Disruptive business model transitioning in B2B: A subscription-based approach for Industry 4.0

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Disruptive business model transitioning in B2B

A subscription-based approach for Industry 4.0

William Fjellström Vladislav Snitko

Industrial and Management Engineering, master's level 2021

Luleå University of Technology

Department of Social Sciences, Technology and Arts

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“You have to think a little bit like Google and search. Nobody pays for the search itself. But everybody knows that Google profits on you searching. And the benefits you receive are worth more than the information you’re giving away. That’s the model you have to pursue. What’s the

value I can create from the information generated by my connected device? That’s where you should focus.”

- CEO Ngenic

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Acknowledgements

This research concludes both authors fifth and final year of a master’s degree within Industrial Engineering & Management at Lulea University of Technology, with a focus towards Industrial Marketing. Hence, we would like to thank all lecturers, supervisors, and classmates who have contributed to this great learning experience.

We would like to extend our most sincere thankfulness to our Lulea University of Technology supervisor, Jeandri Robertson, for providing us with invaluable guidance throughout the duration of this thesis. We would also like to extend an equally large thank you to our favorite Business Developer, Felix Johansson, who provided us with insights, support, tips and gave us the necessary resources needed to conduct our analysis. Further, we would like to thank all case study organization employees who showed interest and supported the project through interesting discussions and informative presentations. We would also like to thank the rest of the team at the case study organization for giving us an opportunity to research this contemporary and interesting topic on their behalf. Lastly, we would like to thank our survey respondents and interviewees for participating in this study.

Without the guidance and assistance from the aforementioned individuals, this study would not have been possible.

_____________________ _____________________

Vladislav Snitko William Fjellström

29th of June, 2021 Lulea

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Abstract

Industry 4.0 has introduced technologies such as machine learning, Internet of Things (IoT), and cloud computing, which has disrupted markets across different industries. These technologies are prime examples of what has come to be known as disruptive innovations. With the advancement of disruptive innovations, organizations are constantly looking for new ways to satisfy customer needs. Therefore, organizations have begun to investigate alternative business models that differ from the conventional way of selling products/services. Within B2C-markets, the rise of subscription-based services has not gone unnoticed and has turned into a prominent business model among Fortune-500 organizations. Within B2B-practices however, subscription-based business models are mostly unheard of. Therefore, the purpose of this study was to examine how Industry Internet of Things (IIoT) solution providers successfully can transition to a subscription-based business model when launching IoT-based disruptive innovations in industry 4.0 B2B-markets.

This purpose was investigated in a quantitative study by constructing a questionnaire based on an adjusted six-factor Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model.

The model was used to stipulate factors influencing buying intention for IoT-technologies. The study was further complemented by qualitative interviews, which aimed to shed more light on buyer’s perception of subscription-based business models for IoT-technologies. The respondents consisted of purchasing function employees within the chemical-, food and beverage-, and life sciences industries in Scandinavia and the US. The quantitative results showed that performance expectancy had the most significant impact on buying intention. The qualitative results stipulated themes based on each UTAUT2 factor while also enlightening how interviewees emphasize the importance of not hiding costs and raised concerns regarding the transfer of data ownership when purchasing IoT-technologies. To conclude, the authors stipulated a conceptual framework for the implementation of subscription-based business models when introducing of IoT-technologies within B2B-markets.

Keywords: Disruptive innovation; Industrial Internet of Things; Subscription-based business models; Software as a Service; Product Service System

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List of figures

Figure 1: Yearly growth of Subscription Economy Index. ... 2

Figure 2: The model of disruptive innovation. ... 6

Figure 3: Model of disruption for the low-end disruption. ... 7

Figure 4: Model of disruptive innovation when a new-market is created. ... 8

Figure 5: The internet of things paradigm represented as a result of the three visions. ... 11

Figure 6: Swallowing the fish. ... 13

Figure 7: Subscription-based business models. ... 14

Figure 8: Model of the transition process from manufacturing to subscription-based business models within the machinery and plant engineering industry. ... 15

Figure 9: Benefits of implementing SaaS. ... 17

Figure 10: Unified Theory of Acceptance and Use of Technology 2 framework. ... 22

Figure 11: Relationship linking the conceptual model and the research questions 1, 2, and 3. .. 23

Figure 12: Illustration of the utilized research strategy. ... 28

Figure 13: Conceptual coding framework. ... 32

Figure 14: Conceptual coding of the factor performance expectancy. ... 43

Figure 15: Conceptual coding of the factor effort expectancy. ... 44

Figure 16: Conceptual coding of the factor social influence. ... 45

Figure 17: Conceptual coding of the factor facilitation conditions. ... 46

Figure 18: Conceptual coding of the factor price value. ... 47

Figure 19: Conceptual coding of the factor habit. ... 48

Figure 20: Path model with results ... 66

Figure 21: Implementation process for subscription-based business models ... 72

List of tables

Table 1: Disruptive innovation characteristics derived from innovation diffusion theory. ... 10

Table 2: Overview of subscription-based business model literature for manufacturing companies. ... 16

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Table 3: The buying center roles... 20

Table 4: Conceptual table of an adjusted UTAUT 2 model. ... 24

Table 5: Descriptive statistics of ownership method ... 50

Table 6: Descriptive statistics of factors ... 51

Table 7: Detailed descriptive statistics of the factors ... 52

Table 8: Descriptive statistics of mean variables ... 53

Table 9: Test of Normality ... 54

Table 10: Test of normality - Logarithmic... 55

Table 11: Ordinal regression - model fit statistics ... 55

Table 12: Ordinal regression - pseudo r-squares ... 55

Table 13: Ordinal regression - parameter estimates... 56

Table 14: Ordinal regression - test of parallel lines. ... 56

Table 15: One-way Anova - Between groups ... 57

Table 16: Measurement model ... 58

Table 17: Discriminant validity according to the Fornell and Larcker criterion. ... 61

Table 18: Cross loadings ... 63

Table 19: Direct relationship for hypothesis testing ... 67

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1. Introduction

As contemporary society rapidly changes, industrial practices’ status quo is constantly disrupted and quickly becomes outdated. The fourth industrial revolution (Industry 4.0) further disrupted technological standards and changed the basic understanding of industries as a whole (Culot et al., 2020). Industry 4.0 is characterized by “Smart” products that rely on autonomy, IoT, cloud computing, and machine learning, which now define the competitive landscape as these products focus entirely on individual customer needs (Culot et al., 2020; Vaidya et al., 2018). For enterprises to stay competitive in this environment, relying on innovation is a must. One of the most talked- about management concepts within recent decades has been the rise of disruptive innovation, referring to technologies that allow innovations to become accessible to mainstream markets (Bower & Christensen, 1995). Accepted management principles such as focusing on profit margins and maintaining key customers are situational and have become less relevant, as it in comparison to disruptive innovation leads to failure (Bienenstock, 2016).

