Does Payday Lending Hurt Food Security in Low‐Income Households?

During the study period of 2005–2011, household food insecurity rates as well as regulations over payday lending industry increased in the United States. This study evaluates the association between access to payday lending and the risk of food insecurity using cross‐sectional samples of low‐income households from the 2005 to 2011 Food Security Supplements of the Current Population Survey. The study uses county‐level payday lender density, state‐level legislative status, and county border indicators of cross‐state payday access to examine how payday lender availability affects household food insecurity. The findings suggest that access to payday lending, which may be presumed to provide convenient short‐term credit to underserved consumers, increases the likelihood of household food insecurity by 2.8–6.0 percentage points in absolute terms. Furthermore, the county border effect of payday access has become insignificant later in the sample period, the potential reasons for which are discussed.     

User engagement for mobile payment service providers – introducing the social media engagement model

Twitter is being used by mobile wallet firms for customer acquisition, relationship management, marketing and promotional purposes. This study examines service advertisement and promotional tweets by mobile wallet firm's on Twitter. For this study, timeline data of top four mobile wallet firms of India, Paytm, MobiKwik, Freecharge and Oxigen Wallet were extracted from their Twitter screen (firm generated tweets). The user generated tweets were also extracted, using the search terms as firm's name. This study proposes a Social Media Engagement model for understanding user dynamics. The study provides three interesting inputs for promotional marketing tweets, firstly, firm should post mix of the tweets with respect to content type (i.e. informational, entertainment, remuneration and social). Secondly, a periodic campaigning is needed by the firms; and lastly, firms should focus on increasing their network size. The implications of these findings can help firm's managers and marketers in planning effective social media marketing campaigns.     

Payday lending,crime, and bankruptcy: Is there a connection?

The payday lending industry has been the subject of controversy over the years. This is largely due to the high fee structure of payday loans and the view of some that the industry targets economically vulnerable groups. For these reasons, some states prohibit payday lending, while others impose regulatory restrictions on their operations. Despite the prohibitions and restrictions, the industry nonetheless serves a significant segment of the U.S. population. Our purpose is to determine whether in addition to providing loans to individuals, access to payday lenders is associated with less property crime and fewer bankruptcies. Using a unique data set obtained directly from all state regulatory authorities, we find evidence, contrary to some earlier studies, that the presence of payday lenders may help reduce property crime as well as personal bankruptcies.     

An Investigation of Brand-Related User-Generated Content on Twitter

The big data of user-generated content (UGC) on social media are laden with potential value for brand managers. However, there are many obstacles to using big data to answer brand-management questions. This article presents a framework that automatically derives latent brand topics and classifies brand sentiments. It applies text mining with latent Dirichlet allocation (LDA) and sentiment analysis on 1.7 million unique tweets for 20 brands across five industries: fast food, department store, footwear, electronics, and telecommunications. The framework is used to explore four brand-related questions on Twitter. There are three main findings. First, product, service, and promotions are the dominant topics of interest when consumers interact with brands on Twitter. Second, consumer sentiments toward brands vary within and across industries. Third, separate company-specific analyses of positive and negative tweets generate a more accurate understanding of Twitter users' major brand topics and sentiments. Our findings provide brand managers with actionable insights in targeted advertising, social customer relationship management (CRM), and brand management.     

Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media

The increasing volume of firm-related conversations on social media has made it considerably more difficult for marketers to track and analyse electronic word-of-mouth (eWOM) about brands, products or services. Firms often use sentiment analysis to identify relevant eWOM that requires a response to consequently engage in webcare. In this paper, we show that sentiment analysis of any kind might not be ideal for this purpose, because it relies on the questionable assumption that only negative eWOM is response-worthy and it is not able to infer meaning from text. We propose and test an approach based on supervised machine learning that first decides whether eWOM is relevant for the brand to respond, and then—based on a categorization of seven different types of eWOM (e.g., question, complaint)—classifies three customer satisfaction dimensions. Using a dataset of approximately 60,000 Facebook comments and 11,000 tweets about 16 different brands in eight different industries, we test and compare the efficacy of various sentiment analysis, dictionary-based and machine learning techniques to detect relevant eWOM. In doing so, this study identifies response-worthy eWOM based on the content instead of its expressed sentiment. The results indicate that these machine learning techniques achieve considerably higher accuracy in detecting relevant eWOM on social media compared to any kind of sentiment analysis. Moreover, it is shown that industry-specific classifiers can further improve this process and that algorithms are applicable across different social networks.     

