scholarly journals Relationships Among CEO Narcissism, Debt Financing and Firm Innovation Performance: Emotion Recognition Using Advanced Artificial Intelligence

2021 ◽  
Vol 12 ◽  
Author(s):  
Lan Zhang ◽  
Biming Liang ◽  
Datian Bi ◽  
Yuan Zhou ◽  
Xiaohan Yu
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Positive Impact ◽  
Psychological Research ◽  
Debt Financing ◽  
Innovation Performance ◽  
Firm Innovation ◽  
Main Component ◽  
Ceo Narcissism ◽  
The Impact

Psychological research shows that as the main component of enterprise decision-making, CEOs are not completely rational, cognitive and psychological biases often influence their decision-making process. CEO narcissism has gradually attracted academic attention. Based on upper echelon theory and subconscious theory, this paper uses advanced artificial intelligence technology to quantify CEO narcissism as a kind of emotional intelligence. Taking A-share listed companies in China from 2010 to 2019 as research objects, this paper empirically tests the impact of CEO narcissism on debt financing and innovation performance. The results show that CEO narcissism has a significant positive impact on firm innovation performance. Debt financing plays a mediating role in the relationship between CEO narcissism and firm innovation performance. CEO narcissism can have a positive impact on firm innovation performance through debt financing. Compared with non-SOEs, SOEs' CEO narcissism has a more significant positive effect on debt financing and enterprise innovation performance. The research in this paper enriches psychology and organizational management and provides a reference for an enterprise's management decisions and for investors' investment decisions.

scholarly journals The Impact of R&D Intensity on the Innovation Performance of Artificial Intelligence Enterprises-Based on the Moderating Effect of Patent Portfolio

2020 ◽  
Vol 13 (1) ◽  
pp. 328
Author(s):  
Yuanyuan Dong ◽  
Zepeng Wei ◽  
Tiansen Liu ◽  
Xinpeng Xing
Keyword(s):  
Artificial Intelligence ◽  
Positive Impact ◽  
Moderating Effect ◽  
Innovation Performance ◽  
Three Dimensions ◽  
Practical Significance ◽  
High Level ◽  
The Impact ◽  
The Relationship ◽  
Patent Portfolio

The patent portfolio affects the research and development (R&D) decisions of artificial intelligence enterprises, and provides rights protection for the enterprise’s product market, which is of great practical significance for the realization of innovation performance. The aim of this paper is to discover how the patent portfolio of artificial intelligence enterprises affects the relationship between R&D intensity and innovation performance. Based on the panel data of 164 listed enterprises in the A-share artificial intelligence concept sector of China, using the panel fixed effect regression method, the impact of R&D intensity on innovation performance was analyzed, and the moderating effect of the three dimensions of the patent portfolio on the two was examined. Studies have shown that the impact of R&D intensity on innovation performance is in an inverted U-shaped relationship. In addition, the diversity characteristics of the patent portfolio have a moderating effect on the relationship between R&D intensity and innovation performance, and when the enterprise is at a high level of diversity, the two have a U-shaped flip relationship. The size of the patent portfolio has a positive impact on innovation performance. The research results have theoretical and practical significance for the implementation of effective R&D management in artificial intelligence enterprise organizations.

scholarly journals Civil participation in decision-making on waste management at the local authorities level

2020 ◽  
Vol 18 (1) ◽  
pp. 438-448
Author(s):  
Andrius Stasiukynas ◽  
Vainius Smalskys ◽  
Arvydas Survila ◽  
Volodimyr Yermachenko ◽  
Nataliia Gavkalova
Keyword(s):  
Decision Making ◽  
Theoretical Model ◽  
Waste Management ◽  
Municipal Waste ◽  
Theoretical Assumptions ◽  
Municipal Waste Management ◽  
Participation In Decision Making ◽  
Main Component ◽  
The Impact ◽  
Civil Participation

Civil participation is the main component of effective governance. The topicality of this paper lies in civil participation in decision-making defined by the principles of “new public governance” of the 21st century. This study aims to analyze the impact of civil participation in decision-making on waste management at the municipal level. In this work, the following aspects were considered: theoretical assumptions of civil participation; civil participation in the activities of institutions responsible for environmental protection; issues regarding the development of opportunities of citizens’ involvement in municipal waste management. The article is devoted to the theoretical assumptions about civil participation, theoretical model of analysis, and activity of institutions. To develop a theoretical model of analysis, a classification of civil participation was carried out. Among the methods used, one can mention the questionnaire. The analysis was focused on a legal basis for the activity of institutions; the actual activity of institutions and survey of representatives of the national environmental authorities in Lithuania. The results showed that currently there is no mechanism in Lithuania to ensure civil participation in municipal waste management. Civil participation is still formal and limited. Overall, citizens are informed about the decisions made, but they do not participate actively in decision-making. The analysis of civil participation capacity in waste management leads to the conclusion that Lithuania has no single mechanism to ensure civil participation in waste management. To enhance civil participation in decision-making on waste management, it is suggested to set up a council where the representatives of civil population are delegated.

