Implementing Industry 4.0 Technology in Automotive Industry: Challenges and Opportunities


Underpinned by the rapid advancement of information and communication technologies (ICT), industry 4.0 has therefore gained wide momentum in the manufacturing industry. Being one of the most prominent sectors, the automotive sector has been under tremendous pressure to adapt to the new changes in the environment due to globalisation, diversification of consumers and diversification of products are main drivers for change. Accordingly, the use of digital technologies that align with the industry 4.0 era becomes inevitable. The case is quite similar to the UK’s automotive sector which was once considered a leading automotive exporter and still holds prestige around the world. Yet, there is a lack of research that identifies the challenges and opportunities for the implementation of I4.0 technologies in the UK’s automotive sector. Therefore, this research aimed to identify the potential benefits and opportunities and unveil the expected challenges of adapting I4.0 technologies within the automotive sector. Thus, the research findings are presented along with a few recommendations that would aid the industry in enhancing their business performance and overcoming the anticipated challenges.

1 Introduction

1.1 Background & Overview

In an environment that is characterised by high competition levels, businesses are exerting tremendous amounts of effort to gain a competitive advantage and ensure their growth and prosperity (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021). Accordingly, organisations have been fiercely adopting new technologies and shifting to more automated and interconnected levels to attain sustainability, flexibility, resilience and higher levels of modularisation  (Narayanamurthy and Tortorella, 2021). Underpinned by the rapid advancement of information and communication technologies (ICT), industry 4.0 has therefore gained wide momentum from multiple scholars and business leaders across the globe (Nascimento et al., 2019).


The basis of the Industry 4.0 industrial revolution, referred to as I4.0 hereafter, lies in the integration of manufacturing systems and information and communication technologies (ICT). These are commonly referred to as cyber-physical systems (Dalenogare, Benitez, Ayala and Frank, 2018). The Internet of Things (IoT), big data analytics, smart algorithms, cloud computing, 5G network technologies, wireless sensor networks, (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021), additive manufacturing, simulation, augmented reality, industrial automation (Nascimento et al., 2019), blockchain, and machine learning are technologies that are termed under the Industry 4.0 paradigm (Yadav et al., 2020).


In this context, many developed countries across the globe, such as Germany, France and Italy, have launched industrial plans integrating I4.0 technologies (Muscio and Ciffolilli, 2019). On an international scale, several European countries have been strengthening their capacities to develop and integrate I4.0 technologies; in line with such efforts, Europe has introduced several funds, such as the FP7 fund, and promoted regional networking to develop technologies that contribute to the global value chain (Muscio and Ciffolilli, 2019). Within the manufacturing sector, the utilisation of I4.0 technologies is expected to bring about several benefits including enhanced product quality, process efficiency, and organisational competitiveness (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021), enhanced operational efficiency, reduction of costs and wastes, control over data operations, increased productivity, the possibility of customisation, etc (Nascimento et al., 2019).


The automotive sector is among the most prominent sectors of the manufacturing industry and is considered to be the fourth leading sector across the globe in terms of its contribution to national and international economies (Bhatia and Kumar, 2020). Nevertheless, the industry has been under tremendous pressure to adapt to the new changes in the environment.  Factors such as globalisation, diversification of consumers and diversification of products are main drivers for change. Accordingly, the average life cycle of car models has witnessed a reduction from 8 to 3 years (Winkelhake, 2019). Similar to other industries, the automotive industry has been trying to incorporate and develop I4.0 technologies (Winkelhake, 2019). However, it was evident that such efforts are primarily focused on product development rather than the operational processes within the industry itself; to illustrate, there has been a great momentum towards the integration of technologies that enable assisted and autonomous driving (Bhatia and Kumar, 2020).


Yet, there is a lack of evidence in the literature when it comes to the UK’s automotive industry. Thus, creating an urge to further investigate the UK’s stance in relation to the progress and developments of its automotive sector. Once considered a leading automotive exporter, the UK still holds prestige around the world with regards to automotive manufacturing and exportation. In 2016, the export-generated contribution to the national economy from the automotive industry was about 22 billion British pounds and 18.6 billion British pounds in 2018 (Statista, 2021). The European Union was the main export destination of UK manufactured cars in 2019 where every second car produced by UK-based manufacturers was transported to EU countries (Statista, 2021). In 2016, the UK reached its peak in car production with 1.7 million units of cars being produced yielding an annual turnover of 74 billion euros; however, the production rates witnessed a decline reaching approximately 920 thousand units in 2020 (Statista, 2021); raising concerns with regards to the future performance of the industry.


The case has further been worsened as a result of the adverse impacts of the COVID-19 pandemic. Therefore, automation, resilience and flexibility have become a lifeline for many firms (Bhatia and Kumar, 2020). It has been evident that smart, digitally matured and automated organisations have been able to counteract the negative effects of the pandemic with the aid of I4.0 technologies which has been particularly evidenced in the manufacturing sector (Narayanamurthy and Tortorella, 2021).  Yet, when it comes to the automotive sector, little is known about the role of I4.0 in enhancing the industry’s performance.


Figure 1: Yearly car production in the UK. Source (Statista, 2021).

1.2 Research Gap

From the aforementioned, it is apparent that there is an urgent need to develop new business models in the automotive industry to replace the old robust models (Winkelhake, 2019). Accordingly, the use of digital technologies that align with the industry 4.0 era becomes inevitable. An extensive review of the literature, using multiple search engines and primary keywords, such as “Industry 4.0, Automotive industry, I4.0 technologies, Industry 4.0 Challenges, Industry 4.0 benefits” etc, with a mix of different boolean operators, such as “AND, OR, NOT” was conducted.

It was evident that research pertaining to the discussion of the associated benefits and anticipated barriers and challenges are more generic in nature meaning that they tackle the issue on an industrial level rather than specifically addressing the opportunities and challenges of a specific sector within the manufacturing industry according to its internal and external environment; thus, creating a gap in the literature with regards to the specific opportunities and challenges of I4.0 technologies within the automotive sector. Furthermore, although multiple researchers have attempted to promote the uptake of the 4th industrial revolution within the automotive sector (Winkelhake, 2019; Nascimento et al., 2019; Narayanamurthy and Tortorella, 2021; Fromhold-Eisebith, Marschall, Peters and Thomes, 2021), there are still many ambiguities and uncertainties that need to be addressed. Lin, Lee, Lau and Yang, (2018) found that there is a gap between the theoretical promotion of I4.0 technologies and its practical implementation. A reason for that might be due to the lack of awareness of the associated benefits and solutions to the expected challenges.

