The Role of Artificial Intelligence and Data Network Effects for Creating User Value

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

doi:10.5465/amr.2019.0178

Cited by 258 publications

(139 citation statements)

References 115 publications

(157 reference statements)

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“…The location advantage of lead markets may depend on the attributes of the users that a digital technology acquires. An important challenge for extracting valuable insights from user interactions is to ensure veracity, defined as the extent to which the received insights truly represent reality (Gregory et al 2020). Veracity is critical, as a digital firm can upgrade its technology to appeal to users in multiple countries only if it captures insights from all important user segments in the lead market on every aspect of the digital technology (e.g., features, options, design, user interfaces).…”

Section: Theory and Hypothesesmentioning

confidence: 99%

Revisiting Location in a Digital Age: How Can Lead Markets Accelerate the Internationalization of Mobile Apps?

Siddiqui¹,

Li²,

Priem³

2020

Journal of International Marketing

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Firms strategically expand to countries that offer important location advantages. Yet, for digital firms, which can instantly release their technologies worldwide, it is unclear whether a focus on specific locations can still provide strategic advantages. The authors argue that digital firms reap critical demand-side location advantages for the internationalization of their technologies by strategically interacting with users in lead markets that exhibit either high within-country demand heterogeneity or preference overlaps with several other countries. Simply penetrating a lead market is not enough, however, as both demand-side and supply-side factors influence the digital firm’s potential to take advantage of lead markets. On the demand side, a digital firm should avoid focusing on paying users or acquiring light users. On the supply side, the digital firm must deploy adequate technological and marketing capabilities to benefit from user interactions in lead markets. Thus, the authors link demand-side opportunities and supply-side firm capabilities to develop nuanced theory on how digital firms can spur international expansion. They find empirical support for their arguments by analyzing a large multicounty database of mobile apps in Apple’s App Store.

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Section: Theory and Hypothesesmentioning

confidence: 99%

Revisiting Location in a Digital Age: How Can Lead Markets Accelerate the Internationalization of Mobile Apps?

Siddiqui¹,

Li²,

Priem³

2020

Journal of International Marketing

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“…Managers may need to consider buying powerful hardware or using on-demand cloud computing resources. Additionally, managers may need to invest in building the capability to collect and process very large datasets, while taking measures to safeguard data quality (Gregory, Henfridsson, Kaganer, & Kyriakou, 2020). Moreover, development and application of DL algorithms require specialized data science skills, and the shortage of data scientists in the labor market is becoming a serious constraint on implementing such algorithms (Patil & Davenport, 2012).…”

Section: Economic Challengesmentioning

confidence: 99%

Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

Shrestha

Krishna

Krogh

2021

Journal of Business Research

140

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The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organizations. In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their analytical capabilities and perhaps help their transition to more creative work. We conceptualize the decision-making process in organizations augmented with DL algorithm outcomes (such as predictions or robust patterns from unstructured data) as deep learning-augmented decision-making (DLADM). We contribute to the understanding and application of DL for decisionmaking in organizations by (a) providing an accessible tutorial on DL algorithms and (b) illustrating DLADM with two case studies drawing on image recognition and sentiment analysis tasks performed on datasets from Zalando, a European e-commerce firm, and Rotten Tomatoes, a review aggregation website for movies, respectively. Finally, promises and challenges of DLADM as well as recommendations for managers in attending to these challenges are also discussed.

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“…7 ML tends to centralize communications for faster exploitation of large datasets. In 2010, technology executive David McCrory coined the term data gravity to explain that 'as data accumulates (builds mass) there is a greater likelihood that additional Services and Applications will be attracted to this data' (McCrory, 2010; for a scholarly version of this idea, see Gregory, Henfridsson, Kaganer, & Kyriakou, 2020). This effect occurs because information technology, in general, performs better with low latency -achievable by pooling data for swift exploitation by digital applications.…”

Section: Overview Of Machine Learning Technologymentioning

confidence: 99%

Decentralized vs. Distributed Organization: Blockchain, Machine Learning and the Future of the Digital Platform

Vergne

2020

Organization Theory

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The terms decentralized organization and distributed organization are often used interchangeably, despite describing two distinct phenomena. I propose distinguishing decentralization, as the dispersion of organizational communications, from distribution, as the dispersion of organizational decision-making. Organizations can be distributed without being decentralized (and vice versa), and having multiple management layers directly affects only distribution – not decentralization. This proposed distinction has implications for understanding the growth of digital platforms (e.g. amazon.com ), which dominate the global economy in the 21st century. While prominent platforms typically use machine learning as their core technology to transform inputs (e.g. data) into outputs (e.g. matchmaking services), blockchain has emerged as an alternative technological blueprint. I argue that blockchain enables platforms that are both decentralized and distributed (e.g. Bitcoin), whereas machine learning fosters centralized communications and the concentration of decision-making (e.g. Facebook Inc.). This distinction has crucial implications for antitrust policy, which, I contend, should shift both its analysis and its target of action away from the corporate level and focus instead on the data level. Based on this essay’s framework, I make several predictions regarding the future of competition between centralized and decentralized platforms, the evolution of government regulation, and broader implications for managers in the digital economy and for the business schools charged with their education. I conclude with reflections on the opportunity to revive cybernetic thinking for preventing a dystopian future dominated by a handful of platform behemoths.

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The Role of Artificial Intelligence and Data Network Effects for Creating User Value

Cited by 258 publications

References 115 publications

Revisiting Location in a Digital Age: How Can Lead Markets Accelerate the Internationalization of Mobile Apps?

Revisiting Location in a Digital Age: How Can Lead Markets Accelerate the Internationalization of Mobile Apps?

Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges

Decentralized vs. Distributed Organization: Blockchain, Machine Learning and the Future of the Digital Platform

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