Precautionary demand for cash | Encyclopedia MDPI

Precautionary demand for cash: Comparison

Please note this is a comparison between Version 1 by Grzegorz Maciejewski and Version 3 by Conner Chen.

We have recently seen two conflicting trends in the use of cash. The share of e-payments in retail transactions is steadily increasing, but the same upward trend is true for the share of cash in circulation or at least the cash share remained unchanged. This paper shows the significance of perceived risk for consumers’ precautionary demand for cash after they make a decision to use e-payments. We use data from a study involving Polish consumers. The main conclusions are as follows: surveyed consumers perceive a level of risk associated with card and mobile payments and continue to carry cash for precautionary reasons. Factors such as the consumer’s mental state, lack of trust in e-payments, and attitude to risk influence the decision to maintain cash reserves, while the consumer’s income and age may be considered the main determinants of the value of the cash reserve. Consequently, the decision to use e-payments does not necessarily mean that the demand for cash drops to zero. A degree of difficult-to-reduce, autonomous demand for cash may exist independent of the traditionally studied determinants, in particular, those related to the transaction demand.Niedawno widzieliśmy dwa sprzeczne trendy w wykorzystaniu gotówki. Udział płatności elektronicznych w transakcjach detalicznych systematycznie rośnie, ale ta sama tendencja wzrostowa dotyczy udziału gotówki w obiegu lub przynajmniej udziału gotówki na niezmienionym poziomie. W artykule przedstawiono znaczenie postrzeganego ryzyka dla ostrożnościowego zapotrzebowania konsumentów na gotówkę po podjęciu przez nich decyzji o korzystaniu z płatności elektronicznych. Korzystamy z danych z badania z udziałem polskich konsumentów. Główne wnioski są następujące: ankietowani konsumenci dostrzegają poziom ryzyka związanego z płatnościami kartowymi i mobilnymi i ze względów ostrożności nadal mają przy sobie gotówkę. Czynniki takie jak stan psychiczny konsumenta, brak zaufania do e-płatności i nastawienie do ryzyka wpływają na decyzję o utrzymaniu rezerw gotówkowych, natomiast dochód i wiek konsumenta można uznać za główne wyznaczniki wartości rezerwy gotówkowej. W konsekwencji decyzja o korzystaniu z e-płatności nie musi oznaczać spadku popytu na gotówkę do zera. Stopień trudnego do zredukowania, autonomicznego popytu na gotówkę może istnieć niezależnie od tradycyjnie badanych determinant, w szczególności związanych z popytem transakcyjnym.

precautionary demand for cash
electronic payments
consumer behaviour
perceived uncertainty
perceived risk
sustainable use of resources

1. Perceived Risk Associated with Card and Mobile Payments

In order for the theory of consumer risk to have any explanatory power, it is necessary to exceed a certain minimum threshold of perceived risk, i.e., the consumer must be aware of the possibility of an event that might be unfavourable. At the same time, this is a necessary condition to analyse risk in terms of the determinants of consumer behaviour.

Our research shows that respondents were aware that something might go wrong during a card or mobile payment and that the payment might not be effective despite having sufficient funds in their account. Certainly, this was not true for all respondents, but for a vast majority—nearly 80% of respondents said they perceived a risk of not being able to pay by card or phone.

Those respondents who perceived a level of risk associated with CMP were presented with the scales described above, i.e., the scale showing the level of importance attached to the inability to make a CMP despite having sufficient funds, and the level of likelihood of the inability to make a card or mobile payment despite having sufficient funds. The results are presented in Table 1.

Table 1. Types of perceived risk associated with card and mobile payments and their level (N = 377).

