Digital Transformation at Dutch
Small & Medium-sized Enterprises (SMEs)
The impact on alliances and leadership style
Guus J.D.P. Gruijthuijsen 11418966
Master Thesis: 23.08.2018 | Final version
MSc. In Business Administration: Entrepreneurship & Innovation Supervisor: Dr. G.T. Vinig
Statement of originality
This document is written by Student Guus Gruijthuijsen who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Acknowledgements
During the process of writing this master thesis, some people have made a contribution to com-plete this thesis. Therefore, I would like to express my gratitude to my supervisor dr. G.T. Vinig for providing advice and guidance during the process. I want to thank Sander van Lingen of DellEMC, who has joined me in brainstorm sessions in order to stay critical and challenging in regards to the thesis, as well as providing me with useful literature concerning digital transfor-mation. I would like to thank all the respondents of my survey and interviews who took the time to thoroughly discuss the subject and provide me with clear insights. A special thanks to Hans Doddema for his grammar check and constructive feedback. Lastly, I would like to thank my family and friends who have been enormously supporting in the challenging months of writing a thesis combined with work and job interviews.
Content
Table of figures and tables ... 6
Abstract ... 7
1. Introduction ... 8
2. Literature review ...11
2.1 Defining digital transformation ...11
2.2 Digital maturity ...13
2.3 Small medium-sized enterprises ...14
2.4 Alliances ...15
2.4.1 Resource-based view of alliances: needs versus opportunities ...15
2.4.2 The knowledge-based view of alliances...16
2.4.3 Learning, business and hybrid alliances ...17
2.4.4 Horizontal versus vertical alliances ...18
2.5 The changing role of leadership ...19
2.5.1 Contemporary leadership approaches in the digital age ...20
2.5.2 Transactional Leadership ...21
2.5.3 Transformational Leadership ...21
2.5.4 Authentic leadership ...22
2.6 Conceptual model ...23
3. Methods and Data ...25
3.1 Research approach ...25
3.2 Methodological approach ...26
3.3 Data collection ...27
3.3.1 Questionnaire ...27
3.3.2 Interviews ...29
3.4 Research participants interviews...29
3.5 Data analysis ...30
3.5.1 Questionnaire ...30
3.5.2 Interviews ...31
3.6 Validity and Reliability ...32
4. Results ...33
4.1 Questionnaire ...33
4.1.1 Descriptive statistics, normality, skewness and kurtosis...33
4.1.2 Additional insight...35
4.2. Interviews ...38
4.2.1 SMEs constraints for digital transformation...38
4.2.2 Alliances ...39
4.2.5 Learning, business and hybrid alliances ...42
4.2.6 Horizontal versus vertical alliances ...43
4.2.7 Leadership styles ...44
4.2.8 Transactional versus transformational leadership...44
4.2.9 Authentic Leadership ...46
4.2.10 Digital knowledge leaders ...46
5. Discussion ...48
5.1 Theoretical implications ...48
5.2 Managerial implications ...50
5.3 Limitations and future research ...50
6. Conclusion ...52
7. References ...53
Appendix I: Survey items ...60
Appendix II: Notes during interviews ...62
Appendix III: Transcribed interview - Example ...63
Appendix IV: Codeboom NVivo 12 ...69
Appendix V: Cross-case analysis ...70
Appendix VI: Interview format ...72
Table of figures and tables
Figure 1. Today’s digital business maturity level of MNEs (VansonBourne, 2018)……….…9
Figure 2. Industries 1.0 to 4.0 briefly explained (Prokurat, 2017)………....12
Figure 3. Three types of alliances (Koza & Lewin, 2000)………....18
Figure 4. Disruption caught by new digital technologies (Libert, 2015)………..19
Figure 5. Conceptual model………..24
Figure 6. Overall research design……….25
Figure 7. Histogram of digital maturity level………...33
Table 1. Digital maturity levels of the study……….27
Table 2. Overview of different sectors of respondents……….……….28
Table 3. List of research participating SMEs……….………...30
Table 4. Descriptive statistics digital maturity level………...34
Table 5. Values of the digital maturity levels………34
Table 6. Distribution of firms on the three different digital maturity levels……….34
Table 7. Group statistics of gender on digital maturity level………35
Table 8. Independent t-test for gender on digital maturity level………35
Table 9. One-way ANOVA for the effect of firm age on digital maturity…….………...36
Table 10. Tukey HSD Post hoc test……….………..36
Table 11. Descriptive statistics of firm age……….………..36
Table 12. One-way ANOVA for the effect of sector on digital maturity……….…...37
Table 13. Descriptive statistics of industrial sectors……….……....37
Table 14. An overview of all cases and their number and type of alliances: cross-case analysis………..…………40
Table 15. Quotes of all cases concerning knowledge-based alliances……….……….41
Abstract
Aim: The aim of this research is to examine the impact of digital transformation at Dutch
Small and Medium- sized Enterprises (SMEs) on alliances and leadership style.
Methodology: This study uses a mixed method design. First, the state of digital
trans-formation at Dutch SMEs is measured by digital maturity level using a survey on a 7-point Likert scale. Second, a comparative case study is conducted of six firms on different digital maturity levels.
Contribution: Prior research has focused on digital transformation in the context of
Mul-tinational Enterprises (MNEs). Research is scarce on the impact of digital transformation on SMEs. Therefore, this study contributes to existing literature by providing data on this subject.
Findings: The majority of Dutch SMEs (68,5%) is digitally transforming. Digital
trans-formation is increasing SMEs their alliances: especially learning and hybrid alliances. Besides, a shift from alliances out of needs to alliances out of opportunities is found. No impact was found on horizontal and vertical alliances. It is concluded that knowledge is the main objective to enter alliances in times of digital transformation. Lastly, digital transformation brings a change in leadership style: a shift from transactional to transformational leadership is found. An authentic leadership style is suitable when digitally transforming and leaders’ knowledge is key.
Implications and limitations: Theoretical and managerial implications are given in this
study as well limitations, which results in suggestions for future research. A logical continua-tion of this study would be the use of quantitative research methods including larger samples to further test the effect of digital transformation on alliances and leadership style at Dutch SMEs: in this way, causal relations can be proved.
Key words: Digital transformation, digital maturity, alliances, transformational
1. Introduction
At its inception, only very few people anticipated the speed at which the internet would spread across the world, or its impact on culture and business. Yet, the internet went from something strange to a basic condition for everyday life (Institute for the Future for Dell Tech-nologies, 2017). It is not known what the impact on businesses is going to be of the emerging digital technologies of the last years.
