The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective

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The influence of the Industrial Internet of Things on business models of established manufacturing companies – A business level perspective ⁎

Daniel Kiel , Christian Arnold, Kai-Ingo Voigt Chair of Industrial Management, Friedrich-Alexander University Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany

A R T I C L E I N F O

A B S T R A C T

JEL classification: L00 L60 M15 O32

The emergence of the Industrial Internet of Things (IIoT) poses a large impact on established business models of manufacturing companies. This study aims at analyzing the influence of the IIoT on these business models from a business level perspective. In particular, it focuses on the interrelationships between business model component changes. While the sparse body of extant management literature examines just a subset of business model elements as affected by the IIoT, a framework comprising an entire set of elements is provided. Besides, their direct and indirect interrelationships, and the most important changes in each of the elements, are investigated. For this purpose, an exploratory multiple case study approach is employed, which is based on relevant IIoT-related experiences of 76 German manufacturing companies. By triangulating data from semi-structured expert interviews and archival company material, the study provides in-depth insights and a better understanding of IIoTdriven effects on manufacturing business models. It contributes to extant management literature by revealing the value proposition, internal infrastructure management, and customer relationships predominantly influenced by the IIoT. Moreover, it is shown that IIoT-triggered business model changes are offer-driven, particularly by production and process optimization within customers' production systems. These value proposition changes result in subsequent modifications of the remaining business model elements.

Keywords: Industrial Internet of Things Industrie 4.0 Business model Business model innovation Expert interviews Multiple case study Manufacturing

1. Introduction The article at hand is dedicated to the following research question: How does the Industrial Internet of Things (IIoT) influence business models (BM) of established manufacturing companies? Against the background of today’s multifaceted challenges for manufacturing companies, e.g., shortened technology and innovation cycles, as well as the necessity to offer customized products at the cost of large-scale production, the German government passed the future project Industrie 4.0 in 2011. The term refers to the more internationally known and academically applied IIoT (e.g., Hartmann and Halecker, 2015; Kiel et al., 2016), which underlines the integration of the Internet of Things and Services (IoTS) into manufacturing as well as internet-based communication of objects. It characterizes the proceeding digitized connection of industrial manufacturing resulting in a completely intelligent, connected, and autonomous factory (Kagermann et al., 2013). The IIoT results not only in a production-technical change, but also in extensive organizational consequences and opportunities (Arnold et al., 2016). Established value chains are changing and enabling novel business conceptions and models. Established manufacturers are well

⁎

advised to critically reflect, innovate, and adapt their BMs to stay competitive (Iansiti and Lakhani, 2014; Loebbecke and Picot, 2015). While prior literature on the IIoT has concentrated on technological foundations, challenges, and opportunities, management research has a backlog (Brettel et al., 2014). With regard to the latter, research deals rather with general influences of the IIoT on BMs. These include, for instance, manufacturers providing novel value offers, the importance of collaboration and networking, and changing workforce qualifications. Precise effects of the IIoT on business concepts are yet insufficiently and not systematically examined. There is no scientific work analyzing an entire BM with regard to its interdependent building blocks (Kiel, 2017). However, it is necessary to examine the complete set of BM components since they are closely interrelated and constitute a BM exclusively in their entirety (Schneider and Spieth, 2013). In order to address this gap, the article at hand aims at analyzing the IIoT’s influence on BMs of established manufacturing companies. It thereby reveals concrete changes in each BM component and their relative importance in terms of their absolute change frequencies. In addition, this allows obtaining an indication of the relationships between component modifications in the context of the IIoT. Owing to the lack of prior systematic research on IIoT-driven business model changes

Corresponding author. E-mail addresses: [email protected] (D. Kiel), [email protected] (C. Arnold), [email protected] (K.-I. Voigt).

http://dx.doi.org/10.1016/j.technovation.2017.09.003 Received 26 September 2016; Received in revised form 11 July 2017; Accepted 24 September 2017 0166-4972/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Kiel, D., Technovation (2017), http://dx.doi.org/10.1016/j.technovation.2017.09.003

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consumption, and human work is significantly enhanced (Rehage et al., 2013). Further opportunities and potentials associated with the IIoT are, for example, increased flexibility (Saberi and Yusuff, 2011), optimized decision making (Ganiyusufoglu, 2013), customization (Kalva, 2015), highly profitable BMs (Lee et al., 2014), demography-sensitive job design, and improved work-life balance (Kagermann et al., 2013), just to name a few. In parallel, IIoT business ecosystems emerge which interconnect the virtual and the real worlds, involving companies and individuals in different roles, e.g., module providers, machine-to-machine service providers, network operators, and users, that interact and share connected hardware, software, and platforms with one another (Iansiti and Levien, 2004; Mazhelis et al., 2012).

and their interrelationships, an exploratory and qualitative case study research design based on 76 in-depth expert interviews as a primary source and, e.g., annual reports and company websites for verification purposes as a secondary source is used. The interviewees’ statements were systematically and inductively analyzed and comprehensively displayed by applying the BM ontology (BMO) of Osterwalder et al. (2005), taking a business level perspective. Eventually, this study suggests a framework of IIoT-specific BM component interrelationships. By doing so, current state of research is extended in several ways. Firstly, while prior research has concentrated on technological challenges or merely single effects of the IIoT on distinct BM areas, this article contributes to the literature by integratively identifying BM elements, which take on a key role in this context, i.e., the value proposition, the internal infrastructure management, and the customer relationships. Secondly, the understanding of IIoT-inherent BM influences is enhanced by focusing on the precise BM changes required in each of its constituent elements. This study extends the current state of research by revealing the possibility of offering production and process optimization within customers’ production systems and the emergence of novel contact persons originating in interdisciplinary teams. Thirdly, an integrative illustration of the direct and indirect interrelations of interplaying BM elements contributes to BM and innovation literature since the understanding of relationships between IIoT-triggered BM component changes is enhanced. The remainder of the manuscript is structured as follows. Section 2 outlines the theoretical background of this research by defining the terms ‘IIoT’ and ‘BM’ and explaining the BMO serving as an analytical framework for this multiple case study. The section concludes by displaying the current state of research. Section 3 describes the research design employed and characterizes the examined sample. Section 4 presents the results and Section 5 discusses the findings by illustrating a framework comprising the empirical findings. It concludes with the contributions and managerial implications, along with limitations and further research recommendations.

2.2. Business model Academic literature has begun to agree on some common central characteristics of a BM (Zott and Amit, 2013). These are a BM’s focus on the value creation logic for all stakeholders; the consideration of crucial value creating activities performed by parties external to the company, like suppliers and customers; a comprehensive approach to explain the value creation logic of a company; and the fact that BMs emerge as a new unit of analysis in academia. Nevertheless, Zott and Amit (2013) argue that there still does not exist one established BM concept, which is in line with several other authors (e.g., Casadesus-Masanell and Ricart, 2010; George and Bock, 2011; Johnson, 2010). As one universally valid BM definition would have to be very broad to be appropriate for every case, such a standard definition is not even possible, since it would lack specificity and lead to misunderstandings (Zott and Amit, 2013). Within the multitude of BM definitions emerged to date, Weill and Vitale (2001) regard a BM as the “description of the roles and relationships among a firm’s consumers, customers, allies, and suppliers that identifies the major flows of product, information, and money, and the major benefits to participants” (p. 34). Their BM framework is focused on e-business and is constituted by the addressed target customers, product and service offers, revenue sources, critical success factors, core competencies, channels, and IT infrastructure. Linder and Cantrell (2000) stay more abstract, arguing that a BM is the “organization’s core logic for creating value” (p. 2). Referring to the BM configuration, pricing and revenue model, channels, and value proposition overlap with those elements described by Weill and Vitale (2001). Linder and Cantrell (2000) differently view a commerce process model, relationships, and the organizational form as further necessary BM elements. Afuah and Tucci (2003) represent another view by regarding a BM as a method to build and use a firm’s “resources to offer its customers better value than its competitors and to make money doing so” (p. 4). Since it is necessary to clearly state, define, and explain the BM concept applied to answer research questions in the respective field of interest (Zott and Amit, 2013), this requires agreement on one definition for the further proceeding of the article at hand. Therefore, the BMO of Osterwalder et al. (2005) is applied as analytical framework. In their work, they systematically identify the nine most common BM components in academic literature by synthesizing the models and conceptualizations most often cited, mentioned, and examined (Osterwalder et al., 2005). Furthermore, the BMO has been developed in the context of information systems, which represent a technological core of the IIoT, is comprehensive, and encompasses a comparably high spectrum of BM components necessary for analyzing BMs as completely as possible (Wirtz et al., 2016). According to Osterwalder et al. (2005), a BM is a “conceptual tool that contains a set of elements and their relationships and allows expressing the business logic of a specific firm. It is a description of the value a company offers to one or several segments of customers and of the architecture of the firm and its network of partners for creating, marketing, and delivering this value and relationship capital, to

