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Article Contents

Some background on mobile money and its role in financial inclusion, the economics of mobile money: the micro-view, empirical research, mobile money and the economy: a review of the evidence.

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Janine Aron, Mobile Money and the Economy: A Review of the Evidence, The World Bank Research Observer , Volume 33, Issue 2, August 2018, Pages 135–188, https://doi.org/10.1093/wbro/lky001

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Mobile money is a recent innovation that provides financial transaction services via mobile phone, including to the unbanked global poor. The technology has spread rapidly in the developing world, “leapfrogging” the provision of formal banking services by solving the problems of weak institutional infrastructure and the cost structure of conventional banking. This article examines the evolution of mobile money and its important role in widening financial inclusion. It explores the channels of economic influence of mobile money from a micro perspective, and critically reviews the empirical literature on the economic impact of mobile money. The evidence convincingly suggests that mobile money fosters risk-sharing, but direct evidence of the promotion of welfare and saving is still mostly rather less robust.

“ Leapfrog ”: to improve a position by going past others quickly or by missing some stages of an activity or process.” [Cambridge Business English Dictionary, CUP]

Mobile money is novel : it was barely heard of a decade ago. 1 Yet it has transformed the landscape of financial inclusion, spreading rapidly in developing and emerging market countries (see figure 1 ), and “leapfrogging” the provision of formal banking services. The poor are especially vulnerable to risk (e.g., from illness, unemployment, death of family members, or natural disasters). Enhancing financial inclusion of the unbanked urban and rural poor—a goal of the G20 group of countries—can help to diversify risk. Financial inclusion policy has focused on extending access to formal banking services, but progress has been thwarted by cost and market failure challenges.

Number of Live Mobile Money Services for the Unbanked by Region

Source : Data from the GSMA State of the Industry Report ( 2017 ).

Note : The first mobile money system was launched in the Philippines in 2001, and M-Pesa was launched in 2007.

The new technology helps overcome problems from weak institutional infrastructure and the cost structure of conventional banking. Small size, volatility, informality, and poor governance place constraints on the commercial viability of financial institutions in developing countries ( Beck and Cull 2013 ), see figure 2 . The poor mostly cannot afford the minimum balance requirements and regular charges of typical bank accounts. Mobile phone technology has the advantage that consumers themselves invest in a mobile phone handset, while the (scalable) infrastructure is already in place for the widespread distribution of airtime through secure network channels (see figure 3 ). By adopting mobile money, under-served citizens gain a secure means of transfer and payment at a lower cost, and safe and private storage of funds. Mobile money has filled a lacuna, and has “changed the economics of small accounts” ( Veniard 2010 ). 2

Provision of Banking Infrastructure

Source : G20 Financial Inclusion Indicators database, World Bank and IMF Financial Access Survey.

Note : This shows the position shortly after the adoption of mobile money in Kenya.The first five regions refer to “developing only”.

Fixed Telephone and Mobile-cellular Subscriptions: 2005 and 2017

Source : ITU World Telecommunication, ICT Indicators database.

Note : Subscriptions are per 100 inhabitants. “Mobile phone subscribers” refer to active SIM cards rather than individual subscribers.

The technological innovation has helped ameliorate the perennial asymmetric information constraint faced by conventional banks in lending to the collateral-less poor. 3 The movement of cash into electronic accounts gives a record, for the first time for the unbanked, of the history of their financial transactions in real time. By using algorithms, these records can provide evolving individual credit scores for the unbanked. 4 After a designated period of usage and once a score is available, registered users of mobile money may obtain a pathway to formal banking services accessed only through a mobile phone: to interest-bearing savings accounts that can protect assets; to credit extension to invest in livelihoods; and insurance products that reduce risk.

Apart from reducing asymmetric information, the impact of enhancing transparency through electronic records is far-reaching. Tax collection could be improved by the rise of more visible spending, quite apart from the greater ease of tax collection via mobile money payments. The increased transparency of records protects customers’ rights and fosters trust in business, promoting the growth of efficient payments networks. Mobile money should make international transactions more readily traceable and therefore facilitate identification and better control of money laundering. If the high cost of remittances were reduced by mobile money, this could attract more official remittances, and re-channel “informal” remittances through official channels, raising recorded remittances. 5 In essence, mature mobile money systems and the records they produce help foster the “formalization” of the economy, integrating informal sector users into business networks, formal banking and insurance, and linking them to government through social security, tax, and secure wages payments. However, there are legal data privacy considerations concerning access to and use of mobile money records which have barely begun to be addressed.

The channels through which mobile money can affect the economy are many and complex, and not necessarily well-understood. A burgeoning body of empirical literature has attempted to quantify the possible economic gains for different countries of access to secure financial services through mobile money (e.g., improved risk-sharing, food security, consumption, business profitability, saving, and use of cash transfers), and the factors driving the adoption of mobile money. Demonstrating welfare and risk-sharing gains from mobile money across countries could bolster the case for significant government and donor support, as well as investment.

Unfortunately, interpreting the evidence on the economic impact of mobile money is not straightforward. The empirical literature is burdened by a range of sometimes serious problems with data, methodology, and identification, which some authors underestimate or choose to ignore. Work on mobile money faces “selection” problems since both the “roll-out” of mobile money by Mobile Network Operators (MNO) and their agents and the adoption or usage of mobile money by individuals may be influenced by other factors such as education, wealth, and changes in technology preference. There is mixed success using various methods and data sets in dealing with the resultant ambiguous causality. Although various studies establish statistically significant relationships, they frequently do not test the robustness of their results to different model specifications, measurement errors, and bias due to the possible omission of variables. Furthermore, in practice it is difficult to generalize from these models.

This article introduces the phenomenon of mobile money and its role in financial inclusion. It examines possible channels for the economic influence of mobile money, and reviews the new empirical literature on mobile money, both to obtain a better understanding of the linkages involved and to critically assess the sometimes strong claims made by the authors. Lessons are distilled for improved practice in the future empirical analysis of mobile money.

In economies with deep financial markets like the United States, mobile payments or transfers are predominantly linked with pre-existing bank accounts; mobile payments are rapidly gaining market share after a slow start, catalyzed by new technology and commercial partnerships (e.g., Apple Pay). This is distinct from mobile money payments or transfers in largely cash-based developing or emerging countries, where most users are unbanked. Yet as mobile money systems evolve and smartphones become ever cheaper in less advanced countries, the range of financial services could expand to link with products managed by formal financial institutions such as banks and insurance companies. This will ultimately blur the distinctions between mobile banking and mobile money. Survey evidence suggests that security concerns about mobile payments have diminished in the United States, shaped by industry efforts to enhance security (e.g., Federal Reserve 2016 ). There may be a technological spillover to less advanced economies, and biometrics may allay security concerns (though there are caveats about their use in poor countries). This could catalyze a transformation to a virtually cashless economy, and possibly a new role for some banks beyond traditional payments. 6

The term “financial inclusion” is of recent vintage, and has gained currency with policymakers, most prominently in the Maya Declaration of 2011, when 80 regulatory institutions from 76 countries collectively endorsed a set of financial inclusion principles. The G20 has backed the Maya declaration, promoted indicators to measure “financial inclusion”, and the G20 Summit in 2017 prominently endorsed digital approaches to financial inclusion. Mainstream definitions of financial inclusion share the goal of participation in the formal financial sector, which has severely constrained progress to inclusion. Until recently, the use of electronic mobile money has not been counted as part of financial inclusion under most definitions. Mobile money's role is seen as a pathway for registered users to formal sector financial inclusion via products (insurance, credit and a bank savings account) accessed through a mobile phone.

Aron (2017) argues that a revised definition of financial inclusion should encompass tiers of semi-formal inclusion, and not focus on comprehensive formal banking sector inclusion. Mobile money has transformed the lives of poor consumers who can hold recorded cash privately in non-bank electronic accounts and perform financial transfers easily and cost effectively. Fast-spreading and cheaper smartphones (and recycled smartphone handsets) potentially offer access to sophisticated features and a spectrum of financial services for huge numbers of illiterate people through well-designed applications ( Villasenor 2013 ). Such users may not embrace the formal sector products even if they become available, for example, if they qualify for credit, the loans may be small and not adequate to purpose, creating a disincentive to participate. Moreover, the actual number of informal users may be far higher than is formally reported. South Asia has close to 90% of the global unregistered mobile money customers, using an over-the-counter (OTC) model where the challenges and costs of establishing identity in registering were circumvented in favor of a drive for early market share ( Scharwatt et al. 2015 ). 7 In practice, the proliferation of mobile money services and the sheer numbers of new users actively signed up has become integral to achieving ambitious targets under the 2011 Maya Declaration. A revised set of G20 indicators in 2016 has raised the prominence of mobile money, reducing the bias to formality.

In box 1 , the Kenyan mobile money system M-Pesa is summarized and serves to explain the “nuts and bolts” of a profitable mobile money system. Instead of bank branches, mobile money systems rely on a large network of agents. These are linked under various contractual arrangements with a parent MNO, usually in partnership with a prudentially-regulated bank. 8 The nature of agent network structures and the design of the individual agent contracts are crucial for the successful development of mobile money systems ( Aron 2017 ). The typical authorized agents of the mobile money services provider are shops or outlets staffed by small business owners. 9 Mobile money systems were initially dominated by domestic money transfers, but have expanded into a broader payments platform for utility bills, rent, taxes, school fees, and retail payments. Business usage is expanding rapidly through special networks for the payment of suppliers, wages payments, and potentially pensions. Government usage for the payment of wages and social security has lagged, though the cost savings or reductions, especially in insecure environments, could be significant.

Kenya's mobile money system originated in 2005 as an experiment for loan payments via mobile phones in micro-credit schemes, in a public-private partnership between DFID (UK), the Kenyan Government, and Vodafone. In March 2007, Safaricom, the Kenyan subsidiary of Vodafone, launched a commercial payments service, M-Pesa, with the slogan “send money home”, exploiting the proliferation of mobile phone ownership. A decade later, there were six operators, though Safaricom controlled 65% of the market. The FinAccess (2013) survey revealed that 67% of the adult population used financial services in 2013 versus 41% in 2009, driven by mobile money. There were 27 million registered M-Pesa customers by 2017, of whom 19 million were (30-day) “active”. M-Pesa revenue grew by 33% to Kshs 55bn (US$536m) in the year to Mar. 2017, over one-quarter of Safaricom's total service revenue. The Bank of Kenya recorded in 2015, for all operators, a monthly value of transactions of Kshs 227.9bn (US $2.2bn), or about one-half of average monthly GDP.

In August, 2014, the National Payment System Regulations were issued under the National Payment System Act, providing a legal framework for mobile money. These regulations formalized and extended prudential and market conduct requirements for mobile money providers as previously articulated in simple letters of no-objection from the Central Bank of Kenya (CBK). The CBK has duties of oversight, inspection, and enforcement. There are mechanisms for consumer protection, redress, and confidentiality of data.

In Kenya, banks and non-banks, including mobile network operators (MNOs), may provide mobile money services. The net deposits from customers have to be invested in prudentially-regulated banks for safe-keeping in “Trust” accounts, which back 100% of the money of the participants in the mobile money service; the banks are required to satisfy fiduciary responsibility in all transactions concerning the Trust funds ( Greenacre and Buckley 2016 ). No investment of Trust funds is allowed; the funds are strictly separated from the service provider's own accounts and safeguarded from claims of its creditors. Safaricom's Trust account interest income is covenanted to charity.

The early agent exclusivity arrangement for M-Pesa was formally outlawed in July 2014; the CBK ordered Safaricom to open the agent network to other operators to improve competition and to lower fees for customers. Interoperability of platforms was implemented in April 2018; before this, users of mobile money services had to affiliate with multiple mobile providers.

By 2017, there were 136,000 M-Pesa agents countrywide (compared with about 2.43 commercial bank branches per 1,000 km 2 in 2013, or 1,410 total branches). Establishing an agency network and the training and payment of agents is a considerable early investment by operators to develop the market. Retail cash agents transact with their own cash and electronic money in their own M-Pesa accounts to meet customer demand. Wholesale agents (banks or non-bank merchants) are allowed higher limits on electronic money stored in their M-Pesa accounts; they perform a liquidity management service for retail agents, who typically transact daily with wholesalers. Retail agents open accounts observing identity checks required by anti-money laundering legislation, and the cash provision function spans in-store cash merchants to street-based merchants. M-Pesa agents are compensated from transaction fees charged to customers.

Mobile phone users purchase a SIM card with the mobile money “app” for their phone, register with a retail agent using a national identity card and acquire an electronic mobile money account. They deposit money into the account by giving cash to the agent, and receive, in return, equivalent value “electronic money” via their mobile phone. To withdraw money, they transfer electronic money via their mobile phone to the cash merchant's mobile money account, and receive cash in return. Electronic money can be transferred instantly from a customer's account to any other individual, whether registered or not, without using formal bank accounts. The transactions are authorized and recorded in real time. A secure text message (SMS) with a code is sent to the recipient, authorizing a retail agent to transfer money from the remitter's account into cash for the designated recipient. The maximum allowed account balance is Ksh 100,000 (US $970), the maximum daily transaction is Ksh 140,000, the maximum per transaction is Ksh 70,000, and the minimum allowed transfer is Ksh1 (US 10cents). The main transactions are non-bank payments services such as buying airtime, paying bills and school fees, and domestic transfers.

Depositors do not receive interest on their electronic accounts and bear the risk of loss of value through inflation. They pay the cost of transferring and withdrawing money, but there is no charge for depositing money. The graduated withdrawal fee pays for the cost of the M-Pesa account, ranging from about 0.5% for large transfers to 20% for the smallest. The costs of transfer are 10% for the smallest transfers, falling to 0.5% at transfers of Kshs 20,000, and to 0.16% for Kshs 70,000. Costs are greater to transfer to unregistered users.

Safaricom has pioneered a business payments platform and this is an important growth area for the company. The “Lipa na M-Pesa” business network has built a critical mass of consumers using retail payments providing dedicated business till numbers and low transaction fees, and it enables bulk disbursements such as promotional payments or salary payments. For Safaricom, customer-to-business payments accounted for 10.5% of the average monthly value of all payments in 2016.

M-Shwari is a savings and loan product operated entirely from the mobile phone, launched in 2012 by partners Safaricom and Commercial Bank of Africa. By 2016, (30-day) active customers numbered 3.9m, with Kshs 8.1bn on deposit. Customers can move funds between their M-Pesa account and M-Shwari bank savings account (with no minimum balances or charges, and paying graduated interest rates of 2% to 5%). The new Lock Box service pays higher interest rates for fixed deposits. M-Pesa subscribers of 6 months standing can apply for an M-Shwari loan without fees or paperwork. An initial credit score and loan limit is calculated using an algorithm from the stream of recorded financial actions. Loan disbursement and repayment is via M-Pesa, without loan interest charges, but with a facility fee of 7.5%. Loan sizes range from US $1 to US $235 with a 30-day term but can be rolled over at a monthly fee of 7.5% (this resembles an interest rate at a high annual compounded rate of 138 percent). Progressively larger loans can be extended when a loan is successfully repaid. By 2016, there was Kshs 7.4 bn on loan; non-performing loans numbered 1.93% of the portfolio, with an average loan size of Ksh 4,000 ($39).

In 2015, an M-Pesa health micro-insurance product, launched during the previous year, was discontinued through failure to gain traction. The annual premium (Kshs12,000) had bought family cover worth Kshs 290,000 for maternity, dental and optical care, and hospital and funeral expenses. In late 2015, M-Tiba (“mobile care”), a dedicated health savings “wallet” to pay for care at selected affordable health providers, was launched by Safaricom with two partners, enabling users to save and pay for healthcare. Donors and insurers can use M-Tiba for targeted products including vouchers, managed funds, and low-cost health insurance.