Focusing on harnessing and identifying disrupting innovations is critical as it not only avoids failure but also displaces competition (Christensen & Bower, 1996; Christensen, 2006). However, the introduction of disruptive products poses several challenges as these innovations often address new markets in unexpected ways (Bower & Christensen, 1995; Rao, Angelo & Nov, 2006).

Disruptive innovation, therefore, remains a hit-or-miss, mainly since product developers tend to focus too much on constructing customer profiles and finding correlation in data rather than focusing on the customer and the actual job for which the product will be used (Christensen et al., 2016). One way to circumvent this is through delivering IoT-based technologies, as these are customer-centric at their core and have significantly risen in popularity during recent years (Gubbi et al., 2013). In a B2B context, recent research regarding IoT-based technologies often mentions the term Industrial Internet of Things (IIoT) (Khan et al., 2020). The base premise being that IIoT allows organizations to develop intelligent systems connected to a cloud network, which can thereafter be used to satisfy individual customer needs (Perera et al., 2014; Khan et al., 2020).

As a result of Industry 4.0, businesses are now looking for new opportunities to meet customer needs. Consequently, the search for new business models suiting the crucial transition from

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outdated business models to new value capturing business models in respective industries is looked at. One of these transitions concerns subscription-based business models as these business models are customer-centric at their core and leave product-oriented organizations with little to no potential to compete (Tzou & Weisert, 2018). It is important to note that customer orientation has proven to be a critical success factor for launching disruptive innovations (Yu & Hang, 2010).

Purely product-oriented firms which lack this customer orientation have to project and estimate their upcoming revenue to make investments, which can often be difficult, especially during uncertain time periods. Instead, as an example of a customer-oriented business model, firms that deal with subscription-based services attain money at the start of each year or month, with which they can plan and execute future investments, partly disregarding the volatile nature of contemporary macro-environmental factors (Tzou & Weisert, 2018). As seen in figure 1, subscription-based business models prove to be resilient in times of crisis as industry growth remains primarily unscathed. Most noteworthy is that their growth remains steady amidst the Covid-19 pandemic, whereas many other industries have suffered.

Figure 1: Yearly growth of Subscription Economy Index.

Source: Zuora (2020)

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1.1 Problem discussion

Tzou and Wesiert (2018) argue that every contemporary company’s main goal should be to pursue a business model that effortlessly generates value through information creation. The concept is simple yet difficult to execute. Progress within product manufacturing and distribution enterprises stems from the ability to sustain innovation (Christensen & Bower, 2003). To remain competitive, enterprises are forced to adapt to changes in the competitive landscape (Christensen, 1997). A currently relevant research area to frame this conundrum is disruptive innovation, which constantly alters business practices and industries as a whole. Thus, disruptive innovation serves as a foundational framework for this study. In the field of disruptive innovation, Christensen has laid a theoretical foundation while other authors have extended his work (Adner, 2002; Bower &

Christensen, 1995; Christensen, 1997; Christensen & Raynor, 2003; Danneels, 2004; Schmidt &

Druehl, 2008). However, disruptive innovation can be introduced to markets in numerous different ways. A contemporary, relevant way to do so is through subscription-based services, which have recently been employed as a core part of business models to many fortune 500 organizations (Tzou

& Weisert, 2018). This implementation, however, may prove to be difficult for firms. For starters, convincing board members and investors why initially decreasing revenues for a long-term gain is a good idea might be just as difficult as it sounds. To illustrate this, Lah and Wood (2016) introduced the concept of “swallowing the fish” as a result of Adobe swallowing the conceptual fish in 2011, which became a trailblazer for a new era of subscription-based business models (Tzou

& Weisert, 2018). Further resistance towards subscription-based services stems from integration issues, such as the fact that these business models measure success with untraditional key performance indicators (KPI’s), which does not align well with most firms' traditional financial measurement instruments. Instead, organizations can benefit from focusing on annual recurring revenue (ARR) when measuring subscription income statements (Tzou & Wesiert, 2018).

Subscription-based services involve Software as a Service (SaaS), Leasing, and Product Service Systems (PSS), which are all business models that revolve around the concept of recurring payments (Dubey & Wagle, 2007; Ebi et al., 2019; Sun et al., 2007). Subscription-based services in the B2C-markets are a well-researched area, with cases such as Netflix, Spotify, Amazon, etc.

However, the introduction of subscription-based services in B2B-markets is only now beginning to be researched and is defined by hurdles of its own, especially for organizations that deal in

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product manufacturing and distribution (Schuh et al., 2019), where teams often become stuck defending an outdated business model (Lah & Wood, 2016). Theory in this area is scarce, and the only academic work within the B2B transition to subscription-based business models is the process model as proposed by Riesener et al. (2020). However, this process model does not provide insights into practical implications/requirements. Further, it does not enlighten the customer's point of view when implementing subscription-based business models in industry 4.0 markets.

Therefore, more research needs to be conducted to close the existing knowledge gap and prepare firms for implementing subscription-based business models within industry 4.0.

1.2 Research problem

The purpose of this study is to examine how IIoT-solution providers successfully can transition to a subscription-based business model when launching IoT-based disruptive innovations in industry 4.0 B2B-markets. Hence, the following research questions are proposed:

➢ RQ1: What factors impact the purchasing preferences for IoT-based disruptive innovations among buyers within B2B-markets?

➢ RQ2: What are the most significant drivers of intention to purchase IoT-based disruptive innovations among buyers within B2B-markets?

➢ RQ3: What business model best suits IIoT-solution providers when introducing IoT-based disruptive innovations in B2B-markets?

1.3 Delimitations

This study is limited to determine what subscription-based model, organizations should employ when selling IIoT-technologies, meaning that the study will have limited generalizability to other industries. The study is also limited to investigating organizations close to the partnering organization’s value chain. As focus lies within IoT-solutions in a B2B-environment, this study will focus on intention to purchase within B2B purchasing function employees. Further, because of convenience, the questionnaire distribution is limited to respondents within the US while all interviewees were residents in Scandinavia.