About three centuries ago, the first industrial revolution started with the invention of the steam engine. The present is a new time of change where the lines - that once separated the digital, physical and biological - are blurred (Simmons, 2017). The business world is transform-ing into a digital world: This is believed to become the next industrial revolution. However, many businesses are not ready for this transformation. A recent example is the Dutch Ware-house V&D, which was unprepared and ended in bankruptcy. For years, V&D was a market leader. But the company failed to cope with digital transformations and fell behind its compet-itors. Competitors executed a 24/7 web shop strategy and used big data, whereas V&D con-ducted a nine-to-five web shop strategy. Traditional processes, skills, capabilities and whole strategies are often impossible to translate into the digital world (Edelman, 2010). Louden (2017) agrees with this statement and states: ‘90% of the information technology (IT) leaders state that traditional legacy systems prevent their digital services from evolving into the disrup-tive enterprise growth and efficiency drivers their organization needs’. These opinions indicate that the transformation of a company to the digital world is poorly understood and needs further research.
Firms in almost every sector have tried to explore new digital technologies and exploit their benefits. The Internet of Things (IoT), Big Data, Robotics, Artificial Intelligence (AI) etc. are all digital technologies which need organizational change. In the last decade, digital transfor-mation has affected the approach to innovation by organizations and how they create and cap-ture value in daily businesses (Bresciani et al., 2017). Digital technology involves transfor-mations of key business operations and affects products and processes, and even affects and transforms management concepts and whole organizational structures (Matt et al., 2015). Dig-ital transformation is driving many firms, academics and entrepreneurs to envision futures in which its impacts on society will be nothing short of transformative: 82% of 3,800 global busi-ness leaders believe that humans and machines will work as integrated teams within the organ-ization in the coming five years (VansonBourne, 2018). Boogert (2018) states that digital trans-formation is becoming top priority for CEO’s of firms with a revenue from over € 50 million.
In a study of VansonBourne & Dell Technologies (2016), 45% of 4,000 senior decision makers across the world, indicated that they are concerned about becoming obsolete in just three to five years. Also, 48% did not know what their industry would look like in three years, while 73% believed that their firms needed to become more digital in order to succeed in the coming years. The transformation is happening right now; Figure 1 shows that 34% of compa-nies is experimenting and gradually embracing digital transformation.
Figure 1. Today’s digital business maturity level of MNEs. Source: VansonBourne (2018)
Prior literature (Bresciani et al., 2017; Li et al., 2017; Berman & Marshall, 2014) has indi-cated that firms are engaging in alliances because of digital transformation. Multinational en-terprises (MNEs) aim to explore and exploit new digital technologies and, in order to do so, new types of knowledge and skills are needed and must be managed. Entrepreneurs strengthen their social networks, which is important for a larger social capital that provides possibilities for acquiring information, resources and knowledge (Li et al., 2017). Berman & Marshall (2014) argue that organizations will only be as relevant as their ability to deliver the best cus-tomer experience through the right collaborations. In this era of digital transformation, consum-ers require services from multiple suppliconsum-ers and demand that firms engage in collaborations (Berman &Marshall, 2014). The ability to connect with the right organizational partners is cru-cial for MNEs to prosper in a digital world.
A number of researchers (Sultan & Van de Bunt-Kokhuis, 2012; Kane et al., 2015; Agar-wal et al., 2010) have perceived organizational changes in response to digital transformation, especially, in terms of leadership (Matt et al., 2015; Li et al., 2017)and management (Bonnet et al., 2014; Gurusamy et al., 2016; Berman, 2012).However, academic research has scarcely
explored how the way firms operate is changing due to the digital transformation. Previous work has mainly been limited to MNEs, while there is scarce information on the impact of digital transformation on Small and Medium- sized Enterprises (SMEs). Therefore, this study is aimed at the impact of digital transformation of Dutch SMEs on alliances and leadership style. This results in the following research question:
How does digital transformation at Dutch SMES impact alliances and the style of leadership?
The main objective of this study is to obtain new insights and explore how digital transfor-mation affects Dutch SME’s alliances and leadership styles.
This thesis is structured as follows: First, relevant studies and leading theories on digital transformation, alliances and leadership will be reviewed in a literature review. Next, the meth-ods chapter presents an overview of the data collection process and the methodology used to answer the research question and its subquestions. The result chapter provides a description of all findings, followed by a discussion, in which theoretical and managerial implications are discussed, as well as the limitations of the present research and suggestions for future research. In the final chapter, the conclusion, the research question will be answered and some key sug-gestions will complete this thesis.
2. Literature review
The literature review has four aims. First, definitions of digital transformation (measured by digital maturity) and digital maturity will be discussed, whereupon a definition is given that is used in this study. Second, the current literature about digital transformation (measured as dig-ital maturity) and its effect on firms is discussed, especially on parts of an organization that are affected by digital transformation. Third, concepts of alliances and leadership style are framed and clearly defined. Fourth, the conceptual framework used in this study will be presented.
2.1 Defining digital transformation
Although, various scholars have examined the concept of digital transformation, there is no consensus about its definition. Hassani et al. (2017) define digital transformation as ‘integrating and exploiting new digital technologies’. Westerman et al. (2011) agree with this definition, but add that ‘the use of new digital technologies should radically improve the performance of firms’. Thus, digital transformation concerns not only exploiting and integrating, but also per-formance improvement. Li et al. (2017) define digital transformation as a transformation pre-cipitated by a transformational technology. Compared to organizational transformation, digital transformation focuses on the impact of IT on information flow, routines, organizational struc-ture and firms’ capabilities to settle and adapt to IT (Li et al., 2017). Another comparable defi-nition is: ‘The journey of reinventing daily businesses practices in order to fully exploit infor-mation technology (IT) and to facilitate supply chain collaboration which should result in op-erational excellence’ (Bowersox et al., 2005). Although the above definitions of digital trans-formation are comprehensive, they are formulated merely at an abstract level. The authors agree that digital transformation is about exploring, exploiting, accommodating and adapting to new digital technologies (Hassani et al., 2017; Westerman et al., 2011; Li et al., 2017; Bowersox et al., 2005). However, it remains unclear what exactly is meant by these new digital technologies. Therefore, in this paragraph, ‘industry 4.0’ will be briefly discussed, and the definition of digital technologies will be given.
Industry 1.0 is seen as the ‘first’ industrial revolution that emerged with water and steam power. Industry 2.0 uses mass production using electrical energy, and industry 3.0 is about use of computers, electronics and automation (figure 2).
Figure 2. Industries 1.0 to 4.0 briefly explained. (Source:Prokurat, 2017)
Industry 4.0 is happening today, which makes it a most interesting topic for research. More than a hundred different definitions of industry 4.0 have been given (Moeuf et al., 2017). How-ever, Schumacher et al. (2016) provide an overarching definition of industry 4.0: ‘recent tech-nological advances where the internet and supporting technologies serve as a backbone to inte-grate physical objects, human actors, intelligent machines, production lines and processes across organizational boundaries to form a new kind of intelligent, networked and agile value chain’ (p. 162).
To make ‘recent technological advances’ concrete, the nine main technologies and methods of industry 4.0 (Ruessmann et al., 2015) are used in this study, in random order: Big Data (i.e. Social media), Robotics, Simulations, IoT, Cybersecurity, Cloud computing, Augmented real-ity, Additive manufacturing and Horizontal and vertical system integration.
