Innovations in the payment card market: The case of Poland

Electronic Commerce Research and Applications xxx (2015) xxx–xxx

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Innovations in the payment card market: The case of Poland Ewelina Sokołowska Department of Corporate Finance, University of Gdansk, Gdansk, Poland

a r t i c l e

i n f o

Article history: Received 17 March 2015 Received in revised form 17 July 2015 Accepted 17 July 2015 Available online xxxx Keywords: Card services Decision-making E-commerce Econometrics Empirical research Forecasting Payment cards Poland

a b s t r a c t The Polish payment card market is important given the current position of Poland as the largest Central European country. The purpose of this research is to determine directions for the development of the payment cards market in Poland. An econometric model describing some important aspects of this market is presented. Short-term forecasts of this market’s development will be estimated using a set of constructed empirical econometric models, with particular attention paid to the intensity measures of payment card use, as well as to the use of payment card devices. The findings from these models are meant to provide answers to the question of what can be expected from observing the current Polish market for electronic payments. At the same time, the methodology that is applied is universal and can be used to study the directions of development for electronic payments market elsewhere in the world. It should also be emphasized that selection of an appropriate method requires the testing and matching of such models, which will describe market development most effectively. Ó 2015 Elsevier B.V. All rights reserved.

1. Introduction The development of the market for financial services is connected with the development of the sector that produces financial innovations. The term innovation is derived from Latin word innovare, which means ‘‘to create something new.’’ Innovation also can be understood as the application of new knowledge in a production process (Begg et al. 1997). Various entities operating in such a market often attempt to diversify their products and services in a manner that corresponds with sudden and gradual changes in the economy (Tufano 2003). Financial innovations entail new financial instruments, new kinds of financial institutions and financial services, and new niche markets, as well as innovations associated with how financial services are delivered (Sokołowska 2010, 2014a) – including the move to the cashless society (Bank of Netherlands 2006). This definition is broad and has been evolving over time, and includes innovative products and processes. The products include derivatives (Sokołowska 2009, Wis´niewska 2003, 2007), for example, and the processes include novel ways of distributing the products, innovative ways of financial transaction settlement, and new payment techniques. Financial innovation also is a functional approach. New products and services allow financial services market participants to

E-mail address: [email protected]

reduce their costs and risks. Such innovations also can better satisfy the expectations of participants compared to traditional products and services. Improvements that are introduced thus should target the need to meet the higher expectations in the marketplace (Frame and White 2014). Some of the factors that have been driving financial innovations include: progressive globalization processes; the deregulation of financial markets; increased competition; and technological progress. New technologies seem to have increased scale economies in the production of financial services, creating opportunities to improve efficiency and increase value through consolidation (Berger et al. 1999, Berger and DeYoung 2006, Hancock et al. 1999, Hauswald and Marquez 2003, Li et al. 2006). One of the most visible trends in financial innovation is the development of the payment card market. The need for online payments was first addressed by using payment methods from the offline world for online payments (Vincent et al. 2010). In recent years, a shift away from traditional forms of payments, such as cash and checks, toward more innovative and electronic means of payment has been occurring. Past research by Bolt et al. (2008) indicates that the number of payments using payment cards increased by 140% in 11 European Union countries in the years from 1987 to 2004. The first plastic credit card appeared in 1958 in the United States, and banks quickly recognized the advantages of this modern means of payment. Magnetic stripe cards in Poland were introduced in the 1980s. The first payment cards in Poland

http://dx.doi.org/10.1016/j.elerap.2015.07.005 1567-4223/Ó 2015 Elsevier B.V. All rights reserved.

Please cite this article in press as: Sokołowska, E. Innovations in the payment card market: The case of Poland. Electron. Comm. Res. Appl. (2015), http:// dx.doi.org/10.1016/j.elerap.2015.07.005

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appeared in the late 1960s. For many years payment cards in Poland were unobtainable though, mainly due to the economic and political system that blocked modern and innovative solutions. Foreign banks were the ones to have issued these cards, but their usage was very limited. The economic changes that took place in Poland after 1989, and the entry of Poland into the European Union gave the country more opportunities for developing the payment card market. Serious development of the payment card market only began to take place in Poland in the 1990s though. This was possible mainly because of the reforms carried out after 1989. These changes shaped the national banking sector of the time, and initiated the transformation of retail banking. At the same time, banks began to offer their customers new products and services, including payment cards. Thus, despite the fact that such payment instruments in Poland were a kind of untested financial innovation, the card market grew very fast. Data from the (Polish National Bank 2014) indicate that, in the 3rd quarter of 2013, there were more than 863,000 cash withdrawals at the bank’s counters, for a total of 99 million PLN. Recent changes encourage the assessment of the payment card market in the near future, and the Polish case is an interesting subject for research. On the one hand, the average number of payment cards in Poland per user is lower compared to the average of the European Union countries. On the other hand, the Polish market for electronic payments is one of the most advanced and innovative markets in Europe, and it offers its participants a wide spectrum of payment tools, as well as access to the newest technologies in electronic payments. What is more, after the transformation of the system in Poland, very rapid development of the financial market was observed. The relatively rapid enrichment of the society, which has had significant impacts on the development of the market of financial instruments and services, also has taken place. The growth of gross domestic product (GDP), the enrichment of selected social groups, and the prospects of growing prosperity in the middle Eastern European countries have caused them to become a very attractive market segment for this type of services. For example, in 2013, the value of household savings in Poland exceeded 1 billion PLN (with 1 PLN = 0.33 USD). This growth indicates, that in the years 2001–2011, the savings of the Poles were increasing on average by 11.4% annually (Sokołowska 2014b). Currently, the payment card market in Poland operates within the national payments structure as part of the retail payment system. Cards play an important role by being an integral part of the payments capabilities in Poland, and the number of payment card subscribers has been increasing with each year. This is determined by universal access, and because payment cards are an excellent alternative to traditional cash. Modern computer and telecommunication technologies have fostered further development of the payment card market due to the innovative solutions that have emerged. The two largest payment organizations, MasterCard and VISA, have proved to be the most popular channels for the distribution of their services. Moreover, Poland was one of the first countries in Europe after Great Britain and France to introduce near field communication (NFC)-based card payments. The high level of the fees that banks charged for payment card services involving interchange payments hindered market development. The fees amounted to around 1.50% of the transaction value for credit card payments, and around 1.60% of the transaction value for debit card payments. The average in the European Union countries amounted to 0.84% and 0.70% for credit cards and debit cards, while in many others countries these services were available free of charge. An amendment of the Payment Services Act of August 30, 2013 reduced interchange fees to a maximum of 0.5% of the value of domestic transactions using a payment card (Sejm of Poland 2013). The new fee structure went into force starting

on January 1, 2014. These changes were meant to increase the availability of non-cash payment mechanisms for all interested consumers. In addition, the improvement of safety and the facilitation of lost payment card claims were other stimulators for market development. The Polish Banks Association launched a new system card cancellation system for this, for example. Analysis of the payment card market is particularly important given the current position of Poland, since it is the largest country in Central Europe. So studying the direction of development for payment cards in Poland is valuable and interesting – something that this research can achieve. To this end, an empirical model describing some important aspects of this market will be presented. Short-term forecasts of this market’s development, estimated using econometric analysis, also will be reported. Particular attention will be paid to intensity measures for payment card use, as well as the use of payment card devices. These models are meant to provide an answer to the question of what can be expected from observing the current Polish market for electronic payments. At the same time, the methodology is universal. It can be used to study development in electronic payments markets elsewhere in the world. It should also be emphasized that selection of an appropriate method requires the testing and matching of such models, which will describe market development well. We will use quantitative data related to the intensity of payment card usage and electronic payments infrastructure, compared with quantitative variables that describe the characteristics of the market. These include cards, payment-related devices, ATMs, and card-accepting merchants.