Kenya received an estimated US $1.7bn of international remittances in 2016 (World Bank Migration Brief 27). In 2014, Safaricom partnered with MoneyGram to enable remittances from over 90 countries worldwide to be sent to M-Pesa users, and now has similar agreements with Western Union and several other partners. In 2015, Vodafone and MTN announced an interconnection of mobile money services enabling affordable regional remittances between M-Pesa customers in Kenya, Tanzania, Democratic Republic of Congo, and Mozambique, and MTN Mobile Money customers in Uganda, Rwanda, and Zambia. In 2016, Vodafone partnered with HomeSend (a joint venture created by MasterCard, eServGlobal and BICS) to extend remittances for M-Pesa users in Africa, Albania, and Romania.

Governments could securely pay policemen and other officials their wages; the national revenue authority could accept payments for taxes, licenses, and fines, and municipalities for parking payments; and public transport could use mobile money payments. Delivery of social welfare or aid with mobile payments could reduce “leakage” and ghost recipients. Some of these are a reality in Kenya, with M-Pesa and Airtel, through pilots or fully-functioning systems, but government salary and social payments have lagged relative to Afghanistan, Tanzania, and Malawi. Donor and commercial initiatives increasingly use the technology; for example, affordable solar energy-powered electricity systems in rural areas can be fully purchased remotely on a pay-as-you-use basis using mobile payments (M-Kopa Solar launched in 2014 in Kenya).

Vodafone has concentrated on the proliferation of its mobile money platform in markets that are heavy cash users. M-Pesa is used in several countries other than Kenya, by order of roll-out: Tanzania, Fiji, South Africa, Fiji, Democratic Republic of Congo, India (launched in 2013), Mozambique, Egypt, Lesotho, Romania (2014), Albania (2015), and Ghana (2015).

A fast-growing product is international remittance through mobile money channels. The size of officially-recorded remittance flows to developing countries and the high transactions costs suggest that the potential gains from transparent and cheaper methods of remittance are significant. 10 Security concerns present a challenge because of poor compliance to international law at the receiving end. If the local compliance challenge can be overcome, mobile money (bound by “know your client” legislation and electronic recording of transactions) should facilitate remittances to war-torn countries with weak governance and limited or no functional banking, like Somalia.

The novelty of mobile money and its recent introduction in many countries means few studies have examined the economics of mobile money. 11 The mobile money storage and payments system, and its further linkages to bank savings accounts, micro-insurance, and credit via algorithmic credit scores, could affect households and businesses through several different channels. Mobile money potentially helps ameliorate several areas of market failure in developing economies. 12

Reducing Transactions Costs

Mobile money reduces the transactions costs of sending and receiving money, especially given the inadequate and expensive transport infrastructure. Jack and Suri , (2014) observe that in Kenya, where families and social networks are widely-dispersed from internal migration, remittances on average travel 200 km. 13

Transactions costs include the transport costs of travel, for example, to a bank, utility company, or government office; the travel time and the waiting time in long queues; the coordination costs between individuals, between firms and suppliers or customers, and between government and individuals, which can be extensive in time and money lost; and the costs of delays and “ leakages ” through corruption or middlemen, acting like a tax (or complete loss through theft from insecure methods of money transfer) . There is also an opportunity cost to lost money and time. The money could have been invested, spent, or saved; the time could have been spent in productive activities. The automated delivery of cash transfers, wages, social security funds, and private remittances by electronic transfer increases the certainty of the timing of cash receipts, which helps planning. This further reduces coordination costs, the costs of delays, and hence the opportunity costs.

Reducing Asymmetric Information and Improved Transparency

Recording financial transactions creates greater financial transparency and reduces asymmetric information. Asymmetric information and the fixed costs of servicing an account lie at the heart of the failure of the formal banking sector to advance credit to poor customers who lack collateral and financial histories. Moving cash from under the mattress into an electronic account turns it into recorded cash. Every deposit, withdrawal, transfer, or payment transaction through mobile money creates a recorded financial history. Linking algorithmic credit scores and the granting of small loans was discussed above (see box 1 ).

An electronic record of payments potentially protects consumers against theft, fraud, and misinformation. Such protection can reduce transaction costs for consumers and increase the use of business through trust. For example, Radcliffe and Voorhies (2012) note how the “anonymity of cash” may inhibit trust between traders and new vendors. Greater transparency through records can help regulate the service, including the dissemination and posting of information on transactions costs to promote competition. Recorded transfers with appropriate ID documentation (“know your customer”) also facilitates cheaper international remittance transfers.

Changing the Nature of Saving and Increasing Savings through Digital Means

There are several motives for saving. Life-cycle motives compensate for differences in timing between income and expenditure streams, and these include saving for education, leisure, marriage, consumer durables, housing purchases, retirement, and funeral expenses. Precautionary motives (buffer stock saving) reflect the uncertainties of future income and expenditures, and include saving for unemployment, illness, accidents, natural disasters, and risks associated with old age. Finally, there is saving for a bequest motive, to give gifts in one's lifetime or to leave a legacy to heirs. Saving thus helps to allocate consumption over time, and to reduce risk.

For the unbanked poor, their “immersion in physical cash creates considerable frictions in their financial lives” ( Radcliffe and Voorhies 2012 ). Cash-based households have informal saving options, which carry risks of theft or “liquidation”: cash under the mattress; accumulation of assets such as jewelry or livestock; and storing savings with informal savings groups. The loss of savings in this manner is common. Mobile money electronic accounts offer the safe storage of cash, though without the payment of interest.

Another advantage is privacy. Compared with cash receipts, the reduced observability of the timing and sizes of mobile transfers and the accumulated electronic balances could protect savings for the recipient ( Aker et al. 2016 ). Moreover, in an economic psychology literature on how the poor could be encouraged to accumulate savings, for example, the use of “commitment” savings accounts ( Dupas and Robinson 2013 ), mobile money accounts offer a practical template.

Risk and Insurance

Living standards of the poor are at risk of multiple communal shocks including flooding, droughts, pestilence, other natural disasters, sometimes conflict, and medical epidemics, as well as idiosyncratic shocks including theft, damage to the homestead, illness, and death in the family. There are very limited opportunities for insuring against these risks. Formal insurance is typically absent, but family, clan, and network ties can create informal insurance networks, ameliorating such risks by periodic transfers and monitored by trust relationships amongst members of the network ( De Weerdt and Dercon 2006 ). Jack and Suri (2011) suggest several ways by which mobile money can facilitate risk-spreading. For example, the geographic reach of networks can enlarge, while timely transfers of money can arrest serious declines that may be impossible or hard to reverse. The mobile money technology allows small and more frequent transfers of money that allow a more flexible management of negative shocks. Thus, informal insurance networks may function more effectively. In turn, more efficient investment decisions can be made, improving the risk and return trade-off. Where mobile money develops sufficiently to allow access to micro-insurance (see box 1 ), there is potentially an additional buffer against negative shocks.

Incomplete Property Rights, Changing Family Dynamics and Changing Social Networks

Women or minority groups may face limitations in their opportunities and their access to property, an aspect of inequality often resulting in more widespread economic inefficiencies. Mobile money could change bargaining power within the family. Greater privacy may influence both inter-household allocations ( Jakiela and Ozier 2016 ) and intra-household allocations ( Duflo and Udry 2004 ). If the nature of expenditure by gender differs ( Chattopadhyay and Duflo 2004 ), there could be welfare changes in the household ( Aker et al. 2016 ).

Little research has been done on network formation or dissolution, or on migration and remittance decisions using network data ( Chuang and Schechter 2015 ). Mobile money could change the nature of social networks. The cohesion of a network could be strengthened or weakened. The size of networks could be expanded with the greater geographical reach of the transfer mechanism. Morawczynski and Pickens (2009) note the greater autonomy of rural Kenyan women as they can more easily solicit funds from their husbands and other contacts in the city. The reduced transactions costs of remittances might create a more liberal attitude to migration from the homestead ( Jack and Suri 2011 ), though distant migrants are also less observable and accountable. Johnson (2014) stresses the continued importance of rotating credit schemes for perpetuating trust and coordination in communities. There is evidence of substitution away from these schemes due to mobile money ( Mbiti and Weil 2016 ), but also evidence that the schemes themselves use the mobile money transfer and storage mechanism ( Wilson, Harper, and Griffith 2010 ).

Improving other Aspects of Economic Efficiency

The combination of better communication and coordination with mobile phones and instantaneous mobile payments could improve business planning and efficiency. Indeed, mobile payments facilitate trade. Access to credit, informally and through banking services linked to mobile money, can improve investment decisions. Improved risk sharing and cheaper, secure, long-range remittances can expand the scope of labor decisions to encompass higher-risk but higher-return occupations, or migration to higher-return labor markets ( Suri and Jack 2016 ). There could be better allocation of savings and labor within the household and in businesses, and more efficient investment decisions affecting agriculture and business, and education and skills. Returns to investment could rise, with a feedback to greater savings.

“Perhaps the ‘holy grail’ of demand side data is the impact question. How can we understand whether branchless banking services are making a positive difference in client's lives?” McKay and Kendall (2013) .

The rapid global growth of payments, transfers, and international remittances speaks of mobile money providers satisfying a demand for financial services not previously adequately met. This revealed preference suggests a net welfare improvement. Moreover, positive externalities imply a larger total than private benefit, as greater connectedness in the system occurs with each adoption. But are empirical studies able to measure economic benefits, as well as local if not system-wide externalities?

Given its novelty, few academic studies have examined the economics of mobile money. The bulk of empirical work employs survey data at the household- or firm level. To reach robust conclusions on the economic benefits, the bar is set very high for empirical analysis. First, it is important to analy z e the appropriate data , but often this is hard to achieve. Second, there are considerable methodological challenges in the empirical work, so that results need to be carefully assessed, and not taken at face value. An analytical typology table summarizes the empirical studies ( table 1 ). A more in-depth analysis of the studies is presented in Aron (2017) .

A typology of Micro-empirical Studies on the Economic Effects of Mobile Money

Source : Constructed by the author from sourced papers in column 1.

Notes : 1. Disentangle technology/service: Some RCT studies are able to disentangle the mobile money services delivery from ownership of a mobile phone by providing new phones to both treatment and control groups, or by considering only participants with a mobile phone number. Other studies achieve this by introducing a dummy for ownership of a mobile phone into regressions. 2. Definition of M-money usage: For the unwary, there are definitional ambiguities using both telecoms and self-reported data, see section on Challenges for Data. If individuals own multiple, valid SIM cards with different providers, this will exaggerate users. If registered customers are inactive (and globally two thirds of registered accounts are inactive with a generous 90 day definition), this will exaggerate the participation. On the other hand, there is undercounting of overall usage where unregistered customers intensively use an over-the counter service, as in South Asia.

Challenges for Data

Definitional ambiguities could cause mis-counting when measuring mobile money “usage”. If the precision of the variable is compromised, measurement bias is introduced into regressions (see table 1 , column 1). Using the number of mobile money accounts or the number of registered customers may induce multiple counting of the same individual if several accounts are held with different providers. If registered customers are inactive (and globally two-thirds of registered accounts are inactive with a generous 90-day definition), this will exaggerate the true participation (see figure 4 ). Where unregistered customers intensively use the service, as in over-the-counter (OTC) services, overall usage will be underestimated.

Registered and Active Total Accounts

Source : Data from the GSMA State of the Industry report ( 2017 ).

Some data are unobservable. Empirical regressions will be mis-specified when omitting hard-to-measure variables linked to mobile money, such as spillover learning effects in the community, and technological and quality changes. Important

“observables”, such as education (where quality is not assessed) and wealth are typically poorly measured in household surveys, which may exacerbate the biases.

Institutional and political regime changes also affect the uptake of mobile money. For example, adoption is enhanced with more liberal registration requirements below a low threshold of use. In Côte d'Ivoire, the cessation of conflict and onset of greater growth and stability from 2012 was a key to driving mobile money adoption ( Pénicaud and Katakam 2014 ). There are likely to be shifts over time in the relevance of particular determinants, for example, cheaper, more capable smartphones widen access and ownership. Shifts can be proxied by carefully-dated dummy variables; interaction of these dummies with explanatory variables introduces non-linearities and tests whether the effects of the variables alter with regime changes.

Data may be proprietorial, and it may be difficult to design surveys optimally in advance. Against these difficulties, if privacy concerns can be overcome, new access to a rich seam of “big” data on the administrative mobile money transactions from both businesses and individuals presents an enormous research opportunity. Mobile money transactions data could have a wealth of potential applications of which four examples follow: to help forecast hard-to-gauge household assets and expenditure that otherwise rely on self-reported data (this has been done using mobile phone data, see Blumenstock, Cadamuro, and On 2015 ); to derive proxies for migration patterns from geotagged data ( Blumenstock 2012 ); to link GPS data with administrative data to examine price discrimination schemes ( Economides and Jeziorski 2016 ); and to explore evolving social networks with changing remittances ( Aron 2017 ; Aker and Blumenstock 2015 ).

Challenges for Empirical Methods

The quantitative empirical work on mobile money falls into two categories: studies which assess the determinants of the adoption of mobile money (i.e., where a proxy for usage of mobile money is the dependent variable) and studies of the effects of mobile money on micro-economic outcomes (i.e., where usage of mobile money is not the dependent variable). Examples of the latter include whether mobile money promotes improved risk-sharing, food security, consumption, business profitability, saving, and effective use of cash transfers.

Research on mobile money faces two “selection” problems, raising the problem of endogeneity in empirical analysis. 14 The “roll-out” of mobile money by MNOs and their agents may not be random if they select into areas on the basis of household and village characteristics. For instance, there will be an upward bias on the effect of mobile money on consumption if the wealth of a village determines agent selection into that village (and that wealth is not controlled for in regressions). It is difficult to disprove self-selection by the agents toward more profitable locations. Several authors contend there is little statistical correlation between agent “roll-out” and household observable characteristics that might have been associated with future outcomes; but they use partial correlates only, which is not decisive. In Jack and Suri (2014) , such bivariate correlations between agent density at 1 km, 2 km, or 5 km and a range of observables also include location-by-time and rural-by-time fixed effects. 15 But this is rather different from trying to explain agent density with a full range of the variables and all relevant interaction effects to prove it is exogenous or “unpredictable”. Moreover, it does not rule out correlation between agent roll-out and unobservables or poorly-measured observables (such as wealth) that also affect outcomes.

One factor suggesting that roll-out may have been non-random is that Jack and Suri (2014) themselves suggest the following: “. . .many of the agents had business relationships with Safaricom prior to the advent of M-PESA, and about 75 percent report sales of cell phones or Safaricom products as their main business.” As Aker and Blumenstock (2015) imply for the prior telecom infrastructure, “. . . decisions regarding expansion of ICT infrastructure and ICT-based programs are typically driven by private sector or policy criteria.” Thus, even if the bias is likely to be low for Kenya, there may be greater selectivity biases in countries such as Niger, Tanzania, and Uganda, with relatively less developed technological infrastructure.

A second selection problem is undisputed: the adoption of mobile money by individuals is influenced by factors both observable (e.g., education, wealth, urban dwelling, and the use of banking services) and unobservable (e.g., susceptibility to risk, community learning spillover effects, and changes in technology preference) that may be correlated with mobile money use.

Given the selection problems, the dominant empirical methodologies are Randomized Controlled Trials (RCT), quasi-experiments with a Difference-in-Differences estimation strategy or the non-parametric method of Propensity Score Matching, and Instrumental Variables (see box 2 ). The choice amongst methods is not uncontroversial. The methods have differing degrees of success in dealing with heterogeneity at the individual or household level. 16 A consideration is whether results can be “scaled-up” or “transported” to allow generalization to other contexts. Since institutional structures, regulation and demand patterns differ across countries, generalizations of evidence need to be made cautiously (e.g., generalizability may depend on the extent and quality of the agent network). Econometric modelling difficulties imply that the conclusions drawn are often suggestive only.

