The Influence of Globalization on Open Innovation
Practices and a Firm’s Innovation Performance
Master Thesis by Niklas Koch - S3558185 Supervisor: Dr. S. R. Gubbi Co-Assessor: Dr. C. Schlägel Rijksuniversiteit Groningen University of Groningen Faculty of Economics and Business
Master of Science in International Business and Management
21.01.2019
open innovation. This study expands the scope of existing literature by adding the effect of globalization. It examines whether globalization has increased the need for firms to engage in open innovation practices and whether those practices have influenced the firm’s innovative performance. The research was conducted in the context of the automotive industry and investigated the eight largest car manufacturers. The collected data is analyzed with the help of a Poisson regression model and examined. Resultingly, this study found that there is no causal relationship between OI practices and a firm’s innovative performance. Moreover, globalization has not significantly moderated the relationship between the two variables. However, due to the increase in product launches in the industry, other OI practices than the ones used in this study seem to shape the industry. Therefore, future studies could define industry specific OI practices that shape the market player’s innovative performances.
Key words:
CI – Closed Innovation
EBIT – Earning before interests and taxes F.C.A – Fiat Chrysler Alliance
FDI – Foreign Direct Investment IMF – International Monetary Fund IP – Intellectual Property
JV – Joint Venture
M&A – Mergers & Acquisitions
OECD – Organization for Economic Co-operation and Development OI – Open Innovation
RoW – Rest of the World
3. Literature Review ... 9
3.1. Innovation ... 9
3.1.1. Contrasting concepts: Closed & Open Innovation ... 100
3.1.1.1. Outbound and Inbound Open Innovation ... 103
3.2. Globalization ... 14 4. Methodology ... 17 4.1. Conceptual Model ... 17 4.2. Sample ... 18 4.3. Data ... 18 4.3.1. Independent Variables ... 18 4.3.2. Moderator Variable ... 19 4.3.3. Dependent Variable ... 19
4.3.4. Control and Dummy Variables ... 20
4.4. Regression Model ... 22
4.4.1. Fixed Effects Model ... 22
5. Results and Discussion ... 24
5.1. Descriptive Statistics ... 24
5.2. Multicollinearity ... 25
5.3. Multiple Regression Analysis ... 26
5.4. Discussion ... 28
6. Limitations and Future Research ... 33
7. Conclusion ... 33
8. References ... 33
Figure 3: Internationalization of Volkswagen’s worldwide automobile production ... 8
Figure 4: The Closed Paradigm for Managing Industrial R&D ... 10
Figure 5: The Open Innovation Paradigm for Managing Industrial R&D ... 12
Figure 6: Conceptual Model ... 17
Table of Tables Table 1: Different Forms of Openness ... 14
1. Introduction
One of the more important developments in the field of innovation in the recent times is that firms have started to source knowledge and technology from their external environment instead of researching and developing in-house (Laursen & Salter, 2006; Gambardella, Giuri, & Luzzi, 2007). This shift from an internal focus to external outreach has been appropriately captured by the concept of open innovation (OI). OI is a paradigm suggesting that firms can and should use a combination of external ideas as well as internal ideas to create new pathways to the market, as the firm looks to advance its innovative performance (Chesbrough, 2006). New sources of input for OI practices arise when markets globalize and therefore, reveal their business potential.
Consequently, globalizing markets accompanied by competitive pressures have increased the need for firms to expand to new markets (Hirst, Thompson, & Bromley, 2015). The emergence of new markets offers new external knowledge input for firms, a key component of OI, which should lead to an improvement of a firm’s innovative performance (Chesbrough, 2006). In contrast to this assumption, Levitt (1993) states that globalization is leading towards a convergence of markets through altering cultures and hence resulting in also converging consumer preferences. Hence, one could assume that a firm’s efforts to gain insights in consumer preferences through internationalization would become less important. These two contrasting statements of the effect of globalization will be considered and reviewed in this thesis.
this idea, this thesis will address two key questions. The first, “Does globalization embrace OI?”. The second question “Does an engagement in OI practices improve a firm’s innovative performance?” is based on the two previously highlighted differing assumptions of globalization. Those key questions lead to the underlying research question of this thesis, namely: “Has globalization accelerated a firm’s need to engage in open innovation practices? If so, how have they influenced the firm’s innovative performance?”
The primary goal of this thesis is the investigation of the moderating effect of globalization on OI practices and the resulting innovative performance of firms across different markets. More precisely, whether stronger globalizing markets offer more knowledge inputs that lead to a better innovative performance in the particular market. To do so this will be accomplished by focusing on the automotive industry as it is one of the most globalized industries (Sturgeon, Van Biesebroeck, & Gereffi, 2008). Automotive manufacturing is one of the most globalized industries (Sturgeon et al., 2008). Additionally, it is one of the highest in research and development (R&D) investing industries, indicating innovation to be a key success factor in the industry (“R&D Expenditures by Industrial Sectors”, 2010; Sturgeon, Memedovic, Van Biesebroeck, & Gereffi, 2008). Both globalization and the high R&D intensity are key components of the previously introduced research question, justifying the fit of the automotive manufacturing industry to this research. Additionally, the majority of papers dealing with OI have focused on either the pharmaceutical or the high-tech industry (Bianchi et al., 2011; Chesbrough & Crowther, 2006; Chiaroni et al., 2009; West & Gallagher, 2006). Hence this research will supplement this OI research with insight from the automotive industry.
