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Delivering a New Business Model in the Data-sharing Economy:
What Motivates Consumers to Share their Personal Information
MSc. Business Administration
Digital Business Track
Master Thesis
Author:
Auke Kilian Holtrop
11422890
aukeholtrop@gmail.com
Supervisor:
Dr. Somayeh Koohborfardhaghighi
s.koohborfardhaghighi@uva.nl
Second reader:
Prof. Peter van Baalen
1 Abstract
Companies try to stay ahead with competitive business models that create more value for consumers than competitors. Nowadays value co-creation arises between companies which provide digital services and those which are involved in the production of goods to create connected services. New business models in the digital or data-sharing economy should encourage consumers to share their personal information free so that it could increase value in the entire business eco-system. Instead of paying for the received digital service over a physical device, consumers are supposed to exchange their personal data with the service providers. If enabled correctly, by knowing the habitats of its consumers a business could easily share its users data and this new revenue stream could be worth a lot. Another form of revenue could be knowledge creation by the aggregation of the obtained data. The contribution of this thesis is twofold. First, this research will address the urge for the development of new business models based on the concept of digital co-creation and connected services. Second, it will create an understanding of how such new business models creates new customers experience. Specifically, it investigates how such new business models encourage customers to share their personal data despite the perceived risk of doing so. To illustrate the importance and potentials of the value co-creation process an example about wearable technology ecosystem (e.g. smartwatches) is provided. I collected my survey results of 148 participants on their willingness to share personal data in exchange of the received free digital services over a smartwatch. I aim to capture the influencing factors which facilitate the trading between free data and received benefits. The results of my analysis show that the reputation of the service provider and the benefits offered by the service provider are very important for this purpose.
This thesis will also have some practical implications for managers. The first practical implication will be the answer on how to participate in the value co-creation process with other existing digital services in the market to create more value for every actor in the business ecosystem. That could be the solution to create a competitive advantage because of delivering an incredible customer experience. Secondly, it will become clear what potential barriers consumers could have for sharing their personal data by offering connected services, despite the perceived risk of doing so.
2 Statement of Originality
This document is written by Student Auke Kilian Holtrop who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Auke Kilian Holtrop - Amsterdam June 21th 2018
3 Table of Contents Abstract ... 1 Statement of Originality ... 2 List of Figures ... 4 List of Tables ... 4 1. Introduction ... 5
2. Literature related to business models ... 11
2.1 Business models and their artefacts ... 11
2.2 New sources of revenue models ... 13
3. Designing a new business model around the concept of value co-creation ... 15
3.1 Definition of value co-creation ... 15
3.2 Digital value co-creation ... 16
3.3 Digitalization, sharing personal data and privacy issues ... 17
3.4 Research gap ... 21
4. Conceptual model ... 25
5. Research methodology and data collection ... 28
5.1 Sample and participants ... 28
5.2 Survey design ... 29 5.3 Measures ... 29 6. Results... 31 6.1 Analytical procedure ... 31 6.2 Sample Data ... 32 6.3 Hypotheses testing ... 33
7. Conclusion, discussion and implications ... 35
7.1 Conclusion and Discussion... 35
7.2 Limitation and suggestions for future research ... 38
7.3 Managerial implications ... 39
Bibliography ... 40
Acknowledgements ... 45
4 List of Figures
Figure 1 - Illustration of the proposed business model of digital co-creation and connected services ... 9
Figure 2 - Conceptual model ... 26
Figure 3 - the ecosystem of companies based on digital co-creation and connected services ... 36
List of Tables Table 1 - Summary of literature review ... 22
Table 2 - Variable definitions and measurements ... 30
Table 3 - Rotated Component Matrix ... 32
Table 4 - Means, Standard Deviations, Correlations ... 32
5 1. Introduction
Business models have changed in recent years so that companies stay competitive and remain profitable in dynamic markets. Some researchers argue that a shift in the static business model where the core logic of the company does not change is needed towards a situation where the core logic does change a lot and that the business model became a journey model specifically for the consumers (Linder & Cantrell, 2000).
New business models are focusing on the increasing importance of value creation. Meanwhile, this focus elaborates on the interest in value creation for all different actors and partners within the company’s ecosystem to maximize profit (Nenonen & Storbacka, 2010). In recent years, research goes into detail of increasing added value, whereas the term ‘value co-creation’ arises. In short, adding value by combining values and resources of different companies. Via ‘co-creation’, a combined product or service could emerge, with more value for consumers (Järvi & Pellinen, 2011; Ng, 2010). An understanding of different types of business logic for companies is needed to understand and see the potential of value co-creation. In 2004, Vargo and Lusch (2004) introduced a new dominant logic for organizations. They acknowledged the change in rethinking the orientation for organization (Vargo & Lusch, 2004). This implies a locus of control shift from the simple exchange of products to consumers (i.e., good-dominant logic). to a complicated locus of control which focusses more on creating value for customers by creating an experience (i.e., service-dominant logic) (Vargo & Lusch, 2008b). In a service-dominant (S-D) logic, customer co-creation starts to evolve whereas the consumer has an important role within a company. Grönroos (2008) argues that consumers can create value for themselves as they can cooperate with the company and that companies could benefit because they can create value together with consumers. A specific example of this kind of value co-creation together with a consumer is where a good-dominant logic company (i.e. manufacturer of wearable tech devices) is testing its products together with consumers, whereas they have the ability to help developing the final product. In these spheres, the company could increase the value for consumers by listening to the specific needs of them and implementing new features, which increases the value of the product (Grönroos, 2008; Payne, Storbacka, & Frow, 2008).
6 To address this phenomenon of value co-creation, new business models are needed. In addition to the value co-creation concept, technology has also become an important factor for the development of business models. It changes every aspect of the way a business operates. Valjakka et al. (2012) already introduced the value co-creation concept with the use of a case study. Meanwhile, technologies were not combined within this research. Embedding technologies and services from other enterprises within the existing business models could improve business processes and create potential for growing and expending. In other words, the usage of new technologies within old business models could increase the value of products and services.
The use of technologies like Artificial Intelligence, Machine Learning, Cloud Computing and Security Services are compulsory when companies decide to be digital. The difficulty in these spheres is when a company does not have the capacity and ability to augment such technologies into their business models to improve their products and services. However, companies do have the capacity to create new business models with the concept of value co-creation. Companies could try to acquire each other’s services or solutions to create more value for their consumers. These combinations and integration of services are more useful when combined with hardware products. Therefore, it is very important to integrate the old way of doing business with new technologies and existing solutions in the whole business eco system for further value creation and delivering better customer experience (Osterwalder, Pigneur, & Tucci, 2005). To illustrate the importance and potentials of the value co-creation process an example about the wearable technology ecosystem (e.g. smartwatches) will be introduced.
