Abstract
The increased complexity in the current business environment connected with the globalisation of economies and rapid technological developments makes firms depend on innovation and, in the process, develop dense networks of relationships, making collaboration an essential requisite for innovation. Thus, collaboration develops based on complex social networks from which innovation emerges. From this perspective, collaboration takes a systematic approach, where social relationships are crucial. This article describes the innovation behaviour of firms operating in India and introduces collaboration as a system drawing from the systems theory and triple helix innovation model. The results of the mixed methods study conducted pointed toward a fragile collaboration framework. Triangulation was employed to provide a deeper understanding. Furthermore, limited understanding of collaboration as a social system has constrained social interactions, leading to limited knowledge production, application, and knowledge sharing, with technological development and innovation delays. The article lists crucial factors from the perspectives of industry and academia to foster a collaboration framework.
1 Introduction
Over the past decades, globalisation and the accelerating pace of scientific and technological development have transformed how firms and markets operate (OECD, 1999; Möller, 2010; Salimi and Rezaei, 2015). Examples are short product cycles, increased competition and the rise of knowledge complexity. Emerging markets are the most affected because firms in these countries require developing capabilities to build on their innovative abilities (Pai et al., 2012; Cirera and Sabetti, 2019). As a result, innovation becomes compulsory (Pai et al., 2012).
Innovation in India is particularly significant because the country has recently extended its market-oriented economy in which increased importance is given to the private sector, affected previously by a tightly regulated economy (Ahluwalia, 2019; Krishnan and Prashantham, 2019), and to universities fostered by higher dependence on knowledge production.
Innovation is a process dependent on the production, distribution and application of knowledge (Pai et al., 2012), highly reliant on complex relationships (Powell et al., 1996, Datta and Saad, 2011). Hence the most successful businesses will be the ones capable of establishing complex relationships for the production and application of knowledge to foster the creation of complex technological processes and products (Rycroft and Kash, 1999). These complex relationships introduce a social dimension into innovation known as collaboration, which is crucial for firms (von Hippel, 1988, Doz, 1996, Chesbrough, 2004; Gillier et al., 2012; Salimi and Rezaei, 2015; Hsieh et al., 2018; Mascarenhas et al., 2018; Schilling and Shankar, 2020: 179) because it enables faster learning (Laursen and Salter 2006; Un and Asakawa, 2015; Haus-Reve et al., 2019).
The inclusion of a social dimension in innovation paved the way for its renewed conceptualisation to encompass a system dimension. As such, several models were developed to investigate innovation and technological changes based on relations and flows of knowledge, and the national innovation system is one of the most well-known models (Nelson, 1988; Lundvall, 1992; Freeman, 1995; Edquist, 1997; Lu, 2008; Etzkowitz, 2008: 8; Saad and Zawdie, 2008; Kashani and Roshani, 2019). More recently, scholars realised the significance of academia (Etzkowitz and Leydesdorff, 2000; Heaton et al., 2019) in the innovation process, which led to the emergence of the triple helix innovation model. The model analyses innovation by focusing on the government, industry, academia relationships and knowledge dynamics. The performance of the innovation model depends on the strength of the collaboration based on relationships and knowledge flows occurring between these organisations. However, the current amount of literature published associating innovation with the triple helix model suggests it has come chiefly from developed markets, with very little research conducted in emerging economies (Egbetokun et al., 2017).
Additionally, the literature on collaboration between industry and academia has recognised the significance of academia in innovation systems in developed countries (Heaton et al., 2019); but in emerging markets and India, the discussion has been far and between (Schiller and Brimble, 2009; Joseph and Abraham, 2009; Mascarenhas et al., 2018). Thus, there is a need to understand more about innovation in India and the contribution of collaboration to this process. This article was built based on the literature on collaboration between academia-industry and innovation systems as not much literature was found related to India. In doing so, it informs the extent of work conducted earlier.
This article aims to explore the innovative behaviour of firms in India and investigate the contribution of collaboration to innovation from a systematic perspective using a systems theory approach and the triple helix as an analytical model. Two main research questions guide the study: how are firms innovating in India and how does collaboration contribute to innovation. We conducted a small preliminary literature review to assess the extant literature, followed by a sequential explanatory mixed methods study composed of a quantitative and qualitative strand. The first strand explores the innovative behaviour of firms operating in India by measuring variables associated with innovation activities. The second strand was conducted subsequently to develop a deeper understanding of the statistical models and investigate the contribution of collaboration by examining the current relationships and knowledge flows between firms and academia.
The remainder of the article is organised as follows: in the next section, we define collaboration from the perspective of systems theory, including the triple helix, and summarise how collaboration is relevant to knowledge production, innovation and social capital, and why the triple helix is helpful to analyse innovation and how it is a unique form of collaboration. The methods section describes the mixed methods design used in this study. We introduce the findings of each phase of the investigation in the results section and then analyse them in the discussion section. Finally, conclusions are drawn in the final section, summarising the most significant findings, discussing limitations and identifying future research directions.
1.1 Motivation for the Study
1.1.1 Innovation in India: Historical Overview and Setting Trends
India got its independence from Britain in 1947. The new government believed that technology and innovation would spur economic growth (Li and Nair, 2007; Nair et al., 2015). The investment in research and innovation was implemented soon after based on the Soviet model through the setting up of government run and owned factories, infrastructure, and R&D laboratories (Li and Nair, 2007; Nair et al., 2015; Krishnan and Prashantham, 2019) and over time India’s economy became public-sector-dominated (Li and Nair, 2007; Nair et al., 2015). However, the investments made did not produce the effects anticipated. Between 1950 and 1980, India was growing slower than other emerging markets such as Korea, Malaysia and Singapore at a sluggish annual growth of around 3%, 3.5% instead of the typical 7% in Asian markets (Li and Nair, 2007; Nair et al., 2015; Jain et al., 2015). One of the explanations is associated with the over-regulated system that produced inefficiencies, shortages and corruption (Li and Nair, 2007). This period of tight regulation has isolated Indian firms from innovation occurring elsewhere (Nair et al., 2015). For example, sourcing raw materials or importing technology requires regulatory approvals by the government (Madanmohan et al., 2004; Nair et al., 2015; Krishnan and Prashantham, 2019). Firms were not incentivized to explore foreign market options, and because of the large home market, there was no need to develop abroad (Sahasranamam et al., 2019). This environment would lead to a slow innovation pace with consequences in developing an innovative structure as it was not essential. New economic reforms were introduced in the mid-1980 that opened the economy. During this period, trade barriers were reduced, focusing on export policies to attract foreign direct investment and expanding the private sector (Krishna et al., 2000). Many firms adopted foreign technology, and multinationals (MNC s) entered the country more significantly, increasing competition. The awareness of the importance of innovation in driving economic growth also increased (NKC, 2007; Nair et al., 2015).