Common in medical research, RCT was little used in economics before 2003, and has generated heated debate. This critique is pertinent to the reliability and generalizability of mobile money RCT studies. An RCT evaluates whether a specific, controlled change has a discernible impact on a treated group relative to a control group. RCTs focus on small interventions that apply in certain contexts so that inferences for other settings, or even scaling up based on the results, may be invalid. Identifying a causal connection in one situation might be specific to that trial and not a general principle; even the direction of causality can depend on the setting. Deaton (2010) argues that there are actually two stages of selection. In the first, a group is chosen from the entire population that will in the second stage be randomly divided into the treated and control groups. The first stage is not random, but may be determined by convenience or politics, and therefore may not be representative of the entire population. Deaton and Cartwright (2016) further argue that randomization does not guarantee that the treatment and control groups are identical except for the treatment, that is, it does not guarantee that other causal factors are balanced across the groups at the point of randomization. a The studied populations in RCTs are typically very small, so an outlier in the experimental group can have a large distortionary effect. Further, the trial or intervention itself ( Gillespie 1991 ), and the nature and quality of information provided about the intervention, can affect behavior. Standard errors are often erroneously computed and spurious inferences are made, as t-statistics for estimated average treatment effects from RCTs do not in general follow the t-distribution.

A second approach, more widely-used in mobile money research, tests specific theoretical hypotheses using a Difference-in-Differences (DD) estimation, which mimics an experimental approach by comparing differences in the changes of a control and a treated group after an intervention (here, the adoption of mobile money). The restrictive assumption is made that in the absence of the intervention, the average change in the outcome for the affected and control groups would have been the same. This is the “parallel or common trends” assumption. The DD estimates typically derive from an Ordinary Least Squares (OLS) regression for repeated cross-sections or for a panel of data on individuals (appropriately sampled to avoid selection bias) for one or more periods before and after an intervention. A dummy variable is included for the intervention and a set of control variables. The method has the appeal of simplicity, and when the interventions are approximately random, conditional on the time and location fixed effects, and also on household fixed effects in the context of household panels, it can reduce the (time- invariant ) endogeneity problems from comparing heterogeneous individuals. b What remains is time- variant , unobserved household heterogeneity. This may be partially mitigated with appropriate controls for time-variant household characteristics (demographics, for instance) and location-by-time fixed effects (accounting for only part of the time- variant , unobserved heterogeneity, since these dummies average over households in a location). c Further problems arise when the intervention is not random, when the linear assumption under OLS is inappropriate, and from serial correlation problems exaggerating levels of significance in standard errors when several years of data are involved ( Bertrand, Duflo, and Mullainathan 2004 ). One useful test of the DD strategy is the placebo test; it uses data from prior periods before the intervention, and the DD is redone aiming for a close-to-zero placebo effect for the included intervention.

Several mobile money studies present supplementary evidence from Propensity Score matching methods. These methods mimic characteristics of an RCT in the context of an observational (or non-randomized) study, using non-parametric rather than regression techniques to estimate the effects of an intervention (e.g., use of mobile money) on outcomes between treated and control groups. Where baseline characteristics of treated subjects often differ systematically from those of untreated subjects, Propensity Score matching can match samples of subjects who are as similar as possible on observed (pre-treatment) characteristics. Differences in post-treatment outcome variables between the matches are averaged and are attributed to the treatment. There are two crucial assumptions for the validity of the technique. There should be no hidden bias from unobserved heterogeneity and the criteria for adequate balance should be clear and satisfied. However, conditioning on the Propensity Score need not balance unmeasured covariates; and even the balance-checking between measured co-variates is problematic because the criteria for adequate balance are ill-defined (see Hill (2008) on the “rampant lack of good practice”, and Austin (2011) ).

IV can be used for consistent estimation when correlation between explanatory variable/s and the error term is suspected. An endogenous variable is replaced by the predicted value from a set of instruments that are strongly correlated to the explanatory variable (informative or strong), but uncorrelated with the errors (valid or exogenous). Finding credible exogenous instruments for mobile money usage is a challenge. Several instruments have been used in the mobile money empirical literature but statistical tests tend to find them weak, which may introduce bias. d Instruments based on agent density and network connectivity assume that the roll-out of mobile money and network coverage itself was “random”.

See a non-technical version at: http://voxeu.org/article/limitations-randomised-controlled-trials , Nov. 2016.

A dummy variable is included for every household or entity (bar one entity).

A national time effect is a common effect across time experienced by all regions , for example, from macro-fluctuations. But disaggregating to two regions, North and South say, where North is less affected by drought, then interacting both regional dummies with time allows their differential response over time to be captured. With location-by-time fixed effects (without a national time effect), there is a location (e.g., district, region, or country) dummy for each year (bar one location and one year).

Instruments used for mobile money usage ( table 1 ) are as follows: the log of the distance to the closest agent and the number of agents within 5 km of the household ( Jack and Suri 2014 ), the distance to and cost of reaching the nearest mobile money agent ( Riley 2018) , and the log of the distance to the nearest mobile money agent ( Munyegera and Matsumoto 2016a ); the fraction of respondents in the sub-location registered with M-Pesa ( Demombynes and Thegeya 2012 ) and the proportion of households using mobile money and for those owning a mobile phone at the village level ( Kikulwe, Fischer, and Qaim 2014 ); household-specific mobile phone network connectivity and the size of the information exchange network of the household ( Murendo and Wollni 2016 ); and 2006 survey responses (before M-Pesa was introduced) about riskier, slower, and more costly transfer methods ( Mbiti and Weil 2016 ).

Many studies fail to “disentangle” the adoption of the technology (the phone) from adoption of the service (mobile money) it provides ( Aker et al. 2016 ). How and whether the different studies address this to reduce bias is explicitly clarified in table 1 (column 4). Whether clustered standard errors are reported ( Bertrand, Duflo, and Mullainathan 2004 ) is noted in column 3 of table 1 .

To explore the factors that determine the adoption of mobile money (i.e., where a proxy for usage is the dependent variable), Probit or Tobit regressions or OLS regressions are commonly used. The principal empirical problem is the identification of causal relationships. This encompasses biases introduced by poorly measured determinants, omitted observable variables, and omitted unobservables. Examples of hard-to-measure unobservables are the following: spillover effects; technological and quality changes of the handset and services; the quality of agents and trust in the system; and the effects of advertising campaigns and incentives to register. 17 , 18 Non-linearities are crucial in adoption empirics (e.g., adoption can be catalyzed by the cessation of conflict), but are typically ignored. Network effects also matter since a critical mass of users and a critical mass of reliable agents fosters sustainable adoption.

Given these challenges, it is unsurprising that studies of adoption in different countries have been conducted by non -economists focused largely on qualitative aspects, or have examined mobile money adoption correlations with firm and household surveys ( Aker and Mbiti 2010 ). 19 These studies find that adopters of mobile money are more likely to be younger, wealthier, better educated, have a bank account, own a mobile phone and reside in urban areas. One convincing econometric study has supported these links ( Munyegera and Matsumoto 2016a ) and deserves attention; this panel study removes time-invariant household heterogeneity with household fixed effects and some time-variant household heterogeneity with location-by-time dummies in a panel context in rural Uganda. 20 These authors include many individual controls (e.g., control for ownership of a mobile phone, distance to the nearest mobile money agent and a migrant worker in the family) further helping to reduce endogeneity. 21 The authors find no gender effect or age effect for rural adopters, but distance to the nearest mobile money agent proved important, as did education and wealth; both the dummies for the ownership of the phone and the migrant worker are significant (all with a 1% significance). It is still possible that there is some time-variant household heterogeneity that is not controlled for, as location-by-time dummies only address an average over households in a location. 22

Private Mobile Money Transfers and Risk Sharing

Amongst the most convincing analyses of the impact of mobile money are the panel data studies using a Difference-in-Differences approach that explore how mobile money has fostered improved risk-sharing amongst informal networks after large shocks. The proposed mechanism operates via lower transaction costs (compared to alternatives) for money transfer, influencing the size, frequency, and (sender) diversity of domestic remittances. The intervention is a negative shock, and such shocks are probably random. 23 The focus is not on the direct effect of mobile money usage on outcome variables like consumption, but rather on the interaction of mobile money usage with the shock (while controlling for household characteristics to interact with the shock). This puts less emphasis on the endogeneity of the mobile money usage dummy. The best of these studies fully exploit the panel data to remove sources of unobserved time-invariant household heterogeneity using household fixed effects (see box 2 ), include location-by-time dummies and rural-by-time dummies to help control for time- varying heterogeneity according to location or the rural-urban divide, and (mostly) include appropriate controls.

All the reviewed risk-sharing studies disentangle the impact of the mobile phone technology from the transfer mechanism, either by considering only participants with a mobile phone number (though this introduces a new selection criterion), or by introducing a dummy for ownership of a mobile phone into the regressions.

A sophisticated study by Blumenstock, Eagle, and Fafchamps (2016) uses a Difference-in-Differences approach to analyze the transfer of airtime: the authors call it a “rudimentary form of mobile money” but it is not convertible for cash. These authors exploit the random timing and location of earthquakes in Rwanda in a natural experiment to identify covariate economic shocks. 24 Their study relies solely on administrative telecoms data and lacks survey measures of welfare or wealth. 25 The link between risk-sharing and money transfer is instead implied, given the consistency between observed patterns of transfers and the characteristics of their theoretical models of reciprocal risk sharing. All regressions include a shock dummy and time fixed effects. Location fixed effects in regional-level regressions are replaced by recipient fixed effects in individual-level regressions, and by a fixed effect controlling for the average intensity and direction of transfer flows between two users in dyadic regressions. In extended regressions these authors allow for heterogeneity between individuals and different types of sender-recipient pairs, and cross the characteristics with shock dummies (see table 1 ).

Blumenstock, Eagle, and Fafchamps (2016) find, perhaps surprisingly, that as well as geographical proximity, transfers to victims near the epicentre after the Lake Kivu earthquake of 2008 are determined by a past history of reciprocity between individuals, and the transfers decrease in the wealth of the sender and increase in the wealth of the recipient. The opposite would be obtained in the case of charity or altruism. There are possible selection issues. Selection is induced because wealth itself determines the ownership of phones in Rwanda in 2008 ( Blumenstock and Eagle 2012 ). Further, the wealth of the recipient is likely be correlated with the size of his or her geographical network. Ideally, the differences in such networks should be controlled for, as airtime does not in this sense have the same utility in times of disaster for the wealthy and the poor.

A path-breaking study by Jack and Suri (2014) exploring risk sharing and mobile money finds total consumption of Kenyan mobile money users is unaffected by a range of negative (self-reported) income shocks, while that of non-users drops by 7% (with 10% significance). 26 The effect is more evident for the bottom three quintiles of the income distribution. A similar result is found when isolating the impact of health shocks on total consumption. 27 A Difference-in-Differences approach is applied to a panel specification controlling for household fixed effects, location-by-time dummies, and rural-by-time dummies. There is a dummy for a negative shock to income in the last six months, and a dummy for an M-Pesa user in the household, and the two dummies are crossed to test whether M-Pesa users are better able to smooth risk. An included vector of controls (though not including wealth, see table 1 ) is crossed with the shock dummy to help control for correlations of M-Pesa with observables that might help smooth risk.

For Tanzanian mobile money users, a very similar set-up by Riley (2018) takes matters a stage further by examining the potential beneficial spillover effects (local externalities) of mobile money to the village community (which includes non-users) following an aggregate shock (either a self-reported shock such as droughts or floods, or a measure of rainfall deviations from a long-term mean, see table 1 ). 28 The regressions include a dummy for mobile money use by an individual in a village, and one for the proportion of mobile money users in a village, so that there are three interaction effects with the shock dummy, including its interaction with the vector of controls. Unlike in Jack and Suri (2014) , wealth, expected to be time-varying, is here included as a control.

Riley (2018) finds that there are spillover effects in the absence of a shock, as mobile money users share remittances with the village, resulting in per capita consumption of everyone in the village increasing. After an aggregate shock, however, households using mobile money benefit from an 8% to 14% increase in consumption (with 5% significance) compared with non-users, cancelling the effect of the negative shock on users; but there are no spillover effects to the community of non-users. The benefits to users and to communities (in the absence of a shock) are found to be highest in rural areas and to decrease sharply with distance to the nearest mobile money agent. The included district-by-time dummies are important in helping to control for heterogeneity from the self-selection into districts by mobile money services providers, for localized spillover effects, and for unobservable differential effects of rainfall (e.g., for different occupations by district).

All three studies conduct placebo tests supporting the common trends assumption of the DD specification. In Riley (2018) , Propensity Scoring was used to try to match users and non-users with similar characteristics, confirming results. Attempts by both Riley (2018) and Jack and Suri (2014) to apply the IV technique (see box 2 ) and instrument the usage dummy and its interaction with the shock are less successful, typically with weak instruments based on agent rollout data such as agent density (see box 2 ). The IV regressions do not contradict the conclusions, but in Riley (2018) , although a Sargan-Hansen test determines the instruments are valid (exogenous), they are found by Cragg-Donald Wald F statistic tests to be statistically weak, which may potentially introduce a large bias. The former test is missing in Jack and Suri (2014) .

Using data from a survey of nearly 7,700 M-Pesa agents, Jack and Suri (2014) also compare consumption responses in reduced-form panel regressions with fixed effects, substituting “access to an agent” for M-Pesa usage, and claim that the results reinforce their conclusions. However, the crucial assumption of exogeneity of the agent density proxy rests only on bivariate correlations, discussed critically above.

It remains possible that time-variant household heterogeneity (e.g., changing risk preference or changing technology preference) may still confound the results. One specific example of time-variation in characteristics would be where in the first wave of the panel, a fifteen-year old is not in work, but by the second wave, three years later, she is working, which affects her ability to purchase a mobile phone and use mobile money. It would be important to control properly for age structure in this case. More difficult to deal with is systematic unobserved heterogeneity from interaction effects. If there are missing interaction effects from time-varying unobservables or time-varying excluded observables (e.g., wealth) that could help households to smooth risk, then the effect of M-Pesa in smoothing consumption could be exaggerated. For instance, there could be an upward bias if a household that is wealthier in the second period is better placed to withstand a negative income shock; or if households wealthier in the second period than the first tend to experience smaller negative income shocks.

Mobile Money Transfers and Welfare

Far less satisfactory are the (non-RCT) welfare studies reviewed, where results are generally judged unreliable by this survey. Endogeneity problems for the usage dummy are center stage, and the use of instrumentation and other methods to mitigate it by removing as many sources of heterogeneity as possible are not always convincing.

Of the six studies, only three disentangle the impact of the mobile phone technology from the transfer mechanism by including a dummy for ownership of a mobile phone into regressions: Munyegera and Matsumoto (2016a) , Murendo and Wollni (2016) , and Sekabira and Qaim (2016) . One cross-sectional study faces serious problems of controlling for unobserved heterogeneity ( Murendo and Wollni 2016 ). Two panel studies use inappropriate linear specifications that are likely to introduce heavy biases ( Sekabira and Qaim (2016) and Kikulwe, Fischer, and Qaim (2014) ), see discussion in Aron (2017) . A fourth study employs propensity scoring with a very small cross-sectional sample, but is subject to unobserved heterogeneity ( Kirui, Okello, and Njiraini 2013 ). The full critical analyses of these studies can be found in Aron (2017) , and details are summarized in table 1 .

The two remaining studies use panel data. Of these two, one fully exploits Ugandan panel data to control for heterogeneity where possible (see table 1 ), and claims an increase of 9.5% (with 5% significance) in the monthly real per capita household consumption for mobile money users ( Munyegera and Matsumoto 2016a ). The Difference-in-Differences specification requires the mobile money intervention to be random, which is questionable. Their IV regression to address this problem shows the above coefficient in the regression for consumption increasing four-fold , which casts doubt on the results. Similar to Jack and Suri (2014) , the authors rely on bi variate correlations only to validate the agent density-based instrument. Using fixed effects regressions, the authors find a similar coefficient for food consumption as for total consumption, but greatly higher coefficients for non-food. Given the ambiguous results, propensity score methods are applied to try to match comparable households, and weighted regressions are run for total and food consumption. This recovers a coefficient of around 7% (at the 5% level) for overall consumption, but the coefficient for food consumption is poorly measured. Too little information is given to properly evaluate the method, however (see box 2 ).

