Automated Author ProfileJamorn Jaroencheep
Jamorn Jaroencheep
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Clothing category is key driver of E-commerce growth in Thailand (Thansettakij, 2019). The result shows that most of consumer in Thailand buy fashion products online and receive product at home or an office. However, there are a new way of online shopping that is Click-and-Collect service. This new solution is popular in Europe, Australia, and U.S.A (Mortimer & Grimmer, 2017). It is interesting to find out key factors that impact to Thai fashion shoppers using Click-and-Collect. Objectives of this research are to understand fashion shopper profile, to determine fashion shopper’s buying behavior, and key attributes for ordering fashion products through the online and collecting fashion products at a physical store. This study used both exploratory and descriptive research to gain an overview of the industry and insights from fashion online shoppers. Exploratory research included secondary research and focus group. Descriptive research used online questionnaires to gather data.Results showed four key factors that influence ordering online and collecting offline for fashion products. Respondents concern about Online Experience, In-store Experience, Pick-up Duration, and No Additional Fee as the key attributes before buying fashion products via Click-and-Collect service. Moreover, respondents were categorized into three segments that are In-Store Experience Seekers, Convenience Seekers, and Online Experience Seekers. Even if respondents can be separated into three clusters, there are indifferent buying behavior and key factors that influence three clusters buy fashion products online via Click-and-Collect. Therefore, fashion brands are able to apply same marketing strategies to all three segments.
Authors
- Jamorn Jaroencheep