Following the liberalisation of the economy, India’s economic growth would initially be driven by low-cost labor, a rise in the expansion of the private sector and foreign investment, which allowed many businesses across industries to upgrade their technology and improve productivity and meet demands (Nair et al., 2015). Conversely, businesses would also experience weak technological capabilities due to the absence of incentives for firms to build on their competencies associated with the technology barriers imposed during the regulated economy (Krishnan and Jha, 2011). The entrance of MNC s into the country with well-oiled innovation processes built over more developed innovation structures implied that Indian businesses had to develop fast catching-up strategies and processes not to fall behind. Trying to adapt fast to the new business environment and the market demanding affordable products, the focus of Indian businesses has been on incremental products and process innovation (Krishnan and Prashantham, 2019). Catching up on the knowledge ladder led many Indian businesses to adopt strategies such as becoming suppliers or distributors of MNC s subsidiaries operating in the country. This strategy was especially significant in the Information and Technology sector that flourished. For example, this was how Indian firms such as Infosys and TCS began (Bhagavatula et al., 2019). Other firms used the services of external consultants in their innovation efforts (Krishnan and Jha, 2011; Krishnan and Prashantham, 2019), and some became globally competitive (Sahasranamam et al., 2019). However, some firms could not move forward without a foreign partner as the knowledge flows were slow (Bhagavatula et al., 2019). In this sense, institutional contexts in emerging markets impact strategies followed by firms (Chari and Parthiban, 2012; Sahasranamam et al., 2019) and, in the same way, impact the development of their innovation structure. India, for example, the innovation structure was nearly inexistent because innovation was limited. After liberalisation, some development started occurring when the MNCs entered the country and became significant knowledge partners (Sahasranamam et al., 2019).
Innovation in India attracts increased interest from scholars and policymakers (Sahasranamam et al., 2019). Nevertheless, the topic is still under-researched (Ernst et al., 2015; Nair et al., 2015; Chatterjee and Sahasranamam, 2017; Krishnan and Prashantham, 2019; Sahasranamam et al., 2019). One reason is that perhaps innovation is rooted in specific institutional contexts (Sahasranamam et al., 2019) paired with innovation constraints developed from earlier heavy regulated periods. For this reason, getting a complete portrait of India’s current state of innovation is challenging. Therefore developing an explorative quantitative study seemed appropriate to get a more in-depth understanding. Because of that, propositions were not formulated at this time. However, some suppositions were extracted from the literature review and guided our understanding of innovation in emerging markets. This exercise helped in choosing the variables to include in the quantitative study described in the methods section.
1.1.2 Innovation in India: Collaboration Overview
Research on collaboration appears to be at an early development stage in India as very few studies were found emerging in the last 20 years. A small preliminary literature review was conducted using the Thomson Reuters Web of Science1 database. The body of literature found mainly concentrates on National Innovation Systems (Dolfsma and Leydorsff, 2001; Sharma et al., 2012) and literature on developing countries (Bartels et al., 2012; Coussi et al., 2018; Choi and Zo, 2019) is general. Lesser studies used the triple helix model in the Indian context (Perumal et al., 2020) or focused on collaboration between industry-academia in a developing country (Fischer et al., 2018). None of the studies has empirically addressed collaboration from a systems approach, defined according to the systems theory and based on the triple helix innovation model.
2 Theoretical Framework
2.1 Collaboration
2.1.1 Definition
This article defines collaboration according to the systems theory and the triple helix innovation model. Based on the systems theory, a system centres on organised relationships between a set of actors that interact with each other and function to work towards a common purpose (von Bertalanffy, 1968: 8), containing:
Components: a set of actors (academia, government, industry)
Relationships among the actors: a set of actions taken by the actors that involve diverse interactions to perform a function
Function: the outcome of these relationships
The triple helix innovation model provides the actors that interact with each other, namely industry, academia and government, and a detailed view of their relationships based on knowledge exchanges that perform the function of an outcome that can be of knowledge production and innovation (Ranga and Etzkowitz, 2013).
From this perspective, collaboration is then perceived as a social system in which the relationships established by the different actors facilitate knowledge exchanges to perform a function that will be the outcome of such relationships. The outcome can be either a tangible outcome, including knowledge production and innovation or intangible in the form of trust. As such, while the systems theory provides a general framework to study collaboration from a structured point of view, the triple helix model offers the components and identifies relationships and the outcomes, providing contextual understanding to investigate collaboration.
2.1.2 Importance of Collaboration to Knowledge Production
The knowledge production process can be divided into tacit and explicit dimensions (Seidler-de Alvis and Hartmann, 2008). Polanyi put forward the definition of tacit knowledge based on the premise that “we know more than we can tell” (Seidler-de Alvis and Hartmann, 2008). From this perspective, tacit knowledge is personal, highly subjective and therefore difficult to capture in the form of text. It derives from actions, experience, and involvement in specific contexts (Alavi and Leidner, 2001). On the other hand, explicit knowledge is articulated; it can be expressed in words or numbers, is shareable in the written form found in books, data, reports, and the like; it is codified, formal, and systematic (Nonaka and Konno, 1998; Nonaka and Von Krogh, 2009).
Understanding the process of knowledge generation from a structural perspective helps comprehend that collaboration is crucial for knowledge production because of its tacit dimension that enables knowledge sharing (Ahuja, 2000). Individuals acquire knowledge by getting involved in networks where “elements of know-how” are shared (Johnson et al., 2002) which increases learning (Powell et al., 1996). It is suggested that tacit knowledge fosters “many competitive capabilities” as it lies in the head of individuals (Leonard and Sensiper, 1998). As noted by Johnson et al. (2002), product complexity combining different disciplines and technologies makes know-how increasingly important associated with the access to different sources of knowledge. However, the usefulness of this knowledge is bounded by context, involving social capital in the form of trust, collaboration and openness (Johnson et al., 2002). As Nahapiet and Ghoshal (1998) positioned it, stating Spender (1996: 52): “[C]ollective knowledge is the most secure and strategically significant kind of organisational knowledge.” The importance of collaboration in the development and acquisition of collective knowledge is particularly significant for firms operating in the life sciences industry (Zucker et al., 1996, as cited in Nahapiet and Ghoshal, 1998), assuming that individuals work as a team so that knowledge can be produced (Nahapiet and Ghoshal, 1998).
