AMOS RUN SAMPLE DATA | SmartPLS
The dataset originates from the article by Hayat et al. (2022) – “Eco-advertising and Ban-on-Plastic: The Influence of CSR Green Practices on Green Impulse Behavior.” The study investigates the relationship between environmental advertising and the ban on plastic for socially responsible businesses (CSR) and their impact on consumers’ green impulse behavior. The paper also examines the mediating role of environmental advertising, the ban on plastic, consumers’ subjective awareness, and their green shopping behavior in these relationships.
Abstract:
Consumers are becoming increasingly aware of the environmental and social impacts of their consumption habits. To engage in eco-friendly transactions, consumers require product information related to the environment. Consequently, marketers and companies are utilizing eco-friendly advertising tools to integrate detailed environmental information into their products, such as environmental advertising and bans on plastic. This study aims to examine the direct influence of environmental advertising and the ban on plastic on green impulse behavior and the mediating role of CSR green practices. Data were collected from a sample of participants, and the results suggest that environmental advertising and the ban on plastic significantly and positively influence green impulse behavior directly and through flexible dimensions of CSR green practices as a mediating mechanism. The study provides essential insights for companies and marketers to explore issues related to green impulse behavior and CSR green practices, thereby identifying the roles of environmental advertising and the ban on plastic.
Hayat, K., Jianjun, Z., Ali, S., & Ageli, M. M. (2022). Eco-advertising and ban-on-plastic: The influence of CSR green practices on green impulse behavior. Journal of the Knowledge Economy, 1–30.
Starting from this dataset onwards, we will not publicly disclose the results to ensure data confidentiality. If you are interested, please contact us for more detailed information about this dataset. In this post, we only show the final model results. We assure you of the data’s integrity. If you find any discrepancies, we will support you in rectifying the dataset free of charge.
♥ Payment and Notes on the Sample Dataset
The sample dataset is compatible with the model presented here. The dataset is fixed and does not have custom options. If you have any other requests, please use our Data Processing service.
Please note that this dataset is not unique, so many people may have purchased it.
Payment method: Payment in advance to the account:
Account holder: NGUYEN THANH TIEN
ACB account 4707776 (Thanh Da branch)
Payment note: Data code + space + your email (only the part before @)
For example, if your email is hathu2212@gmail.com, the payment note is as follows: Data23070101 hathu2212
After making the transfer, please send an email with the payment confirmation to phantichamos@gmail.com or send a direct message to Zalo number 0971.202.308. We will send you the dataset file within 30 minutes of receiving the payment information. The dataset file includes an encoded data file in SPSS and any related model files if available. In the file we send you, we also include the article used to initiate the sample data (if available).
♥ Dataset Information
Data code: Data23070101
Price: 1,169,000 VND
♥ Dataset Description
- The model runs CB SEM using AMOS 28 software. (For this dataset, you should run it with SmartPLS 4 for very good results, better than running with CB SEM)
Source: Hayat et al (2022)
- Sample size: 479
• Scale: 5-point Likert
- Description: The structural model has 6 first-order latent variables, including 1 moderator variable, measured by 28 observed variables. For details, see the research model. CB SEM analysis results all reached OK.
- Statistical information: In this sample data set, we will not show basic information about qualitative variables. If you need it, please contact us, we will create it for you for free to suit your research context (free of charge).
We hope you are satisfied with this dataset!
