We present evidence that many product reviews at a private label retailer’s website are submitted by customers who have not purchased the product they are reviewing. We show that these reviews are significantly more negative than other reviews. Moreover, because the reviews are influential, the negative ratings reduce sales of these items.

Deception is generally considered a more cognitively complex process than merely stating the truth leading deceivers to use less complex language. The complexity of the language is often measured by the length of the words used.

Because it is often difficult for deceivers to create concrete details in their messages, they have a tendency to include details that are unrelated to the focus of the message. For example, in a study of deception in hotel reviews report that deceptive reviews are more likely to contain references to the reviewer’s family rather than details of the hotel being reviewed. Other indicators of deception reported in hotel reviews include using more exclamation points “!”

The results indicate strong evidence of deception in the reviews written without a prior transaction. Recall that the word count is one of the most commonly used linguistic cues used to detect deception. The word count for the reviews without prior transactions is approximately 40% higher than in the reviews with prior transactions. We also observe significant (p<0.01) differences on each of the other linguistic cues. We caution that these differences do not indicate that all of the reviews without prior transactions are deceptive. Instead we conclude that these reviews are more likely to contain linguistic cues that are consistent with deception, suggesting that at least some of these reviews are deceptive.

Amazon. The sample of 80 books had a total of 7,219 reviews, averaging 90.2 reviews per book. This included an average of 52.7 reviews tagged as an Amazon Verified Purchase and 37.6 that were not tagged. We report the average rating and the distribution of ratings for these two samples of reviews. We see that the low rating effect is replicated using these reviews from a separate retailer in a different category. The magnitude of the effect is similar to the findings reported before, with approximately twice as many ratings equal to 1 amongst the reviews without a verified Amazon transaction (9.38% versus 4.77%).

Why people write such stuff: The second explanation is in some respects the reverse of the upset customers explanation. It is possible that these customers are acting as “self-appointed brand managers”. They are loyal to the brand and want an avenue to provide feedback to the company about how to improve its products. They will even do so on products they have not purchased.

Third explanation is that reviewers are simply writing reviews to enhance their social status. This explanation is related to the more general question of why do customers ever write reviews with or without prior transactions? In an attempt to answer this more general question some researchers have argued that customers are motivated by self-enhancement. Self-enhancement is defined as a tendency to favor experiences that bolster self-image, and is recognized as one of our most important social motivations. Self-enhancement may explain why reviewers write reviews for items they have not purchased. However, it does not immediately explain why these reviews are more likely to be negative. One possibility is that customers believe that they will be more credible if they contribute some negative reviews.

Check at full at http://web.mit.edu/simester/Public/Papers/Deceptive_Reviews.pdf

We end up with the evaluation spreadsheet. Each row lists some feature or sub-feature that someone has determined the technology must have. These features are all weighted according to their importance.

The columns then reflect all the options. Fill each cell with a number assessing how well this option delivers this feature, and you calculate a score for each option. Select the one with the highest weighted score, and you have the best technology for your needs.

Problem is, this rarely works.

For a start, the features and weightings aren’t objective. Someone gathers requirements, filtering them as they go. People argue about weightings. Ultimately, the person with the most power decides. We’ve just shifted the politics into the structure of the spreadsheet.

Research into the way experts make decisions shows that they rarely think through a set of options and assess them against objective “decision criteria”.

In situations where they need to integrate a lot of information, deal with uncertainty, and balance the concerns of diverse perspectives, experts go through the following stages:

They imagine themselves into the situation.

1. They identify a single option that will most likely meet their needs.
2. They test this option, mentally, against the situation.
3. If this option works well enough, they don’t waste time on further analysis. They select it and get into action.
4. If the option doesn't work, they adapt and adjust it in their minds. If they can find a way to make it work, they use it.
5. If they can’t find a way to make it work, they look for other options.
6. They may go through this loop several times, using the selection and testing and adaptation of options to improve their understanding of the situation. They evolve a workable solution.

Experts can do this remarkably quickly. Fire fighters and other emergency service workers go through this loop in life-and-death situations in fractions of a second.

We’re not fire fighters Lives don’t depend on the split-second timing of our decisions. So it makes sense to balance scenario-based decision-making with some thought about features, functionality and weighing off the different options.

We should make it easy to imagine ourselves into the situation we’re trying to address. What would it be like to use this system, work with this agency, etc? Write scenarios rather than feature lists, and ask vendors to explain how their technology fits each scenario.

We need to give ourselves hands-on time with each option, so we can experience how they really work. Vendor-driven presentations aren’t enough. Pilots are ideal, or hands-on workshops where we can try the options for ourselves during the course of the procurement.

Via: http://econsultancy.com/ru/blog/62153-how-we-buy-technology