• Home
  • Browse
    • Current Issue
    • Browse Issues
    • Browse Subjects
    • Browse Keywords
    • Browse Authors
  • Submit Paper
  • Journal Info
    • Editorial Board
    • Editorial Staff
    • Peer Review Process
    • Related Links
    • Facts & Figures
  • Guide for Authors
  • Contact Us
  • Register
  • Login

Advanced Search
Reduce Font Increase Font
Home Articles Article Details
Print
  • Recommend Journal Recommend
  • |
  • Alert E-Alert
  • |
  • Order JournalOrder Journal
  • |
  • Track Your ArticleTrack your article
    Abstracting/Indexing   
      p-ISSN: 1735-1472
    
e-ISSN: 1735-2630
    
    (In Press)
Volume 10 (2013)
Volume 9 (2012)
Volume 8 (2011)
Volume 7 (2010)
Volume 6 (2009)
Volume 5 (2008)
Volume 4 (2007)
Volume 3 (2006)
Volume 2 (2005)
Volume 1 (2004)
Optimization of product line design for environmentally conscious technologies in notebook industry
Article 7: Volume 7, Number 3, Summer 2010, Pages 473-484 (12) XML PDF (777 K)
Authors
K. H. Lin; L. H. Shih; S. C. Lee
Abstract
Promotion of green technologies related to notebook computer will have significant benefits in the environment. Notebook companies need to make a careful market assessment for green technologies. Due to the variety of consumer preferences for green technologies, as well as a hot competitive climate in notebook market, consumer preferences should be taken into consideration during the assessment process. This study classifies the green technologies of notebook industry. Some green technologies are not controlled by the environmental regulations but are popular among customers. This study named this kind of technologies niche green technologies. The product line design model can evaluate the design scheme based on customer preferences. Therefore, this study uses conjoin analysis to investigate the consumers’ preferences for assorted technology. Subsequently, product line design model is utilized to seek the optimal scheme of niche green technologies adoption based on the consumers’ preference. Results of conjoint analysis reveal that consumers value two attributes, including price and size. Furthermore, the preferences for niche green technologies in solid state drive disk and light emitting diode backlight surpass the former technology. After the assessment of market situation with product line design model, two types of niche green technologies, including lithium polymer battery and light emitting diode backlight are suggested for the adoption of new products design.
Keywords
Conjoint analysis; Consumer preferences; Environmental regulations; Green technologies; Notebook computer
Main Subjects
Environmental technology; Notebook industry
References
1. Alexouda, G., (2002). An evolutionary algorithm based method for the product line design using the share of choices criterion. Second Hellenic Conference on Artificial., , 321-330 (10 Pages)
2. Alexouda, G., (2004). An evolutionary algorithm approach to the share of choices problem in the product line design. Comput. Oper. Res., 31 (13), 2215-2229 (15 Pages), DOI: 10.1016/S0305-0548(03)00173-4. Abstract | Full Text (223 K)
3. Alexouda, G., (2005). An user-friendly marketing decision support system for the product line design using evolutionary algorithms. Decis. Supp. Sys., 38 (4), 495-509 (15 Pages), DOI: 10.1016/j.dss.2003.09.002. Abstract | Full Text (819 K)
4. Alexouda, G.; Paparrizos, K., (1999). A genetic algorithm approach to the buyer’s welfare problem of product line design: An comparative computational study. Yugoslav J. Oper. Res., 9 (2), 223-233 (11 Pages) Abstract
5. Alexouda, G.; Paparrizos, K., (20001). A genetic algorithm approach to the product line design problem using the seller ’s return criterion: An extensive comparative computational study. Eur. J. Oper. Res., 134 (1), 165-178 (14 Pages), DOI: 10.1016/S0377-2217(00)00246-0. Abstract | Full Text (122 K)
6. Balakrishnan, P. V.; Gupta, R.; Jacob, V. (2004). Development of hybrid genetic algorithms for product line designs. IEEE T. Syst. Man Cyb., 34 (1), 468-483 (16 Pages), DOI: 10.1109/TSMCB.2003.817051. Abstract | Full Text (424 K)
7. Balakrishnan, P. V.; Jacob, V. S., (1996). Genetic algorithms for product design. Manage. Sci., 42 (8), 1105-1117 (13 Pages), DOI: 10.1287/mnsc.42.8.1105. Abstract | Full Text
8. Bandyopadhyay, G.; Chattopadhyay, S., (2007). Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. Int. J. Environ. Sci. Tech., 4 (1), 141-150 (10 Pages) Abstract | Full Text (121 K)
9. Chen, C. C., (2009). Environmental impact assessment framework by integrating scientific analysis and subjective perception. Int. J. Environ. Sci. Tech., 6 (4), 605-618 (14 Pages) Abstract | Full Text (646 K)
