Assessing organisational capabilities of incumbent car manufacturers in light of current influencing factors

Keywords:

Abstract:

The interrelation of organisational resources, such as organisational capabilities, and competitive advantages as well as profits has triggered great efforts in the field of strategy research (Schilke, Hu & Helfat, 2018). Dynamic capabilities are a type of organisational capabilities that have been traditionally defined as "the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments" (Teece, Pisano & Shuen, 1997). Recent scholars however argue that there is a lack of a common understanding of dynamic capabilities (Peteraf, Di Stefano & Verona, 2013). In fact, research concerned with the capability lens has been criticized for insufficient empirical underpinning and lack of operationalizability (Priem & Butler, 2001; Easterby-Smith, Lyles & Peteraf, 2009). Key in sharpening the capabilities perspective is to detach from the focus on conceptual development and to refine methodologies to improve empirical evidence. This idea led to several approaches to improve our understanding of how to assess organisational capabilities (Ulrich & Smallwood, 2004; Achtenhagen, Melin & Naldi, 2013) and further underlying factors of value creation (Argyres, Mahoney & Nickerson, 2019; Lieberman & Dhawan, 2005).
How can organizational capabilities be assessed holistically? A holistic approach to assessing organisational capabilities refers to the entire company as level of analysis and includes the aspects of capabilities, such as leadership and agility, to production technologies as unit of analysis relevant for determining the corporate strategy. Previous studies have either considered particular individual capabilities or within a specific capability deployment context (de Bakker & Nijhof, 2002; Lieberman & Dhawan, 2005; Wheeler, 2002; Trejo et al., 2002; Helfat & Raubitschek, 2018). It is a question of operationalising the capabilities concept in the resource-based view (RBV) context. The RBV is the view of firms as the sum of their resources for establishing and maintaining advantages over competitors (Penrose, 1959; Barney, 1991; Peteraf, 1993; Teece, Pisano & Shuen, 1990). Operationalisation of the RBV refers to how the view of a company's resources in form of all its assets, knowledge, organisational processes, technologies, information, firm attributes and capabilities (Barney, 1991) can be applied and utilised for problem-solving. Answering is a contribution to explaining the underlying argument of certain capabilities as sources of sustained competitive advantages and sustained profits (Peteraf, 1993; Teece, Pisano & Shuen, 1997; Barney, Ketchen & Wright, 2011; Barney, 1991) over longer periods of time in our nested system of individuals, organisations and industries on a tangible level.
Developing a holistic approach to assessing organisational capabilities has become increasingly pressing and important for academics and practitioners as environments have become more Volatile, Uncertain, Complex and Ambiguous (VUCA). VUCA refers respectively to the dynamics of change, lack of predictability, multiplicity of forces at play and haziness of reality (van Tulder, Jankowska & Verbeke, 2019; Buckley, 2019; Schoemaker, Heaton & Teece, 2018; Petricevic & Teece, 2019; Teece, Peteraf & Leih, 2016). The automotive industry today is an example of an industry with such a risky environment.
After a century of hardly any change in the business model of car manufacturers (also known as Original Equipment Manufacturers, hereafter OEMs), today's value chains of the automotive industry are being challenged. Experts are calling it the automotive industry at a crossroads, the second automotive revolution or mobility's second great inflexion point (Simoudis & Zoepf, 2019; Cornet et al., 2019; Hauptmeier, 2010). The industry at present is characterised by a high degree of dynamism and change in business models.
This dynamism is largely based around the four core themes of connectivity (C), autonomy (A), sharing (S) and electrification (E), referred to by the acronym CASE (Eisele et al., 2019). Connectivity is about connecting the car to the internet and its environment, such as other cars and transport infrastructure, to improve the car use experience. Autonomy describes the ever-growing support of drivers to ultimately replacing them by fully autonomous operating systems in cars without steering wheels. Sharing is the joint use of a passenger car, offering the end-user flexibility in a network of available vehicles. It mainly aims increasing vehicle utilisation. Electrification is the shift to an electrified drive, away from internal combustion engines (ICEs) used predominantly today. It is almost emission-free at the location of use. An overview of the traditional automotive value chain and new electric car value chain is attached as Appendix 1. A comparison of the two value chains highlights that the new value chain involves an increased number of stakeholders, such as new players, new value creation potentials for car manufacturers, for example related to electric vehicle infrastructure and battery recycling, as well as reduced value added related to the conventional ICE drive train.
CASE opens opportunities to end customers and providers of mobility (related) products and services. Each CASE factor itself offers advantages and benefits to end consumers. This includes productive travel time and cheaper rides, and also more reliable technology and potentially security. Moreover, it opens value creation and capturing potentials for businesses, such as automotive manufacturers and suppliers, for example, through business model transformation and digital service offerings. Most notably and promising for users and providers, synergies arise from innovations associated with combination of the four CASE dimensions, such as shared vehicles without human operators, connected autonomous vehicles (CAVs). Currently, automotive manufacturers are divided about promising strategies to participate in the CASE market participate and pursue different strategic approaches (Malnight, Buche & Dhanaraj, 2019).
This paper engages with research on capabilities in the RBV context (Barney, 1991; Teece & Pisano, 1994; Teece, Pisano & Shuen, 1997; Adner & Helfat, 2003; Peteraf & Barney, 2003). It draws in particular on existing literature on the assessment of capabilities and measurement of corporate performance indicators (Wheeler, 2002; Lieberman & Dhawan, 2005; Barreto, 2010; Ethiraj et al., 2005; Ozcan, 2018). Moreover, it participates in the ongoing debate in the Management and Organization Review (MOR) on Tesla and the automotive CASE topics (Perkins & Murmann, 2018; Macduffie, 2018; Teece, 2018, 2019; Jiang & Lu, 2018; Välikangas, 2018).
Three research gaps are addressed: The first one concerns the structured and holistic assessment of capabilities at firm level. Existing research either considers certain capability deployment contexts only (de Bakker & Nijhof, 2002; Lieberman & Dhawan, 2005), certain capability types (Wheeler, 2002; Trejo et al., 2002; Helfat & Raubitschek, 2018), or is narrow in terms of perspectives covered and data considered (Achtenhagen, Melin & Naldi, 2013; Ulrich & Smallwood, 2004) to assess capabilities. This study argues that the holistic assessment of organisational capabilities is a crucial link between the capability lens, its operationalisation and underpinning empirics. Second, current literature lacks a comprehensive and structured assessment and understanding of CASE at present, especially regarding implications for incumbent car manufacturers. Previous studies have either studied CASE in a more general fashion (Teece, 2018; Jacobides, 2018; Pütz et al., 2019; Modi, Spulber & Jin, 2018; Fard & Brugeman, 2019), or investigated only certain CASE factors or aspects (Teece, 2019; Jiang & Lu, 2018; Perkins & Murmann, 2018; Jacobides, Macduffie & Tae, 2013; Schulze, MacDuffie & Täube, 2015; Ajanovic & Haas, 2016; Zoepf & Riggs, 2019; Firnkorn & Müller, 2012; Wesseling et al., 2015; Bohnsack, Kolk & Pinkse, 2015; Faria & Andersen, 2017; Cohen & Hopkins, 2019). Third, the CASE capabilities matrix developed in the course of this MOR debate (Teece, 2018) has potential for being extended and therefore providing a more holistic view. This includes considering inter-firm differences, sharpening of the unit of analysis as well as creation of transparency regarding assessment process and argument structure and taking into account different contextual scenarios. The matrix is attached with Appendix 2. The paper aims to develop a holistic capabilities assessment approach applicable to incumbent car manufacturers.
The main research question guiding this paper is: How can organisational capabilities of incumbent car manufacturers be assessed holistically? Three sub-questions are considered: What are the characteristics of current influencing factors prevalent in the automotive industry? What do these imply for incumbent car manufacturers capabilities? How can organisational capabilities of incumbent car manufacturers be assessed as a basis for corporate strategy development and for analysing inter-firm differences in capabilities? The managerial practice is thus supported in strategy problem-solving in quest of finding answers to “What should I do?” or specifically “What should my strategy be?” (Brown, Bradley & McLean, 2019).
Secondary data from the 20 largest incumbent car manufacturers in terms of number of cars sold worldwide are compiled for the period past three years. These 20 car manufacturers produce about 88 per cent of vehicles sold worldwide (International Organization of Motor Vehicle Manufacturers, 2018), see Appendix 3 for details. The most recent annual reports, sustainability reports, industry reports, news based on officially confirmed data and further shareholder information from incumbent car manufacturers are considered in light of the research questions.
Apart from providing a holistic approach to assessing capabilities, the approach developed also informs managerial practice and provides application guidance through automotive examples. However, the approach has potential application to other industries. The operationalisation of assessing capabilities may be appreciated as operationalisation of the capability lens more broadly, providing key information for strategic and operative decisions. Finally, this paper provides a comprehensive CASE analysis considering implications for automakers.
This paper is organized in six sections. The first section introduces links to the previous debate on CASE capabilities of automakers. Subsequently, an analytical framework for managing car manufacturers in a risky VUCA environment is introduced, serving as structure for this paper. Next, the CASE factors, development trajectories and their implications for automobile manufacturers are analysed. The core of the paper is the development and application of a framework for assessing capabilities of incumbent car manufacturers. A conclusion and outlook close this paper.

