Статті в журналах: "Predicting Innovation"

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Schwabsky, Nitza, Ufuk Erdogan, and Megan Tschannen-Moran. "Predicting school innovation." Journal of Educational Administration 58, no. 2 (December 23, 2019): 246–62. http://dx.doi.org/10.1108/jea-02-2019-0029.

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Purpose The purpose of this paper is to examine the role of collective teacher efficacy, academic press and faculty trust, all of which are components of academic optimism (AO), in predicting school innovation. In addition, the authors explored the extent to which faculty trust mediates the association between collective teacher efficacy and academic press with school innovation. Design/methodology/approach In all, 1,009 teachers from 79 schools in Northern Israel completed anonymous questionnaires about AO and innovation. Aggregation, descriptive statistics, bivariate correlation analyses and mediation analysis were performed to analyze the data. Findings Results showed that the components of AO, i.e., collective teacher efficacy, academic press and trust, were positively correlated to school innovation, and that trust mediated the relationship between collective teacher efficacy and school innovation. The study results confirmed that AO holds a significant predictive value in school innovation and highlights the importance of trust in supporting innovation. Practical implications As school leaders are challenged to foster innovative new practices in their schools, the findings suggest that they will need to know how to cultivate collective teacher efficacy, academic press and faculty trust. Originality/value This is the first study to examine the role of the components of AO in predicting innovation. By using a robust sample, the authors were able to examine the proposed school-level model with respect to the factors that affect school innovation. Originality also lies in the organizational approach to educational innovation in relation to faculty’s beliefs and behaviors.

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Blagojevic, Marija, Zivadin Micic, and Momcilo Vujicic. "Cluster analysis of knowledge sources in standardized electrical engineering subfields." Serbian Journal of Electrical Engineering 13, no. 3 (2016): 405–22. http://dx.doi.org/10.2298/sjee1603405b.

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The paper presents a cluster analysis of innovation of knowledge sources based on the standards in the field of Electrical Engineering. Both local (SRPS) and global (ISO) knowledge sources have been analysed with the aim of innovating a Knowledge Base (KB). The results presented indicate a means/possibility of grouping the subfields within a cluster. They also point to a trend or intensity of knowledge source innovation for the purpose of innovating the KB that accompanies innovations. The study provides the possibility of predicting necessary financial resources in the forthcoming period by means of original mathematical relations. Furthermore, the cluster analysis facilitates the comparison of the innovation intensity in this and other (sub)fields. Future work relates to the monitoring of the knowledge source innovation by means of KB engineering and improvement of the methodology of prediction using neural networks.

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Hang Do, Thuy, Tim Mazzarol, Thierry Volery, and Sophie Reboud. "Predicting anticipated rent from innovation commercialisation in SMEs." European Journal of Innovation Management 17, no. 2 (May 6, 2014): 183–208. http://dx.doi.org/10.1108/ejim-12-2012-0113.

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Purpose – The purpose of this paper is to examine the relationship between the expectations that small business entrepreneurs hold in relation to the future returns from the commercialisation of innovations, and key organisational elements including inputs, knowledge, culture, strategy, portfolio, project management and commercialisation. More specifically, this research aims to deepen the knowledge of how small- and medium-sized enterprises (SMEs) manage their innovation and identify critical factors determining the potential innovation outcomes. Design/methodology/approach – This study draws on a large sample of innovative SMEs from multiple Organisation for Economic Co-Operation and Development countries. Data were collected using a questionnaire administered face-to-face with owners-managers or executives of SMEs who made critical decisions for the innovation management of the firm. First, a factor analysis is conducted to identify the most appropriate measures for each variable. Second, the authors test for multicollinearity among independent variables. The final step integrates results from the general linear model analysis that measures the relationship between organisational factors and the anticipated returns. Findings – Findings suggest that positive expectations over future investment in innovation – as measured by the anticipated rent – are influenced by organisational factors, including innovation strategy, portfolio management, project management, and organisational culture and commercialisation process. Conversely, the resource endowment is not perceived as a barrier to innovation and to the development of a competitive advantage. In addition, industrial knowledge management has an indirect effect on the anticipated returns. Originality/value – Despite extensive research in innovation management, the role of organisational factors on anticipated returns in SMEs has not been investigated to date. The study provides researchers with new insights into the resource-based view and the theory of entrepreneurial rent from the perspective of innovation management. The findings offer guidance to managers as to potential success factors in enhancing the rent, but also reflect entrepreneurial optimism in the management of innovation.

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Rojas-Córdova, Carolina, Boris Heredia-Rojas, and Patricio Ramírez-Correa. "Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach." Symmetry 12, no. 9 (August 19, 2020): 1381. http://dx.doi.org/10.3390/sym12091381.

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In the Industry 4.0 scenario, innovation emerges as a clear driver for the economic development of societies. This effect is particularly true for the least developed countries. Nevertheless, there is a lack of studies that analyze this phenomenon in these nations. In this context, this study aims to examine the impact of perceived barriers to innovation to predict companies′ innovative intentions in an emerging economy. This study is a preliminary effort to use data mining and symmetry-based learning concepts, especially classification, to assist the identification of strategies to incentivize intention to innovate in companies. Using the decision tree classification technique, we analyzed a sample of Chilean companies (N = 5876). The sample was divided into large enterprises (LEs) and small and medium enterprises (SMEs). In the group of large companies, the barriers that most impact the intention to innovate are innovation cost, lack of demand innovations, and lack of qualified personnel. Alternatively, in the group of small-medium companies, the barriers that most impact the intention to innovate are lack of own funds, lack of demand innovations, and lack of information about technology. These results show how the perceptions of barriers are significant to predict the intentions of innovation in Chilean companies. Furthermore, the perceptions of these barriers are contingent on the organizational sizes. These findings contribute to understanding the effect of contingencies on innovative intention in an emerging economy.

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Park, Kyungbo, Jeonghwa Cha, and Jongyi Hong. "Developing a Framework for Evaluating and Predicting Management Innovation in Public Research Institutions." Sustainability 15, no. 9 (April 27, 2023): 7261. http://dx.doi.org/10.3390/su15097261.

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As the external environment changes rapidly, organizations need management innovation to adapt to and exploit change as an opportunity. To innovate, it is necessary to evaluate management innovation, because if an organization can measure the degree of management innovation, it can also achieve it. Moreover, if management innovation is predictable, profits can be maximized, and costs can be minimized by allocating efficient resources and establishing appropriate strategies. Therefore, this study attempts to predict the management innovation in public research institutions. Basic data mining and ensemble data mining techniques were used for the prediction. This analysis targeted public research institutes in South Korea. The results showed that the predictive power of public research institutions with high innovation was high. This study suggests that management innovation can be predicted in highly innovative public research institutions. Furthermore, this study’s framework can be applied to other industries.

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Ramiz Abdinov, Vidadi Akhundov, Ramiz Abdinov, Vidadi Akhundov. "METHODOLOGY FOR ASSESSING THE IMPACT OF INNOVATIONS ON THE PRODUCTION OF THE REGION'S FINAL PRODUCTS." PIRETC-Proceeding of The International Research Education & Training Centre 21, no. 04 (November 9, 2022): 33–38. http://dx.doi.org/10.36962/piretc21042022-33.

