Purchase intent

Brewer M S, Zhu L G and McKeith F K (2001), Marbling effects on quality characteristics of pork loin chops consumer purchase intent, visual and sensory characteristics , Meat Sci, 59, 153-163. 

In order to optimize purchase intent, consider the sequence of steps shown in Table 5. Note that the computer attempts to find the highest point on the surface to maximize purchase intent, subject to the constraint that the formula ingredients (alcohol, absinthe, cassia) all must lie within the tested limits, rather than exceeding those limits. The constraints insure that in the study the optimum product represents a value for which there exist actual observations. (It does little or no good to project an optimally acceptable product which has a formulation lying very far away from any existing products. In that case the extrapolation may lose its validity.)... 

In Table 5 note the different steps or iterations, representing successive attempts to improve the purchase intent score. Each successive attempt represents a new formulation which achieves a higher purchase intent rating than previously. Note the rapid improvements in purchase intent initially, for the first set of iterations. However, as we reach the top of the hill the increments in purchase intent for the product become smaller and smaller. This occurs because at the top of the hill we find less room for improvement. 

The results from Table 5 illustrate the most acceptable product, from the point of view of purchase intent. Note that the product models in Table 4 also allow the product developer to estimate the likely sensory attribute profile of the optimum product, which also appears in Table 5. Thus, it becomes possible to rapidly achieve an optimum product, whilst at the same time predict its profile on sensory and image characteristics. 

A consumer validation of the various benefits of shower gel and liquid hand soap formulations, evaluated in the various tests mentioned above, is critical to the success of the product in the marketplace. The consumer test not only provides this validation, but it also provides much more valuable information, such as consumer likes and dislikes about product attributes and aesthetics and purchase intent. 

The softener performance perceived by consumers is the balance between the absolute efficacy determined in the laboratory and the product aesthetics. In other words, the consumer perception of the product performance is heavily influenced by aesthetic attributes such as fragrance and viscosity. Consumer tests indeed show that perfume, and more precisely perfume substantivity on fabrics, is the main reason for preferring one product among several delivering the same softness. Consumers appreciate both the odor of the product itself, which generates the appeal and causes the purchase intent, and the smell of the laundered fabrics, which settles the repurchase intent. 

Oily feel 6. Purchase intent 6. Feels clean 6. High-quality product... 

A higher rating would Indicate a greater potential of adoption/ purchase Intention. 

This comparative process results in evaluations of quality and/or satisfaction (and subsequent effects, e.g., future purchase intentions). 

The bioflavor compounds of blue cheese, obtained from fermentation of Aspergillus spp., were encapsulated in soy lecithin liposomes and spray-dried to obtain the powder form by Santana et al. (2005). A sensory evaluation was performed, by adding the liposome-bioflavor powder in a base of light cream cheese, which was spread on toasts. Flavor intensity, acceptance by the consumers, and purchasing intention were the tests done in the sensory evaluation. The results showed that the encapsulation maintained the characteristic flavor of blue cheese and the product was classified by the consumers as acceptable. The dried liposome-stabilized flavor was useful to add in foods and to be kept in storage. 

Three distinct facets of product use are commonly measured in food choice research purchase intent, acute intake, and habitual intake. The first construct, purchase intent. 

Bower JA, Saadat MA and Whitten C. (2003) Effect of liking, information and consumer characteristics on purchase intention and willingness to pay more for a fat spread with a proven health benefit. Food Qual Prefer, 14 65-74. 

Guinard JX, SmiciklasWright H, Marty C, Sabha, RA, Taylor-Davis S and Wright C. (1996) Acceptability of fat-modified foods in a population of older adults Contrast between sensory preference and purchase intent. Food Qual Prefer, 7 21-28. 

Mucci A, Hough G and Ziliani C. (2004) Factors that influence purchase intent and perceptions of genetically modified foods among Argentine consumers. Food Qual Prefer, 15 559-567. 

Figures 5.11 and 5.12 plot the exponential logarithm (loge) of 52-week sales (adjusted for differences in distribution and merchandising) versus weighed purchase intent (a transformation of the proportion of definitely would buy and probably would buy ) and liking (the proportion of respondents rating the product in the top three boxes on a 9-point hedonic scale). These metrics are fairly standard in volumetric research.

For purchase intent (Fig. 5.11), the coefQcient of determination B ) is 0.28 (nowhere near significant) with only 8% of variation in the data explained by the model fitted. With liking (Fig. 5.12), is 0.38 (not significant) with only 15% of variation explained. If these findings are geneiaUsable, they would indicate that using threshold values of purchase intent or liking as action standards in new product evaluation would be suboptimal. 

Figure 5.13 plots the relationship between adjusted sales versus the derived index of fit-to-brand for the 15 products. The R value at 0.46 is significant (albeit at >90% level of confidence) and the variance explained is 21%. Whilst this is hardly a superb model, the relationship is on the cusp of significance and the variance explained is greater than for liking and purchase intent. We believe that a much better fit could have been obtained had the execution of the conceptual profiling not been so heavily compromised. 

J Yegge. Influence of sensory and non-sensory attributes of Chardonnay wine on acceptance and purchase intent. Ph. D. Dissertation. University of California, Davis, 2001. 

---