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The relation between managerial cognition and the adoption of open innovation

A comparative case study in the automotive industry

Master Business Administration – Digital Business & Innovation Isabelle Mansour

11032561 Master Thesis

Thesis Supervisor: Kevin Heij University of Amsterdam Final version

June 24, 2021

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Statement of originality

This document is written by Isabelle Mansour who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Table of contents

Abstract 4

1. Introduction 5

2. Literature review 8

2.1 The automotive industry 8

2.2 Open innovation 9

2.3 Open innovation in the automotive industry 12

2.3.1 Inbound OI in the automotive industry 12

2.3.2 Outbound OI in the automotive industry 13

2.3.3 Coupled OI in the automotive industry 14

2.4 Managerial cognition 15

2.4.1 Managerial attention towards the automotive industry 16

2.5 CEO Narcissism 18

3. Research design 22

3.1 Sample 22

3.2 Analysis 25

3.2.1 Independent variable 25

3.2.2 Dependent variables 26

3.2.3 Moderating variable 28

4. Results 29

5. Discussion 37

6. Conclusion 42

7. Reference list 43

Appendix A 55

Appendix B 59

Appendix C 61

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Abstract

The automotive industry is experiencing a revolutionary discontinuity driven by megatrends electrification, autonomous driving, connectivity, and the sharing economy. The increasing innovation and cost pressure have pressured the automotive industry into open innovation (OI). Besides that, managerial cognition has shown that managerial attention is important for making strategic choices and actions when responding to changing environments (e.g., the automotive industry) which requires costly and risky investments. However, little is known about the relationship between managerial cognition and the adoption of OI activities. Therefore, this study aims to answer how managerial cognition relates to the adoption of inbound-, outbound-, and coupled OI. Literature shows that narcissism was influencing CEO decision-making, especially when there is a considerable amount of uncertainty.

Consequently, this research hypothesized that CEO narcissism positively influences the relationship between managerial attention and the adoption of inbound-, outbound-, and coupled OI. A comparative case study based on a qualitative content analysis examined annual reports from manufacturers BMW, Ford, Tata and FCA from 1 January 2010 till December 2019. Interestingly, this research could not support a relation between managerial cognition on the adoption of inbound-, outbound-, and coupled OI nor the moderating effect of CEO narcissism as this may be a result of the incompleteness of annual reports. Although the hypotheses are not supported, this study shows that for measuring OI, annual reports are not a reliable source. Also, CEOs pay more attention to the four megatrends over the last ten years, and automatically so do their inbound- and coupled OI activities.

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1. Introduction

IBM Corporation stated , ‘’For more than 100 years, the automotive industry has created a competitive advantage through engineering excellence, but this will not be sufficient’’(IBM, 2015, p.5). Indeed, the automotive industry is captured by cost and innovation pressure, and it is about to experience a revolutionary discontinuity in developing innovations (Ettabaa et al., 2019). This revolution is directed by the convergence of megatrends (Accenture, 2018), which have emerged for various reasons. The urgency to move toward more sustainable innovation projects pushes car manufacturers to start developing lower-emission alternatives for the internal combustion engine, mainly electric, hybrid, and fuel-cell vehicles (Ferràs-Hernández et al., 2017; Pinkse et al., 2014). Furthermore, a host of new startups are conquering every niche of what seems to be the new architecture in the automotive industry:

shared and autonomous driving cars (Ferràs-Hernández et al., 2017). The growing innovation and cost pressure has accelerated the incumbents to look outside their boundaries (De Massis et al., 2012), in which open innovation (OI) is necessary. OI is defined by Chesbrough (2006, p.1) as ‘’the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively.’’

According to Chesbrough (2003), OI implies that firms more and more rely on external sources and individuals by emphasizing that ideas, resources, and individuals flow in and out of the organization.

Chesbrough (2003) outlined the term OI in two directions, inbound OI and outbound OI (Chesbrough, 2003). Inbound OI is when a firm receives external knowledge, whereas outbound OI is often exploiting existing knowledge outside the firm boundaries by licensing IP or cross-industry innovations (Chesbrough, 2003). Gassman and Enkel (2004) added a third direction, namely coupled OI. Coupled OI combines the inbound and outbound processes where two or more companies jointly develop a new idea and try to bring it to the market (Gassman and Enkel, 2004).

Besides the specific OI, the word innovation in general can be seen as a socio-cognitive process that consists among other aspects as the creation of mutual mental representations in organizations and innovation management (Bergman et al., 2015). Managerial cognition has shown that managerial

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6 attention is especially important for making strategic choices and actions when responses to a changing environment (e.g., revolutionary character automotive industry) require costly and risky investments (e.g., open innovation) (Barr 1998, Cho and Hambrbick, 2006, Kaplan et al. 2003, Ocasio1997). All three directions of OI activities are often characterized by a considerable amount of uncertainty and ambiguity (Euchner, 2013). Different studies have examined managerial cognition and innovation (Manral, 2011; Vecchiato, 2017). For example, Manral (2011) discusses the relationship between managerial cognition and innovation through Kanter’s model (1988). Kanter’s model (1988) includes tasks in innovation, but it does not include OI’s concept. Furthermore, the research of Vecchiato (2017) explored why incumbent firms fail to identify new markets in the mobile communication industry.

Meanwhile, managerial cognition influences a response to external environmental change and is predicting organizational behavior and firm performance (Barr 1998, Cho and Hambrbick, 2006, Kaplan et al. 2003, Ocasio 1997). However, little is known about the relationship between managerial cognition and the adoption of inbound-, outbound- and coupled OI. Instead, research has shown that opening knowledge boundaries could be especially difficult for organizations located in traditional and asset- intensive industries, such as the automotive industry (Chiaroni et al., 2011). Therefore, this study aims to close the gap by researching the relation between managerial cognition and the adoption of inbound- , outbound-, and coupled OI in the automotive industry.

