Explanation of Exceptional Values in Multi-dimensional Business Databases

“How can the functionality of multi-dimensional business databases be extended with diagnostic capabilities to support managerial decision-making?” This question states the main research problem addressed in this thesis. Before giving an answer, the question first requires clarification and delineation. In this chapter, the research question is placed briefly into context, both regarding academic and business relevance. This leads to the formulation of three specific research questions. Subsequently, a section is dedicated to each specific research question. An outline of this thesis concludes the chapter

Analysis for Detecting and Explaining Exceptions in Business Data

In this paper we describe the concepts of automatic analysis for the exceptional patterns which are hidden in a large set of business data. These exceptions are interesting to be investigated further for their causes and explanations, as they provide important decision support. The analysis process is driven by diagnostic drill-down operations following the equations of the information structure in which the data are organised. Using business intelligence, the analysis method can generate explanations supported by the data. The methodology was tested on a case study and was reflected in considering the practical aspects of its application procedure

SLA-aware operational efficiency in AI-enabled service chains: challenges ahead

Service providers compose services in service chains that require deep integra tion of core operational information systems across organizations. Additionally, advanced analytics inform data-driven decision-making in corresponding AI-ena-bled business processes in today’s complex environments. However, individual partner engagements with service consumers and providers often entail individu-ally negotiated, highly customized Service Level Agreements (SLAs) comprising engagement-specific metrics that semantically differ from general KPIs utilized on a broader operational (i.e., cross-client) level. Furthermore, the number of unique SLAs to be managed increases with the size of such service chains. The resulting complexity pushes large organizations to employ dedicated SLA management sys-tems, but such ‘siloed’ approaches make it difficult to leverage insights from SLA evaluations and predictions for decision-making in core business processes, and vice versa. Consequently, simultaneous optimization for both global operational process efficiency and engagement-specific SLA compliance is hampered. To address these shortcomings, we propose our vision of supplying online, AI-supported SLA analyt-ics to data-driven, intelligent core workflows of the enterprise and discuss current research challenges arising from this vision. Exemplified by two scenarios derived from real use cases in industry and public administration, we demonstrate the need for improved semantic alignment of heavily customized SLAs with AI-enabled operational systems. Moreover, we discuss specific challenges of prescriptive SLA analytics under multi-engagement SLA awareness and how the dual role of AI in such scenarios demands bidirectional data exchange between operational processes and SLA management. Finally, we discuss the implications of federating AI-sup-ported SLA analytics across organizations

Studies on Determinants and Consequences of Financial Reporting Quality

The purpose of this dissertation is to investigate determinants and consequences of financial reporting quality. The first study examines the impact of high quality financial reporting on private firms’ access to bank debt and trade credit capital. The results suggest that high quality financial reporting does have economic consequences even in the presence of private communication that serve as alternative information asymmetrymitigating mechanisms. The second study investigates the relation between auditor size and audit quality for private firms in a setting in which large auditors have materially weak incentives to retain their audit quality advantage. My analysis provides evidence that the otherwise positive relation between auditor size and audit quality reverses when the incentives of large auditors to deliver superior audit quality become sufficiently weak. The third study examines the effect of the political connections of auditors on audit quality. The results suggest that politically connected offices deliver superior audit quality than nonpolitically connected offices. However, the positive relation between audit office political connectedness and audit quality dissipates for those clients that are politically connected themselves. Overall, the three studies of this dissertation highlight the importance of auditor incentives in shaping financial reporting quality as well as the significance of accounting information even in settings with low demand for high quality financial reporting