By using the industry 4.0 framework of Ruesmann et al. (2015), new digital technologies are made concrete. Thus, a definition of digital transformation was formulated for this study:
‘Digital transformation means the exploration, exploitation and adaptation of new (emerging) digital technologies’. However, digital transformation is hard to measure since it concerns a
process. When comparing different stages in a process, different characteristics of the process can be described that can be used for measurement. To measure a firms’ digital transformation, Schumacher et al. (2016), Kane et al. (2015) and De Carolis et al. (2017) have used digital
2.2 Digital maturity
Maturity refers to a state of being complete, ready or perfect (Schumacher et al., 2016) which implies progress in the development of a system. Schumacher et al. (2016, p.162) state that: ‘Maturity models are commonly used as an instrument to conceptualize and measure maturity of an organization or a process, regarding some specific target state’. As digital transformation itself is intangible and hard to measure, digital maturity is used in this study to measure the state of digital transformation. Therefore, the definition of Remane et al. (2017) of digital ma-turity is used: ’Digital mama-turity is the status of companies’ digital transformation’. A survey by Hoberg et al. (2015) confirmed the high degree of uncertainty that digital transformation is accompanied by: managers admit the urgency of digital transforming, but only a few of them have developed a strategy and implementation plan to accomplish the transformation. Consul-tancy companies and practice-oriented research have addressed this uncertainty by developing digital maturity models (Forrester, 2016; Capgemini, 2011; Berghaus and Back, 2016; Vanson-Bourne, 2018). These models allow a more thorough understanding of a firms’ status of digital transformation, and provide companies with the possibility to evaluate their current position in the transformation and to define the necessary steps to reach a higher level of digital transfor-mation.
The government of Australia (2016) developed a digital maturity assessment tool (DMAT) which enables firms to self-assess their digital maturity level. The objective of the Australian Government (2016) is to obtain a picture of companies’ level of digital maturity and identify where organizations are in their digital transformation process. The tool enables to identify on which aspects organizations are doing well and where improvements can be made. The follow-ing aspects are examined in DMAT: Governance and leadership, people and culture, capacity and capability, innovation and technology. Li et al. (2017) expect that leadership plays a major role when companies are transforming. Without support of the top, MNEs are unable to em-brace digital transformation. Governance and leadership measure the executive support, author-ization and reporting processes and detailing of roles and responsibilities. People and culture focusses on the firms’ culture, including customer focus, innovation, risk appetite and attention to manage digital transformation (Government of South Australia, 2016). Capacity and capa-bility are the abilities to be or become digitally mature. Staff numbers, resources, skills, access to the right technology, training and supporting procedures and policies for digital transfor-mation are questioned. An important aspect (Berghaus & Back, 2016) is that digital change is a result of commitment and staff affinity with digital transformation. Innovation measures the willingness and ability of organizations to imagine new products and services and new ways of
service delivery, as well the level of proactivity to assess new technologies and business pro-cesses (Government of South Australia, 2016). Finally, technology indicates the suitability of the underlying technology platforms, programs and systems that support the above described aspects. Thus, to measure the status of Dutch SMEs digital transformation, the first sub-ques-tion of this study is formulated:
S1: What is the digital maturity level of Dutch SMEs?
This study aims to explore the impact of digital transformation on alliances and leadership style. However, to be able to examine this impact, it needs to be measured. Therefore, the DMAT of the Australian Government is adapted into a questionnaire to measure the digital maturity level of Dutch SMEs. In Appendix I, all survey items and different aspects are de-scribed. The method chapter clarifies how the different maturity levels are used to answer the research question.
2.3 Small medium-sized enterprises
99.8 % of officially registered Dutch companies is considered to be an SME (with 1 to 250 employees). Dutch SMEs produce 66% of the gross national product (GNP) and provide work for 67% of the working population (CBS, 2017). In 2016, there were about three million full-time jobs within the SME sector, which is 70% of the overall employment (NCOF, 2016). These numbers indicate the importance of the Dutch SME sector: They are the motor of the Dutch economy and the largest job providers. This makes the sector interesting for research, as there are many knowledge gaps in the literature on digital transformation.
As described in the introduction, research on digital transformation of SMEs is scarcely done, although SMEs are considered very vulnerable because of digital transformation. In geral, SMEs tend to suffer the most from changes because of the turbulent and competitive en-vironment in which they operate (Chen et al., 2016). Competitive advantage in digital technol-ogies is hard to gain due to a lack of financial resources (Schumacher et al., 2016; Chen et al., 2016; Brouthers et al., 2015). Whereas multinationals can afford to experiment with new digital technologies and its risks, SMEs need to be sure of the impact of a new technology before they can invest. Next to the lack of financial resources, research has indicated that SMEs suffer from lack of time and technologies and skills. Most of the time, employees of SMEs have to perform multiple job tasks, which leaves barely time to learn new technologies and skills (Chen et al.,
2016). Arendt (2008) concurs with this: SMEs experience greater problems in finding e-Busi-ness solutions than MNEs due to the lack of time, knowledge and information of SMEs. Be-cause of the lack of time, SMEs are reluctant to engage in internet-based technologies (Brouth-ers et al., 2015). The number of companies in the Dutch SMEs sector and their importance to the national economy indicate the necessity to cope with digital transformation. According to Kane (2017), firms have to cope with digital transformation, in order to cope with other players in its value chain like partners, competitors, suppliers, customers, employees, et cetera.
2.4 Alliances
Bresciani et al. (2017) found that the Internet of Things (IoT) affects the way MNEs innovate and how value creation is captured in daily businesses. IoT is one of the new digital technolo-gies that is explored and exploited by firms in their digital transformation. Bresciani et al. (2017) state as their main result that MNEs are building alliances to gain missing resources. Hoffmann (2007) supports this statement by showing that building alliances improve explora-tion and exploitaexplora-tion activities, like new innovative technologies. Berman & Marshall (2014) state that digital transformation will result in an everyone-to-everyone economy instead of an individual-centered economy. This means that organizations will partake in collaborations and only will be as relevant as their ability to create the best customer value through the right col-laborations (Berman & Marshall, 2014). Moreover, firms have to reinvent themselves to remain competitive. Research is mostly done on MNEs engaging in alliances because of new digital technologies, but, as Bresciani et al. (2017) suggest, research should be conducted on the expe-riences of SMEs which are exploring and exploiting alliances for its digital transformation.
The following paragraphs define alliances and describe what type of alliances are examined in this study.
2.4.1 Resource-based view of alliances: needs versus opportunities
Multiple definitions of alliances are given in literature and different terms are used to refer to alliances. Hoffman & Schlosser (2001) use the terms collaborations, strategic alliances and joint ventures synonymously and refer to all of these as alliances. Alliances appear in all forms and shapes: informal coalitions, joint ventures, mergers, multiparty networks etc. (Koza & Lewin, 2000). Varadarajan & Cunningham (1995) define strategic alliances as ‘the pooling of specific skills and resources by the partnering firms to achieve common goals and goals specific to individual partners’. Alliances reflect the collective use of inter-organizational information
flows and resources, that assist alliance firms achieve a future desired goal (Jarratt, 1998). Many theories are applied in research to approach alliances: Two of these provide the basis for under-standing and defining alliances in this study.
