The document discusses partial least squares structural equation modeling (PLS-SEM) as a statistical technique for second-generation research. It explains that PLS-SEM can handle complex models with latent variables and mediators better than first-generation techniques like regression. The document also compares PLS-SEM to other second-generation techniques like covariance-based SEM and discusses the advantages and disadvantages of each approach.
Data and Behavior Change: Is Seeing Believing, and is That Enough?Sustainable Brands
How do we drive behavior change by enabling easy digestion of one's consumption behavior, or the context of one's social circles? What hard evidence do we have that 'recently liberated,' easily accessible data in energy use, water use, nutrition, and life/work satisfaction is changing behavior? What have we learned about user/consumer reactions to different types of data visualization and what advice do respective solution providers have for brands entering their own data transparency journeys aiming to attract or change customers, employees and other stakeholders?
Data and Behavior Change: Is Seeing Believing, and is That Enough?Sustainable Brands
How do we drive behavior change by enabling easy digestion of one's consumption behavior, or the context of one's social circles? What hard evidence do we have that 'recently liberated,' easily accessible data in energy use, water use, nutrition, and life/work satisfaction is changing behavior? What have we learned about user/consumer reactions to different types of data visualization and what advice do respective solution providers have for brands entering their own data transparency journeys aiming to attract or change customers, employees and other stakeholders?
Multi Objective Optimization of PMEDM Process Parameter by Topsis Methodijtsrd
In this study, MRR, SR, and HV in powder mixed electrical discharge machining PMEDM were multi criteria decision making MCDM by TOPSIS method. The process parameters used included work piece materials, electrode materials, electrode polarity, pulse on time, pulse off time, current, and titanium powder concentration. Some interaction pairs among the process parameters were also used to evaluate. The results showed that optimal process parameters, including ton = 20 µs, I= 6 A, tof = 57 µs, and 10 g l. The optimum characteristics were MRR = 38.79 mm3 min, SR = 2.71 m, and HV = 771.0 HV. Nguyen Duc Luan | Nguyen Duc Minh | Le Thi Phuong Thanh ""Multi-Objective Optimization of PMEDM Process Parameter by Topsis Method"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23169.pdf
Paper URL: https://www.ijtsrd.com/engineering/manufacturing-engineering/23169/multi-objective-optimization-of-pmedm-process-parameter-by-topsis-method/nguyen-duc-luan
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Covariance-based Structural Equation Modeling (CB-SEM) Model Using SmartPLS4Teetut Tresirichod
1. First CB SEM Model using SmartPLS4
2. Data Screening and Imputation
3. Basic CFA Model with SmartPLS4
4. What Fit Indices to use for Model Fit?
5. Understand and Interpret Construct Reliability and Convergent Validity
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7. Simple Structural Model Analysis
8. Moderation Analysis using CBSEM Model in SmartPLS4
9. Mediation Analysis using CBSEM Model in SmartPLS4
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Improving profitability for small businessBen Wann
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Cracking the Workplace Discipline Code Main.pptxWorkforce Group
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5. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 5
สำหรับเทคนิคที่ใช้ในกำรวิจัยเมื่อพิจำรณำจำกผลงำนที่ตีพิมพ์ในวำรสำรที่มี
ชื่อเสียง (Management Information Systems Quarterly [MISQ), Information Systems
Research [ISR], Journal of Management Information Systems [JMIS], and Journal of
the Association for Information Systems JAIS) ในช่วงปี ค.ศ. 2000-2008 พบว่ำส่วน
ใหญ่ ให้ควำมสำคัญในกำรใช้เทคนิคกำรวิเครำะห์ ยุคที่สอง (Gerow, Grover, Roberts,
& Thatcher, 2010) หลังจำกนั้นแล้วตั้งแต่ปี ค.ศ. 2005 เป็นต้นมำเริ่มมีกำรนำ PLS-SEM
มำใช้อย่ำงจริงจังเพรำะตัวโปรแกรมใช้ง่ำย ข้อมูลมีข้อจำกัดน้อย และให้ผลกำรวิเครำะห์
ในระดับที่ยอมรับได้ทำให้นักวิจัยได้ใช้ PLS-SEM มำกขึ้นอย่ำงเห็นได้ชัด (Ringle,
Sarstedt, & Straub, 2012)
13. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 13
Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using Partial Least Squares Path Modeling in International Advertising Research:
Basic Concepts and Recent Issues. In S. Okazaki (Ed.), Handbook of Research in International Advertising (pp. 252–276). Cheltenham:
Edward Elgar Publishing.
14. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 14
Product1
Product2
Product3
Loyalty1
Loyalty2
Service1
Service2
Service3
Weighting Scheme: Factor or Centroid
(Currently factor is used)
Product Satisfaction
Service Satisfaction
Loyalty
22. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 22
For Mode A: Loadings are defined as covariances between the latent variables values and the individual indicators values
Product1 0.880
Product2 0.798 0.502
Product3 0.888
1.808 Loyalty1
1.808 Loyalty2
Service1 2.213 0.754 <- r²
Service2 2.293 0.429
Service3 2.147
ATTENTION: The Excel-LINEST function returns the parameters in reversed order. http://office.microsoft.com/en-gb/excel-help/linest-function-HP010069838.aspx
ACHTUNG: Auf deutsch heisst die Funktion RGP. Siehe http://office.microsoft.com/de-de/excel-help/rgp-funktion-HP010342653.aspx
Product Satisfaction
Loyalty
Service Satisfaction
For Mode A: Loadings are defined as covariances between the latent variables values and the individual indicators values
Product1 0.896
Product2 0.777 0.510
Product3 0.891
0.938 Loyalty1
0.962 Loyalty2
Service1 0.843 0.776 <- r²
Service2 0.893 0.434
Service3 0.842
ATTENTION: The Excel-LINEST function returns the parameters in reversed order. http://office.microsoft.com/en-gb/excel-help/linest-function-HP010069838.aspx
ACHTUNG: Auf deutsch heisst die Funktion RGP. Siehe http://office.microsoft.com/de-de/excel-help/rgp-funktion-HP010342653.aspx
Product Satisfaction
Loyalty
Service Satisfaction
23. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 23
PLS เป็น variance based model พัฒนำตำมวิธี OLS ที่ใช้principal component
regression (PCR) เป็นเครื่องมือตำมวิธี least square
ตัวแบบสมกำรโครงสร้ำง (structural equation modeling, SEM) เป็น Second
Generation Model คือเป็นตัวแบบที่สำมำรถวิเครำะห์ควำมสัมพันธ์ระหว่ำงตัวแปรได้
หลำยระดับของ SEM ทั้งวิเครำะห์ควำมสัมพันธ์ใน inner model (structure model)
และวิเครำะห์ควำมสัมพันธ์ใน outer model (measurement model) ไปในครำวเดียวกัน
25. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 25
จำกกำรติดตำมกำรใช้ second generation modeling ในวำรสำรด้ำน IS พบว่ำมี
กำรนำ ซอฟท์แวร์ PLS และ LISREL ไปใช้ในงำนวิจัย 39 % เท่ำกัน ที่เหลืออีก 23%
เป็นซอฟท์แวร์ SEM อื่นเช่น AMOS EQS และอื่น ๆ (Gefen, Straub and Boudreau,
2000)
อ้ำงอิงจำก: มนตรี พิริยะกุล. (2010). PartialLeast Square Path Modeling (PLS Path Modeling)
Chatelin,Y.M. (2002), Vinzi, V.E., and Tenenhons, M. (2002). State of Art on PLS Path Modeling through the available
software,Retreived Feb 12, 2010, from http://www.sinopai.com/sinopai2/repository/ppt/20041227005.pdf.
Lauro, C. and V.E.Vinzi. (2004). Some Contributionsto PLS Path Modeling and System for the European
Customer Satisfaction,RetrievedJan 13,2010,from http://www.sis-statistica.it/files.pdf/atti/RMi0602p201-
210.pdf.
68. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 68
There are many ways of modeling the moderating effects (interaction effects)
of multi-item constructs (Dijkstra & Henseler, 2011). Fassott et al. (2016) provide the
most current guidelines on how to model the moderating effects of composites. In order
to avoid multicollinearity issues in the context of moderating effects, users can
orthogonalize the interaction term (see Henseler & Chin, 2010). Analysts using
ADANCO 2.0.1 should use a two-stage approach to model moderating effects:
1. Estimate the model without the interaction. Extract the construct scores.
Create an interaction term.
2. Estimate the model, including the interaction.
84. ผศ.ดร.ธีทัต ตรีศิริโชติ Page 84
Van Riel et al. (2017) provide the most current guidelines on how to model
second-order constructs using variance-based SEM. Analysts using ADANCO 2.0.1
should use a two-stage approach to model second-order constructs:
1. Estimate the model containing only the first-order constructs. Extract the
construct scores.
2. Estimate the model containing the second-order construct(s). Use the
construct scores of the first-order constructs as indicators of the second-order
construct(s). If necessary, adjust the reliability of the second-order construct manually.