The Impact of Intellectual Capital on the Firm Performance of Russian Manufacturing Companies
Abstract
:1. Introduction
2. Theoretical and Empirical Background of the Research
2.1. The Concept of Intellectual Capital and Its Relation to Risks
- Disclosure of information on intellectual capital in the company’s financial statements;
- Intellectual capital in universities, education, and the public sector;
- Knowledge management;
- The impact of intellectual capital on the market value of companies and their performance.
2.2. VAIC Model Measuring and Evaluating the Intellectual Capital of an Enterprise
2.2.1. VAIC Model Overview
- Easily assesses the effectiveness of intellectual capital and enables comparative analysis between different sectors and countries;
- The model uses data from a company’s financial statements, which helps management accurately assess the effectiveness of added value creation through capital employed and intellectual capital;
- If the perfect competition assumption is relaxed, the VAIC captures the ability of a firm to generate profits;
- VAIC can be used to identify general trends; it is, however, not capable of any deeper investigation.
- The VAIC model does not include the capital of a company’s relations with counterparties, nor a company’s ability to innovate in its business processes or the products it produces;
- The VAIC model measures only the operating efficiency of a company; the depreciation cost included in added value does not depend on the profits generated by the firm;
- Structural capital does not describe relationship capital, defined as the difference between added value and human capital;
- Human capital efficiency is defined as the ratio of added value to the magnitude of human capital, in which case the smaller the capital, the greater its efficiency. So it must be ensured that the measuring points belong to the same general salary level. One cannot therefore compare high- and low-salary companies or countries with each other;
- Analysis of integrated reports, business models and key performance indicators of a particular company does allow deeper understanding of the contribution of intellectual capital to the value creation process, but this is a time-consuming and resource-intensive process.
- The main components of IC: structural capital efficiency (SCE), human capital efficiency (HCE), and capital employed efficiency (CEE);
- Additional components of IC: relationship capital efficiency (RCE), R&D expenditure efficiency (RDE);
- Complex measures of IC: value-added intellectual coefficient (VAIC), modified value-added intellectual coefficient (MVAIC).
2.2.2. Human Capital Efficiency (HCE)
2.2.3. Capital Employed Efficiency (CEE)
2.2.4. Structural Capital Efficiency (SCE)
2.2.5. Relationship Capital
2.3. Trends in the Development of the Manufacturing Industry in Russia and the World
3. Materials and Methods
3.1. Data
3.2. Dependent Variables