2. The Automotive Industry
et al., 2008). The simultaneous increasingly global but also regional integration are indicators for firm’s shifting away from traditional closed innovation (CI) towards OI practices.
However, such a globally integrated supplier network is vulnerable as shown by the global economic recession after the financial crisis in 2007, passenger car profits significantly decreased, as shown in figure 1. What figure 1 also highlights, following 2009 profits in the industry recovered.
After the financial crisis, market profit development shifted. As the North American market, Brazil, Russia, India, China, South Africa markets (BRICS), and Rest of the World (RoW) markets grew, they gained significance and attractiveness for car manufacturers resulting in increased manufacturing efforts in those markets (Mohr, Müller, Krieg, Gao, Kaas, Krieger & Hensley, 2013). The development of growing revenues from the emerging BRICS markets was forecasted to continue into the future with North American, BRICS, and RoW markets more likely to grow (Mohr et al., 2013). The shifting significance of markets is also displayed in Figure 2 and Figure 3 which highlight the country shares of production of Toyota and Volkswagen, two of the largest car manufacturers in the world (Quest, 2018). On the one hand, both figures show mostly a decreasing or stagnating production volume for Europe and Japan from 2000 to 2016, so in their home markets. One the other hand, they display a growing
12% 10% 5% 18% 26% 31% 9% -11% 6% 16% 16% 23% 15% 6% 0% 3% 7% -1% 5% -3% -6% -4% -6% 1% -20% -10% 0% 10% 20% 30% 40% 50% 60%
BRIC & RoW North America Europe Japan & South Korea
globalization and how they have affected a firm’s innovative performance will be examined in the following sections.
0% 10% 20% 30% 40% 50% 60% 70% 80%
China USA Japan India Brazil Mexico Canada Others 2000 2016 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
China Germany Brazil Mexico Spain Others 2000 2016
Figure 2. Internationalization of Toyota’s worldwide automobile production, by Quest (2018)
3. Literature Review 3.1. Innovation.
Innovation is a wide concept with a variety of different facets. Since Schumpeter (1939) differentiated between invention and innovation, various authors have defined innovation and discussed its relevance (Jacobs, 2007; Nelson & Winter, 1977; Utterback, 1994). For this thesis, invention is defined as the first occurrence of an idea and innovation as the first
commercialization of the invention (Fagerberger, 2004). Innovations can also be classified by new products, new methods of production, new sources of supply, exploitation of new
markets, and new ways to organize business (Schumpeter, 1939). Most studies preceding Schumpeter’s work have focused on the first two types of innovations, namely new products or new production methods (Fagerberger, 2004).
Subsequently, Jacobs’ (2007) popular division of innovation into product innovation, process innovation, and transaction innovation gained significance. Product innovation refers to the introduction and creation of new products or services, in terms of technological
improvements and completely new products. Process innovation, as the name implies, is the innovation of new production processes or techniques, rather focused on the ‘how’ in the production process. Transaction innovation refers to a new approach of product distribution. Product innovation, process innovation, and transaction innovation build up on each other, where product innovation forms that basis of process and transaction innovation, making it the most relevant among the three (Jacobs, 2007).
Another study based on Schumpeter’s research (1939), was developed by Freeman and Soete in 1997, who classified innovations by how radical they are compared to the existing context in which they are introduced. They characterized innovations as either
“incremental/marginal” innovations when the innovation as such, only had a slight impact in its context where it was introduced. The counterpart “radical” innovations or “technological revolutions made a significant impact or change in the context they were introduced in (Freeman & Soete, 1997; Fagerberger, 2004).
3.1.1. Contrasting concepts: Closed & Open Innovation. According to Henry Chesbrough (2006), closed innovation (CI) is the antithesis to his concept of OI. CI can be understood as the traditional innovation model in which firms develop new product and innovations internally, as illustrated in Figure 4 (Laursen & Salter, 2006). Chesbrough
himself defines CI as a vertically integrated model “[...] in which internal innovation activities lead to internally developed products and services that are then distributed by the firm”
(Chesbrough, 2006).
