ESIC Market


Review of the measurement of Dynamic Capabilities: a proposal of indicators for the automotive industry

Pablo Martínez de Miguel

Universidad Rey Juan Carlos

Antón García Martínez

Universidad Córdoba

José-Luis Montes-Botella

Departamento de Economía Aplicada I

Universidad Rey Juan Carlos

Received: 10-10-2021; Accepted: 09-12-2021; Published: 31-03-2022


Objective: The objective of the paper is to identify relations between digital transformation and the micro-foundations of the dynamic capabilities within the automotive sector.

Methodology: To achieve the previous goal, the analysis is based on a literature review and expert judgments through a survey. Then, from a quantitative methodology of exploratory analysis the correct assignment of the indicators as well as a SEM analysis of structural equations with latent variables as a statistical technique has been used.

Results: Therefore, using the indicators already presented, it has been possible to establish the relationship model. We have been able to present how all these indicators correspond to dynamic capabilities, and it is the digital transformations that generate them.

Limitations: the research presents some limitations that should be considered when contextualizing the work done. The most representative one is the difficulty of obtaining a larger sample, because out of 142 surveys, only 42 responses were obtained, due to the limited time respondents had to attend to the researcher.

Practical implications: the automotive industry is continuously impacted with the introduction of new technologies, which makes it necessary for organizations to adapt to the fast pace of growth. Furthermore, companies that understand the importance of digital transformation show more modern work styles, consider user preferences and the information they can obtain from the context.

Keywords: digital transformation; dynamic capabilities; automotive industry; competitive advantages; innovation capabilities

JEL codes: O15; J14; L26



研究目标:: 本研究的目标是确定数字化转型与汽车行业动态能力的微观基础之间的关系。

分析方法:: 为实现本研究的目标,本分析基于文献回顾和专家判断的调查结果。 随后,通过探索性分析的定量方法,正确使用了指标的分配,以及用潜在变量的结构方程的 SEM 分析作为统计技术。

研究结论:: 因此,基于已经提出的指标,建立关系模型。 已及在其模型的基础上展示这些数字化转型产生的指标如何与动态能力相对应。

研究局限:: 本研究的局限性,最具代表性的是难以获得更大的样本,由于受访者参加调查的时间限制,在 142 次调查中仅获得了 42 份答复。

实际应用:: 汽车行业不断受到新技术引入的影响,因此组织有必要适应快速增长的步伐。 此外,了解数字化转型重要性的公司表现出更现代的工作方式,考虑用户偏好以及他们可以从环境中获得的信息。


JEL 分类号: O15; J14; L26

1. Introduction

The automotive industry is an important driver of growth and prosperity worldwide, due to its social contribution, by facilitating people’s mobility in an efficient, safe and affordable way, and its economic contribution, as a driver of innovation, a generator of quality employment and a pillar of international trade. In the case of Spain, it has become a mainstay of our industry and a benchmark on a global scale, thanks to a large production capacity and high productivity, derived from a skilled workforce and a high level of plant automation. The economic crisis generated by the pandemic has taken its toll on a sector that is in the midst of a technological transformation towards electrification (Montoriol-Garriga & Díaz, 2021)

Organizations need to be able to reconfigure themselves in the face of such a competitive market. These Dynamic Capabilities (DC) allow organizations to feel and shape opportunities and threats, seize opportunities and maintain competitiveness through the enhancement, combination, protection, reconfiguration of intangible and tangible assets (Rotjanakorn et al., 2020).

Besides, the need for companies at critical and unexpected moments, such as the one we are currently experiencing as a result of the Covid-19 pandemic, to develop processes that allow operating in the virtual context is being one of the motivations of this work. In addition, 57% of automotive executive directors believe they will not recover pre-covid turnover until after 2022 (KPMG, 2021)

Dynamic capabilities enable business enterprises to create, deploy and protect the intangible assets in a sustainable performance (Teece, 2009). For analytical purposes in the paper, we are going to disaggregate dynamic capabilities into the capability of sensing and shaping opportunities and threats, seizing those opportunities and to maintain competitiveness through innovation. Considering the investments the automobile industry has done in digital transformation in the last decades (Faconauto, 2019 special edition about mobility and connectivity), determining whether the degree of digital transformation produces an improvement in financial results, greater customer satisfaction and therefore allows obtaining a sustainable competitive advantage over time is key.

