Wearable Devices : A Technological Trend with Implications for Business Models

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Linköping University | Department of Management and Engineering Master’s thesis, 30 credits | MSc Business Administration - Strategy and Management in International Organizations

Spring 2018 | ISRN-number: LIU-IEI-FIL-A--18/02845--SE

Wearable Devices

-A Technological Trend with

Implications for Business

Models

Katharina Koschell

Kristina Dubs

Supervisor: Heiko Gebauer

Linköpings University SE-581 83 Linköping, Sweden

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Linköping University | Department of Management and Engineering Master’s thesis, 30 credits | MSc Business Administration - Strategy and Management in International Organizations

Spring 2018 | ISRN-number: LIU-IEI-FIL-A--18/02845--SE

English title:

Wearable Devices – A Technological Trend with Implications for Business Models

Authors:

Koschell, Katharina & Dubs, Kristina

Advisor:

Gebauer, Heiko

Publication type:

Master’s thesis in Business Administration

Strategy and Management in International Organizations Advanced level, 30 credits

Spring semester 2018

ISRN-number: LIU-IEI-FIL-A--18/02845--SE Linköping University

Department of Management and Engineering (IEI)

www.liu.se

Katharina Koschell

Kristina Dubs

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Copyright

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According to intellectual property law the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement.

For additional information about the Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page: http://www.ep.liu.se/.

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II

Abstract

Background

Wearable technology, which is a part of the Internet of Things (IoT), appears to be an upcoming trend with increasing importance within the business world. Nevertheless, no clear business model for companies working with wearables had been defined yet taking the influences wearables have on businesses and especially their value proposition into consideration.

Purpose

The purpose of this thesis is to offer input to the lack of existing literature within business models and wearables technology. The aim is to unfold a general business model that can be used within wearable companies/IoT businesses and show the influence these technologies have on them.

Methodology

In order to conduct an empirical research a multiple case study has been conducted, based on semi-structured interviews with eight companies, which core business consists out of wearable technology. The frameworks on business models by Gassmann et al (2014) and Osterwalder and Pigneur (2010) serve as the basis for this study and its analysis, which is based on a grounded theory approach.

Results

It appears that a great amount of similarities can be found through the cross-case analysis between the cases. This makes the construction of a new business model possible. The unfolded model gives also a new contribution to the theory of Hui (2014) regarding a new area of value creation and value capture within IoT businesses.

Keywords

Business Model, value proposition, wearables technology, wearable devices, Internet of Things.

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Acknowledgement

This master thesis was written during the final semester of both authors master program MSc Strategy and Management in International Organisations at Linköpings University. The idea of writing this thesis about wearable devices came into our minds, as we are both owner and engaged user of a Fitbit fitness tracker. During the last two years of our master studies the fitness tracker was always with us, tracking our physical fitness and activity. This

gave the incentive to dive deeper into the studies of wearables. The thesis would not have been possible without the help of several people.

First of all, we would like to thank all the companies, who participated in the interviews and provided the great material to work with. You are the heart of our research.

Further, we would like to thank our families and friends, who supported us not only during the development of the thesis but also during the last two years in the master program. All

your support and help has led to this final result and made us to who we are today. Special thanks also to our supervisor Heiko Gebauer, who gave us new insights, which made it possible for us to keep going and think outside the box. Without your input and advice, the

thesis would not be the way it is now.

Last but not least we would like to thank each other for the dedication, effort, understanding and respect we showed to each other during the whole process. The friendship we developed during the last two years grew even stronger through the challenge of working

together on this big project. We are proud of the personal as well as professional development we got through this journey.

Linköping, May 2018

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Figures and Tables

Figures

Figure 1: World of Wearable Technology Application ... 7

Figure 2: Business Model Triangle... 9

Figure 3: The 9 Building Blocks ... 9

Figure 4: B2C Elements of Value ... 16

Figure 5: Porter´s Generic Value Chain ... 17

Figure 6: Value Added ... 18

Figure 7: Analytics Maturity Model ... 20

Tables Table 1: Structure of the Thesis ... 4

Table 2: Overview of Wearables ... 6

Table 3: Merge of the two Business Model Approaches. ... 10

Table 4: Merge of the Business Model Triangle, the Nine Building Blocks and Business Components ... 13

Table 5: New Value Creation ... 23

Table 6: New Value Capture ... 23

Table 7: Sample Overview – Main Characteristics of the Samples Chosen ... 27

Table 8: Within-case Analysis Persona. ... 35

Table 9: Within-case Analysis Business Model... 37

Table 10: Within-case Analysis Value Creation and Capture. ... 39

Table 11: Persona End Customer Cross Case. ... 40

Table 12: Persona B2B Cross Case... 41

Table 13: Cross-case Analysis Business Model. ... 47

Table 14: Cross-case Analysis Value Creation and Capture. ... 51

Table 15: Archetypal Business Model defined through the Analysis... 56

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1 Introduction

1.1 Background

“It’s clear that we are in the early stages of a business trend that is starting at the consumer-level, as they tend to do these days. [...] The point is there are real business implications for

the adoption of wearable technology. We are likely to see a host of business model disruptions to come, as we do with any number of important technological advancements.”

- Tom Archer, US Technology Industry Leader, PwC US (2011-2016 PwC)

Wearable devices. A technology that is worn on the human body and includes a sensor technology that is able to collect and deliver information about their surroundings in real-time (Techopedia Inc, 2018). During the last years wearable devices experienced a strong increase within the tech market. Achieving a breakthrough with the Bluetooth headset in 2002, the following years some of the most iconic wearable devices came to the market, produced by Nike+ and Fitbit (Grace College, 2018). With the following growth of activity trackers and the introduction of the Apple watch, 2014 got named “The Year of Wearable Technology” (Grace college, 2018). Being so closely connected to the Internet of Things (IoT), insiders forecast wearables since than to be the “next big thing for businesses” (Leung, 2014). The range of wearable products is highly diversified and while some users see them as a fun gadget, others realize the high potential to change the business world, as they change the way consumers are interacting with the environment (Ericsson AB, 2016). The idea behind the development of wearables is that they become so unobtrusive that users do not even think about them being there (O´Brien, 2016).

