Spinning: Zooming in an Atypical Consumer Behavior

Abstract

The present article introduces the concept of consumer spinning. This occurs when consumers have lost track of their initial motivation to consume and engage unwittingly in hamster-like spinning in a “wheel of misfortune”, being encouraged to spend through sweetheart deals and thus incur debt without even realizing it. This article takes for example the Global Financial Crisis of 2007-2009 (GFC) during which the use of such sweeteners by sellers of predatory mortgages and advocates for lax regulations led U.S. consumers to spin and, in the process, ignore the debt trap they were falling into. We discuss the results of a functional magnetic resonance imaging (fMRI) experiment we conducted to test the ratio of greed over risk aversion and the hypothesized phenomenon of spinning. We believe that this is the first academic attempt to examine spinning resorting to an experimental tool used regularly in neuroscience and psychology research. Our framework indicates that government regulators should take the role of marketing sweeteners into account when devising policies. Marketing managers, who strive to educate consumers and help them reduce their debt load, thus promoting better purchasing power, stand to benefit from an understanding of these possible spinning behaviors and adapt their campaigns accordingly. We believe that there should be legal consequences for encouraging consumer spinning, as this at times may be an intentional and deceitful invitation for consumers to harm themselves financially.

Keywords

risk aversion greed regulations market contagion debt trap market friction spinning

Introduction

Since the very beginning of the financial market system, there have been numerous examples of market downturns around the world. Famous instances include the Dutch Tulipomania in the 17th century in Holland, the Mississippi Bubble in France in the 18th century and the savings and loan fiasco in the United States in the 1980s (Kindleberger 1996, Rajan 2010, Sorescu et al. 2018).

Indeed, markets and corporations are prone to dubious behaviors (Brunnermeier and Sannikov 2014) and, in some cases, even illegal corporate activities (Cloninger and Waller 2000). During these crises, many consumers first become overly excited and lose track of proper risk assessment. Then, when things go sour, they panic and make decisions that further accelerate their downfall and the market crash that follows. They spin, in the sense that they lose focus on their initial motivation due to market forces (such as promotional sweet deals) and frictions (such as volatility).

While the absence of appropriate regulatory institutions and measures (Campbell 2019), including with respect to advertising, from the 17th to the beginning of the 20th century jeopardized financial stability, this gap has since been mitigated by the creation of, for example, the U.S. Federal Reserve Bank¹ (Grossman and Meissner 2010) and the introduction of laws regulating or banning misleading advertising, such as Section 15 of the Federal Trade Commission Act.

However, the questionable management of government-run businesses such as Fannie Mae and Freddie Mac and the lack of proper contemporary measures² cannot escape scrutiny (Calomiris and Wallison 2008), nor can the use of heavy, often deceptive advertising and maneuvering during the Global Financial Crisis (GFC) (Ben-David 2011; Brunnermeier and Pedersen 2009).

The 2007-2009 global financial crisis (GFC) is one example among many others that illustrates how speculation and the publicity around toxic products that comes with it have eroded the confidence of businesses, communities, and consumer confidence (Samoa and Shoaf 2005, Brown 2010). In the present paper, we use this case to exemplify the concept of spinning.

The U.S. subprime crisis left the economy carrying a trillion dollars in dubitable mortgages (Frame, Lehnert, and Prescott 2008, p. 4). In parallel, between 2005 and 2008, the Federal Reserve increased its key lending rate, from roughly 1 to 5 percent, which caused housing construction to slow down sharply, from 2 to 1.5 million units, to 500,000 units³ built in 2010.

With the start of the economic downfall, consumer debt built up steadily, resulting in numerous delinquencies and foreclosures, 50% of which were linked to predatory loans, the equivalent of 2% of U.S. GDP⁴. Both the International Monetary Fund (IMF 2009b, Chap. 2) and the Financial Crisis Inquiry Commission report (2011, p. XVI) criticized the lack of proper regulations and the reckless promotion of risk-free and easy credit (Fostel and Geanakoplos 2012). Academics blamed advertising and promotion practices for magnifying efforts and tactics to hide the real risk associated with subprime mortgages (West and Prendergast 2009).

The crisis did not emerge out of nowhere (Razin and Rosefielde 2011); it was a consequence of “unruly deregulation” (Krugman 2009). In the late 1970s, the U.S. Community Reinvestment Act (CRA) opened the door to leniency, with banks permitted to grant credits to unqualified clients, a tendency that became even more pronounced in the 1990s with the Glass-Stegall Act revision (Taylor 2007, White 2009).

This paper explains how various market agents – not only regulators and sellers of predatory mortgages but also buyers-turned-sellers who flipped their houses at a profit – fostered greedy consumer behaviors by using various “sweeteners” (sweet deals).

Sweeteners enticed naive, gullible consumers into ignoring impending debt traps and jumping into the house-buying frenzy by making their offers more appealing than were justified (Miles 2013). An example of the influence of sweeteners is that of teaser rates, by which mortgagees benefited from subprime rates for a short period (usually one year); research has indeed shown that risk-free rates motivated buyers to take on more risk (Ganzach and Wohl 2018). Hill and Kozup (2007) convincingly describe how the subprime mortgages that invaded the U.S. market during the period 2007-2009 were consumer loans with at least one of the following elements always present: “aggressive and deceptive marketing, lack of concern for the borrower’s ability to pay, high interest rates and excessive fees, unnecessary provisions that do not benefit the borrower…large prepayment penalties, or faulty underwriting…” (p. 29). The authors recognize that some lenders or house-flippers targeted vulnerable people who were exploited by way of incomplete disclosure of information, thus favoring “irrational choices” (p. 32) tied to unfair contracts (p. 40).

In the first section, we introduce our framework of spinning and sweeteners and explain its operation. We thereafter dedicate a section on sweeteners because we postulate consumers would not be distracted from their initial motivation to consume and start spinning their “wheel of misfortune”, like a clueless hamster, if they did not have something that encourages them to do so. In the third section, we present the findings of an exploratory functional magnetic resonance imaging (fMRI) study of 19 participants, which reinforce the notion that fear affects financial decision-making (see the Appendix). The outcome reinforces previous work showing that people do indeed make more mistakes when vulnerable (Starcke and Brand 2012). We correlate fear with greed and the perceived risk of a debt trap (Scherbina and Schlusche 2013) or, in other words, risk aversion (Lucas 1978). Our study furthermore assumes the following: We consider greed to be the fear of not entering the booming housing (bull) market on time to make a quick profit, and we interpret the perceived risk of a debt trap as the fear of not exiting a collapsing (bear) market that would cause housing assets to lose their value quickly, making it impossible to repay debt and thus causing foreclosures. Our study does not directly measure the influence of sweeteners during the GFC, as doing so would have required an extensive longitudinal study with many uncontrollable variables. Rather, the study only points in the direction that, perhaps, given the right sweeteners, people would tend to ignore the real risk and start spinning. Our research is exploratory and aims to prepare a path for future research.

In the fourth section, we discuss our results and outline their meaning. In the U.S. market during the GFC, greed or the fear of not entering the booming market on time overwhelmed the perceived risk of a debt trap. We contend that this greed or fear occurred in part under the influence of the various sweeteners that filled the market and enticed consumers to spin. Our concept of spinning being an emerging one, we cannot firmly establish that it took place during the GFC, but we contend that it could be the case and that future research is warranted. In our conclusion, we emphasize the importance of regulators understanding how marketing tactics such as sweeteners influence the market when devising economic policies and explore avenues for future research.

Our Framework of Marketing Sweeteners

We present a framework of sweeteners that attempts to illustrate how the U.S. market behaved in 2000-2009. Models invariably are simplifications of reality and inevitably rest on key assumptions (Samuel 2009). Ours, consistent with the works of Mesly, Chkir, and Racicot (2018) and Huck, Mavoori, and Mesly (2019), are as follows: Our first assumption is that the market comprises four market agents: two policy-driven variables – government actions as defined by the Federal Reserve’s interest rates, and the ratio of predatory to total mortgages – and two agent-driven variables – sellers of predatory mortgages as measured by the ratio of shadow⁵ to traditional banking, and buyers of those predatory mortgages as measured by [1 - foreclosures]. This last equation describes a healthy population that will eventually become prey to the sellers of predatory mortgages. We assume these measures plausibly represent market forces for the purposes of our modeling effort. The interest rates act as a predator of sorts towards the predatory mortgages; as the rates increase, the attractiveness of these toxic products diminishes. Previous authors have shown that the U.S. subprime market exhibits Lotka-Volterra predator-prey relationships between these policy- and agent-driven market variables (Brady 2017, Huck, Mavoori, and Mesly 2019, Lotka 1920, 1925, Volterra 1926, 1931) (see Figure 1):

Figure 1.

U.S. market data exhibiting Lolka-Volterra (LV) patterns. Notes: Curves based on actual market data in the U.S. pointing to LV patterns.

The market data used to create Figure 1 provide a summative portrait of what happened in the U.S. during the GFC. Predatory mortgages kept rising due to the lack of regulations, and with them rose heavy advertising aimed at luring potential customers into buying one or multiple houses. The Federal Reserve raised the interest rate to cool the overheated market, and the result was consistent with typical Lotka-Volterra predator-prey dynamics. As the number of product mortgages increased, so did the number of sellers of these products. With the end of the grace period (usually a year or two for subprime mortgages), buyers of houses suddenly faced the harsh reality of having to renew their mortgages at much higher rates. Their inescapable debt transformed them from overly optimistic buyers to prey, as signaled by the spike in foreclosures. The “owners’ equity in real estate” curve illustrates the entire market dynamic when coupled with that of debt-to-disposable income; debt kept increasing even though buyers stopped investing in real estate. Clearly, their predatory mortgages caught up to them.

Our second assumption is that the Federal Reserve increased the interest rate with the main objective of avoiding debt accumulation. The Federal Reserve sought to avoid an unmanageable debt-to-disposable income ratio; consumers in that circumstance would not be able to repay their debt and pay their income tax. Excessive consumer debt is harmful to the economy; as stated by Bordo and Meissner (2012), “Overall, there is a strong positive relationship between real credit growth and the probability of having a banking crisis”.

Our third and last assumption is that in a hot market, greed is at the heart of the typical buyer. We define greed as the fear of not entering the hot (bull) market on time and its opposite, the fear of a possible debt trap, as the fear of not exiting a bust (bear) market on time. Fear is thus at the heart of our framework, like two sides of a coin. When buyers become fearless upon facing the risk of debt traps, fearing instead that they will not be able to benefit from profit-generating booming markets, they become vulnerable, which soon translates into unbearable debt (Keltner and Gross 1999; Kunzmann, Kappes, and Wrosch 2014).

Our core, simplified framework, which places the principal market agents in their dynamic relations with one another (Nejad 2016), is illustrated in Figure 2.

Figure 2.

A simplified version of the process leading to consumer spinning. Note: This stylized and simplified framework shows how marketing sweet deals (sweeteners) nurture greed to the detriment of proper risk assessment (hence, the ratio greed/risk aversion), leading customers towards a debt trap through the process of spinning, which leads to an end point when the debt becomes unsustainable (and forces foreclosure, in the case of the GFC).