The objective of the paper is to identify relations between digital transformation and the micro-foundations of the dynamic capabilities within the automotive sector. The paper aimed at investigating the nature of building dynamic capabilities for business model innovation towards the ongoing digital transformation. These benefits are in line with those proposed by De Pablos and López (2012), although the author also refers to the importance of information as an asset, the process of change in companies and organizational learning.

After the introduction, the following is a conceptual framework where we will conduct a full review of the literature on the measurement of these capabilities to identify indicators specific for automotive sector. Then, a structural equation model (SEM) has been built on to contrast the relationships between the indicators and the latent variables. To finalize, a discussion on how these capabilities will allow obtaining knowledge about each operation compared with the average and with the best practices.

2. Conceptual Framework

The analysis of the literature carried out in this paper allows us to corroborate that there are few previous written works that relate the digital transformation and the dynamic capabilities applied to the automotive sector. However, there are studies that show that digital transformation helps to create value (Reddy & Reinartz, 2017), and academic articles that confirm the influence that digitalization has on innovation processes in companies (Rachinger et al., 2018).

Based on this, we propose the following research design that will be addressed through the Figure 1.

Figure 1. Research design

Source: Own source, 2020

2.1. Digital Transformation

With the rise of new digital technologies such as artificial intelligence (AI), internet of things (IoT), mobile and social internet, blockchain and big data, firms in almost all industries are conducting multiple initiatives to explore and exploit the benefits of these technologies (Fitzgerald et al., 2013; Ross et al., 2017). This necessarily entails transformations of key business operations and affects products and processes, as well as organizational structures and management concepts to conduct these complex company-wide transformations (Matt et al., 2015)

Meanwhile, the society is facing fast and radical changes due to the maturation of digital technologies and their power to penetrate markets rapidly, while customers’ demands are increasing, and organizations face tougher competition due to globalization (Bharadwaj et al., 2013; Li et al., 2018).

Warner & Wäger (2019) have remarked that digital transformation is inconsistently used by leaders within and across the industries to describe various strategizing and organizing activities, which reaffirms the importance of defining this concept, as no formal categorization exists in academic literature and the boundaries of the term are often blurred (Reis et al., 2018)

The Table 1 shows a full overview of typical definitions according to the literature.

Table 1. Definitions of Digital Transformation



Gaiardelli et al. (2011)

Digital transformation is a process of reinventing and reengineering a business to digitize a company. This transformation is the deliberate and continuous digital evolution of a company’s business model, strategically, tactically and operationally.

Schaible and Bouée (2015)

It is a process of changing the way people think, in which organizations must discover the opportunities provided by the modern world in terms of digitalization.

Matt et al. (2015)

Transformation strategies focus on the transformation of products, processes and other organizational aspects, including user interaction with technology as an integral part of the product or service.

Organizational aspects, which include user interaction with technology as an integral part of the product or service and allows defining products, services and business models jointly.

Heilig et al. (2017)

It is only an application of ICT to the organization’s processes.

Crespo and Pariente (2018)

It is the management process that directs the culture, strategy, methodologies, and capabilities of an organization based on digital technologies.

Morakanyane et al. (2017)

Comprehensive Understanding of Digital Transformation based on Evolutionary Process, Digital Capabilities, Digital Tecnologies busines models, operational processes and customer experiences and Value Creation

Linares (2018); Sánchez (2015)

It arises with a change in the way of thinking, which requires a modification in the culture, structure and operations of the organizations.

Skog et al. (2018)

It is total disruption and chaos in the business world.

Source: Own source, 2020

2.2. Dynamic Capabilities

Teece et al. (1997, pp. 509-510)) defined dynamic capabilities as “the ability of an organization to integrate, build and reconfigure internal and external competencies to address exceedingly changing environments”. A capability to be strategic, it must cover a customer need (so there is a source of income), it must be unique (so the products/services produced can be assigned a price without considering too much the competition), and difficult to replicate (so the benefits will not have competition). Table 2 indicates the definition of Dynamic Capabilities.