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2. Literature Review

This chapter contains a review of relevant literature and can be separated into two main concepts:

disruptive innovation and subscription-based business models. First, disruptive innovation and its characteristics are discussed as this serves as the foundational framework for the considered line of products and services. Secondly, a review on subscription-based models is conducted where three main areas are reviewed, namely software as a service, product service systems, and leasing.

Lastly, the UTAUT2-framework is utilized to provide a conceptual model, which further serves as the analysis’s foundation.

2.1 Disruptive innovation

The term disruptive technology was first coined by Christensen and his co-author Bower (Bower

& Christensen, 1995). The works of Christensen and Bower (1995) laid the foundation for one of the most talked-about areas in management and has since become a term that far too many use loosely (Danneels, 2004; Hopp et al., 2018). At the onset, the term was referred to as disruptive technology, focusing on the rise of a specific technology. However, in later years the concept was refined as disruptive innovation to broaden the concept by including services and business models as well (Christensen & Raynor, 2003). A common misconception that has taken place in recent years is to overuse the term disruptive innovation as any innovation (Christensen, 2015).

Therefore, the term disruptive innovation is overused as a synonym for any posed threat to an industry, which is inaccurate as it ignores the theoretical meaning of the term disruptive innovation (Christensen et al., 2018). The definition of disruptive innovation has been a hot debate throughout recent years, with researchers not appreciating Christensen’s attempt to define the concept (Danneels, 2004; Markides, 2006; Schmidt & Druehl, 2008). However, by reviewing existing literature, this study will define the concept as “An innovation that significantly alters how businesses operate by offering a value proposition suiting price-sensitive markets”.

A conceptual paper by Yu and Hang (2010) noted that disruptive innovation is built upon and inspired by earlier works (Christensen, 1992; Christensen, 1993; Henderson & Clark, 1990;

Moore, 2014; Schumpeter, 2013). The authors described a similar phenomenon but with inconsistent denominations. The most influential book on disruptive innovation throughout the

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literature is The Innovator’s dilemma (Christensen, 1997). The book originates from a managerial perspective and is built upon insights into how small and medium enterprises (SMEs) can recognize disruptive technologies. The book focuses on why excellent and successful companies lose their position as front runners by reflecting the concept of using performance over time. Figure 2 visualizes how disruptive innovations transpire from below, working their way upwards through the more mainstream segments (Christensen, 1997).

Figure 2: The model of disruptive innovation.

Source: Christensen et al. (2018)

In addition to recognizing disruptive technologies, Christensen conveys how companies can decide whether to cater to their current customers or adopt technologies that change their customers’

future needs (Christensen, 1997). A follow-up to The Innovator’s Dilemma named The Innovator’s Solution was later published (Christensen & Raynor, 2003), containing refined thoughts on the theory after researchers had criticized it for not being specific enough (Schmidt & Porteus, 2000).

Thus, Christensen and Raynor (2003) introduced low-end and new-market disruptions and further expanded the concept to innovations as a substitute for technology. The inevitable evolution of the theory brought Christensen and Raynor (2003) to refine it, which puts forward additional views to consider. With the divide of disruptive innovation, Christensen and Raynor (2003) further identified emerging characteristics of low-end disruptions and new-market disruptions. Low-end disruptions confront the overused segments, which often are the most price sensitive. As seen in

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figures 3 and 4, there are fundamental differences between low-end disruption and new-market disruption, depending on the product or service itself, one either approaches the low-end or new- market disruption. Furthermore, low-end disruption tends to serve customers in a way that is “good enough”, to further down the line shift focus upstream in search for greater profit margins (Christensen & Raynor, 2003).

Figure 3: Model of disruption for the low-end disruption.

Source: Christensen and Raynor (2003)

As the name suggests, new-market disruptions create an entirely new-market with an approach that is more convenient due to the customer setting being more approachable, see figure 4 (Christensen & Raynor, 2003). As defined by Bower and Christensen (1995), disruptive innovation historically gives rise to new markets due to the current market conditions not appreciating the given value. This phenomenon creates emerging markets where applications are seen as more valuable due to their more accessible nature.

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Figure 4: Model of disruptive innovation when a new-market is created.

Source: Christensen and Raynor (2003)

Complementing Christensen and Raynor (2003), Schmidt and Druehl (2008) suggested that when disruptive innovations go from low-end towards high-end a pattern, low-end encroachment occurs, which is a gradual process. On the opposite, high-end encroachment strikes the markets immediately by introducing high-end products (Schmidt & Druehl, 2008). According to Schmidt and Druehl (2008), low-end encroachment creates three different scenarios: fringe-market, detached-market, and immediate scenarios. In line with Schmidt and Druehl (2008), Carr (2005) suggests that top-down disruptive innovation or a high-end encroachment approach could outperform low-end encroachment if conducted per existing theory. However, this research is criticized by Christensen et al. (2018) for being incompatible with Christensen's existing theory’s fundamental principles. Christensen is determined that disruptive innovation can only be introduced through the low-end entry, as those with lower or zero willingness to pay are the ones who are dissatisfied with current technology (Christensen et al., 2018). The author further argues that all forms of high-end innovation may not, therefore, be classified as disruptive innovation.

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2.1.1 Characteristics of disruptive innovation

Why do we care about the characteristics of disruptive innovations? For a company to stay relevant, it is crucial to be able to identify disruptive innovation. Hence, its characteristics give an insight into what to look for within possible disruptive innovation candidates. Danneels (2004) theorized that it could be hard to see this in advance and that disruptive innovation often becomes identified post hoc.

Christensen (1997) states that a characteristic of disruptive innovation is that it changes the underpinning of the nature of competition. Furthermore, Christensen mentions two essential characteristics that affect the product life cycles and competitive dynamics in the general market:

● The characteristics that make a product viable in emerging markets are usually the ones making it unavailing in established markets.

● Disruptive innovations are often simpler, cheaper, more reliable, and more convenient than well-established products.

When investigating why some firms succeed (Christensen, 1997) found that a lesson to be learned is that products that “sit in a lab” constantly trying to be upgraded to fit the mainstream market’s needs will never be disruptive. The only way it can be disruptive is if that product can instead be embraced for its disruptive attributes, whereas it can create a new market (Christensen, 2003).