Next to the managerial cognition gap, there is also a lack of information about how a CEO’s personality plays a critical role in dealing with external contextual factors and making strategic choices. In particular, narcissism influences decisions-making such as implementing internationalization and adopting radical new technologies (Chatterjee & Hambrick, 2007; Gerstner et al., 2013). Narcissism is the extent to which a person has an inflated view of itself and cannot recognize this view him or herself (Campbell et al., 2004). Several studies have examined the relationship between CEO narcissism and innovation. For example, Kashmiri et al. (2017) noted a positive relationship between new product innovation and radical innovations in a product portfolio. Additionally, Chatterjee and Hambrick (2011) have shown that narcissistic CEOs spend more on research and development and acquire new companies more often. Furthermore, Zhu and Chen (2014) show a positive relation between CEO narcissism and

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7 risk-taking spending. CEO narcissism is interesting to look at in the automotive industry specifically since innovation in this sector involves high costs and rapid changes (Matricano et al., 2019). However, previous research has never studied the moderating effect of CEO narcissism between managerial cognition and the adoption of OI. Given the pressure for OEMs to invest more in OI activities (Ettabaa et al., 2019) which go along with risks and high investments (Euchner, 2013), CEO narcissism needs to be implemented as a moderating factor to cover the second gap. Ultimately, this study aims to answer how managerial cognition relates to the adoption of inbound OI-, outbound OI- & coupled OI activities in the automotive industry, with the moderating effect of CEO narcissism. Here, managerial cognition is measured in terms of the attention a CEO devotes to the four megatrends emerging in the automotive industry. Therefore, the research question is:

RQ: What is the relation between managerial cognition and the adoption of inbound-, outbound- and coupled open innovation in the automotive industry; the moderating effect of CEO narcissism.

Contributions

These results contribute to the literature on managerial cognition by outlining circumstances under which CEOs affect OI adoption in a changing industry. Previous research about the relation between CEO managerial cognition and innovation was explicitly focused on OI. Furthermore, research about OI in the automotive industry is mostly done via case studies where data was collected via interviews.

No studies have examined OI in the automotive industry with annual reports as a source. Annual reports aim to communicate applicable information about past, current, and future firms’ activities (Breton, 2009), and might be a reliable source to measure OI in the automotive industry. Suppose this reliability assumption will be confirmed after this study, companies in other mature industries, such as the automotive industry, could use this study for measuring the adoption of their inbound-, outbound-, and coupled OI activities.

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2. Literature review

2.1 The automotive industry

The automotive industry has experienced significant restructuring worldwide in the last twenty years and now serves as one of the most globalized industries (Dicken, 2015). OEMs used to lead the industry with very high barriers to entry for new potential entrants (Accenture, 2018). The automotive industry plays a big role in the international source of employment, which is primarily derived from its connections within the domestic and global economy but also from its complex value chain (Broding &

Fartasch, 2019). According to Sirkin and colleagues (2015), the automotive industry will be at the forefront of the ‘fourth industrial revolution’ as one of the biggest capital-intensive industries.

This study aims to measure CEO managerial cognition regarding the attention a CEO devotes to the four megatrends in the automotive industry. The convergence of the four megatrends, electrification, autonomous driving, connectivity, and the sharing economy, revolutionizes today’s automotive businesses (Gao et al., 2016). Autonomous driving means that a vehicle can act alone at all times and in all traffic situations. The person who used to drive the car is here out of the loop (Hermann et al., 2018).

The term electrification refers to developing and integrating systems and components that enable electric energy to be used for transportation (Tate and Savagian, 2008). Connectivity refers to the connected car, which is the next generation of car technologies making use of the Internet and making it possible for the vehicle’s passengers to take advantage of numerous new services and features (Möller and Haas, 2019). Finally, the term sharing economy refers to the sharing activity of unused assets together with IT-based technology (Görög, 2018). The combination of the four megatrends is very disruptive and forms the need for change (Accenture, 2018). Automotive players aim to go along with the megatrends through strategic partnerships and other collaborations. This results for example in firms who look for competitors to build up gaps in their technology portfolio or are creating global innovation hubs to identify startups that can support meaningful change across the organization (Dutt et al., 2020). The four megatrends in the automotive industry force OEMs to look outside their boundaries, and OI can be seen as a good opportunity (Lazzarotti et al., 2012; Gassmann, 2006; Ili et al., 2010).

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9 2.2 Open innovation

Chesbrough (2003) introduced the concept of OI. He saw firms changing their innovation strategies and later business models from closed in-house innovation activities to actively opening up and collaborating with external actors to create a shared value (Chesbrough & Crowther, 2006). West & Bogers (2014) describe OI as when a company either actively seeks to access knowledge developed by other parties to incorporate it in its innovation efforts or add knowledge it has acquired itself to others for more development. The primary idea behind the OI model is that even large companies can no longer carry all the capabilities and resources to create innovation by themselves and need to exploit external knowledge (Gassmann, 2006). Gassman (2006) and West & Gallagher (2006) argue that OI activities are linked to the pooling of collaborative R&D activities or engaging in trading intellectual property rights to spin or spin out strategic innovation resources. A more recent study by Cano-Kollmann et al.

(2018) argues that OI splits the vertically integrated model of innovation that was always the key to success for big organizations. Incorporating external knowledge sources as fully integrated components of the firm’s creative endeavor shows a major shift in the practice and study of innovation (Cano- Kollmann et al., 2018).

Chesbrough (2003) makes a distinction between inbound OI and outbound OI. Inbound OI is defined as

‘’the practice of exploring and integrating external knowledge for technology development and technology exploitation’’ (Parida et al., 2019, p. 288). Inbound OI assigns to innovation activities focusing on acquiring external knowledge (Spithoven et al., 2011); ‘in-licensing’, ‘R&D collaborations’,

‘Merger and Acquisition (M&A)/strategic alliance’ and ‘user involvement’ belong to inbound OI (Ahn et al., 2013). Licensing is a transfer of rights from a licensor (seller) to a licensee (buyer). In the case of in-licensing, the licensee uses licensor’s intellectual property (Bogers et al., 2012). R&D collaboration projects are defined as ‘’the union of two or more parties, institutions or individuals that develop a technological project’’ (Arranz & Fdez. De Arroyabe, 2009, p.3). In the context of OI, acquisitions refer to the relations with other parties that add ideas or generate different types of input to facilitate the continuity of acquired firms, their ideas, and developments (Weber and Tarba, 2016). User involvement is the information and feedback that firms receive from their users in the product development process

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10 (Bosch-Sijtsema and Bosch, 2014). Outbound OI is ‘’the practice of exploiting technology capabilities by utilizing not only internal but also external paths of commercialization’’ (Parida et al., 2019, p. 299).

Outbound OI links to the exploitation of knowledge in different ways. By revealing internal knowledge via outbound OI, innovation flows out of the organization via commercialization (Mortara & Minshall, 2011). Next to the commercialization through licensing, there are also other options for exploiting unused internal technologies by using a spin-off form. Especially for technologies that are not within the technological core competencies of a company or their commercial exploitation does not fit the company’s strategy (Simic, 2013). Examples of outbound OI given by Mortara & Minshall (2011) are

‘licensing-out’ and ‘spin-off’. A spin-off is a company formed by former employees of an existing firm who have left this firm, but maintains loose ties with this mother organization (Koster, 2004). Licensing- out can take place in the form of sharing internal IPs, content, material and expertise with potential collaborators to stimulate innovation (Mortarta & Minshall, 2011). In several firms, outbound OI is a part of corporate strategy, and it exceeds a marginal activity of commercializing new technologies (Fosfuri, 2006). This strategy, however, goes hand in hand with major risks because it may weaken a firm’s competitive position based on sharing relevant knowledge (Chesbrough & Crowther, 2006).