2. Theoretical background 2.1. The Industrial Internet of Things The IIoT integrates recent trends from the information and communication technology (ICT) area in industrial manufacturing. It is based on the IoTS, which refers to the “seamless integration of physical objects such as sensors or home appliances (i.e., things) and services” over online networks (de Leusse et al., 2009, p. 47). It serves as a key enabler for the creation of networks comprising manufacturing processes and consequently converting factories into a smart manufacturing environment. In other words, the IIoT involves the integration of both Cyber-Physical Systems (CPS), which connect the physical and the virtual worlds, and the IoTS into industrial processes. This results in several novel implications for value creation, business models, service orientation, and job design (Kagermann et al., 2013). Correspondent to the definition of Bauer et al. (2014), the IIoT is defined as the “real-time capable, intelligent, horizontal, and vertical connection of people, machines, objects, and ICT systems to dynamically manage complex systems” (p. 18). In this context, the IIoT refers to recent developments with regard to the creation of a novel manufacturing paradigm and environment comprising intelligent and selfcontrolling objects: smart products are constantly identifiable and steadily locatable, as well as being aware of their latest condition and alternative paths to their destination. Envisioning an extensive penetration with this manufacturing approach, orders guide themselves through entire value chains autonomously and machines set-up automatically as well as rescheduling production on their own if an error is predicted. This so-called ‘smart factory’ is in control of complexity and is less vulnerable to losses of production. Consequently, resource efficiency in terms of material usage, energy 2

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concerns on

Value configuration

allows

promote

Relationships

maintained with

Fig. 1. Relationships of BM components (Source: based on Osterwalder (2004)).

receive

relies on value for

Partner network

Core competencies

allow based on

Value proposition

deliver

Distribution channels

deliver to

Target customers

supports

Cost structure

built on

Revenue model

of particular interest. The final set of literature was analyzed in depth with regard to its respective key findings concerning the impact of the IIoT on manufacturers’ BMs. Since the IIoT represents a relatively young research field, an inductive classification of the articles’ findings and development of topical categories was chosen. In the final step of the analysis, all three authors of the article at hand compared, critically reflected, and discussed these categories. Given the descriptive nature of the review, there is no application of statistical methods for the classification of the sample. The review clearly revealed that possibilities to remodel BMs and organizational consequences of the IIoT are still not examined sufficiently. Nevertheless, Lee et al. (2014) suggest that the integration of digital, web-based production systems into manufacturers’ value chains is associated with changing BMs. Dependent on the intensity of the integration of the IIoT into value creation, it will partially trigger significant changes in established BMs. This affects companies from a multitude of industrial sectors which are equipping their products, services, and processes with digitization and connectivity technologies (Arnold et al., 2016). Table 1 summarizes the main categories identified in the relevant literature. Value offers are characterized by the provision of individualized products, advanced mass customization, and batch size one production (Kalva, 2015; Petrick and Simpson, 2013). The provision of individualized and complex products requires a complete understanding of customer needs and expectations. More direct interactions and consultations between suppliers and customers lead to improved, longer term, and intensified customer relationships (Kagermann et al., 2013; Kans and Ingwald, 2016). In addition, the expansion of innovative and customized service offers, i.e., product/service combinations representing so-called hybrid solutions that are particularly data-based, also aim at an increasing and consequent customer orientation (Fleisch et al., 2014). Despite individualized product manufacturing and service offers, the IIoT allows optimization of production systems regarding costs, reliability, time, quality, efficiency, and similar aspects (Kagermann et al., 2013). The collection, monitoring, and analysis of machine condition data allows for remote and predictive maintenance to reduce stoppages in production as far as possible (Kirazli and Hormann, 2015). In particular, purposeful knowledge is extracted from big data by applying data mining to detect patterns, which enables automated decision-making (Brettel et al., 2014). In this context, manufacturers are required to offer modular combinations of hardware and software. In addition to their former focus on hardware development, they now have to deal with software development activities (Sendler, 2013). The combination of production-related hardware and software represents a crucial novel resource (Zhang et al., 2014). They require large investments in IT infrastructure that can be offset by hybrid solutions, which enable new income sources based on, e.g., service level agreements and long-term contracts (Dijkman et al., 2015). Data as a key resource of the IIoT enables dynamic pricing as well as new

generate profitable and sustainable revenue streams” (p. 17f.). More briefly speaking, “[a] business model describes the rationale of how an organization creates, delivers, and captures value” (Osterwalder and Pigneur, 2010, p. 14). Fig. 1 illustrates the nine elements/components constituting the BMO, including their interrelations. The center is formed by the value proposition that describes products and services that add value for particular customer segments, which in turn receive the value proposition. These target customers represent the different groups of customers the company intends to address. The distribution channels characterize how the company reaches its customers and communicates with them. The channels thereby deliver the value proposition to the customers. Relationships maintained with the company’s customers are described in the element relationships and promote the value proposition. The value configuration describes the activities a company has to perform to create the value proposition and run the entire BM. The core competencies are those resources that are mandatory to perform the crucial activities and are therefore the basis of the BM’s value proposition. Moreover, these two BM elements allow the value proposition. The firm’s network of crucial suppliers and other important partners is described in the partner network. This concerns key activities, which are facilitated or replaced by partners. Hence, key partners support the offering of the value proposition. The revenue model and the cost structure give some indication of the financial aspects, where the former is built on the value proposition and the latter contains costs related to value creation, marketing, and delivering (Osterwalder et al., 2005).

2.3. The influence of the Industrial Internet of Things on business models: current state of research At the beginning of this research, relevant literature published between 2011 and 2016 comprising both high quality journal papers and relevant and reliable collected editions, book chapters, working papers, and research reports (to account for the IIoT’s degree of novelty in research) was systematically reviewed. The documents were obtained by a search in the databases ABI/Inform, Business Source Complete (EBSCO), ScienceDirect, and Google Scholar, using relevant keywords (for example, ‘industry 4.0’, ‘internet of things and services’, ‘industrial internet of things’, ‘cyber-physical systems’, ‘business model’, ‘business model innovation’, ‘business model development’, and ‘business logic’). Due to the wide application of the term ‘IIoT’ in Germany, these keywords were extended by their respective German synonyms. The full-text review approach of Rashman et al. (2009) identified 87 relevant articles to be analyzed in detail. In order to ensure the high quality and reliability of the literature review, the articles’ relevance and quality was discussed in several sessions consisting of the authors and two independent research fellows in their department. The research goals, definition of key terms, methodological rigor, results, and conclusions, as well as the relevance to BMs in the context of the IIoT, were 3

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Table 1 Literature covering IIoT-triggered BM changes. Topic

Brief description

Representative sources

Value offers

– Increasing provision of individualized and innovative service offers complementing traditional products – Offering integrated product/service solutions based on the collection, monitoring, and analysis of (e.g., machine condition) data

Fleisch et al. (2014), Kalva (2015), Kans and Ingwald (2016), Kirazli and Hormann (2015), Petrick and Simpson (2013)

Collaboration and networking

– Novel solution offers require the integration of partners, such as customers and suppliers, in an interactive and collaborative way – Increasing customer integration into product and service engineering and design – Novel suppliers compensate for unavailable resources required for the provision of novel products and services

Arnold et al. (2016), Kagermann et al. (2013), Porter and Heppelmann (2014), Stockinger et al. (2015)

Human resources

– Increasing employee qualifications are required regarding software development and IT competencies, as well as data analysis skills – Low and middle qualified staff are predicted to be subject to reductions – Employees’ roles will change from operators to problem solvers

Bonekamp and Sure (2015), Erol et al. (2016), Richert et al. (2016), Rogers and Trombley (2014)

analysis of affected BM components and their internal relationships.

