The Effects of Social Networking Sites on Consumer–Brand Relationships

In the era of a vastly changing marketing communications environment, social media and social networking sites (SNS) have become an attractive outlet for brand promotions and advertising. As traditional media such as television, radio, and newspapers prove ineffective in establishing a valuable rapport with consumers, it is social networks which are expected to build bonds and engage online users into an amusing dialog, play, and intercourse with brands. This study applies an experimental design to assess the effects of SNS on the relationship between brands and consumers. Customer-based brand equity is adopted in this study as a reflective measure of consumer–brand connection. In order to evaluate hypothesized interactions, the linear mixed effects models are used. The results show very little (if any) impact of SNS on brand equity. Relevant explanations are discussed and further research directions are indicated.     

Media multitasking and the effectiveness of combining online and radio advertising

Research on the effectiveness of cross-media campaigns has shown that combining online advertising with advertising in offline media can result in more positive consumer responses than using only one medium. However, when using computers, people increasingly engage in more than one media activity at a time (i.e. media multitasking), which might influence how consumers respond to advertisements they encounter in these media. Therefore, this paper investigates advertising effects during media multitasking. More specifically, the paper gives insight into the effectiveness of simultaneous exposure to online and radio advertising, because simultaneously surfing the internet and listening to the radio is a common media multitasking combination. Results of an experimental study with 111 participants showed that combining online and radio advertising resulted in more positive affective and behavioral responses than using only one medium. However, media multitasking seemed to have a negative influence on the recall and recognition of auditory information as combining media did not result in superior cognitive responses compared to using online ads alone.     

The role of persuasion knowledge,assessment of benefit and harm,and third-person perception in coping with online behavioral advertising

Online behavioral advertising (OBA) provides Internet users with potential benefit (relevant ads) and harm (privacy infringement) through personalized covert persuasion tactics, making it an interesting case for understanding perceptions of media effects. In study 1 (survey), we found that subjective persuasion knowledge of OBA was positively related to third-person perception (TPP). Importantly, the assessment of the potential harm and benefit of OBA mediated the relationship between subjective persuasion knowledge and TPP. Objective persuasion knowledge however was only indirectly related to TPP via subjective persuasion knowledge. TPP related to personal (accept, avoid OBA) but not social-level (pro-regulation) coping measures. Study 2 (experiment) replicated study 1 and showed that the perceived effect of OBA on self (not others) drove the TPP and the responses to personal outcome measures. Implications for understanding perceived media effects and response to OBA are discussed.     

Business engagement on Twitter: a path analysis

Social media services, such as Twitter, enable commercial businesses to participate actively in online word-of-mouth communication. In this project, we examined the potential influences of business engagement in online word-of-mouth communication on the level of consumers’ engagement and investigated the trajectories of a business’ online word-of-mouth message diffusion in the Twitter community. We used path analysis to examine 164,478 tweets from 96,725 individual Twitter users with regards to nine brands during a 5-week study period. We operationalized business engagement as the amount of online word-of-mouth messages from brand and the number of consumers the brand follows. We operationalized consumers’ engagement as the number of online word-of-mouth messages from consumers both connecting to the brand and having no connection with the brand as well as the number of consumers following the brand. We concluded that the business engagement on Twitter relates directly to consumers’ engagement with online word-of-mouth communication. In addition, retweeting, as an explicit way to show consumers’ response to business engagement, indicates that the influence only reaches consumers with a second-degree relationship to the brand and that the life cycle of a tweet is generally 1.5 to 4 hours at most. Our research has critical implications in terms of advancing the understanding of the business’s role in the online word-of-mouth communication and bringing insight to the analytics of social networks and online word-of-mouth message diffusion patterns.     

“I like them,but won't ‘like’ them”: An examination of impression management associated with visible political party affiliation on Facebook

Unlike traditional media, our interactions with political parties via social media are generally public, subject to scrutiny by others and, consequently, a self-presentation concern. This paper contributes to theory on impression management within social network sites (SNSs) by providing an understanding of the effect of visible affiliation on page ‘Liking’ behavior in the context of political parties; specifically, the possible association with social anxiety and the use of protective impression management. We predict that while users may be motivated to ‘Like’ a political party, some may feel socially anxious about the impressions their friends may derive from this action, and so ultimately choose to refrain from ‘Liking’ the party. Furthermore, we propose a new function of ‘Secret Likes’ (i.e. ‘Likes’ that others cannot see) as a means to increase gateway interactions. A survey of eligible voters (n = 225) was conducted in the month prior to the 2015 UK general election, examining behavior associated with the Facebook pages of the two largest political parties. Results support that conspicuous affiliation with political parties indeed hinders intention to ‘Like’ political pages and is associated with social anxiety. ‘Secret Likes’ were found to be a successful method to increase gateway interactions. In addition to the theoretical contribution, implications for political party communications and site designers are considered.     

