Digital innovation in the periphery

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Digital innovation in the periphery

A qualitative case study of Sogn og

Fjordane from a regional innovation system perspective

Emma Kyte & Yasmina Fatima Machrouh

Master Thesis

Entrepreneurship and Innovation 30 credits

Department of Informatics

Faculty of Mathematics and Natural Sciences UNIVERSITY OF OSLO

June 2020

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Thesis title: Digital innovation in the periphery

Delivered: 15.06.2020

Authors: Emma Kyte og

Yasmina Fatima Machrouh Master’s degree: Master of Science in

Innovation and Entrepreneurship Supervisor: Rune Njøs

Topic of study: Digital innovation in Sogn og Fjordane

Method: Qualitative case study Abstract:

The aim of this thesis is to contribute to a better understanding of digital innovation in the periphery from a regional innovation system (RIS) perspective. We have conducted a single case study on the peripheral region of Sogn og Fjordane to find the barriers for digital innovation. The empirical data was gathered by interviewing informants with different backgrounds from various industries across Sogn og Fjordane. Our findings illustrate that digital innovation can be hard to do alone, as it is time-consuming and requires change. The main barriers for digital innovation in Sogn og Fjordane are lack of communication between various actors, low levels of education and lack of diversity as well as local knowledge. This thesis concluded that the RIS-model can contribute to the changes required for digital

innovation. RISs can be a great facilitator to stimulate innovation activities through

interactive learning and knowledge sharing. However, there is a need for the actors in Sogn og Fjordane to access more external knowledge from milieus outside of the region to generate more knowledge flow and increase their innovativeness.

Keywords: regional innovation system, rural regions, digital innovation, innovation, Sogn og Fjordane

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Digital innovation in the periphery

Emma Kyte and Yasmina Fatima Machrouh http://www.duo.uio.no/

Trykk: Reprosentralen, Universitetet i Oslo

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Abstract

The aim of this thesis is to contribute to a better understanding of digital innovation in the periphery from a regional innovation system (RIS) perspective. We have conducted a single case study on the peripheral region of Sogn og Fjordane to find the barriers for digital innovation. The empirical data was gathered by interviewing informants with different backgrounds from various industries across Sogn og Fjordane. Our findings illustrate that digital innovation can be hard to do alone, as it is time-consuming and requires change. The main barriers for digital innovation in Sogn og Fjordane are lack of communication between various actors, low levels of education and lack of diversity as well as local knowledge. This thesis concluded that the RIS-model can contribute to the changes required for digital

innovation. RISs can be a great facilitator to stimulate innovation activities through

interactive learning and knowledge sharing. However, there is a need for the actors in Sogn og Fjordane to access more external knowledge from milieus outside of the region to generate more knowledge flow and increase their innovativeness.

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Acknowledgments

This master thesis marks the end of an important era in our lives. Writing this thesis has been challenging but very fulfilling with many ups and downs. It has been filled with stress, laughter, frustration up until the very end. We feel lucky to have had each other through this process, it has helped us keeping sane. We would like to take this opportunity to thank those who have helped us during this journey.

First, we are extremely grateful for our supervisor, Rune Njøs for giving us the guidance and motivation needed throughout the writing process. Thank you for making this time enjoyable and we hope that you did not get tired of our endless questions. Through our state of nerves, you have calmed us down and stayed patient.

Secondly, we would like to thank VRI4 for the support that made it possible to travel across Sogn og Fjordane. We would also like to thank Teknoløft and Erik Kyrkjebø for giving us a flying start to this project.

Thirdly, we would also like to thank our informants for taking the time out of their busy

schedules to speak with us. Your contributions have truly been a great resource for this thesis.

Forth, we would like to thank our families for their love and support through this time. Thank you for your continuous encouragement despite our mood swings.

Last, but not least, we would like to thank each other. We have motivated and encourage each other through our 5 years of studying. We have travelled the world together and are so

grateful to have had these experiences together.

#Whānau4Life

Emma Kyte and Yasmina Machrouh

Bergen, June 2020

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List of Tables

Table 1: Barriers for digital innovation in rural regions from a RIS perspective ... 16

Table 2: Informants and their positions... 20

Tabel 3: Master thesis timeline ... 22

Table 4: Findings from the empirical analysis ... 36

Table 5: Barriers for digital innovation in Sogn og Fjordane through a RIS perspective ... 37

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Abbreviations

DUI Doing, Using and Interacting

HVL Western Norway University of Applied Science IT Information technology

NSD Norwegian Centre for Research Data RIS Regional innovation system

R&D Research and development

SME Small and medium sized enterprise SSB Statistics Norway

STI Scientific, Technology and Innovation

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1 Introduction

Innovation differs from region to region and some are more innovative than others.

However, a common factor for innovation is often that a varied and solid business environment improves innovation in a region (Gulbrandsen, Bye, Finne, & Njøs, 2013).

Innovation can be defined as an interactive learning process through collaborations between various actors (Asheim, Isaksen, & Trippl, 2019). These collaborations often consist of research and development institutions, government institutions and businesses, and is often described as a regional innovation system (Karlsen, Isaksen, Larsen, Skogseid, & Nygaard, 2013).

Today's society has an increasingly globalised and knowledge-intensive economy, and Norway needs to be able to innovate and adapt its business sector to get a competitive edge and create value (Nystad, 2013). Society is always evolving and with this, comes challenges that businesses need to face to stay ahead. One of the biggest challenges, or rather an

opportunity is digitalisation (Isaksen, Trippl, Kyllingstad, & Rypestøl, 2019). Digitalisation is the process of using technology to improve or find new solutions. Innovation and profitability are often facilitated by digitalisation to be able to increase productivity (Kommunal- og moderniseringsdepartementet, 2014). However, digitalisation requires changes that need to be implemented by actors, in terms of competency, processes as well as in business models. It can be hard to change existing routines for Norwegian businesses, as most of them are small and medium sized enterprises. These businesses have a lack of available resources and

competence to digitise. Therefore, it is important that Norwegian businesses use collaboration as a tool for digital innovation (Næringslivets Hovedorganisasjon, 2018).

