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      Innovation and organizational performance: A perspective among Chinese enterprises

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          Abstract

          The influence of innovation on the performance of Chinese enterprises still remains inconclusive in the literature of innovation management. The aim of this research therefore is to examine the link between innovation and performance of Chinese enterprises, and explore the influence of sentiment expressed by investors in this relationship. The data for our study are drawn from 3,500 Chinese listed firms, operating within the periods, 2009–2017. Panel autoregressive models (fixed and random effects) are employed in our empirical analyses. We further performed Hausman tests in order to ascertain which of the models is more suitable for our dataset. Results from the analysis show that innovation significantly influences the performance of Chinese enterprises and it is moderated by sentiment expressed by investors. Specifically, it is found that Chinese enterprises tend to be innovative as feedback to sentiment expressed by investors and this consequently results in higher performance.

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          Most cited references125

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          Issues in Assessing the Contribution of Research and Development to Productivity Growth

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            Investor Sentiment in the Stock Market

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              Econometric Analysis of Cross Section and Panel Data, second edition

              The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                07 November 2022
                2022
                : 13
                : 927617
                Affiliations
                [1] 1School of Finance and Economics, Jiangsu University , Zhenjiang, China
                [2] 2School of Management, Jiangsu University , Zhenjiang, China
                Author notes

                Edited by: Ying Han Fan, Curtin University, Australia

                Reviewed by: Muhammad Sadiq Shahid, Bahauddin Zakariya University, Pakistan; Syed Abdul Rehman Khan, Tsinghua University, China

                *Correspondence: Chengpeng Zhu, 2111719001@ 123456stmail.ujs.edu.cn

                This article was submitted to Performance Science, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2022.927617
                9677191
                36420382
                d6c5d63c-9548-4154-a4a8-8ec4029e65db
                Copyright © 2022 Zhu, Isaac and Edmund.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 24 April 2022
                : 21 September 2022
                Page count
                Figures: 0, Tables: 7, Equations: 7, References: 128, Pages: 14, Words: 12218
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                innovation,enterprise performance,moderating role,investor sentiment,chinese enterprises

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