Table 2. Definitions of Dynamic Capabilities



Collins (1994)

Ability to innovate and develop quickly.

Helfat (1997)

Competencies or capabilities that the company has to create new products and thus respond to changing market demands.

Teece et al. (1997)

Ability of the organization to integrate, build and reconfigure internal and external competencies for immediate application to changing environments.

Eisenhard and Martin (2000)

Strategic habits of the organization by means of which it makes new configurations of its resources to keep pace with the market.

Zahra and George (2002)

These are the capabilities of organizations to re-deploy and reconfigure their resource bases to meet customer demands and to cope with competitive demands.

Winter (2003)

Capabilities to act and thus expand, modify or generate extraordinary capabilities.

Vivas (2005)

These are complex, high-level organizational processes that create the appropriate conditions for modifying and renewing the organization’s assets.

Peláez et al.(2008)

Coordination of internal and external competencies, in order to adapt the organization to a rapidly changing environment

Tondolo et al. (2015)

It is the creation, renovation or integration of resources, assets, capabilities, competencies and routines that will allow the company to keep pace with the changes offered by the competitive environment.

Teece and Leih (2016)

These are high-level activities that allow a company to focus on the production of goods and services that already have or may have a high market demand.

Rotjanakorn et al. (2020)

The ability of an organization to change and modify the current resource base through exploration.

Source: Own source 2020

2.3. Digital Transformation, Dynamic capabilities, and Automotive sector.

The objective of the part was to identify the relations between digital transformation with the micro-foundations of the dynamic capabilities (sensing, seizing and innovation) within the automotive sector.

The increasing pace of digital technology development affects and brings major changes to all industries (Schwertner, 2017). The emergence of digital innovations is accelerating and intervening existing business models by delivering opportunities for new services. Drawing on the automotive industry, leading trends like self-driving cars, connectivity and car sharing are creating new business models. These are simultaneously giving rise for innovative market entrants, which begin to transform the automotive industry (Llopis et al., 2021).

We have developed table 3, a synthesis of the found literature is made:

Table 3. Research result: Digital Transformation, Dynamic Capabilities and Automotive sector




Digital Transformation


Cohen and Schmidt (2013)

Gaiardelli et al. (2011)

Schaible and Bouée (2015)

Warner and Wäger (2019)

Loonam et al. (2018)

Reis et al. (2018)

Fitzgerald et al. (2013)

Ross et al. (2017)

Matt et al. (2015)

Bharadwaj et al. (2013)

Li et al. (2018)

Kaufman and Horton (2015)

Schuchmann and Seufert (2015)

Hess et al. (2016)

Abdelaal and Zaki (2018)

Venkatraman (1994)

Vendrell-Herrero et al. (2016)

Nochta et al. (2019)

Hanelt et al. (2015)

Rijswijk (2020)

Digitalization, digitization, and digital transformation

Kääriäinen et al. (2017)

Brennen and Kreiss (2014)

Bloomberg (2018)

Stolterman and Fors (2004)

Henriette et al. (2015)

Jacobi and Brenner (2017)

Schwertner (2017)

Elements of the Digital Transformation: IoT, Artificial Intelligence and Big Data

Ashton (2009)

Gubbi et al. (2013)

Mukherjee et al. (2017)

Makridakis (2017)

Schwab (2016)

McAfee et al. (2012)

Dynamic Capabilities


Henderson and Cockburn (1994)

Kogut and Zander (1992)

Teece et al. (1997)

Qaiyum and Wang (2018)

Bendig et al. (2018)

Roy and Khokle (2016)

Karimi and Walter (2015)

Kevill et al. (2017)

Rotjanakorn et al. (2020)

Helfat et al. (2007)

Ambrosini and Bowman (2009)

Huang and Li (2017)

Cezarino et al. (2019)

Akram and Hilman (2018)

Tondolo et al. (2015)

Schwertner (2017)

Wagner and Wäger (2019)

Eisenhardt and Martin (2000)

Bharadwaj et al. (2013)

Teece (2007)

Kindström et al. (2013)

Fisher et al. (2010)


Bendig et al. (2018)