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1.1 Background

This thesis is linked to the research and development (R&D) collaboration project Teknoløft. The R&D institutions involved are Western Norway University of Applied Science (HVL), Western Norway Research Institute, Kunnskapsparken Sogn og Fjordane and

SINTEF Digital. The Teknoløft project aims to increase knowledge and capacity around digitalisation and automation between R&D institutions and businesses in Sogn og Fjordane to stimulate innovation. The digital shift is forcing businesses to drastically change the way they operate in order to survive. Therefore, Teknoløft is focusing on a wide range of small and medium sized entreprises (SMEs) in Sogn og Fjordane, so that they can take an active part in the digital shift (Høgskulen på Vestlandet).

1.2 Motivation

The motivation behind this thesis is that we want to research digital innovation as it is a highly relevant topic in today's society. As society is becoming more digital, businesses have to innovate to stay up to date with new technology. We find the research project Teknoløft interesting as their topic of research is relevant to our background as electronics engineers. We hope that our thesis can contribute with valuable information that can aid Teknoløft in achieving their aim of doubling the innovation activity in Sogn og Fjordane by 2024 (Høgskulen på Vestlandet).

1.3 Research question

In this thesis we wish to get a deeper understanding on how regional innovation systems (RISs) can stimulate digital innovation in peripheral regions with a focus on Sogn og Fjordane. RIS is “a system consisting of regional networks of interaction, within or across industries, public and private, whose purpose is to promote R&D and innovation activities”

(Larsen, Nesse, & Skogseid, 2017, p. 25). We want to get a clearer picture as to what affects innovation in rural regions, as well as the barriers for digitalisation in Sogn og Fjordane through a RIS perspective. The RIS approach can be used to analyse and give a clearer view of both strengths and weaknesses of a region (Tödtling & Trippl, 2005). The RIS-model should be adapted to the different circumstances in different regions, due to regions having various challenges and opportunities across industries (Karlsen et al., 2013; Njøs & Fosse,

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3 2019). Karlsen et al. (2013) mentioned that peripheral regions should concentrate on

developing collaboration activities between actors to evolve their innovation capabilities. In short, the RIS-model focuses particularly on the collaboration between R&D institutions, businesses and government institutions to stimulate innovation within a region. This model allows us to take an approach where we focus on how different actors interact to stimulate digital innovation on a regional level. Subsequently, we have defined one theoretical and one empirical research question to investigate. This will give us a broader understanding of the topic of study. The research questions we intend to investigate are the following:

Theoretical research question:

What is the role of regional innovation systems for digital innovation in rural areas?

Empirical research question:

What are the barriers for digital innovation in the regional innovation system in Sogn og Fjordane?

We will use existing theories as well as conduct an empirical case study to answer our research questions. The case study will give us a greater picture of the different views from R&D institutions, businesses and government institutions regarding barriers for digital innovation. Surfacing from this, we will discuss if the RIS-model fits the region as well as how Teknoløft can address these barriers in Sogn og Fjordane.

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1.4 Chapter overview

Chapter 2 – Theory - Introduces literature and theories used in this thesis, as well as an analytical framework used in the analysis in chapter 4 and discussion in chapter 5.

Chapter 3 - Method - Presents the choice of research design and methodology, as well as the data collection process.

Chapter 4 - Empirical Analysis - Presents the empirical findings from our data collection.

Chapter 5 - Discussion – Discusses our empirical findings in relation to our theoretical findings to highlight the barriers for digital innovation in Sogn og Fjordane.

Chapter 6 - Conclusion and future work - Summarizes the main findings. This chapter provides the theoretical and practical contributions of this thesis. We also discuss the limitations of our thesis as well as suggestions for future work.

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2 Theory

In the following chapter we will introduce the literature and theories used in our thesis.

We conducted a literature review to find relevant information to answer our research

questions. The literature review helped us discover previous findings on our topics of study, as well as the gaps in the literature. The main topic discussed in our literature review is regional innovation systems with a focus on digital innovation. The literature review is narrowed down into a Norwegian context, where we empirically shed a light on the rural region of Sogn og Fjordane. This chapter will also provide an analytical framework applied in both the empirical analysis and discussion chapters.

2.1 Innovation systems

Joseph Schumpeter defined “innovation” as “new combinations” of existing resources and the commercialisation of these “new combinations”. This can be in the form of a new service, product or technology (Abelsen, Isaksen, & Jakobsen, 2013). According to Larsen et al. (2017) “innovation” is defined as something that can contribute to value creation both in the public and private sector. This definition is in accordance with Schumpeter’s definition.

Jakobsen & Lorentzen defines an innovation process as follows “Innovation is an interactive process involving different actors: ‘a collective achievement that requires key roles from numerous entrepreneurs in both the public and private sector’” (Jakobsen & Lorentzen, 2015, p. 81).

Shearmur and Doloreux (2016) referred to several research papers where there was a consensus on what makes businesses innovative. The most common factors found were interaction, collaboration and knowledge sharing. Geographical proximity between actors can be a facilitator for innovation, as it makes it possible to have more frequent interactions face- to-face. This gives small and medium size enterprises (SMEs) the opportunity for a better knowledge exchange between actors, which can stimulate innovation. Therefore, SMEs can compensate for their lack of resources by being geographically close to other businesses and organisations (Aslesen & Harirchi, 2015). Public schemes frequently emphasize collaboration as a tool to stimulate innovation and business development. The intent is to achieve

collaboration between businesses and R&D institutions through open innovation. This involves a more systematic approach to utilising expertise within a business as well as

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external knowledge from different actors (Gulbrandsen et al., 2013). Jakobsen and Lorentzen (2015) argue that external inputs such as suppliers, rivals, customers and R&D institutions will positively impact the innovativeness of businesses. This is because an innovation process is interactive, and therefore businesses need complementary resource inputs in this process. In line with this Asheim et al. (2019) and Gulbrandsen et al. (2013) convey that collaboration is without a doubt an essential part of innovation, it is a widespread phenomenon and it is almost impossible to find businesses that innovate alone.

An innovation system is a collective and interacting process, where both public and private actors are involved. These actors consist of businesses, R&D institutions and

government institutions, also known as the triple helix. A triple helix refers to the interaction between the actors involved in an innovation system. The triple helix model views innovation as something that happens in the intersection between these actors, rather than something that happens by one actor alone. These actors cooperate by sharing ideas and knowledge about innovations. However, for the model to work, the activities need to be beneficial for the actors involved, in terms of their own innovation capabilities (Larsen et al., 2017). According to Larsen et al. (2017) innovation systems can be categorised into three different geographical levels: regional, national and global. This thesis will concentrate on the innovation system on a regional level. RISs focus on regional configurations, and the significance of geographical proximity when it comes to knowledge sharing and trust between actors collaborating (Asheim et al., 2019).

