{"id":124,"date":"2022-02-03T10:23:57","date_gmt":"2022-02-03T10:23:57","guid":{"rendered":"https:\/\/truthbegold.co.uk\/?post_type=article&p=124"},"modified":"2022-02-13T21:45:06","modified_gmt":"2022-02-13T21:45:06","slug":"sub-conscious-thoughts-about-cashless-payments","status":"publish","type":"article","link":"https:\/\/truthbegold.co.uk\/article\/sub-conscious-thoughts-about-cashless-payments\/","title":{"rendered":"Sub-conscious thoughts about cashless payments"},"content":{"rendered":"\n

Are cashless payments making it easier or harder to feel in control of our finances?<\/h2>\n\n\n\n

This report uses two innovative research techniques to understand how in control consumers feel about newer cashless payments such as contactless cards and Apple Pay when compared to traditional cash. We have chosen these techniques to get a better emotional quantification of such feelings. We believe that asking consumers how they feel about such issues (in standard explicit quantitative surveys and qualitative focus groups) only scratches the surface and often does not elicit the true subconscious feelings of the individual. We use Implicit Response Test (IRT) methodology to quantify the strength of associations consumers have for control with both cashless and cash payments. We find that when consumers are stimulated or \u2018primed\u2018 with images of cashless payments, they find it harder to sort the word \u2018Control\u2019 into the correct \u2018Control\u2019 domain. In the same test, when also primed with cashless payment images they also find it easier to sort the word \u2018Uncontrolled\u2019 into the correct \u2018Uncontrolled\u2019 domain. Using different forms of cashless and cash primes, we find that consumers implicitly feel cashless payments are more uncontrolled than controlled. We use Mind Metaphor Interview (MMI) methodology to qualify the different dimensions of control consumers feel for cashless payments. Using images to describe how they feel about cashless payments, consumers tell us that there are four negative dimensions of control and one positive. The four negative dimensions include overspending, criminal activity, embarrassment when the technology fails them and an invasion of privacy from the financial institutions. The one positive dimension for control is a feeling of convenience and ease, where cashless payments help their daily lives flow more easily. We conclude by discussing the implications of these results for financial institutions and the growing fintech community.<\/p>\n\n\n\n

1. Introduction<\/h3>\n\n\n\n

As adoption of new cashless payment technologies takes off, understanding how consumers feel about using contactless cards, online payment systems like Paypal, phones via apple and android pay and even voice activated purchases via devices such as amazon Echo is of increasing importance to both the established financial institutions and the growing fintech sector.<\/p>\n\n\n\n

Since the early 1990s, a number of observers foresaw the increased use of a cashless payment system and predictions of a cashless society (Humphrey and Berger 1990; Humphrey et al 1996; Olney 1999; Klee 2004; Garcia-Swartz et al 2006). There is no doubt of the significant benefits that a cashless society can bring such as cost savings and efficient use of resources. However, some commentators have put forward their cases that a cashless society will have an overall adverse affect, leading to an increase in overall consumption, increasing personal debt and a negative impact upon savings levels (Nocera, 1994; Libow, 1955; Taylor and Tilford, 2000; Zavestoski 2002; MacDonald et al 2006).<\/p>\n\n\n\n

Whilst this study does not tackle the macro economic impact of cashless payments, we did want to understand if these new payment methods helped consumers feel more, or indeed less, in control of their finances.<\/p>\n\n\n\n

Interestingly, at the same time as the accelerated take up of cashless payments, we\u2019ve also seen a new area of economics take off. Underpinned by psychology, this new field encompasses how consumers really feel and behave and the term \u2019behavioural economics\u2019 is now more commonly known and increasingly understood.<\/p>\n\n\n\n

This exciting new field challenges the traditional economics models of rational agent behaviour, making decisions in an environment of perfect information and perfect trust. Instead behavioural economics proposes that consumers often act irrationally or \u2018non-rationally\u2019 in their decision making. Nobel Prize winning psychologists Kahneman and Tversky (Kahneman 2011) and economists Thaler and Sunstein (Thaler and Sunstein 2008) propose that humans possess two thinking systems \u2013 one fast and operating deep in the subconscious and the second one acting far slower, more deliberative and sits within in our consciousness. They go on to suggest that the majority of our behaviour is in fact driven by our fast thinking system, often making decisions for ourselves, without really taking time to think at all.<\/p>\n\n\n\n

This simple model goes a long way to explaining what many researchers have believed for a long time \u2013 asking consumers why they do the things they do might not give us the full answer.<\/p>\n\n\n\n