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The article developed an algorithm for calculating the coefficient of the impact of innovations on the growth rate of the final product. This mathematical apparatus is a tool for adequate adjustment of the economic system, taking into account the potential of its innovative development. Next, the problem of predicting the regional final product is solved using the Cobb-Douglas(innovation) model. In the calculations, the values of the parameters are determined by the Gradient method. The result obtained indicates the adequacy of the approach used. In addition, the assumptions and hypotheses put forward in the study create prerequisites for further expanding the amount of knowledge in the field of studying the innovative impact on the economy. The practical significance of the proposed models of innovative development of economic systems lies in the emergence of new opportunities for building the innovation policy of the state. Keywords: innovation activity, fuzzy model, Gradient method, innovation index

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Swart, Rachelle R., Maria JG Jacobs, Cheryl Roumen, Ruud MA Houben, Folkert Koetsveld, and Liesbeth J. Boersma. "Factors predicting timely implementation of radiotherapy innovations: the first model." British Journal of Radiology 94, no. 1117 (January 1, 2021): 20200613. http://dx.doi.org/10.1259/bjr.20200613.

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Objective: The improvement of radiotherapy depends largely on the implementation of innovations, of which effectivity varies widely. The aim of this study is to develop a prediction model for successful innovation implementation in radiotherapy to improve effective management of innovation projects. Methods: A literature review was performed to identify success factors for innovation implementation. Subsequently, in two large academic radiotherapy centres in the Netherlands, an inventory was made of all innovation projects executed between 2011 and 2017. Semi-structured interviews were performed to record the presence/absence of the success factors found in the review for each project. Successful implementation was defined as timely implementation, yes/no. Cross-tables, Χ2 tests, t-tests and Benjamin-Hochberg correction were used for analysing the data. A multivariate logistic regression technique was used to build a prediction model. Results: From the 163 identified innovation projects, only 54% were successfully implemented. We found 31 success factors in literature of which 14 were significantly related to successful implementation in the innovation projects in our study. The prediction model contained the following determinants: (1) sufficient and competent employees, (2) complexity, (3) understanding/awareness of the project goals and process by employees, (4) feasibility and desirability. The area Under the curve (AUC) of the prediction model was 0.86 (0.8–0.92, 95% CI). Conclusion: A prediction model was developed for successful implementation of innovation in radiotherapy. Advances in knowledge: This prediction model is the first of its kind and, after external validation, could be widely applicable to predict the timely implementation of radiotherapy innovations.

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Gashema, Bruce. "Predicting innovative work behaviors through transformational leadership." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 1 (February 11, 2021): 69–84. http://dx.doi.org/10.20525/ijrbs.v10i1.999.

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To stay competitive and viable in today's rapidly evolving and highly hostile market climate, companies need to concentrate more on innovation. In this regard, the specific quality of transformational leadership has been strongly linked to organizational innovativeness. However, the purpose of this study is to examine the relationship between transformational leadership and the innovation behavior of employees. The literature review developed in the current study is fundamentally centered on how transformational leaders value innovation by facilitating organizational learning to foster innovative behavior among employees. Corporate social responsibilities as an organizational level factor that can drive employee attitudes were also examined as a moderating effect in this relationship. The rationale of the relationship of the variables is supported by both transformational leadership theory (TFL) and social exchange theory (SET). Data were collected from corporate social responsibility engaged SMEs operating in Rwanda. Using Cranach’s alpha reliability and validity were tested while structural equation modeling (SEM) was used in analyzing data. The findings of this study intend to fill some gaps in the current literature such as the introduction of CSR in the relationship between top managerial level leadership, organizational learning, and innovation behavior of employees, also introducing SET to explain such important relationship

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Rani, Ruchi, Sumit Kumar, Rutuja Rajendra Kadam, and Sanjeev Kumar Pippal. "A machine learning model for predicting innovation effort of firms." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (August 1, 2023): 4633. http://dx.doi.org/10.11591/ijece.v13i4.pp4633-4639.

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Classification and regression tree (CART) data mining models have been used in several scientific fields for building efficient and accurate predictive models. Some of the application areas are prediction of disease, targeted marketing, and fraud detection. In this paper we use CART which widely used machine learning technique for predicting research and development (R&D) intensity or innovation effort of firms using several relevant variables like technical opportunity, knowledge spillover and absorptive capacity. We found that accuracy of CART models is superior to the often-used linear parametric models. The results of this study are considered necessary for both financial analysts and practitioners. In the case of financial analysts, it establishes the power of data-driven prototypes to understand the innovation thinking of employees, whereas in the case of policymakers or business entrepreneurs, who can take advantage of evidence-based tools in the decision-making process.

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Pan, Han, Wu Xin, and Yuping Li. "A review on the concept of consumer innovativeness." E3S Web of Conferences 251 (2021): 01080. http://dx.doi.org/10.1051/e3sconf/202125101080.

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Beginning in the 1970s, academia began to study consumer innovation and used it as an important indicator for predicting consumers’ new product adoption behavior. This article makes a more comprehensive summary and evaluation of the definition of consumer innovation from three aspects (innate innovativeness; special fields innovativeness; actualized innovativeness), summarizes the relationship between the three innovations, and builds a consumer innovation integration model.

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Godenhjelm, Sebastian, and Jan-Erik Johanson. "The effect of stakeholder inclusion on public sector project innovation." International Review of Administrative Sciences 84, no. 1 (March 14, 2016): 42–62. http://dx.doi.org/10.1177/0020852315620291.

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The delivery of public services in collaborative agency networks has given rise to an increasing use of projects in administering policy and service delivery. Projects are assumed to provide mechanisms by which flexibility can be achieved and innovative solutions produced. The aim of the article is to advance the understanding of collaboration between stakeholders and its effect on innovation. It analyses stakeholders’ influence on the creation of project innovations in 275 European Union-funded projects by using content analyses and logistic regression analyses. The results show that projects can act as hubs where valuable information is produced but that few projects produce innovations. Project stakeholder network, knowledge dissemination and project influence, as well as sources of advice, play a role in predicting project innovations. The article concludes that the overly optimistic view of collaboration as a remedy for a lack of innovation in the public sector can be questioned. Points for practitioners The results of the article help practitioners to compose public sector development projects that foster innovation. The results suggest that it pays to include representatives of research and education facilities among project staff as their inclusion predicts the possibilities of achieving innovations. The empirical findings provide insight into project innovation and indicate which practices to avoid. It is suggested that when managed correctly, stakeholder inclusion has an effect on public sector project innovation.

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Carbon, Claus-Christian. "Predicting Preferences for Innovative Design: The “Repeated Evaluation Technique” (RET)." GfK Marketing Intelligence Review 7, no. 2 (November 1, 2015): 34–39. http://dx.doi.org/10.1515/gfkmir-2015-0016.

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Abstract How do you realistically assess the success potential of innovative products? This task is quite challenging because the Average Joe generally has an aversion to innovation. Therefore it is not really possible to get valid innovation evaluations from typical consumers. Only when we feel secure and have time to become familiar with a new thing can innovation become exciting and attractive. The “Repeated Evaluation Technique” (RET) was developed especially for the purpose of systematic familiarization with products to be evaluated. Subjects in an RET, for example, typical consumers, are encouraged to think explicitly and intensively about a product and its competitors. By forcing the subjects to engage with the material, known as the “elaboration,” the procedure helps consumers understand the product better and distinguish differences. The ascertained judgments come closer and closer to real everyday assessments that one would usually only gain after weeks and months of dealing with products.

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Yaakobi, Erez, and Jacob Weisberg. "Individual, group and organizational efficacies in predicting performance." Personnel Review 47, no. 2 (March 5, 2018): 535–54. http://dx.doi.org/10.1108/pr-08-2016-0212.