2.2 Digital innovation

Digital innovation can be defined as innovation where digital technology is used to create “new combinations” (Isaksen et al., 2019; Nambisan, Lyytinen, Majchrzak, & Song, 2017; Räisänen & Tuovinen, 2020). The outcome of a digital innovation does not necessarily have to be digital. The main focus is the use of digital technologies and digitised processes to innovate. This can be done either through new products, processes or even altering business models to achieve innovation (Ciriello, Richter, & Schwabe, 2018; Isaksen et al., 2019;

Nambisan et al., 2017). According to Isaksen et al. (2019), there are three types of knowledge that can be used for digital technology: scientific knowledge, experience-based knowledge and scientific- and experience-based knowledge. Scientific knowledge focuses on developing specific technologies based on scientific principles. Experience based knowledge is knowing

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knowledge is used when developing new or upgraded versions of existing products or services (Isaksen et al., 2019).

Digital innovation has become progressively more important for businesses and is no longer just used by software businesses. The five most valuable businesses in the world today are in the digital sector, as a result digital technology contributes to altering large parts of the world's economy (Ciriello et al., 2018). Digital transformation has gained a lot of attention from various industries. This is because digitalisation is no longer viewed as just the development of new technology, but rather seen as an important element to remaining

competitive in industries, which can lead to innovation (Isaksen et al., 2019). A key factor for digital transformation is the development of new skills and competence in the workforce (Isaksen et al., 2019). According to Räisänen and Tuovinen (2020) SMEs invests less in digital technologies than larger businesses and therefore, they struggle to keep up with digital transformation. According to Nambisan et al. (2017) digital innovation has radically changed how innovation occurs. Digital innovation also enables collaborations for innovation, which can bring actors from various backgrounds together, and possibly reshape existing industries (Nambisan et al., 2017). However, digital innovation requires change and a support from RIS to gain knowledge to create and adapt new digital solutions (Isaksen et al., 2019).

2.3 Regional innovation systems

Regional innovation system (RIS) is “the institutional and organizational infrastructure interacting and supporting innovation within the production system of a region” (Asheim et al. 2015, p.274, cited in (Njøs & Fosse, 2019, p. 420)). RIS can be defined in many ways, it has also been defined as a facilitator for innovation development, where different actors in a region engage in interactive learning through an institutional milieu (Steinmo, Lauvås, Eidem, Salamonsen, & Paulsen, 2018). According to Larsen et al. (2017) RIS is “a system consisting of regional networks of interaction, within or across industries, public and private, whose purpose is to promote R&D and innovation activities” (Larsen et al., 2017, p. 25). RISs can be approached in a broad or narrow way. On the one hand, a broad approach focuses on every aspect that affects the production system of a region (Njøs & Fosse, 2019). This is in line with the definition from Asheim et al. (2015). On the other hand, a narrow approach is more

concerned with encouraging development of relations between a handful of actors in a region.

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Where the aim is to stimulate innovation and shared understandings (Njøs & Fosse, 2019).

This is in accordance with the definition from Larsen et al. (2017). This thesis will focus on the different actors involved in RIS from a narrow approach.

For a RIS to be well-functioning it is important to have knowledge development and an interactive learning process between the actors involved in innovation activities (Asheim et al., 2019; Steinmo et al., 2018). Cooperation can be more achievable on a regional level, due to proximity, not only spatial proximity but the mutual core values from the actors involved.

Thus, making proximity an important factor for innovation. This is one of the reasons as to why RISs exist (Asheim et al., 2019). In RIS different types of organisations cooperate in the so-called triple helix (Larsen et al., 2017). Through this interaction, the actors involved develop a sharing and learning culture which can be beneficial for the region. This way of viewing RIS focuses on collaboration between the actors from the different spheres (Njøs &

Fosse, 2019).

According to Tödtling and Trippl (2005), there are several different types of RIS failures that can arise when trying to innovate. On the one hand, underdeveloped set up of organisations or institutions can lead to RIS failures. This can occur because essential parts are either missing or unsuitable and will therefore have negative consequences on the

potential for innovation in a region. This obstacle can be either on a business level or system level, and it varies from lack of innovative clusters and specialised knowledge, to

overspecialised and outdated technology. On the other hand, if different actors and

organisations are not interacting properly in an innovation process, this can contribute to a RIS insufficiency as well. In RISs there are two types of networking problems that can arise.

There can either be poor communication between the different actors involved, leading to a lack of knowledge that can result in poor innovative performance. Ties that are too strong between actors can lead to a lock-in effect, which can undercut innovation capabilities in regions (Tödtling & Trippl, 2005). Actors can become too dependent on each other if a collaboration is too long-lasting. This can also lead to a “lock-in” of innovation opportunities, where the ability to renew their perspective is reduced, as they become too preoccupied with established routines (Gulbrandsen et al., 2013).

Karlsen et al. (2013) refer to innovation studies showing that businesses very rarely innovate alone. Business should interact with R&D institutions such as universities, public and private research institutions, as it is considered a great benefit for all actors involved

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9 (Njøs, Jakobsen, Fosse, & Engelsen, 2014). Businesses that participate in regional

collaboration through networks generally achieve more innovation (Johansen et al., 2013).

Small- and medium-sized enterprises (SMEs) should not solely depend on local knowledge, as this can cause a lock-in effect. External resources give businesses an opportunity to innovate by exchanging knowledge with other businesses, or with R&D institutions.

Numerous studies have shown that external collaborations, particularly technological

collaborations are pivotal for the innovativeness of SMEs. SMEs tend to build deep and long- lasting collaborations, as it can be difficult to find collaboration partners. However, these long-lasting collaborations might not be the most beneficial for SMEs. Studies indicate that these enterprises would benefit more from frequent change in collaboration partners (Aslesen

& Harirchi, 2015). The ability to utilise external resources available in a network can be a greater advantage for staying competitive than the size of a business(Aslesen & Harirchi, 2015; Johansen et al., 2013).

Business networks are a great way of facilitating collaboration between SMEs to help develop their innovativeness and competitive edge (Innovation Norway, 2019). Participation in networks requires a high degree of commitment from the businesses involved. For business networks to be successful, it may be necessary for public facilitators to be involved (Johansen et al., 2013). The need for support from public actors is necessary, as low levels of both collaboration and knowledge sharing can occur if there is a lack of support for networking activities (Miörner & Trippl, 2017). According to Gulbrandsen et al. (2013), networks require a great amount of time invested for the actors involved, and the results are unpredictable.