As one great advertising great, David Ogilvy once said \u2018Consumers don\u2019t think how they feel. They don\u2019t say what they think and they don\u2019t do what they say\u201d.<\/p>\n\n\n\n

And therein lies the heart of this research challenge, if we asked consumers for their explicit attitudes around cashless payments and control, how close to the truth would their answers really be? In focus groups and surveys, often addressing our rational minds, would they truly acknowledge if they felt out of control using cashless payments and could they accurately define how they felt when using these new methods of payment?<\/p>\n\n\n\n

To help us overcome this challenge, alongside the take-off of behavioural economics, in parallel there has been considerable growth in neuroscience, biometric and implicit research techniques. Due to the constraints of funding for this project we felt it appropriate to investigate the use of new implicit research techniques and new qualitative interview methodologies to overcome these problems.<\/p>\n\n\n\n

Our ambition was to go deeper into consumer subconscious decision making and understand how consumers really felt about cashless payments and whether or not they felt more or less in control when compared to cash.<\/p>\n\n\n\n

Implicit research techniques came to the fore as a result of a provocative study and subsequent book, The Blind Spot by Anthony Greenwald, Professor of Psychology at the University of Washington and Mahzarin Banaji, a social psychologist at Harvard investigating bias and stereotypes in regards to race and ethnicity. (Greenwald and Banaji 2011).<\/p>\n\n\n\n

They have conducted extensive research on cognition and have collaborated to create the implicit association test. This measures the extent to which an individual will associate two individual concepts.<\/p>\n\n\n\n

These new tests seek to ask consumers to sort different words or images to the left or right and depending on the speed of their response, it\u2019s proposed that the deep associations in the brain can be elicited.<\/p>\n\n\n\n

These new techniques are not without their challengers. A recent article in The Cut discussed the reliability and repeatability of such techniques and the summary highlighted that more work needs to be done in this area to increase the significance of the findings. We also recognise that the techniques used in implicit research are not the only predictors of consumer behaviour. Simply because consumers have a feeling of a lack of control at an implicit level does not necessarily lead to uncontrolled cashless payment spending patterns as other decision making processes may override this feeling in different contexts.<\/p>\n\n\n\n

We are aware of the currently active debates about the application of another Implicit Response paradigm\u2019s (the Implicit Association Test) to predicting socially discriminatory behaviour, often called \u2018implicit bias\u2019 and controversially used to ascribe racist motives to law enforcement personnel.<\/p>\n\n\n\n

However, published meta-analyses of a range of Implicit Response measures have established their reliability, validity and predictability.<\/p>\n\n\n\n

For example, a paper published in 2012 (Cameron, Brown-Iannuzzi and Payne 2012) examined findings from 167 academic studies wrote that: \u201cSequential priming measures have incremental predictive validity over explicit measures.\u201d, \u201cA comprehensive examination of all available data suggests that priming tasks, as a class, are indeed valid measures.\u201d <\/p>\n\n\n\n

And that \u201cPriming measures were associated with controllable and uncontrollable behaviours, and with socially sensitive and socially innocuous behaviours.\u201d<\/p>\n\n\n\n

For this research, we have used the Cloud Army Reactor Implicit apps to measure the priming effect of cash and cashless imagery. Cloud Army are also conducting ongoing science research to optimise and enhance the robustness of their Reactor tests.<\/p>\n\n\n\n

For example, we optimised number of replicates, i.e. the number of times each respondent sees the same combination of a prime image and a particular sorting task, strongly affects the reliability of the test results, as measured by a statistic named Cronbach\u2019s Alpha.<\/p>\n\n\n\n

We have found that some paradigms used by other vendors use too few replicates to achieve acceptable reliability.<\/p>\n\n\n\n

For the research that follows, we will refer to the test as the Implicit Response Test (IRT).<\/p>\n\n\n\n

For our second technique, we have been inspired by the work of Dr Gerald Zaltman and his work understanding deep metaphors that he has identified as driving our conscious and especially non-conscious thoughts. It was developed by Zaltman at the Harvard Business School in the early 1990s. As Zaltman described it \u201cA lot goes on in our minds that we\u2019re not aware of. Most of what influences what we say and do occurs below the level of awareness. That\u2019s why we need new techniques: to get at hidden knowledge, to get at what people don\u2019t know they know. The technique that he developed is called the Zaltman Metaphor Elicitation Technique or ZMET and has been used for a variety of marketing organisations, academic researchers and social science professionals.<\/p>\n\n\n\n

Zaltman began researching the power of using imagery whilst on holiday in Nepal in 1990. Zaltman at the last minute opted not to bring his camera on the trip and instead asked local residents to help him to chronicle the trip by asking them to take pictures that would explain what life was like in their villages with disposable cameras. After developing the pictures Zaltman returned the village to ask residents to explain the meaning of the photographs through an interpreter. The pictures they had taken revealed ideas that would have been unacceptable of impossible to put into words.<\/p>\n\n\n\n

