Supporting servitization via the Internet of Things: the ES provider viewpoint : A case study of the multinational ES provider IFS and its customers

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Linköping University | Department of Management and Engineering Master Thesis, 30 hp | Industrial Engineering and Management – Economic Information Systems Spring 2018 | LIU-IEI-TEK-A--18/03200--SE

Supporting servitization via the Internet of

Things: the ES provider viewpoint

- A case study of the multinational ES provider IFS and its customers

Joakim Gernelin Wallin Fredrik Oskarsson

Supervisor: Fredrik Lindeberg Examiner: Alf Westelius

Linköping University SE-581 83 Linköping, Sweden +46 13-28 10 00, www.liu.se

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Abstract

Servitization is a popular term referring to the innovation of an organization’s capabilities and processes to better create mutual value through a shift from selling products to selling Product-Service Systems. It is generally regarded as an innovative business model with great potential for smoother revenue streams, higher profit margins and longer, more closely-knit customer

relationships. It is seldom easy to reengineer the business model of an entire organization and the servitization concept does not come without risks and uncertainties. One of the biggest changes when servitizing is the ownership of products; it is no longer necessarily the customer who owns the product, but the producer. This entails new uncertainties surrounding the use and care of the product, leaving the provider with a risk because of a lack of information about how the product is being used. The technology Internet of Things (IoT) can potentially solve this problem by enabling a constant remote data flow from the products in use back to the provider.

IFS, the company at which the research of this study took place, develops and supplies enterprise systems (ES) such as enterprise resource planning systems (ERP) and can be described as forward-thinking and innovative. The ERP is a central point for any manufacturing firm, governing data and enabling an organization to automate and integrate a comprehensive part of their business processes, and to produce and access information in real time. In order to build functionality which can be referred to as the “best practice”, and to make a profit doing it, ES providers, such as IFS, need to stay in touch with the market and develop attractive applications in line with the general demand. One way to do this is to collaboratively develop functionality together with customers.

In recent years, IFS has developed the product IFS IoT Business Connector in such a collaborative way with a number of pioneer customers. The product enables the collection and analysis of IoT data as well as seamless integration into IFS’ other products. This has opened up possibilities to utilize IoT functionality to support a more efficient provision of services, but the continued successful

development of the Business Connector in this direction entails several more collaborative ventures into many different industries.

To assist in this, we have concretized how IoT can be used to support a servitization process, as well as how the market has realized and adopted this. This has rendered a conceptual model for judging the suitability of a company, based on the readiness to utilize IoT and servitization respectively. We have then presented which types of customers an ES provider should aim to collaborate with for joint development of functionality in the enterprise system, to enable IoT solutions which can support servitization. This is followed by a presentation of a short market review, and the

visualization of the market in the model. From this we draw conclusions about the fit and usefulness of the model, and the market maturity in general. Finally, we present a number of suggestions on how ES providers can work to develop such support in a more efficient manner.

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Copyright

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organizational systems, built around packaged enterprise systems software, enabling an organization to automate and integrate a comprehensive part of their business processes, to share common data and practices, and to produce and access information in real time. The most important class of ES are enterprise resource planning system (ERP systems) (Svejvig, 2009). When we talk about ES or ES providers we hence include and mainly refer to ERP or ERP providers.

Internet of Things We define the Internet of Things as sensors and actuators connected by networks to computing systems, which can monitor and manage the state and actions of connected objects (Manyika et al., 2015)

Product Service System A Product-Service system is an integrated bundle of products and services which aims at creating customer utility and generating value (Boehm and Thomas, 2013)

Servitization Servitization is the innovation of an

organization’s capabilities and processes to better create mutual value through a shift from selling products to selling Product-Service Systems (Baines et al. 2009, Dinges et al. 2017)

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1 Introduction

This chapter provides an introduction to the relevance of the topic studied, and an overview of the major areas which are of interest given the topic of the thesis. It also introduces notions regarding how an enterprise software provider needs to work to incorporate new trends and technology into their offering. Further, a background of the case company which is the focus object of this study is presented together with a

description of the problems that have been identified. These then form the basis for the purpose of the study, which are boiled down to a number of specific research questions. Lastly, the delimitations which have scoped the study are presented.

1.1 Background

Traditionally, manufacturing organizations have focused on product innovation and reduction of costs to compete with their adversaries. Such strategies can lead to great success, such as how Toyota in many ways revolutionized manufacturing with their Lean production concept, aimed at reducing costs without sacrificing productivity. Similarly, gas turbine manufacturers such as Siemens AG work on continuously improving the fuel efficiency and function of their products so as to become cheaper and better in use for their customers (Baines and Lightfoot, 2013). A manufacturer mainly focused on such product innovation and cost reduction can be said to follow a product-centric competitive strategy. However, increased globalization and ever-increasing competition have resulted in manufacturing organizations seeking to create additional value through the provision of services (Green, Davies, and Ng, 2017). In this context, services constitute activities that a manufacturer can perform to complement the physical products they offer, in contrast to so-called pure service providers such as banks and schools which do not base their services around a physical product. The basic rationale for offering services is to exploit the competences around the product that the manufacturer possesses, to be able to provide improvements in efficiency and effectiveness to their customers (Baines and Lightfoot, 2013).

