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Using Mixed Methods to Evaluate the Role and Contribution of Using Mixed Methods to Evaluate the Role and Contribution of

Disciplined Innovation Processes (DIPs) for Start-Up Growth and Disciplined Innovation Processes (DIPs) for Start-Up Growth and

Development. Development.

Saad Ahmed Technological University Dublin, saad.ahmed@tudublin.ie

Anthony Paul Buckley Technological University Dublin, anthony.buckley@tudublin.ie

Francis Behan Corning Incorporated USA, behanfm@corning.com

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Recommended Citation Recommended Citation Ahmed, S, Buckley, AP & Behan, F (2021). Using Mixed Methods to Evaluate the Role and Contribution of Disciplined Innovation Processes (DIPs) for Start-Up Growth and Development. 20th European Conference on Research Methodology for Business and Management Studies, University of Aveiro, Portugal 17-18 June.

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Using Mixed Methods to Evaluate the Role and Contribution of Disciplined Innovation Processes (DIPs) for Start-Up Growth and Development Saad Ahmed1, Dr Anthony Paul Buckley1 and Francis Behan2 1Technological University of Dublin, Dublin, Ireland 2Corning Inc. Corning, New York, U.S. saad.ahmed@tudublin.ie anthony.buckley@tudublin.ie behanFM@Corning.com

Abstract According to Ohr and Frank-Mattes (2017), there appears to be limited knowledge or appreciation of disciplined innovation frameworks, tools or techniques for the scale-up phase of commercialisation, especially from those with technical backgrounds. The research questions in this study therefore asks: Can a more disciplined approach to the innovation process make a positive contribution to improved commercialisation outcomes for start-ups and corporate venturing? Additionally, it asks if this disciplined process can be configured into a reliable and repeatable process across multiple contexts?

For complex research questions such as this, quantitative or qualitative research approaches alone cannot adequately address these questions. This paper demonstrates how a mixed methods research design and data analysis strategy can address the research questions outlined above.

The approach taken in the study is best described as a multiphase sequential exploratory research design (Saunders et al. 2012). In this sequential exploratory design, qualitative data collection and analysis takes place first (Cross- case analysis), followed by quantitative data collection and analysis (Qualitative Comparative analysis). The final stage in the study is the interpretation of the combined results from both phases.

This research, when completed, will serve as the empirical basis for the development of a proprietary approach to the disciplined innovation process. This mixed method research on disciplined innovation processes can help in ‘upping the batting average’ by identifying those factors which must necessarily be present in the start-up to increase their chances of survival. Findings will have implications, in particular, for future entrepreneurial support services to entrepreneurs which are provided by the State and/or Universities and HEIs.

Keywords

Diciplined Innovation Process, Mixed method, QCA, start-up incubator, incubation, entrepreneurial ecosystem.

1. Introduction

Innovations, driven by technological change are critically important for economic growth (Storey and Greene, 2010). Indeed, there appears to be a clear positive relationship between GDP and innovation in both developed and developing economies (Ulku 2004). Innovations are therefore essential for increased productivity and value creation at an enterprise level as well as at a national level. Entrepreneurs – both corporate and individual, operating within transnational innovation and entrepreneurial ecosystems are the primary conduits for improved productivity levels, value creation and capture (Acs et al. 2009; Acs et al. 2013). At the firm level, Fagerberg, Mowery, and Nelson (2006) are careful to distinguish between invention and innovation. They define invention as ‘the first occurrence for a new product or process’, whilst Storey and Greene (2010) define innovation as ‘………. the first commercialisation of the idea’. This ability to commercialise being the key differentiator in the successful outcome or otherwise of the innovation process (Aulet, 2013). Yet commercialisation (as the terminal stage of the innovation process) is the least developed area in the innovation literature (Adams et al., 2006).

