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2019
DOI: 10.1007/s11123-019-00562-9
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How to survive and compete: the impact of information asymmetry on productivity

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Cited by 9 publications
(5 citation statements)
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References 55 publications
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“…The findings indicate that FDI benefits may be gained mainly by firms with sizable absorptive capacity (often high-tech or large), as they can capitalize on the presence of MNCs to increase productivity and lower inefficiency. This finding is in line with Jin et al [17].…”
Section: Technology Adoption Effectsupporting
confidence: 93%
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“…The findings indicate that FDI benefits may be gained mainly by firms with sizable absorptive capacity (often high-tech or large), as they can capitalize on the presence of MNCs to increase productivity and lower inefficiency. This finding is in line with Jin et al [17].…”
Section: Technology Adoption Effectsupporting
confidence: 93%
“…According to Jin et al [17], FDI in LT sectors may harm domestic firms' production through demonstration effect. Our findings on demonstration effects (HSpill) for lowtech are similar to those of Suyanto et al [11], who discovered that sectors such as food and beverages and textiles (low-tech) gained technical efficiency but not productivity spillovers as a result of increased foreign presence.…”
Section: Demonstration Effect (Hspill)mentioning
confidence: 99%
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“…The rapid inflow of FDI is widely believed to benefit Indonesia in fresh capital, new employment, source of funds for government spending, and source of funds for savings-investment gaps. Another benefit at the micro-level, which is equally important, is that FDI is, in fact benefiting local firms’ productivity through knowledge transfer in the forms of advanced technology, managerial expertise, and knowledge of scale-setting of production ( Jin et al., 2020 ; Khalifah et al., 2015 ; Malik, 2015 ). Such benefits increase the productivity of local firms, as local firms can imitate the technology possessed by FDI and adapt managerial knowledge through the recruitment of managers whom multinational corporations have trained ( Drahokoupil and Fabo, 2020 ; Kim, 2016 ; Newman et al., 2015 ).…”
Section: Introductionmentioning
confidence: 99%
“…The general framework for the ultimate analysis of the stochastic frontier was provided by Kumbhakar and Lovell (2000); before that, Battese and Coelli (1995) had attempted to apply the analysis of the stochastic frontier to the case of panel data. More recently, this production function has been applied by Jin et al (2020), using the following formulation:…”
Section: 2-study Designmentioning
confidence: 99%