All posts by tedbolsius

Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions


Online word of mouth (WOM) reviews are becoming increasingly important for both consumers and firms. Different types of reviews for different kinds of products can have an impact on the ultimate purchase decisions of the review readers. This paper focusses on the linguistic content, also called explanation type, of the online WOM reviews by focusing in what way review writers explain their ‘actions’ or ‘reactions’ to certain product types.


The article makes a distinction between utilitarian products and hedonic products and the distinction between explained actions (‘I chose this book because..’) and explained reactions (I love this book because..’). Utilitarian products are bought out of necessity and exhibit more cognitive and functional attributes, whereas hedonic products are primarily more luxury products exhibiting more emotional and sensory aspects. Therefore, a compatibility between explanation types (explained actions vs. explained reactions) and products types (utilitarian vs. hedonic) is suggested.

It is hypothesized that review readers find explained actions more helpful for utilitarian products and explained reactions more helpful for hedonic products, because of an increase in the ability to make their attitudes towards the product more predictable. Thereby, increased attitude predictability and review helpfulness will increase the ultimate product choice of the review reader (see figure 1). Increased attitude predictability makes individuals more certain of how they will like the reviewed product. Whereas review helpfulness increases the level of understanding of the product and being able to better assess to products due to having read the review. As mentioned before, an increase in the two above mentioned variables will most likely also increase sales, which is interesting from a managerial point of view.

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The main hypothesis is tested along five different studies, each having a different set up and focusing on different aspects.

Study 1
The first study provides insights in whether review readers favor – and review writers provide – different explanations across products types. Reviews from different books, both nonfiction (utilitarian) and fiction (hedonic), were gathered from Amazon and studied. It was found that nonfiction reviews included more explained actions sentences and fiction reviews contained more explained reactions sentences, they were also found to be more helpful, in line with the hypothesis (see figure 2).

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Study 2
The second study was done through AmazonTurk and 132 participants were assigned the role of review writer or reader and had to fill out the blanks in reviews for certain product types. They found that product type significantly influenced the sentence choice, as predicted. Nonfiction reviews contained more explained actions, whereas fiction reviews contained more explained actions.

Study 3
159 Participants from a panel had to imagine writing a review about a photo camera for professional use (utilitarian) or fun (hedonic). It was found that participants chose more explained actions for the professional camera and more explained reactions for a camera used for holidays. Review readers perceived these explanations also as more helpful.  This study also proved that the hypothesis holds in a different product category.

Study 4
Study four finds that explained actions allow review readers to better predict their attitude towards utilitarian products, whereas explained reactions increase the attitude predictability for hedonic products. Thereby, increase in attitude predictability increases the likelihood of buying the product by the review reader.

Study 5
The last study finds that review readers prefer explained actions for utilitarian products and explained reactions for hedonic products, as this increases attitude predictability. In turn, increased attitude predictability increases review helpfulness and the intention the purchase the products.

Strengths & Weaknesses

As the article dives into the aspect of the linguistic features of WOM, it contributes valuable information to the existing literature. One of the strengths of the article is that it tests its assumptions in different set ups and through different channels. This enhances the reliability of the study and its findings. Thereby, the implications provide helpful managerial insights for consumers and marketers. By knowing what kind of specific language is used and perceived as most helpful in WOM, online retailers can encourage review writers to write with the most desired linguistic features in order to boost sales.

Although the research focusses on explanation type, the explanation content can also be very valuable, especially when consumers are deciding on particular product specifics or choosing between similar products. Thereby, the article assumes that ‘attitude prediction’ and ‘review helpfulness’ both influence the product choice and purchase intention. However, other variables such as price, perceived brand perception and many others variables can influence whether people buy the product or not, this is however not considered in the article. Thereby, purchase intentions can yield significantly different results in real life, as real purchasing decisions can differ from the intention of purchase. This could have been tackled by monitoring real life purchasing decisions, instead of asking for purchase intentions.


In conclusion, the linguistic features in WOM reviews are very important when it comes to utilitarian and hedonic products, as each category requires different explanations types, as to be viewed as most helpful and increase likeliness of product choice. This research clearly states that explained actions are most valuable for utilitarian products and explained reactions are most valuable for hedonic products. Further research in this field of WOM can increase the implications for both consumers and marketers, as to increase satisfaction and sales.




