2. Literature review
A review of the literature in the research on absorptive capacity, innovation and results of previous innovation projects was broad-based and varied. Cronin et al. (2008) suggested that an effective literature review should include multiple sources. The search was carried out using the Discover Durham connections, Durham University databases and search engines using EBSCOhost, LexisNexis, Science direct and Google Scholar. The keyword search terms included terms such as innovation management, innovation projects, innovation failure, innovation collaboration, open innovation, innovation success, organizational innovation, non technological innovation, innovation paradigm, absorptive capacity influence on innovation, absorptive capacity of SME, organizational learning, organizational competitiveness, project failure, innovation project failure, effect of project failure and resilience.
The remaining part of the chapter is a review on the published literature on innovation, its dimensions and definition; the impact of the results of the previous innovation project; and absorptive capacity, its dimensions, how to measure absorptive capacity and the questions developed from the literature review.
Innovation is widely regarded as the one of the main process driving economic growth and sustainable competitive advantages for the manufacturing industry. An innovation is the implementation of a new or improved product either goods or service or a process. The Oslo Manual (2005) describes the minimum requirement for a product, process or organizational method to be innovative is that it should be new or significantly improved by the organization. Innovation is closely linked to the concept of uncertainty and unpredictable, making the innovation management a complex task (Candi et al. 2013). However, Innovation is important for organization to have a competitive advantage over their competitors and to become leaders in their field (Cho and Pucik 2005; Gunday et al., 2011). Therefore, engaging in these activities is become a vital imperative for many organizations.
The increase in competition among organizations, large and small in the same field of work is forcing them to innovate to stay ahead or even remain in the industry. This increasing competence is the key to improvement and innovation. Such competition has therefore pushed SMEs to improve their competitiveness in customer service and innovation (Sing et al. 2008). Thakkar et al. (2008) argues that smaller organizations have to differ from larger organizations in essentially three main aspects – evolution. uncertainty and innovation.
The acquisition of knowledge from external sources, supplier network, competition, research partners even customers is vital to continuing innovation by an organization (Tallman and Phene 2007). The organization’s ability to capitalise on this external knowledge and commercialize it is related to its prior knowledge and absorptive capacity (Cohen and Levinthal 1990). By making use of the external knowledge and thus expanding boundaries, organizations can swiftly react to change in the market and make use of the information.
Traditionally, innovation has stemmed out the R&D department of the organization, from the hard work of the R&D employees and the organization’s commitment and expenditure in their R&D efforts (Arora et al. 2001). Much of the literature discussion on innovation examines the individual or organizational aspects. Many of the existing process models for innovation are linear in nature (Cheng and Van de Ven 1996). Furthermore, many authors focus on only one specific type of innovation like technological, product or process (Barrett and Walsham 1995). Cheng and Van de Ven (1996) propose an innovation model that presents innovation in non linear and as a complex and dynamic process. Van de Ven (2017) argues that innovation managers can not control the innovation success but can help it succeed by developing and practicing skills for the obstacles which may be encountered. He compares the innovation process to an uncharted river, which while going with the flow of the process the managers can look ahead to see if there are any obstacles and have to decide which course is best for the organization. In innovation literature there is a theme of the recognition of the novelty of the innovation project and the resulting uncertainty (Hall et al. 2011). There is a need to understand the concept of novelty and other dimensions of innovation before the definition of innovation.
2.2 Dimensions of Innovation
This section explains the view of novelty and the dimensions of innovation. Typically, innovation needs to be seen from different perspectives (Garcia and Calantone, 2002; Hall et al., 2011):
- Content or object dimension: What is new? For example, the business model, market, culture, strategy, product, service, etc.
- Context or subjective dimension: New for whom, when seen from which point of reference? People, organization, resource base, industry, etc.
- Intensity dimension: How new? How is the innovativeness of the innovation project measured?
- Process dimension: Where and when does the innovation process begin and finish?
- Normative dimension: When is the innovation successful or not? Good or bad?
The contest dimension classifies the innovation based on its type, that is the type of innovation. It is most often classified based on their resulting object or content. It may be classified separately into product or process innovation. Some researchers classify innovation as a complex system, which is built from components, interfaces, relationships, systems and a network of systems (Gatignon et al., 2002; Hernderson and Clark 1990). Others classify innovation based on their functional areas, such as production, marketing, logistics, etc. or related to the main subject of novelty such as organizational, strategic, technological, market or service innovation. More recently, researchers have focused on business model innovation which is classified based on the content dimension of innovation (Markides 2006).