Bibliography

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Full Text:

INTRODUCTION
The interrelation of organisational resources, such as organisational capabilities, and competitive advantages as well as profits has triggered great efforts in the field of strategy research (Schilke, Hu & Helfat, 2018). Dynamic capabilities are a type of organisational capabilities that have been traditionally defined as "the firm’s ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments" (Teece, Pisano & Shuen, 1997). Recent scholars however argue that there is a lack of a common understanding of dynamic capabilities (Peteraf, Di Stefano & Verona, 2013). In fact, research concerned with the capability lens has been criticized for insufficient empirical underpinning and lack of operationalizability (Priem & Butler, 2001; Easterby-Smith, Lyles & Peteraf, 2009). Key in sharpening the capabilities perspective is to detach from the focus on conceptual development and to refine methodologies to improve empirical evidence. This idea led to several approaches to improve our understanding of how to assess organisational capabilities (Ulrich & Smallwood, 2004; Achtenhagen, Melin & Naldi, 2013) and further underlying factors of value creation (Argyres, Mahoney & Nickerson, 2019; Lieberman & Dhawan, 2005).
How can organizational capabilities be assessed holistically? A holistic approach to assessing organisational capabilities refers to the entire company as level of analysis and includes the aspects of capabilities, such as leadership and agility, to production technologies as unit of analysis relevant for determining the corporate strategy. Previous studies have either considered particular individual capabilities or within a specific capability deployment context (de Bakker & Nijhof, 2002; Lieberman & Dhawan, 2005; Wheeler, 2002; Trejo et al., 2002; Helfat & Raubitschek, 2018). It is a question of operationalising the capabilities concept in the resource-based view (RBV) context. The RBV is the view of firms as the sum of their resources for establishing and maintaining advantages over competitors (Penrose, 1959; Barney, 1991; Peteraf, 1993; Teece, Pisano & Shuen, 1990). Operationalisation of the RBV refers to how the view of a company's resources in form of all its assets, knowledge, organisational processes, technologies, information, firm attributes and capabilities (Barney, 1991) can be applied and utilised for problem-solving. Answering is a contribution to explaining the underlying argument of certain capabilities as sources of sustained competitive advantages and sustained profits (Peteraf, 1993; Teece, Pisano & Shuen, 1997; Barney, Ketchen & Wright, 2011; Barney, 1991) over longer periods of time in our nested system of individuals, organisations and industries on a tangible level.
Developing a holistic approach to assessing organisational capabilities has become increasingly pressing and important for academics and practitioners as environments have become more Volatile, Uncertain, Complex and Ambiguous (VUCA). VUCA refers respectively to the dynamics of change, lack of predictability, multiplicity of forces at play and haziness of reality (van Tulder, Jankowska & Verbeke, 2019; Buckley, 2019; Schoemaker, Heaton & Teece, 2018; Petricevic & Teece, 2019; Teece, Peteraf & Leih, 2016). The automotive industry today is an example of an industry with such a risky environment.
After a century of hardly any change in the business model of car manufacturers (also known as Original Equipment Manufacturers, hereafter OEMs), today's value chains of the automotive industry are being challenged. Experts are calling it the automotive industry at a crossroads, the second automotive revolution or mobility's second great inflexion point (Simoudis & Zoepf, 2019; Cornet et al., 2019; Hauptmeier, 2010). The industry at present is characterised by a high degree of dynamism and change in business models.
This dynamism is largely based around the four core themes of connectivity (C), autonomy (A), sharing (S) and electrification (E), referred to by the acronym CASE (Eisele et al., 2019). Connectivity is about connecting the car to the internet and its environment, such as other cars and transport infrastructure, to improve the car use experience. Autonomy describes the ever-growing support of drivers to ultimately replacing them by fully autonomous operating systems in cars without steering wheels. Sharing is the joint use of a passenger car, offering the end-user flexibility in a network of available vehicles. It mainly aims increasing vehicle utilisation. Electrification is the shift to an electrified drive, away from internal combustion engines (ICEs) used predominantly today. It is almost emission-free at the location of use. An overview of the traditional automotive value chain and new electric car value chain is attached as Appendix 1. A comparison of the two value chains highlights that the new value chain involves an increased number of stakeholders, such as new players, new value creation potentials for car manufacturers, for example related to electric vehicle infrastructure and battery recycling, as well as reduced value added related to the conventional ICE drive train.
CASE opens opportunities to end customers and providers of mobility (related) products and services. Each CASE factor itself offers advantages and benefits to end consumers. This includes productive travel time and cheaper rides, and also more reliable technology and potentially security. Moreover, it opens value creation and capturing potentials for businesses, such as automotive manufacturers and suppliers, for example, through business model transformation and digital service offerings. Most notably and promising for users and providers, synergies arise from innovations associated with combination of the four CASE dimensions, such as shared vehicles without human operators, connected autonomous vehicles (CAVs). Currently, automotive manufacturers are divided about promising strategies to participate in the CASE market participate and pursue different strategic approaches (Malnight, Buche & Dhanaraj, 2019).
This paper engages with research on capabilities in the RBV context (Barney, 1991; Teece & Pisano, 1994; Teece, Pisano & Shuen, 1997; Adner & Helfat, 2003; Peteraf & Barney, 2003). It draws in particular on existing literature on the assessment of capabilities and measurement of corporate performance indicators (Wheeler, 2002; Lieberman & Dhawan, 2005; Barreto, 2010; Ethiraj et al., 2005; Ozcan, 2018). Moreover, it participates in the ongoing debate in the Management and Organization Review (MOR) on Tesla and the automotive CASE topics (Perkins & Murmann, 2018; Macduffie, 2018a; Teece, 2018b, 2019; Jiang & Lu, 2018; Välikangas, 2018).
Three research gaps are addressed: The first one concerns the structured and holistic assessment of capabilities at firm level. Existing research either considers certain capability deployment contexts only (de Bakker & Nijhof, 2002; Lieberman & Dhawan, 2005), certain capability types (Wheeler, 2002; Trejo et al., 2002; Helfat & Raubitschek, 2018), or is narrow in terms of perspectives covered and data considered (Achtenhagen, Melin & Naldi, 2013; Ulrich & Smallwood, 2004) to assess capabilities. This study argues that the holistic assessment of organisational capabilities is a crucial link between the capability lens, its operationalisation and underpinning empirics. Second, current literature lacks a comprehensive and structured assessment and understanding of CASE at present, especially regarding implications for incumbent car manufacturers. Previous studies have either studied CASE in a more general fashion (Teece, 2018b; Jacobides, 2018; Pütz et al., 2019; Modi, Spulber & Jin, 2018; Fard & Brugeman, 2019), or investigated only certain CASE factors or aspects (Teece, 2019; Jiang & Lu, 2018; Perkins & Murmann, 2018; Jacobides, Macduffie & Tae, 2013; Schulze, MacDuffie & Täube, 2015; Ajanovic & Haas, 2016; Zoepf & Riggs, 2019; Firnkorn & Müller, 2012; Wesseling et al., 2015; Bohnsack, Kolk & Pinkse, 2015; Faria & Andersen, 2017; Cohen & Hopkins, 2019). Third, the CASE capabilities matrix developed in the course of this MOR debate (Teece, 2018b) has potential for being extended and therefore providing a more holistic view. This includes considering inter-firm differences, sharpening of the unit of analysis as well as creation of transparency regarding assessment process and argument structure and taking into account different contextual scenarios. The matrix is attached with Appendix 2. The paper aims to develop a holistic capabilities assessment approach applicable to incumbent car manufacturers.
The main research question guiding this paper is: How can organisational capabilities of incumbent car manufacturers be assessed holistically? Three sub-questions are considered: What are the characteristics of current influencing factors prevalent in the automotive industry? What do these imply for incumbent car manufacturers capabilities? How can organisational capabilities of incumbent car manufacturers be assessed as a basis for corporate strategy development and for analysing inter-firm differences in capabilities? The managerial practice is thus supported in strategy problem-solving in quest of finding answers to “What should I do?” or specifically “What should my strategy be?” (Brown, Bradley & McLean, 2019).
Secondary data from the 20 largest incumbent car manufacturers in terms of number of cars sold worldwide are compiled for the period past three years. These 20 car manufacturers produce about 88 per cent of vehicles sold worldwide (International Organization of Motor Vehicle Manufacturers, 2018), see Appendix 3 for details. The most recent annual reports, sustainability reports, industry reports, news based on officially confirmed data and further shareholder information from incumbent car manufacturers are considered in light of the research questions.
Apart from providing a holistic approach to assessing capabilities, the approach developed also informs managerial practice and provides application guidance through automotive examples. However, the approach has potential application to other industries. The operationalisation of assessing capabilities may be appreciated as operationalisation of the capability lens more broadly, providing key information for strategic and operative decisions. Finally, this paper provides a comprehensive CASE analysis considering implications for automakers.
This paper is organized in six sections. The first section introduces links to the previous debate in the MOR. Subsequently, an analytical framework for managing car manufacturers in a risky VUCA environment is introduced, serving as structure for this paper. Next, the CASE factors, development trajectories and their implications for automobile manufacturers are analysed. The core of the paper is the development and application of a framework for assessing capabilities of incumbent car manufacturers. A conclusion and outlook close this paper.
PRECEDING MOR DEBATE
An ongoing debate in the Management and Organization Review (MOR) discusses recent developments and disruptions in the automotive industry (Perkins & Murmann, 2018; Macduffie, 2018a; Teece, 2018b, 2019). This paper ties in particularly with a CASE capabilities matrix developed by Teece in course of this debate (Teece, 2018b). The matrix draws conclusions about capabilities of incumbent automobile manufacturers focusing on CASE. This paper complements, by taking a holistic approach, an internal perspective to the external and considers inter-firm differences of automotive manufacturers.
Recap of the Preceding MOR Debate
Based on Tesla and the Model S, Perkins and Murmann (2018) argued that any company investing one to two billion USD can design, develop and manufacture an electric vehicle (EV) in 3 to 5 years. IT Multinational Enterprises (MNEs), such as Alibaba and Tencent, are mentioned as potential new entrants and competitors to incumbents. Not acknowledged are their already serious efforts in automotive capabilities, mainly with a focus on autonomous driving (Dai, 2018; Sun, 2018; Deng, 2018).
MacDuffie (2018) responded, arguing that the competitive strength of established OEMs is stronger than described and clarifying what is (not) unique about Tesla. He points out that new car manufacturers would have to establish and master system integration capabilities. Tesla, providing the Model 3 production as an example, has not yet achieved this to the extent of established manufacturers. MacDuffie supports his argument emphasising that several new mobility service providers, such as Waymo, Uber and Lyft, stated that they are opposed to producing vehicles in the future. Underlining that their business model is much closer to that of an automotive OEM than that of an (arbitrary) IT company outside the industry. In line with Christensen’s theory of disruptive innovation (Christensen, 2013), MacDuffie believes that influential new car manufacturers will work up their way from the low-price segment.
Lu (2018) discusses entry barriers of the automotive industry focusing on China (Jiang & Lu, 2018). He elaborates why it is unlikely that today’s successful OEMs or financially strong internet companies, but rather a “new species”, may be successful in the future automotive industry. He notes that the future automobile will be produced and delivered in a new form of ecosystem requiring capabilities largely not congruent with those required and present with OEMs today. According to the paper, future success will depend on mastering service integration in EVs and managing the ecosystem around the vehicle. Why incumbent car manufacturers and IT giants already involved in the automotive industry will not be able to develop future required capabilities remains an unanswered question.
Välikangas (2018) complements that another challenge for established car manufacturers is limited management attention. She highlights, in line with Christensen (2013), the challenge of simultaneously ensuring present success and preparing for the future. Moreover, she suggests the MOR debate as starting point for discussions on entrepreneurial disruption more broadly.
