مشخصات مقاله | |
ترجمه عنوان مقاله | سرمایه انسانی و نوآوری در سطح شرکتی: شواهدی از چین |
عنوان انگلیسی مقاله | Firm-level human capital and innovation: Evidence from China |
انتشار | مقاله سال 2020 |
تعداد صفحات مقاله انگلیسی | 15 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
2.525 در سال 2019 |
شاخص H_index | 61 در سال 2020 |
شاخص SJR | 1.052 در سال 2019 |
شناسه ISSN | 1043-951X |
شاخص Quartile (چارک) | Q1 در سال 2019 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | مدیریت |
گرایش های مرتبط | مدیریت منابع انسانی، مدیریت عملکرد، سیاست های تحقیق و توسعه، مدیریت استراتژیک، مدیریت کسب و کار |
نوع ارائه مقاله |
ژورنال |
مجله | بررسی اقتصادی چین – China Economic Review |
دانشگاه | School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, PR China |
کلمات کلیدی | ثبت اختراعات، نوآوری، سرمایه انسانی، نیروی کار ماهر، سرمایه انسانی مدیریتی، آموزش، تحقیق و توسعه، جغرافیا |
کلمات کلیدی انگلیسی | Patents، Innovation، Human capital، Skilled labor، Managerial human capital، Education، R&D، Geography |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.chieco.2019.101388 |
کد محصول | E14362 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract
1- Introduction 2- Firm level human capital and innovation 3- Empirical models and estimation strategies 4- Data and human capital measures 5- The estimated effects of human capital on patents 6- Results from control function approach 7- Innovation propensity and intensity 8- Concluding remarks References |
بخشی از متن مقاله: |
Abstract This paper examines the role of human capital in firms’ innovation. Based on a World Bank survey of manufacturing firms in China, we use two firm-level datasets: one from large metropolitan cities, and one from mid-sized cities. Patents are used as an indicator of innovation. The human capital indicators we use include the number of highly educated workers, the general manager’s education and tenure, and the management team’s education and age. We use the Negative Binomial and Instrumental Variables estimators to estimate patent production function models that are augmented by our human capital variables. We also use the zero-inflated Negative Binomial model to examine the likelihood of innovation. We find that the human capital indicators play an important role in influencing patenting, and that some of the human capital variables appear to have a greater impact on patenting in mid-sized cities. Our human capital estimates are obtained after controlling for firms’ R&D, size, market share, age, and foreign ownership, as well as fixed effects to control for industry-specific characteristics, and firms’ location and geography. Introduction Identifying the underlying factors that make firms more innovative is of considerable importance and has seen substantial research. The literature has identified the roles played by R&D expenditures, firm size, financing constraints, among other characteristics. In part, driven by data availability, much of this literature has focused on firms in the relatively more developed countries. Our focus in this paper is to examine some of the key determinants of firms’ innovation output in a developing and rapidly growing large economy, China. More importantly, our objective is to focus on the contribution of various aspects of firms’ human capital – as measured by the firms’ managerial and workforce skill-levels – on their innovation output and on their heterogenous effects across different market environments. This is particularly important for a country like China which has made significant investments in R&D related resources to boost its development process, but still lags the developed economies. For example, China was ranked as the second largest R&D spender in the World in 2017. |