20-AM-04: ST7.2 - Innovation Is the Trigger of Industry 4.0. What About the Opposite?
Industry 4.0 is a paradigm based on the Cyber-Physical Systems concept – a fusion of the physical and virtual worlds - the Internet of Things and the Internet of Services, which will have a disruptive impact on every aspect of manufacturing companies (Almada-Lobo, 2015). Accordingly, enterprises are now facing the challenge and the opportunity provided by new technologies, within the Industry 4.0 paradigm, to evolve towards the new industrial concept of intelligent factory. This paradigm can be broken down into several themes: systems to enable customized production; strategies, methods and tools to promote sustainability; systems for the enhancement of the role of the people in factories; high efficiency production systems; innovative production processes; production systems that can evolve and adapt; strategies and management approaches to develop next-generation production systems (Industrie 4.0., 2014).
Industry 4.0 has become a popular buzzword among different communities such as manufacturers, governmental institutions, policy makers and academics. However, the understanding of this phenomenon is mostly limited to the implementation of technological innovation aimed at process automation and an overall performance improvement. Further, the path towards the development of novel technologies and services compliant with the Industry 4.0 paradigm has been structured through different approaches such as open innovation, business model innovation, and industrial organization. However, the role of the Industry 4.0 design principles – such as interoperability, transparency in information, technical assistance, decentralization of decision - in supporting and shaping novel innovation approaches is still poorly investigated.
In line with these considerations, the track will call for both conceptual and empirical papers that address, but are not restricted to, the following questions:
- How do the Industry 4.0 technologies impact the way organizations drive innovation? How do they change existing innovation models and create new ones?
- How can the enterprise organizational structure and culture support innovation in an Industry 4.0 environment?
- How can novel business models foster innovation in an Industry 4.0 context?
- Which new skills and capabilities are necessary to appropriately address and integrate the new technologies and business models required for implementation of Industry 4.0?
- How can the characteristics of interoperability, transparency in information, technical assistance, decentralization of decision, typical of the Industry 4.0, drive new innovation processes?
- What kind of systems for the enhancement of the role of the people in factories can foster innovation in an industry 4.0 context? What kind of new innovation models do they create?
- What kind of strategies and management approaches to develop next-generation production systems can support new innovation models and processes in an industry 4.0 context?
The changing role of public intermediaries in the context of emerging digital technologies: evidence from France and the UK
1University of Florence, Italy; 2Birkbeck College, University of London, UK; 3University of Modena and Reggio Emilia, Italy; 4NEOMA Business School, France
Intermediary organisations that support firm-level and collaborative innovation, often called knowledge or innovation intermediaries, have broadly two types of roles – (i) the supply of ‘content’ (knowledge, information), and (ii) the supply of ‘contact’ (networking opportunities) to actors involved in or aiming to be involved in innovative activities.
Indeed, intermediaries usually offer matchmaking services, as well as a wider range of knowledge-intensive services (Bessant and Rush, 1995; Lynn et al., 1996; Hargadon and Sutton, 1997; Den Hertog, 2000; Howells, 2006; Doganova, 2013). For instance, intermediaries facilitate the diffusion of information about collaboration opportunities with other actors (Bougrain and Haudeville, 2002), as well as about useful and applicable techniques or technologies for product and service development (Howard Partners, 2007; Rosenkopf and Nerkar, 2001). They can help firms to address their capabilities failures (Bessant and Rush, 2005; Knockaert et al., 2014) by providing training and knowledge and technology mapping services. Very often, intermediaries are required to address interaction failures in the innovation system (Russo et al., 2016).
New technologies such as the Internet of Things (IoT) – where innovation has necessarily a collaborative scope – require the presence of intermediaries capable of integrating complex technologies and different pieces of knowledge in a same system. Who these intermediaries are remains underexplored in the extant literature.
This paper aims at answering two research questions: (i) what kind of innovation intermediaries can be found in industry 4.0? and, (ii) what kind of roles do these intermediaries play?