2.1.3 Importance of Collaboration to Innovation
There is a consensus among researchers that collaboration is essential for fostering innovation (Powell et al., 1996; Teece et al., 1997; Pai et al., 2012; González-Benito et al., 2016; Skute et al., 2019). One of the main reasons is that rapid technological developments lead to limitations in firms’ resources (Powell et al., 1996; González-Benito et al., 2016) which makes very few firms “go alone” in the development of newer technologies (Tether, 2002). Earlier studies found that collaboration plays a significant role in innovation by enabling the engagement of different actors in social networks from which partners can access resources and capabilities (Powell et al., 1996; González-Benito et al., 2016).
2.1.4 Social Capital as an Outcome of Collaboration
With the “locus of innovation” shifting to networks, called “networks of learning” (Powell et al., 1996), previous innovation theories were fine-tuned to accommodate social interaction as an innovation component. Accordingly, social capital enters innovation studies (Landry et al., 2002). Social capital relies on relationships and exchanges created during the collaboration process, but for that reason, it is highly affected by factors shaping the evolution of social relationships, such as time, interaction, interdependence and closure (Nahapiet and Ghoshal, 1998). The contribution of collaboration to social capital is based upon the premise that it fosters teamwork (Nahapiet and Ghoshal, 1998), which will develop into trust over time and the building of networks (Landry et al., 2002) while facilitating knowledge sharing and consequently new knowledge production.
2.1.5 Triple Helix as a Theoretical Framework to Analyse Collaboration
Innovation and technological developments originate from a complex set of relationships and knowledge dynamics (Lu, 2008); however, research emphasises R&D as the primary factor for technological innovation. In recent years, the growing knowledge complexity has expanded the research on innovation to accommodate different perspectives to capture a broader reality. Von Bertalanffy, a German biologist, was one of the first scholars to identify the importance of understanding complexity through systems. In 1968, the researcher published the General System Theory, in which he describes a system as “a set of elements standing in interaction” (1968: 38); a self-organised structure where the whole is more than a sum of its parts and the function is to work towards a common purpose.
One of the first examples of systems applied to innovation emerging in the innovation studies was the national innovation system (Nelson, 1988; Edquist, 1997; Lundvall, 1992; Freeman, 1995), whose purpose was to enhance innovative industry capability (Etzkowitz and Leydesdorff, 2000; Chung, 2002; Etzkowitz, 2003; Lu, 2008; Datta and Saad, 2011). Over time, variations of this model (for example, regional innovation system) emerged based on the argument that regions might shape innovation differently, for example, Silicon Valley (Edquist, 1997; Lu, 2008). However, this model has some shortcomings, such as considering the industry as the leading innovator and relegating government and academia to a more supporting and traditional role (Etzkowitz, 2003; Datta and Saad, 2011). Government supports innovation by releasing appropriate industrial and technology policies, and academia contributes as an educator and trainer of talent (Datta and Saad, 2011).
Other similar innovation systems have emerged, such as the technological systems by Carlsson and Stankiewicz in 1991 (Edquist, 1997) and the sectoral innovation system primarily suggested by Pavitt (Edquist, 1997) and later extended by Malerba (2002). Systems boundaries such as type of sectors and technologies were created to explain the contribution of technological development to economic growth and innovation (OECD, 1999).
More recent attention has been focusing on understanding the role of academia in fostering economic development as industry partners (Etzkowitz and Leydesdorff, 2000; Lu, 2008; Ranga and Etzkowitz, 2013), and two reasons can be highlighted: 1) global markets and business activities are increasing the dependence of economies in knowledge production; 2) economies are changing from production to more socio-economic interconnected systems based on complex networks (Lu, 2008; Ranga and Etzkowitz, 2013; Mascarenhas et al., 2018). These factors have contributed to the emergence of the triple helix innovation model, whose purpose is to capture the complex dynamics of relationships between government, industry and academia to increase innovation activity and foster economic growth (Etzkowitz, 2003; Teece, 2017; Yoda and Kuwashima, 2020). The model contextualises the knowledge flows (production, transfer and use) emerging from complex relationships in the context of innovation (Lu, 2008; Etzkowitz, 2008: 8; Saad and Zawdie, 2011; Ranga and Etzkowitz, 2013; Cai, 2014; Yoda and Kuwashima, 2020).
The triple helix model highlights three important aspects: first, that innovation is no longer restricted to the industry as it can be achieved by academia as well; second, that the government can go beyond the role of creating policy; and third, that innovation practices result from the reciprocal relationship among the three actors (Etzkowitz, 2003; Etzkowitz, 2008: 8; Ranga and Etzkowitz, 2013).
Conversely, it has been reported in the literature that it is difficult to establish the results solely based on the triple helix model (Ranga and Etzkowitz, 2013). The main reason is the loosely defined triple helix concept (Ranga and Etzkowitz, 2013). However, our approach differs from collaboration in the triple helix model introduced by Ranga and Etzkowitz (2013). From our perspective, the triple helix model helps identify the actors, the relationships and the functions based on the systems theory-based definition of collaboration.
2.1.6 Summary of the Theoretical Framework
The concept of collaboration system format is introduced based on the systems theory and the triple helix innovation model. Drawing from the systems theory, the system provides the structure – actors, relations and outcomes – and the triple helix provides the context, such as informing on the type of actors and relations based on knowledge exchanges and outcomes. In this way, the collaboration framework seeks to provide a model to analyse knowledge exchanges emerging from complex relationships and their outcomes in the context of innovation. The outcomes emerging from the model can be tangible and intangible. Tangible outcomes include the production of knowledge and innovation, which can be technological or non-technological. Conversely, intangible outcomes include knowledge capacity development among the actors and trust improvement for the actors involved in the system and therefore, adding social capital as an intangible output of the collaboration system framework.
Figure 1 describes the theoretical framework used in the study.