2. Literature Research on the subject of electronic banking appeared in the literature as early as the 1990s. Printing, distribution, and cash controls were estimated to cost a developed economy about 0.75% of annual gross domestic product (GDP), and an emerging economy 1–2% (Cobb 2003). As of mid-2015, the number of studies on the subject of electronic payments has grown much larges. The study performed by Asokan et al. (1997) analyzed electronic payment systems and focused on the issues related to security. Clemons et al. (1996), Kreltszheim (1999), Panurach (1996), Shon and Swatman (1998), and Westland et al. (1998) also conducted electronic payments-related research in the 1990s. Some other empirical studies include work by Borzekowski et al. (2006), Borzekowski et al. (2008), Hyytinen and Takalo (2004), Stavins (2002), van Hove et al. (2005), Zinman (2005a,b), and Jonker (2007). Most studies on the adoption and usage of a new electronic payment technology have been based on the technology acceptance model (TAM) (Davis 1989) and innovation diffusion theory (IDT) (Rogers 1995), without any explicit consideration of the two-sided market structure of the payments market. See-To et al. (2007) developed a model of a two-sided market, and found that a survival mass of merchants and consumers is required for a payment system to be sustainable, and a critical mass for the acceptance level to take off and remain stable. With regard to the directions for changes in the market for electronic payment forms, it is worth noting the study by Humphrey et al. (1996). They attempted to estimate how consumer payments break down between different payment mechanisms in various countries. They aimed to explain the determinants of these choices. The results of their study confirmed that dynamic growth of electronic payment forms is associated with a lower cost of their use compared to other payment forms. The authors also tried to explain why payment systems differ across various countries. Major factors influencing those differences reflect the variety of the options available to consumers, as well as the institutional

Please cite this article in press as: Sokołowska, E. Innovations in the payment card market: The case of Poland. Electron. Comm. Res. Appl. (2015), http:// dx.doi.org/10.1016/j.elerap.2015.07.005

E. Sokołowska / Electronic Commerce Research and Applications xxx (2015) xxx–xxx

and cultural differences among the countries. Their differences can be explained based on cultural and institutional factors. For instance, the usage of non-cash payment systems depends on per capita income, the availability of new payment systems, as well as the prevalence of volatility. Research analyses of the increase in transactions using debit and credit cards has assessed the influence of demographic factors on the development of electronic payment transaction-making in the market. Some interesting studies on customer behavior in the payment card market were conducted in the U.S., for example. Using the Survey of Consumer Finances (SCF), Kennickell and Kwast (1997) (SCF 1995), Stavins (2002) (SCF 1998) and Zinman (2005a,b) (SCF 2001) have shown that using new technologies and electronic payments is typical for young and well-educated customers. These studies have found that there is no linear relationship between the number of payment card transactions and the level of income though. Additionally, no significant increase in payment transactions has been observed for the wealthiest households. Klee (2006), Carow and Staten (1999), and Rysman (2004), who studied debit card use and diffusion, extended these research findings. Rysman (2004) focused on the role of demographic factors that influence credit card brands. Borzekowski et al. (2008) considered the rapid increase in the number of debit card transactions, which exceeded the number of credit card transactions, and investigated the patterns, preferences and price responses of debit card consumers using a nationally representative consumer survey. Amromin and Chakravorti (2009) demonstrated that greater use of debit cards caused a decline in demand for smalldenomination bank notes and coins used to give change. Payment cards offer benefits to consumers and merchants in terms of safety and income insurance. Besides that, payment cards played an important role in the overall success of the Single European Payment Area (SEPA). Moreover, Hussain et al. (2007) noted that the trends in the electronic payments markets of the developed countries constitute a sort of a behavior pattern for the developing countries also. They investigated the factors affecting emergence and development of e-business in China, India and Pakistan, using a logit model for payment method preferences. The first study of European payment systems scale economies study was carried out by Khiaonarong (2003). Beijnen and Bolt (2009) investigated economies of scale for the European payments processing industry also. They suggested that the creation of SEPA will stimulate consolidation and mergers among these organizations, similar to what has been observed elsewhere in the world. Some details and empirical regularities will give the reader with additional understanding of European payments. Fig. 1 presents data on card payment penetration across Europe. Notice in particular that a large group of people in Poland remained outside the market for non-cash payments as recently as 2012. That year, the indicator for the number of payment cards per capita in Poland was 0.86. In the European Union, in contrast, only Romania had a lower rate of 0.64, while the average number of cards that people held in other European Union countries was 1.46. Despite rapid growth of non-cash transactions using cards, and in spite of the increase of devices supporting payment cards per million inhabitants, Poland still was significantly lower compared to the more developed European Union countries. This implies that the market will grow at a rapid pace. The present research views Poland as a developing market, but we apply knowledge on card payments based on trends in other developed countries. Our purpose is to develop new knowledge that goes beyond existing studies of these issues, such as Hussain et al. (2007). We also will attempt to verify the hypothesis that Poland, as a developing market, will follow the path of developed markets in the growth of its card market. The quantitative and

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qualitative methods that we use will require multiple tests in order to select the models that most effectively describe the mechanisms that underlie the Polish market.

3. Dataset and methodology Currently there are several kinds of payment cards in Poland. The following cards can be distinguished: credit cards, debit cards and charge cards. The differences depend on how they accomplish funds settlement. It is also possible to classifying the cards according to their data storage technologies, including: magnetic stripe cards; magnetic stripe and microprocessor cards; and virtual cards. Virtual cards are intended for Internet payments only, or for other payments not requiring the physical presentation of the card, such as through the use of a mobile phone. This classification is important based on how payment card data acquisition works. Our analysis uses statistical data regularly provided by the Payment Systems Department of the Polish National Bank. The data refer to Polish payment cards, and are from banks and non-bank entities. The Electronic Payment Instruments Act of September 12, 2002 is the legal basis for obligatory reporting, and this enabled us to acquire the data for this study. The Act came into force after October 12, 2003, and the following organizations were required to report to the Polish National Bank:  Payment card issuers – including banks and non-bank entities – must report on: the type and the number of payment cards issues; the operations that use those cards; the monetary value of payments that are run through the cards; the number of ATM machines in the market; and the number and the monetary value of ATM transactions.  Settlement agents also must report information regarding the card-accepting network, in terms of: the number of accepting merchants and organizations; the number of retail locations and service points; the number of devices that support electronic payment transactions; and the transactions executed in this network. The bank acquires up-to-date information from the banking sector, non-bank entities and settlement agents. For the purpose of capturing the most important variables to characterize Poland’s payment card market, we selected the period from the 4th quarter of 2004 till the 4th quarter of 1013 as the period for analysis with data from the Polish National Bank (2015). This enabled us to acquire data on a quarterly basis. Settlement agents, who fall under the 2003 Act, also provided relevant data. Secondary data used in the study are analyzed though the use of the methodology of statistics and econometrics for the description and analysis of changes in the payment card market (Wis´niewski 2009, 2013). In order to show the dynamics of the changes in the payment card market, we will present some of the data to enable the reader to become familiar with the payment card market during the study period. In addition to graphical methods that allow visual assessment of the changes that have been taking place, we also apply a number of econometric models with different functional forms. The basic model used is linear, as follows:

yt ¼ a0 þ a1 xt1 þ . . . þ aj xtj þ . . . þ ak xtk þ gt

ð1Þ

where: yt is the model’s dependent variable; xt1, . . ., xtj, . . . xtk represent the model’s explanatory variables; a0, a1, . . ., aj, . . ., ak are the model’s structural parameters; gt is a random component; t (t = 1, . . ., n) representing the n periods of time t for the observations; and j (j = 0, 1, . . ., k) represents the number j of explanatory variables, with 0 as the intercept term.

Please cite this article in press as: Sokołowska, E. Innovations in the payment card market: The case of Poland. Electron. Comm. Res. Appl. (2015), http:// dx.doi.org/10.1016/j.elerap.2015.07.005

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E. Sokołowska / Electronic Commerce Research and Applications xxx (2015) xxx–xxx

Fig. 1. Number of payment cards per capita in Europe, 2012. Source: Own elaboration via data that are available at the following URL: www.ipso.ie/?action=statistics& sectionName=EUStatistics&statisticCode=EU&statisticRef=EU05.

In addition, seasonal dummy variables, a time variable, and autoregressive variables with lags of 1, 2, 3 and 4 quarters are included among the variables. Time-series data are used to represent the quarters of a year. We also will estimate a non-linear trend model in the following hyperbolic form:

yt ¼ a0 ½ðt  a2 Þ=ðt þ a1 Þ þ gt

ð2Þ

The signs of Eq. (2) are the same as in Eq. (1), while the variable t in Eq. (2) represents the time period and also acts as the subscript t on the other variables. The methodology allows the estimation of the dependent variable in each empirical model, which underpins decision-making in the business entities concerned – mainly banks. It also supports forecasting future values of the variables that are considered, which makes it a useful tool in decision-making. Short-term forecasts of the characteristics of the payment card market in Poland, up until the end of 2015, will be presented. Tables 1–3 summarize the data on the payment card market (see Table 4).

4. Results and findings The results of this study confirm that the decade between 2004 and 2013 was a period of dynamic development in the Polish payment card market. In the 4th quarter of 2013, the value of transactions using payment cards reached 113.6 billion PLN and was higher by approximately 163.3% compared to the value of such transactions in the 4th quarter of 2004. We will present information on the intensity processes of payment card transactions in the period from the 4th quarter of 2004 till the 4th quarter of 2013. The transactions using debit cards were dominant, exceeding 90% of overall payment card transactions. We also will analyze the share of credit card transactions, which accounted for just a small percentage in each of the periods that were analyzed. The quarterly time-series of the monetary value of credit card transactions per credit card supports very specific assessments. Up until 2009, there was a downward trend of this variable. However, after 2009, a trend of dynamic growth in the monetary value of transactions per credit card appeared, reaching nearly

Please cite this article in press as: Sokołowska, E. Innovations in the payment card market: The case of Poland. Electron. Comm. Res. Appl. (2015), http:// dx.doi.org/10.1016/j.elerap.2015.07.005

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E. Sokołowska / Electronic Commerce Research and Applications xxx (2015) xxx–xxx Table 1 Payment cards and transactions in Poland, 2004–2013. Year, quarter

Payment cards

Debit cards

Credit cards

Overall transactions

Debit card transactions

Credit card transactions

2004 2005 2005 2005 2005 2006 2006 2006 2006 2007 2007 2007 2007 2008 2008 2008 2008 2009 2009 2009 2009 2010 2010 2010 2010 2011 2011 2011 2011 2012 2012 2012 2012 2013 2013 2013 2013

16,911 18,188 18,844 19,492 20,370 21,169 21,904 22,633 23,848 24,344 24,919 25,910 26,496 27,283 28,195 29,166 30,276 31,080 31,745 32,486 33,213 32,803 32,207 32,095 31,984 31,956 32,886 31,727 32,045 32,031 32,674 33,167 33,291 33,644 34,402 34,897 34,659

14,283 14,574 14,899 15,288 15,369 15,909 16,194 16,483 16,944 17,221 17,516 17,916 18,256 18,793 19,193 19,814 20,456 20,974 21,392 21,621 21,981 22,191 22,226 22,619 22,752 23,092 24,137 24,233 24,785 24,969 25,743 26,312 26,550 27,037 27,767 28,299 28,236

1996 2995 3344 3613 4384 4695 5137 5600 6354 6601 6893 7493 7813 8071 8571 8927 9405 9701 9955 10,476 10,858 10,249 9623 9137 8901 8536 8424 7167 6949 6770 6631 6554 6448 6304 6334 6302 6134

173,848 175,140 195,511 196,601 208,937 207,503 229,293 230,161 243,410 247,003 268,939 271,337 288,470 279,393 314,769 313,139 324,960 317,580 352,783 357,991 365,972 358,000 384,878 398,691 407,934 402,316 449,028 450,306 470,655 461,140 497,781 505,599 519,271 508,556 559,349 580,637 591,464

154,923 155,808 173,119 172,885 183,655 181,590 200,379 199,600 211,349 214,543 233,474 233,499 247,656 239,874 270,729 266,999 277,236 271,562 302,739 304,456 313,376 307,685 332,956 344,792 354,162 350,633 393,162 394,129 413,810 405,800 439,428 445,442 457,900 449,949 492,693 510,253 522,072

13,448 14,357 16,957 18,398 19,986 20,868 23,728 25,713 27,288 28,037 30,855 33,426 36,388 35,459 39,676 42,004 43,628 42,280 46,123 49,610 48,578 46,837 48,344 50,419 50,277 48,403 52,320 52,732 53,394 52,161 55,037 56,938 58,111 55,542 63,384 67,213 66,151

