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Marketing Letters

, Volume 29, Issue 1, pp 61–72 | Cite as

What else can your payment card do? Multifunctionality of payment modes can reduce payment transparency

  • Rufina GafeevaEmail author
  • Erik Hoelzl
  • Holger Roschk
Article

Abstract

Payment modes (e.g., cash vs. credit card) vary in the transparency of the outflow of money. Smartcards (multifunctional cards), which bundle payment with non-payment functions (e.g., loyalty programs, identification, and other information functions), have become an increasingly popular payment mode. This shift toward multifunctionality in payment modes is assumed to reduce payment transparency and consequently to decrease consumers’ recall accuracy of past expenditures. We employ a field study to examine recall accuracy for recent purchases with cash, a single-function card, and a multifunctional card. We find that recall accuracy is lower when using a single- or a multifunction card than cash. We also find that it is not the multifunctionality of the card that results in a higher recall error but the individual usage patterns: A higher usage frequency of the non-payment functions results in a higher recall error.

Keywords

Money Multifunctionality Payment distinctiveness Transparency Recall error Digital payment modes 

1 Introduction

Smartcards are plastic cards with an embedded processor or chip that can store and process data, thus enabling multiple applications (Shelfer and Procaccino 2002). Therefore, smartcards can be regarded as a multifunctional payment mode that bundles payment with loyalty programs, identification, and other information functions. Consumer payment with smartcards is evolving. Globally, it is estimated that over three billion new smartcards with a payment function will be issued in 2017, reflecting a compound annual growth rate of 20% between 2000 and 2017 (Eurosmart 2016). The idea of bundling payment with other functions can also be increasingly found for digital devices such as mobile phones or smartwatches (Weber 2007). Overall, marketers expect multifunctional payment modes to provide added convenience to the user, consequently increasing sales. However, there is a broader concern as to how multifunctional payment modes will affect consumers’ financial well-being. Researchers warn that consumers may perceive the transaction with a multifunctional payment mode as less salient than paying with cash or credit cards (e.g., Shah et al. 2016).

The literature offers little evidence on multifunctional payment modes, despite their recent proliferation. Previous research examined primarily whether spending differs between cash and credit card payment (e.g., Inman et al. 2009; Raghubir and Srivastava 2008; Soman 2003; Thomas et al. 2011). The evidence is often based on laboratory settings (e.g., Prelec and Simester 2001; Runnemark et al. 2015), and little attention is given to consumers’ payment recall accuracy. However, an accurate recall of past expenditures is important because it affects the willingness to spend money in the future (e.g., Heath and Soll 1996; Sussman and Alter 2012).

In this paper, we focus on multifunctional card payment and explore the following research questions: (1) Does card payment decrease the recall accuracy of expenditures in comparison to cash payment? (2) Does payment with a multifunctional card decrease the recall accuracy of expenditures in comparison to a single-function payment card? (3) Do specific usage patterns for a multifunctional card reduce the recall accuracy of expenditures?

To address our research questions, we conduct a field study investigating consumers’ recall of a recent purchase using three payment modes: cash, a single-function card, and a multifunctional card. Our findings contribute to the literature in two ways. First, we add to the research on cash versus card payment by studying actual payment transactions in a field setting. So far, initial evidence has been provided by Soman (2001), who shows in a small field study that the amount spent on books is better remembered for cash (vs. credit card) transactions. Thus, we provide added validity to Soman’s (2001) results with regard to another industry (cafeteria) and to a new payment mode (multifunctional card). Second, we extend the research stream on digital payment modes. Focusing on their multifunctional characteristic, we introduce payment distinctiveness as a previously unidentified aspect of payment transparency, which allows us to predict the recall accuracy of consumer expenditures.

2 Theoretical background

2.1 Transparency of payment modes

Considerable empirical evidence suggests that payment modes influence consumer behavior. For instance, in comparison to a cash payment, a credit card payment increases the willingness to spend (e.g., Inman et al. 2009; Prelec and Simester 2001; Raghubir and Srivastava 2008; Runnemark et al. 2015; Soman 2003; Thomas et al. 2011) but decreases post-transaction commitment and perception of ownership toward the purchased object (e.g., Kamleitner and Erki 2012; Shah et al. 2016).

