Exploring Retailers’ Behavioural Intentions Towards Using M-payment: Extending UTAUT with Perceived Risk and Trust

Abstract

This study aims to identify the factors influencing retailers’ intentions to use m-payment. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted and extended with two additional variables: trust and perceived risk (PR). The sample of 682 respondents was obtained through an online survey distributed to retailers throughout Egypt. The partial least squares-structural equation modelling method was employed to analyse the data and test the hypotheses of this study. In terms of outcome, performance expectancy, effort expectancy, social influences (SI), trust and PR are found as significant factors in the retailers’ behavioural intentions to use m-payment (BIU). Moreover, trust and SI were the most significant factors influencing the retailers’ BIU m-payment. This study provides insight into Egypt’s retail sector regarding BIU m-payment. Accordingly, decision-makers, m-payment providers and stakeholders can benefit from the results by looking at the significant factors and understanding the retailers’ BIU m-payment, especially when formulating strategies for the m-payment ecosystem. This study investigated the ‘pay pay cash’ initiative, which is being implemented for the first time in Egypt after the spread of the Coronavirus.

Keywords

Egypt retailers m-payment perceived risk trust UTAUT model

Introduction

With the rapid development in communications and information technology, m-payments may provide the first widely accepted cashless transaction, thus offering tremendous value to multiple stakeholders such as consumers, retailers, mobile service providers and financial institutions (Mallat, 2007). Nevertheless, the network externalities that can be created between and within the various sides are crucial for the success of a platform (Lee et al., 2019) and increasing adopters of m-payment (Dahlberg et al., 2015).

Retailers have been addressed as the crucial players influencing the diffusion of m-payment (Pisani & Moormann, 2018). Lee et al. (2019) have argued that when retailers are more accessible via an m-payment service, consumers’ use of m-payment will increase, and they will value the service more. That is to say, if retailers reject m-payments, it may lead to the demise of m-payment growth in the real economy (Dahlberg et al., 2015). However, various studies have identified barriers to retailers’ behavioural intention to use (BIU) m-payment (Moghavvemi et al., 2021), such as a lack of trust and increased risk perceived in m-payment transactions .

Despite the growing body of research, the factors influencing the adoption of m-payment are primarily unknown and require more investigation (Al-Saedi & Al-Emran, 2021). Moreover, based on the literature review, there are gaps that this study will strive to fill.

First, according to (Khan & Khan, 2021), most studies have used younger users in their sample sizes, limiting the results’ generalizability to the entire society.

Second, prior research is lacking in this area from a retailers’ point of view and is intensive on customers (Dahlberg et al., 2015; Khan & Khan, 2021).

Third, in contrast to developed countries, emerging markets such as Egypt face persistent issues with weak banking infrastructure (Rahman et al., 2020) and the dominance of cash transactions over purchases, which reached 55% of the total transactions .

Fourth, it remains controversial regarding the significant effect of PR on BIU (Agarwal, 2020; Goyal et al., 2020; Widyanto et al., 2021) and trust on BIU (Alkhowaiter, 2020). Furthermore, a few previous studies have attempted to partially incorporate PR and trust to determine retailers’ BIU, but none have examined the relationships between these constructs concurrently to assess the predictors of retailers’ BIU m-payment.

Fifth, to the best of the researcher’s knowledge, no research paper has yet investigated the acceptance of the m-payment initiative (Pay Pay Cash) in Egypt (Khan & Khan, 2021).

Taken together, this study is important as it would add to the existing literature by (i) trying to identify external factors that influence the retailers’ BIU m-payment; (ii) extending theoretical comprehension of the retailers’ BIU m-payment; (iii) offering empirical evidence of the trust and PR impacts on retailers’ BIU m-payment; (iv) proving that does age, gender and educational qualification moderating the influence of the latent constructs on BIU; and (v) providing insight for the Egyptian government and stakeholders and help to illuminate essential drivers for retailers’ BIU m-payment they may use it to decide on future m-payment guidelines and strategies.

The following is how this paper is organized. First, a brief overview of the literature review is provided. The elaboration of the framework and the research hypotheses built on the theoretical foundation are then proposed. The following section explains the study methods. The results of the analysis are in the following section. A discussion and conclusions follow a section containing theoretical and practical implications—finally, the paper’s limitations and recommendations for future research.

Literature Review and Hypotheses Development

The Egyptian government is imposing financial inclusion (crci.sci.eg, 2015). On 10 May 2020, the Central Bank of Egypt launched the Pay Pay Cash initiative to facilitate citizens and merchants in their daily transactions using m-payment (Central Bank of Egypt, 2020). According to Esawe and Elwkeel (2020), rapid growth and expanding digital stages, such as m-payment, can provide all of the mechanisms needed to achieve the desired level of financial inclusion. Moreover, successful m-payment acceptance is critical for enhancing financial inclusion (Mohamad & Kassim, 2017) and vice versa (Simatele, 2021).

