Background: The complex, multidimensional nature of healthcare quality makes provider and treatment decisions based on quality difficult. Patient-reported outcome (PRO) measures can enhance patient centricity and involvement. The proliferation of PRO measures, however, requires a simplification to improve comprehensibility. Composite measures can simplify complex data without sacrificing the underlying information. Objective and methods: We propose a five-step development approach to combine different PRO into one composite measure (PRO-CM): (i) theoretical framework and metric selection, (ii) initial data analysis, (iii) rescaling, (iv) weighting and aggregation, and (v) sensitivity and uncertainty analysis. We evaluate different rescaling, weighting, and aggregation methods by utilizing data of 3145 hip and 2605 knee replacement patients, to identify the most advantageous development approach for a PRO-CM that reflects quality variations from a patient perspective. Results: The comparison of different methods within steps (iii) and (iv) reveals the following methods as most advantageous: (iii) rescaling via z-score standardization and (iv) applying differential weights and additive aggregation. The resulting PROCM is most sensitive to variations in physical health. Changing weighting schemes impacts the PRO-CM most directly, while it proves more robust towards different rescaling and aggregation approaches. Conclusion: Combining multiple PRO provides a holistic picture of patients’ health improvement. The PRO-CM can enhance patient understanding and simplify reporting and monitoring of PRO. However, the development methodology of a PROCM needs to be justified and transparent to ensure that it is comprehensible and replicable. This is essential to address the well-known problems associated with composites, such as misinterpretation and lack of trust.

Purpose - Increased business competition requires even more rapid and sophisticated information and data analysis. These requirements challenge performance management to effectively support the decision making process. Business analytics is an emerging field that can potentially extend the domain of performance management to provide an improved understanding of business dynamics and lead to a better decision making. The purpose of this positional paper is to introduce performance management analytics as a potential extension of performance management research and practice. The paper clarifies the possible application areas of business analytics and their advantages within the context of performance management. Design/methodology/approach - The paper employs a literature based analysis and from this a conceptual argument is established. Finally, a business analytical model is presented to be used to undertake future research. Findings - The paper clarifies the possible application areas of business analytics and their advantages within the context of organizational performance management. Originality/value - The main implication is that the paper provides evidence of the use of business analytics for understanding organizational performance. Several insights are provided for management accounting research and education.

In an age of increasing access to and power of artificial intelligence (AI), ethical concerns, such as fairness, transparency, and human well-being have come to the attention of regulators, standard setting bodies, and organizations alike. In order to build AI-based systems that comply with new rules, organizations will have to adopt systems of governance. This study develops, based on existing frameworks and a multiple case study, a governance framework specifically designed with these challenges in mind: The St. Gallen Governance Framework for Artificial Intelligence focuses on identifying stakeholder concerns and strategic goals, building a management control system, assigning roles and responsibilities, and incorporating dynamism into the system of governance.

The purpose of this paper is to explain the interrelation between formal control and trust in dyadic buyer-supplier relationships when facing appropriation concerns. From a methodological perspective our starting point is not to take as given an often suggested humped-shaped relationship between theses control devises (Dekker, 2004, Tomkins, 2001), but to provide a dynamic model that generates endogenously such a pattern. This model involves trust generation mechanisms that interrelate with formal control in the contract and execution phase of a buyer-supplier relationship. The model structure involves feedback loops that contribute to diminishing causality concerns and is able to go beyond a comparative analysis. From a theoretical perspective, we reconsider the control problem of appropriation concerns when buyers face a performance risk. Our simulation results imply that a performance shock triggers a complementary (substitutive) relationship between goodwill (contractual) trust and monitoring frequency. Efficiency or effectivity gains are able to restore the humped-shaped relationships between the trust types and formal control. From a practical perspective and a transaction costs economics view, these results imply that giving a second chance to a supplier in favor of a long-term relationship may establish cost saving trust as a control devise despite the shock. Two directions determine the next steps for theory development induced by the model. First, the model is extended by asset specificity and partner selection. Asset specificity via idiosyncratic investments is regarded as a further antecedent of appropriation concerns; partner selection, is considered as an alternative to formal control. Second, the causal structure of the dynamic model may be tested empirically with a survey-based, non-recursive structural equation model. We consider these recommondations as invitations for future work. In conclusion, we consider our dynamic model that involves a humped-shaped relationship between the control devises as a candidate for extending existing theories regarding interfirm relationships.