Inbound OI is where new ideas move into a company, and outbound OI, where new technologies can be acquired by ‘external organizations with business models that are better suited to commercialize a given technology’ (Chesbrough & Crowther, 2006, p. 229). Gassmann & Enkel (2004) introduced a third direction to OI by adding the term coupled OI. The term coupled OI is defined as ‘’coupling the outside- in and inside-out processes by working in alliances with complementary partners in which give and take is crucial for success’’ (Gassman & Enkel, 2004, p.6). As mentioned earlier, coupled OI combines inbound and outbound OI together where two parties jointly develop or commercialize innovation by collaborating with other organizations in innovation networks, such as strategic alliances and joint ventures (Mazzola et al., 2012; Kobylinksi et al., 2013). In the research of Gassmann & Enkel (2004), the concept of coupled OI is in line with the model created by Piller & West (2014). Both studies show that knowledge creation occurs through co-creation between multiple organizations and an external community (Gassman & Enkel, 2014; Piller & West, 2014). The collaboration in coupled OI activities

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11 is often in a profound form and takes place for a long time (Kobylinksi et al., 2013). Coupled OI activities result, for example, in alliances, cooperation, and joint ventures. An alliance is a cooperation agreement between two organizations that go along with contract and trust-building measures (West, 2014). Joint ventures are created when two parent companies contribute resources to form a new entity (Chen et al., 2018). Joint ventures are a powerful mechanism to promote the creation and acquisition of knowledge between borders for lowering costs (Chen et al., 2018). But on the other the side the process of joint development or co-creation goes along with different risks that firms can face (Gorbatuyuk et al., 2016). Sharing knowledge with competitors in joint OI projects can be risky because of the shared knowledge competitors can use against each other (Rouyre and Fernandez, 2019). In the literature, most of the studies cover only the terms inbound OI and outbound OI (e.g., Ahn et al., 2013; Chesbrough, 2003; Mortara & Minshall, 2011). As a result, it is not always clear which activities belong to coupled OI or inbound OI/Outbound OI. For example, Ahn et al. (2013) state that activity ‘R&D collaboration’

belongs to inbound OI. But, looking at the definition of R&D collaboration given by Arranz & Fdez.

De Arroyabe, (2009, p.3), this activity has more something of coupled OI. In the next chapter OI activities will be discussed in the context of the automotive industry specifically.

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12 2.3 Open innovation in the automotive industry

Ili and colleagues (2010) show that OI is already applicable for the automotive industry and that OI will be essential in the following decade. Despite the need for open innovation in all directions (inbound-, outbound- and coupled), the research of Martins & Kaminski (2019) shows that inbound OI activities are the most pro-dominance. The study of Martins & Kaminski (2019) is confirmed by Lazzarotti and colleagues (2013), who show that companies in the automotive industry tend to look outside their boundaries for external sources to increase innovativeness (inbound OI). However, external paths to the current outside business with own intellectual property are still rare (outbound OI). A more precise examination will be given of the current inbound-, outbound- and coupled OI activities in the automotive industry in the next section.

2.3.1 Inbound OI in the automotive industry

There are three studies about OI in the automotive industry that describe the activities belonging to inbound OI and outbound OI (Ili et al., 2010; Karlsson and Sköld, 2013; Mazzola et al., 2012). The first study is the study of Ili and his colleagues (2010). In this study, Ili and colleagues (2010) examined whether the concept of OI creates a better R&D productivity for firms in the automotive industry than a closed innovation model. Examples of inbound OI in this study are the following: development request;

online portal for ideas; competition and venture capital (Ili et al., 2010). An online portal for ideas is an online platform for organizations, innovators and customers where new and innovative ideas are gathered. Firms are able to get the knowledge from innovative talents from all over the world (Çubukcu and Gümüş, 2015). Competitions are for example innovation contests. In an innovation contest, a firm publishes an innovation-related problem that the firm is facing. Independent agents (the solvers) can generate solutions for the firm’s problem, and the agent that provides the best solutions gets an award (Terwiesch and Xu, 2008). Venture Capital occurs when investors provide risk capital to high-potential entrepreneurs by gaining substantial influence over the entrepreneurial firm (Wijbenga et al., 2007). The study of Karlsson and Sköld (2013) focused on vertical and horizontal OI in global automotive groups.

They also give examples of inbound OI: systems for assessing offerings (e.g., online portal); open to initiatives; partnering and cooperation with firms. Additionally, the inbound OI practices given by

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13 Mazzola and colleagues (2012) are supplier, university and government collaborations; national public funding; in-licensing and acquisition. Table 1 gives an overview of the inbound OI activities that Ili and colleagues (2010), Karlsson and Sköld (2013) and Mazzola and colleagues (2012) mentioned in their studies.

Research Inbound Open innovation

Ili et al. (2010) Trend scouts; learning journeys; common research laboratory; online portal for ideas; competitions and venture capital

Karlsson and Sköld (2013) Open to initiatives; open to initiatives; partnering and cooperation with firms

Mazzola et. al. (2012) Suppliers, university and government collaborations;

in-licensing and acquisition Table 1 Inbound OI Automotive (Ili et al., 2010; Karlsson and Sköld, 2013; Mazzola et al., 20212),

2.3.2 Outbound OI

Outbound OI can be seen more as a strategic activity by firms, who can profit from their own innovations without investing in complementary assets (Mazzola et al., 2012). More companies want to increase the exploitation of own IP within and outside the current business (Ili et al., 2010). Relevant examples of outbound OI activities in the automotive industry given by Ili and colleagues (2010) are: reciprocal license agreements; licensing; patent sales and grant-back license. Patents are protections through exclusive means for the absorption and implementation of innovations (Patra & Raju, 2020). The research of Karlsson and Sköld (2013) shows that outbound OI mainly arrives in vertical relations.