Wearable tech devices sales are increasing every year. According to Statista (2018), in 2017 there were 453 million wearables connected and the expectation will be 929 million connected wearables in 2021. These wearables could add value for consumers in many ways e.g. health tracking, sport activities etc.). Also business and lifestyle activities and functionalities are added in these wearables by having email, messaging and other API’s that increase the value of the wearable tech device. A requirement for these wearables is that they actually track personal activities of the consumers and show some useful information to them (e.g., heart rate, blood pressure, counting the
7 steps etc.). As a result the usability of a wearable tech device goes hand in hand with proper software and services. These products are almost worthless for consumers without the right applications. As some good-dominant companies (G-D) do not have enough capacity or resources to create a service in-house, they need the help of other S-D service-dominant companies to integrate the right services with their products. Such business models are based on connected services and have the potential to deliver added value to all actors within a business setting. Connectivity between services has the potential to deliver innovative solutions and to generate benefits for all service providers (Koohborfardhaghighi & Altmann, 2014). This is actually the main idea of value co-creation.
The idea of delivering new business models based on connected services could also be seen as a form of ‘digital co-creation’, because services of other companies can be delivered in a digital format as well (e.g. financial services and security services).
Most services a wearable tech company delivers are free after buying a wearable tech device (e.g. receiving messages of missed phone calls, emails, alerts from specific news topics etc). However, the idea of digital co-creation opens a new space for companies to renew their traditional business mode and to get post-revenue. Think about all personal activities that are tracked by the wearable tech device. Such devices generate a huge amount of data which can be considered as big data. This stream of data could be a post-revenue stream for the company and can be seen as a resource for generating added value (e.g. location based advertising and marketing but also selling aggregated research to create market knowledge).
The more data generated out of wearable tech devices, the better the profit of the company. However, there are two main concerns in this scenario one is related to big data processing and the other is around security and privacy in sharing personal data. The two mentioned issues which will be addresses in the following.
First, when it comes to data collection, processing and knowledge discovery, it is difficult for a wearable tech company to compete with big competitors in the industry (i.e., due to lack of resources or even change in the activity focus). These days, companies like Google, Amazon and Facebook are
8 delivering platform or services for processing big data and data knowledge discovery. A wearable tech company can use the services of such big enterprise to generate knowledge out of the collected data of their customers.
Second, security and privacy have always been the main concerns of consumers especially when it comes to sharing their personal data (Kim, Sankar, Wilson, & Haynes, 2017; Phelps, Nowak, & Ferrell, 2000; Troiano, 2017). Consumers know that they can be tracked by companies collecting their personal data and habitats can use such information against them (Kokolakis, 2015; Troiano, 2017). Consumers want to make sure that they have a certain level of control on their personal data and check whether they are shared with others. According to the research by Quint and Rogers (2015), 85 percent of consumers want to know more about the personal information companies collect, and even 86 percent want to exercise greater control (Quint & Rogers, 2015). This addresses another challenge for wearable tech companies to address the delivery and security concerns of the consumers.
To illustrate how a business model based on the value co-creation concept can improve its ability to both create and capture value, a specific example will be introduced for a wearable tech manufacturer. To address the first issue, the manufacturing company could use a analytics software package service to create knowledge about its own generated data. To address the second issue, security services could be bought to let the consumers feel that their data is safe and secured (e.g. Security services by Fujitsu (Fujitsu, 2016)). Both services are not for free and the wearable tech company has to pay for them, but the generated revenue out of the collected data can be considered as the new added value creation through this proposed new business models. Since the security of the processed data is guaranteed through a third party, it also has the potential to impact the willingness of consumers to share their personal data. This proposed business model is summarized in Figure 1.
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Figure 1 - Illustration of the proposed business model of digital co-creation and connected services
Based on the above mentioned line of arguments, I present the following research question:
How does a new business model based on digital value co-creation (connected services) increases business value in the entire ecosystem and impact the willingness of consumers to share their personal data?
This thesis encompasses a new business model based on the idea of digital co-creation and connected services. To illustrate the importance and potentials of the value co-creation process an example about wearable technology ecosystem (e.g. smartwatches) is provided. Moreover, in this thesis I investigate how the proposed business model impacts the willingness of consumers in sharing their personal data.
10 The contribution of this thesis is twofold. First this research will address the urge for development of new business models based on the concept of digital co-creation and connected services. It will create an understanding of digital value co-creation, value creation via new business models and creating connected services. Second it will create an understanding of how such new business models create new customers experience. Specifically, it investigates how new business models encourage customers to share their personal data despite the perceived risk of doing so.
This thesis will also have some practical implications for managers. The first practical implication will be the answer on how to create a digital value co-creation with other services to create more value for every actor in the business ecosystem. Explicitly, which kind of revenue creation is possible using digital co-creation within the development of new business models to maximize business profits and values for consumers. That could be the solution to create a competitive advantage because of delivering an incredible customer experience.
Secondly, it will become clear what value connected services will deliver to consumers and how that could impact the customer experience. What potential barriers consumers could have for sharing their personal data by offering connected services, despite the perceived risk of doing so. What factors are most important for them? In this way, companies could target and manipulate the customers to increase the willingness to share their data.
The rest of this thesis is organized as follows. In chapter 2 the current literature on business models and revenue streams will be discussed. Chapter 3 will go through the literature on co-creation, value co-creation, privacy issues and the willingness to share of personal data and its influencing factors. The research gap identified in the literature will be discussed in this section too. In chapter 4, the conceptual model and hypothesis will be presented. This is followed by an extensive explanation of the research design and method in chapter 5. The results of the analysis will be presented in chapter 6. Chapter 7 will provide the conclusion and discussion over the findings and implications for managers.
11 2. Literature related to business models
2.1 Business models and their artefacts
A good possibility to create new businesses in the fast changing environment is with new technologies arising. But what kind of business model should fit a business like this? Therefore, I will discuss what a business model consist of and which specific elements are important before starting a business with the help of digital co-creation. A description will be given of the basic artefacts what a business model consist of as theoretical concepts. Regarding the digital environment where this research is focussing on, Bradley et al. (2015) indicates multiple business models. The authors describe three major categories of the disruptive business models by creating value. Firstly, they argue that a business model could be focussed on ‘cost value’, whereas cost reduction in an existing market has the primary focus. Secondly, they indicate a category which focusses on experienced value for the consumer and lastly they indicate another business model category that focusses on platform value. Creating value by sharing and combining knowledge and resources to create value for the consumer (Bradley, Loucks, Macaulay, Noronha, & Wade, 2015).