The first theory is the resource-based view, which explains firms as a bundle of resources of a firm (Eisenhardt & Schoonhoven, 1996). Resources are all strengths and assets of a company, and may be tangible or intangible: Tangible resources might be capital or technology, whereas intangible resources might be reputation or managerial skills. When a firm engages in alliances, it is argued that firms are either in vulnerable strategic positions in need of resources, or firms are in strong social positions and they capitalize on their assets to create alliance opportunities (Eisenhardt & Schoonhoven, 1996). In the resource-based view, alliances can be seen as coop-erative relationships driven by strategic resource needs and social resource opportunities (Ei-senhardt & Schoonhoven, 1996). The strategic resource needs are driven by the fact that firms cannot acquire the resources by themselves internally with acceptable cost risk or within an acceptable time (Hoffman & Schlosser, 2001). Therefore, required technological know-how may be obtained by strategic alliances when it is too expensive for an SME to develop this technology or now how by itself. Social resource opportunities are driven by status, reputation, and the expansion of a firm’s network (Eisenhardt & Schoonhoven, 1996). When obtaining reputation and status, it is easier to adopt new digital technologies, since strategic alliances are easier reached (Kane, 2017). The second sub-question of this study is:
S2: What is the impact of digital transformation on building strategic resource needs vs. social resource opportunities alliances?
2.4.2 The knowledge-based view of alliances
Based on the resource-based view, several studies have viewed alliances as a quest for re-sources (Eisenhart & Schoonhoven, 1996; Rothaermel, 2001; Gulati, 1999). Knowledge is a specific resource which appears to be important to alliances, especially in technology manage-ment (Hagedoorn, 1993; Dickson and Weaver, 1997). Thus, over the years, a knowledge-based view of alliance has emerged, which is the second theory used in this study. This knowledge-based view argues that alliances provide the best context for creating value combining or ex-changing dispersed knowledge (Hoffman & Schlosser, 2001). Many studies have identified knowledge including technological knowledge sharing as a dominant objective for alliances (Kale et al., 2000; Dyer and Nobeaka, 2000; Larsson et al., 1998). However, it remains the
unknow if knowledge sharing can be considered as the dominant objective for alliances, where new digital technologies are concerned.
The literature on the knowledge-based view has shown that knowledge management exists out of two conceptually distinct dimensions (Grant & Badenfuller, 2004). The first dimension concerns activities that increase a firm’s stock of knowledge, seen as exploration and knowledge generation. In the context of alliances, knowledge generation means that each par-ticipating firm can use the alliance for learning purposes and to absorb and transfer a partner’s knowledge base (Grant & Badenfuller, 2004). The second dimension refers to activities that deploy existing knowledge to create value, considered as exploitation and knowledge applica-tion. Knowledge application, in the context of alliances, indicates a form of knowledge sharing in which each participating firm can access its partner’s knowledge stock to exploit comple-mentarities (Grant & Badenfuller, 2004). Thus, two types of knowledge sharing within alliances are distinguished: knowledge generation and knowledge application. Therefore, the third sub-question of this study is:
S3: What is the impact of digital transformation on the role of knowledge (generation vs. application) when engaging in alliances?
2.4.3 Learning, business and hybrid alliances
According to Koza & Lewin (2000), exploitation and exploration are two basic reasons for entering alliances. Exploiting alliances serve as a source of incremental value from pooling complementary resources that none of the partners wants to develop by themselves (Koza & Lewin, 2000). In general, these exploitation alliances are implemented as joint ventures. How-ever, exploration alliances rather serve for learning unknown technologies, new geographic markets or new product domains. Koza & Lewin (2000) argue that three types of alliances arise because of this exploitation and exploration (figure 3). First, learning alliances which are mainly explorative. This type of alliance can reveal insights and new information about markets, core competencies and new technologies. The second type are business alliances which are based on strong exploitation intents and barely on exploration intent. These alliances are often struc-tured as networks: ‘a form of collaboration among multiple companies in which, typically, the network members are each specialized, bringing unique value-adding resource to the network such as market access or skills’ (Koza & Lewin, 2000, p. 149). The third type are hybrid alli-ances which have both exploration as exploitation intents. Hybrid allialli-ances start as a combina-tion of learning and business alliances. Both partners aim to simultaneously maximize
opportunities for capturing value by leveraging assets and existing capabilities, as well captur-ing value by learncaptur-ing from of each other (about market, technologies etc.) This leads to the fourth subquestion:
S4: What is the impact of digital transformation on learning, business and hybrid alliances?
Figure 3. Three types of alliances (source: Koza & Lewin, 2000)
2.4.4 Horizontal versus vertical alliances
The literature on alliances makes a distinction between horizontal and vertical alliances. Horizontal alliances are formed by firms operating in the same business area. The firms in-volved used to be competitors, and now partner to improve their positions against other com-petitors (Belderbos et al., 2012). An example is cooperation between firms in research and de-velopment. Vertical alliances is a partnership between a firm and its distributors and suppliers. These alliances benefit customers by lower prices. This vertical alliance deepens the relation-ship between the firm and suppliers/distributors by exchanging knowledge (Belderbos et al., 2012). The benefits of vertical alliances are more perceptible because of the economic benefits along the supply chain. The benefits of horizontal alliances are more invisible and informal, like social benefits. Horizontal alliances consist mainly out of information and social exchange (O’Donnell et al., 2001). Literature on the impact of digital transformation on horizontal vs. vertical alliances is lacking, therefore the fifth sub question of this study is:
2.5 The changing role of leadership
As mentioned in the introduction, prior research focused on the impact of digital transfor-mation on MNEs. Matt et al. (2015) state that, to accommodate digital transfortransfor-mation, four dimensions are important: the use of technologies, changes in value creation, structural changes and financial aspects. The use of technologies concerns the attitude of the company towards new digital technologies and their ability to explore and exploit these. The way firms are man-aged by the top is important in this matter. According to Matt et al. (2015) the use of new digital technologies indicates a change in value creation, however, how precisely the value creation changes remains unclear. Digital transformation leads to structural changes to provide a solid basis for the adoption of new technologies and value creation. As in almost every business situation, financial resources are key to the ability to undergo any business transformation. Matt et al. (2015) conclude that digital transformation and its relation with business development and business models needs to be assessed from a leadership perspective, because it is still un-known what impact the transformation will have on leadership management. VansonBourne (2018) found that 75% of respondents believe that the majority of leadership roles will be filled in by digital natives (i.e. people who grew up with digital technology). Also, 15% of the re-spondents indicated that the senior management lacks a sufficient digital mindset and approach. 86% would give the advice to give top management the task to spearhead digital change. This study points at the importance of the role of leadership management in digital transformation. Libert (2015) concurs with this as he concluded that new digital technologies also require new leadership styles (figure 4).
