Scalability in Startups : A Case Study of How Technological Startup Companies Can Enhance Scalability

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Linköping University│Department of Management and Engineering (IEI) Master Thesis, 30 ECTS│Strategic Management and Control Spring Term 2020│LIU-IEI-TEK-A--20/03855—SE

Linköping University SE-581 83 Linköping, Sweden 013-28 10 00, www.liu.se

Scalability in Startups

- A Case Study of How Technological Startup Companies

Can Enhance Scalability

Erik Foogel Jakobsson

Andreas Jenefeldt

Supervisor: Thomas Rosenfall Examiner: Christina Grundström

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Abstract

Startups and new businesses are important for the development of technology, the economy, and the society as a whole. To be able to contribute towards this cause, startups need to be able to survive and grow. It is therefore important for startups to understand how they can scale up their business. This led to the purpose of this study: to determine success factors for technological startup companies to increase their scalability. Five areas were identified to have an impact on the scala-bility of a business, namely; partnerships, cloud computing, modularity, process automation and business model scalability. Within these areas, several subareas were found, which were certain areas of interest within the theory. Together, these subareas helped answer how companies can work with scalability in each area. These areas, and their subareas, went into an analytical model that formed the structure of the empirical and analytical parts of the study. The study is a multi-case study, consisting of 15 B2B companies, of varying size and maturity, whom all offered soft-ware as a part of their solutions.

The study concludes that there are six important factors for succeeding with scalability. An im-portant factor to succeed with scalability is to adopt partnerships, since this will allow for outsourc-ing, and give access to resources, markets and customers. It is also concluded that cloud computing is a very scalable delivery method, but that it requires certain success factors, such as working with partners, having a customer focus, having the right knowledge internally, and having a standardized product. Further, modularity can enable companies to meet differing customer needs since it in-creases flexibility, can expand the offer, and make sales easier. The study concludes that process automation increases the efficiency in the company, and can be done through automating a number of processes. Focusing both internally and externally is another important factor for success, by allowing companies to develop a scalable product that is demanded by customers. Lastly, a scalable business model is found to be the final objective, and that it is important to work with the other areas to get there, something that includes trial and error to find what works best for each individual company.

The six important factors formed the basis for the recommendations. The study recommend that startups should utilize partnerships and process automation. Startups should also be aware of, and work with, the success factors of cloud computing, use modularity when selling to markets with different customer needs, automate other processes before automating sales, keep customer focus when developing the product, and work actively to become more scalable.

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Acknowledgements

This master thesis is the last step towards our MSc degree in Industrial Engineering and Manage-ment. It has been a big project for us and included both its ups and downs. Especially the situation with Covid-19 and everything that followed meant some big changes when it came to the inter-views, but luckily it has worked well to conduct the interviews online, using tools such as Teams instead. We would not have been able to complete this thesis alone, however, and we would there-fore like to thank everyone that have helped us along the way.

First and foremost, we would like to thank our supervisor and examiner at Linköping University, Thomas Rosenfall and Christina Grundström, for helping us with all our questions and giving val-uable insights, that have helped us produce a better report.

Further, we want to thank our employer iMatrics and our supervisor Mari Ahlquist, both for letting us do this study, but also for being so welcoming and making us feel at home. We have enjoyed this spring with you, especially things like the daily Dutch words and digital fikas during the last two months when we had to work from home.

We would also like to thank all our respondents for giving us their time and letting us interview them, especially in the challenging times caused by Covid-19.

Finally, we would like to thank Viktor Hakegård and Johan Karlsson for giving us helpful feedback and valuable ideas during our peer-review seminars.

________________________ ________________________

Erik Foogel Jakobsson Andreas Jenefeldt

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List of figures

Figure 1. An embryo for an analytical model. ... 5

Figure 2. The analytical model. ... 29

List of tables

Table 1. Resource types and preferable partnerships for a firm (Das & Teng, 2000, p.45). ... 7

Table 2. Areas within partnerships that will be used in the analytical model. ... 9

Table 3. Areas within cloud computing that will be used in the analytical model. ... 13

Table 4. Areas within modularity that will be used in the analytical model. ... 18

Table 5. Areas within process automation that will be used in the analytical model. ... 24

Table 6. Areas within business model scalability that will be used in the analytical model. ... 28

Table 7. Information about the interviews. ... 37

Table 8. Actors in partnerships. ... 44

Table 9. Type of partnerships. ... 46

Table 10. Benefits from partnerships. ... 48

Table 11. Companies that use cloud and a partner to handle the servers. ... 50

Table 12. Success factors for cloud computing. ... 52

Table 13. Benefits from cloud computing. ... 54

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Table 15. How modularity affects partnerships... 57

Table 16. Benefits of modularity. ... 59

Table 17. Areas within process automation. ... 62

Table 18. Benefits from process automation. ... 64

Table 19. Work towards scalability. ... 67

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1 Introduction

This chapter starts with giving a background to the problem and an analysis of the important as-pects of said problem. This introduction chapter then continues by describing the purpose of the thesis as well as outlining the rest of the thesis.

For the last 20 years, fast growing startup companies have been able to create new industries and dominate these, while also disrupting existing industries (Davila & Epstein, 2014). The reason for their fast growth, which is enabled by the disruption and creation of new industries, is their ability to come up with, and develop, breakthrough innovations (ibid.). New businesses are also crucial for the development of both national economies and society as a whole (Szarek & Piecuch, 2018). Almus (2002) points to the fact that smaller firms, such as startup companies, have an especially high potential for growth. It therefore seems important to deepen the understanding of how startups function.

1.1 Growth in Startup Companies

Startup companies can emerge from both an existing company and an independent business idea (Kolosok & Koniukh, 2018). There are therefore many different definitions of what constitutes as a startup. One definition is that a startup is a company that is trying to gather resources, establish an innovative product, develop the business and enter a market (ibid.). An example of the startup process can be described as beginning with the forming of a business idea, followed by founding the company, growing the business, expanding and lastly exiting the startup phase (Kolosok & Koniukh, 2018).

It is important for businesses to seek continuous growth; those who do not will be overtaken by another company and eventually die (Quelle, 2012). Accordingly, Smit, Thompson and Vi-guerie (2005) describe how businesses that do not grow often face the risk of acquisition by another company, and that companies therefore must continue to grow in order to survive. Rapid growth can be seen in various organizations, but to survive in the long term, the organizations also need to be able to sustain this growth (Weiss, 2012). There are often multiple risks connected to the expe-rienced growth of companies. For example, a company can grow faster than its capacity to support this experienced growth, leading to a growth that is neither sustainable nor repeatable for the or-ganization (Weiss, 2012). Thus, considering the importance of startups for the societal develop-ment, and the importance of growth for businesses to survive, it is important to understand how startups can capture and utilize their growth potential, in a sustainable fashion, to prosper.

