TECHNOVATE: Journal of Information Technology and Strategic Innovation Management

Understanding Consumer Behavior Through Big Data Analytics: Evidence from the Smartphone Industry

Bouaddi Mohamed, Beddaa Mohammed, Khaldi Siham — Mon, 26 Jan 2026 00:00:00 +0000

In an era dominated by digital interactions, the utilization of Big Data has become essential for businesses striving to comprehend and anticipate consumer behavior. This study investigates the impact of Big Data on understanding consumer behavior and enhancing customer satisfaction within the smartphone market. A quantitative research design was employed, involving a sample of 300 smartphone users surveyed through a structured questionnaire. Utilizing Structural Equation Modeling (SEM), the analysis revealed significant positive relationships: Big Data positively influences the understanding of consumer behavior and consumer satisfaction, while a deeper understanding of consumer behavior also enhances satisfaction. The strong statistical significance of these findings underscores the strategic value of Big Data for businesses aiming to optimize customer experiences and maintain competitive advantage. Furthermore, the study highlights key recommendations for organizations looking to leverage Big Data effectively. Companies should invest in robust data analytics platforms, implement advanced analytics tools for effective customer segmentation, and utilize machine learning algorithms to anticipate consumer trends. Ethical considerations are paramount; organizations must ensure transparency in data collection and comply with privacy regulations to foster consumer trust. Future research should explore the long-term effects of Big Data utilization on consumer satisfaction and examine its applications across different industries. Overall, this study affirms that effective data analytics not only enhances consumer insights and satisfaction but also strengthens relationships between businesses and their customers in an increasingly competitive market landscape.

A Profile Matching-Based Decision Support Framework for Selecting Generative AI Tools in Higher Education

Sat, 31 Jan 2026 00:00:00 +0000

This study aims to develop a decision-making model for determining the best AI application, specifically for students, in selecting the best alternative based on various criteria. This study used the Profile Matching Method to rank alternatives. Data was collected from students through an online survey, with criteria including ease of use (C1), task completion support (C2), creativity and idea support (C3), output quality and accuracy (C4), flexibility of use (C5), and access cost (C6). A decision-making analysis was conducted to determine the application that best suited students' preferences and identified the factors that most influenced their choice. The results showed the ChatGPT application alternative (A1) as the best student choice.

Algorithmic Forecasting of Tourist Mobility: Implementation of Fuzzy Time Series in High-Variability Aviation Data

Sat, 31 Jan 2026 00:00:00 +0000

Accurate forecasting of tourist arrivals is a critical determinant for strategic planning and operational efficiency in island destinations. However, forecasting domestic tourist mobility through airport gateways, such as Lombok International Airport (LIA), presents a significant challenge due to the high volatility and non-linear characteristics of aviation data. Conventional statistical models often fail to capture these dynamic fluctuations effectively. To address this issue, this study proposes an algorithmic forecasting framework using the Fuzzy Time Series (FTS) Chen model. The methodology involves processing monthly arrival data through a structured sequence: defining the universe of discourse, partitioning intervals, fuzzification, establishing Fuzzy Logical Relationships (FLRs), and performing defuzzification. The model's performance was rigorously evaluated using the Mean Absolute Percentage Error (MAPE). Empirical results demonstrate that the FTS Chen algorithm is highly effective for stable datasets, achieving a forecasting accuracy with a MAPE as low as 9.23% for foreign tourist arrivals. In contrast, the model exhibited higher error rates for domestic tourist data, attributed to significant seasonal volatility and external shocks. These findings confirm that while the proposed soft computing approach is robust for detecting trends in stable tourism flows, highly fluctuating domestic markets may require hybrid optimization. Practically, this study provides airport authorities with a quantitative tool to anticipate visitor volume and optimize resource allocation in the post-pandemic era.