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Society as a Laboratory to Experiment with New Technologies
Published in Diana M. Bowman, Elen Stokes, Arie Rip, Embedding New Technologies into Society, 2017
Learning-by-doing does not require anticipation because it takes place after a technology has been introduced in society. It might therefore be a possibility to discover unexpected things and to learn from actual developments. At the same time, learning-by-doing might be quite costly. It might be costly for two main reasons. First, although failure or the occurrence of undesirable effects may offer good learning opportunities they incur considerable social costs. A disaster like Fukushima might be epistemologically an excellent opportunity for learning; from a social and ethical point of view, it is primarily a disaster that is undesirable. Second, learning-by-doing might be costly because usually it takes place when a technology is already well embedded in society; changing the design of the technology or adapting certain institutions might at that stage have become quite costly. Or, if the introduction of a technology has failed, it might be impossible to revive the technology even if the failure of embedding might have led to learning that would allow a better embedding of the technology.
Technological development of the passenger car industry (1981–2000)
Published in Jatinder Singh, Global Players and the Indian Car Industry, 2018
It is a known fact that firms in developing countries can acquire technology through different channels.3 Broadly, these channels are classified into three categories. First, the internal efforts through in-house R&D activities are an important source of technological development. Spending in in-house R&D activities does not only mean the production of new technology (i.e. innovation), as a large chunk of R&D spending in developing countries aims at modifying existing technology, and upgrading products and processes according to changing market needs (Gumaste 1988; Cohen and Levinthal 1989; Bell and Pavitt 1992; Ernst, Ganiatsos and Mytelke 1998; Mani 2011). Second, the imports of technology can also supplement the internal efforts of firms towards technological development. It is argued that the adaptation of imported technology in accordance with the local raw material conditions would require the same kind of technical efforts as developing new technology on their own; therefore, the importance of technological imitation in the process of technological development cannot be ignored.4 Third, learning by doing is another way through which firms can strengthen their technological capabilities. Learning by doing is a long-run process through which firms get to know about better usage of available technology and thereby can produce more output with the same amount of resources (Bell 1984). Evidence from developing countries has strongly supported views emphasising the contribution of learning by doing in developing technological capabilities (Fransman and King 1984; Bell 1984; Lall 1984, 1987; Tyabji 2000).
“Made in China 2025” and the recent industrial policy in China
Published in Shigeru Thomas Otsubo, Christian Samen Otchia, Designing Integrated Industrial Policies Volume I, 2020
Kucuk Ali Akkemik, Murat Yülek
The industrialization pattern above is also related to industrial revolutions and accompanying technological changes. Another view of the stages of industrialization emphasizes technological sophistication in each stage (Yülek, 2018, pp. 176–181). According to this view, in the first stage, the technologies are related to IR1; production is mechanized through imports of machinery; and the output share of capital increases. Labor-intensive industries, such as textiles and clothing, are examples of such industries. In the second stage, technological sophistication and productivity increase as the country learns the technologies. It is common at this stage for developing countries to attract investments for assembly or parts production as well as production under licenses from advanced countries’ firms due to lower production costs. At the end of this stage, capital intensity and productivity increase a great deal and the country learns and masters the technologies embedded in machines. In the third stage, highly sophisticated (hi-tech) products of the advanced countries are imitated and hi-tech industries such as automotive and electronics are established. These industries are also characterized by learning-by-doing, which enables upgrading of labor skills. National production capacity increases and the technology creation capacity of the country improves. In the fourth, and last, stage, innovation is the key and new industrial products need to be developed by intensive research and development. The country is competing at the technological frontier with advanced countries and total factor productivity growth becomes the most important source of long-run growth. Most of the economies in the middle-income trap still remain at the second stage while industrial policies have helped East Asian economies such as Korea, Singapore, and Taiwan to successfully progress to the third stage and subsequently to the fourth stage.
Circular economy practices and corporate social responsibility performance: the role of sense-giving
Published in International Journal of Logistics Research and Applications, 2023
Tao Hong, Jinghua Ou, Fu Jia, Lujie Chen, Ying Yang
The long-term implementation of ECO (RL) practices can be measured as the frequency of the firm adopting them over a past period (Bergh and Lim 2008). More frequent ECO (RL) practice adoptions mean a continuous investment in CE. The same measurement has been widely employed to represent the accumulation of experience in many organisational learning articles (e.g. Gao and Pan 2010; Ding 2014). Organisational learning is defined as a process where experience is obtained from performing a task, which consequently influences the firm behaviours and performance (Bergh and Lim 2008). Firms can acquire experience from different sources, including direct knowledge, previous activities and decisions, and other firms’ experience, in which learning-by-doing is one of the most important mechanisms (Gao and Pan 2010). Based on this, continuous CE practice adoptions allow firms to iterate and update the experience towards CE change and to achieve better CSR performance (Al-Sheyadi, Muyldermans, and Kauppi 2019; Termeer and Metze 2019).
The coopetition effect of learning-by-doing in outsourcing
Published in International Journal of Production Research, 2021
Sijing Deng, Xu Guan, Jiayan Xu
Learning-by-doing (or learning curve) is a well-known economic concept which relates the productivity growth to the accumulation of production experience by firms. According to learning-by-doing (LBD), the unit production cost decreases in the cumulative production quantity due to the accumulated experience from repetition of work. LBD is a passive learning process which is unrelated to firms' active investment in capital or labour. LBD is prevailing in various industries, including airframes (Wright 1936), machine manufacturing (Baloff 1971) and semiconductors (Webbink 1977). The Japanese automobile manufacturer Toyota is famous for its management philosophy requiring continuous improvement, known as Kaizen, which is built upon LBD (Shingo 1981). LBD is also an important consideration for firms' competing strategies. For example, Jarmin (1994) shows that in the early rayon industry of the U.S.A., firms in competition take the strategic effects of LBD into account when choosing their output strategies.
Creating Effective Automation to Maintain Explicit User Engagement
Published in International Journal of Human–Computer Interaction, 2020
Jason M. Bindewald, Michael E. Miller, Gilbert L. Peterson
Each design decision imposes risk to the ability of the final automation to enhance system performance as it is difficult to project the effect these decisions will have on the behavior of the human-automation team. This risk occurs as a result of many factors. For example, functions can exist at multiple levels within a functional hierarchy and the allocation of functions within teams of humans are often time dependent (Miller & Parasuraman, 2007). Therefore, improper allocation can impose an untenable load on the human or remove the human from a control loop at one level of the hierarchy which significantly influences behavior at a different level of the hierarchy. Additionally, the inclusion of automation is known to change operator behavior (Bradshaw, Hoffman, Woods, & Johnson, 2013; Wiener & Curry, 1980b; Woods, 1996). These changes go beyond the addition of teamwork functions that arise due to inherent interaction with the automation (Bindewald, Miller, & Peterson, 2014; Pritchett, Kim, & Feigh, 2014). Humans learn new methods to utilize the system through interaction, a process referred to in the innovation literature as “learning by doing” (Rosenberg, 1982). Therefore, it becomes important to develop techniques to understand the impacts of each of these design decisions on the human-automation team to overcome the risks associated with our inability to project these changes. Of particular importance to the current research is the risk that the operator will disengage from a safety critical task, improperly relinquishing control of this task to a less than foolproof automation which guarantees an eventual system failure.