Looking at the numbers, ABI research forecasts 530 million shipped wearable devices in 2018 (Loftus, 2016) and the tech market will double its shipping of wearables from current 125.5 million shipped devices per year to 240.1 million in 2021 (Lamkin, 2017). Drivers for this increase are forecasted to be the most popular categories within wearables: Wristbands and watches (Lamkin, 2017). Nevertheless, there is also an increase in the range of wearable technology to different body parts from head to toe (Wade, 2017). And although wearable

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2 devices started as an addition to the smartphone, many of these technologies often do not need to be connected to a smart device anymore, but can process data on their own (Leung, 2014). A study on consumer views on wearables made by Ericsson AB in 2016 showed that in many cases wearables are starting to replace the smartphone as the most personal device (Ericsson AB, 2016). Looking at the high increase within wearables, the wearable market and the possibility to use them as a lifestyle decision makes entirely new business models possible (O´Brien, 2016).

1.2 Research Problem

Although the wearable market has grown intensively during the last years, the market for wearable devices is still in an early phase (Ericsson, 2016). Currently dominated by the tracking of health, wellness and activity not only the sport and healthy industry is affected by wearables, but also other industries, such as media, communication, retail, education or law enforcement, where virtual glasses will be used for example by the police to identify criminals via facial recognition software (Boitnott, 2016). Furthermore, depending on which wearable device is chosen, there can be factors outweighing the need for an excellent technology making customers choose the product. One of these factors can be for example fashion or entertainment. Another area wearables are having a big influence on are retail financial services. Wearables lay the foundation to revolutionize the experience of customer identity, customer understanding and customer data, leading the next wave of technological transformation (Bray, 2018). One of the managerial contributions that can be obtained from studies in this area, is that wearable devices will have to fulfil different needs to the customer. Customers are not only interested in the functional value of the product, but also in aspects of beauty and social needs (Hui-Wen Chuah et al, 2016). While some studies suggest that the value proposition of wearables seems unclear, as many users stop wearing them shortly after purchase (Kim, 2016), the continuous improvements of these devices in terms of standalone connectivity and functionality in daily life have a great potential to make them “one of those

devices you don't leave behind” (Mottl, 2015).

Although, it is often pointed out that wearables also have an influence on businesses (O'Brien, 2016; Archer, 2014), most research has been done focusing on the implication the customer

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side faces due to the wearable market and a clear picture of the impact these devices have on companies and their way of operating with them is missing. A new technology that has so clear implications on customers includes also that businesses need to switch to a customer-centric approach and to re-evaluate their value proposition presented to the customer (Teece, 2010). This includes not only the side of value creation towards the customer but also the side of value capture by providing these new technological products and services. Taking into account that IoT has already degraded business models within the industries of music recording and news, especially companies working in this area, including wearable technology, also need a well-developed business model (Teece, 2010).

1.3 Purpose

Based on the above described problem the fundamental purpose of this research study is to focus on the development of a business model for companies with wearable technology as their core business, in order to fill the gap in literature. Additionally, this study is also looking beyond wearables as a core business to see if the findings can be related and used within other IoT businesses. In order to develop a general business model this thesis is analyzing the patterns of several companies using wearable devices in their business model and to examine (1) if differences between the business models exists, (2) if similarities between the business models exists, (3) what caused these differences and similarities within the models, (4) if it is possible to generalize these outcomes to one common business model and (5) if this model can also be used within other IoT sectors. Hence the research question is as follows:

How can the business model for wearable devices be unfolded?

The research is conducted by a multiple case-study with eight companies who include wearable devices in their business model from several countries. Each case and its business model are described and analyzed in a within-case analysis. By a cross-case analysis patterns within the data are going to be identified. The answer to this question will be not only interesting to academics, but also to executives within different industries, who´s core business consists of wearable technology or similar IoT technologies as well as for entrepreneurs planning to introduce new wearable devices to the market.

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1.4 Thesis Structure

Table 1 shows the general overview of the master thesis, which is divided in seven different parts. The main objectives of each part are further pointed out and explained shortly.

Chapter Description

1 Introduction

- Identification of the research problem: Missing business model for companies with wearable technology as their core business - Research question: How can the business model for wearable

devices be unfolded?

- Purpose of the study 2 Theoretical

Framework

- Definition of the terms IoT and wearables - Characterization of the term business model

- Characterization of the terms value creation and value capture - Transformation of value creation and capture over time

3 Methodology

- Qualitative research based on a multiple case study - Introduction of the eight conducted cases

- Data collection method: Semi-structured interviews - Data analysis: Within- and cross-case analysis - Validity and reliability of the study

4 Findings - Presentation of the within-case analysis results

- Presentation of the cross-case analysis results based on coding

5 Discussion

- Development of the end-customer persona - Development of the B2B persona

- Archetypal business model based on the model of Osterwalder and Pigneur (2010)

- Archetypal value creation and value capture model 6 Limitations

and Further Research

- Limitations of the research study

- Suggestions for further research, including a larger sample, wider range within IoT and an extra focus on the interference of profitability

7 Summary - Summary of the results gathered in this thesis - Research question and sub-questions are answered

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2 Theoretical Framework

2.1 IoT Landscape and Wearables

The term the Internet of Things (IoT) has been discussed a lot in recent days. To give the reader a better of understanding of the IoT concept and its landscape, an overview of the term and its interference with wearable devices is given.

“The Internet of Things (IoT) is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment” (Gartner IT Glossary, 2018). To express this definition in other words the IoT

connects things, such as mobile phones, coffee machines or lamps, with the internet and also with each other to enable major convenience effects (Morgan, 2014). These effects are ranging from the connected alarm clock in the morning that brews coffee right when it is time to wake up, to the connected car that already knows the fastest way to the next meeting. There are almost no limits to what the IoT is able to do (Morgan, 2014).

A part of the IoT are wearable devices, also called wearable technology or simply wearables. Wearables are technologies, which are incorporated into items that are worn on the body, such as watches, bracelets or glasses, but also sensors that are incorporated in clothing or shoes (Wareable, 2014-2018). Wearables provide additional features, that other devices, like smartphones for example, cannot offer and the “purpose of wearable technology is to create

constant, convenient, seamless, portable, and mostly hands-free access to electronics and computers” (Wareable, 2014-2018). Wearable technology tries to connect directly to its users

by helping them to achieve goals or to make their daily life easier (Wareable, 2014-2018). An overview of the most common wearable devices can be found in Table 2 and Figure 1.

