The paper aims to state the research protocol for the innovation-seeking behavior of Small- to Medium-sized Enterprises (SMEs), related to the classification of knowledge needs expressed in the networking databases. The dataset of 9301 networking offers as the outcome of proactive attitudes represents the content of the Enterprise Europe Network (EEN) database. The data set has been semi-automatically obtained using the rvest R package, and then analyzed using static word embedding neural network architecture: Continuous Bag-of-Words (CBoW), predictive model Skip-Gram, and Global Vectors for Word Representation (GloVe) considered the state-of-the-art models, to create topic-specific lexicons. The proportion of offers labeled as Exploitative innovation to Explorative innovation is balanced with a 51%–49% proportion. The prediction rates show good performance with an AUC score of 0.887, and the prediction rates for exploratory innovation 0.878 and explorative innovation 0.857. The performance of predictions with the frequency-inverse document frequency (TF-IDF) technique shows that the research protocol is sufficient to categorize the innovation-seeking behavior of SMEs using static word embedding based on the description of knowledge needs and text classification, but it is not perfect due to the general entropy related to the outcome of networking. In the context of networking, SMEs place a greater emphasis on explorative innovation in their innovation-seeking behavior. They prioritize smart technologies and global business cooperation, whereas current information technologies and software are more of interest to SMEs that adopt an exploitative innovation approach.
BT - Heliyon DO - 10.1016/j.heliyon.2023.e14689 M1 - 4 N2 -The paper aims to state the research protocol for the innovation-seeking behavior of Small- to Medium-sized Enterprises (SMEs), related to the classification of knowledge needs expressed in the networking databases. The dataset of 9301 networking offers as the outcome of proactive attitudes represents the content of the Enterprise Europe Network (EEN) database. The data set has been semi-automatically obtained using the rvest R package, and then analyzed using static word embedding neural network architecture: Continuous Bag-of-Words (CBoW), predictive model Skip-Gram, and Global Vectors for Word Representation (GloVe) considered the state-of-the-art models, to create topic-specific lexicons. The proportion of offers labeled as Exploitative innovation to Explorative innovation is balanced with a 51%–49% proportion. The prediction rates show good performance with an AUC score of 0.887, and the prediction rates for exploratory innovation 0.878 and explorative innovation 0.857. The performance of predictions with the frequency-inverse document frequency (TF-IDF) technique shows that the research protocol is sufficient to categorize the innovation-seeking behavior of SMEs using static word embedding based on the description of knowledge needs and text classification, but it is not perfect due to the general entropy related to the outcome of networking. In the context of networking, SMEs place a greater emphasis on explorative innovation in their innovation-seeking behavior. They prioritize smart technologies and global business cooperation, whereas current information technologies and software are more of interest to SMEs that adopt an exploitative innovation approach.
PY - 2023 EP - e14689 T2 - Heliyon TI - Seeking Innovation: The Research Protocol for SMEs Networking VL - 9 SN - 24058440 ER -