Theoretical framework of the study
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
3 Methods
3.1 Design
Mixed methods are a suitable alternative to capture the complexity of social sciences problems and improve the analysis, increasing the significance of the findings and enhancing their explanation and generalisation. This study employs a mixed methods design whose advantages are already well documented in the literature (Ivankova et al., 2006; Creswell, 2014: 203; Molina-Azorin, 2011; Hollstein, 2014). As it leans on the strengths of both methods, it helps to minimise their weaknesses (Stange et al., 2006; Driscoll et al., 2007).
A sequential explanatory mixed methods study was conducted first through a quantitative strand followed by a qualitative strand. The first strand explores the innovative behaviour of firms operating in India by measuring several variables associated with collaboration for innovation and other related variables. The qualitative study provides a more profound understanding as it explains the factors that promote or hinder innovation by investigating the contribution of collaboration to innovation.
3.1.1 Integration
The integration in this study was implemented at the level of design, methods, and interpretation, followed by the observation of studies from Fetters et al. (2013). Concerning the design, the second study was built and followed the results from the first, so the integration was performed through “building” (Kelle, 2006; Creswell and Tashakkori, 2007). In terms of methods, the quantitative strand database informed the data collection approach of the qualitative strand (Fetters et al., 2013). Finally, results are discussed at the interpretation level through a contiguous narrative (Fetters et al., 2013).
3.1.2 Priority
Priority in mixed methods relates to the degree of importance given to each strand “throughout the data collection and analysis”, including the study’s objective (Ivankova et al., 2006). This study prioritises the qualitative strand for its ability to explain the results from the quantitative strand. While the first strand is helpful to provide patterns of trends (Driscoll et al., 2007), it is the qualitative strand performed after that, that complements the understanding of the results emerging from the statistical models (Driscoll et al., 2007; Fetters et al., 2013).
3.1.3 Triangulation
Triangulation is a way of understanding an issue from a minimum of two points and involves data integration. The use of this technique in a mixed methods study has several purposes. It can illustrate a case, validate a study, or extend understanding of phenomena (Fielding, 2012; Flick, 2017). According to Fielding (2012), illustration relies on data from interviews to help policymakers, for example, to get a glimpse of what is going on in the “real world”, supposing that quantitative data is “dry”. Convergent validation is a way of corroborating or confirming results using a second lens to confirm what is already known (Flick, 2017). It raises concerns because quantitative and qualitative studies have different epistemological assumptions (Fielding, 2012). Fielding and Fielding (1986) and Flick, (2017) suggest that triangulation is instead a strategy employed that provides a deeper and more comprehensive understanding of a phenomenon, something Flick (2017) calls “extra knowledge”. This study employs triangulation based on the premise that it can enhance a deeper understanding of the phenomena under study. The qualitative study was built on the quantitative results from the first study.
3.2 Setting and Sample
3.2.1 Quantitative Study: First Strand
Innovation surveys are good instruments to measure innovation (Rogers and Rogers, 1998; Dodgson, 2014: 81), for which good and reliable data are essential. The World Bank provides good quality data due to applying global accepted standards and norms, making this data a reliable source of information.2 For this reason, the 2014 World Bank Enterprise Survey microdata dataset3 and specifically its innovation module that describes the innovative behaviour of firms in India, was downloaded from the institution’s website, cleaned and analysed. The sample was composed of 3488 interviews with organisations from India’s manufacturing and service (retail and other services and non-retail) industries and carried out between June 2013 and December 2014. The unit of analysis was the firm, following a sample stratification for micro (< 5), small (> = five and < = 19), medium (> = 20 and < = 99), and large (> = 100). Concerning The World Bank questionnaire, the survey aimed to provide an overview of the innovation behaviour of firms in India with questions addressing different types of innovation: product, process, marketing and organisational.
3.2.2 Qualitative Study: Second Strand
A second strand followed the previous one with an embedded case study with four subunits (Yin, 2018). Previous literature has shown that embedded case studies may be helpful to comprehend “complex social phenomena” (Yin, 2018) within real-life contexts (Merriam and Tisdell, 2009: 259; Gustafsson, 2017; Yin, 2018) and to gain a more in-depth and concrete understanding of an event (Yin, 2018) due to richness of data (Eisenhardt and Graebner, 2007). The subunits were composed of three highly-innovation intensive firms in the life sciences industry (biotechnology and pharmaceuticals), and one subunit constituted by faculty of diverse, high education institutes. Life sciences are one of India’s top three most innovative industries (Baskaran, 2016) and experiencing fast growth in the Asia Pacific region4 and globally.
3.2.3 Development of Materials and Methods
Figure 2 summarises the instrument development, the sample and data collection and the study’s data analysis.
Flowchart with the development of the materials and methods elaborated by the authors
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
3.3 Data Collection
3.3.1 Quantitative Study: First Strand
The World Bank collected the data by administering a survey to manufacturing and service industries (retail and other services and non-retail) to assemble evidence on the nature and determinants of innovation, identify projects that can generate innovation, and stimulate policy dialogue in emerging markets.5 The World Bank has used stratified random sampling as the methodology for sampling data. The broad perspective of the services and manufacturing industries would set the trends to inform the following study and the nature of industries and organisations to include.
3.3.2 Variables
An initial total of 38 binary dependent and independent variables related to product and process innovation and associated with intellectual property, novelty, technology, collaboration, business operations, and human capital were primarily selected based on the literature reviewed and included in Tables 1, 2 and 3.
The variables were recoded, and missing values were dropped before further analysis. Therefore, only values from ‘yes’ or ‘no’ answers were included.
During the analysis process, the number of variables came down to 32 after it was understood they were not statistically significant and hence not contributing to the investigation, followed observation of studies from Hosmer et al. (2013: 91–99). A total of four models emerged after initial exploration and assessment of ROC (receiver operating characteristic),6 classification, Variance Inflation Factor (VIF) and Wald test. Table 1, Table 2 and Table 3 identify and describe the dependent and independent variables used in the models. The World Bank is mentioned as TWB for readability.
Summary of dependent variables
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Source: Adapted from the 2014 World Bank Enterprise Survey
Summary of independent variables based on product innovation
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Source: Adapted from the 2014 World Bank Enterprise Survey
Summary of independent variables based on process innovation
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Source: Adapted from the 2014 World Bank Enterprise Survey
3.3.2.1 Dependent and Independent Variables
3.3.2.1.1 Qualitative Study: Second Strand
Data collection was conducted through phone or video calls. Interviews were recorded upon the consent of the participants, or notes were taken. Interviews had an average duration of one hour.