Roy and Khokle (2016)

Kevill et al. (2017)

Teece (2007)

Dixon et al. (2014)

Kindström et al. (2013)

Helfat and Peteraf (2015)

Battisti and Deakins (2017)


Teece (2007)

Helfat and Peteraf (2015)

Roy and Khokle (2016)

Akram and Hilman (2018)

Zhou et al. (2019)

Bendig et al. (2018)

Battisti and Deakins (2017)

Jacobi and Brenner (2017)


Matysiak et al. (2018)

Teece (2007)

Roy and Khokle (2016)

Helfat and Peteraf (2015)

Teece et al. (2016)

Rigby et al. (2016)

Kindström et al. (2013)

Wang et al. (2018)

Yeow et al. (2018)

Karimi and Walter (2015)


Teece (2007)

Helfat and Peteraf (2015)

Bendig et al. (2018)

Kindström et al. (2013)

Hodgkinson and Healey (2011)

Yeow et al. (2018)

Eisenhardt and Martin (2000)

Rotjanakorn et al. (2020)

Teece et al. (1997)

Digital transformation and Dynamic Capabilities


Jacobi and Brenner (2017)

Schwertner (2017)

Helfat et al. (2007)

Jacobi and Brenner (2017)

Teece et al. (1997)

Matt et al. (2015)

Eisenhardt and Martin (2000)

Automotive sector


Llopis et al. (2021)

Fichman et al. (2014)

Yoo et al. (2010)

Simonji-Elias et al. (2014)

Hanelt et al. (2015)

Gao et al. (2016)

Letiche et al. (2008)

Perrott (2008)

Möller et al. (2011)

Berman and Bell (2011)

Chanias and Hess (2016)

Hildebrandt et al. (2015)

Keller and Hüsig (2009)

Piccinini et al. (2015)

Fitzgerald et al. (2013)

Lucas et al. (2013)

Gregory et al. (2015)

Remane et al. (2016)

Digital transformation and Automotive Sector

Fichman et al. (2014)

Gao et al. (2016)

Yoo et al. (2010)

Hanelt et al. (2015)

Letiche et al. (2008)

Perrott (2008)

Möller et al. (2011)

Fitzgerald et al. (2013)

Lucas et al. (2013)

Berman and Bell (2011)

Matt et al. (2015)

Riasanow et al. (2017)

Böhm et al. (2010).

Remane et al. (2016)

Hildebrandt et al. (2015)

Llopis et al.(2021)

World Economic Forum (2016)

CCOO (2018)

Farahani et al. (2017)

Keller and Hüsig (2009)

Ben-Zeev et al. (2017)

Rubio et al. (2019)

Rubio and Llopis-Albert (2019)

Piccinini et al. (2015)

Kern and Wolff (2019)

Pfleeger and Pfleeger (2003)

Dynamic capabilities and Automotive sector

Rotjanakorn et al. (2020)

Leite et al. (2017)

Tondolo et al. (2015)

Leite (2013)

Teece and Leih (2016)

Camuffo and Volpato (1996)

Leite (2013)

Christensen (2011)

Alves (2011)

Mesquita et al. (2013)

Lee (2012)

Maynez et al. (2018)

Nakano et al. (2013)

Makkonen et al. (2014)

Mamun et al. (2017)

Source: own source 2019

3. Material And Methods

From the methodological perspective, a review of the literature aimed at identifying dynamic capabilities and indicators that measure these in the automotive sector has been carried out. The terms searched are: “Digital Transformation & Dynamic Capabilities”, “Digital Transformation & Automotive Sector”, “Dynamic Capabilities & Automotive Sector”. The most important databases used were: ABI Research, Econlit, Academic Search Premiere, Google scholar, Springer, Science Direct from the period between years 2001 and 2020.

For this purpose, firstly, from literature review and expert judgments are assigned. Secondly, from a quantitative methodology of exploratory analysis the correct assignment of the indicators to each latent variable (capabilities) is verified.


In our main basis for this study, it is assumed that dynamic capabilities are capable of developing sensing, capturing and innovation capabilities in organizations, for which the following hypotheses have been developed. A SEM analysis of structural equations with latent variables is also performed, as a statistical technique that has been used mainly in the marketing and market research sectors (Caballero, 2006). Consequently, this paper aims to corroborate whether dynamic capabilities emerge in automotive companies as a consequence of digital transformation.