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Purpose The purpose of this paper is to develop a framework for predicting three facets of employee performance (quality, innovation and efficiency) based on the evaluation of individual (self and occupational), group (collective) and organizational (means) efficacies. Design/methodology/approach A sample of 109 managers employed mainly in high-tech industries evaluated their employees’ quality, innovation and efficiency performance. The employees’ efficacies were also evaluated on three organizational levels. Findings Evaluation of employees’ self-efficacy accounted for most of the explained variance for all performance facets. Evaluation of group efficacy added incremental explained variance to the general performance as well as to the innovation performance and efficiency performance. Evaluation of means efficacy (provided to employees) added incremental explained variance to the general performance as well as to the innovation performance and the efficiency of performance. Male managers differed from female managers in their predictions of employees’ performance. Originality/value This is the first study to examine the concurrent effects of four types of efficacies, based on three organizational levels, in predicting performance. It also examines three facets of performance instead of only a general performance measure. It presents a model of the relative importance of these efficacies in predicting facets of performance.

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Honcharov, Yu V., M. V. Dykha, V. Voronina, A. Milka, and N. Klymenchukova. "Forecasting the innovation of Ukraine’s economic development in a global dimension." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 1 (February 28, 2023): 174–81. http://dx.doi.org/10.33271/nvngu/2023-1/174.

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Purpose. To analyze the state and trends of innovative development of the countries of the world, to define the place of Ukraine in the international arena and forecast its innovation. Methodology. The methodological basis was formed by general scientific theoretical methods: generalization, explanation, grouping; statistical method (for a comprehensive assessment of the state and trends of innovative activity of the economies of the countries of the world and Ukraine); methods of analysis and synthesis, economic and mathematical methods of forecasting, methods of abstraction and visualization (for a visual demonstration of the actual data and the results of the study). Findings. The level of innovativeness of the world’s economies and Ukraine’s position in the ranking of the Global Innovation Index (GII) are analyzed. The positive/stimulating influence of globalization processes on the spread of innovations, as well as discriminatory methods of innovative development (“dumping” of second-order technologies in developing countries) are noted. The GII forecast is determined using a regression model of the dependence of this indicator on the specific weight of innovation-active enterprises in the total number of industrial enterprises and innovation costs. The forecasting results showed a slight downward trend in the value of the GII of Ukraine. It is argued that in order to increase the level of innovativeness of the national economy, to strengthen Ukraine’s competitive position, and taking into account the fact that the economies of other countries do not stop in their development, it is necessary to significantly intensify innovative activity in Ukraine. Originality. A scientific-methodical approach to predicting the innovativeness of economic development is proposed depending on the specific weight of innovatively active enterprises in the total number of industrial enterprises and innovation costs. There is an opportunity to carry out predictive and planning calculations of the selected forecasting object, to investigate the behavior of the macroeconomic system in the context of the innovativeness of its development, to make adjustments to the strategic management system at the state level in order to stimulate innovative processes and influence the results of the innovative development of Ukraine in the global dimension. Practical value. The proposed scientific-methodical approach makes it possible to assess the trends of the level of innovativeness of development in the future and becomes an informative basis for developing a system of measures for the activation of innovative activity.

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Kiseleva, I. A., N. E. Simonovich, and E. D. Solomatina. "Social well-being of people and the strategy of innovative development of the enterprise." Proceedings of the Voronezh State University of Engineering Technologies 81, no. 1 (July 18, 2019): 402–11. http://dx.doi.org/10.20914/2310-1202-2019-1-402-411.

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The article describes the problems of introducing innovations in Russia from the point of view of the development of standardization processes in the field of risk management and the psychological development of the profession of a risk manager. The features of the development of innovative development of the enterprise, taking into account the state of social well-being of people, are considered. Innovation strategy, above all, implies strategic management, as an activity of forming and implementing the mission, the main goals of the company, which guarantee its development in an environment of constant competition and instability. Strategic management is an ongoing process of selecting and executing the organization’s activities, taking into account the social well-being of employees, based on predicting changes in the external environment, predicting problems based on expert decisions aimed at maintaining the company's created advantage.

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Нигматуллин, Ш. И. "Analysis of innovative development of Russian regions." Audit and Financial Analysis, no. 5 (November 30, 2020): 144–47. http://dx.doi.org/10.38097/afa.2020.69.72.020.

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Важным аспектом в современной политике управления инновационной деятельностью в регионах является система информационного обеспечения и мониторинга исполнения ключевых индикаторов эффективности, характеризующих инновационный потенциал и развитие инновационных процессов в регионах Российской Федерации, на основе которых строятся прогнозные оценки и разрабатываются новые механизмы инновационного развития. Научная статья посвящена анализу инновационного развития на мировом уровне и оценке положения (рейтинговая) России с точки зрения инновационной составляющей. Также представлены результаты рейтинговая эффективности инновационной деятельности в субъектах Российской Федерации и анализ качества инновационных политик регионов, входящих в Приволжский федеральный округ. Автором статьи предложены основные направления и даны рекомендации по повышению инновационного развития на региональном уровне и трансформации системы государственного регулирования инновационной политики в аспекте систематизации ключевых индикаторов эффективности инновационной деятельности и инновационной активности предприятий и компаний регионов и на их основе прогнозирования инновационного развития, а также организации системы мониторинга исполнения инновационной политики в регионах. An important aspect in the modern policy of innovation management in the regions is the system of information support and monitoring of the implementation of key performance indicators characterizing the innovation potential and development of innovation processes in the regions of the Russian Federation, on the basis of which predictive estimates are built and new mechanisms of innovative development are developed. The scientific article is devoted to the analysis of innovative development at the world level and the assessment of the position (rating) of Russia in terms of the innovation component. Also presented are the results of the rating efficiency of innovation in the constituent entities of the Russian Federation and the analysis of the quality of innovation policies of the regions included in the Volga Federal District. The author of the article proposes the main directions and gives recommendations for increasing innovative development at the regional level and transforming the system of state regulation of innovation policy in the aspect of systematizing key indicators of the effectiveness of innovation and innovative activity of enterprises and companies in the regions and, on their basis, predicting innovative development, as well as organizing a monitoring system implementation of innovation policy in the regions.

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Нигматуллин, Ш. И. "Analysis of innovative development of Russian regions." Audit and Financial Analysis, no. 5 (November 30, 2020): 144–47. http://dx.doi.org/10.38097/afa.2020.69.72.020.

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Важным аспектом в современной политике управления инновационной деятельностью в регионах является система информационного обеспечения и мониторинга исполнения ключевых индикаторов эффективности, характеризующих инновационный потенциал и развитие инновационных процессов в регионах Российской Федерации, на основе которых строятся прогнозные оценки и разрабатываются новые механизмы инновационного развития. Научная статья посвящена анализу инновационного развития на мировом уровне и оценке положения (рейтинговая) России с точки зрения инновационной составляющей. Также представлены результаты рейтинговая эффективности инновационной деятельности в субъектах Российской Федерации и анализ качества инновационных политик регионов, входящих в Приволжский федеральный округ. Автором статьи предложены основные направления и даны рекомендации по повышению инновационного развития на региональном уровне и трансформации системы государственного регулирования инновационной политики в аспекте систематизации ключевых индикаторов эффективности инновационной деятельности и инновационной активности предприятий и компаний регионов и на их основе прогнозирования инновационного развития, а также организации системы мониторинга исполнения инновационной политики в регионах. An important aspect in the modern policy of innovation management in the regions is the system of information support and monitoring of the implementation of key performance indicators characterizing the innovation potential and development of innovation processes in the regions of the Russian Federation, on the basis of which predictive estimates are built and new mechanisms of innovative development are developed. The scientific article is devoted to the analysis of innovative development at the world level and the assessment of the position (rating) of Russia in terms of the innovation component. Also presented are the results of the rating efficiency of innovation in the constituent entities of the Russian Federation and the analysis of the quality of innovation policies of the regions included in the Volga Federal District. The author of the article proposes the main directions and gives recommendations for increasing innovative development at the regional level and transforming the system of state regulation of innovation policy in the aspect of systematizing key indicators of the effectiveness of innovation and innovative activity of enterprises and companies in the regions and, on their basis, predicting innovative development, as well as organizing a monitoring system implementation of innovation policy in the regions.