10. Chien, M. K.; Shih, L. H., (2007). An empirical study of the implementation of green supply chain management practices in the electrical and electronic industry and their relation to organizational performances. Int. J. Environ. Sci. Tech., 4 (3), 383-394 (12 Pages) Abstract | Full Text (215 K)
11. Dobson, G.; Kalish, S., (1988). Positioning and pricing a product line. Marketing Sci., 7 (2), 107-125 (19 Pages), DOI: 10.1287/mksc.7.2.107. Abstract | Full Text
12. Green, P. E.; Krieger, A. M., (1985). Models and heuristics for product line selection. Market. Sci., 4 (1), 1-19 (19 Pages) Abstract
13. Green, P. E.; Krieger, A. M., (1989). Recent contribution to optimal product positioning and buyer segmentation. Eur. J. Opl. Res., 41 (2), 127-141 (15 Pages), DOI: 10.1016/0377-2217(89)90375-5. Abstract | Full Text (1204 K)
14. Green, P. E.; Krieger, A. M., (1991). Product Design Strategies for Target Market Positioning. J. Prod. Innovat. Manage., 8 (3), 189-202 (14 Pages), DOI: 10.1016/0737-6782(91)90026-U. Abstract
15. Green, P. E.; Srinivasan, V., (1978). Conjoint analysis in consumer research: Issues and outlook. J. Cons. Res., 5 (2), 103-123 (21 Pages) Abstract
16. Gross, R. A.; Kalra, B., (2002). Biodegradable polymers for the environment. Science., 297 (5582), 803-807 (5 Pages), DOI: 10.1126/science.297.5582.803. Abstract | Full Text (316 K)
17. Hsu, C. W.; Hu, A. H., (2008). Green supply chain management in the electronic industry. Int. J. Environ. Sci. Tech., 5 (2), 205-216 (12 Pages) Abstract | Full Text (268 K)
18. Huang, P. S.; Shih, L. H., (2009). Effective environmental management through environmental knowledge management. Int. J. Environ. Sci. Tech., 6 (1), 35-50 (16 Pages) Abstract | Full Text (324 K)
19. Kang, Y.; Lee, W.; Suh, D. H.; Changjin, L., (2003). Solid polymer electrolytes based on cross linked-polysiloxaneg- oligo (ethylene oxide): Ionic conductivity and electrochemical properties 119-121,. J. Power. Sour., 119-121, 448-453 (6 Pages) Abstract | Full Text (171 K)
20. Kohli, R.; Sukumar, R., (1990). Heuristics for product-line design using conjoint analysis. Manage. Sci., 36 (12), 1464-1477 (14 Pages) Abstract
21. Krieger, A. M.; Green, P. E.; Wind, Y. J., (2004). Adventures in conjoint analysis: A practitioner ’s guide to trade-off modeling and applications. Monograph, University of Pennsylvania. , 1-24 (24 Pages) Abstract | Full Text (194 K)
22. Li, H.; Azarm, S., (2002). An approach for product line design selection Under Uncertainty and Competition. J. Mech. Design, 124 (3), 385-392 (8 Pages), DOI: 10.1115/1.1485740. Abstract
23. Masuda, Y.; Nakayama, M.; Wakihara, M., (2007). Fabrication of all solid-state lithium polymer secondary batteries using PEG-borate/aluminate ester as plasticizer for polymer electrolyte. Solid. State. Ionics., 178 (13-14), 981-986 (6 Pages), DOI: 10.1016/j.ssi.2007.04.009. Abstract | Full Text (502 K)
24. Nair, S. K.; Thakur, L. S.; Wen, K. W., (1995). Near optimal solutions for product line design and selection: Beam search heuristics. Manage. Sci., 41 (5), 767-785 (19 Pages) Abstract
25. Nnorom I. C.; Osibanjo O., (2009). Heavy metal characterization of waste portable rechargeable batteries used in mobile phones.. Int. J. Environ. Sci. Tech., 6 (4), 641-650 (10 Pages) Abstract | Full Text (136 K)
26. Steiner, W.; Hruschka, H., (2003). Genetic algorithms for product design: How well do they really work?. Int. J. Market. Res., 45 (2), 229-240 (12 Pages) Abstract
27. Szymanski, D.; Bharadwaj, S.; Varadarajan, R. (1993). An analysis of the market share profitability relationship. J. Market., 29, 1-18 (18 Pages) Abstract
28. Tehrani, S. M.; Karbassi, A. R.; Ghoddosi, J.; Monavvari, S. M.; Mirbagheri, S. A., (2009). Prediction of energy consumption and urban air pollution reduction in eshopping adoption. J. Food, Agri. Environ., 7 (3 and 4), 898-903 (6 Pages). Full Text (356 K)
29. Tsai, H. C., (2007). An investigation into EMI-induced noise in nanometer multi-quantum well InGaN LEDs. Opt. Commun., 273 (2), 311-319 (9 Pages), DOI: 10.1016/j.optcom.2007.01.038. Abstract | Full Text (269 K)
30. Tuzkaya, G.; Gülsün, B., (2008). Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach. Int. J. Environ. Sci. Tech., 5 (3), 339-352 (14 Pages) Abstract | Full Text (208 K)
31. Wittink, D. R.; Cattin, P., (1989). Commercial use of conjoint analysis: An update. J. Marketing., 53 (3), 91-96 (6 Pages) Abstract
32. Zufryden, F. S., (1979). ZIPMAP-A zero-one integer programming model for market segmentation and product positioning. J. Oper. Res. Soc., 30 (1), 63-70 (8 Pages) Abstract

Home | About Us | Sitemap | News | Glossary | Privacy Policy | Help | Contact Us

© 2004 - 2013 IAU. All rights reserved.

Top of Page