How to Predict the Innovation to SMEs? Applying the Data Mining Process to the Spinner Innovation Model

Ronnie Figueiredo (), Carla Magalhães and Claudia Huber
Additional contact information
Ronnie Figueiredo: Research Center in Business Sciences, NECE (UBI), 6200-209 Covilhã, Portugal
Carla Magalhães: TRIE—Transdisciplinary Research Center for Entrepreneurship & Innovation Ecosystems, Lusófona University, 4000-098 Porto, Portugal
Claudia Huber: TRIE—Transdisciplinary Research Center for Entrepreneurship & Innovation Ecosystems, Lusófona University, 4000-098 Porto, Portugal

Social Sciences, 2023, vol. 12, issue 2, 1-17

Abstract: Despite the importance of small and medium-sized enterprises (SMEs) for the growth and development of companies, the high failure rate of these companies persists, and this correspondingly demands the attention of managers. Thus, to boost the company success rate, we may deploy certain approaches, for example predictive models, specifically for the SME innovation. This study aims to examine the variables that positively shape and contribute towards innovation of SMEs. Based on the Spinner innovation model, we explore how to predict the innovation of SMEs by applying the variables, namely knowledge creation, knowledge transfer, public knowledge management, private knowledge management and innovation. This study applied the data mining technique according to the cross industry standard process for data mining (CRISP-DM) method while the Statistical Package for the Social Sciences (SPSS_Version28) served to analyze the data collected from 208 SME employees in Oporto, Portugal. The results demonstrate how the Spinner innovation model positively influences the contributions of the SMEs. This SME-dedicated model fosters the creation of knowledge between internal and external interactions and increases the capacity to predict the SME innovation by 56%.

Keywords: Spinner innovation; data mining; predictive; model; open innovation (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2076-0760/12/2/75/pdf (application/pdf)
https://www.mdpi.com/2076-0760/12/2/75/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jscscx:v:12:y:2023:i:2:p:75-:d:1052457

Access Statistics for this article

Social Sciences is currently edited by Ms. Yvonne Chu

More articles in Social Sciences from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().