Tang et al. (2021, p. 3) refer to m-payment ‘to which consumers pay bills, goods, and services through mobile apps using mobile devices such as a smartphone.’

Even though m-payments provide convenience and benefits, their use and adoption remain sluggish in developed countries, as they are in developing countries (Ketokivi & Mahoney, 2017). The unexpectedly low diffusion of m-payments, even with the government incentive programmes to support retailers (Singh & Sinha, 2020), and the broader application of technological solutions necessitates more investigation into what obstructs the adoption and use of m-payments (Talwar et al., 2020). Agarwal (2020) has explained that retailers have a fear of tax increases and are unwilling to disclose their transactions. In addition, many retailers work in sectors outside the formal economy system, and many of them are illiterate.

Many previous studies investigated BIU m-payment from the consumer and retailer perspectives. For example, Mehta et al. (2021) have shown that perceived ease of use PEOU, perceived usefulness PU and SI influences the BIU m-payment. Furthermore, they found significant moderating effects of PR on the associations among SI, PEOU and BIU m-payment. Hasan and Gupta (2020) proved that P value, trust, compatibility and SI influence the BIU m-payment. Moreover, they found that trust and compatibility have a more powerful influence on the BIU m-payment. Lee et al. (2019) have proposed an integrated model in which adoption from the consumer and retailer perspectives influences each other’s demand relying on investigating the factors affecting m-payment adoption from the consumer and retailer perspectives. Moghavvemi et al. (2021) have conducted in-depth interviews to gain insight into merchants’ motivational drives, barriers and challenges. Their results indicate that among the factors influencing merchants’ adoption of m-payment are reducing the time and fees of transaction processing, convenience and security features improvement. Ariffin et al. (2020) have shown that PP, PE, SI, FC, habit and P-security influence the retailers’ BIU m-payment. Agarwal, (2020) has investigated the factors which affect behavioural intention among small merchants in India, and his results show that PE, EE and habit influence the small shop keepers’ intention to adopt a future digital payment service. Liébana-Cabanillas and Lara-Rubio (2017) have used logistic regression modelling and neural network analysis to determine the main factors influencing m-payment adoption. The results reveal that those factors were the number of employees of a particular company, its net income, the experience regarding traditional payment systems and, finally, the perceived and actual advantages, utility and usefulness of the m-payment systems.

Moreover, both trust and PR are complex concepts. Concerning trust, there is interaction between both people and technology (Xu et al., 2014). In addition, trust can be divided into initial trust and experiential trust (Kim et al., 2008). Each of them is affected by a range of factors; for example, m-payments are considered as a new technological innovation (Boateng & Sarpong, 2019), especially for Egyptian retailers. Therefore, experiential trust is lacking. However, the initial trust of retailers can be based on the reputation of the service providers. As a result, retailers can trust, or not trust, the distinct parts that make up the m-payments service.

It has been evidenced that when PR is excessive, retailers will avoid using virtual services (Moghavvemi et al., 2021). However, the more retailers trust in m-payment service, the less risk they perceive and the more likely they will utilize the service (Liébana-Cabanillas & Lara-Rubio, 2017; Singh & Sinha, 2020).

Research Model

Why use the UTAUT model and its extensions?

The UTAUT model was adopted since it (among the adoption theories) is presented as one of the most important and popular models available for assessing user acceptance and use of technology (Harris et al., 2019; Karsen et al., 2019). UTAUT is a synthesis of eight prior technology acceptance models. Moreover, it outperforms the eight individual models with an adjusted R² of 70% (Venkatesh et al., 2003).

In addition, many previous studies have used UTAUT to predict the BIU of m-payment from the consumer and retailer perspectives as both can be considered (Al-Saedi & Al-Emran, 2021; Khan & Khan, 2021) as users of technology. In addition, Magsamen-Conrad et al. (2020, p. 5) emphasizes that most of UTAUT’s early research focused exclusively on organizational contexts. However, many prior studies have followed Venkatesh’s suggestion to extend the model in different ways: indifferent (countries, age groups and technologies); in identifying other relevant constructs to serve as exogenous variables or endogenous theoretical mechanisms.

Appendix I presents the theories, models and factors examined in studies that investigated retailers’ BIU m-payment. Even though all these studies examine the same technology, they develop various determinants of m-payment acceptance, which supports the idea that these determinants would differ depending on the context. Moreover, the recent study extends the UTAUT model by incorporating the constructs of trust and PR. Fortunately, past studies have never adequately addressed this topic , particularly in the Egyptian retailers’ context.

