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Top1. Introduction
Since the start of this new century, various leading nations have engaged in industrial transformations from automated manufacturing to intelligent manufacturing. The so-called fourth industrial revolution has been seen (Industry 4.0) as reflected in a manufacturing sector backed by much more advanced intelligence; manufacturing firms will adopt intelligent manufacturing technologies and processes, including cyber-physical systems (CPS), the internet of things (IoT), cloud computing, cognitive computing and artificial intelligence (AI). Based on a lesson learned from the past successes of several rounds of industrial revolutions (Rostow, 1960; Wen, 2016), these nations have been actively orchestrating their desired transformations by introducing and implementing various policies and providing governmental supports. Examples of those efforts include the following:
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Since 2011, the USA has launched a series of strategic plans and acts, such as the “Advanced Manufacturing Partnership Program” (PCAST, 2012), “Revitalizing the US Manufacturing and Innovation Act 2014”, the “National Manufacturing Innovation Network Strategic Plan” (OWH, 2016), and established the “Industrial Internet Alliance” in 2014;
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In 2013, German industry and academia jointly proposed and advocated for such a platform known as “Industry 4.0”;
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In 2015, the concept of ultra-smart “Society 5.0” appeared in Japan (Government of Japan, 2015) in an effort to ensure Japan’s dominance in manufacturing. The materialization of this concept was expected to rely on the nation’s superior technology and relevant systemic foundations;
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In 2015, China announced “Made in China 2025” (State Council of the PRC, 2015). In the following year, China put forward the “Guiding Opinions on Deepening the Integral Development of Manufacturing Industry and Internet” (State Council of the PRC, 2016) and “Development Plan of Intelligent Manufacturing (2016-2020)” (MIIT, 2016a); and
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In 2017, the government of the United Kingdom made a plan for “Growing the Artificial Intelligence Industry in the UK” (Hall & Pesenti, 2017).
With these developments transpiring, the following research question arises naturally: how do government policies affect the economic performance of manufacturing firms? Answers to this question are important for developed nations, because these nations desire to maintain their leading positions in the world economy. Simultaneously, answers to this question are also important to developing and undeveloped nations, because they face a strong possibility of further trailing behind the developed nations economically, socially, and politically. To address this question, the current work attempts to provide relevant answers by using the logic and methodology of systems science. In terms of the literature, this logic and methodology represents an unconventional approach.
The logic and methodology used here merits special attention, in particular due to their contrast with other approaches to answering the same or similar questions. The logic parallels the logical reasoning used in mathematics (Kline, 1972) and are not constrained data and/or anecdotes (Kuhn, 1962). By contrast, empirically developed conclusions are tethered to specific data, making extrapolation to other facts potentially less reliable. Thus, the goal of the propositions established below is to allow generation of reliable policy recommendations – instead of suggestions. As for systemic reasoning, an increasing number of business studies, such as Porter (1985), Mazzei et al. (2017), Forrest (2018), etc., have employed such an approach.