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Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies

Year 2017, Volume: 1 Issue: 2, 99 - 108, 29.12.2017

Abstract



The Industrial Internet of Things (IIoT)
represents a novel, future-oriented paradigm of industrial value creation,
which facilitates the creation of networks across and within manufacturing
companies. Consequently, the IIoT is associated with an adjusted characterization
of respective business ecosystems. As current research has primarily focused on
the IIoT’s technical fundamentals, economic research is still in its infancy.
This article aims at examining the effects of IIoT on manufacturing companies’
business ecosystems by applying a mixed-method approach. Thus, we carried out a
quantitative survey among 198 German manufacturers from several industries
based on insights of 15 expert interviews. This study contributes to the sparse
body of scientific IIoT literature from an economic perspective by revealing
that IIoT adoption is associated with greater openness of manufacturers toward
participants of all analyzed ecosystem dimensions, i.e., customers, suppliers,
organizations external to the own industry, and research institutions.
Moreover, an intensified ecosystem integration is expected over time.




References

  • 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(8), 1640015-1-1640015-25.
  • Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805.
  • Bauer, W., Schlund, S., Marrenbach, D., & Ganschar, O. (2014). Industrie 4.0 – Volkswirtschaftliches Potenzial für Deutschland. BITKOM & Fraunhofer IAO: Berlin & Stuttgart.
  • Berman, S., & Korsten, P. (2014). Leading in the connected era. Strategy & Leadership, 42(1), 37–46.
  • Bonekamp, L., & Sure, M., (2015). Consequences of Industry 4.0 on Human Labour and Work Organisation. Journal of Business and Media Psychology, 6(1), 33-40.
  • Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering, 8(1), 37-44.
  • Breznitz, D. (2014). Why Germany Dominates the U.S. in Innovation. Available at https://hbr.org/2014/05/why-germany-dominates-the-u-s-in-innovation.
  • Cooper, D. R., & Schindler, P. S. (1998). Business Research Methods. Burr Ridge, IL: Irwin/McGraw-Hill.
  • Creswell, J. W. & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research. Los Angeles, CA: Sage.
  • Dais, S. (2014). Industrie 4.0 – Anstoß, Vision, Vorgehen. In Bauernhansl, T., ten Hompel, M. & Vogel-Heuser, B. (Eds.), Industrie 4.0 in Produktion, Automatisierung und Logistik – Anwendung, Technologien, Migration (pp. 625-634). Wiesbaden: Springer.
  • Eloranta, V., & Turunen, T. (2016). Platforms in service-driven manufacturing. Leveraging complexity by connecting, sharing, and integrating. Industrial Marketing Management, 55, 178–186.
  • Engelken, M., Römer, B., Drescher, M., & Welpe, I. (2016). Transforming the energy system: Why municipalities strive for energy self-sufficiency. Energy Policy, 89, 365-377.
  • Federal Bureau of Statistics. (2016). Statistisches Jahrbuch 2016. Wiesbaden, Germany: Federal Bureau of Statistics.
  • Fowler, F. J. (1993). Survey Research Methods (2nd. ed.). Thousand Oaks: Sage.
  • Geisberger, E. & Broy, M. (2012). agendaCPS (acatech STUDIE). Heidelberg: Springer.
  • Frazzon, E. M., Hartmann, J., Makuschewitz, T., & Scholz-Reiter, B. (2013). Towards Socio-Cyber-Physical Systems in Production Networks, Procedia CIRP, 7, 49-54.
  • 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.
  • Graebner, M. E. & Eisenhardt, K. M. (2004). The seller’s side of the story: acquisition as courtship and governance as syndicate in entrepreneurial firms. Administrative Science Quarterly, 49(3), 366–403.
  • Iansiti, M. & Levien, R. (2004). The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Boston: Harvard Business Press.
  • Johnson, R. B. & Onwuegbuzie, A. J. (2004). Mixed Methods Research: A Paradigm Whose Time Has Come. Educational Researcher, 33(7), 14-26.
  • Kelley, D. J., Peters, L., & O'Connor, G. C. (2009). Intra-organizational networking for innovation-based corporate entrepreneurship. Journal of Business Venturing, 24(3), 221-235.
  • Kiel, D., Arnold, C., Collisi, M., & Voigt, K.-I. (2016). The Impact of the Industrial Internet of Things on Established Business Models. In Proc. Int. Conf. Association for Management of Technology IAMOT 2016 (673-695). Orlando, FL.
  • Kurniawan, S. (2008). Older people and mobile phones: A multi-method investigation. International Journal of Human-Computer Studies, 66(12), 889-901.
  • Mazhelis, O., Luoma, E., & Warma, H. (2012). Defining an internet-of-things ecosystem. In Andreev, S., Balandin, S., & Koucheryavy, Y. (Eds.), Internet of Things, Smart Spaces, and Next Generation Networking (pp. 1-14). Berlin, Heidelberg: Springer.
  • Miles, M. B. & Huberman, M. A. (1994). Qualitative Data Analysis. Thousand Oaks: Sage.
  • Moore, J. F. (1997). The death of competiton. Leadership and strategy in the age of business ecosystems. New York, NY: HarperBusiness.
  • Müller, J. W. (2000). Possible Advantages of a Robust Evaluation of Comparisons. Journal of Research of the National Institute of Standards and Technology, 105(4), 551-555.
  • Nagar, B. & Raj, T. (2013). An analytical case study of an advanced manufacturing system for evaluating the impact of human enablers in its performance. Journal of Advances in Management Research, 10(1), 85-99.
  • Obermaier, R. (2016). Industrie 4.0 als unternehmerische Gestaltungsaufgabe. Betriebswirtschaftliche, technische und rechtliche Herausforderungen. Wiesbaden: Springer Gabler.
  • Peltoniemi, M. & Vuori, E. (2004). Business ecosystem as the new approach to complex adaptive business environments. Proceedings of eBusiness research forum, 18, 267-281.
  • Ramsey, E., Ibbotson, P. & McCole, P. (2008). Factors that impact technology innovation adoption among Irish professional service sector. International Journal of Innovation Management, 12(4), 629-654.
  • Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37, 21-54.
  • Vowles N., Thirkell, P., & Sinha, A. (2011). Different determinants at different times: B2B adoption of a eadical innovation. Journal of Business Research, 64(11), 1162-1168.
  • Weill, P. & Woerner, S. L. (2015). Thriving in an Increasing Digital Ecosystem. MIT Sloan Management Review, 45(4), 27-34.

Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies

Year 2017, Volume: 1 Issue: 2, 99 - 108, 29.12.2017

Abstract

The Industrial Internet of Things (IIoT)
represents a novel, future-oriented paradigm of industrial value creation,
which facilitates the creation of networks across and within manufacturing
companies. Consequently, the IIoT is associated with an adjusted characterization
of respective business ecosystems. As current research has primarily focused on
the IIoT’s technical fundamentals, economic research is still in its infancy.
This article aims at examining the effects of IIoT on manufacturing companies’
business ecosystems by applying a mixed-method approach. Thus, we carried out a
quantitative survey among 198 German manufacturers from several industries
based on insights of 15 expert interviews. This study contributes to the sparse
body of scientific IIoT literature from an economic perspective by revealing
that IIoT adoption is associated with greater openness of manufacturers toward
participants of all analyzed ecosystem dimensions, i.e., customers, suppliers,
organizations external to the own industry, and research institutions.
Moreover, an intensified ecosystem integration is expected over time.




References

  • 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(8), 1640015-1-1640015-25.
  • Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805.
  • Bauer, W., Schlund, S., Marrenbach, D., & Ganschar, O. (2014). Industrie 4.0 – Volkswirtschaftliches Potenzial für Deutschland. BITKOM & Fraunhofer IAO: Berlin & Stuttgart.
  • Berman, S., & Korsten, P. (2014). Leading in the connected era. Strategy & Leadership, 42(1), 37–46.
  • Bonekamp, L., & Sure, M., (2015). Consequences of Industry 4.0 on Human Labour and Work Organisation. Journal of Business and Media Psychology, 6(1), 33-40.
  • Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Aerospace, Industrial and Mechatronics Engineering, 8(1), 37-44.
  • Breznitz, D. (2014). Why Germany Dominates the U.S. in Innovation. Available at https://hbr.org/2014/05/why-germany-dominates-the-u-s-in-innovation.
  • Cooper, D. R., & Schindler, P. S. (1998). Business Research Methods. Burr Ridge, IL: Irwin/McGraw-Hill.
  • Creswell, J. W. & Plano Clark, V. L. (2011). Designing and Conducting Mixed Methods Research. Los Angeles, CA: Sage.
  • Dais, S. (2014). Industrie 4.0 – Anstoß, Vision, Vorgehen. In Bauernhansl, T., ten Hompel, M. & Vogel-Heuser, B. (Eds.), Industrie 4.0 in Produktion, Automatisierung und Logistik – Anwendung, Technologien, Migration (pp. 625-634). Wiesbaden: Springer.
  • Eloranta, V., & Turunen, T. (2016). Platforms in service-driven manufacturing. Leveraging complexity by connecting, sharing, and integrating. Industrial Marketing Management, 55, 178–186.
  • Engelken, M., Römer, B., Drescher, M., & Welpe, I. (2016). Transforming the energy system: Why municipalities strive for energy self-sufficiency. Energy Policy, 89, 365-377.
  • Federal Bureau of Statistics. (2016). Statistisches Jahrbuch 2016. Wiesbaden, Germany: Federal Bureau of Statistics.
  • Fowler, F. J. (1993). Survey Research Methods (2nd. ed.). Thousand Oaks: Sage.
  • Geisberger, E. & Broy, M. (2012). agendaCPS (acatech STUDIE). Heidelberg: Springer.
  • Frazzon, E. M., Hartmann, J., Makuschewitz, T., & Scholz-Reiter, B. (2013). Towards Socio-Cyber-Physical Systems in Production Networks, Procedia CIRP, 7, 49-54.
  • 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.
  • Graebner, M. E. & Eisenhardt, K. M. (2004). The seller’s side of the story: acquisition as courtship and governance as syndicate in entrepreneurial firms. Administrative Science Quarterly, 49(3), 366–403.
  • Iansiti, M. & Levien, R. (2004). The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Boston: Harvard Business Press.
  • Johnson, R. B. & Onwuegbuzie, A. J. (2004). Mixed Methods Research: A Paradigm Whose Time Has Come. Educational Researcher, 33(7), 14-26.