As an explanation for these differences in spending between cash and credit cards, Soman (2003, p. 175) introduces the concept of payment transparency, defined as the “relative salience of the payment.” Accordingly, a high payment transparency creates an aversion to spending because consumers experience the “pain of paying” (Prelec and Loewenstein 1998). Payment modes can differ in their degree of transparency in terms of physical form and amount (Soman 2003). For example, a credit card is less transparent than cash because it does not resemble banknotes (physical form) and does not involve counting (amount). Card payments are also less transparent due to the decoupling effect. This effect describes the separation of payment and consumption (Prelec and Loewenstein 1998), which is, in the case of card payments, for instance, achieved by combining multiple distinct purchases on a single bill.

As a consequence of reduced payment transparency, individuals may experience the outflow of money as less salient and recall their spending less accurately (Soman 2001). Hence, we examine whether card transactions result in a higher recall error than cash transactions do (research question 1).

2.2 Multifunctionality of payment modes

Multifunctional payment modes are not used exclusively for payment. Their multifunctional characteristic may weaken the association between the device and its payment function. In other words, the other functions dilute the identity of a multifunctional device as a payment mode, which may be understood as another form of decoupling (i.e., separation between the device and its payment function) and reduced payment transparency. Consequently, consumers may perceive transactions with a multifunctional payment mode as less salient than payments with cash or credit cards (Shah et al. 2016). Therefore, we examine whether payment with a multifunctional card results in a higher recall error than payment with a single-function card (research question 2).

2.3 Distinctiveness of the payment function

Individual usage patterns of multifunctional payment modes vary in the extent to which they make the payment function less distinct from other non-payment functions. We propose the factor payment distinctiveness to describe the extent of the decoupling effect between the device and its payment function for multifunctional payment modes. To capture payment distinctiveness numerically, we use the following equation to calculate the difference between the usage of the payment function (u payment) and the average usage of the non-payment functions (u non-payment), with a higher number indicating higher payment distinctiveness.
$$ \mathrm{Payment}\ \mathrm{distinctiveness}={u}_{\mathrm{payment}}-\frac{\sum \limits_{i=1}^n{u_{\mathrm{non}-\mathrm{payment}}}_i}{n} $$
(1)

Literature from cognitive psychology makes it possible to explain the relationship between payment distinctiveness and recall accuracy. Numerous studies have shown that information that is distinct and therefore perceptually salient is better memorized (e.g., Brunel et al. 2010; Geraci and Rajaram 2004, 2006). For instance, in a classical study by von Restorff (1933), participants recalled a syllable from a list more accurately if it was distinct from other, rather homogenous items. In addition, bizarre sentences (e.g., McDaniel et al. 2013) and words with unusual orthographies (e.g., Hunt and Elliot 1980) are better memorized than normal sentences or words.

Drawing on this well-established effect of distinctiveness on memory, we suggest that the payment distinctiveness of multifunctional payment modes may facilitate the recall accuracy of expenditures. Like information, which is better memorized if distinct, payment that is distinct from other functions is also likely to be better memorized. Therefore, for the multifunctional card, we examine whether payment distinctiveness and recall error exhibit a negative relation (research question 3).

3 Methodology

3.1 Design, procedure, and sample

Our field study took place at two time points in cafeterias at a German university. At time point one of data collection (summer 2015), these cafeterias accepted both cash and a single-function cafeteria card (i.e., a prepaid card that could be used voluntarily only for cafeteria purchases). With the start of the winter term of 2015, the single-function cafeteria card expired for all students and was replaced by a multifunctional student card that incorporated the previous payment function with additional non-payment functions, such as student ID, public transport ticket, and library card. At time point two of data collection (summer 2016), students could choose at the university cafeterias between payment by cash or multifunctional student card.

In order to control for seasonal differences in purchase patterns, we deliberately set the two time points for data collection close to the end of the respective academic year. By doing so, we also assured that the students could become familiar with the single-function cafeteria card and the multifunctional student card. Furthermore, we chose two university cafeterias that sold the same products for identical prices, but which were located on opposite sides of the university campus, so that students from different schools could be included in the study. Finally, to rule out weekday-related effects, each wave of data collection comprised for each cafeteria a period of one week, covering all working days and business hours.

During data collection, an interviewer waited in close proximity to the register and observed which products a person placed at the checkout. We only approached individuals who purchased two or more fixed-price items. Hence, a person was not approached if the purchase included products with variable prices (e.g., a salad with a price based on weight). The minimum of two items was set to increase the difficulty of the payment recall task. The limitation to items with fixed prices was necessary to determine retrospectively the actual amount of the purchase. It may be argued that this selection criterion led to the exclusion of certain students purchasing items with variable weight-based prices, such as the salad. However, the sampling procedure was identical across payment modes and should not have introduced any bias. The limitation to fixed-price items might be expected to lead to a more conservative assessment of the recall error since there is less variability in prices, and such prices also remain constant over time.

