E-commerce transactions, the installed base of credit cards, and the potential mobile E-commerce adoption

ABSTRACT

Mobile e-commerce (m-commerce) relaxes consumers’ temporal and geographic purchasing constraints and encourage the establishment of omnichannel markets. It is often argued that rapid increase in smartphone penetration is the primary driver of m-commerce adoption, whereas others contend that early adoption of m-commerce applications are mostly by “relatively heavy” Internet commerce users. Brynjolfsson et al. (2013) argue that rapid increase in smartphone penetration is the primary driver of m-commerce adoption, whereas Einav et al. (2014) contend that early adoption of m-commerce applications are mostly by ‘relatively heavy’ Internet commerce users. This article explores strength of the influences within a nested multiple-service framework, where the reduced-form econometric analysis allows for interdependency between m-commerce and e-commerce services, and the installed base of credit cards. The results reveal a complex situation in which credit cards facilitate e-commerce services, whereas m-commerce adoptions are driven by prior e-commerce and online transaction activity. Also, higher respondent incomes are negatively associated with proposed m-commerce adoption. Surprisingly, privacy concerns do not affect proposed adoption independently; however, an interaction term suggests privacy remains an adoption barrier for the older persons.

KEYWORDS:

• M-commerce adoption
• multi-service framework
• U.S. consumer preferences
• trivariate probit model
• survey data

JEL CLASSIFICATION:

• C25
• C93
• D12
• L96
• O51

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ While it goes by many names, a ‘digital wallet’ containing encrypted credit or debit card information is loaded into mobile devices.

² The U.S. Census Bureau defines e-commerce as ‘transactions sold on-line whether over open networks such as the Internet or proprietary networks running systems such as Electronic Data Interchange.’ Electronic data interchange is defined as ‘the structured transmission between organizations by electronic means … without human intervention.’ Electronic data interchange is more applicable to B2B e-commerce than the B2C transactions that define the retail sector (Lieber and Syverson 2012).

³ While contactless transactions are convenient in a variety of uses, the two most popular forms of m-commerce globally are undoubtedly POS payments and fund transfers. Starting in late 2014, a variety of high-profile smartphone makers launched own versions of mobile POS payment systems using similar NFC and authentication technologies. These include Apple Pay from Apple, Inc., Android Pay from Google, Inc. and Samsung Pay from Samsung (presently limited only to selected Samsung mobile devices). Although these systems are not yet universally available, significant use of them is already being (or will shortly be) made in the U.S., U.K. and elsewhere. Implementing systems require collaboration and revenue-sharing among smartphone makers, mobile network operators, credit card-issuing banks and financial institutions and other intermediaries. PayPal Holdings, Inc., a leading international online payments company, has just become the latest entrant into the mobile payments field. With already millions of users and several hundred thousands of participating retailers and merchants, the prognosis for mobile POS payment growth worldwide is encouraging.

⁴ The interested reader should also consult Bronnenberg and Ellickson (2015) for an excellent review.

⁵ Support for the ‘search cost’ approach can also be found in Ghose, Goldfarb and Han (2012).

⁶ The primary decision maker is previously identified by Luth Research, Inc. The survey was conducted in February 2013. 183 responses are omitted. 180 responses have no mobile phone and three questionnaires are incomplete.

⁷ The variables SECURITY and PRIVACY are constructed from the sample data. Section VI (Electronic and Mobile Commerce) of the questionnaire (p. 39) contains questions concerned with online security and privacy. The categorical variable SECURITY is obtained from Question 06-A-6, which asks: ‘Using a cell phone to make purchases or bill payments may involve some risks. Knowing your personal habits and circumstances, are you likely to experience the following risk.’ Five scenarios are available to be presented, however, only one scenario is presented to each respondent. For example, ‘I, or someone using my cell phone, may charge something by accident (tap when not intended).’ The scenarios are rotated and presented to different respondents. Available responses are: very likely; somewhat likely; neither likely nor unlikely; somewhat unlikely and very unlikely. Data for the variable PRIVACY is obtained from Question 06-A7B, which asks: ‘How much are you deterred (or prevented) from making payments or transferring funds over the Internet by you concerns about ..’ Seven scenarios are available to be presented. Again the available responses are rotated and presented to different respondents. For example, Scenario 2 is ‘unauthorized payments’, whilst Scenario 6 is ‘Theft by fraudulent websites.’

⁸ Additionally, they argue that mobile application purchases are not simply purchases that would have been made otherwise on the regular Internet platform, viz. regular online and mobile e-commerce are not substitutes.

⁹ Importantly, smartphones are inherently less secure than wired desktop or laptop computers as limited bandwidth; memory and processing capability inhibit the installation of conventional data encryption and security software.

¹⁰ On the role of trust in online environments, see Belanger, Hiller and Smith (2002), Lu et al. (2011) and Huang, Lu and Ba (2016).

¹¹ The model would be consistent and identified and hence would be estimated by the method such as the instrumental variable method, were it to include the latent variables () on the right-hand side of the equations instead of the observed binary variables (). In our context, such model would specify the net utility from the proposed m-commerce use to depend not on the past experience of online and credit card transactions but on the unobserved net utility from these experiences. We found such specification unrealistic and hence decided to specify the net utility from m-commerce as a function of the past experience of the online and credit card transactions.

¹² This transformation of a multivariate binomial model into a multinomial model has been long known since Cox (1972) and Amemiya (1981). However, it has not been appreciated widely with only a small number of its application to the analysis of empirical data, see for example, Velandia et al. (2009).

¹³ For details of the algorithm see Train (2003, p. 126–137).

¹⁴ Cappellari and Jenkins (2003) argue that when the number of draws is more than the square root of the sample size, the parameter estimates are robust to the initial seed value. Furthermore, within this framework the variances of the disturbances are normalized to unity.

¹⁵ Full estimation results on the multinomial logit model are provided in Appendix, Table A1.

¹⁶ Regulators have mostly acted to protect competition and ensure unhindered access to money transfers across national borders and enforcement of privacy requirements.