2. Research Context

Within the commercialisation domain, it is clear that many knowledge-driven organizations and entrepreneurs have not mastered the practice of implementing reliable and repeatable processes for the innovation process (Kumar, 2012). This is evident from the extremely high failure rates of innovation projects, with only four percent of projects reportedly succeeding in one research study in the corporate arena (Tuff and Wunker, 2014). Patel (2015) also reports high failure rates for innovation-driven start-ups. One of the main reasons for failure here is the inability of entrepreneurs to commercialise their ideas into a scalable or sustainable business models (CB Insight, 2017). According to Ohr and Frank-Mattes (2017), there appears to be limited knowledge

or appreciation of disciplined innovation frameworks, tools or techniques for the scale-up phase of commercialisation especially from those with technical backgrounds. Given the dearth of success for corporates and start-ups at the innovation and commercialisation process, there would seem to be scope for the development of robust frameworks, tools and techniques for innovation process success (Lerner 2012).

Simply put, ‘the process and practice of innovation’ needs to be mastered if value is to be created and captured by entrepreneurs, corporate and individual alike. A small number of academic authors have championed a more disciplined approach to innovation and suggested that this would ‘up the batting average’ for innovation success (Kumar, 2012; Lerner, 2012; Aulet, 2013). They suggest (based on their own anecdotal and case experiences) that the innovation process can be successfully managed through all stages from idea generation to commercialisation on a reliable and repeatable basis (Lerner, 2012; Sull, 2015; Gabriel and Euchner 2017). For example Kumar (2012) in his framework suggests four principles that all entrepreneurs should consider for effective value based innovation, these are:

1. Build innovation around experience. 2. Think of innovation as system. 3. Cultivate an innovation culture 4. Finally, adopt a Disciplined Innovation Process (DIP).

Similarly, Cooper (2012) in his globally used Stage-Gate® innovation process emphasised the importance of a structured (disciplined) ‘Idea to Launch System’ as a key ingredient of successful corporate innovation. However, in an increasingly VUCAH (Volatility, Uncertainty, Complexity, Ambiguity, and Hyper-connectedness) environments overly deterministic frameworks may be increasingly inappropriate (Rao and Chuan 2013; Rao 2018).

3. Managing the Innovation Process

Innovations are at the centre of technical, economic, ecological, social and political progress. Therefore, different research disciplines have been focussing on this subject for decades. Academic research emphasises the importance of the commercialisation aspect of innovations, thus clearly distinguishing them from inventions. Innovation management organises all of the innovation-related tasks and processes and creates the culture in which innovations can flourish. A major challenge for innovation management is in trying to assure a continuous flow of ideas to make innovation repeatable and sustainable.

Accordingly, innovation management can be defined as the institutional planning and control process of all transactions by persons carrying managerial responsibilities which cover the development and implementation of company’s subjective new products and processes. Therefore, the overall mission of innovation management is to manage all innovation activities to ensure long-term sustainable competitive advantages (Pleschak and Sabisch 1996).

Trott (2002) suggests that innovation is not just a singular event, but a series of activities that are related to each other. Innovation can be understood as a process which has several stages (Gopalakrishnan and Damanpour 1997). Several studies suggest that companies with a high performance in innovation generally have a formal process for developing new products and services (Griffin, 1997; Tatikonda and Rosenthal, 2000), therefore considering and identifying the stages of these processes can be useful. Narvekar and Jain (2006) argue that although many models of the innovation process exist, the process is still an “enigma‟; therefore, this process cannot be pinned down easily. Joe et al. (2005) believe that although innovations vary in scale, degree of newness and nature, it is possible to observe similar fundamental processes common to all firms at a level of generalisation.

Vahs and Burmester (2002), illustrate under a management lens (Figure 1) the scope of innovation management in comparison to research and development (R and D) and technology management that can help to resolve future terminological misinterpretations, even on a broader and multidisciplinary understanding of the innovation phenomenon.

Figure 1. Scope of Innovation Management

(Source Vahs and Burmester 2002)

3.1. Innovation Process Models Framework Following the development of innovation concepts, models of innovation and innovation processes evolved. broad range of innovation process models have been developed over the recent decades. All models share the common understanding that innovation activities can more or less correctly be described and visualized in process models. Some models describe the life cycle of innovation by S-shaped logistic function, which consists of three separate phases reflecting the application aspect of its development: the emergence, growth and maturity (Howard and Guile 1992). Other studies emphasize the characteristics of innovation which are defined according to innovation development stages, e.g. Maidique (1980) identifies the recognition of the invention, development, realization and distribution as phases of the innovation process (Maidique, 1980). Linear models of innovation in general highlight the discovery (invention), the definition of areas of application of the results of innovation, its development, design and use as phases of the innovation process. A plethora of process models describing the development and commercialisation of new products, services or processes can be found in the academic literature. Innovation process models can be categorised into various generations of models (Rothwell 1994; Cooper 1994). Figure 2 presents the seven generations of innovation process models based on classification of Rothwell (1994).

