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The impact of top management team tenure heterogeneity on declining firm's innovation efficiency

Cite this dataset

Gao, Qilin; Huang, Xunjiang (2024). The impact of top management team tenure heterogeneity on declining firm's innovation efficiency [Dataset]. Dryad. https://doi.org/10.5061/dryad.sn02v6xcs

Abstract

A firm maybe experiences a flourishing or declining period, and it also probably survives or even dies in future. Most firms have experienced decline in their development process. The impact of demand contraction and COVID-19 has caused more firms to face decline. Under the dilemma of resources reduction and recovery, the declining firm pays more and more attention to the efficient utilization of limited resources. It attempts to achieve turnaround through CEO substitution, adjustment of ownership concentration, innovation-oriented and other strategies. Based on Upper echelons theory and resource-based view, using the data of A-share listed manufacturing firms in Shanghai and Shenzhen from 2015 to 2019, the impacts of TMT tenure heterogeneity and ownership concentration on the innovation efficiency were investigated. The results show that the TMT tenure heterogeneity promotes the innovation efficiency of declining firms, while the relatively centralized ownership structure negatively moderates this effect. The robustness analysis of variable replacement and the solving of endogenous problem also further support this conclusion. This study not only enriches the application areas of Upper echelons theory, but also provides practical guideline for declining firms to replace top management members and to dilute equity in order to achieve recovery.

README

README: Variables

Dependent variable

Technological innovation efficiency (Tfpch): DEA-Malmquist Index by firm, 2016-2019

Compared with DEA model, Malmquist Index model can measure technological innovation efficiency in multiple periods. Our research uses Malmquist Index to express technological innovation efficiency. R&D input is the capital stock of R&D expenditure calculated by the perpetual inventory method and the number of R&D personnel of the firm in the current year. Considering the long patent granting cycle, the innovation output is selected from the firm's current year's business revenue and the number of patents granted in the lagging period.

R&D capital stock: K_it=K_(it-1) (1-δ_it )+Ι_it (1)

Base period capital stock: K_0=I_0/(g+δ)

K_it is the R&D capital stock of firm i in year t. Ι_it is the actual R&D capital investment of firm i in year t, deflated using 2015 as the base period. δ is the depreciation rate and g is the average growth rate of R&D investment. Based on the existing research, δ is 15% (Hu et al., 2005). The price conversion index for R&D inputs is: 0.45*investment in fixed assets price index + 0.55*consumer price index. Due to the reduction of R&D expenses by some declining firms, g is taken as a negative value. Therefore, when calculating the base period capital stock, firms with a true g value less than 0 are calculated as 0.

Independent variable

TMT tenure heterogeneity (Ten): Coefficient of variation

The main measures of TMT tenure heterogeneity are the Herfindal-Hirschman (Niebuhr, 2010) coefficient and the coefficient of variation (Anderson et al., 2011). Considering that TMT tenure heterogeneity is a continuous variable, we choose the coefficient of variation to denote TMT tenure heterogeneity, with larger values indicating greater TMT tenure heterogeneity. The TMT members mainly include the chairman, (vice) president, (vice) general manager, assistant to the general manager, secretary of the board of directors, directors, supervisors, functional department directors and other managers (Khosravi et al., 2019; Lee et al., 2021).

Moderator variable

Ownership concentration (Top1): Shareholding ratio of the largest shareholder

The proportion of shares held by the first largest shareholder is used to represent the ownership concentration within the firm (Zulfiqar and Hussain, 2020).

Control variables

Enterprise size (Esize): Ln (total assets at the end of the year)

TMT size (Tsize): Ln (number of TMT+1): 

Board size (Dsize): Ln (number of board members+1)

Solvency (*Asse*t): Asset-liability ratio

Growth ability (Tobinq): Tobin q

Operational capability (Growth): Total asset turnover ratio

Profitability (Roa): ROE

Executive shareholding ratio (ES): Ratio of shares held by executives to total shares

Subsidy intensity (Sub): Ratio of subsidy amount to operating revenue

Capital intensity (CI): Ln (net fixed assets per capita)

Proportion of state-owned shares (State): Ratio of state-owned shares to total shares

Equity balances degree (BS): Sum of shareholdings of the second to tenth shareholders

Age of firm (Age): Ln (time lag between inspection period and listing period)

Nature of ownership (Nature): Dummy variable, state-owned firm=1; private firm=0

Industry (Industry): Dummy variable, 1-7 dummy variables based on 2012 SEC industry classification

Year (Year)

Methods

The data mainly comes from the annual data of Shanghai and Shenzhen A-share listed manufacturing firms from 2015 to 2019. Referring to Teixeira et al. (2019), firms that meet the following criteria are defined as declining firms: (1) ROA decreases for at least two consecutive years; (2) ROA is negative for at least three consecutive years. Firms with missing key indicators such as R&D expenditures and number of patents granted are excluded. The patent data are obtained from the CNRDS database, and the financial data are obtained from the CSMAR and WIND databases. Meanwhile, to ensure the comparability of samples, 534 firm observations are finally selected after PSM nearest-neighbour matching on a 1:1 no-put-back basis, of which 267 are declining firms and 267 are normal firms. The DEA-Malmquist index is calculated using Deap2.1. Finally, 2136 sample observations are obtained from 2016 to 2019.