3.3. Independent Variables: Value-Added Intellectual Coefficient (VAIC)
3.4. Control Variables
3.5. Model Specification
+ b7GRPi,t + b8∆URi,t + b9IndustryInGRPi,t + b10RNDtoGRPi,t + ɛi,t;
b3Sizei,t + b4Size2i,t + b5Levi,t + b6Lev2i,t + b7GRPi,t + b8∆URi,t + b9Industry
InGRPi,t + b10RNDtoGRPi,t + ɛi,t;
+ b8∆URi,t + b9IndustryInGRPi,t + b10RNDtoGRPi,t + ɛi,t;
b3Sizei,t + b4Size2i,t + b5Levi,t + b6Lev2i,t + b7GRPi,t + b8∆URi,t + b9Industry
InGRPi,t + b10RNDtoGRPi,t + ɛi,t;
b8∆URi,t + b9IndustryInGRPi,t + b10RNDtoGRPi,t + ɛi,t;
b4Size2i,t + b5Levi,t + b6Lev2i,t + b7GRPi,t + b8∆URi,t + b9IndustryInGRPi,t +
b10RNDtoGRPi,t + ɛi,t;
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Authors | Sample | Y | SCE | HCE | CEE | VAIC |
---|---|---|---|---|---|---|
(Marzo and Bonnini 2022) | 335 Italian companies operating in non-financial sectors between 2009 and 2018 | ROA | N | + | + | / |
ROE | + | N | N | / | ||
MtBV | N | N | N | / | ||
(Nejjari and Aamoum 2021) | 29 companies from Morocco, belonging to 8 sectors of the economy, from 2013 to 2019 | ROE | N | + | + | / |
ROA | N | + | + | / | ||
MtBV | N | + | + | / | ||
(Ardiansari et al. 2021) | 56 Indonesian real estate firms, from 2014 to 2018 | ROE | + | N | N | / |
MtBV | N | N | N | / | ||
(Sumiati 2020) | 43 companies with the strongest reputation in knowledge management in Indonesia in 2016 | ROA | N | + | + | / |
(Petković et al. 2020) | 548 large French wine companies in the period 2015 to 2019 | OPERAPROFIT | - | + | + | + |
NETINCOME | - | + | + | + | ||
(Fawzi Shubita 2019) | 73 manufacturing companies from Jordan from 2005 to 2017 | MtBV | N | + | N | N |
(Xu and Liu 2019) | Renewable energy companies from 2010 to 2016 | ROA | N | + | + | + |
(Smriti and Das 2018) | 710 service and manufacturing companies from India from 2001 to 2016 | ATO | + | + | - | + |
ROA | N | - | + | + | ||
SG | + | - | + | + | ||
TQ | + | - | + | + | ||
(Nadeem et al. 2017) | 6045 publicly listed firms in BRICS economies for the period of 2005 to 2014 | ROE | + | + | + | + |
ROA | + | + N | + | + N | ||
(Bryl and Truskolaski 2015) | 21 Polish IT companies from 2010 to 2013 | ROA | + | - | + | / |
ROE | + | - | + | / | ||
(Maditinos et al. 2011) | 96 Greek companies from the construction, industrial goods and services, food and household goods sectors from 2006 to 2008 | ROA | / | / | / | + |
ROE | / | / | / | + | ||
(Clarke et al. 2011) | 2 161 materials, financial and industrials companies from Australian from 2003 to 2008 | ROA | + | + | + | + |
ROE | + | + | + | + | ||
(Zéghal and Maaloul 2010) | 342 British companies during 2005 | ROA | / | / | / | + |
Authors | Sample | Y | SCE | HCE | CEE | RCE | RDE | MVAIC |
---|---|---|---|---|---|---|---|---|
(Li et al. 2021) | Top 100 companies of the 2016 ranking published by Forbes for the 2011–2015 period | ROA | N | + | + | N | / | + |
ROE | N | + | + | N | / | + | ||
Value creation | N | N | N | + | / | N | ||