As Figure 4 shows, in CI, ideas flow into the firm and out into the market without influence from the external environment on the organization’s innovation process, as highlighted by the solid lines that describe the boundaries of the firm (Chesbrough, 2006). Consequently, all of the research remains inside the firm’s boundaries. In order to further define CI, Chesbrough attached the following characteristics, that underline and support the process displayed in Figure 4:
• The smart people in our field work for us
• To profit from R&D, we must discover it, develop it, and ship it ourselves • If we discover it ourselves, we will get it to market first
• The company that gets an innovation to market first will win • If we create the most and the best ideas in the industry, we will win
Despite firms rarely following a pure CI process, developments in the field of innovation make it necessary to open the innovation process (Huizingh, 2011). Accordingly, industry trends such as outsourcing result in firms reconsidering their strategies, shifting from a ‘do-it-yourself attitude’ towards ‘outside-in’ thinking (Gassmann, 2006). ‘Outside-in’ thinking links with Henry Chesbrough’s concept, OI (2006). OI is the utilization of purposeful in and outflows of knowledge, accelerating internal innovation and expanding the markets for external innovation (Chesbrough, 2006; figure 5). Schumpeter’s first model of marketing innovation (1939), where a lone entrepreneur manages the innovation process himself, has been reconditioned by a new, rich concept where highly interactive actors successfully exploit new ideas and sought innovation (Laursen & Salter, 2006). OI highlights the importance of interaction between a firm and its external environment during the innovation process and underlines the need for cooperation among innovators. Additionally, OI has different implications for organizing innovation (Chesbrough, 2006). However, as the roots of OI extend back in history most of the activities described are not new (Huizingh, 2011).
In order to further frame his concept Chesbrough attributed characteristics to the concept of OI (2006):
• Not all the smart people work for us. We need to work with smart people inside and outside our company
• External R&D can create significant value
• Internal R&D is needed to claim some portion of that value • We don’t have to originate the research to profit from it
• Building a better business model is better than getting to market first • If we make the best use of internal and external ideas, we will win
As shown in figure 5, OI uses external and internal ideas for value creation. The goal of OI is the capitalization on internal and external ideas leading to increased market knowledge and boosting a firm’s innovative performance (Gassmann and Enkel, 2004). Further, in OI the firm’s boundaries are porous and allow interaction with the firm’s external environment (Chesbrough, 2006). Therefore, the term OI practices, as used in the research question of this study, can be defined as practices where a firm interacts with its external market environment and therefore opens its innovation process. However, as stated, Chesbrough’s broadly defined concept was nothing new since OI practices were already used by a variety of companies (Huizingh, 2006). So why did the concept become so attractive to scholars? According to this question, Huizingh (2006) identified two main reasons.
Huizingh states that Chesbrough (2006) labeled a variety of different developments with a single term at the right point in time when these trends strongly emerged. Furthermore, he linked the acquisition of external knowledge and exploitation of internal knowledge to the terms ‘inbound’ and ‘outbound open innovation’ adding two fundamental dimensions to the concept (Huizingh, 2006).
3.1.1.1. Outbound and Inbound Open Innovation. Innovation literature highlights that OI is characterized by purposive knowledge in- and outflows. As illustrated in figure 5, the flow of knowledge occurs outside-in but also inside-out. Consequently,
Chesbrough’s terms inbound and outbound open innovation, which refer to knowledge exploration, retention, and exploitation can be performed within or outside a firm’s walls (Lichtenthaler & Lichtenthaler, 2009). Van de Vrande et al. (2009) identified the outflows of knowledge or technology exploitation as leveraging existing technological capabilities outside the borders of the organization. Whereas, the purposive inflows of knowledge or technology exploration refers to capturing and benefiting from external sources of knowledge to improve technological capabilities (Van de Vrande et al., 2009).
However, those two flows of knowledge are linked as every inbound innovation stream should generate an outbound effort (Chesbrough & Crowther, 2006). Surprisingly, empirical studies have found that companies are more likely to perform inbound OI practices than outbound OI (Ili et al., 2010; Schroll & Mild, 2011; Van De Vrande et al., 2009). This imbalance has several explanations (Huizingh, 2011). Firstly, the methodology of the studies that explored this imbalance may have been biased. Secondly, organizations are willing to use external knowledge but hesitate to provide it themselves, despite the relationship between outbound and inbound OI being a two-way interaction.
Table 1
Different Forms of Openness
Inbound Innovation Outbound Innovation
Pecuniary Acquiring Selling
Non-pecuniary Sourcing Revealing
Source: (Dahlander & Gann, 2010)
3.2. Globalization.
Globalization is a broad concept which impacts cultural, economic, or social dimensions of life (Appadurai, 1996; Levitt, 1993; Robertson, 1992). This paper will focus on an
economic perspective of globalization. However, there is no final or clear-cut definition for globalization. Clark and Lewis (2003) stated that the unclear definition of ‘globalization’ is due to the wide range of independent studies and many differing perspectives, leading to the ongoing and intense debate among academics regarding the definition and significance of globalization.
Kacowicz (1999) broadly defines globalization as cluster related changes on an economic, ideological, technological, and cultural level. He further sets out his statement and explains that globalization is leading to the internationalization of production, freely moving capital and an intensified economic interdependence (Kacowicz, 1999). Furthermore, Levitt (1993) defines globalization as a force that “drives the world toward a converging commonality”. Furthermore, Levitt (1993) claims that globalization is leading to global markets and standardization of consumer products. According to Rodrik (1997), globalization is defined as the international integration of markets, goods, services, and capital, pressuring society to alter traditional practices. This integration of markets by Rodrik (1997) is supported by Govindarajan and Gupta (2001), who describe globalization to have growing economic interdependence effects among countries, such as the enhancement of the flow of goods and services, capital, and know-how between countries. Moreover, Dunning and Lundan (2008) address that the technological advances have enhanced cross-border exchange of knowledge and increased the understanding for societal and cultural diversity or commonality, leading to altering cultures.
the free flow of capital is promoted. Due to the convergence of markets, altering traditions and standardization of consumer products, it can be assumed that firms do not increasingly engage in OI practices since they do not need new knowledge inflows on consumer preferences, markets or cultural diversity. Therefore, the overall number of OI practices performed is assumed to stagnate or even decrease. Resultingly, no increase in a firm’s innovative performance can be expected. Consequently, the first Hypothesis follows.