The other panel study, by Suri and Jack (2016) , argues strongly for a causal role for mobile money on welfare. 29 The effect of mobile money in Kenya is explored for categories of outcomes, measured in 2014 (see table 1 ). Unlike the other studies in this sub-section, these authors use the change in agent density between 2008 and 2010 to proxy or substitute for mobile money usage (i.e., they are not using agent density as an instrument in an IV regression). 30 By pre-dating the proxy relative to 2014 outcomes, the authors hope to make their proxy exogenous. There are two problems with this. First, the measure may not be highly correlated with later usage (which is like having a weak instrument in an IV regression). Second, the crucial assumption of exogeneity of the agent density proxy rests on bivariate correlations conducted in Jack and Suri (2014) . That being said, placebo tests support the common trends assumption of the DD specification.

To estimate the marginal effect of an increase in agent density for females, a gender dummy and the change in agent density are crossed. The change in agent density is also crossed with household (or individual) characteristics to rule out cases where the gender effect was in fact driven by these other characteristics.

Suri and Jack (2016) do not use household fixed effects or location-by-time dummies, but control only for location fixed effects—upon which a great deal then rests to try to mop up household heterogeneity. There are controls for age and gender, but controls such as dummy for ownership of a mobile phone, household physical and financial wealth, education, and possession of a bank account are excluded. Their analysis is at its most convincing in a differenced specification for consumption (their table 1 ), which at least then effectively excludes household time-invariant fixed effects through differencing (the level regressions are likely to have considerable unexplained heterogeneity). Nevertheless, even in the differenced specification, time-varying heterogeneity from unobservables (and omitted wealth) may still introduce bias. With these caveats in mind, we present their results for consumption. These authors find that for households using mobile money, consumption growth for male-headed households was negative, while that of female-headed households was positive and statistically significant. They suggest that the latter could be driven by increased labor or capital income, or by transfers between individuals with different propensities to consume. They draw implications for the reduction of poverty (affecting 2% of Kenyan households), and shifts in occupations out of farming, particularly for female-headed households. However, if there is unobserved heterogeneity of the type discussed above, for example, if wealth which is not controlled for is correlated with mobile money services, then they may be over-estimating the reduction in poverty.

Of the few RCT studies reviewed, see table 1 , some deal with very small transfers and small and specialized samples, and results are not easily generalizable. Two papers exploring the impacts of public or employer mobile money cash or wages transfers are Aker et al. (2016) and Blumenstock et al. (2015b) . Both identify cost savings from reduced transactions costs for the disbursing party. But there are different results for the recipient: there are cost savings in Aker's study based in Niger, and possible cost increases in the Blumenstock et al. study in the more insecure environment in Afghanistan. Both studies disentangled mobile money delivery from ownership of a mobile phone, providing new phones to treatment and control groups.

The impressive RCT study on household welfare by Aker et al. (2016) finds improvements in household welfare after drought for the recipients of cash transfers through mobile money accounts in Niger, one of the world's poorest countries. Intra-household bargaining power for women was promoted and their productivity improved through reduced transport costs, and reduced travelling and queuing time. 31 Recipients were more likely to cultivate and market cash crops conventionally grown by women, and had fewer depleted durable and non-durable assets. Household and child diet diversity was 9% to 16% higher among households who received mobile transfers, mostly due to increased consumption of beans and fats (1% significance level), and children consumed one-third more of a meal per day (5% significance level). These authors emphasize that the mobile money “infrastructure” has to be working well to reap the benefits. Repeating such RCT studies across many locations, cultures, continents, and time periods may help reinforce the conclusions and generalizability.

Given the short time period of observation and the small sample size, the Blumenstock et al. (2015b) study, which was able to distinguish changes in the saving behavior of recipients of wage transfers in Difference-in-Differences estimates of the treatment effect, was not able to find improvements in welfare indicators such as consumption and self-reported satisfaction.

Analyses of Savings Behavior

There are several qualitative studies with localized implications for saving behavior. For instance, Wilson, Harper, and Griffith (2010 ) describe how members of informal savings groups in Nairobi find it cost- and time-effective to move their cash (especially with larger savings) into a group M-Pesa account each week from the deposit collector's own account. Further, Jack and Suri (2011) find that by 2009, 90% of early adopters used M-Pesa for saving (amongst other savings instruments and use of cash) for reasons of improved security, greater privacy, increased ease of use, reduced transactions costs, and precautionary saving against emergencies.

Three non-RCT studies encompassing a variety of techniques all suggest the beneficial influence of mobile money on reported savings by method, and on saving flows ( table 1 ). Two of these studies use cross-sectional survey data ( Demombynes and Thegeya 2012 and Munyegera and Matsumoto 2016b ), and one makes a balanced panel of locations, not individuals ( Mbiti and Weil 2016 ). None of these studies disentangles the technology from the service it provides by controlling for the ownership of a mobile phone. Attempts to instrument the mobile money dummy are not successful in these studies, but an approach employing the residual of an adoption regression by Munyegera and Matsumoto (2016b) is supportive, though in a cross-sectional context. No robust and conclusive results are reached, therefore. There are serious concerns with how the saving flow is measured and from the implications of the use of log specifications (see details in Aron (2017) ).

Probit regressions for saving by Demombynes and Thegeya (2012) with various controls ( table 1 ), find reported saving by any method is more likely for older individuals who are male, rural, married, and with higher levels of education, reported income, and wealth. With these controls, and instrumenting for M-Pesa usage, M-Pesa users are 20% more likely to report having savings (1% significance). The instrument (the fraction of respondents in the sub-location registered with M-Pesa) averages over individuals within locations, and eliminates only some unobserved district-level heterogeneity. This caveat suggests that the result is indicative only. The authors also apply IV estimation to the log of average monthly saving (a flow) on similar controls and with the same instrument (see table 1 ). The coefficient for M-Pesa usage is not statistically significant. It is unclear whether the endogeneity is severe and the instrument is so successful in dealing with it that mobile usage is not relevant to saving, or whether it is simply a poor instrument for M-Pesa usage.

A related exercise for Uganda using Probit regressions for reported saving yields no significant variables at the 1% significance level, save for the mobile money usage dummy ( Munyegera and Matsumoto 2016b ). The specification is not comparable to that of Demombynes and Thegeya (2012) , which included log income (highly significant), wealth quintiles, and marital status for a far larger survey ( table 1 ). Whether the significance of mobile money usage for Uganda is indeed important or whether the coefficient is biased strongly upwards as it proxies for unobservables is unclear. The log of annual saving (a flow) is modelled in Tobit regressions, with similar controls. 32 Two approaches are adopted to help address endogeneity (though not the IV approach). A residual from a first-stage Probit regression for mobile money adoption is added to the Tobit, and is significant at the 1% level. The coefficient on the mobile money usage dummy remains fairly stable, and is positive and significant, which is a supportive test. Second, to reduce observable (time-invariant) household heterogeneity, propensity-score matching is applied (though with scant information on methods used and robustness). These authors run OLS regressions weighted by the propensity score with various controls ( table 1 ), but nothing proves significant except the mobile money usage dummy and the value of assets (at the 5% level). The authors suggest this is because heterogeneity has been successfully removed and suggest a role for mobile money in encouraging saving. The conclusions require the proverbial “large pinch of salt” because despite the authors’ heroic attempts, in cross-section it is very difficult to control for unobserved heterogeneity, and the propensity result is also subject to unobserved heterogeneity concerns (see box 2 ).

A potentially interesting finding from the quantitative work of Mbiti and Weil (2016) is that adopting M-Pesa reduces both the use of informal savings groups and the need to hide cash in secret places. These authors use a first-differenced IV regression for saving methods with various controls ( table 1 ), the differenced specification removing biases due to any time-invariant unobservables. However, it is difficult to draw firm conclusions as the set of instruments used is not intuitive (see Aron (2017) ); and biases might arise from correlation with unobserved, time- varying characteristics of households.

Two RCT studies were the only saving studies that disentangled the mobile technology from the service it provides ( table 1 ). One RCT experimental study ( Batista and Vicente 2016 ) uses cross-sectional data and narrows the type of population tested in its selected sample; it is subject to the problem of interpreting a treatment effect when the intervention depends also on the type of training information provided. Both aspects limit the generalizability of the finding that mobile money increases the willingness to save, though the narrowing of selection helps deal with heterogeneity. A second RCT panel study controlling for individual and survey wave fixed effects, based in Afghanistan ( Blumenstock et al. 2015b ), was applied to a small and specialized sample. Increased usage of mobile savings differed by the prior banking status and size of salary of recipients, and liquidity preference and savings withdrawal rose with perceptions of physical insecurity. However, recipients had to incur the costs of finding liquid agents (where adequate mobile network and agent coverage actually existed), and some had privacy concerns for security reasons. Again, the results are suggestive but not generalizable.

One cross-country study tries to relate “enabling” regulation to the usage of mobile money for 35 countries. Gutierrez and Singh (2013) use self-constructed ( de jure ) regulatory indices in a logit regression controlling for both country characteristics and individual (micro-) characteristics. 33 , 34 By using location (country) fixed effects to reduce omitted variable bias, these authors are unable to include the indices themselves, but only their interaction with individual characteristics. 35 The interaction effects nevertheless yield some plausible insights. A regulatory framework that supports interoperability appears to promote higher usage among the poorest. Stronger consumer protection appears to reduce usage by the poorest, perhaps through raised costs, while amongst the educated, greater consumer protection promotes usage. But heterogeneity remains present in the cross-section, and the direct effect of regulation could only be tested if a panel of Global Findex usage data should become available.

The main contribution of this survey has been to explore the channels of economic impact and to critically survey a new body of economic research in order to answer the following question: Are empirical studies able to measure the economic benefits and local if not system-wide externalities? As a reality check for policy-makers, there is an important role for micro-studies in evaluating the often optimistic assumptions underlying macro-studies that link digital finance and economic growth and inequality. These include assumptions about the barriers to adoption, the welfare impact, the uptake of diversified services including credit, and the government's tax take. For instance, a highly optimistic study by McKinsey (2016) applies a proprietary general equilibrium macroeconomic model to macro-data for seven countries and extrapolate the results globally for all emerging market countries; these authors predict that adoption and use of digital finance (banking in general) could increase the GDP of all emerging economies by 6%, or $3.7 trillion, by 2025.

The survey has distilled lessons for improved practice in the empirical analysis of mobile money. Studies should demonstrate that they take the data issues seriously, including correctly measuring the usage of mobile money, or else providing caveats. It is important to disentangle phone ownership from usage of its services, such as mobile money. The survey suggests that studies do grapple with unobserved heterogeneity but often not sufficiently. The wary policy-maker should give the greater weight to micro-studies using balanced panel data , and which apply their considerable potential advantages for control of time-invariant and some time-variant (e.g., by location) heterogeneity (see box 2 ). Ideally, these should include appropriate controls for potentially time-variant household characteristics (e.g., demography, wealth , having a migrant worker in the family, and being formally banked) and location-by-time dummy proxies. Such a panel approach is probably “as good as it gets” in terms of ameliorating biases from unobserved heterogeneity. Some residual time-variant unobservable heterogeneity may still confound results, but in shorter time periods the bias is likely to be small. In areas where mobile money is fairly new, panel survey data collection should be encouraged. Controlling for heterogeneity and finding exogenous instruments in cross-sectional studies is a heroic exercise: these studies are likely to be compromised and unreliable.

Finding credible exogenous instruments for the endogenous mobile money usage measure in instrumental variable (IV) methods has proved highly challenging. Most are based on agent density and network connectivity, assuming the “random roll-out” of mobile money, and of network coverage. Statistical F tests often find the instruments weak, leading to potentially biased results. An increasing trend is to present propensity score analysis to reinforce the results when IV results prove ambiguous. However, more detail and clarity on evaluation and assumptions is required given the debate and controversy in the literature, so that the propensity score application is transparent and not a black box result.

Given drawbacks with all the techniques, it would be most satisfactory if studies could apply and contrast a range of techniques. 36 Applying a best practice approach to panel data both with and without fixed effects can ascertain the size and direction of the bias of OLS methods. The bias may be positive or negative; authors need to consider the direction of the bias, since then OLS methods can provide useful upper or lower bounds on estimates. Not controlling for unobserved heterogeneity and a lack of instrumenting or weak instruments probably results in an upward bias of the importance of mobile money for the level of consumption or saving. But, if looking at interactions with a negative shock, there is more likely to be a bias to zero; hence, the micro-studies could be under-stating the absolute size of the beneficial effect of mobile money on risk-sharing. 37 And while Suri and Jack (2016) characterize the risk-sharing result as more short-term in nature, if illness and death are prevented by improved insurance of this type, then there are long-term implications as well. With a range of techniques, the potential biases of IV methods and of the propensity score matching can also be ascertained. Where there is an under-statement of the bias, this qualitatively strengthens policy conclusions from noisy micro-studies.

Another problem, universally neglected by the surveyed studies, is non-constant parameters, e.g., because of spillover effects and technological improvements. By its nature, the evolution of mobile money entails regime changes. These shifts introduce potential non-linearities that need to be tested for in both micro- and macro-work. The changes could result in earlier estimates being an underestimate of later effects. Structural breaks can mean the findings of studies can be hard to generalize. The micro-studies ignoring spillover effects may be picking up only part of an effect, and hence may be a poor guide to the economy-wide effect of a policy.

Robustness testing and testing of the validity of instruments (their strength and exogeneity) are patchy over the studies. 38 Researchers should try harder to illuminate those dimensions where welfare improvements are greatest by checking for differences in responses between more and less affluent households and other types of non-linearity (e.g., urban versus rural, by occupation, and by education level), and by gender ( Suri and Jack (2016) ). Areas for future research, where there has been little quantitative work as yet, include building on Riley (2018) in exploring community spillover effects, and on Jack, Ray, and Suri (2013) and Blumenstock et al. (2016) on little-studied network effects, as well as on timely investigation of the new products of digital credit ( Francis, Blumenstock, and Robinson 2017 ) and insurance through mobile money channels.

Focusing on the studies that apply best practice, the most convincing evidence is from the panel studies of Riley (2018) and Jack and Suri (2014) , suggesting that mobile money fosters improved risk-sharing amongst informal networks in Kenya and Uganda after large shocks, through lower transaction costs of domestic transfer. On mobile money adoption, the Ugandan panel study of Munyegera and Matsumoto (2016a) deserves attention, supporting widespread qualitative evidence that education and wealth matter, but these authors found no gender or age effect for rural adopters. Generalizability of all these results may depend on the extent and quality of the agent network. Though all the non-RCT studies claim the beneficial influence of mobile money on reported savings (by saving method), and on saving flows, the results are compromised by a lack of balanced panel data and appropriate instruments, and no robust and conclusive results can be reached. RCT studies in Mozambique and Afghanistan suggest that saving did not increase, though the saving method switched to mobile money; these studies use small and specialized samples and are probably not generalizable. Far less satisfactory are the (non-RCT) welfare studies reviewed, where results are generally judged unreliable by this survey. A Ugandan panel study suggests an improvement in consumption for mobile money users ( Munyegera and Matsumoto 2016a ); the IV regression casts doubt on the claimed result, but it is supported by a propensity score analysis. A panel study for Kenya by Suri and Jack (2016) is at its most convincing in a differenced specification for consumption; consumption growth for male-headed households was negative and of female-headed households was positive with access to mobile money, but the result is tempered by probable bias from the limited control of heterogeneity. The RCT study by Aker et al. (2016) found the receipt of cash transfers through mobile money accounts promoted intra-household bargaining power for women and their productivity in Niger, with reduced transactions costs. Child nutrition improved and increased diet diversity for the household, with fewer depleted durable and non-durable assets than for control groups. The generalizability of this study is uncertain and depends on a functioning agent network. Repeating such RCT studies across many locations, cultures, continents, and time periods may help reinforce the conclusions and generalizability. 39

Digital finance is one of few areas where there has been a real revolution in services and leapfrogging over deficient traditional infrastructure. However, improved access to financial services is compromised by economic obstacles, significant amongst which are corruption, a lack of electricity generation, and appalling road infrastructure. 40 Complementary action is required to address such problems. The micro-studies show how difficult it is to quantify outcomes accurately and to extrapolate from individual studies of different countries, scaling up the effects to make policy pronouncements. Given the lack of complementary inputs, there could be strong returns to scale in the short-run from mobile money, but not in the long-run, given the constraints. On the other hand, the micro-benefit established by several studies could be multiplied greatly through spillover effects in the presence of well-functioning general infrastructure and transparency (lack of corruption)—especially if mobile money itself reduced corruption.

