A technology delivery system for characterizing the supply side of technology emergence: Illustrated for Big Data & Analytics

https://doi.org/10.1016/j.techfore.2017.09.012Get rights and content

Highlights

  • It provides a promising chain-link approach to the supply side of innovation.

  • It offers a systematic approach to building a technology delivery system (TDS).

  • It focuses on improving techno-centric assessment and foresight approaches.

  • It draws on multiple data sources related to political, economic, academic, technical, and commercial market factors.

  • It presents the empirical results of the Big Data & Analytics case study.

Abstract

While there is a general recognition that breakthrough innovation is non-linear and requires an alignment between producers (supply) and users (demand), there is still a need for strategic intelligence about the emerging supply chains of new technological innovations. This technology delivery system (TDS) is an updated form of the TDS model and provides a promising chain-link approach to the supply side of innovation. Building on early research into supply-side TDS studies, we present a systematic approach to building a TDS model that includes four phases: (1) identifying the macroeconomic and policy environment, including market competition, financial investment, and industrial policy; (2) specifying the key public and private institutions; (3) addressing the core technical complements and their owners, then tracing their interactions through information linkages and technology transfers; and (4) depicting the market prospects and evaluating the potential profound influences on technological change and social developments. Our TDS methodology is illustrated using the field of Big Data & Analytics (“BDA”).

Introduction

One can view technology development from a number of perspectives. The supply chain perspective views technology development as an attempt to deliver a system to meet specific human needs or wants, while the market embedding perspective looks at technology development in terms of uptake, adoption, and the wider use of technology. Through this perspective, innovation is strongly affected by the dynamics of the economic and the social/political contexts that shape the transformation of new technology into products and services that are well embedded in markets and society. Both perspectives are techno-centric, in that they start with a technology option and explore the future pathways of development and uptake.1 When facing constantly fluctuating economic environments and swiftly changing markets, industrial actors are driven to pursue continual technological innovation as a response to maintaining a competitive edge (Wang et al., 2008). However, the process of technological innovation, which takes place through highly complex socio-techno-economic systems, is marked by the increasing role played by other factors, such as regulation and marketing. The social acceptability of innovation, especially where organized critical groups are concerned, must also be considered (Giget, 1997). Addressing these important relationships in the process of socio-technical change associated with complex technologies, thus, becomes a thorny problem for decision makers, both in government and industry.

Over the past few decades, a large number of innovation system approaches to explicate innovation in complex competitive environments have emerged. We have found the “Technology Delivery System (TDS)” conceptual model, first proposed in the 1970s, offers a helpful techno-centric approach for understanding what translates an idea into an effective innovation.2 The TDS offers an important framework for gathering and organizing information, and for drawing conclusions about the implications that can be used for decisions around emerging technology supply chains.3 It also helps those involved in technological forecasting to organize and communicate the critical problem-structuring phase of a forecast (Roper et al., 2011). The resulting system model can help public and private sector decision makers grasp key structures and processes and how these can be tuned to enhance the prospects of successful innovation.

Until now, we have not formulated a systematic approach to understand the new and emerging science and technology (NEST) and its associated TDS modeling, including technological regimes, technology architectures, and socio-technical systems (Porter et al., 2015). This paper presents a systematic framework for building a TDS model to explore empirical insights that draws upon different types of documents (e.g., policy reports, funding proposals, scientific articles, and patent assignment information).

The remainder of this article is organized as follows: following this introduction, the theoretical background of this study and a short overview of related literature is provided in Section 2. Section 3 explores the research objectives; then the systematic approach of building a TDS framework is developed in Section 4. Section 5 describes the search strategy and data retrieval for the present case analysis, leading into Section 6, which presents the empirical results of the Big Data & Analytics (“BDA”) case study. This is followed by a discussion and the managerial implications in Section 7. Finally, our conclusions are drawn in Section 8.

Section snippets

Literature review

The notion of a TDS was employed by the National Academy of Engineering to represent the complex processes by which knowledge of the consumer is deliberately applied to achieve amenities and social values (Wenk, 1973). In this model, the innovative process is driven by the market, where the government attempts to minimize the barriers that impede the TDS and to support struggling industries through an innovation policy of fixing market failures (Branscomb, 1973).4

Research objectives

Innovation is currently considered increasingly crucial to driving jobs and growth, as well as effectively dealing with the negative ramifications of historical economic drivers that have led to inequality, unsustainable manufacturing systems, and a polluting energy system that is based on unsustainable energy sources (Hekkert and Negro, 2009). Studying innovation is therefore important.

As the literature review above highlights, several of the proposed approaches to innovation systems offer

Framework and methodology

Our systematic approach for building a TDS model is constructed in four stages.