payment and license models due to the possibility of consumption- and performance-based billing (Xu, 2012). Hence, efficient individualization of prices and billing is ensured. Collaboration and networking serve as strategic success factors in the context of the IIoT. Nevertheless, manufacturing companies often show resistance with regard to opening up to external partners, such as suppliers and customers, or competitors (Kagermann et al., 2013). Thus, a strong, secure, and reliable platform-based partner network is a prerequisite. An essential aspect is the order-related flexibility of value creation processes, which requires ad hoc connections to key partners via such platforms (Holtewert et al., 2013). According to Porter and Heppelmann (2014), the IIoT results in the necessity of novel suppliers. These serve to compensation for unavailable resources in terms of, for example, knowhow, hardware, software, etc. Here, strategic partnerships with suppliers of data analytics, IT systems, software development, and ICT equipment have to be highlighted (Stockinger et al., 2015). Customers serve as collaborative partners concerning the development, engineering, and design of products and services (Kagermann et al., 2013). Moreover, the IIoT facilitates, besides the previous business-to-business (B2B) relationships, a new form of business-to-business-to-customer (B2B2C) relationships that are characterized by a direct reaching of end customers, i.e., the customers of the company’s own customers. This new constellation can be formed in two ways. On the one hand, it allows the bypassing of present customers; on the other hand, it is possible to approach the end customer together with the manufacturer’s own (intermediary) customer and thereby providing the end customer with better products and services (Burmeister et al., 2016; Pfoertsch and Chen 2011). With regard to human resources, companies face increasing employee qualification requirements in terms of extensive IT knowhow, which can be built up either by human resource development or recruiting instruments, dependent on the available level of internal employee qualification (Erol et al., 2016). To give an example, data analysis specialists play an important role in the detection of data patterns. In this context, staff reductions among lower and middle qualified and salaried employees are predicted. The role of employees will change from operators to controllers and problem solvers. The human being is required as a decision-maker, sensor, and actor. Due to the complete network-like linkage of production entities, single objects may cause conflicts, whereupon the human being, supported by assistance systems, has to intervene. Moreover, CPS are not able to completely conceive their environment, though equipped with intelligent sensors. As a consequence, the human being’s sensorial capabilities have to compensate for this imperfection (Bonekamp and Sure, 2015). The current state of research shows that there is no systematic and integrative (i.e., a combined consideration of all BM components)

3. Methodological overview The article at hand employs a qualitative empirical approach, which is of an exploratory nature. This is justified by the fact that no sufficient integrative and systematic investigation of IIoT-related influences on the components, which constitute a BM only in their entirety, of manufacturers’ BMs exists. A multiple case study approach primarily based on inductively analyzed in-depth expert interviews, supposed to contribute to developing theoretical knowledge, was applied (Edmondson and McManus, 2007; Eisenhardt and Graebner, 2007). According to Almeida (2011) and Dubé and Paré (2003), the application of case study research is particularly well suited to organizational research in the context of information systems. Since these systems are among the key enablers of the IIoT and this study deals with issues of an organizational nature, this approach is appropriate for this article’s purpose. Also, the IIoT qualifies for exploratory case study research since it is a contemporary and complex phenomenon to be studied within its real-life context (Benbasat et al., 1987; Yin, 2009). Case studies are particularly useful in that they give in-depth and deeprooted information to answer ‘how’ and ‘why’ research questions (Stokes and Bergin, 2006; Yin, 2009). The decision to rely on multiple cases rather than a single one is based on the fact that the former provide greater rigor, more robustness and reliability, less dependency on the particular context, and better generalizability of findings compared to the latter (Eisenhardt, 1991; Yin, 2009). Semi-structured expert interviews with knowledgeable and experienced managers form the primary source of empirical data. The decision to rely on semi-structured expert interviews suits the exploratory nature of this study. This kind of interview allows structured data collection but still follows the principle of openness. The investigator serves as an inquiry and interpretation instrument, providing the interviewee with sufficient space to disclose his experiences, knowhow, and opinions without being externally controlled (Cannell and Kahn, 1968; Yin, 2009). As recommended by Flanagan (1954), a modified version of the critical incident technique (CIT) is used to meet the requirements of the BM analysis. The method has proved successful in numerous government, business, industrial, and educational research projects, and in doctoral dissertations and professional papers. Although case study research is not sampling research (Benbasat et al., 1987), this article follows Yin’s (2009) recommendation for multiple case study sampling by applying a replication logic to counteract possible negative effects of sample bias on this study’s findings as far as possible. In order to obtain a more generalizable picture of the influence of the IIoT on established BMs in the manufacturing context, the sample includes manufacturers of different size and from different 4

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Appendix B). The first part included general and personal questions in terms of the expert’s job position and company tenure to verify his/her reliability and knowledgeability. The second part dealt with questions about the characteristics of a project which served as a critical incident according to the aforementioned modified CIT. The experts were required to refer to a typical and comparable project in which aspects of the IIoT had to play a high or very high and decisive role for its success. The third part of the interview guide contained questions about the changes of the single components constituting the BMO triggered by the project and the IIoT respectively. In this context, it was ensured that the experts exclusively referred to the most important BM modifications by explicitly requesting it. To allow for better tracing of these changes, the experts were asked about both their former non-IIoT-affected and their IIoT-affected BM in which the project was run. The interviews were audio-recorded and transcribed, resulting in almost 1100 pages of text material to be analyzed according to qualitative content analysis, as suggested by Miles and Huberman (1994). Qualitative content analysis is a method to analyze empirical material systematically by following specific rules in order to control the process of analysis. Since it is not the intention of this method to produce statistical significance, its systematic and transparent procedures support the identification of patterns, themes, and categories, helping to answer the research question. While the expert interviews constituted the main data source, secondary data was also included. Whenever it was available, secondary data was collected from annual reports, company magazines, internal project reports, and company websites (altogether about 550 pages), but was not coded since it was exclusively used for triangulation purposes and to verify the experts’ statements in order to strengthen their validity. Triangulating is a well-accepted method to provide a stronger and more valid foundation for emerging topics and their relationships (Maxwell, 1996; Yin, 2009), since the findings and conclusions of a case study are more convincing and correct if they rely on different sources of information (Dubé and Paré, 2003). In the qualitative content analysis of the interview transcripts, the developed categories were informed by extant literature but defined inductively (Gioia et al., 2013; Kelley et al., 2009) in order to allow concepts to emerge from this process. This is in accordance with Krippendorff (2013), who suggests exclusively applying deductive coding schemes if the overall context is researched sufficiently. Additionally, by identifying consistencies and patterns in the collected data (Greening et al., 1996), inductive coding allows for the contribution to theory building and does not aim at theory testing (Edmondson and McManus, 2007). In order to demonstrate rigor in the qualitative study, this article follows the scientifically recognized and recommended works of Gioia et al. (2013). In inductive coding, the first step is to perform an initial data coding, which maintains the integrity of first-order (informant-centric) concepts. Thus, the interview

branches. These branches, i.e., machinery and plant engineering, automotive, electrical engineering, ICT, and medical engineering, represent the most important industries in Germany in terms of their contribution to the gross domestic product (Federal Bureau of Statistics, 2016). The interviewed companies are located in Germany since the IIoT, which is a synonym for the German term ‘Industrie 4.0’, is a central column of the German ‘high-tech strategy 2020’. The requirement was exclusively to select companies that have already been organizationally affected by the IIoT. This enables obtaining reliable, competent, and professional information. The manufacturers were selected based on their high innovativeness and leadership in a variety of mature and emerging market segments. The diversity of the case study material was supposed to extend the variety in the data to enable a contribution to theory building (Eisenhardt, 1989). Building on the CIT, the key criterion for a final integration of a company into the sample was to be able to report on a project in which the IIoT played a predominant and decisive role for its success. Also, the project had to be supported by top management, perceiving it as being highly relevant for the company’s future strategic orientation. The experts had to possess a middle or top management position, be closely involved in the reported projects, and know the companies’ BMs well to strengthen the reliability of recalled important issues (Graebner and Eisenhardt, 2004; Huber and Power, 1985). Overall, the sample comprises 76 interviews, conducted between February and June 2016: 31 interviews with machine and plant engineering companies (40.8%), 20 interviews in the automotive industry (26.3%), ten interviews with representatives from electrical engineering manufacturers (13.2%), nine interviews with firms from the ICT industry (11.8%) and six interviews within the medical engineering sector (7.9%). Fig. 2 provides an overview of the distribution of the sample in terms of number of employees and sales. The majority of the firms (89.5%) have more than 1000 employees. Sales of the firms vary across a broad range, whereas 56.2% of the companies achieve more than 1000 million euros sales volume. The interviewees’ names are anonymized for confidentiality reasons (see Appendix A for an overview of the informants and interviews). The interviews lasted between 55 and 75 min. All interviews were conducted in German, the native language of the interviewers and all interviewees. Corresponding to the exploratory nature of this study, the development of the interview guide was informed by literature but followed the principle of openness and flexibility to allow unexpected and novel topics to emerge (Ananthram and Chan, 2013; Kasabov, 2015). Subsequently, the initial interview guide was pilot tested with three independent corporate representatives in order to verify its duration, understandability, content structure, reasonableness, and the value of the provided information for the research goal. After having revised small areas, the final interview guide was organized in three parts (see

