Finnish “state of mind” on inter-organizational integration

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value 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.