1.3 Research Aims & Objectives

In light of this discussion, this research aims to identify the potential benefits and opportunities and unveil the expected challenges of adapting I4.0 technologies within the automotive sector in an attempt to present solutions that would aid in promoting the adoption of I4.0 technologies; hence, the research objectives are as follows:

  1. To discuss what Industry 4.0 is and the opportunities of its implementation in the automotive industry.
  2. To identify the challenges faced in adopting I4.0 technologies in the automotive industry.
  3. To provide recommendations on how these challenges could be addressed.

1.4 Research Significance

With the outbreak of the COVID-19 pandemic, new ways of getting work done became inevitable; thus, this research would provide insights into practical and theoretical implications pertaining to the automation of the automotive industry to enhance its performance and outcomes. Thus, this research would contribute to the literature by shedding light on the realised benefits, potential opportunities and expected barriers. It would provide insights into the possible means to address obstacles so that organisations willing to adopt I4.0 technologies could account for them in their strategic plans. Furthermore, it would pave the way for the implementation of the technology to become an integral part of the industry in the post-pandemic world.


2 Literature Review

2.1 Introduction

With the rapid evolution of technologies, adaptation to the 4th industrial revolution is inescapable and is bound to happen to sustain firms’ existence and growth. This is where the role of I4.0 technologies come into play. As aforementioned, several countries are exerting massive amounts of effort to facilitate the development and implementation of I4.0 technologies. To illustrate, Germany had launched the “High-Tech Strategy 2020”; the USA, France, China and Brazil have similar programs to promote industrial development (Dalenogare, Benitez, Ayala and Frank, 2018). The following paragraphs present an overview of I4.0 technologies along with the associated opportunities and challenges within the manufacturing industry in general and the automotive industry in particular.

2.2 Industry 4.0 Technology- Overview

As defined by Muscio and Ciffolilli, (2019), Industry 4.0 is “a word that identifies innovative technologies, processes and products, typical of a Fourth Industrial Revolution characterised by a massive and pervasive use of interdependent digital technologies and the rise of cyber-physical spaces or smart factories.” (p.169). Therefore, I4.0 technologies employ digital tools that aid in coordinating all value chain interactions (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021). Thus, it offers real-time internet-based connectivity of people, machines and objects, connecting and digitising value streams and turning businesses into smart and autonomous firms  (Narayanamurthy and Tortorella, 2021). There are many drivers for the adoption of I4.0 technologies including managerial, economic, social and environmental drivers (Yadav et al., 2020). The technology is capable of providing intelligence and connectivity in four main dimensions. These are smart products, smart manufacturing, smart supply chains and smart working  (Narayanamurthy and Tortorella, 2021).

2.3 Industry 4.0 in the manufacturing Industry

2.3.1 Smart Manufacturing

The concept of I4.0 is also known as smart manufacturing where “human beings, machines, and resources communicate with each other as naturally as in a social network” (Bhatia and Kumar, 2020, p.3). There is a shift towards smart factories in the manufacturing industry that entails machine-to-machine and man-to-machine interactions through the transformation of objects into intercommunicating intelligent systems (Muscio and Ciffolilli, 2019). Yadav et al., (2020) identified 29 I4.0 enablers that could be incorporated into the manufacturing industry to achieve sustainable development goals; these include machine learning, advanced information-sharing systems, digitising of supply chains, reverse logistics, real-time tracking of suppliers, etc.

Figure 2: Industry 4.0- based enablers to enhance the sustainability of the manufacturing industry. Source: Yadav et al., (2020).


Nascimento et al., (2019) found that I4.0 paves the way for novel economic development paths; through their study, the authors proved that smart production systems, using web technologies reverse logistics and additive manufacturing could be integrated into the circular economy approaches in the manufacturing industry; this is since such technologies facilitate the reuse and recycling of scarce resources (Nascimento et al., 2019).

2.3.2 Opportunities of its implementation

As aforementioned, I4.0 enables industries and businesses to incorporate cost-effective technologies that would enable them to have full control over the firm’s performance. The use of IoT and other related tools facilitates the integration of product design and production, and the monitoring and optimisation of production processes (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021). Thus, I4.0 technologies increase control over data operations, productivity, product quality, and process efficiency, and optimise workflows and material flows leading to cost efficiency and augmentation of operational outcomes (Yadav et al., 2020).  Moreover, they are capable of significantly reducing set-up and processing times, labour and material costs and other indirect costs leading to higher productivity rates and cost reductions (Dalenogare, Benitez, Ayala and Frank, 2018).


Also, I4.0 technologies prevent the depletion of valuable natural resources and mitigate the adverse impacts on the environment. Hence, they ensure the sustainability of businesses by minimising wastes and maximising the reuse and recycle of waste materials. They also increase material recovery rates (Nascimento et al., 2019) and aid in saving natural resources such as scarce raw materials, energy, water, etc (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021).


Furthermore, I4.0 technologies could aid supply chains in attaining higher sustainability levels. Supply chains act as an integral part of business operations as it interferes in all product development stages from its inception until it reaches its end users (Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, 2020). As stated by Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, (2020), I4.0 technologies could enhance the sustainability of supply chains through “product and process design, manufacturing planning and control, cooperation with suppliers, shared information and customer energy and value through flexibility and process re-engineering” (p.7) enabling better management of the whole supply chain (Dalenogare, Benitez, Ayala and Frank, 2018).



Generally speaking, I4.0 technologies enables digitised companies to offer their clients novel and innovative digital solutions giving them a competitive advantage over their competitors (Dalenogare, Benitez, Ayala and Frank, 2018).  In the I4.0 era, Digital Twin (DT) technology became an integral part of the manufacturing industry. The technology facilitates the simulation of real-time working conditions and aids in having an intelligent and cost-effective decision-making system (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021). Besides, other benefits include enhancement of decision-making process, process visualisation and control, reduction of launching time, enhanced workplace safety and reduction of labour claims (Dalenogare, Benitez, Ayala and Frank, 2018).


Additionally, Narayanamurthy and Tortorella, (2021) found the implementation of I4.0 technologies, where virtual connection practices have been enforced, had a positive impact on employees’ job performance, specifically in terms of quality output amid the COVID-19 pandemic. Accordingly, the authors suggested that organisations should intensify their digitisation efforts and adopt new working routines that would integrate other technologies as well.

Figure 3: Significant relationship between I4.0 technologies and employees’ work performance. Source: Narayanamurthy and Tortorella, (2021).



2.3.3 Challenges Faced

Despite the massive potential of I4.0 along with the perceived benefits, many industries are reluctant to shift to I4.0, at least for the near future. However, business leaders are being reluctant to acknowledge the possibility of full digitisation since they tend to primarily rely on their acquired experiences and the know-how skills developed over the years  (Nascimento et al., 2019). It was also evident that small and medium business enterprises are more prone to the challenges of adopting digitised routines due to the lack of adequate funds (Narayanamurthy and Tortorella, 2021). However, similar problems and challenges are also encountered by large organisations and firms as such investments are capital and resource-intensive (Yadav et al., 2020). Moreover, technologies are rapidly evolving leading to a great risk of obsolescence; accordingly, firms are expected to invest heavily in newly developed technologies to upgrade their existing operations; further, they are also expected to invest in research and development to advance their procedures and make use of the full potential of these technologies (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021).


