Data Network Effects: Key Conditions, Shared Data, and the Data Value Duality

Gregory, Robert Wayne; Henfridsson, Ola; Káganer, Evgeny; Kyriakou, Harris

doi:10.5465/amr.2021.0111

Cited by 41 publications

(18 citation statements)

References 12 publications

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“…Yet, a large portion of digitalized innovations are consumed by organization themselves. There are, as well, interesting mechanisms of innovation and value creation in the offer that do not necessarily result from direct intervention on it e.g., data networks effects [96]. Are these dynamics just a matter of novelty or are they evidence pointing towards a deeper change in how organizations approach innovation and how the gain value from it?…”

Section: Discussionmentioning

confidence: 99%

Interactions Between Innovation and Digital Transformation: A Co-Word Analysis

2022

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Recent literature suggests there is a natural connection between innovation and digital transformation, two key topics of interest in management and organization that have spawned large, independent and well-defined areas of study. While this connection might be analytically straightforward and notable examples are not hard to find, in everyday life, it materializes in a multiplicity of ways. A need emerges, then, to better understand the interaction between innovation and digital transformation so that it can be explored and exploited by actors in academia and the public, private and third sectors. In this article, we use a co-word analysis, a text mining technique that permits to systematically map the intellectual structure of a research field, to characterize the most notable dynamics of 'innovation-driven digitalization' and 'digitalized innovation'-the two major dimensions of interaction between innovation and digital transformation. The text identifies the relevant themes, subthemes and concepts that appear in the literature, as well as their relationship and level of development. It, then, aggregates them in a taxonomy that, on the one hand, readily displays their connection and, on the other hand, (i) informs about current or potential controversies, (ii) gaps, (iii) lines for novel and further research, and (iv) alternatives to bridge to other areas of study.

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Section: Discussionmentioning

confidence: 99%

Interactions Between Innovation and Digital Transformation: A Co-Word Analysis

2022

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“…Our findings can explain the mixed the result in the literature. First, we acknowledge the feedback loop logic in Gregory et al (2021). However, Proposition 1 shows that the stock of available data produced by the feedback loop has a finite oracle size because of time dependency.…”

Section: Discussionmentioning

confidence: 99%

“…All these theories attest that more data is always better, and securing a vast amount of such resource leads to the firm's success in the long run. In addition, recent research hypothesizes a feedback loop [Gregory et al (2021), Ichihashi (2021)] between the size of available data and the quality of AI-based solutions. Gregory et al (2021) theorize and compare this data externality to network effects, where the value of a service or product increases in user-base size.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, recent research hypothesizes a feedback loop [Gregory et al (2021), Ichihashi (2021)] between the size of available data and the quality of AI-based solutions. Gregory et al (2021) theorize and compare this data externality to network effects, where the value of a service or product increases in user-base size. In this "data network effect" [Gregory et al (2021), Hagiu and Wright (2020), Prufer and Schottmuller (2017)], more data leads to a higher accuracy of algorithms, which means better services [Hestness et al (2017)].…”

Section: Introductionmentioning

confidence: 99%

“…Gregory et al (2021) theorize and compare this data externality to network effects, where the value of a service or product increases in user-base size. In this "data network effect" [Gregory et al (2021), Hagiu and Wright (2020), Prufer and Schottmuller (2017)], more data leads to a higher accuracy of algorithms, which means better services [Hestness et al (2017)]. Better service then leads to a higher user engagement or a larger user-base, which creates even more data.…”

Section: Introductionmentioning

confidence: 99%

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Time and the Value of Data

Valavi¹,

Hestness²,

Ardalani³

et al. 2022

Preprint

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Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of keeping around an infinite supply of older (less relevant) data. In addition, we argue that increasing the stock of data by including older datasets may, in fact, damage the model's accuracy. Expectedly, the model's accuracy improves by increasing the flow of data (defined as data collection rate); however, it requires other tradeoffs in terms of refreshing or retraining machine learning models more frequently.Using these results, we investigate how the business value created by machine learning models scales with data and when the stock of data establishes a sustainable competitive advantage. We argue that data's time-dependency weakens the barrier to entry that the stock of data creates. As a result, a competing firm equipped with a limited (yet sufficient) amount of recent data can develop more accurate models. This result, coupled with the fact that older datasets may deteriorate models' accuracy, suggests that created business value doesn't scale with the stock of available data unless the firm offloads less relevant data from its data repository. Consequently, a firm's growth policy should incorporate a balance between the stock of historical data and the flow of new data.We complement our theoretical results with an experiment. In the experiment, we use the simple yet widely used machine learning task known as next work prediction. We empirically measure the loss in the accuracy of a next word prediction model trained on datasets from various time periods. Our empirical measurements confirm the economic significance of the value decline over time. For example, 100MB of text data, after seven years, becomes as valuable as 50MB of current data for the next word prediction task.

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Digitalization of cross‐border R&D alliances: Configurational insights and cognitive digitalization biases

Bouncken

Fredrich

Sinkovics

et al. 2022

Global Strategy Journal

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Firms implement digital technology for improving coordination and communication in cross-border R&D alliances. However, there is great ambivalence regarding how digitalization influences cross-border knowledge transfers. Our analysis clarifies some of this ambivalence by providing different configurations of absorptive capacity in cross-border R&D alliances. The fuzzy-set qualitative comparative analysis (fsQCA) reveals only low absorptive capacity achievement in most configurations of digital technology implementation. The findings indicate effects of cognitive digitalization biases, under which firms take the benefits of digital technology for granted while ignoring deep-level challenges rooted in the contextuality of international ties. However, high absorptive capacity is achievable when (1) allying with bigger and younger partners, (2) under technological similarity, and (3) coping with the associated digitalization biases. Managerial Summary: Firms are eager to grasp the potential of digital technology. Within R&D alliances, digital technology is deemed to facilitate better coordination and communication. However, advantages from

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Data Network Effects: Key Conditions, Shared Data, and the Data Value Duality

Cited by 41 publications

References 12 publications

Interactions Between Innovation and Digital Transformation: A Co-Word Analysis

Interactions Between Innovation and Digital Transformation: A Co-Word Analysis

Time and the Value of Data

Digitalization of cross‐border R&D alliances: Configurational insights and cognitive digitalization biases

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