Topic Modelling of Contemporary Management Information Systems Research: A Latent Dirichlet Allocation Approach

Öz

This research thoroughly explores the expansive academic discourse on Management Information Systems (MIS), investigating its branches, influential theories, and diverse topics. Employing a topic modeling approach, the study analyzes 7852 article abstracts from the Scopus database (2022-2023) using the Latent Dirichlet Allocation algorithm. The goal is to identify research gaps and delineate key areas of interest. The findings suggest that the corpus of articles can be distinctly classified into varied thematic categories, each representing significant facets within the MIS domain. The analytical outcome postulates the existence of seven distinct thematic clusters such as “Digital Innovation and Business Performance”, “Cost Optimization and Demand Analysis”, “Advancements in Machine Learning and Deep Learning”, “Sustainable Practices in Green Logistics and Supply Chain Management”, “Impact of the COVID-19 Pandemic on Social Interaction and Crisis Management Strategies in Businesses”, “Decision Making Systems and the Efficacy of Optimization Models”, and “Consumer Satisfaction and Behavioral Analysis in Online Services”. These insights not only underscore the heterogeneity and breadth of the MIS field but also underscore the dynamic nature of the discipline. The derived results hold the potential to steer future inquiries and foster novel contributions within the realm of MIS.

Anahtar Kelimeler

• Management Information Systems
• Topic Modeling
• Latent Dirichlet Allocation
• Text Mining
• Machine Learning

Proje Numarası

222K181

Güncel Yönetim Bilişim Sistemleri Araştırmalarının Konu Modellemesi: Bir Gizli Dirichlet Ataması Yaklaşımı

Öz

Bu araştırma, Yönetim Bilişim Sistemleri (YBS) üzerine geniş kapsamlı akademik söylemi derinlemesine incelemekte, dallarını, etkili teorileri ve çeşitli konuları araştırmaktadır. Bir konu modelleme yaklaşımı kullanan çalışma, Scopus veri tabanından (2022-2023) 7852 makale özetini Gizli Dirichlet Ataması algoritması kullanarak analiz etmektedir. Amaç, araştırma boşluklarını belirlemek ve temel ilgi alanlarını tanımlamaktır. Bulgular, makale külliyatının, her biri YBS alanındaki önemli yönleri temsil eden çeşitli tematik kategoriler halinde belirgin bir şekilde sınıflandırılabileceğini göstermektedir. Analitik sonuç, 'Dijital İnovasyon ve İş Performansı', 'Maliyet Optimizasyonu ve Talep Analizi', 'Makine Öğrenimi ve Derin Öğrenmedeki Gelişmeler', 'Yeşil Lojistik ve Tedarik Zinciri Yönetiminde Sürdürülebilir Uygulamalar', 'COVID-19 Pandemisinin İşletmelerde Sosyal Etkileşim ve Kriz Yönetimi Stratejileri Üzerindeki Etkisi', 'Karar Verme Sistemleri ve Optimizasyon Modellerinin Etkinliği' ve 'Çevrimiçi Hizmetlerde Tüketici Memnuniyeti ve Davranış Analizi' gibi yedi farklı tematik kümenin varlığını varsaymaktadır. Bu görüşler sadece YBS alanının heterojenliğini ve genişliğini vurgulamakla kalmıyor, aynı zamanda disiplinin dinamik doğasının da altını çiziyor. Elde edilen sonuçlar, gelecekteki araştırmaları yönlendirme ve YBS alanında yeni katkıları teşvik etme potansiyeline sahiptir.

Anahtar Kelimeler

• Yönetim bilişim sistemleri
• Konu modelleme
• Gizli Dirichlet Ataması
• Metin madenciliği
• Makine öğrenmesi

Destekleyen Kurum

TÜBİTAK | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Proje Numarası

222K181

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