4 research outputs found
AStudy on the Mechanism of Open Innovation on Enterprise InnovationAbility
The implement of enterprises open innovation has great significance on improving its innovation ability. The main purpose of the enterprises implement of open innovation is to obtain the ten key resources, and the measurement of enterprise innovation ability mainly focused on the enterprise inject, production, management, marketing, output five aspects. The thesis constructed the relationship model between open innovation and enterprise innovation ability, and used structural equation model to verify the rationality of the conceptual model of relationship, the result of which shows that both the horizontal and vertical cooperation between enterprises, the government-industry-university-research cooperation as well as the public innovation platform construction is conducive to the improvement of enterprise innovation ability
The Mechanism and Empirical Test on the Effect of Technological Innovation on International Service Outsourcing in China
Technological innovation can promote the growth of international service outsourcing in China by advancing enterprise ability to undertake international service outsourcing, human resource quality, upgrading of international service outsourcing industry and base building of international service outsourcing. Based on the relative data from 10 areas where the international service outsourcing is developed best in China, this article builds the regression model to study the effect of technological innovation on international service outsourcing. The result indicates that technological innovation can promote obviously the development of international service outsourcing. Some suggestion should be taken to accelerate the technology innovation ability of china, such as adding the input to technological innovation, encouraging talent engaged in the international service outsourcing industry, optimizing environment of technological innovation
A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications
Enterprise financial risk analysis aims at predicting the enterprises' future
financial risk.Due to the wide application, enterprise financial risk analysis
has always been a core research issue in finance. Although there are already
some valuable and impressive surveys on risk management, these surveys
introduce approaches in a relatively isolated way and lack the recent advances
in enterprise financial risk analysis. Due to the rapid expansion of the
enterprise financial risk analysis, especially from the computer science and
big data perspective, it is both necessary and challenging to comprehensively
review the relevant studies. This survey attempts to connect and systematize
the existing enterprise financial risk researches, as well as to summarize and
interpret the mechanisms and the strategies of enterprise financial risk
analysis in a comprehensive way, which may help readers have a better
understanding of the current research status and ideas. This paper provides a
systematic literature review of over 300 articles published on enterprise risk
analysis modelling over a 50-year period, 1968 to 2022. We first introduce the
formal definition of enterprise risk as well as the related concepts. Then, we
categorized the representative works in terms of risk type and summarized the
three aspects of risk analysis. Finally, we compared the analysis methods used
to model the enterprise financial risk. Our goal is to clarify current
cutting-edge research and its possible future directions to model enterprise
risk, aiming to fully understand the mechanisms of enterprise risk
communication and influence and its application on corporate governance,
financial institution and government regulation
Developing retail performance measurement and financial distress prediction systems by using credit scoring techniques
The current research develops a theoretical framework based on the ResourceAdvantage Theory of Competition (Hunt, 2000) for the selection of appropriate
variables. Using a review of the literature as well as to interviews and a survey, 170
potential retail performance variables were identified as possible for inclusion in the
model. To produce a relative simple model with the aim of avoiding over-fitting, a
limited number of key variables or principal components were selected to predict
default. Five credit-scoring techniques: Naive Bayes, Logistic Regression, Recursive
Partitioning, Artificial Neural Network, and Sequential Minimal Optimization (SMO)
were employed on a sample of 195 healthy and 51 distressed businesses from the
USA market over five time periods: 1994-1998, 1995-1999, 1996-2000, 1997-2001
and 1998-2002.Analyses provide sufficient evidence that the five credit scoring methodologies
have sound classification ability in the year before financial distress. Moreover, they
still remained sound even five years prior to financial distress. However, it is difficult
to conclude which modelling technique has the highest classification ability
uniformly, since model performance varied in terms of different time scales. The
analysis also showed that external environment influences do impact on default
assessment for all five credit-scoring techniques, but these influences are weak.
These findings indicate that the developed models are theoretically sound. There is
however a need to compare their performance to other approaches.To explore the issue of the model's performance two approaches are taken. First,
rankings from the study were compared with those from a standard rating system—in
this case the well-established Moody's Credit Rating. It is assumed that the higher
the degree of similarity between the two sets of rankings, the greater the credibility
of the prediction model. The results indicated that the logistic regression model and
the SMO model were most comparable with Moody's. Secondly, the model's
performance was assessed by applying it to different geographical areas. The original
USA model was therefore applied to a new US data set as well as the European and
Japanese markets. Results indicated that all market models displayed similar
discriminating ability one year prior to financial distress. However, the USA model
performed relatively better than European and Japanese models five years before
financial distress. This implied that a financial distress model has potentially better
prediction ability when based on a single market.Following this result it was decided to explore the performance of a generic global
model, since model construction is time-consuming and costly. A composite model
was constructed by combining data from USA, European and Japanese markets. This
composite model had sound prediction performance, even up to five years before
financial distress, as the accuracy rate was above 85.15% and AUROC value was
above 0.7202. Comparing with the original USA model, the composite model has
similar prediction performance in terms of the accuracy rate. However, the composite
model presented a worse prediction utility based on the AUROC value. A future
research direction might be to include more world retailing markets in order to
ensure the model's prediction utility and practical applicability