Like, Comment and Share: The Impact of Type of Posting About Customer Interaction with Brand in a Virtual Social Network

Transcription

1 Like, Comment and Share: The Impact of Type of Posting About Customer Interaction with Brand in a Virtual Social Network ABSTRACT This study aimed to measure the impact of the type of posts on the metrics Like, Comment and Share on a social network, through an econometric approach with an estimate by Minimum Ordinary Squares. The investigation considered eight brands of Brazilian beers on Facebook, totaling 2583 observations. The main results show that three types have a positive impact on comments, shares, and likes, which suggests a pattern and indicates a theoretical and empirical way for future works. I. Introduction From the establishment of the Internet as a marketing communication (Hoffman & Novak, 1996), online social media such as blogs, forums and virtual social networking platforms called the attention of professionals (Kumar, Bhaskaran, Mirchandani, & Shah, 2013), who now include systems such as Facebook on their strategies, especially after evident increases in sales and return on investments (Kumar et al., 2013; Kumar & Mirchandani, 2012). Virtual social networks are changing the way that companies interact with their customers through actions that include recommendations from friends and contacts, user-generated content and reviews about products and services (Rohm, Kaltcheva, & Milne, 2013). It's becoming more and more common marketing research operated with the help of data coming from virtual social networks (e.g. Aral and Walker, 2011; Ransbotham, Kane, & Lurie, 2012; Sun, 2012; De Vries, Gensler, & Leeflang, 2012). Despite this growth, the authors highlight that companies have not been able to measure the effectiveness of strategies based on these data through tangible measurements (Kumar et al., 2013). In the specific case of Facebook, authors such as Casteleyn, Mottart and Rutten (2009), Peters et al. (2013) and Ramsaran-Fowdar (2013) advocate for the incorporation of virtual social network on research from the use of techniques to understand the volume of data available, while others point to the lack of investigations that consider the role of brands in these locations (Smith, Fischer & Yongjian, 2012). Based on this gap and fundamented on studies that categorize content in virtual social networks (e..g Caseiro & Barbosa, 2011; De Vries et al, 2012;. Rauschnabel, Praxmarer, & Ivens, 2012; Smith et al, 2012;. Swani, Milne, & Brown, 2013), this article aims to measure the impact of the type of posts on the metrics Like, Comment and Share on a social network, through an econometric approach that seeks to answer the following research problem: what is the impact of the type of posts based on the Likes, Comments and Shares of the user with the brand on the social networking?

2 II. Method The object of analysis in this study consisted of 2583 posts published on fan pages of eight Brazilian beer brands between December 2012 and February 2013, considering this figure significantly higher than the overall quantitative analysis in other researches like De Vries et al. (2012) and Swani et al. (2013). The choice of the fan pages followed two criteria: (a) the brand chosen should have an effective participation in the Brazilian mass market, (b) the brand should also regularly participate in social networking, posting something at least once a week. The object of analysis in this study consisted of 2583 posts published on fan pages of eight Brazilian beer brands between December 2012 and February 2013, considering this figure significantly higher than the overall quantitative analysis in other researches like De Vries et al. (2012) and Swani et al. (2013). The choice of the fan pages followed two criteria: (a) the brand chosen should have an effective participation in the Brazilian mass market; (b) the brand should also regularly participate in social networking, posting something at least once a week. Data collection was performed on one single day, through a procedure of web browser that allows you to save all the data from the page as a file. Such file had the variables divided into two blocks: the variables of interest (posts, likes, comments and shares) and the independent variables (brand of beer, time of day and time of the week where the posts occurred, month of posts, quantity of posts published that day and duration of posts regarding the day on which the data was collected. Data was then taken to an Excel spreadsheet to be analyzed in gretl (version ) and Stata softwares (version 11.2). The posts collected were classified according to a typology to consider the content of their messages. This classification is a sample improvement on the work of De Vries et al. (2012) and Swani et al. (2013), since it presents eight kinds of posts based on their content and on who posted the comment, as shown in Figure 1. Variable Abreviation Description Application Enquiry Event APP ENQ EVE It is the post that will link directly to an application created by the company holding the beer brand, which aims at providing a software with specific goals. Creating applications is a tool offered by Facebook to the pages. Example: application for tracking all carnival blocks in the city of Salvador. Posts in which direct questions are made to followers of the brand with the help of platform available on the social network. Options in which the follower selects an alternative are presented. The amount of options and responses was not considered in this work. Posts covering the brand or event linked to the brand and that include media like photos and videos. In this category there are mainly photos and albums produced during the Carnival period in 2013.