Hiphotesis 1. The sensing capability influences the seizing capability.

Garrido et al. (2020) state that the nature of the capability of the sensing dimension implies investing in research efforts, information search and resource allocation that do not yield immediate returns, i.e., sensing involves activities that require investments. That is why, according to these authors, the sensing dimension is a prerequisite for the other dimensions, i.e. seizing and threat management and reconfiguration. According to Lee and Yoo (2019), sensing capability acts positively on seizing capability. Consequently, sensing capability influences seizing capability because it provides insight into identified opportunities and resources in order to take advantage of them and transform ideas into new products, services and processes that, strategically employed through a well-organized business plan, will influence the organization’s performance. Organizations that frequently participate in market detection are prepared to take advantage of the opportunities presented to develop competitive skills (Teece, 2007).

Hiphotesis 2. The seizing capability influences the innovation capability.

Seizing directly influences the capability for innovation, because the new opportunities identified are used to create new products and services (Garrido et al., 2020). With the adoption of new technologies and appropriate business models, competitive advantages are achieved, since the company produces combinations and arrangements of assets in a particular way that would be difficult for other organizations to imitate (Teece, 2007). For innovation, the transformation of knowledge acquired from the outside is essential, which requires merging new knowledge acquired with existing knowledge and improving adaptation to the evolution of the environment, using existing resources within the organization as new tools to deal with changes in the environment (Pavlou & Sawy, 2011). Consequently, the most appropriate opportunities are used at the right time to create innovative outcomes (Lee & Yoo, 2019).

Hiphotesis 3. The sensing capability influences the innovation capability.

Innovation activities are carried out with the purpose of maintaining the survival and growth of the company, because a company that offers superior value to the competition, intervenes in the purchase intention and behavior of customers, resulting in competitive advantage (Morgan et al., 2004). Opportunities for product innovation depend on knowledge of the external environment, i.e., sensing capability, which means that firms should pursue product innovation strategies that will enable them to grow in the short, medium, and long term (Lee & Yoo, 2019). A firm’s awareness of its external environment is going to depend on the purpose; that is, whether exploration activities are developed for the development of new products, or whether exploitation activities are carried out to improve existing ones (Hwang & Lee, 2010).

Hiphotesis 4. The process sequence of adapting dynamic capabilities is sensing, seizing and innovation.

The capability of searching and forming opportunities (sensing) allows managers to know the current challenges in dynamic competitive environments (Jiao et al., 2011); this implies developing the dynamic capabilities to implement strategies and actions that allow taking the best advantage of opportunities and facing challenges and threats in a changing environment (Miranda, 2015). Once the opportunities have been identified, managers take advantage of their potential (seizing) to transform and exploit knowledge in the creation, innovation, process improvement and definition of strategies to combine new knowledge with existing knowledge. Seizing is related to strategic processes to raise organizational performance and competitive advantage (Foss et al., 2013). The seizing capability is applied to the configuration of the business model (Teece, 2007), so organizations use dynamic competencies to create, reconfigure or modify the competencies and resources they have according to the changes occurring in the context (Mezger, 2014).

The relationship between the hypotheses is shown in Figure 2 according to the indicators assigned

Figure 2. Indicators assigned to each latent variable

Source: Own source, 2020

The Sample

A total of 142 requests were sent to respond to the survey, corresponding to the companies identified that met the requirements of belonging to the automotive and components sector. Of these, a total of 42 responses were obtained, which constitute the sample with which the model was estimated. The target audience is the profiles of executives and managers working in these companies. Of the companies consulted, three of them operate only in Spain, two in the European Union and the rest, 37 are recognized as multinationals operating in a global environment. On the other hand, 30 are purely automotive companies, while the remaining 12 are focused on the spare parts sector, such as batteries, tires, etc.