(Majumder et al. 2021) | 14 cement producers from China from 2009 to 2018 | ROA | - | + | + | - | / | N |
MtBV | - | + | + | - | / | N | ||
NPM | - | + | + | - | / | N | ||
(Ge and Xu 2021) | 204 pharmaceutical companies listed on the Shanghai and Shenzhen stock exchanges from 2013 to 2018 | EBIT | + | + | + | + | N | + |
EBITDA | N | + | + | N | N | + | ||
NPM | N | + | + | - | + | + | ||
GPM | N | + | N | N | N | + | ||
EPS | N | + | + | N | N | + | ||
ROIC | N | + | + | - | + | + | ||
ROA | N | + | + | N | N | + | ||
ROE | N | + | + | N | N | + | ||
SG | N | + | + | N | N | N | ||
ATO | N | + | + | N | N | + | ||
MtBV | N | N | + | N | N | - | ||
(Xu and Liu 2020) | 415 manufacturing firms from Korea from 2013 to 2018 | ROA | N | + | + | - | - | / |
ROE | N | + | + | - | - | / | ||
ATO | N | N | + | N | N | / | ||
MtBV | N | N | N | N | N | / | ||
(Soetanto and Liem 2019) | 127 Indonesian firms from 2010 to 2017 | ROA | + | N | + | N | / | + |
MtBV | N | N | N | N | / | N | ||
(Xu and Wang 2019) | 29 and 37 textile companies in China and South Korea over the period 2012–2017 | EBITDA | + N | - + | + | N | / | + |
ROA | + | + | + | + N | / | + | ||
ROE | + | N + | + | + | / | + | ||
ATO | N | - | + | N | / | N + | ||
(Xu and Li 2019) | 496 (116 high-tech and 380 non-high-tech) SMEs in China’s manufacturing sector listed on the Shenzhen stock exchanges duringthe period 2012–2016 | EBIT | + | + | + | N | / | + |
ROA | + | + | + | + | / | + | ||
NPM | - N | + | + | N | / | + | ||
ATO | N + | - | + | N + | / | N + |
Appendix B
Industry Name in Accordance with OKVED-2 | Industry Code in Accordance with OKVED-2 | Frequency | Share, % | Cumulative Share, % |
---|---|---|---|---|
Food production | 10 | 4264 | 18.15 | 18.15 |
Manufacture of fabricated metal products, except machinery and equipment | 25 | 2253 | 9.59 | 27.74 |
Manufacture of machinery and equipment, not included in other groups | 28 | 2150 | 9.15 | 36.89 |
Manufacture of other non-metallic mineral products | 23 | 2015 | 8.58 | 45.47 |
Manufacture of rubber and plastic products | 22 | 1936 | 8.24 | 53.71 |
Repair and installation of machinery and equipment | 33 | 1557 | 6.63 | 60.33 |
Manufacture of electrical equipment | 27 | 1243 | 5.29 | 65.63 |
Manufacture of chemicals and chemical products | 20 | 1021 | 4.35 | 69.97 |
Manufacture of motor vehicles, trailers, and semi-trailers | 29 | 735 | 3.13 | 73.10 |
Printing and copying of information media | 18 | 636 | 2.71 | 75.81 |
Beverage industry | 11 | 634 | 2.70 | 78.51 |
Manufacture of paper and paper products | 17 | 579 | 2.46 | 80.97 |
Manufacture of computers, electronic, and optical products | 26 | 543 | 2.31 | 83.28 |
Manufacture of basic metals | 24 | 532 | 2.26 | 85.55 |
Manufacture of textiles | 13 | 512 | 2.18 | 87.72 |
Furniture manufacturing | 31 | 495 | 2.11 | 89.83 |
Manufacture of other manufactured goods | 32 | 483 | 2.06 | 91.89 |