By combining quantitative and qualitative analysis, we analyze who these new operators are and what their characteristics are. The interview transcripts have been analysed with the tools of automatic text analysis (Bolasco, 2005) and content analysis (by using Atlas.ti and NVivo).
Building on 20 in-depth interviews with different types of organizations involved in the implementation of complex IoT applications, we analyse the different intermediary roles that are performed in these processes, from the stages of ideation of the project through to partnership creation and project implementation.
We identify several types of intermediary roles, each of which can be performed by different types of organizations. In particular, three types of intermediary roles emerge as important. The first is that of the provider of an IoT platform. Such providers can be either new software companies or large-scale companies that were already in the production of hardware. The second role is that of the system integrator that develops the whole system architecture - a role that can be played both by relatively small companies that specialize in a specific field (e.g. in smart cities projects) and by large-scale general operators. The third role - on which we focus most of our attention – involves bringing IoT to companies in different fields. This role can be played by a range of actors operating within the intermediaries mentioned above, and in dedicated organizations.
Contribution to Scholarship
This research is among the first to analyze Industry 4.0 innovation intermediaries. Based on fieldwork conducted in four countries - UK, France, Italy and Germany - we show that these intermediairies operate at three main levels.
Contribution to Practice
Parallel to the analysis of intermediaries we conduct an analysis of industrial and innovation policies implemented in Italy, UK, France and Germany that support the diffusion of the technologies of the fourth industrial revolution. The specific goal is to model which types of intermediaries are currently funded by those national and regional policies, which objectives are targeted through their actions, and which impact they are expected to produce.
The paper explores a topical issue of innovation intermediaries both from innovation management and from innovation policy perspective. It is in line with the theme 7 and in particular with the track 7.2.
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Bolasco, S. (2005). Statistica testuale e text mining: alcuni paradigmi applicativi. Quaderni di Statistica, 7.
Bougrain, F., Haudeville, B. (2002). Innovation, collaboration and SMEs internal research capacities. Research Policy, 31(5): 735–747.
Den Hertog, P. (2000). Knowledge-intensive business services as co-producers of innovation, International Journal of Innovation Management, 4 (4): 491–528.
Doganova, L. (2013). Transfer and exploration: Two models of science-industry intermediation. Science & Public Policy, 40(4): 442-452.
Hargadon, A., Sutton, R. I. (1997). Technology brokering and innovation in a product development firm, Administrative Science Quarterly 42(4): 716-749.
Howard Partners (2007). Study of the Role of Intermediaries in Support of Innovation, Department of Industry, Tourism and Resources, Australia.
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Knockaert, M., Spithoven, A., Clarysse, B. (2014).The impact of technology intermediaries on firm cognitive capacity additionality, Technological Forecasting and Social Change, 81: 376-387.
Lynn, L H., Mohan Reddy, N., Aram, J. D. (1996). Linking technology and institutions: the innovation community framework, Research Policy, 25(1): 91-106.
Rosenkopf, L. and Nerkar, A. (2001). Beyond local search: boundary‐spanning, exploration, and impact in the optical disk industry. Strategic Management Journal, 22(4): 287-306.
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Exploring IT investment decisions of SME in the Fourth Industrial Revolution
LUISS Guido Carli University, Italy
The German government initiative "Industry 4.0" has drawn attention across different countries and partners. In Italy, the government has designed policies to support enterprises in adopting new technologies for Industry 4.0. In this paper, we focus on the propensity of SMEs in IT investment in an industry 4.0 context.