The content dimension takes into consideration the perspective of the subject of innovation to which the innovation appears new. As innovation is a relative term, the innovativeness of an innovation project also depends on the person or organization or context perspective (Garcia and Calantone 2002). The classification of innovation based on context dimension makes innovation more objective and comparable and can be divided into different units, from the organization to the individual level. The typical unit of analysis in management research is organizational level. In an organization, the innovation managers decide what is innovative for the organization. Managers have the choice to put an innovation project on hold to further consideration. Holding a project can be relevant when the evidence is ambiguous, meaning they can be interpreted in multiple ways. They are influenced by the fit of the innovation project to the organization’s resources or the organization’s strategy and familiarity of the innovation project for example used technology (Danneels and Kleinschmidtb 2001). Based on their ability to broaden the familiar domain of the company the managers choose to rate innovation higher in the markets and technology used which are closer to the existing businesses. Garcia and Calantone (2002) suggest that the next large context level is industry. Innovativeness in this context is anything that is considered new for the industry. The new innovative product may be innovative for the organization entering into a new market, but it is not necessarily innovative for the organizations in that industry. Thus, this demonstrates that the innovation depends not only on the context in time and space but also on the unit of comparison, as novelty changes over time. The world perspective is the only one which eliminates time and space constraints, as it describes innovations that have been introduces for the first time for the whole world (Garcia and Calantone 2002).
The intensity dimension measures the novelty of the object of innovation. The measure of the degrees of ‘newness’ of an innovation is most frequently used as ‘Innovativeness’. Low innovative products are considered as products with low degree of newness while on the other hand highly innovative products are considered to have a high degree of newness. However, there is no indication of whose perspective and what is considered to be new. Innovativeness measures the magnitude of novelty as the degree of change from the preceding to the next state. Garcia and Calantone (2002) suggest that from a macro level perspective innovativeness is the ability of an innovation to create a paradigm shift in the market structure or science and technology. Considering a micro level perspective, innovativeness is the ability of the innovation to influence the organization’s existing technological or marketing resources, skills, capabilities, knowledge or strategy.
The process dimension of innovation represents the outcome and the process that eventually leads to the novel outcome of the innovation. From this view, the innovation process can be understood as all the activities involved in the introduction and creation of the innovation. The results of each phase are the input to the next phase. However, the findings in the later phase may require returning to in a previous phase. Hence, the process is most often nonlinear, interactive and repetitive (Lynn et al. 1996). This is a broader understanding of the process dimension which consists of all the activities right from idea generation to the introduction to the market. It includes the diffusion and impact of the innovation following Schumpeter’s distinction of invention, diffusion and innovation (McCraw 2007). One must be able to distinguish between the concept, realization and impact stages inside process innovation.
Normative dimension checks whether the innovation is a change is better or worse. Normative dimension is subjective to the reference individual or organization and the stage of the innovation process. Compared to the novelty perspective, the normative dimension takes into consideration the final assessment on the failure or success of the innovation after completion of the innovation process.
2.3 Definition of Innovation
The word “innovation” is derived from the Latin noun “innovates”. The modern interpretation of innovation started from Schumpeter’s work in 1934 (McCraw 2007). He defined innovation as new combinations of existing and new knowledge, equipment, resources and other factors. He states that it is about the changes that occur in the production process, development of products, materials, organizational forms and resources. With Schumpeter definition of innovation in 1934, a scientific discipline emerged from the late 1950s for innovation and has been developing fast, with many researchers contributing to innovation (Chen 2017).
An organizations innovation performance is a directly affected by the specific strategies and actions the organization employs to increase its innovative capability. In management and strategy literature, top management’s commitment to innovation, change in leadership, improving technological competencies, innovation culture and improving the employee’s creative skills are found to affect the organization’s innovation performance (Aragón-Correa et al., 2007; Herzog and Leker, 2010; Laforet, 2008). While, in operations management the organization’s innovation performance has been linked to how operational related strategies and activities can improve the innovation performance of the organizations (Maine et al., 2012; Terziovski et al., 2002;).
Traditional innovation paradigms introduced by researchers were mainly due to the changes in the industrial revolution and developments in information technology. These paradigms have focused on technology, science and the economy. Acs et al. (2017) explains that in developed countries only innovation can continuously stimulate new economic growth, while in developing countries through innovation the industrial infrastructure of the countries will improve and further improve their national competitiveness.
Innovation activities of the organization have been traditionally perceived to be mainly focused on product or process innovation. Both these types of innovation are associated with the development or the application of new technologies. New products usually have a new technical feature or many new features that offer some new functionalities for the product, allow new applications of the product or increases the product quality to meet the market needs. Liao et al. (2007) suggest that product innovation relates to all other categories of innovation which are radical, incremental and systems innovation. New process usually relies on the use of new technologies to achieve a greater efficiency of production or to produce better products or provide better services (Damanpour 1996). The first and second Oslo Manual directly link both product and process innovation to technological innovation (Schmidt and Rammer 2007).
The technological or technical view on innovation has been criticised by researchers. Firstly, it is usually biased towards innovation in manufacturing and does not fully capture innovation in services. (Hipp and Grupp 2005). Secondly, innovation in organizations is not only about developing and applying new technologies, but more importantly it is also to reorganize and adopt business routines, external relations, internal organization and marketing (Boer and During 2001). Thirdly, innovation management literature focuses on the importance of integrating all process, product and organizational innovation to successfully achieving market success by transferring new business opportunities and new ideas into new products (Cozzarin and Perzival 2006). Fourthly, innovation literature also stresses on the significant role of linking R&D, new marketing approaches and technological innovations (Griffin and Hauser 1996). Marsili and Salter (2006) suggest that the positive impact of innovation is not restricted to the practices which use technology or invest highly in internal R&D. Hence to get a complete picture of innovation in an organization, the concept of innovation much also be extended to non technological innovation (Schmidt and Rammer 2007).