Woodworking and manufacture of articles of wood and cork (except furniture) and manufacture of articles of straw and materials for plaiting | 16 | 466 | 1.98 | 93.87 |
Manufacture of clothing | 14 | 443 | 1.89 | 95.76 |
Manufacture of medicines and materials used for medical purposes | 21 | 432 | 1.84 | 97.60 |
Manufacture of other vehicles and equipment | 30 | 267 | 1.14 | 98.73 |
Manufacture of leather and leather goods | 15 | 225 | 0.96 | 99.69 |
Manufacture of coke and petroleum products | 19 | 60 | 0.26 | 99.94 |
Manufacture of tobacco products | 12 | 13 | 0.06 | 100.00 |
Total | 23,494 | 100.00 |
Region Name | Frequency | Share, % | Cumulative Share, % |
---|---|---|---|
Moscow | 3118 | 13.27 | 13.27 |
Moscow Region | 2690 | 11.45 | 24.72 |
Sverdlovsk Region | 1286 | 5.47 | 30.19 |
Krasnodar Region | 828 | 3.52 | 33.72 |
Saint Petersburg | 764 | 3.25 | 36.97 |
Chelyabinsk Region | 764 | 3.25 | 40.22 |
Novosibirsk Region | 724 | 3.08 | 43.30 |
Samara Region | 675 | 2.87 | 46.18 |
Bashkortostan (Republic) | 664 | 2.83 | 49.00 |
Perm Region | 601 | 2.56 | 51.56 |
Rostov Region | 580 | 2.47 | 54.03 |
Voronezh Region | 524 | 2.23 | 56.26 |
Republic Of Tatarstan | 518 | 2.20 | 58.47 |
Nizhny Novgorod Region | 485 | 2.06 | 60.53 |
Kaluga Region | 481 | 2.05 | 62.58 |
Yaroslavl Region | 440 | 1.87 | 64.45 |
Krasnoyarsk Region | 378 | 1.61 | 66.06 |
Saratov Region | 374 | 1.59 | 67.65 |
Tula Region | 358 | 1.52 | 69.18 |
Vladimir Region | 345 | 1.47 | 70.64 |
Lipetsk Region | 319 | 1.36 | 72.00 |
Belgorod Region | 312 | 1.33 | 73.33 |
Volgograd Region | 293 | 1.25 | 74.58 |
Stavropol Region | 283 | 1.20 | 75.78 |
Leningrad Region | 266 | 1.13 | 76.91 |
Ulyanovsk Region | 240 | 1.02 | 77.93 |
Ivanovo Region | 219 | 0.93 | 78.87 |
Orenburg Region | 211 | 0.90 | 79.77 |
Tver Region | 210 | 0.89 | 80.66 |
Udmurt Republic | 204 | 0.87 | 81.53 |
Tyumen Region | 202 | 0.86 | 82.39 |
Chuvash Republic-Chuvashia | 200 | 0.85 | 83.24 |
Bryansk Region | 198 | 0.84 | 84.08 |
Altai Region | 196 | 0.83 | 84.92 |
Ryazan Oblast | 194 | 0.83 | 85.74 |
Mari El (Republic) | 181 | 0.77 | 86.51 |
Irkutsk Region | 171 | 0.73 | 87.24 |
Vologda Region | 164 | 0.70 | 87.94 |
Mordovia (Republic) | 164 | 0.70 | 88.64 |
Smolensk Region | 154 | 0.66 | 89.29 |
Novgorod Region | 152 | 0.65 | 89.94 |
Penza Region | 143 | 0.61 | 90.55 |
Republic Of Crimea | 144 | 0.61 | 91.16 |
Kurgan Region | 139 | 0.59 | 91.75 |
Omsk Region | 119 | 0.51 | 92.26 |
Kaliningrad Region | 115 | 0.49 | 92.75 |
Kirov Region | 115 | 0.49 | 93.24 |
Arhangelsk Region | 109 | 0.46 | 93.70 |
Kemerovo Region | 107 | 0.46 | 94.16 |
Kostroma Region | 103 | 0.44 | 94.59 |
Kursk Region | 103 | 0.44 | 95.03 |
Primorsky Krai | 98 | 0.42 | 95.45 |
Tambov Region | 95 | 0.40 | 95.85 |
Khabarovsk Region | 87 | 0.37 | 96.22 |
Oryol Region | 76 | 0.32 | 96.55 |
Pskov Region | 65 | 0.28 | 96.82 |
Tomsk Region | 65 | 0.28 | 97.10 |
Karelia (Republic) | 63 | 0.27 | 97.37 |