Atkinson (2015) has argued that economic inequality is often aligned with differences in access to, use of, or knowledge of information and communication technologies. This author stressed that researchers, firms, policymakers and governments have the possibility to shape the direction and path of technological change. Aid agencies, other donors, charitable foundations, and international agencies have played a key role in the beneficial growth of mobile money and the associated financial inclusion ( Aron 2017 ). Creative coalitions and the investment in multi-stakeholder partnerships can prompt deeper change, learning, and practical action. An important application is for academic research on mobile money. Poor quality data and sub-optimal data collection and analysis severely compromise the conclusions that can be reached from empirical work. A concerted attempt by donors, regulators such as central banks, the regulated MNOs, and academics could harness the appropriate data for timely best practice analysis. If anonymizing procedures were accepted, then the benefits from research analysis using anonymized disaggregated data could be reaped. The survey has highlighted the best practice techniques that when applied to empirical analysis could reach more reliable conclusions and bolster the case for significant government and donor support, and commercial investment.

Janine Aron is a Senior Research Fellow at the Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, UK; Centre for the Study of African Economies, Department of Economics, Manor Road Building, Oxford OX1 3UQ. This work was supported by the Gates Foundation (grant number MQRYDE00), the Open Society Foundations and the Oxford Martin School. Special thanks go to John Muellbauer (Nuffield College, Oxford University). The author also thanks Chris Adam (Oxford University), Tony Atkinson (Oxford University), John Duca (Federal Reserve Bank of Dallas, USA), Colin Mayer (SAID Business School, Oxford University), Ggombe Kasim Munyegera (IGC), David Porteous (Bankable Frontier Associates), Emma Riley (Oxford University), Federico Varese (Oxford University) and Sebastian Walker (IMF) for their helpful comments.

The phenomenal growth since 2007 of Kenya's M-Pesa system has brought mobile money to international prominence (“M” is for mobile, and “pesa” is Swahili for money), see box 1 .

Prior to mobile money in Kenya, there were fewer than three bank branches per 100,000 people. Saving was mostly in the form of cash under the mattress. Domestic transfers used scarce post office branches, or insecure intermediaries such as bus-drivers. International remittances were received expensively via money transfer companies or Hawala.

Rotating savings and credit associations and cooperatives address the problem of asymmetric information, allowing small accumulated sums by groups to help individual members spread risk. The related micro-credit movement offers collateral-free loans to marginalized borrowers at near-market interest rates. However, assessing such micro-finance in a long-running evaluation in India, Banerjee et al. (2015) conclude it has had limited success.

The FICO scores in the United States, decisive in 90% of U.S. lending decisions by 2015, are created in a similar manner (Financial Times 2015).

The official remittances statistics would improve as well as the economic management of remittances. In highly dollarized economies (see Corralesa et al. (2016) for the extent of this phenomenon in Africa), mobile money through lower transactions costs may reduce currency substitution, thereby deepening their financial systems.

On the merits of a cashless economy, including fighting corruption and money-laundering, see Rogoff (2016) .

Registration aids financial inclusion toward formal sector products. By contrast, an OTC transaction is conducted through an agent's account on behalf of the customer.

Regulation of mobile money is discussed in detail in Aron (2017) , especially prudential regulation by the central banks; see also Di Castri (2013) .

Third party merchants are not “agents” in a strict legal sense of having the legal authority to act for the service provider—this depends on the local regulation requirements.

Remittances to developing countries are projected to reach US$444 billion in 2017. The true size of remittances, including unrecorded flows, is likely to be significantly larger (World Bank Migration and Development Brief no. 27).

The following authors have examined aspects of the economics of mobile money: Mas and Klein (2012) , Jack, Suri, and Townsend (2010) , Jack and Suri (2011) and Weil, Mbiti, and Mwega (2012) .

See Karlan et al. (2016) on market failure in a more general context of financial services.

Mobile money halves the cost of sending compared to Western Union, and is about a third lower than the postal bank or bus delivery cost, excluding transportation or time costs (see also Morawczynski (2009) ).

An endogeneity problem in econometrics occurs when an explanatory variable is correlated with the error term as a result of simultaneous causality, omitted variables, and/or measurement error. There are several statistical methods that aim to correct the resulting bias in the regression estimates (see box 2 ).

The log of wealth is one of the observables and there is weak evidence for a correlation with wealth.

Heterogeneity refers to variation across individual units of observation, some of which can be observed (e.g., age and education), and some of which is difficult to measure (e.g., changing technological preferences). Thus, omitted heterogeneity is an omitted variable, and hence a kind of endogeneity (see box 2).

On agent quality, see Balasubramanian and Drake (2015) .

Work in progress by Blumenstock and co-authors explores the negative effects of violence on the adoption of mobile money in Afghanistan. Available at: http://www.jblumenstock.com/ .

Not on adoption per se but with implications for adoption, Economides and Jeziorski (2016) match administrative transactions data with GPS data in Tanzania, quantifying motivations for usage, such as willingness to pay to avoid walking with cash or to avoid storing money at home to alleviate criminal risk.

These authors take two approaches, and find similar results, using first a Probit regression, and then a linear probability model with fixed effects. The mobile money “usage” measure in the dependent variable does not match the preferred definition of active (90-day) users, however, and this could bias the results.

Note that agent density may not be exogenous.

The results of a related study on adoption by Weil, Mbiti, and Mwega (2012) should be regarded as suggestive, and of supporting correlations, see Aron (2017) and table 1 . The study cannot control for individual fixed effects and suffers from an omission of controls.

This is a reasonable assumption if unexpected shocks are reported, and not systematically correlated with most household characteristics. Though unlikely in a short time frame, if shocks are correlated with changes in unobservable household characteristics then they would not be random.

Idiosyncratic shocks affect individuals or households; covariant shocks affect groups of households, communities, regions, or even entire countries.

The average amount transferred over the two-month period is small at around US $1; the total additional influx (explicit transfers to all 15 cellular towers within 20 km of the epicentre) measured about US $84.

Food consumption, however, appears to be equally well-smoothed by both users and non-users in the sample.

User households can finance health care expenditures from remittances without compromising other consumption, but non-users must reduce non-medical spending for this; see also Suri, Jack, and Stoker (2012) .

A broadening of networks is likely ( Chuang and Schechter 2015 ), but Riley (2018) more restrictively assumes the sharing social network is village-wide, rather than across villages by lineage, for instance, and that it is constant over time.

“…Thus, although mobile phone use correlates well with economic development, mobile money causes it,” ( Suri and Jack (2016) , my italics).

Agent density is defined as the number of agents within 1 km of the household. This change variable approximates to the level of agent density in 2010, as agent density would have been low in 2008.

Cash-transfer recipients were temporarily able to conceal the arrival of the transfer, increasing bargaining power.

This technique serves to censor observations at zero as the lower limit since households not using financial services will not yield an outcome.

De facto rather than de jure regulations should enter an index, so that it is the quality or performance of the existing regulations that matter rather than merely their existence ( Aron 2000 ).

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Mobile banking: a bibliometric analysis

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• Published: 01 February 2024

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• Kamlesh Kohli 1 ,
• Monika Kashyap 1 ,
• Mahendra Babu Kuruva 1 &
• Sunil Tiwari   ORCID: orcid.org/0000-0002-0180-9237 2  

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This study aims to analyze the keywords related to mobile banking by focusing on its development between 2003 and 2023. To do so, a systematic bibliometric analysis is undertaken, for which, the screening of Scopus database was done to identify the publication (institutions, most-prolific authors, year-wise, countries-wise and most-cited papers). Additionally, keyword occurrence analysis was also done using the VOSviewer software. The results of the study indicate that there are worldwide trends and increased production that have resulted in various changes. One prominent topic that consistently emerges in relation to mobile banking across different time periods is mobile telecommunication technologies. Through the demonstration of the origination of major terminologies in mobile banking, notable shifts in the evolution of the field's terminological framework were discernible. Hence, it is imperative to investigate the progress in forthcoming decades, specifically the impact of recent global events on the evolution of mobile banking usage worldwide. Furthermore, the current study adds substantial value to the existing body of literature by presenting a conceptual framework that might guide future research endeavors. The framework offers researchers the potential to investigate the many study streams in forthcoming studies. The present study represents a novel contribution in terms of its methodology. A thorough examination of research databases, such as Scopus and Google Scholar, reveals a lack of published literature that comprehensively and extensively addresses the topic of mobile banking (m-banking) in a multi-period context, particularly in an applied manner. Moreover, the present study addresses this research gap by the implementation of a bibliometric analysis and content analysis.

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Mobile Banking Adoption: Key Challenges and Opportunities and Implications for a Developing Country

Exploring the key drivers behind the adoption of mobile banking services.

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Kamlesh Kohli, Monika Kashyap & Mahendra Babu Kuruva

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All authors have approved the manuscript title “RECAPITULATION OF SCHOLARLY RESEARCH ON MOBILE BANKING FROM 2003 TO 2021: A BIBLIOMETRIC ANALYSIS” and agree with its submission to this journal. The current work has not been published and is not under consideration for publication elsewhere. Moreover, there is no conflict of any interests for this study.

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Kohli, K., Kashyap, M., Kuruva, M.B. et al. Mobile banking: a bibliometric analysis. J Financ Serv Mark (2024). https://doi.org/10.1057/s41264-024-00267-7

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International Journal of Bank Marketing

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Article publication date: 2 October 2017

Most empirical studies of m-banking seek to understand the factors and motivations that influence the adoption or behaviour intention. The purpose of this paper is to focus on analysing and synthesising existing studies and make recommendations to researchers and practitioners.

Design/methodology/approach

Few papers focus on the m-banking use and individual performance, but on the determinants of adoption measures, instead. This research examines 64 journal articles published between 2002 and 2016 in top journals. Following a comprehensive review of the literature, the authors propose a research agenda.

The importance of use and individual performance has long been recognised by academics and practitioners in a variety of functional disciplines. The present review indicates that the topics of m-banking adoption and behavioural intention dominate the majority of research, but finds very few studies on post-adoption. The two most significant drivers of intentions to adopt m-banking are perceived ease of use and perceived usefulness. Considering several m-banking definitions, the authors propose a new, broader definition that takes into account the technological changes that have occurred over time. m-banking is a service or product offered by financial institutions that makes use of portable technologies.

Originality/value

This paper assembles this diverse body of knowledge into a coherent whole. The authors expect that this review will be of benefit to anyone interested in m-banking research and that it will help to stimulate further interest. In order to advance research in m-banking, future research should consider other theories uncovered in our findings.

• Individual performance
• M-banking definition evolution
• M-banking theory framework

Tam, C. and Oliveira, T. (2017), "Literature review of mobile banking and individual performance", International Journal of Bank Marketing , Vol. 35 No. 7, pp. 1044-1067. https://doi.org/10.1108/IJBM-09-2015-0143

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Analyzing the Effect of People Utilizing Mobile Technology to Make Banking Services More Accessible

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Many firms in the modern world utilize m-banking systems to communicate with their consumers. The word m-banking refers to a widespread method of providing financial services and localization to customers. Since m-banking is important to both banks and users, it has been included in numerous literary works. As a result, embracing financial services via the m-banking platform is critical. This article's technique is mostly descriptive research that investigates common views, current situations, modern tactics, tangible emerging consequences, etc. The main objective here is to analyze the benefits of this study by investigating the past. Since this article analyzes what exists and is descriptive, the data is being retrieved by conducting a cross-sectional survey method about different features that are relevant by sampling the population. The main aim of this study is to explore the adoption of mobile banking technology by consumers. Based on the values of different variables such as affective commitment (AC), transaction convenience (TC), perceived ease of use (PEU), perceived reliability (PR), pre and post benefits (PPB), service, system, and information quality (SSIQ), bank trust (BT), and profitability (P), the inter-relationship between them and the adoption of m-banking technique by the users in banking technology. The model is investigated by examining the hypothesis and identifying the relationship that exists between these different parameters. A simple linear regression method is implemented using the Statistical Package for the Social Sciences (SPSS) software.

Introduction

Nowadays, mobile technology plays a vital role in many industries such as banking on managing stocks and finances, business firms sell their products through applications and websites. Mobile banking services such as payment systems influence people's lives more dramatically than other innovations in the recent times ( 1 ). Mobile technology made banking services such as payment, transaction, and other services easy through the online mode instead of offline mode. Even the adoption of mobile banking services increased day-by-day by the consumers, still mobile payments do not become ordinary to many of the public ( 2 ). Due to the growth of mobile network technology, there is a need for security infrastructure for mobile payments. The security measures give trust to the consumers to adopt mobile banking at anytime from anywhere.

For achieving a successful outcome in business, mobile technology is recognized as competitive and creative equipment that provides better online transaction options ( 3 ). Recently, cellphones are being widely used as a tool by people for online shopping, banking, paying bills, etc. Our day-to-day routine lives have been changed through the adoption and development of mobile technology that modifies the way of communication, approaches for selling and buying goods and services, and also the mode of information collection. Such corresponding correlation arises in a constructive environment that has no limitation regarding the time or place ( 4 ).

The rapid development in mobile technology and the protection framework facilitate the people to perform m-banking transactions at any time from anywhere, whereas m-banking comprises financial service which is implemented directly or indirectly over a network of wireless telecommunication ( 3 ). In the financial sector, the services applied by mobile are referred to as mobile financial services (MFSs) they also include mobile banking and mobile payment. Several studies were conducted to examine the peoples' mindset in view of understanding the mode of adopting mobile banking as a unique and usual service ( 5 ). According to the statistics, the level of usage of smartphones has increased from 35 to 80% in 2020, also worldwide, the number of mobile phone beneficiaries is about 6.8 billion, though each mobile was fully activated with the internet. Thus, mobile banking helps in the development of bank access rates which influences bank perception ( 6 ). In the banking sector, it is observed that most of the customers have switched to smartphone, apps, or tablets which are highly utilized in their day-to-day life for shopping, entertainment, learning, socializing, and jobs, and these mobile-centric customers are preferred by more bankers ( 7 ). For example, a bank in Japan, Jibun Bank whose principal communication with customers is through the mobile channel and was the first bank that affords their complete banking services and products over mobile channels. With the help of cameras and mobile phone, customer has the facility to start a new account with their bank. This mode of banking enables the bank to improve their customer service over anywhere at any time basis. Also, the different mobile banking services are payment, web-related shopping, fund transfer, and web-related marketing. This Jibun Bank has reached more popular for its efficient services within the short duration of their mobile banking introduction ( 8 ). Through this, they have attained above 500,000 new bank accounts in their bank. In the initial days, the bank had the facility to send usual text messages related to money transfer and withdrawal and these services were updated to provide its customer a complete functional banking experience ( 9 ).

Mobile banking is adopted by many consumers as it is easy to open an account, pay bills, transfer funds, do mobile-based shopping, etc. Many banks in China and America who introduced mobile banking for all their services experienced high demand for their services through mobile banking ( 10 ). But many challenges were faced by these banks due to no face-to-face interactions. Trust is an important factor in mobile banking but lack of interaction led to the risk of uncertainty and anonymity. On the other hand, the consumers also suffer from uncertainty whether the bank is trustworthy are not.

During the last decades, mobile banking became relevant all over the world. The number of fund transfers through mobile banking also increased since last year, but mobile banking has many security problems. Sometimes, the security setting of mobile phones can be overridden by the virus in smartphones ( 11 ). Many banks open their banking applications, these apps should be updated frequently otherwise the consumers can be vulnerable to the attacks such as DDoS attacks, phishing, spoofing, corporate account takeover, and skimming ( 12 ).

