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To conduct this research, a wide variety of approaches were used. The study used a total of 10 cumulative steps that were determinant of the result. The first three approaches were based on the factor of time allocated by individuals while using the Internet, as well as the diminishing marginal returns that followed, listed by equations on nodes [1], [2], and [3] (Zhang et al. 3-4). To retrieve the necessary information, the research “focus[ed] on only two types of sites, e-commerce, and social networks, and two types of value ‘commodities’…informational value and entertainment value” (Zhang et al. 3), thus determining the maximum utility, as listed on node [1]. The study then considered the factor that “marginal returns… diminish[ed] with more time spent on the site” (Zhang et al. 3) and adjusted their calculation model, as listed on nodes [2] and [3]. This resulted in the “time spent on social networking sites [being] proportional to the ratio of contributions…produce[d]” (Zhang et al. 4).

With the above calculations, the study continues to “explain consumers’ buying behavior[s]” (Zhang et al. 4). While e-commerce sites expose viewers to their products or labels, social networks allow the same viewers to see “other consumers’ experiences [and] recommendations” (Zhang et al. 4). Due to these reasons, by expanding on an individual’s informational value, the penchant to purchase online would burgeon accordingly. However, these two commodities are similar to a double-edged sword as there is both a potential negative and positive correlation. In the short run, the negative correlation comes from the risk that “higher entertainment value found on social networking sites may decrease the attractiveness” (Zhang et al. 4) of e-commerce sites. However, in the long run, the positive correlation states that consistent usage of social networking sites will increase informational value as time passes.

After all previous considerations, empirical data is gathered under the headings of “online purchase records”, “web-browsing history” and “demographics and control variables”, listed on pages 5-9. From here, the equation for purchasing decisions is drawn to “examine the relationship of social network usage and engagement with purchase activity” (Zhang et al. 10), as listed in node [7]. Node [8] then acts as a control equation for errors, such as activity bias, as it controls for “unobserved individual-level heterogeneity and correlated unobservable…including individual-level random effects” (Zhang et al. 10).

To conclude the study, the report models the equation for the number of purchases and retailers purchased from in equation nodes [9] and [10]. Ultimately, the methodology process of the study takes into consideration “other potential endogeneity concerns, such as time-varying unobservable” (Zhang et al. 12) and incorporates the theorem into equation nodes [7]-[10].

Regarding the results of the study on the impact of social media on online shopping, it has been divided into two (2) subcategories labeled results for focal social network variables and other variables. The first subcategory explored how “social network activity is associated with consumers’ purchase decisions” (Zhang et al. 14). During the testing, there were two categories the subject could fall into which could be: social networking sites result in a decrease in the subjects purchasing activity, (Zhang et al. 14). Or, a positive correlation between cumulative social network usage and shopping activities. Upon investigation, the results from this test show a positive correlation between the length of time the subject spends on social media and the number of purchases made. The findings “point” that there is an immediate, short-term negative relationship correlated with social networking and online shopping which “suggests a substitution effect”. The results indicate a “cumulative, longer-term positive relationship” in terms of the subject’s engagement with social networking and increased purchased activity.

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In terms of other variables, another relationship was considered in terms of internet-related variables with purchase activity. Short-term effects were controlled during this study in terms of seeing a correlation with immediate purchase. It was discovered that search engines also have a positive correlation with the probability of purchase meaning that search activity is interrelated with the subject’s purchasing activity (Zhang et al. 14).

In conclusion, the study displayed a prominent correlation between social media usage and online sales. Despite IBM indicating social media platforms are ineffective in driving e-commerce sales, the study suggests the expected positive payoff may be more of a longer-term effect. The use of social media to promote a product or a service’s information is highly effective when it comes to exposing a brand. The engagement a firm displays on social networks with its potential customers is related to the impulse of the customers to shop (Zhang et al. 16)

Managers should recognize the importance of the relationship between social networking and e-commerce. According to the findings of the conducted research, managers should consider cumulative consumer interaction with their brands on social media. Consistent and cumulative interaction translates positively to shopping activity. The focus on consumer interaction contributes to marginal values gained which are outlined in the research; people allocate their time accordingly. This positive relationship is stated to be stronger with products and services that are more likely to be shared online. Therefore, managers should advertise on specific product categories; like chocolates.

As social network use continues to extend, a crucial question for marketers is whether or not consumers’ online-looking activities are associated with their use of social networks and, if so, what the character of this relationship is. To test the link between social network use and online booking, the authors leverage a novel shopper panel knowledge set that tracks people’s browsing of looking and social network websites and their online buying activities over one year. Although companies are progressively victimization social networks in their promoting methods, analysis by IBM suggests that social media has very little impact on e-commerce, with only .34% of online sales referred by social media websites. Despite the growing body of selling literature on social networks, researchers have paid very little attention to the interaction between social network usage and e-commerce activity.

On the one hand, victimization on social networks might be related to buying as a result of shoppers on social networks are oftentimes exposed to data regarding merchandise and consumption-related activities, starting from product ads by brands to friends’ conversations and opinions regarding recent looking experiences (e.g., Chevalier and Mayzlin 2006; Moe and Trusov 2011; writer and Galak 2012). The people and the companies use social networks to share data that may be generally represented as “consumption connected.” for instance, on their social network accounts, shoppers typically post photos of recent purchases, share stories regarding looking experiences, and describe merchandise that they require to buy within the future. 

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