Examples given here are: continuous scanning of small high-tech companies and start-ups asking for funds on their home page. Above that, outbound OI activities mentioned in Mazzola et al. (2012) are out-licensing; external technology commercialization; co-patent and R&D alliances. Brunswicker &

Chesbrough (2018) show that firms are more willing to gain external knowledge for free than sharing their own knowledge to others. In general, organizations tend to look outside their boundaries for external sources to increase innovativeness, while there are barely external paths to outside the business with own intellectual property (Ili et al., 2010). In table 2, an overview is given of the outbound OI activities that Ili and colleagues (2010), Karlsson and Sköld (2013) and Mazzola and colleagues (2012) mentioned in their studies.

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14 2.3.3 Coupled open innovation in the automotive industry

In the studies of Ili and colleagues (2010) and Karlsson and Skold (2013), activities that belong to coupled OI are not discussed. Although the literature is rare in coupled OI the automotive industry, more joint development agreements and joint ventures appear between OEMs (Reid, 2016). The growing amount of collaborations between OEMs results from the need for OEMs to move along with the four megatrends (electrification, autonomous driving, connectivity, and the sharing economy). These megatrends are forcing OEMs to abandon their long-standing investments into, for example, fossil- fueled cars (Yüksel et al., 2019). In the beginning, investments in lithium-ion batteries came along with high risk and uncertainty. Therefore, many OEMs such as General Motors, Toyota, Honda, and Ford used strategic partnerships as a way to spread risk (Deloitte Development, 2012). For example, the three OEMs Nissan, Renault, and Daimler joint forces together in the Daimler-Renault-Nissan alliance, where they collaborate on producing electric vehicles but also to explore other new technologies (Reid, 2016).

Another example of coupled OI in the automotive industry is given in the multiple case study by De Massis and his colleagues (2012). They provide an example of an automotive supplier who uses coupled collaborations to acquire ideas and knowledge and transfer its knowledge by making available its knowledge to benefit from the cooperation.

Research Outbound Open innovation

Ili et al. (2010) licensing; alliances; joint ventures; patent sales;

external training; grant-back license;

Karlsson and Sköld (2013) Continuous scanning of small high-techs and start- ups asking for RFT

Mazzola et. al. (2012) Out-licensing; external technology

commercialization; co-patent and R&D alliances Table 2 Outbound OI Automotive (Ili et al., 2010; Karlsson and Sköld, 2013; Mazzola et al., 2012)

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15 2.4 Managerial cognition

The terms managerial cognition and managerial attention are often used with the same definition (Barr 1998, Cho & Hambrbick, 2006, Kaplan et al. 2003, Ocasio1997). Managerial cognition has shown that managerial attention is especially important for making strategic choices and actions when responses to an uncertain environment call for costly and risky investments (Barr 1998, Cho & Hambrbick, 2006, Kaplan et al. 2003; Ocasio, 1997). Considering that attention is the ‘’noticing, encoding, interpreting, and focusing of time and effort by organizational decisionmakers ‘’ (Ocasio 1997, p.189), it captures the presently evolving focus of a CEO’s cognitive effort. Furthermore, in the research of Eggers and Kaplan (2009), cognition is measured in terms of the attention a CEO devotes to an emerging technology and the technology’s industry. Hence, managerial cognition and managerial attention can be used interchangeably (Eggers & Kaplan, 2009). Therefore, in this study, managerial cognition will be measured in terms of the attention a CEO devotes to the four megatrends; electrification, autonomous driving, connectivity and the sharing economy. According to managerial cognition literature (Eggers &

Kaplan, 2009, Kaplan, 2011; Porac & Thomas, 2012), executives’ cognitive or mental model influences their attention allocation, their interpretation of the environment, and thus their reaction to external environmental change, and is a fundamental predictor of a firms behavior and performance (E.g., Eggers

& Kaplan, 2009; Peng & Liu, 2016; Porac & Thomas, 2012; Tripsas & Gavetti, 2000; Vecchiato, 2017).

Furthermore, Barr (1998) argues that a crucial component in a firm’s strategic response to new environmental events is the interpretations managers develop about the event itself and about key dimensions of their strategy. In line with this, Yang et al. (2019) show that managers’ focus on proactive environmental approach transforms their perceived business and social pressures into their firm’s innovation capability. Furthermore, Vecchiato (2017) examined the role of managerial attention in the organizations Motorola and Kodak, who both failed to identify emerging markets. This study shows the importance of managerial attention to adapt to change (Vecchiato, 2017). To conclude, in turbulent environments (e.g., innovation pressure, revolutionary character of the automotive industry), CEO’s first need to pay attention to their environment before they can act (Eggers & Kaplan, 2009; Peng & Liu, 2016; Porac & Thomas, 2012; Tripsas & Gavetti, 2000 Vecchiato, 2017).

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16 2.4.1 Managerial attention towards the automotive industry

A firm’s response to changing industry (e.g., the automotive industry) has been examined by several scholars. Previous studies established a link between cognition and technology adaption. However, as mentioned before, this study shed light on the relationship between managerial cognition and the adoption of inbound-, outbound-, and coupled OI. Since previous researches are not focused on cognition and OI specifically, therefore a comparison will be made with technology adaption and innovation in general.

Kaplan and Eggers (2008) showed that top managers with solid experience in an industry would be more aware of competitive behavior, thus directing the firm to adopt technologies that would fit with their customers’ demands. Huang and colleagues (2019) studied the impact of CEO’s environmental awareness on technological innovation. This study concluded that a CEO’s environmental awareness could significantly promote the technological innovation of the enterprise. CEOs who are more environmentally conscious invest more in R&D activities, receive more patents, and achieve greater innovative success (Huang et al., 2019). Lavrynenk and colleagues (2017) showed that for managers it becomes more essential to make links between different divisions, functions, and external firms in terms of being able to screen and use external sources and capacities that are complementary to the existing internal competency base (Lavrynenk et al., 2017). In order to achieve this, firms need to pay attention to changing innovation trends (e.g., four megatrends in the automotive industry) in the market (Lavrynenk et al., 2017). Since inbound OI refers to the process of exploring and integrating external knowledge for technology development (Parida et al., 2019), a link between the study of Layvrynenk and his colleagues (2017), and inbound can be made. Therefore, based on the theories of Kaplan and Eggers (2008), Huang and colleagues (2019), and Layvrnenk and colleagues (2017), the assumption is that greater CEO attention towards the four megatrends in the automotive industry, a specific manifestation of managerial cognition, will be associated with a higher degree of inbound OI within the OEM. This has led to the following hypothesis.

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17 Hypothesis 1a: Greater CEO attention towards the four megatrends in the automotive industry will be associated with a higher degree of inbound innovation adoption within the OEM.