The third edition of the Oslo Manual (2005) incorporates this view and introduces two new types of non technological innovations, marketing innovation and organization innovation, which complement product and process innovations. Marketing innovation is the implementing new marketing methods which have significant changes in design, packaging, pricing, placement and promotion (Oslo Manual 2005). While organizational innovation is the implementing of new organizational methods and routines which were not used in the organization earlier (Oslo Manual 2005).
As the Oslo Manual (2005) had introduced these new types of non technological innovations in 2005 and taking into consideration that many researchers have focused on technological innovation much more that non technological innovation over the years. The author wants to establish how different manufacturing organizations perceive innovation, if they consider it as technological innovation or they also recognize and implement non technological innovation. Therefore, the author suggests the following hypothesis:
Hypothesis 1 (H1): Perceiving innovation as a combination of both technological and non technological innovation has a positive effect on innovation in the organization.
2.4 Results of previous innovation projects undertaken by the organization
In the innovation context, innovation projects for creating or adapting new processes, products or services are more often terminated before they are completed (Balachandra et al., 1996; Shepherd and Cardon, 2009). Usually the project team members are allocated to new projects or other duties. Some innovation projects that fare well are sometimes terminated before they are completed due to change in the organizations strategy or considering the organizations strategic portfolio management (Krishnan and Ulrich 2001). These terminations may be due to avoid unnecessary losses, to align the innovation project portfolio of the organization, or simply release the resources associated with an unpromising initiative (Krishnan and Ulrich 2001).
The termination of an innovation project is considered to be a negative result or failure of an innovation project. Terminating products or project failure can have strong detrimental effects on the members of the innovation projects and the organization itself (Shepherd et al., 2011; Shepherd et al., 2013). Most employees perceive such termination as a personal failure (Shepherd et al. 2014). There is a lot of research on dealing with innovation project termination which focuses on how and when a project should be terminated (Patzelt et al., 2011; Sarangee et al., 2013). A few researchers have addressed the negative effects of terminating innovation projects on the individuals involved which may counteract the intended positive effects of ending the project early (Shepherd et al., 2011; Shepherd et al., 2013; Shepherd et al. 2014). The negative results of an innovation project as well as the trauma associated with it, is likely to leave scars which may impact the individuals’ commitment to the following projects (Shepherd and Cardon, 2009. Valikangas et al. 2009). This can be a major problem for the organization as the commitment of the team members is vital to the success of an innovation project (Shepherd et al., 2013). Thus, the way the organization reacts after the termination of an innovation project is important.
Most innovation projects have high time constraints and therefore the need for the team members to be committed to the innovation projects is more important than other organizational commitments (Hoegl et al. 2004). Hoegle suggest that commitment is defines as the acceptance of the goals and values of the project, the willingness to take part in the project with a desire to remain part of the project and work towards the projects goal. Thus, previous research shows that commitment towards a project and its final goal is one of the key success factors for innovation projects (Ehrhardt et al.,2014; Hoegl et al., 2004). Valikangas et al. (2009) outlines how the inability to commit to future innovation projects can be an outcome of innovation project terminations. While Shepherd et al.(2013) investigates the negative effects of project terminations.
Shepherd et al. (2009) finds that individuals can learn and recover from negative results of previous projects. The experience of loss tends to make people more fearful and aware to make sure the mistakes are not repeated in a new project. In the context of resilience, the commitment to a project after an unfavourable outcome of the previous project can be seen as the individual’s innovative capability (Hoegl et al. 2004). Shepherd et al. (2013) identifies social support can help the individual’s commitment towards a project. Shepherd et al. (2011) found a relationship between the time span between projects and learning from the mistakes of the project which was a failure. Shepherd el al. (2011) propose that it takes time to build an account of the failed project and the individuals need time to gain a deeper understanding of the relationships between the failed project and the contribution of the team. The influencing factor of social support can be providing resources like instrumental aid, emotions concern or appraisal.
Literature from learning and recovering from failure suggest that social support is a potential positive factor on the individual (Shepherd and Cardon, 2009; Shepherd et al., 2009; Shepherd et al., 2013). Social support from leaders especially can take different forms, emotional support or informational support. Emotional support can primarily help the individual to feel better and involves empathy, trust, care and opportunity for emotional expression (Cohen 2004). While, in contrast providing feedback is more take focused like giving advice and guidance is informational support (Cohen 2004). Providing social support shows care and empathy and is expected to have a positive effect on the individual, makes the individual feel valued and part of the team and will foster positive emotions. When negative emotions are prevalent, then positive emotions from social support are important to counteract the negative effects (Shepherd and Cardon 2009).