The major contribution of the article is:

• To analyze the benefits of this study by investigating the past.
• To explore the adoption of mobile banking technology by consumers.

The remaining article is arranged accordingly. Section Literature Review presents the related work and the hypothesis is formulated in Section Hypothesis Development and Research Methodology. The results in terms of linear regression and descriptive analysis are provided in Section Results and the article is concluded in Section Conclusion and Discussions.

Literature Review

In the modern world, many firms use systems of mobile banking to communicate with the customers. The payment system in mobile banking services benefited people's lives. This article aims to analyze the effect of people using the technology of mobile to make banking services more accessible. Literature on mobile banking adoption, electronic banking services, mobile payment service user acceptance are reviewed.

Hamidi et al. ( 13 ) studied the influence of mobile banking adoption on consumer engagement and satisfaction utilizing the customer relationship management (CRM) system, which is the most essential aspect in banking industry. CRM is also seen as a critical role for enhancing client satisfaction in mobile banking. The statistical study performed evaluated the dialogue between the bank's customer sector and their client. The statistical analysis findings have a favorable influence on consumer interactions and satisfaction.

Geebren et al. ( 14 ) investigated the importance of consumer satisfaction in mobile eco-systems that used electronic banking services, particularly in developing nations. This entailed researching consumer satisfaction in mobile banking, with a focus on the importance of trust. To determine consumer satisfaction, structural modeling using partial least squares (PLS–SEM) methods were employed to examine the data, and trust demonstrated that customer contentment had a beneficial influence.

The relevance of an early trust theoretical model in mobile payment service user acceptance was highlighted by Gao et al. ( 15 ). The initial trust theoretical model highlighted the facilitators and barriers to user trust in m-payment services. The links in the original trust theoretical model were assessed using partial least squares structural modeling (PLS–SEM). The findings may be used in m-payment adoption research and practice in a variety of ways. In total, 52.3% of the difference in usage intention was explained by the current model.

Hentzen JK et al. ( 16 ) offered a mobile technology that allows for vital involvement, as well as an explanation of how a retirement app might assist people in planning for their post-retirement strategy. The available literature survey data were used to evaluate a sample of 440 Australian pension fund members. The findings show that consumers' perceived financial security, financial self-efficacy, retirement planning involvement, future consequences consideration, and perceived usefulness with a mobile retirement app have direct and indirect effects on their expected engagement through their goal to adopt the app.

Zhu et al. ( 17 ) investigated existing technology designs, including mobile banking, used by rural communities in six Chinese regions. According to the findings, interpersonal and mass communication channels have a bigger influence than organizational communication channels. Mobile banking should be examined since it can assist alleviate the lack of access to financial goods and financial infrastructure in rural areas.

Afeti et al. ( 18 ) developed a mobile payment technology for payment for micro-businesses. The study draws on the transaction cost theory and the task-technology fit (TTF) theory as the assumed lens. In total, 20 micro-businesses based on qualitative data were analyzed and the research findings denote those micro-businesses adoption of mobile payments results in strategic and operational benefits.

Alamoudi et al. ( 19 ) proposed that the mobile technology acceptance lookalike was changed as we investigated consumers' acceptance of mobile shopping in general stores by examining transaction convenience, usefulness, attitudes, ease of use, transaction speed, optimism, and personal innovativeness. A total of 351 respondents completed the questionnaire evaluation. Consumers are willing to use mobile shopping channels if the system is clear and straightforward to use.

Jebarajakirthy et al. ( 20 ) used a comprehensive moderated mediation framework to evaluate the influence of online convenience aspects on mobile banking uptake. Covariance-based structural equation modeling and the process macro are utilized to test these predictions. This study examines how convenience characteristics influence mobile banking adoption intentions.

Abdinoor et al. ( 21 ) studied the adoption of mobile financial services in Tanzania with the use of a technology acceptance model. To select the sample from data collection, a random sampling technique was used. The user and non-user of mobile financial services were included in the sample. Zhang et al. ( 3 ) investigated customers' use of mobile technology to help them with banking services and activities, as well as the variables that impact their adoption and engagement. Here, the analysis is done by the structural equation modeling technique to know the consumers' intentions toward mobile banking. The result examines the adoption of mobile banking apps to facilitate bank consumers' banking services.

Tiwart et al. ( 22 ) researched the variable that influences adoption in Commercial Bank Ethiopia. The author used factors such as perceived ease of use (PEOU), infrastructure (INF), security (SEC), trust, and e-banking adoption. Here, a structural equation model based on least partial square analysis is used. The result of this analysis proves that the trust of customers mediates between PEOU, INF, SEC, and e-banking adoption and realized the factors that guess the purpose and adoption of e-banking by Ethiopian customers.

The problem identified in mobile banking services is critical financial services through mobile banking because it has security problems.

Hypothesis Development and Research Methodology

Participant's demographic information.

This study is mainly conducted in China by taking the top e-banks that utilize m-banking. The questionnaire is distributed to both the users and staff of the bank. Within 6 months, 293 questionnaires were completed. These details were taken into consideration in this study. The details of the questionnaires were obtained online and also provided to the users on their official bank pages. The information obtained in the questionnaire is provided in Tables 1 , ​ ,2 2 .

Demographic analysis.

Participant details.

Participant Details

The information of the customers is mainly obtained based on their m-bank usage. The five main banks such as state-owned, joint-stock, postal savings, commercial city bank, and the agricultural bank were selected. Each participant was asked to say about their experience of their usage.

The amount of time spent by each user on average is provided in Figure 1A . As seen in Figure 1A , more than 39.38% of users are using more than 3 h a day and a minimum of is online Conceptual Model for less than 1 h. In total, 8.21% of the users only use the application for a limited amount of time (below 30 min).

Usage analysis of m-banking application (A) Users using m-banking during the day and (B) users' friends and references using mobile banking.

Figure 1B presents the percentage of people the users have referred for mobile banking. Based on Figure 1B , a total of 48.52% has more than 100 contacts who have been referred to the m-banking. The increase in m-banking contact can improve efficiency.

Conceptual Model

The main aim of this research is to identify the usage of mobile technology of consumers for the banking system. The hypothesis provided for each variable is explained in detail in this section and the conceptual model is provided in Figure 2 . The measurement instrument used for this study is presented in Table 3 .

Conceptual modeling structure.

Measurement instrument to analyze different parameters.

Hypothesis Modeling

Service, system, and information quality (ssiq)".

System quality mainly measures the efficiency of the overall m-banking system in terms of service provider anonymity. The system quality of the m-banking mainly depends upon the ease of use, the attractiveness of the application, trust of the user, etc. If the user trusts the system quality, then they mainly select the system for future transactions. Service Quality mainly relies on the service provided by the m-banking system and it also evaluated the effectiveness of the service in terms of personalization, reliability, delay, etc. Hence, service quality is crucial to improve the quality of m-banking since poor-service quality can result in minimal user trust and satisfaction. Information quality mainly implies that high-quality information is reliable, complete, accurate, relevant, and accessible. The information quality is an important variable that improves the usage, trust, and satisfaction of the customers in the m-banking system.

Hypothesis (H6): The service, system, and information quality had a positive relationship in improving the m-banking quality and user satisfaction.

Affective Commitment

Affective obligations between business partners reflect mental reliance on others and are founded on emotions, loyalty, and dependency. The psychological bond in this example demonstrates an emotional commitment. Support for differences in particular data among new and long-term clients. Based on their differing expectations, new and loyal clients behave in the same way.

Hypothesis (H1): The interaction of customers helps to develop a good relationship with the affective commitment of a person to the bank which is focused on interaction.

Pre- and Post-benefit Convenience

The time and attempt to acquire some benefits of services is said to be benefit convenience. Some of the components of benefit convenience are fast service, timely services, and bank employee attitudes. Sometimes in banks, the consumer needs to visit repeatedly to avail specific service. But in m-banking, consumers can avail any services on the go from the home itself. So, the time and attempt to acquire the service became very easy. Therefore, benefit convenience shows a positive result in mobile banking adoption ( 23 ). The time and attempt to contact a service provider for a particular service is said to be post benefit convenience. In m-banking, the service provider can be contacted and solve the grievances within a few clicks through various means such as email, live chat, and toll-free numbers. But in offline banking, the consumer needs to visit the branch and wait for the service provider to solve the problem in service. So, the post benefit convenience shows a positive result in mobile banking adoption ( 20 ).

Hypothesis (H5): The pre- and post-benefit convenience are positively interrelated with m-banking.

Transaction Convenience

The fast and simple way to complete the transaction is said to be transaction convenience. Acquiring the service of transaction in a lesser time is the main component of transaction convenience. Other components of transaction convenience are uninterrupted transaction, transaction confirmation, easy checkout, and price inconsistency. By using m-banking, the transaction can be done in any place at any time in a few clicks and can do several transactions simultaneously. But in offline banking, consumer needs to wait in a queue for availing a transaction service. Hence, transaction convenience shows a positive result in mobile banking adoption ( 24 ).

Hypothesis (H2): The transaction convenience is positively interrelated with m-banking.

Profitability

Customer participation in an organization's mobile banking may increase customer value and profitability ( 25 ).

Hypothesis (H8): Profitability shows positive results in mobile banking adoption.

Perceived Reliability

Perceived reliability is specified as the limit to which the individual has independently considered the technology as a faithful one. In order to offer a trustworthy technical service, the system reliability is important that enables the user to attain the aimed objective. An undependable technology that is employed by an individual seems to be low confidence and such technology utilization should be restricted ( 26 ). Research related to mobile technology and information system has revealed that a technology with perceived reliability possesses has an important impact on customers' perceived satisfaction, detected value, and observed quality. Also, customers' understanding level of trust is examined and analyzed as an antecedent of confidence in an organization through the mobile commerce setting. Though mobile is a wireless technology, it is easily susceptible to violations or attacks ( 27 ). Mostly, the customers were insisted to share their personal data while consuming and shopping services performed through mobile technology such as date of birth, debit and credit card details, funds, and address. The reliability of mobile technology should be improved thereby increasing customers' belief in securing their personal data. The postulated hypotheses are mentioned as follows:

Hypothesis (H4): Perceived reliability in m-banking helps to improve the consumer's trust and ease of use of banking services.

Banking Trust

In this hypothesis, trust is considered as a faith of competence, integrity, ability, and benevolence that the individual has toward each other. Trust has become an important reducing feature of risk, whereas the e-commerce connection is considered as a naturally risky factor ( 28 ). Also, trust in banking is specified that it is linked to reliability and perceived privacy. Generally, the primary interaction between the customer and banker will significantly affect the trust progress in them.

Hypothesis (H7): Based on their usage of mobile devices and attitude, the consumer trust toward M-banking increases.

Perceived Ease of Use (PEU)

Perceived ease of use evaluates the increase in the amount of work required to acquire a job utilizing new technologies on an individual basis. In the effort condition, the simplicity of use of mobile innovation is critical for including customers in the co-creation of value experiences ( 29 ). Mobile technology's ability to increase advantages for bank clients has been widely shown, and its simplicity of use is a key factor in consumer acceptability. PEU has been linked to advanced tackling new technologies in the banking industry in several research. However, the launch was difficult, and it was claimed that customers' inclination to utilize mobile banking services is not intrinsically related to employment prospects. As a consequence, it indicates that study into PEU and its influence on consumer attitudes, as well as the interactive method, are both worthwhile endeavors ( 30 – 32 ).

Hypothesis (H3): Customers' views about the usage of mobile devices for facilitating financial services are favorably associated with their perceived ease of use (PEU).

Scale Validity and Reliability

The reliability of the scales was assessed using Cronbach's alpha values. Goodness-of-fit approaches were used to assess the overall model fit for confirmatory factor analysis (CFA). The RMSEA was 0.09, the comparative fit index (CFI) was 0.99, the goodness-of-fit (GFI) was 0.98, and the standardized root mean square residual (RMR) was 0.06. The X2 to df ratio was 4.14, the root mean square error of approximation (RMSEA) was 0.09, the CFI was 0.99, the GFI was 0.98, and the standardized RMR was 0.06. As indicated in Table 4 , the reliability coefficients of all constructs were more than 0.87, which is higher than the 0.9 thresholds. The extracted average variance was used to assess convergent validity (AVE). Because the AVE values ranged from 0.58 to 0.92, convergent validity was not an issue. The AVE scores were lower than the squared correlations between pairs of constructs, indicating discriminant validity in the data set. Table 4 shows the results of the Cronbach's alpha and AVE tests.

Hypothesis testing.

Linear Regression Test

Regression tests are run when the allowable alpha coefficient for the scales has been determined. Table 5 shows the regression test results for each of the assumptions discussed in the following sections. Affective commitment (AC) and interaction were investigated, and the correlation coefficient 0.297 and likelihood of significance 0.000 indicate that there is a positive and significant relationship between the two. The regression test supports this positive relationship, showing that the interaction variable may be able to predict AC variation.

The results of the linear regression test.

With a correlation value of 0.333 and a probability of significance of 0.000, the following test demonstrated a positive relationship between perceived ease of use and satisfaction (PEU). The correlation coefficient between the variables perceived reliability (PR) and interaction was 0.172, with a probability of significance of 0.000, demonstrating a positive link between the two variables. The correlation value of 0.430 and the probability of significance of 0.000 indicated a positive relationship between satisfaction and service, system, and information quality (SSIQ) ( 3 ).

Descriptive Data

The items in this questionnaire were answered using a five-point Likert scale, with the alternatives being: very low (1), low (2), medium (3), high (4), and very high (5). Table 6 shows the SD, middle value (MV), and lowest value (LV) of the descriptive data received from the surveys ( 3 ).

Descriptive data analysis.

Conclusion and Discussions

Mobile banking is a cost-effective way to reach customers. The different activities that can be performed by the consumers in m-banking are checking their bank balance, making transactions, making investments, getting account statements, paying bills, etc. The motivation for user adoption and use of mobile banking is examined in this research. Positive connections between profitability, trust, and transaction convenience to utilize mobile banking have been found in previous studies. Users' confidence in using mobile banking services, enjoyment, utility of the system, etc., are not the same thing. The participants were unconcerned about the danger of fraud, system dependability, or perceived privacy while building and extending faith in their banks and mobile services. Because of today's technology-driven lifestyle, individuals are receptive to embracing new technologies that are compatible with their mobile phones.

To add value to practice, we must obtain convincing and widespread results from both users and mobile banking service providers. Like mobile phones, smartwatches have become more popular among people nowadays. With this wearable technology, banks have the greatest responsibility to fulfill hedonic and social needs. The purpose and use of this research are common to all consumers irrespective of various countries, populations, and socio-economic status. The variables used in this work are, namely, affective commitment (AC), transaction convenience (TC), perceived ease of use (PEU), perceived reliability (PR), pre- and post benefits (PPBs) service, system, and information quality (SSIQ), bank trust (BT), and profitability (P). Consumer behavior such as untrustworthiness, fraudulence, and so on, as well as mobile banking transaction risks such as financial, privacy, and cyber security, are important risk concerns that may be addressed in future research developments.

The problems and risks that arise by the consumers and banking sector for adopting mobile banking services would be reduced by the scientific researchers using various technologies and methodologies in the future. The results in this article show the different statistical surveys and believed that it is true for both mobile banking consumers and non-mobile banking consumers with various socioeconomic environments. But when we consider globally each country had its own rules and regulations regarding mobile banking transactions, services, and products. And these rules and regulations react differently in different countries.

Data Availability Statement

Author contributions.

Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Research Proposal on Mobile Banking.pdf

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ORIGINAL RESEARCH article

Analyzing the effect of people utilizing mobile technology to make banking services more accessible.