For most industrial firms, external technology exploitation is not their primary focus in business (Davis and Harrison, 2001). As a result, firms keep their potential knowledge and do not share it in a domain other than their own business (Kutvonen, 2011). In the context of out-licensing, sensing capacity describes finding new opportunities for licensing (Hu et al., 2015). Technology-intensive industries are often characterized by market imperfection and information asymmetries (Zeckhauser, 1996) which run in both directions: the licensee misses knowledge about the technology, while the licensor misses expertise about the technology’s market potential. For this reason, managerial attention is needed for recognizing possible market opportunities for out-licensing (Hu et al., 2015). Therefore the assumption is that greater CEO attention towards the four megatrends will be associated with a higher degree of outbound OI adoption within the OEM. This has led to the following hypothesis:

Hypothesis 1b: Greater CEO attention towards the four megatrends will be associated with a higher degree of outbound open innovation adoption.

As the literature shows, coupled open innovation combines inbound OI and outbound OI, where the innovation and ideas exchanges occur in both directions by forming relationships with complementary partners (Kobvlinksi et al., 2013; Lazzarotti et al., 2010; Mazzola et al., 2012). Huang and colleagues (2019) concluded that a CEO’s environmental awareness could significantly promote the firm’s technological innovation, which results in, for example, receiving more patents. Kaplan and Eggers (2009) confirmed this by stating that managerial attention affects how an organization reacts to technological responsiveness to competitors. In the last years, automotive players needed to deal with an immense transformation in the industry, where also new disruptive players (e.g., Tesla) entered the automotive market (Yüksel, 2019). The competitive pressures in the automotive industry are forcing OEMs to change their traditional scope (Mondragon et al., 2006). The OEMs could respond to the transformation through strategic partnerships and targeted acquisitions to spread risk and stay competitive in the market (Dutt et al., 2010). Managerial attention could support these collaborations with other organizations by focusing other managers on alternative operations and making sure that the

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18 right resources are available (Gifford, 2012). This attention can increase the degree of awareness, anticipation, and action concerning what matters to the managers (Hambrick and Mason, 1984).

Consequently, managerial attention could have a major influence on the strategic decision-making processes of a firm (Zeng and Mackay, 2018). As a response to the market dynamics, a strategic decision could result in coupled OI activities such as joint ventures or joint R&D projects (Gorbatyuk et al., 2016). Therefore, the assumption is that greater CEO attention toward the changing automotive industry will lead to a higher degree of coupled OI within the OEM.

Hypothesis 1c: Greater managerial attention toward the four megatrends will be associated with a higher degree of coupled open innovation adoption.

2.5 CEO narcissism

Management scholars largely agree with top management’s relevance about innovation to building innovation capability (Wang & Wang, 2012). In particular, discretionary and intangible decisions, such as open innovation initiatives, are among the important strategic choices that are highly dependent on a manager’s preference, values, and characteristics (Candelo et al., 2018; Chatterjee & Hambrick, 2007).

More specifically, the CEO’s personality plays a critical role in classifying and dealing with external contextual factors (e.g., changing industry), which finally affects decision-making on the adoption of OI activities. Narcissism has proved to be an influential aspect of CEO decision-making (Chatterjee &

Hambrick, 2007; Gerstner et al., 2013). Narcissism is the extent to which a person has an inflated view of itself and is obsessed with having that self-view (Campbell et al., 2004). Kashmiri and colleagues (2017) showed that CEO narcissism positively influences new product innovation and radical innovation. In line with this, Mata and Khan (2020) examined the influence of CEO narcissism on aggressive innovation strategies, which proved to be positive as well. The relation between CEO narcissism and innovation can be explained by the results Gerstner and colleagues (2013), who show that CEO narcissism enables greater managerial attention to a new technology, as narcissistic CEOs will be greatly observant to the new development and will activate their management teams to do as well.

As OI is often characterized by a considerable amount of uncertainty and ambiguity, more narcissistic

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19 CEOs will convey that they are able to deal with this ambiguity. Whereas less narcissistic CEOs tend to be more risk-averse and tend to see OI as risky (Kashmiri et al., 2017).

Looking at the relationship between managerial cognition and inbound OI, the assumption is that CEO narcissism has a strengthening moderating role in the relationship between managerial cognition and inbound OI. If CEO narcissism is low, CEOs tend to be more risk-averse, and they are less likely to invest in R&D projects (Kashmiri et al., 2017; Zhu & Cheng, 2014). Inbound OI activities are types of openness in which external resources can be added to the internal processes of a firm via, for example, acquiring. Acquiring is an entry that involves monetary exchanges, including all forms of purchasing technology and R&D efforts (Leitão et al., 2020). This goes along with high costs and rapid changes (Matricano et al., 2019). Furthermore, Gerstner and colleagues (2013) showed that CEOs who score lower on narcissism pay less attention to new technologies and developments. Therefore, the assumption is that in the case of low CEO narcissism, it will weaken the relationship between managerial cognition and the adoption of inbound OI. On the other side, if a CEO scores high on narcissism previously, research showed that they feel more comfortable with taking a risk and tackling ambiguities (Kashmiri et al., 2017; Mata & Khan, 2020; Zhu & Cheng, 2014). This could directly affect the relationship between managerial cognition and the adoption of inbound OI, since CEOs will feel more comfortable adopting new technologies. As mentioned before, research also showed that more narcissistic CEOs would pay more attention to new technologies and other developments in the market (Gerstner et al., 2013). Concluding, it means that CEO narcissism has both a positive influence on managerial attention and the adoption of innovation. Therefore the assumption is that CEO narcissism positively influences the relationship between managerial cognition and the adoption of inbound OI. The above has led to the following hypothesis;

Hypothesis 2a: The relationship between managerial attention towards the four megatrends and adoption of inbound open innovation is amplified by the degree of CEO Narcissism.

Firms can execute outbound OI activities in multiple ways, but the most common practices are revealing and selling. Revealing is a practice without monetary exchange and focuses on knowledge-sharing with the partner network without actual financial benefit. In contrast, selling involves monetary exchange,

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20 where the results of a R&D investment are brought to the market. (Leitão et al., 2020). While outward external knowledge brings excellent financial opportunities, it also risks of negative effects on innovation performance (Fosfuri, 2006). For example, if a manufacturer has produced a unique product or found a rare source, there is the tendency not to share the know-how with other organizations. In such cases, there is a clear risk of loss of IP (Von Krogh et al., 2018). As mentioned in the previous hypothesis, CEOs who score low on narcissism tend to be more risk-averse compared to CEOs who score high on narcissism. Since research shows that outbound OI practices come along with risks (Kashmiri et al., 2017; Zhu & Cheng, 2014), the assumption is that low CEO narcissism will weaken the relationship between managerial cognition and the adoption of outbound OI. On the other side, CEOs scoring higher on narcissism will be able to take more risks (Kashmiri et al., 2017; Mata & Khan, 2020; Zhu & Cheng, 2014). Again, this could directly affect the relationship between managerial cognition and the adoption of outbound OI, since narcissistic CEOs will feel more comfortable with sharing knowledge or technologies (Leitão et al., 2020). Concluding, it means that CEO narcissism has a strengthening role in the relation between managerial cognition and the adoption of outbound OI. This has led to the following hypothesis:

Hypothesis 2b: The relationship between managerial attention towards the four megatrends and adoption of outbound open innovation is amplified by the degree of CEO Narcissism.