Growth is also common within companies in high-technology sectors (Enjolras, Camargo & Schmitt, 2019). A technology company can be defined as a company that is devoted to, and earns the majority of its revenues, from creating and selling technology or a technological service, such as a software or a social media platform (Baca, 2019). According to Kittlaus and Clough (2009),

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the element that characterizes software businesses most, compared to other types of services, is that the production cost almost only consists of the fixed development cost, while the variable costs for a single unit, such as shipping and documentation, is neglectable. These characteristics make it easy to achieve economies of scale by producing more, but also requires that a minimum number of products are sold to pay for the fixed cost and reach break-even (Kittlaus & Clough, 2009). Further, the initial investment in the software industry is low, leading to lower entry barriers and a high number of companies entering, only to quickly fail (Kittlaus & Clough, 2009), highlighting the potentials and risks within this sector.

Company growth is not limited to external factors, as economic condition or industry maturity (Deans & Kroeger, 2004). Seeing that many companies manage to grow even in poor economic conditions, growth is dependent on internal factors, like organization, culture, and operations, which all need to support the growth initiatives (ibid.). This is also supported by Olson, van Bever and Verry (2008), who found that when companies experience growth stall, the causes for this are most often within managerial control, and could therefore have been prevented if management would have acted in time. Further, sustainable growth is a result of long-term strategy, rather than short-term changes (Deans & Kroeger, 2004). Thus, it seems like startups can affect their growth and keep it sustainable, rather than having to rely fully on external factors, further indicating the importance of understanding the startup growth process.

Internally, Freytag (2019a) found that an important first step is to develop a viable product. Once this product has been developed, a startup company should change focus towards scaling up its revenue as fast as possible (Freytag, 2019a). Startup companies usually are funded by external venture capital and have limited resources (Davila & Epstein, 2014). Thus, profitable growth would seem at an utmost importance for them, since continuous negative results from aggressive expan-sion is not an option, due to the limitations in resources and dependence on external funding. While growth simply entails an increase in revenues alongside an equal increase in costs, scaling up the business entails an increased revenue that is larger than the increase in costs (Carucci, 2016), which would suggest that a profitable growth can be achieved through scalability. Hence, using scalability should be an effective way to grow for startup companies.

1.2 Scalability as a Mean to Grow for Technological Startup Companies

Current research in scalability covers a diversity of aspects, both from a technical point of view, as well as an organizational one (Dano, 2019; Kittlaus & Clough, 2009; Maghsoudloo & Khoshavi, 2020; Nair & Blomquist, 2019). Currently, research has examined how different areas of a business relates to and enhances scalability, which in turn influences growth.

Research has found a connection between partnerships and scalability (Cannone & Ughetto, 2014; Davilla & Epstein, 2014; McDougall, Shane & Oviatt, 1994). Davila and Epstein (2014) state that successful startup companies, in addition to innovations, has both resources and a network. This is especially evident in firms that manage to grow quickly on a global scale; they develop and utilize

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close collaborations with various stakeholders all over the globe as a mean to obtain all necessary resources (Cannone & Ughetto, 2014; McDougall, et al., 1994).

A second area, found to increase scalability, is cloud computing (Chopra, 2018; Kittlaus & Clough, 2009; Ross & Blumenstein, 2015). Cloud computing allows companies to get an easier access to international markets, reduce costs of exploring new opportunities and make the service more avail-able for the customers (Chopra, 2018; Kittlaus & Clough, 2009; Ross & Blumenstein, 2015). In addition, it has also led to many startups by allowing entrepreneurs to start businesses in a variety of industries (Chopra, 2018).

Modularity has also been found to enhance scalability (Dano, 2019). Modularity can be defined as the possibility to divide the product into different building blocks, with different functions, called modules (ibid.). With these modules, an organization can relatively easy customize a product and deliver the wanted functionality to the customer (Dano, 2019). Modularity is a well-known ad-vantage for software providers and one of the more important aspects when designing the software (Benkoczi, Gaur, Hossain & Khan, 2018). Having modularity in the product will reduce lead time and increase the economies of scale (Benkoczi, et al., 2018).

Sharma (2017) found that by automating processes, businesses can achieve advantages such as increased consistency, quality and cost-efficiency. Automation also reduces the labor intensity (Coupe, 2019). These four factors all enhance scalability (cf. Carucci, 2016). Although process automation has mostly been examined with respect to industrial businesses, as in the study of Sharma (2017), processes such as sales, marketing and some sort of production are also present in other businesses, like software. Software businesses, that offer a solution, are more interconnected, sales are more complex, and the different functions in this type of business should therefore be seen more as a process compared to only selling a good (Storbacka, 2011).

Finally, Nair and Blomquist (2019) found that most of the startups that fail to leave the startup phase successfully does not do it because of technological shortcomings, but rather because of not having a scalable business model. Casadesus-Masanell and Ricart (2010) concludes that a business model describes the logic of the firm, meaning how it operates and creates value for its stakehold-ers. By having a scalable business model, startups can focus their efforts on organizational chal-lenges connected to growth (Nair & Blomquist, 2019), which leads to a higher chance of success (Carucci, 2016).

As can be seen above, previous studies have identified several areas of interest, relevant to scala-bility. However, none has taken a holistic perspective, by including all these areas together, to examine how these areas are related and affect one another. This study tries to fill this gap by studying several interrelated parts, instead of examining each part individually, thereby giving startups a better understanding of what can be done to increase their scalability.

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4 1.3 Purpose

The purpose of this study is to determine success factors for technological startup companies to increase their scalability.

1.4 Disposition

Chapter 2 Frame of reference, contains the theory that is used to construct the analytical model. This chapter is mainly written for researchers who want to know what theories that have been used when forming the analytical model, and what research questions that will be examined in chapter 4 Empirical Data and Analysis.

Chapter 3 Methodology presents the scientific view this study is based on and describes how the study is performed, from choice of literature to data collection and analysis. The chapter ends with an evaluation of the quality of the study. This chapter is mainly written for researchers who want to know how the study was conducted and want to be able to evaluate the validity and reliability of the study.

In chapter 4 Empirical Data and Analysis, the data from the study, collected through interviews, is presented and analyzed based on the theory in chapter 2 Frame of reference. Chapter 4 Empirical Data and Analysis is mainly written for both researchers and managers who want to see the data from the interviews with other companies and how this data is analyzed, to determine how startups can work to become more scalable.