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

1500 PLN quarterly. We will study this based on an empirical estimation model. The variables in our estimation model include: #Merchants is the number of merchants that accepted payment cards in a period; CreditCardTransValuet1 is the mean of the monetary value of credit card transactions delayed by 1 quarter; and Q2 and Q3 are dummy variables equal to 1 during the 2nd and 3rd quarters, and 0 during the 1st and 4th quarters. In addition: R2 represents the typical explanatory capability of the model with respect to the dependent variable; volatility, Vol, is a random coefficient in percentage terms for the regression standard error, StdErr, relative to the mean of the variable CreditCardTransValuet; and e represents the 0 mean regression residuals. The estimated empirical model describing the quarterly volatility of the monetary value of credit card transactions has the following form:

increase in the mean transaction value for credit cards by 1 PLN in a previous quarter, there also was an increase in transaction value in the current quarter by almost 0.7 PLN. What is more, there were seasonal fluctuations indicating that the value of credit card transactions in the 2nd quarter of each year increased on average by over 0.09 PLN for cards during the 2nd quarter, and on average by 0.08 PLN during the 3rd quarter. Eq. (3) explains the variation in the variable CreditCardTransValuet with high accuracy, since almost 95% of the variation is described by the explanatory variables in this equation. Eq. (3) also can be used to estimate the future CreditCardTransValue, however, it is necessary to obtain information for the estimation of the number of card-accepting merchants, #Merchants. With this in mind, we constructed an autoregressive model with seasonal fluctuations to describe this volatility from period to period of the variable #Merchants, as follows:

CreditCardTransValuet ¼ 0:147 þ 0:004 #Merchantst

#Merchantst ¼ 5:655 þ 0:566 #Merchantst1

ð3:119Þ

ð1:140Þ

ð5:786Þ

þ 0:320 #Merchantst3 þ 0:410 t þ t

þ 0:693 CreditCardTransValuet1

ð2:412Þ

ð9:725Þ

þ 0:094Q 2 þ 0:080Q 3 þt ð3:710Þ

ð4:182Þ

ð2:129Þ

ð4Þ

ð3Þ

ð3:264Þ

R2 ¼ 94:6%; StdErr ¼ 0:060; Vol ¼ 6:63% Eq. (3) indicates that an increase in the number of merchants who accepted credit cards during the periods that were considered resulted in an increase of the average monetary value of transactions for credit cards by 4 PLN. There was a strong inertial effect in transaction value though. This indicates that, along with an

R2 ¼ 97:8%; StdErr ¼ 3:587; Vol ¼ 3:71% Seasonal fluctuations were eliminated in Eq. (4), while a positive linear trend and autoregressive terms with one and three period lags remain. There is high descriptive accuracy of the variable #Merchantst, as confirmed by the value of R2 = 97.8%. The information presented in Table 2 allows the variable CreditCardTransValue to be estimated. Forecasts of the quarterly average monetary value of credit card transactions are presented in Table 5. The

Please cite this article in press as: Sokołowska, E. Innovations in the payment card market: The case of Poland. Electron. Comm. Res. Appl. (2015), http:// dx.doi.org/10.1016/j.elerap.2015.07.005

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Table 2 Card transaction value; merchants, branches, payment card devices, Poland, 2004–2013. Year, quarter

Value of transactions (million PLN)

Value of debit card transactions (million PLN)

Value of credit card transactions (million PLN)

Merchants

Branches

Payment card devices

2004 2005 2005 2005 2005 2006 2006 2006 2006 2007 2007 2007 2007 2008 2008 2008 2008 2009 2009 2009 2009 2010 2010 2010 2010 2011 2011 2011 2011 2012 2012 2012 2012 2013 2013 2013 2013

43,165 41,556 46,358 48,266 51,066 47,780 54,078 56,683 60,399 59,054 65,523 68,437 72,145 66,639 77,111 79,741 80,798 74,336 83,691 86,197 86,188 79,794 87,866 92,719 93,443 86,469 98,210 100,659 103,698 95,215 103,494 106,432 106,009 97,941 107,867 113,666 113,634

38,788 37,408 41,616 43,331 45,749 42,679 48,299 50,743 54,111 52,932 58,815 61,316 64,381 59,389 68,894 71,169 72,030 66,216 75,061 77,275 77,536 71,677 79,353 83,912 84,545 78,260 89,211 91,580 94,393 86,384 94,256 97,045 96,397 89,116 97,894 103,294 103,312

2463 2436 2891 3148 3518 3335 3902 4176 4528 4481 5024 5547 6200 5842 6648 7104 7292 6806 7262 7588 7258 6879 7218 7523 7596 6996 7670 7785 7965 7613 7956 8180 8384 7653 8722 9215 9113

68,611 70,446 72,824 74,415 76,199 74,101 74,627 76,105 78,024 78,616 64,932 67,359 80,092 71,280 74,626 77,485 81,867 85,080 87,266 90,372 92,989 95,707 99,835 101,897 104,127 105,691 109,442 110,198 114,214 120,419 123,991 126,450 130,440 130,272 133,181 136,147 139,808

118,783 121,400 126,105 129,655 133,796 129,321 130,090 133,416 136,330 138,846 121,740 128,279 135,502 139,319 143,916 146,782 153,070 161,832 165,989 170,855 175,629 177,441 184,876 188,419 189,064 198,088 201,916 202,680 210,888 220,579 229,246 233,443 237,409 242,453 250,518 253,355 261,293

143,230 146,791 151,833 158,392 165,924 161,514 163,269 169,136 176,475 170,836 163,248 176,508 186,610 192,922 198,226 203,084 212,338 213,675 217,363 223,069 230,576 228,969 241,944 245,438 252,652 252,118 259,733 261,191 267,407 274,979 285,356 293,440 290,461 298,380 311,820 310,542 326,346

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

information in Table 5 allows inferences to be made: for example, a slight increase in the value of transactions per credit card, up to around 1691 PLN in the second quarter of 2015, can be expected in the future. The related forecast is shown in Fig. 2, which depicts the estimated number of merchants accepting cards in 2014 and 2015 (see Fig. 3). Also, forecast estimations of the variable CreditCardTransValue indicate a decrease in the value of the average monetary value of credit card transactions, as a result of seasonal fluctuations during the related trimesters (4th quarter 2014 and 1st quarter 2015). Thus, a systematic increase in the value of the forecasted variable cannot be expected. These forecasts are acceptable since the average prediction errors are small. The characteristic of intensity of transactions using major debit cards is different. An upward trend in the values transactions for debit cards is observed, and the increments diminish. This signifies that the process is approaching the saturation level. The following empirical model of a hyperbolic trend illustrates this process:

CreditCardTransValuet ¼ 3633:6½ðt þ 1:0297Þ=ðt þ 1:974Þ þ et ð5Þ In Eq. (5), as before, CreditCardTransValuet represents the monetary value of quarterly transactions per debit card, and et represents the regression residuals. The coefficient a0 = 3,633.6 PLN, which is the saturation level. It follows that the average monetary value of quarterly transactions per debit card is about 3633 PLN. Thus, we can expect this variable to become stable when the

saturation level is reached.The estimated empirical model describing the volatility of the quarterly average monetary value of transactions for debit cards is as follows:

DebitCardTransValuet ¼ 3:244 þ0:242 DebitCardTransValuet1 ð9:696Þ

ð3:081Þ

 0:0097 #Facilitiest þ0:056t 0:324Q 1 þ t ð6:669Þ

ð7:408Þ

ð11:94Þ

ð6Þ R2 ¼ 96:6%; StdErr ¼ 0:064; Vol ¼ 1:91% The variable DebitCardTransValue is characterized by a first-order autoregressive lag and by seasonal fluctuations during the first year. Along with an increase in the average monetary value of transactions for debit cards by 1 PLN in the previous quarter, there was an increase on average of that value in the current quarter by 0.242 PLN. During the first quarter of the year, there is a decrease in the value of the variable DebitCardTransValuet by around 0.324 PLN relative to the systemic component. A positive trend seems to be occurring, which signifies an average quarterly increase in the monetary value of transactions for debit cards of about 0.056 PLN. In addition, the coefficient of random volatility, Vol, as a percentage of the standard error of the arithmetic average of the dependent variable is 0.064; and the fit of the model is very good, as signified by R2 = 96.6%. The number of the facilities, #Facilities, where payment card payments can be used is a significant factor that influences DebitCardTransValue. As a result, with an increase in the number of such facilities by 1000, there is an associated decrease in the

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E. Sokołowska / Electronic Commerce Research and Applications xxx (2015) xxx–xxx Table 3 Payment card market in Poland: non-cash transactions, ATMs, and cash withdrawals. Period

Number of non-cash transactions

Value of non-cash transactions (million PLN)

Number of ATMs

Cash withdrawal transactions

Value of cash withdrawals (million PLN)

2004 2005 2005 2005 2005 2006 2006 2006 2006 2007 2007 2007 2007 2008 2008 2008 2008 2009 2009 2009 2009 2010 2010 2010 2010 2011 2011 2011 2011 2012 2012 2012 2012 2013 2013 2013 2013

56,653 55,793 64,824 67,590 77,165 77,385 88,335 92,389 102,070 101,910 113,569 117,783 129,863 130,110 146,199 147,942 158,906 154,490 175,119 179,980 191,689 187,765 203,202 213,789 225,859 221,063 245,502 251,319 270,310 270,261 295,000 301,713 318,844 314,061 353,311 365,219 392,298

7593 6732 7890 8359 9765 8466 10,254 10,916 12,284 11,276 13,236 14,003 15,561 14,575 16,858 17,257 18,550 16,184 19,099 19,571 20,918 18,804 20,824 22,208 24,037 21,413 24,594 25,141 27,858 25,386 28,145 28,762 30,645 27,541 30,541 33,579 36,474

8054 8263 8467 8544 8776 8964 9148 9400 9938 10,269 10,578 11,098 11,542 12,174 12,670 13,208 13,878 14,477 14,548 15,353 15,714 16,256 16,017 16,281 16,463 16,508 16,953 17,057 17,500 17,761 17,950 17,979 18,667 18,665 18,759 18,795 18,903

117,638 115,656 128,678 126,993 131,517 127,543 140,846 141,171 143,423 141,013 156,129 154,520 156,033 145,798 162,900 159,773 164,102 158,829 172,682 169,401 165,324 160,004 170,830 168,904 169,298 164,751 182,358 178,497 177,519 174,227 190,984 190,110 187,080 180,798 197,512 198,185 195,210

37,294 33,623 37,802 38,988 40,897 37,898 43,465 46,572 48,013 46,448 52,002 54,656 55,082 49,928 60,682 59,278 60,447 56,197 62,338 63,034 61,285 57,539 63,121 65,094 65,393 60,445 68,215 69,916 69,139 64,517 71,567 73,612 71,388 66,019 72,901 76,643 76,716

Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Table 4 Quarterly forecasts of number of merchants, 2014–2015 (000s). Year, quarter

Forecasts

Average prediction error

95% confidence interval

2014 2014 2014 2014 2015 2015 2015

142,883 145,980 149,312 152,589 155,843 159,158 162,491

3,5872 4,1215 4,2785 4,6402 4,9298 5,1006 5,2673

135,557–150,209 137,563–154,397 140,574–158,050 143,113–162,066 145,775–165,911 148,741–169,575 151,733–173,248

Q1 Q2 Q3 Q4 Q1 Q2 Q3

average monetary value of debit card transactions by around 9.7 PLN. This may signify an increase in the frequency of payments using payment cards instead of cash payments. The high accuracy of the model for the dependent variable makes it appropriate for forecasting and estimation, but we also need to forecast the value of #Facilitiest. To do this, we constructed an autoregressive trend model with seasonal fluctuations that describes #Facilitiest:

#Facilitiest ¼ 8:790 þ 0:917 #Facilitiest1 þ 0:480t þt ð1:319Þ

ð13:45Þ

ð1:747Þ

ð7Þ

Source: own calculations using the GRETL package.

R2 ¼ 99:1%; StdErr ¼ 4:364; Vol ¼ 2:49% Table 5 Quarterly forecasts, monetary value of transactions per credit card, 2014–2015 (000s PLN). Forecast period

Forecasts

Average prediction error

95% confidence interval

2014 2014 2014 2014 2015 2015

1.461807 1.551484 1.613632 1.589816 1.586275 1.691242

0.060016 0.073023 0.078509 0.081013 0.082188 0.082747

1,339,403–1,584,211 1,402,553–1,700,416 1,453,511–1,773,752 1,424,589–1,755,042 1,418,651–1,753,899 1,522,478–1,860,006

Q1 Q2 Q3 Q4 Q1 Q2

Source: Own calculations using the GRETL package. Too short of a time-series applied in the GRETL package allowed the forecast estimations for a single credit card only up to the second quarter of 2015. This was caused by autoregression, which decreases the number of the degrees of freedom in the regression equation.

The volatility of the #Facilities variable is described by one-lag autoregression and a linear trend. The model fits the data well, based on R2 = 99.1%, which allows Eq. (7) to be used to estimate the future value of this variable. The forecast results are presented in Table 7. Forecasts for Poland suggest a further increase in the number of #Facilitiest. Payment cards are being used more commonly now, as more new offerings become available on the market, which must adapt to the needs of customers who would like to pay using a payment card. As a result, it also is likely that changes in DebitCardTransValuet will occur during subsequent periods. Our forecasts of this variable are presented in Table 6 and Fig. 4. The estimates indicate that the quarterly forecast of the average monetary value of transactions for debit cards during the quarters of 2014 and 2015 will oscillate around the saturation level

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Fig. 2. Quarterly forecasts of number of merchants, 2014–2015.

