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The role of management control is frequently emphasized in connection with inter-organizational relationships and value networks. For example, boundary-spanning cost and accounting control techniques have been studied in multifaceted empirical settings. The prevalence of such techniques is, however, currently unknown in conjunction with companies’ interests to increase inter-organizational integration in general. Additionally, also the nexus between the internal state of cost management and the company’s willingness to develop inter-organizational relationships requires further investigation. The paper aims to discuss these issues.

The study is based on an extensive survey that was responded to by more than 1,500 CEOs and CFOs from large, medium-sized and small Finnish enterprises in a variety of industries. As the authors chose the mixed-methods approach, both quantitative and qualitative data were collected for the study.

The findings suggest that companies can be allocated to five clusters: “the cost experts,” “the trustful,” “the holdouts,” “the trailblazers” and “the uncertain”. When the networking-oriented clusters, “the trustful” and “the trailblazers” are combined, the authors can conclude that 40 percent of the studied companies are interested in increasing inter-organizational integration. However, only 7 percent have boundary-spanning techniques in use. There is also a correlation between interest in integrating and developing cost management.

This paper contains several theoretical implications, although further research, e.g. comparative studies, is required to verify the findings. The scarcity of managerial implications can be regarded as a limitation.

This paper fills several untapped research gaps by studying inter-organizational integration in the cost management context from multiple, complementary perspectives with a particularly large set of data.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

While companies worldwide are largely comprised of small and medium-sized enterprises (SMEs), a significant amount of management accounting (MA) research focuses on larger organisations, thus leaving MA practice in SMEs relatively under-researched. This paper aims to examine MA adoption (MAA) and its interfaces with MA challenges and business performance from a small business perspective.

A sample of 502 small businesses is investigated with an embedded mixed methods research design comprised of qualitative content analysis, factor analysis and analysis of variance.

Up to 78% of small businesses are facing MA challenges that stem from organisation, systems, personnel and/or resources. Based on the present findings, MA challenges do motivate small businesses to at least consider investing in MAA as small businesses facing challenges are more likely to acquire systems and services than those reporting no issues at all. Hence, small business managers seem to not only recognise where their challenges lie, but also seek ways to improve the situation through MAA. The analysis also reveals that companies with the highest MA know-how have the best average solvency, suggesting that small businesses indeed benefit from MAA. Interestingly, the performance at medium levels of know-how declines while investments increase, revealing a “decreasing solvency phenomenon”. Potential explanations are, e.g. the MA not fitting the company’s exact needs, or information usability and use being limited by poor MA understanding.

The originality of the research lies in exploring the interfaces between MA challenges, MAA and small business performance using distinctive embedded mixed methods research design.

Big data and related technologies are expected to drastically change the way industrial maintenance is managed. However, at the moment, many companies are collecting large amounts of data without knowing how to systematically exploit it. It is therefore important to find new ways of evaluating and quantifying the value of data. This paper addresses the value of data-based profitability of maintenance investments.

An analytical wasted value of data model (WVD-model) is presented to quantify how the value of data can be increased through eliminating waste. The use of the model is demonstrated with a case example of a maintenance investment appraisal of an automotive parts manufacturer.

The presented model contributes to the gap between the academic research and the solutions implemented in practice in the area of value optimization. The model provides a systematic way of evaluating if the benefits of investing in maintenance data exceed the additional costs incurred. Applying the model to a case study revealed that even though the case company would need to spend more time in analyzing and processing the increased data, the investment would be profitable if even a modest share of the current asset failures could be prevented through improved data analysis.

The model is designed and developed on the principle of eliminating waste to increase value, which has not been previously extensively discussed in the context of data management.