Modeling the clickstream across multiple online advertising channels using a binary logit with Bayesian mixture of normals

The evaluation of online marketing activities using standalone metrics does not explain the development of consumer behavior over time, although it is of primary importance to allocate and optimize financial resources among multiple advertising channels. We develop a binary logit model with a Bayesian mixture approach to demonstrate consumer clickstreams across multiple online advertising channels. Therefore, a detailed user-level dataset from a large financial service provider is analyzed. We find both differences in the effects of repeated advertisement exposure across multiple types of display advertising as well as positive effects of interaction between display and paid search advertising influencing consumer click probabilities. We identify two consumer types with different levels of susceptibility to online advertising (resistant vs. susceptible consumers) and show that the knowledge of consumers individual click probabilities can support companies in managing display advertising campaigns.     

Contrast effects in online auctions

Prior research has shown online auction features can serve as information cues and affect consumers’ willingness-to-pay. We argue that auctions are not only affected by their information cues but also by contrasting, peripheral information cues from adjacent auction listings. Applying contrast effects theory, we examined the moderating effects of time urgency and persuasion intent on the processing of contrasting peripheral information from adjacent auctions. Using two controlled experiments and an empirical field study, we showed that time urgency experienced by bidders in online auctions resulted in increased heuristic processing of contrasting information from adjacent auction listings. Under time pressure, bidders were more likely to be affected by this contrasting peripheral information. We also found that bidders will discount contrasting peripheral information if they perceive salient persuasion intents in advertising presented by the auctioneers. The resulting contrast effects ultimately lead to changes in willingness-to-pay and underscore the importance of peripheral information from adjacent auctions in impacting auction outcomes.     

Travellers’ acceptance of consumer-generated media: An integrated model of technology acceptance and source credibility theories

Despite the rising influence of social media, the use of consumer-generated media (CGM) for the task of travel planning still meets with scepticism among certain online travel consumers. Hence the need to further explore the psychological factors underlying this aspect of online behaviour. The study proposes a model of consumer-generated media acceptance for the purpose of travel planning which integrates the Technology Acceptance Model with the Source Credibility Theory. Using an online survey of 661 valid responses and structural equation modelling, the findings highlight the critical factors relevant to the cognitive processes which determine online travellers’ affective and conative responses to the use of consumer-generated media for travel planning. The results suggest that integrating technology acceptance factors with that of information adoption can enhance the understanding of consumer-generated media usage in the vacation planning context. The study outcome holds implications for theory and practice.     

Political Expression and Action on Social Media: Exploring the Relationship Between Lower‐ and Higher‐Threshold Political Activities Among Twitter Users in Italy

Scholars and commentators have debated whether lower‐threshold forms of political engagement on social media should be treated as being conducive to higher‐threshold modes of political participation or a diversion from them. Drawing on an original survey of a representative sample of Italians who discussed the 2013 election on Twitter, we demonstrate that the more respondents acquire political information via social media and express themselves politically on these platforms, the more they are likely to contact politicians via e‐mail, campaign for parties and candidates using social media, and attend offline events to which they were invited online. These results suggest that lower‐threshold forms of political engagement on social media do not distract from higher‐threshold activities, but are strongly associated with them.     

Multiple categorizations of products: cognitive modeling of customers through social media data mining

As various forms of social media are spreading, we often witness that an idea of an individual user drives macroscopic changes. From the perspectives of product development and marketing, the opinions left by potential consumers in online social network can generate big ripple effects. This study analyzes the user opinions in online space to grasp preferences toward various products psychologically categorized by users. We also suggest an aspect of the market mentally configured by users using network modeling while following the framework of economic sociology. Existing analyses on online market place are mainly dealing with structural issues such as inter-actor relationships and status measurement. This study, however, analyzes complex preferences regarding diverse products and brands and derives a new model for inter-market connections. We expect that our study will provide important consequences on digital marketing and community design of corporations planning word of mouth effect in online space.     