Even though the project termination might be the in the best interest of the organization and might be the best decision it may not always be a failure for the organization as they can learn from the mistakes and improve the way they take on new projects. Social support in an organization can be offered by managers, colleagues or friends in the organization. When a manager offers their support after a project failure, it can be in terms of being available and accessible to the team members and provide a provision of empathy, trust and care, along with the opportunity for emotional expression (Cohen 2004).
This research project will focus on the result of the previous innovation project, if it was terminated or abandoned, the innovation project is considered to be successful if it has financial benefits to the organization. If the results of previous innovation project effects the organization in terms of taking on new innovation projects and if there was any social support offered in the organization by managers when the previous innovation project was a failure. Therefore, the author suggests the following hypothesis:
Hypothesis 2 (H2): Social support after an innovation project failure in an organization has a positive effect on the organization taking on new innovation projects.
2.5 Absorptive Capacity
Absorptive capacity is defined as the ability to identify the worth of new information, assimilate it and then apply it to meet commercial ends (Cohen and Levinthal 1990). Since Cohen and Levinthal’s (1990) article on Absorptive Capacity (ACAP), it has received considerable attention among researchers. Building on Cohen and Levinthal’s initial work, researchers have shown that ACAP influences transfer of knowledge in an organization (Gupta and Govindarajan 2000) and interorganizational learning (Lane et al. 2001). Most of them find a positive impact of ACAP on innovation in an organization and performance either in a direct way (Caloghirou et al. 2014; Lichtenthaler 2009) or a moderating way (Fernhaber and Patel 2012; Tsai 2001). Cohen and Levinthal (1990) argue that with prior related knowledge, ACAP is very likely to build new knowledge which stimulates innovative activities.
Seminal authors Cohen and Levinthal (1990) found three main factors which influence the organization’s ACAP. First having diverse expertise within the organization. Second having prior knowledge which is associated with the new knowledge coming into the organization. Third the process of engaging the organization’s external environment such as sending the employees for advance training, learning from customers, supplier or competitors. Possessing these factors, especially prior knowledge will help the organization in identifying and accumulating new and valuable knowledge over a period. Organizations which can effectively develop their expertise, accumulate new knowledge and learn to be quick and expeditious when faced with unpredictable environments which may offer new business opportunities.
Cohen and Levinthal establish three main dimensions which correspond to the three abilities from their definition of absorptive capacity.
First is the ability to recognize and identify the value or the worth of the new external knowledge by the organization. To use the new external knowledge the organization has to have some basic prior knowledge.
Second, the organization has to be able to assimilate the new external knowledge, that is the organization has to be able to internalize the new knowledge. This is easier if the both the organizations knowledge processing system are similar.
Third, the organization has to be able to commercialize the new external knowledge. Here the more experienced organizations in problem solving and new product development will have an advantage over other organizations.
Many researchers relate to the same definition of the ACAP as Cohen and Levinthal (1990) however, they establish different numbers of dimensions for ACAP. While the definition of ACAP has been changing based on research done by subsequent researchers, the dimensions have also changed. Table 1 shows the main dimensions of absorptive capacity
Table 1. Main dimensions of Absorptive Capacity
|Cohen and Levinthal (1990)
|Recognize the value
|Lane and Lubatkin (1998)
|Recognize the value
|Lane, Salk, and Lyles (2001)
|Zahra and George (2002)
|Todorova and Durisin (2007)
|Assimilate or Transform
|Recognize and assimilate
|Maintain and reactivate
|Transmute and apply
Lane and Lubatkin (1998) start from the same definition of Cohen and Levinthal (1990) and establish same dimensions. They however, stress on the first dimension is the similarity of academic or technical knowledge of the organization. The know how part of the organizations knowledge base is the second part dimension, while the final dimension focuses on the similarity of the organizations commercial focus.
In the subsequent study by Lane et al. (2001), they focus their research on ACAP in the context of International joint ventures and further expand and adapt the three dimensions. The first dimension they state is the ability to recognize the knowledge, which according to them depends on the trust and type of relation between the joint venture partners, their past knowledge base and their cultural compatibility. The ability to assimilate the new knowledge, which is their second dimension depends on support from higher management, the organizations flexibility, adaptability, specialty and the official objectives of the organization. Finally, the third dimension stated by them is the ability to apply the external knowledge, is based on the training programs and the business strategies of all the organizations involved in the joint venture. They however, relate the third dimension to the performance of the joint venture and not to the knowledge learned as in the other two dimensions. They further conclude that the first and second dimension, the ability to understand and the ability to assimilate the external knowledge are independent and different from the third component, the ability to apply the knowledge. This result is consistent with the results of Heeley (1997). Heeley states that there are two dimensions to absorptive capacity, acquisition of external knowledge and the distribution of the knowledge in the organization. Therefore, ACAP could have only two dimensions.
