Does public support moderate the relationship between firms’ external knowledge sourcing and innovation? Evidence from manufacturing firms in Thailand
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DOIhttp://dx.doi.org/10.21511/ppm.23(4).2025.15
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Article InfoVolume 23 2025, Issue #4, pp. 203-219
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Type of the article: Research Article
Abstract
The primary objective of this study is to examine how public support moderates the effect of external knowledge sourcing on firms’ innovation performance. It contributes to the literature on external knowledge acquisition, as the moderating effect of public support on external knowledge sourcing for innovation has rarely been investigated. This study uses postal survey data from 423 manufacturing firms in Thailand, collected between March and August 2021, with key respondents including senior managers and firm owners. The Negative Binomial Regression is used for data analysis, as the dependent variable – the number of registered intellectual property rights – is a count variable with a non-normal distribution. The key findings reveal that public support has a positive direct effect on firms’ innovations. However, its interaction with external knowledge sourcing is negative to innovation performance. Thus, contrary to expectation, public support negatively moderates the relationship between external knowledge sourcing and innovation, suggesting that receiving more support weakens the effect of external knowledge sourcing on innovation performance. Moreover, public support does not positively moderate the inverted U-curve relationship between external knowledge sourcing and innovation by augmenting the optimal efficiency of firms’ knowledge sourcing activities. Instead, firms that receive more support tend to achieve optimal efficiency in knowledge sourcing faster than those that receive less. Therefore, rather than complementing external knowledge sourcing, public support appears to serve as a substitute for it: receiving public support reduces firms’ need to seek external knowledge to strengthen their innovative capabilities.
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JEL Classification (Paper profile tab)O31, O36, O38, H20
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References54
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Tables4
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Figures2
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- Figure 1. The moderating effect of public support for Model 2
- Figure 2. The moderating effect of public support for Model 3
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- Table 1. Number of innovations introduced by sample firms in the last three years
- Table 2. Descriptive statistics and bivariate correlations
- Table 3. NBR results
- Table A1. RDI programs covered by the current study
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Energy efficiency and green solutions in sustainable development: evidence from the Norwegian maritime industry
Viktoriia Koilo
doi: http://dx.doi.org/10.21511/ppm.18(4).2020.24
Problems and Perspectives in Management Volume 18, 2020 Issue #4 pp. 289-302 Views: 1416 Downloads: 490 TO CITE АНОТАЦІЯThe maritime industry plays a special role in Norway. In recent years, it became subject to increasingly stronger requirements to reduce emissions. However, the most important is that the Norwegian maritime industry in several areas can deliver and further develop technology and products that provide lower emissions, nationally and globally. Going forward, technology development will be more important with time. Thus, it is important to find out what impact it will have on the industry’s sustainable development and estimate the efficiency of new technologies.
This paper primarily aims to find a new optimization tool, which allows monitoring progress in the maritime industry towards sustainable development.
The present study reveals many new possible zero-emission solutions in the maritime industry, such as battery-electric architectures, ammonia, hydrogen, biofuel, and liquefied natural gas (LNG), liquefied petroleum gas (LPG), autonomous ships, etc. Moreover, it was highlighted that without active coordination between governance, academia, and industry, it is impossible to achieve international climate commitments and associated targets for reducing the emissions in the maritime industry.
In addition, in this study, a twofold model was proposed: the first part is the calculation of the Sustainable Development Index (SDI), and the last one is mathematical modeling, where the optimization variable carbon dioxide (CO2) emissions and Sustainable Development Index (SDI) should be maximized.
The investigation results prove that the model should be tested, and further research in this area is needed.Acknowledgment
The research is supported by a grant from the Research Based Innovation “SFI Marine Operation in Virtual Environment (SFI-MOVE)” (Project no: 237929) in Norway.