Specification	Importance of a Negative Consequence				Opinion on the Likelihood of Risk Occurrence				Additive Risk Model
Specification	Me	Mo	M	SD	Me	Mo	M	SD	Additive Risk Model
1. The payment terminal might fail	5	7	4.47	1.899	4	4	4.48	1.763	8.95
2. I might only learn of the absence of a payment terminal at the point of sale (POS) at the checkout	5	7	4.53	1.943	4	5	4.26	1.900	8.79
3. The Internet connection might be cut off	4	5	4.05	1.902	4	4	4.14	1.750	8.19
4. The payment terminal might be faulty	4	5	4.04	1.820	4	3	3.96	1.797	8.00
5. My bank might have a maintenance break	4	4	4.05	1.849	4	4	3.89	1.814	7.94
6. Other unpredictable problems might occur when making a card payment	4	4	3.88	1.733	4	4	3.64	1.683	7.52
7. The power might be cut off	4	1	3.74	1.977	3	3	3.53	1.728	7.27
8. My card might get blocked	4	1	3.70	2.043	3	2	3.26	1.806	6.96
9. I might enter the incorrect PIN	3	1	3.16	1.987	3	1	3.23	1.961	6.39
10. My card might be damaged when inserted into the payment terminal	2	1	2.92	1.994	2	1	2.60	1.600	5.52

Where: Me—median, Mo—modal value, M—arithmetic average, SD—standard deviation. Additive Risk Model: the arithmetic average of the opinions regarding the importance of negative consequences of card and mobile payments + the arithmetic average of the opinions regarding the likelihood of occurrence of negative consequences of card and mobile payments. Data source: Collected by this research.

In the context of making card or mobile payments, the surveyed consumers were mostly concerned about only learning of the absence of a payment terminal at a given POS at the register, as well as about a possible failure of the terminal. These consequences were also considered most likely, although the possibility of terminal failure was considered more likely. The data contained in Table 1 also show that the surveyed consumers have different attitudes towards negative consequences and the likelihood of their occurrence in real life. They also attach more importance to the significance of negative consequences than to the likelihood of their occurrence, which has already been observed in previous studies ^[1].

Then, using the ARM to measure the magnitude of the perceived risk associated with CMP, we determined that the greatest risk identified in our research is the risk of payment terminal failure (value 8.95). Also noteworthy are the relatively high values attributed to the risk associated with only learning of the absence of a payment terminal at the POS at the register (value 8.79) and the risk of the internet connection being cut off (value 8.19). In contrast, the lowest values were attributed by the respondents to the risk of damaging the card when inserting it into a payment terminal (value 5.52) and the risk of entering an incorrect PIN (value 6.39)—see Table 1.

Analysing the magnitude and types of consumer risk when making a card or mobile payment, we considered it useful to also draw attention to the measures taken by consumers to reduce the risks that they do not accept. Therefore, those respondents who declared making payments by card or phone and who perceived the risk associated with the possibility that the payment might not be effective despite having sufficient funds in their account were asked about how they deal with such potential threat.

The responses show that consumers use many different methods of reducing risk, depending on the consumer’s degree of involvement in the purchasing process (active and passive prevention strategies) and the magnitude of risk being reduced (strategies to reduce uncertainty and strategies to reduce the likelihood of negative consequences)—see Figure 1.

Sustainability 12 07977 g001 550

Figure 1. Strategies used by respondents to deal with the risk of being unable to pay by card or phone (N = 385, in %). Note: The percentage of responses does not add up to 100% because respondents could indicate more than one prevention strategy. Data source: Collected by the authors.

According to respondents, the most effective strategy for reducing the risk associated with card and mobile payments is carrying a certain amount of cash, as indicated by almost 8 out of 10 respondents. More than half of the respondents make sure that a given POS accepts e-payments before shopping and 1 in 3 always shop in the same familiar places (Figure 1).

The strategy of carrying a certain amount of cash is assessed by respondents as the most effective prevention strategy. Nearly 60% of respondents gave this strategy the highest rank among all strategies aimed at reducing the risk associated with card and mobile payments (Table 2).

Table 2. Effectiveness of used strategies to reduce the risk associated with card and mobile payments—respondents’ assessment (N = 385, in %).

Strategy	Rank I	Strategy	Rank II	Strategy	Rank III
Model 1.2				Model 1.3				Model 1.4		Model 2.1Model 1.5	Model 1.6	Model 1.7
Model 2.2				β	se	p	OR	β	se
Carrying cash	58.9	Making sure that the POS accepts card or mobile payments before shopping	26.4

We started with investigating the determinants of the decision to maintain a cash reserve. The response variable was an event in the form of making a decision on maintaining the cash reserve. We studied the significance of the variables obtained in the EFA as well as variables such as past experiences with technical problems; declared attitude to risk, age, and education; and declared level of income. Definitions of the variables are presented in Table 3.