Employees

Sales (mil. EUR)

> 100,000

> 50,000

(50,000 - 100,000]

(10,000 - 50,000]

(10,000 - 50,000]

(5,000 - 10,000]

(5,000 - 10,000]

(1,000 - 5,000]

(1,000 - 5,000]

(500 - 1,000]

(500 - 1,000]

(100 - 500]

(200 - 500]

(50 - 100]

(0 - 200]

(0 - 50] 0%

10%

20%

30%

40%

0%

Percentage of interviewed companies

10%

20%

Percentage of interviewed companies

Fig. 2. Employees (n = 76) and sales in million euros (n = 73).

5

30%

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affected by the IIoT. The most frequently affected BM component is the value proposition, since 65 of the 76 interviewees underline its respective importance. Here, customer value in terms of product and process optimization, as well as new product, service, and solution offers, experience an impact due to the IIoT. Occurring less often, but not less important, is the dimension of the company’s internal infrastructure, represented by the core competencies (n = 60), particularly IT knowhow, and value configuration (n = 53), in most cases technology and software development. The relationships to customers are likewise important, being affected 56 times. Changes in this component are primarily related to an increasing degree of intensity in terms of communicating with, understanding, and satisfying customers, treating them as partners, and integrating customers early. In contrast, the further two components constituting the customer interface of the BM, i.e., the distribution channels (n = 15) and the target customers (n = 12), seem to play a minor role regarding their frequencies of relevant change. The IIoT does not immediately influence the way of reaching established customers since, especially in the B2B market, traditional communication ways, such as face-to-face communication, telephone, and e-mail, serve as a crucial basis for establishing and maintaining personal relationships. A similar subordinate importance manifests for the revenue model (n = 22). Until this study, more than two-thirds of the cases did not adjust their income sources due to the IIoT. The cost structure occupies a middle position, emphasized by 42 experts. Comparing the partner network, representing the external side of the company’s infrastructure, with the internal infrastructure, it becomes apparent that the IIoT mainly impacts a firm’s own activities and resources in terms of absolute frequencies.

transcripts were analyzed against the background of their respective context both in a team (the first 15 interviews) and, later on, individually. In particular, the former allows discussing category labels and coding procedures, which resulted in a common understanding of the coding process. The latter was not only consistently checked and revised, but also modified and, if necessary, inductively expanded based on team discussions. Afterwards, the revised category system was applied to all 76 interviews to enhance the comparability of the results. In a second step, first-order concepts, which were perceived as highly relevant for the respective companies, were organized into secondorder themes. Against the background of this article’s research goals, these second-order themes were inspired by the author’s previous knowledge based on having consulted literature, and were therefore rather theoretical. In a third and last step, the authors distilled the second-order themes into overarching BM dimensions/components. Appendix C shows the full set of first-order concepts, second-order themes, and aggregate BM dimensions constituting the data structure, representing “a key component of demonstrating rigor in qualitative research” (Gioia et al., 2013, p. 20). Next, the application of a frequency analysis according to Holsti (1968) provided not only the possibility to observe how often the BM components were changed in the sample, but also how often they changed simultaneously allowing to recognize apparent interrelationships. Eventually, a suggested framework was developed, which comprises the most prevalent changing BM components and their interrelationships in the context of the IIoT. To ensure data reliability during the entire interview analysis procedure, the first two authors of this article coded the data separately, serving as the fundament for the calculation of the inter-coder reliability (Holsti, 1969). Since it resulted in a high value, the validity of the coding process was strengthened. Potential key informant and retrospective biases were addressed by the aforementioned triangulation of archival data as well as the selection of reliable and knowledgeable experts. These were motivated to provide accurate statements since the authors promised them to treat their personal information confidentially so that they would not have to fear negative consequences from, e.g., supervisors (Eisenhardt, 1989; Yin, 2009). Likewise, having requested the interviewees to report on both their former BM and their IIoT-affected BM and the single BM elements constituting a BM only in their entirety accounts for routine criticisms of data collection relying on expert interviews, e.g., regarding retrospective bias (Schüßler et al., 2014). In doing so, the validity and robustness of the findings was further enhanced (Block et al., 2016; Denzin, 1970).

4.2. Interrelations between different business model components Table 2 shows how often a change in one BM component is simultaneously associated with changes in the other components. By way of example, besides the insight that every change in target customers goes hand in hand with an alteration in the value proposition, it is obvious that ten out of twelve target customer changes are related to respective changes concerning relationships to customers. In addition, in 51 out of 56 cases in which the relationships are influenced by the IIoT, there is a change in the product and/or service offers as well. Such examples are labelled as noticeable if: a) there is a high portion of simultaneous change between two BM elements and b) the relationship between these two BM elements is reasonable and supported by the case material. If one of these two criteria is not fulfilled, the interrelationship is considered as not noticeable. By equally applying this systematic interpretation to the remaining BM elements, the following noticeable interrelated BM component changes are indicated (Fig. 4).

4. Empirical findings and interpretations 4.1. Prevalent business model component modifications driven by the Industrial Internet of Things Fig. 3 consolidates the absolute frequencies of the BM component changes. As described in the previous section, the applied coding procedure exclusively considers changes that were of particular relevance to the interviewed companies. This reveals a rank order of BM elements

Table 2 Overview of simultaneous BM component changes.

VP TC RS DC VC CC PN RM CS

VP

TC

RS

DC

VC

CC

PN

RM

CS

65 12a 51a 14a 46a 55a 34a 22a 37a

12 12 10 3 9 12 8 5 5

51 10a 56 13a 42 48 29 20 33

14 3 13 15 11 14 9 7 8

46 9 42 11 53 45a 26a 17 35a

55 12 48 14 45 60 30 22 37a

34 8 29 9 26 30 35 14 23

22 5 20 7 17 22 14 22 13

37 5 33 8 35 37 23 13 42

VP: Value proposition; TC: Target customers; RS: Relationships; DC: Distribution channels; VC: Value configuration; CC: Core competencies; PN: Partner network; RM: Revenue model; CS: Cost structure. a Noticeable relationships.

Fig. 3. Frequencies of BM component changes (n = 76).

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Fig. 4. Indicated interrelationships of BM components.

Value configuration

Partner network

Core competencies

Cost structure

Relationships

Value proposition

Distribution channels

Target customers

Revenue model

In the following, the interrelationships are explained by closely referring to the most frequent and relevant BM changes stated by the interviewed experts (see Appendix C for exact change frequencies). Results show, in the first instance, that the IIoT affects the set of products and services generating value for customers. New markets or customers, whether in existing or new industries, are not reached without modification of the value proposition. Based on data mining and analytics comprising the collection, processing, and handling of relevant data for production and process traceability purposes, novel products, services, and solutions can be offered. In this context, customers benefit from condition monitoring, cloud computing, machine-tomachine platforms, and augmented reality devices.

constituted by an increasing orientation toward direct sales, is ascribable to changing customer relationships or a modified value proposition. Products equipped with technological components, such as embedded systems and CPS serving as the basis for IIoT-readiness, tend to be more complex. Suppliers of these products make rather direct and therefore intensified contact with customers to meet increased consultation requirements.

“The IIoT allows us to generate new service offers. For instance, the topic of condition monitoring and predictive maintenance. Machine-to-machine communication and interconnected machines provide us with information about their status. As a consequence, we are able to offer a respective service package. […] This allows us to address new customers, particularly small and medium-sized enterprises.” (Interview no. 68)

In contrast, changing customer relationships are not necessarily causing modified distribution channels if a company already operates with direct sales channels whose ability proves sufficient to meet changing relationship requirements. For the sake of completeness, it has to be mentioned that in very few cases the interviewees’ statements show an increasing orientation toward indirect sales in order to facilitate market access drawing on sales partners. With regard to the infrastructure management of the BM, Fig. 4 indicates that the value configuration is closely related to the product and service portfolio. The offer of novel data-driven solutions requires IIoT-specific steps concerning technology development, manufacturing activities, and a consequent service orientation. For instance, manufacturers that formerly produced mainly hardware, now have to deal with software development and data mining required for utilizing CPS, enabling novel value propositions, i.e., modular combinations of hardware and software in the form of hybrid solution packages. Since CPS comprise sensors, actuators, central processing units, and respective software, such activities turn out to be crucial.