Therefore, the question becomes: How does one identify the market that values the anomalies in a company's product? Like Christensen’s attributes of disruptive innovation, Thomond and Lettice (2002) suggest that it begins within an emerging or niche market where the customer needs are not being fulfilled. Thomond and Lettice (2002) further propose that the niche market adoption leads disruptive innovation to flourish due to enabling increased investments in the product/service.

Danneels (2004) states that the proposed characteristics may be typical but not necessary for it to be recognized as a disruptive innovation. Additionally, some characteristics might be essential, while others are industry specific (Danneels, 2004). This conclusion was derived from Chesbrough (n.d.) where the author mentions that most of the studies carried out on disruptive innovation cases are industry-specific and that their generalizability was not addressed.

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A study by Nagy et al. (2016) redefine disruption innovation by suggesting the characteristics:

functionality, technical standards, and ownership. Each of the three characteristics corresponds to earlier works on innovation diffusion theory, which is the precursor where innovation attributes have previously been suggested and aims to explain why, how, and at what rate innovations spread (Attewell, 1992; Rogers, 2010). The definition of each characteristic is shown in table 1.

Table 1: Disruptive innovation characteristics derived from innovation diffusion theory.

Functionality Functionality refers to the ability to perform a previously impossible task before the invention of the innovation (Nagy et al., 2016).

Technical standards Technical standards change with technology. Thus, new complex technology inhabits characteristics that create knowledge barriers, forcing users to overcome these to maximize their effectiveness (Attewell, 1992; Nagy et al., 2016).

Ownership In essence, ownership is not a physical characteristic compared to functionality and technical standards. Ownership affects market prices, innovation-related services, and the interaction with a marketplace due to the owner’s decisions (Nagy et al., 2016).

Nagy et al. (2016) suggest that the redefinition of disruptive innovation derives from the argument that disruptive innovation characteristics are intangible. Thus, suggesting that previous characteristics (cost, quality, performance, etc.) proposed by Christensen and others are external attributes in nature. To properly characterize disruptive innovations Nagy et al. (2016) states that the three intrinsic characteristics, which are based on innovation adoption literature, help identify marketplace disruptions. The characteristics attempt to give firms a better tool to decide whether their innovation is disruptive or not. Hence, by utilizing the characteristics of Nagy et al. (2016), firms can compare their existing innovation characteristics to their newly developed ones. If their proposed disruptive innovation inhabits existing innovation characteristics, the likelihood of it being disruptive is low.

Even though there have been many different perspectives on Christensen’s original definition of the disruptive literature (Christensen, 1997; Thomond & Lettice, 2002; Christensen & Raynor, 2003; Danneels, 2004), similarities in line with Christensen's original characteristics can be identified more frequently than others, such as with Nagy et al. (2016). However, this might be due to the recent nature of the contribution proposed by Nagy et al. (2016). Thus, this paper will

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not neglect either of the above-mentioned characteristics due to all of them corresponding to the paper’s aim.

2.2 IoT, IIoT, and Industry 4.0

Iot, IIot, and Industry 4.0 are three concepts that are closely related to one another, but not indistinguishable. The internet has drastically changed the environment for both businesses and people in the last decades and continues to do so. The International Telecommunication Union (2012) defines the Internet of Things as: “A global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies”. The basic premise for the Internet of Things (IoT) is that it provides real-time sensing using the power of technology (Gubbi et al., 2013). One of the core theoretical contributions, Atzori et al. (2010), suggests that the IoT paradigm can be divided into three distinct visions; things, internet, and semantic oriented visions.

Figure 5: The internet of things paradigm represented as a result of the three visions.

Source: Atzori et al. (2010)

As illustrated in figure 5, things-oriented vision deals with the sensors surrounding the technology.

The internet-oriented vision serves as a middleware between the software and the application, while semantic-oriented vision is the knowledge that creates an infrastructure for the massive

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amounts of information that is stored via real-time sensors (Atzori et al., 2010). As argued by Atzori et al. (2010), IoT only occurs where these visions interlace. Thus, IoT grants the opportunity for manufacturing organizations to develop intelligent production systems and connect production sites to one another (Wortmann & Flüchter, 2015). In industrial practice, IoT technologies utilize sensors or devices which monitor and collect any form of environmental data, i.e., temperature, level measurement, or current location, and then proceed to transfer the data to a cloud-based solution (Perera et al., 2014). After that, the IoT-technology processes the data, which is then presented to the end-user in a retroactive and useful manner (Perera et al., 2014). The industrial adoption of these technologies has come to be known as IIoT (Khan et al., 2020). These technologies are a cornerstone in Industry 4.0 and have become widely popular since their introduction to markets (Perera et al., 2014; Wortman & Flüchter, 2015). To further separate the different concepts, Industry 4.0 mainly refers to a complete automation of industry, while IIoT- technologies facilitate this automation process by collecting large amounts of data which is used to further enhance the product/service in a customer-centric manner. The rise in popularity may be a result of focus lying mainly on the daily activities of the end-user, which makes IIoT- technologies considerably more customer-centric than conventional, mass-produced products.

Hence, this paper will refer to IIoT-technologies when discussing the B2B-provision of IoT- technologies.

2.3 The rise of subscription-based business models

When dealing with disruptive innovations, the solution’s provision is arguably equally as important as the product itself. One relevant method of provision is by providing frequent delivery of a value proposition at the exchange of periodic payments via subscription-based services (Ebi et al., 2019). Tzou and Weisert (2018, p. 15) elegantly frame that the contemporary age is the age of the customer, where customers have significantly increased expectations, which is where subscription-based business models must meet these altered needs. One major advantage of subscription-based business models is the predictive revenue, as firms that deal with subscriptions gain income at the start of each year/month, which allows for flexible resource planning (Ebi et al., 2019; Tzou & Weisert, 2018). As an arbitrary IIoT-solution provider, the transition from a product orientation to subscription-based business models may be unexplored territory for most organizations. However, this is the case with all disruptive innovations, which the introduction of

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these business models in a B2B-environment can be classified as (Nagy et al., 2016). The arguments for subscription-based business models includes the fact that producing physical products is more capital intensive than subscription-based business models, which also relies on predictive revenue (Schuh et al., 2019). Further, subscription-based business models have the ability to continually transform to stay up to date with current customer needs (Schuh et al., 2019).

However, the implementation of subscription-based business models is commonly characterized by a hurdle known as “swallowing the fish” (Lah & Wood, 2016).