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Li, Yang, Qiuyi Zhang, and Tianzhuo Gong. "Quantitative Influence Analysis of the Development Scale of Market Economy on the Level of Music Innovation." Discrete Dynamics in Nature and Society 2022 (July 11, 2022): 1–8. http://dx.doi.org/10.1155/2022/4524811.

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Music art is a form of conveying cultural information and humanistic emotions. From ancient times to the present, the form of music has undergone great changes. The change of music form is closely related to the change in dynasties, the development of the market economy, and the change in humanistic spirit. Today, the development of music has reached a relatively prosperous stage, which is closely related to the rapid development of the market economy. At the heart of the quantitative analysis is the study of associations between data. However, it will be a difficult task to analyze the relationship between the development scale of the market economy and the form of music innovation only by artificial means. This research mainly uses a convolutional neural network and long and short-term memory neural network technology to quantitatively analyze the relationship between the development scale of the market economy and the form of music innovation, which is mainly a quantitative analysis of classical music, pop music, and rap music. The research results show that convolutional neural networks and long short-term memory neural networks have sufficient capabilities to quantitatively analyze the relationship between the market development scale and the form of music innovation. Neural networks with LSTM layers have lower errors in predicting market economic correlates than neural network methods without LSTM layers. The error is reduced by 0.27%. For the prediction of innovative forms of music, the largest prediction error is only 2.93%, which is closely related to the variability of popular music. The linear correlation indices for the predictions of the three forms of musical innovation also all exceed 0.955.

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Potsulin, A. S., and I. G. Sergeeva. "Assessment of the Quality of Entrepreneurial Innovation Projects of Russian Universities Based on a Machine Learning Model (In Russ.)." Economics. Law. Innovaion, no. 2 (June 30, 2023): 47–54. http://dx.doi.org/10.17586/2713-1874-2023-2-47-54.

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By 2030 it has been planning to support more than 30 thousand entrepreneurial and innovative projects of students and graduate students, this idea has become one of the government initiatives in the sphere of social and economic development of Russia. It is known that the pursuit of quantity can lead to a decrease in quality and the subsequent closure of innovative projects. However now there is no generally accepted methodology that allows preliminary assessment of the quality of innovative projects of universities. The presented study empirically illustrates the ap-plication of machine learning methods for assessing the quality of entrepreneurial innovation projects of universities and predicting their success. The study analyzed the main reasons for the closure of Russian startups, resulting in a list of criteria for evaluating the quality of university innovation projects. A survey of managers of innovative projects implemented in universities in St. Petersburg, Moscow and Vladivostok was conducted, on the basis of which a sample was formed for training the quality assessment model of entrepreneurial innovation projects. As a result of the study, a machine learning model was built and trained, and its accuracy was evaluated. Random Forest was chosen as the ma-chine learning method; the reason for its choice was its high accuracy, resistance to outliers, and ability to process data with a large number of features and classes. The application of these results will simplify the process of assessing the quality of entrepreneurial innovation projects and allow predicting their success. Assessing the quality of entrepreneuri-al innovation projects at the early stages of their implementation will be beneficial not only for project managers, but also for business incubators at universities.

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Strong, Robert, John Thomas Wynn, James R. Lindner, and Karissa Palmer. "Evaluating Brazilian Agriculturalists’ IoT Smart Agriculture Adoption Barriers: Understanding Stakeholder Salience Prior to Launching an Innovation." Sensors 22, no. 18 (September 9, 2022): 6833. http://dx.doi.org/10.3390/s22186833.

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The study sought to: (1) evaluate agriculturalists’ characteristics as adopters of IoT smart agriculture technologies, (2) evaluate traits fostering innovation adoption, (3) evaluate the cycle of IoT smart agriculture adoption, and, lastly, (4) discern attributes and barriers of information communication. Researchers utilized a survey design to develop an instrument composed of eight adoption constructs and one personal characteristic construct and distributed it to agriculturalists at an agricultural exposition in Rio Grande do Sul. Three-hundred-forty-four (n = 344) agriculturalists responded to the data collection instrument. Adopter characteristics of agriculturalists were educated, higher consciousness of social status, larger understanding of technology use, and more likely identified as opinion leaders in communities. Innovation traits advantageous to IoT adoption regarding smart agriculture innovations were: (a) simplistic, (b) easily communicated to a targeted audience, (c) socially accepted, and (d) larger degrees of functionality. Smart agriculture innovation’s elevated levels of observability and compatibility coupled with the innovation’s low complexity were the diffusion elements predicting agriculturalists’ adoption. Agriculturalists’ beliefs in barriers to adopting IoT innovations were excessive complexity and minimal compatibility. Practitioners or change agents should promote IoT smart agriculture technologies to opinion leaders, reduce the innovation’s complexity, and amplify educational opportunities for technologies. The existing sum of IoT smart agriculture adoption literature with stakeholders and actors is descriptive and limited, which constitutes this inquiry as unique.

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Torbey, Michel T., Romergryko Geocadin, and Anish Bhardwaj. "Predicting outcome after cardiac arrest: Time for innovation." Resuscitation 66, no. 1 (July 2005): 109–10. http://dx.doi.org/10.1016/j.resuscitation.2005.02.003.

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Alas, Ruth, Ulle Ubius, and Mary Ann Gaal. "Predicting Innovation Climate using the Competing Values Model." Procedia - Social and Behavioral Sciences 62 (October 2012): 540–44. http://dx.doi.org/10.1016/j.sbspro.2012.09.089.

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Kinne, Jan, and David Lenz. "Predicting innovative firms using web mining and deep learning." PLOS ONE 16, no. 4 (April 1, 2021): e0249071. http://dx.doi.org/10.1371/journal.pone.0249071.

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Evidence-based STI (science, technology, and innovation) policy making requires accurate indicators of innovation in order to promote economic growth. However, traditional indicators from patents and questionnaire-based surveys often lack coverage, granularity as well as timeliness and may involve high data collection costs, especially when conducted at a large scale. Consequently, they struggle to provide policy makers and scientists with the full picture of the current state of the innovation system. In this paper, we propose a first approach on generating web-based innovation indicators which may have the potential to overcome some of the shortcomings of traditional indicators. Specifically, we develop a method to identify product innovator firms at a large scale and very low costs. We use traditional firm-level indicators from a questionnaire-based innovation survey (German Community Innovation Survey) to train an artificial neural network classification model on labelled (product innovator/no product innovator) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict whether they are product innovators or not. We then compare these predictions to firm-level patent statistics, survey extrapolation benchmark data, and regional innovation indicators. The results show that our approach produces reliable predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity.

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CHEN, TE FU. "TRANSFORMING KNOWLEDGE INTO ACTION TO REACH INNOVATION CAPACITY IN HIGH-TECH SMES." International Journal of Innovation and Technology Management 09, no. 01 (February 2012): 1250005. http://dx.doi.org/10.1142/s0219877012500058.