“While a conventional and simple bearing can easily be sold via indirect sales, i.e., a distributor buys a bearing from us and resells it, the new [IIoT-ready] product is more complex. Therefore, we are more directly in touch with the customer.” (Interview no. 16)

Also, higher efficiency regarding resource usage, energy, and time, as well as increased overall equipment effectiveness, can be achieved. Manufacturers offer product and process optimization within their customers’ production systems as well as novel service solutions. This inevitably requires a high degree of understanding customers’ problems and expectations. In this environment of data-driven servitization, customer relationships become more intensive. Fig. 4 indicates that changing customer relationships are usually based on changes in the customer value. Manufacturers increasingly establish longer term, communicative, and collaborative contacts with customers. “The customer relationship gets more intense because we have to talk earlier to the customers as we already need their design data. We connect closer with the customers, meaning that we are able to be part of their projects earlier. We strengthen the strategic collaboration.” (Interview no. 50)

“Formerly, our key activity was to produce electronic hardware. This is dramatically changing toward software development, IT, and data provision.” (Interview no. 9) Pursuing respective activities concerning the development of both hardware and software serving as a modular combination is highly important to address contemporary customer requirements.

Both their integration into development phases from the very beginning and the implementation of interdisciplinary teams are geared to the understanding and satisfaction of customer needs. While formerly mechanical and plant engineering companies in particular were in touch with production staff, the interdisciplinary nature bears new contact persons originating in IT and research and development (R & D). Although Fig. 4 suggests a direct connection between the customer relationships and the target customers, it is merely of an indirect nature since there is no plausible explanation based on the case study data. The suggested connection only exists because it represents a subset of both the links between customer relationships and the value proposition, as well as between target customers and the value proposition. With regard to the distribution channels, Fig. 4 indicates that a modification of this BM element, which is in the sample at hand mainly

“We have to stop product-oriented thinking but rather pursue a software development and software business direction.” (Interview no. 52) Additionally, manufacturing-related activities regarding standardization/modularization, customization, and application of simulations lay the foundations for novel value propositions. These activities reflect the fusion of the real and the virtual worlds, being a core feature of the IIoT since tangible production activities refer to the former while intangible simulations refer to the latter. Closely associated with the value configuration are the core competencies. Within these, knowhow and culture are found to be the most important resources in order to meet the requirements of modified value configurations. The acquisition and development of employee 7

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“Recently, we have had to cope with rising costs for software development, but also for production hardware. […] Concerning our service business, we are facing rising costs for our staff. […] Finally, there are rising investments into our strategic partnerships to pursue the IIoT movement.” (Interview no. 27)

qualifications in order to match technological and economic IIoT requirements plays a central role. Employees affected by the IIoT need to possess specific IT, development, data analytics, and software knowhow, as well as market competence and understanding of customers. “The raw material we use is intellectual property, i.e., IT expertise. These are people capable of programming software and people capable of utilizing analyzed data purposefully. […] Well, I think, on the one hand, we need more employees with a technical education, i.e., in the area of IT or ICT. […] On the other hand, we have to develop more knowhow about new customers to which we try to sell our new solutions. Hence, we need a very strong market expertise.” (Interview no. 9)

Fig. 4 indicates a close and direct relationship between the costs and the core competencies, since a changing cost structure is reducible to modified resources. Admittedly, the results indicate a close relationship between costs and the value configuration as well, but due to the explanations presented above regarding the constitutional character of the core competencies for the value configuration, it is supposed to be indirect. The same systematic holds true for the link between the value proposition and the cost structure. Last but not least, the analysis shows that manufacturing companies hardly experience any changes of their revenue models. Among the small number of those companies confronted with a change of their revenue model, there is no revenue model adjustment without a respective offer of novel solutions. Novel billing concepts, e.g., for condition monitoring, such as subscription (i.e., selling continuous access to services equivalent to continuous revenue streams) and pay-by-usage (i.e., performance-based billing relying on effective usage rates) models necessitate products equipped with IIoT-ready technologies. Whereas the former were more frequently mentioned, the latter are hardly applied, although the data-driven nature of the IIoT significantly facilitates the implementation of usage fees.

Human resources and corporate culture are highly important for a future-oriented maintenance of BMs subject to the IIoT. Additionally, production equipment and infrastructure in terms of IT hardware, as well as machines equipped with sensors, actuators, computing capacity, and web-enabled interfaces, are critical resources enabling an IIoTadapted value configuration. “Particularly regarding big data and their analysis, we lacked competencies, as we did not have the necessary technologies. But recently, we are using such technologies for the analysis of data. […] Moreover, our machines have to be equipped with sensors and connectivity features.” (Interview no. 72) Like the customer interface, Fig. 4 again suggests a direct relationship between these competencies and the value proposition, which is, according to this study’s analysis, of a merely indirect nature. This is due to the fact, that the core competencies enable the value configuration, which then again enables the provision of novel product and service solutions. The impression of a direct relationship between the crucial resources and the value proposition is given, but is not fully reasonable. The close link between the partner network and both the value proposition and configuration can be ascribed to the circumstance that manufacturers who are willing to offer a novel portfolio may not perform adequate respective activities by themselves or do not possess the available and critical resources.

“As we are offering IIoT-oriented services, we will definitely have service revenues. Consequently, we will not have revenues exclusively from the sale of hardware, machines, and components, but rather from our service offers. This will lead to more continuous [i.e., subscription fees] and less one-time income streams.” (Interview no. 61)

5. Discussion and conclusion 5.1. Theoretical implications and contributions Fig. 5 summarizes the empirical results presented in the previous chapter in the form of a refined framework comprising three central aspects constituting the main objectives of this study:

“As you can see, we are confronted with other issues and other customer expectations. But we are not able to address these requirements by ourselves, because our product is only a part of the solution package. Consequently, we have to cooperate with other companies and especially with suppliers in order to be able to offer the complete solution. […] We need somebody who is able to handle data and somebody capable of programming software interfaces. Furthermore, we need data analysts. To sum up, we deal with completely new partners.” (Interview no. 9)

• The BM elements’ relative importance subject to the IIoT in terms of absolute frequencies of relevant change. • The most important specific modifications of each BM element. • The interrelations of BM elements and their respective modifications.

Manufacturers have to draw on specialized and appropriate knowledge as well as value-adding actions from external partners in terms of both the supply of IIoT-specific technologies and collaborative development activities. For instance, externally sourcing cloud technologies, platforms, locating technologies, software, connectivity, ICT, hardware, and complete system solutions becomes increasingly important. Also, as already explained in the context of the customer relationships, customers are increasingly treated like collaborative partners and are as early as possible integrated into value creation. With regard to the financial aspects of the BM, the cost structure is subject to changes as well. These can most frequently be identified regarding IT infrastructure and production. The former refers to consistently rising expenses for IT, software, and platforms. Concerning the latter, the interviewees disagree on whether production costs experience an increase or a reduction, especially in terms of different time horizons.

This study shows that, with reference to the value proposition, the implementation of the IIoT into manufacturers’ established BMs results in the possibility of offering novel products, services, or even a combination of them in terms of comprehensive solution packages. These are, e.g., condition monitoring, predictive maintenance, and cloud computing. This clearly demonstrates the high technology and service orientation associated with the integration of the internet into the manufacturing environment, which is in accordance with the current state of research (Fleisch et al., 2014). Furthermore, manufacturers increasingly apply data mining and analytics, serving as a prerequisite for offering respective solution packages. In relation to this, Kagermann et al. (2013) show that the IIoT provides the possibility of optimizing production systems and processes with regard to costs, reliability, time, quality, and efficiency. Nevertheless, their findings refer to the optimization of the suppliers’ own value creation processes, whereas this article’s empirical results go even further by revealing that manufacturers also aim at optimizing their customers’ production systems, representing a novel value offer. Contrary to, for example, Kalva (2015) and Petrick and Simpson (2013), customer-oriented individualization

“Of course, we are able to save money due to the minimization of production downtimes and errors, enabling us to reduce variable and fixed costs.” (Interview no. 26) 8

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Value configuration

Fig. 5. Framework of IIoT-specific BM component interrelationships.