Collaborating with other firms can result in coupled OI activities, which enables a firm to exchange knowledge to minimize innovation costs. In this way, a firm spreads risks with another party which can be beneficial (Chen et al., 2018). But on the other side, sharing knowledge with competitors can be risky because the shared knowledge can be used against each other. Risk-averse managers would probably minimize these risks by reducing their knowledge sharing or by reducing participation in other coupled OI projects (Rouyre and Fernandez, 2019). As shown in the previous hypotheses, more narcissistic CEOs tend to take more risks, while less narcissistic CEO have a risk-averse attitude (Kashmiri et al., 2017). As it turns out, engaging in coupled OI activities will involve several risks such as knowledge and source sharing (Rouyre and Fernandez, 2019). Therefore, the assumption is that a lower level of CEO narcissism will weaken the relationship between the variable managerial cognition and the

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21 adoption of coupled OI activities, and a higher level of CEO narcissism will strengthen the relationship between the variable managerial cognition and the adoption of inbound OI activities.

Hypothesis 2c: The relationship between managerial attention towards the four megatrends and adoption of coupled open innovation is amplified by the degree of CEO Narcissism.

Figure 1 shows the conceptual model of the hypotheses. The pluses in the figure show the positive relationships between the variables. The arrows of H1a, H1b and H1c show the assumptions that greater CEO attention towards the four megatrends, a specific manifestation of managerial cognition, will be associated with a higher degree of inbound-, outbound-, and coupled OI within the automotive industry.

The arrows of H2a, H2b and H2c show the assumption that CEO narcissism will amplify the relationships between managerial attention towards the four megatrends and the adoption of inbound-, outbound-, and coupled OI.

Figure 1: Conceptual model

Managerial attention towards the four

megatrends

CEO Narcissism

H2a +, Hb+, and Hc +

Coupled OI

H1a +

H1b +

H1c +

Outbound OI Inbound OI

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22

3. Research design

To give an answer to the main research question, a qualitative content analysis was used in this comparative case study with secondary data. Comparative case studies are undertaken over time and highlight comparison within and across conditions (Goodrick, 2014). Content analysis is often used to interpret meaning in speech and could involve linguistic ‘quantification’ where words are units of analysis (O’Leary, 2017). Furthermore, a content analysis could also refer to thematic analysis through coding as it is often used in studies where the occurrence is assumed to indicate important trends (O’Leary, 2017). For example, counting the words “best boss” to label and measure the amount of narcissism in a report.

3.1 Sample

The content that was used for this analysis were annual reports and letters to shareholders (LTS). An annual report is a report made by an organization every year that consists of the organization’s audited accounts, together with statements of profits or loss and how the management sees the organization in the future (CambridgeDictionary, 2013). The annual report aims to communicate with the outside simply and understandably with applicable information on the past, current, and future firms’ activities (Breton, 2009). The LTS is part of an annual report. CEOs or top management write the LTS to explain the most prominent corporate events and achievements, their situation, beliefs, and values (Aerts & Yan, 2017).

The time scope of this study was from 1 January 2010 till 31 December 2019, considering that the automotive industry slowly started changing in 2010 (Gao et al., 2016; International Labour Organization, 2021) and it has turned into the current occurring revolution (Gao et al., 2016; Pardi, 2019). The available data were collected over a decade and therefore longitudinal.

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23 This comparative case study was based on four OEMs in the automotive industry worldwide. The selection criteria were based on the availability of the annual reports and LTS, and a small pre-test. A pre-test was done to make sure there will be some difference between the cases. When looking at the fifteen biggest OEMs in the automotive industry globally (Global 500, 2020), five were already removed from the list of potential OEMs to research, considering that some annual reports were not accessible.

Two pre-tests were conducted on the remaining ten OEMs. The pre-tests were conducted to find one OEM that would score low on managerial cognition and one OEM that would score high on managerial cognition, one OEM that would score low on CEO narcissism, and one that would score high on CEO narcissism. Managerial attention towards the changing automotive industry in the pre-test was measured according to Eggers and Kaplan (2009), Kaplan (2008) and Osborne and colleagues (2001). Words related to the four megatrends in the automotive industry were searched for (Accenture, 2018). For example, words such as electrification, autonomous driving, and plug-in hybrid vehicles were included.

An overview of the words related to the four megatrends in the pre-test is shown in appendix A, tables 14 and 15. The raw word count was divided by the total number of words and then multiplied by a hundred. This normalization allows for comparison across companies and years, and for more interpretable regression coefficients (Gertner et al., 2014; Eggers & Kaplan, 2009). The pre-tests were done by the following OEMs: BMW, Volkswagen, Toyota, FCA, Tata, Daimler, Ford, Mitsubishi, Renault, and Volvo. This resulted in a ranking of scores, where BMW had the highest score on managerial attention towards the changing industry and Tata had the lowest score on managerial attention towards the changing industry. Furthermore, the variable CEO narcissism was measured based on the approach of Chatterjee & Hambrick (2007). Chatterjee & Hambrick (2007) developed four indicators of narcissistic tendencies: the prominence of CEO’s photograph in annual reports, CEO prominence in press releases, first-person singular pronouns in interviews and CEO relative pay. In this study only the prominence of the CEO’s photograph in annual reports was used. BMW had the highest score according to the indicators of narcissistic tendencies created by Chatterjee & Hambrick (2007), whereas FCA had the lowest score on CEO narcissism. Considering that BMW had the highest score on both variables, Ford was added to the selected group since Ford had the second highest score on the variable managerial cognition in the pre-test. The pre-tests resulted in the following cases: BMW (high

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24 Tata

BMW

FCA

Ford

Figure 2 Overview selected cases after pre-test based on CEO narcissism score and managerial cognition score

score CEO narcissism), FCA (low score CEO narcissism), Ford (high score managerial cognition) and Tata (low score managerial cognition). The selected cases are reflected in a matrix in figure 1. Here, you can see the OEM scores’ differences in managerial cognition and CEO narcissism.

