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The role of risk in e-retailers’ adoption of payment methods: evidence for transition economies

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Abstract

We use logit analysis to exploit a self-collected dataset on the payment and delivery options offered by the vast majority of B2C websites in five Central Asian transition economies. Specifically, we conduct a supply-side test of (elements of) the Transaction Context Model, which highlights the role of perceived risk. Our results confirm that e-retailers in sectors with higher average transaction values are more likely to adopt ‘pay in advance’ instruments—such as debit cards—that have a lower payment risk for the seller. We also find that merchants who offer higher-risk delivery options are more prone to also adopt higher-risk payment instruments (such as credit cards). Our control variables also yield interesting results. In particular, in line with the network externalities theory, we find evidence that the offline penetration of a payment instrument positively affects online merchant adoption.

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Notes

  1. Transition economies can be defined as “economies that are in transition from a communist style central planning system to a free market system” [53].

  2. As described in Arango and Taylor [2, p. 6], payment risk can be seen as comprising the risk of fraud/counterfeiting, the risk of theft (internal or external), the risk of human error during the exchange, as well as finality risk, “the risk that a provisional transfer of funds or securities will be rescinded” (CPSS [7, p. 24]).

  3. As Arango and Taylor [2, p. 6] explain, “consumers have a certain number of days to dispute a credit card transaction, whether it is because of an unresolved dispute with the merchant or because there is a fraudulent claim (i.e., the card was used without the cardholder’s consent). In these cases, the transaction will be reversed through a chargeback”. Note that in the Dutch survey multiple answers were possible, so the 3 need not equate with 3 out of the 5 credit card accepting merchants.

  4. There are other relevant papers. However, these papers are empirical in nature and do not really test a unified theoretical model. Instead they focus on a specific factor—costs, network externalities, etc.—and take a pragmatic approach in their selection of other explanatory variables. Again most papers overlook the role of risk; see [23, 35]. Bounie et al. [6], for their part, do include a card fraud variable in their regressions but do not find a significant impact on card acceptance by French merchants. The authors blame this on the fact that their variable is of an absolute nature and does not take into account the incidence of fraud with alternative payment instruments. Arango and Taylor [2] are another exception, but this paper is discussed lower in the main text.

  5. Note that in the published version of the Li et al. paper—Zhang and Li [64]—the theoretical model is no longer there.

  6. From a consumer perspective, this could be called vendor risk [34].

  7. See Fig. 3 in Liezenberg et al. [34] for a schematic presentation of the model.

  8. Where r l is concerned, this statement is qualified when developing our second hypothesis, in Sect. 5.1.

  9. In their experiment, Mascha et al. [37] use product price as the mechanism for manipulating r p .

  10. Product uncertainty is defined as “the difficulty of buyers in evaluating the characteristics and future performance of products” [20, p. 2].

  11. There is a link here with the well-known classification of payment instruments as “pay before”, “pay now”, or “pay later”. However, this distinction only relates to the settlement of the transaction from the payer’s perspective and does not necessarily reveal much about the chronological sequence of payment and receipt of the goods. Indeed, a consumer who pays by means of an online bank transfer uses a pay now payment instrument in terms of settlement: her current account is debited immediately. But in terms of the A–P–D sequence, she in fact pays first and will receive the goods only a couple of days later. In other words, in the TCM such a scenario classifies as “pay in advance” [34, Fig. 2, p. 221].

  12. Hu et al. [20, p. 2] erroneously classify credit cards as “pay-before delivery”, together with debit cards.

  13. More generally, the Zhang and Li [64] article contains an interesting Table—Table 1 on p. 1079—that provides an overview, for the case of the US, of the protection that individual payment methods provide to both buyers and sellers. Zhang and Li’s conclusion from the table is straightforward: for buyers, credit cards provide more protection than cash-equivalents such as cash, money order, cashier’s check, etc.; for sellers, it is the opposite. Given the higher card fraud levels, this holds a fortiori in the countries that we study.

  14. It goes without saying that this can be mutually beneficial for seller and buyers, as anecdotal evidence for Nigeria shows: “Millions of people in [Lagos] are prospering and many are shopping online for the first time. But in a country that has become synonymous with online fraud, they would sooner hand money to a courier than enter their credit-card numbers on a website. So online shopping site DealDey.com employs of fleet of motorcyclists to dart through gridlocked streets to meet online shoppers waiting to pay for their purchases with cash” [19]. There is a clear link here with Stavins’ [57] finding, mentioned in Sect. 4, that 63 % of US consumers view in-person payments as ‘very secure’.

