The Business Transformation of Incumbents in the Transition to new Basic Technologies

Type de publication:

Conference Paper

Auteurs:

Falk, Fabian

Source:

Gerpisa colloquium, Paris (2021)

Résumé:

Purpose

During discontinuous changes to new basic technologies, not only the velocity but also uncertainty, complexity and ambiguity are high. This is because the achievement of a new stable industrial architecture (e.g. Jacobides/MacDuffie, 2013) with a new “dominant design” (e.g. Christensen et al. 1998) is a lengthy process - witness the transitions from sailing ship to steam ship or from internal combustion engine technology to electric mobility (e.g. Donada/Attias, 2015). Therefore, the current research into business transformation demands highly nuanced perspectives (e.g., Eggers/Park 2018; Bigelow et al 2019).

Incumbent companies possessing insufficient information under uncertainty have a particular need to accelerate the transformation. If they wait too long for the uncertainty to be reduced, they may be forced out of the market (cf. Porter and Rivkin 2000; Raffaelli et al. 2019). Waiting is only an option if the old technology is likely to remain more profitable than the new technology for a long time.  

In long-term business transformation, incumbent companies have to make several independent decisions simultaneously. On the one hand, they have to make decisions on the timing of their entry into the new business under uncertainty (e.g., Lieberman/Montgomery 2013), i.e. they have to create timing strategies (cf. Bigelow et al. 2019). They also have to make decisions on the content of shrinkage of the existing business and growth strategies for the new one (ambidextrous management, based on Raisch/Tushman 2016).

The research questions in this article are therefore: 

  1. What alternative decisions are possible in the long-term transition to new basic technologies based on a combination of process-oriented timing and content-related transformation?
  2. On what do the decisions on the alternative strategies for business transformation depend? and
  3. What influence do these decisions have on the extent of the business transformation at a given point in time in the long-term transition to a new basic technology?

 

Design

In order to answer the first research question:

  • decisions relating to the process of timing the entry into the new business are explained by breaking down models of the evolution of industries towards a new dominant design (e.g., Christensen et al. 1998 or Klepper 2002) into the early and late entry decisions made by individual companies and
  • decisions relating to the content of different transformation strategies are explained by market- and competence-based approaches in Strategic Management which offer (rational) decision-making alternatives for investing in the ramp-down of old businesses and the ramp-up of new businesses (e.g., Becker et al. 2016): 1. exit and leapfrogging, 2. harvest strategy and leapfrogging, 3. harvest strategy and internal competence development and 4. consolidation strategy and internal competence development.

The two decisions / strategies can then be combined.

For the second research question, we can use a model explaining how to improve decision-making quality under uncertainty (Csaszar/Eggers 2013): by (1) improving the level of information depending on particularly dynamic sensing capabilities (e.g. Teece 2007) or by (2) waiting for more certainty depending on, e.g., sunk costs in the traditional and new business or resource re-deployment (e.g. Csaszar and Eggers 2013; Lieberman et al. 2017).

The third research question can also be answered by the theoretical analysis. i.e.: what influence do the (eight) individual alternative decisions have on the extent of a business transformation at a given point in time in the long-termin transition to a new basic technology? Possible answers are formulated as assumptions.

The research questions will be checked empirically by a survey conducted in the German automobile supplier industry at a given point in time (March 2021). The survey is being distributed by the German Association of the Automotive Industry (VDA) among members companies between March 10th and 24th. It therefore has not yet been fully completed, but it apperas to be showing a high return.

 

Findings

The timing and transformations strategies, their influencing factors as well as their effects on the degree of automotive transformation will be empirically analyzed using a multiple regression analysis by May 2021. We will therefore be able to report on the results and answers to the research questions at the Gerpisa Colloquium in June.

 

Practical and theoretical implications

The submission makes a threefold contribution to previous research: 1. it expands the research into decision-making under uncertainty during long-term changes by combining timing and transformation decisions, 2. it expands the research on ambidexterity by ambidextrous transformation strategies and 3. it offers guidance to incumbent companies which have little information under high uncertainty in order to improve and therefore accelerate their decisions on business transformation and avoid being pushed out of the market.

By zooming in on the transition process, it also provides companies with an overview of the status of the business transformation of incumbent automotive suppliers in the transition to electric mobility and of the starting point for acceleration.

 

References 

Becker, A./Stolltz, R./Stäblein, T. (2016): Strategic ramp-up planning in automotive production networks. In: International Journal of Production Research, 55(1): 59-78.

Bigelow, L./Nickerson, J.A./Park, W.-Y. (2019): When and how to shift gears: Dynamic trade-offs among adjustment, opportunity, and transaction costs in response to an innovation shock. In: Strategic Management Journal, 40(3): 377-40.

Christensen, C.M./Suárez, F.F./Utterback, J.M. (1998): Strategies for survival in fast changing industries. In: Management Science, 44(12): 207-220.

Csaszar, F.A./Eggers, J.P. (2013): Organizational decision making: An information aggregation view. In: Management Science, 59(10): 2257-2277.

Donada, C./Attias, D. (2015): Food for thought: which organization and ecosystem governance to boost radical innovation in the electromobility 2.0 industry? In: International Journal of Automotive Technology and Management, 15(2):105-125.

Eggers, J.P./Park, K.F. (2018): Incumbent adaptation to technological change. The past, present, and the future of research on heterogeneous incumbent response. In: Academy of Management Annals, 12(1): 357-389.

Jacobides, M.G./MacDuffie, J.P. (2013): How to drive value your way. In: Harvard Business Review, 91(7): 92-100.

Klepper, S. (2002): The capabilities of new firms and the evolution of the U.S. automobile industry. In Industrial and Corporate Change, 11(4): 645-666.

Lieberman, M.B./Montgomery, D.B. (2013): Conundra and progress: Research on entry order and performance. In: Long Range Planning, 46(4-5): 312-324.

Lieberman, M.B./Garcia-Castro, R./Balasubramanian, N. (2017): Measuring value creation and appropriation in firms: THE VCA model. In: Strategic Management Journal, 38(6): 1193-1211.

Porter, M.E./Rivkin, J.W. (2000): Industry Transformation. Boston/Mass.: Harvard Business School.

Raffaelli, R./Glynn, M A./Tushman, M. (2019): Frame flexibility: The role of cognitive and emotional framing in innovation adaptation of incumbent firms. In: Strategic Management Journal, 40(7): 1013-1039.

Raisch, S./Tushman, M.L. (2016): Growing new corporate businesses: From initiation to graduation.

            In: Organizational Science, 27(5): 1237-1257.

Teece, D.J. (2007): Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. In: Strategic Management Journal, 28(13): 1319-1350.

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