Li et al. (2017) state that the role of top management has always played an important role in organizational changes, including digital transformation. Top managers’ knowledge about and understanding of new digital technologies is key to successful adoption and implementation of these technologies. Prior research (Matt et al., 2015; VansonBourne, 2018) indicates that digital transformation might affect the leadership style. However, research on this matter (Li et al., 2017) has been done in a different context. Digital transformation is defined as digitalization, thus, going from analog to digital. This study examines digital transformation defined as en-gaging in new digital technologies (Big data, Internet of Things, etc.) The focus in this research on change in leadership styles because of digital transformation, was mainly on MNEs with hardly any data on SMEs. Therefore, the seventh sub question of this study is:
S6: How does digital transformation at Dutch SMEs impact the leadership styles?
To answer to this subquestion, relevant leadership approaches will be discussed in the next paragraphs.
2.5.1 Contemporary leadership approaches in the digital age
Leadership has been given a lot of attention in literature. Already in 1930 the trait theory was formulated with the question whether people are either made or born with the traits that would make them excel as leaders (Bain, 1930 in: Gehring, 2007). This nature/nurture debate whether people are born or made as leaders has continued since. Different leadership theories have emerged: The contingency, situational, behavioral and participative theories. However, in the present digital age, leadership is seen as key to survive in the rapid changing environment (Uhl-Bien, 2007), whereas the theories mentioned above are based on traditional contexts (Northouse, 2004; Bass & Stogdill, 1990). Since this study focuses on the impact of digital transformation on leadership styles, relevant and recent theories will be discussed. As the digital age is changing environments and needs different leadership skills, new leadership approaches evolve (Uhl-Bien, 2007). Two leadership theories are dominant in the literature on leadership, i.e. transformational and transactional leadership. In the next paragraphs these will be dis-cussed. Authentic leadership will also be disdis-cussed.
2.5.2 Transactional Leadership
Transactional and transformational leadership were first introduced by Burns (1998). Trans-actional leaders focus on the proper exchange of resources, they give followers something they want in exchange for something they want (Judge & Piccolo, 2004). Followers agree, accept or comply with the leader in exchange for rewards, praise and resources or the avoidance of dis-ciplinary action (Bass et al., 2003). Characteristic for transactional leadership are control ori-entation, focus on efficiency & time and risk avoidance (Bass, 1985). Three leadership behav-iors for this model are identified by Bass & Stogdill (1990):
1. Contingent reward
2. Management by exception (active vs. passive) 3. Laissez-Faire
In contingent reward leadership, the leader promises rewards for good performance, recog-nizes accomplishments and contracts exchange of rewards for effort. Undesired behavior will be punished, whereas desires behavior will be rewarded with e.g. promotions (Bass, 1990). Active management by exception describes the behavior of the leader who is watching and searching for deviations from the rules and standards, and if founded, taking corrective action. Passive management by exception means that the leader just waits for errors and intervenes if they occur (Bass, 1990). The last type transactional leadership behavior is laissez-faire, which means that the leader abdicates his or her responsibilities and avoids making decisions (Judge & Picollo, 2004). Laissez-faire management is devastating in times of rapid change, like the present (Matt et al., 2015).
2.5.3 Transformational Leadership
Dozens of studies have found a positive relationship between transformational leadership style and organizational effectiveness. Whereas transactional leaders focus on proper exchange of resources, transformational leaders focus on engagement with their subordinates to reach a higher level of inspiration and motivation (Den Hartog, 2001). Transformational leaders tend to motivate people, show integrity and are considered to be charismatic and capable of shaping a strong vision (Stone et al., 2004). Four transformational leadership styles are identified by Bass & Stogdill (1990):
1. Charismatic 2. Inspirational
3. Intellectual stimulation 4. Individualized consideration
By charismatic influence a leader provides vision and a sense of mission. Instilling pride and gaining respect and trust are also typical aspects (Bass, 1990). Charismatic leaders be-come a ‘role model’ and followers admire them (Bass & Avolio, 1994). Inspirational leaders inspire followers by communicating high expectations and expressing important purposes in understandable and simple ways (Bass & Stogdill, 1990). Also, they inspire followers by cre-ating challenges and providing meaning. The third transformational leadership style by intel-lectual stimulation, is executed by promoting and stimulating followers to be assertive, inno-vative and independent (Bass & Stogdill, 1990). This is realized by experimentation, sharing of ideas, and questioning assumptions. The last transformational leadership style is individu-alized consideration, where leaders give much personal attention, creating a supportive cli-mate (Bass, 1990). Followers perceive individual attention, coaching and advise of the leader. Eisenbach et al. (1999) found that transformational leadership is positively correlated with or-ganizational change. Since digital transformation asks for an oror-ganizational change (Sultan & van de Bunt-Kokhuis, 2012; Kane et al., 2015; Agarwal et al., 2010), transformational leader-ship in all four styles is examined in this research.
2.5.4 Authentic leadership
Recently, authentic leadership has gained much attention, especially in the academic litera-ture. Digital transformation is happening right now, in challenging and turbulent times. There is growing recognition among scholars of authentic leadership as relevant and urgently needed in these challenging and changing times (Seligman, 2002; Luthans & Avolio, 2004). George (2003) has shown that authentic leaders show others that they want to understand their own leadership, because that way they can serve others more effectively. These leaders are open about their feelings and opinions, as well as their strengths and weaknesses (George, 2003). This creates sympathy, trust and respect by their followers, thus these leaders are perceived as authentic (Avolio et al., 2004). As many scholars state (Luthans et al., 2006; Li et al., 2017), followers’ trust in their leaders is essential in a rapidly changing world. Authentic leadership enables leaders to embrace transformation and it creates acceptance of change by their
followers. Therefore, authentic leadership is examined in this research. Walumba et al. (2008) have identified four authentic leadership styles:
1. Internalized-regulation
2. Balanced processing of information 3. Relational transparency
4. Self-consciousness
Internalized-regulation means that an authentic leader is able to self-regulate one’s behavior by using intrinsic motivation and moral standards as guidance (Gardner et al., 2005). Kernis (2003) uses the term ‘unbiased processing’ to refer to the self, without denials, private knowledge, distortions and exaggerations. However, as Gardner et al. (2005) state, humans are inherently biased as information processors, especially when processing self-relevant infor-mation. Therefore, they use the term ‘balanced processing’ instead of unbiased processing. Bal-anced processing assumes that someone is able to objectively analyze data and people’s opin-ions, and make a decision based on this analysis. The third authentic leadership style, relational transparency, means that the leader shows high levels of openness, self-disclosure and trust (Walumba et al., 2008). Lastly, self-consciousness is linked to self-reflection: ‘by reflecting through introspection, authentic leaders gain clarity and concordance with respect to their core values, identity, emotions, motives and goals’ (Gardner et al, 2005, p. 347).
