Optimization of product line design for environmentally conscious technologies in notebook industry
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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.
KeywordsConjoint analysis Consumer preferences Environmental regulations Green technologies Notebook computer
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- Alexouda, G., (2002). An evolutionary algorithm based method for the product line design using the share of choices criterion. Second Hellenic Conference on Artificial Intelligence, 321–330.Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- 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 (11 pages).Google Scholar
- 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).Google Scholar
- Szymanski, D.; Bharadwaj, S.; Varadarajan, R. (1993). An analysis of the market share profitability relationship. J. Market., 29, 1–18 (18 pages).Google Scholar
- 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 e-shopping adoption. J. Food, Agri. Environ., 7(3and 4), 898–903 (7 pages).Google Scholar
- 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).Google Scholar