As seen in Figure 2, marketing sweetener deals are created to maximize greed but to minimize risk (notably by hiding risk as far as possible along the chain of financial institutions), thus encouraging consumption. In the process of overconsuming, consumers walk blindly into a debt trap. They ignore it and keep buying for the sake of buying, thus spinning their wheel of misfortune, until such time as the economic system itself spins out of control (crisis), as exemplified during the GFC.

Figure 3 offers a complex version of Figure 2, while keeping the same logical sequence of behaviors.

Figure 3.

The Consolidated Model of Financial Predation and the use of sweeteners. Notes: In this complex framework based on dynamical systems, psychological constructs are in a hexagonal. “Predatory mortgages sold by sellers” is the starting point and the debt trap is the end point of the framework.

The framework in Figure 3 uses a systems dynamics approach, which previous researchers have identified as a more appropriate and more dynamic approach than structural equation modeling (SEM) when dealing with complex phenomena (Chintagunta et al. 2006, Rutz and Sonnier 2011). SEM focuses mostly on static psychological constructs and needs to be tested with data retrieved from questionnaires addressing latent and actual constructs in a single scenario (e.g., how respondents feel at a particular time). On the other hand, dynamic systems can test a single psychological phenomenon, such as fear, and see how it evolves over time given different scenarios (Forrester 1994).

The framework in Figure 3 reads as follows: Buyers of predatory mortgages are greedy (at least, some of them), that is, geared towards materialistic values. Their search for houses and attractive borrowing terms amounts to foraging, as animals do in the wild (Bonsall and Hassell 2007). By foraging, we mean that eager borrowers look for the least threatening lender, one that can eventually and unfortunately become a predator. Unlike in nature, where animals forage for food (e.g., a squirrel digging the ground to recover a hidden nut) and hide or run away from their natural predators, in a financial setting, things are quite different. Eager borrowers seek money (forage) but they must seek it from people who can become their predators (the lenders). Lacking a real choice, they seek out the least threatening lender, one they are inclined to put the most trust in.

As in the wild, foragers must always weigh the risks (of getting caught by the predator) and opportunities (to feed). In the financial world, ultimately the risk is the prospect of a debt trap; the opportunity is to flip houses and make money fast. When greed is high and the perceived risk of a debt trap is low, buyers engage in a contagion wheel (or what will become a wheel of misfortune). First, they borrow money; lenders make money access as attractive as possible by easing access, notably by lowering their qualifying standards. Buyers then acquire houses, contracting subprime mortgages in the process. These mortgages are “sweet deals” in their own right, with teaser rates and other such incentives.

The buyers upgrade their recently-bought houses to make them as attractive as possible on the market, for example, by renovating the kitchens. They raise the prices on the recently purchased properties. At this point, the buyers become sellers, joining the population of predators. To encourage potential buyers, they sweeten their offers with advertising and buying incentives. In some reported cases, a seller offered a free lawnmower or similar inducement. Excited with the profit they have just made, the buyers-turned-sellers use their assets as collateral and buy even more houses, motivated as they are by greed and blinded by sweeteners that hide the risk of a debt trap. In this way, the wheel starts turning. The buyers-cum-sellers increase their exposure to real risk (Claessens and Kodres 2014), making them increasingly vulnerable to market volatility (Soman and Cheema 2002).

Other potential buyers join in, and soon buyers compete against each other, with the overall effect that each buyer becomes greedier (Hoffmann, Krause, and Laubach 2012). Contagion takes place. However, this entire mechanism opens the faucet that fills in the “bathtub” of debt, with no drain available. The buyers do not repay their debts. Instead, they keep borrowing because they think there is more money to be made. When in a state of spinning, however, even the thought of making more money becomes elusive; there comes a point where consumers are so baffled by market events that they simply spin the wheel of misfortune, not realizing they are increasing their debt load.

Our framework identifies four opportunities to activate sweeteners (sweet deals), which we posit are the necessary ingredients to entice consumers to spin (like an apple given to a horse as a reward for having performed in a circus show): during the borrowing, purchasing, renovating and actual selling. At each stage, it is to the profit seekers’ best advantage to find means of sweetening their actions (sugarcoating their poison pills) and luring their prey into becoming greedier and less vigilant about the debt-trap risk.

Assuming consumers are not greedy and that the risk of a debt trap is high, would they borrow money? No, not unless the cost of money was unusually low. Would they buy houses they do not really need? No, not unless they can access very low rates, at least for a short while. Would they invest in renovating the house? No, not unless the market shows a tendency for prices to go up. Would they sell the houses and offer incentives out of their own pockets? No, not unless they can turn in a rewarding profit that allows them to acquire access to easy credit. In short, we posit that the GFC could not have taken place without these four sweeteners.

Of course, financial literacy can be a source of help to assist trapped consumers to get out of their financial misery. However, things are not that simple. In markets where moral hazard (defined as, “the failure to either behave diligently or in good faith at any point in the exchange”; Ericson and Doyle 2003, p. 11) accompanies the “fear factor” of price fluctuations (Obi, Dubihlelaband, and Choi 2012), consumers at times build their wealth expectations on faulty heuristics and biases that confound their judgments (Chaiken 1980, Kahneman and Tversky 1979) as well as on poor investment habits, exhibiting a significant lack of self-control (Helweg-Larsen and Shepperd 2001). Their appetite for a quick profit or greed (Shefrin 2000) silences any reasonable efforts at adequate risk assessment (Hoffmann, Krause, and Laubach 2012).

As suggested in Figures 2 and 3, we contend that greed and an absence of risk aversion were the psychological cores of the GFC (Fostel and Geanakoplos 2012; Reinhart and Rogoff 2009). From 2000 to 2010, over fifty percent of all foreclosures were due to subprime loans, only 12% of which were first-lien mortgages. Excited consumers bought multiple houses that they could not afford to carry over the medium or even the short term (Frame, Lehnert, and Prescott⁶ 2008). However, we assume that past a certain point, consumers became desensitized to the market forces and frictions, were overloaded with information and sweet deals, and thus engaged in spinning.

Buyers of predatory mortgages acted as prey (hamsters of sorts). Their excessive overconfidence in the market mechanisms was fueled by extraordinarily aggressive marketing campaigns (Ben-David 2011) and gullibility (Sama and Shoaf 2005, Frame, Lehnert, and Prescott 2008). They could not resist the appeal of what a house represented to them: shelter, safety, belonging, wealth, status, and power. Their greed pushed them towards more extravagant living (Shiller 2005). To achieve such a lifestyle, they spent beyond their means.

Concerning the sellers of predatory mortgages, Shiller (2005, p. 76) describes them as follows: “When clever persons become professionals at deceiving people, and devote years to perfect their act, they can put seemingly impossible feats before our eyes and fool us, at least for a while.”

The predatory mortgages served as toxic (Wiles et al. 2010), deceitful instruments presenting “terms and conditions that ultimately harm[ed] borrowers”⁷. Unknowingly, excited home buyers got themselves in a debt trap, one that revealed itself when the Federal Reserve increased the interest rates rather suddenly to cool off the market.

In our sweetener framework whereby sweet deals are designed to hide the toxicity of the market, the household debt-to-disposable income ratio constitutes the main market force that dictates the Federal Reserve’s intervention. Indeed, “Financial crises are ultimately related to two problems: insolvency and illiquidity” (Hinds 2009).

From 2000 to 2007, the U.S. household mortgage debt to consumption ratio rose from roughly 2.5 to 4.5, declining to 3 by 2015. At the same time, the ratio of housing rent to consumption decreased from approximately 0.08 to 0.07, whereas the ratio of consumption to income increased from 0.90 to 0.94 in 2005 and leveled at 0.87 in 2015. Mortgage debt indeed ballooned approximately twofold for predatory mortgages (Albanesigiacomo De Giorgi, and Nosal 2017).

In a debt-trap scenario, the opportunity to avoid one’s financial obligations is nil – hence the term “trap” (or wheel of misfortune). This may not apply to all buyers, but it certainly applied during the GFC to all those who ended up having to go through foreclosures: unable to meet their financial obligations, they had to relinquish their houses (and homes). Figure 1 illustrates this scenario with actual market data, showing the progression of debt over time and the lagged increase in foreclosures (a process that takes time to implement). More generally, some consumers tend to overborrow, thus becoming “more likely to enter a cycle of debt”, simply because they become fearful of the scarcity of the asset they long for (Cook and Sadeghein 2018, p. 78) – namely, houses in the case of the GFC.

Worse still, for most home buyers during the GFC in the U.S., the debt incurred with house purchasing was associated with substantial credit card expenses (Case, John, and Shiller 2005, Mishkin 2007, Guiso, Sapienza, and Zingales 2009, Elul et al. 2010). The system represented in Figures 2 and 3 is thus highly toxic (Carroll, Otsuka, and Slacalek 2011). As an example, house buyers usually buy a new car within two years of moving in (Mian and Sufi 2009). The debt trap is best exemplified by the U.S. phenomenon of food stamps; between 2004 and 2009, they⁸ rose from 25 to some 50 million units. During the GFC, consumers across all income levels spun the wheel of misfortune (the contagion circle in Figures 2 and 3) and filled the bathtub of their accumulated debt to the point of overflow (Antoinette and Schoar 2016, Gelain, Lansing, and Natvik 2018).

In the introduction, we defined greed as the fear of missing out on the opportunity to enter a hot market on time (Lux 1995), one where one can make money fast by flipping houses⁹ (Joo and Grable 2004). Seuntjens et al. (2015), for their part, define greed as follows: “Although greed is both hailed as the motor of economic growth and blamed as the cause of economic crises, very little is known about its psychological underpinnings.” Greed is an unmonitored appetite for predatory utility maximization; as such, it induces an exaggerated level of need (with loaded tension) for what should be a normal need (Lewin 1951, Maslow 1954). That exaggerated level of need is the fear of not accessing necessary resources (or not entering the market on time), counterbalanced by the fear of not exiting a threatening market on time (Fay and Xie 2010), that is, perceived risk (Hoover, Green, and Saegert 1978).

This behavioral mechanism is the financial version of foraging in nature. Animals constantly weigh fulfilling their physiological and procreation needs (greed) against the fear of being caught by a predator (the debt trap) (Paulssen, Roulet, and Wilke 2014).

When greed exceeds perceived risk (or risk aversion), consumers forego the proper search for valuable information (Bikhchandani and Sharma 2000, Calvo and Mendoza 2000, Shiller 2005), and sink into an optimistic profit-making view of the market (Abreu and Brunnermeier 2003). Thus, they end up acting against their own welfare (Devereux and Sutherland 2010). During the GFC, eager house buyers became excited by the sweet deals that were offered to them, ignoring the toxicity that at some point down the road would harm them. In fact, by narrowing their investment efforts to housing and by buying into the sweet deals (e.g., predatory mortgages), consumers made themselves vulnerable to market volatility.

We argue that asymmetric risk attitude, biased information search, illusions of quick profits, minimizing loss aversion¹⁰, and overconfidence (Gärling et al. 2009) all fostered that vulnerability, which, as Figure 1 illustrates, led to vast numbers of foreclosures in the wake of the contagion effect (Grether and Plott, 1979). Aggressive publicity in favor of subprime mortgages, spreading from as early as the year 2000 to 2018, helped blur the market, making toxic products look appealing (Ben-David 2011).

The Marketing Sweeteners (Sweet Heart Deals)

We can conceive of sweeteners as smoothies of sorts in times of impending crisis (Dutt and Padmanabhan 2011), which entice consumers to lose track of their initial consuming motivation and foundation and drive them to simply behave, that is, in a GFC type of market, to spin the wheel of misfortune, much like a hamster.