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6 Wearable Device Short Explanation

Smart Cloth Clothes with sensors, that measure body stats

Smart Glasses Glasses, that have additional features build in, such as small displays in the corner Medical Techniques Often used in the health care sector Sport and Fitness Tracker Wristbands, that measure daily activity,

heart rate and other sport related variables 3D Motion Tracker Sensors, that are able to detect special

movements of the body and are mostly incorporate into clothing

Smart Watches Watches, that have additional features, like receiving and reading text messages

Head Mounted Displays Displays, that can be worn on the head, like augmented reality (AR) glasses

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Figure 1: World of Wearable Technology Application (Beecham Research, 2018).

As said above, wearables are part of the IoT and therefore connected to the internet and other devices. Many wearables do not directly operate through the internet, but they are linked via Bluetooth to a smartphone and to the smartphone´s internet access (Investopedia, 2018). Via this connection wearables can receive information from the smartphone, as for example text messages, but can also send information they have gathered during the day back to the smartphone. Wearable devices often include sensors, that measure information about their environment (Techopedia Inc, 2018). To analyze the information gathered, the internet connection from the smartphone is used.

Wearables are also used in the B2B sector. The development of IoT allows companies to be more efficient, reduce costs and enhance revenue (Pütter, 2018). Sensors in bigger machines, such as engines or turbines, monitor the performance and predict maintenance time and failure rate (D´Emidio et al, 2015). Smart glasses, worn by an employee in Argentina, can

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8 display the helping hand of an engineer in Germany or they can show employees the position of a product in the warehouse (Hauptfleisch, 2014).

Predictions say that the sale of smartwatches will reach 81 million units in 2021, which represents a number of 16% of the total wearables market (Gartner, 2017). Increased access to wearables leads to a higher acceptance around consumers and in the long run also in the B2B sector (Muse, 2016). It is only natural, that businesses want to tap into that market and develop a business model around it.

2.2 Components of a Business Model

“A business model is a story about how an organization creates, delivers, and captures value”

(Kaplan, 2012, p. 15). Nowadays a company’s long-term success depends on its business model (Gassmann et al, 2014). The business model is one of the first decisions made in the lifecycle of a company, which has a major impact on the economics of the company (Urbanowska-Sojkin, 2011), but needs to be distinguished from the strategy of a company (Períc et al, 2017). Strategies address the competitive environment and the competitive advantage of a company, while business models focus on the customer (Períc, et al, 2017). In their book, Gassmann et al (2014) describe 55 business models, which are implemented in 90% of all successful companies. The core of every successful business model is therefore the customer (“Who?”). The targeted customer segment determines the other three angles,

“What?” (The Value Proposition), “How?” (The Value Chain) and “Value” (The Profit

Mechanism). The overview of the Business Model Triangle by Gassmann et al (2014) can be seen in Figure 2.

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Figure 2: Business Model Triangle (Gassmann et al, 2014, p. 7).

According to Osterwalder and Pigneur (2010) “a business model describes the rationale of how

an organization creates, delivers, and captures value”. This description is explained within nine

separate building blocks: Customer segments, value proposition, distribution and marketing channels, customer relationships, revenue streams, key resources, key activities, key partnerships and cost structure. These building blocks developed by Osterwalder and Pigneur (2010) can be seen in Figure 3.

Figure 3: The 9 Building Blocks (Osterwalder and Pigneur, 2010, p. 24-25).

Many more definitions and frameworks of business models can be found in literature, but in this thesis a combination of the business model triangle by Gassmann et al (2014) and nine

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10 building blocks by Osterwalder and Pigneur (2010) will be used as a starting point. A merge of both models can be seen in Table 3.

Business Model Triangle by Gassmann et al (2014)

Business Model Canvas by Osterwalder and Pigneur (2010)

Who? Customer Segments

Customer Relationships

What? Value Proposition

How? Distribution Channels

Key Resources Key Activities Key Partnerships

Value? Revenue Streams

Cost Structure

Table 3: Merge of the two Business Model Approaches.

The customer is the heart and center of every business model (Gassmann et al, 2014; Osterwalder and Pigneur, 2010). “Who” the business serves and which customer segment the company is targeting is very important to know (Gassmann et al, 2014), because without customers there would be no business (Osterwalder and Pigneur, 2010). Customer segmentation defines the narrowing down of customers with similar characteristics (Bain and Company, 2018). Types of customer segmentation can be for example, a mass market or a niche market (Osterwalder and Pigneur, 2010). To maximize the customer experience, the relationship maintained with the customer is an important tool. Depending on the customer segment, different types of relationships are expected by the customer. The more trust a customer needs to buy a product, for example in the private banking sector, the longer and deeper the relationship needs to be (Osterwalder and Pigneur, 2010).

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Understanding the customer needs is essential to create an irresistible value proposition (Kaplan, 2012), the “what” of the business model (Gassmann et al, 2014). The value proposition describes the offerings of a company, which can be products and/or services that serve the customer’s needs (Osterwalder and Pigneur, 2010; Gassmann et al, 2014; Períc et al, 2017). The value created for the customer can be quantitative or qualitative as Osterwalder and Pigneur (2010) stating some examples for. Performance-based value propositions focus on products, that are higher performing than the ones from competitors. Price-based value propositions have the price, usually very low, as their core offer. Other value propositions are focusing on the aesthetic value of products like the design, but also on selling the brand, which functions as a status symbol. Convenience and accessibility is another value proposition that targets customers’ needs. The value proposition is the first step towards successful sales (Chesbrough and Rosenbloom, 2002).

After having decided on a specific value proposition, decisions on “how” to produce it need to be taken (Gassmann et al, 2014). The operational value chain with all its activities, processes, resources and capabilities needs to be described. The ability of a business to create value for the customer depends on its key resources and activities (Períc et al, 2017; Kaplan, 2012). Key resources are resources that are substantially important to the company's business model (Osterwalder and Pigneur, 2010). Depending on the business model, they vary and “can be

physical, financial, intellectual or human resources” (Osterwalder and Pigneur, 2010, p. 40).