Some actors participate symbolically without contributing anything to the networks. Despite the learning potential involved, businesses can be reluctant to collaborate as the benefits may come too far ahead in the future. Recently, there has been an increase attention on learning as an important tool in innovation processes. For businesses to be able to innovate and adapt both in the short and long term, it is crucial for them to be able to promote learning (Båtevik et al., 2013).

Businesses in peripheral regions usually have a lack of local knowledge and it is important for them to compensate for this by using external resources productively. The competence and R&D capacity of a workforce determines the level of absorptive capacity in businesses. As well as the level of R&D capacity from public actors within a RIS. These are important factors for innovative capabilities of businesses through external resources (Asheim

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et al., 2019). For businesses to innovate they are dependent on a solid RIS where features such as openness, trust, collaboration and interaction are fundamental. These features are often combined with modes of innovation, as this can contribute positively to the

innovativeness of a business (Cooke, 2016).

According to (Jensen, Johnson, Lorenz, & Lundvall, 2007) there are two modes of innovation, STI (Scientific, Technology and Innovation) and DUI (Doing, Using and Interacting). The STI mode is based on scientific and technical knowledge to enhance their innovative activities (Asheim et al., 2019). The DUI mode is based on learning through informal activities and experience-based know-how. Knowledge learned through DUI

depends on existing knowledge and is usually acquired on the job. This mode utilizes learning by doing, using and interacting and therefore knowledge is attained both intentionally and unintentionally. Knowledge sharing through DUI activities can develop skills in employees by promoting learning activities and interactions both within and outside businesses (Asheim et al., 2019; Jensen et al., 2007).

Businesses who combine a DUI innovation mode and the use of external resources through R&D are shown to be innovative. This indicates that the innovativeness of a business correlates with how much diverse knowledge is used (Asheim et al., 2019). New skills and knowledge around the use of digital technology needs to be learned and adapted by businesses and R&D institutions to digitise. RISs can be considered to play a role in adapting and using these new digital technologies (Isaksen et al., 2019). Absorptive capacity can be used when acquiring new innovative skills in existing and new employees in a workforce (Cooke, 2016).

RISs are shaped by their resources, competences, skills and experience, and has a focus on supporting regional businesses with their current practices (Isaksen et al., 2019;

Miörner & Trippl, 2017). However, when discussing digitalisation, there may also be a need to reconfigure not only how businesses innovate but also the support system for innovation.

Thus, RISs need to change and make shifts in technology by changing routines and create a change in institutions and organisations (Isaksen et al., 2019). It is important to understand the role of different actors in the RIS, and their role when it comes to transforming existing regional arrangements. A hindrance for change in RISs is that institutions tend to change at a slow pace. The lack of understanding from private and public actors when it comes to future needs to stay competitive also needs to be addressed (Miörner & Trippl, 2017). These changes can be challenging, partly due to RISs having a set way of doing things, which can be difficult

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11 to alter. However, change needs to happen in all part of RISs in order to support and stimulate digital innovation. This can be in the form of new organisations or changes to already existing organisations, such as new study programmes offered. Another way of transforming RISs is through increased knowledge flow and interactive learning to help retrain current employees and train the new employees in new digital technologies (Asheim et al., 2019; Isaksen et al., 2019). As a highly educated workforce is an essential part of a business’s potential for development. This type of workforce also facilitates important dialogue and networking with various actors both inside and outside their own region (Båtevik et al., 2013).

2.4 Innovation in urban and rural regions

Eder (2019) point out that studies done on innovation have had a big focus on high-tech innovations and therefore rural regions might be overlooked. Nonetheless, it has been shown that many industries have the ability to innovate, and not only high-tech industries that are located in urban areas. Eder (2019) pointed out that the literature found today on innovation in periphery regions is considered relatively lower, in comparison to literature written on innovation in cities. However, the extent of it found today is wide- ranging. It scopes from small villages to big cities all around Europe. Consequently, this makes the research on innovation in the periphery more varied than once assumed (Eder, 2019).

Tödtling and Trippl (2005) point out that there is no single “best practice” approach to innovation. There are different barriers to overcome in different regions. Therefore,

approaches applied will differ greatly from region to region. Meaning that every region will have different challenges, problems to face, as well as different opportunities. Hence, there is not only one innovation approach that is applicable everywhere. Previous research indicates that innovative regions have an environment and culture that encourages interaction and generation of new ideas, which is facilitated through open innovation. These regions typically have the support from regional institutions. This suggests that innovation can occur in both urban and rural areas, providing that they have these features. Thus, diversity, external resources and R&D institutions correlate with how conducive to innovation a region is (Shearmur & Doloreux, 2016). However, there are opposing views in the literature about which type of areas are more conducive for innovation. More specifically whether specialised or diversified areas have more advantages. Innovators can be classified as either fast or slow.

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Fast innovators are dependent on frequent interaction, access to various knowledge sources and R&D. Therefore, it is often located in urban areas. Whereas slow innovators are often found in periphery regions, because one does not require the latest information and interaction is not always a necessity (Eder, 2019).

Peripheral regions are usually characterised by many SMEs, few R&D institutions, low levels of R&D activities and long distances. Thus, rural regions are in a weaker position to innovate than urban regions, as they have weakly developed RIS prerequisites, known as

“organisational thinness” (Asheim et al., 2019; Larsen et al., 2017; Tödtling & Trippl, 2005).

Organisationally thin RISs have few support organisations and usually rely less on businesses to create new innovation to transform RISs. To strengthen thin RISs there is a need for a system-level change, where organisations take more initiative to change parts of RISs (Asheim et al., 2019). Low levels of R&D activities are preventative of innovation activities as well as leading to a lower absorption capacity of the businesses in a region (Tödtling &

Trippl, 2005). It is pivotal for businesses in organisationally thin RISs to use absorptive

capacity, especially in regions that rely on experience-based knowledge (Asheim et al., 2019).

Karlsen et al. (2013) stated that a key factor that can contribute to innovation in businesses is collaboration with R&D institutions. According to Jakobsen and Lorentzen (2015), collaboration between businesses and R&D institutions is more used in rural regions, even though R&D institutions are mainly located in urban areas. Thus, suggesting that collaboration for innovation and open innovation increases when the regional size decreases.