Moreover, business executives are facing multiple challenges in defining the changes in procedures and the needed resources for I4.0 technology (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021). This is since the development and implementation of such technologies require precise and detailed strategic plans that could identify the needs of the business and align such needs to the developmental and implementation efforts to attain the desired outcomes (Yadav et al., 2020). Also, the lack of qualified human capital and experience in operating Computer Numerical Controlled (CNC) machines are also among the highest perceived challenges for the adoption of I4.0 technologies  (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021). Other more general barriers that obstruct the adoption of I4.0 are manifested through the lack of comprehensive legal frameworks that address data handling issues, strong IT infrastructure (Fromhold-Eisebith, Marschall, Peters and Thomes, 2021), business culture, level of education, and economic and political instability (Dalenogare, Benitez, Ayala and Frank, 2018).

2.4 Industry 4.0 in the automotive industry

It is evident that digital transformation and I4.0 technologies are gaining momentum in the automotive industry (Winkelhake, 2019). Customers are increasingly demanding innovative and customised solutions that adhere to their unique needs and requirements. Not only does the automotive industry need to enhance the digitisation and automation of its processes but also, there is an increasing urge to augment the connectivity and integration within the industry (Bhatia and Kumar, 2020). The following paragraphs discuss a few of the identified challenges and opportunities for the use of I4.0 technology in the automotive industry.

2.4.1 Opportunities of I4.0 in the automotive industry

To start with, Bhatia and Kumar, (2020) identified four main areas where benefits due to the incorporation of I4.0 technologies are perceived within the automotive sector namely, operational performance, product performance, economic performance and responsiveness. Thus, similar to other manufacturing industries, I4.0 technologies could enhance information sharing, integration of processes, coordination and collaboration (Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, 2020).  Moreover, several economic benefits are realised with the incorporation of I4.0 technologies including a reduction in production and operational costs, and better consumption of resources (Bhatia and Kumar, 2020).


Regarding product performance, I4,0 is capable of enhancing product quality, and product customisation (Bhatia and Kumar, 2020). Concerning operational performance, I4.0 could aid in increasing productivity levels, process efficiency and flexibility, visualisation and control processes, etc (Bhatia and Kumar, 2020). Besides, the I4.0 technologies are expected to enhance productivity, improve competitiveness levels and increase the industry’s profitability rates (Winkelhake, 2019). Also, the use of data-sharing technologies, such as blockchain technologies and IoT, is associated with higher reliability rates that lead to a reduction in product preparation and delivery times  (Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, 2020).


Interestingly, Bhatia and Kumar, (2020) have also indicated that such technologies aid in enhancing the responsiveness of the automotive sector meaning that the industry would be capable of decreasing launching time, augment responsiveness to consumer demands, and aid in having flexible production processes where last-minute modifications could be easily introduced. Furthermore, as identified by Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, (2020), I4.0 technologies are among the leading initiatives in promoting the efficiency and effectiveness of the supply chain within the automotive industry.

Figure 4: Facilitators for the development of a sustainable supply chain within the automotive industry. Source (Bhatia and Kumar, 2020).


Additionally, Hidayatno, Rahman and Daniyasti, (2019) identified six potential benefits for the adoption of I4.0 technologies namely, competitiveness boost, acquisition of knowledge and the technical know-how, GDP growth, decrease in technological costs, long-term contractual partnerships, and profitability gains.

2.4.2 Challenges of I4.0 in the automotive industry

Again, the incorporation of I4.0 technologies within the automotive sector is associated with several challenges. To start with, the automotive industry is characterised by its short-term objectives. Thus, as an implication, the industry is often reluctant to invest in technological developments that require intensive capital investments with long-term returns on investment (Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, 2020). Similarly, Winkelhake, (2019) found that such reluctance is due to the fact that there is no guaranteed and immediate return on investment which exposes companies to multiple risks. Thus, creating an urge to increase the awareness of the potential benefits. Lin, Lee, Lau and Yang, (2018) found that the lack of appropriate methods to quantify the benefits is another challenge; their results further indicate that the automotive firms’ willingness to adopt I4.0 technologies increase when they are fully aware of the opportunities and advantages, regardless of their size.


Moreover, research has shown that the industry lacks specific transformation strategies that clearly outlines future steps, the resources needed and the anticipated risks. This is particularly apparent in the case of small and medium-sized enterprises that lack the needed resources (Winkelhake, 2019). Also, there is a lack of the technical capabilities needed to integrate old and new technologies in a smooth manner that results in the emergence of compatibility issues (Arcidiacono, Ancarani, Mauro and Schupp, 2019). The lack of an experienced workforce, effective legislation, and funding initiatives are also among the barriers to the implementation of I4.0 technologies (Ramirez-Peña, Mayuet, Vazquez-Martinez and Batista, 2020). Other external challenges include the high levels of competition and price pressures from customers, lack of collaboration along the value chain, lack of technology suppliers, and institutional factors such as business culture and employees’ resistance to change (Lin, Lee, Lau and Yang, 2018).



3 Methodology

3.1 Introduction

This research aims to identify the potential benefits and opportunities and unveil the expected challenges and obstacles to the adoption of I4.0 technologies within the automotive industry. This chapter highlights the methodology used in conducting this research. This study has referred to the research onion, developed by Saunders (2009), as a guide for the different decisions and strategies chosen to develop the methodology framework.


Figure 5: Research onion. Source (Saunders, 2009).

3.2 Research Philosophy

Among the widely used research philosophies are positivism and interpretivism (Paterson et al., 2016). The former is based on the objectivism and functionalist paradigms meaning that the research’s topic is considered as an independent physical object; where the researcher is detached from what is being researched and maintains an objective stance (Paterson et al., 2016). Therefore, law-like generalisation and conclusions about such a subject are obtained through precise and unbiased scientific research methods that are observable, measurable and value-free (Saunders, 2009).


Interpretivism, on the other hand, is a subjective research philosophy where research subjects are regarded as social, interdependent, and complex worlds to add multiple meanings, interpretations and realities to the findings of the research (Saunders, 2009). Thus, the researcher’s own beliefs and values play a crucial role in the interpretation of the research findings (Paterson et al., 2016). Since this research aims to identify the status of I4.0 technologies within the UK’s automotive industry along with the exact opportunities and challenges encountered during its adoption and implementation, a positivist research philosophy is chosen.

