Smart Device in Human Behavior Manipulation

Description

This dataset, titled Smart Device in Human Behavior Manipulation, is derived from a structured survey-based study aimed at exploring the influence of smart devices on human behavior, psychological patterns, decision-making, and privacy awareness. The dataset captures responses from individuals across diverse demographics and provides insights into how various features of smart devices (e.g., personalized ads, notifications, recommendation systems) potentially shape or manipulate user behavior in subtle yet impactful ways. The data includes variables related to: Demographics (age, gender, education level, occupation) Device Usage Patterns (screen time, preferred apps, device dependency) Behavioral Influence (mood changes, impulsive decisions, productivity impact) Advertisement and Content Exposure (ad personalization, engagement behavior) Privacy and Security Awareness (understanding of data tracking, permission settings) Each entry represents an anonymized response from a single participant, and all responses were collected via a voluntary online survey with informed consent. The dataset is intended for use in behavioral science, digital psychology, AI ethics, and human-computer interaction research.

Steps to reproduce

To reproduce the findings or conduct further analysis using this dataset, follow the steps below: Download the Dataset Obtain the CSV file titled "Smart Device in Human Behavior Manipulation.csv" from this Mendeley Data repository. Understand the Variables Refer to the header row in the CSV file for variable names. Each row represents an anonymized participant response. Key variable groups include: Demographic information Smart device usage habits Perceived behavioral impact Advertisement and algorithmic content exposure Privacy and data awareness levels Preprocess the Data (Optional) Handle missing values if present. Convert categorical variables to appropriate types (e.g., one-hot encoding for modeling). Normalize or standardize data for statistical or machine learning applications. Run Statistical or Machine Learning Analyses Perform exploratory data analysis (EDA) to identify trends and correlations. Apply regression, clustering, classification, or association rule mining techniques depending on your research goals. Tools such as Python (Pandas, Scikit-learn), R, or SPSS can be used. Replicate the Study or Extend It Replicate the behavioral patterns analysis done in the original study. Extend the dataset with longitudinal studies or cross-cultural comparisons. Combine with qualitative data or experimental interventions to enhance findings. Cite This Dataset If you use this dataset in your research, please cite it using the citation provided by Mendeley Data.

Institutions

• State University of Bangladesh
• Dhaka International University

Categories

Funders

• Skill Morph Research Lab

Licence