Incidental Exposure,Selective Exposure,and Political Information Sharing: Integrating Online Exposure Patterns and Expression on Social Media

Political information sharing in social media offers citizens opportunities to engage with news and express their political views, but how do different patterns of online political information exposure, including both incidental and selective exposure, affect sharing? Using two‐wave panel survey data collected in the United States, we examine the relationship between incidental and selective exposure and their consequent links to political information sharing, across different levels of strength of political party affiliation. Our results demonstrate that incidental exposure to counter‐attitudinal information drives stronger partisans to more actively seek out like‐minded political content, which subsequently encourages political information sharing on social media. The results highlight the need to consider both types of political information exposure when modeling citizens' political behavior online.     

How Can Our Tweets Go Viral? Point-Process Modelling of Brand Content

People create and share content via online social networks, which provide an unparalleled opportunity for brands to gain visibility, promote products or services and drive revenue growth. Much research has focused on why, how, or what social content is popular, trending and “hype”. One central challenge is to forecast the spread (cascades) of information that leads to the popularity of content throughout a social network. Online content tends to have bursts and spikes, experiencing a different cascading pattern depending on the viral propagation. In this paper, we propose and test a flexible framework capable of modelling such patterns and trends. We take temporal and network perspectives and develop a model based on the multivariate Hawkes processes that account for social behaviour and network elements such as follower counts, and activity variation observed in collective re-sharing behaviour. We focus on Twitter as the most widely used micro-blogging online social network and measure the popularity of a brand's tweet by analysing the time-series path of three types of subsequent activities (retweets (RTs), replies (REs) and likes (LKs)). The specific model that we propose in this paper is the multidimensional epidemic-type aftershock sequence (METAS) model, a particular case of the multivariate Hawkes process. It consists of a power-law relaxation governing the timing of activities. It also includes an exponential boost as a reinforcement mechanism for the response amplitude to model the impact of influential users on their followers. Earlier attempts to model online cascades have treated all online responses as one type of activity. Rather than aggregating all the activities into one stream, and therefore, ignoring exciting effects among different types of activities, we incorporate the activity variation into the predictive models of content popularity, explicitly accounting for such excitation effects. We develop epidemic-type mutually exciting Hawkes point processes models to quantify such effects and to predict more accurately the number of follow-up activities (i.e., RTs, REs and LKs) on a brand tweet after it is posted. Our results suggest that the proposed model outperforms the state-of-the-art models in terms of prediction accuracy, as it is able to account for mutual excitations and cross-interactions between sequences of users’ activities from one type to another. These results are relevant for developing and executing a plan for online activities by the brand owners.     

“What I’ve received doesn’t match what I saw online”: Effects of purchase-risk notices on reducing online returns

Many online returns are caused by mismatches between what consumers see online and what they actually receive. This paper discusses e-sellers’ use of purchase-risk notices (PRNs) for possible mismatches as a preemptive action to avoid returns. Two one-factor (purchase-risk notice: presence vs. absence) scenario experiments were conducted via two studies (Study 1 and Study 2). The pre-purchase effects and post-purchase effects of PRNs were examined in Study 1 and Study 2, respectively. One-way ANOVAs were used to test the hypotheses. It was found that returns can be avoided by using PRNs without negatively affecting consumers’ purchase intentions. Additionally, using PRNs can make consumers more tolerant of minor mismatches, attract more repurchases, and reduce consumers’ dissatisfaction and regret about purchase decisions.     

‘You talking to me?’ The influence of peer communication on adolescents’ persuasion knowledge and attitude towards social advertisements

This article examines how peer communication among adolescents (14–16 years) affects the evaluation of social advertising (i.e. targeted ad that adopts the social proof heuristic by using an individual’s social ties as endorsers for a brand) on social networking sites (SNSs). More precisely, the focus lies on how engaging in online peer chatting on these social platforms alters persuasion knowledge and attitude towards the ad. In order to test this, two between-subjects experiments were conducted in which adolescents chatted with peers on a mock SNS that contained a social ad. In Experiment 1, results reveal that a social ad generates a more positive attitude among adolescents when they have engaged in online peer communication, and at the same time, triggers less persuasion knowledge. In Experiment 2, the depth of the relationship between the chatters (tie strength) plays a significant moderating role as peer communication with strong ties yields greater effects, compared to communication with weak ties. These findings reveal important social influence dynamics that may alter the elaboration of persuasive communication, leading to valuable theoretical and practical implications.     