The businesses have undergone significant change due to embedded systems that create hybrid physical-cyber systems (Porter & Heppelmaa, 2014). Industry 4.0 is viewed as combination of different technologies; big data and analytics, autonomous and cooperative robots, computing technology, simulation, Internet of things, additive manufacturing, augmented reality, integration of systems and cybersecurity (Rüßmann et al., 2015). Given that the embeddedness of digital technologies in organizations has extensively changed businesses (Rudtsch et al. 2014; Burmeister, 2016), we take the view that IT strategy is integrated with business strategy (Bharadwaj et al., 2013) rather than the traditional view of IT alignment which limits IT strategy at functional level strategy that need to be aligned with business strategy (Henderson & Venkatraman, 1999). Prior research highlights alignment is a continuous process (Yeow et al., 2018), that organizations need to align the business resources, strategic decisions and IT investment with business strategy (Mithas et al., 2013).
While the concept of Industry 4.0 has been widely investigated in large enterprises (Arnold et al., 2016), few studies focus on SMEs (Moeuf et al., 2018). Yet to date, little is known about how SMEs with limited financial resources make strategic decisions in particular about IT investment on diverse technologies.
To close this gap, this paper seeks to answer of what are the main IT investments of SMEs in an Industry 4.0 strategy and why.
In this paper we analyze quantitatively the responses of SMEs to Italian Government policies designed to facilitate SMEs in adopting technologies for Industry 4.0.
We analyze 1890 Italian SMEs that benefit from bank loan support package out of 5000 applications made in 11 months from 2017 till 2018.
The findings illustrate the propensity of SMEs in embedding advanced technologies for Industry 4.0 across different industrial sectors and regions. Furthermore, the cluster analysis shows the distribution of IT investment on tangible and intangible advanced technologies. In particular, we found few SMEs invest on cyber security which might be due to lack of awareness of managers making IT strategic decisions.
Contribution to Scholarship
This study aims to contribute to alignment literature by highlighting the importance of IT investment as strategic decision in Industry 4.0.
Contribution to Practice
Moreover, the implications of this paper for practice are in two-fold. First, the results can help policy makers understand how SMEs are likely to embed new technologies in an industry 4.0 context. Second for practitioners, the results highlight the importance of integration of IT investment in new emerging technologies with business strategy.
This paper aims to address the R&D conference call on Industry 4.0 by highlighting importance of alignment in strategic decisions in developing next generation production systems.
Arnold, C., Kiel, D., & Voigt, K. I. (2016). How the industrial internet of things changes business models in different manufacturing industries. International Journal of Innovation Management, 20(08), 1640015.
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights. MIS quarterly, 471-482.
Burmeister, C., Lüttgens, D., & Piller, F. T. (2016). Business model innovation for Industrie 4.0: why the “Industrial Internet” mandates a new perspective on innovation. Die Unternehmung, 70(2), 124-152.
Henderson, J. C., & Venkatraman, H. (1999). Strategic alignment: Leveraging information technology for transforming organizations. IBM systems journal, 38(2.3), 472-484.
Mithas, S., Tafti, A., & Mitchell, W. (2013). How a firm's competitive environment and digital strategic posture influence digital business strategy. MIS quarterly, 511-536.
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), 1118-1136.
Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard business review, 92(11), 64-88.
Rudtsch, V., Gausemeier, J., Gesing, J., Mittag, T., & Peter, S. (2014). Pattern-based business model development for cyber-physical production systems. Procedia CIRP, 25, 313-319.
Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9(1), 54-89.
Yeow, A., Soh, C., & Hansen, R. (2018). Aligning with new digital strategy: A dynamic capabilities approach. The Journal of Strategic Information Systems, 27(1), 43-58.
Collaborative Robots as Structuring Devices in Work Processes. An Organizational Perspective
University of Genoa, Italy
In recent years a new generation of «collaborative robots» (cobots) has been introduced in the work environments and domestic settings. Their main applications are in logistic activities (e.g.: warehouse transportation), defense tasks (e.g.: demining), agriculture (e.g.: milking), public relation (e.g.: customer service), medical care and rehabilitation (exoskeletons) (IFR, 2018).