  15. Kz = 12.8 % and Uz = 44.4 %, compared to Kg = 4.3 %; χ2-test p < 0.000.

  16. The classification of PayPal as ‘e-money’ can be criticized, as the current version of PayPal is mainly used not as an electronic wallet but rather as a way for small merchants to indirectly accept credit cards. However, we specifically did not want to lump PayPal together with the straightforward acceptance of credit cards. This said, we did run robustness checks by reclassifying PayPal as a credit card; cf. infra.

  17. Kz = 28.6 % and Uz = 36.1 % vs. Kg = 8.7 %; χ2-test p = 0.065.

  18. See http://www.internetretailer.com/top500/.

  19. After the facts we did send out an e-mail survey—in order to, at the very least, be able to check whether our ratings made sense. As anticipated, we only received six complete responses out of a total of 181 surveys sent. Of these six responses, five were consistent with our coding and one was not.

  20. Note that reusing the industry classification of Basu and Muylle [5] was not an option as their sectors do not cover all the product categories that we encountered.

  21. We also ran a robustness check with a set of three dummy variables. This yielded similar results.

  22. As robustness checks, we repeated the regressions in Table 5 for a sample that progressively excluded Turkmenistan (N = 191), Tajikistan (N = 185), and Kyrgyzstan (N = 162). This was motivated by the observation that, respectively, none, none, and only one of the websites in these countries offer a credit card payment option. Reassuringly, none of the results disappeared and in several cases the significance levels even improved. Moreover, the variant of our TRX_SIZE variable with three rather than two categories also has a negative impact in all models, be it that its significance drops to the 5 % level in models 3 and 9.

  23. The same is true for offline bank transfers (N = 194); results not reported.

  24. The fact that ANYE contains PayPal, which is nowadays more akin to a high-risk credit card than low-risk e-money, could also have been part of the explanation. However, upon reclassifying PayPal as a credit card, the negative sign remained. Incidentally, the reclassification left unchanged—and even improved somewhat—the results for ANYC. Given that there were only 3 sites that accept PayPal but not credit cards the latter is not surprising.

  25. Also, if TRX_SIZE is replaced by its variant with three categories, it only remains significant (at the 10 % level) in models 9 and 12. In all other models, the positive sign remains, but significance hovers just above 10 %.

  26. Two Uzbek online merchants whom we contacted mentioned 3.5 and 3.6 %, respectively.

  27. The fact that merchants and commercial banks in certain of the countries pointed out that cardholders pay part of these fees—and in some cases apparently even the entire fee—does not solve our problem.

  28. There are no fees for cardholders either. Banks only charge companies 1–2 % for transferring the salaries of employees to their debit cards.

  29. Uzbek holders of MasterCard and Visa credit cards need a USD account.

  30. Removal of these 7 sites from models 1–6 in Table 6 does not fundamentally alter the results. In fact, the only change is that none of the variables that try to capture the international orientation of a site (INTERNAT_CUR, etc.) remain significant, but this is only normal. As an aside, given that adopting credit cards is apparently not an option for many Uzbek e-retailers, in an additional robustness check we also re-estimated the regressions for ANYC (in Table 5) without Uzbekistan (N = 158; results not reported). This leaves unchanged our fundamental results; that is, those for TRX_SIZE and DELIVERY_RISK—be it that there are drops in the significance levels. TRX_SIZE is now only significant at the 5 % level (and only at the 10 % level in model 2; that is, the P value is 0.051). The significance of DELIVERY_RISK drops to 5 % in model 9, but stays at the 1 % level in models 7–8. This said, the significant results for OFFLINE_PRESENCE disappear completely and those for the INTERNAT variables are severely reduced in number.

  31. In their Table 2 with behavioral criteria for scoring context examples Liezenberg note, for buyer risk, “Can the buyer reverse the transaction? How well secured is the solution?” (emphasis added), but the latter type of risk does not appear explicitly in their model. Also, in the same Table there is no mention of intrinsic security of a payment instrument under the heading seller risk.