Moore, S. (2015). Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions. Journal of Consumer Research, 42(1), pp.30-44.

GoMetro – Real Time Passenger Data for Public Transportation Systems

Cape Town, South Africa.

Three million commuters use the metro rail on a daily basis in South Africa. The underdeveloped public transportation system has frequent delays, however there are no notifications of cancellations or changes in the schedule due to the fact that there are no sources of real time travel information. The South African startup, GoMetro, provides commuters with mobile services through the mobile web, apps, socials networks and sms-services and thereby they connect transit operators with commuters who are at the center of the platform. Commuters in their turn log on to the platform and share their real time location, stops, delays and any cancelations. In return GoMetro can provide and exchange real-time arrival and departure information, current locations of vehicles, early notifications of operational breakdowns and travel disruptions of the public transportation system. Hereby, GoMetro, transit operators and commuters co-create value through the sharing of real time data information creating a platform using a customer-centric approach.

Although GoMetro started in South African cities with underdeveloped transportation systems, the scope of the business model reaches much further. Personal mobile devices are being used and have changed information distribution paradigms, however they have not yet been used in the public transportation domain (Nunes, Galvao and Cunha, 2014). When consumers interact with service providers, in this case GoMetro, a win-win situations is created. This business model incorporates three different roles for the commuters in the co-creation of value; they need to use the information, provide real time information and validate given information (Nunes, Galvao and Cunha, 2014). This business model can be used in many different countries and cities, as the use of mobile devices has risen substantially over the recent years and will continue to do so (, 2018).

The three parties involved in the platform of GoMetro are the commuters, the transit companies and GoMetro itself, each creating value with each other as to create joint profitability. The commuters create value through sharing the real time travel data and use the travel data of others, thereby creating value for the platform as a whole. The transit companies can provide incentives such as discounts on travel fares for the commuters, as to incentivize them to share their travel data. GoMetro contributes by the creation of the platform and bringing all users together so they can co-create value, in return they make money through advertisements on the platform. The creation and development is done in cooperation with Intel, who provide technical support and insights. All these elements are linked to the customer-centricity of the platform and the interaction between the parties creates joint profitability for all players involved.

The institutional environment GoMetro faces in South African cities has been positive ever since GoMetro started with the idea. With millions of people commuting each day in South Africa and many cannot afford a car themselves, efficient public transportation could be a lifesaver. GoMetro helps to improve the efficiency and commuters adopt the platform in large numbers, as already near to a million people are registered users. Looking at the support the company is getting from both governmental institutions as well as private companies the platform seems to be beneficial for all. Increasing the use of public transportation in the big South African cities helps to reduce the use of private cars, air pollution and frees up space in the cities, as less cars enter the urban areas. All these elements contribute to a more efficient infrastructure of large cities. Having no legal boundaries or complications, makes the institutional environment even more advantageous for the platform of GoMetro.

One drawback is the issue of privacy concerns. Sharing real time personal travel information reveals where you are at a given moment in time and this captures valuable information which can also be used for undesirable purposes. Consumers have to consider whether sharing their real time travel data is worth the costs of sharing private information with the platform. As long as the benefits outweigh the costs, the platform has a sustainable business model and a bright future (Karwatzki et al., 2017).

The extent to which the business model of co-creating value by customers sharing their real time travel information with a platform can reach is yet to be determined. The need for a more efficient public transportation system and the willingness of commuters to share their real time travel data are the least requirements for the business model to succeed. ‘As cities grow, they are in need of a flexible mobility platform to service their mobility needs’ (Justin Coutzee, Founder of GoMetro, 2016). Big cities in Africa, Asia and the Middle East are likely to adopt such business models as they want to improve the way people move within their urban areas.






Nunes, A., Galvao, T. and Cunha, J. (2014). Urban Public Transport Service Co-creation: Leveraging Passenger’s Knowledge to Enhance Travel Experience. Procedia Social and Behavioral Sciences, 111, pp.577-585. (2018). Mobile Phone, Smartphone Usage Varries Globally – eMarketer. [online] Available at: [Accessed 13 Feb. 2018].

Karwatzki, S., Dytynko, O., Trenz, M. and Veit, D. (2017). Beyond the Personalization–Privacy Paradox: Privacy Valuation, Transparency Features, and Service Personalization. Journal of Management Information Systems, 34(2), pp.369-400.