Fundamental Tradeoffs in Scaling

Objective

This project aims to investigate some of the most fundamental, yet to date largely unexamined and poorly understood, trade-offs in the scaling process of high growth firms via two interrelated studies: 

-        Speed of Scaling and Adherence to Standards (Study 1): How does speed of scaling affect the organizational capacity to maintain adherence to required operational standards/routines? 

-        Open-Source Software and Time to Scale-Up (Study 2): How does the use of standard open-source development tools affect the timing of scale-up and scaling outcomes in digital startups?

 

Extant research suggests that organizations seek to scale rapidly (spatial replication) while at the same time aiming to ensure compliance with required standards/practices (temporal replication) by introducing intermediate (middle management) organizational units that relieve the capacity constraints and overload at the top of the organization (DeSantola & Gulati, 2017; Jansen et al., 2023; Tippmann et al, 2023; Coviello et al., 2024; Bohan et al., 2024). Such middle layer organizational units play an essential role with respect to both scaling up and monitoring compliance with required standards/practices. Yet, we still know little about the mechanisms that determine their capacity to simultaneously navigate both competing demands, a research gap which serves as the basis for Study 1.

With respect to the timing of scale-up (Study 2), extant literature predominantly looks at the timing of the transition point between experimentation and scaling on firm performance with contradictory findings (Lee & Kim, 2024). Some argue that startups should start scaling early to gain a competitive advantage vis-à-vis existing or would-be competitors (Coviello et al., 2024). Others suggest that premature scaling is an even bigger risk as insufficient experimentation  and limited validation of product-market fit sharply increase the odds of a costly pivot or even outright failure (Coviello et al, 2024; Janssen et al., 2023). Yet, we still know little about the determinants of the timing of scale-up and how they condition subsequent scaling performance.

Desired Impact / Expected Output

This research line provides scale-ups, founders, managers, policymakers with a blueprint for understanding and effectively managing the most fundamental trade-offs in scaling, thus significantly enhancing their capacity to generate significant, sustainable economic and social value through scaling. In addition to the above managerial and policy implications and insights, it seeks to make foundational theoretical and empirical scholarly contributions targeted for publication in top-tier academic journals, such as Organization Science, Strategic Management Journal, and the Journal of Business Venturing, thus advancing scholarly knowledge in management and entrepreneurship. The results will also be disseminated through a practitioner workshop, online repositories/social media, and articles in the popular press, ensuring accessibility and real-world application. We plan to also cooperate with industry associations, universities and their entrepreneurship centers, local and regional institutions (e.g., chambers of commerce, incubators) to promote the findings widely. This work aligns with the United Nations Sustainable Development Goals, particularly SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure).

 

Methodology

This research line adopts a quantitative methodology:

-       Study 1 leverages a unique survival-bias-free proprietary longitudinal dataset that tracks the scaling of the thousands of franchised outlets of one of the world’s largest franchise organizations on a monthly basis over a period of an entire decade. We set out to causally identify how attention switching and experiential learning mechanisms govern and moderate the effect of speed of scaling on adherence to required standards, including via leveraging instrumental variables and geospatial data that provide exogenous sources of variation. The methodology used will enable us to shed new light on the fundamental, yet to date underexplored/misunderstood, trade-off between quantity (speed) and quality (adherence to standards) of scaling faced by every scaling organization. 

-       Study 2 will utilize a proprietary dataset acquired from a globally leading labor analytics firm (Lightcast) which includes 250 million online job postings in the period from 2010 to 2020 and represents over three million U.S. companies, covering virtually the entire universe of job openings / labor market data in the USA. The dataset will be supplemented with data from Crunchbase, recognized for its extensive database of early-stage companies, making it a particularly valuable resource in the technology and startup sectors. The dataset will be additionally augmented with USPTO (United States Patent Office) patent data. We will leverage DiD (difference-in-differences) methodology to identify the effect of the TensorFlow AI shock on the timing of scaling of digital (particularly digital AI) startups and key moderators of said relationship. The methodology will allow us to shed light on key factors that influence the timing of when a startup begins its scaling efforts and how this timing affects its subsequent performance outcomes. It will, thereby, further understanding of the essential trade-off between scaling early (timing of the start of scaling) and scaling well (the performance outcomes of the subsequent scaling process). 

 

Workplan & Schedule 

 

Funding needs

 

The main funding needs are related to data acquisition (Lightcast database) and conference and research team travel as detailed in the attached budget worksheet. 

 

 

References

Bohan, S., Timppmann, E., Levie, J., Igoe, J. and Bowers, B. (2024). What is Scaling? Journal of Business Venturing, 39(1): 106355. 

Coviello, N., Autio, E., Nambisan, S., Patzelt, H., & Thomas, L. D. (2024). Organizational scaling, scalability, and scale-up: Definitional harmonization and a research agenda. Journal of Business Venturing, 39(5), 106419.

DeSantola, A., & Gulati, R. (2017). Scaling: Organizing and growth in entrepreneurial ventures. Academy of Management Annals, 11(2), 640-668.

Jansen, J. J. P., Heavey, C., Mom, T. J. M., Simsek, Z., & Zahra, S. A. (2023). Scaling-up: Building, leading and sustaining rapid growth over time. Journal of Management Studies, 60(3), 581-604.

Lee, S., & Kim, J. D. (2024). When do startups scale? Large‐scale evidence from job postings. Strategic Management Journal, 45(9), 1633-1669.

Levinthal, D. A., & Marino, A. (2015). Three facets of organizational adaptation: Selection, variety, and plasticity. Organization Science, 26(3), 743-755.

Tippmann, E., Ambos, T. C., Del Giudice, M., Monaghan, S., & Ringov, D. (2023). Scale-ups and scaling in an international business context. Journal of World Business, 58(1), 10139.