3.3 Research Type

There are two research types that are often used in business research namely primary and secondary research. Primary research is based on the collection of original and first-hand raw data that specifically address a particular research question. Thus, data for primary research could be collected from interviews, surveys, experiments and observations (Gideon, 2012). Secondary research, on the other hand, is dependent on second-hand raw data that was originally gathered by other researchers for other purposes. Data could be obtained from readily available sources such as governmental reports, legislation, archival records, etc (Gideon, 2012). Although in some cases secondary research could be more cost-effective and time-efficient as compared to primary research  (Gideon, 2012), this study is based on the collection of primary data that are tailored to address the specific objectives and needs of the research.

3.4 Data Collection Technique

There are multiple primary data collection techniques including interviews, surveys, experiments, observations, questionnaires, etc (Madsen, 2018). Interviews allow for the gathering of accurate and high-quality data. This is since the interaction between the researcher and research participants are maintained throughout the interview allowing for further discussion and clarifications. However, Interviews are time-consuming, might provide misleading data if participants get biased, and are restrictive in terms of the time and location that are most convenient to the research participants  (Gideon, 2016). Electronic data collection methods, on the other hand, have become the most prevailing nowadays. It uses the internet as the basis for collecting data. This could be done through e-mails, online surveys or mobile messages (Madsen, 2018). Online surveys are the most efficient means of gathering information where respondents could participate at the time and location that are most convenient to them (Gideon, 2016). The choice of conducting an online survey was due to the fact that the research aims to obtain a large amount of data. By using this survey type, the researcher ensured accessing the maximum number of participants. The research participants are employees and business leaders who are directly involved in the UK’s automotive industry; thus, the researcher reached out to them and provided them with a brief introduction that explains the aims and objectives of this study. Upon their consent to participate, the research survey was sent through email.


Figure 6: Source (O’Gorman and MacIntosh, 2020)

3.5 Data type

There are two primary data types namely quantitative and qualitative data (Farrell, 2021). Qualitative data are descriptive in nature and focus on gathering data such as opinions, experiences, attitudes and attributes. Thus, such data is subjective meaning that they are open to the researcher’s interpretations and inferences (Farrell, 2021). On the other hand, quantitative data is primarily based on gathering countable and measurable information; this research method is objective in nature meaning that conclusions drawn are based on fixed factual data (Farrell, 2021).  This research makes use of both qualitative and quantitative data to obtain concrete results that are further supported by insights into the participants’ opinions and perceptions. Therefore, the survey included a mix of close-ended and semi-close ended questions along with several scale assessment questions, using a 5-point Likert scale, for the quantitative data; whereas, qualitative data obtained from secondary sources using the literature would complement the data obtained from the survey adding depth to the interpretation of the results.

3.6 Survey Questions

As aforementioned, the survey uses a mixed-method approach through the use of closed-ended and semi-open-ended questions. While developing the questionnaire, emphasis was placed on the conceptual framework developed by Gideon, (2016). Accordingly, the survey underwent three main screening phases. Firstly, the survey was checked for clarity and relevance to ensure that the participants comprehend each question as intended. This was achieved by ensuring that the questions were short and precise with no vague words, jargon or highly technical terms. Secondly, a broadness check was made to ensure that all questions were specific and directly relates to the objectives of the study. Thirdly, the questions were checked for duplication based on the anticipated answers; thus, when two questions were perceived to have similar answers, one of them was eliminated. Following the development of the survey, a pilot study was conducted where peers were allowed to have the survey to identify areas that needed improvement. Thus, five peers were asked to answer the survey questions before sending the survey to the industry’s experts and their feedback was obtained and documented. Accordingly, the survey was updated before it was sent to the research participants.

Figure 7: A basic theoretical framework for answering questions. Source (Gideon, 2016).

3.7 Data analysis

There are two primary data analysis methods namely deductive and inductive approaches; the former is often associated with quantitative data where possible relationships between concepts and variables are identified (Saunders, 2009). Also, this data analysis approach allows for the quantitative measuring of concepts. Furthermore, it allows the researcher to generalise the research findings to a certain extent (Saunders, 2009). On the other hand, an inductive analysis approach is often concerned with a particular context; thus, a small sample size is more appropriate allowing the researcher to have a multidimensional explanation of the problem at hand (Saunders, 2009). Since this research is based on obtaining factual results based on numerical data, a deductive approach will be used. Thus, descriptive statistics usings means, variances, standard deviations and such are used for the analysis of the data gathered. Furthermore, box and whiskers plots are used for the visualisation of the gathered data and to provide a deeper understanding of the numerical data gathered and analysed.

3.8 Ethical Considerations

Considering the research’s ethical standards, approval to the “Engineering Systems and Management ‘Self-Certificate’ ethics form for final year projects” (appendix 1) was obtained. The ethical form contains four primary questions that are related to the direct involvement of research participants, the existence of clinical and physical procedures, potential public controversies and the interaction with human beings. Since the answer to all the four questions is “No”, no further approvals were needed. Furthermore, all necessary measures were taken to ensure that the best interests of the research participants were always protected. To start with, all participants were presented with an information sheet where information with regards to this research was transparently communicated; this is essential to prevent any forms of deception (Farrell, 2021). The research participants were then requested to consent to participating and they were informed that they were free to withdraw at any time without giving reasons. Moreover, the research participants were also assured of their anonymity and the confidentiality of any personal information they provided.


4 Results, Analysis & discussion

4.1 Introduction

This chapter illustrates the results obtained from surveying 31 experts working in the UK’s automotive industry. The chapter starts with a brief discussion of the demographics of the research participants including their roles, years and experience and the number of employees employed within their respective organisations. Also, a brief description of the participant’s current knowledge of I4.0 technologies is presented. This is followed by a detailed analysis and discussion of the research findings.

4.2 General Information

As aforementioned, 31 responses from experts within the UK’s automotive industry were gathered. The survey included five primary roles namely, consultants, directors, managers, leaders and engineers with the majority (32%) being managers followed by directors (29%); whereas, the remaining three roles had four participants each. Furthermore, the survey revealed that the roles were related to automotive manufacturing and automotive operations. The average years of experience of the research participants are 14.25 years with a standard deviation of 7.7 years; thus, indicating that they possess a high level of expertise in their respective areas which further adds to the reliability and accuracy of the research’s findings.

4.3 Participants’ knowledge & implementation of I4.0

With regards to the participants’ knowledge of Industry 4.0 technologies, the participants were asked to check all the technologies that they believe are related to their I4.0. Eleven options were presented. Out of the 31 respondents, only 1 has indicated that all of the presented technologies are related to I4.0. The majority of the respondents (32%) choose 10 out of the 11 technologies indicating that there is a wide knowledge of the technologies associated with I4.0. Moreover, it was evident that “Big data analytics” was the most recognised I4.0 technology (87%) followed by “Machine learning” (83.8%), “Industrial automation” (80.6%)  and  IoT (71%).