Key activities or processes link a company's resources together and form capabilities, that create value for the customer (Períc et al, 2017). These activities include production, networks and problem solving for the customer (Osterwalder and Pigneur, 2010). Important for the value chain are also key partnerships. A network of partners and suppliers make the whole business model work (Osterwalder and Pigneur, 2010) and influences the value creation positively (Períc et al, 2017; Kaplan, 2012). Osterwalder and Pigneur (2010) determine four different types of partnerships: Alliances with non-competitors, alliances with competitors, joint ventures and buyer-supplier relationships. A big part of the value chain are channels, mainly communication, sales and distribution channels, which need to be adapted to the business model (Osterwalder and Pigneur, 2010). Customer segmentation is the base for the adaptation of channels, marketing and pricing (Bain and Company, 2018).

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12 The last dimension of a business model is the profit mechanism. It clarifies why a business is financially viable and how it creates “value” (Gassmann et al, 2014). First, revenue sources need to be determined (Kaplan, 2012). Often, the actual value recipient is not paying for the product or service. An example are students as value recipients, but the actual bill is paid by the state. There are also different revenue streams a company can implement (Osterwalder and Pigneur, 2010). Often a company sells a product as an asset sale and gets cash right away. However, there are also different models, like leasing, licensing or subscription, where the company will get constant revenue from one single sale. Another issue to consider is the price, the product should sell for. Prices can be fixed, negotiable (Osterwalder and Pigneur, 2010) or reduced (Kaplan, 2012). Finally, costs are playing an important role for financial survival. Some business models are based on a cost-driven structure, where the goal is to offer the lowest cost on the market, whereas other models operate on high margins. Cost advantages can be generated through economies of scale and scope, depending on what is more appropriate for the operating business model (Osterwalder and Pigneur, 2010).

Table 4 summarizes the different components of a business model based on the business model canvas of Osterwalder and Pigneur (2010). They mention that the value proposition has near infinite types and only the most important ones are listed. A detailed description of every component can be found in the Appendix.

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Business Model Triangle by Gassmann et al

(2014)

The Nine Building Blocks by Osterwalder and Pigneur (2010)

Business Model Components by Osterwalder and Pigneur (2010)

Who?

Customer Segments

Customer Relationships

Mass market, niche market,

segmented, diversified, multi-sided platforms

Personal assistance, dedicated personal assistance, self-service, automated services, communities, co-creation

What?

Value Proposition Newness, performance,

customization, “getting the job done”, design, brand/status, price, cost reduction, risk reduction, accessibility, convenience/usability

How?

Distribution and Marketing Channels

Key Resources Key Activities Key Partnerships

Direct sales force, direct web sales, indirect via own stores, partner stores or wholesalers

Physical, intellectual, human, financial Production, problem solving, platform Optimization and economy of scale, reduction of risk and uncertainty, acquisition of particular resources and activities

Value?

Revenue Streams

Cost Structure

Asset sale, usage fee, subscription fees, lending/renting/leasing,

licensing, brokerage fees, advertising Cost-driven, value-driven

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14 To conclude, a business model describes “who your customers are, what you are selling, how

you produce your offering, and why your business is profitable” (Gassmann et al, 2014, p. 7).

It addresses both, external and internal aspects and if one of the three triangles are adjusted, the other two automatically do too (Gassmann et al, 2014).

2.3 Concept of Value and its Creation/Capture

A strong customer focus - which should always be pursued (Gassmann et al, 2013) - directs companies to the notion of value (Golub et al, 2000) and offerings, which are significantly valuable to all stakeholders and are leading to the success of a business (Åman, 2017). The following part will further give an overview what value is in general and the theoretical concepts behind value creation and value capture. In the end, insights into recent changes in value creation and capture are given.

2.3.1 Definition of the Term Value

Statements like “Business is about creating value. Value is the monetary worth of a product or

asset” (Grant, 2010, p. 35) or “Value is what consumers are willing to pay” (Porter, 1985, p. 3)

indicate that value equals money. However, there are also scholars who see value in a different, philosophically, perspective. Åman (2017) distinguishes five different types of philosophical value: Instrumental, ethical, intrinsic, aesthetic and inherent value. Intrinsic value is the value a product has in itself without outside influences. Instrumental value on the other hand assumes a goal or objective that can be achieved with the product. The product alone does not have a specific value. An example are different resources in a company. Each resource does not hold the worth, but all resources together hold it when combining them to create a product for the customer.

Following the statements above, value is also an economic concept. Bowman and Ambrosini (2000) defined two different types of value, use value and exchange value. The perceived use value determines what the customer is willing to pay for a product. It refers to the usefulness of the product to the customer and is subjective, as it depends on the individual customer

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needs. A customer bases a buying decision on rational, but also on emotional factors (MacKay, 1999). Customers assess a product due to its functional abilities (functional value), the ability to gain pleasure from it (aesthetic value) and its social message (social value) sent to the customers environment. Exchange value, on the other hand, is the concrete amount of money the company receives from the customer and realized upon sale (Bowman and Ambrosini, 2000). Exchange value needs to be distinguished from the profit of a company as at the time when exchange value is realized it is not known who the exact receiver of the return of sale is (Åman, 2017).

The two concepts above are rather broad and Bain and Company (2016) created a more detailed description of value. Customers evaluate goods and services based on their price and the perceived use value, whereas companies tend to focus more on the price as it is easier to measure and to understand. Therefore, it is important to know what customers value can maximize revenue and ensure loyalty of the customers. It is not easy and psychologically complicated to evaluate that (Almqvist et al, 2016) and therefore, Bain and Company (2016) developed the B2C Elements of Value, which can be seen in Figure 4.

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Figure 4: B2C Elements of Value (Bain and Company, 2016).

The pyramid is based on Abraham Maslow's hierarchy of needs published in 1943 and consists of 30 elements. The base of the pyramid is built by elements that inherit the functional value, which represents the basic needs of a person, such as reduce costs, quality or variety. The second layer is emotional value, whose elements should meet emotional needs of the buyer. As emotional value design, attractiveness or fun could be perceived. Some products go a step further and are considered life changing, as they provide hope or motivation. The top of the pyramid is value as social impact. By helping society, one can as well draw self-transcendence from it and products can be world changing (Bain and Company, 2016). The more value elements of the pyramid a product can offer, the higher is the chance the customer will buy it again and the companies enhances its revenue (Almqvist et al, 2016). Considering the definitions of value, one can say that “the purpose of a business is to create value (through

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work), sell or trade it to customers, and capture some of that value as profit” (Jorgenson,

2015).