Finally, in chapter 5 Conclusions and Discussion the conclusions and recommendations from the study are presented. The contribution from this study is also presented, together with a discussion regarding limitations and possible future research. Chapter 5 Conclusions and Discussion is written for both researchers and managers who want to see the final conclusions and recommendations of how a technological startup company can work with scalability in their organization, as a tool for growth, as well as what this study contributes with to research, and interesting future research connected to the study.

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2 Frame of reference

This chapter examines the areas found relevant to scalability in the introduction further. Using theories in these areas, hypotheses regarding how these areas are related to each other and affect scalability are then presented in the form of an analytical model and research questions later in the chapter.

From the introduction, it was found that areas relevant to scalability is partnerships, cloud compu-ting, modularity, process automation and business model scalability. These five areas will be ex-amined in more detail, to get a deeper understanding of previous research regarding these areas, and results in an analytical model, describing scalability in startups from the theoretical perspec-tive. Firstly, the area of partnerships will be studied, to lay the foundation for startups to scale the business. Secondly, the high potential for scalability from working with cloud computing will be examined. Following this, modularity will be the third area to be examined to get an understanding of what a modular product and organization can entail for scalability. The fourth area in the frame of reference is the area of process automation where it is examined how process automation can increase scalability for startups. Lastly, the fifth area to be examined is business model scalability and how this influences startups ability to scale up the business. An embryo of the analytical model, highlighting which areas that will be examined with respect to scalability, and in which order they will be examined, can be seen in Figure 1.

Figure 1. An embryo for an analytical model.

2.1 Partnerships

Startups often have limited resources and need to find ways of overcoming these limitations (Pangarkar & Wu, 2012), and often do not make use of all aspects in the business model but rather only offer a product (Teece, 2010). Partnerships can give startups access to resources, financial

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capital and markets as well as improve their credibility, and thereby increase growth and scalability (Kohler, 2016). Startup companies will therefore usually form a network consisting of partners, suppliers, distributors, agents, customers and competitors (Cavusgil & Knight, 2015; Li & Deng, 2017), to obtain the necessary resources (Holmes Jr, 2008). The ability to receive resources from the network will even influence the choice of which market to go to (Ojala, Evers & Rialp, 2018). Especially for digital platform providers, it is important to consider all stakeholders on the multi-sided markets, both the companies creating the content to the platform and the companies distrib-uting and enabling end users to take advantage of the content on the platform (Ojala, et al., 2018). Seemingly, relationships and partner networks are therefore important for startups. A company should try to let its partners do a part of the job, and thereby leverage their resources (Nielsen & Lund, 2018b). Examples of this is to let customers sell the product, give each other access to re-sources, share distribution methods, and build customer loyalty (Nielsen & Lund, 2018b). Further-more, common activities that are outsourced by startups are development of prototypes as well as the product development and infrastructure for operations (Nguyen Duc & Abrahamsson, 2017).

Another way of handling having scarce resources is called bootstrapping, a common financing method where startups try to minimize costs and make use of internal capital (Mac an Bhaird & Lynn, 2015; Nguyen Duc & Abrahamsson, 2017). Examples of this is getting payed in advance from customers, reducing personnel costs, working from home or having a low rent space and using personal guarantees for credits and loans (Mac an Bhaird & Lynn, 2015). However, these activities are somewhat limited in providing enough capital to fully exploit market opportunities, thereby limiting scalability (Patel, et al., 2011). To counteract the limited scalability of bootstrapping, which in turn might decrease returns and growth, companies can engage in strategic alliances, and thereby retrieve the resources needed to grow (Patel, et al., 2011).

2.1.1 How startups can work with partnerships

Depending on what resources an organization, and the partner organization, have, Das and Teng (2000) states that different type of partnerships is preferred. If the business has property-based resources, i.e. legal properties owned by the company such as physical and human resources, and the partner firm has knowledge-based resources, i.e. know-how and skills, an equity joint venture is to be preferred (Das & Teng, 2000). A joint venture is an independent entity created by several parent companies (Chen, Lin, Lin & Hsiao, 2020). Joint ventures are used to integrate the partners significantly, as the joint venture will allow the parties to work as one organization (Das & Teng, 2000). Moreover, joint ventures is a tightly coupled relationship mostly used in established markets where economies of scale is important and as an opportunity to gain access to international markets (Barringer & Harrison, 2000). If the partner firm, however, has property-based resources, a unilat-eral contract-based alliance is preferred (Das & Teng, 2000). A unilatunilat-eral contract-based alliance is an alliance where the two firms do not work together to a great extent and instead conduct their part of the partnership independently of the other firm, such as when exchanging licenses for money

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(Das & Teng, 2000). When the business has knowledge-based resources on the other hand, a mi-nority equity alliance is preferred if the partner firm has property-based resources and a bilateral contract-based alliance if the partner firm has knowledge-based resources (Das & Teng, 2000). A minority equity alliance is an alliance where one party has an equity stake in the other party (Das & Teng, 2000). A bilateral contract-based alliance is defined as an alliance where both parties work together regularly, such as joint R&D, marketing or production (Das & Teng, 2000). These three types of alliances are all loosely coupled in accordance with how Barringer and Harrison (2000) describes alliances. The preferred types of partnerships can also be seen in Table 1.

Table 1. Resource types and preferable partnerships for a firm (Das & Teng, 2000, p.45).

Partner Firm

Property-based Resources Knowledge-based Resources

Firm

Property-based Resources Unilateral Contract-based Alliances Equity Joint Venture

Knowledge-based Resources Minority Equity Alliances Bilateral Contract-based Alliances

Nguyen Duc and Abrahamsson (2017) on the other hand, state that the type of relationship rather depends on the firms’ maturity and that, typically, outsourcing is at first based on contracts and concerns smaller projects. As the company reach the end of the startup phase and starts to scale up the business, deeper partnerships become a better alternative for firms when outsourcing (Nguyen Duc & Abrahamsson, 2017). Additionally, alliances are less complex type of partnership without common ownership and are often beneficial to use in marketing and technology aspects (Barringer & Harrison, 2000). A factor for success in partnerships is that there is a person with understanding of both sides and can be responsible for handling the relationship between startup and the partners used for outsourcing (Nguyen Duc & Abrahamsson, 2017). Further, it is important for startups to consider challenges regarding geography, culture as well as temporality when choosing a partner-ship (Nguyen Duc & Abrahamsson, 2017).

Networks are common in industries where knowledge is important for competitive advantage. Net-works also allow each business to focus on its core competency as well as making it more flexible (Barringer & Harrison, 2000). Furthermore, partner networks provide several advantages for small and medium-sized enterprises, SMEs, and thereby increase their competitiveness (Polyantchikov,

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Shevtshenko, Karaulova, Kangilaski & Camarinha-Matos, 2017). Networks can for example ena-ble startups to gain access to sales channels, customers, investments and international markets (Freytag, 2019b).