The individuals were approached inside the cafeteria right after they had paid at the register. This allowed us to avoid any delay that might have introduced additional influences on their memory. The approached individuals were asked whether they were enrolled at the university as a student and were willing to take a short survey about their recent purchase, including an opportunity to participate in a lottery. We surveyed only students because it was only students who had experienced the transition from the single-function cafeteria card to the multifunctional student card. Given the design of the field study, we could have approached some people multiple times. However, due to the high customer frequency in both cafeterias and their distinct locations, it seems very unlikely that we “double-counted” any respondent.

The survey was conducted as a face-to-face interview. All interviewers (five research assistants and the first author) followed standardized interview guidelines. Overall, 501 individuals took part. Five participants were excluded due to missing values, resulting in 496 usable participants.

3.2 Independent, dependent, and control variables

The independent variable payment mode is represented by participants’ group affiliation according to their chosen mode of payment: paying with cash, the single-function cafeteria card (cardSF), or the multifunctional student card (cardMF). In addition, we calculated payment distinctiveness according to Eq. (1) for participants who used the multifunctional card. For this purpose, participants reported on their usage frequency of all card functions (student ID, public transport ticket, payment at the cafeteria, and library card) on a 7-point scale (1 = never, 7 = very often).

To measure our dependent variable recall error, participants completed a payment recall task. In the first step, they were asked to recall the total amount spent on their recent purchase. In order to incentivize participants to recall the amount accurately, the chance to participate in a lottery of 10 × 20 € (approx. US$23) was offered. Participants were informed that the number of lottery tickets would depend on their recall accuracy. In the second step, the purchased items were photographed and documented. After the interview, the actual price of each item was identified on the basis of the photograph, allowing the calculation of the actual purchase amount and its comparison to the recalled amount. For this purpose, we followed Zeithaml’s (1982) recommendation and calculated the recall error as the absolute percentage deviation between the actual purchase amount and the recalled purchase amount, with a higher number indicating lower recall accuracy:
$$ \mathrm{Recall}\ \mathrm{error}=\left|\frac{\mathrm{actual}\ \mathrm{purchase}\ \mathrm{amount}-\mathrm{recalled}\ \mathrm{purchase}\ \mathrm{amount}\ }{\mathrm{actual}\ \mathrm{purchase}\ \mathrm{amount}}\right| $$
(2)

While field studies make it possible to investigate consumer behavior in a natural setting, they offer limited control over field conditions. Specifically, participants’ chosen payment mode could not be randomized. Since this could have led to potential self-selection effects, we took them into account by including a set of control variables capturing differences in individual and purchase characteristics.

To control for individual characteristics, participants reported demographic data such as age, gender, and school affiliation. They reported on their cafeteria visit frequency (1 = hardly ever, 2 = once per month, 3 = 2–3 times per month, 4 = once per week, 5 = 2–3 times per week, 6 = once per day, 7 = more than once per day) and their preferred payment at the cafeteria (cash/card). They evaluated the payment with card on two items adopted from Voss et al. (2003) capturing usefulness (1 = useful, 7 = useless) and pleasantness (1 = pleasant, 7 = unpleasant), reverse coded afterward so that a higher number on this scale indicates higher usefulness/pleasantness. Participants reported their general tendency to experience the pain of paying on the following item of the “Spendthrift-Tightwad” scale (Rick et al. 2008): “Which of the following descriptions fits you better?” (1 = Tightwad—difficulty spending money, 11 = Spendthrift—difficulty controlling spending).

To control for characteristics specific to the purchase, we recorded the number of items purchased, the purchase amount, and the cafeteria location (cafeteria1/cafeteria2). Participants reported whether they paid attention to the price list and/or to the point of sale display (yes/no). They further reported the purchase-specific pain of paying on one item, adopted from Thomas et al. (2011), in response to the question “How did you feel while paying today?” (1 = sad face [☹], 7 = happy face [☺], reverse coded afterward so that a higher number on this scale indicates greater pain of paying) and on two items adopted from Bagchi and Block (2011), asking “How painful was it to spend money at the cafeteria today?” (1 = not painful at all, 7 = very painful) and “How do you rate your purchase today?” (1 = not expensive at all, 7 = very expensive).

