Influence Factors Of Innovation Research Articles

Exploration of Optimisation Mechanism of Innovation and Entrepreneurship and Student Management for College Students Driven by Big Data

Abstract The current employment situation is getting tighter and tighter, and innovation and entrepreneurship of college students have gradually become the focus of attention of the government and colleges and universities. This paper preprocesses the student data, constructs the data matrix using the gray prediction algorithm, uses the least squares method, establishes the differential equation solution, and obtains employment prediction data. For the employment prediction can be known to the innovation and entrepreneurship situation of college students, this paper further introduces the Logistic regression analysis, establishes a multivariate regression analysis algorithm based on the influencing factors of innovation and entrepreneurship, and provides theoretical support for the study of the influencing factors and the optimization mechanism of the management of innovation and entrepreneurship student management. This paper takes University H in Zhejiang Province of China as the research site to explore the current situation of innovation and entrepreneurship of its college students in depth. The students who choose self-help entrepreneurship in 2022-2023 only accounted for 2.42% and 2.04%, and the predicted percentage in 2024 is 1.93%, so the current situation of innovation and entrepreneurship is dismal. In the analysis of college students’ innovation and entrepreneurship influencing factors, the factors of specialty, personal work experience, and buddy entrepreneurship experience all showed significant differences (P<0.05). In the regression analysis of entrepreneurship education and entrepreneurship policy on entrepreneurial intention, the regression coefficients of entrepreneurship education and entrepreneurship policy are 0.276 and 0.136, respectively, which both reach the level of significance, and all of them are in a significant positive proportional relationship.

Research on the influence factors of innovation and entrepreneurship education based on neural network model

Abstract This paper selects the evaluation indexes that fit with the teaching mode and evaluation objectives of university innovation and entrepreneurship education in the new media environment. The limitations of the existing evaluation indexes are analyzed and combined with the characteristics of students’ physical and mental development and cognitive level; the indexes are screened and integrated, and a new evaluation system is constructed. Based on the improved particle swarm algorithm, the BP neural network is optimized to establish the evaluation model of students’ innovation ability and improve the defects of the particle swarm algorithm, covering the selection of the network topology, the number of neurons in each layer, the setting of the parameters of the improved particle swarm algorithm, and the dynamic adjustment of the weights and thresholds, so as to build the evaluation model. Analyzing the influencing factors of college innovation and entrepreneurship education in terms of college factors and individual factors, 45.7% of students want college innovation and entrepreneurship education courses to focus on entrepreneurial opportunities or environment analysis in the teaching process, 41.4% of students feel average in terms of teaching resources, and more than 50% of college students are conservative in the direction of employment or entrepreneurship in terms of personal factors. Comprehensive analysis shows that the influencing factors, such as inflexible teaching methods, unsound curriculum systems, imperfect practice platforms, and college students’ own ideological awareness, are very significant and worth noting.

Informal institution and corporate innovation: From the perspective of social trust

Purpose: This study examines how social trust, as an informal institution, creates a cooperative and honest atmosphere to improve managers’ willingness for firm innovation. This study also addresses that the informal system may substitute for the formal institutions in promoting corporate innovation. Design/methodology/approach: This study opts a sample of Chinese listed firms over the period of 2007–2014. We obtain the detailed patent application information from the official website of China Patent and Intellectual Property Office. The social trust information is based on the results of Chinese Enterprise Survey System. Findings: The results indicate that firms located in regions of high social trust tend to have more innovation output, and is robust to a battery of sensitivity tests. The authors further document that this effect is more pronounced for firms with poor short-term financial performance, which are located in regions with weak legal environments and firms having lower information transparency. In addition, the authors also demonstrate that social trust can help firms to get adequate funds to reduce financial pressure and encourage firms to pay more attention to long-term benefits, alleviating investors as well as management myopia. Research limitations/implications: Limited by the data sources, when measuring innovation activity, the authors only use patent numbers represent for innovation output. Then, this study measures the patent quality through classifying patent categories (invention patent or others), since in China, the citation data are imperfect and inadequate. Practical implications: The results suggest that social trust means a good culture of honesty and cooperation can promote firm’s innovation engagement. Especially, in emerging markets, where formal mechanisms are relatively less effective, informal institutions can serve as an alternative system for alleviating information asymmetry. Originality/value: This study contributes to the literature in two ways. First, this study finds the positive effect of social trust on corporate innovation, which provides a new perspective for deepening the understanding of the influence factors of innovation. Second, this study further clarifies the mechanism of social trust promoting firm innovation, indicating that social trust can effectively alleviate outsiders’ concern about moral hazard risk. Then the stakeholders are more willing to provide financial support and pay more attention to the firm’s long-term performance.

Analysis of scientific and technological innovation influence factors affect enterprise performance

With the development of the society, science and technology innovation becomes the development foundation of the equipment manufacturing industry enterprises. According to the questionnaires of 29 equipment manufacturing industries in Shenyang, the paper summarises the factors that affect the enterprise performance based on grounded theory. In order to study the relationships between the influencing factors and enterprise performance, multiple linear regression analysis is conducted by Statistical Product and Service Solutions (SPSS) software.

Analysis of scientific and technological innovation influence factors affect enterprise performance

With the development of the society, science and technology innovation becomes the development foundation of the equipment manufacturing industry enterprises. According to the questionnaires of 29 equipment manufacturing industries in Shenyang, the paper summarises the factors that affect the enterprise performance based on grounded theory. In order to study the relationships between the influencing factors and enterprise performance, multiple linear regression analysis is conducted by Statistical Product and Service Solutions (SPSS) software.