Low managerial cognition Low CEO

narcissism

High CEO narcissism High managerial

cognition

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25 3.2 Analysis

3.2.1 Independent variable managerial attention towards the changing industry

For measuring the independent variable managerial attention towards the changing industry, the same method was used as is described in the pre-test. A quantitative content analysis approach was used.

First, a list of words related to the four megatrends was created and can be found in appendix A. This list was based on studies related to the four megatrends in the automotive industry (Accenture 2018, Görog, 2018; Möller and Haas, 2019) and after analyzing some LTS’ to get familiar with the text. After forming lists, the words in the LTS that were corresponding with words in the list were counted. After counting the words related to the four megatrends, the raw word count was divided by the total number of words and then multiplied by a hundred. The total number of words was measured by copying the LTS to a Word Document and check the total amount of words. To make sure the total word count was done in the same way in every LTS, all the words in the LTS were counted except for the name of the CEO. The letter’s salutation and the description of pictures were included in the total counted words.

To ensure reliability, the words that were counted needed to correspond with words in the created list.

By matching the words in LTS’s with the list, it ensured consistency since the words in different LTS’s were counted in the same way every time. Furthermore, this process was done manually by the author of this study. Manual research was chosen to be sure that all relevant words that were counted were related to the four megatrends and not to something else. For example, in the LTS of the FCA Group 2011, there is the following sentence: ‘’We made significant progress in converging architectures and components, integrating purchasing activities, sharing best practice in manufacturing processes, and optimizing utilization of our combined production capacity’’ (FCA group, 2011, p.10). Here, the word sharing has nothing to do with the megatrend sharing economy. To further ensure reliability, an extra researcher (graduated student Business Administration at the University of Amsterdam) did the same measurement to a sample of LTS’s. Her results were compared with the researcher’s results, but no differences in measurements were found. The measurements of the extra researcher are located in appendix A.

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26 3.2.2 Dependent variables adoption of inbound OI, outbound OI and coupled OI

The initial plan for measuring the adoption of the variables inbound- outbound- and coupled OI was by carrying out quantitative content analysis. But, after scanning the annual reports of some of the OEMs, it became immediately clear that the quantitative content analysis would not measure what it should measure, namely the amount of inbound-, outbound- and coupled OI activities written in the annual reports. For example, the word ‘’acquisition’’ appears in the annual report of Tata 2014 52 times (annual report Tata Motors, 2014) while in that year only two big acquisitions took place. It also became clear that measuring the three directions of OI in annual reports comes with an intensive investigation to look for activities related to inbound- , outbound- and coupled OI. In order to measure the right, the following was done. First, an overview was made with all the activities that belong to inbound- , outbound- and coupled OI activities. These activities were based on previous OI studies (De Massis et al., 2012; Ili et al., 2010; Karlsson & Sköld, 2013; Mazzola et al., 2012) and after scanning annual reports of all the OEMs used for this study. The activities that were looked for in the annual reports are structured in table 3. The definitions of the activities related to the three directions of OI are located in appendix B.

Inbound OI Outbound OI Coupled OI

Acquisitions Spin-offs Joint development/research

In-licensing Out-licensing Strategic alliance

Venture capital/start-ups Technology commercialization Joint ventures Research laboratory External trainings

University partnerships Innovation contest

Trend scouts/learning journeys

Table 3 Inbound-, outbound- and coupled OI activities

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27 Second, to get familiar with the annual report and the company, the LTS was read again. This gave a first impression of all the activities the company was engaged in that specific year. After reading the LTS one more time, activities related to inbound- , outbound-, and coupled OI activities were looked for. The first step in this process was by searching the words related to three directions of OI with the function control + F. For example, for the activity acquisition, the word ‘acquisition’ was put in the search function of the PDF. This gave a first impression of where acquisitions were discussed in the annual reports. Subsequently, all the acquisitions were looked for in that specific year by an intensive investigation. Most of the annual reports of the OEMs are build every year in the same structure. For example, in the annual reports of BMW, all the major agreements relating to change of control or other acquisitions are found back under the head ‘’significant agreements of the company taking effect in the event of a change in control following a takeover bid’’ (annual report BMW Group, 2017, p. 114).

Activities done by an OEM in earlier years were not noted. This applied, for example, for the measurement of joint ventures, an activity that relates to coupled OI. Only joint ventures that were new in a specific year were noted or if there was a major change in the composition of the joint venture. If it was unclear to which direction of OI an activity belonged to, then the activity was discussed with an extra researcher (graduated student master Business Administration at the University of Amsterdam).

The process of intensively analyzing the annual reports and looking for the specific activities was done twice to ensure no activities were overlooked. The noted activities were placed next to each other per year in an overview in Excel. An overview of the inbound-, outbound,- and coupled OI can also be found in appendix C. Once all the data was gathered, another overview was created in a Word document together with the extra researcher. In this document, only the activities that were adopted a specific year were left. For example, FCA entered in 2012 in a joint venture together with Tata. The joint venture with Tata is discussed in every annual report, but for this study it is only counted in the year 2012. The activities were discussed one more time and were placed under each other per direction, per year. The discussion ensured that the right activities were placed among the right direction of OI. Finally, per year and per direction, a ranking was made. For example, one OEM was chosen for most the inbound OI activities in 2010 and one OEM was chosen for the least inbound OI activities in 2010. This ranking was made for every year and for every direction OI and can be found in the result section. The ranking

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28 was based on the amount of activities per inbound- , outbound- and coupled OI activities. If the amount was the same, then two OEMs were placed in for the ‘most’ of ‘least’ activities of inbound- , outbound- and coupled OI.

3.2.3 Moderating variable CEO Narcissism

For measuring the degree of CEO narcissism, the same method was used as is done in the pre-test.

Following Gerstner et al. (2013) and Chatterjee and Hambrick (2007), this study measures the photographic presence of a CEO in annual reports. The pre-test was based on the photograph of the CEO in the annual report of 2019. For measuring CEO narcissism in the period between 2009 and 2019, the annual reports of the OEMs in this time scope was used. The photograph of the CEO is often placed above or next to the LTS. In addition, Gerstner and colleagues (2013) used the CEO narcissism indicator in their research and conducted additional validation tests. The rating of this indicator is described in the table below. When a CEO has a rating of 1, it means that it scores low on CEO narcissism. When a CEO has a rating of 4, it means that it scores high on CEO narcissism. Besides making an overview of the narcissisms scores, also an overview was made of the CEOs per OEM per year. In case of noticeable changes in scores, the change of CEO could be looked at.