2.3.2 Value Creation

Another important element of every business model, besides the customer, resides in value creation (Hui, 2014). Often, customers pay more for a product than its materialistic value is worth. Satisfied customers pay even more than the total cost of the product leading the company to a profit. This phenomenon results from companies being able to add value for the customer in order to enhance the amount the customer is willing to pay. Further, it will be described how this value is created.

Value creation refers to activities performed by the company that enhance the value of a product (Hui, 2014). According to Grant (2013) value can be created through either production or repositioning of products in the market. Production alone is not a guarantee for success as wide options exist in the markets and products are hardly ever unique (Jorgenson, 2015). A common form of displaying value creation is the value chain, which can be seen in Figure 5.

Figure 5: Porter´s Generic Value Chain (Mind Tools Ltd, 1996-2018).

The value chain logic sees value creation as a transformation of inputs into outputs. Porter's value chain visualizes business processes within an organization, where each process has the

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18 goal to create value for the customer. The value chain consists of primary activities and secondary activities. Primary activities (logistics, operations, marketing and sales and service) are mainly responsible to create value for the customer. Secondary activities, such as human resource management or technology development support the main activities. How much value for the customer has been generated can be measured by the margin on products (Jorgensen, 2015).

2.3.3 Value Capture

The value captured by the company can easily be mistaken as the company’s revenue (Jorgensen, 2015). Value captured however is the amount that actually stays with the firm, which is the revenue lowered by material input, wages, salaries, interest, rent, royalties, taxes and dividends as can be seen in Figure 6 (Grant, 2010).

Figure 6: Based on the Concept of Value Added explained by Grant (2010).

A company can create value for the customer but does not necessarily need to make a profit (Jorgensen, 2015). Big companies like Amazon or Tesla did not achieve to capture their value the first years the firms became active, even if they are successful companies now. Besides Tesla's high costs for batteries, both companies decided to focus on growth rather than profit. Amazon developed its own movie studio, a consumer electronics business and started an ocean shipping company and an airline (Markman, 2017) while Tesla focused on developing

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an attractive product for the costumer (Archer, 2017). Profit depends a lot on the priorities of the company, as can be seen through the examples presented.

2.3.4 Transformation of Value Capture and Value Creation

Since the upcoming of the internet and the IoT a lot has changed for businesses. Traditionally, value creation meant to react to customer needs with a solution (Hui, 2014; IEEE, 2016). Competition was based on features and as soon as there was a feature innovation, products became obsolete (Hui, 2014). Emerging technologies opened the path to new ways of value creation and value capture and companies need to take it to avoid getting lost in competition (Wei, 2016). The IoT is an enabler for new value propositions and as such also an enabler for new ways of creating and capturing value (Wei, 2016). To enable these potentials, companies have to restructure their value creation and value capture to compete in the new era of the IoT (Hui, 2014).

The changes in value creation can be summarized in three major points: Customer needs, offerings and the role of data. A company's success depends on its ability to address their customers’ needs (Strategyn, 2018). Spacey (2017) defined 19 common customer needs: Functionality and features, price, time and convenience, terms, experience, look, status and identity, reliability and durability, performance, efficiency, safety, risk, formulations, sustainability, packaging integration and compatibility, standards and compliance. However, it is not easy to capture customer needs (Strategyn, 2018). First, a company has to identify their customers based on interviews or focus groups (Conductor Learning Center, 2018). It is important to know which jobs the customers have to finish and what support they need for them (Strategyn, 2018). The result of these interviews are desired outcomes, which can be translated into customer needs. During the upbringing of the IoT, the customer needs have changed. Traditionally, companies reacted to solve existing needs for customers, whereas now companies solve needs on demand in real time.

This is only possible with the evolution of the product offering. Before the emergence of the IoT, companies sold one product at a time. Nowadays, products, and especially their features, can be changed after the original sale happened. Companies can react to customers’ needs in

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20 time and can create new additional features, which are pushed to the product via updates. This offers new possibilities of value creation through offerings, such as subscriptions to service updates.

Connected products offer as well new possibilities for analytics, forecasting, process optimization and customer service experiences (Hui, 2014). Traditionally data was only used at a single point in time for future product requirements (Hui, 2014). It emerged to a method of value creation for the customer. Data is used to better understand customer needs, to reduce costs and to provide more value to customers through features and services. Gartner (2016) has developed an analytics maturity model, which categorizes where a company stands in the use of data. The model can be seen in Figure 7.

Figure 7: Analytics Maturity Model (Gartner, 2016).

Companies usually start with descriptive analytics to see what has happened in the company. Data is solely used for information. A step further is diagnostic analytics taking the business. With diagnostics a company wants to know, why specific incidents happened. These two steps are easily accomplished by computer systems and can be used to analyze customer behavior and trends. The next step, predictive analytics, is more challenging. The software has to predict what will happen in the future. With the complexity of the analytics, also the value for the company rises as customer behaviors can be predicted more accurately. The last step, the

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prescriptive analytics, is analyzing how things can be achieved and are more foresight than insight (Gartner, 2016).

Data does not only provide a source of value creation, but also of value capture as data can be sold to other businesses. In the field of value capture the major changes can again be addressed in three points: Path to profit, control points and capability development (Gartner, 2016).

The traditional company relied on the sale of a product as single revenue stream (Hui, 2014). The IoT offers possibilities for different methods to gain profit, such as advertising, subscription, fee-for-service, licensing (Afuah and Tucci, 2003) or value-added services like apps (Hui, 2014). The goal for a company is always to “enable recurring revenue” to not be dependent on the loyalty of customers (Hui, 2014). An example from the wearable device market is Fitbit. Fitbit sells its devices for a fixed price, but through a premium service additional features can be bought for a fee (Angelidis, 2015).

Recurring revenue can also be assured with new control points that come with the introduction of the IoT. Control points are strategic decisions that enable control of customer-side value and deliver return (Huawei, 2017). The IoT Connctd GmbH (2018) defined three control points of the IoT: End customer experience, trusted access points and an operational standard, which makes development of application accessible. Personalized products enhance customer experience and generate information about and for the customer. While the customer is busy, networks between products within a company, but also with partnerships outside the company, are established to make the customers life easier and to lock him into the network (Hui, 2014).