Influencing Factors of Knowledge Workers' Innovation Behavior Based on Self-management

Knowledge workers' innovative behavior is an important driving force for enterprise development. The paper explores the influencing factors of knowledge worker's innovation behavior through the self-control ability and achievement motivation of knowledge workers. In the paper we introduce achievement motivation in the relationship between self-control ability and innovative behavior, built a relationship model for knowledge workers' influencing factors of innovation, and the model is validated by regression analysis of questionnaire data. The results show that the self-management ability of knowledge workers has a significant positive effect on innovation behavior, and the achievement motivation plays an intermediate role between them. Enterprises can influence knowledge workers’ innovative behavior by interfering with self-management and achievement motivation of them.

The impact of executives’ perceptions of environmental threats and organizational slack on innovation strategies

Purpose The paper aims to study the relationship between executives’ perceptions of environmental threats and innovation strategies and investigate the moderating effect of contextual factor (i.e. organizational slack) on such relations. It proposes a dualistic relationship between executives’ perceptions of environmental threats and innovation strategies, in which different perceptions of environmental threats will lead to corresponding innovation strategies, and dyadic organizational slack can promote such processes. Design/methodology/approach The paper is based on a survey with 163 valid questionnaires, which were all completed by executives. Hierarchical ordinary least-squares regression analysis is used to test the hypotheses proposed in this paper. Findings The paper provides empirical insights about that executives tend to choose exploratory innovation when they perceive environmental changes as likely loss threats, yet adopt exploitative innovation when perceiving control-reducing threats. Furthermore, unabsorbed slack (e.g. financial redundancy) positively moderates both relationships, while absorbed slack (e.g. operational redundancy) merely positively influences the relationship between the perception of control-reducing threats and exploitative innovation. Originality/value The paper bridges the gap between organizational innovation and cognitive theory by proposing a dualistic relationship between executives’ perceptions of environmental threats and innovation strategies. The paper further enriches innovation studies by jointly considering both subjective and objective influence factors of innovation and argues that organizational slack can moderate such dualistic relationship.

Personal Health Record Use in the United States: Forecasting Future Adoption Levels.

BackgroundPersonal health records (PHRs) offer a tremendous opportunity to generate consumer support in pursing the triple aim of reducing costs, increasing access, and improving care quality. Moreover, surveys in the United States indicate that consumers want Web-based access to their medical records. However, concerns that consumers’ low health information literacy levels and physicians’ resistance to sharing notes will limit PHRs’ utility to a relatively small portion of the population have reduced both the product innovation and policy imperatives.ObjectiveThe purpose of our study was 3-fold: first, to report on US consumers’ current level of PHR activity; second, to describe the roles of imitation and innovation influence factors in determining PHR adoption rates; and third, to forecast future PHR diffusion uptake among US consumers under 3 scenarios.MethodsWe used secondary data from the Health Information National Trends Survey (HINTS) of US citizens for the survey years 2008, 2011, and 2013. Applying technology diffusion theory and Bass modeling, we evaluated 3 future PHR adoption scenarios by varying the introduction dates.ResultsAll models displayed the characteristic diffusion S-curve indicating that the PHR technology is likely to achieve significant market penetration ahead of meaningful use goals. The best-performing model indicates that PHR adoption will exceed 75% by 2020. Therefore, the meaningful use program targets for PHR adoption are below the rates likely to occur without an intervention.ConclusionsThe promise of improved care quality and cost savings through better consumer engagement prompted the US Institute of Medicine to call for universal PHR adoption in 1999. The PHR products available as of 2014 are likely to meet and exceed meaningful use stage 3 targets before 2020 without any incentive. Therefore, more ambitious uptake and functionality availability should be incorporated into future goals.

Application of Management Bionics to Technological Innovation in Agriculture

A study was done on agricultural enterprises technology innovation based on the management bionics method. From the perspective of bionics, this study investigates influence factors, mechanisms and team building of agricultural enterprises technology innovation based on the scientific and normative analysis of management bionics. Through standardized management bionic analogy analysis, the following analogies were drawn: agricultural enterprises technology innovation influence factors and the onion biological form, agricultural enterprises technology innovation mechanism and the biological enzyme catalytic reaction mechanism, agricultural enterprises technology innovation team building with the bee biological group effect. Therefore, three bionic element models of agricultural enterprises technology innovation were built, namely agricultural enterprises technology innovation onion model, technology innovation enzyme catalytic reaction bionic model and the bees’ team effect bionic model. This study ends with the corresponding bionic strategies and suggestions about agricultural enterprises technology innovation, which may provide references for the development of agricultural enterprises technology innovation in China. At the same time, this study also enriches the research range and theoretical system of management bionics.

System Modeling and Simulation for High-End Equipment Manufacturing Industry Technical Innovation Influence Factors

Analysis the influence factors of high-end equipment manufacturing industry technical innovation, divide them into three dimensions which are the internal control factors, internal and external coordination factors and external development factors, establish a 18-indicator-index system, and use system dynamics method to modeling and dynamic simulation, in order to provide theoretical guidance for improving the high-end equipment manufacturing industry technical innovation ability, and promote the development of high-end equipment manufacturing industry.