• Capital University of Economics and Business, Beijing, China

Many firms in the modern world utilize m-banking systems to communicate with their consumers. The word m-banking refers to a widespread method of providing financial services and localization to customers. Since m-banking is important to both banks and users, it has been included in numerous literary works. As a result, embracing financial services via the m-banking platform is critical. This article's technique is mostly descriptive research that investigates common views, current situations, modern tactics, tangible emerging consequences, etc. The main objective here is to analyze the benefits of this study by investigating the past. Since this article analyzes what exists and is descriptive, the data is being retrieved by conducting a cross-sectional survey method about different features that are relevant by sampling the population. The main aim of this study is to explore the adoption of mobile banking technology by consumers. Based on the values of different variables such as affective commitment (AC), transaction convenience (TC), perceived ease of use (PEU), perceived reliability (PR), pre and post benefits (PPB), service, system, and information quality (SSIQ), bank trust (BT), and profitability (P), the inter-relationship between them and the adoption of m-banking technique by the users in banking technology. The model is investigated by examining the hypothesis and identifying the relationship that exists between these different parameters. A simple linear regression method is implemented using the Statistical Package for the Social Sciences (SPSS) software.

Introduction

Nowadays, mobile technology plays a vital role in many industries such as banking on managing stocks and finances, business firms sell their products through applications and websites. Mobile banking services such as payment systems influence people's lives more dramatically than other innovations in the recent times ( 1 ). Mobile technology made banking services such as payment, transaction, and other services easy through the online mode instead of offline mode. Even the adoption of mobile banking services increased day-by-day by the consumers, still mobile payments do not become ordinary to many of the public ( 2 ). Due to the growth of mobile network technology, there is a need for security infrastructure for mobile payments. The security measures give trust to the consumers to adopt mobile banking at anytime from anywhere.

For achieving a successful outcome in business, mobile technology is recognized as competitive and creative equipment that provides better online transaction options ( 3 ). Recently, cellphones are being widely used as a tool by people for online shopping, banking, paying bills, etc. Our day-to-day routine lives have been changed through the adoption and development of mobile technology that modifies the way of communication, approaches for selling and buying goods and services, and also the mode of information collection. Such corresponding correlation arises in a constructive environment that has no limitation regarding the time or place ( 4 ).

The rapid development in mobile technology and the protection framework facilitate the people to perform m-banking transactions at any time from anywhere, whereas m-banking comprises financial service which is implemented directly or indirectly over a network of wireless telecommunication ( 3 ). In the financial sector, the services applied by mobile are referred to as mobile financial services (MFSs) they also include mobile banking and mobile payment. Several studies were conducted to examine the peoples' mindset in view of understanding the mode of adopting mobile banking as a unique and usual service ( 5 ). According to the statistics, the level of usage of smartphones has increased from 35 to 80% in 2020, also worldwide, the number of mobile phone beneficiaries is about 6.8 billion, though each mobile was fully activated with the internet. Thus, mobile banking helps in the development of bank access rates which influences bank perception ( 6 ). In the banking sector, it is observed that most of the customers have switched to smartphone, apps, or tablets which are highly utilized in their day-to-day life for shopping, entertainment, learning, socializing, and jobs, and these mobile-centric customers are preferred by more bankers ( 7 ). For example, a bank in Japan, Jibun Bank whose principal communication with customers is through the mobile channel and was the first bank that affords their complete banking services and products over mobile channels. With the help of cameras and mobile phone, customer has the facility to start a new account with their bank. This mode of banking enables the bank to improve their customer service over anywhere at any time basis. Also, the different mobile banking services are payment, web-related shopping, fund transfer, and web-related marketing. This Jibun Bank has reached more popular for its efficient services within the short duration of their mobile banking introduction ( 8 ). Through this, they have attained above 500,000 new bank accounts in their bank. In the initial days, the bank had the facility to send usual text messages related to money transfer and withdrawal and these services were updated to provide its customer a complete functional banking experience ( 9 ).

Mobile banking is adopted by many consumers as it is easy to open an account, pay bills, transfer funds, do mobile-based shopping, etc. Many banks in China and America who introduced mobile banking for all their services experienced high demand for their services through mobile banking ( 10 ). But many challenges were faced by these banks due to no face-to-face interactions. Trust is an important factor in mobile banking but lack of interaction led to the risk of uncertainty and anonymity. On the other hand, the consumers also suffer from uncertainty whether the bank is trustworthy are not.

During the last decades, mobile banking became relevant all over the world. The number of fund transfers through mobile banking also increased since last year, but mobile banking has many security problems. Sometimes, the security setting of mobile phones can be overridden by the virus in smartphones ( 11 ). Many banks open their banking applications, these apps should be updated frequently otherwise the consumers can be vulnerable to the attacks such as DDoS attacks, phishing, spoofing, corporate account takeover, and skimming ( 12 ).

The major contribution of the article is:

• To analyze the benefits of this study by investigating the past.

• To explore the adoption of mobile banking technology by consumers.

The remaining article is arranged accordingly. Section Literature Review presents the related work and the hypothesis is formulated in Section Hypothesis Development and Research Methodology. The results in terms of linear regression and descriptive analysis are provided in Section Results and the article is concluded in Section Conclusion and Discussions.

Literature Review

In the modern world, many firms use systems of mobile banking to communicate with the customers. The payment system in mobile banking services benefited people's lives. This article aims to analyze the effect of people using the technology of mobile to make banking services more accessible. Literature on mobile banking adoption, electronic banking services, mobile payment service user acceptance are reviewed.

Hamidi et al. ( 13 ) studied the influence of mobile banking adoption on consumer engagement and satisfaction utilizing the customer relationship management (CRM) system, which is the most essential aspect in banking industry. CRM is also seen as a critical role for enhancing client satisfaction in mobile banking. The statistical study performed evaluated the dialogue between the bank's customer sector and their client. The statistical analysis findings have a favorable influence on consumer interactions and satisfaction.

Geebren et al. ( 14 ) investigated the importance of consumer satisfaction in mobile eco-systems that used electronic banking services, particularly in developing nations. This entailed researching consumer satisfaction in mobile banking, with a focus on the importance of trust. To determine consumer satisfaction, structural modeling using partial least squares (PLS–SEM) methods were employed to examine the data, and trust demonstrated that customer contentment had a beneficial influence.

The relevance of an early trust theoretical model in mobile payment service user acceptance was highlighted by Gao et al. ( 15 ). The initial trust theoretical model highlighted the facilitators and barriers to user trust in m-payment services. The links in the original trust theoretical model were assessed using partial least squares structural modeling (PLS–SEM). The findings may be used in m-payment adoption research and practice in a variety of ways. In total, 52.3% of the difference in usage intention was explained by the current model.

Hentzen JK et al. ( 16 ) offered a mobile technology that allows for vital involvement, as well as an explanation of how a retirement app might assist people in planning for their post-retirement strategy. The available literature survey data were used to evaluate a sample of 440 Australian pension fund members. The findings show that consumers' perceived financial security, financial self-efficacy, retirement planning involvement, future consequences consideration, and perceived usefulness with a mobile retirement app have direct and indirect effects on their expected engagement through their goal to adopt the app.

Zhu et al. ( 17 ) investigated existing technology designs, including mobile banking, used by rural communities in six Chinese regions. According to the findings, interpersonal and mass communication channels have a bigger influence than organizational communication channels. Mobile banking should be examined since it can assist alleviate the lack of access to financial goods and financial infrastructure in rural areas.

Afeti et al. ( 18 ) developed a mobile payment technology for payment for micro-businesses. The study draws on the transaction cost theory and the task-technology fit (TTF) theory as the assumed lens. In total, 20 micro-businesses based on qualitative data were analyzed and the research findings denote those micro-businesses adoption of mobile payments results in strategic and operational benefits.

Alamoudi et al. ( 19 ) proposed that the mobile technology acceptance lookalike was changed as we investigated consumers' acceptance of mobile shopping in general stores by examining transaction convenience, usefulness, attitudes, ease of use, transaction speed, optimism, and personal innovativeness. A total of 351 respondents completed the questionnaire evaluation. Consumers are willing to use mobile shopping channels if the system is clear and straightforward to use.

Jebarajakirthy et al. ( 20 ) used a comprehensive moderated mediation framework to evaluate the influence of online convenience aspects on mobile banking uptake. Covariance-based structural equation modeling and the process macro are utilized to test these predictions. This study examines how convenience characteristics influence mobile banking adoption intentions.

Abdinoor et al. ( 21 ) studied the adoption of mobile financial services in Tanzania with the use of a technology acceptance model. To select the sample from data collection, a random sampling technique was used. The user and non-user of mobile financial services were included in the sample. Zhang et al. ( 3 ) investigated customers' use of mobile technology to help them with banking services and activities, as well as the variables that impact their adoption and engagement. Here, the analysis is done by the structural equation modeling technique to know the consumers' intentions toward mobile banking. The result examines the adoption of mobile banking apps to facilitate bank consumers' banking services.

Tiwart et al. ( 22 ) researched the variable that influences adoption in Commercial Bank Ethiopia. The author used factors such as perceived ease of use (PEOU), infrastructure (INF), security (SEC), trust, and e-banking adoption. Here, a structural equation model based on least partial square analysis is used. The result of this analysis proves that the trust of customers mediates between PEOU, INF, SEC, and e-banking adoption and realized the factors that guess the purpose and adoption of e-banking by Ethiopian customers.

The problem identified in mobile banking services is critical financial services through mobile banking because it has security problems.

Hypothesis Development and Research Methodology

Participant's demographic information.

This study is mainly conducted in China by taking the top e-banks that utilize m-banking. The questionnaire is distributed to both the users and staff of the bank. Within 6 months, 293 questionnaires were completed. These details were taken into consideration in this study. The details of the questionnaires were obtained online and also provided to the users on their official bank pages. The information obtained in the questionnaire is provided in Tables 1 , 2 .

Table 1 . Demographic analysis.

Table 2 . Participant details.

Participant Details

The information of the customers is mainly obtained based on their m-bank usage. The five main banks such as state-owned, joint-stock, postal savings, commercial city bank, and the agricultural bank were selected. Each participant was asked to say about their experience of their usage.

The amount of time spent by each user on average is provided in Figure 1A . As seen in Figure 1A , more than 39.38% of users are using more than 3 h a day and a minimum of is online Conceptual Model for less than 1 h. In total, 8.21% of the users only use the application for a limited amount of time (below 30 min).

Figure 1 . Usage analysis of m-banking application (A) Users using m-banking during the day and (B) users' friends and references using mobile banking.

Figure 1B presents the percentage of people the users have referred for mobile banking. Based on Figure 1B , a total of 48.52% has more than 100 contacts who have been referred to the m-banking. The increase in m-banking contact can improve efficiency.

Conceptual Model

The main aim of this research is to identify the usage of mobile technology of consumers for the banking system. The hypothesis provided for each variable is explained in detail in this section and the conceptual model is provided in Figure 2 . The measurement instrument used for this study is presented in Table 3 .

Figure 2 . Conceptual modeling structure.

Table 3 . Measurement instrument to analyze different parameters.

Hypothesis Modeling

Service, system, and information quality (ssiq)".

System quality mainly measures the efficiency of the overall m-banking system in terms of service provider anonymity. The system quality of the m-banking mainly depends upon the ease of use, the attractiveness of the application, trust of the user, etc. If the user trusts the system quality, then they mainly select the system for future transactions. Service Quality mainly relies on the service provided by the m-banking system and it also evaluated the effectiveness of the service in terms of personalization, reliability, delay, etc. Hence, service quality is crucial to improve the quality of m-banking since poor-service quality can result in minimal user trust and satisfaction. Information quality mainly implies that high-quality information is reliable, complete, accurate, relevant, and accessible. The information quality is an important variable that improves the usage, trust, and satisfaction of the customers in the m-banking system.

Hypothesis (H6): The service, system, and information quality had a positive relationship in improving the m-banking quality and user satisfaction.

Affective Commitment

Affective obligations between business partners reflect mental reliance on others and are founded on emotions, loyalty, and dependency. The psychological bond in this example demonstrates an emotional commitment. Support for differences in particular data among new and long-term clients. Based on their differing expectations, new and loyal clients behave in the same way.

Hypothesis (H1): The interaction of customers helps to develop a good relationship with the affective commitment of a person to the bank which is focused on interaction.

Pre- and Post-benefit Convenience

The time and attempt to acquire some benefits of services is said to be benefit convenience. Some of the components of benefit convenience are fast service, timely services, and bank employee attitudes. Sometimes in banks, the consumer needs to visit repeatedly to avail specific service. But in m-banking, consumers can avail any services on the go from the home itself. So, the time and attempt to acquire the service became very easy. Therefore, benefit convenience shows a positive result in mobile banking adoption ( 23 ). The time and attempt to contact a service provider for a particular service is said to be post benefit convenience. In m-banking, the service provider can be contacted and solve the grievances within a few clicks through various means such as email, live chat, and toll-free numbers. But in offline banking, the consumer needs to visit the branch and wait for the service provider to solve the problem in service. So, the post benefit convenience shows a positive result in mobile banking adoption ( 20 ).

Hypothesis (H5): The pre- and post-benefit convenience are positively interrelated with m-banking.

Transaction Convenience

The fast and simple way to complete the transaction is said to be transaction convenience. Acquiring the service of transaction in a lesser time is the main component of transaction convenience. Other components of transaction convenience are uninterrupted transaction, transaction confirmation, easy checkout, and price inconsistency. By using m-banking, the transaction can be done in any place at any time in a few clicks and can do several transactions simultaneously. But in offline banking, consumer needs to wait in a queue for availing a transaction service. Hence, transaction convenience shows a positive result in mobile banking adoption ( 24 ).

Hypothesis (H2): The transaction convenience is positively interrelated with m-banking.

Profitability

Customer participation in an organization's mobile banking may increase customer value and profitability ( 25 ).

Hypothesis (H8): Profitability shows positive results in mobile banking adoption.

Perceived Reliability

Perceived reliability is specified as the limit to which the individual has independently considered the technology as a faithful one. In order to offer a trustworthy technical service, the system reliability is important that enables the user to attain the aimed objective. An undependable technology that is employed by an individual seems to be low confidence and such technology utilization should be restricted ( 26 ). Research related to mobile technology and information system has revealed that a technology with perceived reliability possesses has an important impact on customers' perceived satisfaction, detected value, and observed quality. Also, customers' understanding level of trust is examined and analyzed as an antecedent of confidence in an organization through the mobile commerce setting. Though mobile is a wireless technology, it is easily susceptible to violations or attacks ( 27 ). Mostly, the customers were insisted to share their personal data while consuming and shopping services performed through mobile technology such as date of birth, debit and credit card details, funds, and address. The reliability of mobile technology should be improved thereby increasing customers' belief in securing their personal data. The postulated hypotheses are mentioned as follows:

Hypothesis (H4): Perceived reliability in m-banking helps to improve the consumer's trust and ease of use of banking services.

Banking Trust

In this hypothesis, trust is considered as a faith of competence, integrity, ability, and benevolence that the individual has toward each other. Trust has become an important reducing feature of risk, whereas the e-commerce connection is considered as a naturally risky factor ( 28 ). Also, trust in banking is specified that it is linked to reliability and perceived privacy. Generally, the primary interaction between the customer and banker will significantly affect the trust progress in them.

Hypothesis (H7): Based on their usage of mobile devices and attitude, the consumer trust toward M-banking increases.

Perceived Ease of Use (PEU)

Perceived ease of use evaluates the increase in the amount of work required to acquire a job utilizing new technologies on an individual basis. In the effort condition, the simplicity of use of mobile innovation is critical for including customers in the co-creation of value experiences ( 29 ). Mobile technology's ability to increase advantages for bank clients has been widely shown, and its simplicity of use is a key factor in consumer acceptability. PEU has been linked to advanced tackling new technologies in the banking industry in several research. However, the launch was difficult, and it was claimed that customers' inclination to utilize mobile banking services is not intrinsically related to employment prospects. As a consequence, it indicates that study into PEU and its influence on consumer attitudes, as well as the interactive method, are both worthwhile endeavors ( 30 – 32 ).

Hypothesis (H3): Customers' views about the usage of mobile devices for facilitating financial services are favorably associated with their perceived ease of use (PEU).

