Modern Statistical Techniques and Sustainability Studies: Selected Papers from the 9th International Conference on PLS and Related Methods (PLS'17)
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (20 October 2017) | Viewed by 130478
Special Issue Editors
Interests: tourism and consumption; information technology and management; carbon reduction and transportation
Interests: sustainable cities; competitive and sustainable strategy; information systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
By comparing with first-generation statistical techniques (e.g., linear regression, ANOVA, MANOVA), second-generation techniques can provide a more rigorous analysis by assessing the structural model and the measurement model at the same time. With the proliferation of second-generation statistical techniques in nutrition, technology, energy, economics, sustainable cities, management, consumption and other sustainability disciplines, it is time to have a a dearth of understanding regarding the role that second-generation statistical techniques can play toward achiving social and environmental sustainability studies.
This Special Issue will address wide-ranging topics on modern statistical techniques and sustainability studies, espeically the original methodological contributions in modern statistical techniques with application in areas and disciplines related to management, social sciences, chemometrics, sensory analysis, industry, tourism and life sciences including genomics. It is hoped that the broad scope of this Special Issue will encourage submissions from across several disciplines.
Dr. Ivan K. W. Lai
Dr. Yide Liu
Guest Editors
Manuscript Submission Information
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Keywords
- Environmental sustainability
- Social sustainability
- Applied statistics
- Sustainable development index and index research
- Partial least squares
- Structural equation modeling
- Tourism and consumption
- Information technology and management
- Sensory analysis
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