While control points determine high profits, capabilities and their development are the base for competing in a market (Huawei, 2017). While capability development used to take place in one's own company by strengthening core competencies and existing resources, it is now important to understand business models of competitors and partners as well (Hui, 2014). If a software development company wants to sell its app, they also need to know how the app

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22 store and the android store works. Otherwise they would not be able to sell their product. As a base, there are four types of capabilities a company can have (Capstera, 2018):

● Strategic Capabilities: Responsible for competitive differentiation

● Core Capabilities: Fundamental for a company's existence and hardly replicable by competitors.

● Context Capabilities: Responsible for handle tasks that are obligatory, such as accounting.

● Foundational or Commodity Capabilities: Capabilities that ensure the functioning of the company, but do not add significant value.

Capabilities of a company change, and it may be possible that a foundational capability is transformed into a strategic capability (Capstera, 2018). An example is Apple, who transformed design from a foundational into a strategic capability, which ensured Apple its success.

As said above, the IoT mindset requires companies to establish partnerships. Partnerships have a lot of advantages. Start-Ups can close knowledge gaps or use existing resources, like platforms or solutions, from partners (Chan, 2017). Depending on the type of exchange between the partners, there are various categories of partnership styles (Chan, 2017):

● OEM (Original Equipment Manufacturer) Partnership: This type of partnership occurs when a developer creates a solution and sells it to another company, which resells it under their name. An example could be a device manufacturer who buys a special code from a developer and includes the code in his device.

● Technology Partnership: Technology partnerships base on the creation of core technology and the wish that this core technology is compatible with complementary solutions. Both partners may have to alter their technologies to make both compatible. ● Channel Partnership: A company does not want to sell its product directly to the end customer, but through a channel. They create a network of different channels and provide training of the product, support and marketing.

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● Co-marketing Partnership: Two companies decide to sell their solutions together and market them as complementary solutions. This partnership may require alterations on either side of the solutions to make them compatible.

Table 5 and Table 6 present an overview of the changes in value creation and value capture during the shift from a traditional product mindset to the new Internet of Things mindset.

New Value Creation Traditional Product Mindset Internet of Things Mindset

Customer Needs

Solve for existing needs and lifestyle in a reactive

manner

Address real-time and emerging needs in a predictive manner

Offerings Stand-alone product that becomes obsolete over time

Product refreshes through over-the-air updates and has synergy value

Role of Data

Single point data is used for future product

requirements

Information convergence creates the experience for current products and services

Table 5: New Value Creation (Adapted from Hui, 2014).

New Value Capture Traditional Product Mindset Internet of Things Mindset Path to Profit Sale of the next product Enable recurring revenue

Control Points

Potentially includes commodity advantages, IP ownership and branding

Adds personalization and context; network effects between products

Capability Development

Leverage core

competencies, existing resources

Understand how other ecosystem partners make money

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24

3 Method

3.1 Research design

According to Flick (2015) there are two different ways to collect data in the area of Business Administration: Quantitative and qualitative data. A quantitative research is based on clear measurement as data to find out differences and a better understand in an existing problem. The researchers therefore know directly what they are looking for (Verhoeven and Reed, 2011). These qualitative methods include numerical and quantified data, as social surveys and experiments. A qualitative research on the other hand is adapted from non-numerical data and is used to understand a problem and discover new aspects by analyzing and interpreting interviews, observations or cases (Flick, 2015). This approach is widely used when the researches have only a basic idea about the direction of the research and are flexible towards the results (Verhoeven and Reed, 2011). While reviewing the literature used in the theoretical framework it became clear that the study of the influence the wearable industry has on business models shows a literature gap, which requires further research to build the theory. Therefore, this exploratory research is based on a qualitative approach rather than on a quantitative one.

Nevertheless, qualitative data can also lead to a theory that can be used in a quantitative approach later in the process (Eisenhardt, 1989). A combination of both methods can therefore help to compensate for the limitations of each other. As said by Flick (2006):

“Quantitative and qualitative approaches should not be separated, even if they are being explained separately.”

This paper is going to be based on a case study research conducted by semi-structured interviews. Case study research can be beneficial when the objectives of a research go beyond description and explanation (Woodside, 2010), especially appropriate in new research areas (Eisenhardt, 1989). A case study is an empirical research that focuses on a contemporary phenomenon within a real-life context, especially when the boundaries between these two factors are not clear (Yin, 1994). Eisenhardt (1989) and Dyer and Wilkins (1991) associate case study research with the building of a theory, but other researches also show that case study

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research is also appropriate for theory testing (Howard and Morgenroth, 1968; Gladwin, 1989). In order to build a new business model out of the cases conducted, this research will focus on the process of building a theory.

The decision to choose a single-case or a multiple-case study depends on the issue in question. For cases which cannot be replicated in other cases a single-case design can be used. Cases that can be adopted with real-life events having various sources of evidence through replication are a better fit to a multiple-case design (Zainal, 2007). Yin (1994) adds that the theory building from multiple designs occurs rather on theory than on the population. Therefore, multiple-case studies can enhance and support previous results by replication. Looking at the different advantages and disadvantages a multiple-case study has, which are explained in detail in the limitations in Chapter 6, the purpose of this study matches best with a multiple-case study. Because of that also a multiple-case study is used.

3.2 Sampling

Within a multiple-case design rather a replication process is used than the sampling logic used in statistical sampling. This process can be also described as theoretical sampling and is based on replication by pattern-matching, which is a technique linking different pieces of information from the cases to some theoretical proposition (Eisenhardt, 1989). A theoretical sample is mostly rather small and consists of the cases picked by the researcher (Yin, 1994). Selecting the right population helps to limit irrelevant variables with the aim to find theoretically favorable cases that demonstrate the studied phenomenon (Eisenhardt, 1989). It may not be preferable to choose cases randomly but rather to choose cases showing extreme situations and polar types, which are likely to either replicate or extend a theory and makes it possible to observe contrasting patterns in the collected data (Pettigrew, 1988). In this process we work with cases build out of interviews, with the aim to extend the theory on value creation within the wearable device industry.

Taking this into consideration our sample was chosen through theoretical sampling to extend the theory on value creation and value capturing based on business models including wearable devices. All the samples we included in our study are companies which work within the

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26 technology and/or application software industry and include a wearable device through different approaches into the business model. This method of qualitative sampling makes possible analytic generalizations possible, as it provides the opportunity to select and examine chosen processes in the cases and to define its implications on theory, which will be further discussed in Chapter 6 (Tsang, 2013).