Adygea (Republic) (Adygea) | 62 | 0.26 | 97.63 |
Amur Region | 60 | 0.26 | 97.89 |
Komi (Republic) | 61 | 0.26 | 98.15 |
North Ossetia-Alania (Republic) | 50 | 0.21 | 98.36 |
Khakassia (Republic) | 47 | 0.20 | 98.56 |
Buryatia (Republic) | 39 | 0.17 | 98.73 |
Dagestan (Republic) | 40 | 0.17 | 98.90 |
Kabardino-Balkarian Republic | 41 | 0.17 | 99.07 |
Kamchatka Krai | 33 | 0.14 | 99.21 |
Transbaikal Region | 26 | 0.11 | 99.32 |
Karachay-Cherkess Republic | 27 | 0.11 | 99.44 |
Murmansk Region | 26 | 0.11 | 99.55 |
Sakha (Republic) (Yakutia) | 27 | 0.11 | 99.66 |
Astrakhan Region | 22 | 0.09 | 99.76 |
Sakhalin Region | 16 | 0.07 | 99.83 |
Altai (Republic) | 15 | 0.06 | 99.89 |
Jewish Autonomous Region | 8 | 0.03 | 99.92 |
Magadan Region | 8 | 0.03 | 99.96 |
Chechen Republic | 7 | 0.03 | 99.99 |
Kalmykia (Republic) | 3 | 0.01 | 100.00 |
Total | 100.00 |
1 | World Competitiveness Ranking is compiled by the World Competitiveness Center by calculating an annual index of key indicators in four areas: Economic Performance, Government Efficiency, Business Efficiency, and Infrastructure. |
2 | https://spark-interfax.com/ (accessed on 2 February 2023). |
3 | https://www.fedstat.ru/ (accessed on 2 February 2023). |
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Variable Abbreviation | Variable Definition | Measurement | Expected Relationship | Works Using the Indicator |
---|---|---|---|---|
Dependent variables | ||||
LnEBIT | Natural logarithm of EBIT | Logarithm of Rubles | \ | (Ge and Xu 2021) |
ROA | Return on assets | Ratio | \ | (Majumder et al. 2021; Nejjari and Aamoum 2021; Sumiati 2020; Xu and Liu 2019, 2020; Soetanto and Liem 2019; Smriti and Das 2018) |
ATO | Asset turnover | Ratio | \ | (Ge and Xu 2021; Xu and Liu 2020; Smriti and Das 2018) |
Independent variables | ||||
VAIC | Sum of HCE, SCE and CEE | Coefficient | + | (Fawzi Shubita 2019; Xu and Liu 2019, 2020; Smriti and Das 2018) |
HCE | Human capital efficiency | Coefficient | + | (Ge and Xu 2021; Nejjari and Aamoum 2021; Ardiansari et al. 2021; Majumder et al. 2021; Sumiati 2020; Xu and Liu 2019, 2020; Fawzi Shubita 2019; Soetanto and Liem 2019; Smriti and Das 2018) |
SCE | Structural capital efficiency | Coefficient | + | (Ge and Xu 2021; Nejjari and Aamoum 2021; Ardiansari et al. 2021; Majumder et al. 2021; Sumiati 2020; Xu and Liu 2019, 2020; Fawzi Shubita 2019; Soetanto and Liem 2019; Smriti and Das 2018) |
CEE | Capital employed efficiency | Coefficient | + | (Ge and Xu 2021; Nejjari and Aamoum 2021; Ardiansari et al. 2021; Majumder et al. 2021; Sumiati 2020; Xu and Liu 2019, 2020; Fawzi Shubita 2019; Soetanto and Liem 2019; Smriti and Das 2018) |
Control variables | ||||
Size | Natural logarithm of total assets | Logarithm of Rubles | + | (Ge and Xu 2021; Xu and Liu 2020) |
Lev | Leverage | Ratio | - | (Ge and Xu 2021; Xu and Liu 2020) |
LnGRP | Natural logarithm of Gross regional product in constant 2016 prices | Logarithm of Rubles | + | (Ge and Xu 2021; Xu and Liu 2020) |