Teece (2018) applied his dynamic capabilities framework, through sensing and sensemaking, analysing CASE and arising challenges for incumbent car manufacturers. He introduced a matrix for assessed traditional car manufacturers capabilities and their distance to each of the four CASE topics. The approach is based on the three dimensions technology, business model and market. He infers that the capability gaps, i.e. the distance to the CASE topics, of traditional car manufacturers are not particularly large. His bottom line is that established car manufacturers may also dominate the upcoming automotive industry transformation if they demonstrate strong dynamic capabilities and system integration skills.
Building on his prior paper, Teece (2019) subsequently focused on China as a market and origin of promising automobile manufacturers. His analysis considered Geely (EVs), Didi Chuxing (ridesharing) and Baidu (autonomous vehicles, hereafter AVs). Apart from Geely, no Chinese automotive OEM has been able to gain significant market shares in a non-Chinese car market. For foreign automotive MNEs in China, the Chinese government was highlighted as a particular (potential) challenge, restricting operations of foreign car manufacturers in the past.
Areas for Extension
Teece (2018) developed a matrix suggesting it indicates distances between present and for CASE required capabilities of traditional car manufacturers in the dimensions technology, business model and market. The matrix can be improved in several ways. I will take a look at the following elements of the analysis: reasoning for classifications, “traditional car manufacturer” (unit of analysis), “distance” (classification), “dimensions of capability distance” (horizontal axis), “CASE capabilities” (vertical axis), scope of analysis, time dimension and content.
No reasoning for classifications is provided, e.g. data sources, metrics and classification process transparency. Therefore, trustworthiness or reliability cannot be verified.
It is not clarified what a “traditional car manufacturers” is. In the business realm, “traditional” may refer to a variety of dimensions, including founding year, strategic focus, business operation, customer structure, sales or profit pools, number of employees or number of cars sold (Kley, Lerch & Dallinger, 2011; Millet, Yvars & Tonnelier, 2012; Amey, 1995). Regardless of the dimension(s) considered, no homogeneous “traditional car manufacturers” picture can be drawn. BMW and Volkswagen are examples of how apparently similar and “traditional” manufacturers have different opinions on their capabilities. BWM, led by Oliver Zipse, considers its capabilities as those of an automobile builder and announced (Zipse, 2019) to focus on the traditional automotive business of manufacturing cars. The company will also sell its shares in the ShareNow joint venture (JV) with Daimler and disengage from further MaaS business (ibid.). Volkswagen (VW) views itself as mobility facilitator and service provider and published a presentation entitled “Leadership in Mobility-as-a-Service” (Jungwirth, 2018). Then Chief Digital Officer Johann Jungwirth described how VW intends to conquer the MaaS market. These two established and presumably in every respect “traditional” automobile manufacturers perceive themselves to have already today different capabilities. Based on their differentiated strategies, those will presumably diverge even more in the future.
“Distance” as well as the gradations of near, medium and far are not specified or quantified. In theoretical models, vague classifications are being accepted (e.g. Ansoff, 1980; Schaltegger & Wagner, 2011). However, this practical application requires specification of what is considered or even quantification of gradations for unambiguity (e.g. Töytäri et al., 2011; Nissen, 2019). Distance is the difference between something and something else. The paper indicates that distances between capabilities are considered. Indirectly indicated is the consideration of present capabilities of traditional car manufacturers. Indistinct is the second distance dimension. Conceivable is that distance to the industry leader or an ideal alignment of capabilities in the sense of optimal effectiveness and efficiency may be examined. Besides considering capabilities from a productivity operative lens, capabilities may be considered through a financial lens by observing actual and optimal possible value creation.
Categorising the dimensions of capability distance as technology, business model and market without specification is problematic. “Technology” may refer to all organisational capabilities enabled by technology or certain capabilities solely to be described as technology (Bharadwaj, 2000), e.g. a Machine Learning (ML) capability. “Business model” (Chesbrough, 2010) may describe something like building cars and selling them to end-users. According to an analysis by the Boston Consulting Group (BCG), 30 per cent of OEM profits in 2017 came from the traditional components business, 35 per cent from the sales of new cars, 11 per cent from financial services, such as financing and leasing, and 24 per cent from the aftermarket business, such as sales of parts and accessories (Andersen et al., 2018:p.7), indicating the challenge of defining automakers business model straightforward. The “market” (Day, 1994) dimension may include current customers willingness to purchase, a metric difficult to concretise.
It is ambiguous what examining a CASE factor includes. All established car manufacturers offer electric, to some extent autonomous and connected cars, and partly MaaS services today (Miao et al., 2019; Athanasopoulou et al., 2019). What is an electric car capability dimension? The sum of all capabilities required to build an electric car? If so, it would be necessary to consider what is being done internally, utilising know-how and competences available. Additionally, components and expertise purchased would have to be taken into account. For example, concerning battery technology, approaches among established OEMs in the make-or-buy (MoB) decision differ greatly (Olivetti et al., 2017; Chae, Yan & Yang, 2019). Regarding autonomous and connected cars, the degree or level of autonomy or connectedness requires specification. For example, the Society of Automotive Engineers (SAE) offers autonomy levels (SAE, 2019) and McKinsey suggested concretisations of connectivity levels (Bertoncello, Husain & Möller, 2018). Personal Mobility needs specification analogously. Moreover, CASE do not occur isolated in practice but rather interact with and influence each, a decisive analysis aspect to consider. For example, MaaS can only be used with a car connected to the internet (Mahmassani, 2016). Strong adoption and financial viability of MaaS might emerge when fully autonomous electric robo-taxis are used in masses (Talebian & Mishra, 2018; Taiebat et al., 2018; Bansal & Kockelman, 2017). These kinds of synergies and their importance for analysing capabilities cannot be captured with CASE isolation.
CASE cannot be considered technologies. Rather, each factor contains technology components (Taiebat et al., 2018; Bansal & Kockelman, 2017). For example, components of autonomy are applied Artificial Intelligence (AI) utilising various technologies like ML or Vision, sensor technology, and vehicle IT architecture. Although sharing is enabled and implemented by technology bundles, itself is rather a shift in how mobility is used: consumed on-demand from a pool of available means of transport (Balasubramanian et al., 2016; Firnkorn & Müller, 2012).
The analysis scope can be questioned. Focus is purely on internal factors and external environmental factors, like regulatory and customer needs, are ignored. External factors have to be considered as they may pose a high degree of VUCA in the capabilities context (Gu, Liu & Qing, 2017; Harrison & Thiel, 2017).
The analysis does not consider a time dimension. Particularly in such a rapidly changing automotive industry (Xia, Govindan & Zhu, 2015; Nicholds & Mo, 2018), it does make a difference whether a CASE factor with certain characteristics is required by a car manufacturer in one, three or five years. Therefore, considering CASE factors time dimension is of vital importance in determining a distance or capability gap.
Although classifications and distances lack transparency, two statements are explicitly made: AVs do not constitute a change for the business model dimension, and connected cars do not lead to a change in the market dimension of traditional car manufacturers.
It can be challenged that AVs do not pose a change for the business model dimension. It is unlikely that fully autonomous vehicles (SAE, 2019) will be used in the same or a similar way with a similar utilisation like current passenger cars. Increased car productivity, i.e. capacity utilisation, likely leads to a reduction in the total number of passenger cars required and sold (Heineke et al., 2020; Tschiesner et al., 2020). Moreover, shifting market structures due to AVs will lead to changes in OEMs business models (ibid.). Conceivable is that MaaS platform operators will become the main customers of car manufacturers and provide end consumers with on-demand mobility or OEMs may offer MaaS themselves.
Questionable is also that connected cars do not lead to a change in market structures. Indeed, all new cars today are already equipped with some form of connectivity. An advanced level of connectivity, e.g. level 5 “Virtual chauffeur: All occupants’ explicit and unstated needs fulfilled by cognitive AI that predicts and performs complex, unprogrammed tasks” (Bertoncello, Husain & Möller, 2018:p.2) is unlikely not to affect the automotive market or potential customer base due to enormous additional customer benefit. Rather, an individualised offer of this or a similar form may attract customers previously using other modes of transport, such as rail or bus, which cannot be individualised to the same or a comparable extent.
Approaches, such as the reviewed CASE capability matrix, reflect the academic interest in and learning potential of the automotive industry. Although the approach and its added value for academia and managerial practice has been challenged, it provides a valuable baseline for further developments pursuing a greater degree of sharpness - a quest embraced by this paper.
MANAGING AUTOMAKERS IN A RISKY VUCA ENVIRONMENT
The current VUCA environment of the automotive industry poses risks for incumbent car manufacturers. Incumbents face challenges of developing and deploying capabilities in this rapidly and profoundly changing environment.
Characterising our world as volatile, uncertain, complex, and ambiguous (VUCA) was coined by the U.S. military in the late 1990s (Whiteman, 1998). In recent years, the acronym has been recognised academically to characterise global environments, e.g. the macroeconomic context in International Business (IB). For companies, VUCA associates business risks. Risks are known outcomes occurring with uncertain probabilities, mirroring uncertainty where outcomes are unknown (Teece, Peteraf & Leih, 2016). Effectiveness may be the entrepreneurs focus facing uncertainty, while efficiency when dealing with risk (ibid.). For this paper, VUCA is considered an external business factor resulting from entrepreneurs’ perceptions of external influences. Those include macro-environmental PESTEL (political, economic, social, technological, environmental and legal) factors and industry forces.
Competition and innovation are concepts relevant for incumbent automotive manufacturers in the VUCA context. Incumbents competitors are increasingly becoming asymmetrical in terms of origins, organisational forms, operating speeds, and degrees of efficiency and effectiveness (Millar, Groth & Mahon, 2018) increasing uncertainty. Mobility service providers such as Uber, Lyft, DiDi, Gojek, Grab, Ola Cabs and Waymo may become incumbent automakers future competitors (Clewlow & Mishra, 2017; Hensher, 2017; Teece, 2019; Financial Times, 2019). Today, their future role and profitability remain uncertain. Innovation is a key success factor in the automotive industry (Millar, Groth & Mahon, 2018). VUCA characteristics are both drivers and products of disruptive innovation (Millar, Groth & Mahon, 2018). Open Innovation, i.e. collaboration on innovations with external partners, is one approach to enhancing innovation efforts in a VUCA world acknowledging that promising ideas and talents often reside external (Bogers et al., 2019).
Management and leadership may pose great challenges to automakers in a VUCA world. It may require considering, understanding and coordinating organisational and divisional levels (e.g. financial performance, operational effectiveness, and capability usage) as well as individuals (e.g. purpose, well-being, and creativity; Millar, Groth & Mahon, 2018). The auto industries size, complexity, pace and magnitude change fires challenge. Leadership and management in such environments may thrive by testing different hypotheses about market changes and technologies surpassing detailed and rigid plans (Conger, 2004). Automakers are balancing flexibility and efficiency, e.g. concerning EVs energy storage systems. Fords global head of product development and purchasing, Hau Thai-Tang, describes incumbents innovation dilemma as “But we have a hundred-year-old home that we’re trying to update while we live in it” (Thai-tang & Schwartz, 2019:p.4), emphasising technological pace of change and future uncertainty.
Rapid changes in supply, demand, innovation or regulation are referred to as shocks (Garcia-Sanchez, Mesquita & Vassolo, 2014; Porter & Rivkin, 2000). Those might disrupt established sources of competitive advantage and profits as well as reinforce the need for repositioning of incumbents. Shocks associated with innovation may pose great opportunities and challenges. One theoretical lens for managing companies in a VUCA world is dynamic capabilities (Schoemaker, Heaton & Teece, 2018; Buckley, 2019; Millar, Groth & Mahon, 2018).
VUCA is a concept disaggregating car manufacturers environmental influences and risks. Linked to dynamic capabilities concept and building on previous approaches (Teece, 2007, 2018b), the analytical framework below guides this paper. Dashed ovals are environmental factors influencing all other elements. Dark ovals are addressed in this paper (in-scope) and connected by solid arrows. In the dynamic capabilities literature, they are all elements of the sensing capability (Teece, 2018b). Light ovals with dark writing are subsequent elements not specifically addressed in this paper (out of scope). Dotted arrows indicate their relationships.