4 Results

4.1 Descriptive statistics

Analyses are based on 496 participants (n t1 = 244; n t2 = 252; 59.7% women; age M = 24.07, SD = 4.05). Cash payment from both time points was combined into one group. Descriptive statistics and group comparisons between payment modes are shown in Table 1. The results show that recall error differed significantly between payment modes, but purchase amount did not. Thus, we can rule out purchase amount as a driver of recall accuracy.
Table 1

Descriptive statistics (n = 496)

 

Payment mode

 

Cash

CardSF

CardMF

 

M

SD

M

SD

M

SD

p

n

268

 

110

 

118

  

Percent

(54.03)

 

(22.18)

 

(23.79)

  

Recall error (%)

2.50a

7.24

7.15b

16.74

4.95b

8.60

***

Individual characteristics

 Age

24.36

4.36

24.12

3.59

23.38

3.65

n.s.

 Gender (female, %)

65.67a

 

50.00b

 

55.08b

 

**

 School (%)

  Human Sciences

50.00a

 

20.00b

 

39.83a

 

***

  Law

21.64a

 

36.36b

 

20.34a

 

**

  Arts and Humanities

18.28

 

18.18

 

17.80

 

n.s.

  Other

10.07a

 

25.45b

 

22.03b

 

***

 Cafeteria visit frequency (%)

  2–3 times per month or less

17.91a

 

5.45b

 

9.32b

 

**

  Once per week

23.88a

 

9.09b

 

19.49a

 

**

  2–3 times per week

35.82

 

44.55

 

42.37

 

n.s.

  Once per day

14.93

 

22.73

 

16.10

 

n.s.

  More than once per day

7.46a

 

18.18b

 

12.71

 

**

 Preferred payment (card, %)

29.10a

 

89.09b

 

91.52b

 

***

 Usefulness

5.11a

1.99

6.07b

1.48

6.46b

0.98

***

 Pleasantness

5.60a

1.65

6.45b

1.07

6.57b

0.97

***

 Spendthrift

6.39

1.78

6.33

1.90

6.42

1.93

n.s.

Purchase characteristics

 Number of items

2.32

0.69

2.39

0.83

2.27

0.53

n.s.

 Purchase amount (US$)

2.70

1.01

2.80

1.15

2.83

1.00

n.s.

 Cafeteria (cafeteria1, %)

60.45a

 

30.00b

 

44.07c

 

***

 Price list (yes, %)

32.84

 

32.73

 

32.20

 

n.s.

 Display (yes, %)

40.30a

 

50.91a

 

64.41b

 

***

 Sad

2.57a

1.48

2.88

1.37

3.09b

1.47

**

 Painful

2.21

1.46

2.03

1.45

2.34

1.55

n.s.

 Expensive

2.81a

1.36

3.13b

1.48

3.25b

1.50

*

Usage frequency

 Student ID

 

 

3.06

1.93

 

 Public transport ticket

 

 

6.25

1.40

 

 Payment at the cafeteria

 

 

6.01

1.26

 

 Library card

 

 

3.19

2.23

 

Payment distinctiveness

 

 

1.84

1.63

 

Significance of group differences is shown in the last column. Means within a line that have different letters are statistically different from each other at or below the p < 0.05 level. For “Other” school affiliation, the answer categories Management, Economics and Social Sciences, Mathematics and Natural Sciences, and Medicine were grouped together due to cell size. For visit frequency of “2–3 times per month or less”, the answer categories hardly ever, once per month, and 2–3 times per month were grouped together due to cell size. Currency translation was 1 € ≙ US$1.137 on the last day of data collection (06/03/2016)

* p < 0.05. ** p < 0.01. *** p < 0.001

4.2 Payment mode

To answer research questions 1 and 2, we used a two-stage regression analysis with recall error as the dependent variable. Results are shown in Table 2, column (1).
Table 2

Hierarchical multiple regressions for recall error

Predictor

(1) All participants

(2) Multifunctional card users

(n = 496)

(n = 118)

B

SE

β

B

SE

β

Constant

0.005

0.055

 

− 0.023

0.116

 

Payment mode

 C 1 (cash vs. both card groups)

0.046

0.012

0.217***

  

 C 2 (cardSF vs. cardMF)

− 0.020

0.014

− 0.065

  

Payment distinctiveness

  

− 0.013

0.006

− 0.245*

Individual characteristics

 Age

0.001

0.001

0.031

− 0.002

0.002

− 0.086

 Gender (0 = male, 1 = female)

0.017

0.011

0.077

0.016

0.017

0.093

 Schoola:

  Law

0.011

0.021

0.047

− 0.004

0.038

− 0.017

  Arts and Humanities

− 0.004

0.017

− 0.013

− 0.042

0.034

− 0.187

  Other

− 0.025

0.019

− 0.089

− 0.058

0.036

− 0.282

 Cafeteria visit frequencyb

  2–3 times per month or less

0.003

0.021

0.008

0.041

0.037

0.139

  Once per week

0.038

0.019

0.144

0.056

0.032

0.258

  2–3 times per week

0.006

0.017

0.028*

0.059

0.028

0.341*

  Once per day

0.003

0.019

0.010

0.036

0.030

0.153

 Preferred payment (0 = cash, 1 = card)

0.003

0.013

0.014

− 0.006

0.030

− 0.018

 Usefulness

− 0.003

0.004

− 0.046

0.007

0.011

0.079

 Pleasantness

0.004

0.005

0.057

0.001

0.011

0.016

 Spendthrift

0.002

0.003

0.027

− 0.003

0.005

− 0.066

Purchase characteristics

 Number of items

0.006

0.008

0.043

0.029

0.016

0.181

 Purchase amount

− 0.001

0.005

− 0.013

0.006

0.010

0.064

 Cafeteria (0 = cafeteria1, 1 = cafeteria2)

0.002

0.017

0.010

0.044

0.031

0.253

 Price list (0 = no, 1 = yes)

− 0.019

0.010

− 0.086

− 0.040

0.019

− 0.220

 Display (0 = no, 1 = yes)

− 0.015

0.010

− 0.070

− 0.019

0.017

− 0.106

 Sad

− 0.009

0.003

− 0.121*

0.006

0.006

0.101

 Painful

0.000

0.004

− 0.004

0.007

0.006

0.133

 Expensive

0.004

0.004

0.054

− 0.011

0.007

− 0.195

B unstandardized regression coefficient, SE standard error, β standardized regression coefficient

aHuman Sciences used as reference category

bMore than once per day used as reference category

* p < 0.05. ** p < 0.01. *** p < 0.001

Payment mode was the first variable entered. To compare recall error between payment mode groups, we followed Cohen et al. (2003, pp. 332–341) and used contrast coding to combine payment mode groups according to our respective research questions. To compare the two card groups with the cash group, we constructed the code variable C 1 [cardMF = 1/3, cardSF = 1/3, cash = − 2/3]. To compare the multifunctional card group with the single-function card group, we constructed the code variable C 2 [cardMF = 1/2, cardSF = − 1/2, cash = 0]. The two contrast code variables (C 1 and C 2) for payment mode, entered at stage one, explained unique variance in recall error, with R 2 = 0.03, F(2, 493) = 8.33, p < 0.001. Specific comparisons for the payment mode groups showed that recall error was significantly lower in the cash group than that in both card groups, with B = 0.04, p < 0.001. Furthermore, the recall error of the single-function card group did not differ significantly relative to the multifunctional card group, with B = − 0.02, p = 0.11.

Control variables capturing the individual and purchase characteristics were entered at stage two but were not statistically predictive of recall error, with R 2 = 0.09, ΔR 2 = 0.06, ΔF(21, 472) = 1.50, p = 0.07. Furthermore, recall error remained significantly lower in the cash group than that in both card groups, with B = 0.05, p < 0.001, and did not differ between both card groups, with B = − 0.02, p = 0.15. This result indicates that the control variables did not account for the effect.

Our findings show that card payments decrease the recall accuracy of expenditures as compared to cash payments (research question 1). However, payment with a multifunctional card does not decrease recall accuracy of expenditures compared to a single-function card (research question 2).

4.3 Payment distinctiveness

In order to answer research question 3, we focused on participants’ usage of their multifunctional card. Therefore, only participants who used the multifunctional card for their purchase (n = 118) were used for the analyses. Preliminary analyses showed a negative correlation between payment distinctiveness and recall error, with r(116) = − 0.22, p = 0.02. Similar to payment mode, payment distinctiveness did not significantly correlate with the purchase amount, with r(116) = 0.05, p = 0.58.

We next conducted a two-stage regression with recall error as the dependent variable, shown in Table 2, column (2). Payment distinctiveness, which was entered at stage one, explained unique variance in recall error, with R 2 = 0.05, F(1, 116) = 5.75, p = 0.02, and as expected was negatively related to recall error, with B = − 0.01, p = 0.02. Control variables capturing the individual and purchase characteristics were entered at stage two but were not statistically predictive of recall error, with R 2 = 0.29, ΔR 2 = 0.24, ΔF(21, 95) = 1.51, p = 0.09. Furthermore, higher payment distinctiveness remained predictive of a significantly lower recall error, with B = − 0.01, p = 0.03. This result indicates that the control variables did not account for the effect.