Picking the right sample size for a study depends on the aim of the study and the suitability of cases for the research (Hamel et al, 1993). Because of that the opinions of the optimal number of cases vary a lot. While Donaldson (2000) defines 20 cases as an extreme as many other studies done are based on only one study, a single-case study can be problematic due to control reasons (Yin, 1994). Roscoe (1975) suggests a number of ten to twelve cases and in addition to that Eisenhardt (1989) recommends between four and ten cases for a valid case study. Both recommendations are further supported by Stake (2006, p. 22): “The benefits of a

multicase study will be limited if fewer than, say, 4 cases are chosen, or more than 10 [...]. 15 or 30 cases provide more uniqueness of interactivity than the research team and readers can come to understand.” Further, Eisenhardt and Graebner (2007) the chosen cases should

include polar types that include differences and makes it possible to observe contrasting patterns in the collected data.

For our study we therefore selected eight cases, referred to as Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta and Theta. Within each case a leading role and thus a key decision maker within the company has been interviewed. Although one more interview (Iota) was taken, it was decided to not include the case report Iota into the study, as the case did not include any wearable device in its business model yet. An overview of the eight chosen cases and its characteristics can be seen in Table 7.

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Company Country Industry Product/ Service/ Software Development Stage Distinctive Characteristic Alpha Canada Health Technology and Application Software

Smart Bra Presale Combines technology with fashion industry

Beta Finland Measuring Technology and Application Software Wearable Devices and Motion Sensor Presale and Sale Creation of a new motion sensor, which

will be sold with a development kit Gamma USA Application software and Health Technology

Swim App and Spire Health Tag

Development and Sale

New development of the spire health tag, high

expertise within swimming Delta USA GPS Technology and Application Software GPS and Tracking Products Sale

High variety of niche markets covering multiple areas of indoor/outdoor sports Epsilon Sweden Wearable Technology and Application Software

Smart Band Presale

Goal to make everyday life easier, product can be combined with other

already existing technologies Zeta Denmark Application

Software

Digital

Marketing App Sale

Use of data, partnership with device companies

for tracking Eta USA Chip Technology Micro Chips and

Smart Glasses

Development and Sale

Development of the new smart glasses, one stop

shop Theta Denmark Health technology and application software Virtual Running Coach Sales

Wearable gives not only data but translates data

into instructions

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28 The eight chosen companies show differences in terms of their country of origin, product and development state in order to observe contrasting patterns. The similarity between the companies are as mentioned before, the inclusion of a wearable device into the business model, which is also the basis for this study. Alpha combines its wearable device with the lingerie for women and therefore with the fashion industry. Beta is selling on the one hand its wearable devices and on the other hand a development kit for developers from the outside. Gamma uses its expertise in swimming to develop a new wearable spire, which can be invisibly worn in the swimming suits or swimming trousers the company sells in its partnered retail store and Delta is special due to its high variability within special wearable niche markets. Epsilon is launching a new wearable that is not directed towards the sports industry but towards needs in daily life as payment and entrances. Zeta is based on an application software but is using wearable devices from a partner company to improve its application and receive data to generate revenue. Eta is a very big company that develops all products in-house, produces various wearable devices and launches soon completely new designed smart glasses. Finally, Theta stands out through a special developed chip within its wearable device that gives not only data but also translates the data directly into instructions.

A multiple case design relies rather on theoretical than on random sampling (Eisenhardt, 1989). Yin (2009) adds that cases should be chosen with the expectation to either provide similar results or contrasting results, as similar results make it possible to generalize the study and contrasting results helps to establish boundary conditions. In this study all the mentioned cases were chosen as they were expected to show similar results, regardless their contrasts in the background. This makes generalization possible and reduces therefore the risk of bias.

3.3 Data Collection Method

The data for this project was collected through semi-structured in-depth interviews via Skype with one representative of each companies. Semi-structured interviews are based on a formal interview for which the interviewer developed an “interview guide” with questions and topics that need to be covered. The interviewer is able to follow the topics but can also let the conversation go into new areas if the respondent strays in a new direction with its answers (RWJF, 2008). According to Bernard (1988), semi-structured interviews can provide reliable

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and comparable qualitative data. The questions were open-ended to assure that the interviewee can talk freely and that the answers are rich in detail (Dörnyei, 2007).

Skype as a conduction method for the interviews was used, as it gave the opportunity to conduct interviews with companies from all over the world and receive an international sampling as it nullifies the need “to visit an agreed location for interview” (Rowley, 2012, p. 264). Because of that it offers therefore the opportunity of a wider geographical spread of participants in a safer, cheaper and quicker way than face-to-face interviews do (Oates, 2015). A communication program like Skype mimics also face-to-face interaction and makes an authentic presentation possible in a similar way than face-to-face-exchange (Sullivan, 2012). He adds, that with modern society's dependence on technology in nowadays world, so much individual time is spend on the web that presentations online have a higher potential of being accurate than in previous decades (Sullivan, 2012). In total nine interviews have been conducted via Skype between February 2, 2018 and February 28, 2018, from which eight ones have been used for this study.

One important aspect we considered during the design process of the interview was wording. The interviews were conducted in English (the case Iota, that was not used for this study, was conducted in German as seen in the Appendix) with simple and short questions following Barbour and Schostaks (2005, p. 43) recommendation “the shorter the interviewer’s questions

and the longer the subject’s answers, the better an interview is”. All the questions were

non-directive, so the interviewees were not forced to answer in a specific way but could tell and present their own opinions and ideas (Sue and Ritter, 2012).

The interview guideline for this study was structured in three different parts. The first part was related to basic questions about the background of the company. We opened the interview with these questions in order to establish an appropriate atmosphere and a natural flow (Harrell and Bradley, 2009). Nevertheless, we kept the section short to maintain the

“interviewee´s motivation by keeping boredom at bay” (Berg, 2007, p. 210). The second part

of the interview was based on the current business model of the company the interviewee works in or is leading. These questions were structured by the combined approaches of Gassmann et al (2014) and Osterwalder and Pigneur (2010), as shown in Chapter 2. We

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30 decided to base this part on these two combined approaches to get a general understanding within the Who-What-How-Why approach by Gassmann et al (2014) but also reach a deepness within each category through Osterwalder´s and Pigneur´s (2010) nine building blocks. The last part was focusing on trends within the wearable device industry especially the model

“device as a service”, to see if this trend is used within the companies or if there are other

specific developments happening.