Figure 2. Development of Generations of Innovation Management Systems.

(Source: Adapted by author based on Gaubinger et al. 2015)

4. Startups and Entrepreneurial Ecosystem

The entrepreneurial process can be broken down in three stages (Van der Veen and Wakkee 2004). The first stage is opportunity recognition and developing ideas into an actual business opportunity. The second stage is assessing the needs in the market and matching those with the resources available to the entrepreneurs and their networks, evolving the idea and business opportunity into a proper business plan. The third stage is exploiting the opportunity to create value by providing a product or service that the market can buy in. In contrast to traditional entrepreneurial activities, like opening a restaurant or a grocery shop, a knowledge-driven startup is a newly emerged and fast-growing business seeking to meet the marketplace by developing a business model around an innovative idea (Blank 2010). The projects pursued by startups are risky, so their survival rates are quite low. However, the small number of startups that survive and succeed can have significant economic impact in the short to medium term (Storey and Greene, 2010, Guzman and Stern, 2016). Innovative high potential growth startups are at the centre of policy interest. The key questions remain as to how public policy can improve the framework and systemic conditions of entrepreneurial ecosystems and facilitate more fast-growth startup creation and development (Van Roy and Nepelski 2016, 2017). The framework conditions include, among others, the institutions and the physical infrastructures, while the systemic conditions mostly relate to the presence of networks, leadership, finance, talent or new knowledge

(Stam and Spigel 2016). Thus, the framework conditions can be improved by measures like changes to the regulatory environment. Indeed, the role of the public sector is less overt in the improvement of the systemic conditions (Fuerlinger et al. 2015). Ways to improve the systemic conditions may be, for example, by increasing the density of connections or by facilitating the network of different actors in the entrepreneurial ecosystem.

4.1. 3B framework of policy support to startups Entrepreneurial activities are extensively affected by the context in which they take place. Hence, understanding the institutional, informational and socioeconomic factors–commonly denoted as framework conditions – is essential to collect relevant insights about the entrepreneurial processes of creation, survival and growth. The evolution and life of startups are marked by a series of outcomes and milestones. Policy actions designed to improve the outcomes of the entrepreneurial process should be tailored to the distinct needs of entrepreneurs along the different milestones (Autio and Rannikko 2016).

Figure 3. introduces the 3B (buffering, bridging and boosting) framework of policy support to startups along their development phases (stand-up, start-up and scale-up). The underlying assumption behind the 3B framework of policy support to startups is that every entrepreneurial journey originates from opportunity or problem recognition. To pursue this opportunity, prospective entrepreneurs must take action to actually become entrepreneurs. Hence the first milestone is the creation of the start-up. At this stage, public support should aim to buffering startups from adverse external conditions. The rationale behind buffering lies in a resource-based perspective in which firms are resource-constrained entities. Buffering policies aim to create adequate conditions for the provision of vital resources in order to lower the firm dependency on external providers. The resource endowment is particularly salient at the creation of startups to ensure that they "do not run out of fuel". Public support through buffering can include seed-stage access to financial capital, low-cost office space, tax deductions, and initiatives to lower the regulatory burden of establishing new firms, among others.

The second milestone that entrepreneurs need to achieve during their journey is the retention of the business venture leading to survival. Once achieved the retention milestone, buffering and sheltering barriers against the hostile external environment are no longer appropriate. At this stage, the bridging public support comes into play. In sharp contrast with buffering activities, these policy instruments promote and facilitate networking relationships with external partners. Bridging activities relate to the facilitation of inter-organizational networks, collaborations and the flow of knowledge and resources across organisations.