3 Fan Information Promotion Publicity Services FA INF PROM PUB SER Publication with content created by follower / fan. The follower is either the responsible for the central idea or has sent the photograph used in the post; their participation is mentioned in any of the cases. It is also considered the post in which the brand attracts the follower to contribute with ideas that will become a future post, all of that without previously announced rewards. The publication is characterized in that category when publishing content gathers data about events, places, opportunities, celebrities, musicians, etc., being directly or indirectly connected to brands. Example: informative posts on the street carnival blocks from the city of Rio de Janeiro. Posts that advertise contests and raffles, ie, promote and encourage the participation of the follower with a reward Posts aiming to promote the brand on the social networking, presenting advertisements crossing the digital sphere (those are also conveyed in traditional media and reproduced in social network).it is also considered posts with fun content, in order to attract the attention of their followers. Link or advertising that strictly make direct connection with the virtual store service or with information about how to purchase a product, containing phone numbers. Another example refers to the Skol brand, which has its own online radio in which posts make a direct link to the page on which the radio is performed. Figure 1 Categories of posts built to the eight brand profiles Source: The authors After rating three econometric models were built taking into consideration dependent variables: Comments (Model 1), Shares (Model 2) and Likes (Model 3). The models incorporated both quantitative and qualitative independent variables as well as reference variables (type of posting, "bb" for brands of beer, "dp" duration of posting, npp number of posts published, "mor. / aft. / nig." for the time of day, "pw" for the period of the week, "ow" for posts published over the weekend, and" Dec / Jan / Feb "for the month of posting). The estimate was performed by the method of MOS. The representations of the parameters of intercept and rise of the models are set out in equations (1), (2) and (3) below: comen = β 0 + β 1 app + β 2 enq + β 3 eve + β 4 fa + β 5 inf + β 6 prom + β 7 pub + β 8 ser + β 9 bb1 + β 10 bb2 + β 11 bb3 + β 12 bb4 + β 13 bb5 + β 14 bb6 + β 15 bb7 + β 16 dp + β 17 npp + β 18 mor + β 19 aft + β 20 nig + β 21 pw + β 22 ow + β 23 dec + β 14 jan + β 25 feb + u (1) comp = β 0 + β 1 app + β 2 enq + β 3 eve + β 4 fa + β 5 inf + β 6 prom + β 7 pub + β 8 ser + β 9 bb1 + β 10 bb2 + β 11 bb3 + β 12 bb4 + β 13 bb5 + β 14 bb6 + β 15 bb7 + β 16 dp + β 17 npp + β 18 mor + β 19 aft + β 20 nig + β 21 pw + β 22 ow + β 23 dec + β 14 jan + β 25 feb + u (2) cur = β 0 + β 1 app + β 2 enq + β 3 eve + β 4 fa + β 5 inf + β 6 prom + β 7 pub + β 8 ser + β 9 bb1 + β 10 bb2 + β 11 bb3 + β 12 bb4 + β 13 bb5 + β 14 bb6 + β 15 bb7 + β 16 dp + β 17 npp + β 18 mor + β 19 aft + β 20 nig + β 21 pw + β 22 ow + β 23 dec + β 14 jan + β 25 feb + u (3)