scholarly journals Impact of Behavioral Biases on Investment Decision Making with Moderating Role of Financial Literacy

2018 ◽  
Vol 6 (2) ◽  
pp. 34-41
Author(s):  
Rizwan Khalid ◽  
◽  
Muhammad Javed ◽  
Khurram Shahzad ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Financial Literacy ◽  
Positive Impact ◽  
Investment Decision ◽  
Structure Questionnaire ◽  
Investment Decision Making ◽  
Overconfidence Bias ◽  
The Impact ◽  
Moderating Role

The objective of this study is to examine the Impact of Overconfidence bias and Herding bias on Investment Decision Making with Moderating Role of Financial Literacy. The population was Investor, Employee and Graduate Student. A sample of 200 was selected using convenience technique. Data were collected through structure questionnaire adopted from different papers. Correlation and Regression analysis were performed to examine the result. The Results show that overconfidence bias and herding bias have a positive impact on investment decision making and Financial Literacy has positive impact on investment decision making. Based on the results and discussions of the study findings as well as the limitations, theoretical and practical implications of the study have been provided.

scholarly journals Stochastic Fuzzy Algorithms for Impairment of Assets Management

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Marcel Ioan Bolos ◽  
Victoria Bogdan ◽  
Ioana Alexandra Bradea ◽  
Claudia Diana Sabau Popa ◽  
Dorina Nicoleta Popa
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Numbers ◽  
Cash Flows ◽  
The Other ◽  
Dual Purpose ◽  
Fuzzy Algorithms ◽  
Tangible Assets ◽  
The Impact

The present paper aims to analyze the impairment of tangible assets with the help of artificial intelligence. Stochastic fuzzy numbers have been introduced with a dual purpose: on one hand to estimate the cash flows generated by tangible assets exploitation and, on the other hand, to ensure the value ranges stratifications that define these cash flows. Estimation of cash flows using stochastic fuzzy numbers was based on cash flows generated by tangible assets in previous periods of operation. Also, based on the Lagrange multipliers, were introduced: the objective function of minimizing the standard deviations from the recorded value of the cash flows generated by the tangible assets, as well as the constraints caused by the impairment of tangible assets identification according to which the cash flows values must be equal to the annual value of the invested capital. Within the determination of the impairment value and stratification of the value ranges determined by the cash flows using stochastic fuzzy numbers, the impairment of assets risk was identified. Information provided by impairment of assets but also the impairment risks, is the basis of the decision-making measures taken to mitigate the impact of accumulated impairment losses on company’s financial performance.

scholarly journals An Analysis of Behavioral Biases in Investment Decision-Making

2019 ◽  
Vol 10 (4) ◽  
pp. 55 ◽  
Author(s):  
Geetika Madaan ◽  
Sanjeet Singh
Keyword(s):  
Decision Making ◽  
Positive Impact ◽  
Stock Exchange ◽  
Investment Decision ◽  
Investment Decisions ◽  
Individual Investors ◽  
Behavioral Biases ◽  
Investment Decision Making ◽  
Survey Responses ◽  
The Impact

Individual investor’s behavior is extensively influenced by various biases that highlighted in the growing discipline of behavior finance. Therefore, this study is also one of another effort to assess the impact of behavioral biases in investment decision-making in National Stock Exchange. A questionnaire is designed and through survey responses collected from 243 investors. The present research has applied inferential statistics and descriptive statistics. In the existing study, four behavioral biases have been reviewed namely, overconfidence, anchoring, disposition effect and herding behavior. The results show that overconfidence and herding bias have significant positive impact on investment decision. Overall results conclude that individual investors have limited knowledge and more prone towards making psychological errors. The findings of the study also indicate the existence of these four behavioral biases on individual investment decisions. This study will be helpful to financial intermediaries to advice their clients. Further, study can be elaborated to study other behavioral biases on investment decisions.

scholarly journals Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

2019 ◽  
Vol 33 (2) ◽  
pp. 31-50 ◽  
Author(s):  
Ajay Agrawal ◽  
Joshua S. Gans ◽  
Avi Goldfarb
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Labor Market ◽  
Market Impact ◽  
Recent Advances ◽  
The Impact ◽  
Decision Tasks

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

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