Passyn, K., Diriker, M., & Article Critique

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"As in the pre-testing, Amazon.com (M= 6.87) and Barnes & Noble (M= 7.18) brands were found to be significantly more familiar than Maximo.com (M= 4.44) and Suprema (M= 5.26; ps < .001).5 Lastly, 92.3% of respondents correctly classified the seller into the appropriate physical presence category. A 4 (consensus) x 2 (physical presence) x 2 (brand familiarity) ANOVA was computed for all dependent variables."(Benedicktus, Brady, Drake & Voorhees 2010)

For further clarification, an ANOVA is an analysis of the variation present in the experiment. This is a means for testing a hypothesis that implies the variation in an experiment is no greater than that due to normal variation of individuals'characterstics and error in their measurment. The ANOVAs performed in the study indicated significant main effects of the consensus manipulation on each measure which I found significantly important to the discovery of amount of trustworthiness in consumer population. The comparisons made from Scheffe designate support for H1a -- c. This led to an overall conclusion that the higher the concurrence, the greater the trust which gave way to higher rates of purchase aim.

The results of the two tests imply that consensus data is able to provide a "broad cue" that communicates believability and offers the potential for increase buyer intentions for brands that consumers are accostomed to or not. These results apply to the hybrid retailer and etailer companies.In contrast, the conclusion of physical being and brand awareness yielded results that were narrower in scope. Despite the information found, it was seen that consensus data was not enough to shield against flowing, established doubts on the internet. The conclusion then is that a mixture of high consensus and brand familiarity is needed to serve the purpose of trustworthiness. The article finishes with approbations on how etailers can communicate a trustworthy persona through customer service and how the use of further research can aid in these conclusions.

Suspicion played in overall negative part in creating consumer trustworthiness.

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The findings of the studied pointed out the need for there to have a combination of high concurrence and a familiar brand for their to exist the least amount of suspicion which as previously stated was a deterrant. I found that concurrence and brand familiarity had increased benefits in not just the overall consumer experience, but in the effectiveness of marketing. it's been proven that information like with these studies really does benefit any kind of retailer including etailers regardless of physical presense or not. It didn't hurt the retailers that they had physical stores for customers to go into, but it didn't hurt etailers as much that they didn't. The results in the article indicated that "consensus information had the broadest effects on trustworthiness and purchase intentions, whereas brand familiarity and physical presence were more limited in scope." (Benedicktus, Brady, Drake & Voorhees 2010)

In terms of how research courses should approach the difference between the two procedures, there really isn't much to alter or change. Both techniques offer consistent results. Both have results that convey information that benefit and help prove or disprove a hypothesis effectively. Personally the general linear model could be improved to make it more comprehensible to me and others by adding more examples instead of endless streams of data. I think that the equations and the formulated hypothesis show the reseached data well, but having an example of the data in use would help people understand it easier than just giving the data.

I believe ANOVA is the best kind of method to approach my dissertation because of the philosophy applied to the testing of the hypothesis. It creates an easier and linear understanding of the material and the hypothesis. The basis for evey statistical test is to phrase a question in terms of null hypothesis, attempting to prove that everything is equal. ANOVA helps me better configure the data to reach my goal of figuring.....

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