Relational foundations of an unequal consumer credit market: Symbiotic ties between banks and payday lenders

Banks have financially supported payday lenders for decades. In this article I qualitatively demonstrate how these financial relationships have reinforced and expanded a bifurcated consumer credit market, and why these relationships matter for consumer access to financial services. I use archival financial documents from publicly traded payday lending companies between 1996 and 2014, available through the Securities and Exchange Commission, to construct the bank-payday lender network and reveal motivations for these financial relationships. I find that bank-payday lender relationships are sustained over many years, and that these relationships are mutually beneficial, enabling payday lender expansion and providing a way for banks to quietly profit from high-interest lending in the face of other regulatory constraints. Further, I show that these relationships have significantly reshaped the consumer credit market over the past two decades. I conclude by considering broader implications of symbiotic institutional relationships in contemporary markets.     

Making new products go viral and succeed

Why are some new product introductions more viral and successful than others? This research integrates theories of interpersonal communication and consumer learning to explore this question. Analyzing a unique data set of millions of consumer word-of-mouth transmissions (eWOM) on social media regarding 345 new automobile products introduced during 2008–2015, we find that more innovative products generate more eWOM volume but surprisingly less positive sentiment. These effects vary in magnitude across eWOM channels. However, the use of rich-content communication, pre-announcement, and cobranding strengthens (weakens) the positive (negative) effect of product innovativeness on eWOM volume (sentiment). The results further indicate that eWOM sentiment is a stronger predictor of new product success than eWOM volume. Experimental results reveal more insights into how product innovativeness influences eWOM metrics in several product categories and shed light on the role of excitement and perceived risk as mechanisms underlying these effects. The research offers useful implications for firms to design effective viral marketing campaigns to enhance new product success.     

Comparing automated text classification methods

Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.     

Starbucks’ marketing communications strategy on Twitter

Many scholars have found that social media are extraordinarily useful platforms for marketers to engage their stakeholders. Much research has developed frameworks for and proposed empirical guidelines on how brands should communicate and interact with audiences on social media platforms. However, a comprehensive study that is based on a successful and reputable brand is still lacking. The objective of this research was to categorise the types of content used by one of the most admired brands in the world, Starbucks, and evaluate its effectiveness on Twitter. In total, the author studied 565 tweets and retweets and 1392 replies. This research found that Starbucks’ marketing communications strategy includes three types of original and retweeted content: information-sharing, emotion-evoking, and action-inducing content. The author also found six types of replies: information, Apology and suppot, positive comment, question and enquiry, chit-chat, and gratitude. To be successful on the social media platforms, this research suggests that brands should use various types of content, focus on visual content, and prepare for and manage customer interactions appropriately. The findings present useful and practical information for brands that seek to engage audiences using an inexpensive but compelling marketing communications strategy on social media.     

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. We used the chi‐square automatic interaction detection (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: (1) using message structure variables only, (2) source variables only, (3) message content and sentiment variables only, and (4) 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, while 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. We make a methodological contribution by illustrating how CHAID predictive modeling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context.     

Ethical Codes in Sports Organizations: Classification Framework,Content Analysis,and the Influence of Content on Code Effectiveness

Although there is a growing body of research on social media, only few studies have considered organic products. Therefore, this study mapped the diffusion path of the social media resources for organic products in Mexico and South Korea through Twitter and compared the contents of tweets about organic products in terms of their semantic and hyperlink networks using webometric methods. The results indicate that for organic products, Koreans sent tweets much more frequently than Mexicans. Mexican tweets focused on basic food products in street markets, whereas Korean tweets highlighted promotions and firms, revealing the corporatist structure of its economy. In both cases, the findings support Twitter as a useful tool for Word-of-Mouth Communication on the online environment, among product consumers, and between consumers and enterprises.     

‘We (don’t) know how you feel’ – a comparative study of automated vs. manual analysis of social media conversations

Abstract

The ever-growing volume of brand-related conversations on social media platforms has captivated the attention of academics and practitioners, as the analysis of those conversations promises to offer unparalleled insight into consumers’ emotions. This article takes a step back from the hype, and investigates the vulnerabilities related to the analysis of social media data concerning consumers’ sentiment. A review of the literature indicates that the form, focus, source and context of the communication may negatively impact on the analyst’s ability to identify sentiment polarity and emotional state. Likewise, the selection of analytical tool, the creation of codes, and the classification of the data, adversely affect the researcher’s ability to accurately assess the sentiment expressed in a social media conversation. Our study of Twitter conversations about coffee shows low levels of agreement between manual and automated analysis, which is of grave concern given the popularity of the latter in consumer research.     

Going digital? The impact of social media marketing on retail website traffic,orders and sales

Given the rapid proliferation of digital technology, social media has become a key digital marketing strategy to promote business products, with the ultimate aim of maximising profits. Yet, empirical evidence on the impact of social media as a marketing tool remains underexplored. Using unique data over a 12-month period from a major online retailer, this paper examines the impact of daily social media activity on daily business outcomes: website traffic, orders and sales. Key findings reveal that social media leads to increased web traffic, but it does not produce a significant rise in product orders and sale income. Though, larger social media campaigns tend to result in significantly higher number of orders and sale income, and Facebook emerges as the most effective channel. Our results also reveal that the effectiveness of social media marketing varies across products depending on their complexity, cost and brand status. Taken together, these results offer a better understanding of the ways social media marketing impact businesses and provide intelligence on how to allocate resources to develop marketing campaigns.     