Table 3. Definitions of vari

Variable	Definition
Model 2.3				p	OR	β	se	p	OR	β	se	p	OR	β	se	p	OR	β	se	p	OR	β	se	p	OR
β	se	p	OR	β	se	p	OR	β	se	p
Shopping in the same familiar places	20.6
PDC
OR
1PC1precautionary demand for cash; an event consisting of maintaining a cash reserve despite having made a decision to use CMP, 1—yes or sometimes yes; 0—no	−0.039	0.190	0.839	0.962																						Making sure that the POS accepts card or mobile payments before shopping	11.1
PDC_v	Carrying multiple payment cards	13.4	Carrying cash	15.0
1PC2volume of precautionary demand for cash; the range of the value of the cash reserve 1. PLN 50 and less; 2. PLN 51-100; 3. PLN 101 and more	0.652	0.221	0.003	1.920																						0.224	0.107	0.036	Shopping in the same familiar places	7.3	Making sure that the payment terminal is in working order at the POS before shopping	13.4	Shopping in places where banks or ATMs are also located	13.9
1.244	1PC1
1PC3risk of technical problems with the terminal	0.189	0.181	0.297	1.208
1PC3				−0.120		0.121	0.320	0.887								Carrying multiple payment cards	6.8
	1PC2Shopping in the same familiar places	13.1	Making sure that the POS accepts card or mobile payments before shopping	13.1
2PC1risk associated with how the consumer handles a transaction
2PC1	0.048			0.402	0.200	0.116	0.680	1.049	0.044	1.495					0.385	0.194	0.047	1.470															Shopping in places where banks or ATMs are also located	5.1	Shopping in places where banks or ATMs are also located	12.8	Making sure that the payment terminal is in working order at the POS before shopping	12.0
	1PC3
2PC2risk of other technical problems					0.504	0.200	0.012	1.655					0.484	Using a card blocking sleeve, which protects it against contactless theft	5.1
0.198	0.014	1.622									0.420	0.203	0.038
2PC2	−0.064	0.120	0.594	0.938	1.522							2PC1Carrying cash	9.5	Shopping close to home	7.5
		2PC3unusual purchase situation (commute, high value, new card)					0.310	0.173	0.073	1.363
2PC3	−0.156	0.115	0.176	0.855									Turning off Near Field Communication (NFC) immediately after making a mobile contactless payment	3.0	Shopping close to home	5.9
2PC4		Using a card blocking sleeve, which protects it against contactless theft	7.1

2PC4	0.025	0.192	0.897	1.025					−0.037							Making sure that the payment terminal is in working order at the POS before shopping	1.9	Using a card blocking sleeve, which protects it against contactless theft	3.0	Carrying multiple payment cards	0.8926.0
			Shopping close to home	0.8	Turning off NFC immediately after making a mobile contactless payment	2.4	Turning off NFC immediately after making a mobile contactless payment	4.5

Where: Rank I—the strategy that is the most effective in the respondents’ opinion; Rank II and Rank III—other highest ranked strategies in terms of effectiveness, in the respondents opinion. Data source: Collected by the authors.

The fact that respondents use prevention strategies might prove that they are extremely prudent, but it might also mean that they do not trust e-payments due to their perceived unreliability. The question is whether the perceived unreliability is linked to problems that they have actually experienced in the past. To answer this, respondents were also asked to indicate how many times in the last three years they had encountered problems when making a card or mobile payment despite having sufficient funds in their account. These responses indicated that 39.0% of respondents could not recall any such situation, 32.3% admitted that they encountered such a situation not more than once or twice, and 28.7% indicated that they had problems with e-payments at least three times. The most common reasons for the payment not being effective were: payment terminal failure (49.8%), internet connection being cut off and a related problem with data transfer (23.4%), or a failure/maintenance break at the bank (14.7%).

