current status of their BIA landscape, making it explicit that a BIA program is a technology endeavor as well as an organizational development.

Maturity models of Business Intelligence and Analytics (BIA) have been previously used to assess BIA development progress in organizations in many sectors, such as healthcare and business. However, there is a lack of studies reporting up-to-date knowledge on applying maturity assessment in Higher Education Institutions (HEI). It remains unclear precisely to what extent and how HEI employ maturity assessment and the benefits of such exercises. This paper addresses this gap by reporting a case study at a large Norwegian university. A domain-specific maturity model is used as a lens to observe and reflect on the BIA implementation at the Norwegian University of Science and Technology. This paper reports the assessment results and discuss the implications of the maturity assessment. The findings and discussions in the case can cater to a broader audience of BIA practitioners and researchers, contributing to understanding the value and adoption dynamics of BIA in Higher Education.

This article addresses the complex challenges that public organizations face in designing, implementing, and evaluating their strategies, where public interest and regulatory compliance often intertwine with strategic objectives. This research investigates the application of ontologies in the field of public sector strategy management to enhance the capacity of organizations to make informed data-driven decisions, efficiently allocate resources, and effectively navigate the intricate landscape of the public sector. The LNEC - National Laboratory for Civil Engineering’s strategy is used as an exploratory case study. Semantic web technologies are used to perform strategy analysis, including validating the strategy formulation and supporting the strategy execution by assessing performance indicators, verifying the design of cause-and-effect relationships between strategic objectives, and monitoring and empirically validating these relationships. The increased interoperability of these technologies enables information sharing across systems and organizations. Following the strategy analysis, recommendations are provided, leading to a more robust and data-driven strategic management approach, enabling accurate, traceable, and continuous monitoring of an organization’s strategy. Theoretical and practical implications are discussed, along with limitations and future work. This research offers a blueprint for public sector organizations seeking to optimize their strategies, foster transparency, and deliver more effective services to the public they serve.

The Balanced Scorecard, developed in 1992 by Kaplan and Norton, has evolved into a communication and strategy execution system widely adopted by organizations across various industries. This article explores the use of an ontology to bridge the gap between strategy management and data within the Balanced Scorecard framework. The Balanced Scorecard Ontology is introduced to store, validate, and analyze knowledge, containing information about the Strategy Map and Quantification Frameworks, essential for evaluating the strategy execution. The proposed ontology is designed, developed, and evaluated using competency questions, and further validated by an online tool. Specifically, the proposed formalization of the Balanced Scorecard framework provides a semantic layer aimed at facilitating an effective Balanced Scorecard implementation, enabling accurate, traceable, and continuous monitoring and improvement of the strategy execution, based on a data-driven approach. The formalization of this knowledge through an ontology encompasses several advantages, such as improved interoperability and validation of the framework's elements, inference of new knowledge, and enhanced communication between different stakeholders. Additionally, managerial implications include ensuring alignment between the Balanced Scorecard and organizational goals, supporting compliance and governance efforts, improving communication and knowledge transfer, enhancing the strategic decision-making process, and facilitating the integration of data into the Balanced Scorecard.