Zahra and George (2002) find four dimensions of absorptive capacity which is in unison with their definition of the phases of absorptive capacity. They define absorptive capacity as the set of strategic processes and procedures of an organization by which it can acquire, assimilate, transform and exploit the knowledge to create value. The first dimension is acquisition which was defined as the recognition of value of knowledge but is redefined by them by focusing not only on the use of the external knowledge but also its transfer from one organization to the other. Assimilation is the second dimension, which focuses on the organization to understand and use the external knowledge by its own internal processes. The third dimension is transformation, which focuses on the ability of the organization to combine the newly acquired knowledge with the existing knowledge of the organization. The final dimension is exploitation, which is strategic for an organization and generates the results after the three previous dimensions. It is the development of processes which can apply the newly acquired knowledge to either improve existing goods and services or create new ones.
They further combine the dimensions into two subsets, potential and realized absorptive capacity. Potential capacity allows the organization to be receptive to the external knowledge, that is to acquire, examine and understand this new knowledge. This involves the knowledge acquisition and assimilation dimensions. Realized capacity is the organizations capability to transform and exploit the new knowledge by incorporating this with the existing knowledge into the operations of the organization. This is therefore determined by the knowledge transformation and exploitation dimensions. They emphasize that if an organization acquires knowledge from another organization and evaluates it, it may not exploit this newly acquired knowledge.
Todorova and Durisin (2007) make some changes in the definition developed by Zahra and Gerorge (2002) which leads to change in dimensions and further propose to reconceptualize absorptive capacity. They propose that the first dimension is the ability to recognize the value of external knowledge as done by Cohen and Levinthal (1990) and emphasize recognition as the first important step in acquiring new knowledge. They further suggest that transformation is an alternative step to assimilation but not subsequent since organizations can acquire new knowledge which would be more compatible with the business operations if they have some prior knowledge. Furthermore, the transformation process assumes that those business process and ideas which were not understood or not compatible with the old frameworks and business models, the organization can now understand them and use them. Therefore, the four dimensions stated by Todorova and Durisin (2007) are recognize the value, acquire the new external knowledge, transform or assimilate the knowledge and exploit it.
The research by Lichtenthaler (2009) also suggests three complementary learning processes under ACAP: recognize and assimilate which is part of the exploratory learning; maintain assimilated knowledge and reactivate this knowledge which is part of the transformative learning; and finally transmute and apply which is part of the exploitative learning. These stages have already been analysed by previous researchers however, Lichtenthaler (2009) has grouped them in a different way.
Volberda et al. (2010) endorse a framework for ACAP which is based on the model suggested by Zahra and George (2002). However, they emphasize that is it important to consider the managerial and intra organizational antecedents as drivers for ACAP of an organization.
Considering all the foregoing contribution, the author defines the four dimensions of ACAP as:
Acquisition is the organization’s capability to seek, recognize, evaluate and acquire the external knowledge which is important for the development its business operations (Lane and Lubatkin, 1998; Zahra and George, 2002)
Assimilation is the organization’s ability to understand and realize the importance of the external knowledge extracted from outside the organization. The ability to investigate, organize, develop, interpret and finally internalize and understand this new knowledge (Cohen and Levinthal 1990).
Transformation is the ability of the organization to facilitate the transfer of the assimilated knowledge (new and prior knowledge). This is done by combining the new and existing knowledge in a new and different way, eliminating and binding knowledge and by interpreting it in a different way (Jansen et al., 2005; Todorova and Durisin 2007).
Exploitation is the organization’s capability to integrate the newly acquired, assimilated and transformed knowledge into its business operations and routines for the organization’s application and use. This will improve the quality of products and services and may develop new product and services (Lane et al. ,2001; Zahra and George, 2002).
The author suggests the following hypothesis:
Hypothesis 3 (H3): Engaging in all four dimensions of absorptive capacity will have a positive effect on the innovation in an organization.
There is a need to access the organizations performance in all the four dimension of ACAP, which is suggested in section 2.6.
2.6 Measures of Absorptive Capacity
There have been many ways in which researchers measure ACAP, many have chosen to measure ACAP directly by considering it to be unidimensional. Research and Development (R&D) effort is the most commonly used measure, which is the R&D expenditure by the organization divided by the annual sales of the organization (Cohen and Levinthal, 1990; Stock et al., 2001; Tsai, 2001; Zahra and Hayton, 2008). They used a simple method of measuring ACAP, however, it does not completely reflect the ACAP of the organization (Zahra and George 2002).
While other authors have used one or two measures which are related to the investment in R&D by the organization. The number of full time R&D personnel (Muscio 2007), number of patents and R&D expenditure (George et al. 2001), the knowledge management of information technology (IT) (Boynton et al. 1994), the annual expenditure of an organization on research publications (Cockburn and Henderson 1998), R&D effort and the effort of training staff, which is the expenditure of training the staff divided by the annual sales (Petroni and Panciroli 2002) and R&D activities which develop new knowledge and dissemination of this knowledge (Spithoven et al. 2010)
Other researchers have used a wider set of variables to measure ACAP. Mangematin and Nesta (1999) use R&D expenditure, number of researchers, duration of R&D activities, R&D laboratories, number of publications, number of patents and links with research institutes. Nieto and Quevedo (2005) use a scale of 32 items to measure the organization’s knowledge and experience, communication, diversity and strategic position of the organization.