In order to find out if the interview guideline works and collects valid and reliable information a pretest was done to find mistakes or unclear expressions. The responses of the pilot testing provide the researcher with a valuation of the reliability and suitability of the interview questions (Saunders et al, 2009). In this research study a pretest has been done on January 31, 2018 in Linköping. As one of the authors of this study was employed at Campushallen during that time, one of the coordinators of the fitness institution in Linköping came forward to do the pretest in person, which gave the opportunity to make some small modifications and develop the final structure after reviewing the pretest and getting feedback in from the test candidate.

All the final interviewees have been recorded and to ensure that all the information gathered are going to be interpreted in a correct way and the meaning of the answers is not being transformed, a case report for each interview has been written. To write these reports all the answers of the interviewees have been discussed detailed by the authors of these thesis in order to ensure that the responses are understood clearly and the interpretation in the written cases align with the actual statements made. All the case reports can be found in the Appendix of this study. Out of the case reports three different tables have been formed that can be seen in Chapter 4. The first table shows a detailed definition of each cases´ persona, which will give the basis in order to construct the new business model. The second table is pointing out the most important factors for each case regarding each company’s current business model, structured according to Gassmann et al (2014) and Osterwalder´s and Pigneur´s (2010) nine building blocks. The third table concentrates on the outcomes regarding the value proposition with focus on value creation and value capture by Hui (2014).

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To ensure that all the information gathered is complete we consulted the interviewees again if information were missing or some general clarifications have been needed to complete the tables. This ensured that the used data is valid and correctly interpreted to limit the risk of bias. These finished tables gave the basis for the analysis presented in Chapter 5.

3.4 Data Analysis and Interpretation

The data analysis within this study is based on a within-case perspective and a cross-case perspective. The within case analysis focuses on a narrative perspective shown in the cases, in order to describe and demonstrate how wearable devices are integrated into the business model of each firm. A within case approach is important to understand each case in depth and to become familiar with the data collected (Eisenhardt, 1989). Each case helps therefore to identify “unique patterns of each case” (Eisenhardt, 1989). The within case analysis is done through a narrative strategy, which includes to describe each case in detail, focusing on the business model of each company and the implementation of wearables within it. To describe the business model in a detailed and complete way the information within the cases have been structured into three different tables as mentioned above in Chapter 3.4. Eisenhardt and Graebner (2007) point out that the use of tables or visual applications that summarize the case evidence to emphasize the depth of the empirical grounding of the theory. In each table the columns show the main characteristics each business model shows in terms of persona, the business model canvas by Osterwalder and Pigneur (2010) and the characteristics enabling value creation and value capture within the business models based on Hui (2014).

The generated tables of the within-case analysis build the basis for the cross-case analysis. The table format makes it possible to look beyond assumptions and observe similarities and differences between the eight business models. This method of identifying patterns is called grounded theory and initially developed by Glaser and Strauss (1967). This approach is based on the interplay between empirical data collection and data analysis with the aim to develop a concept through coding. This procedure makes it possible to generalize theory. The use of multiple cases allows it to understand concepts better and to get new insights or refine previous insights (Glaser and Strauss, 1967). This study is using the general understanding of the grounded theory, which means to find patterns in a pool of data based on several case

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32 studies. The raw data presented in the persona table is used to point out noticeable patterns by categorizing them. The raw data in the tables for the business model and the value creation and capture is additionally coded, based on the definitions and characteristics described in the theoretical part of this study in Chapter 2. The use of coding is an important part of the qualitative research process, as it helps to capture similarities of experiences across cases (Coffey and Atkinson, 1996).

The categorizations within business models are coded according to the different categories of business model components of the nine building blocks by Osterwalder and Pigneur (2010). A detailed description of the categories can be seen in Chapter 2.2 and in the Appendix. The categorizations within value creation and value capture are based on the two frameworks for value creation and value capture by Hui (2014) and coded due to the different frameworks presented in Chapter 2.3, as well as based on the findings from the within-case analysis. This includes the B2C elements of value by the Bain and Company (2016), the value chain by Porter (1985), the framework of value added presented by Grant (2010) and the analytics maturity model by Gartner (2016).

The cross-case analysis is done by comparing the different codes within the categories per case. This makes is possible to observe if specific elements are appearing in multiple cases or contrasting each other. This is done through the help of cross case analysis matrices, which are presented in Chapter 4. Both categories and codes are means to answer the research question in the end.

3.5 Reliability and Validity

The validity and reliability of the collected data shows if an empirical research is of high quality and can therefore achieve the same results by replication (Yin, 2014). This research study ensures reliability through the generation of a database build on the literature data and the interviews that have been recorded as well as transcribed into cases. Regarding this study to be replicable, the cross-case analysis builds reliable findings, as they are supported by theory about business models, value creation and value capture as well as interferences between the eight cases. Nevertheless, there could be also further reasons explaining the discovered

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patterns, which have not been taken into account in this study and can therefore reduce the reliability of the research. This means replication of the study can be seen rather as an extending of the findings and therefore as a contribution.

Validity refers to the “extent to which data collection method or methods accurately measure

what they were intended to do” and if these findings have been presented veridic (Saunders

et. al., 2009: 603).On the one hand there is internal validity referring to correctness of results and conclusion and on the other hand there is external validity referring to the generalization of the results (Yin, 2014). As mentioned before in Chapter 3.2 case studies can have a lack of external validity. Nevertheless, by using a multiple case study supported through existing literature a certain degree of generalization can be achieved (Yin, 2014; Eisenhardt 1989). As the purpose of this research is also an exploratory one, it aims to get a better understanding of our research problem and therefore meant to be the basis for further studies in similar areas, which makes our business model presented in the outcome not conclusive. Instead, the study aims for a better understanding of the problem and research gap, by discovering a special set of patterns (Yin, 2014).

Regarding the internal validity, this study is based on a multiple case study. This makes is possible to achieve a larger scope in order to back up the findings (Eisenhardt, 1989; Eisenhardt and Graebner, 2007; Yin, 2014). Using semi-structured interviews based on a framework and a narrative approach for the within-case analysis ensures to present the data truthfully and set equal measurements for each case. The additional fact, that all the findings can be linked to existing literatures, justifies further the studies validity (Eisenhardt, 1989).

Wearable Devices : A Technological Trend with Implications for Business Models