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The study aims to explore how high-tech small-to-medium-sized enterprises (SMEs) utilize integrative innovation resource strategy to transforming knowledge into action to reach innovation capacity via integrating innovation culture, knowledge management (KM), managing and organizing innovation, to achieve sustained innovation and value creation, and then to reach KM performances. The study uses multidimensional perspectives to construct an integrative innovation model, and through four case studies to examine this model, compares the distinct features between smaller and larger high-tech SMEs in utilizing integrative innovation resource strategy. The finding indicates predicting whether an innovative action will progress through an organization, and effectively planning the action, requires managers to identify, understand, and analyze all individual and organizational support that might bear on the action. Management and other integrated processes set up support for action that protect the organization's identity.

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Mazambani, Last, and Emmanuel Mutambara. "Predicting FinTech innovation adoption in South Africa: the case of cryptocurrency." African Journal of Economic and Management Studies 11, no. 1 (October 21, 2019): 30–50. http://dx.doi.org/10.1108/ajems-04-2019-0152.

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Purpose Financial technology innovation within the developed world is driving financial markets, yet its adoption is lagging among consumers in emerging markets. At the same time, most African economies continue to be at the tail end of global financial innovations adoption. Given lagging consumer adoption of cryptocurrency in South Africa, the purpose of this paper is to apply the theory of planned behaviour (TPB) to predict behavioural intention to adopt cryptocurrency. Design/methodology/approach A survey instrument based on the TPB was used to collect quantitative data for predicting adoption from adult distance students at the Mancosa, Cape Town campus. For data analysis, the two-step structural equation modelling approach was used. Findings The findings indicate that attitude and perceived behavioural control positively impact the intention to adopt cryptocurrency. Subjective norm showed a negative non-significant influence. Overall, the results of the study show that the model has a good model fit and can be used to explain the theory. Research limitations/implications The results of this study may not be generalisable to the wider population as it is only based on a cross-sectional study of a sample of adult students at a single institute in South Africa. Originality/value The contribution of this paper is threefold: it is one of a few studies on the behavioural intention to adopt cryptocurrency in South Africa using the TPB model, it contributes towards the use of predictive behavioural economics models in understanding consumer behaviour critical to accelerating the adoption of financial innovations, and the results of the study also inform behaviour change strategies that can be applied by practitioners or policymakers to improve adoption. Studies of this nature may lead to the development of financial innovation in emerging markets through a nuanced understanding of consumer behaviour.

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Lewis, Laurie K., and David R. Seibold. "Communication during intraorganizational innovation adoption: Predicting users' behavioral coping responses to innovations in organizations." Communication Monographs 63, no. 2 (June 1996): 131–57. http://dx.doi.org/10.1080/03637759609376383.

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Zang, Jinjuan. "Structural holes, exploratory innovation and exploitative innovation." Management Decision 56, no. 8 (August 13, 2018): 1682–95. http://dx.doi.org/10.1108/md-05-2017-0485.

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Purpose Existing research has demonstrated that the innovation implications of structural holes are inconsistent. The diverse and broad resources associated with structural holes facilitate innovation. On the contrary, brokerage will also hinder trust and increase the opportunism behaviors among partners, which will damage innovation. Inspired by the conflicting conclusions, the purpose of this paper is to analyze the roles of structural holes on exploratory innovation and exploitative innovation. Design/methodology/approach To test the model, the paper used a panel of 305 US computer focal firms and 6,894 alliances from the period spanning 1993 to 2004, and adopted the Heckman two-stage selection procedure in predicting the results. Findings The results show that structural holes help firms to develop exploratory innovation while negatively impacting exploitative innovation. Originality/value This study offers precise insights on inconsistent understandings between structural holes and innovation by differentiating exploratory innovation from exploitative innovation. Furthermore, it contributes to the burgeoning literature on exploration and exploitation from the network perspective.

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Fischer, Sebastian, Michael Frese, Jennifer Clarissa Mertins, Julia Verena Hardt, Thomas Flock, Juri Schauder, Michael Schmitz, and Jette Wiegel. "Climate for Personal Initiative and Radical and Incremental Innovation in Firms: A Validation Study." Journal of Enterprising Culture 22, no. 01 (March 2014): 91–109. http://dx.doi.org/10.1142/s0218495814500046.

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We examine whether organizational climate for personal initiative (PI climate) is conducive to firm innovation in small and medium-sized firms. Employees with PI are self-starting, proactive, and persistent, and a PI climate is characterized by common norms of encouraging PI at the workplace. A climate that fosters PI among employees would enhance the innovation output of firms, since it increases not only proactive thinking about future opportunities and problems but self-starting action as well. This PI climate is distinct from the team climate inventory (TCI, Anderson and West, 1996). We contrast the PI climate measure (Baer and Frese, 2003) with the TCI for predicting radical and incremental innovations in firms. Findings reveal (with 25 firms, N = 82 employees) that PI climate was related to radical innovation, but not incremental innovation. On the other hand, the TCI (unrelated to radical innovation) was related to incremental innovation. Our study results imply that different organizational climates account for the different forms of innovation in firms.

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Shcherbak, Valeriia, and Polina Puzyrova. "THE FORECASTING THE LEVEL OF DEVELOPMENT OF INNOVATIVE POTENTIAL OF TEXTILE ENTERPRISES." Management 32, no. 2 (April 16, 2021): 65–83. http://dx.doi.org/10.30857/2415-3206.2020.2.5.

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Background and objectives. The analysis of the main shortcomings of modern forecasting of innovative development of textile enterprises shows the need to build a well-functioning system for forecasting innovative development; development of a methodology for forecasting promising areas of innovative development, taking into account the peculiarities of the functioning of textile enterprises. Therefore, an urgent problem arises of finding effective mechanisms for objectively identifying the weak and strong aspects of the innovative activity of textile enterprises, establishing further priorities for the formation and development of the innovative potential of enterprises, determining the main vectors of innovative development, taking into account the competitiveness and results of R&D (research and development work).Methods:The methods used: factor analysis – to identify the most significant indicators that affect the level of use of innovative potential; cluster analysis of K-means – for a reasonable division of enterprises into groups according to the levels of integrated business performance / use of innovation; taxonomy method – to determine the boundary value of the levels of development of innovative potential as a result of the integration of individual business objects for each of the clusters into one structure; discriminant analysis – for object recognition for deciding which business objects to integrate into the business structure.Findings: As a result of predicting the level of development of the innovative potential of textile enterprises, groups of factors were identified that stimulate and discourage the development of the innovative potential of textile enterprises.Conclusion: To improve the efficient operation of textile enterprises, it was proposed to use special tools for managing innovation potential: active expansion of business activities, attraction of innovations, containment of achieved market positions, search for effective methods of using innovations, selective growth of certain types of activities, differentiated attraction of innovations, and revision of certain activities.

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Agarwal, Ruchi, and Babeet Gupta. "Innovation and Leadership: A Study of Organizations Based in the United Arab Emirates." Foundations of Management 13, no. 1 (January 1, 2021): 73–84. http://dx.doi.org/10.2478/fman-2021-0006.

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Abstract The successful practice of innovation in any organization demands the integration and interaction of individual, collective, and organizational climate levels of management. Leadership styles have an impact on all these three levels of management. However, little work has been done to assess this impact, especially in the context of United Arab Emirates (UAE)-based organizations. This study was conducted with the purpose of assessing whether leadership processes, tools, and techniques differ in innovative organizations and, thereafter, propose general leadership requirements for innovative projects in the UAE. This study adopts statistical data-processing methods, such as descriptive statistics, the reliability of the instrument, correlation, and regression analysis. Data obtained through a survey of UAE-based organizations were used for analyzing whether innovativeness (as perceived by employees) is statistically correlated with leadership dimensions. The research results show that the transformational leadership style scores better over transactional leadership when it comes to predicting innovations.