From August to December 2020, twenty-six participants contributed to the study across the Indian states of Maharashtra, Karnataka and Tamil Nadu. In-depth interviews were the instrument selected for this strand with two interview protocols.7
3.4 Data Analysis
3.4.1 Quantitative Study: First Strand
Data analysis was conducted using STATA statistical software package version 14. In analysing the data, logistic regression was used based on the type of data identified. The use of this technique in data analysis is to find a decent model that “describe[s] the relationship between an outcome […] variable and a set of independent […] variables” (Hosmer and Lemeshow, 2000: 1) and is helpful to analyse “dichotomous outcomes” (Peng et al., 2002). The entire process was conducted based on the works of Hosmer et al. (2013: 91–99), as described in Table 4.
Summary of the data analysis process
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Source: Adapted from Hosmer et al. (2013: 91–99)
To access the effect of the interaction of both control variables FIRMSIZE and FIRMAGE1, the Variance Inflation Factor (VIF) was used for it is a standard tool for accessing multicollinearity (O’Brien, 2007; Vatcheva et al., 2016). The threshold used in the analysis was adopted after observation of the work of O’Brien (2007) as there is no agreement in the literature on how high VIF must be to constitute a problem; the rule of thumb considered was then VIF < 4.
3.4.2 Qualitative Study: Second Strand
In the second strand, an iterative data analysis process was conducted involving transcription of interviews, revision, writing of memos and report writing using Atlas T.I. software version 8. The data collected was analysed through a grounded theory approach as this method is helpful to explore social relationships and understand social problems and concerns (Strauss and Corbin, 1998: 81).
Grounded theory is a qualitative technique that employs “inductive analysis as the principal technique” (Bowen, 2006). Grounded theory is a method of analysis (Charmaz, 2012) often employed when less is known about a phenomenon (Corbin and Strauss, 2014; 32; Chun et al., 2019) to get novel insights into long-standing problems or to study phenomena from a holistic perspective (Corbin and Strauss, 2014: 27–32). Grounded theory is employed when the purpose is to generate a theory grounded in the data (Creswell, 2014: 662; Corbin and Strauss, 2014: 29; Charmaz and Belgrade, 2019; Chun et al., 2019) and is not selected prior to the start of the research (Corbin and Strauss, 2014: 29).
The process involved three steps: open, axial and selective coding. First, open coding was performed by dividing sentences into smaller pieces to summarise data in a few words. Next, similar meaning codes were merged. Second, the axial coding was performed by organising the data generated by these codes to create relationships. Third, a process of selective coding was performed by summarising codes into higher categories.
4 Results
This section summarises the results of the quantitative and qualitative analysis. We first summarise the results obtained from the statistical models related to product and process innovation presented in Table 5 and Table 6, respectively. Next, a sample of the qualitative analysis results is presented in Table 7 based on the grounded theory approach, describing the key categories. The entire set of findings can be accessed in Appendix C.
Table 5 summarises the results for product innovation and associated variables described in Table 1 and Table 2 in the methods section. The models’ (M1, M2, M3) summary consists of odds ratio (OR), standard error (St.E), confidence interval (CI), with the details at the bottom providing information on the results related to the validation of the models. Similarly, Table 6 summarises the results for process innovation. The methods section describes the variables used for measuring process innovation in Table 1 and Table 3.
Results across Table 5 and Table 6 suggest collaboration as the primary key pattern for product and process innovation. Strong evidence was found that collaboration with foreign firms conduces to product innovation in the national market (p < 0.05) and copyright (p <0.01) and process innovation in the international market (p < 0.05). By contrast, a negative association between collaboration with the government for product innovation in the national market was found, significant at the p < 0.05 level though with a small odds ratio. Surprisingly, no results were found for the remaining collaboration variables measured, such as collaboration with domestic firms, domestic academia, foreign academia, and consultancies.
Other results suggest that cost reduction highly contributes to product innovation in the national market (p < 0.01) and international market (p < 0.05), as well as cost-efficiency to process innovation in the international market (p < 0.05). Employment showed a negative association with product innovation in the international market (p <0.01), however, the odds ratio is small to include it for further analysis.
Other findings in Table 5 reported a negative association between copyright and market demand (p < 0.05). On the other hand, compliance showed a positive association with product innovation in the national market to some extent (p < 0.1).
Logistic regression summary based on product innovation
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Logistic regression summary based on process innovation
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Sample of the qualitative results with respective categories and sub-categories
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Source: Compiled by the authors. The comprehensive results can be consulted in Appendix C
Similarly, Table 6 indicates that production speed (production efficiency) is negatively associated with process innovation, significant at the p < 0.01 level. Conversely, speed of delivery showed some evidence of affecting process innovation, but not very significant (p < 0.1). Finally, no results were found associated with the technology variables measured.
A sample of the qualitative analysis results is presented in Table 78 around a core category: “Collaboration as a social framework”, and will be discussed next.
5 Discussion
This section presents the interpretation of the findings identified in the results section, first discussing the quantitative analysis by examining the results from Table 5 and Table 6, summarised in Table 8. Next, the qualitative analysis described in Table 7 will be analysed. Table 7 describes a sample of the qualitative results, with the remaining findings in Appendix C. Results will be analysed and discussed based on each theme and category.
Based on Table 8, results indicate that collaboration with foreign firms is strongly associated with product innovation in the national market and copyright, and a similar association was found for process innovation in the international market. Two reasons may explain this finding: 1) the availability of skilled personnel and technology in foreign firms (Cirera and Cusolito, 2019); 2) the intention of foreign firms to expand their product portfolio in multiple markets (Un and Asakawa, 2015) for which they set up subsidiaries or engage in collaboration with national firms.
Quantitative strand results and explanation summary based on product and process innovation
Citation: Triple Helix 10, 3 (2024) ; 10.1163/21971927-bja10039
Source: Compiled by the authors
Results also indicate that product innovation in the national market is less likely to originate from collaboration with the government, suggesting that policy might not fully address industry requirements. A possible explanation is that innovation policies may be affected due to a silo approach to policy-making, as innovation is usually under the supervision of multiple offices instead of a single one (UNESCO, 2021). Another probable reason is that governments experience difficulties developing programs targeting emerging technologies and ensuring that firms access them for competitiveness (UNESCO, 2021).
Surprisingly, no results for the remaining collaboration variables measured were found, and a similar result emerged from the variables measuring technology for process innovation. The explanation is perhaps associated with emerging markets being primarily technology importers, and the ease of access may decrease internal research and development requirements (Cirera and Cusolito, 2019).