The transformation process of increasingly offering services integrated with the products an organization manufactures can broadly be called servitization, based on the definition by Baines and Lightfoot (2013). An organization following such a strategy can be said to be servitizing (Baines and Lightfoot, 2013). A key aspect of servitization is the focus on integrating services with products, as compared to viewing them as separate entities, forming what Lindahl, Sundin, and Sakao (2014) call Integrated Product-Service Offerings (IPSOs). Traditionally, the tendency for managers has been to view services purely as add-ons to products, with the main part of total value creation stemming from the physical product (Baines et al., 2009). For companies following a servitization strategy, however, the value proposition instead includes services as fundamental value-adding activities where the product is just one aspect of the overall offering (Gebauer, Friedli, and Fleisch, 2006). Baines and Lightfoot (2013) describe how servitization represents a shift from focusing on the product itself to the outcome and actual value that is created for the customer by using the product that is, the functionality that the product offers. For example, a gas turbine manufacturer can instead of just selling a turbine focus on the value created by the turbine in use, such as the energy generated and other aspects as reliability of operation and a low down-time (Baines and Lightfoot, 2013). As such, servitization implies a strong customer centricity, where customers are provided with tailored solutions as compared to just receiving the product. Among other things, this customer centricity entails a shift of the nature of customer interaction to be relationship-based instead of transaction-based. This includes moving towards a closer customer contact where the manufacturer maintains responsibility for the product after it has been delivered to the customer, while at the same time

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gaining control over the entire product lifecycle. The services provided by the manufacturer can typically include different maintenance and repair operations. Other value creating opportunities from services include using information and knowledge about the product to be able to help the customer optimize the product operation(Baines et al., 2009). One critical factor in being able to offer these services is to be able to be in remote contact with the products delivered. Receiving and analyzing real-time data about status, performance, usage and the location of a product in use enables the manufacturer to be more in control of it, and through that, ensure optimal product performance and availability (Grubic, 2014). The technologies enabling remote constant data flow and this type of detailed product information, generally referred to as the Internet of Things, have gained a lot of traction in the recent years, and the possibility of integrating them into enterprise systems has emerged (Rymaszewska, Helo, and Gunasekaran, 2017).

The Internet of Things (IoT) is a technology which can broadly be described as the movement of more and more products being connected, and the opportunities they can represent (Manyika et al., 2015). The technology builds on advanced technological sectors, such as the increasing possibilities to connect physical products via gateways to the internet, and thus being able to reliably transfer data in a secure manner from a product to other products and systems. A central aspect is thus to facilitate an information flow from and to products in a way which not previously being possible. The resulting connected products are sometimes referred to as “smart” products (Porter and Heppelmann, 2014). Further, improvements in data analytics makes it possible to analyze large amounts of data gathered to make informed decisions. Porter and Heppelmann (2014) describe how the IoT and the

possibilities that arise from embedding sensors, processors, software and connectivity to improve products will potentially reshape markets and the value chains of companies radically.

The term IoT includes applications of connected products in many areas, such as consumer applications in smart homes and wearables, sometimes referred to as consumer IoT. When talking about the industrial applications of IoT, the term “Industrial Internet of Things (IIoT)” or “Industrial Internet” is commonly used (Gierej, 2017). For this study, we are concerned with the latter, and will hence when further referring to IoT mean it in the context of industrial applications. It should be noted that industrial applications of IoT includes both IoT used in a factory-internal context and in business-to-business uses, where the primary focus of this study is on the latter, but will still encompass the factory-internal context.

From a servitization viewpoint, IoT provides a number of apparent possibilities, or affordances, which can be used to create increased value for the customer. It can increase visibility of a product in terms of operating characteristics, condition, time in use and location. By having remote access to this information, the service provider can deliver faster maintenance and repair actions and reduce the need for manual observation. In addition to this, by analyzing historical data of both the specific product in operation as well as other products of the same type, faults can be detected before they occur and service work be scheduled to avoid a product breaking down, resulting in increased reliability and up-time of the product (Baines and Lightfoot, 2013).

An important part of delivering IoT solutions is to connect data and insight from smart products with various business systems, such as ERP and CRM systems (Porter and Heppelmann, 2014). In this report, we refer to such systems of packaged enterprise software using the umbrella term Enterprise System (ES), where companies which develop and deliver them will be referred to as ES providers. ES providers who want to deliver systems that fit the changing needs of customers need to follow trends in the industry in order to remain relevant in the marketplace. This includes identifying opportunities of emerging technologies and tendencies, such as IoT and servitization, and innovating

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and providing novel solutions that are well integrated with other parts of the applications the ES provider delivers (Michel, 2017). In this study, we will hence discuss the terms IoT and servitization in relation to ES providers in the context of the ES provider delivering software products which can help their customers to utilize IoT and servitization, not how the ES provider in itself can servitize.

1.2 IFS

IFS is a multinational ES provider which develops, supplies and implements software for

manufacturing, service and asset management, projects and supply chain management, forming the combined software product IFS Applications. IFS has grown organically as well as through acquisitions and historically, IFS Applications has been an ERP system, but through these acquisitions, the offering has been extended to Field Service Management (FSM). IFS has a special focus on expertise and culture; commitment of employees in the projects and work, together with a focus on detailed industry knowledge supposedly providing the company with its edge and skill in the ES-market. This has been affirmed by modules in the system being repeatedly named as Leader in the Gartner Research Magic Quadrant (IFS, 2018a), as well as receiving consistently high ratings in Gartner collected user reviews. This focus and agility has enabled them to target customer segments of both more traditional manufacturers looking for effective and best-practice production, as well as more innovative industries with a tendency for previously unsupported business processes, requiring development of special functionality in the enterprise system.

Whatever the customers strategic mindset toward innovation, there are constant improvements and development of functionality in IFS Applications; two of the latest additions to the list of requested functionalities from the market are the previously mentioned IoT and servitization. While these developments in technology and business models have a great promise for businesses, they also entail new ways of working, new processes in the company and a subsequent support in IFS Applications. A few years ago, the experimental R&D department, IFS Labs, started looking into IoT, attempting to grasp the possibilities and demands of the technology and the developments. The customer needs and requirements were not clear, but through joint efforts with manufacturers it became apparent that there was an abundance of collected data, but not many further developments. The acquisition of data was there, but the analysis of it into information for meaningful action was not. A decision was made as to what position IFS would take in this, where a focus on discovery through business

optimization was chosen; this entails data processing, creating reactions and enhancing business in

different ways. This was the conception of the IFS Business Connector, an IoT Hub with a direct link into the heart of IFS Applications and its many modules. The solution has been released to market for some time now and a handful of implementations have taken place; some new values have been realized which were not possible before, where raw data and data flows are now being transformed into information and actions in the enterprise system such as preventive and more precise actions. But the majority of companies in the market and the IFS customer base are not realizing or are not making a conscious effort to realize the inherit value of their data collection.