According to Liedtke et al. (2017) a business model consist of three different artefacts. The first one is the value proposition, that states the actually offered product or service to the customer. The second one is the value chain, how is the value created and the third one is the revenue model, how the revenue stream is created (Liedtke et al., 2017). There are multiple articles and authors that have the same view on these three artefacts (Osterwalder & Pigneur, 2010; Osterwalder et al., 2005; Pourabdollahian & Copani, 2017). As these artefacts do sound quite straightforward, they are more complex, especially the revenue model.
2.1.1 Value Proposition
Simply stated, the value proposition is what value you deliver for a specific consumer segment. In other words, what kind of product or service is delivered to the consumer, by the combination of the elements; value, infrastructure and network, the relationship capital between parties and the sustainability (Osterwalder & Pigneur, 2010).
12 In the case of digital co-creation, the combination of different products and services which create a new service, could be stated as the value for the consumer. This value proposition could be related to the theory of competitive advantage whereas a specific combination of resources could lead to competitive advantage (i.e. Resource Based View of the Firm (Barney, 1991)), but also the five market factors that could lead to competitive advantage (i.e. Five Forces Model (Porter, 1979)).
2.1.2 Value chain
The value chain of the business model is the way how the value proposition is created. Liedtke et al. (2017) explains the value chain as follows; “A firm has to master and orchestrate several processes and activities, resources and capabilities in its internal value chain to build and distribute the value proposition.” (Liedtke et al., 2017, p. 42). In other words, this value chain (the mastering and orchestrating of the value chain), is the way of how the value proposition could be delivered and which is sustaining for the business.
2.1.3 Revenue Streams
In the business model context, there are tons of different options to create revenue from a product or service. For example continuous revenue streams, which generate a continuous income. But there are also possibilities like monthly subscriptions, selling a product once, pay-per-use, yearly revenues etc.. In every specific market, series of different revenue streams are applicable. For example, in the Social Software Industry, revenue models like advertising, membership licensing, affiliate programs, donations and merchandise are systems to create a revenue stream from (Chai, Potdar, & Chang, 2007). Software-as-a-Service (SaaS) has for example three major revenue models namely Pay-per-Use models, Software Rental model and Software Licensing models (Ojala, 2013). Even for Open Source Software Businesses, revenue streams are possible such as a revenue in resources or licensing for enterprises and free use for private users (Rajala, Nissilä, & Westerlund, 2007). Another example for a specific business is E-learning. Besides the ‘normal’ revenue streams like subscriptions, pay-per-use and memberships, this market has also to do with indirect revenue streams. This indicates possibilities not only in the E-learning market but also other markets.
13 In this case, indirect revenues are created like an increase in subscriptions because of seeing an advertisement. Another indirect revenue stream is that companies would like to sponsor a well-known platform, to increase their own brand awareness. Finally, the distribution of content, or sharing knowledge for free, result in more traffic of consumers and more subscriptions and memberships (Mendling, Neumann, Pinterits, Simon, & Wild, 2005). Of course, all these forms of globally accepted revenue streams do have their own specific completion within a business model and a specific organization.
2.2 New sources of revenue models
Respectively new revenue models refer to the revenue models where a gap could be identified and where not too much research has taken place. It is called respectively new because there are some authors started writing already earlier about the topic of revenue streams by data. For example, Gallaugher, Auger and BarNir (2001) already point to the development of syndication which implies bundling large number of unrelated data that could be very profitable. The hypothesis which were significantly tested was a positive relationship between the performance online and the syndication of content to other online firms and services.
There are different types of data which can be shared. Therefore, some political and ethical debates arise. Major topic, selling personal healthcare data (Kaplan, 2014). Because of strict regulations, finding a new business revenue stream on selling data with a personal touch, is not sustainable. Lambrecht et al. (2014), indicates three different revenue streams for online companies that offer digital goods. One of these revenue streams is regarding selling data which mainly implies selling personal data to direct marketing companies. This kind of selling data (or in the form of cookies) is already widely accepted, but are constantly tortured by new privacy regulations and customer feedback. To get around these privacy issues, a privacy firewall needs to be added such that personal data is not personal anymore. From that source of big data, data analytics combined with data science, knowledge about the original group of consumers can be a huge gain. A strict privacy problem is the relation of selling data which is ‘anonymized’, but is actually completely related to a specific person. Of course, this brings problems while this data is not that much worth.
14 In contrast of tracing back the original person, another form of selling data is possible. Selling aggregated data and knowledge. In this form, it is not possible to trace a specific person and privacy issues can be discarded. Within the industry of mobile sensing, Li, Cao and La Porta (2012) tried to aggregate data and being privacy aware using a specific aggregator.
Schüritz, Seebacher & Dorner (2017) are seeming to be the first researchers indicating a pay-with-data structure, as well as a multisided revenue model. This method implies that a consumer is going to pay for using a service, by providing personal data. The multisided revenue model is focussed on for example a service like Youtube with uploaders and streamers, which need to pay both for using the service. So this implies that this concept is not new.
Regarding the topic of revenue streams, to my knowledge, there are still no researchers or presented articles that focus on the business model of connected services and use the source of data, aggregated to pass the privacy issues, to get revenue from. Selling it as a anonymized version of aggregated data is a possibility to compete with corporate multinationals like Google and Facebook. The knowledge extracted could be sold in niche markets and advertisers. Other kinds of knowledge could be totals of usages to create value for another specific markets.
15 3. Designing a new business model around the concept of value co-creation
3.1 Definition of value co-creation
Co-creation, or value co-creation, could take place in different contexts. Co-creation could be business-to-consumer (B2C), whereas together with the consumer a certain products’ value can be increased which could lead to a positive performance (Gruner & Homburg, 2000; Wong, Peko, Sundaram, & Piramuthu, 2016; Zhang, Jiang, Shabbir, & Du, 2015). Another form of possible value co-creation is business-to-business (B2B) whereas businesses create together more value for the consumer (Jouny-Rivier, Reynoso, & Edvardsson, 2017).