Figure 1 Analytical framework – Dynamic capabilities in a VUCA environment
Source: Own illustration based on (Teece, 2007, 2018b).
Based on sensing the environment, (hypothesis-driven) scenarios are developed, constituting input for interpretation (sensemaking). Previous approaches (Teece, 2007, 2018b), did not link sensing and scenario building. However, environmental sensing is a central prerequisite for developing solid and insightful scenarios. Moreover, previous approaches (ibid.), ignored the influence of all VUCA dimensions on dynamic capabilities. Sensing, scenario building and sensemaking are being elaborated, focusing incumbent car manufacturers.
SENSING: CASE ANALYSIS
Since its emergence in the early 20th century, the automotive industry has had profound, largely beneficial, influences peoples live. The next ten years may encompass more change than the past 50 years (Hill, 2019; Baltic, Hensley & Salazar, 2019). Change and transformation are shaped and driven by connectivity, autonomy, sharing and electrification (CASE) – partly augmented by hydrogen-based energy supply as potentially viable add to electrification.
Academia yet lacks a comprehensive CASE understanding (Teece, 2018b; Miao et al., 2019; Cohen & Hopkins, 2019; Talebian & Mishra, 2018; Taiebat et al., 2018; Bansal & Kockelman, 2017). This paper discusses CASE shedding light on the research question focusing on the link to incumbent automobile manufacturers guided by the matrix below. The matrix is employed for examining CASE individually and aggregated promising a holistic and rich picture. The aggregated view, requiring a preceding individual CASE understanding, is considered relevant reflecting their occurrence in reality. Scenarios building and assessing implications for incumbent automakers requires considering CASE interdependencies and synergies.
Why? How? What? Past Present Future
Connectivity
Autonomy
Sharing
Electrification
CASE combination
Table 1 CASE analysis structure
Source: Own illustration.
The analytical structure considers four layers per CASE aspect. Starting with why, how and what, inspired by Simon Sinek’s Golden Circle (Sinek, 2011), definitions, analysis scope, relevance and effects are considered. CASE aspects are studied chronologically from historical context to emergence of present structures. Incumbent automobile manufacturers, their CASE strategies and stakeholders, are being emphasised. Quantifications through sales, market shares and profits are considered where applicable. An outlook, incorporating development trajectories and forecasts, indicate where the industry is heading. The analytical structure is applied in the next section in terms of (1) Why-How-What, (2) Past, (3) Present, and (4) Future per CASE component.
Connectivity
Vehicle connectivity refers to communication, sending and receiving information, between systems in and outside the vehicle’s local area network (LAN; Zohdy & Rakha, 2016). The term Connected Car is frequently used for vehicles accessing the internet and often wireless LAN (WLAN; Woo, Jo & Lee, 2015). Vehicle sensors are providing data for communication. Connectivity is considered a core element of the automotive customer experience and an enhancer or even enabler of further CASE topics.
No one widely adopted approaches to classifying and specifying vehicle connectivity exists. A classification based on communication partners (Center for Advanced Automotive Technology, 2019) considers Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), Vehicle to Pedestrian (V2P), Vehicle to Cloud (V2C) and Vehicle to Everything (V2X) communication (Abboud, Omar & Zhuang, 2016). McKinsey defined five levels of vehicle connectivity based on the degree of customer benefit (Bertoncello, Husain & Möller, 2018), inspired by the five levels of autonomy (SAE, 2019). Those reach from general hardware connectivity (Level 1), including exchange of basic vehicle usage data, to virtual chauffeur (Level 5), utilising AI to assess and meet customer needs proactively.
In 1996, General Motors launched OnStar (Barabba et al., 2002; Ettlie & Rosenthal, 2012), one of the first connected car features on the market. The subscription-based communication service aimed at improving accident response. In the following years, car connectivity adoption gradually increased, utilising improved technology. In 2001, the first remote diagnosis systems were introduced and in 2003 more advanced (turn-by-turn) navigation systems (Richter, Tomko & Winter, 2008). Widely implemented connectivity features include speed limit warnings and accident alerts. More recently, focus shifted on predictive intelligence systems (Ding et al., 2015).
Vehicle connectivity opens opportunities for creating and capturing economic, social and ecologic value. Automotive manufacturers and suppliers access already today large amounts of car data improving customer experience and the vehicle as a product (Woo, Jo & Lee, 2015; Möller & Haas, 2019). Advanced connectivity features store data about car users and create individual usage profiles, utilised by e.g. Apple CarPlay and Android Auto (Modi, Spulber & Jin, 2018; Möller & Haas, 2019). ML and other forms of AI engage with users proactive providing recommendations and enhancing available services customer-centric. Future vehicle connectivity may integrate the automobile, today largely transportation-focused, greater socially, e.g. by integrating social media. Vehicle connectivity improvements open opportunities for insurance companies. Improved individual risk profiles for usage-based insurance may tighten the link between individual risk and insurance premium (Pütz et al., 2019; Matley & Gandhi, 2016). ML may improve accident prevention by detecting fatigue symptoms indicated through conspicuous driving behaviour. Such efforts promise reduced social costs, e.g. arising from infrastructure repairs and hospital treatments (Abra et al., 2019). Connectivity may lead to improvements in the overall transport system. Technology-enabled reductions in safety distances and traffic flow optimisations could increase the number of vehicles on the road (capacity) (Duvall et al., 2019). The future of traffic and infrastructure management remains uncertain.
Vehicle connectivity also poses challenges. Data security and user acceptance remain uncertain (Heineke et al., 2020). Current development clock speeds suggest agile approaches to developing and deploying services. Automotive incumbents likely have to adapt their ecosystems and partnerships to meet new capability requirements. An overview of current partnerships is provided in Appendix 4. Continuous improvements of car usability may be enabled by regular software updates, known from our smartphones, challenging technical capabilities, like in-vehicle IT architecture, of incumbents (Apostu et al., 2019).
Autonomy
AVs are cars operating with little or no human interference. Terminologies comprise connected and autonomous vehicles (CAVs), self-driving or driverless vehicles, robo/robotic cars and robo-taxis (Miao et al., 2019; Cohen & Hopkins, 2019). Core capabilities of AVs include environmental sensing, interpreting and steering. Core components include sensors, such lidar and GPS, advanced software for interpreting data inputs, technical infrastructure, e.g. computing capacity, and adequate vehicle steering systems (Kampshoff & Padhi, 2017; Christian Gerdes, Thornton & Millar, 2019; Ding et al., 2015).
The Society of Automobile Engineers (SAE) published the accepted classification of autonomous driving levels in 2014 (SAE, 2019). It is based on the required driver attention and degree of intervention to operate the car. The six levels of autonomous driving range from a fully manual (level 0) to fully autonomous without a steering wheel and no human intervention required (level 5). Advanced autonomy may redefine the user vehicle relationship.
In 2004, the first Defense Advanced Research Projects Agency (DARPA) Grand Challenge was run (Davies, 2017). The desert race competition with autonomous (driverless) cars was one of the first impulses of the autonomous driving development era. Carnegie Mellon University’s Red Team got furthest with a converted Humvee completing nearly 12 of the 240 km long racetrack (ibid.).
Today, autonomous driving is a competitive space. Highly capital and time intensive research is conducted by well-financed start-ups, spin-offs and subsidiaries of incumbents. Promising opportunities for new entrants are rare and led to consolidation (Talebian & Mishra, 2018; Miao et al., 2019). Especially the kilometres (physically and digitally) covered autonomously are considered an indicator of progress. Alphabet Inc. subsidiary Waymo LLC is leading the field (Bayern, 2019; Financial Times, 2019). In fall of 2018, the company reported (Korosec, 2018) over ten million autonomous miles covered on public roads and over ten billion in a virtual simulator (Etherington, 2019b). Waymo announced (DeBord, 2018) its AV taxi service offering in Phoenix, Arizona, in late 2018. This milestone for autonomous driving enhanced trust and confidence of politicians and media. Alibaba, Tencent (Dai, 2018; Deng, 2018) and the majority of incumbent automakers and Tier-1 suppliers recognized the potential of AVs and invested accordingly.
The future of AVs suggests challenges to and opportunities for businesses. Insurance companies may change due to reduced accidents and decreases in insurance premiums (Pütz et al., 2019). Cities may experience drops in revenues from vehicle taxes and other fees, e.g. fines for driving violations. Set-up and operation of charging infrastructure in conjunction with increased energy consumption may challenge partied involved (Knupfer, Ramanathan & London, 2019). The increased capacity utilisation of AV may lead to fewer vehicles posing quantity challenges for car manufacturers and suppliers (Heineke et al., 2020; Tschiesner et al., 2020). Moreover, those developing AVs may face hardware and software capability gaps (Apostu et al., 2019).
Promising opportunities offset challenges. McKinsey estimates AV associated revenues could amount to $1.6 trillion in 2030 (Baltic, Hensley & Salazar, 2019). This would be more than double the combined 2017 sales of Toyota, Volkswagen, General Motors and Ford (International Organization of Motor Vehicle Manufacturers, 2018). Moreover, social benefits such as alternative use of parking spaces, productive travel time and safer roads may improve.
Sharing
In the mobility context, sharing refers to shared and/or joint use of a means of transport (Esztergár-Kiss & Kerényi, 2019; Pütz et al., 2019). In the following, sharing is considered in terms of the automobile (carsharing). Sharing changes the private car purchase and ownership to on-demand consumption transportation. Main motivators for car-sharing include shared costs, environmental advantages and social benefits (ibid.).
The first automobile sharing service was launched as community project in Zurich in 1948. It was still analogue at the time and aimed at providing cars to people who cannot afford buying one (Portland Bureau of Transportation, 2011). New technical possibilities and adoption of sharing economy in other areas, such as peer-to-peer accommodation and travel advising, fostered mobility sharing related developments.
Carsharing adoption is hardly widespread and accounts for about one per cent of total vehicle miles travelled (VMT) in the US (Meyer & Shaheen, 2017). Prominent providers today include ShareNow, a merger of BMW’s DriveNow and Daimler’s car2go, Lyft and Uber. All are struggling with profitability, especially due to driver costs involved and yet lower rates compared to traditional taxi companies (Lu, Chen & Shen, 2018; Gilibert & Ribas, 2019). Increasing the geographic coverage, number and distance of rides as well as business expansion, like delivery of goods, are levers to changing cost and revenue structures (Bösch et al., 2018).
Future potential attracted enormous investment from venture capital (VC) companies, established businesses and other financial backers. AVs promise increased ridesharing adoption due to added customer benefit and lower operating costs (Miao et al., 2019). Around $55 billion have been invested in ridesharing over the past seven years (Möller et al., 2019; GP Bullhound Research, 2019). In the US alone, the market potential is estimated to be $30 billion annually (Möller et al., 2019). Today, ridesharing costs per mile in the US are $2.5 (ibid.).
Increasing carsharing adoption impacts car manufacturers' core business. Car manufacturers are at risk of being further separated from end-users and their data (Burkacky et al., 2018), losing bargaining power over the customer interface provider. Changing vehicle requirements, like more robust, interchangeable interiors and stricter focus on vehicle costs, are uncertain (Hensley, Padhi & Salazar, 2017; Christian Gerdes, Thornton & Millar, 2019).
Electrification
Electrification refers to the shift from internal combustion engines (ICEs) to electric drives (Song & Aaldering, 2019). Lower operating and maintenance costs, quieter operation and environmental benefits are among the main motivation for electrification (Borén et al., 2017; Noel et al., 2019). Environmental impact of EVs depends on production and use emphasising the relevance of electricity generation. Energy storage, today largely lithium-based, accounts for most costs associated with EVs (Olivetti et al., 2017; Baur & Gan, 2018). Currently, EV production costs are not competitive with ICE cars (Gu, Liu & Qing, 2017; Heid et al., 2017).
First forms of EVs were introduced in the early 19th century (Meyer & Shaheen, 2017; Hauptmeier, 2010). Since, EVs experienced waves of greater and lower attention. However, electrification has not succeeded in mass-market adoption over ICEs. An EV milestone was the introduction of the Tesla Roadster in 2008 (Chen, Chowdhury & Donada, 2019; Hayes et al., 2011).