Relationships • Intensification • New contact persons • Longer term relationships

• Technology development • Manufacturing activities • Servitization

Partner network

Core competencies

Value proposition

Distribution channels

Target customers

• IT suppliers • Development partners • Customers

• Knowhow and culture • Production equipment and infrastructure

• Product/process optimization • New product/service/ solution offerings • Data mining and analytics

• Direct sales • Indirect sales

• New customers in new industries • New customers in existing industries

Boxes:

Cost structure

Revenue model

• IT infrastructure • Production

• Subscription model

Most often affected BM elements Medium affected BM elements

Arrows:

Direct interrelationships Indirect interrelationships

Least affected BM elements

implementation of interdisciplinary teams bear new contact persons originating in IT and R & D for manufacturers, while they were formerly particularly in touch with production staff. The visionary deliberations made by Burmeister et al. (2016) regarding the emergence of B2B2C relations, i.e., direct reaching of end customers, enabled and triggered by the IIoT are not yet observable in the analyzed sample. Applying such B2B2C relations implies, based on the BMO, approaching new customers, since they were previously not addressed directly. Less developed B2B2C relations are explained by the fact that the interviewed manufacturers hardly reach new customer segments at all. Regarding the value configuration, integrative solution packages encompassing a modular combination of hardware and software components require hardware manufacturers in particular to perform respective technology development activities. Furthermore, the application of simulations, along with standardized and modularized manufacturing activities, constitutes novel IIoT-specific value propositions. These value-adding activities concerning, for example, research activities, product and process design, and software development, are in accordance with Sendler (2013), who recognizes it as a challenge for hardware manufacturers. As mentioned above, this study shows the importance of a consequent service orientation associated with an extended customer-oriented communication and intensification of customer relationships, which complies with the findings of Kans and Ingwald (2016). Surprisingly, and in contrast to extant literature underlining the IIoT-inherent importance of data security and safety activities in order to protect the production environment from abuse (Bonekamp and Sure, 2015), none of the sample companies report on such activities. Hence, awareness in terms of high standards regarding the prevention of unauthorized access, data manipulation, and data destruction has to be raised. Manufacturers may still perceive data and its associated mining and analysis as the basis for novel solution packages, but are not aware of its importance as part of the core competencies. Since the core competencies constitute the value configuration of the BM, an adequate adaption of workforce qualifications is critical. Employees need to possess, for example, specific IT, CPS, and data

based on the IIoT, which is part of the value proposition, plays a subordinate role. This is because only two companies are affected (see Appendix C). Based on the analyzed case material, there seems to be no increasing customer-oriented individualization/customization of the product portfolio and batch size one production. This can be explained by the fact that the sample at hand comprises companies already offering highly individualized products and services by nature, so that individualization may not be directly ascribable to the IIoT but largely facilitated by it. This results in a consequent and individualized service orientation. Contrary to literature stating that the IIoT enables the addressing of new markets and customer segments (Burmeister et al., 2016), the interviewed manufacturers hardly seem to be either able or willing to address new target customers, since there are only twelve respective cases observable (see Fig. 3). This applies to both already served and new industries. Although the value proposition is the key enabler for attracting new customer segments, the manufacturers particularly address their existing customers with IIoT-enabled values, since they know their problems and expectations best. This makes it easier to implement the relatively young approach of the IIoT into their businesses. Regarding the distribution channels, there is an increasing orientation toward direct sales if manufacturers do not yet employ sufficiently developed direct channels. These and their inherent direct customer contact are necessary to meet the increased consultation requirements of complex IIoT-ready products and technological components. As a consequence of the increasing service orientation and solution offers being in need of explanation, relationships with customers experience both an intensification and a somewhat partner-like collaboration. With that, customer relationships not only improve but also become longer term, aiming at the complete satisfaction of customers’ needs. The work of Kans and Ingwald (2016) fully supports these findings by emphasizing the need for direct interactions between suppliers and users, a complete comprehension of customer expectations, and an early integration of customers into product and service engineering and design. A novel insight of this empirical research is that both the customers’ early integration into development phases and the

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analytical knowhow, which agrees with the findings of Erol et al. (2016). Additionally, corporate culture and mindset have to be tailored to market competence and understanding of customers. Hence, regarding concerns that the IIoT and the internet-based connectivity and decentralized production will probably result in decreasing importance of the human being compared to smart and intelligent machines (Bonekamp and Sure, 2015), the expert statements from the respective companies prove otherwise. To a greater degree, future employees’ roles will change from operators to sophisticated controllers and problem solvers. In accordance with Zhang et al. (2014), this study confirms the relevance of IT systems and cloud technologies implemented in production processes constituting further key resources. Likewise, it emphasizes the importance of software components as a fundamental resource for BMs subject to the IIoT. If manufacturers do not have these critical resources internally, they have to rely on external partner networks. These are predominantly characterized by IT suppliers and development partners. This is in line with Porter and Heppelmann (2014), who emphasize the need for novel suppliers. Furthermore, this article supports extant literature by revealing the importance of customers as collaborative partners to be integrated into the development, engineering, and design of products and services as early as possible (Kagermann et al., 2013). Manufacturers try to meet the challenges of the IIoT particularly by adapting and adequately preparing their internal infrastructure, resources, and activities. In any case where they are not able or willing to do so, they rely on the valuable support of external key partners. Nevertheless, there is still the potential to make more use of the IIoT-inherent horizontal connectivity throughout value chains by more frequently and intensively establishing beneficial partner networks. On closer examination of the revenue models, this study surprises with the finding that most of the manufacturers experience hardly any changes although the IIoT strongly enables novel revenue streams such as performance-based pay-by-usage models (Xu, 2012). Correspondingly, dynamic pricing and new payment and licensing models are still a long way off. Two possible reasons for this circumstance originate from the customer’s and the manufacturer’s perspective. Firstly, referring to the former, manufacturers still predominantly address existing customers while hardly approaching new markets. By doing so, they experience resistance to unfamiliar billing models on behalf of the customers. Secondly, referring to the latter, the production costs of machinery arise in the full amount but are not immediately balanced by customers. Hence, the manufacturer has to go into payment in advance, resulting in financial risk. Regarding the cost structure, this study’s findings comply with

Dijkman et al.’s (2015) discussion of rising expenses for IT facilities, software, and platforms associated with the technology-driven character of the IIoT. In addition, although there is no consensus among the interviewed experts regarding an increase or decrease of productionrelated costs, a trend toward decreasing production costs can be observed. Thus, the hypothesis of Rogers and Trombley (2014) concerning cost reduction potentials can be supported. Surprisingly, costs related to human resources and R & D activities seem to play a subordinate role. Due to aforementioned remarks concerning the importance of employees and the focus on internal technology development activities, a higher relevance of these cost components was expected. Table 3 provides an overview of the most prevalent changes of BM elements due to the IIoT. To sum up, the study at hand contributes to the literature on IIoT and BMs concerning three main aspects. 1. On the conceptual basis of the BMO, this article contributes to literature by identifying BM elements taking on a key role within the IIoT. The value proposition, relationships, value configuration, and core competencies are the focus. Correspondingly, the IIoT aims at both value creation and value capture. In contrast, the revenue model, target customers, and distribution channels seem to play a subordinate role. 2. Besides confirming several observations of recent literature on IIoTrelated BM changes shown above, the presented findings extend the literature in two ways. Firstly, this study reveals the possibility of offering production and process optimization within customers’ production systems, supplementing Kagermann et al. (2013), who mainly aim at a supplier’s internal value creation activities. Secondly, this study highlights the emergence of interdisciplinary teams originating in IT and R & D serving as novel contact persons for manufacturers on their customers’ side. 3. Lastly, there is a contribution to extant BM and innovation literature by addressing the absence of research concerning the interrelationships of single BM components. From a general perspective, the proposed framework integratively illustrates direct and indirect interrelations between BM elements. More precisely, the understanding of relationships regarding changing BM elements due to the IIoT is enhanced. The IIoT primarily focuses on and changes the value proposition. The modification of the remaining BM areas is triggered by this change subsequently. Hence, BM changes due to the IIoT are offer-driven.