IFS has further identified servitization as a potential growth area, where the aim is to provide products which can support new functionality requirements from customers which are shifting from being product-centric to increasingly delivering offerings which integrates services with products. As a part of this, IFS are interested in utilizing their product IFS IoT Business Connector to provide functionality which can support new operations for companies moving towards such Integrated Product-Service Offerings (IPSOs) as described by (Lindahl et al., 2014).

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The existing implementations of IFS IoT Business Connector mentioned have been a collaborative effort between IFS and a number of their customers, which have resulted in a number of

interrelations between the Business Connector IoT hub and other modules in IFS Applications. The implementations are mainly based on the customers data while ideas for analysis and

operationalization has originated from IFS. While some reusable parts and synergies between different implementations have appeared, the development has mainly been new development so far. What proves to be reusable is usually functionality that is more common and is found useful by several customers and is naturally a good addition to the system because it satisfies the need of the general customer without too much additional adaptations. As mentioned, customers do not necessarily know what insights the data can give them, and as such they do not know what functionality they want from the system. For IFS this means that some trial-and-error is needed in order to achieve a product that is complete; and as IFS is a company aiming to turn a profit, it is implicit that they would want to make the most wanted functionality with the least possible resources. In doing this, there might be some priorities or directions regarding which customers these collaborations are done with that would result in a higher probability for reusability and more attractive offering.

We, the researchers behind this study, are two master’s students at Linköping University who have done our final thesis over a period of 20 weeks. The case company IFS has shown to be a good collaborative partner to students of Linköping University in the past; several previous master’s theses have come out of the cooperation. Adding to this, their strategy of forward thinking and innovation, as well as shown interest in the areas, tells us that it is a fitting collaborative partner for a thesis of this nature. This study has hence been carried out together with IFS, where we as researchers have been located physically in IFS main office in Linköping.

1.3 Problem Description

The development of IoT technology, how it can potentially be used to support a servitization process and which affordances (the possibilities a particular technology offers prospective users (Westelius, 2013)) that it carries with it are three closely connected areas. These areas have gained attention in a number of studies and reports recently (Baines and Lightfoot, 2013; Bureca, 2017; Porter and

Heppelmann, 2014; Seregni, Sassanelli, Cerri, Zanetti, and Terzi, 2016; Thornberry, 2016). However, it is not apparent if companies in the market are utilizing these affordances; nor is it clear at what stage companies are at in the readiness to utilize IoT and to what extent they are fit to servitize. From an ES provider viewpoint, it is possible to take an active role for IoT by developing functionality to enable analytics of data and provide insight as a part of their solutions package, which can enable their customers to utilize the potential of IoT. One example of this is how the large German ES provider SAP markets a portfolio of IoT solutions, which stipulates to let customers develop, deploy and manage IoT applications (SAP, 2018a). Several other ES providers such as Oracle, Salesforce and IFS are following a similar path (IFS, 2018b; Oracle, 2018a; Salesforce, 2018). The ES provider IFS is developing such new functionality in collaboration with several customers. Determining which customers to work with in order to develop functionality and possibilities of IoT to support servitization is however not straightforward; there seems to be certain aspects which makes a customer a better fit for such collaboration than others. Albeit gaining traction in industry magazines and blog posts (Hill, 2017; McKendrick, 2018; van Heur, 2015), our impression is that the area of IoT in relation to ES providers remains largely unresearched in academia. This holds especially true in regard to how ES providers can provide functionality for IoT that can work as support for a servitization process of the ES provider’s customers. This setting, in terms of recent developments

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and potential, is seen as an interesting case and opportunity from our perspective. From an academic standpoint we see this as an opportunity to contribute to the base of knowledge in the intersection of these areas. Finally, our review of the areas shows that there is a lack of previous academic research regarding how ES providers can and should develop such functionality.

1.4 Purpose

The previous sections have presented the research area of this study and the case company which it has been performed at, together with a concretization of the problems we have identified. To address and research these problems, we have stated the following purpose of the study:

This study investigates how enterprise system providers can support customer servitization with the Internet of Things.

The purpose of this study gives rise to three specific research questions, which are stated below together with a short description of how we aim to proceed to answer them.

1.4.1 Research Questions

1) Considering affordances by IoT in a servitization context:

a. Which are the most relevant affordances, and which are the most essential factors enabling manufacturers to utilize them?

b. Are there tendencies of companies on the market of adopting such affordances, and what degree of maturity do they exhibit in regard to these enabling factors?

To address the first part of this research question, we have reviewed current literature and

attempted to establish a set of affordances that are enabled by IoT to support servitization. Following this, we aimed to evaluate the critical elements in the servitization process as described in literature to find which factors that determine a firm’s ability to successfully undergo the process. In a similar fashion, we have determined which factors that are essential for successful utilization of IoT based on previous research on the technology. Based on this, we present a model which can be used to place companies according to these two set of factors.

To address the second part of the research question, we gathered and interpreted data from internal sources within the IFS ecosystem, as well as complementary, external ones. This data was posed as empirical findings, evaluated in context of the findings from RQ1a.

2) Considering these affordances and factors from an ES provider viewpoint, are there segments of the market which appear more or less relevant to engage with to further development of functionality for IoT supporting servitization?

This research question is addressed by collecting information through literature and empirics to form an appreciation for the role of the ES provider, and subsequently, in addition to the results from RQ1, present it in the generated model. Any areas in the model found to be favorable or non-favorable for an ES provider is shown and motivated.

3) From an ES provider viewpoint, what are some important aspects to keep in mind when developing functionality for IoT supporting servitization?

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Analysis of the current situation within the case company and its customers resulted in a set of suggestions for an effective furtherance of the development of this functionality in the case company’s product, and how generalizable they are to ES providers in general.

1.5 Delimitations

To achieve successful business transformation in a servitization process, there are a multitude of different factors which impact the probability for success. One of these is to take advantage of technical advancements such as IoT technology, but this is by no means the only relevant factor (Baines and Lightfoot, 2013). The main focus of this study is however on how IoT solutions can work to support a servitization process, and as a consequence of this, other areas of servitization take a less prominent role.