Tweet categorization by combining content and structural knowledge

Twitter is a worldwide social media platform where millions of people frequently express ideas and opinions about any topic. This widespread success makes the analysis of tweets an interesting and possibly lucrative task, being those tweets rarely objective and becoming the targeting for large-scale analysis. In this paper, we explore the idea of integrating two fundamental aspects of a tweet, the proper textual content and its underlying structural information, when addressing the tweet categorization task. Thus, not only we analyze textual content of tweets but also analyze the structural information provided by the relationship between tweets and users, and we propose different methods for effectively combining both kinds of feature models extracted from the different knowledge sources. In order to test our approach, we address the specific task of determining the political opinion of Twitter users within their political context, observing that our most refined knowledge integration approach performs remarkably better (about 5 points above) than the textual-based classic model.     

Internet,News, and Political Trust: The Difference Between Social Media and Online Media Outlets

What is the relationship between Internet usage and political trust? To answer this question, we performed a cross‐sectional analysis of Eurobarometer survey data related to 27 countries and a supervised sentiment analysis of online political information broadcast during the Italian debate on the reform of public funding of parties. The results disclose the differences between Web 1.0 websites and Web 2.0 social media, showing that consumption of news from information/news websites is positively associated with higher trust, while access to information available on social media is linked with lower trust. This has implications for the debate on social media as a public sphere and for the tension between professional and citizen journalism.     

Assessing the reTweet proneness of tweets: predictive models for retweeting

The problem of assessing the mechanisms underlying the phenomenon of virality of social network posts is of great value for many activities, such as advertising and viral marketing, influencing and promoting, early monitoring and emergency response. Among the several social networks, Twitter.com is one of the most effective in propagating information in real time, and the propagation effectiveness of a post (i.e., tweet) is related to the number of times the tweet has been retweeted. Different models have been proposed in the literature to understand the retweet proneness of a tweet (tendency or inclination of a tweet to be retweeted). In this paper, a further step is presented, thus several features extracted from Twitter data have been analyzed to create predictive models, with the aim of predicting the degree of retweeting of tweets (i.e., the number of retweets a given tweet may get). The main goal is to obtain indications about the probable number of retweets a tweet may obtain from the social network. In the paper, the usage of the classification trees with recursive partitioning procedure for prediction has been proposed and the obtained results have been compared, in terms of accuracy and processing time, with respect to other methods. The Twitter data employed for the proposed study have been collected by using the Twitter Vigilance study and research platform of DISIT Lab in the last 18 months. The work has been developed in the context of smart city projects of the European Commission RESOLUTE H2020, in which the capacity of communicating information is fundamental for advertising, promoting alerts of civil protection, etc.     

Can consumers be persuaded on brand microblogs? An empirical study

Building upon the persuasion theory in the social psychology literature, this study investigates how companies can use microblogging services to influence consumers’ participation and brand loyalty. We develop and empirically test our research model using an online survey in China. Our findings show that persuasion factors, including information quality, perceived similarity, and source credibility, increase community commitment. Perceived similarity and source credibility exhibit bias effects on information quality. Community commitment affects participation and brand loyalty, which is also influenced by participation. We expect that this research can contribute to the existing literature and provide marketers with important practical suggestions.     

Examining the impact of social media advertising using ANOVA analysis

Social media has become a significant part of daily life and nowadays it has started to turn out to be common practice for consumers and companies to employ social media to form, establish, and connect relationships. In addition, social media is based on web and mobile-based technologies for creating highly interactive paradigms by which communities and individuals co-create, share, modify and discuss user-generated content. However, it is never easy for businesses to create social media advertising that attracts people and encourages them to buy their products. This article intends to present a statistical analysis that focuses on social media marketing among people of different ages. Six categories—personal profile, usage, assessment, higher-order data, trustworthiness, and elements of social media ads—are included in the analysis of social media marketing. The initial phase focuses on questionnaire preparation depending on social media advertising. Subsequently, the planned questionnaire is collected from diverse respondents at various educational levels utilizing a Google form. Then, these respondents' responses are collected for examination. To determine whether the outcomes of the experiment are meaningful, the evaluation in this piece of study is based on an ANOVA analysis.     

A lightweight clustering–based approach to discover different emotional shades from social message streams

With the explosion of social media, automatic analysis of sentiment and emotion from user-generated content has attracted the attention of many research areas and commercial-marketing domains targeted at studying the social behavior of web users and their public attitudes toward brands, social events, and political actions. Capturing the emotions expressed in the written language could be crucial to support the decision-making processes: the emotion resulting from a tweet or a review about an item could affect the way to advertise or to trade on the web and then to make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion-based classification of textual data from a social network by using an extended version of the fuzzy C-means algorithm called extension of fuzzy C-means. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results.