4. Results

The criteria for selecting these indicators have been mainly their validity, according to the relevant literature, for the measurement of the dynamic capabilities that have been proposed. For this purpose, we have chosen nine representative elements. The capabilities were selected based on previous work on dynamic capabilities. Besides, the estimation of the capabilities has been used, in turn, to determine their influence on the respective indicators through which they are manifested. Hence, we have built Table 4 as summary of the literature revision for our model.

Table 4. SUMMARY Literature revision of the indicator use in our model





How often do you use BIG DATA for purchasing behavior analysis?

Hofacker et al. (2016)

Kennett et al. (2011)

Montgomery (2007)

How often is it able to advance in the PRODUCT DESIGN to acceptance according to real tastes, education, geographical areas, etc. through Big Data?

Chuang and Lin (2015)

Cooper and Kleinschmidt (2011)

Lichtenthale (2016)

Zhan et al. (2018)


How often has digital technology enabled the sales force to OPTIMIZE ROUTES?

Kotler and Armstrong (2003)

Sandhusen (2002)

How often has digital technology developed solutions to PREVENT ACCIDENTS?

Bhatti et al. (2019)

Pansambal (2020)



Sandoval Almazán (2011)

Utz and Breuer (2019)

Wolff and Moser (2006)


How often do you use sensor integration or data management to make COMMERCIAL ALLIANCES with suppliers or/and customers?

Akter,et al. (2016)

Holmlund et al. (2020)

LaValle et al. (2011)

How often does digital technology allow you to estimate THE DURABILITY of the different parts that make up the product?

Ran et al. (2019)

Zhang et al. (2019)


How often does technology UNIFY SYSTEMS GLOBALLY across your plants and logistics centers?

Dvorak et al. (2013)

Laudon and Laudon (2006)

How often has digital technology made it possible to CONNECT ALL BUSINESS DIVISIONS under one direction?

Bharadwaj et al. (2013)

Hildebrandt et al. (2015)

Lucas et al. (2013)

Yoo (2010)


How often do YOU USE ALERTS installed in customers’ vehicles?

Abulkhair et al. (2015)

Koo et al. (2016)


How often do insurance companies contact your company to offer a CUSTOMIZED PRODUCT depending on the driving style by the data you collect directly from the vehicle?

Bian et al. (2018)

How often are PREDICTIVE MODELS used to ANTICIPATE WEAR of parts have had an impact on the vehicle’s maintenance cost?

Candanedo et al. (2018)

Murat et al. (2020)


How often has digital technology allowed us to make decisions about ORGANIZATIONAL CHANGES?

Attaran et al. (2019)

Dority (2016)

How often do you use digital technology to know when the customer WILL CHANGE THE PRODUCT, the type of product you are going to look for, color, features in order to anticipate it and thus launch the user a communication that makes you purchasing it?

Ahmadinia et al. (2015)

Anderson and Bolton (2015)

Foroudi et al. (2017)

Pantano and Timmermans (2014)

How often does the digital transformation allow your company to ANTICIPATE FUTURE CAR FAILURES allowing the connection with the workshop, being able to make an appointment, even before such a failure occurs?

Borgi et al. (2017).

Murat Çınar et al. (2020).


How often is the DEGREE OF INNOVATION OF COMPETITION IDENTIFIED through networked devices?

Koch and Windsperger (2017)

Hana (2013)

Source: own source, 2020/2021

To test the relationships between the indicators and the latent variables, as well as the relationships established between the hypotheses and the constructs representing the capabilities, a structural equation model (SEM) was specified and estimated. All of the capabilities present in the model - sensing, seizing and innovation - are reflected in Figure 3. The coefficients, on the arrows of the model schematic, are shown on a standardized scale from -1 to 1.

Figure 3. Structural equation model among the capabilities identified

Source: own source (2021)

Table 5 indicates the results of the acceptance or rejection of our hypothesis:

- Hypothesis 1 (H1): the sensing capability measured through the indicators (BD_SENS_1; BD_SENS_2; DT_SENS_1; DT_SENS_2; IOT_SENS) positively influences the seizing capability is accepted since this relationship is considered proven.

- Hypothesis 2 (H2): seizing capability measured through the indicators (BD_SEIZ_1; BD_SEIZ_2; DT_SEIZ_1; IOT_SEIZ) positively influences innovation capability is accepted since this relationship is considered proven.