Likewise, a complete IoT implementation has many components and aspects that need to be considered. Many of these more technical cornerstones have not been considered in this study, for example the security aspects of an IoT system.

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2 Methodology

The methodology section aims to explain how the research has been structured and conducted, and how results and conclusions have been derived. First, the general process is described, with sub-steps detailed, to explain the proceedings in the work. Secondly, the design of the research is explained, including how we handle the relationship between empiric reality and theory and which considerations we have taken when conducting and evaluating our research. Following this, the different phases of the study such as literature review and data collection are detailed.

2.1 Research process

This section aims to provide an overview of the research process of this study, where we highlight the distinct steps taken during the course of performing our research. Aspects are presented at a higher level in chronological order, where more detailed information is presented in the subsequent sections of this chapter. An illustration of the research process can be found in Figure 1, the

components of which is explained below.

Figure 1 – Steps undertaken in the research process.

As Figure 1 illustrates, this study started with an initial prestudy. The prestudy phase began with open discussions with an employee at the designated case company IFS to get a brief overview of his view of the potential problems associated with the area of research chosen. This resulted in a high-level idea that we wanted to understand how our case organization IFS could be connected to the area IoT and servitization, which they both had shown interest in and also developed basic functionality to

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support. This thus framed our study to include three major areas, namely Internet of Things, servitization and Enterprise System (ES) providers. Our previous knowledge of these areas was relatively limited, and we also identified that the areas were “umbrella” terms that could be

interpreted differently depending on the source. To deepen our understanding of the concepts, and the potential relation between them, we did a preliminary literature review over a span of one week. The preliminary literature consisted of reading numerous different reports and studies on the three areas described above, which we then saved for later use.

Armed with an improved theoretical understanding of the areas we wanted to study, we proceeded to perform a number of pilot interviews at our case company, as illustrated in Figure 1. These pilot interviews were mainly aimed to help us understand the current situation at the case company, including what the status and sentiment towards the concepts IoT and servitization were. This firstly showed that our initial understanding of the areas might have been a bit glorified by the theory we read – the pilot interviews indicated that companies were not utilizing IoT and servitization to the extent we had initially come to believe but were rather quite immature. This phase also included gathering information from our case company’s intranet and website.

After this, we went back to the drawing board and tried to set up a general plan for the study, where we tried to create a problem description and generate a number of research questions. This aimed to guide our work and put the focus on what we wanted to find answers to. At this stage, the research was loosely defined as us wanting to investigate both which possibilities IoT technology could present to support a servitization process in general, and also how an ES provider who wanted to develop functionality to support this in their business systems could proceed.

At this point, we proceeded to follow a route of iterating between improving our theoretical understanding together with performing further interviews at the case company, illustrated by the loop between the box “Interviews” and “Literature and theoretical support” in Figure 1. Overall, the interviews improved our understanding of the context of our case company, which in turn led us to both try to cement notions we had seen and also resulted in new questions we wanted to

investigate, which led us on to interviewing new people. Our interviews also had a large focus in getting information about customers of our case company. The general aim was for us to get both an understanding of the internal workings of our case company, but also to find out how their

customers and potential customers looked like. As these companies showed to be relatively immature in reference to utilizing IoT to support servitization, we also performed external

interviews with a company which seemed to be mature and rather advanced in this regard. Coupled with this work, we referred back to literature to a large degree to continuously try to analyze our empirical findings based on previous research.

Around halfway through our study, the work to date was subjected to a peer review, led by two master students and also including a professor and a PhD student at Linköping University, who also were the examiner and supervisor for the master’s thesis this study is a part of. This gave us feedback on the direction of our work.

Then, we reiterated the problem description and research questions. We also generated a model, with the aim to relate factors of servitization and IoT respectively to our case company. We decided that we had gathered sufficient empirical data to be able to answer our research questions, and shifted focus onto analyzing all our empirical findings more thoroughly against literature, described by the box “Analysis” in Figure 1. During this stage of the research, we were still open to exploring new literature to be able to analyze our results, but refrained from gathering more empirical data. All in

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all, this resulted in a number of conclusions of the study and suggestions, aimed to answer our research questions, as illustrated by the last box in Figure 1, “Conclusions & Suggestions”.

2.2 Research design

Generally, the study has been somewhat iterative between literature and empirical observations. As described in the previous section we have attempted to understand the areas servitization and IoT, first by a brief literature review and a round of interviews, after which we went back to the literature for a deeper understanding and support for the phenomena observed. We then returned to the empirical observations, and as a last step, tried to explain our context in terms from the literature, before finally visualizing the empirical observations in our model-explanation of the context.

Considering the iterative and explanatory relationship between literature and empirical observations, we see a resemblance to what Walton (2013) refers to as abductive reasoning. He also mentions that abductive reasoning has been defined as “[…] the process of forming an explanatory hypothesis”. This is our intention, forming a reasonable explanatory hypothesis for our context. Another point that is made by Walton (2013) is that abductive inference, unlike inductive ditto, does not need to be based on finding the most, or countably many cases. As we have based our study on empirical observations to support our explanation, but have not tried to make this empirical examination exhaustive, this is fitting and accurate.

Secondly, a note about the data and nature of the area under study is that it is a complex meeting point of areas with a lot of dependencies in themselves. We acknowledge that the study takes place in a domain in which social context plays an important role, where culture and organizational aspects are vital in the development and implementation of new business practices. Bryman and Bell (2013) notes that when social context plays a role, and the area of research is not black and white, the interpretation of the empirical situation is important. We have had this in mind, and have collectively discussed and interpreted the results and empirical material to contextualize and be able to draw meaningful conclusions. Considering this, the study has also focused on handling what Bryman and Bell (2013) refer to as qualitative data; the ambiguity of less structured interviews with questions regarding complex areas are suitable for empirical research of a wider context.