There are certain steps that are important for SMEs when working towards becoming a part of a partner network that is sustainable over time (Shevtshenko, Mahmood & Karaulova, 2019). It is therefore important for firms to first define their business processes and how the organization op-erates for the firm to be able to outsource as well as to obtain resources from partner firms (Shevtshenko, et al., 2019). The firm then needs to form key performance indicators to define how to measure the performance of the network, followed by defining any risks that can be associated with being a part of the network (Shevtshenko, et al., 2019). Lastly, SMEs should evaluate which activities are adding value to customers and which are not, and, with the help of the network, try to eliminate time spent on activities that does not add any value (Shevtshenko, et al., 2019). Fur-thermore, Kask and Linton (2014) state that there are three aspects affecting the success for startups in their endeavor of finding a business partner, namely the management style, the situation in that marketplace as well as how unique and original the solution is. In an established market with in-cremental changes, startups can increase the chances of finding a business partner by not having a radical solution and by managing the firm in a way that takes advantage of current resources, while in a new market, the chances of finding a business partner are increased by having a radical solution or having a management style that explores new opportunities (Kask & Linton, 2014).

2.1.2 Synthesis of Partnerships

According to both Cavusgil and Knight (2015) and Li and Deng (2017) startups can have partner-ships with many different actors, namely partners, suppliers, distributors, agents, customers and competitors. From the discussion regarding partnerships, one can see that previous research can be divided into three main benefits startups can receive from engaging in partnerships. The first ben-efit is gaining access to resources (Freytag, 2019b; Holmes Jr, 2008; Kohler, 2016), markets (Freytag, 2019b; Kohler, 2016) and customers (Freytag, 2019b). A second view on how startups can utilize partnerships is by outsourcing parts of their business (Nguyen Duc & Abrahamsson, 2017; Nielsen & Lund, 2018b). Nguyen Duc and Abrahamsson (2017) point out that development of prototypes, product development and infrastructure for operations are common activities to out-source. By outsourcing parts of the business to partners the focal firm does not require the resources to perform these parts themselves, which is similar to the benefit Holmes Jr (2008) and Kohler (2016) describe. By doing this, startups can probably use their limited resources more efficiently. A third perspective on why partnerships are important for scalability in startups is that a common financing method, called bootstrapping, can limit resource possibilities (Mac an Bhaird & Lynn, 2015; Nguyen Duc & Abrahamsson, 2017) and therefore inhibit scalability. To avoid this, startups should instead be involved in strategic alliances (Patel, et al., 2011). This third view differs some-what from the first two, that are similar to each other. Determining how to work with partnership

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and who to form partnerships with, will allow startups to scale up their business despite the lack of resources, thus helping to answer the purpose of the study.

When engaging in partnerships, two prominent sides regarding what influences the type of part-nership were found. Das and Teng (2000) describes that different types of partpart-nerships are to be preferred depending on what type of resource each firm in the partnership contributes with. The other perspective on what type of partnership startups should choose argues that the level of ma-turity of the startup is the aspect that ought to decide the type of partnership (Nguyen Duc & Abrahamsson, 2017). At first, partnerships are usually based on contracts, and develop into deeper partnerships as companies reach the end of the startup phase (ibid.). The two sides on when to use what type of partnership are very different from each other. Since there are two different opinions on when to use what type of relationship, it will be important for startups to get clarity in what type of partnership that is to be preferred. An interesting question that arises is how this works for startups in practice, and by bringing clarity to this, startups can utilize the potential for scalability that comes with forming partnerships. Areas regarding how partnerships affect scalability that are needed in the analytical model, and the following analysis, is therefore with which actors a startup can form partnerships, what influences the type of partnerships and what benefits startups gain from partnerships. An overview of these areas can be seen in Table 2.

Table 2. Areas within partnerships that will be used in the analytical model.

Area Parts Sources

Actors to engage in a partner-ship with

Partners, suppliers, distributors, agents, customers, and competitors

(Cavusgil & Knight, 2015; Li & Deng, 2017)

Type of partnership

Depends on resources (Das & Teng, 2000)

Depends on maturity (Nguyen Duc & Abrahamsson, 2017)

Benefits of engaging in a part-nership

Access to resources, markets and customers

(Freytag, 2019b; Holmes Jr, 2008; Kohler, 2016)

Possibility to outsource (Nguyen Duc & Abrahamsson, 2017;

Nielsen & Lund, 2018b)

Mitigate drawbacks of bootstrapping

(Mac an Bhaird & Lynn, 2015; Nguyen Duc & Abrahamsson, 2017;

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10 2.2 Cloud Computing

The business potential should not have constraints in the form of material or physical assets, like machine time, man hours, storage, cash liquidity and similar forms of capacity (Lund & Nielsen, 2018a). Cloud computing allows startup companies to lower entry barriers (Shan, Jia, Zheng & Xu, 2018), increase product quality and remove technological constraints in their offering (Ferri, Spanò & Tomo, 2020). Therefore, the scalability of an organization’s business operations is enhanced by cloud technology (Juntunen, et al., 2018), which frees a company from any physical constraints (Hallowell, 2001). Cloud computing can also be beneficial for flexibility, product development and profits, and therefore reduce the need for outsourcing that startups commonly make use of (Ferri, et al., 2020). Cloud technology thereby helps the organization to benefit from economies of scale more quickly (Juntunen, et al., 2018), as well as with receiving venture capital (Ferri, et al., 2020).

Cloud computing is, according to Sultan and van de Bunt-Kokhuis (2012), comprised of the three areas of infrastructure, platform and software as a service, where the area of software as a service is the largest with respect to sales and revenue (International Data Corporation, 2013). Further, Stampfl, Prügl and Osterloh (2013) found that the software as a service business model shows almost perfect scalability, mostly thanks to the fact that no physical goods have to be shipped. Therefore, software as a service will examined in more detail.

2.2.1 Software as a Service

Software as a service, known as SaaS, means that the user subscribes a software from an owner, rather than buying the actual software (Laplante, Zhang & Voas, 2008; Li, Cheng, Duan & Yang, 2017). This means that the owner hosts the service and lets its customers use it over the Internet or an intranet, while charging the customers on a per-use basis or alternatively on a rental basis for a certain subscription period (Laplante, et al., 2008; Li, et al., 2017; Ojala, 2013). Furthermore, it is important to have a price model that sets prices after the benefit that is generated for the customer and that the model is flexible depending on the customers situation (Floerecke, 2018). SaaS means that the customer does not have any control over the underlying infrastructure in the cloud, such as storage, operating systems, network or servers (Miyachi, 2018). It will therefore be important to explain how cloud computing and SaaS works to the customer to prevent trust issues, regarding for example data security, and improve the customer relationship (Floerecke, 2018).

