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Recent studies in the accounting literature have investigated the economic consequences of R&D capitalization. Discretionary R&D capitalization for target beating can be characterized as a firm signaling private information on its future economic benefits or as opportunistic earnings management. R&D capitalization also has an impact on a firm’s marginal costs and product market competition. The purpose of this paper is to address how firms choose R&D levels for the purpose of meeting or beating their earnings targets and how this influences sequential product market competition.

The authors study this issue in a stylized game-theoretic model where R&D choices of a firm are not only strategically made but also used to convey proprietary information to its rival. The model provides a rationale for a firm distorting its R&D level to earn more profits and meet its earnings target.

The equilibrium result indicates that before the realization of common cost shock, a firm can influence the output of its accounting system (i.e. meeting an earnings target) through adjusting its R&D choices. This firm will overinvest in R&D, and this will give an opportunity to create some reserves to be used later to earn a higher profit and reach the earnings target.

This paper contributes to the research on real earnings management in terms of how R&D capitalization affects a firm’s R&D choices by influencing the output of its accounting system through adjusting its R&D choices and the strategic impact of those choices.

This paper aims to explore the relationship between the types of R&D‐activities within science‐based firms and the knowledge transfer channels used for industry‐science collaboration. Rooted in a contingency approach, it seeks to identify patterns in the organization of knowledge transfer and to disclose ways that may support R&D‐managers in achieving effective knowledge transfer.

The paper is an exploratory study in order to obtain a deep understanding of the relationship. At first, both the types of R&D‐activity and the knowledge transfer channels were conceptualized based on an extensive literature review. Second, data were collected by means of semi‐structured interviews with 17 (assistant) R&D‐managers of ten large European chemical firms.

The analysis suggests that almost each of the knowledge transfer channels used for industry‐science collaboration has a more or less unique link to a specific type of R&D‐activity. An empirically based model is developed that visualizes the linkages. In addition, explanations for observed links are proposed.

The empirical analysis reported focuses on multinational firms in the science‐based European chemical industry, because they invest heavily in R&D and are hence more interested in collaboration with scientific partners. Further research is needed to determine the model's applicability in other empirical settings, both within and outside science‐based industries.

The paper provides R&D‐managers with a model that may support them in deciding how to organize their collaboration with scientific partners based on the type of their internal R&D‐activity to achieve effective knowledge transfer.

The paper is one of the first studies that empirically assesses the relationship between the types of R&D‐activities in firms and the knowledge transfer channels that are used for industry‐science collaboration.

The purpose of this paper is twofold: to analyze to what extent innovation output of R & D collaborations, proxied by co-patenting activities in terms of quantity, characteristics and value, differs depending on whether the engaged R & D partners have a certain type of relationship (allies, suppliers and subsidiaries); to identify possible automakers co-patenting patterns taking into account the differences in the innovation output with their R & D partners.

To reach the aims, the authors matched two types of data: co-assigned patent portfolio of four automakers and relationship type between automakers and their co-assignees. Matching the company names of the two data sources allowed the authors to obtain the final data set used to carry out extensive descriptive and regression analysis, both on a firm- and patent-level.

Results show differences in the characteristics and the technological value of patented inventions in relation with the type of collaboration partner; they also support the authors in the identification of four co-patenting patterns (contingent, purposive, watchful and advanced) according to the co-patenting propensity and the presence of a preferred relationship type.

The paper contributes to the literature by investigating the presence of differences across the patenting activities of a selection of automakers and their supplier, allied and subsidiary firms. The issue related to patent value represents an emerging area of interest in the field of collaborations for innovation. The methodology constitutes a novelty by matching two different sources and standardizing the company names (“name game”) through an automated algorithm and a double manual check, by searching company web sites and corporate trees.

Gives an in depth view of the strategies pursued by the world’s leading chief executive officers in an attempt to provide guidance to new chief executives of today. Considers the marketing strategies employed, together with the organizational structures used and looks at the universal concepts that can be applied to any product. Uses anecdotal evidence to formulate a number of theories which can be used to compare your company with the best in the world. Presents initial survival strategies and then looks at ways companies can broaden their boundaries through manipulation and choice. Covers a huge variety of case studies and examples together with a substantial question and answer section.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

This paper analyzes the productivity effects of structural capital such as research and development (R&D) and organizational capital (OC). Innovation work also produces innovation-labor-biased technical change (IBTC) and knowledge spillovers. Analyses use full register-based dataset of Finnish firms for the period 1994–2014 from Statistics Finland.

Intangibles are derived from the labor costs of innovation-type occupations using linked employer-employee data. The approach is consistent with National Accounting and offered as one method in OECD (2010) and applied in statistical offices, e.g. in measuring software. The EU 7th framework Innodrive project 2008–2011 extended this method to cover R&D and OC.