The collected research results do not justify such a high level of respondents’ prudence. However, as indicated earlier, nearly 80% of respondents perceive a risk associated with card and mobile payments and have worked out ways to deal with it. These results are the same as those obtained in the regression analysis (model 1.5).

2. Cash Reserves of Surveyed Consumers

Those respondents who declared making payments by card or phone and who perceived a risk associated with the possibility that the payment might not be effective despite having sufficient funds in their account were asked whether they also carried cash apart from their card/phone since they were aware of such risk. Respondents confirmed this, with 9 out of 10 (89.9%) answering “yes, always” (37.7%) or “yes, sometimes” (52.2%). Then, we asked them about the amount of cash they were carrying. The responses were in the range between PLN 10 (approximately EUR 2, ca. 0.42% of the modal and 0.24% of the median monthly salary in Poland before tax) and PLN 2000 (approximately EUR 400, ca. 84% of the modal and 49% of the median salary in Poland before tax) [76]. The median and the modal value amounted to PLN 100 (approximately EUR 20). We observed that the responses were clearly linked to the denominations of notes in circulation. Consequently, a cash reserve of PLN 20 was declared by 5.0% of the respondents, PLN 50—21.9%, PLN 100—32.7%, and PLN 200—14.9%. Therefore, the amounts corresponding to the values of the individual denominations represented almost ¾ of all variants of the responses.

3. Determinants of Cash Reserves of Surveyed Consumers

We then examined the determinants of cash reserves maintained by consumers who declared making payments by card or phone and who perceived a level of risk associated with the possibility that the payment might not be effective despite having sufficient funds in their account.

2PC2
perception of lack of mental comfort during payment



3PC2	0.018	0.123	0.882	1.018


					−0.289	0.138	0.037	0.749	−0.347	0.129	0.007	0.707

Data source: Collected by this research.

Models taking into account individual groups of variables identified during the factor analysis show that when making a decision to maintain cash reserves, considerable significance is attached to concerns related to handling e-payments (model 1.1), the perception of mental distress during e-payments, and unusual purchase situations (model 1.2), as well as trust in the e-payment system, understood as a product of trust in the banking system and technology (model 1.3). The perception of corruption in model 1.3 is also quite surprising. Model 1.4 shows the results of the backward stepwise regression for all variables identified during the factor analysis.

It is worth noting that maintaining cash reserves is not a result of past experiences with the unreliability of payment systems (model 1.5), but rather of the perception of associated risk (such perception does not necessarily have to result from past experiences). However, it is the level of acceptance of the risk from this group of variables that determines whether one maintains cash reserves (model 1.6). Those consumers who accept higher risk levels are less likely to do so.

Finally, model 1.7, which we treat as a baseline, takes into account all the tested variables which, in a statistically significant way, determine the decision to maintain a cash reserve despite having made the decision to use e-payments.

The most important results can be summarized as follows. Assuming the standard significance level at p < 0.05, the decision to maintain a cash reserve despite having made a decision to use e-payments depends on the:

mental state of the respondent, i.e., mental reaction to the purchase situation (stress, haste, bad mood); the higher the perception of this phenomenon, the more likely the consumer is to make the decision to maintain a cash reserve;

trust in electronic payment systems (this trust is the result of both trust in the banking system and in technology); this dependence is negative, i.e., the higher the trust, the less likely the consumer is to make the decision to maintain a cash reserve;.

attitude to risk; consumers declaring a higher level of risk acceptance maintain a cash reserve less frequently.

Some results indicate the role of the perception of corruption (the higher this perception, the less likely the consumer is to make the decision to maintain a cash reserve). This surprising result is interpreted under “Discussion”.

The results show that the decision to maintain a cash reserve is not dependent on the perception of the magnitude of the various types of risk associated with e-payments (payment terminal failure etc.) and consumer’s past experiences. It is worth noting that although the perception of specific types of risk has no significance, the very fact that such risk exists is the cause of maintaining a cash reserve, as indicated in our research. Age, education, and declared level of income are also insignificant.