Human-robot interaction (HRI) lies on decades of research either in the industrial domain (Villani et. al., 2018) and household settings (Xu et al., 2015). Many scholars of different disciplines contributed to this debate, but a clear technological orientation is evident. However, the notion of collaboration is mostly relevant in many disciplines in social sciences, as it has been adopted in order to understand interactions among individuals. In organization studies a fundamental contribution is Chester Barnard’s definition of organizations as “systems of cooperation” (Barnard, 1938: 79). In fact, the notion of collaboration has several overlapping spaces with other related constructs such as cooperation, teamwork and coordination ((Malone, 1990; Malone and Crowstone, 1994). Bedwell et al., 2012 provide a multidisciplinary state of the art of the literature and a working definition of collaboration and Patel et al., 2012 trace the main factors (and sub-factors) of collaborative work.
This new generation of robots is portrayed as «collaborative», thus putting an accent on the organizational facets of the interaction with the workers. The organizational implications of this «collaboration» have not been researched yet, although a clearer sense of our understanding of this aspect of collaborative robotics would be essential.
In this article we aim at discussing the idea of collaboration in collaborative robotics from an organizational perspective. More specifically, our general research questions can be outlined as follows: are collaborative-robots collaborative? What are the organizational implications of collaborating with a collaborative robot?
This contribution is either conceptual, as we question the idea that collaborative-robots can be actually considered collaborative from an organizational point of view, and practical, as it discusses the organizational implications of collaborating with a robot on the base of a qualitative analysis from fieldwork. We are gathering data using semi-structured interviews with key informants and qualitative-interpretive focus groups. Transcripts are analysed following a traditional qualitative approach focused on the importance of understanding the point of view of the key informants.
Fieldwork is currently underway. Two different domains are researched: rehabilitation services (robotic prosthesis; motorized exoskeletons, robotic rehabilitation platforms and other robotic rehabilitation systems) and the manufacturing industry (bio-inspired robots for handling and manipulation in advanced industrial automation).
Although many collaborative ingredients occur in the human/cobot interaction, the provisional analysis tend to challenge the idea that collaborative-robots can be actually considered collaborative from an organizational point of view. More appropriately, we should think of these new generation of robots as innovative means of coordination of working activities, whose adoption needs to be studied from an organizational perspective. As of the organizational implications of collaborating with a robot, one element is emerging: the adoption of these technologies in manufacturing and domestic setting is coupled with either techno-optimism or techno-scepticism when not techno-panic. However, none of these approaches seem sustainable, as the actual adoption of these technologies enables changes in the organizational regulation that are clearly not technology determined.
Contribution to Scholarship
The adoption of collaborative robots is challenging the domain of organization as a discipline, demanding a new wave of basic research, as many issues at the heart of the organizational knowledge are challenged, including collaboration and the traditional notion of technology. Studying the adoption of collaborative robots in the work settings allow to discuss the conventional idea of rationality and the implications of data-driven/cyber-human decision processes. Also, coordination and interdependences change in a virtualized working environment. The new paths of knowledge creation and the learning processes in a digitalized world are transformed, together with the changes in the idea of communication, identity and teamwork itself.
Contribution to Practice
Understanding the organizational implications of human/cobot interaction is essential as the integration of collaborative robots in the work settings has many implications that need to be managed. These typically relate to design, appropriation and use choices (Masino and Zamarian, 2003) that are extremely relevant for technology acceptance and the transformation of work: the technical, operational and physical features of the technology itself; the involved areas of activities, work processes, interdependencies with other activities or other technologies as well as the way end-users interact with the cobots.
This contribution focuses the transformation of work and the challenges that are connected to the adoption of technology innovation in the work settings. Its relevance concerns many themes that are focused in the conference such as digital technologies, industry 4.0, the adoption and diffusion of deep-tech; education for innovation.
Barnard, C. (1938). The Functions of the Executive, Cambridge, MA: Harvard University Press.
Bedwell, W.L., Wildman, J.L., DiazGranados, D., Salazar, M., Kramer, W.S., Salas, E. (2012). Collaboration at work: An integrative multilevel conceptualization. Human Resource Management Review, 22(2), 128–145.