2.6 Conceptual model
In this paragraph, a conceptual model is presented based on the research question, the liter-ature review and the identified knowledge gaps in the literliter-ature. The conceptual model demon-strates the variables in this study and their interrelationships. The independent variable is digital transformation at Dutch SMEs (measured by digital maturity level). The effect of gender, firm age and sector on digital maturity level is quantitative controlled. The influence of financial and time constrains for SMEs, is controlled qualitatively. The impact of the independent variable (digital transformation at Dutch SMEs, measured by digital maturity level) on the dependent variables alliances and leadership style, have been examined using interviews. The conceptual model also presents what types of alliances and leadership styles are examined.
3. Methods and Data
The methods and data used in this study, are described in this chapter.
3.1 Research approach
The aim of this research is to answer the question: ‘What is the impact of digital transfor-mation on the alliances and leadership style of Dutch SME’s’. This study exists out of two research phases. First, the state of digital transformation at Dutch SMEs will be measured by a questionnaire which provides an indication of their digital maturity level. Secondly, the impact of digital transformation on alliances and leadership style has been investigated using semi-structured interviews (figure 6).
Figure 6. Overall research design.
Eisenhardt (1989) states that starting research with a completely blank knowledge of theo-retical is impossible. Therefore, this study started with a pre-study. Besides identifying the main research gap, the pre-study helped to gain a deeper understanding of digital transformation, a topic which requires specific knowledge. This pre-study existed out of meetings with Dell EMC, a leading Fortune 500 company in the digital transformation era, two lectures about new digital technologies and two meetings with Dell EMC to provide more context and specific knowledge about digital transformation. This pre-study phase contributed to determine the aim of this study, the research question and the expected academical and managerial contribution. Besides, it provided an indication of related topics that should be examined within this study. The literature review provided scope for the research and the development of the concepts to be examined. This resulted in a solid theoretical framework and data could be collected. As described above, this occurred in two different phases.
Plenty of literature is available about digital transformation and its impact on MNEs. How-ever, according to Bresciani et al. (2017), little research has been published on the relation between digital transformation and SMEs. There is also a lack of studies addressing the link between digital transformation and leadership and/or alliances. According to Saunders et al. (2012), an exploratory research approach is suitable when the examined topic has not been studied before, and literature is lacking. This study aims to gain new insights about the never studied impact of digital transformation on leadership style and alliances in SMEs. Therefore, an exploratory approach is considered appropriate for this study.
3.2 Methodological approach
When adopting a certain methodological approach, the first question is what the form and nature of studied reality is. The two main approaches are objectivism vs. constructionism (Saun-ders et al., 2012). The constructionism approach assumes that reality is subjective and socially constructed. The truth can be based on subjective meanings and social phenomena, which is called interpretivism (Saunders et al., 2012). Shah & Corley (2006) state that the process of constructionism is an inductive process that provides interpretive understanding of the studied phenomenon. Given the above, an constructionism approach is adopted for this study, since the research requires interpretive understanding of the phenomenon. The study aims to explore how leadership and alliances are affected by digital transformation. The chosen approach will help to clarify what leadership styles and types of alliances will arise because of digital transfor-mation. This approach provides a chance to clarify the experiences and opinions of different firms at different levels of digital maturity about digital transformation, constraints for SMEs, types of alliances and styles of leadership. As mentioned earlier, the subject matter of this re-search is still new and there are no existing frameworks or general methods for examining the impact of digital transformation. Therefore, an inductive method is used in this the study, and empirical data will be used as a source. This study will contribute to developing theory. The literature review provided specific codes concerning different types of alliances and leadership models: therefore a deductive approach is used as well (Hyde, 2000).
3.3 Data collection
This study involves qualitative research and has used a mixed method design (Cresswell & Cresswell, 2017). First, a survey was conducted, which provided quantitative data to measure the independent variable. Second, interviews were executed to obtain qualitative data to exam-ine the impact of the independent variable on the dependent variables.
3.3.1 Questionnaire
Questionnaires have a more confirmatory character, whereas interviews are suitable for ex-ploratory purposes (Harris & Brown, 2010). Therefore, in the first phase of this research data have been gathered using a questionnaire to measure digital transformation with an adapted Digital Maturity Assessment Tool (DMAT) (Australian government, 2016). Often, maturity models fail if their construction is too complex (Schumacher et al., 2016). Therefore, the DMAT is transformed into a practical and representative questionnaire, to assess the maturity level of SMEs. The questionnaire used a 7-point Likert scale ranging from 1=’strongly disa-gree’ to 7=’strongly adisa-gree’, with a Cronbach’s alpha of .863. Participants had to indicate to what extent they agreed with an item about digital maturity (see Appendix I for all items). Prior literature does not provide a scale for maturity levels based on Likert-scales. Therefore, the data of the questionnaire were divided in three different clusters: low on digital maturity, average on digital maturity, high on digital maturity (see table 1).
Table 1. Digital maturity levels of the study
Digital maturity Scale
Low < 𝜇 − 1s
Average ³ 𝜇 − 1s 𝑎𝑛𝑑 £ 𝜇 + 1s
High > 𝜇 + 1s
Before sending out the questionnaire, a pre-test was conducted for content validation and overall user experience. Five SMEs and ten master students have filled out the questionnaire and provided feedback. For this study, it was very important to approach people who had knowledge about the state of digital transformation of their firm. Dell EMC has provided a list of Dutch SMEs and their contacts. The contacts selected from this list were either CEO’s or people with knowledge about the state of digital transformation. The international standard classification for SMEs is used in this study, expressed as the number of employees (Brouthers et al., 2015). Firms between 1 and 250 employees are considered small or medium sized
enterprise. This study was conducted at a firm level, where one respondent is assumed repre-sentative for one firm. The questionnaire was sent by e-mail using a convenience sampling approach to 820 firms, only 27 email addresses bounced. Out of the remaining 793 firms, 108 firms completed the questionnaire, i.e. a response rate of 13.61 %, which is sufficient (Baruch & Holtom, 2008). The responses included firms from various sectors (see table 2). The re-sponses from different sectors provided a good cross-sector overview.
Table 2. Overview of different sectors of respondents
Sector Percentage
Health Care 8.9%
Production and manufacturing 9.8%
Service providers 9.8%
Legal 2.7%
Retail 8.9%
Financial services 7.1%
Education 1.8%
Logistics and Transportation 8.0%
Consultancy 17.0%
Energy 3.6%
Construction and Real Estate 4.5%
Food and Catering 5.4%
3.3.2 Interviews
Conducting interviews is a common method for collecting qualitative data and is suitable when the study needs a deeper understanding of a subject matter (Cho & Trent, 2006). In this study, a deeper understanding is required. The interviews with firms gave insights in how digital transformation impacts alliances and leadership. Saunders et al. (2012) make a distinction be-tween structured, semi-structured and unstructured interviews. Semi-structured interviews have questions that are determined beforehand but give the opportunity to ask more and new ques-tions when new insights arise during the interview (Collis & Hussey, 2013). Semi-structured interviews are an adequate tool for conducting the interviews in this research as they give the opportunity to deviate from the standard questions and/or add topics: Participants were asked about roles and types of leadership, but there was also room to tell more than the pre-developed questions asked.