A reason for this might be that rural areas have a lack of complementary knowledge inputs, as well as potential innovation partners (Jakobsen & Lorentzen, 2015). External resources are important for rural regions as they acquire knowledge not available in the region, which can increase their innovativeness. However, to acquire this knowledge, rural regions need to be able to have absorptive capacity, thus there is a need to strengthen the R&D activities in the region. Another way to try an overcome low level of innovativeness for rural regions is to strengthen their lack of external resources. This can be done by connecting external resources to potential clusters or networks in the region. (Tödtling & Trippl, 2005).

According to Park (2017) rural areas have a digital disadvantage compared to urban areas, due to distance, infrastructure and socio-demographics. These factors can contribute to the lack of digital innovations in rural areas. The lower level of education in rural areas compared to urban areas is also a contributing factor. However, the digital shift has also given

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13 rural areas benefits. The barrier of distance is no longer as demanding, due to digital tools (Park, 2017). However, in order to utilise these tools there is a need for competence within technology (Isaksen et al., 2019). Through innovation research it has been shown that there is a positive correlation between the level of education in the workforce and the rate of

innovation in a region. In rural regions the education level can be a barrier to overcome for innovation (Båtevik et al., 2013). As peripheral regions have fewer R&D institutions, they have a lack of higher education and more access to low and medium level education (Tödtling

& Trippl, 2005). The competence needed in businesses in a region do not always match with the competence available due to the education programmes offered at local universities. This mismatch can also arise with knowledge generated from R&D institutions in a region. Young adults move from rural regions to get a higher level of education and relevant job

opportunities (Båtevik et al., 2013; Håvold, Nesse, & Årethun, 2017; Miörner & Trippl, 2017).

2.4.1 Innovation in urban and rural regions in Norway

The Norwegian nature such as the fjords have made urban and rural areas more distant from each other compared to other countries. This, along with the Norwegian climate makes communication over land an obstacle. Norway's size and nature leads to a lack of population and therefore of businesses. Places are further away from each other, leading to more

isolation. These factors have been viewed to give businesses lock-in, as well as lower generation and dispersion of knowledge which leads to a decrease in innovation. However, the wealth of the country and its human capital have been viewed as great assets in the development of innovation (Rodríguez-Pose & Fitjar, 2013). Although, Cooke (2016) addresses that Norway scores low on innovation based on international measures. It is explained that Norwegian businesses often uses a DUI mode in their innovation activities.

Thus, innovation that occurs in Norwegian businesses often go under the radar as it originates from more knowledge-based learning activities, and are not registered as innovation (Cooke, 2016).

Over the last decades collaboration has been a vital part of industrial policy debate for regional development in Norway (Gulbrandsen et al., 2013). Public schemes are necessary for regional development. Each year, Norway spends a large amount of public funds to promote knowledge sharing between actors through collaboration (Johansen et al., 2013). Innovation

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collaboration through policies has improved collaborations at a local level, and research has shown that local connections have been a contributing factor. Norwegian cities take great pride in their cities, which has led to a great amount of collaborations on a local and regional level. There is a high level of trust in Norway and this impacts positively for collaboration in close proximity, as people trust each other and also want to support each other(Rodríguez- Pose & Fitjar, 2013). Rodríguez-Pose and Fitjar (2013) pointed out in their research on Norwegian cities that Norwegian businesses are cooperative with other businesses located in close geographical proximity. The businesses that were surveyed remarked that geography is of great significance when it comes to making partnerships. Consequently, collaborations and networks where usually made between local and regional businesses, as opposed to with national actors. However, these strong local connections may not be good for long-term development for city regions, as it can gradually suppress innovation. They also indicated that these types of connections did not make businesses more innovative than other businesses. On the contrary, innovation tends to occur in businesses where collaboration with actors outside of their geographical proximity (Rodríguez-Pose & Fitjar, 2013).

One of the most important challenges for economic development in Norway is the uneven regional growth in competence-intensive workplaces. The meaning behind

competence-intensive is that both the demand for higher educated workforce differs across regions in Norway, and that there are challenges connected to recruiting such workforce in peripheral regions (Båtevik et al., 2013, p. 10). Businesses are seeking a good combination of prior learning and formal expertise for their personnel. Achieving this can be pivotal for development within a business. Studies conducted by Statistics Norway (SSB) show that lack of qualified personnel is one of the main factors that can hinder innovation activity in a business. These studies showed that not only rural areas have this issue, but also businesses in urban areas. Even though in urban areas the access to highly educated personnel is higher than in rural areas, the demand is also higher. Therefore, access to qualified personnel is a

challenge for businesses both in urban and rural areas, but in different ways (Båtevik et al., 2013). However, it can be easier to find national and sectoral innovation systems rather than regional innovation systems in Norway. This is because Norway has a very concentrated university sector, and a relatively decentralised industry (Asheim et al., 2019).

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2.5 Analytical framework

In this chapter, we have introduced various theories and concepts within innovation, RIS and digitalisation literature. This literature review was conducted to find out what research and literature has been previously done on the subject. We have set up an analytical framework based on the theory in this chapter, to guide our empirical investigation. This analytical framework will also be the basis for our empirical analysis in chapter 4 and the discussion in chapter 5.

Through the literature, it is shown that there are different barriers to overcome for different regions to become innovative. Some of the main factors that stimulate innovation are collaboration and knowledge sharing between regional businesses. The theory pointed out that RIS prerequisites are weakly developed in peripheral regions, which can make them less innovative. Through the theory it came to light that the RIS-model should be adapted to different circumstances in different regions. The aim of RISs is to get different actors to cooperate and share knowledge to stimulate digital innovation. Table 1 shows various

dimensions that affect innovativeness in rural areas from a RIS-perspective. These dimensions will help us to find what barriers exist for digital innovation in peripheral areas and how to address them. The dimensions used in this framework is based on what theories and previous studies have shown. Analysing these dimensions up against our data collection, will give us a good basis to answer our empirical research question.

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Table 1: Barriers for digital innovation in rural regions from a RIS perspective

Dimensions Findings from the literature

Actors

Innovation activities Slow innovators Rarely innovate alone

Businesses High amount of SMEs

R&D institutions and

facilitators Few

Collaboration

Networks Need to be strengthened

Geographical proximity Sharing and trust culture

Communication Long distances

Collaboration patterns Fixed patterns Knowledge and

competence

Mode of innovation DUI

Education Emphasis on low to medium level

Workforce Lack of competence

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3 Method

In this section we will discuss our choice of research design and methodology used in our master thesis. We will give a theoretical insight into case studies, as well as explain our data collection process. Finally, we will provide an insight into the validity and reliability of our case study.