Figure 6: Swallowing the fish.

Source: Lah and Wood (2016)

As illustrated in figure 6, the introduction of subscription-based business models will initially drive revenues down as part of the implementation process, which might sound unappealing to a board of directors or investors. An example of this was when Mark Garret, CFO at Adobe in 2011, attempted to explain to Wall Street investors why he intended to stop selling Adobe Suite, their Cash-cow, to transition to a subscription-based business model instead (Tzou & Weisert, 2018, p.

72). Needless to say, Garret was met with great resistance, followed by a tanking stock. After the dust had settled, Adobe became a trailblazer as other firms quickly realized the benefits of transitioning from a product orientation to a subscription-based business model. Caterpillar, PTC, and Arrow are examples of organizations that took note, disrupted the market by introducing subscription-based business models, and consequently experienced extensive growth. What defines subscription-based business models is the deliverance of value to customers in exchange

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for a periodically recurring fee (McCarthy et al., 2017). As this definition is quite broad, there are multiple paths that an organization can pursue. Leasing, Software as a Service (SaaS), and product service system (PSS) all come to mind. These alternatives share the trait of being defined by recurring periodic payments (Fabozzi, 2008; Riesener et al., 2020; Seethamraju, 2015), and are thus categorised as subscription-based business models in this study. Both operational leasing and PSS allows for continuous updates of the provided software by the product/service provider, while financial leasing provides you with the software but not the continuous service, which is illustrated for further clarity in figure 7.

Figure 7: Subscription-based business models.

2.3.1 Subscription-based business models in B2B

When considering an organization that deals with IIoT-solutions, the choice regarding which subscription-based business model to provide and how to implement it may be ambiguous. To shed light on the matter, Riesener et al. (2020) conceptualized a model, as seen in figure 8, which conceptually illustrates how manufacturing firms within machinery and plant engineering can transition to subscription-based business models and the different activities which are involved in the process, regardless of the chosen subscription-based business model.

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Figure 8: Model of the transition process from manufacturing to subscription-based business models within the machinery and plant engineering industry.

Source: Riesener et al. (2020)

Figure 8 illustrates the initial stages of Riesener et al’s. (2020) subscription-based business model implementation model which is applicable on IoT-platforms and involves conceptualizing the subscription model, gaining customer insights, and specifying which subscription-based business model to use. Next comes the implementation of a subscription-based service, whereas the incorporated software then records and analyses received data (Riesener et al., 2020). Based on the data, the software service is then continuously altered to suit customer needs better. Although this model seems relatively straightforward, one has to pay attention to subscription-based services’ characteristics, which should act as a framework for implementing the process (Schuh et al., 2020). The author further states that four main characteristics define an advanced subscription- based service model within a mechanical and plant engineering industry 4.0 market. These are periodic payments, continuous performance improvement of customer benefits, knowledge of the change in individual customer value, and a long-term collaborative relationship (Schuh et al., 2020).

Tzou and Weisert (2018) propose their operational framework for subscription-based business models, which the authors named the PADRE/PPM-framework. Padre is an acronym for Position (web, PR, events), Acquire (sales team, reseller), Deploy (Implement, customer training, and

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adoption), Run (account, tech support), and Expand (increase capabilities and consumption through up-and cross-selling) (Tzou & Weisert, 2018). The PADRE/PPM-framework also consists of three core subsystems (PPM) that monitor operations. These subsystems include People (recruiting, training, career development), Product (R&D, product marketing, beta innovation), as well as Money (finance, operations, and legal counsel). This framework can be used as a checklist for organizations who use subscription-based business models in order to measure progress (Tzou

& Weisert, 2018). Table 2 below summarizes the most relevant and recent sources related to subscription-based business models within B2B-practices.

Table 2: Overview of subscription-based business model literature for manufacturing companies.

Authors and year Subject area

(Riesener et al., 2020) Argues for a methodology for designing and implementing subscription models within machinery and plant engineering.

(Schuh et al., 2020) Proposes four characteristics that define advanced subscription- based models within the machinery and plant engineering industry.

(Schuh et al., 2019) Proposes a management model for managing the transition to subscription-based services within the machinery and plant engineering industry.

One aspect which recent subscription-based business model literature agrees on however, is the fact that the software has to be introduced as a service, and not in the form of conventional enterprise resource planning (ERP) (Riesener et al., 2020, Schuh et al., 2020). Hence, Software as a Service (SaaS) will be of interest in this study. Further, none of the three aforementioned studies involve empirical research, which indicates that a theoretical gap exists.

2.3.2 Software as a service

SaaS is a delivery model that disrupted markets with its introduction and has since become a contemporarily relevant innovation utilized by organizations to provide customers with business application software from an external source. SaaS originated from the application service provisioning (ASP) model, which involved vendor hosting and managing application services

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from a single point through the internet (Valente & Mitra, 2007). In the early years, ASP did not have sufficient infrastructure to support good economies of scale (Kern et al., (2002). However, with new and better disruptive innovation, applications now enable vendors to host multiple customers, creating what is called multi-tenancy. This has emerged to be one of SaaS’s most significant advantages as it allows economies of scale due to a standard code used throughout all customers. Beneficial consequences are cost reduction and process quality improvements while also making way for rapid innovation (Loukis et al., 2019). Waters (2005) visualized the benefits of SaaS by showing “unknowable” costs often found within traditional licensing, see figure 9.

Figure 9: Benefits of implementing SaaS.

Source: Waters (2005)

SaaS can be defined as: “an application or service that is deployed from a centralized data center across a network, providing access and use on a recurring fee basis, where users normally rent the applications/services from a central provider” (Seethamraju, 2015, p.476). To grasp the concept of SaaS, Mäkilä et al. (2010) summarized various definitions of SaaS in their study and concluded five common characteristics among different definitions of Saas:

● The product is used through a web browser.

● The product is not tailor-made for each customer.

● The product does not include software that needs to be installed at the customer’s location.

● The product does not require special integration and installation work.

● The pricing of the product is based on the actual usage of the software.