For instance, the photographers tended to cut off peoples feet in the images. This was intended, as bare feet is a sign of poverty in Nepal. Zaltman believed that had he asked the villagers to describe life in just words this topic would not have emerged as it carried such a stigma.<\/p>\n\n\n\n

ZMET is a hybrid market research methodology that is grounded in a number of different disciplines including verbal and non-verbal communication, visual sociology, visual anthropology, semiotics, mental imagery, cognitive neuroscience and phototherapy. The principles behind the approach are that thought occurs as a pattern of neural activity, not as words; most human meaning is exchanged nonverbally; emotion and reason are equally important and are combined in decision making; most thought, emotion and learning occur without awareness; mental models guide the selection of, process of and response to stimuli; cognitions are shared socially, and non-literal language and especially metaphor is central to understanding which means that ZMET is a great tool to use to understand customer experiences.<\/p>\n\n\n\n

Inspired by the work of Zaltman, we used a non-patented approach, similar to the above studies and what we call a Mind Metaphor Interview (MMI). For the context of the research there are seven deep metaphors as follows;<\/p>\n\n\n\n

BALANCE: <\/strong>Balance\u2014and imbalance\u2014involves ideas of equilibrium, adjusting, maintaining or offsetting forces, and the idea of things as they should be. People express psychological imbalance when talking about being out-of-sorts, down, and feeling off, and psychological balance when they say they feel centered, feel inner peace, or are back on track.<\/p>\n\n\n\n

JOURNEY:<\/strong> People talk about many aspects in life as a journey. We often frame life itself as one big journey, including, for many, an afterlife. Sometimes we think of our lives as a brief journey, as in \u201clife is short\u201d; other times we view it as lengthy, as in \u201che still had so much life to live,\u201d when a journey ends prematurely.<\/p>\n\n\n\n

CONTAINER:<\/strong> Containers perform two functions: keeping things in and keeping things out. We find ourselves in or out of physical shape or condition, in a good or bad mood, stuck in a rut, or born into a social class and family. Memories are one of the most vital containers because they store our individual histories and identities.<\/p>\n\n\n\n

RESOURCE: <\/strong>We need resources to survive\u2014we would die without food and water or a nurturing adult in our infancy. Products and services are also important resources: we might refer to a smartphone as a \u201clifeline,\u201d or motor oil as a truck\u2019s \u201clifeblood.\u201d An intelligent person is a \u201cfountain of knowledge\u201d; gaining an education is the \u201ckey\u201d to one\u2019s future.<\/p>\n\n\n\n

TRANSFORMATION:<\/strong> Transformation involves changing states or status. Physically we can go from being \u201claid low by a cold\u201d to being \u201cup and about.\u201d Money can be \"matured,\" or \"grown.\" Emotionally, if we undergo a major life change, we talk about needing \u201cattitude adjustments\u201d or \u201cturning over a new leaf.\u201d<\/p>\n\n\n\n

CONNECTION:<\/strong> Connection\u2014and disconnection\u2014 encompasses feelings of belonging or exclusion: being kept in or out of the loop, identifying with others, being drawn to celebrities, or breaking up a relationship. Themes of connection and disconnection quite often express themselves through connection to others, connection to self, connection to a moment and connection to something larger, among other themes.<\/p>\n\n\n\n

The final metaphor is of particular relevance for this research and helps us understand how in control consumers feel about cashless payments.<\/p>\n\n\n\n

CONTROL:<\/strong> We all need to feel in control of our lives. Sometimes we succeed, sometimes we don't. In our lives, we sometimes feel events \u201cspiraling out of control.\u201d When life is calm, we cruise on \u201cauto pilot.\u201d Social norms arise to control group interactions, and we sanction those who don't adhere to these norms.<\/p>\n\n\n\n

In what follows, we bring together both quantitative and qualitative results to describe how consumers feel about cashless payments, are they feeling out of control and what makes them feel this way?<\/p>\n\n\n\n

2. Methodology<\/h3>\n\n\n\n

2.1 Implicit Response Test (IRT)<\/h4>\n\n\n\n

The Implicit Response Test uses the Cloud Army Reactor Implicit App to measure automatically evoked associations and attitudes by measuring changes in reaction times in a word sorting task when participants are \u2018primed\u2019 by exposure to images. (for a summary explanation of the priming effect, see: https:\/\/en.wikipedia.org\/wiki\/Priming_(psychology))<\/a><\/p>\n\n\n\n

