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Journals / Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Cilt: 28 Sayı: Özel Sayı Özel Sayı

Assessing Sectoral Exposure to the Transformative Effects of Macrotrends Using Multi-Criteria Decision-Making Methods: Evidence from the Future of Jobs Report 2025

Makrotrendlerin Dönüştürücü Etkilerine Sektörel Maruziyetin Çok Kriterli Karar Verme Yöntemleriyle Değerlendirilmesi: Future of Jobs Report 2025 Temelli Bir İnceleme

Ezgi Dilan Urmak Akçakaya(ARDAHAN ÜNİVERSİTESİ)0000-0003-3472-1837
Gözde Koca(BİLECİK ŞEYH EDEBALİ ÜNİVERSİTESİ)0000-0001-6847-6812
Pages
153–168
Publication Date
2026-10-21
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AI Summary & Key Takeaways

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

The findings indicate that Mining and Metals has the highest overall transformation intensity, followed by Automotive and Aerospace and Supply Chain and Transportation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to assess the intensity of sectoral exposure to the transformative effects of global macrotrends using Multi-Criteria Decision-Making (MCDM) methods.
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Key Findings & Data:The analysis draws on data from the Future of Jobs Report 2025 and covers 22 sectors evaluated across 12 macrotrend criteria.
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Conclusion & Impact:The findings indicate that Mining and Metals has the highest overall transformation intensity, followed by Automotive and Aerospace and Supply Chain and Transportation.
🔬Design:Araştırma Makalesi
📍Scope:2025 Verileri
⚙️Variables:ÇKKV · Sektörel Dönüşüm · Geleceğin İşleri

Abstract

This study aims to assess the intensity of sectoral exposure to the transformative effects of global macrotrends using Multi-Criteria Decision-Making (MCDM) methods. The analysis draws on data from the Future of Jobs Report 2025 and covers 22 sectors evaluated across 12 macrotrend criteria. Because the criterion values represent the extent to which each macrotrend is expected to affect sectoral transformation, all criteria were defined as maximization-oriented impact criteria. Accordingly, a higher value indicates greater transformation exposure rather than better sectoral performance. Criterion weights were determined using WENSLO, SPC, and equal weighting approaches, while sectoral rankings were obtained using PIV, CRADIS, and ARTASI. The rankings produced under the three weighting scenarios were integrated using the Borda method, and the consistency between the MCDM methods was examined through Spearman correlation analysis. The findings indicate that Mining and Metals has the highest overall transformation intensity, followed by Automotive and Aerospace and Supply Chain and Transportation. Conversely, Real Estate and Accommodation, Food, and Leisure exhibit the lowest relative levels of exposure to the macrotrends included in the analysis. The correlations between the rankings are positive, statistically significant, and very strong, with the highest agreement observed between PIV and CRADIS (ρ = 0.984). The study provides a multidimensional framework for identifying sectors facing the greatest cumulative transformation pressure and offers relevant insights for policymakers, sector representatives, and strategic decision-makers.

Özet

Bu çalışma, küresel makrotrendlerin dönüştürücü etkilerine yönelik sektörel maruziyet yoğunluğunu Çok Kriterli Karar Verme (ÇKKV) yöntemleriyle değerlendirmeyi amaçlamaktadır. Analizde Future of Jobs Report 2025’ten elde edilen veriler kullanılmış ve 12 makrotrend kriteri kapsamında 22 sektör incelenmiştir. Kriter değerleri, her bir makrotrendin sektörel dönüşümü etkileme düzeyini gösterdiğinden, tüm kriterler maksimizasyon yönlü etki kriterleri olarak tanımlanmıştır. Buna göre yüksek bir değer, daha iyi sektörel performansı değil, daha yüksek dönüşüm maruziyetini ifade etmektedir. Kriter ağırlıkları WENSLO, SPC ve eşit ağırlıklandırma yaklaşımlarıyla belirlenmiş; sektör sıralamaları ise PIV, CRADIS ve ARTASI yöntemleri kullanılarak elde edilmiştir. Üç ağırlıklandırma senaryosu altında oluşturulan sıralamalar Borda yöntemiyle bütünleştirilmiş, ÇKKV yöntemleri arasındaki sıralama tutarlılığı ise Spearman korelasyon analiziyle incelenmiştir. Bulgular, Mining and Metals sektörünün en yüksek toplam dönüşüm yoğunluğuna sahip olduğunu; bu sektörü Automotive and Aerospace ile Supply Chain and Transportation sektörlerinin izlediğini göstermektedir. Buna karşılık Real Estate ile Accommodation, Food, and Leisure sektörleri, analize dâhil edilen makrotrendlere görece en düşük maruziyet düzeylerini sergilemiştir. Yöntemler arasındaki korelasyonların pozitif, istatistiksel olarak anlamlı ve oldukça güçlü olduğu, en yüksek uyumun PIV ile CRADIS arasında gerçekleştiği belirlenmiştir (ρ = 0,984). Çalışma, en yüksek toplam dönüşüm baskısıyla karşı karşıya olan sektörlerin belirlenmesine yönelik çok boyutlu bir çerçeve sunmakta; politika yapıcılar, sektör temsilcileri ve stratejik karar alıcılar açısından önemli çıkarımlar sağlamaktadır.

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Cite This Article

Akçakaya, E. D. U., Koca, G. (2026). Assessing Sectoral Exposure to the Transformative Effects of Macrotrends Using Multi-Criteria Decision-Making Methods: Evidence from the Future of Jobs Report 2025. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 28 Sayı: Özel Sayı Özel Sayı, 153–168. https://doi.org/10.33707/akuiibfd.1966395

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