The analysis of the questionnaire resulted in three levels of digital maturity level (low, av-erage, high). At each level two interviews have been executed, i.e. six interviews in total, with an average length of about 35 minutes. Each firm in the different clusters is considered as a single case, which makes this research a multiple case study (Gustafsson, 2017). This gives the opportunity to understand differences and similarities between the cases (Baxter & Jack, 2008).
3.4 Research participants interviews
In the first phase of the research, respondents had to indicate in the questionnaire whether they were willing to be interviewed. This included five firms in the category low (on digital maturity), eight in the category average, and six in the category high. As much as possible, the firms (cases) were selected in different sectors, to gain the most representative view of Dutch SMEs. Founders and/or digital savvy respondents with knowledge of digital transformation, alliances of the firm and leadership styles were chosen for the interviews, as that knowledge was required to gather useful data.
The above procedure was used to select appropriate cases for this research. This means that purposeful sampling, a non-probability technique is used (Suri, 2011). More specifically, max-imum variation sampling is used in order to capture a wide range of perspectives relating to the impact of digital transformation on alliances and leadership. This gives the opportunity to gain greater insights and look at the subject matter from multiple angles (Palinkas et al., 2015). Table 3 gives an overview of the cases (SMEs), function of respondents, with the SME maturity level and a short characterization of each SME.
Table 3. List of research participating SMEs
SMEs Digital maturity level:
Function: Short description company:
Case 1 (C1) Low; 1,81 Manager Firm that provides the maintenance of all sorts of floors in buildings
Case 2 (C2) Low; 2,63 Region manager Firm that provides courses in reanima-tion
Case 3 (C3) Average; 3,69 Owner Lamps & lightening firm with multiple stores
Case 4 (C4) Average; 4,38 Region Manager Firm that sells and produces suits with multiple stores
Case 5 (C5) High; 6,19 Sales Consultant Sales intelligence platform elevating prospecting through account insights Case 6 (C6) High; 6,31 Digital
Consult-ant
Digital marketing consultancy improv-ing the digital marketimprov-ing strategy of cli-ents
3.5 Data analysis
In this paragraph, the analysis of both the questionnaire and the interviews are described. 3.5.1 Questionnaire
As described earlier, the survey served as an indication of the maturity levels of Dutch SMEs. When all the data was collected, it was exported from Qualtrics to SPSS. It is assumed that the data is normally distributed, based on the completed surveys (N=108) (Field, 2013). A skew-ness and kurtosis test was executed to confirm the assumption of the data being normally dis-tributed. The skewness test is a measure of symmetry whereas the kurtosis test measures whether the data are light- or heavy tailed relative to a normal distribution. High kurtosis indi-cates heavy tails, whereas low kurtosis indiindi-cates to have light tails (Field, 2013). Then, the descriptive statistics of the collected data were analyzed and applied to the different digital maturity levels: low, average and high (table 1).
The literature does not lead to expectations about the demographics: gender, firm age and sector. For example, there is no indication given in prior research whether man or woman tend
to be more progressive concerning digital transformation. Older firms could have more finan-cial resources to digitally transform and thus become digital mature, but younger firms have directly started in this digital world and could therefore be more digitally mature. Lastly, it might be obvious that the service providers sector is more involved in digital transformation than the education sector. However, as Matt et al. (2015) stated, digital transformation is dis-rupting the way of doing business in every sector.
Therefore, differences in responses are controlled for gender, firm age and sector. The effect of gender on digital maturity is tested by an independent samples T-test. The effect of firm age and sector on digital maturity is tested by an one-way between subjects ANOVA.
3.5.2 Interviews
Interviews were recorded, notes were only made when something interesting was said (see Appendix II). The interviews were transcribed shortly after they were finished (see Appendix III), and the transcripts were used to categorize and analyze the data to enable a constant com-parison (Dye et al., 2000). According to Johnston (2006), NVivo 12 coding is commonly used and accepted among researchers to analyze data. NVivo 12 coding enables to efficiently struc-ture and code large numbers of long texts and identify important themes patterns (Richards, 1999). A code is: ‘assigned to a section of text when the researchers identify a phenomenon present it as a theme (Kreiner et al., 2006, p. 1036). In this study, the coded text length ranged from one sentence to multiple sentences. The analysis of the data within NVivo 12 consisted out of two rounds. First, selective coding (Huberman & Miles, 2002), since preliminary main codes were already formed. Second, open coding (Miles & Huberman, 2004), which provided the possibility to reveal new statements and new codes which did not appear during the selective coding in the first round. Two new codes were formed: digital transformation general and knowledge sharing (see Appendix IV for a complete overview of all codes). In this study, a cross-case analysis has been executed (see Appendix V). Creation of different word tables dis-play the data from single cases in a uniform framework. This enables a search for similarities and differences across the data (Yin, 2009). Saldaña (2015) states that there is no holy grail for the analyses of qualitative data, or how it should be coded. However, the coding process is executed as efficiently as possible in this study.
3.6 Validity and Reliability
Validity determines whether the results of a study are in line with the objectives (Gibbert et al., 2008). Specifically, internal validity refers to the causal relationship between the variables and results (Gibbert et al., 2008). To guarantee internal validity, while at the same time increase the rigor and quality of the study, a few steps are taken throughout the research process. First, internal validity is ensured by using the combination of two methodologic approaches within this study, which is called triangulation (Thurmond, 2001). The combination of both quantita-tive and qualitaquantita-tive research provides deeper insight, and the combination provides an better understanding of the research subject (Cresswell & Creswell, 2017). However, the mixed method design is very time consuming. Second, peer scrutiny during the project is used too, to ensure validity (Shenton, 2004). To control for common method bias, the respondents of the survey and interviews were assured that responses will remain anonymous and that the ques-tionnaires and interviews would only be used to draw conclusions at aggregate level (Conway & Lance, 2010). Lastly, the purpose and scope of the research was discussed briefly with re-spondents before the interview, to avoid interviewer and interviewee bias.
External validity refers to the ability to generalize findings of a study to other settings and contexts (Saunders et al., 2012). Gibbert et al. (2008) describe that multiple case studies do not allow for statistical generalization, but this does not mean they are devoid of generalization possibilities. Analytical generalization refers to generalization from empirical observations to theory. Eisenhardt (1989) states that case studies can serve as a starting point for theory devel-opment and cross-case analysis between four to ten cases may provide a sufficient base for analytical generalization. With six case studies, this research contributes to the academical lit-erature. However, Shenton (2004) acknowledges that understanding a phenomenon is growing gradually, through several studies rather than one major research. Therefore, this study contrib-utes to theory, but further research will be required.