3.1 Qualitative Case study

The empirical method we chose for our master thesis is a qualitative single case study.

Considering the amount of time we had at our disposal, we chose to use a qualitative method to efficiently collect the relevant data needed.

A case study is a method that thoroughly examines one isolated case. The benefit of this is that it allows a researcher to investigate in-depth a “case”, resulting in a real-world perspective (Yin, 2018). A vital element in a case study is to find out why decisions are made, how they were implemented and with what results (Yin, 2018). The results of using this method are working hypotheses, interpretations of results from other studies and defining new research for potential new case studies. According to Yin (2018), a drawback of using a single-case study is that it can be challenging to pinpoint a general view about a group, as one person's opinion might not be the entire groups opinion.

It is beneficial to utilise a case study when studying a set of events in the recent past and the present (Yin, 2018). Yin (2018) explains that when doing a case study, one should do research in the field, this can be done through interviews with informants, respondents, businesses or organisations that have knowledge on the topic of study.

3.1.1 Choice of case

We decided to use a single case study, because we wanted to get an in-depth understanding around the barriers of digital innovation for businesses in peripheral regions from a RIS perspective. Our focus was narrowed down to the rural region of Sogn og

Fjordane, where we reached out to a wide range of different businesses in various industries, as well as R&D institutions and facilitators. Thus, getting a broader understanding of the barriers that exists across various industries in the region.

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The reasons we chose our case is because of the research project Teknoløft. The research project focuses on Sogn og Fjordane, which is why we focused on this region. The advantages of basing our thesis on an existing research project is, that it gives us the

opportunity to get easier access to data, and informants relevant to our case.

3.2 Data collection

In this subsection we will explain how we collected data from different sources, to be able to answer our research question. To obtain the data needed, we used both primary and secondary data. We started out with collecting secondary data through our literature review.

Thereafter, we collected primary data using qualitative interviews.

3.2.1 Secondary text data

Secondary text data is often used as supplementary data in a research project but can also be used on its own. This data already exists, which makes it useful and efficient to use, as it is not as time consuming as primary data. It is also important to be able to assesses whether the secondary data has good credibility and quality (Easterby-Smith, Thorpe, & Jackson, 2015). Our secondary text data was collected through our literature review, to get an understanding of the topic of study, and for our future research. To make sure the data was credible, we only used peer reviewed articles. The secondary text data was used as a foundation to help us with our research questions as well as to help construct our interview guides for our qualitative interviews.

3.2.2 Qualitative interviews

Qualitative interviews have a purpose of achieving in-depth information about the research topic, with a goal of a conversation between the interviewer and interview object.

The data of interest that is collected through these interviews is with a focus on the interview object's own perspective through their experiences and perceptions. In-depth interviews can be done either face-to-face or remotely. Face-to-face interviews are done in person, this type of interview is mostly used when the purpose is to seek in-depth insight of more complex topics and events that have happened in the past. Face-to-face interviews makes it is easier to do follow-up questions, in addition to observing the body language of the interview object. A weakness with a face-to-face interview is how time consuming they are to complete. This

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19 makes it a challenge to be able to get all the information that is needed, if there is not enough time to complete all the interviews one had in mind. A less time-consuming interview is remote interviewing, this is done through phone, email, video conference or chat. The benefit of remote interviews is that it is more flexible and cost effective for the people involved. The disadvantages are that it can limit the time you spend on an interview, and you will not have the possibility to observe the surroundings of the interview, as well as the body language of the interview object. Lack of time can also be a barrier for the possibility of the depth of information from the interview object (Easterby-Smith et al., 2015).

Our primary data was collected through semi-structured in-depth interviews. We did face-to-face interviews because we wanted an in-depth insight of the topic of study, as well as it being a lower threshold to do follow-up questions. Semi-structuring our interviews gave us an opportunity to ask our informants open questions. As a result, the informants were able to talk freely about the topics at hand. This created more of a flow and a conversation rather than a formal interview. The question we asked in the interviews was based on an interview guide we made (see Appendix A and Appendix B). Most of the interviews were conducted in Sogn og Fjordane at the offices of our informants. However, three interviews were conducted remotely due to convenience for those informants. With these interviews, we experienced less of a connection with the informants, and were not able to have the same flow as with the face- to-face interviews. We also experience that the interview time was much shorter.

3.3 Selection of informants

Our selection criteria whilst choosing our informants, were actors that are involved with Teknoløft. Teknoløft has a total of 25 different actors involved in the project from various industries (Høgskulen på Vestlandet). Since there are 25 different actors involved in Teknoløft, we have a broad selection of actors that have knowledge of interest around our topic of study. To get a RIS perspective from our informants, we decided to interview informants from all the spheres of the triple helix.

To determine which actors to contact we spoke to the project leader of Teknoløft, Erik Kyrkjebø. He gave us a list of actors involved in the project, as well as a contact person for each actor. The actors were sorted by Erik Kyrkjebø into three categories based on how much engagement they had in the project. Out of all the actors involved we decided to contact those

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with different levels of involvement with the Teknoløft project. We contacted 14 actors, ranging from low to high engagement. Due to a tip during an interview, we also ended up contacting one business outside of Teknoløft. Through mail correspondence and phone conversations we set up interviews with our informants. Unfortunately, we could not interview three of the actors we contacted. One of the businesses did not have time for an interview, due to their busy schedule. The two other actors were not interviewed, due to late responses and time constraints on both sides, our schedules did therefore not match. In total we conducted 12 in-depth interviews with two facilitators, two R&D institutions and 8 businesses. The informants had specialised knowledge needed for our research question, where their position ranged from researchers to founders of businesses. In Table 2 we present the informants and their positions. We have categorised the informants in four different groups based on what type of actor they represent. Our informants will be referred to by the names presented in Table 2 in our empirical analysis in chapter 4.

