Journal of Quantitative Research in Social Sciences

Microclimate Marketing: A Real-Time, Weather-Based Application for Influencing Consumer Behavior

Murat KAÇAR — 2025-07-31

This article provides an in-depth examination of the "microclimate marketing" concept, defining it as an application for real-time, weather-based influencing of consumer behavior. The study presents a framework for the concept, tracing it from its ecological origins to its contemporary definition within the marketing context. It explores the complex effects of weather on consumer psychology and behavior through psychological mechanisms such as mood, purchase intention, and spending habits. Relevant theoretical frameworks, including the Stimulus-Organism-Response (S-O-R) Model and Mood Congruence Theory, are employed to explain the underlying drivers of this interaction. The paper details how dynamic content personalization is achieved through the integration of real-time and predictive weather data, showcasing applications across various sectors and detailing concrete examples of success. Furthermore, it comprehensively discusses the technical challenges in weather data management—such as data accuracy, reliability, and integration—along with the ethical dimensions, including location data privacy and algorithmic bias. Finally, the article assesses how emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) will shape the future of microclimate marketing. It thereby offers a holistic perspective that highlights both the potential and the challenges of this strategic approach.

Evaluation of Social Policy Scenarios Against Declining Fertility Rates Through a System Dynamics Model: The Case of Turkey

Dilber Yeliz YILMAZ — 2025-07-31

In recent years, the decline in fertility rates in Turkey has emerged not only as a demographic phenomenon but also as a structural transformation process that threatens the sustainability of social and economic systems. This study analyzes the underlying causes of the fertility decline in Turkey and evaluates the potential impacts of social policy scenarios developed in response to this issue through a system dynamics modeling approach. First, historical fertility trends in Turkey are examined; subsequently, the demographic and socio-economic consequences of the current fertility structure are revealed. Then, the adequacy of the current social policy instruments offered by the government to address this decline is discussed. In the modeling phase, three main scenarios are defined: continuation of current policies, increase in financial incentives and development of structural supports such as childcare services and parental leave. According to simulation results covering the years 2024–2044, financial incentives alone are not sufficient, while structural supports generate more lasting and positive effects on fertility in the long term. The findings indicate that fertility policies cannot be limited to economic incentives alone and emphasize the need for comprehensive and multidimensional social policy approaches. In this regard, the study provides a significant contribution to policymakers in developing data-driven and long-term strategies.

Forecasting Traffic Accident Numbers Using Time Series Models

Serhat TOPUZ — 2025-07-31

This study aims to forecast the number of traffic accidents that may occur in the jurisdiction of the Gendarmerie in the Western Black Sea Region using time series models. Statistical data from April 2019 to December 2023 obtained from the official website of the General Command of Gendarmerie have been utilized. This data includes traffic accidents occurring in the provinces of Bartın, Bolu, Düzce, Karabük, Kastamonu, Sinop, and Zonguldak. The Autoregressive Integrated Moving Average (ARIMA) method has been employed to predict future accident numbers. In this context, data analyses and modeling have been conducted using Minitab and EViews software. The results of the study are supported by trend analysis, seasonality analysis, and forecasting models. According to the findings, predicting the number of traffic accidents will contribute to more informed and effective decision-making by managers in personnel allocation and economic planning. Furthermore, the forecast results obtained will assist in taking necessary measures to prevent traffic accidents, which are significant for public health and the state economy, in future similar studies relevant to academic and public institutions.