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Based on Mäkilä et al’s. (2010) five characteristics one can further expand these to make them easier to grasp. First, by definition, SaaS must include periodic subscription fees on a continuous time frame (Dubey & Wagle, 2007; Sun et al., 2007). Second, the SaaS is provided by an outside vendor providing the software; hence, all applications and software are bought online, saving additional costs compared to traditionally buying on-premise software and maintaining it themselves (Choudhary, 2007; Dubey & Wagle, 2007; Waters, 2005). Similar to the second characteristic, the third is automated updates and services of the software. Because updates and maintenance are automated and solved by the provider, no additional staff is needed to maintain the software running (Choudhary, 2007; Waters, 2005). Fourth, SaaS is not customizable at its core (Valente & Mitra, 2007), but can in its meta-data (upper layer code) be customized to fit customer-specific needs (Cusumano, 2010). Fifth, due to internet usage, information sharing activities are high (Valente & Mitra, 2007).

2.3.3 Product service systems

When the intangible software service is accompanied by a tangible product, product service systems (PSS’s) are often utilized as a means to sell this mix of product/service offering (Kjaer et al., 2019). A PSS is a product offering where the value proposition focuses on the job conducted and the customer’s needs rather than focusing on the product itself (Annarelli et al., 2016). Baines et al. (2007), who provided a significant literature review regarding the topic, defined it as “a market proposition that extends the traditional functionality of a product by incorporating additional services”. Although PSS concepts were provided as early as 1999, it has still not been employed widely. At this time, however, literature was dealing with a closely related subject known as servitization. As both terms revolve around providing additional value from service offerings, the difference was that servitization focused solely on the economic perspective while the focus of PSS revolved around the sustainability aspect of a product offering (Goedkoop et al., 1999).

Recent literature elaborates how PSS provides environmental and social benefits and provides firms with an incentive to prolong the service life of products by re-using parts and making them as material and cost- and eco-efficient as possible (Tukker, 2015). Tukker (2015) further argues

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that the drawbacks of PSS include lack of accessibility and a lower intangible value than competing products, as it restricts behavioral freedom for customers, which may be one of the reasons why it has not been utilized to an extent since its initial introduction to academic literature. As global competition for resources has grown in recent years, the focus has shifted towards PSS once more, now emphasizing its incorporation into achieving circular economy businesses (Fernandes et al., 2020). More importantly is the fact that PSS focuses on the actual “sale of use” rather than the sale of the product (Annarelli et al., 2016; Baines et al., 2007), which in essence is more customer- oriented as the focus lies in the use of the product (Christensen et al., 2016). Tukker (2015) proposes three types of PSS: product-oriented, use-oriented, and result-oriented PSS. However, as this report revolves around customer needs, use-oriented PSS is the concept that will be considered as customer centricity is essential in the modern-day era for organizations to strive when implementing subscription-based business models (Tzou & Weisert, 2018).

2.3.4 Leasing

One common alternative for selling industrial equipment is through a leasing business model.

When compared to conventional sales, leasing is less capital intensive, more economically efficient, and facilitates the introduction of innovations to the market (Fabozzi, 2008; Vasilescu, 2006). Fabozzi (2008, p.823) defines leasing as “... a contract wherein, over the term of the lease, the owner of equipment (the lessor) permits another entity (the lessee) to use that equipment in exchange for a promise by the lessee to make a series of lease payments”. As periodic payments characterize leasing, it can be classified under a subscription-based business model. Within a lease there can be numerous optional considerations such as length of the lease, delivery, and installation charges (Fabozzi, 2008).

It is important to note that leasing is more frequently used among small- and medium enterprises because of the market-penetrating characteristics that leasing has (Nechaev et al., 2020; Vasilescu, 2006). Further, leasing can be divided into two models, namely financial leasing and operational leasing. Financial leasing is bound to the rules of the Generally Accepted Accounting Principles (GAAP), which states that ownership remains with the lessor, but must be transferred to the lessee at the end of the lease (Dhaliwal et al., 2011; SEC, 2020). The lease must contain a bargain purchase option and the lease life must exceed 75% of the asset’s economic life (SEC, 2020). If a

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lease does not fulfill one of these requirements, the lease will be classified as an operational lease and taxed accordingly (SEC, 2020). Operational leases are use-oriented and more flexible than financial leases (SEC, 2020), which in essence makes these types of leases more customer centric and thus of interest in this study. However, to accurately investigate purchasing preferences among buyers concerning subscription-based business models, the role of the buyer needs to be clarified.

2.4 B2B buying roles

A key aspect of the purchasing process within B2B markets is that the customer who intends to use a product or service is not necessarily the one who purchases it. Due to organizations purchasing functions being spread across different roles, the stakeholders in the purchasing process need to be discussed. Extensive literature within the area has been proposed, the most noteworthy being Keller (2009), who proposed seven different roles which constitute a B2B buying center.

These roles are defined in table 3.

Table 3: The buying center roles Source: Keller (2009)

Initiators The initiators suggest a product or service purchase. Initiates the buying process.

Users The end-users are the ones who will use the product or service (often the ones who initiate the buying process).

Influencers Those who influence the buying process by defining specifications or providing information for evaluating certain alternatives.

Deciders Decides on product requirements or suppliers.

Approvers The approvers are the ones who have the final say in whether or not to buy the product or service. Authorizes buyers and deciders propositions.

Buyers The buyers are individuals who have formal authority to select vendors and alter the purchasing terms/conditions.

Gatekeepers Gatekeepers have the power to prevent purchases from happening by not providing information to other roles in the buying center.

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When considering the proposed buying center roles, the purchase function and the end-user differ from one another. In this case, the end-user occupies the roles of the Initiator, User, Influencer, and Decider, while an individual within a purchasing function occupies the roles of the Approver, Buyer as well as Gatekeeper. Here, the individual who possesses the role of buyer is of interest. In other words, the purchasing function will be of interest in this study.

2.5 Theoretical Framework - UTAUT 2

To determine the buying preferences of purchasing functions, investigating the different factors that affect behavioral intentions is an alternative (Venkatesh et al., 2003). The Unified Theory of Acceptance and Use of Technology framework (UTAUT) was first devised by Venkatesh et al.

(2003) as a means for organizations to predict adaptation and use of a specific technology applicable in both B2C- and B2B-markets (Chang, 2012). The original UTAUT framework’s core constructs revolve around four factors that predict use- and behavioral intentions (Venkatesh et al., 2003). The first factor is Performance Expectancy, which is defined as the degree to which the individual using the technology believes it will increase their job performance. Second is Effort Expectancy, the degree to which an individual finds the technology easy to use. The third is Facilitating Conditions, the degree to which an individual believes that the organization they are part of has an infrastructure that supports the implementation of the technology. The fourth factor is Social Influence, which is the degree to which an individual believes that others think that he/she should use the technology.