A SaaS application can serve hundreds of organizations with thousands of users each (Tsai, Huang, Bai & Gao, 2012). Technological scalability is therefore one of the most important requirements for cloud-based products, including SaaS (Ahmad & Andras, 2019). The high potential for scala-bility is also one of the main advantages with SaaS (Aleem, Batool, Ahmed, Khatak & Ullah, 2017; Mac an Bhaird & Lynn, 2015; Marston, Li, Bandyopadhyay and Ghalsasi, 2011; Trinh Phuong, Cong & Tran, 2015). Another benefit with cloud computing and SaaS is that it has smaller entry

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barriers, which is especially beneficial for SMEs (Mac an Bhaird & Lynn, 2015). Scalability in this technological context can be defined as the possibility for the cloud-based software to increase its capacity when demand increases over time (Ahmad & Andras, 2019). This can be achieved with a

Multi-Tenancy Architecture, where the owner maintains a single code base that supports all users, while each customer perceives as if their software is custom-made (Aleem, et al., 2017; Marston,

et al., 2011; Tsai, et al., 2012). Having a system with independent functions and auto-scaling are

also important aspects of having a scalable SaaS software (Aleem, et al., 2017).

In addition to these aspects, the value proposition should also be characterized by ease of use as well as a comprehensive customer service, and preferably be developed with the help of customers (Floerecke, 2018). By involving customers in the product development of a software, and thereby obtaining customer feedback, companies will be able to develop a more general product (Mac an Bhaird & Lynn, 2015). Apart from helping the company to build a reputation, which helps attain customers and leverage sourcing, it can improve company scalability by allowing the company to acquire additional customers through vertical integration (Mac an Bhaird & Lynn, 2015). It is also important to have a high availability on the service and have qualified employees with enough knowledge to maintain the service (Floerecke, 2018). What type of customer segment is targeted can differ on what type of software is produced, where smaller businesses often demand smaller and isolated products while big businesses demand a product that is more integrated (ibid.). Addi-tionally, larger businesses entail more money, but they also require more personal support (Floerecke, 2018).

To measure the scalability of the software there are several different metrics that can be used, such as processing time, consumption of resources, the ratio between resource consumption and perfor-mance as well as how these metrics varies over time (Aleem, et al., 2017). In addition to scalability, customization is also an important aspect of SaaS solutions, to meet the tenants demands, but there are options to do this automatically (ibid.). However, the multitenancy aspects of SaaS applications make it extremely difficult for individual subscribing customers to customize the software (Li, et

al., 2017), something that may entail that the technological scalability is even more important for

business growth. Furthermore, successful SaaS businesses are also able to gain extensive knowledge as well as industry expertise of both the technology and customers to make sure that they provide customers with value, and build a network of partners that can help with sales, mar-keting and provide services so that the software provider can focus on the core business and not have to travel to each customer (Floerecke, 2018). Another important success factor for SaaS pro-viders is to focus on one customer segment, although an overall ambition should be to have as many customers as possible (Floerecke, 2018). Moreover, cloud-based software is proving itself in most markets, sectors and size of organizations (Ferri, et al., 2020). In this context the multitenancy aspect can be beneficial, since it will allow SaaS providers to benefit from economies of scale (Li,

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SaaS is seemingly a way to achieve scalability in the product but as Li, et al. (2018) mention, SaaS is not always the optimal solution. When the customers cost of not customizing the software, and therefore not getting the optimal product, is higher than the cost of actually customizing it, selling the software both through retailers, i.e. with the help of a third party, and SaaS channels is the most profitable (ibid.). However, when the customers cost of not customizing the software is low in comparison to the cost of customization, SaaS is the most profitable distribution channel (ibid.). Finally, when targeting small and medium sized businesses or a wide market segment of customers, SaaS will be the most profitable revenue model (Ojala, 2013).

2.2.2 Synthesis of Cloud computing

Having the same code base for many customers can also result in fewer products, thereby making product development easier.

According to Ferri, et al. (2020), cloud computing can be found in a wide range of markets, sectors and organizational sizes. Similar to this, Floerecke (2018) argues that a company providing SaaS should aim for many customers from several segments, suggesting that the sector of the focal com-pany does not matter. A question is raised whether this is the case in practice, or if companies from certain sectors are keener on adopting cloud computing. It is therefore important to examine what type of companies that use cloud computing, to better understand to whom cloud computing is a suitable way to increase scalability.

While Hallowell (2001) identifies that cloud computing is a way to eliminate physical constraints, Ferri, et al. (2020) point out that cloud computing removes both technological and financial con-straints for organizations. Cloud computing allows the software owner to maintain one code base for all customers (Aleem, et al., 2017; Marston, et al., 2011; Tsai, et al., 2012), and serve thousands of users simultaneously with one application (Tsai, et al., 2012). Having the same code base for many customers can result in fewer products, thereby making product development easier. Ferri, et al. (2020), also state that cloud computing benefits product development, and further state that it benefits flexibility, product quality and reduce the need for outsourcing. We believe that is similar to what Mac an Bhaird and Lynn (2015) and Shan, et al. (2018) mean when they argue that cloud computing and Saas lower the entry barriers for a company. Juntunen, et al. (2018) further claims that cloud computing will allow the organization to quickly reach economies of scale, and that by bypassing constraints in resources, startups can increase their scalability. Many different views can be seen regarding the benefits stemming from cloud computing. For startups to really understand which benefits the adoption of cloud computing brings and how this contributes to increased scala-bility, it is therefore important to examine and bring clarity into. Examining the benefits stemming from cloud computing thusly helps us answer the purpose of this report.

The largest area of cloud computing is software as a service (International Data Corporation, 2013), which also is a business model with exceptional scalability (Ahmad & Andras, 2019; Aleem, et al., 2017; Stampfl, et al., 2013). Floerecke (2018) found that there are some important aspects for

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startups as they work to become a SaaS provider, for example working with partners, focusing on the customer and addressing issues such as availability, data security, the price model and knowledge amongst employees. There are several views on how to work towards cloud computing however, for example working with partners contradicts what Ferri, et al. (2020) said about cloud computing reducing the need for outsourcing. Further, while some research aims for the company to create a more generalized product (Mac an Bhaird & Lynn, 2015), others strives for customiza-tion (Aleem, et al., 2017). Li, et al. (2017) further describe the difficulties of achieving customer customization as a problem. Due to these differences in opinions amongst previous research on what makes for a successful SaaS business, it is important to get clarity in what success factors that are helpful in practice, and through this help startups to adopt cloud computing and thereby increase their scalability.