The process is that participants are given an objective sorting task whereby they are told words will appear in a sequence in the centre of their computer screen at timed intervals and they should \u2018sort\u2019 each word that appears by matching it to one of two \u2018pole\u2019 words, constantly displayed on either side of the screen. In this instance the pole words were \u2018Uncontrolled\u2019 (displayed on the left of the screen) and \u2018Controlled\u2019 (displayed on the right of the screen). These poles represent opposite attitudinal concepts or associations. One at a time, either the word \u2018Controlled\u2019 or 'Uncontrolled\u2019 appeared in the centre of the screen, for sorting, using a display algorithm that ensures each word appears an equal number of times, but in a random sequence.<\/p>\n\n\n\n

After a short practice to familiarise themselves with the task, a baseline measurement is made to obtain the average reaction speed per participant for sorting the words to either pole. These reaction speed times are measured accurately down to the millisecond level.<\/p>\n\n\n\n

These baseline measures are important, in order to establish the typical response time for each individual to the sorting task. For example, different individuals will have different average natural response times, and they may typically respond slightly faster to one pole word than the other. Then the main task is introduced. Now individual images of cash or cashless payments types flash up before each word, the participants are instructed to ignore the images and continue to only sort the words. Again, their response times are measured, but now for the average of each image-word pairing.<\/p>\n\n\n\n

Stimulus included visuals of the following;<\/p>\n\n\n\n

Cash<\/strong> \u2013 2 pence, 10 pence, 20 pence, 50 pence, \u00a31 coin, \u00a35 note, \u00a310 note, \u00a320 note.
Cashless<\/strong> \u2013 Contactless logo, Direct Debit logo, Visa \/ Mastercard logos, Android Pay logo, Paypal logo, Amazon Echo logo, Apple Pay logo.<\/p>\n\n\n\n

In contrast to their baseline average response times per pole, these times represent the image-primed response time per pole. If a priming effect occurs whereby the image primes an association with the word that subsequently appears such that it helps them react faster (compared to their natural baseline response time, and to their primed response time to the opposite word), then we can say that the image has facilitated their response and represents a greater association with that pole than its opposite.<\/p>\n\n\n\n

Similarly, a slowed response to the opposite pole is also an indication of an association with the attribute. For example, after seeing an image strongly associated with effective control, the reaction time sorting the word Controlled\u2019 will be slightly faster, compared to baseline, than the sorting time for the word \u2018Uncontrolled\u2019 compared to baseline.<\/p>\n\n\n\n

Finally, a second baseline measurement is made (without the appearance of the priming images). This is done in order to capture any natural drift in response times that may have occurred either due to improvements through practice through the course of the test, or slowing of response times due to fatigue.<\/p>\n\n\n\n

When comparing primed responses to baseline, we use the average of the baseline trials that occur before and after the primed trials. <\/p>\n\n\n\n

Incidentally, a side-benefit of the objective nature of the task (i.e. that it has right and wrong responses, and participants are prompted to respond accurately if they click incorrectly), is that participants must attend to the task and can\u2019t \u2018randomly\u2019 or inattentively simply click their way through the test.<\/p>\n\n\n\n

For each individual within the sample of 200 people we now have average baseline response times per pole word, and then per priming-image when paired with each word.<\/p>\n\n\n\n

The effect size calculation used is Cohen\u2019s D, which can be represented as:<\/p>\n\n\n\n

d = (M1 - M2)\/StdDev<\/p>\n\n\n\n

Where M1 & M2 are the average response times for baseline and priming-image respectively, and StdDev is the standard deviation of the set of measurement times. This effectively divides the differences in response times yielded from the primed-task by the variance of scores.<\/p>\n\n\n\n

Cohen\u2019s D values are calculated for the sorting of each word when paired with each pole, and then one pole score is subtracted from the other, to show the difference between the poles (i.e. is the effect of facilitation stronger for one pole or its opposite, or is there no difference between them).<\/p>\n\n\n\n

Finally, t-test statistical calculations are made to determine the statistical significance of the difference between the priming effect sizes.<\/p>\n\n\n\n

The Evaluative Decision Task<\/h5>\n\n\n\n

Cloud Army\u2019s Reactor Implicit Apps currently use variations of a paradigm called the Evaluative Decision Task (EDT) which was described in the literature in 1986 by Fazio and Williams (Fazio and Williams 1986) and has subsequently been used in a large number of published academic studies.<\/p>\n\n\n\n

IRT Recruitment Criteria<\/h5>\n\n\n\n

Respondents were recruited via on-line panels according to the following criteria;<\/p>\n\n\n\n