∆UR | Change in the unemployment rate in the region | % | - | \ |
IndustryInGRP | Share of manufacturing industry in GRP | % | + | \ |
RNDtoGRP | Share of investment in the development of new technologies in GRP | % | + | \ |
Variable | Number of Observations | Average | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|---|
lnEBIT | 23,494 | 16.385 | 2.185 | 6.908 | 25.492 |
ROA | 23,494 | 0.096 | 0.115 | −0.243 | 0.7 |
ATO | 23,494 | 2.035 | 1.457 | 0.002 | 9.982 |
VAIC | 23,494 | 2.751 | 1.675 | −4.179 | 13.995 |
CEE | 23,494 | 0.423 | 0.395 | −0.243 | 3.794 |
SCE | 23,494 | 0.343 | 0.344 | −4.368 | 2.5 |
HCE | 23,494 | 1.984 | 1.443 | −1.626 | 13.034 |
Size | 23,494 | 19.073 | 1.761 | 9.105 | 27.084 |
Lev | 23,494 | 0.521 | 0.289 | 0 | 1 |
∆UR | 23,494 | 0.045 | 0.735 | −1.8 | 5.1 |
IndustryInGRP | 23,494 | 20.972 | 8.467 | 0.6 | 42.9 |
RNDtoGRP | 23,494 | 1.335 | 1.036 | 0.04 | 5.49 |
Indicator | Pooled OLS | Fixed Effects | Random Effects | Pooled OLS | Fixed Effects | Random Effects |
---|---|---|---|---|---|---|
HCE | 0.564 *** | 0.496 *** | 0.554 *** | |||
(0.015) | (0.018) | (0.015) | ||||
HCE2 | –0.049 *** | –0.035 *** | –0.042 *** | |||
(0.001) | (0.002) | (0.001) | ||||
SCE | 1.080 *** | 0.662 *** | 0.834 *** | |||
(0.028) | (0.029) | (0.025) | ||||
SCE2 | 0.387 *** | 0.279 *** | 0.323 *** | |||
(0.011) | (0.012) | (0.010) | ||||
CEE | 2.879 *** | 2.798 *** | 2.720 *** | |||
(0.037) | (0.072) | (0.044) | ||||
CEE2 | –0.808 *** | –0.615 *** | –0.702 *** | |||
(0.016) | (0.026) | (0.018) | ||||
Size | 1.025 *** | 1.256 *** | 0.964 *** | 1.176 *** | 0.702 *** | 1.017 *** |
(0.047) | (0.185) | (0.067) | (0.052) | (0.192) | (0.074) | |
Size2 | 0.000 | –0.003 | 0.002 | –0.005 *** | 0.006 | –0.001 |
(0.001) | (0.005) | (0.002) | (0.001) | (0.005) | (0.002) | |
Lev | 1.209 *** | 1.121 *** | 1.242 *** | 1.882 *** | 1.331 *** | 1.787 *** |
(0.088) | (0.161) | (0.109) | (0.097) | (0.171) | (0.119) | |
Lev2 | –1.660 *** | –1.990 *** | –1.797 *** | –2.723 *** | –2.628 *** | –2.746 *** |
(0.085) | (0.152) | (0.104) | (0.092) | (0.161) | (0.113) | |
LnGRP | 0.012 * | –1.079 *** | 0.015 | 0.015 * | –0.678 ** | 0.014 |
(0.006) | (0.198) | (0.009) | (0.007) | (0.210) | (0.010) | |
∆UR | –0.047 *** | –0.039 *** | –0.048 *** | –0.050 *** | –0.033 *** | –0.047 *** |
(0.008) | (0.007) | (0.006) | (0.009) | (0.008) | (0.007) | |
IndustryInGRP | 0.004 *** | 0.002 | 0.004 *** | 0.004 *** | 0.002 | 0.004 *** |
(0.001) | (0.003) | (0.001) | (0.001) | (0.004) | (0.001) | |
RNDtoGRP | 0.017 * | –0.030 | 0.022 * | 0.027 *** | –0.028 | 0.033 ** |
(0.007) | (0.041) | (0.011) | (0.008) | (0.044) | (0.012) | |
VAIC | 0.828 *** | 0.646 *** | 0.708 *** | |||
(0.010) | (0.012) | (0.010) | ||||
VAIC2 | –0.053 *** | –0.031 *** | –0.039 *** | |||
(0.001) | (0.001) | (0.001) | ||||
Constant term | –5.966 *** | 14.601 ** | –5.420 *** | –6.369 *** | 13.986 ** | –4.590 *** |