- Hypothesis 3 (H3) sensing capability measured through the indicators (BD_SENS_1; BD_SENS_2; DT_SENS_1; DT_SENS_2; IOT_SENS) does not positively influence innovation capability and therefore is not accepted as this relationship is not considered proven.

- Hypothesis (H4) The sequence of dynamic capabilities adaptation process, uptake and innovation is accepted as this relationship is considered proven. Although H3 does not have a direct influence on innovation, it does have an indirect influence on innovation, the influence is twofold, direct and indirect.

Table 5. Hypothesis results


Parameter Value


Acceptance/Rejection of the hypothesis

H1 Function




H2 Function




H3 Function



No accepted

H4 Function




Source: own source (2021)

5. Discussion

From the study developed it was possible to verify the fulfillment of hypothesis 1, that is, the sensing capability measured through the indicators BD_SENS_1; BD_SENS_2; DT_SENS_1; DT_SENS_2; IOT_SENS, positively influences the capability of seizing, it is accepted since this relationship is considered proven. This statement is made based on the theoretical review, which highlights the importance of the information that companies obtain from part of the context; proof of this is the Big Data, which is used to know the opportunities and threats presented by the environment; because from the data, the company can learn about the needs of customers (Zhan et al., 2018). Big data also provides information about the number of customers visiting in website. In addition, graphs can be used to analyze customer satisfaction surveys (Kennett et al., 2011) and understand the decision-making process of consumers (Hofacker et al., 2016).

On the other hand, the automation of processes carried out within the organization, such as the sales force, managed through technological resources such as cell phones and tablets, allows the company to maintain contact with customers, perform sales operations and keep informed of what is happening in the market (Kotler & Armstrong, 2003). Also, the use of social networks helps companies to share information with people interested in their products in real time and build a closer relationship with their customers (Utz & Breuer, 2019). In the case of the automotive industry, moreover, digital technology has enabled the introduction of sensors in vehicles that allow contact with customers, detection of dangerous situations and accident prevention, even applications for cell phones are developed to estimate the dangerous situation on roads and assist the driver to avoid accidents (Bhatti et al., 2019).

Consequently, the capability of sensing positively influences the capability of seizing as expressed by Lee and Yoo (2019), because the company by developing the capability can know the opportunities and needs of the environment and take advantage of this information to create new products and processes that will make it develop competitive advantages (Teece, 2007).

Regarding hypothesis 2 (H2), the seizing capability measured through the indicators BD_SEIZ_1; BD_SEIZ_2; DT_SEIZ_1; IOT_SEIZ, positively influences the capability for innovation, it is accepted, since this relationship is considered proven. This statement is made based on the fact that Big Data analysis has become a source of innovation and competition, because it brings more and more value thanks to the information it obtains from customers (Holmlund et al., 2020), this data is transformed into knowledge to make business decisions and address customer problems (Akter & Wamba, 2016). In the automotive industry, the evolution of digital technology through tools such as the internet, artificial intelligence and sensing technology have influenced the maintenance model. Thanks to technology, predictive maintenance has become a solution to address smart manufacturing and estimate the condition of equipment, diagnosing failures and assessing the remaining lifetime (Zhang et al., 2019). The possibility of manufacturing and placing sensors in vehicles will increase as the rise of the internet of things does, because with the increase of sensors, the amount of data that will be a source for the development of machine learning algorithms for preventive maintenance will also increase (Borgi et al., 2017).

Therefore, in companies that go hand in hand with digital transformation, the available technological resources, such as technical equipment, data storage devices, software, communication networks, among others, are used to provide customer service (Laudon & Laudon, 2006). In this regard, authors such as Lucas et al., (2013), and Lee et al., (2012) believe that digital technologies offer more flexible environments to create new organizational forms with customers, and as expressed by Hildebrandt et al., (2015), vehicle OEMs that have heterogeneous knowledge of digital technologies, that can integrate them into their companies and commercialize this knowledge, are better prepared to face the digital transformation.