By establishing the frequency of cloud computing amongst companies, give startups awareness of important factors when working with cloud computing and what benefits that comes with cloud computing, startup companies can be able to increase their scalability through cloud computing, thereby answering the purpose of the report. These areas are therefore needed in the analytical model and the following analysis, as can be seen in Table 3.

Table 3. Areas within cloud computing that will be used in the analytical model.

Presence of cloud computing

Sectors and markets (Ferri, et al., 2020; Floerecke, 2018)

Organizational size (Ferri, et al., 2020)

Success factors for cloud com-puting

Customer focus (Floerecke, 2018)

Working with partners (Ferri, et al., 2020; Floerecke, 2018)

Availability (Floerecke, 2018)

Knowledge (Floerecke, 2018)

Standardization (Mac an Bhaird & Lynn, 2015)

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Price model (Floerecke, 2018)

Data security (Floerecke, 2018)

Benefits of using cloud compu-ting

Removes constraints (Ferri, et al., 2020; Hallowell, 2001)

Increases flexibility (Ferri, et al., 2020)

Improves product development

(Aleem, et al., 2017; Ferri, et al., 2020; Marston, et al., 2011; Tsai, et

al., 2012)

Reduces need for outsourcing (Ferri, et al., 2020)

Lowers entry barriers (Mac an Bhaird & Lynn, 2015; Shan, et al., 2018)

Improves product quality (Ferri, et al., 2020)

2.3 Modularity

According to Rahikka, Ulkiniemi and Pekkarinen (2011) many service firms struggle with the par-adox of having a cost-efficient standardized service and offering a flexible service, tailored to the customer’s needs, especially since customers nowadays do not accept having to settle with a stand-ardized product, or having to pay extra for a customized one. For many companies, mass customi-zation has become the go-to way to handle the balance between customicustomi-zation and standardicustomi-zation (Bask, Lipponen, Rajahonka & Tinnilä, 2011). Bask, et al. (2011) emphasizes that there are other ways of handling this. One way is using service modularity, which can simplify design and devel-opment processes, as well as allowing employees to specialize on certain tasks to a higher degree (Rahikka, et al., 2011). Modularity is the division of products or systems to independent, discrete and self-contained modules, while still maintaining complementarities and synergies between the components (Elia, Massini & Narula, 2019) or the “degree to which a system’s components can be separated and recombined” (Schilling, 2000, p. 312). From a more technically point of view, it can

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be seen as the structure of a product, process or system whose design elements are divided and placed into modules, while having distinct interfaces based on a formal architecture or plan (Kuula, Haapasalo & Tolonen, 2018). For a service firm, the measure of customization is the profoundness of the customer involvement and the measure of modularity is the number of products offered, based on different modules and service levels (Bask, et al., 2011). From a service network perspec-tive, customization is based on the suppliers’ degree of dedication, and modularity is based on the responsibilities of the suppliers (Bask, et al., 2011).

2.3.1 Components of Modularity

Within modularity design, an important aspect is the interfaces that connects modules, components and subsystems, which together forms the modular architecture (Brax, Bask, Hsuan & Voss, 2017). These interfaces in modular services can be defined as “the set of rules and guidelines governing the flexible arrangement, interconnections, and interdependence of service components and service providers” (de Blok, Meijboom, Luijkx, Schols & Schroeder, 2014, p.186). Service architecture usually tells which modules that is a part of a system and what functions they bring to the system (Avlonitis & Hsuan, 2017). By using a product platform, consisting of a set of interfaces and sub-systems, many different products can be offered by reusing and matching different modular com-ponents, and a common structure when developing a family of products can be obtained (Brax, et al., 2017). Platform thinking can also be used to identify the structure of modules and logic of activities, as well as customer offerings (Kuula, et al., 2018). For a service company, the service platform is the service production and service process, that is, the company’s capabilities that are invisible to the customer (ibid.). This platform, together with modularity, enable repeatability and scalability (Kuula, et al., 2018). There are two concepts that are important regarding modularity within software, namely cohesion, the coupling inside a module, and coupling, the coupling be-tween different modules (Skiada, Ampatzoglou, Arvanitou, Chatzigeorgiou & Stamelos, 2019; Xiang, Pan, Jiang, Zhu & Li, 2019). To design a modular software, a module should not depend on another module, i.e. low coupling, but everything inside each module should be dependent on each other, i.e. high cohesion (Skiada, et al., 2019; Xiang, et al., 2019).

A process connected to modularity, that further develops the service process and that enables re-peatability and scalability, is productization (Kuula, et al., 2018). Through the process of produc-tization the service becomes more tangible and repeatable (ibid.). In this process, the service is first defined, to find customer value, service promise, and what is the core and additional services in the service package (ibid.). Next, it is decided what parts of the service that can be standardized, and what parts that have to be tailored, and then the service is made more concrete for customers, ensuring that the whole company communicates the same information about the service and that customer expectations are managed to the right level (Kuula, et al., 2018). Carlborg and Kindström (2014) emphasizes that managers should be aware of the services their company offers, and what subprocesses that goes into these services. Hyötyläinen and Möller (2007) find that the

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cation between the sales people and production managers need to be improved, especially in com-panies focused on the customers, as this would help many production managers who currently have to develop complex customized solutions based on the salesman’s perception of the customer needs, without any consideration of production feasibility.

The service architecture can have various levels of decomposition, varying from an integral to modular service (Mikkola, 2006). According to the “mirroring” hypothesis, a firm that has a mod-ular product will have modmod-ular, more loosely-coupled, relationships with other partners, while more integral companies will have a more integral, or tightly-coupled, relationships (MacCormack, Baldwin & Rusnak, 2012). As opposed to this, Avlonitis and Hsuan (2017) found that companies that have a modular offering, can exhibit an integral organization, or service delivery system, or integral relationships with partners, meaning that different parts of the architecture can be either modular or integral independently of one another.

2.3.2 Working with Modularity

Bask, et al. (2011) shows that in a “non-modular regular” company, the network is generic and the suppliers have limited responsibility. Further, the customer then has a few options to choose from, and only gets involved late in the process (Bask, et al., 2011). Compared to this, in a mass custom-ization, “modular customized” company, the suppliers have a high responsibility in a customized network and produce modules, enabling the focal company to focus on the actual assembly and creating customer-specific modules (Bask, et al., 2011). Apart from these types of companies, a company can also produce fully customized products to specific customers, doing most activities in-house, or move toward more generic modules, creating a more standardized offer (Bask, et al., 2011). By having knowledge of what modules that build the services, both service delivery and development of new and existing services can be improved (Carlborg & Kindström, 2014).