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Fung Ivan Chan, Tan, Marianne Borja, Brett Welch, and Mary Ellen Batiuk. "Predicting the Probability for Faculty Adopting an Audience Response System in Higher Education." Journal of Information Technology Education: Research 15 (2016): 395–407. http://dx.doi.org/10.28945/3548.

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Instructional technologies can be effective tools to foster student engagement, but university faculty may be reluctant to integrate innovative and evidence-based modern learning technologies into instruction. Based on Rogers’ diffusion of innovation theory, this quantitative, nonexperimental, one-shot cross-sectional survey determined what attributes of innovation (relative advantage, compatibility, complexity, trialability, and observability) predict the probability of faculty adopting the audience response system (ARS) into instruction. The sample of the study consisted of 201 faculty at a university in the southeastern United States. Binary logistic regression analysis was used to determine the attributes of innovation that predict the probability of faculty adopting the ARS into instruction. Out of the five attributes, compatibility and trialability made significant contributions to the model. The implication of the findings is that, in order to maximize adoption, the faculty needs to be given the opportunity to pre-test the ARS prior to implementation, and they need to know how the technology will assist them in achieving their pedagogical goals. Recommendations were made to leverage these attributes to foster faculty adoption of the ARS into instruction.

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Edeh, Friday Ogbu, Nurul Mohammad Zayed, Vitalii Nitsenko, Olha Brezhnieva-Yermolenko, Julia Negovska, and Maryna Shtan. "Predicting Innovation Capability through Knowledge Management in the Banking Sector." Journal of Risk and Financial Management 15, no. 7 (July 17, 2022): 312. http://dx.doi.org/10.3390/jrfm15070312.

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Purpose: The purpose of this study was to investigate the effects of knowledge management on innovation capability in the banking sector. Research methodology: Cross-sectional research design was employed in this study as it supports the use of questionnaire for data collection. Fifteen deposit money banks constitute the accessible population. Questionnaire was used as an instrument for data collection. A sample size of 272 was drawn from the overall population of 920. Overall, 259 staff participated in the study. Demographic characteristics of participants were analysed with frequency distribution while linear regression was used to analyse formulated hypotheses with the aid SPSS. Findings: This study found that knowledge management has significant positive effects on innovation capability. Research limitations: The research limitation is associated with cross-sectional survey and geographical scope. Future studies should employ longitudinal survey that support data collection for a year. Secondly, future studies should be carried out in other countries other than Africa. Practical implications: The implication of the finding is that managers and directors of banks should encourage knowledge management practices in their workplaces as this has proven by this study to improve innovation capability in terms of marketing innovation capability, product innovation capability and process innovation capability. Originality/Value: There is no research that has investigated the effects of knowledge management on innovation capability. Thus, this study provides new insight on promoting innovation capability through knowledge management.

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Bayadilova, Bakyt, Arsen Nassyrkhanov, Elvira Tlessova, Lyazzat Parimbekova, Maigul Tolymgozhinova, and Tursynzada Kuangaliyeva. "The Effectiveness of Innovative Infrastructure: The Case of Kazakhstan." Quality Innovation Prosperity 24, no. 1 (April 1, 2020): 69. http://dx.doi.org/10.12776/qip.v24i1.1406.

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Purpose: The development of an innovative economy is constrained by the problems of science funding, modernization of scientific institutions and innovative training of specialists. This article focuses on the problem of evaluating the effectiveness of the innovation infrastructure of the Republic of Kazakhstan using a systematized set of performance indicators.Methodology/Approach: Approaches to assessing innovation infrastructure have been analysed. Based on the analysis, correlation and regression assessment model has been developed.Findings: A forecast has been made for innovative infrastructure development based on the obtained performance indicators. This forecast is of high practical significance, as it allows predicting the outcomes of innovation.Research Limitation/implication: In the light of globalization, it is extremely urgent to develop an innovative economy along with regional innovation systems. If combined, these systems can accelerate the innovation processes in the regions, ensure competitiveness and expedite the socio-economic development. The formation of an innovative economy should be in line with the productive forces and production relations.Originality/Value of the paper: Through categorization, this study establishes a set of underlying indicators, which are used to measure the performance of the innovation infrastructure. A model of correlation and regression analysis is built, which allows evaluating the effectiveness of the innovation infrastructure of Kazakhstan.

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Chen, Shuting, and Dengke Yu. "The impact of R&D effort on business model innovation: Evaluating chain mediation through collaboration breadth and depth." PLOS ONE 18, no. 6 (June 5, 2023): e0286715. http://dx.doi.org/10.1371/journal.pone.0286715.

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Drawing on a novel theoretical framework, we explored the impact of research and development (R&D) effort on business model innovation via external collaboration breadth and collaboration depth in sequence. We empirically analyzed a sample of 94 Chinese innovative enterprises by applying hierarchical regression analysis and chain mediation analysis. The results indicate that R&D effort positively influences business model innovation. The influencing mechanism is that R&D effort positively affects external collaboration breadth, which in turn positively stimulates external collaboration depth, and ultimately benefits the implementation of business model innovation. Therefore, the breadth and depth of external collaboration play a chain-mediating role. The study develops a new framework for understanding the relationship between R&D effort, external collaboration, and business model innovation. It combines enterprises’ internal behavior (R&D) and external behavior (collaboration) to establish an inside-out mechanism for predicting business model innovation. It enriches the theory of business model innovation. It also provides insights for managers and governments to optimize policies in innovation-driven development.

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KLEINKNECHT, ALFRED, and GERBEN VAN DER PANNE. "PREDICTING NEW PRODUCT SALES: THE POST-LAUNCH PERFORMANCE OF 215 INNOVATORS." International Journal of Innovation Management 16, no. 02 (April 2012): 1250011. http://dx.doi.org/10.1142/s1363919611003544.

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New product sales are hard to predict. Our analysis of sales performance two years after market launch reveals that three groups of factors do not increase the accuracy of predicting new product sales: (1) A firm's general experience and experience with innovation; (2) High technological competences and strong knowledge networks; (3) Customer involvement in new product development. R&D managers should realise that experience with innovation as well as high technological competences, while possibly helpful during the development stage, do not necessarily enhance an accurate prediction of new product sales. Moreover, other than intuitively expected, networking can be ambiguous: It reduces uncertainty about future sales performance by providing information; but it may also enhance knowledge leaking to competitors, thus increasing probabilities of unexpected failure.

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Soloviev, V. P., and V. L. Rozaliev. "Simulation of knowledge development in an innovation system based on neural network model." Journal of Physics: Conference Series 2094, no. 3 (November 1, 2021): 032044. http://dx.doi.org/10.1088/1742-6596/2094/3/032044.

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Abstract Innovative development is impossible without the support of scientific research capable of forming fundamentally new technological approaches. The process of knowledge development cannot be fully formalized, therefore, management decisions are made in conditions of uncertainty. The models developed on the basis of self-organization are the most effective for predicting the development of knowledge in the innovation system. On the basis of the developed recurrent neural network, the analysis of the influence of the state scientific and technical policy on the formation of the strategies of the actors of the national innovation systems of the BRICS countries is carried out. The determinants of the influence of the state scientific and technical policy on the development of knowledge in the innovation system are revealed.

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Ben-Haim, Yakov. "Does a better model yield a better argument? An info-gap analysis." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2200 (April 2017): 20160890. http://dx.doi.org/10.1098/rspa.2016.0890.