As for hypothesis 3 (H3), the sensing capability measured through the indicators BD_SENS_1; BD_SENS_2; DT_SENS_1; DT_SENS_2; IOT_SENS, does not positively influence the capability for innovation and therefore it is not accepted because this relationship is not considered proven. Although it should be noted that according to Teece (2007), with the information about the opportunities and threats obtained from its environment, the organization can make decisions to modify or create new products and processes, so seising does influence the capability for innovation, but in this study this relationship was not proven through the indicators used to measure it.

Finally, for hypothesis (H4), it is accepted that the sequence of the adaptation process of dynamic capabilities is sensing, seizing and innovation, since this relationship is considered proven. Although H3 does not exert a direct influence on innovation, it does have an indirect influence, therefore, the influence is twofold, direct and indirect. This supports the view of Teece (2007), who states that the development of dynamic capabilities is primarily about detecting, seizing new opportunities, as well as transforming or reconfiguring resources to increase performance, rather than analyzing and optimizing the current resource base. Dynamic capabilities do not necessarily have a direct effect on performance, but rather an indirect effect through their influence on a firm’s resource base (Battisti & Deakins, 2017). Consequently, these dynamic capabilities are essential to promote creativity, and when strong, they make any firm able to cope with the uncertainty of innovation and competition (Rotjanakorn et al., 2020). In relation to this point, it should be noted that the automotive industry is continuously impacted with the introduction of new technologies, which makes it necessary for organizations to adapt to the fast pace of growth. Consequently, it is necessary to take into account the dynamic capabilities that these companies have, which also exceed the core competencies, to be in continuous observation of the changes in the environment and thus ensure the permanence of the industry in the market.

6. Conclusion

Digital transformation has become a source of innovation and competition, because it brings more and more value thanks to the information it obtains from customers, then this data is transformed into knowledge to make business decisions and address customer problems.

It should be noted that the automotive industry is continuously impacted with the introduction of new technologies, which makes it necessary for organizations to adapt to the fast pace of growth. Consequently, it is necessary to take into account the dynamic capabilities that these companies have, which also exceed the core competencies, to be in continuous observation of the changes in the environment and thus ensure the permanence of the industry in the market.

Companies that understand the importance of digital transformation show more modern work styles, consider user preferences and the information they can obtain from the context. The proper treatment of this information is the key to competitive advantage, so employees must have access to the right information to be able to properly execute their tasks and thus increase quality and productivity.

Although there is consensus in the academic literature on the importance of the development of dynamic capabilities to achieve sustainable competitive advantages over time, this review carried out on the measurement of dynamic capabilities reveals the diversity of studies in this field, with defined indicators to measure these capabilities in practice.

All innovation actions seek to improve economic performance either by increasing sales or reducing costs, in other words, innovation is intended to increase profits, so it is expected that new innovations will surpass the technical and economic solutions that already exist in the market and therefore generate huge profits for the company

In conclusion, and in compliance with each specific objective set out in this paper, it can be said that when evaluating the effectiveness of the indicators used to determine each of the dynamic capabilities, the indicators used to determine the influence of seizing and innovation capability have been adequate for this purpose, since they have been able to determine the relationship between each of the dynamic capabilities and their effects within the automotive sector.

In this paper, applying the concepts of different types of dynamic capabilities of the automotive sector, nine indicators were identified as adequate to perform this measurement. Therefore, this study offers a useful tool for the academic sector and the market that allows automotive companies to measure their dynamic capabilities and compare them with other regarding the digital transformation.

However, the research presents some limitations that should be considered when contextualizing the work done. The most representative one is the difficulty of obtaining a larger sample, because out of 142 surveys, only 42 responses were obtained, due to the limited time respondents had to attend to the researcher.

This measurement will help them to develop active process improvement strategies to raise their market sensing, seizing and innovation capabilities, and in this way, improve their managerial performance and seek a better positioning in the sector.

A future line of research would be to extend this study to other types of companies, in order to be able to measure the success of organizations based on their dynamic capabilities.


The authors wish to thank the peer reviewers and the editors of this journal for their comments, which helped to improve this document.

Declaration of Conflicting Interests

All the authors made significant contributions to this study and have agreed to its publication. Further, all the authors state that there are no conflicts of interest in this study.


The author(s) received no financial support for the research, authorship, and/or publication of this article.


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