As the second step, we analysed the determinants of the value of the maintained reserve. We divided the reserve values declared by the respondents into three ranges, organized by the value of the reserve, from smallest to largest (Table 5). Then, we calculated the ordinal regression. In principle, the research strategy was the same as in the first step; however, since the results were not dependent on the selection of variables for the model, we have only published two intermediate models and the final model. Models 2.1 and 2.3 have undergone basic testing (including the Test of Parallel Lines). Model 2.2., which does not pass some of the tests, was published for information only and it has not been the subject of further interpretation. The results are presented in Table 5.

Table 5. Ordinal regressio

Variable
Variable	1PC1	−0.164	0.116	0.156	0.848
1PC2	0.354	0.128	0.006	1.425

		0.114		0.744	0.964
2PC3	sales channel considered risky (internet, abroad)
	2PC4
3PC1							0.165	0.180	0.360	1.179					low level of trust, e.g., due to making a purchase for the first time

3PC1	−0.114	0.111	0.305	3PC2							−0.484	0.201	0.016	0.616	−0.473	0.207	0.022	0.623	−0.523	0.213	0.014	0.593	3PC1	trust in the state and its actions, e.g., in the economy and in the scope of cybersecurity
3PC3					3PC2	trust in the payment system, understood as a product of trust in the state and technology
			3PC3	risk of armed conflict
3PC4	perception of corruption
0.131	0.178	0.463	tech_prob	technical problem with the payment
unac_risk	risk of the transaction not being accepted despite having sufficient funds; 1PC1 + 1PC2 + 1PC3
att_risk	attitude to risk
gender	gender of respondents
age	age of respondents
income	subjective assessment of respondents’ own financial situation
education	declared higher education

Data source: Collected by this research.

Unfortunately, the selection of variables for the model is hindered by the fact that there is no commonly accepted theory of which its conclusions could be tested during the research. Our research is mostly exploratory. Therefore, our strategy was to test the significance of those groups of variables that were interesting to us, either on account of relations described in literature or due to their usefulness in achieving our research objectives. We then built a model through a backward stepwise regression (other methods of variable selection produced identical or similar results). Both model building strategies led us to the baseline model, which was subject to final interpretation. All models have undergone basic testing (including the Hosmer–Lemeshow test).

The results of the logistic regression have been presented in Table 4. Models 1.1–1.6 show the results of testing individual groups of variables. Model 1.7 is our final model. In each case, age, education, and declared income level were used as control variables. However, these were not statistically significant in any model.

Table 4. Logistic regression results.

Variable	Model 1.1
Variable
1.140




3PC3	−0.151	0.111	0.172	0.860									3PC4
3PC4	−0.090	0.113				−0.409	0.426	0.914	0.191	0.033	0.664	−0.523	0.204			0.010	0.593									−0.555	0.207	0.007	0.574
	Technical problem with the payment
technical problem with the payment													0.228	0.084	0.674							0.254	0.397	0.523	1.289
Unacceptance risk
Unacceptance risk						0.063							0.683	0.242	0.114	0.033	1.274	0.193	0.116	0.098	1.212
0.975					Attitude to risk
Attitude to risk												0.078	0.918	0.992
age	0.038	0.008	0.000	1.038	0.036	0.009	0.000	1.037	0.036	0.008	0.000	1.036
income	0.291	0.107	0.006	1.338	0.300	0.105	0.004	1.350	0.283	0.103	0.006	1.335
education	0.144	0.230	0.532	1.155	0.025	0.214	0.907	1.025

Data source: Collected by this research.

The most important results can be summarized as follows. Assuming a standard significance level at p < 0.05, the decision on the value of the cash reserve maintained despite having made a decision to use CMP does not depend on the assessment of the magnitude of risk (except for problems due to consumer’s mistakes), past experiences, and attitude to risk. The main determinants are income (higher income increases the likelihood of maintaining larger reserves) and age (the likelihood of maintaining larger reserves increases with age).

