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Accounting for quality and improved organizational performance has recently received attention in management control research. However, the extent to which process innovation changes have been integrated into management control research is limited. This paper contributes to that integration by drawing from institutional adaptive theory of organizational change and process innovation strategies. The paper utilizes a 2 by 2 contingency table that uses two factors: environmental conditions and organizational change/learning strategies, to build a process innovation framework. A combination of these two factors yields four process innovation strategies: mechanistic, organic, organizational development (OD) and organizational transformation (OT).

The four process innovation typologies are applied to characterize innovations in accounting such as activity based costing (ABC). ABC has been discussed as a multi-phased innovation process that provides an environment where both the initiation and the implementation of accounting change can occur. Technical innovation can be successfully initiated as organic innovation that unfolds in a decentralized organization and requires radical change and double loop learning. Implementation occurs best as a mechanistic innovation in a hierarchical organization and involving incremental change and single loop learning. The paper concludes that if ABC is integrated into an OD or OT intervention strategy, the technical and administrative innovation aspects of ABC can be utilized to manage the organization’s operating activities.

The mechanistic-organic assumptions of SF address those organizational factors related to structural arrangements, contextual factors, job-task work activities, and human resources management policies. Organizations adopt structures and procedures in search of legitimacy and institutionalization (Riebero & Scapens, 2006, p. 96). Structures manifest themselves in centralized (mechanistic) and/or decentralized (organic) forms. These structures can be loose or tightly controlled; they can involve independent or interdependent tasks. These conditions have a direct impact on the operation of management information and control systems that will, in turn, impact organizational learning and process innovations, which, ultimately, affects organizational performance.

Strange and Soule (1998) outlined the processes of innovations as follows. “Innovations are novel (at least to the adopting community), making communication a necessary condition for adoption. Innovations are also culturally understood as progressive, strengthening the hand of change agents. And since innovations are risky and uncertain, adopters carefully weigh the experience of others before acting” (p. 267).

The paper extends the organizational learning framework: Structural-Functional (SF)-single-loop or Conflictual-Radical (CR)-double-loop learning to the management accounting literature. The sociological approach of organizational learning is utilized to understand those contingent factors that can explain why management accounting innovations succeed or fail in organizations.

We view learning as enhancing an organization’s strategic competitive advantage by making it better able to adopt and diffuse innovation in respond to changes in its environment in order to manage improved performance. The success of management accounting innovations is contingent upon whether its learning process involves SF-single-loop or CR-double-loop learning to adopt and diffuse process innovation.

The paper suggests that the learning strategy that the organization chooses is the reason why some management accounting innovations are more successfully adopted than others and why some innovations are easily diffused in some organizations but not in others. We propose that the sociological approaches to learning provide an alternative framework with which to better understand the adoption and diffusion of process innovations in management accounting systems.

It has become evident that management accounting researchers need to pay particular attention to an organization’s approach to adoption and diffusion of innovation strategies, particularly when they are designing and implementing process innovation programs for an organization. According to Schulz (2001), there are two interrelated stages of the learning that can shape the outcome of the innovation process in an organization. The first stage is related to the acquisition/production (adoption) of knowledge that results in gathering information, codification, and exploration. This is followed by the second stage which is the distribution or dissemination (diffusion) processes. When these two stages – adoption and diffusion – are applied within an accounting context, they address issues that are commonly associated with the successes and/or failures of management accounting innovations.

Although innovation involves learning, the nature of the learning process does not completely describe the manner in which an innovation affects the organization. Accordingly, we suggest that the two interrelated organizational sociological dimensions of innovations processes, namely, (1) the adoption and diffusion theories of Rogers (1971 and 1995), to approach organizational learning, and (2) the SF (single loop) and CR (double loop) approaches to learning be used simultaneously to describe management accounting innovations.

When an innovation is implemented, it initially can be introduced as an incremental change, one that can be limited in both in its scope and its breadth of administrative changes. This means that situations which are most likely to benefit from its initiation can serve as the prototype for its adoption by the organization. If successful, this can be followed by systemic accounting innovations to instituting broader administrative changes within the existing accounting reporting and control systems.

