Assessing innovation capabilities of manufacturing companies by combination of unsupervised and supervised machine learning approaches

Akman, Gülşen; Yörür, Bahadır; Boyacı, Ali İhsan; Chiu, Ming‐Chuan

doi:10.1016/j.asoc.2023.110735

Cited by 6 publications

(5 citation statements)

References 76 publications

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“…Their findings reveal complex relationships between intelligence innovation, economic factors, and unemployment. Akman et al (2023) presented a method for assessing innovation capabilities in companies using unsupervised and supervised machine learning algorithms. Their research categorizes companies into different groups based on their innovation capabilities, and they employ a range of classification algorithms, including KNN, GaussianNB, RandomForest, and others.…”

Section: Related Workmentioning

confidence: 99%

“…This information corroborates the affirmation by de Carvalho et al (2011, p. 11), which states, 'usually, the more innovative a company is, the greater its competitiveness and better its position in the market in which it operates.'. In the same vein, Akman et al (2023) cite that it is important for business that companies engage in innovation activities, not only for the company but for the overall economic development of a country.…”

Section: Introductionmentioning

confidence: 99%

“…The only requirement for applying KDD is that there be manipulable data (Fayyad et al, 1996), and that the innovation area can generate data on the level of maturity of companies with great potential for exploration. Data mining techniques may include machine learning algorithms in managing innovation in various fields, especially in economic research and predicting technological evolution (Akman et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Clustering techniques and innovation-based comparison in Londrina and Region companies

Paula Barbosa de Morais,

Santos Dias,

Samways dos Santos

et al. 2024

Semin., Exact Tech. Scienc.

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Innovation is often considered a cornerstone for success across various companies. However, research focused on measuring and describing innovation frequently relies on classical statistical techniques. In this context, this study applied unsupervised machine learning techniques to cluster companies in the Londrina region, investigating how variables related to innovation differ among the identified clusters. Data were collected through a survey instrument adapted from CIS 4 and PINTEC, encompassing 26 responding companies, although 23 were analyzed in this study. Four clustering algorithms were employed: k-means, k-means+PCA, hierarchical, and hierarchical+PCA. Regarding the results, the hierarchical+PCA algorithm showed improved separation between service and manufacturing companies. Clusters identified with the value ``"1" indicated concerns related to regular investment in R&D, collaborations for innovation, and requests/registrations of patents in the last three years. Analyzing demographic characteristics revealed that clusters identified by hierarchical+PCA exhibited a higher presence of service sector companies, while cluster 1 showed a prevalence of industries, suggesting that these possess more innovative characteristics in the Londrina region.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Clustering techniques and innovation-based comparison in Londrina and Region companies

Paula Barbosa de Morais,

Santos Dias,

Samways dos Santos

et al. 2024

Semin., Exact Tech. Scienc.

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“…Given the exponential growth of social media data, the quest for more sophisticated techniques to facilitate effective knowledge discovery becomes imperative (Shu and Ye, 2023). Traditional approaches often rely on shallow and structured training frameworks (Akman et al ., 2023; Zerveas et al ., 2021). Conversely, competitive analysis leverages supervised or unsupervised computing methodologies to autonomously enhance feature representation, thereby enabling substantial data extraction and classification (Yang et al ., 2022).…”

Section: Introductionmentioning

confidence: 99%

Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling

Shahidzadeh,

Shokouhyar

2024

IMDS

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PurposeIn recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).Design/methodology/approachLeveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.FindingsTo substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.Originality/valueThis scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

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“…The creation of innovative enterprises is also important [16]. The assessment of the innovative opportunities of manufacturing companies is presented in the work [17]. The search for innovative development strategies for enterprises in a competitive market was carried out in the work [18].…”

Section: Introductionmentioning

confidence: 99%

Formation of innovative infrastructure in the context of project-oriented management of the oil and gas industry

Kinash,

Tarayevska,

Petryna

et al. 2024

E3S Web Conf.

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Innovative infrastructure of oil and gas enterprises involves the use of modern technologies, systems, and approaches aimed at improving efficiency, safety, environmental sustainability, and competitiveness in the production and operation of oil and gas. This study investigates the creation of a project-oriented supervisory company in the oil and gas industry in the context of innovative development, which is relevant and promising. The key challenges faced by consulting firms in the oil and gas industry and possible strategic directions for their resolution, which will require supervisory firms to be more flexible, innovative, and strategic in their approaches, considering the specifics of the industry and the needs of clients were identified. The study also develops a tree of problems for the creation of a project-oriented supervisory company in the oil and gas industry and determines that this direction is sufficiently relevant and necessary for the formation of innovative infrastructure of oil and gas enterprises. It gives an assessment of the economic efficiency of creating a project-oriented supervisory company based on the analysis of both basic methods – payback period and return on investment. The study’s calculations confirm the feasibility of creating a project-oriented supervisory company as an innovative infrastructure that will support the development of oil and gas structures for both the country as a whole and individual enterprises.

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Assessing innovation capabilities of manufacturing companies by combination of unsupervised and supervised machine learning approaches

Cited by 6 publications

References 76 publications

Clustering techniques and innovation-based comparison in Londrina and Region companies

Clustering techniques and innovation-based comparison in Londrina and Region companies

Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling

Formation of innovative infrastructure in the context of project-oriented management of the oil and gas industry

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