Hypothesis0: The relationship between OI practices and a firm’s innovative
performance is not moderated by globalization. Furthermore, there is no relationship between OI practices and firm’s innovative performance, regardless of globalization.
However, opposing statements to this Hypothesis can be made. Weiss (2005) argues that it would be misleading to believe that globalization is withdrawing nation-states’ scope or responsibility. The author further states that through globalization, domestic institutions are reinforced, highlighting the firms’ need for target market embeddedness in order to gain valuable market insights (Weiss, 2005). Moreover, contrasting to Levitt’s (1993) and Rodrik’s (1997) statements on altering traditions and converging commonality, Knight (1999) argues that the ongoing globalization and trade liberalization leads to a consumer preference towards home country, or domestically produced goods. Knight’s (1999) argument, therefore, leads to the assumption that firms need to internationalize to satisfy consumer needs, hence engage in OI practices. Further, Dunning and Lundan (2008) argue, that globalization along with technological advances has drastically widened the economic marketplace leading to constantly increasing competition in the global market. Such a development enhances the need for firms to engage in OI practices such as joint ventures (JVs), mergers and acquisitions (MA’s) or strategic alliances in order to maintain or gain market share in their competitive environment.
These assumption lead to HypothesisA.
HypothesisA: The relationship between OI practices and a firm’s innovative
4. Methodology
The purpose of this research is to investigate the influence of OI practices on the innovative performance of firms moderated by the forces of globalization. The following section will discuss the methodology used within this study. The chosen sample for
investigation will be introduced and the definition of the variables and its measurements to test the hypothesis will be proposed. Lastly, the empirical model for the quantitative analysis will be explained.
4.1. Conceptual Model.
The overall research question of this study consists of two interrelated questions. Namely, “Has globalization accelerated a firm’s commitment towards open innovation practices?”, and “If so, how has globalization influenced the firm’s innovative performance?”. Therefore, the conceptual model will consist of two interrelated relationships and connected variables (Figure 6). The two variables representing OI practices are M&As and Joint Ventures (JVs). As presented in the introduction of this study, the importance of the moderating effect of globalization was highlighted. Consequently, it is used as the moderator variable in this body of research. The dependent variable, representing the innovative performance of a firm, is the number of products launched in a certain market.
Figure 6. Conceptual Model
Moderator variable: Flow of FDI Independent variable (1):
Mergers & Acquisitions
Independent variable (2): Joint Ventures
Dependent Variable: Number of Product
4.2. Sample.
As justified, this study focuses on the automotive industry. Therefore, the dataset covers the largest car manufacturers in the industry by revenue. Due to the assembly of the data for the dependent variable, which was collected from the annual reports of the respective company, it was found that the largest companies show more relevant data. Therefore, the number of car manufacturers was chosen upon the availability of the data. Resultingly, the sample consist of eight of the ten largest car manufacturers, as shown in Appendix A. The time span investigated covers an eight-year period, from 2009 to 2017 for the key automotive markets in, North America (NA), the European Union (EU) and the Brazilian, Russian, Indian, Chinese and South African (BRICS) market according to profits (Mohr et al., 2013). The time span chosen was based on data availability and the course of the global financial crisis. In 2009 the automotive industry reached its negative peak and markets started to shift, as highlighted in Figure 1 (Mohr et al., 2013). Therefore, market players rethought their business models. More background information on the automotive industry and its market characteristics can be found in Section 2.
4.3. Data
4.3.1. Independent Variables. In section 2.2.1 it became clear that OI is a broad concept comprised of numerous practices, all relevant to OI. This study covers two of those variables, outbound and inbound OI, that together represent the two main knowledge flows introduced in section 2.2.1.1.
As Figure 6 shows, the first independent variable in this study is M&A’s, representing the purposive inflows of knowledge. As derived from section 2.2.1.1. which introduces the two flows of innovation in the context of OI, ‘acquiring’ was identified as a technology exploration or inbound OI activity, making M&As suitable as an independent variable. M&A can be defined “[…] as the combination of two or more companies into one new company or corporation” (Roberts et al., 2003), and includes several complex steps until the complete integration of companies. The data for the first independent variable will be provided by two databases, Orbis and Zephyr. These databases include all relevant M&A deals for the sample introduced.
independent variable, the data will be provided by the database Orbis. Orbis includes the relevant JVs for the sample introduced.