Fig. 3. Quarterly forecasts of number of facilities, 2014–2015 (000s).

established by Eq. (5). The forecasts are 3363.38 PLN in the first quarter of 2014 up to the highest value of 3704.40 PLN during the fourth quarter of 2014. The next characteristic of development intensity in the payment card market is the quarterly average monetary value of ATM withdrawals for payment card (ATMTransValuet). Eq. (8) describes this process:

ATMTransValuet ¼ 0:376 þ 0:620 ATMTransValuet1 ð0:550Þ

ð3:668Þ

þ 0:350 ATMTransValuet2 0:017 t ð1:929Þ

ð2:706Þ

þ 0:0078 #Merchantst  0:323 Q 1 ð2:386Þ

ð5:138Þ

þ 0:384 Q 2 þ 0:292 Q 3 þ t ð4:020Þ

ð3:021Þ

ð8Þ

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R2 ¼ 0:935; StdErr ¼ 0:119; Vol ¼ 2:82% Eq. (8) contains a traditional autoregression, a trend, seasonal fluctuations, and variables characterizing the development of non-cash card payments. We evaluated a regression that included the number of accepting merchants, the number of devices executing payment card transactions, and the number of facilities handling payment card payments. Only the #Merchants turned out to be statistically significant though. Table 8 and Fig. 5 present forecasts of the average quarterly monetary value of ATM withdrawals Table 6 Quarterly forecasts, monetary value of transactions per debit card, 2014–2015 (000s PLN). Year, quarter

Forecasts

Average prediction error

95% confidence interval

2014 2014 2014 2014 2015 2015

3,363,378 3,619,477 3,685,414 3,704,398 3,389,437 3,638,897

64,349 66,204 66,311 66,317 66,318 66,318

3,232,137–3,494,619 3,484,453–3,754,501 3,550,172–3,820,656 3,569,143–3,839,653 3,254,181–3,524,692 3,503,642–3,774,153

Q1 Q2 Q3 Q4 Q1 Q2

Source: Own calculations using the GRETL package.

during 2014 and 2015. A systematic increase in the value of the variable ATMTransValuet can be expected. This likely results from the increasing wealth of payment cardholders, and from their increasing habituation to substituting payments with payment cards for cash transactions (see Fig. 6). Finally, the intensity mechanism of using payment card transaction devices, especially the average monetary value of quarterly non-cash transactions on card-related devices, NonCashTransValue in thousands of PLN is described by Eq. (9). The related model includes autoregressions up to and including the fourth order, a trend, and seasonal fluctuations, as well as explanatory variables characterizing the development of non-cash payment card use. These include: the number of the accepting merchants; the number of the devices executing payment card payments; and the number of the facilities handling payments using payment cards. The following equation describes this process:

NonCashTransValt ¼ 544:48  2:118 #Facilitiest þ 9:687 t ð11:958Þ

ð17:83Þ

ð19:68Þ

 27:443 Q 1 þ uVncut

ð9Þ

ð9:089Þ

R2 ¼ 0:935; StdErr ¼ 0:119; Vol ¼ 2:82%

Table 8 Quarterly forecasts of monetary value of ATM withdrawals per payment card, 2014–2015 (000s PLN).

Table 7 Quarterly forecast, number of facilities, 2014–2015 (000s). Forecast period

Forecasts

Average prediction error

95% confidence interval

Year, quarter

Forecasts

Average prediction error

95% confidence interval

2014 2014 2014 2014 2015 2015 2015 2015

266,657 272,057 277,489 282,951 288,439 293,953 299,489 305,047

43,639 59,211 69,662 77,366 83,295 87,973 91,722 94,761

257,779–275,536 260,011–284,103 263,316–291,662 267,210–298,691 271,493–305,386 276,055–311,851 280,828–318,151 285,768–324,326

2014 2014 2014 2014 2015 2015 2015 2015

3,691,407 4,170,976 4,255,898 4,192,147 3,867,461 4,359,425 4,466,847 4,415,414

118,882 139,853 164,843 183,166 199,885 214,399 227,491 239,309

3,4474,81–3,935,332 3,884,022–4,457,931 3,917,669–4,594,127 3,816,321–4,567,972 3,457,330–4,277,591 3,919,516–4,799,334 4,000,074–4,933,619 3,924,393–4,906,436

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Source: Own calculations using the GRETL package.

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Source: Own calculations using the GRETL package.

Fig. 4. Quarterly forecasts of monetary value of debit card transactions, 2014–2015 (000s PLN).

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Fig. 5. Quarterly forecasts of monetary value of ATM withdrawal transactions per payment card, 2014–2015 (000s PLN).

Fig. 6. Quarterly forecasts of monetary value of non-cash transactions per payment card device, 2014–2015 (000s PLN).

The autoregressive processes turned out to be statistically insignificant. In Eq. (9), only the number of the facilities, a linear trend, and seasonal fluctuations in the first quarter were statistically significant. The equation describes the volatility mechanism of the variable NonCashTransValt, which allows the forecast estimation of that variable (see Table 9).

The forecasts indicate that during 2014 and 2015 seasonal fluctuations of non-cash transactions should were still to be expected. The variation of this variable was predicted to range between a little over 313,000 PLN during the first quarter of 2015 up to 346,000 PLN during the second quarter of 2014.

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Table 9 Quarterly forecasts of monetary value of non-cash transactions per card payment device, 2014–2015 (000s PLN). Forecasted period

Forecasts

Average prediction error

95% confidence interval

2014 2014 2014 2014 2015 2015 2015

320,411,348 346,105,294 344,288,360 342,407,893 313,029,252 338,481,769 336,444,585

8,879,517 8,656,508 8,737,343 8,823,547 9,207,320 9,011,810 9,113,744

302,345,835–338,476,862 328,493,497–363,717,091 326,512,103–362,064,617 324,456,251–360,359,534 294,296,820–331,761,685 320,147,104–356,816,435 317,902,533–354,986,637

Q1 Q2 Q3 Q4 Q1 Q2 Q3

Source: Own calculations using the GRETL package.

The Polish payment card market is likely to be similar to those of other large European Union countries. The changes occurring in this market, however, require systematical observations and research, since new non-cash payment technologies are emerging, which can displace current technologies. 5. Limitations and directions for future research The relatively short period of payment card usage, especially in new European Union countries, is a significant limitation for carrying out studies on payment cards. Thus, statistical inference must be viewed with increased caution. As such, it seems necessary to study the relationship between cash payments and card payments in the retail consumer market, since it will affect decisions regarding cash issuance. The most important challenges in the Polish payment market include: interpretation and national implementation of the Payment Services Directive; increases in banking penetration into Polish society; and the introduction of innovations associated with payment instruments. Further increases in transactions in the electronic payment market require increased banking competition, more participation in the market by non-banking competitors, as well as an increase in efficiency through operational and cost optimization, and especially the optimization of customer migration to new SEPA instruments. The current state of the market suggests that Polish consumers are willing to use innovative solutions. As such, it is necessary to conduct marketing activities to popularize additional services, by which consumers will be informed about new opportunities due to their possession of payment cards. Information on card safety and usage is also important. Increasingly widespread usage of the mobile Internet, GSM network standards such as USSD and SMS, and contactless transactions, including contactless cards and NFC mobile payments, are of particular importance among innovations in payment services. Further research ought to address the impact of demographic factors on the development of the electronic payment market, but this requires collection and access to much more detailed data. The future of payment cards undoubtedly entails additional services that will go beyond payments. The possibility of connecting a payment card with another identity document or a public transportation card is also noteworthy. Modern solutions also appear in increasingly effective card security measures, such as the rapidly-changing CVV/CVC codes. Lack of permanent access to the card and the possibility of reading the current codes that block payment authorization may result in a significant reduction of cyberspace crime. Contactless payment cards, which verify the identity of the user through an embedded fingerprint, also has proven to be a valuable innovative. Innovations arising in the payment card market are challenging and undoubtedly will help to movitate new lines of research. Additionally, in recent years, at least three parallel mobile payment systems have appeared in the Polish market, however, the direction of their development has not been sufficiently specified.

