Analysis of Organizational Management Factors Affecting the Innovation Capability of Small and Medium-Sized Enterprises
Keywords:
Innovation capability, determinants of innovation, small and medium-sized enterprises, data envelopment analysis, multiple linear regressionAbstract
The purpose of this article is to evaluate how management characteristics affect small and medium-sized enterprises' (SMEs) capacity for innovation. A total of 315 SMEs in Uzbekistan were analyzed between 2022 and 2023. Leadership, information and knowledge management, customer relationship management, business-society relationship management, results, age, and size were among the internal aspects taken into account. Innovation capability was evaluated using sectoral innovation level. The correlations were analyzed using data envelopment analysis and multiple linear regression. The results showed that an organization's efficiency and capacity for innovations are positively impacted by information and knowledge, customer interactions, leadership, and society.
References
Alves, A. C., Barbieux, D., Reichert, F. M., & ... (2017). Innovation and dynamic capabilities of the firm: Defining an assessment model. … de Administração de …. https://www.scielo.br/j/rae/a/h4x4m9gWJXy4F6rkfY9YPjH/?lang=en
Elj, M. El, & Abassi, B. (2014). The determinants of innovation: An empirical analysis in Egypt, Jordan, Syria and Turkey. … of Development Studies/Revue Canadienne d’ …. https://doi.org/10.1080/02255189.2014.934787
Farace, S., & Mazzotta, F. (2015). The effect of human capital and networks on knowledge and innovation in SMEs 1. Journal of Innovation Economics &Management. https://www.cairn.info/revue-journal-of-innovation-economics-2015-1-page-39.htm
Galende, J., & de la Fuente, J. M. (2003). Internal factors determining a firm’s innovative be-haviour. Research Policy. https://www.sciencedirect.com/science/article/pii/S0048733302000823
Ganau, R., & Maria, E. D. (2014). Determinants of technological innovation in SMEs. Firm-level fac-tors, agglomeration economies and the role of KIBS providers. econstor.eu. https://www.econstor.eu/handle/10419/124398
Genis-Gruber, A., & Öğüt, H. (2014). Environmental factors affecting innovation strategies of companies: Customers and suppliers effect. Procedia-Social and Behavioral Sciences. https://www.sciencedirect.com/science/article/pii/S1877042814050824
Horsager, J. (2020). Brain-first versus body-first Parkinson’s disease: A multimodal imaging case-control study. Brain, 143(10), 3077–3088. https://doi.org/10.1093/brain/awaa238
Huang, C. (2021). 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. The Lancet, 397(10270), 220–232. https://doi.org/10.1016/S0140-6736(20)32656-8
Karikari, T. K. (2020). Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. The Lancet Neurology, 19(5), 422–433. https://doi.org/10.1016/S1474-4422(20)30071-5
Keil, A. P. (2020). A quantile-based g-computation approach to addressing the effects of expo-sure mixtures. Environmental Health Perspectives, 128(4). https://doi.org/10.1289/EHP5838
Kim, J. H. (2019). Multicollinearity and misleading statistical results. Korean Journal of Anesthesi-ology, 72(6), 558–569. https://doi.org/10.4097/kja.19087
Labrague, L. J. (2020). COVID-19 anxiety among front-line nurses: Predictive role of organisa-tional support, personal resilience and social support. Journal of Nursing Management, 28(7), 1653–1661. https://doi.org/10.1111/jonm.13121
Laforet, S., & Tann, J. (2006). Innovative characteristics of small manufacturing firms. Journal of Small Business and Enterprise …. https://doi.org/10.1108/14626000610680253
Li, K. (2019). Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proceedings of the National Academy of Sciences of the United States of America, 116(2), 422–427. https://doi.org/10.1073/pnas.1812168116
Ling, Y. (2020). Persistence and clearance of viral RNA in 2019 novel coronavirus disease reha-bilitation patients. Chinese Medical Journal, 133(9), 1039–1043. https://doi.org/10.1097/CM9.0000000000000774
Paget, J. (2019). Global mortality associated with seasonal influenza epidemics: New burden estimates and predictors from the GLaMOR Project. Journal of Global Health, 9(2). https://doi.org/10.7189/jogh.09.020421
Reitsma, M. B. (2021). Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. The Lancet, 397(10292), 2337–2360. https://doi.org/10.1016/S0140-6736(21)01169-7
Rudd, K. E. (2020). Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. The Lancet, 395(10219), 200–211. https://doi.org/10.1016/S0140-6736(19)32989-7
Ruiz, J. B. (2021). Predictors of intention to vaccinate against COVID-19: Results of a nationwide survey. Vaccine, 39(7), 1080–1086. https://doi.org/10.1016/j.vaccine.2021.01.010
Schumpeter, J. (1982). A teoria do desenvolvimento econômico (1912). São Paulo: Ed.
Wafa, H. A. (2020). Burden of Stroke in Europe: Thirty-Year Projections of Incidence, Preva-lence, Deaths, and Disability-Adjusted Life Years. Stroke, 51(8), 2418–2427. https://doi.org/10.1161/STROKEAHA.120.029606
Wei, J. (2019). Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sensing of Environment, 231. https://doi.org/10.1016/j.rse.2019.111221
Yin, L. (2021). rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study. Genomics, Proteomics and Bioinformatics, 19(4), 619–628. https://doi.org/10.1016/j.gpb.2020.10.007
Zawislak, P. A., Alves, A. C., & ... (2012). Innovation capability: From technology development to transaction capability. Journal of Technology …. https://www.scielo.cl/scielo.php?pid=S0718-27242012000200002&script=sci_arttext&tlng=pt
Zhai, S. (2019). Fine particulate matter (PM2.5) trends in China, 2013-2018. separating contributions from anthropogenic emissions and meteorology. Atmospheric Chemistry and Physics, 19(16), 11031–11041. https://doi.org/10.5194/acp-19-11031-2019
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