Finally, another group of researchers measure ACAP as a process, measuring the various dimensions of ACAP. However, there is no consensus on the number of dimensions of ACAP. Heeley (1997) measures ACAP through the acquisition of new knowledge and it’s dissemination within the organization. Chen (2004) measures how the organization assimilates and replicates the new knowledge. Lin et al. (2002) measures adaptation, production and the application of knowledge, while Lane et al. (2001) uses a scale of 24 items to measure comprehension, assimilation and application of the new knowledge and Jasen et al. (2005) uses a scale of 21 items to measure the potential ACAP and realized ACAP. Table 2 summarizes the different measures used to measure ACAP most by the respected researchers.
Table 2. Measures of ACAP
|Ahuja and Katila (2001)
|Number of patents
|Boynton et al. (1994)
|Knowledge management of Information Technology (IT)
|Scale of 5 items to measure organization’s ability to assimilate and reproduce the new knowledge.
|Cockburn and Henderson (1998)
|Annual expenditure of an organization on research publications.
|Cohen and Levinthal (1990), Stock et al. (2001), Tsai (2001), Zahra and Hayton (2008)
|R&D effort = R&D expenditure / annual sales.
|George et al. (2001)
|R&D expenditure and number of patents.
|Scale of 24 items to measure the acquisition of knowledge and dissemination of the knowledge in the organization.
|Jasen et al. (2005)
|Scale of 21 items to measure the potential ACAP and realized ACAP.
|Lane et al. (2001)
|Scale of 24 items to measure comprehension, assimilation and application of the new knowledge.
|Lin et al. (2002)
|Scale of 15 items to measure capacity of adaptation, production and application of the new knowledge.
|Mangematin and Nesta (1999)
|R&D expenditure, number of researchers, duration of R&D activities, R&D laboratories, number of publications, number of patents and links with research institutes.
|Number of R&D personnel or in house education compared to total employees.
|Nieto and Quevedo (2005)
|Scale of 32 items to measure the organization’s knowledge and experience, communication, diversity and strategic position of the organization.
|Petroni and Panciroli (2002)
|R&D effort and effort in training staff = expenditure of training of staff/annual sales.
|Spithoven et al. (2010)
|R&D activities which develop new knowledge and dissemination of this knowledge.
|Wang et al. (2018)
|Ask managers to evaluate the organization’s capability to acquire, integrate and commercialize the external knowledge.
This research project seeks to measure ACAP capacity by measuring the four dimensions of ACAP which are acquisition, assimilation, transformation and exploitation of knowledge based on Zahra and George (2002). As can be seen from table 2, researchers have used different measures to measure the absorptive capacity of an organization. The author based the questions to assess how the organization engages in the four dimensions of the organization’s ACAP based on the research done by Flatten et al. (2011), Jansen et al. (2005) and Lane et al. (2001). The questions are as follows:
Question 1: Is the search for relevant information concerning your industry an every-day business matter in your company?
Question 2: Does the management motivates the employees to use information sources within our industry?
Question 3: Does the management expects that the employees deal with information beyond your industry?
Question 1: In your company are ideas and concepts communicated cross-departmental?
Question 2: Does the management emphasizes cross-departmental support to solve problems?
Question3: In your company is there a quick information flow? e.g., if a business unit obtains important information it communicates this information promptly to all other business units or departments.
Question4: Does you have periodical cross-departmental meetings to interchange new developments, problems, and achievements?
Question 1: Do the employees have the ability to structure and to use collected knowledge?
Question 2: Are the employees are used to absorb new knowledge?
Question 3: Can the employees successfully link existing knowledge with new insights?
Question 4: Are the employees are able to apply new knowledge in their practical work?
Question 1: Does the management supports the development of prototypes?
Question 2: Does the company regularly reconsider technologies and adapts them accordant to new knowledge?
Question 3: Does the company have the ability to work more effective by adopting new technologies?
3. Methodology and Method
3.1 Research approach
The research conducted in this research project was conducted with approach of abduction, as the author made use of existing theory and research to formulate the hypothesis and direct the data analysis (Saunders et al. 2016) while the findings were derived from the data gathered from semi structed interviews and observations of the manufacturing organizations taking part in the research project. Saunders et al. (2016) suggests that the abduction approach moves back and forth between induction and deduction approaches. According to Saunders et al. (2016, pp. 148) Easterby-Smith et al. (2012) suggest three reasons why selecting the approach to the research is important. Firstly, it enables the researcher to take a more informed decision about the research design, what kind of evidence is required and from where. Secondly, it will help the researcher think about the research strategy and the methodology that is appropriate for the research. Thirdly, knowledge of different research traditions enables the researcher to adapt the research design to cater for constraints.