  • Kelley, D. J., Peters, L., & O'Connor, G. C. (2009). Intra-organizational networking for innovation-based corporate entrepreneurship. Journal of Business Venturing, 24(3), 221-235.
  • Kiel, D., Arnold, C., Collisi, M., & Voigt, K.-I. (2016). The Impact of the Industrial Internet of Things on Established Business Models. In Proc. Int. Conf. Association for Management of Technology IAMOT 2016 (673-695). Orlando, FL.
  • Kurniawan, S. (2008). Older people and mobile phones: A multi-method investigation. International Journal of Human-Computer Studies, 66(12), 889-901.
  • Mazhelis, O., Luoma, E., & Warma, H. (2012). Defining an internet-of-things ecosystem. In Andreev, S., Balandin, S., & Koucheryavy, Y. (Eds.), Internet of Things, Smart Spaces, and Next Generation Networking (pp. 1-14). Berlin, Heidelberg: Springer.
  • Miles, M. B. & Huberman, M. A. (1994). Qualitative Data Analysis. Thousand Oaks: Sage.
  • Moore, J. F. (1997). The death of competiton. Leadership and strategy in the age of business ecosystems. New York, NY: HarperBusiness.
  • Müller, J. W. (2000). Possible Advantages of a Robust Evaluation of Comparisons. Journal of Research of the National Institute of Standards and Technology, 105(4), 551-555.
  • Nagar, B. & Raj, T. (2013). An analytical case study of an advanced manufacturing system for evaluating the impact of human enablers in its performance. Journal of Advances in Management Research, 10(1), 85-99.
  • Obermaier, R. (2016). Industrie 4.0 als unternehmerische Gestaltungsaufgabe. Betriebswirtschaftliche, technische und rechtliche Herausforderungen. Wiesbaden: Springer Gabler.
  • Peltoniemi, M. & Vuori, E. (2004). Business ecosystem as the new approach to complex adaptive business environments. Proceedings of eBusiness research forum, 18, 267-281.
  • Ramsey, E., Ibbotson, P. & McCole, P. (2008). Factors that impact technology innovation adoption among Irish professional service sector. International Journal of Innovation Management, 12(4), 629-654.
  • Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37, 21-54.
  • Vowles N., Thirkell, P., & Sinha, A. (2011). Different determinants at different times: B2B adoption of a eadical innovation. Journal of Business Research, 64(11), 1162-1168.
  • Weill, P. & Woerner, S. L. (2015). Thriving in an Increasing Digital Ecosystem. MIT Sloan Management Review, 45(4), 27-34.
There are 34 citations in total.

Details

Journal Section Makaleler
Authors

Christian Arnold

Kai-Ingo Voigt This is me

Publication Date December 29, 2017
Submission Date November 21, 2017
Published in Issue Year 2017 Volume: 1 Issue: 2

Cite

APA Arnold, C., & Voigt, K.-I. (2017). Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies. Acta Infologica, 1(2), 99-108.
AMA Arnold C, Voigt KI. Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies. ACIN. December 2017;1(2):99-108.
Chicago Arnold, Christian, and Kai-Ingo Voigt. “Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies”. Acta Infologica 1, no. 2 (December 2017): 99-108.
EndNote Arnold C, Voigt K-I (December 1, 2017) Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies. Acta Infologica 1 2 99–108.
IEEE C. Arnold and K.-I. Voigt, “Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies”, ACIN, vol. 1, no. 2, pp. 99–108, 2017.
ISNAD Arnold, Christian - Voigt, Kai-Ingo. “Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies”. Acta Infologica 1/2 (December 2017), 99-108.
JAMA Arnold C, Voigt K-I. Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies. ACIN. 2017;1:99–108.
MLA Arnold, Christian and Kai-Ingo Voigt. “Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies”. Acta Infologica, vol. 1, no. 2, 2017, pp. 99-108.
Vancouver Arnold C, Voigt K-I. Ecosystem Effects of the Industrial Internet of Things on Manufacturing Companies. ACIN. 2017;1(2):99-108.