Hence, it was evident that the research’s participants are highly aware of the 4th industrial revolution. As indicated by Dutt, Natarajan, Wilson and Robinson, (2020), the automotive industry has attained good positions in the digitisation of the physical world with the aid of machine learning and high-speed connectivity solutions. However, all of these participants have missed “additive manufacturing” despite the fact that it appeared to be an integral part of I4.0 technologies within the manufacturing sector (Nascimento et al., 2019). However, it was found that additive manufacturing had lost momentum in the automotive sector in the past two years (Capgemini Research Institute, 2020). This might be due to the irrelevance of 3D printing in the automotive sector.


Figure 8: Most significant I4.0 technologies for the automotive sector. Source (Capgemini Research Institute, 2020).


When asked whether their automotive organisations are implementing I4.0 technologies, the majority of the respondents (96.7%) indicated that I4.0 is used in smart manufacturing (87%), smart supply chains (80.6%), and smart working (70.9%). Ironically, only 16.1% of the participants indicated its use in smart products raising concerns about the status of technological developments within the UK’s cars.  These results are against the findings of the literature that proved that the automotive sector’s primary focus is on product developments (Bhatia and Kumar, 2020). On the other hand, a majority have indicated that I4.0 is being deployed for smart manufacturing which conforms to the findings of the Capgemini Research Institute’s report in 2020. The results further proved that the automotive industry has witnessed a drastic improvement in its smart manufacturing plants between 2017-18 to 2019.


Figure 9: The automotive industry’s progress in smart factories and smart manufacturing. Source (Capgemini Research Institute, 2020).

4.4 Perceived benefits of I.40 in the UK’s automotive industry

To obtain an insight into the perceived benefits of using I4.0 technologies, the respondents were asked to rate thirteen main benefits. Among these benefits, there was a general consensus that I4.0 technologies is perceived to be “extremely beneficial” to the organisation’s profitability which is apparent from the highest mean value and lowest standard deviation (M=4.42, SD=0.8). This is further supported by the ratings’ spread as indicated in the box & whisker plot.


The case is quite similar when it comes to flexibility (M=4.16, SD=0.9), connectivity (M=4.05, SD=1.1), waste reduction (M=4.03, SD=0.9), productivity (M=4.00, SD=1.1), and visualisation (M=4.00, SD=1.0). However, as indicated in the box & whisker plot, a few outliers exist which affect the mean and standard deviation values. Again, the results complement the findings from the literature (Winkelhake, 2019; Capgemini Research Institute, 2020; Bhatia and Kumar, 2020; Dutt, Natarajan, Wilson and Robinson, 2020). Likewise, Gill, (2018) argued that I4.0 technologies are capable of detecting disruptions in automotive manufacturing plants leading to higher profitability rates.


Concerning connectivity, the main advantages of the I4.0 technologies are manifested through three primary forms of integration within industries and businesses. Firstly, I4.0 aids in having vertical ICT integration across the hierarchical levels of an organisation; thereby, integrating the production and managerial levels (Dalenogare, Benitez, Ayala and Frank, 2018). Secondly, the technologies enable horizontal integration which facilitates and promotes coordination and collaboration across enterprises with real-time exchange of data. Finally, end-to-end engineering allows the integration of all value chain levels of a product from its development until it reaches the end-user (Dalenogare, Benitez, Ayala and Frank, 2018).


For cost reductions, 50% of the respondents gave a rating between 3 to 4 while 25% of the respondents gave a rating between 2 to 3. This might be due to the fact that I4.0 technologies are complex and capital intensive meaning that cost reductions might be realised only in the long run. Illogically, the ratings of process efficiency, product quality and enhanced competitiveness seemed to be highly dispersed which indicates that there is no consensus with regards to the extent of perceived benefits of I4.0 technologies in these areas. Similarly, when it comes to enhanced collaboration, the rating values were also highly dispersed which sheds light on the existence of other critical factors that enhance collaboration between different stakeholders within an organisation.

Figure 10: Box & whisker plot of the respondents’ ratings of the perceived benefits of I4.0.


These results vary to some extent from the findings of the literature. To illustrate, as stated by Dr Seshu Bhagavatula, president of the New Technologies and Business Initiatives at Ashok Leyland, one of the largest heavy vehicle manufacturers in India, “The three primary reasons why we took up the smart factory initiative is to improve the productivity, deal with the quality issues and to incorporate made-to-order or mass-customization capabilities” (Capgemini Research Institute, 2020, p.3).


Moreover, as indicated by Soffer, (2019), I4.0 technologies could significantly contribute to the enhancement of process efficiency. This is done through automated root cause analysis where, using different algorithms, the chain of events leading to production failures could easily be traced and rectified. Besides, such technologies are also capable of predicting process inefficiencies prior to their occurrence through the conduction of predictive analysis using big data analytics. This was further evidenced by General Motors’ initiative that involved thousands of robots installed in multiple manufacturing plans to detect flaws, deficiencies and potential failures and disruptions  (Gill, 2018). Besides efficiency, Capgemini Research Institute, (2020) found that intelligent automation and remote monitoring are capable of reducing stocks, eliminating reworks, reducing costs and enhancing the decision process.


Overall, looking at the mean values, it is worth noting that the respondents believed that I4.0 technologies would be in the range of “extremely beneficial” to “very beneficial” for all the given factors. These results suggest that there is a high level of awareness of the potential benefits of I4.0 from individuals at top management positions. This is further indicated by the relatively small standard deviations obtained for all factors as indicated in table 1 below.

Figure 11: Mean values of the perceived benefits of I4.0 technologies in the UK’s automotive industry.



Table 1: Descriptive statistics of the perceived benefits of I4.0 technologies in the UK’s automotive industry.


4.5 Perceived challenges of implementing I.40 in the UK’s automotive industry

Similarly, the research respondents were asked to rate 14 identified challenges that could be encountered during the development/implementation of I4.0 technologies. According to the results, it is apparent that the lack of an experienced workforce (M=3.8, SD=0.5) is the most significant challenge for the adoption of I4.0 which is again evidenced from the data’s spread that is concentrated around the mean value. This result raises concerns with regard to the extent to which training and educational programs are developed and conducted to ensure the availability of the required skills and competencies within the industry. The case is quite similar when it comes to external necessities such as IT infrastructure. As seen in the box and whisker plot, the absence of whiskers shows that the upper and lower quartiles are equal to the maximum and minimum ratings respectively.