The research purpose for this research project is exploratory. Saunders et al. (2016) recognizes that exploratory research studies is a valuable means to ask open questions to discover what exactly is happening in the organization and gain valuable insights about the research topic. They further suggest that exploratory research is particularly helpful when the researcher wishes to clarify their understanding of an issue, phenomenon or problem.
3.2 Research Method
According to Miles et al. (2013) in qualitative research methods while researching a phenomenon, the researcher needs to get as close as possible to get a better understanding of the phenomenon. In this research project the author is researching about innovation and how the manufacturing organizations perceive it, how the result of the previous innovation projects affects the organization on taking on new innovation projects and how absorptive capacity of the organization effects the innovation in the organization.
Qualitative research is suited for this research project as it provides an indication of how the organizational characteristics affect the individual’s actions, in the case of this research project the individual concerned are the managers in the manufacturing organization who can take on new innovation projects. Quantitative research gives the associations between variables or the effect of the individual variables on other variables, which are the main subject of the research (Barbour 2014). Starman (2013) suggests that qualitative research is characterized by an paradigm which is interpretative, subjective to experience and their meanings to an individual and therefore, the subjective views of the participants of the research study plays a vital role. The close collaboration and interaction between the participant and the researcher giver qualitative research methods an advantage as it enables the participants to tell their story or their side of things (Baxter and Jack 2008). By doing so, the participants are able to describe their views of reality and enable the researcher to understand the participant’s actions better.
Opposed to quantitative methods which rely mainly on a research design which is more or less linear, qualitative research methods can be flexible and allows the interview questions to evolve during the research (Barbour 2014). Saunders et al. (2016) recognizes that due to the exploratory nature of a research, interviews are most likely to be relatively unstructured and the research will rely on the quality of the contributions of the participants of the research. This supports the semi structured interview process selected for this research project.
According to Yin (2009) a case study design should be considered when:
- The focus of the research is to answer “How” and “Why” research questions
- The researcher cannot influence the behavior of those involved in the research study
- The background and conditions are important to the research.
The purpose of using case studies in qualitative research is to increase the contextual understanding and create a new knowledge using a single case to study multiple phenomena or using multiple case studies to compare one phenomenon (Barbour 2014). Starman (2013) suggests examining several individual cases that are selected in such a manner that their analysis will provide the most diverse information the researcher will be able to collect. He proposes in selecting contrasting cases instead of the typical cases. Thomas (2011) also suggests selecting an atypical case, where the subject and object have a dynamic relationship. Some researcher criticizes the case selection based on selection bias which is due to the researcher’s prior knowledge about the case. However, Starman (2013) states that selecting the cases based in prior knowledge leads to a better research plan which will make the procedure of theory testing more rigorous. A good case study researcher maintains professionalism, which includes keeping up to date on the related research, understanding the intricacies of the research, striving for credibility and ethical considerations and maintaining full disclosure (Saunders et al. 2016). Yin (2009) states that case studies can be used for exploratory research.
In this research project the author studies one phenomenon, innovation how it is perceived in four different case manufacturing organizations. Case studies are based on detailed analysis on rich empirical data, hence there is a need to have multiple case studies in order to have a high quality analysis (Barbour 2014). Yin (2009) suggests that multiple case studies have the same methodology as a single case study. Saunders et al. (2016) suggests that the rationale for using multiple case studies focuses on if the findings can be replicated across the cases. Yin (2009) proposes that a multiple case study strategy may combine a small number of cases chosen to predict a linear replication and another small set of cases to predict theoretical replication. Linear replication is when cases are selected to where the results are predicted to be similar to one another while theoretical replication is when a set of cases are chosen based on different contextual settings (Saunders et al. 2016). This supports the authors decision to select some different manufacturing organizations based on contextual settings and some similar SMEs.
In this research project the author uses multiple cases to try and comprehend the holistic view of innovation in manufacturing organizations in the North East of England. The author will use case studies of two small manufacturing organizations, one medium manufacturing organization and one large multinational manufacturing organization all based in the North East of England. The implementation of a remediation plan minimized the risk of exposure for the research project, which included interviewing techniques such as being a good listener, asking good questions and staying adaptive (Yin 2009)
3.3 Data collection
The data was collected in the form of semi structured interviews supported by observations of the manufacturing plants of the participating organizations.
The data was collected in line with the qualitative method chosen for this research project as suggested by Saunders et al. (2016). Yin (2009) explains that the individual conducting the research serves as the instrument in qualitative research methods. An interview guide (Appendix 1) was used as support throughout the interviews to ensure all the questions and parts which were required to fulfil the research objectives were covered. The interview guide was more of an informal list of topics and questions opposed to structured interviews where a manuscript of the exact questions in a certain order have to be followed (Barbour 2014). The interview guide consists of 13 open ended question for innovation and the results of the previous innovation project undertaken by the manufacturing organization and 14 questions with regards to the organization’s absorptive capacity. When collecting data through questions either from questions or surveys, the researcher has to consider how the questions are formulated as they will determine the answers the researcher gets (Yin 2009). Figure 3 represents the overall details of the data collection protocol flowchart.