In recent years, state subsidies often subsidised a lump sum per EV (Altenburg, 2014), subsidizing lower price EVs proportionally higher. Differences in subsidies are also the main reason for great differences across geopolitical regions (Wesseling et al., 2015; Oliver Wyman, 2018). To further increase EV adoption, some cities, like Oslo and Madrid (Ajanovic & Haas, 2019), are imposing restrictions on ICE vehicles. The current dependence of EV sales on subsidies became apparent in China in 2019. A reduction in subsidies led to a massive drop in EV sales (Guan et al., 2019). Norway is leading in EV sales, driven by massive government subsidies. In 2019, 42 per cent of all new vehicles sold in the country were electric – a considerable increase on the already high 31 per cent in 2018 (Holter, 2020). EV sales, still inferior to ICE cars, are rising at rapid pace. In 2017, 1.3 million electric cars were sold worldwide. Forecasts suggest a rise to 3 million by late 2020 (Baltic, Hensley & Salazar, 2019).
Incumbent automakers invest heavily in EVs. Since 2010, investments of $19 billion in EVs and charging, as well as $14.3 billion in battery technologies, have been disclosed (Möller et al., 2019). Moreover, incumbents announced around 300 new EV models until 2025 (ibid.). Manufacturers of EVs invest in optimising production and models fully geared to electric propulsion (native electric vehicle). However, a production cost gap between ICE cars and EVs of around $8000 for comparable models remains (Baltic, Hensley & Salazar, 2019). Volkswagen introduced the modular electric drive matrix (MEB, German: “Modularer E-Antriebs-Baukasten”) platform (Volkswagen AG, 2019) to improve its cost structure. This platform serves as basis for several models – an established approach for ICE cars. Volkswagen opened the MEB to other manufacturers, such as Ford (Etherington, 2019a).
Looking ahead, developments like decreasing production cost gaps and learning curves in R&D and manufacturing may benefit EV sales posing challenges for incumbents to adapt business models, organisations and operations. Hydrogen fuel cell adoption as a potentially promising energy storage solution remains uncertain (Ajanovic & Haas, 2019).
CASE Combination
Motivators for CASE (Pütz et al., 2019) compared with those for the ICE car introduction over 100 years ago are similar: lower transport costs and improved customer experience. However, today focus shifted towards ecological and social sustainability emphasised by increased numbers of sustainability reports in recent years (Olawumi & Chan, 2018; Gusmão Caiado et al., 2018; Landrum & Ohsowski, 2018).
Combining the CASE threads may change the way we think about cars and transport. Future mobility seems to be more interconnected, smarter and better integrated into the everyday life of users (Grazia Speranza, 2018; Pütz et al., 2019). Developments suggest that the automobile has to regain status of being “a car for every pursue and purpose” as former GM president Alfred Sloan once coined (Dale, 1956:p.46).
CASE aggregation has implications for car manufacturers. Competitive structures and modes of operation appear to be breaking up, shifting from a hierarchical waterfall-oriented manufacturer-supplier relationship to agile forms of collaborating and competing in ecosystems (Hill, 2019; Jacobides, Macduffie & Tae, 2013; Chen, Chowdhury & Donada, 2019). Changes are impacting revenue sources, cost structures, operational efficiency, user experience, innovation, security and sustainability. Future automotive revenue pools may rise to $5.5 trillion in 2015 and $7.7 trillion in 2030. $4.3 trillion (56 per cent) of the later may come from disruptive sources like CASE (Dhawan et al., 2019). “If I had asked people what they wanted, they would have said faster horses.” (Vlaskovits, 2011) is a quote attributed to Henry Ford highlighting the challenge of disruptive innovation like CASE. Car manufacturers may avoid breeding horses in the age of cars.
SCENARIO BUILDING
Scenario building, the concretisation of potential future developments, is a success critical tool for strategic management of companies in risky VUCA environments (Heinonen et al., 2017; Sharif & Irani, 2017). Considering time and degree of VUCA, three cases for assessment capabilities may provide managerial insights relevant to business strategy and operation. Table 2 below considers these three aspects, providing a framework for simulating alternative scenarios of future developments by internal groups, such as the business development department, or external parties, such as consultants or market analysts. Deciding for a case provides the baseline for developing scenarios, answering: What is the required time dimension for this analysis? What degree of VUCA should be incorporated in this analysis?
Case no. Cases for assessing capabilities (time dimension) Degree of VUCA
#1 Present capabilities in the present context Lowest
#2 Present capabilities in an assumed future context Medium
#3 Assumed future capabilities in an assumed future context Highest
Table 2 Cases for assessing capabilities – time and VUCA dimensions
Source: Own illustration.
Present or future organisational capabilities may be considered by firm internal or external stakeholders for strategic and operational decisions in present or future context, involving varying degrees of VUCA. Future scenarios may be extrapolations of developments adjusted for assumed future influences providing the management of an enterprise with potentially valuable insights about what future value existing capabilities have. Modelling future scenarios involves incomplete and imperfect information and therefore a latitude for interpretation with respect to bounded rationality (Simon, 1962; Williamson, 1975; Amit & Schoemaker, 1993; Kahneman, 2003) and subjectivity (Ma, 2016; Amit & Schoemaker, 1993; Schubert, Baier & Rammer, 2018). These factors are particularly relevant for high degrees of VUCA, irrespective of any attempts of quantification and objectification.
Quantifications can be particularly helpful when assessing organisational capabilities in making more informed and deliberate business decisions. For example, numerical scales can be used to quantify the state of development of capabilities. Value add of capabilities may be expressed financially, e.g. expressed by Economic Value Added (EVA) over the cost of capital (Gupta & Sikarwar, 2016; Mielcarz & Mlinarič, 2014).
In essence, modelling of the future includes two steps (Goerlandt, Ståhlberg & Kujala, 2012; Bañuls, Turoff & Hiltz, 2013). First, factors influencing the development are identified to model future capabilities and external development scenarios. Second, the degree of influence or degree of development of these internal and external factors is estimated over time. Definition of different scenarios, like best, worst and most likely, helps to illustrate the range and probability of possible developments. Digital tools, like Microsoft Excel or more dedicated modelling software such as Carta, support this exercise (Ulrich & Smallwood, 2004).
Automotive-specific external influencing factors for modelling future scenarios of incumbent car manufacturers include assumed future stakeholder needs and regulations (Hannon et al., 2019; Arena, Spera & Laguardia, 2017; World Economic Forum, 2018). Car emission regulations have been a powerful means of exerting pressure on automobile manufacturers, influencing their strategic orientations considerably. Since automobiles are large, heavy, fast-moving and potentially dangerous objects in public spaces (MacDuffie & Fujimoto, 2010), it can be assumed that it will take some time to agree on definitive regulations for AVs, providing a framework for car manufacturers and further stakeholders to operate.
For the application of the proposed extended framework in this paper, considering present capabilities in a future context is deemed to be suitable for assessing capabilities of incumbent car manufacturers. This is because current capabilities are the tangible starting point for a first analysis of capabilities and identification of potential future capability gaps. The consideration of future development scenarios is essential, as the future of the automotive industry will be different from the present in terms of actors, roles and CASE factors involved (Miao et al., 2019; Cohen & Hopkins, 2019; Pütz et al., 2019; Talebian & Mishra, 2018). At a later stage, a tangible example of a future scenario will be developed to apply the proposed approach for assessing capabilities. For this purpose, the following chapter provides the foundation and context of car manufacturers.
SENSEMAKING: INCUMBENT AUTOMAKERS CONSTRAINTS
Sensemaking (Schilke, Hu & Helfat, 2018; Teece, Pisano & Shuen, 1997; Teece, 2018b) is employed to perceive and interpret current CASE developments from incumbent car manufacturers perspectives to determine constraints they are facing. Constraints demonstrate why an assessment of capabilities of incumbent car manufacturers on an organisational level is useful. The capability assessment developed in the next section concretises constraints in terms of present capabilities, future capability requirements and identifies capability gaps.
OEMs future role concerning CASE involves challenges and risks. Taking a position beyond the role of an automobile supplier could enable OEMs to capture a greater share of the value chain (Lejarraga et al., 2016; Songthaveephol & Mohamad, 2020; Balasubramanian et al., 2016). This may be appealing to all OEMs. How beneficial is the starting position of incumbent OEMs to operate successfully in fields beyond automobile (hardware) provision? What hurdles do OEMs face in comparison to, for example, new technology players? Three factors may be particularly prevalent when considering these and related issues: talent pool, corporate culture and required investment capital.
A change in talent pool – the sum of all skills within a company (Collings & Mellahi, 2009) – is necessary due to various developments (Lytle, Skarbek & Robinson, 2019). On the one hand, this includes a change in the drive train from diesel and gasoline engines to electric drive. The latter is associated with new skill requirements and lower value-added by OEMs (Hayes et al., 2011). On the other hand, the automobile as a product is becoming more digital. Car manufacturers will have to accelerate, especially regarding software development capabilities (Apostu et al., 2019).
The corporate culture – reflected in beliefs and behaviours (Schmid & Grosche, 2008) – poses a further challenge. Historically, in the “traditional” automotive business, no such fundamental changes in the business model and ways of operating occurred (Rao, 2009). For example, agile approaches are required (Hill, 2019) to deliver digital solutions effectively and efficiently contrasting established waterfall approaches.
A third challenge is the capital intensity of operating and competing in advanced CASE markets (Möller et al., 2019). The conventional and long-established automotive business is characterised by long cycles and high capital intensity (Lejarraga et al., 2016). Developing digital solutions, such as autonomous driving, is highly capital intensive (Bösch et al., 2018; Cohen & Hopkins, 2019), challenging automotive incumbents while maintaining their core business.
Those three constraints relate to a concept Clayton Christensen describes as “The Innovator’s Dilemma” (Christensen, 2013). The concept explains why established companies may fail while apparently doing everything right. It builds on the premise that innovations often undergo an S-shaped development over time in terms of their performance. This entails that innovations often perform less well in their initial phase and deliver less added value to the customer than their established counterparts. Thus, innovations may be less interesting for established companies than improvements in the existing portfolio. However, in the middle part of the innovations S-progression, there is a disproportionate value-added increase and the innovation outperforms iterative improvements of the established offering. Clayton Christensen describes this as a challenge for established companies, independent of their industry, as they often have to meet certain short-term performance metrics, such as sales volumes and profit targets. Furthermore, they have obligations through existing liabilities and, unlike some new entrants, may not be able to cope with lean periods in early innovation stages (ibid.).
Extended Conceptual Framework for Assessing Automakers Capabilities
The constraints outlined above highlight the challenges facing automotive manufacturers in terms of capabilities and change. This section introduces a conceptual framework that extends Teece’s “CASE capabilities matrix” and enables the identification of future capability gaps in a holistic way. It serves as a basis for determining suitable strategies and implementation on the quest for sustained value creation for automakers (Achtenhagen, Melin & Naldi, 2013).
The framework for identifying untapped capabilities gaps is based on two dimensions. On the one hand, a present internal and external dimension of the company is assessed. The external dimension is relevant because it directly affects internal factors, such as external emission regulations affecting car engine choices and thereby required internal R&D and production capabilities (Gu, Liu & Qing, 2017; Harrison & Thiel, 2017). On the other hand, future or future required internal and external factors are considered based on a specific future scenario. Comparing the two assessed dimensions identifies capability gaps. An outline of the matrix is illustrated below.