In terms of the data and the nature of it, the situation and context of the study has what Bryman & Bell (2013) refers to as a “case study design”, meaning the study focuses on a detailed and current phase of a single case. The case is chosen to be on the organizational level, focusing on the workings within the case, but with an emphasis on the external market of customers and potential customers. In terms of different types of cases, we argue that this is what Bryman & Bell (2013) refer to as “the informational or revealing case”, which would mean that the research opportunity is somewhat unique, which we agree on, as mentioned in the background (section 1.1). A brief note on the context of our case, IFS is what we refer to as the case. While IFS is the place of the study, and the ES provider plays a big role in the study, IFS’s customers together with some external companies are used as examples of the market situation. So, while the customers and external companies are cases in themselves, they are considered as part of the IFS case. The unit of analysis in our study, as discussed by Nuri Yurdusev (1993), is a twofold phenomenon, which differs between the research questions. The first queries of the study are those of the market, by means of customers and external cases, and what affordances there are by IoT supporting servitization, and to what extent the market has adopted them. In this aspect, the unit of analysis as well as the unit of observation (Nuri Yurdusev, 1993) is our observation of the market, in comparison to the frame of reference; although, the market is observed on an individual level and the analysis is on more of a general level. Further, the second and third queries are those of IFS’s relation to the current situation in terms of

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their state of development and the market. In these queries, the unit of analysis and the unit of observation is IFS and the market respectively, both being reviewed on a general level. Even though Nuri Yurdusev (1993) originally regarded the expressions in a more social setting, where people, groups or systems of groups are concerned, we find that it is applicable on a business setting where people, organizations and markets are the corresponding levels.

The generalizability, and applicability – or lack thereof – of our results and conclusions to various other cases, will be covered next. The discussion is based on an article by Lee & Baskerville (2003) where the authors cover the use and misuse of generalization. They present a framework for classifying its four different forms, which concern the distinction between empirical and theoretical statements, and the means of transferring knowledge and reasoning from one to another –

generalizing.

As mentioned in the research process section, the study was initialized with a prestudy, during which we set out to form an opinion and appreciation of the subjects in question; in accordance with Lee & Baskerville (2003), this would be considered “TE” generalizing. This means that we gathered a lot of theoretical works concerning the areas in question, and from this created a vision and an idea of how the world should work according to the theory. In other words, we tried to generalize the theory to the empirical setting we would be in at IFS. When we were faced with the real situation and were introduced to the case setting, and performed the pilot interviews, we realized that parts of the image we had did not match the reality. Instead of the image we had, which proved to be a “best case” in a theoretically perfect world, some concepts from the theory were mere notions in the real climate. This is entirely in line with what Lee & Baskerville (2003) note, and shows that the theories were crafted in different empirical settings, and cannot be directly transferred to ours, which is understandable. We accepted this, and kept the theories in mind when continuing the research, but with a more empirical, data collection-mindset. It should be noted that the theory gathered was by no means totally disproved by the reality as we found it, and was still of use for us, even though we needed to complement it with new theory to form a coherent picture of the world. One example of this was that we thought companies would exhibit a larger move towards servitization, by for example exhibiting tendencies of service offerings such as advanced services (which will be presented in the frame of reference, 3.1), rather than the reality with less developed offerings that we found. Our initial conception of servitization being more adopted was a result of reading literature propagating the concept, with too little of a critical view against it.

In the main part of the study, as mentioned, we pursued a more empirically guided approach to the study, and instead attempted to describe the empirical setting that we saw in the case. What Lee and Baskerville (2003) note about generalizing from description to theory, ET generalizability, is the main effort of this thesis work; where generalizing from case study findings to theory is the most

applicable description used. For example. the model generated in this study (see section 4.6) can be considered to be theory, where we as described by Lee and Baskerville (2003) have generalized our empirical observations in the study to a concept model.

One important aspect and note from Lee & Baskerville (2003) to take away is the generalizability of the resulting theory, where they pose that it is illogical to presume that the theory will remain valid beyond the observed case, since the theory is crafted from analysis of empirics of only one case. In conclusion, we have viewed the resulting theories and descriptions of the empirics solely as a conceptual description of our observations, and encourage the reader to do the same. As Lee & Baskerville (2003) mention, the only way to test ET generalizability is that of testing the resulting

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theory in beyond the sample or domain, which is left as a suggestion for future research and we welcome as a useful and affirming complement to this study.

2.2.1 Klein and Myers seven principles

Klein and Myers (1999) presents a set of principles for both conducting and evaluating interpretive field research in information systems. The principles aim to summarize important insights in

interpretivist research, and are targeted at practitioners in the IS area. The study of Klein and Myers (1999) is widely accepted and reviewed, with over 5000 citations, wherefore we will not present critique of the principles presented themselves, but instead assume them being potentially relevant and useful. Klein and Myers (1999) mention how some of the principles can be more or less applicable depending on the specific research project, but also notes that they are to some extent interdependent. Our interpretation of this is that an understanding of all the principles are important, and we have thus gone through all the different principles in the initial phase of our research, and reflected over how they apply to our specific research. The resulting understanding was then used to guide our work and allowed us to continuously reflect over the implications of the current stage of the research process.

The first principle, the fundamental principle of the hermeneutic circle, suggests that in order to understand a complex whole, we use preconditions about the meanings of the parts and their interrelationships that make up the whole. Further, Klein and Myers (1999) argue that it is by iterating between the whole and the parts that makes up the whole that all human understanding is achieved. A large part of this study has consisted of investigating how seemingly relatively

disconnected areas as IoT, servitization and the role of enterprise software providers can be related. To do this, we have iterated between zooming in and trying to understand the parts on their own with a bird’s view where we use that information to connect the pieces and draw relevant

conclusions, which is in line with Klein and Myers (1999).