Our findings suggest that for multifunctional cards, low payment distinctiveness reduces the recall accuracy of expenditures (research question 3).

5 Discussion

Our contribution to the literature is twofold. First, we add to the research on cash versus card payment, which is primarily based on laboratory settings (e.g., Prelec and Simester 2001; Runnemark et al. 2015), by studying actual payment transactions. In line with previous research suggesting that card payment is less transparent than cash due to decoupling between payment and consumption (e.g., Prelec and Loewenstein 1998; Soman 2001), we find that individuals paying with a single-function card and a multifunctional card are more inaccurate in their purchase amount recall than are those paying with cash (research question 1). Thus, our results provide added validity to existing research by documenting the recall bias for another industry setting (i.e., cafeteria instead of bookstore) and for another payment mode (i.e., the multifunctional card as a relatively new payment option). Although the field study lacked in randomization, we can rule out self-selection effects because the results showed the individual and purchase characteristics to be barely predictive in their explanatory power.

Second, we extend the theory on payment transparency by introducing multifunctionality as an important characteristic of payment modes and suggest that it may reduce payment transparency. We proposed that the multifunctional characteristic of payment modes introduces another form of decoupling between the device and its payment function. Hence, we expected a lower recall accuracy for individuals paying with a multifunctional card than for those with a single-function card. However, we did not find significant differences in recall accuracy between the two payment modes (research question 2). In direction, the multifunctional card actually showed a lower recall error. Comparing both card groups, participants using the multifunctional card paid more attention to the display (see Table 1). Although not predictive in subsequent analyses, it may be speculated that this points toward a possible source for the lower recall error, which may be detectable by methods that are more sophisticated, such as eye tracking.

Nonetheless, taking into account that individual usage patterns of multifunctional payment modes vary, the within-group comparison of multifunctional card users showed patterns that support our hypothesis. In line with the well-established effect of distinctiveness on memory, we find that if usage patterns make the payment function more distinct from the other non-payment functions, the expenditures are likely to be better memorized (research question 3). In sum, our findings suggest that multifunctionality in payment modes does not increase payment recall error per se but only alongside the high usage frequency of other non-payment functions, i.e., a low distinctiveness of the payment function.

We also find that the purchase amount did not differ significantly, either between payment modes or for each level of payment distinctiveness. Our university cafeteria setting restricted purchases to food items for immediate consumption and resembled Bagchi and Block’s (2011) cafe setting. While our results confirm Bagchi and Block’s (2011) findings that payment mode does not affect the purchase amount of items for immediate consumption, we show that even in our limited setting, payment mode affects the recall accuracy of past expenditures.

Our research has two limitations. The first limitation is the use of a multifunctional card. Future research could analyze multifunctional payment modes that are more complex, such as smartphones or smartwatches. These digital devices offer more non-payment functions, such as games, potentially further decreasing the distinctiveness of the payment function. The second limitation is the observation of a single purchase with the recall being measured right after payment. Therefore, future research could collect longitudinal data to study the recall accuracy over time for multiple purchases with different time delays after the purchase.

Our findings offer recommendations for marketing practitioners and policy makers. Results for the multifunctional card showed that a high usage of the non-payment functions leads to lower payment distinctiveness. Accordingly, marketers can decrease the transparency of a card payment by offering non-payment functions that are intended for frequent use (e.g., identification or transport ticket). On the other hand, policy makers can apply the knowledge of payment distinctiveness to increase payment transparency. Hence, payment designs that separate the payment function from non-payment functions or that emphasize the outflow of money, such as instant payment notifications and financial summaries, could increase financial awareness.

Notes

Acknowledgments

We thank Michael Blens, Marc Heise, Paula Risius, Leonie Schiedek, and Oliver Zabel for their help with data collection. We also thank Katherine Burson, Scott Rick, and the members of the research seminar in economic and social psychology of the University of Cologne for comments on previous versions of this manuscript.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Cologne Graduate School in Management, Economics and Social SciencesUniversity of CologneCologneGermany
  2. 2.Institute for Sociology and Social PsychologyUniversity of CologneCologneGermany
  3. 3.Department of Service ManagementAlpen-Adria-Universität KlagenfurtKlagenfurtAustria

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