Results have also shown cost as a significant factor explaining innovation for products and processes (Davenport; 1993: 22; Reichstein and Salter, 2006; Reguia, 2014).
There is a slight indication that higher levels of innovation (product innovation in the international market) may reduce employment. Prior research has shown mixed results as, on the one hand, some studies indicate that innovation can generate higher levels of employment (Hall et al., 2008; Harrison et al., 2014; Mehta, 2016), and on the other, some evidence points to a decrease in employment, mainly associated with skill-biased technology change and degree of innovation (Cirera and Sabetti, 2019).
It was suggested that compliance might contribute to product innovation in the national market when firms need to conform to regulation changes (Blind, 2012).
Some negative associations were observed, suggesting that these variables are unlikely to contribute to innovation. For example, market demand is unlikely to create intellectual property as association with copyright is negative. The result means that firms may accomplish minor improvements in products or services not directly related to market demand but with the firm’s intention to lower costs (Mowery and Rosenberg, 1979).
In conclusion, the results showed mixed results associated with collaboration’s contribution to innovation. Additionally, technology development appears to be limited. Such findings may point toward an inadequate collaboration framework for innovation. The limitations offered by the statistical models led to further investigation through a qualitative method in searching for potential explanations. The qualitative analysis will now be discussed, starting with the core category and then each category in a total of six.
6 Core Category: “Collaboration as a Social Framework”
Results from the qualitative analysis indicate that collaboration’s social framework to support innovation is deficient. The explanation is described by the emerging categories pointing towards a set of issues associated with strategy and implementation aspects. From a collaboration perspective, problems arise due to inadequate mechanisms to foster social interaction. For example, the current relationships are primarily transactional, incompatible and competitive. Transactional because there are knowledge exchanges between the parts involved in the transaction but with not enough relevant value being generated. One example is the firms’ focus on the acquisition of foreign technology. Relationships are also incompatible related to the absence of organisational mechanisms in place and competitive because of a limited comprehensive set of government mechanisms to address intellectual property issues. As a result, firms and academia find it challenging to align a common purpose which, for firms, it is associated with adequate incentives and autonomy and for academia, with financial rewards and learning opportunities.
Consequently, the development of trust by firms in academia gets impacted. On the one hand, trust can be challenging to achieve when connected with technological aspects and data-related issues; on the other, it can be built based on earlier social interactions experienced directly through partnerships or indirectly associated with the reputation of academia or faculty. Knowledge-sharing practices are also affected as trust is challenging to develop. In this case, firms may prefer to hide critical knowledge in connection with potential knowledge leakages to competitors and favour practices such as networking.
The above factors help to explain the weaknesses in the current collaboration framework. Three key ideas emerge from the analysis: first, that the current relationships discourage knowledge sharing leading to delays in knowledge creation processes, thus slowing down technological development and innovation; second, that minimal social interactions affect the development of problem-solving skills and contribute to a slowdown of technical capabilities, and three that innovation and technological development are not increasing fast due to the limited introduction of mechanisms that improve the social framework. Based on the above examination, the core category identified in our analysis is “Collaboration as a social framework”.
Each category in a total of six is now going to be discussed.
6.1 Category 1: Challenges over Knowledge Production and Application
Firms and academia obtain limited skills from experience and differ in the type of knowledge pursued. Firms prefer to acquire explicit knowledge in the form of foreign technology transfers or ICT outsourcing (Lin, 2003; Liefner and Schiller, 2008; Nair et al., 2015). To a lesser extent, firms acquire tacit knowledge associated with outsourcing resources which helps minimise skill shortages and improve cost-efficiency (Wang et al., 2008). However, as firms’ knowledge production and applicability reduce, so does their problem-solving capabilities. Consequently, firms search for academia’s support to minimise such deficiency.
Conversely, academia mainly focuses on knowledge production associated with tacit knowledge; however, the applicability of knowledge related to technology development is minimal. As a result, experience may be less developed. The explanation might be related to historical contexts that constrained technological development and knowledge transfer in academia, generating gaps between teaching and research and breaking the ability of academia to develop capabilities to effectively contribute to industrial needs (Datta et al., 2011). Another explanation is related to the technological readiness level in academia (TRL), as usually, academia focuses on TRL 1–4 while firms focus on TRLs 7–9.9
6.2 Category 2: Lack of Organisational Collaborative Standard Mechanisms
Results suggest an absence of organisational collaborative standard mechanisms to facilitate interaction between firms and academia suggested by the sub-categories “misalignment of business and academic goals”, “limited collaborative innovation culture”, and “limited collaborative organisational processes”. One explanation is probably associated with inadequate mechanisms in place by government authorities to facilitate interactions. Another possibility is firms and academia’s inability to accommodate each other’s requirements as they operate in distinguished markets and pursue different incentives (Partha and David, 1994; Bruneel et al., 2010; Hemmert et al., 2014; Un and Asakawa, 2015). The limited collaborative, innovative culture associated with restricted knowledge applicability, as suggested in category one, can explain why firms and academia find it challenging to align distinct cultures. It is not new that culture is a major obstacle in collaboration (Hemmert et al., 2014; Ankrah and Al-Tabbaa, 2015; Mascarenhas et al., 2018; Rybnicek and Königsgruber, 2019; Azman et al., 2019). However, culture as an obstacle is vague because it is contextual. In this case, the limited applicability of knowledge in both firms and academia impacts the development of an innovative culture that enables collaboration.
6.3 Category 3: Limited Mechanisms Related to Intellectual Property Rights (IPR)
Firms and academia face challenges over IPR mechanisms demonstrated by sub-categories “IPR related issues”, “government schemes”, and “weak IPR policies”. Challenges over IPR and collaboration are not new and affect both emerging and developed markets (Hertzfeld et al., 2006; Schiller and Brimble, 2009; Mascarenhas et al., 2018). For example, the collaboration literature suggests an almost inexistent comprehensive regulation to govern collaboration between firms and academia (Schiller and Brimble, 2009). Another possible explanation might be associated with governments decreasing the allocation of funds to academia, which generates the search for funding from other sources. As a result, academia may develop a more business-driven mindset that may discourage firms from collaborating.