Since the initial introduction of the UTAUT framework, Venkatesh et al. (2012) has incorporated three additional factors to the model, Hedonic Motivation, Price Value, and Habit, which resulted in the construct of the UTAUT 2 framework. Hedonic Motivation refers to the fun or pleasure which derives from the usage of a product (Tamilmani et al., 2019); Price Value refers to the quality, cost, and price of the product (Venkatesh et al., 2012); and Habit, which bases itself in prior behavior, refers to the degree of which an individual believes that their behavior is automatic (Huang & Kao, 2015). These additions substantially increased the variance explaining behavioral intentions (from 56% to 76%) (Chang, 2012). Considering the increased explanatory level, the UTAUT 2 framework will therefore be used for the purpose of this report.

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Figure 10: Unified Theory of Acceptance and Use of Technology 2 framework.

Source: Venkatesh et al. (2012)

The UTAUT 2 framework is illustrated in figure 10. It is important to note that the moderating factors (age, gender, and experience) will not be applicable in this study, as the focus will lie within purchasing functions. Instead, this study will attempt to stipulate moderating factors relevant in the context of a B2B purchasing function. Furthermore, as the UTAUT 2 model revolves around the acceptance of technologies in a B2C context, some influencing factors which may affect behavioral intention might not be fully applicable in this study either. With this in mind, hedonic motivation was removed as an influencing factor, as the authors consider that fun and pleasure have little to do with the decisions of a B2B purchase function employee. Based on the UTAUT2 model, a conceptual framework was constructed to address purchasing function employees.

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2.6 Frame of Reference

A conceptual framework was developed to further explain the questionnaire’s components and their relevance to the proposed research question. Here, factors, moderating factors, and the hypotheses are visualized.

Figure 11: Relationship linking the conceptual model and the research questions 1, 2, and 3.

Figure 11 illustrates the hypothesized relationships between the factors and buying intention, as well as the relationship between the moderating factors and their impact on buying intention.

Each of the factors were operationalized to increase practical relevance for both the respondents and for the study as a whole. These operationalizations are stipulated in table 4 below.

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Conceptual Area Conceptual Definition Dimensions Operational Definitions Purchasing function

acceptance and purchasing intention of disruptive

innovations

A purchasing functions acceptance and

consequently, purchasing intentions regarding products of services which are simpler, cheaper, more reliable, and convenient than well- established products, thus creating a new market

Performance expectancy

The degree to which purchasing function employees perceive a disruptive innovation to increase job performance within the organization Effort

expectancy

The degree to which purchasing function employees perceive that a disruptive innovation will be easy to use for the

organization’s users.

Social influence

The degree to which purchasing function

employees is influenced by peers within the organization (hierarchical orders, co- workers, etc.) when purchasing a disruptive innovation

Facilitating conditions

The degree to which purchasing function

employees believe that their organization of employment has sufficient infrastructure to implement the solution Price value The degree to which

purchasing function employees perceive the quality, cost, and price of a disruptive innovation to be in line with the proposed value offering

Habit Based on prior purchases, the degree to which purchasing function

employees believe that their buying behavior is automatic

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Based on the modified UTAUT2 model as seen in figure 11, the following hypothesis was formulated:

H1a: Performance Expectancy positively influences intention to buy IoT-based disruptive innovations.

H2a: Effort Expectancy positively influences intention to buy IoT-based disruptive innovations.

H3a: Social Influence positively influences intention to buy IoT-based disruptive innovations.

H4a: Facilitating Conditions positively influences intention to buy IoT-based disruptive innovations.

H5a: Price Value positively influences intention to buy IoT-based disruptive innovations.

H6a: Habit positively influences intention to buy IoT-based disruptive innovations.

Since this study looks at buying intentions from a B2B perspective on disruptive innovations, the UTAUT2 model has been modified accordingly. Hence, the moderators have been reconsidered as buying decisions on a business level are purely professional. Thus, age and gender become irrelevant as moderating factors. Due to companies’ need to shift and adjust to the changes in ecosystems and move to dynamic experiments, firms can no longer rely on rigid strategies (Jacobides, 2019). Thus, in line with the study’s context, new moderators have been recognized.

Due to the nature of competition changing, ownership is not what it used to be, especially regarding disruptive innovation. As ownership is a key consideration within purchasing functions, ownership is becoming increasingly more important as purchasers become more involved producing long- term relationships (Paesbrugghe et al., 2017), which also is a characteristic of subscription-based business models (Schuh et al., 2020). The importance of physical ownership is dwindling as customers consider the job which a product or service provides is of greater importance (Tzou &

Weisert, 2018). In addition to the dwindling physical ownership, the ecosystem on various levels is shifting to asset and resource ownership (Jacobides, 2019; Vargo et al., 2020). Further, ownership is one of the main characteristics of disruptive innovation (Nagy et al., 2016).

Therefore, ownership was stipulated as a potential moderating factor.

H1b: The influence of Performance Expectancy on buying intention is moderated by Ownership.

H2b: The influence of Effort Expectancy on buying intention is moderated by Ownership.

H3b: The influence of Social Influence on buying intention is moderated by Ownership.

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H4b: The influence of Facilitating Conditions on buying intention is moderated by Ownership.

H5b: The influence of Price Value on buying intention is moderated by Ownership.

H6b: The influence of Habit on buying intention is moderated by Ownership.

Although age and gender are not relevant in the B2B context, purchasing may be seen as a habitual behavior. When regarding habitual behavior, experience is a critical factor (Huang & Kao, 2015).

A technology adoption study by Morris and Venkatesh (2000) also showed that experience moderated the impact of social influence on behavioral intention. Also, an internet usage study conducted by Fuksa (2013) showed that all of the factors in the UTAUT2 model as well as behavioral intention were moderated by experience. Based on this, experience was stipulated as a moderating factor.

H1c: The influence of Performance Expectancy on buying intention is moderated by Experience.

H2c: The influence of Effort Expectancy on buying intention is moderated by Experience.

H3c: The influence of Social Influence on buying intention is moderated by Experience.

H4c: The influence of Facilitating Conditions on buying intention is moderated by Experience.

H5c: The influence of Price Value on buying intention is moderated by Experience.

H6c: The influence of Habit on buying intention is moderated by Experience.