New services can, for example, be created using current modules and resources (Carlborg & Kindström, 2014). For software companies, the services usually require a high level of technical skills and focuses on the customers processes, while the customers usually want a customized ser-vice (Carlborg & Kindström, 2014). According to Carlborg and Kindström (2014) such a process might not be linear, but can be more iterative, making it important to understand the interaction between the modules. To increase efficiency, firms should work with standardizing the service routines, considering that some autonomy in service processes is required due to customer unique-ness and that technical complexity might make standardization infeasible (Carlborg & Kindström, 2014; Kuula, et al., 2018). Further, other complex parts of the service, as planning and logistics, can be difficult to standardize (Carlborg & Kindström, 2014). For these complex companies, focus is usually on customer service instead of efficiency, which makes it important for them to have a wide range of modules and being able to combine them and fulfill customer needs (Araujo & Spring, 2011; Carlborg & Kindström, 2014). As opposed to this, Hyötyläinen and Möller (2007) found that a smaller customer service offering will not lead to lower customer satisfaction. Even with fewer products, differing customer needs can still be fulfilled, if service modules are used

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(ibid.). This can actually improve functionality and reduce delivery time, thus leading to both in-creased customer satisfaction and lower costs (Hyötyläinen & Möller, 2007).

2.3.3 Benefits of Modularity

By using modularity, customer unique solutions can be divided into standardized modules and re-used, thus lowering service production costs (Bask, et al., 2011; Hyötyläinen & Möller, 2007; Kuula, et al., 2018). Apart from adding flexibility and personalization to the offer (Brax, et al., 2017; Kuula, et al., 2018; Rahikka, et al., 2011), modularity might also increase customers’ will-ingness to purchase additional services and to outsource, thus increasing their value perception of the service (Rahikka, et al., 2011). By dividing not just the product, but also the production process into modules, using modularity and platform thinking, a complicated process can be split up to several producers, and the same components can be used to create several different experiences to different markets (Kuula, et al., 2018). Similarly, having developed interfaces enhance decompo-sition of service production systems and thereby facilitates relationships between organizations, like outsourcing (Brax, et al., 2017). Finally, standardization and modularization create a basis for scalability and repeatability (Kuula, et al., 2018).

2.3.4 Synthesis of Modularity

It can be established from existing studies about modularity that it enhances both standardization and scalability (Kuula, et al., 2018), thus making it important to consider when trying to describe how startup companies can become more scalable. One can see that several authors connect mod-ularity partnerships, both that it enables complex processes to be split up to several parties (Kuula, et al., 2018), and that modularity enhances relationship between firms, like outsourcing (Brax, et al., 2017). While some studies show that having a modular product means having a modular part-nerships (MacCormack, et al., 2012), other studies show that there is no such connection (Avlonitis & Hsuan, 2017). Especially the partnership with suppliers, with respect to modularity, has been examined to some extent by Bask, et al. (2011), where a higher degree of modularity was found to put a bigger responsibility on the suppliers. Partnerships is seemingly an important area when it comes to modularity. By further examining how modularity affects all types of partnerships, valu-able insight can be provided to startups on how to approach both modularity and partnerships, thus helping them to increase their scalability.

The different definitions of modularity are similar in great parts, that modularity is about dividing a product or organization into several different part, which can be re-connected in several ways (Elia, et al., 2019; Kuula, et al., 2018; Schilling, 2000). However, while Schilling (2000) views modularity solely as the degree to which components of a system can be separated and recombined in different ways, Elia, et al. (2019) emphasize that the parts should still have synergies. The latter is something we interpret that Brax, et al. (2017) and Kuula, et al. (2018) believe is maintained through interfaces, which in turn are governed by the service architecture. In the case of software modularity, it is of importance for the modules to be independent of one another, while everything

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within a module is highly dependent on each other (Skiada, et al., 2019; Xiang, et al., 2019). This seems contradictory with being able to maintain synergies and raises questions regarding how soft-ware companies work with modularity in practice. Apart from having a modular product, modu-larity can also be applied to the offer as a whole, as can be seen by the debate whether a company can use modularity to decrease the scope of its offering, without leading to a lower customer satis-faction (Hyötyläinen & Möller, 2007), or if it should be used as a mean to create an even wider range of offerings for differing customer needs (Araujo & Spring, 2011; Carlborg & Kindström, 2014). One can see that there can be modularity in both the product, organization and offer, and that it is somewhat unclear how to utilize modularity. It is therefore important to examine what types of modularity startups utilize in practice, to help them increase their scalability. However, due to the small size of startups, organizational structure and modularity in the organization will not be considered to the same extent.

That the division of products, into modules, will lead to a bigger flexibility by re-arranging pre-existing modules into new products is widely recognized in current research (Bask, et al., 2011; Brax, et al., 2017; Hyötyläinen & Möller, 2007; Kuula, et al., 2018; Rahikka, et al., 2011). Modu-larity means that the same components can be used to create several different experiences to dif-ferent markets (Kuula, et al., 2018), which probably is connected to previously mentioned flexibil-ity. Other benefits are easier outsourcing (Brax, et al., 2017; Rahikka, et al., 2011) and standardization (Kuula, et al., 2018). Lastly, modularity can increase customers’ value perception and willingness to purchase additional services (Rahikka, et al., 2011). Determining how modular-ity affects partnerships, what types of modularmodular-ity that can be used and what benefits modularmodular-ity result in, can help companies in their work with modularity and thereby increasing their scalability. These three areas will therefore be needed in the analytical model, see Table 4.

Table 4. Areas within modularity that will be used in the analytical model.