4 III. Results and Discussion In a descriptive analysis of the dependent variables, likes has the highest average among the variables ( ), followed by shares ( ) and comments ( ). These results suggest that, from the available options on Facebook analyzed for interaction between customer and brand, likes are more frequent, perhaps by the mechanism of activation of this option on the platform. Instead, shares have the lowest average frequency between the three dependent variables. Comments also appear to be of lower standard deviation variable, revealing lower dispersion of data. III.I Specification tests, F test and quality of adjustment in models As a first step in the analysis, we realized specification tests and interpreted the multiple linear restriction tests (the F test) and the results of the coefficients of determination of the models (R 2 and R 2 Adjusted). Later the Breusch and Pagan (1979) test was performed, which returned high values of chi-square statistics, which allow us to reject the null hypothesis of constant variance (chi-square = on Model 1, chi-square = on Model 2 e chisquare = on Model 3). The values of the F test allowed us to reject the null hypothesis that the angle coefficients are simultaneously equal to zero (Gujarati & Porter, 2011). The R-squared and Adjusted R- squared explain the variance of the dependent variables in a reasonable manner for Model 1 (F value = 34.76, p-value < 0.01, R 2 = 9,16% e R 2 Adjusted = 8,41%) Model 2 (F value = 28.4 p-value < 0.01, R 2 = 17,6% e R 2 Adjusted = 16,9% ) and Model 3 (valor F = 72.56, p- value < 0.01, R 2 = 12,5% e R 2 Adjusted 11,8% ). III.II Analysis of the impact of the variables of interest The second step of the analysis involved the results of hypotheses tests about coefficients of individual estimates of regression, and found that posts to the category FA and PROM are statistically significant at a confidence level of 99.9% in all three models and are characterized by linear and positive impact on the dependent variables. These results support the arguments of researchers who highlight the importance of user generated content in virtual social networks activities(rohm & Kaltcheva 2013) and confirm the leading role attained by clients, customers or fans of brands in these spaces (Hennig-Thurau et al., 2010). The four other types of posts analyzed (APP, ENQ, EVE and INF) did not promote statistically different impacts of the SER reference variable. These results bring together early indications of non-significance of these typologies and additional studies are needed to identify the recurrence of this pattern with brands from other sectors and other product categories. Specifically on informative posts, literature had already given evidence of no statistical significance in this category. De Vries et al. (2012) did not fail to reject the hypothesis that informative posts and comments get more likes than those characterized as non-informative. The decision to include it in the group of eight variables of interest was due to the need to test a more comprehensive set of typologies than De Vries et al. (2012) and Swani et al. (2013). III.III Analysis of the impact on the variables of interest Regarding the quantitative variables that control, respectively, the amount of posts per day and duration of posting, we observe the following: multiple daily publications in the profiles of the brands do not interfere on likes, comments or shares, which reveals some independence

5 between the frequency of exposure of posts and the dependent variables. In contrast, the exposure time of a post promotes linear and negative impacts on shares and likes. The third group of control variables considered periods of the day, week and month of the posts. However, the only period that was distinguished in a statistically significant manner, was the morning period, only in Model 2: posts in the morning to a 95% level of confidence. Posts published during the week are statistically identical to the weekend. That means they do not cause impacts in comments, shares, and likes. These results confirm what was found in the study by De Vries et al. (2012), which also included day of the week as a control variable and found no statistically significant results in 355 posts distributed in six product categories. Finally, in general, there was no statistically significant difference between the months in which the postings occurred. Posts published in December, January and February are statistically equal when considering comments and likes. Particularly when one analyzes the Model 2 it appears that posts in December received, on average, more shares than January and more shares than February. IV. Conclusions and Implications for Theory and Practice Posts from fans, promotional and advertising have a linear and positive impact on the dependent variables, which suggests a pattern and indicates a theoretical and empirical way for future studies that are intended to classify and identify the influence of these typologies. It was concluded that virtual social networks like Facebook are more efficient when used as a means of promotion that provides hedonic benefits to users, instead of utilities benefits through direct promotion of products, services and prices (Chandon, 1995; Chandon et al. 2000). Typologies that were statistically significant possibly promote what Subramani and Rajagopalan (2003) classify as engagement and emotional connection of people with the message conveyed. This connection allows the diffusion of the post to the social network of friends in one s social circle. The main theoretical and empirical implication of this paper is to identify a pattern in the impact of types of posts on metrics that measure the interaction between customer and brand on Facebook. This impact is linear and positive, but further research can test non-linear models in this respect. In practical terms, this study advances by presenting types of posts that might serve as a benchmark for stocks of companies and brands in this social network. Additional investigations into this type are needed to test whether the impact of these typologies remains in other social networks, and from the model presented in this paper consider, for example, the dependent variables as part of simultaneous equations or a system in which the terms disturbance are highly correlated (Zellner, 1962).

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