Does research pay? An analysis of R&D expenditure and economic performance in small and medium sized enterprises

In order to develop or protect competitive advantages, companies, besides other initiatives, increasingly invest more and more in research and development (R&D) to foster innovation and dynamic capabilities. Consequently, discussions about the effectiveness and benefit of R&D expenditure have become popular in the recent years.
In the first part of this book the theoretical basis for innovation management, calculation of benefit, and evaluation methods of R&D initiatives in companies are given. The second part presents a quantitative analysis of the efficacy of R&D expenditure and economic performance of SME based on a data set produced from the Statistisches Bundesamt Germany and by using MS Excel the programming language "R". Finally, this research provides a summary, an outlook, and a recommendation for further research.

Autorentext

Axel Jörn, B.Eng., wurde 1982 in Güstrow geboren. Nach seiner Berufsausbildung als Anlagenmechaniker in der Handwerksbranche, entschied sich der Autor, seine fachlichen Qualifikationen im Bereich der Technik durch einen staatlich geprüften Techniker für Maschinenbaü und ein nein nebenberufliches Studium zum Bachelor of Engineering Mechatronik- weiter auszubauen. Bei seiner Vollzeit Tätigkeit in der Entwicklung und im Projektmanagement bei Airbus sowie bei Siemens sammelte der Autor umfassende praktische Erfahrungen in der Luftfahrt- und der Windenergiebranche. Darüber hinaus vertiefte er sein Projektmanagement know how mit einer PMI Zertifizierung und sein ökonomisches Verständnis mit einem MBM Studium.

Leseprobe
Text sample:
Chapter 2.2 Methods to measure efficiency of innovation and R&D expenditure:
The following paragraph deals with the measurement of the benefit of innovation and lists the different calculation methods. According to Schmeisser, there is a gap in scientific knowledge related to calculation methods to determine the success of innovation. The practical statistics about the success of inventions (ideas) show the importance of measuring their sense and prosperity. An interdisciplinary, empirical, and long-term study into the ideas of 116 companies reveals that only 11 out of 1.919 are actually successful. Fewer than 10% of the ideas have entered the market. The market disclosed 70% of them as unsuccessful. From the remaining products, 46% were sold with deficit, 33% did not bring significant profit, and only 21% were finally successful and soled with profit. There are different dimensions to the success of an idea or innovation. One can refer to the success of a project in relation to an innovation, or the total project portfolio of several innovation projects, or even on company level. As the given data set provides figures mirroring the overall performance of the companies and their related R&D spending, for this study a literature review of measurement methods for success at the company level was performed.
According to Schmeisser, the contemplation at the company level by using performance indicators like turnover, growth in turnover and revenue is difficult due to two reasons. Firstly, the success of a company is influenced by several other internal (degree of dynamic capability, innovation, ect.), and external (micro- and macro- environmental) factors. Therefore, there is no causal relationship between a successful innovation management and the success of a company. The second weak point of this approach is that the actual numbers for turnover, revenue, or income returns are based on the innovations or achievements of the past, which, therefore, give no evidence of the present innovation initiatives. Moreover, the success of the resulting product depends on factors like the related marketing mix, after-seals service, etc. Consequently, the academic measurement of the success of innovations is done at project not at company level. Nevertheless, this assignment approaches science at this stage by developing formulas and calculation models based on quantitative methods using MS Excel and the statistical analysis software "R" in order to determine the success of R&D spending based on figures at the company level. The literature offers several techniques of measuring the value of innovations and R&D spending. These are:

• Rating and solvency check.
• Technology cost analysis.
• Technology balancing.
• Technology portfolio matrix.
• Target costing.
• Berliner balance score card.
  All these methods are used to answer the scientific question of this assignment using different methods and with several advantages and disadvantages. As none of these research models is applicable to the given dataset, a further description of these techniques is not needed.
  3 Analysis of R&D expenditure of SME`s:
  This section presents statistical analyses by applying several functions of the programming language "R". Based on the Excel data set described in section 1.2 the embedded "rcmdr-package" is used to answer the question "Does research pay?" The following "R" functions are applied:
• Regression analysis: Scatter diagram.
• Descriptive statistical analysis: Correlation matrix (R²).
• Mean value investigation: T-test for independent samples.
  With the first two functions, it is possible to identify whether there are any statistical connections between two or more variables in a sample, to understand how strong these connections are, and to test the probability that these relationships also exist within the whole population.
  The purpose of the T-test is to check whether the means of two variables differ significantly or no