Ironically, the lack of resources, in terms of machines and equipment, had the second-highest mean and standard deviation values (M=3.6, SD=0.7); while this could provide an insight into the special needs and required modifications to the existing technologies, it is worth noting that the ratings are highly dispersed. Concerning the lack of needed resources in terms of finances, human resources, and the needed technology, it has been acknowledged that, over the past 50 years, the automotive sector has been investing billions of dollars in automation, enterprise systems and advanced product technologies (Dutt, Natarajan, Wilson and Robinson, 2020) indicating that resources are not a major challenge. Furthermore, Yadav et al., (2020) suggested that the use of a time-tested approach, where small problems are tackled first, is the most effective approach to the technological transformation journey in terms of the availability of resources.


In the same way, the mean and standard deviation values for the lack of effective strategic plans are (M=3.6, SD=1,1); whereas 50% of the respondents’ ratings were below 3. However, Yadav et al., (2020) have found that the industry is plagued with a shortage of talent to plan, execute and maintain new digital systems. While the case might not be applicable to the UK’s industry, there is a lack of clear evidence proving otherwise. Overall, there are several solutions to the aforementioned challenges that could be readily implemented by organisations. Moreover, obsolescence risks attained the lowest mean and standard deviation values (M=2.4, SD=0.6) with 75% of the respondents’ ratings being below 3 and lower. This could also be indicative of the lack of awareness of the pace at which these technologies are progressing and evolving.


Figure 12: Box & whisker plot of the respondents’ ratings of the perceived challenges of I4.0.


Other highly perceived challenges included lack of financial resources, return on investment, compatibility issues, and collaboration indicating that the issues encountered are multi-dimensional issues that involve several stakeholders and departments and requires an overarching approach that considers different aspects simultaneously. Bhatia and Kumar, (2020) identified multiple challenges to the automotive sector in attaining the highest performance outcomes when incorporating I4.0 technologies; these include organisational leadership, IT infrastructure, financial aspects, external support from academics and researchers, data governance, collaboration and teamwork, training and development, strategic integration and legal aspects (Bhatia and Kumar, 2020). Thus, it is apparent that several challenges conform to the findings of this research.


Interestingly, the lack of research and development within the field was perceived to be a neutral challenge with 75% of the respondents’ ratings below 3; thereby, shedding light on the efforts being exerted for the development and adoption of I4.0. As indicated by Bhatia and Kumar, (2020), the automotive sector’s investments seem to be highly dependent on technology providers and suppliers which decreases the efforts spent towards education and training of the workforce. Therefore, shifting focus to education would not only ensure the availability of an experienced workforce but also would ensure having the capabilities to develop new technologies. Whereas, Winkelhake, (2019) suggested that the automotive industry should consider investing more capital in infrastructure projects and research, development and innovation (RDI) activities to modify the existing work procedures to adapt to the digital transformation.


Figure 13: Mean values of the perceived benefits of I4.0 technologies in the UK’s automotive industry.


The respondents were then asked whether they have rolled back after the implementation of I4.0 technologies with only four respondents claiming that the major issues encountered were technical, operational and managerial issues. These results complement the aforementioned findings with regards to the most challenging aspects faced. When asked about the possible solutions to tackle these issues, two of the respondents claimed that awareness should be raised about the potential benefits of the technology. Also, one respondent claimed that there is a need to implement specific analysis frameworks that identify the main aims and objectives of the organisation along with the development of specific Key Performance Indicators (KPIs) that aid in assessing the outcomes. Finally, two other respondents claimed that more research is needed due to the lack of technological advancements in the sector indicating that the technology is yet immature. However, none of the respondents claimed that financial, maintenance and control issues and lack of benefits were reasons for the rollback. This was further supported by the respondents’ replies that indicated that the costs of implementing I4.0 technologies are commercially sustainable.







Table 2: Descriptive statistics of the perceived benefits of I4.0 technologies in the UK’s automotive industry.


4.6 Essential factors for the successful implementation of I4.0 in the UK’s automotive industry

After identifying the perceived opportunities and challenges of implementing I4.0 technologies within the automotive industry, it was crucial to investigate the most influential factors for the successful implementation of I4.0 technologies. The highest mean value was attained by the leadership style (M=4.4, SD=0.8) where only 25% of the respondents’ ratings were between 3 to 4; thus, indicating that the leadership style is the most important driver for the successful implementation of I4.0 technologies. This further shows that effective leadership and management approaches including the planning, implementation and monitoring of diverse aspects is essential. Although having a business culture that is supportive of change is somehow related to having an effective leadership style and employees’ engagement, the ratings obtained are highly dispersed with 50% of the respondents’ ratings falling between 2 to 4 while 25% were from 2 to 3. As stated by Dutt, Natarajan, Wilson and Robinson, (2020), leadership and management styles are among the most significant factors influencing the success of I4.0 technologies. These findings are further supported by the results of Capgemini Research Institute, (2020) that indicate that having a proper transformational vision along with an effective leadership approach are crucial.


This was followed by education and training (M=4.3, SD=0.6) which shows that competencies and skills are fundamental factors to ensure success and the achievement of the desired outcomes. Furthermore, another reason why such skills are vital might be for sustainability reasons to maintain long-term growth and prosperity. Employees’ engagement in the decision-making process was also perceived to be an indispensable factor (M=4.3, SD=0.8) which further strengthens the aforementioned result in relation to the significance of leadership and management styles. Likewise, the existence and development of the needed infrastructure were also perceived to be pivotal for the success of I4.0 technologies (M=4.1, SD=0.8); however, the relatively lower ratings may be due to the fact that several I4.0 technologies might not be solely dependent on IT infrastructure.



Figure 14: Box & whisker plot of the most influential factors for the successful implementation of I4.0 in the UK’s automotive industry.


Ironically, the formation of long-term partnerships and strengthening the relationship between different stakeholders along the value chain was perceived to be of lower significance with 75% of the ratings falling between 2 to 3. Likewise, the ratings obtained for research and development activities were also highly dispersed with 50% of the respondents’ ratings between 2 to 4. These results defied the researcher’s expectations as it was expected that such partnerships would aid in knowledge transfer, acquisition of the needed skills and competencies and facilitate financial investments. On the contrary, it has been acknowledged that strategic partnerships are crucial in the implementation of I4.0 technologies within the automotive sector. This is since such partnerships aid in bridging the knowledge gap in the technology portfolio (Dutt, Natarajan, Wilson and Robinson, 2020). Moreover, there is a general consensus that solving challenges through the collaborative efforts of different stakeholders within the automotive sector would assist in fully realising the value proposition of I4.0 technologies (Dutt, Natarajan, Wilson and Robinson, 2020). Furthermore, Arcidiacono, Ancarani, Mauro and Schupp, (2019) found that the proactive approach to innovation, the development of long-term partnerships, and the early involvement of workers in the change process are among the leading factors that aid organisations in overcoming the anticipated challenges.