4.3.2. Moderator Variable. As expressed in the hypothesis, the moderator variable in this study is globalization. Globalization moderates the effect between OI practices and the innovative performance across markets. According to the Organization for Economic Co-operation and Development (OECD) globalization can be measured in a variety of ways (OECD, 2010). A key measure for globalization is the global flow of Foreign Direct
Investment (FDI), holding a fundamental role in international economic integration since the 1980s and one of the most significant factors in economic integration (OECD, 2010).
Furthermore, freely flowing FDI as an indicator for globalization, is supported by
Govindarajan’s and Gupta’s statement that globalization enhances the free flow of capital (2001).
According to the OECD, foreign investment can be regarded as ‘direct’ if an investor residing in another country “holds at least 10% of the ordinary shares or voting rights of the firm in which it has made the investment” (OECD, 2010). Further, direct investment is measured by the flows and stocks including, equity capital reinvested net earnings, and other capital (OECD, 2010). Therefore, the respective FDI out- and inflows of each market will serve as a measurement for globalization in the frame of this study. The data used for measuring globalization will be provided by the OECD database for each of the markets investigated, namely the EU, NA and BRICS market.
4.3.3. Dependent Variable. The hypotheses’ have highlighted that the innovative performance in this study refers to the number of product launches. Therefore, the products launched in the different key markets such as BRICS, Europe, and North America, as identified in section 1.2, of each company in the sample will be measured.
example: Audi launches a new version of the A6 as a Coupé and Sedan, both models will be counted. Whereas still being aware that a new product could also be a software update or a slight lift of the front lights of an existing model. However, those minor changes are not counted as a product launch.
Secondly, a certain car with same name may be launched in the European market and in the North American. Therefore, one could argue that it is not necessarily a new product. However, in the automotive industry, car models may carry the same name but still vary in their design (Volkswagen, 2018; Volkswagen Deutschland, 2018). Therefore, this assumption will be taken into consideration during the data collection and models with the same name that are launched in different markets are counted as separate launches. Furthermore, in some cases when there is no specific market named in which the product is launched, it will be counted as it was launched in every market.
4.3.4. Control and Dummy Variables. This research includes several control
variables that can have an effect on OI practices due to various reasons. In order to avoid confusing results and to help obtain an unbiased result of the effect of OI practices on the number of new product launches, three control variables were chosen. The first control variable is the firm size by employees, since larger organization have more resources and resultantly more opportunities to engage in OI practices, such as M&As or JVs. The data for this variable will be provided by the Orbis database. The second control variable is R&D intensity, as it controls for the firm’s effort to integrate knowledge gained through OI
practices. Additionally, R&D intensity was chosen due to prior OI studies implementing R&D intensity as a control variable (Lichtenthaler, 2009). R&D intensity is defined as the R&D expenditures divided by the total sales. The data will also be taken from the Orbis database. The third control variable is earnings before interests and taxes (EBIT) margin. The EBIT margin expresses how much of a company’s turnover actually converts into EBIT. As
organizations which are more profitable have more capital to invest in OI practices, therefore one could assume that they are more likely to launch more products, making it relevant to control. The data for the EBIT margin will be provided by the ORBIS database.
effect on later firm performance. Furthermore, one could assume that a company would need to engage in less OI practices in its home-market, due to the longer market presence in its home market. Hence, the company would be able to gain more knowledge in their home market than in other markets. Therefore, companies from a European market are labeled as 1, North American market as 2, BRICS markets as 3 and companies from none of the markets as 0. In the special case of F.C.A, which is an alliance between an American and European firm, the two codes 1 and 2 are simply merged to a 12. The information on the origin of each of the firms in the sample can be found in the database Orbis. An overview of the variables
introduced is provided below, in Table 2.
Table 2
Overview Variables
Type of variable Label Measurement Source
Dependent LAUNCH Number of products launched per year
per manufacturer Annual reports Independent (1) MA Number of M&As per manufacturer Zephyr & Orbis
Independent (2) JV Number of JVs per manufacturer Zephyr
Moderator FDI In- & outward FDI in respective market OECD
Control (1) EMPL Firm's number of employees Orbis
Control (2) EBITM (%) EBIT/Sales Orbis
Control (3) RDINT (%) R&D expenditure/Total sales Orbis
Dummy HOMEM Firm’s home market Orbis
4.4. Regression Model.
As highlighted, this body of research conducts its framework within the timeframe from 2009 to 2017. Therefore, this study is a longitudinal study. Moreover, due to the dependent variable being a count measure, the Poisson and the negative binomial regression model are suitable models (Osgood, 2000; Hilbe, 2011). A count variable can take on integers that are positive or equal to zero (Coxe, West, & Aiken, 2009). The Poisson regression model is based on the assumption that the mean = variance of the data, which is the case in the data. Resultingly, it will be conducted in the context of this study.
The basic Poisson regression model is (Osgood, 2000):
The first equation (1) is the regression equation, relating the natural logarithm of the mean, ln(li) or the expected number of events for case i, to the sum of each explanatory variable’s product, xik. These are multiplied by the regression coefficient, bk (Osgood, 2000). The second
equation (2) indicates that the probability of yi, follows the Poisson distribution (displayed on
the right side of the formula) for the mean from the first equation (1).