The first stage is to identify the relevant macroeconomic and policy environment, including market competition, financial investment, and industrial policy. This provides the overall landscape of the technology under study – in this case, Big Data – and is where value chains will emerge (if they emerge). It is the world of investment and policy, and, thus, is populated by those interested in the intelligence the TDS

Data

As shown in Table 2, this paper explores empirical insights drawn from different types of documents (e.g., policy reports, funding proposals, scientific articles, and patent assignment information) to contribute to an enriched TDS model for the study of the supply side of BDA – with a focus on the United States (US). The US is a good locale because their innovation system is well placed to take advantage of the advances in BDA. Over the past decade, the US has been prominent in the digital

Case study: the TDS for Big Data & Analytics in the US

A case study of BDA in the US illustrates how the TDS methodology can be used to identify the elements that are most likely to be affected by the speed, customization, and volume of an innovation. Below we provide, in advance, the composite TDS that is the output of the analyses that follow, shown as Fig. 2.

Discussion

In this paper, we propose a TDS model to characterize the supply side of technology emergence. It proves to be useful in identifying the different elements of an emerging technology supply chain, as well as some of the macroeconomic and policy factors that impinge on its development. We also identify the potential application areas to which the supply chain will provide added value and socio-economic benefit. We draw on multiple data sources related to political, economic, academic, technical,

Conclusions

This paper contributes to technology management and opportunity identification for complex innovations (see Fig. 9). The presented approach focuses on improving techno-centric assessment and foresight to describe emerging and evolving supply chains and to make clear the relevant dynamics to inform decision making and intervention. The approach helps pull together many different types of data into a coherent map of elements that will shape the eventual supply chain of an emerging technology as

Acknowledgements

We acknowledge support from the US National Science Foundation (Award No. 1527370), the Junior Fellowships of CAST Advanced S&T Think-tank Program (Grant No. DXB-ZKQN-2017-020), the General Program of the National Natural Science Foundation of China (Grant No. 71673024) and the European Union Seventh Framework Programme for ICT (Grant No. 285593). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the supporters or the

Ying Huang is a Ph.D. candidate in the School of Management and Economics, Beijing Institute of Technology. He was in School of Public Policy at Georgia Tech as the visiting scholar from September, 2014, to September, 2015. His specialty is technology innovation management and technology forecasting, particularly for study of emerging technology. He is the reviewers of academic peer-reviewed journals including Technological Forecasting and Social Change, Technology Analysis & Strategic

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    Ying Huang is a Ph.D. candidate in the School of Management and Economics, Beijing Institute of Technology. He was in School of Public Policy at Georgia Tech as the visiting scholar from September, 2014, to September, 2015. His specialty is technology innovation management and technology forecasting, particularly for study of emerging technology. He is the reviewers of academic peer-reviewed journals including Technological Forecasting and Social Change, Technology Analysis & Strategic Management, Scientometrics, International Journal of Technology Management.

    Alan L. Porter (PhD) is Professor Emeritus of Industrial & Systems Engineering, and of Public Policy, at Georgia Institute of Technology, where he remains Co-director of the Technology Policy and Assessment Center (TPAC). He is also Director of R&D for Search Technology, Inc., Norcross, GA. He is author of some 230 articles and books. Current research emphasizes measuring, mapping, and forecasting ST&I knowledge diffusion patterns.

    Scott W. Cunningham (PhD) is the associate professor of Policy Analysis in the Department of Technology, Policy and Management, Delft University of Technology. He received a Ph.D. in Science, Technology and Innovation Policy from the Science Policy Research Unit. He is interested in operations research and decision sciences approaches for policy making, especially in probabilistic models of social exchange. Other interests include building multi-actor systems theory through the economic sociology and innovation policy literatures. Besides, He is the associate editors of Technological Forecasting and Social Change.

    Douglas K. R. Robinson (PhD) is a researcher and consultant on the dynamics, management and policies of innovation, with a particular focus on emerging technologies. He weaves together three axes of research: the dynamics of technology emergence (particularly the collective dimension); future-oriented analyses informed by dynamics of socio-technical change; and policy and strategic intelligence on research and innovation. He has recently written reports for the OECD, NASA, ESA and the French Ministry of Research and Higher Education on emerging technologies and new industrial dynamics.

    Jianhua Liu (PhD) is the associate professor of National Science Library, Chinese Academy of Sciences (CAS). Besides, she is a librarian in National Science Library CAS. She was in School of Public Policy at Georgia Tech as a visiting scholar from October 2015 to May 2016. Her research focus on information extraction and text mining.

    Donghua Zhu is a professor in the School of Management and Economics, Beijing Institute of Technology, where he remains director of the Laboratory of Knowledge Management and Data Analysis. His main academic research fields include science and technology data mining, technology innovation management, technology forecasting and management. He was the main technical backbone and project designers of Department of Defense project and NSF program shouldered by Technology Policy & Assessment Centre (TPAC) in Georgia Institute of Technology.

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