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Niedawne zmiany w stosowaniu płatności gotówkowych i elektronicznych są trudne do wyjaśnienia. Z jednej strony stale rośnie udział płatności elektronicznych w transakcjach detalicznych [ 1 ]. Rozprzestrzenianie się różnych form e-płatności, a także innowacji technologicznych, wypełnia istniejące luki w infrastrukturze (np. Płatności niskimi kwotami, peer to peer - P2P) i pozwala na wygodniejsze korzystanie z płatności elektronicznych i przyczynia się do coraz większej liczby konsumenci rezygnujący z płatności gotówkowych.

Jednocześnie jednak gotówka nadal pozostaje znaczącym środkiem płatniczym. Jego znaczenie jest różne w różnych krajach, ale często zachowuje swoje podstawowe znaczenie przy płatnościach w punkcie sprzedaży (POS) [ 1 , 2 , 3 ]. Co więcej, w ostatnich latach pojawiło się nowe zjawisko, polegające na tym, że wzrost wolumenu gotówki w obiegu lub przynajmniej udziału gotówki pozostał na niezmienionym poziomie. Tendencja ta jest widoczna zarówno przy analizie udziału środków pieniężnych w produkcie krajowym brutto (PKB), jak i udziału środków pieniężnych w szerokich agregatach monetarnych [ 4 , 5]. Udział gotówki w PKB rośnie w większości krajów co najmniej od początku kryzysu 2007+, ale w wielu przypadkach tendencja uległa zmianie jeszcze przed kryzysem (np. W strefie euro). Szwecja jest jednym z nielicznych krajów, w których udział gotówki w PKB spada.

Najnowsze dane potwierdzają te trendy. Na przykład w strefie euro stosunek pieniądza w obiegu do PKB wyniósł 10% w 2015 r. I 10,3% w 2019 r .; w Polsce było to 8,2% w 2015 roku i 9,9% w 2019 roku [ 6 ]. Ostateczny wpływ pandemii na obsługę gotówki nie jest znany i brakuje odpowiednich danych. Co prawda względy sanitarne mogą skutkować zwiększonym zainteresowaniem płatnościami elektronicznymi, ale niekoniecznie wpływa to na zapotrzebowanie na gotówkę. W pierwszym okresie pandemii nastąpił znaczny przejściowy wzrost popytu na gotówkę w wielu krajach w związku z obawami o funkcjonowanie systemów płatniczych [ 7]. Takie ostrożne zachowanie konsumentów dowodzi, że problem, o którym mowa w artykule, jest nadal aktualny. Aktualne dane nie wskazują na spadek ilości gotówki w obiegu, np. W strefie euro w grudniu 2019 r. Było 1270 mld euro, aw lipcu 2020 r. 1311 mld euro [ 8 ], aw Polsce wzrosło z 224 mld zł. w grudniu 2019 r. do 285 mld zł w lipcu 2020 r. [ 9 ].

Jobst i Stix [ 4 ] przeanalizowali determinanty obecnych trendów w wykorzystaniu gotówki i nie znaleźli satysfakcjonującego wyjaśnienia, biorąc pod uwagę tradycyjne podejścia badawcze. Determinanty korzystania z gotówki i e-płatności są zatem nadal otwartym i interesującym obszarem badań.

W naszym podejściu dążymy do ukazania znaczenia percepcji ryzyka dla prewencyjnego zapotrzebowania konsumentów na gotówkę po podjęciu przez nich decyzji o korzystaniu z płatności elektronicznych (płatność kartą lub telefonem - CMP). W większości dotychczasowych rozważań zakładano pełną substytucyjność użycia różnych form pieniądza, tj. Założenie, że decyzja o wyborze jednej formy pieniądza w danej transakcji oznacza rezygnację z innej formy pieniądza (w konsekwencji wybór płatności bezgotówkowej oznacza spadek zapotrzebowania na gotówkę). Dlatego rozprzestrzenianie się płatności bezgotówkowych powinno stopniowo prowadzić do zaniku gotówki z obiegu. Dotychczas mniej uwagi poświęcano popytowi zapobiegawczemu, który stanowi raczej niewielką część wartościowego całkowitego popytu, ale jednocześnie może wykazywać znaczną inercję.