The advantage of asking questions during a face to face interview is that it is easier for the interviewer to see how the interviewee is answering and reacting to the questions and hence easier to discover and clarify any misinterpretation or misunderstandings regarding the questions (Saunders et al. 2016). To avoid the risk of not getting the correct answers for the research project, the author has contemplated advice found in literature on how to formulate good interview questions (Saunders et al. 2016). The author has then checked the interview questions with the supervisor and friends to see if the questions are understood correctly and edited them if needed before conducting the interviews.
Based on the qualitative research method, the author chose to use non probability sampling techniques as explained by Saunders et al. (2016). The author mainly used a purposive sampling strategy. Saunders et al. (2016) describes purposive sampling as when the researcher has a clear idea of the sample units needed for the research, selects and approaches potential research participants to check if they fulfil the researcher’s requirements. The author had a clear idea of the characteristics of the manufacturing organizations required for this research project . The criteria the author used is as follows:
- Size: Small (10 to 49 employees) to medium sized organizations (50 to 249 employees) and some large organizations ( more than 250 employees)
Motivation: The author selected at least 2 organizations in each size category to compare organizations within the same size bracket.
- Industry: Manufacturing, preferably the organization is an end manufacture or assembles the final product, where the products can be customized.
- Location: The manufacturing organization have to be located in the North East of U.K.
- Interviewee position: Depending on the size and structure of the organization the author selected to interview people who were involved or were responsible for innovation projects of the organization. 1. Innovation lead, 2. Business Development Manager, 3. Design or production Manager, 4. Director, 5. IT Manager
Motivation: The interviewees have to have a holistic view of innovation in the organization, the represent the gate keepers of the organization in terms of taking on new innovation projects.
3.5 Data Analysis
Yin (2009) explains that data analysis in research is challenging, specially when involving case study research. Yin (2009) further suggests that one way to reduce the challenges incurred in analysing a qualitative case study is to have a general strategic approach to the data analysis. All information obtained from data collection was noted Microsoft Excel and Word. The data analysis of this research project included participate tone and observed behaviour from the semi structured interviews. Data analysis involves reducing the collected raw data into a manageable size, developing concise summaries, examining them for patterns, coding or indexing, tabulating, testing or recombining the data to produce empirically based findings (Saunders et al. 2016).
The interview data was analysed as the research project progress as recommended by Saunders et al. (2016). Obtaining background knowledge and understanding of the data included the thorough review of each transcribed recording. After reading each of the transcribed recording and listening to the audio recording of the interviews for understanding, keyword, phrases and sentences which stood out were manually highlighted, thus providing a constant comparison of the emerging relationships.
3.6 Credibility and Dependability
The author has abided by the four principle for data collection as stated by Yin (200) to carry out a high quality case study research which are as follows:
- Use multiple sources of evidence
- Create a case study database
- Use electronic sources with care
- Maintain a chain of evidence
Yin (2009) further described methods for conducting four design tests to ensure that research has validity and reliability and further outline specific tactics which have to be used to maintain the high quality in the case study analysis. In qualitative research methods the research has to determine how the abstract concepts are aligned with research phenomenon and have the ability to prove linkage of the data which has been collected and analysed (Saunders et al. 2016). The researcher must carefully and accurately identify the research participants and portray they correctly to solidify the credibility of the research Starman (2013). Credibility was achieved for this research project through respondent verification to ensure that the represented information is what the participant said.
Dependability is another important criterion for a high quality qualitative research. Dependability is established when the data used in the research remains constant over time (Yin 2009). Saunders et al. (2016) specifies that the dependability of the data in vital for the research to be trustworthy and that the dependability will be higher when others can easily follow the same procedures of the original researcher. Thus, the author has provided the procedure and steps taken in this research project in detail. Dependability was achieved by using standard questioning procedures while interviewing the participants. Furthermore, the data collection was carefully managed to support the dependability of this research project.
3.7 Ethical considerations
There is a long list of ethical disputes that can arise from performing a research, issues from legal to cultural concern or intellectual matter and so forth, all depending on the perspective of the different participants involved in the research. Therefore, the author has followed the ethical guidelines for research suggested by Saunders et al. (2016). In accordance with the Durham University Strategic business project handbook, each participant was given a description of the research project subject and the list of the semi structured interview questions in order for their preparation. They were also offered an opportunity to ask questions before the interview, in an attempt to avoid any deception about the nature of this research project.
Regarding the protection of the research participants, all the participants were treated equally and with dignity as well as provided with a consent form (Appendix 2) , which informed them about the research aim, asked them for their permission to use their name, the organization’s name to ensure the confidentiality of the data and if the interview could be recorded via an electronic device. All the participants were given the option to withdraw from the study at any time. The participants were made aware of the safeguards taken with the data as part of the informed consent process. All the participants gave the author permission to record the interviews for a better analysis of the data however, all did not agree to disclose their and the organization’s names . Therefore, the author has anonymized the participants name and the organization’s name but has mentioned the participants position in the organization with their consent.
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