Payday Loan Marketing in Social Media Networks

By Hao, Jianqiang; Dai, Hongying | The Journal of Consumer Affairs, Summer 2018 | Go to article overview

Payday Loan Marketing in Social Media Networks


Hao, Jianqiang, Dai, Hongying, The Journal of Consumer Affairs


A payday loan (PDL) is a small, short-term loan with a high interest rate. In recent years, online PDL volume and revenue have significantly increased, despite a reduction in PDL storefronts. Stephens Inc. estimated that Internet PDL volume more than doubled from $6.7 billion in 2007 to $14.3 billion in 2010 (Montezemolo, 2013). The literature on PDL lending is wide ranging, covering topics including loan access, geographic distribution, regulation and law enforcement, PDL target population, and social economic burdens (Bertrand and Morse 2011; Bhutta 2014; Bhutta, Skiba, and Tobacman 2015; Li, Mumford, and Tobias 2012). However, limited studies have been conducted on how lenders market PDLs and how consumers perceive these high-cost products, especially through the online channel.

Traditional ways to collect opinion data are slow and expensive. The recently rising social networks provide researches real-time "Big Data" to analyze consumer attitudes and behaviors. There are several advantages of using social media for PDL monitoring: (1) People may behave and express their feelings in a different way on social media networks than they do when they respond to traditional surveys. Therefore, social media posts may contain "back-stage" information that might not be available from a traditional survey. (2) Social media networks provide researchers with "Big Data" to analyze consumer attitudes and behaviors. An average of 500 million tweets (short messages that are fewer than 140 characters) are posted on Twitter each day and 23% of online adults/19% of the entire adult population currently use Twitter (Duggan et al. 2015). Over one billion people use Facebook actively each month (Statista 2015) and 71% of adult Internet users currently use Facebook (Duggan et al. 2015). These networks yield a large sample size and rich information for research, including sentiment assessment, prevalence by geographic locations, and topics of social media posts. (3) Most social media information is real-time and can be analyzed continuously at a relatively low cost. Past studies (Dai and Hao 2017; Ginsberg et al. 2009; Salathe and Khandelwal 2011; Wong et al. 2015) have shown that using social media platforms as a real-time monitoring tool can provide valuable insights into the general public's opinions and attitudes.

The PDL activities from social media networks, including sentiment, opinion, and knowledge along with user count, favorite count, followers, impact factors, and so on, have not been studied in literature. This study seeks to fill gaps in the literature by analyzing the social media data related to PDLs, examining PDL online marketing on Twitter, and investigating the impact of state regulations on PDL marketing and sentiment among Twitter users. To our knowledge, this study is the first of its kind to analyze PDL activities on social networks.

BACKGROUND

PDL Lending Trend

Nonbank lenders offer PDLs through storefronts and over the Internet. The PDL lending industry has grown dramatically since its inception in the 1990s. The Center for Responsible Lending reports that there are over 22,000 PDL locations in the United States and the industry generates an estimated $27 billion in annual loan volume. A PDL typically carries a 14-day loan term for no more than $1,000 with a cost of $10-$20 per $100 borrowed, with annual percentage rates (APRs) often in excess of 400% (Center for Responsible Lending 2015).

Many Americans with good credit or stable income might never use a PDL. However, many other Americans are vulnerable to these high-cost loans, often because they have limited access to mainstream banking services. An estimated 27.7% of all American households representing 67.6 million adults are either "unbanked" (7.7% of all households) or "underbanked" (20% of households) (FDIC 2014). The highest unbanked rate is found among non-Asian minorities, lower-income households, younger households, and unemployed households. …

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