Assuming consumers during the GFC were moderately greedy and were able to perceive a certain level of risk associated with the subprime mortgages, and adopting the classical economic assumptions that they are rational and well-informed (Colander et al. 2009), it makes little sense to believe they would have embarked on a somber journey that saw them accumulating debt and eventually facing foreclosures in the thousands. Logically, the sellers found means to make his toxic products appealing; we call these means sweeteners.

As mentioned previously, our framework includes four sweeteners: (1) ease of credit; (2) incentives provided by sellers; (3) upgrades – sweetening the house; and (4) buying incentives (sweetening the buying appeal). We discuss these four sweeteners and how they interact to favor a contagion (herding) effect long recognized by economists.

In their review of the causes of the GFC, academics and government officials have convincingly established that avid buyers borrowed heavily thanks to the facilitated access to credit (Rajan and Ramcharan 2012); many bought not one but multiple houses (Iyer, Soberman, and Villas-Boas 2005). Consumers and banks alike ignored risk and disregarded their ability to repay, a well-documented precursor of crises (Wellink 2009, Krishnamurthy and Muir 2017). In some instances, excited and deceitful consumers even misrepresented their financial status and altered their documents to benefit from subprime mortgages in what was labeled “predatory borrowing” (Bianco 2008). Plummeting lending criteria led greedy borrowers towards excessive borrowing to frantically buy houses in the hope of flipping them quickly for a hefty profit¹¹.

All pretexts were deemed credible to justify such attraction towards the sweetened credit, ranging from a historical motivation to establish fairness to the poorer class (Gayraud 2011) to achieving financial satisfaction (Moussa and Touzani 2017, Hansen 2014).

There is wide agreement among economists and academics that lenders sugarcoated mortgages via, for example, teaser rates, a trap into which countless buyers fell, based more on emotions than on mere cognition, in what Alan Greenspan, former head of the Federal Reserve, coined “irrational exuberance” (Hoffmann, Krause, and Laubach, 2012; Shiller 2005). Various authors have argued that aggressive advertising exacerbated mortgage appeal (Taute, McQuitty, and Sautter 2011).

Home buyers renovated their properties by financing their efforts through credit; with improved versions of houses on the market and consumer demand ever ascending, selling prices went up. In fact, prices rose faster in geographical areas favoring subprime mortgages (Mian and Sufi 2009, Pavlov and Wachter 2011). Higher prices meant increased profits and, hence, leverage for further borrowing (Wachter 2015). Spending continued at an unprecedented scale. As stated by Greenspan and Kennedy (2008), “personal consumption expenditures towards home equity reached U.S. $136 billion per year from 2001 to 2006, three times the level of the years 1996 to 2000”.

This highly speculative (Del Negro and Otrok 2007), self-reinforcing toxic loop was encouraged by both individuals and astute financial companies, which created sophisticated products for the purpose of hiding risk (e.g., collateralized debt obligations, or CBOs, and special purpose entities or SPEs) (Van den Bulte and Stremersch 2004, In’t Veld et al. 2011).

To lure naïve buyers, house upgrades were occasionally enough. However, in other cases, promoters and buyers-turned-sellers imagined all kinds of promotional tricks, such as gifts, perks and the like (Bikhchandani and Sharma 2000, Dallery and van Treeck 2011). However, soon enough, the Federal Reserve increased the basic interest rate to quell the market agents’ thirst, effectively forcing overstretched borrowers to seek refinancing (if at all available). Otherwise, they could tumble into default or face the shame and pain of foreclosure. As defaults mounted, a glut in the housing supply developed that depressed housing prices and further magnified the downfall of the market.

We posit that the interaction between the policy-driven and agent-driven variables created the contagion process that plagued the U.S. economy starting in 2000 (Dass, Massa, and Patgiri 2008, Diebold and Yılmaz 2014, Olsen 2012).

Contagion was heightened by the fact that many cupid house buyers were naive and gullible (Sama and Shoaf 2005), potentially easily influenced by aggressive advertising campaigns. Indeed, more than 50% of all foreclosures between 1999 and 2011 were tied to predatory mortgages, with a quarter of them caused by poor credit (Frame, Lehnert, and Prescott 2008).

Consumers who became greedier and less vigilant towards the risk of a debt trap were often in the low-income bracket (Iacoviello 2008, Roy and Kemme 2012) and had poor literacy levels, thus making them more susceptible to heavy advertising (Danis and Pennington-Cross 2008, Wang 2009, Kamihigashi 2008). In some sectors, up to eighty percent of fraud victims were aged 65 years or more (Yoon et al. 2005), many of whom experienced the “sharpest increase in bankruptcy filing” (Thorne, Warren, and Sullivan 2009).

Gullible consumers realized towards the end of 2008 that the government could not protect them (Graafland and van de Ven 2011). Legal recourses were scarce or outrageously unaffordable (Ferguson 2012). The price paid for greed and reduced risk perception was indebtedness (MacInnis and Mello 2005, McCoy, Pavlov, and Wachter 2009).

To make matters worse, in an economic system where the fear of not entering the booming market on time far outweighed the fear of falling into default, delinquency or foreclosure, the only logical means of surviving was through deceit, including in advertising (Cox, Cox, and Zimet 2006). Indeed, strategic, regulatory, and legal misstatements constituted over 25% of all messages during the GFC (Brown et al. 2009).

The Research

We laid out three hypotheses, which we tested using a dynamic-system approach that included various investment/fear scenarios:

Sweeteners help in part to hide the risks and encourage greed (e.g., during the GFC and this is probably true in any similar financial crisis).

The fear of not entering a hot market (bull market) on time to earn a quick profit, as well as, on the flip side, the fear of not exiting the market on time to avoid losses due to a declining market (bear market), are influenced by sweeteners, much like a sweet candy will appeal to a child whether in bad or good times (hence the name “sweeteners” or sugar-coated poison pill) (Iyer and Bhaskar 2002).

Participants will make more mistakes when in a “prey” position than when in a “predator” position (Starcke and Brand 2012).

We, of course, could not test our hypotheses during the GFC, as the crisis was long over by the time we conducted our research, but this was not necessary as we could use a proxy: scenario-building operationalized in highly-controlled conditions using dynamic systems modeling.

We could not either directly measure the influence of sweeteners before, during, and immediately after the GFC, a constraint that would necessitate an extensive longitudinal study with many uncontrollable variables. We decided to focus on one key aspect of our sweetener framework – fear. We assumed this focus could help us develop our proposed framework (see Figures 2 and 3). Indeed, in scientific research, it has long been known that it is always better to keep the variables as controllable as possible in order to segregate the influence of each one on the target variable, including in the context of behavioral, decision-making research (Keinan 1987). This cannot be achieved in real-life events, especially not in the case of the GFC where millions of people across the U.S. participated in very diverse and volatile conditions.

Fear is a fundamental piece of the puzzle, be it fear of not entering the booming market on time (greed) or fear of not exiting a collapsing market on time (perceived risk of debt trap). We thus decided to test whether fear would negatively alter decision-making. If indeed fear acts this way, we can hypothesize that sweeteners helped alleviate fear and thus contributed to the explosion of greed and the hiding of risk. In particular, we tested whether the conditions we subjected the participants (university students) to would make them forget about their initial motivation, which was to earn money rather effort-free.

We set up a simple fMRI to test how fear (provoked by random appearances of snakes – see further below; IAPS, 2008) influenced the objective of earning money fast (by collecting yellow circles in a maze in a one-hour experiment) and avoiding getting caught by a moving, fast-acting red triangle (which, when this occurs, would signify a loss). Thus, our proposed set up would test greed in the presence of risk aversion, given the presence of a sweetener (money to be earned at the end). Using a fear factor, we could distract and desensitize the target group of students from the objective of their participation in our experiment, which was to earn money fast with relatively little effort.

We thus designed a simple test that would provide approximations for bubble market conditions. We offered participants the opportunity to act as market agents, that is, as investors (a proxy for home buyers), in an environment in which they could earn or lose money in a series of established scenarios. They could adopt one of two positions: predator or prey.

The predatory position was created by offering the opportunity to earn money by playing through a simple maze. The prey position was created by offering participants two scenarios. In the first, a red triangle chased the participant; if it caught him or her, he or she would lose money. The other scenario added images of a snake appearing randomly. Ahead of time, participants in this study were intentionally screened for their fear of snakes.

In short, we designed a simple investment environment for the purpose of obtaining images of the participants’ brains through the use of fMRI. If we could show that the participants made more mistakes when in a prey position than in a predator position, and if we could show that they made more mistakes when in a high-fear condition (random images of a threatening snake) than in a low-fear condition (no snake images), then it would make sense that the only means of fooling a participant into buying a risky asset would be by sweetening the offer. This purely exploratory research would indicate how market agents act in real life, when market bubbles imply greed (fear of not entering the market) and the hiding of risk (minimizing perceived risk of a debt trap).

We conducted our study over one year. We recorded the neural activity of the participants when they were given the opportunity to earn money (catching a yellow circle in the maze specifically designed for the task) or when facing the risk of losing money (being chased by a red triangle or by a red triangle accompanied by images of threatening snakes).

Our assumptions were as follows: Greedy participants would be measured by the number of yellow circles caught in the minimum amount of time, while displaying a maximum number of wall hits (indicating that they make many errors, which is an indication of fear). In this case, we expect the part of the brain dedicated to the emotions (amygdala) to be very active; the part of the brain dedicated to error detection (anterior cingulate cortex or ACC) to be relatively inactive; and the part of the brain dedicated to computation (prefrontal cortex, PFC) to be moderately active (last image in the Appendix). We assumed that wall hits would be significantly higher in high- compared to low-prey conditions (snake versus no snake), which occurred, and that cortisol levels (measuring stress) before and after would be relatively high. This holding true, we assumed that there would be a maximum of wall hits per unit of time when the participants would experience a high fear of being caught by the red triangle, that is, of not being able to escape. In this scenario, we would expect the ACC to be quite active.

Our research took place near Ottawa (Canada)¹² and followed a strict protocol; a research ethics certificate was obtained from a Canadian university. We recruited the students by placing a poster on the campus’ boards; participants would receive C$ 50 to cover their travel expenses and C$ 30 for participating in the experiment. They would be briefed on the final results in addition to receiving images of their brains. They would also receive a reward calculated by the difference between yellow circles caught and being caught by red triangles. We never specified the exact financial reward, but the advertisement we placed to recruit them specified it would be substantial. We waited for the participants to ask for it to see whether they had somehow lost track of their initial motivation of making money.

Participants had to lie flat on their back on the scanner table for approximately one-half hour. They observed a maze through a set of mirrors. Many scholars have resorted to mazes in fear and decision-making evaluation (e.g., Mobbs et al. 2009). Our laboratory condition represented an extremely simplified market condition, one where we could minimize the number of uncontrollable variables. Participants could respond to the stimuli presented in the maze (catching a yellow circle or running away from a red triangle that was accompanied, at times, by random images of a snake) by handling a pad with their dominant hand. Catching a yellow circle would earn them money (up to a maximum initial allowance of C$ 70), while being caught by a red triangle would cause them to lose money (up to C$ 70). Thus, the participants faced the (moderate) fear of not making money (the amount of which we never disclosed) and the (more intense) fear of losing money in two sets of conditions – low threat with the red triangle or high threat with the red triangle joined by flashing snake images.