Table 3 Conceptual framework for assessing capabilities (outline)
Source: Own illustration.
Application of the structure for identifying capability gaps of incumbent car manufacturers identifies aspects requiring further investigation. Particularly the internal analysis dimension is concretised by a more detailed breakdown of the analysis units. Technological and non-technological organisational capabilities are to be considered (Wheeler, 2002). The differentiation between these two types is relevant, as they involve different requirements on and challenges for the company. Technological capabilities may be structured according to the four CASE topics. Specific forms of organisational capabilities, such as innovation capabilities, integrative capabilities (Helfat & Raubitschek, 2018) and managerial cognitive capabilities (Helfat & Peteraf, 2015), are discussed in the literature. Moreover, strategic factors such as business model and market (Teece, 2018b; Wheeler, 2002) are to be included in the analysis. These strategic factors need to be tailored to the specific scenario. Furthermore, external influences on the company need investigated. The degree of VUCA may be a helpful indication of the magnitude of external influence. Capability gaps result from relative levels of current and future factors, indicated by arrows in Table 4 below.

Table 4 Conceptual framework for assessing capabilities (detailed)
Source: Own illustration.
Three further aspects have to be considered to apply the framework. First, transparency and clarity of the assessment process and data need to be provided to ensure retractability and reproducibility (Aguinis & Solarino, 2019). One way to achieving this is introduced later in this study by a holistic and integral implementation approach. Second, and linked to the first point, transparency about the “distance” and concretisation or even quantification (Töytäri et al., 2011) of the evaluation scales needs to be ensured. Third, the unit of analysis “traditional automobile manufacturer” also requires specification. This paper considers the 20 largest car manufacturers in terms of volume of production. Considering those automakers enables a clear classification based on production data published by the International Organization of Motor Vehicle Manufacturers (2018). In 2018, the top 20 car manufacturers employed approximately 75 per cent of all employees working at car manufacturers worldwide, and about contributed with about 88 per cent of the global vehicle production volume (ibid.).
The proposed framework for identifying capability gaps offers an holistic approach and simultaneously provides new angles of analysis not consider before (Teece, 2018b). First, the time dimension is considered using scenarios, as timing is crucial in implementing strategic and operational measures to achieve a desired outcome. Second, capabilities are broken down into technological and non-technological. Third, the consideration of CASE topics is sharpened by decomposing them down into technical and non-technical capabilities, and thereby achieving a more realistic picture of factors underlying strategic decisions by automakers. Fourth, the analysis includes not only internal aspects and not only CASE but also further aspects and external VUCA context. These external factors, such as customer demand, can have a decisive influence on the development of particular business areas. However, the internal dimension forms the core of the analysis. Combining both dimensions advances research on organisational capabilities due to the more realistic incorporation of influences, compared to considering just one of them.
Application of the Framework
The conceptual framework introduced for identifying capability gaps is exemplary applied and tested in the following. One incumbent car manufacturer among the top 20 is the unit of analysis. Assessments were made based on realistic assumptions derived from a review of the annual reports of the 20 largest incumbent car manufacturers. The future scenario “In ten years, the car manufacturer analysed will become a leading mobility service provider owning and offering fleets of connected autonomous vehicles (CAVs).” is considered. Assessments of factors to be considered are fictitious based on plausible assumptions. A more sophisticated assessment approach for implementation is introduced in the subsequent section. The application is illustrated below.