Jouny-Rivier, Reynoso and Edvardsson (2017) tries to argue which specific determinants indicated a potential in service creation and try to understand what leads to commitment to co-create new services in the business to business logic organizations. Unfortunately, this is difficult to explain because service value co-creation does not have a specific definition. There is an increasing importance of service co-creation in the B2B setting (Lambert & Enz, 2012). Regardless of the importance, B2C co-creation is still easier to quantify which indicates the preference for researchers. Most recent research by Ramaswamy and Ozcan (2018) focusses on the enactments of creation through interaction. This perspective researches deeper than the ‘two human actors coming together in activities’ (Ramaswamy & Ozcan, 2018). Grönroos (2008) does have an entire different perspective on the creation of value. He argues that firms cannot create value for a consumer, but that the firm is only facilitator of a service. The creation of value is done by the actual consumer itself. A contradiction of this statement is that value co-creation happens in all phases of the innovation process (Wong et al., 2016), which relates to the B2C co-creation whereas the consumer involves in the entire product innovation process.
16 Kohtamäki and Rajala (2016) introduced a table of multiple definitions used to explain value co-creation ranging from collaborative production development to joint ventures and co-production. In this research, a clear definition of value co-creation needs to be addressed. Therefore, I combine multiple definitions and create my own definition which is suitable to this research;
Value co-creation is the creation of value by multiple entities via joint production whereas a combination is made of multiple services and if applicable with good-dominant logic companies.
The first part of my definition is created by combining definitions of Terblanche (2014) and Lusch & Vargo (2006). that depicts co-production. Whereas Terblanche (2014) referred perfectly that co-creation happens by a stakeholder participating in the process and Lusch and Vargo (2006) relates to ‘any other partners in the value network’. The second part is based on working together with the consumer or other entities creating value by co-production which are not earlier described by other researchers. Therefore this is part is added to the definition. The definition of Vargo and Lusch (2008) focusses only on B2C logic instead of B2B which are not applicable to this proposed definition of value co-creation in the B2B environment.
In 2010, Ng already pointed towards the importance of value co-creation, rather than creation of value alone. Services are going to be more complex, and yes, this movement has taken place and we are heading towards an even more complex value creation context (Ng, 2010). Even though, extending a business word of mouth via the Web, is also a great opportunity via co-creation to improve the business value (Bughin, Chui, & Manyika, 2010).
3.2 Digital value co-creation
The world is changing rapidly, especially with the use of digital technologies. To illustrate the digital importance of technologies a few strategic predictions for 2018 by Gartner will be highlighted. Gartner is a company specified in technology research and points towards 10 strategic predictions, whereas six of these predictions are focussed on digital technologies to become increasingly important. I will point to four of them, to generate an understanding of this changes. First of all, Gartner predicted “that 5 of top 7 digital giants will fully self-disrupt to create their next leadership
17 opportunity”. Second, blockchain-based cryptocurrencies will create business value. Third, artificial intelligence will create more jobs than it actually deduct. And lastly Gartner predicts that Internet of Things is in everything in the near future (Panetta, 2017). All these predictions relate to the digital aspects of innovations and changes, as well as a new digital era, where digital value co-creation could be part of this modernisation.
The definition of value co-creation stated in this research points to the importance of creating value together with others, in this case focussed on B2B. Co-creation and value co-creation are phenomenon’s that are already decades old. But in this era, services and products could become digital which could have a major impact. That indicates the importance of value co-creation in the digital age. Therefore, digital value co-creation increases importance of technology and its implications which definitely could change markets entirely.
3.3 Digitalization, sharing personal data and privacy issues
As already mentioned, digitalisation became increasingly important. Companies try to acquire as much data as possible of their consumers to gain knowledge about usage, feedback, imperfections and improvements. That means, sharing personal data to service providers via using hardware is needed for companies to obtain that data. In this part it will become clear which characteristics or factors have an impact on the willingness to share personal data.
3.3.1 Willingness to share data
By the development of new technological devices, consumers see opportunities arising by the usage of the devices, combined with platforms to generate knowledge of themselves. This is normally done by obtaining data of lifestyle, health and sport activities. By tracking heartrate, total steps, calories burned etc., consumers try to quantify themselves in the hope to improve life and create an understanding of their habitats. There are number of ways that consumers try to quantify themselves. Whereas woman could try to understand ovulation and menstruation, men try to outperform others on health and sportsmanship (Spiller et al., 2018).
18 An example will be used to understand why people tend to share data combined with a business model regarding sport activities. A smartwatch with GPS tracks a consumers behaviour which starts a cycling activity on his smartwatch with the help of Strava, an API. After finishing his ride, he upload his ride on Strava (i.e. sharing his data), where he can see all his statistics such as heartrate, speed and leverage in the mountains. Afterwards when logging in on his pc, he gets a notification. He is the fastest cyclist on a specific route, faster than everybody else! At that moment, the competition started and this cyclist measured himself compared to others and start sharing his sporting data with Strava again and again. Strava combines all cycling tracks and comes up with a ranking. Unfortunately, not everything is for free. The cyclist could buy a subscription to the platform which gives access to multiple analysis tools to see the progression he made during the season.
3.3.2 Factors that affect consumers’ willingness to share their personal data
Understanding the incentive why consumers wants to share their data, it is important what affects the willingness to share data. Meanwhile, companies try to capture the privacy and data collection within their cookie disclaimers, privacy statements or app permissions, to give sense of control to the consumers. Actually the consumers are still left in uncertainty what is happening with their data (Bastian, 2017). So what are the main drivers for consumers to share their personal data with the use of a service in return and more important, what is influencing it?
