TMT的乐观与企业绩效的关系:基于社会认知的视角

XIONG Jiakuo,ZHAO Weidong,LIANG Ying,WANG Xiaoming

Journal of Industrial Engineering and Engineering Management（2022）

Cited 0|Views7

Abstract

关于乐观的研究近几年出现在国内投资和企业管理文献中.先前的研究并未回答TMT的乐观与企业绩效的关系,在环境波动性较高情景下是否雇佣乐观的TMT以及TMT的管理经验是否越多越好等TMT建设问题.本文以我国2014—2018年沪深上市公司为样本,基于社会认知理论提出OPEE模型来解释TMT的乐观与企业绩效之间的关系,以及二者关系应如何受环境动态性和管理经验的影响.实证结果表明:(1)TMT的乐观负向影响企业绩效;(2)环境动态性会增强TMT的乐观与企业绩效之间的负向关系;(3)管理经验对TMT的乐观与企业绩效之间的关系具有非线性调节效应,即当管理经验较低时,管理经验的提高会抑制TMT的乐观对企业绩效的负向作用;当管理经验突破调节效应临界点后,管理经验的提高会反过来促进TMT的乐观对企业绩效的负向作用.本文拓展了当前的管理者乐观文献.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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