IFR (2018). Executive Summary World Robotics 2018 Service Robots. International Federation of Robotics, Frankfurt, Germany.
Malone, T.W. (1990), What is Coordination Theory and How Can It Help Design Cooperative Work Systems, Proceedings of the Conference on Computer Supported Cooperative Work, Los Angeles, California, October, 1990.
Malone, T.W., Crowstone K. (1994), The Interdisciplinary Study of Coordination, ACM Computing Surveys, 26(1), 87-119.
Masino, G., Zamarian, M. (2003). Information technology artefacts as structuring devices in organizations: design, appropriation and use issues, Interacting with Computers, 15(5), 693–707.
Matuszek, C., Bo, L., Zettlemoyer, L., Fox, D. (2014). Learning from unscripted deictic gesture and language for human-robot interactions. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, North America.
Patel, H., Pettitt, M., Wilson, J.R. (2012). Factors of collaborative working: A framework for a collaboration model. Applied Ergonomics, 43, 1-26.
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Villani, V., Pini, F., Leali, F. Secchi, C. (2018), Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications, Mechatronics, 55, 248-266.
Xu, Dan; Qian, Huihuan; Xu, Yangsheng (2015). The State of the Art in Human–Robot Interaction for Household Services, Household Service Robotics
Managing digital transformation paradoxes: The role & impact of comprehending digitalization for organizational change
1RWTH Aachen University, Germany; 2Radboud University Nijmegen, Netherlands
Despite digitalization’s potential for more radical or business model innovation opportunities, many firms seem to only aim at optimizing existing operations and processes. Therefore, we investigate possible paradoxical tensions in digital transformation, also considering challenges and barriers related to the cooperation in a stakeholder ecosystem and between intra-organizational hierarchical levels.
Andriopoulos, C., & Lewis, M. W. (2009). Exploitation-Exploration Tensions and Organizational Ambidexterity: Managing Paradoxes of Innovation. Organization Science, 20(4), 696-717.
De Rond, M., & Bouchikhi, H. (2004). On the Dialectics of Strategic Alliances. Organization Science, 15(1), 56-69.
Lewis, M. W., & Smith, W. K. (2014). Paradox as a Metatheoretical Perspective:Sharpening the Focus and Widening the Scope. The Journal of Applied Behavioral Science, 50(2), 127-149.
Schad, J., Lewis, M. W., Raisch, S., & Smith, W. K. (2016). Paradox Research in Management Science: Looking Back to Move Forward. Academy of Management Annals, 10(1), 5-64.
Casadesus-Masanell, R., & Zhu, F. (2013). Business model innovation and competitive imitation: The case of sponsor-based business models. Strategic Management Journal, 34(4), 464-482. doi:10.1002/smj.2022
Hargrave, T. J., & Van de Ven, A. H. (2017). Integrating Dialectical and Paradox Perspectives on Managing Contradictions in Organizations. Organization Studies, 38(3-4), 319-339.
Paradoxes as meta-theory help not only in identifying tensions in or between organizations, but also discuss the coping strategies. Digital transformation or business model innovation can be considered as processes, we want to contribute in filling the research gap how companies deal with paradoxical tensions in a fast changing environment.
What is the role and impact of comprehending paradoxical tensions in digital transformation of industrial companies, especially in the context of business model innovation and in the coordination of digital stakeholder ecosystems?
Because of the exploratory character of this study and due to the lack of existing research in this field, we chose a qualitative approach, analysing expert interviews and additional archival data from a public funded research project with several industrial and academic partners.
With regard to the analysis of the data set, we followed an inductive approach by capturing the perspectives of informants. The interview data have been triangulated with project documents.
We conducted 21 interviews with experts in the field of digitalization from industrial companies whereby in some cases several experts from different management levels of each company were asked independently. The experts came from large, as well as small and medium-sized enterprises (SME).