Finally, having data governance frameworks and legal frameworks that regulate the development, implementation and utilization of I4.0 technologies were perceived to be of the lowest significance as compared to all other factors, (M=2.8, SD=0.7) and (M=2.0, SD=0.2) respectively, which is against the findings of Bhatia and Kumar, (2020). This again casts doubt on the respondents’ level of knowledge with regards to multiple factors such as cybersecurity and ethical standards suggesting that more awareness should be raised with regards to the significance of these frameworks. This is since not only would such frameworks adversely impact the performance of organisations but also, might threaten the existence of a business.


Figure 15: Factors to be considered for the successful implementation of I4.0 technology in the UK’s automotive industry.


Table 3: Descriptive statistics of the essential factors for the successful implementation of I4.0 in the UK’s automotive industry


5 Conclusion and Recommendation


This research was driven by the increased momentum towards the adoption and implementation of I4.0 technologies in the manufacturing sector. With the acknowledgement of the significance of the automotive sector along with the identification of an existing gap in the literature, this research aimed to identify the potential opportunities and challenges of implementing I4.0 in the UK’s automotive sector in an attempt to provide recommendations that would raise the sector’s awareness of the technology’s benefits and provide solutions to anticipated challenges. In doing so, a positivist research philosophy was used using an online survey that gathered quantitative data from 31 experts working in different roles in the UK’s automotive sector. The data analysis was performed using descriptive statistics besides box and whisker plots. With regards to the potential benefits, the research’s findings revealed that enhancements to profitability, productivity, flexibility, connectivity, visualisation and waste reductions are the most significant benefits that were expected to be realised in the UK’s automotive sector. Concerning the anticipated challenges, the lack of an experienced workforce, necessary IT infrastructure, needed resources, return on investment, compatibility issues, and collaboration were among the highest perceived challenges for the adoption of I4.0 technologies. Whereas, leadership styles, employees’ engagement in the decision-making process, and education and training were considered as the most influential factors for the successful implementation of I4.0 technologies.

5.1 Recommendations

  • Although the research participants have a good knowledge of the relevant I4.0 technologies, they seemed to lack awareness of the associated benefits and the outcomes of using each technology. This was evidenced from the lack of knowledge of the associated cost reductions and enhancement in process efficiency, product quality and competitiveness. Thus, more awareness should be raised with regards to the means through which these technologies could be used to enhance business performance.
  • The research had also identified the significance of education and training programs to eliminate the shortage of experienced workforce. Therefore, more efforts should be exerted by the automotive industry to develop the required competencies.
  • Similarly, more efforts should be made towards research and development to enable the UK’s automotive sector to develop the needed technologies and reduce reliance on technology suppliers.
  • Also, firms should undertake the formation of long-term partnerships with different stakeholders to aid in knowledge transfer, acquisition of the needed skills and competencies and facilitate financial investments.
  • On an organisational level, firms should ensure having the right leadership and management style to facilitate the technological transformation journey. This includes adopting a decentralised management approach where employees are actively involved in the decision making process. Also, firms should promote a business culture that is supportive and adaptable to rapid changes.
  • Finally, similar to several initiatives that are being made by multiple European countries, the UK’s government should undertake and promote such initiatives and provide the needed facilities such as the required IT infrastructure, legal frameworks, and data governance frameworks.

5.2 Limitations

  • The research’s sample size is the main limitation of this study. Therefore, it is recommended that future research should incorporate a larger sample size to ensure having concrete results that could be generalised to the UK’s automotive sector.
  • The research was conducted using an online survey which may have limited having deeper insights into the underlying factors for the participant’s responses. Therefore, the conduction of face-to-face interviews is recommended for future research to have more conclusive results.
  • While the research is primarily focused on the UK’s automotive industry, a comparison between other countries could be beneficial to have an overarching view of the differences in challenges faced and benefits received. Accordingly, more precise corrective actions could be taken.


6 References


Arcidiacono, F., Ancarani, A., Mauro, C. and Schupp, F., 2019. Where the Rubber Meets the Road. Industry 4.0 Among SMEs in the Automotive Sector. IEEE Engineering Management Review, 47(4), pp.86-93.


Bhatia, M. and Kumar, S., 2020. Critical Success Factors of Industry 4.0 in the Automotive Manufacturing Industry. IEEE Transactions on Engineering Management, pp.1-15.


Capgemini Research Institute, 2020. How automotive organizations can maximize the smart factory potential. [online] Capgemini. Available at: <>.


Dalenogare, L., Benitez, G., Ayala, N. and Frank, A., 2018. The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, pp.383-394.


Dutt, D., Natarajan, V., Wilson, A. and Robinson, R., 2020. Steering into Industry 4.0 in the automotive sector. [online] Deloitte Insights. Available at: <>.


Farrell, P., 2021. Writing your built environment dissertation; practical guidance for students of the built environment. 1st ed. [ebook] Blackwell-Wiley, pp.1-10. Available at:ISBN: 978-1-4051-9851-6. 575>.


Fromhold-Eisebith, M., Marschall, P., Peters, R. and Thomes, P., 2021. Torn between digitized future and context dependent past – How implementing ‘Industry 4.0’ production technologies could transform the German textile industry. Technological Forecasting and Social Change, 166, p.120620.


Gideon, L., 2016. Handbook of Survey Methodology for the Social Sciences. 1st ed. Springer.


Gill, N., 2018. Automotive world and the adoption of Industry 4.0. [online] Capgemini Worldwide. Available at: <>.


Hidayatno, A., Rahman, I. and Daniyasti, D., 2019. Conceptualizing the Promise of Industry 4.0 Technology Adoption: Case Study of Indonesian Automotive Industry. Association for Computing Machinery, [online] pp.334-338. Available at: <> [Accessed 23 July 2021].


Lin, D., Lee, C., Lau, H. and Yang, Y., 2018. Strategic response to Industry 4.0: an empirical investigation on the Chinese automotive industry. Industrial Management & Data Systems, 118(3), pp.589-605.


Madsen, B., 2018. Statistics For Non-statisticians: Springer.


Muscio, A. and Ciffolilli, A., 2019. What drives the capacity to integrate Industry 4.0 technologies? Evidence from European R&D projects. Economics of Innovation and New Technology, 29(2), pp.169-183.


Naoum, S., 2007. Dissertation research and writing for construction students. 2nd ed. Elsevier Ltd.


Nascimento, D., Alencastro, V., Quelhas, O., Caiado, R., Garza-Reyes, J., Rocha-Lona, L. and Tortorella, G., 2019. Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context. Journal of Manufacturing Technology Management, 30(3), pp.607-627.


Narayanamurthy, G. and Tortorella, G., 2021. Impact of COVID-19 outbreak on employee performance – Moderating role of industry 4.0 base technologies. International Journal of Production Economics, 234, p.108075.