Personal advertising is already common in the marketing world, which is based on personal information (Evens & Van Damme, 2016; Martin, Borah, & Palmatier, 2017; Troiano, 2017). Many researches specifically focus on the privacy issues regarding advertising. Leon et al. (2015) also investigated the willingness and unwillingness to share personal data. This research specifically addressed the Online Behavioral Advertising. According to this research, consumers are willing to share if; they receive relevant ads in return, if it is not personal/secret or private and if it simply stated doesn’t matter. These three were indicated by respondents mostly, as there are more reasons to share. Top three reasons not wanting to share data are; if it consist any personal information, if it is none of their business or if the information is unnecessary for advertising. In general, 55% of all respondents were not comfortable sharing any type of data (Leon et al., 2015). There is more research performed
19 regarding the willingness to share. Schudy and Utikal (2017) also conducted a study with financial rewards to find out what privacy is worth. Schudy and Utikal came with interesting results. The most interesting result is that the willingness to share is decreasing with the amount of receivers of that data. Furthermore, data bundling does not have an effect and verification of personal data does, specifically on males’ measured willingness to share. Bastian (2017) investigated if the facilitation of consumer understanding affects the attitude on personal advertisements. Bastian’s findings show that the consumers’ willingness to share data increases when they are provided with clear and comprehensible declarations of data usage. That gives an understanding where the specific threats are within their privacy. That covers the personal level of privacy attention as well. This research also confirms that there is a major concern regarding losing control of personal data. Bastian (2017) focussed further more on the level of personalization as a result sharing personal information based on variables like companies trustworthiness, consumer attitude and data sharing attitude. Companies trustworthiness (i.e. brand trust) is supported by Quint and Rogers (2015) as well. Spiller et al. (2018) added another variable regarding the type of data which is shared and whether the personal attitude is affecting the willingness to share personal data. Phelps, Nowak and Ferrell (2000) already made a connection between the purchase intention and the data which is shared in the contest of shopping behaviour. They also confirm that there are concerns about what is happening with the personal data of consumers. Their input factors are amount of control, type of data that is shared, potential consequences and benefits and the consumer characteristics. The research of Prince (2018) is consistent with the factor of the consumers’ control of their personal data. The basic model of spatial data sharing (Wehn de Montalvo, 2003) indicated drivers of willingness to share spatial data regards the interrelationship between attitude, social pressure and perceived control. Another study focussing on sharing personal data via the internet uses the attributes; data type, benefit of sharing and the extent to which anonymization is guaranteed (Ziefle, Halbey, & Kowalewski, 2016).
Personal data sharing within the topic of healthcare has been the subject of many studies. It is a challenging issue due to regulations, as well as that consumers are more frightened about their personal dossier with health information whereas criminality is quickly associated with this topic
20 (Kaplan, 2014; Kim et al., 2017). The willingness to share personal health data to non-profit organizations and universities is much higher than to share with profit and insurance companies (Pickarda & Swanb, 2014). That also regards to the motivation, which is mainly improving the consumers own health, which implies improving the consumers current state of health.
3.3.3 Privacy issues
As just discussed, privacy concerns arise at consumers when speaking about sharing data for selling purposes, and using personal data. But what kind of privacy issues arise? What is the basis of this privacy issues where consumer-data driven companies need to take into account while developing a new business or business model.
Kokolakis (2015) did a meta-analysis to understand the privacy paradox, but concluded that there is not a clear explanation of a specific paradox as most articles write about. Privacy has to do with personal preferences and personal attitudes and behaviour are affecting the paradox. Kokolakis (2015) related the privacy paradox to different explanation which reaches affection on privacy decisions by cognitive biases up to bounded rationality, incomplete information and information asymmetries. Many studies did research about privacy to understand what privacy is actually worth. By doing field research and asking respondents what they want to see in financial result, by sharing their personal data, most of the times researchers were achieving results as it is generally accepted that people want to share a lot for financial rewards (Acquisti, John, & Loewenstein, 2013; Bastian, 2017; Benndorf & Normann, 2014; Schudy & Utikal, 2017). Unfortunately that could lead to a hypothetical bias (Harrison & Rutstrom, 2008). As a contradiction Beresford, Kübler and Preibusch (2012) studied the unwillingness to pay for privacy. They measured privacy in a field research while buying a DVD on two different web shops. One shop was a bit cheaper, but while purchasing, more personal information needed to be shared. As a result, all consumers bought the cheaper version of the DVD, no matter the amount of personal data they had to share (Beresford, Kübler, & Preibusch, 2012). So, it is difficult to get an understanding about privacy as it is very complex and personal. Wearable industries face privacy issues as well.
21 There are three major concerns about personal privacy. Firstly, the type of data which is collected. Second concern relates to the data storage and the third concern relates to the vulnerability of hackers (Troiano, 2017). Definitely the type of data is one of the most important reasons to concern regarding demographic data, psychographic data and behavioural data (Leon et al., 2015).
3.4 Research gap
Based on the above shown literature review, table 1 shows a summary of all articles related to business models and revenue streams, privacy issues and the willingness to share data which indicates a gap for research. Most studies which focus on sharing personal data are related to a specific market (health or advertising). This elaborate view on this topics shows a gap. Because of the diverse results of the willingness to share data, the digitalisation has an impact. The solution to get passed the privacy issues is aggregating data and letting the users know what the data is used for.
Concluding from table 1, there is a clear distinction in articles that write specifically about business models and revenue streams, as well as articles that only write about privacy, regulations and the willingness to share personal data. Therefore, both well-known topics are well-researched but a combination of both topics is to my existing knowledge never made before, which indicates a gap. This gap will be the focus of research in this thesis.
This research is added as last row to the table to indicate the relationship with the four specified subjects. First of all this research indicates a business model combined with value co-creation. Secondly it takes into account the privacy issues consumers have and finally it test and addresses the willingness to share personal information in the context of connected services (i.e. the combination of value co-creation B2B with a business model).
