1996
DOI: 10.1300/j050v07n02_02
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Quadrant Analysis as a Strategic Planning Technique in Curriculum Development and Program Marketing

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Cited by 19 publications
(8 citation statements)
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“…The median values as a measure of central tendency of score and priority weight are theoretically preferable to the means because a true interval scale may not exist. 0.00000000097 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 24 LG22 0.00000000087 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 This preference is recommended by Lynch et al (1996) and also Martilla and James (1977).…”
Section: Dairy Supply Chain In Indonesiamentioning
confidence: 99%
“…The median values as a measure of central tendency of score and priority weight are theoretically preferable to the means because a true interval scale may not exist. 0.00000000097 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 24 LG22 0.00000000087 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 This preference is recommended by Lynch et al (1996) and also Martilla and James (1977).…”
Section: Dairy Supply Chain In Indonesiamentioning
confidence: 99%
“…However, it must be noted that median values obtained from the results of the questionnaires were used and not the midpoint of the range in the analysis of significance and satisfaction, as a measure of the intersection of the axes. This is because the global median values for the axes reveal the trend of the attributes (Lynch et al, 1996;Martilla & James, 1977). For our work, the cut-off lines calculated in all 7 areas are Me=3.5 for the satisfaction scale, and Me=4.1 for the significance scale.…”
Section: Resultsmentioning
confidence: 92%
“…Our approach differs by establishing a quantifiable link between attributes, benefits, and choices by observing consumer trade-offs among these dimensions, reflecting their relative importance. Quadrant analysis and importance-performance analysis (e.g., Lynch, Carver, and Virgo 1996; Martilla and James 1977), as well as product attribute utility-based models (Gwin and Gwin 2003), provide similar insights by visualizing the correlation and disconnections between attribute importance and brand performance. However, our model does not require respondents to provide the weights that link attributes to perceptions one at a time.…”
Section: Discussion and Future Researchmentioning
confidence: 99%