Furthermore, as highlighted in the hypotheses, globalization is moderating the relationships between the independent variable and the dependent variable. Therefore, a simple slopes test will be conducted, which is a common technique in order to find moderating effects of variables (Preacher, Curran, & Bauer, 2003). Due to the continuous nature of the moderator variable (FDI) the simple slopes test is suitable. In the simple slopes test, one selects values for the moderator at equal to the mean and one standard deviation above and below the mean (Preacher, Curran, & Bauer, 2003). Afterwards, possible changes in the relationships between the independent and dependent variables can be observed.
4.4.1. Fixed Effects Model. A regression model can either be estimated with fixed or random effects (Allison, 2009). When using fixed effects there must be an interest in a time-variant effect (Egger, 2002). As highlighted in previous passages, the prerequisites are given. Therefore, a dummy variable for each target market in the respective year will be used (EU, NA, BRICS). The dummy variable, labeled as ‘mt’, controls for unobserved market-specific effects that may affect the firm’s decision-making to, for example, engage in OI practices.
5. Results and Discussion
Within the following sections the results of the data analysis will be presented. At first, the descriptive statistics will be introduced and the multicollinearity among the variables will be examined. Afterwards, the results of the regression analysis will be presented and
discussed.
5.1. Descriptive Statistics.
Table 3 provides a summary of the panel data used. A total of 216 observations were made for the eight companies across the three key markets over a period of nine years. The variables EMPL, RDINT and EBITM were measured globally and market unrelated. This measurement was carried out, following the assumption that multinational companies work in international teams and also deploy their resources internationally. Therefore, no distinction between the three markets was made for those three variables (EMPL, RDINT and EBITM).
Table 3
Descriptive Statistics
Variable Obs Mean Std. Dev. Min Max
MA 216 .17 .49 0 3 JV 216 .07 .34 0 3 EMPL 72 235,005.4 130,039 95453 642300 RDINT 72 3.99 1.16 1.11 6.68 EBITM 72 4.58 5.19 -20.01 12.16 FDI 216 677,504.9 195,959.8 314,626.3 1,280,990 LAUNCH 216 9.83 9.07 0 44
Among the eight largest car manufacturers in the world, the range for JVs and M&As (Obs = 432) is equal (range: 0-3 = |3|). However, the mean for M&A (M = .17) is higher than for JV (M = .34), implying that the overall number of M&A deals is higher than for JVs. Furthermore, the firm size measured by the number of employees (EMPL) is relatively high (range: 95,453-642,300= |546,847|) meaning that at a certain point in time the largest firm in the sample was nearly seven times larger than the smallest, indicating that some firms in the sample may have or had other capabilities to deploy their resources in order to boost their innovative performance, than others.
unprofitable in certain years (range: -20.01-12.16 = |32.17|). Consequently, the standard deviation was higher for the EBIT margin than for the R&D intensity (SD.= 1.16; SD = 5.19, respectively). Furthermore, car manufacturers have launched nearly ten new product each year across the three target markets the number of product launches per manufacturer across markets (LAUNCH) varies strongly (range: 0-44 = |44|). Although, some or have not launched a product in a certain market in a particular year, the mean for this variable leveled at 0.98.
5.2. Multicollinearity.
Tables 4 and 5 present all the bivariate correlations. The extent to which the variables correlate with each other (Penders, 2016). Collinearity among variables in a multiple
regression model is called multicollinearity and refers to the high correlation between two or more variables (Wooldridge, 2013). According to Farrar and Glauber (1967), high
multicollinearity is a threat to the correct specification and the estimation of the variables unique effects that are examined through regression techniques. This section examines possible correlations among the variable with the help of a correlation matrix, variance inflation factors (VIF) and the resulting collinearity tolerance level (CTL), common tools to do so. As a rule of thumb, the maximum value for the VIF should not be greater than 10 and the threshold for the CTL should not be lower than 0.1 (Penders, 2016). The VIF range for this study is 1.02 to 1.24 (Table 4). As highlighted in Table 4, none of the variables correlate and remain below the threshold. Therefore, no changes will have to be made.
Table 4
Variance Inflaction Factors
Variable VIF CTL (1/VIF)
The correlation matrix in Table 5 underlines the results in Table 4. Specifically, the variables are not significantly correlated. Among the variables displayed, the variables EMPL, representing firm size, and FDI, representing globalization, correlate the strongest. However, the two variables do not exceed the correlation value of 0.7, which is a commonly recognized threshold in research (Cooper & Schindler, 2006). Most importantly, the two independent variables MA and JV show no significant correlation (coefficient = .004), therefore no adjustments will need to be made.