22
Table 1 - Summary of literature review
Author/Year Description / research purpose Context Type of data / method
Focus Points Value co-creation Business model / revenue stream Privacy issue Willing -ness to share data
Ng, 2010 The inclusion of the customer and other
stakeholders for value co-creation Impact on pricing strategies and the creation of new revenue streams
Four movements the author believes that have an impact
√ √
Gruner & Homburg,
2000 Positive performance including customer interaction (B2C) Machinery industry Field interviews and statistical analysis √
Grönroos, 2008 Value co-creation in service dominant logic Service logic as a logic for
consumption and provision Theoretical analysis and conceptual development √ Payne, Storbacka &
Frow, 2008 Understanding and managing value co-creation Customer becomes a co-creator of value Field-research √
Bughin, Chui &
Manyika, 2010 Distributed cocreation moves into the mainstream Technologies that change traditional business models Survey √ √
Lambert & Enz, 2012
Value co-creation in business-to-business relationships
Based on financial outcomes after implementing cross-functional teams
Real data
√ √
Zhang et al., 2015 How value co-creation exerts an effect in the
capabilities-branding link Chinese firms focused on branding Empirical study √
Ramaswamy & Ozcan, 2016
Value co-creation in a digitalized world Brand engagement platforms Case study √
Kohtamäki & Rajala,
2016 Binding organization value-creation strategies together for value creation in B2B systems
Co-creation and co-production Case study
√ √
Wong et al., 2016 Value co-creation in a mobile environment Input from customers Literature review √
Jouny-Rivier, Reynoso & Edvardsson, 2017
Determinants of services co-creation with
business customers French service companies Quantitative study and survey √
Valjakka et al., 2013 Value co-created in B2B networks and how
the compatibility of business models affects value creation
Value co-creation in B2B
networks Qualitative case study, in-depth interviews √ √
Ramaswamy &
Ozcan, 2018 value co-creation as interactional creation and implications Interactive platforms Meta-analysis √
23
Author/Year Description / research purpose Context Type of data / method
Focus Points Value co-creation Business model / revenue stream Privacy
issue Willing-ness to share
data Pourabdollahian &
Copani, 2017
Business models in the healthcare industry Healthcare industry with new
technologies
Proposing a new
business model √
Panetta, 2017 New business models combining
technologies Predictions for 2018 Own data research √
Gallaugher, Auger &
BarNir, 2001 Online revenue streams and performance of a firm’s online efforts Magazine publishing industry Empirical exploration √
Mendling et al., 2005 Indirect revenue models E-learning universities Case study √
Chai, Potdar &
Chang, 2007 Different types of revenue models Software industries Survey √
Rajala, Nissilä & Westerlund, 2007
Different types of revenue models Open Source Software industry Explorative √
Ojala, 2013 Different types of revenue models Software-as-a-Service industry Literature review with
case studies √
Lambrecht et al.,
2014 Reviewing revenue models. What consumers can offer in exchange for digital goods
Digital goods industry Interviews
√
Kaplan, 2014 Selling prescription healthcare data for
pharmaceutical market affects privacy and regulations
Healthcare industry Case study
√ √
Schüritz, Seebacher
& Dorner, 2017 How to turn a value proposition into revenue for data-driven services Start-ups Qualitative research √
Phelps, Nowak &
Ferrell, 2000 The relationship between personal information and beliefs and direct marketing shopping habits
Marketing industry Survey
√ √
Prince, 2018 Consumers want to control their personal
data Willingness to share personal data √ √
Beresford, Kübler &
Preibusch, 2012 People are unwilling to pay for privacy Buying DVD’s online Field experiment √
Li, Cao & La Porta,
24
Author/Year Description / research purpose Context Type of data / method
Focus Points Value co-creation Business model / revenue stream Privacy
issue Willing-ness to share
data Acquisti, John &
Loewenstein, 2013
What is privacy worth and the protection of personal data
Amount of money respondents would accept in change of personal data.
Field experiment
√ Benndorf, Normann,
2014 Willingness to sell personal data Selling Social Media details Laboratory experiment √ √
Kokolakis, 2015 Privacy concerns in the digital age Focus on the privacy paradox Case study and √ √
Schudy & Utikal,
2017 Preferences for privacy and the factors that shape the individual willingness to share personal data
Body and address data Experimental study
√ √
Troiano, 2017 Major concerns involving privacy Wearables and personal health
data Explorative study √ √
Pickarda & Swanb,
2014 Sharing data, willingness to share for research purpose Health data Online survey √ √
Leon et al., 2015 Willingness and unwillingness reasons Advertising industry Online survey √ √
Quint & Rogers,
2015 Understanding how and why consumers are willing to share data Context is the USA, Canada, England, France and India Survey √ √
Ziefle, Halbey & Kowalewski, 2016
Willingness to share data on the internet when using digital services and social network sites
Social Networks and digital services
Empirical research, focus
groups √ √
Gharesifard & Wehn,
2016 Drivers and barriers for sharing data Sharing weather data via personal stations in the Netherlands, UK and Italy
Online survey
√
Kim et al., 2017 Factors influencing the willingness for
sharing electronic health data
Sharing data for healthcare and research
Telephone survey √ √
Bastian, 2017 Personalization after sharing data on
consumer attitude Data driven personalization Online survey √ √
Spiller et al., 2018 Logging personal data and users’ thought on
quantified self-personal data Privacy in relation with personal data Interviews √ √
Holtrop (2018),
Thesis New business models based on connected services in relation with the willingness to share personal data
Wearable tech industry Online survey
25 4. Conceptual model
As it was mentioned in the introduction section of this thesis, the goal of new business models in the data-sharing economy is to create new revenue streams with the use of personal data acquired by physical devices and sharing it with digital service providers to create a unique customer experience. For example, wearable tech companies can deliver interesting digital services (offered by third party digital service providers) on top of their devices to their consumers free of charge in exchange of acquiring their personal data. The wearable tech company is the one who pays for the digital service but the consumers can enjoy the variety of services for free.
According to the literature review and the presented line of arguments, there are multiple factors that have an effect on the consumers’ willingness to share personal data. These factors are mostly found in other contexts like healthcare, business to consumer markets and advertising. I perform this research in the context of smartwatches and my main focus will be on the willingness of the consumers (i.e., smartwatch users) to share their personal data in exchange of the offered services.
Issues may arise when sharing personal data with a service provider. These issues could be related to multiple factors indicated in the literature review of this thesis. For example the type of data that the service provider requests for using its service. Is it worth it for consumers regarding the benefits of the services, are the services useful and is the service provider reputable and safe to share personal data with. These issues motivates to deliver a conceptual model to capture the significance of the process of data sharing. Therefore, I propose the conceptual model presented in figure 2. To understand the conceptual model, a detailed explanation will be presented in the following.
26
Figure 2 - Conceptual model
Type of data
The first variable that will be explained is the type of data that is requested by the service provider (Leon et al., 2015; Phelps et al., 2000; Spiller et al., 2018; Ziefle et al., 2016). The literature about sharing personal data and the collection of data is mostly regarding health data because of privacy issues. Therefore, I make the differentiation between different types of data (both health and non-health). As the literature review suggests, the more private data becomes, the less likely consumers are willing to share their personal data. Is this research it is about the effect of willingness to share personal data within a specific context, namely sharing personal data for free in exchange for using a service for free. Therefore the following hypothesis will be proposed.
H1 – The type of data which is requested by the service provider will have an effect on the consumers’ willingness to share their personal data without getting paid for doing so.
Willingness to share personal data Type of data requested
by the service provider
Benefits offered by the service provider
Reputation of the service provider Control variables: - Age - Gender - Education
27 Benefits offered by the service provider
The second variable is related to the benefits of the service for the consumer. As the consumer is using a specific service of a third party for free in exchange of providing personal data for free, it is supposed that the benefits of the service will influence the consumers’ willingness to share personal data for free (Phelps et al., 2000). An increase in service benefits will probably affect the consumers mindset. Therefore the following hypothesis is formulated.
H2 – The offered benefits by the service provider will have a effect on the consumers’ willingness to share their personal data for free in exchange of using the service for free.