Popyt na pieniądz ze względu na motyw ostrożności tradycyjnie definiuje się jako tę część popytu na pieniądz, która nie wynika z planowanych płatności, ale z niepewności co do wielkości płatności, która będzie musiała zostać dokonana. Z punktu widzenia przedmiotu naszych badań można to również interpretować jako tę część popytu na pieniądz, która (w przypadku wyboru między kilkoma formami płatności) wynika z utrzymywania dodatkowych sald pieniądza jako rezerwy w sytuacji awaryjnej, tj. w przypadku, gdy użycie preferowanej metody płatności nie jest możliwe. Można przypuszczać, że decyzja o wysokości posiadanego z tego powodu salda uzależniona jest m.in. od postrzegania ryzyka braku możliwości dokonania płatności preferowaną metodą. Należy zauważyć, że dotyczy to postrzeganego ryzyka, a nie ryzyka obiektywnego (które w rzeczywistości jest stosunkowo niskie). Zgodnie z naszą najlepszą wiedzą zagadnienie to nie zostało jeszcze zbadane. Istnieje niewiele badań, które pośrednio zajmowały się tym problemem, w tym Karoubi i wsp. [10 ] oraz Oney i wsp. [ 11 ]. W artykule opisujemy determinanty decyzji o utrzymaniu rezerw gotówkowych pomimo podjęcia decyzji o korzystaniu z e-płatności, a także zwracamy uwagę na czynniki determinujące wysokość tych rezerw.

Zagadnienie to jest również niezwykle interesujące w kontekście zrównoważonego rozwoju. W literaturze toczy się dyskusja na temat tego, który system płatności - gotówkowy czy bezgotówkowy - generuje wyższe koszty społeczne i wymaga zaangażowania większej puli zasobów [ 12 , 13 ]. Oba systemy mają zalety i wady, jeśli chodzi o włączenie społeczne i wpływ na środowisko. Na przykład płatności elektroniczne mogą pomóc w przezwyciężeniu zjawiska swobodnej jazdy w gospodarce i wspierać pobieranie podatków [ 14], ale mogą też zwiększać wykluczenie społeczne osób bez dostępu do infrastruktury technicznej (np. internetu). Wnioski nie są jednoznaczne, ale niezależnie od tego, który system jest droższy (pod względem ekonomicznym, społecznym i środowiskowym), stosowanie obu generuje wyższe koszty społeczne i wymaga większego wykorzystania zasobów. Dlatego decyzja o rezygnacji z jednego z tych systemów miałaby pozytywny wpływ na zrównoważone wykorzystanie zasobów.

Celem artykułu jest zbadanie, czy i dlaczego konsumenci mimo podjęcia decyzji o korzystaniu z płatności elektronicznych utrzymują rezerwy gotówkowe rozumiane jako środki pieniężne utrzymywane z pobudek ostrożnościowych. Chcemy także zrozumieć, jakie determinanty leżą u podstaw decyzji o utrzymaniu rezerw i dyktują ich wartość.

Zadaliśmy następujące pytania badawcze (RQ):

Pytanie 1: Czy konsumenci dostrzegają ryzyko związane z płatnościami kartowymi i mobilnymi (CMP), jakie obawy ono powoduje i jakie są strategie prewencyjne konsumentów w celu wyeliminowania tego ryzyka lub przynajmniej zmniejszenia go do akceptowalnego poziomu?

RQ2: Czy konsumenci utrzymują rezerwy gotówkowe pomimo podjęcia decyzji o zastosowaniu CMP? Jeśli tak, jaka jest wartość takich rezerw?

RQ3: Jakie uwarunkowania leżą u podstaw decyzji o utrzymaniu rezerw gotówkowych po podjęciu decyzji o zastosowaniu CMP?

Pytanie 4: Jakie determinanty decydują o wartości rezerw gotówki utrzymywanych pomimo podjęcia decyzji o zastosowaniu CMP?

Artykuł zorganizowany jest w następujący sposób: po przeglądzie literatury dotyczącej wyboru metody płatności, przedstawiamy nasze podejście badawcze, w tym zastosowane metody badawcze i dane, następnie omawiamy wyniki i przedstawiamy końcowe wnioski.