Investigating the Association between Employee Silence and Perceived Organizational Support via Data Mining

Abstract

Modeling human behavior in distinctive contexts is of vital importance today, especially in research in the fields of marketing and management. From a management perspective, modeling employee behavior strengthens decision-makers’ hands in numerous areas such as workforce planning and management. Comprehensive employee profiles obtained offer business organizations extremely important strategic gains, especially sustainable competitive advantage. This study is the first attempt to model the relationship between employee silence behavior and perceived organizational support through data mining techniques, and it aims to extract employee profiles accordingly. Evaluating these two important phenomena within such a relational framework opens a novel door for organizational behavior researchers. The Association Rule Mining technique was applied to achieve knowledge discovery from the data obtained through two scales. Within the research, the rules above a threshold value of 0.50 were evaluated to make the associations representing the employee profiles more meaningful. Accordingly, firstly, the association rules between the factors of the organizational silence scale were evaluated and seven rules were obtained. Next, the perceived organizational support factor was included in the model, and how it affected the obtained association rules was examined. In this manner, ten association rules formed by the factors of the two scales were determined. The inclusion of the perceived organizational support factor revealed quite striking relationships. Especially, an association of over 80% observed in the first five rules enabled the discovery of hidden patterns in terms of determining employee profiles. These valuable inferences obtained through this study reveals the importance of utilizing data mining techniques for future profile extraction studies.

Keywords

• Employee Silence
• Perceived Organizational Support
• Data Mining
• Apriori Algorithm
• Association Rule Mining

Çalışan Sessizliği ve Algılanan Örgütsel Destek Arasındaki İlişkinin Veri Madenciliği ile İncelenmesi

Abstract

İnsan davranışının kendine özgü bağlamlarda modellenmesi bugün özellikle pazarlama ve yönetim alanlarında yapılan araştırmalarda hayati önem taşımaktadır. Yönetim perspektifinden bakıldığında, çalışan davranışının modellenmesi karar vericilerin iş gücü planlaması ve yönetimi gibi pek çok konuda elini güçlendirmektedir. Elde edilen kapsamlı çalışan profilleri işletmelere başta sürdürülebilir rekabet üstünlüğü olmak üzere son derece önemli stratejik kazanımları sunmaktadır. Bu çalışma, çalışan sessizliği davranışı ile algılanan örgütsel destek arasındaki ilişkiyi veri madenciliği teknikleri aracılığıyla modellemeye yönelik öncül bir girişimdir ve bu bağlamda çalışan profili çıkarımı yapılmasını amaçlamaktadır. Bu iki önemli olgunun böyle bir ilişkisel bir çerçevede ele alınması örgütsel davranış araştırmacı için yeni bir kapı aralamaktadır. Çalışma kapsamında kullanılan ölçekler yardımıyla elde edilen verilerden bilgi keşfi yapabilmek için birliktelik kuralları analizi uygulanmıştır. Araştırma kapsamında çalşan profillerini temsilen elde edilen birlikteliklerin daha anlamlı olabilmesi için 0,50 eşik değeri üzerindeki kurallar değerlendirilmiştir. Bu doğrultuda, öncelikle örgütsel sessizlik ölçeğinin faktörleri arasındaki birliktelik kuralları değerlendirilmiş ve yedi kural elde edilmiştir. Daha sonra, algılanan örgütsel destek faktörü modele dahil edilerek elde edilen birliktelik kurallarını nasıl etkilediği incelenmiştir. Böylece her iki ölçeğin faktörlerinin bir arada oluşturduğu on birliktelik kuralı belirlenmiştir. Algılanan örgütsel destek faktörünün dahil olmasıyla araştırma için oldukça çarpıcı ilişkiler ortaya çıkarılmıştır. Özellikle ilk beş kuralda gözlemlenen %80 üzerinde birliktelik ilişkisi, çalışan profillerinin belirlenmesi açısından gizli kalmış örüntülerin keşfedilmesini sağlamıştır. Çalışma kapsamında elde edilen bu değerli çıkarımlar gelecekte yapılacak profil çıkarımı çalışmaları için veri madenciliği tekniklerinin kullanımının önemini ortaya koymaktadır.

Keywords

• Çalışan Sessizliği
• Algılanan Örgütsel Destek
• Veri Madenciliği
• Apriori Algoritması
• Birliktelik Kuralları Analizi

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