Reputation of the service provider
The last variable relates to the affect that the trustworthiness of a service provider could have on the willingness to share personal data. There could be a relationship as the literature review shows, also specifically in the context of connected services (Bastian, 2017; Quint & Rogers, 2015). As consumers know about the business model, trustworthiness (e.g. when it comes to data security and privacy) of the big enterprise that offers the service will impact the willingness to share personal data. Therefore the following hypothesis is formulated.
H3 – The reputation of the service provider have an effect on the consumers’ willingness to share personal data for free.
The techniques and methods related to the research methodology and data collection will be discussed in the next chapter. These methods will indicate the method to test the conceptual model and its hypothesis’.
28 5. Research methodology and data collection
The nature of this study will be observational and therefor a survey will be used to answer the research question and establish the relationship between dependent and independent variables. The consumers’ willingness to share personal data will be tested in a specific context. In this case, an explanation of new proposed business model will be given as a scenario to the respondent. The results of this survey will explain the most important factor for companies using the proposed business model that has an effect on the consumers’ willingness to share personal data.
5.1 Sample and participants
The population of interest are Dutch consumers. The volume of this population is large, and specific knowledge about the sample frame is unknown. Therefore, a non-probability sampling technique will be used conducting this research. Convenience sampling will be used as it has the highest potential for acquiring as many respondents as possible. Respondents will be reached via Social Media like Facebook, LinkedIn and the survey will be spread furthermore via personal email. To have a suitable sample which can be analysed well, a calculation for continuous data is made suggested by Bartlett, Kotrlik and Higgins (2001) which results in 145 respondents1. If a respondent
didn’t complete the whole survey, their results will not be used so around 200 respondents are needed. Because of convenience sampling, the response rate will be difficult to predict. To increase the response rate of the email respondents, a reminder will be send if someone did not respond (Kaplowitz & Hadlock, 2004). 1 𝑁 =( ) ∗( ) ( ) = , ∗ , , = 145 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑑𝑒𝑛𝑡𝑠
29 5.2 Survey design
In this paragraph an explanation will be given about the setup of the survey. First of all the survey will start with an introduction page. This paragraph will consist of two parts. It will stimulate and attract the attention for filling in the survey in combination of explaining the urge of this research as well as that this paragraph will secure the anonymity of the respondent. This will increase the likelihood of completing the survey. Secondly, the respondent will be asked about its demographics (age, gender and education). After the demographics, four different sections with all three question will be asked from the respondent. All four variables will be showed on a separate page with or without a short scenario or introduction to the questions. For example the questions about reputation of the service provider, the introduction will mention Philips healthcare solutions, Cisco’s security services and PayPal’s financial services to create an idea about which service providers could create services. Finally the survey expresses that the services are offered by a third party service provider which is not the wearable tech company. The last questions are to capture the willingness sharing personal data is exchange of using the proposed services for free.
5.3 Measures
The construct ‘type of data requested by the service provider’ will be measured on a predefined scale of faith development. This 8-item scale is reduced to three items considering the topic of connected services and made specific for this research regarding the importance, relevancy and personal identification of types of data. This items will be measured on a 5-point Likert Scale from ‘strongly disagree’ to ‘strongly agree’. An example of an developed item is; it is very important for me to distinguish which type of data I share with the service provider. This items will measure the attitude related to concerns to the differences of data types which could be shared with the service provider (Desimpelaere, Sulas, Duriez, & Hutsebaut, 2009). The scenario will describe different types of data which can be shared. This is a connection of data types which can be collected via wearable tech devices to create an understanding of different data types (International Association of Privacy Professionals, 2018).
30 The benefits of the service offered by the service provider are closely related to the intention to use and the attitude towards using the service. Therefore, the three items are based on the scales used by Heijden (2001). The three items will be measured on a 5-point Likert Scale from ‘strongly disagree’ to ‘strongly agree’. An example; ‘I find the offered services very useful’. The benefits of the service are mentioned in the scenario which need to be read before answering the questions. The scenario is based on three different possible services that could be feasible and interesting for potential users.
The items regarding the reputation of the service provider are based on the items of Malhotra, Kim and Agarwal (2004) (e.g. ‘I do believe these companies are reliable in the security of data’). Three items will indicate the respondents attitude towards three multinationals and third parties that have the possibility to provide a proposed service. The scenario will indicate these highly reputable organizations. This scale will also be measures on a 5-point Likert Scale ranging from ‘strongly disagree’ to ‘strongly agree’.
The last measure regarding the willingness to share personal data is based on the scale by Schoenbachler and Gordon (2002) and Malhotra, Kim and Agarwal (2004) which are made suitable for this research regarding connected services (e.g. ‘I’m very likely to share my personal data free of charge in exchange of the use of the free services offered by the service provider’). This scale is also measured on a 5-point Liker Scale from ‘strongly disagree’ to ‘strongly agree’. For this specific measure a scenario is not needed, as the goal of this scale is to measure the intention of using a service for free in exchange of personal data which may be used by the service provider.
Table 2 - Variable definitions and measurements
Construct Definition Source Items
Type of data requested by the service provider (TD)
Gives an understanding about the importance to have control over the type of data a consumer is sharing with the service provider.
Desimpelaere et al. (2009) 3
Benefits offered by the service provider (BO)
Gives insights if the benefits of the potential service are beneficial.
Heijden (2001) 3
Reputation of the service provider (R)
Do consumers actually trust the services providers with their personal data.
Malhotra, Kim and Agarwal (2004)
3 Willingness to share
personal information (WS)
What is the willingness of the consumers to share personal data regarding the proposed business model.
Malhotra, Kim and Agarwal (2004)
Schoenbachler and Gordon (2002)
31 6. Results
In this chapter the results of the survey will be discussed to find out which factors have a significant influence on the willingness to share their personal data in exchange of the offered digital services by the third party service provider. First of all, the analytical procedure will be extensively discussed to ensure generalizability of this research. To find statistical evidence, a hierarchical regression analysis will be performed to see which factors are effecting the willingness of consumers in sharing their personal data for free.
6.1 Analytical procedure
First, all responses with missing values were structurally deleted from the dataset. Second, one item of the ‘type of data requested by the service provider’ was counter-indicative phrased which needed to be recoded. Also the variable ‘gender’ needed to be recoded so that 1 (male) and 2 (female) became 0 (male) and 1 (female). After recoding the items, reliability checks per scale are executed with the help of a reliability analysis.