Table 2: Informants and their positions

Informants

Small and Medium sized Enterprises

Informant: Position:

SME1 CEO

SME2 CEO

SME3 COO

SME4 Founder

SME5 Project leader

Traditional businesses

TB1 CEO

TB2 Manager

TB3 Manager

R&D institutions

R&D1 Representative

R&D2 Researcher

Facilitators

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F1 Senior advisor

F2 Department manager

3.4 Data analysis and process

To be able to analyse the data found during our interviews, we decided to use a voice recorder rather than take notes. This would make it easier for us to correctly collect all the information our informants relayed to us. During the interviews our goal was to create more of a dialogue as oppose to a formal interview. This was to make our informants feel more at ease and open up to us. Therefore, we did not have specific roles during the interviews. We both led the conversations and ask follow-up question as needed, this created a natural harmony during the conversations.

After we had completed the interviews, we transcribed the recordings. We completed this by dividing the number of recordings equally between us to speed up this process. To be able to analyse all the data collected, we decided to colour coordinate all the data into

categories. This was done to easier understand and see the common denominators in the data collected, as we had a considerable amount of data. The categories were also subcategories to get an overview of the different findings within the different categories. These were put into a table to get a better picture of all the data and made it easier for us to analyse. We created a document for each category where we wrote out our findings, in this document we also

divided it between our four different types of informants. This was to understand if there were different or similar opinions both within the same types of informants but also across different types of informants. Our data process helped us to analyse the data, so we could find the barriers for digital innovation in Sogn og Fjordane. In our analytical framework, and our interview guides we had some topics from previous research that we expected to find, but we were also open for topics that we had not previously considered.

3.4.1 Our data collection experiences

In this subsection we give an insight into our thought throughout the data collection process, where we discuss the strengths and weaknesses we found during this time.

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As we travelled around Sogn og Fjordane to visit our informants, we experienced the geography of the region. This gave us a better understanding and insight into the region as a whole and the distances that exist. Even though our informants are spread across the region, we were able to schedule our interviews in a manner that it aligned with our travel plans, and with informants in proximity. As mentioned, we conducted 12 interviews, which gave us great insight into the region and their businesses as well as their barriers for digital

innovation. We conducted most of our interviews face-to-face. This process was both time- consuming and rewarding. During these interviews we achieved a good dialogue with our informants, and our interview guide was a helpful tool to stay on track with our topics. We also had three remote interviews using video and on reflection of this, we realised that it was not as ideal as face-to-face interviews. We experienced less of a connection with those informants due to it being less personal. Another obstacle we faced was an unstable internet connection, which hindered the flow of the conversation. After we had conducted our interviews, we realised that due to our lack of experience it would take us a great extent of time to transcribing them. However, we managed to transcribe more efficiently as we got more experience.

Table 3 gives an overview of the timeline of the different phases we went through while writing our master thesis.

Tabel 3: Master thesis timeline

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3.5 Validity and reliability

Reliability and validity are important criteria to judge the quality of our case study research. In this subsection, we shed a light on these criteria, as well as how we took into consideration through our data collection.

Validity can be used as a measure of how accurately and relevant a data collection is, to answer the research questions being studied. Internal validity describes the extent to which the results within a case study are valid (Yin, 2018). To ensure internal validity, we made sure that our informants had the relevant knowledge for our topic of study. We were given a list of possible informants; thus, this can have led us to miss out on some who might have good information. During several interviews we were tipped on by other people we should

interview, often this were informants we already had interviewed. However, we did interview an informant that was not on this list due to a tip. Thus, we have a good notion that we have interviewed people with the most relevant knowledge for our topic of study. Furthermore, we have analysed our data collection systematically and thoroughly to ensure the internal validity of our case to identify relevant information.

External validity is the degree to which a case study's findings can be analytically generalised. Analytical generalisation is when results from a case study, can be applied to situations outside the original case, due to similarities in theories or principles (Yin, 2018).

Having said that, case studies are mostly about a specific case at a specific period of time, thus making it harder to generalise the results. However, if the findings from a single case study has similar results to other case studies, it will then increase the probability of

generalising the theoretical concepts studied (Yin, 2012). Yin (2018) describes that results of an analytical generalisation can develop, modify or confirm theoretical concepts. In this master thesis, we intend to see if our empirical results from our case study will either support or challenge the existing theories. However, our thesis is based on only a handful of

informants and it will most likely be difficult to generalise our findings to other situations.

Although, we hope that it will contribute towards theory development.

Reliability has an aim of reducing errors and biases in a study. This is done by ensuring that the same study can be conducted again at a later date by other researchers.

Researchers should be able to arrive at the same conclusion if the same procedures are used, such as the data collection procedure. Therefore, it is important that researchers document the procedures followed in their case study(Yin, 2018). We chose to conduct semi-structured

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interviews; thus, our questions were not identical to each informant. This can make it harder to recreate this study and get the same results. Our case is an ongoing phenomenon, this will affect the conclusions of other researchers if they were to conduct the same study later. Our interpretations of the information from our data collection might not be interpreted the same way by others. However, to try and minimize these challenges we have tried to the best of our ability to be as detailed as possible of our choices in this chapter. Thus, giving researchers an increased chance of conducting the same case at a later date with the same results.

3.5.1 Ethical considerations and privacy

In advance of starting our primary data collection we registered our project at the Norwegian Centre for Research Data (NSD) (see Appendix E –Approval from NSD). The project was approved, which allowed us to start our data collection. With the application we created an information letter (see Appendix C – Information Letter), with a consent form (see Appendix D - Consent form) that the participants signed before starting the interview. Prior to our interviews, we sent our informants an information letter and consent form. This document informed them of the purpose of our study, as well as a confidentiality agreement and their anonymity. All of our informants signed the consent form, and therefore consented to being audio recorded. The recordings and transcribed data will be deleted at the end of the project period.

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4 Empirical Analysis

In chapter 2 we presented literature and theory that were relevant for our theoretical research question: What is the role of regional innovation systems for digital innovation in rural areas? The theoretical findings indicate that collaboration and knowledge exchange are important to stimulate digital innovation. To do so, requires change on a regional level as well as a business level. In this chapter we will present the empirical findings found during the data collection and will refer to our informants by their names presented in Table 2. First, we will give a brief insight of Sogn og Fjordane. Thereafter, we will present our empirical findings that we found through interviewing SMEs, traditional businesses, R&D and facilitators across Sogn og Fjordane. Lastly, we will sum up our key findings from our data collection. This will aid us in answering our empirical research question:

What are the barriers for digital innovation in the regional innovation system in Sogn og Fjordane?