In our study, the fear of snakes acted as a proxy for the fear associated with not entering a hot (bullying) market on time (to earn money) or not exiting a crisis market (bearish) on time (to avoid losing money). As is well known in psychology, most people cannot explain why they have certain fears or phobias: they simply become aware of them as they are faced with the stimulus that generates fear, be it spiders or height, for example. The organic reaction to fear is a basic living organism’s reaction that entices the same physiological response (such as trembling, need to urinate, sweating, etc.), no matter what the stimulus is, and the use of fMRI to examine the brain’s response to a fear-generating stimulus is common in neuropsychological research (Morris, Buchel, and Dolan 2001).

In total, nine females and ten males were chosen out of the 53 who initially showed interest. We selected them based on their fear of snakes, which we measured using the widely-used Snake Anxiety Questionnaire (SNAQ). We used images of snakes retrieved from the well-established International Affective Picture System (IAPS) database. Snakes have been used in many studies (e.g., Nili et al. 2010) because they tend to induce fear among both men and women (Kandel, Schwartz, and Jessell 2000). To be efficient, participants had to have moderate levels of snake phobia. We chose participants aged 18-25 because sensitivity or phobias are usually quite active at that age (Craske et al. 1995). Additionally, their income level was checked and proved to be low to moderate, so that they would be that much more excited by the possibility to earn money by simply participating in a harmless experiment. We opted not to tell participants they would be able to view their fMRI images at the conclusion of the experiment, in order to exclude the possibility of participation solely on that basis. Nineteen or twenty participants are a common number of participants in fMRI studies (e.g., Herwig et al. 2011), so we felt we had an appropriate representation of the general population within the limits of our exploratory research.

We used the following equipment: (1) a Siemens Symphony fMRI Scanner; (2) Siemens headphones; (3) Cedrus touch pad with Lumina part 0TEC4008; (4) Cotton swabs (for cortisol measurement); (5) Siemens A35 software data recording; and (6) SPM-5 for data analysis.

Functional MRI studies are powerful and provide neural evidence of consumer behaviors, especially with respect to measuring emotions and cognition (Vul et al. 2009). Of value to us, fMRI detects unconscious processes (Reimann et al. 2011), which is the main advantage of the present study; participants could not fake fear. Indeed, the emotional response time to animal-related stimuli is approximately 800 milliseconds (Côté and Bouchard 2005), not enough time to think.

The following documents were presented to each participant: (1) the posters; (2) a certificate of ethics; (3) the well-established fear-of-snake SNAK questionnaire (Klorman et al. 1974); (4) a consent form; (5) self-assessment STAI Y1 and Y2 questionnaires (Nili et al. 2010); (6) a confidentiality agreement; and (7) a debriefing questionnaire.

Using a cotton swab, we collected saliva immediately before and immediately after the experiment for the detection of cortisol levels. These levels are a strong indicator of stress and of negative feelings (see McCullough et al. 2007). Indeed, cortisol is a steroid hormone generated by the action of the hypothalamus – the center of defensive and instrumental coping mechanisms in the brain – to deal with stress. Cortisol is known to increase vigilance in the face of danger.

Results and Discussion

We hypothesized that the participants would make more mistakes when in a “prey” position than when in a “predator” position (Starcke and Brand 2012).

To evaluate this hypothesis, we measured the number of yellow circles collected in the maze, the time to collect them when in a predatory position (proxy for greed), and the number of wall hits (errors in trying to escape the red triangle in low- and high-fear conditions) – a proxy for the perceived risk of a debt trap¹³ (See Appendix).

Our main finding was that we had managed to create a spinning condition or something that looked like the concept of spinning. Indeed, at the end of the test, we met each participant and debriefed them at length. Although participants had been offered money for their scores on the test (by catching yellow circles and not being caught by red triangles), none of the participants in the end asked for a tally of gains (yellow circles caught) and losses (being caught by the red triangle) to claim their reward. This omission surprised us; we therefore extended the briefing in the expectation that the participants would claim their reward, but they never did so. We postulate that we had offered enough distractions to them (for example, the novelty of the test and the appearance of the snake) so that in the end, they forgot their initial motivation. We assumed this kind of mechanism occurred in the concept of consumer spinning as we define it.

Without claiming of generalization, our research, with evidence from brain scan analyses of our participants, shows that greed can be influenced with a “fear factor”. Recall we had chosen participants on the basis of their innate aversion of snakes. It appears that when this aversion was fostered to a certain degree, the participants eventually lost track of their initial objective and played the game for the mere sake of playing the game. The initial sweetener we offered (money to low-income students) was not strong enough to prevent them from deviating from their initial goal of making an easy income in the face of a fear stimulus linked to a snake image randomly appearing. This can possibly be turned the other way: if we had eliminated all possible “fear factors” from the experiment and generously rewarded the participants with, say, a bonus for achieving a high score (lots of yellow circles caught and few opportunities to be caught by the red triangle), it is likely that they would have been even more eager to engage in the experiment, which would have translated into the fMRI images, as the reward center (for example) – the VTA or Ventral Tegmental Area – would have been strongly activated (D’Ardenne et al. 2008.) This suggests that marketing sweeteners can have a strong impact of consumers’ behaviors and that spinning can be induced to a certain degree, at least in a portion of the population and given the right conditions.

A consumer spinning status seems maintained by the use of external motivators (sweeteners) that serve to inhibit internal motivators (e.g., the initial need that was supposed to be fulfilled), in particular by making consumers both fearless (they perceive no risks as these are hidden, for example through misleading advertisements or securitization) and resourceless (by stealthily increasing their debt load). This goes against the premises of Self-Determination Theory (SDT), which “broadly characterizes motivation as either intrinsic or extrinsic and suggests that regardless of the context, people have a fundamental need to feel in control of their own destiny and competent in exerting that control” (Drayer, Dwyer, and Shapiro 2019, p. 38). In the case of consumer spinning, consumers give up control and become oblivious as to who and/or what controls them. As such, perhaps consumer spinning could develop into a self-harming condition that could be potentially pathological. Certainly, the level of displayed violence that the media portray during purchasing frenzy, such as the infamous Wal-Mart Black Fridays (which has caused deaths in the U.S.), or the overcrowded parking lots at shopping malls during weekends, suggest that consumers may at times have lost the ability to enjoy their purchasing experience, their need to bond and their sense of satisfaction for fulfilling these needs (Lennon et al., 2018).

In addition to the first finding, we collected more information. Our second finding was that, indeed, participants experiencing high levels of greed (yellow circles condition) and low levels of perceived threat (despite high-stress conditions in which mean images of snakes appeared randomly), displayed an active amygdala, a relatively inactive anterior cingulate cortex (ACC), and a moderately active prefrontal cortex (PFC). In other words, the same pattern occurs whether the condition relates to that of a predator (yellow circle) or that of prey (red triangle with snakes). We assume these participants were intellectually numbed to a certain degree and did not have the necessary coping mechanisms to deal with different scenarios. We also assume that they were greedy, but they disregarded risk; the ratio of greed to perceived risk was high. They would probably respond favorably to sweeteners that appealed to their emotions and dreams of grandeur but not much to their intellect.

Our third finding was that participants in a prey position performed at 25% of their capacity when being prey versus when being a predator. They made more errors when in fear of being caught (proxy for debt trap) than when in fear of not making money (proxy for greed).

If this finding applies in the marketplace and can be generalized to the GFC (something that deserves much more research), then sweeteners would have helped quite substantially to pacify the eager house buyers so that they would become greedier but less sensitive to the real risks associated with their investment decisions. We cannot be certain at this time, but it seems this investigation path is worth pursuing.

Our fourth finding was that participants with a lower cortisol level (fifteen percent) after the experiment made more decision errors (wall hits) than did participants with higher cortisol levels. In other words, their level of vigilance declined. Our results show that the anterior cingulate cortex (ACC), a brain structure linked to error detection (Cardinal et al. 2002), was significantly activated when adopting a prey position¹⁴. We do not know whether these cortisol levels are gender-related or stem from the fact that the participants felt relieved at having completed the experiment. However, one-half of the participants exhibited a change in cortisol levels, indicating some stress-related activity.

Our fifth finding was that participants who were prompted that they were about to become prey (they were warned that the red triangle was coming) showed higher levels of vigilance.

Putting this finding in the context of our sweetener framework and keeping in mind that the research carried out was exploratory, we can speculate that sweeteners may help reduce the level of vigilance of the prey (the buyers of predatory mortgages) and, perhaps, entice spinning.

Finally, we noticed that a certain level of stress improved decision-making (fewer wall hits). However, as stress increased due to the presence of a higher perceived threat level (high-threat scenario with the snake images appearing randomly), the number of wall hits increased by 50%.

Under high stress, participants made significantly more errors. This outcome was not surprising; in the long term, researchers have found that under high levels of stress, the prefrontal cortex (PFC), which is involved in decision-making, suffers from dendritic reduction and retraction (Lupien et al. 2009).

Inserting this finding into our framework, we speculate that it was to the best advantage of the market agents (credit lenders and predatory mortgage sellers) to numb their prey (the naive borrowers and buyers of predatory mortgages) with sweeteners to drive them towards the expected result – incentivizing them to buy, with the hope of turning a quick profit. Indeed, spinning is about consumers becoming desensitized to their initial motivation and risks and simply keeping spinning, that is, doing what they were doing and what other consumers were doing, without raising any questions or concerns.

We conjecture that sweeteners helped blind consumers in the face of risk and enticed them to seek more than they wanted and certainly more than they actually needed. By using sweeteners (sweet deals), lenders and sellers were perhaps able to create the contagion effect that fueled the booming market during the GFC in the U.S. Without sweeteners, it appears unlikely that buyers would have ignored the risks; at least, their greed would have been more moderated by the fear of falling into a debt trap.

When the market started to go awry, consumers most likely made more errors than they normally would, thus possibly making their situation worse (Angie et al. 2011). It is not possible to measure the actual effect of sweeteners on behaviors for an event that took place ten years earlier. However, if our framework holds true, it is likely that uncaring lenders and sellers use sweeteners of all kinds to numb their prey facing the risk of a debt trap and to encourage them to seek more than they need, thus nurturing greed. Predators-sellers have a keen interest in achieving such a result; the greedier the consumers and the more oblivious to risk they are, the more likely it is that they will buy without weighing the consequences of their actions. The more consumers-prey buy, the more the sellers make money. Sweetening is to their advantage.

The phenomenon of spinning is concerning because of the characteristics of at least some of the consumers. They are not all spontaneously inclined to succumb to spinning tactics, but as mentioned, some are particularly prone to gullibility and longing for overconsumption. Academics promoting the consumer learning (CL) theory (Mehta, Rajiv, and Srinivasan 2004) or its revised CL version (Cleeren, van Heerde, and Dekimpe 2013) have recognized that consumers often fail to access the necessary information and instead focus on irrelevant parcels of data. They also point out that many households rely on basic or elementary economic concepts (van Rooij, Lusardi, and Alessie 2011). This makes it that much easier to fool them, that is, to distract them from their initial financial goals and to nurture their greed to the detriment of proper risk assessment, thus leading them towards an unsustainable debt trap.