Additionally, archival data from a cooperative research project with several companies and academic institutions was taken into account. The project aims at developing business models for production-related service systems and can be considered as a microsimulation for a multi-stakeholder digitalization environment. The authors not only have access to meeting logs and project documents but participated partly and conducted workshops with project partners so that observations complement the information base.
We ensured respondents confidentiality and anonymity and they received access to our verbatim transcripts to mitigate bias. Hence, we took several measures to ensure credibility and conformability of our findings (Gioia, Corley, & Hamilton, 2013).
From a first data analysis we derived different categories how the experts comprehend digital transformation and its implications. Similarly, the experts had a different awareness for BMI. The more comprehensive their understanding of digital transformation was, the more awareness for business model changes was exhibited by the experts.
The existence of paradoxes in the context of digital transformation - such as exploitation versus exploration at an organizational level, performing versus learning on a team or divisional level or in-role job performance versus innovativeness on an individual level - could be revealed by a critical view on different methods how companies manage digitalization activities. E.g. most companies maintained strict project approval processes with an extensive definition of project goals and a financial justification before the project start. While companies tried to reduce risk by this strict formalization, many experts expressed the need for faster, more flexible and less monetary guided setting.
Experts with an awareness for BMI, named also more often the opportunity or necessity to collaborate with partners in business ecosystems. They described also joint BMs, which were operated by various partners in a network or an ecosystem, in contrast to other respondents, who mentioned partnerships simply to acquire resources.
Contribution to Scholarship
Similarly to academic literature having not agreed upon a common definition for digital transformation, we could also not find a consistent understanding of it among professionals. Likewise, we pointed to the awareness for BMI as being related to the size of a company and the positions of the respondents.
By creating a common understanding for digital transformation and its far-reaching consequences, as well as by generating an atmosphere to embrace BMI opportunities over different hierarchical levels, companies could reduce resistances, which are typical for disruptive innovations (Cavalcante et al., 2011; Markides, 2006).
We remarked a transition from conventional supplier relationships to partnerships between equal parties. This may lead to confrontations, because the characteristics of the relationships change, e.g. from rigidity to flexibility, from a long-term orientation to a short-term orientation, from vigilance to trust as well as from individualism to collectivism (Das & Teng, 2000; De Rond & Bouchikhi, 2004).
Contribution to Practice
Not only that the mindset of the employees is important and their fears and concerns have to be considered, but also the importance of having highly qualified experts, e.g. digitalization champions to successfully conduct digitalization activities. Especially, they expressed the need for data or information scientists to run data analysis and for technicians or engineers with a domain-relevant knowledge to evaluate and interpret the results.
The experts could confirm the presence of resistances against the disruptive or radical changes which come along with digital transformation and business model innovation, but showed as well that success stories helped to overcome them.
Companies have to face various challenges because of digital transformation in general or Industry 4.0 in specific and our study is a contribution, how to manage with these. We want to discuss the perspective on these topics and their consequences for innovation management.
Cavalcante, S., Kesting, P., & Ulhøi, J. (2011). Business model dynamics and innovation: (re)establishing the missing linkages. Management Decision, 49(8), 1327-1342. doi:10.1108/00251741111163142
Das, T. K., & Teng, B.-S. (2000). Instabilities of Strategic Alliances: An Internal Tensions Perspective. Organization Science, 11(1), 77-101. doi:10.1287/orsc.126.96.36.19970
De Rond, M., & Bouchikhi, H. (2004). On the Dialectics of Strategic Alliances. Organization Science, 15(1), 56-69. doi:10.1287/orsc.1030.0037
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking Qualitative Rigor in Inductive Research:Notes on the Gioia Methodology. Organizational Research Methods, 16(1), 15-31. doi:10.1177/1094428112452151
Markides, C. (2006). Disruptive Innovation: In Need of Better Theory*. Journal of Product Innovation Management, 23(1), 19-25. doi:10.1111/j.1540-5885.2005.00177.x