6.4 Category 4: Divergence over Incentives
Firms and academia divergence over incentives can discourage collaboration, suggested by “common purpose”, “academic incentives”, “industrial learning opportunities”, and “private academia incentives”. In the collaboration between academia and industry literature, the common purpose concept is considered a primary driver for successful collaboration (Weck, 2006; Gillier et al., 2012). One way of aligning goals is through incentives; however, firms and academia diverge in their significance. While for academia, the business potential of projects (Walsh et al., 2008 as cited in Perkmann et al., 2013), costs (Tether and Tajar, 2008) or value (Perkmann et al., 2013) are essential, for firms, factors such as autonomy, decision-making capability and adequate financial resources are the most significant.
6.5 Category 5: Trusting Factors
Academia appears to have limited capability to provide the compliance level or the IT infrastructure for data security required by firms. To the best of our knowledge, research on data security and compliance in connection with collaboration for innovation may be scarce as it was impossible to find any relevant literature related to India. The explanation might be associated with the growing interest in data regulation in academia aligned with the rise of big data (Jim and Chang, 2018; Zhang, 2018). For example, Hina and Domenic’s (2016) study on compliance practices among Malaysian academia found that despite their dependence on technological solutions and security policies to protect the information, information security compliance practices were scarce, resulting in non-compliant behaviours and security breaches with loss of sensitive and valued information. In the U.S., academia often reports security breaches, and a survey conducted in 2004 to 500 colleges and academia found that 40% of these institutes had been hacked (Infosecurity Today, 2004). Other factors identify how trust is developed from social interactions, such as through partnerships or reputation of academia and faculty or adequate financial resources.
6.6 Category 6: Knowledge Sharing is Selective
Firms share knowledge selectively and according to their requirements, described in the sub-categories “knowledge leakage” and “networking”. The collaboration literature has pointed out appropriability issues (Shi et al., 2019), knowledge (Laursen and Salter 2014; Shi et al., 2019) or trade secrets (Mallapaty, 2019; UNESCO, 2021) leakages to competitors as worrisome for firms to engage in collaboration. However, another explanation might be the high costs of acquiring explicit knowledge related to technology transfer. It suggests that explicit knowledge is costly, which challenges earlier works that emphasise the low-cost advantage of this type of knowledge. On the other hand, tacit knowledge appears to be easier to acquire in social contexts but is related to performance (Veugelers, 1997; Álvarez et al., 2009).
7 Conclusion
The increased globalisation of economies and rapid technological developments makes firms depend on innovation and develop dense networks of relationships, making collaboration crucial for innovation.
This study was set out to explore the current state of innovation of firms operating in India and investigate collaboration’s contribution to innovation from a systematic perspective as collaboration is based on social relationships. The qualitative study has emerged from the first study results. Firms’ innovative behaviour was first explored through the investigation of different industries and second by examining the relationships and knowledge dynamics in firms in highly-innovation intensive industries such as pharmaceuticals, biotechnology, and academia. This study defined collaboration as a system based on the systems theory, including the triple helix innovation model, as described in the theoretical framework.
The paper presented the results of the quantitative and qualitative analysis that indicates that the collaboration framework is not fully effective in supporting innovation, explained by a set of limiting and enabling factors. The quantitative analysis suggested that collaboration’s contribution to innovation is limited as only a few variables were contributing to collaboration, with the remaining variables showing either negative associations or no results. In the qualitative analysis, six categories were developed to explain why the collaboration framework shows weaknesses, suggesting the factors constraining or enabling its development.
Considering collaboration from a systematic perspective, one predominant aspect is that social interactions present limitations associated with the type of relations developed. On the one hand, the flaws may result from government authorities’ insufficient mechanisms to enable a more interconnected collaborative environment between academia, firms, and government. On the other hand, it may be due to firms’ reliability in foreign technology and the minimal application of knowledge in academia. Consequently, the development of trust may be difficulted by the nature of these relations. In addition, knowledge sharing practices are also affected by the findings showing that knowledge sharing is currently selective. Firms, particularly, may choose not to share essential knowledge with academia. Consequently, knowledge creation processes will decline, delaying the acquisition of know-how and problem-solving skills, leading to slow advances in innovation and technological development.
The factors presented in the paper are significant as they can be used as a guideline to build relevant mechanisms that can support social interaction. The main idea is that both collaboration and innovation are social activities. Therefore, managing complex networks for innovation requires a structure and a systematic approach.
The study makes relevant contributions to the innovation studies literature, particularly to the innovation systems and collaboration between industry-academia literature in the Indian context: first, a significant contribution is methodological. This research employs a mixed methods design as previous literature identified very few studies conducting this methodology to investigate collaboration (Mascarenhas et al., 2018). Second, it expands the current knowledge on the state of collaboration for innovation in India and uncovers the relationships and knowledge dynamics, barriers and enablers experienced by the actors involved. A third novel aspect is associated with attributing a systems innovation element to collaboration, provided by systems theory and the triple helix. To the best of our knowledge, collaboration has not been approached from a systematic perspective.
Limitations
The qualitative study has a few limitations that need to be addressed. First, only a certain number of regions were included in the research due to constraints caused by the Covid-19 pandemic. Few other outcomes could have emerged if other regions had been included. Second is the lack of access to the organisations connected to the pandemic and subsequent lockdown. It was simply impossible to take additional notes that might have produced supplementary findings.
Future Research
Our study has identified a few areas needing further investigation. For example, more research is required to understand the data governance dynamics associated with collaboration for innovation in India. The topic is particularly relevant because academia is poised to play a more relevant role in economic development in the future. To the best of our knowledge, no such study has been conducted so far.
A comprehensive investigation of the dynamics of technology transfer from academia to industry in the Indian context is required since we have found almost no evidence, and little research has been conducted (Ravi and Janodia, 2021).
Future research should also be directed toward how social capital can foster innovation due to the paucity of results found in our study. The topic appears to be under-researched to the best of our knowledge as we could not find relevant empirical studies.
Acknowledgements
The authors received no financial support for the research, authorship, and publication of this article. We want to thank Dr Nirmal Punjabi, KCDH, IIT Bombay, for the valuable comments that helped to improve this manuscript.
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Appendix A – Quantitative Study Data Analysis Process
This section provides a visual description of the entire quantitative study process in Figure A.1. Then, Table A.1 summarises the 32 variables used in the quantitative analysis process. Next, a brief description of the World Bank survey is included. Tables A.2 to A.13 present the final models for product and process innovation. After, the entire analysis process is described in Tables A.14 to A.32, using the variable copyright as an example. Next, Table A.32 and Figure A.2 provide an example of how classification and ROC was accessed. Finally, Table A.33 describes the variables not used in the study after assessment of ROC, VIF, Wald test and classification.