Conclusion

We presented a framework that attempts to unveil a hypothesized consumer behavior that we name “consumer spinning” and to explain how the use of sweeteners (sweet deals) encouraged a contagion effect and fostered the development of the housing bubble market in the U.S. from 2000 to 2009. We used the GFC because it seemed one of many examples where we can reasonably speculate that spinning took place. Our framework opposes two facets of fear: when foraging (looking to fulfil their needs), consumers balance out the fear of missing an opportunity (the fear of not entering a hot market on time in hopes of making a quick profit) and the fear of a debt trap (perceived threat). In this framework, fear is at the heart of buyers’ behaviors. The findings support the hypothesis that U.S. consumers deployed high levels of greed and subdued their natural tendency to detect danger (of a debt trap) because they were fascinated to some degree by the various sweeteners presented to them: ease of credit, teaser rates and subprime mortgages, upgrades of the houses and buying incentives.

We presented an fMRI study in which most variables could be controlled (as opposed to a real case study where most variables would be uncontrolled, thus severely diluting any possible conclusions concerning the results). We used a highly sophisticated method; our results tend to show that prey in a high-fear condition make more mistakes than do those in a low-fear condition. We showed that participants with moderate use of their prefrontal cortex (PFC), little use of their anterior cingular cortex (ACC) and high use of their amygdala in both extreme conditions (predatory and high-fear prey conditions) could possibly represent a mass of buyers that got caught in a debt trap during the GFC. This finding would corroborate what many economists have said about buyers of predatory mortgages being irrational, uneducated, naive, and vulnerable. The study is a fruitful effort that suggests that tests in virtual reality could be a future path of exploration, combined with fMRI results.

The experiment was designed to distract the participants as much as possible while somewhat replicating some elements of decision-making in a pseudo-market condition augmented by a fear factor (the random image of the snake). We believe we may have identified an untapped consumer behavior in the form of spinning, as the participants clearly lost track of their initial motivation and simply went with the flow. Our sweetener framework tends to be somewhat supported by an fMRI study within the limited parameters involved.

We paralleled our findings with the phenomenon of a booming U.S. housing market and proposed that when the market balloons, greed overwhelms the fear of a debt trap. When markets experience bank runs and accelerating decline, the fear of a debt trap far exceeds greed. In the end, the ratio of greed to risk aversion remains a pivotal factor. In other words, the ratio of the fear not to enter the booming market at the right time to the fear of not exiting the collapsing market at the right time drives consumer behaviors. These behaviors are, we suggest, susceptible to being influenced by sweeteners. Sellers and lenders coat their toxic products with what appeals to consumers, such as ease of credit or fancy borrowing terms in the form of teaser rates. In fact, sweeteners form sugar-coated poison pills. When this technique is used, we posit that it is possible to entice consumers to lose track of their initial consuming motivation and to simply keep spinning their wheel of misfortune, like hamsters spin their wheels cluelessly, being content with simply spinning and nothing else.

We posit that regulations must consider such phenomena or potential behavioral manipulation that would encourage consumers to spin, because in the end, high or uncontrollable household debt-to-disposable income has the potential to harm the entire economy, as was true in the U.S. during and immediately after the GFC. In our view, there is no denying that many customers may spend their weekends shopping for unneeded goods or else forget about why they think they need them. Perhaps this is indicative of a form of spinning behavior. Will consumers realize in the end that they may have lost track of the utility of their buying behavior? Perhaps, too, overconsumption, a behavioral phenomenon anchored deeply in some consumers’ psyche, is indeed a reflection of excessive marketing efforts aimed at inducing spinning. After all, even if some consumers end up in a financial dead-end, there will be other customers filling in for the lost sales (Warren 2004).

We pointed to sweeteners as motivators to enter a consumer spinning state, likely brought about by uncaring marketers and product (including financial products such as predatory mortgages) designers. The behavior that their outputs seem to generate resembles the type of run-around lawyers may give to adversarial parties by clogging the judicial system with endless motions, adamant excuses and procedural defects in order to have them waste their time, effort and money, hoping they will lose interest in their initial, primary motivation and/or the case itself.

Limitations and Opportunities for Further Research

Our research was conducted with a strict control on the variables of interest. Participants were carefully chosen for the potential of triggering fear in them while placing them in a decision-making situation involving gains and losses. We did our study on 19 participants, which is a significant number in fMRI studies (because brain images can be compared against that of a database composed of hundreds of scanned images¹⁵). These participants were university students, not avid house buyers. Therefore, our study was an approximation of the complexities inherent to markets where houses are treated as investment assets and macroeconomic forces (such as foreign debt, exchange rate, etc.) come into play.

While our research was exploratory and examined an emerging concept of consumer behavior, our observations nevertheless merit consideration because they imply that some consumers are potentially manipulated in a predatory manner, that is, in a way that harms them while enriching a few, much like drug dealing. If, indeed, there are marketers out there that specifically design products and advertisement campaigns to lure consumers into buying extravagantly, this should be considered as a form of abuse on consumers’ rights. We propose that further studies should investigate this, which, if tested and accepted, would denote the need for better consumer protection by means of new or tougher laws.

Our Figures 2 and 3 call for multiple studies, notably on the ratio of greed to risk aversion. This has relevance because the accumulated debt eventually becomes unsustainable debt and leads to many household traumas and market upheavals. We suggest taking each component of the model, elaborating workable hypotheses, and testing them across cultures. What if, for example, risk aversion (and greed) was different from one country to the next (as strongly suggested by the various works of Hofstede 1983)?

Perhaps, also, the case of consumer spinning incorporates some dissociative elements, as, according to our definition, it means that consumers have disconnected from their initial needs and from their initial objectives (which was to fulfil these needs). Dissociation is a damming condition in the sense that it isolates individuals, and hence leaves them with even fewer resources, which means they become even more vulnerable. Those who somehow control their spinning state then have even more reason to rejoice. This, we suggest, may have been the case for a portion of the population during the GFC.

Again, if it is the case that some uncaring marketers and financial product designers tap into this mental state to make it an enduring behavior, one that ultimately harms the consumers, then there are reasons to be concerned. Subliminal marketing takes place in a form that has not been named so far.

Certainly, a study of the cunning use of sweeteners and a comparison between extrinsic and intrinsic sources of motivation could further develop the concept of consumer spinning, as is done with other kinds of studies in the field of consumer behavior (Poch and Martin 2015). As hypothesized, this may involve the use of external motivators (sweeteners) numbing consumers’ internal and initial motivators. The role of risk hiding, which tends to reduce vigilance and fear (as a self-protective mechanism), and debt building, which reduces the consumers’ overall resources (e.g., capacity to defend themselves by legal means), have important consequences. Turning consumers into buying machines does not serve the betterment of society, quite the contrary: it encourages waste of intellectual and physical resources.

Casino gambling seems to fall within the description of consumer spinning, at least for some customers. Some older and solitary individuals spend hours simply looking at a screen with colored and noisy wheels that keep spinning. These individuals seem to have forgotten, after a while, their initial goal to find a source of distraction. Instead, they end up confining themselves into a boring, idle state, as if on life-support. They are mesmerized by the random winning of small sums of money that are certainly not enough to build a fortune, but sufficient enough to motivate them to stick to the treacherous, money-swallowing slot machines. These small amounts serve as sweeteners and create a near-like addiction. The exact mechanism of this well-known situation could be further investigated under the lenses of the concept of consumer spinning.

Our research did not address sociodemographic factors, but clearly a mass study on consumer spinning, done through observations in major retailers (such as Wal-Mart, for example) or questionnaires could reveal potential differences between men and women, people with different levels of literacy (including financial literacy), age and consumers’ lifecycles, types of dwelling, and even religion (Buddhism, for example, does not promote overconsumption), or race.

Hence, as evidenced from our previous comments, we suggest that future research on the topic of consumer spinning should consider ethical, legal, marketing, pedagogical, and psychological avenues. When it comes to consumption, the societal choice is simple: encourage consumers to become responsible market agents, including in the financial world, or else sacrifice many of them on the altar of blind capitalism (overconsumption), thus reducing the overall purchasing power of the consumer market and saddling many of these consumers with a “convict ball” (the unsustainable debt) that will impinge on their health, social behaviors and capacity to contribute positively to their community. As discussed, encouraging financial literacy may not be enough or even desired by the consumers-prey: after all, should consumer spinning be a malignant undertow, a hidden truth that affects social consciousness, it is the way we govern our economy that must be reconsidered.

Footnotes

Appendix – Results

Appendix

	There is more activity when the participants are being chased (by a red triangle with or without random images of a snake) than when they do the chasing (catching the yellow circle). Hence, we posit that the basic motivation is fear.
	There is more brain activity when the participants cannot identify the predator – the snake in this instance (that is, when only the red square is chasing them). Knowing that the predator is a snake reduces the ambiguity.
	Seed region 0.005	Negative Connectivity
	Left hypothalamus	-44 -26 -14 Left superior temporal gyrus
	When perceived predation increases with images of snakes appearing randomly, brain functions indirectly related to emotions and language diminish
	Seed region 0.005	Negative connectivity
	Left amygdala	50 2 8 left rolandic operculum ACC 44 24 2 right inferior frontal gyrus 6 -4 6 right thalamus
	When emotions increase due to the surprise appearance of the snake, language, consciousness of self and of the environment, and the capacity to make decisions (including risky decisions) diminish.
	Seed region 0.005	Negative connectivity
	Right amygdala	54 26 26 right inferior frontal gyrus dorsolateral prefrontal cortex 4 2 6 basal ganglia
	When emotions increase due to the surprise effect created by the random appearance of the snake, cognitive and decision capabilities diminish.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Olivier Mesly

Notes

References

Abreu

Dilip

K. Brunnermeier

Markus

(2003), “Bubbles and Crashes,” Econometrica, 71 (1), 173–204.

Acharya

Viral

Richardson

Matthew

(2009), “Causes of the Financial Crisis,” Critical Review, 21 (2–3), 195–210.

Akerlof

A. George

Shiller

Robert. J.

(2009), Animal Spirits: How human Psychology Drives the Economy, and why it Matters for Global Capitalism. New Jersey: Princeton University Press.

Albanesigiacomo De Giorgi

Stefania

Nosal

Jiacomo

(2017), “Credit Growth and the Financial Crisis: A New Narrative,” NBER Working Paper Series No 23740, Bureau of Economic Research, 1–52.

Angie

D. Amanda

Shane

Connelly

Waples

Ethan P.

Kligyte

Vykinta

, (2011), “The Influence of Discrete Emotions on Judgement and Decision-making: A Meta-analytic Review,” Cognition and Emotion, 25 (8), 1393–422.

Antoinette

A. Manuel

Schoar

Felipe S.

(2016). “Loan Originations and Defaults in the Mortgage Crisis: The Role of the Middle Class,” The Review of Financial Studies, 29 (7), 1635–70.

Ben-David

Itzhak

(2011), “Financial Constraints and Inflated Home Prices during the Real Estate Boom,” American Economic Journal: Applied Economics, 3 (3), 55–87.

Bernanke

Ben S

. (2009), “Financial Reform to Address Systemic Risk,” Speech at the Council on Foreign Relations, Washington, D.C. (March 10).

Besanko

David

Ulrich

Doraszelski

Kryukov

Yaroslav

(2014), “The Economics of Predation: What drives Pricing when there is Learning-by-doing?” American Economic Review, 104 (3), 868–97.