‘Reliability refers to the absence of random error, enabling subsequent researcher to arrive at the same insights if they conduct the study along the same steps again’ (Gibbert et al., 2008, p. 1468). To ensure reliability of this study, two pilot interviews were executed to avoid bias, leading questions and potential ambiguity. Feedback has been asked on given answers and the interviewer’s’ interpretation, to avoid observer bias (Saunder et al., 2012). Constant data com-parison, comprehensive data use and inclusive of a deviant case is used to enhance the reliability of this study (George & Apter, 2004). Different research steps are outlined and documented which guarantees the replicability of the study (Yin, 2009). As trustworthiness is a critical factor
4. Results
In this chapter, the results of the questionnaire are presented first, and the three different clusters of digital maturity are formed. Secondly, the results of the interviews are presented, where the large numbers of long text are integrated into one coherent narrative.
4.1 Questionnaire
In the following paragraphs the results of the questionnaire are presented.
4.1.1 Descriptive statistics, normality, skewness and kurtosis
In total, 108 SMEs filled out the online survey completely. 42 respondents were female (38.9%) and 66 male (66.1%). The average age of the respondents was 38 years old, which is fairly coherent with the average age of a Dutch SME owner: 46 years (Dutch Network Group, 2018).The firm’s ages were: 0-2 years (16,1%), 2-5 years (19.6%), 5-10 years (19.6%), 10-20 years (23.2%), 20-49 years (9.8%), 50+ years (11.6%), indicating an even presence of firms with different ages. The histogram for digital maturity shows an approximately normally dis-tribution(figure 7). Table 4 shows that the data was negatively skewed (skewness value= -.372, SE= .233) and the kurtosis was slightly low (kurtosis value= -.722 SE= .461), which confirms the shape and the values of the histogram. Since the skewness and kurtosis value remained between -2 and 2, no mutations of the data were deemed necessary.
Table 4. Descriptive statistics digital maturity level Statistics DM_Total N Valid 108 Missing 0 Mean 4.4074 Std. Deviation 1.29947 Skewness -.372 Std. Error of Skewness .233 Kurtosis -.722 Std. Error of Kurtosis .461
Table 4 also shows that the average score on digital maturity level of Dutch SMEs is M=4.4 with a standard deviation of SD=1.3. With the mean and standard deviation of digital maturity level the values of the different digital maturity levels are calculated (table 5).
Table 5. Values of the digital maturity levels
Digital maturity Scale ????????????
Low < 𝜇 − 1s 4.4074 – 1.29947 = <3.10793 Average ³ 𝜇 − 1s 𝑎𝑛𝑑 £ 𝜇 + 1s ³ 3.10793 £ 5.70687
High > 𝜇 + 1s 4.4074 + 1.29947 = >5.70687
All scores are classified in the above described framework, resulting in the following distri-bution (table 6).
Table 6. Distribution of firms on the three different digital maturity levels
Digital maturity Number of firms Percentage
Low 19 17,59 %
Average 74 68,52 %
High ty 15 13,89 %
Table 6 shows that there are sufficient numbers of SMEs for the comparative analysis of the cases at different digital maturity levels.
4.1.2 Additional insight
For additional insight, differences in responses are controlled for gender, firm age and sector. An independent t-test was conducted to compare digital maturity for female and for male con-ditions (respondents). There was no significant difference in scores for female (M=4.27, SD=1.22) and male (M=4.50, SD=1.35) respondents; t (106)=-.889, p=.376. This suggest that gender does not have an effect on digital maturity (see table 7 and 8).
Table 7. Group statistics of gender on digital maturity level
Table 8. Independent t-test for gender on digital maturity level
A one way between subjects ANOVA was conducted (see table 9) to compare the effect of firm age on digital maturity for the conditions: 0-2 years, 2-5 years, 5-10 years, 10-20 years, 20-49 years and 50+ years. There was a significant effect of firm age on digital maturity at the p<.05 level for the 6 conditions [F(5, 106) = 3.73, p = 0.004]. Because a statistically significant result is founded, a Tukey post hoc test is computed (see table 10). This test is designed to compare each of the conditions to very other conditions. Post hoc comparisons using the Tukey HSD test indicated that mean score for 0-2 years (M=5.14, SD=.92) was significantly different than the 10-20 years condition (M=4.01, SD=1.29), 20-49 years condition (M=3.70, SD=1.33), and the 50+ years condition (M=3.86, SD=.89). However, the 0-2 years condition did not sig-nificantly differ from the 2-5 years and 5-10 years condition (see table 10). Taken together, these results suggest that older firms score lower on digital maturity than the youngest firms (see table 11). However, it should be noted that firms need to be ten years or older to see a significant effect and that this is effect accounts only for the youngest firms (0-2 years).
Table 9. One-way ANOVA for the effect of firm age on digital maturity.
Table 10. Tukey HSD Post hoc test
* The mean difference is significant at the 0.05 level.
** The complete Tuckey post hoc test can be found in Appendix VII.
Table 11. Descriptive statistics of firm age
A one way between subjects ANOVA was conducted to compare the effect of sector (thir-teen conditions) on digital maturity (table 12). There was no significant effect of sector on digital maturity at the p<.05 level for these thirteen conditions [F(12, 99) = 1.36, p= .201]. This suggest that sector does not have a significant effect on digital maturity. The descriptive
statis-Table 12. One-way ANOVA for the effect of sector on digital maturity
4.2. Interviews
The following paragraphs presents the findings of the interviews. The findings are presented according to the sequence of the concepts discussed in chapter 2. The six cases are divided in cases with low (2), average (2), and high (2) digital maturity level. In this chapter the three different categories will be discussed in the same sequence of the concepts as in the literature review. This provides a structured presentation the findings where the different levels can con-tinuously be compared and described on different concepts.
4.2.1 SMEs constraints for digital transformation
In the first part of the interviews (see Appendix VI) respondents were asked to tell about their possibilities to experiment, explore and exploit new digital technologies. The interviews executed at firms that scored low on digital maturity indicate that financial constraints play a significant role in adopting new digital technologies: ‘Until now, it is hard to finance. We are
dependent on third parties or sponsors who would help and favor us to develop certain new technologies in our firm’ (Case 2). Firms depend on third parties or sponsorships to enable
experimentation, and explore and exploit digital technologies. However, these firms with low digital maturity level indicate that they are not yet ready for new digital technologies: ‘That
would be far in the future, not right now. We have phone and e-mail, a brand new website, but in the near future, new digital technologies wouldn’t be something for us yet (Case 1) and: ‘We are thinking about using Virtual Reality, so we can simulate reanimation situations, but this is future music’ (Case 2).
Firms at an average level of digital maturity were slowly engaging in their digital transfor-mation. However, similar to firms with low digital maturity, they experienced financial con-straints which made it hard to adopt digital technologies: ‘If you outsource it to a third party, it
will be a huge investment for a relatively small company. We have thought about to do more with all the data we have, but right now we lack financial resources (Case 3). As expected time
is always scarce within SMEs; nevertheless, firms were willing to invest time in digital trans-formation: ‘Our store in Amsterdam was very successful the first year. Instead of opening
an-other store, we decided focus first on digitalization.’ (Case 4) and: ‘We don’t have time for it right now. But we try to come together once a month and discuss digitalization matters.’ (Case
3).
Firms with a high maturity level and very busy in their digital transformation process expe-rienced financial constraints as well, because of their small size and young age. Everything