Table 5
Correlation Matrix
EMPL FDI EBITM RDINT MA JV
EMPL 1.00 FDI - .596 1.00 EBITM - .099 - .155 1.00 RDINT .141 - .010 .239 1.0000 MA - .130 .019 .045 .014 1.0000 JV - .077 .073 .024 - .080 .004 1.0000
5.3. Multiple Regression Analysis.
In the following section the results of the Poisson regression models will be discussed. The models are derived from the previously introduced hypotheses. In order to assess the overall fit of the model the pseudo R2 will be used as an indicator. The pseudo R2 can be compared in its interpretation to the R2 which indicates the overall fit of the model. However, the pseudo R2 is used when the dependent variable is constrained (Veall & Zimmermann, 1996). In the context of this study, the dependent variable LAUNCH can not be negative, creating an overall fit between variable and model.
The results will be interpreted by looking at the marginal changes in dy/dx, the significance of each variable (at p<0.5%, p<1% and p<5% level), and the overall fit of the model. If the variable’s result is significant (p<0. 05), the protruding sign (positive or
The three regression models follow from the two hypotheses. As explained, in the simple slopes test the moderator variables effects are measured at equal and one standard deviation below and above the mean. However, those will only be conducted if Hypothesis0 is not confirmed. The results of the analysis are displayed in the following table. On the right side of the table the results of the robustness check of the model will be displayed. The robustness check is a common tool among researchers to find evidence for structural validity of the model. Furthermore, it is used to examine how certain regression coefficients behave when some components of the model are modified by removing or adding regressors (Lu & White, 2014).
Table 5
Regression Model 1: Moderating Effect of Globalization on OI practices and Firm Innovative Performance
Regression Model (tm & hm) Robust Standard Errors (tm & hm)
Variable MA -.02 (.04) -.02 (.05) JV -.03 (.06) -.03 (.04) FDI -1.0 (1.0) - 1.0 (1.0) EMPL .01 (1.0) *** .01 (1.0) *** EBITM (%) .27 (.06) *** .27 (.06) ** RDINT (%) -1.43 (.36) *** -1.43 (.36) ** Pseudo R2 .53 .53 Prob > chi2 .00 .00
Note. Significance of the variables for: *p < .05; ** p < .01 =; ***p < .001
The first regression model shows significant results (Prob > chi2 = .00). Furthermore, the pseudo R2 which indicates the explanatory power of the model, is .53. More specifically, it indicates that the first regression model with its variables explains 53% of the variation of product launches practices. The other 47% of the variation is not explained with the variables included in the model, hence are assumingly explained by alternative variables.
In the first regression model in Table 6 one can see that the variable FDI rejects the null hypothesis (p = .40) indicating that there is no causal relationship between the
Furthermore, the results of the two independent variables MA and JV, representing OI practices, are also not significant, due to the two p-values of the regressors which are above the significance threshold of 0.05 (p = .75; p = .44). Those findings are also in line with Hypothesis0. In contrast, the variables EMPL, EBITM, and RDINT show highly significant results (p = .00). This implies that the firm size, the profitability, and the R&D intensity of an organization have a significant impact on its innovative performance. Specifically, the
variables EMPL and EBITM have a positive influence on a firm’s innovative performance. On the other hand, RDINT has a negative influence on a firm’s product launches.
Hence, the control variables in the data have a more significant influence on a firm’s product launches than the two independent variables. Infact, there is no causal relationship between a firm’s performed OI practices and its product launches and the relationship is not moderated by the forces of globalization. Therefore, the simple slopes test is not needed and further examination of the moderating effect of globalization is obsolete. Possible explanation and implications will be discussed in the following section (5.4).
5.4. Discussion.
Prior studies have investigated the influence of OI practices on a firm’s innovative performance. This study aimed to extend the scope of existing OI research by adding the moderating effect of globalization. The main question of this research was, “Has globalization accelerated a firm’s need to engage in open innovation practices? If so, how has it influenced the firm’s innovative performance?” This was completed by literature review and the analysis of data from a sample of the eight largest car manufacturers in the industry. This anlaysis was performed with a Poisson regression model which was conducted due to the nature of the data.
The first regression model has already found no significant relationship between OI practices, globalization and a firm’s innovative performance, supporting Hypothesis0 and the assumptions by Levitt (1993) or Dunning and Lundan (2008) on converging markets and more standardized products. The descriptive statistics reported in Table 3 indicated that the overall magnitude in OI practices was low supporting the assumptions made previous to Hypothesis0.
increasingly engage in OI practices. This development is contrasting to Dunning and
Lundan’s (2008) or Rodrik’s (1998) statement, that globalization leads to altering cultures and less product diversification. The findings in Appendix F together with the results in Appendix E lead to the assumptions that M&As and JVs are not the primarily used OI practices of car manufacturers in the automotive industry. Instead, firms in the industry apparently set up their own venture capital firms and invest in start-up companies to source knowledge and
technology (“How BMW, Audi, GM And The Rest Of Big Auto Are Betting On Startups”, 2019). These findings find support in the results for the variable RDINT. Namely, the results show that an increase in a firm’s investment in its R&D leads to a decrease in its innovative performance. However, as explained, Appendix F illustrates that the overall innovative performance of firms has improved. Consequently, the assumption can be derived that a firm’s internal R&D has lost in significance, which leads to a contrasting assumption to the confirmed Hypothesis0. More precisely, even though this study has found that there no statistically significant relationship between OI practices and a firm’s innovative
performance, the internal R&D has lost in significance and whereas the overall innovative performance of firms in the industry has improved. This statement confirms the findings, that M&As and JVs are not the optimal measures for OI practices in the automotive industry.