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Theories, models and computations underlie reasoned argumentation in many areas. The possibility of error in these arguments, though of low probability, may be highly significant when the argument is used in predicting the probability of rare high-consequence events. This implies that the choice of a theory, model or computational method for predicting rare high-consequence events must account for the probability of error in these components. However, error may result from lack of knowledge or surprises of various sorts, and predicting the probability of error is highly uncertain. We show that the putatively best, most innovative and sophisticated argument may not actually have the lowest probability of error. Innovative arguments may entail greater uncertainty than more standard but less sophisticated methods, creating an innovation dilemma in formulating the argument. We employ info-gap decision theory to characterize and support the resolution of this problem and present several examples.

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Smith, John. "Effects of Machine Learning Algorithms for Predicting and Optimizing the Properties of New Materials in the United States." European Journal of Physical Sciences 6, no. 1 (May 3, 2023): 23–34. http://dx.doi.org/10.47672/ejps.1444.

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Purpose: The aim of this study is to investigate the effects of machine learning algorithms in predicting and optimizing the properties of new materials in the United States. Materials and Methods: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low-cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Results: The research found that machine learning algorithms have a significant impact on materials prediction and optimization in the United States, particularly in energy storage, catalysis, electronics, and aerospace. These algorithms offer advantages in efficiency, scalability, and accuracy compared to traditional methods, but challenges such as data quality, scarcity, interpretability, and reliability need to be addressed to ensure robust and reliable predictions. Recommendations: This study contributes to the understanding of the effects of machine learning algorithms in predicting and optimizing the properties of new materials in the United States. The research advances the knowledge in the field of materials science, materials prediction, and materials optimization. The findings provide insights into the potential of machine learning algorithms for accelerating materials discovery and innovation, and highlight the challenges and opportunities in their application for materials prediction and optimization. The study has practical implications for researchers, engineers, and policymakers involved in materials science, materials design, and materials innovation. The research underscores the importance of leveraging machine learning algorithms as a powerful tool for materials prediction and optimization, and emphasizes the need for further research, development, and integration of these techniques in materials science and engineering practices.

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PILKINGTON, ALAN. "MODELING CITATION DIFFUSION: INNOVATION MANAGEMENT LITERATURE." International Journal of Innovation and Technology Management 10, no. 01 (February 2013): 1350004. http://dx.doi.org/10.1142/s0219877013500041.

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Citation patterns are important to understanding the spread of technological ideas as science is essentially a cumulative activity. One feature only now being appreciated is the obsolescence or ageing patterns in citations and the insights their study can bring. There have been a number of studies examining, predicting, modeling and plotting citation delays, ageing and the publication cycle. These normally apply lognormal, log-logistic and Weibull distributions to scientific papers. This paper adds to this body of work by examining a set of 18 other distributions, and tests their predictive power on a new data set based on 10 years of ISI Citation data for 10 innovation centered journals. The resulting grouping of journals appears to be a useful proxy for academic-practitioner involvement and warrants further investigation. The finding that the three-parameter Inverse Gaussian provides the best fit to the data extends the understanding of this process. As well as allowing the classification of literature, this improved representation of citation obsolescence will allow us to predict earlier and more precisely those scientific ideas which are generating noteworthy attention or may be suitable for early exploitation.

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Adiguzel, Zafer, Fatma Sonmez Cakir, and Masood Nawaz Kalyar. "A study of smartphone companies: do innovation culture and radical innovation affect institutionalization and sustainable competition positively?" International Journal of Innovation 10, no. 1 (March 17, 2022): 95–117. http://dx.doi.org/10.5585/iji.v10i1.20003.

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Objective of the study: The present study examines how innovation culture and radical innovation elicit sustainable competition. The study proposes institutionalization as an important underlying mechanism that originates from innovation culture and radical innovation and provides the basis for sustainable competition.Design/methodology/approach: The data were collected from 763 employees working in smartphone companies located in Istanbul, Turkey. The analysis was performed employing PROCESS macro in order to test the hypothesized relationships.Findings: Results show that both innovation culture and radical innovation have a positive impact on institutionalization and sustainable competition. Moreover, the data also support the mediation effect of institutionalization in predicting sustainable competition.Practical implications: The findings imply that the structure of the firms must be ready for change in order to reap the benefits of sustainable competition. In fact, the creation of an innovation culture is very important in technology-intensive firms because there is a need to create common values that encourage employees to adopt innovative behaviors for sustainable competition. Furthermore, radical innovation can be more advantageous for firm-outcomes when large structural differences are identified and bridged successfully.Originality/value: The study develops a novel framework of innovation-competition using a structural perspective, empirically testing the proposed relationships using data from the technology-intensive industry of Turkey.

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Kim, Hyo-Jung, and Jin-Myong Lee. "Predicting intention to adopt mobile card payment service." Family and Environment Research 58, no. 4 (November 20, 2020): 497–515. http://dx.doi.org/10.6115/fer.2020.035.

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The use of mobile payment services has recently increased in South Korea. Mobile payments allow consumers to purchase items digitally, using a mobile card in an app affiliated with a payment service. This study explores the predictors of intention to adopt mobile payment services. The study employed an A(affective)-B(behavioral)-C(cognitive) model with two antecedent variables: cognitive (perceived usefulness, perceived risk, perceived ease of use, and perceived herding behavior) and affective (satisfaction with the status quo, innovation resistance) responses. An online survey of 405 non-users of mobile payment services aged 20 to 49 years was conducted. The study used SPSS 23.0 for descriptive analysis and Amos 23.0 for confirmatory factor analysis and structural equation modelling. The results are as follows. First, perceived usefulness, perceived risk, and perceived herding behavior significantly influenced innovation resistance. Second, perceived herding behavior significantly influenced subjective norms. Third, innovation resistance and subjective norms significantly influenced the intention to adopt mobile payment services. The findings suggest that the A-B-C model can be useful in understanding consumers’ adoption and resistance behaviors and that cognitive and affective responses are important antecedent variables affecting the decision to adopt mobile payment services.

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Gareeva, Nailya A., and Anna I. Romanova. "Forecasting the innovative development of leading regions of the volga federal district using a synergetic model." Vestnik BIST (Bashkir Institute of Social Technologies), no. 4(57) (October 14, 2022): 50–63. http://dx.doi.org/10.47598/2078-9025-2022-4-57-50-63.

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The most important component of the economic space of the Russian Federation is the innovative space of the regions, characterized by the presence of a high level of uncertainty and inconsistency of the ongoing socio-economic and political phenomena. The article presents the results of predicting the innovative development of the Volga Federal District and its leading regions: the Republic of Tatarstan, the Republic of Bashkortostan, the Nizhny Novgorod region using a synergistic model. The main trends in the change in innovative indicators of the Volga Federal District and the leading regions until 2027 are identified. In particular, the predictive indicators of the volume of production of innovative goods, the cost of innovative activities, gross profit from the sale of innovative products, innovative activity, and the profitability of innovative activities are determined. The practical use of a multifactorial synergetic model can be considered as an objective tool for assessing the innovative development of regional economic systems, identifying promising areas of innovation policy, and an important information resource in making managerial decisions.

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Jackson, Patrick Thaddeus. "Indeterminate Innovation." Perspectives on Politics 21, no. 3 (September 2023): 1013–17. http://dx.doi.org/10.1017/s1537592723001184.