Our research has included an effort in really trying to understand the enterprise software provider that the study was mainly carried out at. In this, we have focused both at the current situation but also tried to form a picture of the historical context of the company. When conducting our interviews, we have included question concerning the background of the person, and how the subjects of the interview has developed over time at the case company. For example, when asking about why servitization and its implications are important, a central point was to understand how IFS has worked with it over time. To strengthen our historical understanding, we have also looked at both documents and white papers the company has produced internally, which gives a hint in how the areas researched have been viewed historically and how that view has changed. In accordance with Klein and Myers (1999) principle of contextualization, this approach has been beneficial by increasing our overall understanding as researchers of the case company’s context, which in turn helped us to be able to understand which areas are of interest and should be studied further. We also believe that it can aid the readers of our work in a similar way, to better follow our thought and work processes and why we have come to draw the conclusions that we did. One example of this is the finding that functionality developed previously by IFS, over time has become standard products, which has impacted our view of how the IoT Business Connector will probably become a standard product over time, and the implications of this.

Klein and Myers (1999) principle of interaction between the researcher(s) and the subjects concerns how a researcher must place himself and the subjects into a historical perspective. Facts are seen to be produced as part and parcel of the social interaction between researcher and participant;

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study, we wanted to avoid pushing our ideas and conceptions onto our interview subjects, as we wanted to capture their current attitude and outlook of the areas we investigated. This is an important aspect as it helped us in getting an understanding of how different people from different departments view the possibilities and potential problems for the case company in areas as IoT and servitization. To do this, we used open questions extensively and tried to allow the interviewees to talk as freely as possible about general areas (see 2.3.2 Data collection for more extensive details regarding how the interviews were performed). The fact that we are students writing our thesis which will eventually be published presents a risk of interviewees not responding truthfully, so as to for example hide failures and shortcomings of either themselves as individuals or the company as a whole. We are aware that this is a risk and that we cannot be totally sure that we have avoided it, however we have tried to convey that we are not trying to judge anyone and that we prefer truthful answers, to mitigate this risk. We have also chosen to not write out the interviewees names in this report and also informed our interviewees of this beforehand, so as to give them anonymity to hopefully be able to speak freer.

With their principle of abstraction and generalization, Klein and Myers (1999) emphasizes the importance of carefully relating theoretical abstractions and generalizations to the field study details as they were experienced by the researcher. Also, theory is explained as playing a crucial role for interpretive research as a “sensitizing device” to view the world in a certain way. In our study, the initial theory review indeed did work this way and gave us an impression of the areas investigated. However, this impression was to some extent conflicted with what we saw in the empirical setting and led us to reevaluate the theory used and also search for new theory which better fitted our findings, which relates to the principle of dialogical reasoning. In accordance with Klein and Myers (1999), we also took an approach of carefully relating the field study details as we experienced them to the theory used to analyze it, to show the readers how we arrived at our theoretical insights. Further, the principle of dialogical reasoning requires the researcher to confront preconceptions that guided the initial research design with the data that emerged through the research process. One reason for the conflict between the theory we read and the empirical setting we investigated can be that the areas mainly investigated, IoT and servitization, are in a very “hyped” phase. The technology is novel and the ways to make it work are as well, with no grounded standards and best practices set. The literature on the areas that we read were in many ways speculative and concerned future

possibilities rather than existing, working real world applications. This led to us having a certain prejudgment in the initial phases of our empirical data gathering, as we tended towards having overvalued how developed companies were in terms of utilizing IoT and servitization as compared with what our interviews revealed. As Klein and Myers (1999) note, prejudices are a necessary starting point of our understanding, and continues by stating that a critical task of hermeneutics is in distinguishing between “true prejudices, by which we understand, from false ones by which we misunderstand”. This realization guided us in trying to look past the “hype” of the investigated areas, and instead look at the “real” world which exists in our case context, and use that knowledge as a basis to find theory that helps us understand it.

According to Klein and Myers (1999) principle of multiple interpretations, an important part of interpretive research is to examine the influences that social context has upon human actions. Researchers should therefore seek out and document multiple viewpoints, together with reasons for them. Also, it is important to seek to understand conflicts related to power, economics or values. The researcher should also confront contradictions that he or she finds, and revise his or her understanding accordingly. In our case study, we interviewed numerous people from different areas of the case company which had very different roles and worked at different levels. In some areas, the

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viewpoints of the interviewees diverged. Our general approach has been to try to understand why the interviewees answered as they did. This has been done by using information about their

professional background and current roles. Also, we tried to affirm or disprove information gathered in earlier reviews by taking up the same subjects in subsequent interviews, and tried to probe why certain interviewees agreed or did not agree with notions we got from other people at the case company. What we found was largely that people who were at a higher level in the organization could often talk in terms of possibilities, and what could be done in the future, while people who were for example working directly with the IFS IoT Business Connector talked about the now, and what is possible today with that product. In the report we have tried to be clear in our distinguishing between these two types of information so as to not create confusion, and have also been critical towards information which seems to be of the first more “visionary” type, and rather used it to show the direction IFS wants to move toward rather than definite truth.

We interpret Klein and Myers (1999) principle of suspicion as being aware on if people might be biased towards a certain world view created by social and political interactions shaping beliefs. In our study, examples of where such bias could appear would be when interviewing people who have a professional role that are taking a certain stance in different subjects. For example, someone who is working with finding opportunities from servitization might convey information in a way which relays a more positive view of how far the work has gone in the organization. To handle such possible bias, we have tried to get to the core of information and asked about specific examples of phenomenon, as compared to assuming truthfulness from people we interview straight off. Further, we have tried to validate information we have gotten by incorporating questions about them in subsequent interviews and analyzing the answers against each other. Other than that, we cannot be sure about the credibility and honesty of our interviewees. We have however tried to be able to discover if a person is being dishonest, to be able to weigh that in when judging the answers. For example, we have paid attention to body language of the interviewee and have also been attentive to if the interviewee tried to change subject or answer in too broad terms, as a means of avoiding giving a straight answer. In this study, we have not suspected any of our interviewees to actively mislead us or give dishonest answers, but we have noticed that people higher up in the organization tend to be drawn towards answering in a more positive way about the functionality of products, which

sometimes is rather possible future functionality and not current functionality already in use being tried and tested.

2.3 Research method

In this area we will go through how we performed the different steps in the study. We will give an overview of how theory was screened and reviewed, and also discuss the credibility of different sources. Further, the different stages of empirical data collection will be presented. The section will cover both how interviews were performed in different stages of the study, and what the purpose for performing them in that way was.