10.

Bianco

Katalina

(2008), The Subprime Lending Crisis: Causes and Effects of the Mortgage Meltdown. New York: Wolters Kluwer Law & Business.

11.

Bikhchandani

Sushil

Sharma

Sunil

(2000), “Herd Behavior in Financial Markets: A Review,” International Monetary Fund WP/00/48, 1–33.

12.

Bonsall

B. Michael

Hassell

Michael

(2007), “Predator-prey Interactions,” in Theoretical Ecology: Principles and Applications, Lord Robert May of Oxford, eds. Great Britain: Oxford University Press.

13.

Bordo

D. Michael

Meissner

Christopher M.

(2012), “Does Inequality Lead to a Financial Crisis?” NBER Working Paper Series No 17896, National Bureau of Economic Research, 1–29.

14.

Borio

Claudio

Drehmann

Mathias

(2009), “Assessing the Risk of Banking Crises – revisited,” BIS Quarterly Review, March, 29–46.

15.

Bougheas

Spiros

Nieboerand

Jeroen

Sefton

Martin

(2015), “Risk Taking and Information Aggregation in Groups,” Journal of Economic Psychology, 51, 34–47.

16.

Brady

E. Michael

(2017), “Modeling Adam Smith’s Analysis of the Very Severe, Negative Impacts of Projectors, Imprudent Risk Takers and Prodigals on the Macro Economy in the Wealth of Nations using a Modified Lotka-Volterra Nonlinear Coupled Model of Differential Equations,” (accessed May 20, 2019), Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3053111.

17.

Brown

J. Kevin

(2010), “The Economics and Ethics of Mixed Communities: Exploring the Philosophy of Integration through the Lens of the Subprime Financial Crisis in the US,” Journal of Business Ethics, 97, 35–50.

18.

Brown

Stephen

Goetzmann

William

Liang

Bing

Schwarz

Christopher

(2009), “Trust and Delegation,” NBER Working Paper No 15529, Bureau of Economic Research, 1–47.

19.

Brunnermeier

K. Markus

Pedersen

Lasse H.

(2009), “Market Liquidity and Funding Liquidity,” Review of Financial Studies, 22 (6), 2201–38.

20.

Brunnermeier

K. Markus

Sannikov

Yuliy

(2014), “A Macroeconomic Model with a Financial Sector,” American Economic Review, 104 (2), 379–421.

21.

Caballero

J. Ricardo

Krishnamurthy

Arvind

(2008), “How Credit Cycles Across a Financial Crisis,” NBER Working Paper Series No 23850, Bureau of Economic Research, 1–55.

22.

Calomiris

W. Charles

Wallison

Peter J.

(2008), “Blame Fannie Mae and Congress for the Credit Mess,” Wall Street Journal, Opinion .

23.

Calvo

A. Guillermo

Mendoza

Enrique G.

(2000), “Rational Contagion and the Globalization of Securities Markets,” Journal of International Economics, 51 (1), 79–113.

24.

Campbell

John

. (2019), “Self-responsibility Gone Bad: Institutions and the 2008 Financial Crisis,” The American Behavioral Scientist, 63 (1), 10–26.

25.

Cardinal

N. Rudolf

Parkinson

John A.

Hall

Jeremy

Everitt

Barry J.

(2002), “Emotion and Motivation: The Role of the Amygdala, Ventral Striatum, and Prefrontal Cortex,” Neuroscience Biobehavioral Review, 26 (3), 321–52.

26.

Carroll

D. Christopher

Otsuka

Misuzu

Slacalek

Jiri

(2011) “How Large are Housing and Financial Wealth Effects? A New Approach,” Journal of Money, Credit and Banking, 43 (1), 55–79.

27.

Case

Karl

Quigley

John

Shiller

Robert. J.

(2005), “Comparing Wealth Effects: The Stock Market Versus the Housing Market,” Advances in Macroeconomics, 5 (1), 1235.

28.

Chaiken

Shelly

(1980), “Heuristic Versus Systematic Information Processing and the Use of Source Versus Message Cues in Persuasion,” Journal of Personality and Social Psychology, 39 (5), 752–66.

29.

Chintagunta

Pradeep

Erdem

Tülin

Rossi

Peter E.

Wedel

Michel

(2006), “Structural Modeling in Marketing: Review and Assessment,” Marketing Science, 25 (6), 604–16.

30.

Claessens

Stijn

Kodres

Laura

(2014), “The Regulatory Responses to the Global Financial Crisis: Some Uncomfortable Questions,” International Monetary Fund WP/14/46 IMF Working Paper, 1–39.

31.

Cleeren

Katleen

Heerde

Harald J. van

Dekimpe

Marnik G.

(2013), “Rising from the Ashes: How Brands and Categories can Overcome Product-harm Crises,” Journal of Marketing, 77 (2), 58–77.

32.

Cloninger

O. Dale.

Waller

Edward. R.

(2000), “Corporate Fraud, Systematic Risk, and Shareholder Enrichment,” The Journal of Socio-Economics, 29 (2), 189–201.

33.

Cochrane

H. John

. (2005), Asset Pricing, revised ed. Princeton, NJ: Princeton University Press.

34.

Colander

David

Föllmer

Hans

Haas

Armin

Goldberg

Michael D.

Juselius

Katarina

Kirman

Allan

Lux

Thomas

Sloth

Brigitte

(2009), “The Financial Crisis and the Systemic Failure of Academic Economics,” Critical Review, 21 (2-3), 249–67.

35.

Cook

A. Laurel

Sadeghein

Raika

(2018), “Effects of Perceived Scarcity on Financial Decision Making,” Journal of Public Policy & Marketing, 37 (1), 68–87.

36.

Corneil

L. Bruce

McNamara

Sue

(2010), “Lessons and Consequences of the Evolving 2007-? Credit Crunch,” Aestimatio – The IEB International Journal of Finance 1, 164–81.

37.

Côté

Sophie

Bouchard

Stéphane

(2005), “Documenting the Efficacy of Virtual Reality Exposure with Psychophysiological and Information Processing Measures,” Applied Psychophysiology and Biofeedback, 30 (3), 217–32.

38.

Cox

D. Anthony

Cox

Den

Zimet

Gregory

(2006), “Understanding Consumer Responses to Product Risk information,” Journal of Marketing, 70 (1), 79–91.

39.

Craske

G. Michelle

Mohlman

Jan

Janet

Glover

Dorothy

Valeri

Sylvia

(1995), “Treatment of Claustrophobias and Snake/spider Phobias: Fear of Arousal and Fear of Context,” Behaviour Research and Therapy, 33 (2), 197–203.

40.

D’Ardenne

Kimberlee

McClure

Samuel M.

Nystrom

Leigh E.

Cohen

Jonathan D.

(2008), “BOLD Responses Reflecting Dopaminergic Signals in the Human Ventral Tegmental Area,” Science, 319 (5867), 1264–67.

41.

Dallery

Thomas

Treeck

Till van

(2011), “Conflicting Claims and Equilibrium Adjustment Processes in a Stock-flow Consistent Macroeconomic Model,” Review of Political Economy, 23 (2), 189–211.

42.

Danis

A. Michelle

Pennington-Cross

Anthony

(2008), “The Delinquency of Subprime Mortgages,” Journal of Economics and Business, 60 (1), 67–90.

43.

Dass

Nishant

Massa

Massimo

Patgiri

Rajdeep

(2008), Mutual Funds and Bubbles: The Surprising role of Contractual Incentives. The Review of Financial Studies, 21 (1), 50–99.

44.

Del Negro

Marco

Otrok

Christopher

(2007), “Luftballons: Monetary Policy and the House Price Boom Across U.S. States,” Journal of Monetary Economics, 54 (7), 1962–85.

45.

DeMarzo

Peter

Kaniel

Ron

Kremer

Iian

(2007), “Technological Innovation and Real Investment Booms and Busts,” Journal of Financial Economics, 85 (3), 735–54.

46.

Devereux

B. Michael

Sutherland

Allan

(2010), “Country Portfolio Dynamics,” Journal of Economic Dynamics and Control, 34 (7), 1325–42.

47.

Díaz

C.F. Alejandro

Reinhart

Carmen M.

(2015), “The Antecedents and Aftermath of Financial Crises,” NBER Working Paper No. 21350, Bureau of Economic Research, 1–39.

48.

Diebold

X. Francis

Yılmaz

Kamil

(2014), “On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms,” Journal of Econometrics, 182 (1), 119–34.

49.

Drayer

Joris

Brendan

Dwyer

Shapiro

Stephen L.

(2019), “Fantasy Millionaires: Identifying At-Risk Consumers Based on Motivation,” Journal of Sport Management, 33 (1), 37–49.

50.

Duca

V. John

Muellbauer

John

Murphy

Anthony

(2010), “Housing Markets and the Financial Crisis of 2007–2009: Lessons for the Future,” Journal of financial stability, 6 (4), 203–17.

51.

Dutt

Pushan

Padmanabhan

Vineet

(2011), “Crisis and Consumption Smoothing,” Marketing Science, 30 (3), 491–512.

52.

Elul

Ronel

Souleles

Nicholas S.

Chomsisengphet

Souphala

Denis

Glennon

Hunt

Robert

(2010), “What “Triggers” Mortgage Default?” American Economic Review, 100 (2), 490–94.

53.

Ericson

V. Richard

Doyle

Aaron

(2003), The Moral Risks of Private Justice: The Case of Insurance Fraud. Risk and Morality. Toronto: University of Toronto Press, Inc.

54.

Fay

Scott

Xie

Jihong

(2010), “The Economics of Buyer Uncertainty: Advance Selling vs. Probabilistic Selling,” Marketing Science, 29 (6), 1040–57.

55.

Fenzl

Thomas

Pelzmann

Linda

(2012), “Psychological and Social Forces Behind Aggregate Financial Market Behavior,” Journal of Behavioral Finance, 13 (1), 56–65.

56.

Ferguson

H. Charles

(2012), Predator Nation: Corporate Criminals, Political Corruption, and the Hijacking of America. New York: Random House.

57.

Final report on the causes of the financial and economic crisis in the United States (2011), Washington, DC: U.S. Government Printing Office. Financial Crisis Inquiry Commission report (2011).

58.

Forrester

Jay

(1994), “System Dynamics, Systems Thinking, and Soft OR,” European Journal of Operational Research, 59 (1), 28–41.

59.

Fostel

Ana

Geanakoplos

John

(2012), “Tranching, CDS, and Asset Prices: How Financial Innovation can cause Bubbles and Crashes,” American Economic Journal: Macroeconomics, 4 (1), 190–225.

60.

Frame

Scott

Lehnert

Andreas

Prescott

Ned

(2008), “A Snapshot of Mortgage Conditions with an Emphasis on Subprime Mortgage Performance. Federal Reserve,” (accessed May 18, 2019), available at http://www.federalreserveonline.org/pdf/mf_knowledge_snapshot-082708.pdf.

61.

Ganzach

Yoav

Wohl

Avi

(2018), “A Behavioral Theory of the Effect of the Risk-free Rate on the Demand for Risky Assets,” Journal of Behavioral and Experimental Economics, 76, 23–27.

62.

Gärling

Thomy

Kirchler

Erich

Lewis

Allan

Raaij

Fred dan van

(2009), “Psychology, Financial Decision Making, and Financial Crises,” Psychological Science in the Public Interest, 10 (1), 1–47.