Type of modularity

Product modularity

(Brax, et al., 2017; Elia, et al., 2019; Kuula, et al., 2018; Schilling, 2000; Skiada, et al., 2019; Xiang, et al.,

2019)

Offer modularity

(Araujo & Spring, 2011; Carlborg & Kindström, 2014; Hyötyläinen &

Möller, 2007)

How modularity affects

partner-ships Enhances outsourcing

(Brax, et al., 2017; Kuula, et al., 2018; Rahikka, et al., 2011)

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Product modularity leads to modular partnerships

(Avlonitis & Hsuan, 2017; MacCormack, et al., 2012)

Increases responsibility on suppliers (Bask, et al., 2011)

Benefits of modularity

Standardization (Kuula, et al., 2018)

Easier outsourcing (Brax, et al., 2017; Rahikka, et al.,

2011)

Increases value perception (Rahikka, et al., 2011)

Increases flexibility

(Bask, et al., 2011; Brax, et al., 2017; Hyötyläinen & Möller, 2007; Kuula, et

al., 2018; Rahikka, et al., 2011)

2.4 Process Automation

Van der Zande, Teigland, Siri and Teigland (2018) found that the jobs most likely to be substituted by automation and digitalization are based in routine and rules, making them easy to codify. This includes both manual tasks, such as assembling and cooking, and cognitive, such as customer ser-vice tasks by phone operators and cashiers (van der Zande, et al., 2018). From a technological point of view, scalability is achieved through automation of projects, reducing the need of human re-sources thus enabling lower personnel intensity and lower fixed costs (Stampfl, et al., 2013). Wallin, Still and Hentonnen (2016), found that labor intensive companies like consulting firms and companies with highly labor-intensive distribution, marketing or sales, for example selling to local governments, had limited scalability. However, some firms that have tried to move towards remov-ing employees from the customer interaction and automatremov-ing this process may still have the need for their human resources as a competitive advantage (Hallowell, 2001). This paradox of needing employees in their services to gain competitive advantages while at the same time wanting to re-duce the need of human interaction to gain scalability is something many business struggle with (ibid.). Furthermore, the most difficult tasks to substitute is the non-routine cognitive jobs, such as negotiating and teaching, since these tasks require creativity and complex communication (van der Zande, et al., 2018). A complex product will therefore often require the need for employees to

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interact with and help the customer, and consequently businesses with this type of offering will not be able to automate the process entirely (Hallowell, 2001).

2.4.1 Sales Force Automation

Hunter and Perreault (2007) found that businesses usually place great importance on sales, with a major focus on the relationship between sellers and customers, followed by a focus on information technology to help the sales personnel. They state that building close relationships with customers will lead to the best returns, something that sales technology can simplify (ibid.). Buttle (2009), describes a sales force automation system as computerized technology to support sales manage-ment and salespeople. These systems can help with keeping track of communication and orders from customers, manage documents, plan events, detect opportunities, configure products and gen-erate proposals (Buttle, 2009). It can be seen as hardware and software that enables manual sales activities to be converted into electronic processes (Stoddard, Clopton and Avila, 2002). Moreover, using sales technology to access information will help with administrative tasks while using it to analyze information will have a negative impact on the administrative tasks (Hunter & Perreault, 2007). Sales technology may help with administrative tasks and can therefore free up time for the customer relationships but increasing the number of administrative tasks because of the increased efficiency, however, may take focus away from the important relationships with customers (ibid.).

According to a study by Engle and Barnes (2000), the adoption of sales force automation systems will lead to an increase in sales. However, it is not sure that this increase in sales will exceed the increase in costs caused by the investment, especially initially when employees are unfamiliar with the computer systems (Engle & Barnes, 2000). The study of Stoddard, et al. (2002), showed that sales force automation does not lower sales costs, but that companies, at the time of the study, did not use all functions available from the sales force automation systems. In a more recent study, it was found that sales technology can facilitate in the relationship-building aspect of sales, and more specifically with the work regarding the analysis and communication of information that help cre-ate the relationship with the customer (Hunter & Perreault, 2007). Sales technology is therefore seemingly a way to improve sales performance if it is used to make non-selling tasks more efficient to free up time for sales personnel to create and build the relationships with customers.

However, automation of sales might be problematic initially for a startup, when both the product and company are unknown. Moore (1999) writes about the problem of overcoming the gap between early adopters and the early majority of the customers when selling new high-tech product, since the latter customer segment is more pragmatic and wants smaller changes based on proven tech-nologies and products. Similarly, Stinchcombe (1965) argues that new companies will experience a liability of newness, that partly is due to the fact that their customers do not know what to expect from the product and through which channels to buy it. This might call for sales personnel to help the customer, making automation problematic.

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2.4.2 Marketing Automation

Similarly, marketing automation can be used to support marketers and marketing managers, with tasks such as planning campaigns, segmenting and targeting customers, keeping track of docu-ments, managing email campaigns, helping with telemarketing, handling loyalty programs and managing internet marketing, for example keyword marketing at Google (Buttle, 2009). A prereq-uisite, however, is, amongst other things, that the organization have enough information for the automated process to work with, and that the data is of good quality (Aquino, 2013). Automation can also be used in a service context, to aid service staff, and complete tasks such as access com-munication history with customers, direct enquiries to the right agent, assist customers with self-service, manage e-mail responses, handle in-house communication, creating adaptive scripts to technical support staff, as well as scheduling and routing (Buttle, 2009). Automating the marketing process will help organizations to give the customers what they want in a much better way than what can be done by hand, making the marketing more precise to the individual customers (Aquino, 2013). With the help of marketing automation businesses can, for example, improve their lead generation, have the possibility of an increased conversion rate, tailor the website to the type of customer that the visitor is and automate emails, aspects that will lead to an increase in both profits and productivity in the organization (Aquino, 2013).

One marketing strategy to enhance growth and business scalability is called Growth Hacking (Bohnsack & Liesner, 2019; Kemell, et al., 2019). The techniques link marketing and technology (Bohnsak & Liesner, 2019; Conway & Hemphill, 2019; Kemell, et al., 2019; Troisia, Maione, Grimaldi & Loia, 2019), such as big data, artificial intelligence, known as AI, and social media (Bohnsack & Liesner, 2019). Growth hacks are based on marketing, data analysis and program-ming, while the implementation process is based on experimenting and the lean startup philosophy (Bohnsack & Liesner, 2019; Conway & Hemphill, 2019). Digital marketing helps generating awareness and acquiring potential customers, which can be assisted by collected customer infor-mation regarding their preferences (Bohnsack & Liesner, 2019). This whole process can also be automated using programming, especially in the near future when artificial intelligence can auto-mate both customer analysis and the coding needed (Bohnsack & Liesner, 2019). For B2B compa-nies, benefits from growth hacking comes from taking strategic and marketing decisions based on data and getting a better understanding of the customers and how they interact with the organization (Troisia, et al., 2019). Due to a longer and more complex process when it comes to B2B sales, big data can be very useful when trying to understand the customer and closing the sale more effi-ciently, as well as creating more personalized marketing (Troisia, et al., 2019).

These techniques involve low cost activities such as using data as metrics and thereafter changing the service accordingly, pulling users to the service and finding creative new applications for ex-isting platforms (Kemell, et al., 2019). Examples can be to follow an organization on social media to gain new followers, to offer a free software trial or to offer a less expensive option when cus-tomer cancel their subscription (Kemell, et al., 2019). For cuscus-tomer acquisition, many of these

Scalability in Startups : A Case Study of How Technological Startup Companies Can Enhance Scalability