This chapter has aimed to indicate some new important emerging policy problems, which have characterised the Covid crisis in the European economy during 2020 and then the bounce-back in 2021. The chapter has illustrated an economic theoretical framework focussed on innovation and structural changes, according to a Schumpeterian and evolutionary or neo-institutional approach, which seems more appropriate than the traditional neoclassical and macroeconomic models, as the basis for a ‘new industrial strategy’ in the European Union. The mainstream economic models are static and point-like, as they do not consider the role of time and of space, such as the existence of asymmetric information and external economies and also the interdependence between the companies and the other ‘stakeholders’ in the process of economic development. On the contrary, the theoretical framework of this chapter considers the factors that act on the structural changes according to four different and interdependent dimensions: the final demand, the intermediate productions and also the supply of labour and the endowment of natural resources. Finally, some preliminary indications on the organisation of a new industrial strategy at the European scale are discussed, different from the focus on just the digital and green technologies, as indicated by the NGEU program by the European Commission.

Purpose – This study explores subsidiaries' local network embeddedness and how it contributes to localised subsidiary innovation output from a social network perspective. In particular, we are interested in analysing the consequences of local network density, diversity and, subsidiaries' network position on its innovation outcomes.

Design/methodology/approach – Data are derived from a longitudinal quantitative study of the entire R&D network within one of the largest life science cluster in Germany, the ‘BioRegion Rhein-Neckar-Dreieck’.

Findings – Our findings indicate that the size (density) of the local network has an inverted U-shaped effect on the innovation outcomes of MNC subsidiaries. Our findings further indicate that a strong brokerage position in the local network has a significant positive influence on the innovation output while a position in the core of the network has a significant negative effect on the innovation output.

Research implications – Our results shed new light on the relationship between local embeddedness, brokerage, the danger of overembeddedness and innovation output of MNC subsidiaries.

An emerging stream of literature has observed that project-based organizations rely increasingly on a network of collaborations originating from the ongoing process of creating and dissolving relationships that bring new project opportunities. Project-based networks are widespread in knowledge-intensive and creative industries, such as life-science and biotechnology, nanotechnology, and software, film, and music industry. This chapter examines the structural characteristics of project-based network-ties in German biotech. We focus on the consequences of local versus international network ties for the innovative success of German biotechnology firms. The findings of our longitudinal event history analysis indicate that the most valuable learning drivers are international research alliances and centrality within the international research network. Surprisingly, we do not find any local effects: neither the density of a local research cluster, nor its diversity or age is of significance. Our results shed new light on the relevance of international linkages for firms that are engaged in project-based learning networks.

Open-Economy models are central to the discussion of the trade-offs monetary policy faces in an increasingly more globalized world (e.g., Marínez-García & Wynne, 2010), but bringing them to the data is not without its challenges. Controlling for misspecification bias, we trace the problem of uncertainty surrounding structural parameter estimation in the context of a fully specified New Open Economy Macro (NOEM) model partly to sample size. We suggest that standard macroeconomic time series with a coverage of less than forty years may not be informative enough for some parameters of interest to be recovered with precision. We also illustrate how uncertainty also arises from weak structural identification, irrespective of the sample size. This remains a concern for empirical research and we recommend estimation with simulated observations before using actual data as a way of detecting structural parameters that are prone to weak identification. We also recommend careful evaluation and documentation of the implementation strategy (specially in the selection of observables) as it can have significant effects on the strength of identification of key model parameters.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

How do ego-networks evolve? How does such evolution affect firms’ innovation output? This chapter uses a longitudinal sample of firms in the biotechnology industry to address these questions. We use social network theory to develop a model of the structure and dynamics of firms’ interorganizational research collaboration ego-networks. Using novel longitudinal methods, this chapter demonstrates how research collaboration ego-networks in the biotechnology industry change over time and how this evolution affects focal firms’ subsequent innovative output. The model is tested on a sample of 482 biotechnology firms over a span of 17 years (1990–2006). The results indicate the significant impacts of ego-network size, ego-network growth, and the inclusion of new members in the ego-network on the innovation output of biotechnology firms. Our results also suggest that enlarging ego-networks by adding new and diverse members presents significant management challenges.