The first commercial offers of NFC payments in Poland appeared at the end of October 2012, as a result of cooperation among T-Mobile, mBank and PolBank, as well as between Orange and mBank. But the lack of deeper involvement and engagement on the part of these entities, which are required for success in operation, has constituted a barrier. The bank, the telecom operator, the organization of payments, and the clients all must be in synch with one another in order to work out effective measures. Meanwhile, consumers face the special task of having to navigate a complex process, while there must be a capable bank and telecom operator to work through. Also, it is necessary for consumers to have mobile phones that are network-compliant, a SIM card, and so on. These things do not make for easy and convenient solutions for consumers. There also has been a noticeable reluctance on the part of consumers to adopt new ways to make payments, so payment cards continue to be a dominant and attractive way to make payments. At the same time, there is the hope that growing competition and increasingly sophisticated customer demand will result in continuous and new developments in the marketplace. Given all of the existing opportunities, the most important potential benefits from the banks’ participation in the Single Euro Payments Area are simplifying payment procedures, and the increasingly popular electronic payment instruments among customers. Improving the safety and security for a variety of payment instruments will lead to an increase of non-cash transactions, as well as ability to have them interoperate throughout the European Union territory. 6. Conclusion The introduction and dissemination of innovations requires meaningful research activities and effective market practices. To make innovations successful, the expectation of key stakeholders and the requirements of retail payment users have to be taken seriously. There are signs indicating that customers see the need for more innovative payment solutions. This study drew a number of conclusions that ought to stimulate activities associated with the further development of innovations in electronic payments and influence improvements in the Polish payment system’s integrity. Our empirical modeling results confirmed seem to describe reality fairly well. At the same time, the analysis work has confirmed their usefulness for describing the mechanisms associated with the current state of the market, the changes occurring within it, and the possibilities of the application of models that were used for forecasting the future directions of market development. Further development of the payment card market, the increasing significance of non-cash transactions, and more growth in the number of payment cards in circulation are expected. The number of transactions overall in Poland is reaching the saturation level though. This means that further increases in the number of transactions is not going to occur at the rate observed in the initial stage of market development. The growing wealth of the society will lead to a further increase in the number of transactions with

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Appendix 1 Modeling notation and definitions. Modeling notation

Definition

yt

Value of the dependent variable at time t in each of the models Structural parameters in the empirical models Values of independent variables at time t; for the jth among k variables overall Random components in the models. Standard of a specific variable in the models Number of facilities where payment cards can be used Number of merchant that accepted payment cards for payment at a point in time The mean of the monetary value of credit card transactions delayed by 1 quarter when its subscript it t  1, or by 2 quarters when its subscript is t  2, and so on The mean of the monetary value of transactions for debit cards delayed by 1 quarter when its subscript it t  1, or by 2 quarters when its subscript is t  2, and so on. A characteristic that proxies for development intensity in the payment card market; measured as the quarterly average of the monetary value of ATM withdrawals for payment cards in the market Another characteristic that proxies for the intensity mechanism associated with using payment card transaction devices; measured as the average monetary value of quarterly non-cash transactions on card-related devices Dummy variable equal to 1 when the period is the 1st, 2nd or 3rd quarter; and 0 otherwise A random coefficient in percentage terms for the regression standard error relative to the mean value of a variable in the regression Regression error term

a0, a1, . . . , ak xt1, . . . xtj, . . . ,xtk

gt StdErr #Facilities #Merchants CreditCardTransValue DebitCardTransValue ATMTransValue NonCashTransValue Q1, Q2, Q3 Vol

e

payment cards. According to many experts, the popularity of payment card usage will cause a decline in the role of cash in the near future (Kukulski and Pluta 2002, p. 18). Even though 82% of transactions are still done using cash, there are signs of the kinds of changes that are coming (Ochocki and Brzeg-Wielunski 2014, p. 149). More modern technologies for making payments are constantly emerging, including mobile payments. The lower level of income in Poland compared with other developed countries is still a developmental barrier for the market though. The increasing wealth of the society will affect the further development of the electronic payment market in Poland. Enabling customers to pay bills, and make transactions related to other public and legal obligations using non-cash transactions will create new opportunities for further development of the payment market in Poland. Appendix A. Appendix 1. References Amromin, G., Chakravorti, S., 2009. Whither loose change? the diminishing demand for small-denomination currency. Journal of Money, Credit and Banking 41 (2–3), 315–335. Asokan, N., Janson, P.A., Steiner, M., Waidner, M., 1997. The state of the art in electronic payment systems. Computer 30 (9), 28–35. Bank of Netherlands (de Nederlandsche Bank). Towards a cashless society? Quarterly Bulletin, Amsterdam, Netherlands, March 2006. Begg, D., Fischer, S., and Dornbusch, R. Economics. Maidenhead, UK, 1997. Beijnen, C., Bolt, W., 2009. Size matters: economies of scale in European payments processing. Journal of Banking and Finance 33 (2), 203–210. Berger, A.N., DeYoung, R., 2006. Technological progress and the geographic expansion of the banking industry. Journal of Money, Credit, and Banking 38 (6), 1483–1513. Berger, A.N., Demsetz, R.S., Strahan, P.E., 1999. The consolidation of the financial services industry: causes, consequences, and implications for the future. Journal of Banking and Finance 23, 135–194. Bolt, W., Humphrey, D., Uittenbogaard, R.A., 2008. Transaction pricing and the adoption of electronic payments: a cross-country comparison. International Journal of Central Banking 4 (1), 89–123. Borzekowski, R., Kiser, E.K., Ahmed, S., 2006. Debit Card Use By U.S. Consumers: Evidence From a New Survey. Board of Governors, Federal Reserve Bank, Washington, DC. Borzekowski, R., Kiser, E., Ahmed, S., 2008. Consumers’ use of debit cards: patterns, preferences, and price response. Journal of Money, Credit and Banking 40 (1), 149–172. Carow, K.A., Staten, M.E., 1999. Debit, credit, and cash: survey evidence on gasoline purchases. Journal of Economics and Business 51 (5), 409–422. Clemons, E.K., Croson, D.C., Weber, B.W., 1996. Reengineering money: the Mondex stored value card and beyond. In: Sprague, R. (Ed.), Proceedings of the TwentyNinth Hawaii International Conference on Systems Science, 4. IEEE Computer Society Press, Los Alamitos, CA, pp. 254–261.