2.3.1 Literature review

The literature review performed had an overall aim to provide a frame of reference for our research. It was also used to further our own understanding of the fields of the academic areas researched, mainly regarding IoT and servitization. One important aspect has been to investigate whether there was consensus in the academic world. Our findings implied that there was not a general way of viewing the world, so instead we tried to understand different viewpoints in the areas and rule out speculations from fact-based findings.

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To find academic articles, we used Google Scholar and the Uni-search engine of the university’s library. Initially we favored articles with high citations as more reliable, and also tried to judge the relevance of sources by assessing their titles and abstract. We used backward search to find new articles by looking in the reference list of interesting articles we found. We also did forward searching by looking up articles that had cited specific articles to find new source material. In some areas, we are leaning heavily on what a few or only one source says, for example we use a book by Baines and Lightfoot (2013) as a basis to presenting and explaining the concept of

servitization. When this is the case, we have provided the reader with some background information and support as to why those specific authors present reliable information, so the reader can for him- or herself make an informed judgement about the source. Further, for all the sources reviewed we have used Google Scholar to see how well-cited the article was, where a lower citation count has resulted in us being more careful in the use of the literature. We argue that citation count is not a definite measure of the quality of the source, but do believe that it gives a decent pointer in that direction.

Some of the sources found and used in this study are not academic in the sense that they are published and peer-reviewed. Rather, they are reports presented by for example large consultant firms such as McKinsey and Accenture. These reports were used as they are based on large market research studies, which ranks how companies work today and also emphasizes how they view the future. This was of value for us as the study to a large extent consider what can be done in areas with novel technology where consensus is not yet reached and best practices not set. A weakness with these articles is that they tend to be visionary and present possibilities in an uncritical way, where the significance of difficulties and problems are toned down. This comes as no surprise, as these types of firms partly make their revenue by selling services to help companies utilize such possibilities. To mitigate this risk, we have tried to source data from several sources and compare them with each other, and also when possible used published literature to support or at least problematize what the less academic sources say.

2.3.2 Data collection

The main form of data collection has been through interviews with persons within, and to some extent outside of the case organization. In accordance with what Bryman & Bell (2013) note, a more loosely structured interview type was deemed most likely to gather all possible information and nuances in our qualitative and interpretive research. This allowed us to ask follow-up questions and direct the questions towards areas where the interviewee appeared to have extra knowledge or interest. Since the interviews were both aimed at revealing useful empirical material for the study, as well as to give the authors a deeper understanding of the areas, this more flexible approach was suitable.

Before each interview, we prepared a quite basic interview guide with questions to ask, and also some potential follow-up questions to ask during the interviews, depending on the answers we got. We also came up with questions during the interviews, that were not prepared at all, if an interesting new area appeared, or if we wanted to dig deeper into a current area. This leads us to believe that our interviews were comprehensive, however as is discussed more in detail below, we could have benefited from going back to interviewees at a later stage of the study to validate our interpretation of answers and our findings.

In transcribing and turning the interviews to empirical material, emphasis was put on the aspect of differing views in different positions in the organization; as Eisenhardt (1989) notes, it is important to

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take into account that every interview is a stand-alone entity, and regard that collected data with a knowledge of whose perspective it reflects.

In total, ten interviews ranging between 90 minutes and two hours were performed. Most interviews were with IFS employees and one with an employee of Siemens. We also had three sessions of around one hour each where an IFS employee did a company presentation and went into detail on the areas IoT and servitization in the IFS context. These sessions took place in the beginning of the study, and we mostly listened, with some follow-up questions added in, to understand IFS’ business and the specific context that was of interest of us.

We have interviewed people holding different positions within IFS, ranging from people developing functionality for IFS IoT Business Connector to Key account managers for customers and others such as industry directors and presale consultants. As discussed previously in section 2.2.1, we have chosen not to list the interviewees in detail in this report. The reasoning behind this is that this report has been done in collaboration with IFS and will hence be read by IFS employees. We believe the anonymity provided by not listing names of specific interviewees have worked to ensure that our interviewees can tell their truthful view of things of potentially sensitive areas, where there might be discrepancies between what people in different parts of the organization thinks. This is also in line with what Bryman and Bell (2013) refer to as avoiding harm to participant, where the participant need not worry about personal exposure, and gives a more truthful answer, as mentioned above. We also mentioned to the interviewee that this was how we would handle the data, avoiding the ethical pitfall which Bryman and Bell (2013) call lack of informed consent, where the interviewee is not informed of any policy regarding the data and the purpose of the study. Instead, we informed the interviewees of the purpose and method of the study, as well as how we would use and present the data. As was discussed in section 2.2.1, we have ourselves also reflected over the role of the interviewee when evaluating their answers, for example by acknowledging that what a person who is actively working with developing IFS IoT Business Connector probably have more correct information about its capabilities and drawbacks compared to an industry director who is organizationally farther from the product.

In our study, we have sometimes not been able to get into contact with people we wanted to. Mostly, we were able to get into contact with people we wanted to interview with the help of our supervisor at IFS. However, a few times we were not able to get an interview despite this, because of reasons such as the potential interviewee being too busy or plainly not interested in taking part in an interview. In these cases, we have tried to mitigate the impact on our study by finding alternate interviewees to get information from another source. There does however exist a risk that we might have missed information which could have affected our conclusions, but we still believe that we have been thorough enough to not miss anything which could impact the conclusions in a major way. In accordance with what Eisenhardt (1989) suggests, multiple investigators and data collection methods were used in the study. Multiple investigators to the extent that the authors have both had opportunities to provide input to the interview questions before and during the interview, as well as that the interviews were complemented and correlated with a collection of written data and

descriptions from the case company in focus by both authors. After the interviews, we have not at once discussed the results with each other, but have instead taken some time to individually process the information gotten before discussing the outcome of the interview with each other. We believe that this has strengthened the empirical findings of the study, as we when converging in our

independent analysis of the interviews enhances the confidence of the findings, according to Eisenhardt (1989). Eisenhardt (1989) also mention taking an opportunistic approach to data

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collection together with a continuous evaluation of field notes, which we have embodied by adjusting the interviews as we gain a better knowledge of the case and study areas.