Table 4 Rating CEO narcissism annual report (Chatterjee and Hambrick, 2007)

Rating CEO Narcissism

The prominence of the CEO’s photograph

4 If the CEO’s photo was of him or her alone and occupied more than half a page 3 If the CEO’s photo was of the CEO alone and occupied less than half a page 2 If the CEO was photographed with one or more fellow executives

1 If there was no photograph of the CEO

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29

4. Results

Part 1 Managerial cognition

In table 5, an overview is given of the scores of managerial attention towards the four trends in the automotive industry.

The crosses in the table mean that for that specific year, no LTS was online available. The zero’s mean that no words related to the four megatrends were found in the LTS. The numbers in bold are the numbers that have the highest score per year. First, it can be seen that the degree of managerial attention for all the OEMs was clearly higher in the years 2016, 2017, 2018, 2019 relative to the years 2010, 2012, 2013. These results confirm the literature (Accenture, 2018; Dutt et al., 2020; Gao et al., 2016), showing that the four megatrends play an increasingly important role in the automotive industry over the last ten years. Ford scored the highest on managerial attention towards the four megatrends in the automotive industry in the years 2010, 2011, 2017, and 2019. In the remaining years, BMW had the highest score for managerial attention towards the four megatrends in the automotive industry. Combining these scores with the cognition literature (Eggers & Kaplan, 2009; Kaplan, 2011, Porac & Thomas, 2012), it suggests that the CEOs of BMW and Ford pay more attention to the four megatrends in the automotive industry compared to the CEOs of FCA and Tata. The results also show that the attention scores of BMW increase almost every year, except for the years 2017 and 2019. Looking at the results of Ford, there is a clear difference of scores between the years 2010 till 2013 and 2016 till 2019. The managerial attention scores of Ford in 2016 till 2019 are more than doubled compared to the scores of 2010 till 2013. The difference in scores shows that the CEO of Ford pays more attention to the four megatrends in the years 2016 till 2019 than in the years 2010 till 2013.

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

FCA 0.115 0.000 0.000 0.000 0.000 0.277 0.837 0.797 0.549 0.265 BMW 0.073 0.245 0.642 1.035 1.183 1.840 2.330 1.592 3.114 1.526 Tata 0.197 0.251 0.000 0.000 0.000 0.090 0.000 0.589 0.731 0.441

Ford 0.637 0.815 0.367 0.000 X X 1.954 1.731 2.609 2.309

Table 5 Scores managerial attention towards the four megatrends in the automotive industry, where X = no LTS online available

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30 Part 2 Inbound OI, Outbound and Coupled OI

Inbound OI

Inbound OI 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 External

supplier

collaboration X XX X Y

WWWW

W W X Z ZZZ

Acquisition

W XX YYY ZZ

X YY

Z X Y W Y Z WW X XX Y Z

XX

YYYY W

In-licensing X Z

Venture capital Z Y W W Y W YY WW Y

Research laboratory/

Researches X Y W Y Z WW Y Z Z

Learning

journeys X W Z Z W Z W Z

University

partnerships X W X Z Y Z ZZ Z WW Z W

National public

funding

Innovation

contest W Z

User

involvement Y W Z Z Z Z

Table 6 overview inbound OI activities OEMs. W = Tata, X = BMW, Y = Ford, , Z = FCA

In table 6, an overview is given of inbound activities founded in the OEMs’ annual reports. The ‘W’

reflects the activity for Tata, the ‘X’ for BMW, the ‘Y’ for Ford, and ‘Z’ for FCA. When there are multiple of the same letter in one box, in that case, an OEM adopted an activity more than once in that year specific year. For example, in 2010, there are two X’s in the box for ‘acquisition’ since BMW acquired in 2010 two companies namely: Simelease and DEKRA Südleasing (annual report BMW Group, 2010). Table 7 shows an overview of the OEMs who adopted the most and least inbound OI activities, compared to OEMs who scored the highest and lowest on managerial cognition. When an OEM was ranked as ‘most’ adopted inbound OI for a specific year, the OEM adopted the most inbound OI compared to the other OEMs. Therefore, ‘most’ and ‘least’ are relative terms. The actual activities and descriptions of these activities can be found in appendix C. The results show that hypothesis 1, Greater CEO attention to the automotive industry will be associated with a higher degree of inbound innovation adoption within the OEM, is not supported. The adoption of inbound OI activities differs per OEM per year. In some years, for example, Tata adopted the most (six till ten activities) inbound

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31 activities (e.g., 2010, 2011), while in other years, Tata adopted a small (zero to two activities) amount of inbound OI activities (e.g., 2019). Therefore, no significant relationship can be found between managerial cognition and the adoption of inbound OI.

2010 2015

Most/highest Least/lowest Most/highest Least/lowest

Inbound OI BMW Tata FCA Ford

Managerial cognition Ford BMW BMW Tata

2011 2016

Most/highest Least/lowest Most/highest Least/lowest

Inbound OI BMW/Ford FCA Tata BMW

Managerial cognition Ford FCA BMW Tata

2012 2017

Most/highest Least/lowest Most/highest Least/lowest

Inbound OI FCA Tata Tata BMW

Managerial cognition BMW Tata/FCA Ford Tata

2013 2018

Most/highest Least/lowest Most/highest Least/lowest

Inbound OI FCA BMW Ford Tata

Managerial cognition BMW X BMW FCA

2014 2019

Most/highest Least/lowest Most/highest Least/lowest

Inbound OI FCA BMW Tata BMW

Managerial cognition BMW X Ford FCA

Table 7 Comparison between most/least adopted inbound OI and highest/lowest managerial cognition score

Although no support was founded for hypothesis 1, the suggested inbound OI activities given in the literature (Bosch-Sijtsema & Bosch, 2014; Ili et al., 2010; Karlsson and Sköld, 2013; Mazzola et al., 20212; Weber & Tarba, 2016) were reflected in the annual reports of the four OEMs which is reflected in table 6. In appendix C, all the inbound OI activities per OEM are described.

Furthermore, the results show that the inbound OI activities are more focused on the four megatrends (electrification, sharing economy, connectivity, and autonomous driving) over the years. For example, the inbound OI activities of Ford were not related to the four megatrends in the years 2010, 2011, and 2012. But, since 2013, more activities were found related to the four megatrends. For example: ‘’We have introduced a Ford Fusion Hybrid automated research vehicle with the University of Michigan and State Farm to study autonomous driving and other advanced technologies’’ (annual report FCA, 2013, p. 2). Seven out of nine inbound OI activities at Ford in 2017, 2018, and 2019 are related to the four

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