Table 5 Conceptual framework for assessing capabilities (applied)
Source: Own illustration.
The exemplary application of the framework highlights various capability gaps and future shortcomings in additional analysis dimensions. In the example and indicated by “(…)” a surplus of capabilities in system integration and leadership arises (Schulze, MacDuffie & Täube, 2015; Xia, Govindan & Zhu, 2015). The application also partly reflects the three constraints, talent pool, corporate culture and required investment capital of incumbent car manufacturers discussed earlier.
Outline for a Holistic and Integral Implementation of the Framework
How does a suitable approach for assessing the analysis aspects, capabilities in particular, look like? An approach for a holistic and integral implementation of the introduced framework is proposed in the following. Holistic (Kley, Lerch & Dallinger, 2011; Nicholds & Mo, 2018; Teece, 2018a) and integral refers to inclusion of internal and external perspectives of the company and consideration of different areas and hierarchical levels within the company concerned. Internal refers to considering the perspectives of employees of the company while external incorporates perspectives of experts outside the company.
The review of the precedent CASE capability matrix (Teece, 2018b) and the CASE analysis revealed that incumbent automakers differ in adopting the “right” CASE strategy. Internal capabilities often are the reason for pursuing a certain strategy (Zipse, 2019; Volkswagen AG, 2020; Daimler AG, 2019; Toyota Motor Corporation, 2019) highlighting the need for a transparent and clear capabilities assessment approach. Figure 2 below offers an approach to implementing the capabilities assessment approach using primary data collection for the purpose of assessment.
The approach involves the five steps defining level and unit of analysis, specifying the capabilities to be analysed, primary data collection, analysis, as well as interpretation and action plan.