Cobb, A. Out of the shadows. Banker Middle East, May 2003. Retrieved from . Davis, F., 1989. Perceived usefulness, perceived ease of use, and consumer acceptance of information technology. MIS Quarterly 13 (3), 319–340. Frame, W.S., White, L.J., 2014. Technological change, financial innovation, and diffusion in banking. In: Berger, A.N., Molyneux, P., Wilson, J.O.S. (Eds.), The Oxford Handbook of Banking, 2nd edition. Oxford University Press, Oxford, UK. Hancock, D., Humphrey, D.B., Wilcox, J., 1999. Cost reductions in electronic payments: the roles of consolidation, economies of scale, and technical change. Journal of Banking and Finance 23 (2–4), 391–421. Hauswald, R., Marquez, R., 2003. Information technology and financial services competition. Review of Financial Studies 16 (3), 921–948. Humphrey, D.B., Pulley, L.B., Vesala, J.M., 1996. Cash, paper, and electronic payments: a cross-country analysis. Journal of Money, Credit and Banking, 914–939. Hussain, Z., Wallace, J., Tassabehji, R., Khan, O., 2007. E-business in the developing world: an empirical study of payment methods and their implications. International Journal of Electronic Business 5 (3), 315–335. Hyytinen, A., and Takalo, T. Multihoming in the market for payment media: evidence from young Finnish consumers. Research Discussion Paper 25, Bank of Finland, Helsinki, Finland, 2004. Jonker, N., 2007. Payment instruments as perceived by consumers: results from a household survey. Economist 155 (3), 271–303. Kennickell, A.B., and Kwast, M.L. Who uses electronic banking? results from the 1995 survey of consumer finances. In Presentation, Annual Meeting of the Western Economic Association, Seattle, Washington, July, 1997. Khiaonarong, T., 2003. Payment Systems Efficiency, Policy Approaches, and the Role of the Central Bank (No. 1/2003). Bank of Finland, Helsinki. Klee, E. Families’ use of payment instruments during a decade of change in the U.S. payment system. Working Paper, Board of Governors, Federal Reserve Bank, Washington, DC, 2006. Kreltszheim, D., 1999. Identifying the proceeds of electronic money fraud. Information Management and Computer Security 7 (5), 223–231. Kukulski, J., Pluta, I., 2002. Credit cards: theory and practice. (Karty płatnicze: teoria i praktyka.). Dom Wydawniczy ABC, Warsaw, Poland. Li, X., Gupta, J.N.D., Koch, J.V., 2006. Effect of technological breakthroughs on electronic markets. Electronic Commerce Research 6 (3–4), 389–404. Ochocki, J., Brzeg-Wielunski, S., 2014. How to use the potential of mobile payments? (Jak wykorzystac´ potencjał płatnos´ci mobilnych?). Bank 6 (256), 149. Panurach, P., 1996. Money in electronic commerce: digital cash, electronic fund transfer, and ecash. Communications of the ACM 39 (6), 45–50. Polish National Bank (Narodowy Bank Polski.) Pay with and paid: a service for people who like card payments and need cash. (Płac´ karta˛ i wypłacaj: usługa dla osób, które lubia˛ płatnos´ci karta˛ i potrzebuja˛ gotówki.), 3, 253, 2014, 134–135. Polish National Bank. (Narodowy Bank Polski.) Payment system. (System płatniczy.), 2015. Available at . Rogers, E., 1995. Diffusion of Innovations, 4t edition. The Free Press, New York, NY. Rysman, M., 2004. Competition between networks: a study of the market for yellow pages. Review of Economic Studies 71 (2), 483–512. See-To, E.W.K., Jaisingh, J., Tam, K.Y., 2007. Analysis of electronic micro-payment market. Journal of Electronic Commerce Research 8 (1), 63–83. Sejm of Poland 2013. The act of 30 August 2013 amending the law on payment services. (Ustawa z Dnia 30 Sierpnia 2013 r.o Zmianie Ustawy o Usługach Płatniczych.). Law No. 199, Item 1271. (Dz. U. Nr 199, Poz. 1271. Warsaw, Poland, 2013. Shon, T.H., Swatman, P.M., 1998. Identifying effectiveness criteria for Internet payment systems. Internet Research 8 (3), 202–218.

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E. Sokołowska / Electronic Commerce Research and Applications xxx (2015) xxx–xxx Sokołowska, E. Weather derivatives in risk management. (Pochodne instrumenty pogodowe w zarza˛dzaniu ryzykiem.) Scientific Papers of the University of Economics in Wroclaw (Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu), 48, 2009, 736–743. Sokołowska, E., 2010. Alternative Forms of Investment in the Securities Market. (Alternatywne formy inwestowania na rynku papierów wartos´ciowych.). Nicolaus Copernicus Publishers (Wydawnictwo Naukowe Uniwersytetu Mikołaja Kopernika), Nicalaus Copernicus University, Torun´, Poland. Sokołowska, E., 2014a. Alternative Investments in Wealth Management. A Comprehensive Study of the Central and East European Market. Springer, Berlin, Germany. Sokołowska, E., 2014b. The role and nature of alternative investments in wealth management. In: Sokołowsja (Ed.), Alternative Investments in Wealth Management: A Comprehensive Study of the Central and Easter European Market. Springer, Berlin, Germany, Chapter 4. Stavins, J., 2002. Effect of consumer characteristics on the use of payment instruments. New England Economic Review 2002 (3), 19–31. Survey of Consumer Finances (SCF). Board of Governors of the Federal Reserve System, Washington, DC, 1995, 1998, 2001. Tufano, P., 2003. Financial innovation. In: Constantinides, G.M., Harris, M., Stulz, R.M. (Eds.), Handbook of the Economics of Finance, 1(A). Elsevier, Berlin, Germany, pp. 307–335, Chapter 6.

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van Hove, L., Loix, E., Pepermans, R., 2005. Survey results for Belgian consumers on electronic payments. (De Belgische consumenten over elektronisch betalen: resultaten van een opinieonderzoek.). Financieel Forum/Banken Financiewezen 1, 16–28. Vincent, O.R., Folorunso, O., Akinde, A.D., 2010. Improving e-payment security using elliptic curve cryptosystem. Electronic Commerce Research 10 (1), 27–41. Westland, J.C., Kwok, M., Shu, J., Kwok, T., Ho, H., 1998. Customer and merchant acceptance of electronic cash: evidence from Mondex in Hong Kong. International Journal of Electronic Commerce 34 (2–3), 5–26. Wis´niewski, J.W., 2009. Microeconometrics (Mikroekonometria). Wydawnictwo UMK, Torun´, Poland. Wis´niewski, J.W., 2013. Correlation and Regression of Economic Qualitative Features. Lambert Academic Publishing, Saarbrücken, Germany. Zinman, J., 2005a. Why Use Debit Instead of Credit? Consumer Choice in a Trillion. Federal Reserve Bank of New York, New York, NY. Zinman, J. Debit or credit? Working Paper, Dartmouth University, Hanover, NH, 2005b.

Please cite this article in press as: Sokołowska, E. Innovations in the payment card market: The case of Poland. Electron. Comm. Res. Appl. (2015), http:// dx.doi.org/10.1016/j.elerap.2015.07.005