Competing Voices: Marketing and Counter-Marketing Alcohol on Twitter

Excessive alcohol consumption constitutes a global health problem, and despite increasing efforts to promote safe drinking, spending on alcohol advertising far outweighs spending on safe-drinking messages. Twitter represents a new channel for social marketing, but its use to promote safe drinking has not been examined. In this study, six Twitter accounts maintained by advocates of safe drinking and/or abstinence were compared with six accounts maintained by alcohol companies using a mixed-method design. The Twitter accounts of alcohol companies were followed by more people, and their tweets were more likely to use interactive features such as hashtags, to be forwarded to others, and to be associated with positive stimuli, suggesting greater interactivity and influence than prohealth Twitter messages. The results suggest social marketers may benefit from adopting the practices of for-profit marketers to increase the visibility of, engagement with, and influence of their tweets.     

Elements of strategic social media marketing: A holistic framework

Social media marketing is an integral element of 21st-century business. However, the literature on social media marketing remains fragmented and is focused on isolated issues, such as tactics for effective communication. The current research applies a qualitative, theory-building approach to develop a strategic framework that articulates four generic dimensions of strategic social media marketing. Social media marketing scope represents a range from defenders to explorers, social media marketing culture includes the poles of conservatism and modernism, social media marketing structures fall between hierarchies and networks, and social media marketing governance ranges from autocracy to anarchy. By providing a comprehensive conceptualization and definition of strategic social media marketing, this research proposes an integrative framework that expands beyond extant marketing theory. Furthermore, managers can apply the framework to position their organizations on these four dimensions in a manner consistent with their overall corporate mission and objectives.     

Composing tweets to increase retweets

Brands tweet not only to communicate with followers but also to reach large audiences rapidly when the tweets are retweeted by the followers. People however will retweet only if they recognize within a few seconds that the tweet is on an interesting topic. Brands therefore need insights into how to compose tweets to facilitate topic recognition even when they are just scanned. This is the issue that we address in this research. Specifically, drawing on findings in psycholinguistics, we empirically investigate if tweets composed such that they include more topic-related words that are located closer to the start get more retweets. Results from an investigation of sales-promotional tweets by sixty brands in four categories indicate that tweets that are composed as above do get more retweets. We repeat the investigation using tweets on several other topics from a natural experiment that generated pairs of tweets where each pair is on the same topic but each tweet in the pair is composed differently. This investigation reconfirms the findings from the analysis of retweets of sales-promotional tweets. We conclude by presenting an approach for how social media managers can compose tweets based on our findings.     

Ten Years of ‘Social Media Marketing’ Research in the Journal of Promotion Management: Research Synthesis,Emerging Themes,and New Directions

The study synthesizes research in emerging social media marketing domains by analyzing published articles on social media marketing in the Journal of Promotion Management published since 2007. We utilized interpretation and synthesis based assessment for examining the usage of social media marketing concepts with a focus on social media, social networking sites' utilization by firms, social media metrics and social media measurement, consumer decision making, consumer perceptions, and utilizing social media marketing across countries and cultures. We found a fair balance of application of social media variables across studies, even though most of these studies were quantitative. After studying the gaps presented in social media based studies of recently published articles, we present emerging social media marketing themes, new research foci and directions, and tried to build on research implications for both theory and practice.     

Seek or destroy? Examining value creation and destruction in behaviour maintenance in social marketing

This article presents an empirical investigation of how behaviour maintenance is supported or hindered through value creation and destruction in social marketing. Using a longitudinal, netnographic approach, we identify and examine the key facilitators and barriers encountered during the maintenance of physical and mental health behaviours. Data were collected over a 12-week observation period via Twitter from a sample of 242 participants. A total of 5212 tweets were analysed using content and thematic analysis facilitated by NVivo software program. The findings identify key barriers to and facilitators of behaviour maintenance at the individual level (downstream), meso level (midstream), and policy level (upstream) that have an influence on value creation and destruction in social marketing. These findings demonstrate the importance of employing a strategic and integrated approach to social change management.     

Social media sentimentas an additional performance measure? Examples from iconic theme park destinations

This analysis quantifies social media data collected for two iconic theme park destinations. Disney World and SeaWorld were studied in-depth using social media analytics, and the findings were compared to publicly available performance measures. The scale and length of social media topics discussed differed significantly, and there was mixed evidence of correlations between social media sentiment and other public performance measures. As the role of social media contributions to selecting retailers and service providers develops, understanding the sentiment around well-known organizations and potential impacts of major events can aid decision makers in the retailing of consumer services.