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3. Methodology

The following chapter is devoted to describing how the methodology was conducted. The section describes the study’s overall purpose, approach and research strategy, measurement items, data collection, and sample collection. The chapter ends by discussing the validity and reliability of the qualitative and quantitative methods.

3.1 Research purpose

As literature in the stated problem area was limited, the purpose of this research was to provide an exploratory angle to the proposed theoretical gap. Therefore, the authors used an agile approach as new theoretical insights or obtained data dictated the direction of the research. This approach resulted in proposed research questions being continuously revised as the research advanced in light of new insights. The purpose of this approach was to provide an exploratory answer to the research problem: to examine how IIoT-solution providers successfully can transition to a subscription-based business model when launching IoT-based disruptive innovations in industry 4.0 B2B-markets.

3.2 Research approach and strategy

The research strategy can be seen as the plan on which the research is to be conducted on to answer the research questions. Saunders et al. (2016) argue that perhaps the most crucial part of the research strategy is to tie the research questions together with the research approach, available resources, and existing literature. A typical divide between different research approaches is quantitative, qualitative, or mixed (Saunders et al., 2016). This study had a mixed-method that first allowed the authors to conduct qualitative interviews to identify relevant factors and general insights, which also maintained high practical relevance in the study (Robson and McCartan, 2016). Later the research arguments and factors were tested by analyzing numeric data through a quantitative analysis. As mentioned by Saunders et al. (2016), a mixed-method can either be simple or complex. A complex version of the mixed-method approach uses multiple phases back and forth between quantitative and qualitative research (Saunders et al., 2016). The simple version does not go back and forth and instead goes from one method to another (Saunders et al., 2016). In line with

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Saunders et al. (2016) this study utilizes the simple mixed-method as the quantitative research is based on the qualitative interviews, as illustrated in figure 12.

Figure 12: Illustration of the utilized research strategy.

Saunders et al. (2016) state that an exploratory study aims to ask open questions to gain insights into the topic of interest. Further, when qualitative research is utilized to gain insights, followed by quantitative research, the research approach becomes sequential exploratory (Saunders et al., 2016). With the paper aiming to explore previous literature and interviews, which formed the foundation for the consequent quantitative research, the researchers had a sequential exploratory approach. This allowed the researchers to use the literature review and interview responses to stipulate hypotheses for the quantitative element, which could thereafter be tested.

Regarding quantitative research, Saunders et al. (2016) state that there are two main strategies to consider, experimental and survey. The quantitative section of this study was conducted as survey research. Thus, tying together with the above-mentioned sequential exploratory approach and more specifically, the deductive part of the quantitative research. Since the survey strategy tends to answer the questions of “what”, “who”, “where”, and “how”, it aligns with the questions formulated in chapter 1.2 (Saunders et al., 2016).

3.3 Measurement Items

The primary method of measurement comes from the constructed questionnaire, which is presented in Exhibit 3. In accordance with Hair (2009), the researchers operationalized the constructs by selecting measurement scale items. Typical for a quantitative design is to use a series of scaled indicator items in a Likert scale format (Hair, 2009). Furthermore, a Likert scale with

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values ranging from 1 to 7 were selected to improve people's likelihood to give valid responses (Robson & McCartan, 2016). The questionnaire items were constructed in line with the UTAUT2- framework but were adjusted slightly to answer the proposed research questions. One adjustment is that behavioral intention was altered to intention to purchase and that the questions regarding each factor were adjusted accordingly. Secondly, ownership was introduced as a moderating factor, while age and gender were removed due to their lack of relevance. However, experience remained as a moderating factor as it complies with the context. Filter questions were used in an attempt to skim out irrelevant responses. A figure of the quality control process is illustrated in Exhibit 1.

3.4 Data collection

Secondary data was collected through market research reports, and scientific articles. When collecting secondary data through scientific papers, Google Scholar, Business Source Premier, and Scopus were primarily used. Throughout the research process, frequently used keywords were:

disruptive innovation, disruptive technology, characteristics of X (disruptive innovations, product service systems, subscription-based services, and software as a services), subscription models, subscription-based service, software as a service (SaaS), product service systems (PSS).

Qualitative primary data was collected from semi-structured interviews. The interviews were held with individuals within the sample population at various organizations in Scandinavia. The interview guide can be observed in Exhibit 2. The interviews were utilized to guide the research when constructing the questionnaire, while also providing a holistic perspective on the subject area. Quantitative primary data was collected through survey responses from respondents in the US. A client database of a technology provider was utilized to reach relevant respondents. The survey was distributed to key management individuals at target organizations via a third-party market research agency. The data was stored in Qualtrics and Microsoft Excel, as this was where the survey was constructed and where the data was cleaned.

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3.5 Sample selection

The sample population consisted of individuals within purchasing functions who operate within life science-, food and beverage-, and the chemical industry. Given that the study is investigating buying intention within purchasers, the researchers interviewed respondents from different organizations since most organizations only have one purchaser within the selected area. Due to limited accessibility during Covid-19 times, the respondents were contacted via phone primarily and email secondarily. In addition to the researchers looking for valid organizations to interview, the researchers got assisted by their supervisor with a contact list of potential organizations to connect with. All of the final respondents resided from Scandinavia and a total of four interviews were conducted. The data from these interviews provided a rounded view of the purchasing process in these industries while also enlightening which factors are most determining when it comes to purchasing decisions. The results from the qualitative part of the study aided in validating the operational definitions for each identified factor, while also providing key insights regarding the research topic. To gain results of high practical relevance, purposive sampling was utilized.

However, this stipulated a result which was not generalizable for a large population (Saunders et al., 2016). To calculate the required sample size “n” for the questionnaire, Slovin’s formula was used.

𝑛 = ^𝑁

1+𝑁𝑒² (1) Here, the total number of commissioned plants within the life science-, food and beverage-, and chemical industry amounted to 63 898 (Industrial Info Resources, 2021). As the intended use for the IoT-technology in question concerns intermediate bulk containers (IBC-tanks), these had to be taken into account when calculating Slovin’s formula. Roughly assumed, half of the commissioned plants utilize IBC-tanks and have a purchasing function employee which deals with these types of purchases in some capacity. Hence, the entire sample population for this study is approximated to N = 31 949. As a confidence interval of 0.05 results in a sample size which is unrealistic for this study, the confidence interval was set to 0.10 for convenience purposes. Inserting N in equation (1) gives:

Disruptive business model transitioning in B2B: A subscription-based approach for Industry 4.0