Table 3 Summary of most prevalent changes of BM elements. BM elements Value proposition

Target customers Distribution channels Relationships

Value configuration Core competencies

Partner network Revenue model Cost structure

Summarized description of crucial IIoT-related changes – New products, services, and solution packages based on application of data mining and analytics – Optimization of production systems and processes, e.g., in terms of costs, reliability, time, quality, and efficiency both for manufacturers and their customers – New target customers are hardly addressed – Increasing orientation toward direct sales due to high customer consultation requirements – Intensification due to required explanation of highly complex solution packages – Early integration of customers into product and service engineering fosters partner-like collaboration – Interdisciplinary teams bear new contact persons originating in IT and R & D – Increasing technology development activities, particularly regarding software development – Consequent service orientation requires extended customer-oriented communication and customer consultation activities – Need for IIoT-appropriate adaption of workforce qualification – Changing role of employees from operators to problem solvers – Increasing relevance of IT systems, cloud technologies, and software – IT suppliers and development partners, along with customers, serve as collaborative partners – Nevertheless, manufacturers draw on own knowhow and key activities rather than on external partners – Experiences hardly any changes due to customer resistance – Rising expenses for IT and software – Decreasing production costs

10

Management position

Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Top Top Middle Middle Top Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Middle Top Middle Middle Middle Middle Top Middle Middle Middle Middle Top Top Middle Middle

Interview no.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

Table A1 Overview of informants and interviews.

30 2 5 4 20 16 16 4 7 11 30 8 8 25 19 11 2 1 17 11 14 2 2 20 16 14 12 5 15 20 8 5 4 4 14 14 18 15 9 15 3 14 25 23 13 25 28 5 5 15 19 35 31 25

Company tenure (years) Medical engineering Electrical engineering Electrical engineering Electrical engineering Medical engineering Medical engineering Automotive Electrical engineering Electrical engineering Medical engineering Medical engineering Medical engineering Automotive Automotive Automotive Automotive Electrical engineering Automotive Automotive Automotive Automotive Automotive Automotive Machine and plant engineering Machine and plant engineering Automotive Electrical engineering Machine and plant engineering ICT ICT ICT ICT ICT ICT ICT Electrical engineering Machine and plant engineering ICT Machine and plant engineering Machine and plant engineering Machine and plant engineering ICT ICT Machine and plant engineering Machine and plant engineering Machine and plant engineering Electrical engineering Electrical engineering Machine and plant engineering Machine and plant engineering Machine and plant engineering Machine and plant engineering Machine and plant engineering Machine and plant engineering

Industry (5000–10,000] (1000–5000] (5000–10,000] (1000–5000] (10,000–50,000] (10,000–50,000] > 100,000 (1000–5000] (5000–10,000] (5000–10,000] (10,000–50,000] (10,000–50,000] (10,000–50,000] (50,000–100,000] > 100,000 (10,000–50,000] (1000–5000] (50,000–100,000] > 100,000 (50,000–100,000] (10,000–50,000] (10,000–50,000] (10,000–50,000] (0–200] (0–200] (5000–10,000] (5000–10,000] (0–200] (1000–5000] (5000–10,000] > 100,000 > 100,000 (1000–5000] (1000–5000] (1000–5000] (10,000–50,000] (5000–10,000] (10,000–50,000] (1000–5000] (200–500] (500–1000] (1000–5000] (1000–5000] > 100,000 (0–200] (200–500] (1000–5000] (1000–5000] (0–200] (1000–5000] (1000–5000] (1000–5000] > 100,000 (10,000–50,000]

Number of employees (in 2015) n/a n/a (1000–5000] (100–500] (10,000–50,000] (10,000–50,000] > 50,000 (500–1000] (500–1000] (500–1000] (1000–5000] (1000–5000] (1000–5000] (10,000–50,000] (10,000–50,000] (5000–10,000] (100–500] (10,000–50,000] (10,000–50,000] > 50,000 (10,000–50,000] (10,000–50,000] (100–500] (0–50] (0–50] (1000–5000] (500–1000] (50–100] (1000–5000] (1000–5000] > 50,000 > 50,000 (100–500] (100–500] (500–1000] (1000–5000] (1000–5000] (5000–10,000] (100–500] (0–50] (50–100] (1000–5000] (100–500] > 50,000 (0–50] (0–50] (100–500] (100–500] n/a (1000–5000] (50–100] (50–100] > 50,000 (1000–5000]

Sales in mil. EUR (in 2015) 72 58 62 58 70 64 60 74 67 60 68 60 72 69 61 61 74 72 72 75 59 75 72 60 59 64 55 57 63 55 68 57 70 70 55 59 63 59 70 60 58 72 65 67 55 62 68 74 58 60 75 71 65 56

(continued on next page)

Interview duration (min)

D. Kiel et al.

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12

(500–1000] (1000–5000] (10,000–50,000] (500–1000] (500–1000] (100–500] (1000–5000] (10,000–50,000] (10,000–50,000] (10,000–50,000] (100–500] (100–500] (10,000–50,000] (10,000–50,000] (1000–5000] (100–500] (10,000–50,000] (10,000–50,000] (10,000–50,000] (500–1000] (500–1000] (100–500] (5000–10,000] engineering engineering engineering engineering engineering

engineering engineering engineering engineering

plant plant plant plant plant

plant plant plant plant

plant engineering plant engineering plant engineering

Automotive Machine and Machine and Machine and Automotive Machine and Machine and Machine and Automotive Automotive Machine and Machine and Machine and Machine and Machine and Automotive Automotive Automotive Automotive Machine and Machine and Machine and Machine and Top Middle Middle Middle Middle Middle Middle Middle Middle Middle Top Top Middle Middle Middle Middle Middle Middle Middle Middle Top Top Middle 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

12 8 10 6 4 4 26 17 30 10 4 48 8 2 10 4 2 17 3 8 15 7 24

Management position Interview no.

Table A1 (continued)

Company tenure (years)

Industry

plant engineering plant engineering plant engineering

Number of employees (in 2015)

Sales in mil. EUR (in 2015)

There are several implications for managerial practitioners. Firstly, the sample at hand provides insights from manufacturers who already had experiences with the integration of the IIoT in their value-adding processes. This serves as a source of learning effects to understand which BM elements are mainly affected and how they are affected. In particular, small and medium-sized companies benefit from the presented findings, since most of them still feel uneasy about the efficiency and effectiveness of the unfamiliar technology approach due to their limited access to resources. The findings help them to timely observe IIoT-driven business model implications and to prepare for them accordingly. Secondly, the changing role of the workforce from operators to problem solvers has to be paralleled by adequate human resource development activities. Here, companies have to make an effort concerning an enhanced interdisciplinary education in the areas of economics, engineering, informatics, and mathematics, e.g., in cooperation with educational institutions. Companies should participate actively in planning and developing educational programs collaboratively with vocational schools, universities, and education and training centers. By doing so, they would benefit from being strongly involved into designing qualifications, competencies, and skills tailored to their IIoTspecific needs. Likewise, companies should establish a corporate culture and mindset characterized by a comprehensive understanding of customers’ problems, requirements, and expectations by taking a customer’s viewpoint, which is in any case, in the era of the IIoT, even more important than it was before. Thirdly, strategic partner networks, e.g., with IT suppliers, will take on increasing importance in interconnected value chains, so they have to be reliable and secure. The importance of close and intensive relationships with customers serving as collaborative key partners required to be integrated into value creation at an early stage is emphasized. The fourth implication addresses the lack of data security and safety actions. These activities are particularly significant in order to protect production systems from unauthorized access, data manipulation, and data destruction. Manufacturers dealing with the IIoT are well advised to take respective action. In this context, increasingly appreciating the value and benefits of data as key resources to be protected is recommended. Fifthly, the connectivity of the IIoT allows the addressing of new customer segments beyond industry boundaries. Here, an operationalized direct sales channel relying on specialized sales teams may meet acquisition and consultation challenges in terms of convincing customers of the beneficial nature of novel offers enabled or facilitated by the IIoT era. Therefore, contrary to recent observations (see, for example, Section 4.1), manufacturers should increasingly balance the pros and cons of these opportunities with strategic foresight. Sixthly, despite the fact that the IIoT requires large investment in IT facilities, it also comes with several cost reduction potentials to be captured. Consequently, managers should not be discouraged from investing in the technological future, as novel income sources ascribed to new product and service offers can be used to finance large IT investments. Against the background of financial markets calling for shortterm financial performance and orientation, this study argues for a long-term strategic vision and sustainable value creation, which is willing to take short-term losses. This implies a change in the mindset of managers and investors to exploit long-term and sustainable benefits of the digitized and connected manufacturing approach. In conclusion, despite the challenge of giving up mental models and dominant logics (Cavalcante et al., 2011; Chesbrough, 2007; Zott and Amit, 2010), established manufacturers should perceive the IIoT as a prospective opportunity for systematically innovating their BMs. This serves as a prerequisite for maintaining and enhancing their international competitiveness and viability. In doing so, it is particularly