The third milestone is the achievement of traction or growth. At this stage, the policy support relates to the boosting of firms' organizational capacities to scale-up the business. It could take the form of public support emphasizing growth motivation and encouraging firms to achieve milestones towards growth (with coaching or mentoring support). Public support of this kind fits particularly well with the growth process of scale-up firms since it boosts them in the pursuit of customer development, market expansion and economic growth.

Figure 3. The 3B Framework of policy support to startups

(Source: Van Roy and Nepelski (2016) based on Autio and Ranniko (2016); Ameczua et al. (2013))

Figure 3. classifies the widespread conceptualisations of the possible policy actions (Autio and Ranniko, 2016) to support different entrepreneurial stages (Autio et al. 2017). Using the 3B framework of policy support to startups, Autio and Ranniko, (2016) presents policy actions into three groups. The classification is based on the

descriptions of the initiatives and how their activities reflect one of the three types of policy support to startups: Buffering: targeting the early stage of entrepreneurial activity; Bridging: helping early startups to reach out to the external actors of the entrepreneurial ecosystem; Boosting: focusing on accelerating the growth of promising ventures. This research aims to identify the necessary policy endeavours to stimulate the entrepreneurial potential for all three different phases mentioned above.

Kirwan, van der Sijde and Groen argue that entrepreneurs require sufficient amounts of four types of capital, namely strategic capital, economic capital, cultural or human capital, and social network capital, for the entrepreneurial process to be “sustainable over time” (2006, p.175). The support provided by accelerators, incubators or other startup/scaleup support mechanisms can bridge that gap and fulfil the entrepreneurs’ needs of capital to successfully develop the firm from the initial idea stage (exploration) through the market research and evaluation into the exploitation of the opportunity into a viable market offering, and beyond.

5. Methodological Choices

The issue to be addressed is – what is the most appropriate research strategy and design (Methodological choice) to answer the research question(s) posed by the researcher. In some cases quantitative approaches may suffice, in others qualitative approaches alone may be most appropriate. Whether to use a quantitative method or methods, a qualitative method or methods, or a mixture of both? Researchers can choose to use a single data collection technique and corresponding analysis procedure, either a mono method (quantitative design or qualitative design). Alternatively, they can use multiple methods. In multiple methods, either researcher can pick multi methods or mixed methods.

Figure 4. Classification of Methodological Choice

(Source: Developed from Saunders et al. 2016)

5.1. Mixed Methods The mixed methods approach, also referred to as the third path (Gorard and Taylor, 2004), the third research paradigm (Johnson and Onwuegbuzie, 2004) and the third methodological movement (Tashakkori and Teddlie, 2003) is widely used and recognised by management scholars. Mixed methods, being the third research paradigm, is known to be a profoundly comprehensive technique for research in social sciences through integration of thematic and statistical data (Tashakkori et al. 1998). Divergent findings created through differing data collection and analysis techniques appear to lead to greater depth and breadth in overall results, from which researchers can make more accurate inferences with increased credibility.

There are two main reasons for the combination of qualitative and quantitative methods in a single study. The researcher can gain a more complete understanding of the phenomenon and achieving complementary results by using the strengths of one method to enhance other one. Furthermore, the significance of combination is that by combining methods in the same study, the researchers can partially overcome the deficiencies or biases that arise from one method. In other words, each method has its own weakness or disadvantage, for instance, a quantitative research method may be unable to capture the deeper meaning of the research problem, whilst a qualitative method may miss the importance of the key variables that have influence on the final results of the study.

It is argued that both approaches can be integrated within one study if the research problem requires methodological triangulation to increase the validity and reliability of the study (Patton, 2002). This can then maximise the ‘knowledge yield’ of the research study (McCall and Bobko 1990). This methodologically

combined approach has increased in popularity in recent years and is now termed ‘Mixed methods’ research (Johnson and Onwvegbozie 2004; Tashakkori and Teddlie 2010; Plano Clark and Creswell 2011).

Molina-Azorín and Cameron (2015) outlined four ways in which using mixed methods can benefit business research: preliminary qualitative data can provide a deeper understanding of context to inform context-specific studies in strategic management and entrepreneurship; attention to both process and outcome through mixed methods benefit theory-building, for example with qualitative methods contributing insights as to the mechanisms through which different variables contribute to a measured outcome; the study of complex organizations would benefit from analyses that are integrated across micro and macro levels; and the use of mixed methods helps to bridge the academic-practitioner divide through enhancing the interpretation and communication of results.