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One of my pet peeves when watching televised sports is when the commentators declare that one or another player or team “has momentum” or that “the momentum has shifted.” Typically, this statement is made shortly after a team or player does something that puts them in a better position to win the game, and the implication seems to be that this change in momentum will carry someone to victory. But there are at least two problems with this all-too-typical sportscaster pronouncement. One is that “momentum” is a mathematically well-defined notion in physics, where it means the mass of an object multiplied by its velocity; linear momentum is also a vector quantity, and has both a magnitude and a direction. It is this complexity that allows momentum and changes in momentum—in conjunction with an account of the various forces at work on the object—to explain the object’s trajectory. A well-kicked football has momentum in the physics sense, but it is quite unclear how the “momentum” of a player or a team might be calculated, to say nothing of the various forces at work on the player or team’s movement through the playing of a game. Hence both the determination of a player or team’s “momentum,” and the use of that “momentum” in explaining or predicting the outcome of a game, necessarily remain at the level of metaphor.

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., Mridul, and Aditi Sharma. "PSYCHOLOGICAL CAPITAL FOR COMPETITIVE ADVANTAGE: A REVIEW." Administrative Development 'A Journal of HIPA, Shimla' 8, SI-1 (October 6, 2021): 297–312. http://dx.doi.org/10.53338/adhipa2021.v08.si01.17.

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Increased competition among businesses demands adequate attention towards steps which would ensure business survival in long run. Businesses must build on their strengths which in turn depend upon their employee’s strengths. One of such strengths is employee’s positive Psychological Capital which comprises of self-efficacy, optimism, hope and resilience. Employee’s Psychological Capital has been linked to key driver of sustainable competitive advantage in today’s business scenario that is innovation. This review focuses upon studies determining the relationship between Psychological Capital and innovation. The review suggests that Psychological capital is a significant predictor of innovation among employees working in various sectors. Positive leadership styles such as Authentic, transformational leadership etc. and favourable organizational climate are found to foster Psychological Capital among employees. Although at individual level there are studies linking Psychological Capital with innovative behaviour, but less number of studies have examined the role of team or collective Psychological Capital in predicting overall innovation within the organizations.

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Barker, Lisa, Meenakshy Aiyer, Andrew Bland, Trina Croland, Robert Jennetten, Gerald Wickham, and Ann Willemsen-Dunlap. "Board #203 - Program Innovation Evaluating Curricular Quality and Predicting Impact." Simulation in Healthcare: Journal of the Society for Simulation in Healthcare 9, no. 6 (December 2014): 441. http://dx.doi.org/10.1097/01.sih.0000459342.57285.62.

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-ur-Rehman, Muhammad Zia, Muhammad Akmal Pasha, and Mudassar Hussain. "DYNAMICS OF AUTHENTIC LEADERSHIP TOWARDS INNOVATION: PREDICTING THE CRITICAL ROLE OF ORGANIZATIONAL CULTURE." Pakistan Journal of Social Research 04, no. 04 (December 31, 2022): 931–39. http://dx.doi.org/10.52567/pjsr.v4i04.902.

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The study is conceptual in nature, as it focuses on innovation as a course undertaken to develop and improve products, processes and markets through creative ideas, techniques, and processes with the aim of enhancing performance of an organization. Organizational culture also plays vital role, where innovation can have direct link with organizational culture, which is by and large shaped up by organizational leadership. In this regard authentic leadership has a special role to play; therefore, the mainstay of this study is to assess and conceptualize association between organizational culture and innovation with special reference to the authentic leadership. Authentic leadership is the “bond” desired to maintain a healthy working environment. “Authenticity” is the combination of four characteristics which include: 1) optimism, 2) productive emotions,3) values and 4) acting in accordance with one’s true self. Around 114 scholarly articles were consulted to study the linkage of organizational culture and innovation especially with references to the effects of a true and authentic leaders in an organization. This was ascertained that authentic leadership can create immensely positive impact on organizational culture in general and organizational innovation in special. Finally, the study proposes a conceptual framework as a foundation for future researchers. Keywords: Change, Performance, Organizational culture, Organizational Innovation, Authentic Leadership.

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Hao, Ying, Mingshun Guo, and Yijing Guo. "Predicting technological innovation in new energy vehicles based on an improved radial basis function neural network for policy synergy." PLOS ONE 17, no. 8 (August 25, 2022): e0271316. http://dx.doi.org/10.1371/journal.pone.0271316.

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Policy synergy is necessary to promote technological innovation and sustainable industrial development. A radial basis function (RBF) neural network model with an automatic coding machine and fractional momentum was proposed for the prediction of technological innovation. Policy keywords for China’s new energy vehicle policies issued over the years were quantified by the use of an Latent Dirichlet Allocation (LDA) model. The training of the neural network model was completed by using policy keywords, synergy was measured as the input layer, and the number of synchronous patent applications was measured as the output layer. The predictive efficacies of the traditional neural network model and the improved neural network model were compared again to verify the applicability and accuracy of the improved neural network. Finally, the influence of the degree of synergy on technological innovation was revealed by changing the intensity of policy measures. This study provides a basis for the relevant departments to formulate industrial policies and improve innovation performance by enterprises.

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Kwon, Kibum, and Daeyeon Cho. "How transactive memory systems relate to organizational innovation: the mediating role of developmental leadership." Journal of Knowledge Management 20, no. 5 (September 12, 2016): 1025–44. http://dx.doi.org/10.1108/jkm-10-2015-0413.

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Purpose The purpose of this paper is to explore the relationship between transactive memory systems and organizational innovation. Several recent studies have discussed the positive relationship between these two entities. Yet, very few studies have demonstrated how transactive memory systems are related to leadership and innovation. This study investigates this tripartite relationship, finding that developmental leadership exerts a mediating effect on the relationship between transactive memory systems and organizational innovation. Design/methodology/approach In examining this relationship, 224 participants from an electronics company in South Korea were surveyed. Structural equation modeling was used to enable the identification of simultaneous interactive relationships among the three research variables. Findings Contrary to previous research results, transactive memory systems were found not to be significantly related to organizational innovation. Results also indicated that transactive memory systems comprise a statistically significant variable that influences developmental leadership. Subsequently, developmental leadership can be considered to be a valid construct in predicting organizational innovation; it can also be seen to fully mediate the relationship between transactive memory systems and organizational innovation. Originality/value These results have theoretical and managerial implications. As transactive memory systems do not always precede organizational innovation, knowledge of “who knows what” is not enough to ensure innovative performances. To accelerate organizational innovation, intentional managerial interventions such as developmental leadership are accordingly necessary.

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Wang, Guan, and Xiaohu Zhou. "Innovation in safety management: A moderated mediation model." Social Behavior and Personality: an international journal 47, no. 9 (September 3, 2019): 1–14. http://dx.doi.org/10.2224/sbp.8166.

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We developed and tested a model linking safety innovation intention with safety innovation behavior and safety performance. Participants were 407 employees of blasting, chemical, mining, and other high-risk industries in China. The results indicated that safety innovation intention was positively associated with safety performance, and safety innovation behavior played a partial mediating role in this relationship. In addition, safety climate moderated the relationship between safety innovation intention and safety innovation behavior, which mediated the interaction of safety climate and safety innovation behavior in predicting safety performance. The results support the importance of the effect of organizations' subjective intention and climate on their innovation behavior and performance in the safety domain.

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Wanyan, Tingyi, Hossein Honarvar, Ariful Azad, Ying Ding, and Benjamin S. Glicksberg. "Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records." Data Intelligence 3, no. 3 (2021): 329–39. http://dx.doi.org/10.1162/dint_a_00097.

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Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions. As clinical data are complex and varied in their structure and components, continued innovation of modelling strategies is required to identify architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model (HGM) on electronic health record (EHR) data and used the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality. We show that the additional information provided by including time as a vector in the embedding captured the relationships between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served as a foundation for future experiments involving different EHR data types on important healthcare prediction tasks.

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