O’Gorman, K. and MacIntosh, R., 2020. Research Philosophy and Paradigm. 1st ed. Pp.1-10.


Paterson, A (ed.), Leung, D, Jackson, W (ed.), MacIntosh, R (ed.) & O’Gorman, KD (ed.) 2016, Research Methods for Accounting and Finance. Global Management Series, Goodfellow Publishers, Oxford.


Ramirez-Peña, M., Mayuet, P., Vazquez-Martinez, J. and Batista, M., 2020. Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review. Materials, 13(24), p.5625.


Saunders, M., 2009. Understanding research philosophy and approaches to theory development. 1st ed. Pp.122-161.


Soffer, A., 2019. The Business Benefits of Industry 4.0 in the Automotive Industry % Seebo Blog. [online] Seebo Blog. Available at: <>


Statista. 2021. Motor vehicles manufacturing turnover in the UK | Statista. [online] Available at: <> [Accessed 22 July 2021].


Winkelhake, U., 2019. Challenges in the Digital Transformation of the Automotive Industry. ATZ worldwide, 121(7-8), pp.36-43.


Yadav, G., Kumar, A., Luthra, S., Garza-Reyes, J., Kumar, V. and Batista, L., 2020. A framework to achieve sustainability in manufacturing organisations of developing economies using industry 4.0 technologies’ enablers. Computers in Industry, 122, p.103280.




7 Appendices


Appendix A: Ethical Consideration Form

Engineering Systems and Management ‘Self-Certificate’ ethics form for final year projects

Guidance for students and supervisors

This ‘self-certificate’ form for ethical approval should only be completed if your answer to all four questions below is NO.

If the answer to ANY of these questions is YES, then the Lower Risk or full, on-line application to the University Ethics Committee is needed.

Project working title: The challenges and opportunities of implementing industry 4.0 in the Automotive industry

Section B Ethics questions

1 – Does the project involve participants selected because of their links with the NHS/clinical practice or because of their professional roles within the NHS/clinical practice, or does the research take place within the NHS/clinical practice, or involve the use of video footage or other materials concerning patients involved in any kind of clinical practice?


2 – Does the project involve any i) clinical procedures or ii) physical intervention or iii) penetration of the participant’s body or iv) prescription of compounds additional to normal diet or other dietary manipulation/supplementation or v) collection of bodily secretions or vi) involve human tissue which comes within the Human Tissue Act? (eg surgical operations; taking body samples including blood and DNA; exposure to ionizing or other radiation; exposure to sound light or radio waves; psychophysiological procedures such as fMRI, MEG, TMS, EEG, ECG, exercise and stress procedures; administration of any chemical substances)?


3 – Having reflected upon the ethical implications of the project and/or its potential findings, do you believe that the research could be a matter of public controversy or have a negative impact on the reputation/standing of X University?


4 – Does the project involve interaction with or the observation of human beings, either directly or remotely (eg via CCTV or internet), including surveys, questionnaires, interviews, blogs, etc?


We confirm that, for the project detailed above, the answer to all four questions is ‘no’.



Appendix B


Statistics Cost reduction Process efficiency Productivity Quality Waste reduction Visualisation Time saving Connectivity Collaboration Customisation Competitiveness Profitability Flexibility
Mean 3.6 3.8 4.0 3.8 4.0 4.0 4.0 4.1 3.8 3.5 3.9 4.4 4.2
Standard Error 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.2
Median 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 3.0 4.0 5.0 4.0
Mode 4.0 5.0 4.0 4.0 4.0 4.0 4.0 5.0 5.0 3.0 4.0 5.0 4.0
Standard Deviation 1.1 1.1 1.1 1.0 0.9 1.0 0.9 1.1 1.2 0.9 0.9 0.8 0.9
Sample Variance 1.2 1.2 1.1 1.0 0.9 0.9 0.9 1.3 1.4 0.8 0.8 0.7 0.8
Range 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0
Minimum 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
Maximum 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0
Sum 111.0 118.0 124.0 119.0 125.0 124.0 123.0 126.0 119.0 107.0 120.0 137.0 129.0
Count 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0




Appendix C


Statistics Financial issues Size of Firm Experienced workforce Resources External necessities Management Reluctance Employees’ reluctance Investment risks Return on Investment Obsolescence risks Research & development Compatibility issues Strategic plans Collaboration
Mean 3.5 3.4 3.8 3.6 3.5 2.5 2.7 2.8 3.2 2.4 2.6 3.4 3.6 3.5
Standard Error 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.1 0.2 0.1 0.2 0.1 0.2 0.1
Median 3.0 3.0 4.0 4.0 3.0 2.0 3.0 3.0 3.0 2.0 3.0 3.0 3.0 4.0
Mode 3.0 3.0 4.0 4.0 3.0 2.0 3.0 3.0 3.0 2.0 3.0 3.0 3.0 4.0
Standard Deviation 0.6 0.6 0.5 0.7 0.5 1.0 1.0 0.7 0.9 0.6 0.9 0.8 1.1 0.7
Sample Variance 0.4 0.3 0.3 0.5 0.3 1.0 1.0 0.5 0.8 0.3 0.8 0.6 1.1 0.5
Minimum 3.0 2.0 3.0 2.0 3.0 1.0 1.0 2.0 2.0 2.0 1.0 2.0 2.0 2.0
Maximum 5.0 4.0 5.0 5.0 4.0 5.0 5.0 5.0 5.0 4.0 4.0 5.0 5.0 5.0
Sum 109.0 106.0 118.0 112.0 108.0 77.0 84.0 86.0 99.0 74.0 81.0 104.0 111.0 110.0
Count 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0



Appendix D


Statistics Research & development Infrastructure Business culture Leadership style Education & training Data governance Legal frameworks long term partnerships Employees’ engagement
Mean 4.0 4.1 4.1 4.4 4.3 2.8 2.0 2.6 4.3
Standard Error 0.2 0.1 0.2 0.1 0.1 0.1 0.0 0.1 0.1
Median 4.0 4.0 4.0 5.0 4.0 3.0 2.0 2.0 4.0
Mode 4.0 4.0 5.0 5.0 4.0 3.0 2.0 2.0 4.0
Standard Deviation 1.0 0.8 1.0 0.8 0.6 0.7 0.2 0.8 0.8
Sample Variance 1.0 0.7 1.0 0.6 0.3 0.5 0.0 0.6 0.6
Minimum 2.0 2.0 2.0 2.0 3.0 2.0 2.0 2.0 2.0
Maximum 5.0 5.0 5.0 5.0 5.0 4.0 3.0 5.0 5.0
Sum 123.0 126.0 127.0 135.0 132.0 87.0 63.0 81.0 132.0
Count 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0 31.0