Unfortunately, the reliability test on ‘type of data requested by the service provider’ became negative due to an item which is probably wrongly interpreted by the respondent which gives a negative Cronbach’s Alpha of -0.244. Whenever this item is deleted from the scale, the scale is seen as good (Cronbach’s Alpha (α) of 0.807). Therefore, this item is completely deleted from the scale and data set. Furthermore, the scales of ‘benefits offered by the service provider’ (α = 0.801), ‘reputation of the service provider’ (α = 0.809) and ‘willingness to share personal data’ (α = 0.880) were all good and sufficient.
To furthermore check the scales, a factor analysis is conducted. Based on the Bartlett’s Test of Sphericity (χ2 (55) = 820,194 with p < 0.001) indicated that the correlations were large enough to use a
factor analysis because the p-value is smaller than 0.001 which indicates that the test is significant. Based on the Eigenvalues (>1), 4 factors could be distracted which is in accordance with 4 scales. Table 3 shows the factor loadings of all items after rotation.
32
Table 3 - Rotated Component Matrix
The means of the scales are computed as ‘TypeofDataTOT’, ‘BenefitsTOT’, ‘ReputationTOT’ and ‘WiltoShareTOT’ which are used in the analysis. The abbreviation (TOT) is used to indicated that these variables are the computed means of the scales. Table 4 summarizes the computed variables with means, standard deviations, correlations and Cronbach’s Alpha’s.
Table 4 - Means, Standard Deviations, Correlations
6.2 Sample Data
The survey has been online for 14 days via Qualtrics and 201 responses were obtained. Unfortunately, not all respondents completed the entire questionnaire. 148 cases were left for analysing after deleting the cases which did not complete the whole survey. As could be seen in the non-completed surveys, most respondents quitted the survey after filling in the control variables. 45 percent of the respondents were female and 55 percent were male. Furthermore, the average age is 30 years old with a standard deviation of 10 years which implies that 68 percent of all respondents are
1 2 3 4 WS_2 0,905 WS_1 0,876 WS_3 0,788 R_2 0,873 R_1 0,843 R_3 0,753 BO_1 0,885 BO_2 0,806 BO_3 0,693 TD_1 0,897 TD_2 0,891
a. Rotation converged in 5 iterations. Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Variable Mean SD 1 2 3 4 5 6 7 1. Age 30,43 11,64 -2. Gender 0,45 0,50 -0,123 -3. Education 4,55 1,37 0,138 -0,031 -4. TypeofDataTOT 3,93 0,95 -,163* 0,013 -0,065 (0,81) 5. BenefitsTOT 3,14 0,90 -0,133 -0,041 0,121 -,299** (0,80) 6. ReputationTOT 2,98 0,97 0,030 -0,055 -0,070 -0,091 ,375** (0,81) 7. WiltoShareTOT 3,09 1,06 -0,021 -0,006 -0,075 -,210* ,481** ,395** (0,88)
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
33 between 19 and 41 years old. Finally, 74 percent of all respondents does have a bachelor’s degree or higher.
6.3 Hypotheses testing
To test the hypothesis presented in this research, a hierarchical multiple regression analysis is executed to predict the level of the ‘willingness to share personal data’ based on the independent variables and the control variables gender, age and education.
In the first part of the regression analysis the three predictors (i.e. control variables) are entered in the first model. As it is shown in Table 5, this model was not leading to any statistically significance (p > 0.05) so there is not enough statistical evidence that the control values do have a sufficient effect on the willingness to share.
At the second part of the analysis the three independent variables are added to the model which as a whole explains 27 percent of the variance in the willingness to share personal data (F (3, 140) = 10.15; p < 0.001). The introduction of the independent variables added 29 percent of the variance explained by the first model. This change is significant as p < 0.001.
As it is shown in Table 5, in the entire model (model 2) two out of three independent variables had a statistically significant effect on the consumers’ willingness to share their personal data. First of all, benefits of the digital service have a statistical significant effect on the willingness to share personal data (β = 0.40; p < 0.001). Second, the reputation of the digital service provider has also a statistical significant effect on the willingness of the consumers in sharing their personal data (β = 0.21; p < 0.01). These results show that whenever the benefits offered by the service provider increase by 1, the willingness of consumers to share their personal data increases by 0.40. Furthermore, if the reputation of a company is 1 point higher that leads to a 0.2 increase on the scale of the willingness to share personal data. The variable ‘type of data’ did not show any significant results (p = 0.245) which suggest that it does not matter for the consumer which type of data they are sharing with the service provider in relation to the use of the service for free. Table 5 summarizes the results of the hierarchical regression analysis.
34
Table 5 - Hierarchical Regression Model of Willingness to Share Personal Data
R R2 R2 Change B SE β t Model 1 0,78 -0,015 Age 0,00 0,01 -0,02 -0,26 Gender -0,05 0,18 -0,02 -0,27 Education -0,05 0,06 -0,07 -0,82 Model 2 0,303 0,273*** 0,297*** Age 0,00 0,01 0,02 0,29 Gender 0,03 0,15 0,01 0,17 Education -0,09 0,06 -0,11 -1,58 TypeofDataTOT -0,10 0,08 -0,09 -1,17 BenefitsTOT 0,47 0,10 0,40*** 4,86 ReputationTOT 0,23 0,08 0,21** 2,67
35 7. Conclusion, discussion and implications
Based on the survey results and the developed knowledge of new business models based on the concept of digital co-creation and connected services in data-sharing economy, conclusions can be made regarding the research question of this thesis. In the following subsections, the obtained results of this research will be discussed, limitations will be recognized and finally implications and suggestions for further research will be described.
7.1 Conclusion and Discussion
As a main contribution of this thesis, I aimed to propose a new business model based on the idea of digital value co-creation and connected services in data-sharing economy which encourages the consumers to share their personal data free of charge. I mainly investigated the influencing factors on the consumers’ willingness to share their personal data. To illustrate the importance and potentials of the value co-creation process an example about wearable technology ecosystem (e.g. smartwatches) was provided. Therefore, the research question in this thesis was the following:
How does a new business model based on digital value co-creation (connected services) increases business value in the entire ecosystem and impact the willingness of consumers to share their personal data?
The research question consists of two parts. The first part is related to the new business model based on digital value co-creation that is proposed, whereas the second part captures the willingness to share personal data for free based on this new business model.
There are some conclusions that could be derived from the literature review which answers the first part of the research question. Within the introduced business model based on the idea of digital value co-creation (connected services), the entire business ecosystem (i.e., consumers, wearable tech producers, digital service providers) could benefit from building a sharing economy around the physical, digital assets and the generated data out of them. As it is shown in figure 3, all actors does have a reliable incoming rewards in such a business model. There will be financial rewards for the digital service provider by offering and connecting their interesting services to the hardware company.