  32. Just as for TRX_SIZE, in a robustness check we reclassified PayPal under credit cards. However, the only impact was a somewhat lower significance of DELIVERY_RISK.

  33. When %CARDS_CREDIT_FINDEX is replaced by either %CARDS_CREDIT_LITS, %CARDS_INTERNAT, or TRANSACTCAP, the variable only remains significant in the simple models 1–2. But then both %CARDS_INTERNAT and TRANSACTCAP are very rough proxies of the penetration of credit cards. As explained in Sect. 2, in reality not all international cards are credit cards. Conversely, when %CARDS_CREDIT_FINDEX is replaced by %ACCOUNT_FI, the results improve: %ACCOUNT_FI has a positive and significant coefficient in models 1–7, and the significance levels are higher. Notice that %CARDS_CREDIT_FINDEX and %ACCOUNT_FI are strongly correlated (0.975), which is understandable.

  34. When we replaced %CARDS_LOCAL with %CARDS_DEBIT_LITS, the latter proved to be equally significant but—puzzlingly—appeared with a negative sign. The same was true for %CARDS_DEBIT_FINDEX. The explanation is that whereas %CARDS_LOCAL focuses on payroll cards, for both %CARDS_DEBIT_LITS and %CARDS_DEBIT_FINDEX a debit card is a debit card. As can be seen in Table 12, the correlation between %CARDS_DEBIT_LITS and %CARDS_DEBIT_FINDEX is near perfect, but the correlation between either of these and %CARDS_LOCAL is low. In other words, they simply do not measure the same phenomenon. The fact that %CARDS_DEBIT_LITS and %CARDS_DEBIT_FINDEX failed to yield results in regressions (not reported) for ANY(D1 + D2)—which measures whether an online vendor accepts Visa Election and/or Maestro cards—confirms that these variables are just not specific enough for our purposes.

  35. The only result that needs some explaining is the negative impact of INTERNAT_CUR on the acceptance of local payroll cards. Indeed, it is not immediately clear why sites with an international orientation would shun what is after all a fairly novel local payment instrument. The explanation lies with the 7 Uzbek websites that sell local handicrafts and are thus not particularly targeting the local market. Once these sites are removed, the impact disappears completely, as already mentioned in footnote 30.

  36. When the password is not entered correctly, the transaction is declined.

  37. In May 2011 UZKART introduced new SmartVista cards that have, and this is a first, e-commerce capabilities. Consumers can make payments online from a personal account that they have opened with ‘Click.uz’ and to which they have linked a bank account.

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Acknowledgments

We are indebted to two anonymous referees for very detailed and helpful suggestions, and to our colleague Malaika Brengman for comments and help in scoring the sectors on transaction size. We also thank Dominique Steenbeek of MasterCard Worldwide (for providing us with information on merchant fees in Central Asia) and several contacts at Kazkommertsbank of Kazakhstan, Central Bank of Kyrgyzstan, National Bank and VneshEconom Bank of Turkmenistan, as well as Hamkor Bank and Asaka Bank of Uzbekistan (for information on local payment methods). An earlier version of this paper was presented at the joint European Central Bank/Banque de France conference on “Retail payments at a crossroads: economics, strategies, and future policies”, Paris, October 11–21, 2013. We thank the conference participants and especially our discussant, Nicole Jonker.

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Authors and Affiliations

  1. Department of Applied Economics (APEC), Vrije Universiteit Brussel, Room C.2.29, Pleinlaan 2, 1050, Brussels, Belgium

    Leo Van Hove

  2. Department of Business, Westminster International University in Tashkent, Room 2.17, Istiqbol str. 12, Tashkent, Uzbekistan, 100047

    Farhod P. Karimov

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Correspondence to Leo Van Hove.

Appendix

Appendix

See Tables 9, 10, 11, 12 and 13.

Table 9 Definitions of explanatory variables
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Table 10 Descriptive statistics
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Table 11 Correlations (N = 194, except indicated otherwise)
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Table 12 Country-level correlations (N = 5, except indicated otherwise)
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Table 13 List of e-commerce sectors and their classification in terms of transaction size
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Hove, L.V., Karimov, F.P. The role of risk in e-retailers’ adoption of payment methods: evidence for transition economies. Electron Commer Res 16, 27–72 (2016). https://doi.org/10.1007/s10660-015-9203-6

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