(1000–5000] (10,000–50,000] (50,000–100,000] (5000–10,000] (1000–5000] (1000–5000] (10,000–50,000] > 100,000 (50,000–100,000] (50,000–100,000] (1000–5000] (1000–5000] (50,000–100,000] > 100,000 (5000–10,000] (1000–5000] (50,000–100,000] (50,000–100,000] (50,000–100,000] (1000–5000] (1000–5000] (1000–5000] (10,000–50,000]

Interview duration (min)

5.2. Managerial implications

75 58 56 62 62 55 60 56 72 64 61 71 68 74 56 59 74 64 75 64 66 68 74

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Table C1 Data Structure. First-order (informant) concepts

Second-order themesa

Aggregate BM dimensionsa

Automation; efficiency (time, energy, and resource); machine availability; overall equipment effectiveness; process simplification; productivity Augmented reality; condition monitoring; hybrid solutions; IIoT-readiness; predictive maintenance; service packages Data analysis; data collection; data consistency; data processing; data traceability; data transparency; data utilization Machine communication; machine diagnostics; operating hours; quality management Cost reduction; cost savings Flexibilization of production; modularity Machine handling; usability; workplace ergonomics Customer retention; lifelong support Individualization New industries Extension of SMEs; new segments within industry E-commerce; elimination of distributors; social media External partners; system integrators Collaboration; communication requirements; consultation requirements; cooperation; customer integration; intensification; joint development Interdisciplinarity; other departments; strategic level Customer retention; life-long interconnectedness; longer term Improvement Automation; standardization Expansion of relationships to end customers (B2B2C) Research and development; software development; software updates; technology design Automation; flexibilization; modularization; simulation; standardization After sales services; commissioning; consulting Customer communication; customer contact; marketing and sales Data analysis; data collection; data mining; data processing Outsourcing; quality control Intralogistics; logistics processes Faster and more efficient current key activities Integration and coordination of external partners

Production and process optimization (37) New product/service/solution offers (26) Data mining and analytics (16)

Value proposition (65)

Integration of customers Diffusion and exchange of knowhow Customer understanding; IT competence; market competence; research and development knowhow; software knowhow; technology knowhow; workforce qualification Industrial facilities; IT infrastructure; machinery Customers; key account management; partnerships Data; real-time data Financial resources Hardware suppliers; IT suppliers; platform providers; software suppliers Research institutes; software coders; software consultants Collaboration with customers Sales specialists Cloud providers; data analysts Third-party logistics providers Continuous service access; membership fee Licensing fees for using external and protected intellectual property Performance-based billing; variable usage fees Hardware sales Turnover share IT; online platforms; software Material; resources; variabilization Service departments Recruiting; staff development Research and development investments Costs related to partner integration Logistics costs a

Quality and performance (13) Cost reduction (7) Flexibility (6) Usability and convenience (5) Customer retention (2) Individualization (2) New customers in new industries (7) New customers in existing industries (6) Direct sales (10) Indirect sales (5) Intensification (41) New contact person (11) Longer term relationships (9) Improvement (3) Automation and standardization (2) Expansion (1) Technology development (15) Manufacturing activities (14) Servitization (12) Marketing (8) Data processing (7) Firm infrastructure (4) Logistics (4) Adjustment of current activities (2) Coordination of value-adding partners (2) Customer integration (2) Knowhow diffusion (1) Knowhow and culture (52) Production equipment and infrastructure (13) Organization (6) Data (4) Financial capital (1) IT suppliers (17) Development partners (6) Customers (5) Distribution partners (3) Data handling partners (2) Third-party logistics providers (2) Subscription model (13) License model (4) Pay-by-usage model (3) Asset sale (1) Commission model (1) IT infrastructure (14) Production (10) Service (8) Human resources (7) Research and development (6) Partnerships (3) Logistics (1)

Target customers (12) Distribution channels (15) Relationships (56)

Value configuration (53)

Core competencies (60)

Partner network (35)

Revenue model (22)

Cost structure (42)

Frequency of mentions indicated in brackets; multiple responses allowed.

results at hand face several limitations. In this article’s methodological section, certain biases and the actions taken to minimize their effect as far as possible have been described. In addition, the detailed findings are not easily generalizable to other situations, since the observed cases did not intend to reflect a representative sample and should not be misinterpreted as such. Nevertheless, even though the sample comprises exclusively German manufacturers, the findings can also serve as an orientation for foreign companies since the examined manufacturers operate globally and their respective BMs reach far beyond national

important to be aware of the interdependencies of single BM elements. Thus, changing one part of a BM most certainly results in changes to the remaining components. Hopefully, the findings and recommendations at hand will support managerial practitioners in dealing with changing BMs in the IIoT era.

5.3. Limitations and further research Due to the exploratory and qualitative nature of this study, the 13

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observed BM component modifications are dependent on the discussed projects, even if it was ensured that they shared common characteristics. As suggested above, a quantitative validation should address this issue. Since the projects only provide a retrospective, future studies should also apply a longitudinal research design in order to observe the evolution of the IIoT within BMs and track the interrelationships of BM elements over a longer period. The sample could be extended from exclusively manufacturing to service companies in order to examine potential divergences along the smooth transition from purely tangible products to hybrid solutions to purely intangible services. Furthermore, future studies should shed some light on the intensities of BM component modifications, since there is probably a difference if a BM element is affected slightly or massively. Despite the presented limitations, the study at hand discloses several well-founded theoretical and managerial implications, which contribute to a better understanding of the IIoT and its effects on established manufacturing BMs. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

borders. In addition, the German manufacturing industry is generally known to have a leading role and draw on about 20 years of experience in relevant core technologies of the IIoT (Jazdi, 2014). Consequently, leading German industry sectors are examined by following a replication sampling logic (Yin, 2009). The identification and explanation of general patterns regarding IIoT-triggered BM changes and their interrelationships can also apply to established manufacturers that are not part of the sample. Yet, it is important that the presented findings have to be reflected reasonably against the background of national, cultural, and organizational differences. Future studies should draw on this deliberations. Moreover, the authors made an effort to follow well-established quality criteria for qualitative research to increase the validity of the results, which serve as a solid foundation for future quantitative research that may test whether the revealed findings hold in a large-scale study of companies within and across industries. Respective samples may also include international companies in order to mirror different understandings and cultural backgrounds related to the concept of the IIoT. Additionally, with regard to the chosen CIT of Flanagan (1954), the Appendix A See Appendix Table A1. Appendix B. Interview guideline 1. 2. 3. 4. 5. 6. 7.

8.

9.

What is your job position? Please also explain your responsibilities and role within your company. Please report on your educational and professional career. Since when do you work for the company? Since when do you have your current job position? What do you understand by the term “Industrial Internet of Things”? Please define and explain. Please think about your experiences in the Industrial Internet of Things. Please describe your experiences on a typical and comparable project, in which aspects of the Industrial Internet of Things played a high or very high and decisive role for the project’s success. How important were aspects of the Industrial Internet of Things for the project’s success? (From “0 = not important at all” to “5 = very important”) Please describe your business model before the project has been run according to the following nine elements. a. Value proposition b. Target customers c. Relationships d. Distribution channels e. Value configuration f. Core competencies g. Partner network h. Revenue model i. Cost structure Please describe the business model after it has been influenced by the project according to the following nine elements. Please also report on significant changes of each business model element ascribable to the project. a. Value proposition b. Target customers c. Relationships d. Distribution channels e. Value configuration f. Core competencies g. Partner network h. Revenue model i. Cost structure How intensive were the business model changes ascribable to the project for each of the following nine business model elements? (From “0 = not important at all” to “5 = very important”) a. Value proposition b. Target customers c. Relationships d. Distribution channels e. Value configuration f. Core competencies g. Partner network h. Revenue model 14

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i. Cost structure 10. Opportunity for comments and further information or questions. Appendix C See Appendix Table C1.

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