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Journals / Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Sayı: 56

IS IT POSSIBLE TO APPLY A DEEP LEARNING ALGORITHM TO INNOVATION MANAGEMENT RESEARCH?

Cemal Öztürk(PAMUKKALE UNIVERSITY)0000-0003-3850-7416
Mustafa İncekara(PAMUKKALE UNIVERSITY)0000-0003-0602-7987
Sezai Tokat(PAMUKKALE UNIVERSITY)0000-0003-0193-8220
Pages
217–226
Publication Date
2023-05-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that LSTM gives the highest performance for all metrics.

📌 3 Key Takeaways

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Objective & Hypothesis:This paper aims to apply a deep learning algorithm to estimate the prediction of various external financial input variables on adopting eco-innovation practices such as renewable energy operations of 5456 SMEs.
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Key Findings & Data:A Long Short-Term Memory Units (LSTM) is applied to the data set to evaluate the performance of different input variables on the adoption of renewable energy.
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Conclusion & Impact:The findings indicate that LSTM gives the highest performance for all metrics.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zeka · Makine Öğrenmesi · Derin Öğrenme

Abstract

This paper aims to apply a deep learning algorithm to estimate the prediction of various external financial input variables on adopting eco-innovation practices such as renewable energy operations of 5456 SMEs. A Long Short-Term Memory Units (LSTM) is applied to the data set to evaluate the performance of different input variables on the adoption of renewable energy. Furthermore, we process the dataset with different machine learning algorithms and compare the results. The findings indicate that LSTM gives the highest performance for all metrics. As a result, some important theoretical implications for management scholars are given.

Özet

Bu makale, farklı dış finansman faktörlerinin KOBİ'lerin yenilenebilir enerjiyi gibi eko-inovasyon uygulamalarını benimsemesini nasıl etkilediğini açıklamayı amaçlamaktadır. Derin öğrenme algoritması uygulanarak 5456 KOBİ'nin yenilenebilir enerji operasyonlarını benimseme konusunda çeşitli dış finansal girdi değişkenlerinin tahmini incelenmiştir. Yenilenebilir enerjinin benimsenmesine ilişkin farklı girdi değişkenlerinin performansını değerlendirmek için veri kümesine Uzun Kısa Süreli Bellek Modeli (LSTM) uygulanmıştır. Ayrıca veri setini farklı makine öğrenme algoritmaları ile karşılaştırılmıştır. Bulgular, LSTM'nin tüm metrikler için en yüksek performansı verdiğini göstermektedir. Sonuç olarak, bazı önemli teorik çıkarımlar verilmiştir.

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Cite This Article

Öztürk, C., İncekara, M., Tokat, S. (2023). IS IT POSSIBLE TO APPLY A DEEP LEARNING ALGORITHM TO INNOVATION MANAGEMENT RESEARCH?. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Sayı: 56, 217–226. https://doi.org/10.30794/pausbed.1127776

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