SmartPLS SAMPLE DATA
This data idea builds on the article by Wang et al (2023) – “Artificial intelligence changes the way we work: A close look at innovation with chatbots”. See full article here.
Wang, X., Lin, X., & Shao, B. (2023). Artificial intelligence changes the way we work: A close look at innovating with chatbots. Journal of the Association for Information Science and Technology, 74(3), 339–353.
♥ Payment and some notes with the Sample Dataset
The sample data set is compatible with the model presented here. The data set is fixed and there is no option to customize according to customer requirements. If you have any other request please use our Data Processing service here.
Note that this dataset is not unique, so many people may have purchased it. However, this dataset is currently unoccupied.
Payment method: pay first, receive data later.
Payment terms: If you buy data, please transfer money to the account:
Account holder: NGUYEN THANH TIEN
ACB Account 4707776 (THANH DA Transaction Office)
Transfer content: Data code + space + your email (only take the part before @)
For example, your email is hathu2212@gmail.com, the transfer content is as follows: Data23060401 hathu2212
After transferring, please send an email with payment documents confirming successful transfer to email address phantichamos@gmail.com or send a direct message to zalo at 0971.202.308. We will send the data file to you within 30 minutes after receiving the transfer information. The data file includes data files pre-encoded in spss and related model files if any. In the file sent to you, we have included an article inspired by creating sample data (if any).
♥ Data Information
Data code: Data23060401
Price: 1,390,000 VND
♥ Data Description
- PLS SEM model
(Source: Wang et al. (2023))
- Sample size: 211
- Description: Higher-order model, cited study scale is the same as Wang et al. (2023).
(Source: Wang et al. (2023))
- PLS SEM model run using SmartPLS: gives good results. The results are similar to the study of Wang et al. (2023).
- Demographic section: Gender, age, education level and Company size. The statistical results are similar to Wang et al. (2023).
| Biến | Chi tiết | Mã hóa | Tổng | % |
| Gender | Nam | 1 | 147 | 69.7% |
| Nữ | 2 | 64 | 30.3% | |
| Age | 18-24 | 1 | 12 | 5.7% |
| 25-34 | 2 | 80 | 37.9% | |
| 35-44 | 3 | 82 | 38.9% | |
| 45-54 | 4 | 31 | 14.7% | |
| 55 or older | 5 | 6 | 2.8% | |
| Education | THPT Trung cấp | 1 | 29 | 13.7% |
| Cao đẳng Đại học | 2 | 120 | 56.9% | |
| Sau đại học | 3 | 62 | 29.4% |
♥ Results of PLS SEM analysis using SmartPLS 4
♪ We only present the main results in the analysis model, but in detail, after you buy the data and run it yourself to get the detailed results.
♪ Evaluate the measurement model for first-order latent variables.
♪ Evaluate reliability based on Outer loadings and CR. We are only interested in indicators related to first-order latent variables.
♪ Assess the level of convergence accuracy through the AVE index. The AVE in the table above of the first-order latent variables are all greater than 0.5, so each measurement model for the first-order latent variable achieves convergence accuracy.
♪ Assess the level of discrimination accuracy (Discriminant Validity).
♪ Evaluate the phase 2 measurement model
Because this is a high-level structural model, the way to run the analysis is also complicated.
♥ WE HOPE YOU ENJOY USING THIS DATA SET ♥
Tip: You should change the sample size to have a set of data that does not overlap with previous buyers, by deleting the number of data lines in the data file, it is recommended to delete within 10 lines → change sample size. change from 200 to 211.