Johnson, Onwuegbuzie and Turner (2007) presented a composite definition:

‘Mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the purposes of breadth and depth of understanding and corroboration. (Johnson et al. 2007: 123).’

A more comprehensive definition is provided by Creswell and Plano Clark (2007: 5) who define mixed methods as follows:

‘Mixed methods research is a research design with philosophical assumptions as well as methods of inquiry. As a methodology, it involves philosophical assumptions that guide the direction of the collection and analysis of data and the mixture of qualitative and quantitative data in a single study or series of studies. Its central premise is that the use of quantitative and qualitative approaches in combination provides a better understanding of research problems that either approach alone.’

Quantitative and qualitative approaches are no longer seen as two discreet opposite approaches. Instead, they represent two ends of a continuum as a study can be seen as more quantitative than qualitative or vice versa. The mixed research approach sits in the middle of this continuum (Creswell 2014; Johnson and Onwuegbuzie 2004).

5.2. Typologies of Mixed Method Designs Mixed methods research designs use both quantitative and qualitative approaches in a single research project to gather or analyse data and several mixed method theorists have developed mixed method typologies (Creswell 2003; Creswell and Plano Clark 2007; Greene and Caracelli 1997; Mertens 2007; Morse 2003; Tashakkori and Teddlie 2003). In the mixed research, a researcher uses a mix of quantitative and qualitative approaches (designs and methods) in one study or a set of related studies. This can be done either concurrently when conducting both parts at the same time or sequentially when conducting one part first and the other second (Antwi and Hamza 2015; Johnson and Christensen 20o4; Molina-Azorin 2016).

The typology of the mixed methods research can be established according to four principal dimensions: the status of each method in relation to the other, the degree of combination of the methods (Johnson et al., 2007), the order of implementation of the methods (Creswell 2003; Johnson and Onwuegbuzie 2004) and the presence or the absence of theoretical framework. Mixed methods research can also be classified according to the order of implementation of the data collection. They can be implemented, for example, in a sequential manner with the objective of exploration or, again, in a concurrent manner with the objective of triangulation. Based on Morse’s (1991) notation system, when the methods are sequential, they are represented under the form QUAL QUAN or QUAN QUAL and when they are concurrent, they are presented under the form QUAL + QUAN.

Creswell, Plano Clark, Gutmann and Hanson (2003) provide one of the most relevant typology of mixed methods designs based on four criteria: the implementation order of data collection (sequential or concurrent), the priority given to quantitative or qualitative research, the research stage of integration of qualitative and quantitative methods and the potential use of a transformational value or action-oriented perspective in the study (framework, advocacy, ideology). This typology specifies six mixed methods designs as explained above in detail are summarized below in the Table 1.

Table 1. Types of Mixed Methods Research Design

Design Type Implementation Priority Stage of Integration Theoretical Perspective

Sequential Explanatory

Quantitative followed by qualitative

Usually quantitative; can be qualitative or equal

Interpretation phase May be present

Sequential Exploratory

Qualitative followed by quantitative

Usually qualitative; can be quantitative or equal

Interpretation phase May be present

Sequential Transformative

Either quantitative followed by qualitative or qualitative followed by quantitative

Qualitative, quantitative or equal

Interpretation phase Definitely present

Concurrent Triangulation

Concurrent collection of quantitative and qualitative data

Preferably equal; can be quantitative or qualitative

Interpretation Phase or analysis phase

May be present

Concurrent Embedded / Nested

Concurrent collection of quantitative and qualitative data

Quantitative or qualitative

Analysis phase May be present

Concurrent Transformative

Concurrent collection of quantitative and qualitative data

Quantitative, qualitative or equal

Usually analysis phase; can be during Interpretation phase

Definitely present

(Source: Adapted by author based on Creswell. et al, 2003)

5.3. Advantages and Challenges of the Mixed Research Approach There are two main advantages of using the mixed research approach (Sale et al. 2002). The first advantage is the "complementary strengths" which means using the strengths of one research method to enhance or support another one. Mixed researchers believe that using only quantitative or qualitative research is limited and incomplete for many research problems. As every approach has its strengths and weaknesses; they should be combined in a way that improves research quality by gaining integral strengths and avoiding overlapping weaknesses (Johnson and Christensen 2012; Sale et al. 2002).

