Competitive Pressure and the Adoption of Complementary Innovations

Kretschmer, Tobias; Miravete, Eugenio J.; Pernías, José C.

doi:10.1257/aer.102.4.1540

Cited by 46 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We assume that 1 and 2 and ν are independent which implies that the model contains all contingency variables and all practices that affect the performance of the two practices x 1 and x 2 . The objective function ( 1) is similar to the the objective function in Kretschmer et al (2012) with two exceptions.…”

Section: Model and Formal Analysismentioning

confidence: 99%

“…First, we assume that the values of x 1 and x 2 are continuous while Kretschmer et al (2012) allow for binary practices. We return to the issue of binary practices as an extension to the current model in section 4.2.3 and 4.4.…”

Section: Model and Formal Analysismentioning

confidence: 99%

“…More sophisticated models are needed to reliably estimate this performance effect. The economics literature has proposed and used a multiple equation model that combines both demand and performance functions (Athey & Stern, 1998;Gentzkow, 2007;Kretschmer et al, 2012;Miravete & Pernias, 2006). Further research can investigate the properties of these statistical models for the management control setting.…”

Section: Going Forwardmentioning

confidence: 99%

See 2 more Smart Citations

Testing for complementarities between accounting practices

Masschelein

Moers

2020

Accounting, Organizations and Society

View full text Add to dashboard Cite

Section: Model and Formal Analysismentioning

confidence: 99%

Section: Model and Formal Analysismentioning

confidence: 99%

Section: Going Forwardmentioning

confidence: 99%

See 1 more Smart Citation

Testing for complementarities between accounting practices

Masschelein

Moers

2020

Accounting, Organizations and Society

View full text Add to dashboard Cite

“…Our model simplifies aspects of the adoption decision to focus on the key issues at stake in our study. While our model incorporates some important factors that have been emphasized in shaping adoption of new technology, such as firm and establishment size and industry as well as location characteristics (e.g., Forman et al, 2005; Griliches, 1957; Hannan & McDowell, 1984), it excludes others, such as the roles of network effects and competition (Karshenas & Stoneman, 1993; Kretschmer et al, 2012). Further, the model does not explicitly incorporate how the benefits and costs of adopting new IT such as ML evolve within firms over time (Brynjolfsson et al, 2019; Brynjolfsson, Rock, & Syverson, 2021).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

How does worker mobility affect business adoption of a new technology? The case of machine learning

Chen,

Balasubramanian,

Forman

2024

Strategic Management Journal

View full text Add to dashboard Cite

Research SummaryWe investigate how worker mobility influences the adoption of a new technology using state‐level changes to the enforceability of noncompete agreements as an exogenous shock to worker mobility. Using data on over 153,000 establishments from 2010 and 2018, we find that changes that facilitate worker movements are associated with a significant decline in the likelihood of adoption of machine learning. Moreover, we find that the magnitude of decline depends upon the size of the establishment, the extent of predictive analytics adoption in its industry, and the number of large establishments in the same industry‐location. These results are consistent with the view that increases in outward worker mobility increase costs for adoption of a new technology that involves significant downstream investments in the early years of its diffusion.Managerial SummarySuccessful business adoption of new technologies such as machine learning requires skilled workers with experience in implementing those technologies. In the early years of technology diffusion workers in early adopting businesses typically acquire these skills through on‐the‐job learning that is paid for by the adopter. So, if such early adopters face an increased risk of those skilled workers quitting, then their incentives to adopt the technology decrease. We examine this possibility using changes in noncompete enforceability as a proxy for changes in worker mobility and find that the likelihood of adopting machine learning decreases as the risk of worker mobility increases, particularly for larger establishments, establishments in industries where adoption may be more beneficial and in locations with many large competing establishments.

show abstract

External Technology Supply and Client-Side Innovation

Peukert

2012

Technology Transfer in a Global Economy

View full text Add to dashboard Cite

Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:http://ftp.zew.de/pub/zew-docs/dp/dp11082.pdf Non-Technical SummaryDynamic market environments and consumer preferences demand firms to assure a high degree of flexibility. At the same time, competitive pressure from globalized markets leads to constantly raising efficiency requirements. Organizations change their design from static to dynamic concepts in order to react adequately to these developments and challenges. A common strategy is to reach beyond the boundaries of the firm to access critical resources.Accordingly, we observe that technology transfer from providers of knowledge intensive business services attracts more and more attention.A specific case is the external supply of information technology. A major reason why firms outsource IT, is the potential to cut cost. We argue that there is also a potential of knowledge transfer in IT outsourcing relationships, leading to client-side innovation. The aim of this paper is to contribute to resolving an empirical puzzle arising from the prior literature. Some authors find beneficial effects of IT outsourcing, others underline that firms often fail to achieve their expected strategic goals.Our stylized theoretical model combines a knowledge production function framework and transaction cost economics. We hypothesize that the right balance between internal and external knowledge is critical for innovation. The empirical application is German AbstractFlexibility in response to competitive pressure from globalized markets and increasingly individualized customer desires has become vital for firms. A common strategy to address this challenge is to employ a dynamic concept of organization and reach beyond the boundaries of the firm. Accordingly, technology transfer from providers of knowledge intensive business services attracts more and more attention. In this context we focus on external supply of information technology and client-side innovation. The aim of this paper is to contribute to resolving an empirical puzzle arising from the prior literature. Some authors find beneficial effects of IT outsourcing, others underline that firms often fail to achieve expected strategic goals. Our stylized theoretical model combines a knowledge production function framework and transaction cost economics. We hypothesize that the right balance between internal and external knowledge is critical for innovation. The empirical application is German firm-level data covering...

show abstract

Competitive Pressure and the Adoption of Complementary Innovations

Cited by 46 publications

References 34 publications

Testing for complementarities between accounting practices

Testing for complementarities between accounting practices

How does worker mobility affect business adoption of a new technology? The case of machine learning

External Technology Supply and Client-Side Innovation

Contact Info

Product

Resources

About