(0.460) | (4.548) | (0.654) | (0.508) | (4.814) | (0.727) | |
0.823 | 0.780 | |||||
0.543 | 0.821 | 0.614 | 0.778 | |||
0.404 | 0.398 | 0.328 | 0.324 | |||
0.559 | 0.86 | 0.64 | 0.819 | |||
N | 23,494 | 23,494 | 23,494 | 23,494 | 23,494 | 23,494 |
Indicator | Pooled OLS | Fixed Effects | Random Effects | Pooled OLS | Fixed Effects | Random Effects |
---|---|---|---|---|---|---|
HCE | 0.168 *** | 0.094 *** | 0.105 *** | |||
(0.019) | (0.014) | (0.013) | ||||
HCE2 | −0.014 *** | −0.005 *** | −0.007 *** | |||
(0.002) | (0.001) | (0.001) | ||||
SCE | 0.269 *** | 0.158 *** | 0.166 *** | |||
(0.035) | (0.023) | (0.022) | ||||
SCE2 | 0.060 *** | 0.061 *** | 0.060 *** | |||
(0.014) | (0.009) | (0.009) | ||||
CEE | 2.534 *** | 2.124 *** | 2.284 *** | |||
(0.046) | (0.057) | (0.046) | ||||
CEE2 | −0.443 *** | −0.288 *** | −0.336 *** | |||
(0.020) | (0.021) | (0.018) | ||||
Size | −0.150 * | −0.929 *** | −0.385 *** | −0.183 ** | −1.952 *** | −0.780 *** |
(0.060) | (0.146) | (0.083) | (0.065) | (0.151) | (0.089) | |
Size2 | −0.002 | 0.017 *** | 0.004 | −0.004 * | 0.037 *** | 0.011 *** |
(0.002) | (0.004) | (0.002) | (0.002) | (0.004) | (0.002) | |
Lev | 2.437 *** | 1.335 *** | 1.783 *** | 3.615 *** | 1.535 *** | 2.276 *** |
(0.112) | (0.127) | (0.109) | (0.121) | (0.134) | (0.117) | |
Lev2 | −1.143 *** | −0.718 *** | −0.853 *** | −2.719 *** | −1.279 *** | −1.789 *** |
(0.108) | (0.120) | (0.104) | (0.116) | (0.127) | (0.110) | |
LnGRP | 0.011 | −0.985 *** | 0.018 | 0.002 | −0.447 ** | 0.007 |
(0.008) | (0.157) | (0.013) | (0.008) | (0.165) | (0.014) | |
∆UR | −0.074 *** | −0.067 *** | −0.083 *** | −0.078 *** | −0.061 *** | −0.080 *** |
(0.010) | (0.006) | (0.005) | (0.011) | (0.006) | (0.005) | |
IndustryInGRP | 0.002 | −0.001 | 0.001 | 0.002 | 0.000 | 0.002 |
(0.001) | (0.003) | (0.001) | (0.001) | (0.003) | (0.002) | |
RNDtoGRP | 0.032 *** | −0.072 * | 0.035 * | 0.043 *** | −0.079 * | 0.042 ** |
(0.009) | (0.033) | (0.014) | (0.010) | (0.035) | (0.015) | |
VAIC | 0.417 *** | 0.264 *** | 0.284 *** | |||
(0.013) | (0.009) | (0.009) | ||||
VAIC2 | −0.031 *** | −0.012 *** | −0.015 *** | |||
(0.001) | (0.001) | (0.001) | ||||
Constant term | 3.114 *** | 33.078 *** | 5.705 *** | 5.151 *** | 34.152 *** | 11.542 *** |
(0.585) | (3.589) | (0.821) | (0.637) | (3.788) | (0.888) | |
0.357 | . | . | 0.221 | |||
0.099 | 0.349 | 0.12 | 0.203 | |||
0.260 | 0.253 | 0.171 | 0.158 | |||
0.106 | 0.380 | 0.135 | 0.227 | |||
N | 23,494 | 23,494 | 23,494 | 23,494 | 23,494 | 23,494 |
AIC | 74,007 | 28,851 | 78,490 | 31,513 | ||
BIC | 7412 | 28,972 | 78,579 | 31,602 | ||
LL | −36,989 | −14,411 | −39,234 | −15,745 | −36,989 | −14,411 |
RMSE | 1.169 | 0.531 | 0.533 | 1.286 | 0.562 | 0.568 |
Indicator | Pooled OLS | Fixed Effects | Random Effects | Pooled OLS | Fixed Effects | Random Effects |
---|---|---|---|---|---|---|
HCE | 0.058 *** | 0.048 *** | 0.056 *** | |||
(0.001) | (0.001) | (0.001) | ||||
