“…Networked literacies are marked by your participation as a peer in these flows and networks — you contribute to them and in turn can share what others provide” – Adrian Miles.
Over the past few decades, the number of network literate individuals has expanded greatly, in turn this has caused the online shopping trend to boom and online revenue continues to improve year after year. The Australia Bureau of Statistics released data that showed online sales reached $246 billion during the 2012-2013 financial year. There are a number of factors which are influencing online sales, and network literacy is one of these key factors, its benefit for companies both exclusively online, and those with physical storefronts is greater than ever before. The ever-growing online trend, particularly in retail is something that will influence any individual intended to work within the media industry.
Chen and Tan (2004) researched what factors determined consumers acceptance and use of online retailers, issues such as security, privacy, shipping costs, and unfamiliarity all impact on consumers Behavioural Intention (BI) to shop online. It has been found that consumer perception of a product is the key determinant of both online and offline purchasing (Darden & Lusch 1983), but particularly the Perceived Usefulness (PU) of online shopping is effected by this. In this particular study, an online survey was used to asses consumer acceptance of online stores. In relation to the Technology Acceptance Model (TAM), the research showed that Perceived Usefulness and Perceived Ease of Use (PEOU) has substantial impacts on consumers acceptance of an online store. The results show that the more user friendly, sophisticated, and helpful an online store is, the better it will contribute to the online shopping network. Lohse and Spiller (1998) found that poorly designed digital storefronts would adversely effect sales, something which appears obvious to us as network literate individuals. It provided evidence that the more network literate a web designer is, the better they can create a simplified online purchasing process, and the more the network “flows”. Another hugely influential factor relating to Behavioural Intention and Perceived Usefulness of online stores is price. In order to retain customers, online stores must keep their prices low so as to compete with offline retailers, and other online companies.
Technology Acceptance Model:
A study by Chen, Wu and Yoon (2001) discovered that product reviews online encouraged sales, particular in products that are less popular, or that have little information about them online. Data was sourced from amazon.com, and showed that products with more reviews sold better than those with a smaller amount, even if that small amount of reviews was largely positive. The reasoning for this is perhaps because consumers base the popularity of a product off the amount of reviews it has, more popular items then receive even more sales, or perhaps it is items with more reviews then become more popular? Online retail can be considered at a disadvantage to offline retail, as consumers do not have the chance to ‘try before they buy’, which is why network literacy becomes so important, as online sellers must be able to give an efficient overview of a product in a simplified way to potential buyers. Search costs refers to the amount of time, energy and money spent by consumers to research a product, the higher the search cost, the less likely an individual will eventually purchase. When information is readily available on a product, consumers are more likely to purchase it, when search costs are too high, consumers often abandon their online purchasing pursuits. Online recommendations are another simple way for retailers to enhance sales, particularly through recommendations like “people who viewed this also liked…” and so on. While the concept has not been researched, it is obvious consumers have perceived trust in the purchases of others online. So recommendations based off other buyers encouraged sales, the more recommended an item, the better its assumed popularity and the more likely an individual is likely to purchase it. It was also found through this study that books with lower prices and a substantial number of reviews were more likely to be recommended to consumers.
While the previous study was conducting using only book related purchases, Gretzel and Yoo (2008) found that three-quarters of people intending to travel considered online reviews while planning their trips. Ye, Law, and Gu (2009) considered this while researching exactly how influential consumer reviews could be for aspects of the hospitality and tourism industry. They discuss the impact of Word of Mouth (WOM) online, which has similarities to Adrian Miles’ quote about contribution online and being able to share what other have provided. For this study they focused on the impact on hotel bookings, based on online reviews on a travel website. They disregarded any reviews that were written over 12 months before the study, to ensure up to date information. The results found that a 10% improvement in online reviews would increase sales by 4.4%. This shows that it is a number of different industries which can be impacted by online reviews.
Forrester Research (Tedeschi 2000) found data showing that two-thirds of online shoppers abandoned their virtual “shopping cart” before completing a purchase. Here is a graph with data showing why consumers stated they stopped before purchasing something online:
Grandon and Ranganathan (2001) studied “the nature and extent of impact of various content and design variables” on online consumers purchases, in order to discover what encourages consumers to buy online and how to improve online revenue. A total of 94 companies with an online presence were subjected to analysis in relation to things such as the complexity of navigation, use of search functions, and information available. The results found that the two factors having the most influence on online sales were the frequency that the website was updated, and the use of ‘decision aids’ to help consumers decide what products were best for them, both of which require network literate employees. They also discovered that complex navigation systems and high level use of multimedia had negative effects for online sales, as both hindered consumers in their online searches. The conclusions of this study reveal the importance of user friendly content and its impact on sales. This shows how thoroughly online sales are influenced by network literacy. There is an implication from this study that websites must be made to cater for consumers who are not particularly network literate, as well as individuals who are. However, perhaps the most interesting finding for this study was that more information on the website did not necessarily encourage online sales, but lowered them. This shows that websites will increase their revenue by giving concise and accurate information, rather than lengthy details. A Ted talk by Sebastian Wernicke, gives some insight into the importance of a few words.
Many surveys determining consumers intention to shop online are conducted online, which disregards any aspect of the population which chooses not to use online retailers. Vijayasarathy (2004) collected his data from the results of a mail survey which questioned consumers use, intention to use, and attitude towards online shopping, it also asked questions relating to their overall internet use. 281 of the 800 questionnaires mailed out were returned complete and deemed useable of the study. Surprisingly in a world now so influenced by the intenet, only 88% of the respondents stated that they had ever used the internet. The personal information provided by the respondents revealed that a majority were well-educated, middle-aged, married individuals with a higher household income than the average American. This may account for the low figures relating to internet use, and ignores a large portion of the population which uses the web, teenagers and children who have grown up learning to be network literate. A key finding in this study was that how consumers perceived the compatibility of their lifestyle with online shopping effected their acceptance of it. For example, individuals who perhaps had time constraints, or simply preferred to shop from the comfort of their home, found online shopping would cater for these needs. In addition to these positive findings, there was also evidence to prove the previous points that outdated interfaces, complex structures, and ineffective search engines contributed to consumer frustration with online retailers. Another issue was the perceived security of websites, consumers were less likely to be satisfied with online transactions when they felt their privacy may be breached.
The world we now live in is constantly begin effected by network literacy, while a few decades ago shopping was a purely offline act, now shopping online is becoming more and more popular due to the large amount of research conducted into how network literacy effects online revenue. Almost all retail industries now have some form of online presence, and there are numerous articles relating to how to improve these sites. Only the future can yield the answers of how the online retail industry is going to adapt and grow.
Miles, A. 2007, “Network Literacy: The New Path to Knowledge”. Screen Education Autumn, No. 45, pp. 24–30
Chen, L, Tan, J 2004, ‘Technology Adaption in E-commerce: Key Determinants of Virtual Store Acceptance’. European Management Journal, Vol. 22, No. 1, pp. 74–86
Chen, P, Wu, S, Yoon, J 2004, ‘The Impact of Online Recommendations and Consumer Feedback on Sales’. International Conference on Information Systems (ICIS), Paper 58
Ye, Q, Law, R, Gu, B 2009, ’The impact of online user reviews on hotel room sales’. International Journal of Hospitality Management 28, pp. 180–182
Grandon, E, Ranganathan, C 2001, ‘The Impact of Content and Design of Web Sites on Online Sales’. Americas Conference on Information Systems (AMCIS), Paper 179
Vijayasarathy, L 2004, ‘Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model’, Information & Management 41, pp. 747–762