63.

Gayraud

Jean-François

(2011), La Grande Fraude. Crime, Subprimes et crises financières16. Paris: Odile Jacob.

64.

Gelain

Paolo

Lansing

Kevin J.

Natvik

Gisle J.

(2018), “Explaining the Boom–bust Cycle in the U.S., Housing Market: A Reverse-engineering Approach,” Journal of Money, Credit and Banking, 50 (8), 1751–83.

65.

Gennaioli

Nicola

Shleifer

Andrei

Vishny

Robert W.

(2013), “A Model of Shadow Banking,” The Journal of Finance, 68 (4), 1331–63.

66.

Glaeser

L. Edward

Gyourko

Joseph

Saiz

Albert

(2008), “Housing Supply and Housing Bubbles,” Journal of Urban Economics, 64 (2), 198–217.

67.

Graafland

J. Johan

van de Ven

Bert W.

(2011), “The Credit Crisis and the Moral Responsibility of Professionals in Finance,” Journal of Business Ethics, 103 (4), 605–19.

68.

Greenspan

Allan

Kennedy

John

, (2008), “Sources and Uses of Equity Extracted from Homes,” Oxford Review of Economic Policy, 24 (1), 120–44.

69.

Grether

M. David

Plott

Charles R.

(1979), “Economic Theory of Choice and the Preference Reversal Phenomenon,” American Economic Review, 69 (4), 623–38.

70.

Grossman

S. Richard

Meissner

Christopher M.

(2010), “International Aspects of the Great Depression and the Crisis of 2007: Similarities, Differences, and Lessons,” NBER Working Paper No 16269, Bureau of Economic Research, 1–45.

71.

Guiso

Luigi

Paula

Sapienza

Zingales

Luigi

(2009), Moral and Social Constraints to Strategic Default. NBER Working Paper No 15145, 1–34.

72.

Hansen

Torben

(2014), “The Role of Trust in Financial Customer-seller Relationships Before and After the Financial Crisis,” Journal of Consumer Behaviour, 13 (6), 442–52.

73.

Hellwig

Martin

, (2008), “Systemic Risk in the Financial Sector. An Analysis of the Subprime-mortgage Financial Crisis.” Preprints of the Max Planck Institute for Research on Collective Goods, 43.

74.

Helweg-Larsen

Marie

Shepperd

James A.

(2001), “Do Moderators of the Optimistic Bias Affect Personal or Target Risk Estimates? A Review of the Literature,” Personality and Social Psychology Review, 5 (1), 74–95.

75.

Herwig

Uwe

Brühl

Anette B.

Viebke

Marie-Caroline

Scholz

Roland W.

Knoch

Dari

Siegrist

Michael

(2011), “Neural Correlates of Evaluating Hazards of High Risks,” Brain Research, 1400, 78–86.

76.

Hill

P. Ronald

Kozup

John C.

(2007), “Consumer Experiences with Predatory Practices,” The Journal of Consumer Affairs, 41 (1), 29–46.

77.

Hinds

Manuel

(2009), “The Poisoned Pool,” International Finance, 12 (2), 269–89.

78.

Hoffmann

Mathias

Krause

Michael U.

Laubach

Thomas

(2012), “Trend Growth Expectations and U.S. House Prices before and After the Crisis,” Journal of Economic Behavior and Organization, 83 (3), 394–409.

79.

Hofstede

Geert

(1983), “Culture’s Consequences: International Differences in Work-Related Values,” Administrative Science Quarterly, 28 (4), 625–29.

80.

Hoover

Robert

Green

Robert T.

Saegert

Joel

(1978), “A Cross-national Study of Perceived Risk: Does Perceived Risk have the Same effect on Consumers in a Foreign Country as in the United States?” Journal of Marketing, 42 (3), 102–25, (accessed February 3, 2019), available at http://fns.usda.gov/pd/snapsummary.htm.

81.

Huck

Nicolas

Mavoori

Hareesh

Mesly

Olivier

(2019), “The Rationality of Irrationality during the GFC in the U.S.,” Economic Modelling. https://doi.org/10.1016/j.econmod.2019.10.033.

82.

Iacoviello

Matteo

(2008), “Household Debt and Income Inequality, 1963–2003,” Journal of Money, Credit and Banking, 40 (5), 929–65.

83.

Igan

Deniz

Mishra

Prachi

Tressel

Thierry

(2011), A Fistful of Dollars: Lobbying and the Financial Crisis. NBER Working Paper No 17076, 1–84.

84.

In’t Veld

Jan

Raciborski

Rafal

Ratto

Marco

Roeger

Werner

, (2011), “The Recent Boom–bust cycle: The Relative Contribution of Capital Flows, Credit Supply and Asset Bubbles,” European Economic Review, 55 (3), 386–406.

85.

International Affective Picture System (IAPS), Lang

P.J.

Bradley

M.M.

Cuthbert

B.N.

(2008), International Affective Picture System (IAPS): Affective Ratings of Pictures and Instructions Manual. University of Florida, Gainesvilles, FL, USA: Technical Report A-8.

86.

International Monetary Fund (IMF) (2009a), World Economic Outlook. Washington, DC, April. http://www.imf.org/external/pubs/ft/weo/2009/update.

87.

International Monetary Fund (IMF) (2009b), “Restarting Securitization Markets: Policy Proposals and Pitfalls,” Chapter 2, in the Global Financial Stability Report (Washington: International Monetary Fund), (accessed June 2, 2017), available at http://www.imf.org/external/pubs/ft/gfsr/2009/02/pdf/chap2.pdf.

88.

Iyer

Ganesh

David

Soberman

Villas-Boas

Miguel

(2005), “The Targeting of Advertising,” Marketing Science, 24 (3), 461–76.

89.

Iyer

S. Balaji

Bhaskar

Kumar R.

(2002), “Investor’s Psychology: A Study of Investor Behaviour in the Indian Capital Market,” Finance India; Delhi, 16 (4), 1357–75.

90.

Joo

So-Hyun

Grable

John E.

(2004), “An Exploratory Framework of the Determinants of Financial Satisfaction,” Journal of Family and Economic Issues, 25 (1), 25–50.

91.

Kahneman

Daniel

Tversky

Amos

(1979), “Prospect Theory: An Analysis of Decision under Risk,” Econometrica, 47 (2), 263–292.

92.

Kamihigashi

Takashi

(2008), “The Spirit of Capitalism, Stock Market Bubbles and Output Fluctuations,” International Journal of Economic Theory, 4 (1), 3–28.

93.

Kaminsky

Graciela

Schmukler

Sergio

(2003), “Short-run Pain, Long-run gain: The Effects of Financial Liberalization,” NBER Working Paper No 9787, 1–63.

94.

Kandel

R. Eric

Schwartz

James H.

Jessell

Thomas M.

(2000), Principles of Neural Science, 4th ed. New York: Mc-Graw Hill Companies, Inc.

95.

Keinan

Giora

(1987), “Decision Making Under Stress: Scanning of Alternatives Under Controllable and Uncontrollable Threats,” Journal of Personality and Social Psychology, 52 (3), 639–44.

96.

Keltner

Dacher

Gross

James J.

(1999), “Functional Accounts of Emotions,” in Handbook of Cognition and Emotion, Dalgleish

Power

, eds. New York: John Wiley and Sons, Ltd, 467–80.

97.

Kindleberger

Charles

(1996), Manias, Panics, and Crashes: A History of Financial Crises, 3rd ed. New York: Basic Books.

98.

Klorman

Rafael

Weerts

Theodore C.

Hastings

James E.

Melaied

Barbara G.

Lang

Peter J.

(1974), “Psychometric Description of Some Specific-fear Questionnaires,” Behavior Therapy, 5 (3), 401–09.

99.

Krahnen

P. Jan

Franke

Günter

(2009), Instabile Finanzmärkte. CFS Working Paper, No. 2009/13, Goethe-Universität Frankfurt.

100.

Krishnamurthy

Arvind

Muir

Tyler

(2017), How Credit Cycles Across a Financial Crisis. National Bureau of Economic Research, Working Paper No 23850. 1–55.

101.

Krugman

Paul

(2009), Reagan did it. International Herald Tribune (June 2).

102.

Kunzmann

Ute

Cathleen

Kappes

Wrosch

Carsten

(2014), “Emotional Aging: A Discrete Emotions Perspective,” Frontiers in Psychology, 5, 380.

103.

Lennon

J. Sharron

Minjeong

Kim

Jaeha

Lee

Johnson

Kim K.P.

(2018), “Consumer Emotions on Black Friday: Antecedents and Consequence,” Journal of Research for Consumers, 32, 70–109.

104.

Lewin

Kurt

(1951) Field Theory in Social Science: Selected Theoretical Papers. New York: Harper.

105.

Lotka

J. Alfred

(1920), “Analytical Note on Certain Rythmic Relations in Organic Systems.” Proceedings of the National Academy of Science USA, 6, 410–415. doi:10.1073/pnas.6.7.410. PMC 1084562.

106.

Lotka

J. Alfred

(1925), Elements of Physical Biology. Baltimore: Williams and Wilkins.

107.

Lucas

E. Robert

Jr . (1978), “Asset Prices in an Exchange Economy,” Econometrica, 46 (6), 1429–45.

108.

Lupien

J. Sonia

McEwen

Bruce S.

Gunnar

Megan R.

Heim

Christine

(2009), “Effects of Stress Throughout The Lifespan on the Brain, Behaviour and Cognition – An Overview,” Nature Reviews, 10 (6), 434–45.

109.

Lux

Thomas

(1995), “Herd Behavior, Bubbles and Crashes,” Economic Journal, 105 (431), 881–96.

110.

MacInnis

J. Deborah

Mello

Gustavo E.

(2005) “The Concept of Hope and its Relevance to Product Evaluation and Choice,” Journal of Marketing, 69 (2), 1–14.

111.

Maslow

H. Abraham

(1954) Motivation and Personality. New York: Harper.

112.

McCoy

A. Patricia

Pavlov

Andrey D.

Wachter

Susan M.

(2009), “Systemic Risk through Securitization: The Result of Deregulation and Regulatory Failure,” Connecticut Law Review, 41, 493–541.

113.

McCullough

E. Michael

Brandon

Ann

Orsulak

Paul

Akers

Linda

(2007), “Rumination, Fear, and Cortisol: An in Vivo Study of Interpersonal Transgressions,” Health Psychology, 26 (1), 126–32.

114.

Mehta

Nitin

Rajiv

Surendra

Srinivasan

Kannan

(2004), “Role of Forgetting in Memory-based Choice Decisions: A Structural Model,” Quantitative Marketing and Economics, 2 (2), 107–40.

115.

Merrouche

Ouarda

Nier

Erland

(2010), What Caused the Global Financial Crisis?—Evidence on the Drivers of Financial Imbalances 1999–2007. IMG WP/10/265, 1–64.

116.

Mesly

Olivier

Chkir

Imed

Racicot

François-Éric

(2018), “Predatory Cells and Puzzling Financial Crises: Are Toxic Products Good for the Financial Markets?” Economic Modelling, 78, 11–31.

117.

Mian

Atif

Sufi

Amir

, (2009), “The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis,” Quarterly Journal of Economics, 124 (4), 1449–96.

118.