Figure 2 Capabilities assessment process
Source: Own illustration.
The proposed five-step process provides transparency and clarity in the assessment of organisational capabilities based on primary data. The process starts with an unambiguous definition of what will be analysed (Ulrich & Smallwood, 2004). Data is collected from both internal and external stakeholders (de Bakker & Nijhof, 2002) to ensure a holistic and integral assessment. This facilitates objectivity, which is particularly relevant for inter-company comparisons. Qualitative and quantitative data appear appropriate, considering the complexity and multiplicity of the assessment. Current as well as future and future required capabilities and further analysis aspects are assessed. Scales for quantifying the assessment and sharpening the capability-related distance and gaps are useful in data collection.
In the analysis phase, on the one hand, internal company data is assessed in order to identify future capability gaps. On the other hand, if data is available, inter-company comparisons may reveal the position relative to the competition and uncover inter-firm efficiency differences (Lieberman & Dhawan, 2005).
Measures from the company’s point of view are derived in the interpretation of the results to prevent future gaps in capabilities (Ulrich & Smallwood, 2004) and to strengthen the company’s competitive position.
The approach introduced above focuses on primary data due to the strong individuality of the analysis. Nevertheless, the use of published data is considered a relevant indicator and suitable supplement – especially for the assessment of future scenarios. This includes annual reports from automobile manufacturers and reports on industry-wide developments.
CONCLUSION AND OUTLOOK
Capabilities as a source of sustained competitive advantages and sustained profits are an established lens in strategy research and strategic management (Schilke, Hu & Helfat, 2018). The concept has been criticised since many years for its difficult operationalisation, high level of abstraction and lack of empirical underpinning (Priem & Butler, 2001; Easterby-Smith, Lyles & Peteraf, 2009; Peteraf, Di Stefano & Verona, 2013). Capabilities assessment is a central link in the nested system of individuals, organisations and industries (Trejo et al., 2002). Based on previous publications, this study extends the Teece (2018b) framework on CASE capabilities of automakers. It demonstrates why a holistic and more nuanced approach to assessing capabilities, taking into account firm internal and external influences, is beneficial, especially for analysing industries that are disintegrated into global value chains, such as incumbent car manufacturers.
The case of the automotive industry is used to operationalise the study of capabilities at corporate level. The comprehensive and structured CASE analysis performed focuses on implications for incumbent car manufacturers. The proposed approach to capabilities assessment was developed offering insights into capabilities, capability gaps and inter-firm differences of incumbent car manufacturers. In contrast to previous studies (Wheeler, 2002; Achtenhagen, Melin & Naldi, 2013; Teece, 2018b), capabilities are considered holistically and with regard to the dimensions relevant for corporate strategy development. The study provides a basis for future practical applications of the capabilities assessment approach in the automotive industry and beyond.
In terms of managerial implications, the approach proposed makes it is easier for managers to understand academic findings and implement them in their organizations to support strategy. Especially in the high-risk VUCA environment of today’s automotive industry (Miao et al., 2019; Pütz et al., 2019; Talebian & Mishra, 2018; Cohen & Hopkins, 2019), a transparent and structured approach to assessing capabilities is essential to meet challenges such as innovation and change. More broadly, the approach can be understood as an operationalisation of the capabilities concept for management practice. Furthermore, the paper offers automotive managers insights into current CASE developments focusing on current strategies of and implications for incumbents.
Testing the holistic and industry-wide approach offers a promising opportunity for future research. Insights into precious (dynamic) capabilities of automakers could provide a more nuanced picture of inter-firm differences of organisational capabilities in the CASE context. Beyond, it is worth considering to what extent the approach introduced could be utilised in other industries, such as financial services or consumer goods. Moreover, the improved understanding of capabilities may be linked to other automotive studies. Those include ethnic aspects of AVs (Keeling et al., 2019), their design (Christian Gerdes, Thornton & Millar, 2019), policies (Macduffie, 2018b) and influences on the labour market (Mudge et al., 2018). Great change in the automotive industry is ahead, opening opportunities to learn more about our RBV of the firm.

APPENDIX
Appendix 1: Automotive value chain
Appendix 1.1: Traditional automotive value chain

Source: Seeberger, 2016:p.69.

Appendix 1.2: New electric vehicle automotive value chain

Source: Own illustration informed by Olivetti et al., 2017; Ganguli, Burns & Goldsberry, 2016; Balasubramanian et al., 2016; Roland Berger, 2015; Kampshoff & Padhi, 2017; Lejarraga et al., 2016.
 
Appendix 2: Teece CASE capabilities matrix

Source: (Teece, 2018b).
 
Appendix 3: Overview of incumbent car manufacturers and production figures
WORLD MOTOR VEHICLE PRODUCTION
OICA correspondents survey
WORLD RANKING OF MANUFACTURERS
Year 2016 Year 2017
Rang GROUP SUM SUM # employees
Total 94,020,883 96,922,080 4,202,000
1 TOYOTA 10,213,486 10,466,051 365,000
2 VOLKSWAGEN 10,126,281 10,382,334 656,000
3 HYUNDAI 7,889,538 7,218,391 120,000
4 G.M. 6,971,710 6,856,880 173,000
5 FORD 6,457,773 6,386,818 200,000
6 NISSAN 5,556,241 5,769,277 139,000
7 HONDA 4,999,266 5,236,842 216,000
8 FIAT 4,681,457 4,600,847 199,000
9 RENAULT 3,373,278 4,153,589 183,000
10 PSA 3,152,787 3,649,742 184,000
11 SUZUKI 2,945,295 3,302,336 45,000
12 SAIC 2,564,786 2,866,913 23,000
13 DAIMLER AG 2,526,450 2,549,142 299,000
14 B.M.W. 2,359,756 2,505,741 135,000
15 GEELY 1,266,456 1,950,382 80,000
16 CHANGAN 1,715,871 1,616,457 50,000
17 MAZDA 1,586,013 1,607,602 46,000
18 DONGFENG MOTOR 1,315,490 1,450,999 150,000
19 BAIC 1,343,682 1,254,483 20,000
20 MITSUBISHI 1,091,500 1,210,263 30,000
21 SUBARU 1,024,604 1,073,057 34,000
22 GREAT WALL 1,094,360 1,041,025 64,000
23 TATA 925,205 932,387 83,000
24 IRAN KHODRO 636,000 710,869 54,000
25 SAIPA 531,000 648,324 48,000
26 MAHINDRA 605,376 612,595 43,000
27 ISUZU 654,480 612,421 8,000
28 CHERY 631,454 605,331 17,000
29 FAW 557,174 592,688 132,000
30 GAC 384,937 513,870 5,000
31 ANHUI JAC AUTOMOTIVE 651,291 493,199 9,000
32 BYD 510,572 421,590 220,000
33 BRILLIANCE 464,210 362,166 4,000
34 HUNAN JIANGNAN 335,585 315,363 1,000
35 CHINA NATIONAL HEAVY DUTY TRUCK 199,941 296,594 1,000
36 CHONGQING LIFAN MOTOR CO. 278,389 214,145 1,000
37 SHANNXI 116,034 189,066 23,000
38 ASHOK LEYLAND 145,434 160,208 12,000
39 SOUTH EAST (FUJIAN) 114,515 159,473 1,000
40 PACCAR 139,605 153,405 25,000
41 CHANGFENG 88,888 135,682 4,000
42 RONGCHENG HUATAI 84,621 132,511 3,000
43 TESLA 83,922 101,027 45,000
44 HAIMA CARS 152,980 94,932 3,000
45 GAZ 87,207 88,902 2,000
46 CHENGDU DAYUN 59,298 79,737 2,000
47 NAVISTAR 48,447 68,258 12,000
48 ZHENGZHOU YUTONG 71,192 67,231 20,000
49 PROTON 73,400 67,170 12,000
50 LEYLAND TRUCKS 14,729 59,795 1,000
Source: OICA 2017 production statistics (International Organization of Motor Vehicle Manufacturers, 2018). 
Appendix 4: Partnerships of car manufacturers for sharing and autonomous driving
Appendix 4.1: Partnerships of car manufacturers (1)

Source: Center for Automotive Research (Fard & Brugeman, 2019:p.3).

Appendix 4.2: Partnerships of car manufacturers (2)

Source: Center for Automotive Research (Modi, Spulber & Jin, 2018:p.33).
 
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