Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Antecedents of retweeting in a (political) marketing context

Word of mouth disseminates across Twitter by means of retweeting; however the antecedents of retweeting have not received much attention. This study uses the CHAID decision tree predictive method (Kass, 1980) with readily available Twitter data, and manually coded sentiment and content data, to identify why some tweets are more likely to be retweeted than others in a (political) marketing context. The analysis includes four CHAID models: (i) using message structure variables only, (ii) source variables only, (iii) message content and sentiment variables only and (iv) a combined model using source, message structure, message content and sentiment variables. The aggregated predictive model correctly classified retweeting behavior with a 76.7% success rate. Retweeting tends to occur when the originator has a high number of Twitter followers and the sentiment of the tweet is negative, contradicting previous research (East, Hammond, & Wright, 2007; Wu, 2013) but concurring with others (Hennig-Thurau, Wiertz, & Feldhaus, 2014). Additionally, particular types of tweet content are associated with high levels of retweeting, in particular those tweets including fear appeals or expressing support for others, whilst others are associated with very low levels of retweeting, such as those mentioning the sender’s personal life. Managerial implications and research directions are presented. The study makes a methodological contribution by illustrating how CHAID predictive modelling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context

Measuring the Influence and Intensity of Customer’s Sentiments in Facebook and Twitter

Organisations these days are actively using social media platforms to engage with potential and existing customers and monitor what they say about the organisation’s product or service. The most important area within social media monitoring lies in how to gain insight for sentiment analysis. Sentiment analysis helps in effective evaluation of customer’s sentiments in real time and takes on a special meaning in the context of online social networks like Twitter and Facebook, which collectively represent the largest online forum available for public opinion. Sentiment Analysis is not about retrieving and analyzing the analytics purely on the basis of positive, negative or neutral sentiment. It is imperative to assess the influencers of the sentiments in terms of Retweet and Share option used by them on Twitter and Facebook platform respectively. Measuring the intensity is other important aspect of sentiment analysis process. What kind of nouns, adjectives, verbs and adverbs are used in the opinion across the Twitter and Facebook platform matters as well since it exhibits the intensity of the underlying emotion in the text written. This study was conducted to propose a framework to identify and analyse the positive and negative sentiments present in Twitter and Facebook platforms and an algorithm was prepared to measure the intensity and influence of the positive, negative sentiment in particular using the document and sentence level analysis technique

Understanding Awareness Diffusion in Microblogging

The word-of-mouth (WOM) marketing has been recognized to be the most credible and important marketing approach, especially when the social network websites have become an internet phenomenon. Previous studies have shown that the dispersion of WOM across various communities has significant positive impact on product adoption (Godes and Mayzlin, 2004). On the other hand, the homophily theory in sociology indicates that people usually feel more comfortable talking with those who are similar to them than those who are not. Such psychological discomfort caused by communicating to dissimilar people may cause information “stuck” in clusters of similar people in a social network (Touchey, 1974). As a result, it is usually more difficult for information to traverse across the boundary of online communities than to spread within a community. However, it appears that the setting at a microblogging website such as twiter.com enables easier cross-boundary message dispersion. The main reason is that when a person retweets a message received from people he follows, the message is broadcasted to his followers, a group of people who might be very different from those this person follows. Given that microblogging has been an important means for organizations to communicate with prospective/existing customers, such retweet behavior becomes crucial for organizations’ online branding endeavors. This study thus seeks to uncover the factors associated with the retweeting behavior of participants at twitter.com by using content and social network analysis technologies. We believe that the results from this project will have both significant contribution to academic research and important implication for practitioners

How open are journalists on Twitter? Trends towards the end-user journalism

The many activities of journalists on Twitter should be analyzed. Are they doing a different kind of journalism? With a content analysis of 1125 tweets, this study reveals trends of some Spanish journalists using Twitter. A traditional role like gatekeeping can be highly amplified in terms of transparency and accountability with actions as retweeting or linking. The landscape offered by this platform is framed with the "ambient journalism", which will help to understand the proposal of this study: the end-user journalism. The findings will show the level of opening with the audience in aspects about replies, requests and linking

Analysis of the Interactive Strategy of Microblog for Snack Food Enterprises

With the development of economic conditions and consumption patterns, snack foods have become the first choice in people\u27s daily diet and consumption, while the market scale is rising, snack foods have also gained high public opinion attention. The existing research results show that the micro-blog interaction effect will positively affect the sales performance, but there are few characterization studies on the effective micro-blog interaction strategy of the snack food enterprises. In this paper, the typical snack food enterprises as an example, mainly through the network crawler to collect micro-blog interactive contents, text analysis, and finally through ANOVA analysis to study the effect of different interaction strategies. The research finds that the strategy of micro-blog interaction of snack food enterprises is better. The characteristic research results of this paper possibly provide reference and enlightenment for the future research of micro-blog interaction strategy of snack food enterprises

Exploring Social Media Marketing In Business: Influence on Product Adoption Perspective

The importance of social media in Business cannot be over emphasized. Social media ensures effective communication between organizations and clients (customers), provides direct and timely contact with a large number of people. As there are many people with varying needs, so are many products with similar offerings competing for the varying needs of the people. This study investigates the influence of social media (Facebook, Youtube and Twitter) marketing on product adoption. 241 staff and customers of Shoprite shopping Mall, Enugu Nigeria who showed interest to participate constituted the respondents. Questionnaire was the study instrument used to elicit response from the sample of 241 respondents that took part in the study. The reliability of the study was established using Cronbach Alpha Statistical tool which yielded 0.886 considered adequate for the study. A 5 point likert structured questionnaire was used to collect data from the affected respondents. The data collected were analyzed and the study hypotheses tested with the use of linear regression. Findings revealed that Facebook, Youtube and Twitter positively and significantly influence product adoption. It was concluded that effective use of appropriate social media platform(s) influence customers’ purchase intention. That proper utilization of social media not only creates product awareness but result to product adoption amongst competing brands. It was recommended that awareness of the existence of a number of social media platforms be widely created and used based on the peculiarities of market segment(s) of the business organization. Keywords: Social Media, Marketing, Effective Communication, Business, Influence, Customer, Product Adoption. DOI: 10.7176/EJBM/12-21-14 Publication date:July 31st 202

Making sense of consumers' tweets: sentiment outcomes for fast fashion retailers through big data analytics

Purpose- Consumers online interactions, posts, rating and ranking, reviews of products/attractions/restaurants and so on lead to a massive amount of data that marketers might access to improve the decision-making process, by impacting the competitive and marketing intelligence. The aim of this research is to help to develop understanding of consumers online generated contents in terms of positive or negative comments to increase marketing intelligence. Design/Methodology/Approach- The research focuses on the collection of 9,652 tweets referring to three fast fashion retailers of different sizes operating in the UK market, which have been shared among consumers and between consumer and firm, and subsequently evaluated through a sentiment analysis based on machine learning. Findings- Findings provide the comparison and contrast of consumers’ response towards the different retailers, while providing useful guidelines to systematically making sense of consumers’ tweets and enhancing marketing intelligence. Practical Implications- Our research provides an effective and systemic approach to (i) accessing the rich data set on consumers’ experiences based the massive number of contents that consumers generate and share online, and (ii) investigating this massive amount of data to achieve insights able to impact on retailers’ marketing intelligence. Originality/Value- To best of our knowledge, while other authors tried to identify the effect of positive or negative online comments/posts/reviews, the present study is the first one to show how to systematically detect the positive or negative sentiments of shared tweets for improving the marketing intelligence of fast fashion retailers