In the spirit of opportunism, we have reviewed much data that has not necessarily had a clear connection to the case as well, mainly to get a grasp of how IFS works and interacts with customers in general. We have in a systematic way discerned what areas of interest we wanted to know more about, and subsequently tried to arrange interviews with people who seemed to have that

knowledge, often based on tips of previous interviewees. Some interactions have been on a more informal level, when we had the chance to talk to a person briefly and ask a few questions, while some have been fully fledged interviews but with people who have an interest in the subject within IFS without having an official role in it. Considering the shift in interview focus during the study, we have, in accordance with what Eisenhardt (1989) suggests, used the freedom of our flexible data collection to make adjustments, and were able to capture some themes and areas we might

otherwise have missed. Although, some questions have remained somewhat unchanged to be able to reference and discern between answer of different interviewees.

One weakness we have identified with our study was that we only interviewed each person once. This had two distinguishable consequences, as we did not validate answers which were ambiguous or unclear, and we also did not ground our subsequent findings of the study with said persons. For some cases, we had brief email contact after the interviews to clarify some things, but in other cases we either did not get in touch again or did not get answers to our emails. If we were to redo the study, we would have planned to have a feedback session with our interviewees, and correspondingly during our interviews have informed our interviewees that we would get back to them at a later stage – preferably we would even have booked the time and place for this during the first interview. This would probably have made it easier to get in touch with the interviewees again.

We have also read a large number of IFS’ company documents and customer case descriptions (it is standard procedure at IFS to generate a brief document about customers and the specifics of the case), which have complemented the information gathered through the personal interviews. For the customer companies of IFS that, as presented above, have been surveyed in this study, we have also gathered information from their respective company website. This method of having multiple data collection methods can have strengthened our study in terms of the constructs presented, as it as described by Eisenhardt (1989) makes it possible to triangulate the collected data, so as to be able to weigh the findings of the different methods against each other. In our case, information about

customer cases presented in IFS internal case description documents have been weighed with what people involved in the project have said, allowing us to form a truer picture of said case.

As described above, we have interpreted and compared information between different interviewees and other data sources such as company documents and case descriptions. In section 4 and 5 of this report, where we present the empirical material gathered in the study, we have generally chosen to present information in this form of interpreted state, where for example the description of a customer company of IFS is based on several of the sources mentioned above. By doing this, we believe that we have been able to present a picture which is as true to reality as possible. In rare cases, we have explicitly stated that information stems mainly from one interviewee, to indicate that this is more of an opinion of an individual as compared to something we have seen a pattern of. A large part of this study has involved the mapping of parts of the market in accordance to their IoT and servitization fit, where we mainly have used IFS customers as a foundation. We have chosen to investigate customers who are involved in implementation projects of IFS IoT Business Connector, as this has provided information about how the product looks and what has been done with it thus far,

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and also showed how the customers who have or are implementing IFS IoT Business Connector have changed over time. Being stationed at IFS, we have also had good possibilities to get information directly from IFS employees who are closely involved with these customers, which would be harder if we for example had chosen to only investigate companies external to IFS. We have however looked at on company without any relation to IFS, namely Siemens. We chose Siemens based on several reasons. First off, we wanted to get in contact with a company not connected to IFS in any way, as a means to not be biased by having looked at only companies which are related to IFS. Secondly, we wanted to investigate a company in which IoT and servitization had been developed to advanced levels, as this was not the case for the companies we surveyed which was IFS customers. Finally, we had a previous good contact with Siemens which allowed us to get to meet a Siemens employee in person, who had a position in the organization that gave good insight in the company’s IoT and servitization efforts.

The information about IFS customers is mainly based on second-hand information, where we used information from IFS employees who have close contact with the respective customer as a source. There are some obvious risks with not sourcing the information directly from the company, for example that an IFS view of specifics in for example an implementation project corresponds badly with the company’s view. We have tried to mitigate this risk by asking questions of the type “What did your company see as the greatest potential of this functionality”, trying to ensure that we got the view of the company to as large extent as possible. We cannot be sure that this actually was the case, as it is of course a difficult ordeal for our interviewees to be able to shift an IFS view to a customer view. From time to time we however noticed more of a sensationalized view from our interviewees, glorifying the possibilities of servitization and IoT. But when concerning real cases and examples, the information was considered to be practical and objective. Asking IFS employees about a customer can also have contributed to getting more truthful answers, as direct contact with the customer could result in the customer wanting to glorify the state of their company. Further, the interviewees we have talked about customers with have worked together with the respective

customer over a prolonged time, giving them a good picture of the state of the customer company. It should be noted that first-hand contact directly with employees of the companies could have given both more and potentially more accurate information in general and also a good ground for comparison between what IFS says about their customers and what those customers say about themselves. Summed up, we believe that the information we have gotten is accurate, but also note this as an area of possible improvement from the perspective if we would have redone the study. It should however be noted that there were some barriers hindering us to seek direct contact with customers. The IFS customers who were not currently using IFS IoT Business Connector can be described to be in a sales phase, where IFS are interested in being able to sell the product to those companies. For us as researchers to approach such customers would potentially convey an image of disparity between marketing of the Business Connector and the real situation, which made it harder for us to use our IFS contacts as a means to get into contact with said customers. For the customer companies which were already using IFS IoT Business Connector, we did some attempts to get in touch directly, but it proved to be difficult. As we already got good information about them from our IFS interviewees, we decided to not pursue direct interviews further.

2.4 Model generation

As part of this study, we have generated a model. In it’s present, final form, the aim of it is to both describe the relation between IoT and servitization (see section 3.4), together with how we see an ES provider’s role in relation to this (section 3.5) as well as having companies mapped into it (section

Supporting servitization via the Internet of Things: the ES provider viewpoint : A case study of the multinational ES provider IFS and its customers