Methodology is implementable at firm-level and offers way to link personnel reporting to intangible assets. The OC-IBTC as well as total resources allocated to OC are relevant for productivity growth. The R&D stock is relatively higher but R&D-IBTC is smaller than OC-IBTC. Public policy should, besides technology policy, account for OC and OC-IBTC and related knowledge spillovers in the industries that are most important among the SMEs (low market-share-firms).

The data are based on remote access to Statistics Finland; the data cannot be disseminated.

Intangible assets are measured from innovation work that encompasses not only R&D work. IBTC is proxied in production function estimation by relative compensations on IA work. The non-competing nature of IAs is captured by IA knowledge spillovers. The sample sizes are much higher than in earlier studies on horizontal knowledge spillovers (such as for SMEs,) thus bringing additional generality to the results.

The purpose of this paper is to analyse cooperation in R&D in the automobile industry in Spain. It first examines to what extent firms cooperate with external actors in the field of technological innovation, and if so, with what type of cooperation partner, paying special attention to the differentiation according to the size of the firms. Second, it aims to study how the firm’s size may affect not only the decision of cooperating but also with which type of partner.

The data in this study came from the surveys done in 2010 and 2013 by the Technological Innovation Panel (PITEC) for firms in the automotive industry. The paper estimates a bivariate probit model that takes into account the two types of cooperation mostly present in such an industry, vertical and institutional, explicitly considering the interdependencies that may arise in their simultaneous choice.

The empirical study confirms that small firms cooperate less frequently than big firms and that giving more importance to information publicly available and having public financial support from local and national governments are important determinants of collaboration agreements, mainly in the case of customers and suppliers.

This paper contributes to the understanding of the motivations of the automotive industry for engaging in R&D cooperation agreements. The authors study how the firm’s size may affect not only the decision of cooperating but also with which type of partner.

This paper aims to understand whether there are different styles of organisation and management of industrial R&D activities. Recent cases of re‐organisation of R&D activities within firms suggest that: R&D activities tend to be carried out in different units of the same organisation and often in different locations, and there seem to be inter‐industry differences among R&D processes. This paper attempts to explore whether and how a certain managerial/organisational style relates to the type of the R&D process (which varies from industry to industry) and to the different nature of the R&D activities. It also identifies the key characteristics of the R&D organisation and the strategic management of technology, associated with each style. The analysis is based upon an empirical study of 19 multinationals operating in various industrial sectors. A classification of the types of R&D process is adopted and the key characteristics of R&D management and organisation in each R&D type are described and discussed.

The purpose of this paper is to provide a patent-based framework for investigating the effect of previous and current open innovation (OI) adoption on firms’ knowledge management strategies and type of innovation output.

Patent data are employed for gauging innovation practices, exploitation vs exploration strategies, specialization vs diversification choices and type of innovation. The study is performed on a sample of 1,280 patents granted to 66 top R&D spending bio-pharmaceutical companies. The year of analysis is 2010.

The previous recourse to specific innovation practices influences the current practice selection. R&D collaboration, outsourcing and mergers and acquisitions are employed to pursue exploration. Past purchase of patents increases the likelihood to achieve architectural and radical innovation in current activities.

The work recommends the use of patent data to gauge many key elements for knowledge and innovation management. Results exhort scholars to investigate innovation practices at the knowledge domain level in order to detect specific behaviors.

The study provides a methodology for supporting decision-makers in assessing firms’ OI adoption, also performing the benchmark with competitors and R&D partners. Given the high computational effort required for applying the methodology, the authors are planning to give access to the software specifically developed for this study.

The work contributes to the current debate considering the effect of a combination of innovation practices on knowledge management strategies and type of innovation output, with a particular focus on OI activities. Moreover, the separation between the impact of previous and current innovation practices provides useful insights.

This paper aims to focus on formation motivations and processes of R&D consortia to appreciate their differential innovative and learning capabilities.

The paper presents its argument in two separate steps. First, a two‐by‐two framework, comprising four consortium types, is developed based on two formation motivations (i.e. risk sharing and networking) and two formation processes (i.e. emergent and engineered). Four case vignettes are used to demonstrate the practical relevance of the two‐by‐two consortium typology framework. Second, the innovative and learning capabilities of each of these consortia are explored and eight propositions are advanced.

The paper introduces four types of consortia: community builders, gamblers, visible hands, and opportunists. It is argued that visible hands generate greater innovation than community builders and opportunists, and community builders and opportunists generate greater innovation than gamblers. It is also argued that government involvement moderates the relationship between consortia type and innovative capabilities in an inverted U shape. Lastly, relative appropriateness of frequency, outcome, and trait imitations to facilitate organization‐level learning among consortium members is explored.

The main contribution of this paper lies in its two‐by‐two typology of consortium formation contextual conditions. Instead of focusing on evolutionary cycles and performance issues of consortia, this paper draws research attention to contextual conditions surrounding consortia formation. Consortium formation contextual conditions are critically important because they predetermine the life cycle and performance trajectory of consortia. This paper also links innovation and learning dynamics in consortia.