Miles

Chris

(2013), “Persuasion, Marketing Communication, and the Metaphor of Magic,” European Journal of Marketing, 47 (11/12), 2002–19.

119.

Milgrom

Paul

Roberts

James

(1982), “Predation, Reputation, and Entry Deterrence,” Journal of Economic Theory, 27 (2), 280–312.

120.

Mishkin

S. Frederic

(2007), “Housing and the Monetary Transmission Mechanism.” FEDS Working Paper 2007-40, Federal Reserve Board.

121.

Mobbs

Dean

Marchant

Jennifer L.

Hassabis

Demis

Saymour

Ben

Tan

Geoffrey

Petrovic

Marcus Gray. Predrag

Dolan

Raymond J.

Frith

Christopher D.

(2009), “From Threat to Fear: The Neural Organization of Defensive Fear Systems in Humans,” The Journal of Neuroscience, 29 (39), 12236–43.

122.

Morris

S. Jeffrey

Buchel

Christian

Dolan

Raymond J.

(2001), “Parallel Neural Responses in Amygdala Subregions and Sensory Cortex during Implicit Fear Conditioning,” NeuroImage, 13 (6), 1044–52.

123.

Moussa

Salim

Touzani

Mourad

(2017), “The Moderating Role of Attachment Styles in Emotional Bonding with Service Providers,” Journal of Consumer Behaviour, 16 (2), 145–60.

124.

Nadauld

D. Taylor

Sherlund

Shane M.

(2008), “The Role of the Securitization Process in the Expansion of Subprime Credit.” Working Paper, Ohio State University.

125.

Neal

Robert

Wheatley

Simon M.

(1998), “Do Measures of Investor Sentiment Predict Returns?” The Journal of Financial and Quantitative Analysis, 33 (4), 523–47.

126.

Nejad

G. Mohammad

(2016), “On the Contributions and the Validation of an Agent-based Simulation Model of Innovation Diffusion,” European Journal of Marketing, 50 (3/4), 647–57.

127.

Nili

Uri

Goldberg

Hagar

Weizman

Abraham

Yadin

Dudai

(2010), “Fear thou not: Activity of Frontal and Temporal Circuits in Moments of Real-life Courage,” Neuron, 66 (6), 949–62.

128.

Obi

Pat

Dubihlelab

Job

Choi

Jeong-Gil

(2012), “Equity Market Valuation, Systematic Risk and Monetary Policy,” Applied Economics, 44 (27), 3605–13.

129.

Obstfeld

Maurice

Rogoff

Kenneth

(2009), “Global Imbalances and the Financial Crisis: Products of Common Causes.” Centre for Economic Policy Research Discussion Paper No. 7606.

130.

Olsen

Robert

(2012), “Trust: The Underappreciated Investment Risk Attribute,” The Journal of Behavioral Finance 13 (4), 308–13.

131.

Paulssen

Marcel

Roulet

Raphael

Wilke

Sina

(2014), “Risk as Moderator of the Trust-loyalty Relationship,” European Journal of Marketing, 48 (5/6), 964–81.

132.

Pavlov

Andrey

Wachter

Susan M.

(2011), “Subprime Lending and Real Estate Prices,” Real Estate Economics, 39 (1), 1–17.

133.

Poch

Martin

(2015), “Effects of Intrinsic and Extrinsic Motivation on User-generated Content,” Journal of Strategic Marketing, 23(4), 305–17.

134.

Portes

Richard

(2009), “Global Imbalances,” in Macroeconomic Stability and Financial Regulation: Key Issues for the G20, Dewatripont

Mathias

Freixas

Xavier

Portes

Richard

, ed. London: Centre for Economic Policy Research.

135.

Posner

A. Richard

(2009), A Failure of Capitalism: The Crisis of ‘08 and the Descent into Depression. Cambridge, MA: Harvard University Press.

136.

Priewe

Jan

(2010), What Went Wrong? Alternative Interpretations of the Global Financial Crisis. in The Financial and Economic Crisis of 2008-2009 and Developing Countries, Dullien

Kotte

D. J.

Márquez

Priewe

, eds. United Nations Publication ISBN 978-92-1-112818-5, 17–52.

137.

Rajan

Raghuram

(2010), Fault Lines. Princeton: Princeton University Press.

138.

Rajan

Raghuram

Ramcharan

Rodney

(2012), “Anatomy of a Credit Crisis: The Boom and Bust in Farm Land Prices in the United States in the 1920s.” National Bureau of Economic Research, NBER No 18027, 1–42.

139.

Razin

Assaf

Rosefielde

Steven

(2011), “Currency and Financial Crises of the 1990s and 2000s.” Working Paper No 16754, National Bureau of Economic Research, 1–36.

140.

Reimann

Martin

Schilke

Oliver

Weber

Bernd

Neuhaus

Carolin

Zaichkowsky

Judith

(2011), “Functional Magnetic Resonance Imaging in Consumer Research: A Review and Application,” Psychology and Marketing, 28 (6), 608–37.

141.

Reinhart

Carmen M.

Rogoff

Kenneth S.

(2009), “The Aftermath of Financial Crises,” The American Economic Review, 99 (2), 466–72.

142.

Roy

Saktinil

Kemme

David M.

(2012), “Causes of Banking Crises: Deregulation, Credit Booms and Asset Bubbles, then and now,” International Review of Economics and Finance, 24, 270–94.

143.

Rutz

J. Oliver

Sonnier

Garrett P.

(2011), “The Evolution of Internal Market Structure,” Marketing Science, 30 (2), 274–89.

144.

Sama

M. Linda

Shoaf

Victoria

(2005), “Reconciling Rules and Principles: An Ethics-based Approach to Corporate Governance,” Journal of Business Ethics, 58 (1), 177–85.

145.

Samuel

Dorit

(2009), “The Subprime Mortgage Crisis: will New Regulations help Avoid Future Financial Debacles?” Albany Government Law Review, 2, 217.

146.

Scherbina

Anna

Schlusche

Bernd

(2013), “Asset Price Bubbles: A Survey,” Quantitative Finance, 14, 589–604.

147.

Seuntjens

G. Terri

Zeelenberg

Marcel

Breugelmans

Serger M.

Ven

Niels

(2015), “Defining Greed,” British Journal of Psychology, 106 (3), 1–21.

148.

Shefrin

Hersh

(2000), Beyond Greed and Fear: Understanding Behavioral Finance and the Psychology of Investing. Boston, MA: Editions Harvard Business School Press.

149.

Shiller

J. Robert

(2005), Irrational Exuberance. New York: Crown Publishing Group, a division of Random House, Inc.

150.

Soman

Dilip

Cheema

Amar

(2002), “The Effect of Credit on Spending Decisions: The Role of the Credit Limit and Credibility,” Marketing Science, 21 (1), 32–53.

151.

Sorescu

Alina

Sorescu

Sorin M.

Armstrong

Will J.

Devoldere

Bart

(2018), “Two Centuries of Innovations and Stock Market Bubbles,” Marketing Science, 37 (4), 507.

152.

Soydemir

Gökçe

Verma

Rahul

Wagner

Andrew

(2017), “The Asymmetric Impact of Rational and Irrational Components of Fear Index on S&P 500 Index Returns,” Review of Behavioral Finance, 9 (3), 278–91.

153.

Starcke

Katrin

Brand

Matthias

(2012), “Decision Making Under Stress: A Selective Review,” Neuroscience and Biobehavioral Reviews, 36 (4), 1228–48.

154.

Stiglitz

Joseph E

. (2003), “Government Failure vs. Market Failure: Principles of Regulation”. JES.Govt.Failure.Mkt.Failure%20(1).pdf.

155.

Taute

Harry A.

McQuitty

Shaun

Sautter

Elise P.

(2011), “Emotional Information Management and Responses To Emotional Appeals,” Journal of Advertising, 40 (3), 31–43.

156.

Taylor

B. John

(2007), “Housing and Monetary Policy.” in Housing, Housing Finance, and Monetary Policy. Kansas City, MO: Federal Reserve Bank of Kansas City (Figure 2 taken from Taylor, 2009, p. 350), Symposium.

157.

Thorne

Deborah

Warren

Elizabeth

Sullivan

Teresa A.

(2009), “The Increasing Vulnerability of Older Americans: Evidence from the Bankruptcy Court,” Harvard Law and Policy Review, 3, 87–101.

158.

Van den Bulte

Christophe

Stremersch

Stefan

(2004), “Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-analytic Test,” Marketing Science, 23 (4), 530–44.

159.

van Rooij

Maarten

Lusardi

Annamaria

Alessie

Rob

(2011), “Financial Literacy and Stock Market Participation,” Journal of Financial Economics, 101 (2), 449–72.

160.

Volterra

Vito

(1926), “Variazioni e fluttuazioni del numero d’individui in specie animali conviventi.” Memoria della Reale Accademia Nazionale dei Lincei, 2, 31–113.

161.

Volterra

Vito

(1931), “Variations and Fluctuations of the Number of Individuals in Animal Species Living Together,” in Animal Ecology, Chapman

R. N

, ed. New York: McGraw-Hill, 409–48.

162.

Vul

Edward

Harris

Christine

Winkielman

Piotr

Pashler

Harold

(2009), “Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, and Social Cognition,” Perspectives on Psychological Science, 4 (3), 274–90.

163.

Wachter

Susan

(2015), “The Housing and Credit Bubbles in the United States and Europe: A Comparison,” Journal of Money, Credit and Banking, 47 (1), 37–42.

164.

Wang

Alex

(2009), “Interplay of Investors’ Financial Knowledge and Risk Taking,” Journal of Behavioral Finance, 10 (4), 204–13.

165.

Warren

Elizabeth

(2004), “The Over-Consumption Myth and Other Tales of Economics, Law, and Morality,” Washington University Law Review, 82 (4), 1485–511.

166.

Wellink

Arnout

(2009), “The Future of Supervision,” Speech given at a FSI High Level Seminar, Cape Town, South Africa, January 29, (accessed June 13, 2015), available at http://www.dnb.nl/en/news/newsand-archive/speeches-2009/dnb212415.jsp.

167.

West

Douglas

Prendergast

Gerard P.

(2009), “Advertising and Promotions Budgeting and the Role of Risk,” European Journal of Marketing, 43 (11/12), 1457–76.

168.

Wheelock

David C.

Wohar

Mark E.

(2009), “Can the Term Spread Predict Output Growth and Recessions? A Survey of the Literature,” Federal Reserve Bank of St. Louis Review, 91, 419–40.

169.

White

R. William

(2009), “Should Monetary Policy “Lean or Clean?” Federal Reserve Bank of Dallas Globalization and Monetary Policy Institute Working Paper No. 3.

170.

Wiles

Michael A.

Jain

Shailenadra P.

Mishra

Saurabh

Lindsey

Charles

(2010), “Stock Market Response to Regulatory Reports of Deceptive Advertising: The Moderating Effect of Omission Bias and Firm Reputation,” Marketing Science, 29 (5), 828–45.

171.

Yoon

Carolyn

Laurent

Gilles

Fung

Helene

Gonzalez

Richard

Gutchess

Angela

Hedden

Trey

Lambert-Pandraud

Raphaëlle

Mather

Mara

Park

Denise

Peters

Ellen

Ian

Skurnik

(2005), “Cognition, Persuasion and Decision Making in Older Consumers,” Marketing Letters, 16 (3), 429–41.