The second advantage is "Triangulation". The purpose of triangulation is to enrich and strength research results by using different methods of data collection and analysis to study the same phenomenon in order to gain a complete understanding of this phenomenon. Triangulation is also used to check on findings from a particular method with finding reached by another one (Greener 2008; Molina-Azorin 2016; Sale et al. 2002).

From the other side, implementing the mixed research approach is faced by two main challenges. First, the mixed research approach needs more time, effort, and money as it includes two phases of research at least (Molina-Azorin 2016). Second, it requires the researcher to expand his research skills, talents and experiences by learning about new research methods and techniques in order to be qualified to conduct both the quantitative and qualitative parts of research (Fetters and Molina-Azorin 2017; Molina-Azorin 2016). This last challenge, in particular, should be seen as an opportunity, as many researchers tend to keep using the same research methods and avoid learning about new ways of doing research which limits their chances of adopting a wide range of research problems (Molina-Azorin 2016).

6. Research Design and Process

This study therefore adopts a mixed methods approach as the most appropriate approach to answer the research questions posed and the research objectives set. The research design can be exploratory and/or descriptive and/or causal (Saunders et al. 2012). The approach taken in the study is best described as a multiphase sequential exploratory research design (Saunders et al. 2012:167). Quantitative analysis techniques will be used after qualitative semi-structured interviews (in exploratory case studies) and archival data to provide the necessary methodological and data triangulation (Patton, 2002). It employs qualitative data analytic techniques (case study and cross case analysis), confirmatory quantitative techniques (QCA - Qualitative Comparative Analysis) and contribution analysis (Structured Meta – analysis), in addition to a proprietary dataset to answer the research questions posed and reach the research objectives set. Figure 5.

depicts the overall research design and design subtypes of the research. The sequential explanatory mixed method design utilised in the research is adapted from Tashakkori and Teddlies’ (2003) typology of mixed method research.

Figure 5. Research Mixed Method Design

(Source: Adapted by author from Tashakkori and Teddlie 2003: 688)

During the first phase of the design (Qualitative), this research employs a multiple-case study (n=20) methodology (Yin, 2016). Secondary and primary data is collected on selected firms from the cohort of firms in the study through archival data and semi-structured interviews with key informants. The collected information is then written-up as descriptive case studies which are then cross-analysed using serial and thematic coding in NVIVO (Saldana 2015).

A Qualitative comparative analysis (QCA) is then undertaken in the second phase (quantitative), using the same case dataset (n=20). QCA is a comparative data analysis methodology that provides causal analysis in a systematic way using a configurational approach to understanding complex phenomena (Ragin 1987). In QCA, cases are transformed into the unique combinations of selected causal conditions and associated outcomes, and then compared and interpreted holistically focusing on their attributes. Output of QCA is then compared and contrasted to the cross-case analyses and as input to the combined analyses (Contribution analysis (Phase 3) (Mayne 2008, 2012; Buckley 2016) to provide the necessary methodological and data triangulation (Patton, 2002).

7. Conclusions

This research will serve as the empirical basis for the development of a proprietary approach to a disciplined innovation process (DIP). The entrepreneurial start-up phase, particularly in technology-based modes, has unacceptably high failure rates. Acknowledging the influence of luck/chance/serendipity (Storey and Greene 2010), this excessively high failure rate appears to be due, in many cases, to the undisciplined processes or poor experimental approach undertaken by technology entrepreneurs in their attempts to commercialise their knowledge and ideas. This mixed method research on disciplined innovation processes can help in identifying the key incubation/acellerator service mechanisms which must be present and which are necessary and sufficient for venture survival and growth post-incubation. This research can therefore assist entrepreneurs, incubator stakeholders and policy makers in ‘upping their batting average’. Findings will have implications, in particular, for future entrepreneurial support services to entrepreneurs which are provided by the State and/or Universities and HEIs.

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