There has been a lot of debate on the new business-oriented role of management accountants during recent years. This paper examines how a case company is trying to change its management accounting culture in practice. Furthermore, it illustrates how accounting practices are woven into the cultural fabric of an organization and the great diversity of practices constituting its business orientation. This longitudinal case study explores and theorizes the multiple cultural change interventions related to management accounting, including how the case company reorganized the management accounting organization, implemented new accounting systems and innovations, pursued a new kind of human resource management (recruitment, training and career planning policies) and set the official corporate values in order to support this change. The deepened decentralization of the business controller function, combined with the effective and increasingly centralized basic accounting systems (such as ERP and consolidation packages) and HRM management, were of high importance in establishing the new business orientation. Moreover, informal interventions such as the role modelling and directing of personal attention - carried out by the top management and top financial executives - and storytelling, contributed to the constitution of cultural practices. Thus, the potential power of these informal change interventions and mechanisms should not be underestimated, with further research being, in fact, in great need. As its major theoretical development, at the end of the report, this study introduces a systematic framework of the cultural change interventions related to management accounting.
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not designed for the goal of estimating the posterior distribution. Our paper addresses this gap in the literature. We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty. We then propose to select the next evaluation location to minimise the expected loss. Experiments show that the proposed method often produces the most accurate approximations as compared to common BO strategies.
This comparative study analyzes institutional logics and categorizes organizational responses to performance measurement systems (PMSs) in two Finnish cities. We refine the typology of organizational responses and suggest how the choice of response depends on the institutionalized logic in the public sector accounting context (cf. Oliver, 1991). Further, we discuss and refine the types of successful co-operation under competing institutional logics (cf. Reay and Hinings, 2009). Our study also sheds light on practice variation in public sector accounting such as how and why PMS use materializes in different forms in units with different institutional logics.
Purpose The purpose of this paper is to focus on how the HR function takes advantage of human resource analytics (HRA), including big data (BD), and discuss factors hindering HRA and data utilization. Moreover, the authors discuss the implications of the HRA-induced role transformation of the human resource (HR) function. Design/methodology/approach This is an explorative case study based on qualitative interviews in nine leading Finnish companies. Findings The results indicate that both technical and human obstacles, operating with very basic HR processes and traditional information systems and poor data quality, hinder adoption of advanced HRA. This, combined with lacking skills in analytics and business understanding, inability to go beyond reporting, misconceptions related to BD and traditional compliance-oriented HR culture pose further challenges for the data analytics capacity and business partner role of the HR function. Senior executives expect no significant advancements of HRA, while HR professionals saw potential value in BD, although skepticism was not uncommon. The results point toward a need for increased cooperation with data analysts and HR professionals in provision and understanding the HR-related data for business-related decision making. Furthermore, cultural change and organizational redesign may be called for, in addition to overcoming technological obstacles related to BD, for it to have an impact on HR practices. HRA utilization and role transition of the HR function seem closely related and this transformation can be mutually reinforcing. Originality/value This study provides and theorizes explorative data on HRA within a group of some of the largest Finnish companies, pointing toward an immature state of the art in BD and HRA utilization and there being a relationship between HRA and the role transition of the HR function in organizations.
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical models are fitted to data, and synthetic likelihood (SL) method is used to form the noisy log-likelihood estimates using computationally costly forward simulations. We frame the inference task as a sequential Bayesian experimental design problem, where the log-likelihood function is modelled with a hierarchical Gaussian process (GP) surrogate model, which is used to efficiently select additional log-likelihood evaluation locations. Motivated by recent progress in the related problem of batch Bayesian optimisation, we develop various batch-sequential design strategies which allow to run some of the potentially costly simulations in parallel. We analyse the properties of the resulting method theoretically and empirically. Experiments with several toy problems and simulation models suggest that our method is robust, highly parallelisable, and sample-efficient.
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible. However, even a single evaluation of a complex model may take several hours, limiting the number of model evaluations available. Modelling the discrepancy between the simulated and observed data using a Gaussian process (GP) can be used to reduce the number of model evaluations required by ABC, but the sensitivity of this approach to a specific GP formulation has not yet been thoroughly investigated. We begin with a comprehensive empirical evaluation of using GPs in ABC, including various transformations of the discrepancies and two novel GP formulations. Our results indicate the choice of GP may significantly affect the accuracy of the estimated posterior distribution. Selection of an appropriate GP model is thus important. We formulate expected utility to measure the accuracy of classifying discrepancies below or above the ABC threshold, and show that it can be used to automate the GP model selection step. Finally, based on the understanding gained with toy examples, we fit a population genetic model for bacteria, providing insight into horizontal gene transfer events within the population and from external origins.
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