4.1 Sogn og Fjordane

Sogn og Fjordane is a former county in Western Norway, this region is now a part of Vestland County. It is a peripheral region with a small population of approximately 110 000 inhabitants (Sogn og Fjordane Fylkeskommune, 2015b). The geography of the region is varied as it extends from the coast to the west and into the mountains to the east. Most of the businesses in Sogn og Fjordane are SMEs and are largely based on the resources that the region offers. This ranges from fruit cultivation, fishing, agriculture to tourism. Industrial businesses have been and still are a core business in the region. A growing industry in Sogn og Fjordane is information technology (IT) businesses. (Sogn og Fjordane Fylkeskommune, 2015a). Given that Sogn og Fjordane is a peripheral region, the businesses there face different challenges compared to urban regions (Karlsen et al., 2013).

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4.2 Actors

Our business informants differ in both size and industry and through our data

collection we found that their approach to innovation also differs. The traditional businesses approach innovation with a focus on new products and to make their processes more efficient from an operational standpoint. The majority of the traditional business informants have invested in production technology and robotization to stay competitive. TB1 views their business as highly innovative when it comes to new products and effectivization. Although, it was stated that there is always room for improvement. Therefore, they have a team that works on various aspect of the business to stay innovative. TB2 indicated that they see the need for change from a competitive standpoint, and digitalisation is an ongoing process in their business. They have a focus on innovation from a customer's point of view, where they try to simplify the customer experience by creating a digital tool. This focus is in line with SME2s vision, which is to have an effortless product for their customers. They strive to be innovative every day to understand the needs of their customers and improve their product. One of our informants pointed out that due to the size of their business, they find it difficult to approach innovation on their own. They addressed the importance of building networks and

collaboration to be able to innovate.

The Research Council of Norway, Vestland County, Innovation Norway, Western Norway Research Institute and HVL were pinpointed as facilitators and R&D institutions available in Sogn og Fjordane to help local businesses. One of our facilitators informed us that they try to aid the development of businesses by facilitating resources necessary for digital innovation. Several of our informants mentioned that Innovation Norway is visible in the region and helpful in connecting businesses in the region to business networks. One of our R&D informants mentioned that businesses have options to get R&D funding, such as

“skatteFunn” managed by The Research Council of Norway. Our data collection suggests that Western Norway Research Institute often collaborates on R&D projects from an early stage, while larger R&D institutions join research projects later in the process.

A consensus from our informants is the importance of digitalisation for businesses to keep up with their competitors. However, it was pointed out that businesses do not always prioritise digitalisation. SME4 defines digitalisation as: “Gathering enough information to increase the knowledge of your business, to make the right decisions, that’s it!”. F1 stated that digitalisation requires change, which is demanding and time-consuming. This change

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acknowledges that businesses approach digitalisation in various degrees, as digitalisation has a broad spectrum. This can scope everything from digitising manual documents to

restructuring an entire business model. According to a handful of our informants, newer businesses have a different perspective on digitalisation. The businesses that have been established in recent years, have digitalisation as a natural part of their business model.

Through our data collection we experienced that locals in Sogn og Fjordane take great pride in their region as most of the businesses are locally owned. Thus, the businesses want to contribute and give back to their community. According to several informants this is a

competitive advantage for the businesses, as they create an identity and a good environment in the region. The facilitator informants stated that the region is dominated by SMEs, where management often focuses on their profitability and production, as their number one goal is to survive. Therefore, they do not always prioritise nor realise the need for digitalisation, which can lead to SMEs being late to digitise their business. It was indicated through our data collection that even though a business is successful today, it is important to be aware and understand that digitalisation is the future. Businesses with a lack of knowledge around the need for digitalisation can face problems in the future to stay competitive. One of our R&D informants believes that businesses need to realise on their own and that it will be more effective coming from other businesses rather than from R&D institutions.

“One does not have time for innovation in good times and cannot afford it in bad times. You have to try even if you have profit and no time, but you should still take the time to think not just one step forward, but five steps forward." (F2)

According to R&D2, a lot of R&D activities are happening in the businesses in the region, but it is described as customer development rather than research. F2 and R&D2 understands that the focus of businesses in the region is to stay profitable and that few businesses prioritize an R&D department. They also expressed that R&D institutions need to convey this to businesses in a better way, as this will help them evolve and renew themselves.

It was mentioned that an employee in charge of R&D might be necessary in a business to understand the purpose and value of R&D or they could potentially lose their

competitiveness. Some of our business informants have an employee in charge of these types of activities. Our data showed that that not all businesses have the capacity for this, and do not know how to access the necessary help to innovate. It was also stated that business do not take

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enough advantage of the resources available and can experience that the public support tools are too bureaucratic and chaotic. Our findings indicate that there can be a need for

organisations that can guide businesses through the “jungle” of the public support tools opportunities. According to F2 it is important that the municipalities know how to connect businesses to relevant public support tools. It was also pointed out that the public support tools available for helping local businesses grow varies within municipalities in the region.

According to R&D2, the threshold is low for businesses in the region to contact facilitators or R&D institutions. It was brought to our attention during an interview that R&D institutions in the region could be experienced as a closed elite group, as businesses might not feel included.

4.3 Collaboration in Sogn og Fjordane

Our findings show that people in Sogn og Fjordane are well acquainted with each other. Resulting in a high level of trust and sharing culture between both individuals as well as between businesses. Our data indicates that in recent years collaboration has improved between businesses across industries in the region. Several informants mentioned this type of collaboration as more common in peripheral regions, as they do not have the same diversity of actors as urban regions. Interactions across industries was pointed out as a strength for the region, as it also lowers the threshold for decision-making. This lower threshold for communication can be seen across the region with various actors, including the interaction with the public support tools. Our data collection shows that there is more cooperation between various facilitators and R&D institutions in Sogn og Fjordane compared to urban regions. It was indicated that businesses in Sogn og Fjordane who take advantage of the R&D resources available in the region get greater help and follow-up on their projects than if they were in urban regions.

Through our data collection it became clear that there is a language barrier to

overcome between businesses and R&D institutions in the region. In the sense that they speak

“different” languages and do not understand each other's needs or available resources. Thus, this creates a mismatch between these actors when it comes to collaborating. Businesses do not necessarily know how to take advantage of resources available at R&D institutions. This was made clear through an example that SME1 pointed out: “If you do not know what a soft robot is, how do you make use for it, and how do you ask for it.”. Some of our informants requested more of a goal-oriented research, as they feel that R&D institutions do not always

Digital innovation in the periphery