Scale Validity and Reliability

The reliability of the scales was assessed using Cronbach's alpha values. Goodness-of-fit approaches were used to assess the overall model fit for confirmatory factor analysis (CFA). The RMSEA was 0.09, the comparative fit index (CFI) was 0.99, the goodness-of-fit (GFI) was 0.98, and the standardized root mean square residual (RMR) was 0.06. The X2 to df ratio was 4.14, the root mean square error of approximation (RMSEA) was 0.09, the CFI was 0.99, the GFI was 0.98, and the standardized RMR was 0.06. As indicated in Table 4 , the reliability coefficients of all constructs were more than 0.87, which is higher than the 0.9 thresholds. The extracted average variance was used to assess convergent validity (AVE). Because the AVE values ranged from 0.58 to 0.92, convergent validity was not an issue. The AVE scores were lower than the squared correlations between pairs of constructs, indicating discriminant validity in the data set. Table 4 shows the results of the Cronbach's alpha and AVE tests.

Table 4 . Hypothesis testing.

Linear Regression Test

Regression tests are run when the allowable alpha coefficient for the scales has been determined. Table 5 shows the regression test results for each of the assumptions discussed in the following sections. Affective commitment (AC) and interaction were investigated, and the correlation coefficient 0.297 and likelihood of significance 0.000 indicate that there is a positive and significant relationship between the two. The regression test supports this positive relationship, showing that the interaction variable may be able to predict AC variation.

Table 5 . The results of the linear regression test.

With a correlation value of 0.333 and a probability of significance of 0.000, the following test demonstrated a positive relationship between perceived ease of use and satisfaction (PEU). The correlation coefficient between the variables perceived reliability (PR) and interaction was 0.172, with a probability of significance of 0.000, demonstrating a positive link between the two variables. The correlation value of 0.430 and the probability of significance of 0.000 indicated a positive relationship between satisfaction and service, system, and information quality (SSIQ) ( 3 ).

Descriptive Data

The items in this questionnaire were answered using a five-point Likert scale, with the alternatives being: very low (1), low (2), medium (3), high (4), and very high (5). Table 6 shows the SD, middle value (MV), and lowest value (LV) of the descriptive data received from the surveys ( 3 ).

Table 6 . Descriptive data analysis.

Conclusion and Discussions

Mobile banking is a cost-effective way to reach customers. The different activities that can be performed by the consumers in m-banking are checking their bank balance, making transactions, making investments, getting account statements, paying bills, etc. The motivation for user adoption and use of mobile banking is examined in this research. Positive connections between profitability, trust, and transaction convenience to utilize mobile banking have been found in previous studies. Users' confidence in using mobile banking services, enjoyment, utility of the system, etc., are not the same thing. The participants were unconcerned about the danger of fraud, system dependability, or perceived privacy while building and extending faith in their banks and mobile services. Because of today's technology-driven lifestyle, individuals are receptive to embracing new technologies that are compatible with their mobile phones.

To add value to practice, we must obtain convincing and widespread results from both users and mobile banking service providers. Like mobile phones, smartwatches have become more popular among people nowadays. With this wearable technology, banks have the greatest responsibility to fulfill hedonic and social needs. The purpose and use of this research are common to all consumers irrespective of various countries, populations, and socio-economic status. The variables used in this work are, namely, affective commitment (AC), transaction convenience (TC), perceived ease of use (PEU), perceived reliability (PR), pre- and post benefits (PPBs) service, system, and information quality (SSIQ), bank trust (BT), and profitability (P). Consumer behavior such as untrustworthiness, fraudulence, and so on, as well as mobile banking transaction risks such as financial, privacy, and cyber security, are important risk concerns that may be addressed in future research developments.

The problems and risks that arise by the consumers and banking sector for adopting mobile banking services would be reduced by the scientific researchers using various technologies and methodologies in the future. The results in this article show the different statistical surveys and believed that it is true for both mobile banking consumers and non-mobile banking consumers with various socioeconomic environments. But when we consider globally each country had its own rules and regulations regarding mobile banking transactions, services, and products. And these rules and regulations react differently in different countries.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Author Contributions

Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: mobile technology, banking, financial service, transaction convenience, mobile banking

Citation: Zhu J and Wang M (2022) Analyzing the Effect of People Utilizing Mobile Technology to Make Banking Services More Accessible. Front. Public Health 10:879342. doi: 10.3389/fpubh.2022.879342

Received: 19 February 2022; Accepted: 17 March 2022; Published: 29 April 2022.

Reviewed by:

Copyright © 2022 Zhu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jiale Zhu, zhujiale8899@126.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• DOI: 10.24036/jtip.v16i2.626
• Corpus ID: 265638158

Analysis of Mobile Banking Acceptance in Indonesia using Extended TAM (Technology Acceptance Model)

• Imam Andhika
• Published in Jurnal Teknologi Informasi… 23 November 2023

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5 Emerging Security Imperatives for Digital Wallets

There are two dueling trends defining 21st century payments: digitization and cybercrime .

And while the two trends are unrelated — particularly given that paper-based payments are more of a fraud risk than their digital counterparts — a new research paper published last week (Aug. 14) finds that, across the digital wallet landscape, the two trends are increasingly butting heads.

The paper, entitled “In Wallet We Trust: Bypassing the Digital Wallets Payment Security for Free Shopping,” posits that an ongoing reliance on outdated authentication methods, and the prioritizing of convenience over security, has left digital wallets vulnerable to attack and abuse by bad actors.

“The implications of these attacks are of a serious nature where the attacker can make purchases of arbitrary amounts by using the victim’s bank card, despite these cards being locked and reported to the bank as stolen by the victim,” the researchers wrote.

In an era where digital transactions are becoming the norm, the convenience of digital wallets is undeniable — and as these wallets become increasingly integral to daily transactions, their security is becoming a top priority.

By adopting emerging security imperatives like biometric authentication, tokenization, end-to-end encryption, multi-factor authentication (MFA) and AI-driven detection and prevention, digital wallet providers can build robust defenses against the growing array of cyber threats.

Read more : Debit Cards In Digital Wallets Gaining Ground Across Sectors

How Digital Wallet Providers Can Protect Users 

The convenience and efficiency offered by digital wallets are undeniable, but with this convenience comes the responsibility to safeguard sensitive financial information. After all, the ease of setting up and using these wallets, combined with the lack of stringent verification processes, has made them an attractive target for illicit activities.

Against this backdrop, end-user reliance on digital wallets is only increasing. What started as a convenient alternative to physical wallets has evolved into a key financial tool, housing sensitive data and enabling seamless transactions across various platforms.

PYMNTS Intelligence finds that digital wallet usage continues to grow across grocery, retail, restaurant and travel sectors, with debit becoming the preferred underlying payment method. In June 2024, consumers paying with digital wallets used stored debit cards in 55% of grocery transactions, as well as 52% of retail transactions, 62% of restaurant transactions and 46% of travel transactions.

As digital wallets continue to gain traction in the financial ecosystem, their security will be a critical factor in determining their long-term success.

Among the emerging security imperatives that are essential for the future of digital wallets, biometric authentication has rapidly gained traction as a reliable and secure method of verifying a user’s identity. Unlike traditional passwords or PINs, which can be easily stolen or forgotten, biometric data — such as fingerprints, facial recognition, and voice patterns — are unique to each individual, making them significantly harder to replicate or misuse.

For digital wallets, the integration of biometric authentication offers a dual advantage: it enhances security while improving user experience by streamlining access to accounts and transactions.

“If you do the facial scan immediately upfront … That means all these transactions will go through seamlessly and you no longer have to confirm your identity after the fact,” Mark Nelsen , senior vice president and global head of consumer payments at  Visa , told PYMNTS.

Read more: 3 Big Ideas From PYMNTS Intelligence’s Digital Wallets UK Report

Reducing Risk in Digital Transactions

Tokenization is another powerful security measure that replaces sensitive data, such as credit card numbers, with a unique identifier or “token” that can be used in transactions without exposing the actual data. This technique minimizes the risk of data breaches, as the token itself is meaningless to unauthorized parties who may intercept it.

“We think tokenized payments have the propensity to penetrate some 70% of transactions as a whole,” Mehret Habteab , senior vice president of product and solutions at  Visa  Europe, told PYMNTS.

In the context of digital wallets, tokenization is particularly effective in securing payment data during transactions. When a user initiates a payment, their sensitive information is not directly transmitted. Instead, a token is generated and used to complete the transaction. This approach significantly reduces the risk of fraud, as even if a hacker gains access to the token, they cannot use it to access the user’s financial information.

And for digital wallets, other methods of end-to-end encryption that involve encrypting payment data, personal information, and other sensitive details at every stage of a transaction can help ensure that even if data is intercepted, it remains unintelligible to unauthorized parties.

Of course, sometimes the simple solutions are the most effective — and for digital wallets, MFA is particularly effective in preventing unauthorized access. Even if a hacker obtains a user’s password, they would still need the second form of authentication to gain access to the wallet. This added layer of security makes it significantly harder for cybercriminals to compromise accounts.

Looking ahead, the rise of artificial intelligence (AI) and machine learning has opened new avenues for enhancing cybersecurity in digital wallets. These technologies enable real-time threat detection and continuous monitoring, allowing digital wallet providers to proactively identify and respond to potential security risks before they escalate.

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New HKS research asks communities what reimagining public safety means to them

In neighborhoods experiencing discrimination from law enforcement, police are viewed both as part of the problem and the solution.

Is it possible for marginalized communities to work together to create thriving neighborhoods? Is there a role for police officers, who are often mistrusted, in this effort?

This was the subject of the latest research from Harvard Kennedy School’s Program in Criminal Justice Policy and Management (PCJ), led by faculty director Sandra Susan Smith.

Smith, the Daniel and Florence Guggenheim Professor of Criminal Justice and the Carol K. Pforzheimer Professor at the Radcliffe Institute, formed the Roundtable on Racial Disparities in Massachusetts Criminal Courts at the PCJ in 2021. Its goal is to influence future policies, practices, and procedures to eradicate the sources of racial inequity and racial disparity in Massachusetts’s courts.

The Roundtable, under the direction of Smith, has studied issues such as jury exclusion and reducing racial disparities through decriminalization. The latest study, This is What Thriving Communities Look Like , looked at four neighborhoods in the greater Boston area to better define what is meant by “reimagining public safety.”

Smith, with Amisha Kambath, a research assistant at the PCJ who is beginning Yale Law School this fall, and Noor Toraif, an assistant professor at UPenn, wanted to understand what it meant for people to feel and be safe in their neighborhoods. Working with focus groups in Roxbury, Dorchester, East Boston and South Boston—communities that have different racial and class compositions but share a troubled relationship with law enforcement—the team gained insights on what residents need to thrive and what role policing can play.

“Ever since the murder of George Floyd in 2020 and the conviction of the Minneapolis police officer who killed him, we have heard this plea to reimagine public safety,” the authors wrote. “In working with the residents of these Boston neighborhoods, we hoped to understand what a safe, thriving community looked like to them. And how law enforcement fits into that picture.”

As Smith revealed in a recent  PCJ report  the residents of these Boston communities report higher rates of police harassment and as a result feel a deep distrust of law enforcement.

“We also knew that creating a thriving community required deliberate and sustained engagements with residents themselves, with a particular emphasis on those most often targeted for aggressive and harmful police interventions but least considered when questions about how to achieve public safety arise,” the researchers wrote.

“What comes through powerfully from these focus group meetings is the level of sophistication that residents have in identifying the very real structural barriers to thriving their communities face ...”

Sandra susan smith.

After a series of focus group sessions, which included participants from each of the four communities, people with diverse experiences and relationships to the state, including a range of age groups as well as formerly incarcerated residents, Kambath, Toraif, and Smith identified three themes:

• What thriving communities look like
• What communities need to thrive
• What role police could play in creating and maintaining safe, healthy, and thriving communities.  

What is “thriving”?  

Inherent in thriving communities, Smith’s team found, was a sense of community cohesion and freedom from harm: as one young adult respondent put it, “everyone kind of having each other’s back.”

The research team noted one striking response when talking about a sense of community.

“Rather than look to more affluent neighborhoods as examples of community cohesion,” the researchers wrote, “these residents most often looked back in time at their own neighborhoods where they were born and raised.”

Respondents pointed out how neighbors would sit on their porches to watch the street, cleaned the areas of trash, and actively engaged with one another in public spaces—all positive signs of a thriving community.

Other respondents noted that local events can enhance a sense of belonging. A formerly incarcerated resident described the East Boston farmer’s market, saying “Every Wednesday they pass out fresh vegetables to the public. It’s a thriving place. It’s a place to bring people together.”

The study indicated that the prevailing threat to safety was easy access to guns, a fear supported by Boston police statistics for the communities. None of the respondents could envision thriving without freedom from violence. 

“Violence, experienced directly or indirectly and with or without guns, impacts the physical and mental health of residents,” said Smith. “It also erodes resident’s ability and willingness to engage in ways that contribute positively to their communities.”

What communities need  

The focus group discussions led to the research team identifying three themes for creating a thriving community: a healthy built environment, greater investments in capital development, and access to high quality physical and mental healthcare.

“Our participating residents focused on specific aspects of the built environment,” wrote Kambath, Toraif, and Smith, pointing to greater access to green spaces, parks and recreation. Many spoke of the need for more trees. The team identified prior research that noted the benefits of green space to help reduce violent crime and found that tree canopy coverage—areas shaded by trees—were especially low in two of the focus group communities.

Neighborhood gentrification—where poorer, urban communities are revitalized by wealthier citizens, often displacing the current residents—was also systemic in these areas, contributing to housing instability for longtime residents. In fact, Boston ranks third among highly gentrified cities in the country, with the four neighborhoods in this study at risk, according to a 2020 study by the National Community Reinvestment Coalition.

Investment in human capital was also top of mind, not only enhanced, free education for the area’s youth, but skill-building opportunities for adults as well. And as one resident argued, there was a sense that jobs and contracts in these neighborhoods should go to community members first.

Residents in all four neighborhoods identified a need for far more resources to address health concerns, especially mental health and substance abuse issues, but feared that more affluent communities with far less need for public support would be privileged in the city’s decision-making.

What can policing do  

“When imagining safe and thriving communities,” Kambath, Toraif, and Smith noted, “Residents offered a wide range of views about the role police can play.” Many saw “police as protectors” equating public safety with police.

Others identified the police as flawed, unresponsive, and failing to treat residents with respect. Nevertheless, the research found residents wanted police to be protective and believed that through training they could become better public servants. As one East Boston resident said, “We need more police officers” to provide a sense of security.

And then some respondents viewed police as “violence workers.” “They terrorize our young people” is how one formerly incarcerated Roxbury-Dorchester resident put it.

"In a related report that the PCJ released in June based on analysis of a representative sample of Boston residents,” Smith wrote, “Our analysis of this Boston survey not only revealed that police harassment is predictive of distrust and eroded feelings of community safety, but police harassment is also predictive of symptoms of trauma and associated with chronic health issues.”

“These findings, which are consistent with prior research in this area, suggest that police harassment might be a direct and indirect contributor in making communities unsafe, thwarting residents’ efforts to thrive,” she continued.

The study, while sobering on many fronts, also offered opportunities for collaboration in these communities. And residents felt they had taken the first important steps, being heard, voicing concerns, and participating in solutions.

"What comes through powerfully from these focus group meetings is the level of sophistication that residents have in identifying the very real structural barriers to thriving their communities face and offering concrete, workable solutions to overcome these,” said Smith. Responses from residents even included photographs of what a thriving community meant to them.

“They are the experts that we should be listening to. In this way, they are no different than generations of residents who have come before them. It is well past time that we take their ideas seriously." 

Banner photograph by Carlin Stiehl/The Boston Globe/Getty Images; inline photograph by Matthew J. Lee/The Boston Globe/Getty Images; portrait by Martha Stewart

Focus group participants submitted photographs that describe a thriving community. The second banner photo is from an East Boston young adult: “I just think it is cool, people can sit down with their friends, or just sit down.”

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