Special Issue in Technological Forecasting and Social Change: Artificial Intelligence as an Enabler for Innovation

Artificial Intelligence (AI) and its associated Machine Learning (ML) capabilities are considered to be the next General-Purpose Technologies (GPTs) that will impact all areas of economy and society at large (Montes & Goertzel, 2019), perhaps at the same or greater magnitude as previous GPTs such as steam engine, electricity, internal combustion engine, and computers (Brynjolfsson, Rock, & Syverson, 2017). The pervasive nature of AI holds considerable potential for disrupting both management practices (von Krogh, 2018) and strategies of business in all industries (Agrawal, Gans, & Goldfarb, 2018). In this call for papers, we focus on the impact of AI on the innovation processes of organizations. While AI can influence both the production and the characteristics of a wide range of products and services, Cockburn, Henderson, and Stern (2018) suggest that AI may impact the innovation process itself by serving as a new general-purpose method of invention. Brynjolfsson and McAfee (2017, p. 19) state that the impact of AI on business and the economy “will be reflected not only in their direct contributions but also in their ability to enable and inspire complementary innovations.” For instance, AI-based learning may “automate discovery across many domains where classification and prediction tasks play an important role” and “radically alter scientific and technical communities’ conceptual approaches and framing of problems” (Cockburn et al., 2018, p.7). Makridakis (2017) proposes that the automation of routine research tasks may allow R&D teams to focus on more creative and innovative tasks. As such, AI can potentially affect the way organizations manage and conduct R&D to develop new products and services. In the pharmaceutical industry, AI has been used to predict candidate selection for trials, identify target proteins, and automate molecule design, all of which have halved the development cost of certain drugs and considerably reduced the time to market through higher approval rate. In marketing, AI can leverage the myriad of consumer data to predict the successful features of a future product or service, which will reduce the notoriously high rate of failure for any new product or service. At an organizational level, AI can lower entry barriers to resource-constrained organizations by drastically reducing R&D costs. In this scenario, an increasing number of small firms will be able to use AI and ML capabilities to produce incremental innovations which otherwise would have been forgone due to inherent high search costs.

This call for papers encourages the submission of work that examines the question of how AI may impact the innovation processes of organizations in all domains. We are particularly interested in work that looks at AI as an enabler for innovation, for instance, when AI improves the ways firms organize and conduct R&D to increase both the quantity and the quality of new products and services, independently of the domain (marketing, finance, production and manufacturing, logistics, etc.), product and service, or industry. However, please note we are not interested in papers that investigate AI-based applications in new products and services. For example, we realize that AI as a GPT may lead to the commercialization of many new products and services that incorporate some of these technologies (see Hengstler, Enkel, & Duelli, 2016). Autonomous cars, smart speakers, and personalized recommendations on e-commerce platforms may be examples of AI-based products and services for consumers, while fraud detection models, targeted advertising, and drones may be examples of AI-based products and services for businesses. These new products and services may be AI-based, but they are not the outcomes of a novel process of innovation. Therefore, we outline below some of the questions that are relevant to this call (this list is non-exhaustive):

Organizing R&D

  • How does AI impact the existing structure and organization of R&D?
  • What potential benefits may AI bring in the organizing or R&D (lower cost, automation of tasks, shorter timelines, etc.)? How do these benefits come around?
  • In contrast, what are the challenges of introducing new structures and organizing in existing R&D units?
  • How might AI impact the role of humans in R&D teams?

Innovation Processes

  • How can AI become a general-purpose method of invention?
  • How are existing innovation processes in a particular domain affected by the introduction of AI capabilities?
  • What are the challenges and key success factors for an organization to implement AI capabilities in its innovation processes?
  • What benefits does AI bring in terms of innovation outputs (number of innovations, nature of innovation, quality of innovation, type of innovation)?

The Role of Data in AI-based Innovation

  • Innovation using AI is highly dependent upon access to data, how can organizations or industries work to facilitate the sharing data?
  • Similarly, how can a “market for data” be created that benefits both users (those who “supply” data) and organizations (those who use data)?
  • What is the role of data in AI-based innovation methods?
  • When do organizations need more or fewer data to innovate? What are both the external and internal conditions that command the volume of data they need?
  • What industries, products, or services are more prone to data management challenges? Who owns the data, and when does data privacy hamper the innovative capability of an organization or industry?

AI as an enabler for small and new organizations

  • Since AI lowers search cost, it facilitates access to innovation activities for firms with constrained resources, but how does this process take place?
  • What are the R&D tasks that will be made more accessible to small firms and new ventures?
  • How might small firms and new ventures access critical data for building their AI algorithms?
Stage Time
Paper submission in Elsevier System
- Invitation (if any) to be sent before the first submission date)
3 Months
Period of peer-review process
- (Begins when the first submission is received + 3 Months)
6 Months
Revised manuscript due + Final acceptance 2 Months
Total 10 Months

Guest editors

Yann Truong, Associate Professor of Digital Management, Burgundy School of Business

Savvas Papagiannidis, Professor of Innovation & Enterprise, Newcastle University Business School

Dirk Schneckenberg, Associate Professor of Innovation Management and Entrepreneurship, Rennes School of Business

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