Description of the quantitative study process followed
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Summary of dependent and independent variables used in the models
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Source: Compiled by the authors
Concerning The World Bank questionnaire, the survey aimed to provide an overview of the innovative behaviour of firms in India with questions addressing different types of innovation: product, process, marketing and organisational.
Finally, an example of the analysis process, from univariate to multivariate logistic regression, is also presented. The dependent variable selected for this analysis was copyright.
Table A.2, Table A.3, and Table A.4 summarise Model 1 final results and assessment based on product innovation.
Model 1: the model selected for the variable psnovelty based on product innovation
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Model 1: assessment of ROC, Classification and VIF
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Source: Compiled by the authors
Model 1: Assessment of the goodness of fit
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Source: Compiled by the authors
Table A.5, Table A.6, and Table A.7 summarise Model 2 final results and assessment based on product innovation.
Model 2: the model selected for the variable psinovelty based on product innovation
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Model 2: assessment of ROC, Classification and VIF
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Source: Compiled by the authors
Model 2: Assessment of the goodness of fit
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Source: Compiled by the authors
Table A.8, Table A.9, and Table A.10 summarise Model 3 final results and assessment based on product innovation.
Model 3: the model selected for the variable copyright based on product innovation
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Model 3: assessment of ROC, Classification and VIF
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Source: Compiled by the authors
Model 3: assessment of the goodness of fit
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Source: Compiled by the authors
Table A.11, Table A.12 and Table A.13 summarise Model 4 final results and assessment based on process innovation.
Model 4: the model selected for the variable proc_inovelty based on process innovation
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Model 4: assessment of ROC, Classification and VIF
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Source: Compiled by the authors
Model 4: assessment of the goodness of fit
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Source: Compiled by the authors
Example of the development of a model adapted from Hosmer et al. (2013: 91–99) based on steps described in this article and presented in Tables A.14 to A.32 and Figure A.2.
A univariate logistic regression is performed in step 1 and step 2 by fitting one dependent and independent variable into a model. The statistical significance level of the variable to include is accessed, and only variables with a cut-off of p-value < 0.25 were considered for the multivariate model.
Univariate logistic regression based on copyright and pscostreduction
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscompetition
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscompliance
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and psmdemand
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscoldfirms
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscolffirms
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscoldacademia
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscolfacademia
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscolconsultancy
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and pscolgovernment
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Univariate logistic regression based on copyright and psnemployees
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
In step 3, step 4 and step 5, the selected variables are fitted into a multivariate model, and significance is accessed, leading to variable dropping.
First iteration of multivariate logistic regression
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
The variables PSCompliance, PSColDfirms, PSNEmployees are dropped, and a new model is run.
New multivariate regression results
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Step 6 uses a new logistic regression, adding the previously rejected independent variables to the last multivariate model (Table A.27).
Multivariate regression including previously rejected variables
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
In step 7, the control variables FIRMSIZE and FIRMAGE1 are added to model one to identify interaction effects.
Assessment of interaction effects with control variable firmsize
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
Assessment of interaction effects with control variable firmage1
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Source: 2014 World Bank Enterprise Survey; compiled by the authors
In step 8, the assessment of the models was performed.
Table A.30 provides an example of assessing interaction effects (VIF).
Accessing interaction effects (VIF)
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Source: Compiled by the authors
An example of accessing goodness of fit with a Wald test can be seen in Table A.31.
Assessment of the goodness of fit
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Source: Compiled by the authors
Table A.32 provides an example of how classification was accessed in the model.
Figure A.2 provides an example of how ROC was accessed.
After the assessment, the model in Table A.26 was considered the final model and included in the study.
Table A.33 describes variables not used after assessment.
Summary of variables not used after assessment
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Source: Compiled b y the authors
Appendix B – Interview Protocols
This section includes the two protocol samples used for industry and academia in the qualitative research based on perspectives on collaboration and partnerships. Table 1 refers to the questions asked to the participants in the industry and Table 2 to the questions asked to the participants in academia.
Interview protocol for firms: perspectives on collaboration and partnerships with academia
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Interview protocol for academia: Perspectives on collaboration and partnerships with firms
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Appendix C – Qualitative Analysis Summary of Results
A comprehensive list of the qualitative results is presented in Tables C.1 to C.6. A visual summary of the findings is described in Figure C.1.
Category 1: Challenges over knowledge production and application
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Source: Compiled by the authors
Category 2: Lack of organisational collaborative standard mechanisms
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Source: Compiled by the authors
Category 3: Limited mechanisms related to Intellectual Property Rights (IPR)
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Source: Compiled by the authors
Category 4: Divergence over incentives
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Source: Compiled by the authors
Category 5: Trusting factors
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Source: Compiled by the authors
Category 6: Knowledge sharing is selective
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Source: Compiled by the authors
Visual description of the qualitative results
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Source: Compiled by the authors
A preliminary literature review was conducted to assess the discussion around the research topic in India. The articles used in this review were retrieved from the Thomson Reuters Web of Science database, as this database has a comprehensive range of relevant and peer-reviewed articles from significant journals (Skute et al., 2019). We have used keywords such as “innovation systems”, “triple helix”, “collaboration academia-industry” and allowed for some variation (university instead of academia) with boundaries such as “emerging markets” (and variations) or India and “manufacturing industry”.
The World Bank, Enterprise Surveys (2014). Available at: https://data.worldbank.org/about (accessed 6 April 2022).
The World Bank, Enterprise Surveys (2014). Available at: https://microdata.worldbank.org/index.php/catalog/2225 (accessed on 14 July 2022).
CBRE (2021). ‘A new era of life sciences growth. Opportunities for occupiers and investors’. Asia Pacific Life Sciences Report 2021.
The World Bank, Enterprise Surveys (2014). Available at: https://data.worldbank.org/ (accessed 6 April 2022).
Is a technique useful to describe the accuracy of the trade-offs between true-positive and false positive rates (Pepe, 2000).
The interview protocol samples are included in Appendix B.
Detailed information of the working process around the qualitative analysis can be seen in Appendix C.
TWI, What are Technology Readiness Levels (TRL)? Available at: https://www.twi-global.com/technical-knowledge/faqs/technology-readiness-levels (accessed 5 April 2022).