Imagine that you are walking in the city center of Rotterdam in the year 2020. A hungry feeling makes you decide to have lunch, so you use your mobile phone to see if there is any nice place to go. A personal profile shows you five restaurants and you decide to pick the one with best recommendations. During lunch you check the newest trends in the online stores and place them in your own shopping list, which results in the shortest route you can walk this afternoon. You pay by using your mobile phone and walk across the street to the first store. During shopping, recommendations of stores nearby pop-up on your screen based on your personal profile. Unfortunately, the article is not available anymore and you scan the product with your shopping app. This evening, the product will be sent home. Shopping in 2020 will be a total different experience than it is today.
As the story describes, shopping experiences will change over the coming years because of the change in needs and possibilities of consumers1,2,3. Consumers want to have the possibility to buy products every moment of the day and the Internet will be of importance in this change2,3. The introduction of applications on mobile phones facilitates this1, and will be the basis for the future shopping experience. Research shows that cross channel retail is an upcoming trend in the future retail industry1,2,4 (figure 1).
Figure 1. Change of consumer behavior on online and offline purchases. Blue: Online purchase; Brown: Offline purchase; Green: Cross channel4
Continue reading Shopping in the future
Product reviews on online platforms are growing in popularity1,2. Platforms like Amazon, Google or the App store use product reviews to show which products have the best experience in usage by other consumers. Most of these product reviews are extremely positive about the product3, but does this indicate that all products are extremely good and that there is no moderate product on the online market? Let’s give it a try to search on Amazon three random product reviews from books, video games and sports. The results are shown in table 1.
As can be seen from the table, two of those random reviews are extremely positive (the book and the sport watch) and one is extremely negative (the video game). An experiment done by Hu et al., (2009) asked customers to rate a music CD on a 5-star scale. This experiment shows an almost normal distribution, which can be expected if the ratings are randomly done by every buyer of the product. Most of the reviews on Amazon (table 1) show a so called J-shape distribution and not the outcome of the explained experiment. What could be the cause of those differences?
The first explanation is the purchasing bias, which states that customers with higher product valuation are more likely to purchase the product than customers with a low product valuation. Continue reading Can you really rely on online product reviews?
The start of the internet era opened a lot of opportunities for people all over the world. People contact each other by social media and news sites can make use of these resources to spread the news of happenings in the world. If a disaster happens at the other side of the world, within a short time the whole world knows of this. The way people respond to disasters is changed because of the internet1. People are more willing to donate money, resources, food and clothing to charity organizations which work in these disaster area’s because of the shared online images and messages1. Because it happens by internet, it is a form of crowdsourcing.
Crowdsourcing is defined as the act of taking a challenge faced by a firm, organization or individual and […] where the firm, organization or individual broadcasts an open call to other individuals […] to solve this challenge2,3. One example of the use of crowdsourcing during disasters is the Fukushima Daiichi nuclear reactor breakdown in Japan. Crowdsourcing platforms started to make maps of the radiation level in Japan and even on the west-coast of the US, where individuals measure these radiation and send them to the platforms4. This gave the citizens of Japan insight in how save it is for them to stay in their home town or if it was better to move to a place with less radiation. Another example is the use of these crowd-sourced maps to give relief workers a clear picture of the current situation, as it was done in Japan after the earthquake5 and after the typhoon in the Philippines in 20131. Continue reading Crowdsourcing as a tool during disasters