Furthermore, the results for the control variables for firm size (EMPL) and profitability (EBITM) yielded significant results. Namely, an increase in firm size and
profitability enhances a firm’s innovative performance. Derived from those findings, one can assume that growing or larger firms use their resources gained and deploy them to improve its innovative performance. However, it could also be argued that more profitable firms also have the financial latitude to engage in such capital intensive OI practices. However, since the results have highlighted that there is no causal relationship between OI practices and a firm’s innovative performance, it seems that the capital generated is rather used for other OI
practices than the ones defined in this study, such as venture capital firms as previously emphasized (“How BMW, Audi, GM And The Rest Of Big Auto Are Betting On Startups”, 2019).
being aware that none of the concepts is performed solely possibly explaining the mixed results. However, indications for other OI practices than M&As and JVs were highlighted. Other reasons for challenges in performing could include issues about intellectual property which are a common risk when engaging in OI, as examined by Chesbrough (2003). However, the pseudo R2 has also shown that the model only explains about half of the
6. Limitations and Future Research
Despite best efforts to conduct rigorous, scientifically sound research, limitations were uncovered during the process of this study. More specifically, three limitations became clear due to the research design and the data collection. Outcomes of these limitations, including their guidance for future fields of research are discussed.
Firstly, the concept of OI has a large scope leading to difficulty separating its numerous interwoven in- and outflows of knowledge. Despite CI and OI being two distinct theoretical concepts, in reality the border between the two concepts is blurred. It is expected that few firms have strict OI or CI processes only. Therefore, the true impact of OI innovation on a firm’s innovative performance is hard to determine as CI processes are continuing to influence performance outcomes. Future research could define clear borders for a firm’s contribution to CI and OI, examining how the relative proportion is affecting a firm’s innovative performance.
Secondly, future studies could try to find industry specific OI practices. This study has found that derived from the theory, M&As and JVs are OI practices. However, other OI practices seem to shape the firm’s innovative performance. Therefore, the investigation of markets and a differentiation between industry specific OI practices may be useful as a basic toolkit for more precise future research. Hence, derived from the definition of OI, every purposive inflow or outflow of knowledge leads to OI engagement. Therefore, choosing the adequate measurements is difficult and never actually frames the concept. Hence, this study was only able to grasp one corner of the concept OI. Future research may try to identify the most representative measurements for OI and further define the term OI practices. Here, a scale or indicator for those OI activities could be the rate of engagement of firms in the industry in such practices. Therefore, OI trends could be observed over time when comparing the engagement of firms in those practices over time and studies like this would yield more precise or representative results.
7. Conclusion
To conclude and to answer the research question, this research found no significant results for the relationship between OI practices and a firm’s innovative performance, furthermore no moderating effect of globalization has been found. Those findings were in line with the
defined Hypothesis0, even though it led to opposing assumptions, as discussed in Section 5.4. The results of the control variables, together with the overall development of product launches, illustrated in Appendix F, have led to the assumption that other OI practices may more significantly influence a firm’s innovative performance, as discussed in section 5.4. Hence, even though the OI practices defined in the context of this study do not significantly influence a firm’s innovative performance and there were no significant relationships between globalization and the product launches, again Appendix F indicates that the overall number of product launches increased significantly from 2009 until 2017. Especially the product
launches in the BRICS market grew by about 100% from 2009 to 2017. Additionally, the European and the North American markets were facing growth. These findings together with the assumed decreasing significance of internal R&D, still support the statement that
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Appendices Appendix A
Revenue of the leading automotive manufacturers worldwide in FY 2017 (in billion U.S. dollars
Rank Car Manufacturer Revenue 2017
1 Toyota Motor 265.17 2 Volkswagen 260.03 3 Daimler 185.24 4 General Motors 157.31 5 Ford Motor 156.78 6 Honda Motor 138.65 7 F.C.A 132.88 8 SAIC Motor 128.92 9 BMW Group 111.23 10 Nissan Motor 107.87
Appendix B
Annual reports: Links to Websites of the Car Manufacturers
Volkswagen: https://www.volkswagenag.com/en/InvestorRelations/news-and-publications/Annual_Reports.html
Daimler: https://www.daimler.com/investors/reports/annual-reports/ General Motors: https://investor.gm.com/investor-relations
Ford Motors: https://shareholder.ford.com/investors/financials/annual-reports/default.aspx Fiat Chrysler Alliance:
https://www.fcagroup.com/en-US/investors/financial_regulatory/financial_reports/Pages/2018.aspx Honda Motors: https://global.honda/investors/library/annual_report.html
Bayrische Motorenwerke: https://www.bmwgroup.com/en/investor-relations/financial-reports.html
Appendix C
Appendix D
Appendix E
Course of OI Practices over time in different markets
Appendix F
Product Launches in the different target markets