HCE2 | −0.004 *** | −0.003 *** | −0.004 *** | |||
(0.000) | (0.000) | (0.000) | ||||
SCE | 0.058 *** | 0.028 *** | 0.043 *** | |||
(0.002) | (0.002) | (0.002) | ||||
SCE2 | 0.033 *** | 0.021 *** | 0.026 *** | |||
(0.001) | (0.001) | (0.001) | ||||
CEE | 0.259 *** | 0.307 *** | 0.252 *** | |||
(0.003) | (0.006) | (0.004) | ||||
CEE2 | −0.069 *** | −0.059 *** | −0.060 *** | |||
(0.001) | (0.002) | (0.002) | ||||
Size | 0.005 | 0.123 *** | 0.009 | 0.014 ** | 0.027 | 0.007 |
(0.004) | (0.015) | (0.006) | (0.005) | (0.017) | (0.006) | |
Size2 | −0.0001 | −0.002 *** | −0.0002 | −0.001 *** | −0.0002 | −0.0003 |
(0.0000) | (0.000) | (0.0000) | (0.000) | (0.000) | (0.000) | |
Lev | −0.038 *** | 0.030 * | −0.010 | 0.031 *** | 0.057 *** | 0.050 *** |
(0.008) | (0.013) | (0.009) | (0.009) | (0.015) | (0.010) | |
Lev2 | −0.032 *** | −0.131 *** | −0.063 *** | −0.138 *** | −0.208 *** | −0.162 *** |
(0.007) | (0.013) | (0.009) | (0.008) | (0.014) | (0.010) | |
LnGRP | 0.004 *** | −0.107 *** | 0.004 *** | 0.004 *** | −0.054 ** | 0.004 *** |
(0.001) | (0.017) | (0.001) | (0.001) | (0.018) | (0.001) | |
∆UR | −0.0003 | −0.0034 *** | −0.0009 | −0.001 | −0.003 *** | −0.001 |
(0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
IndustryInGRP | 0.0003 *** | −0.0007 ** | 0.000 * | 0.0004 *** | −0.0006 | 0.0002 * |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
RNDtoGRP | 0.0008 | 0.0029 | 0.0008 | 0.002 * | 0.002 | 0.002 |
(0.001) | (0.003) | (0.001) | (0.001) | (0.004) | (0.001) | |
VAIC | 0.059 *** | 0.049 *** | 0.052 *** | |||
(0.001) | (0.001) | (0.001) | ||||
VAIC2 | −0.003 *** | −0.002 *** | −0.002 *** | |||
(0.000) | (0.000) | (0.000) | ||||
Constant term | −0.218 *** | 0.597 | −0.279 *** | −0.174 *** | 0.735 | −0.106 |
(0.040) | (0.380) | (0.057) | (0.045) | (0.419) | (0.064) | |
0.523 | . | . | 0.378 | . | . | |
0.087 | 0.516 | 0.142 | 0.375 | |||
0.435 | 0.408 | 0.307 | 0.298 | |||
0.057 | 0.531 | 0.109 | 0.380 | |||
N | 23,494 | 23,494 | 23,494 | 23,494 | 23,494 | 23,494 |
AIC | −52,473 | −76,682 | −46,270 | −71,901 | ||
BIC | −52,352 | −76,561 | −46,182 | −71,813 | ||
LL | 26,251 | 38,356 | 23,146 | 35,962 | ||
RMSE | 0.079 | 0.056 | 0.057 | 0.090 | 0.062 | 0.062 |
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Skhvediani, A.; Koklina, A.; Kudryavtseva, T.; Maksimenko, D. The Impact of Intellectual Capital on the Firm Performance of Russian Manufacturing Companies. Risks 2023, 11, 76. https://doi.org/10.3390/risks11040076
Skhvediani A, Koklina A, Kudryavtseva T, Maksimenko D. The Impact of Intellectual Capital on the Firm Performance of Russian Manufacturing Companies. Risks. 2023; 11(4):76. https://doi.org/10.3390/risks11040076
Chicago/Turabian StyleSkhvediani, Angi, Anastasia Koklina, Tatiana Kudryavtseva, and Diana Maksimenko. 2023. "The Impact of Intellectual Capital on the Firm Performance of Russian Manufacturing Companies" Risks 11, no. 4: 76. https://doi.org/10.3390/risks11040076