Hypotheses Development BIU

According to Chai and Dibb (2014, p. 3), BIU is defined as ‘the degree to which a person has formulated conscious plans regarding whether to perform a specified future behavior’. According to prior research, (e.g. Li & Li, 2020; Moghavvemi et al., 2021), it is critical to investigate the factors influencing retailer’ BIU m-payment to assist not only service providers but also governments in developing the best ways to promote retailers’ adoption and retention of m-payment.

Effort Expectancy (EE)

EE is defined as ‘the degree of ease associated with the use of the system’ (Venkatesh et al., 2003, p. 450). In the present study, EE implicitly denotes the amount of effort retailers expect to invest when using m-payment. It also refers to which a system is straightforward to comprehend and use without requiring any special skills. Some prior studies indicate that EE has a significant positive influence on retailers’ BIU m-payment (Agarwal, 2020; Ariffin et al., 2020; Tang et al., 2021). So, retailers will use m-payment when they see it as effortless, considering that the following hypothesis was posited:

H₁: EE will positively affect retailers’ BIU m-payment.

Performance Expectancy (PE)

PE is defined as ‘the degree to which an individual believes that using the system will help him or her to attain gains in job performance’ (Venkatesh et al. 2003, p. 447). PE refers to how retailers believe that using m-payment will empower and give them an advantage in conducting online transactions, such as speed, security and convenience. Many studies have shown that PE significantly and positively influences retailers’ BIU m-payment (Ariffin et al. 2020; Tang et al. 2021). As a result, this study hypothesizes that:

H₂: PE will positively affect retailers’ BIU m-payment.

Facilitating Conditions (FC)

FC is defined as ‘the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system’ (Venkatesh et al. 2003, p. 453). FC refers to the retailers’ perception of providers’ support of m-payment usage. Moreover, FC assesses whether retailers have the necessary personal knowledge and resources to use the system. Recent studies in m-payment have proven that FC is a direct predictor of retailers’ BIU (e.g. Ariffin et al. 2020). Based on the literature, the following hypothesis is proposed:

H₃: FC will positively affect retailers’ BIU m-payment.

Social Influence (SI)

SI is defined as ‘the degree to which an individual perceives those important others believe he or she should use the new system’ (Venkatesh et al., 2003, p. 451). Many studies have shown that SI significantly and positively influences retailers’ BIU m-payment (Ariffin et al., 2020; Li & Li,2020). However, recent studies do not support this hypothesis (Agarwal, 2020). Accordingly, the following hypothesis is posited:

H₄: SI will positively affect retailers’ BIU m-payment.

Trust

Tang et al. (2021, p. 43) defined trust as ‘trust in the m-payment service provider, banks, and other users, and trust in the m-payment application’. Lee et al. (2019, p. 9) have stated that ‘trust in virtual environments is the primary means of social control, and the importance of trust in virtual environments is more important than in physical environments.’

Harris et al. (2019) have proven that trust is a significant factor in promoting m-payment services and behavioural intention. That implies that increased trust allows individuals to resolve doubts about their own personal motivations, behavioural intentions and the subsequent decisions of those on whom they base their decisions. Xu et al. (2014) have demonstrated technology trust as a specific kind of trust where the technology user lays trust in technology. For this study, we simplified the concept of trust as an aspect of dependability. Trust is defined as the degree to which a retailer considers that m-payment services are trustworthy.

As for the relationship between trust and risk, according to Goyal et al. (2020), there is a degree of risk that users must determine while using m-payment services, and to mitigate risk, a user may invest his or her trust in the service providers. Trust has been proven to positively influence retailers’ BIU m-payment (Liébana-Cabanillas & Lara-Rubio, 2017; Singh & Sinha, 2020). Furthermore, it has been proven that trust has a significant negative influence on perceived risk (PR) (Kim et al., 2008; Lu et al., 2011; Widyanto et al., 2021). Considering these, this study therefore hypothesized:

H₅. Trust will positively affect retailers’ BIU m-payment.

H₆: Trust will negatively affect retailers’ PR of m-Payment.

PR

According to Tan et al. (2010, p. 175), PR refers to ‘users’ perceptions of security when transmitting private information via m-payment services’. Nevertheless, people are worried about such a system’s security and privacy dimensions when doing m-payment transactions. It is observed that while consumers have strong trust in their banks, they have little trust in technology (Roy & Sinha, 2014). In other words, people’s acceptance of m-payment is anticipated by their PR of the technology. According to previous studies (Kim et al., 2008; Lu et al., 2011), the PR of m-payment services negatively influences users’ intention to use these services. However, recent studies have found that PR has an insignificant effect on BIU (Goyal et al., 2020; Widyanto et al., 2021). Hence, we proposed:

H₇. PR will negatively affect retailers’ BIU m-payment.

Moderating Effect

Gender and age played varying moderating roles in some prior studies to explain m-payment adoption behaviour (Sahi et al., 2021). Venkatesh et al. (2003) have argued that older people have lower PE because they put forth less effort to learn a new system. On the other hand, younger workers are more concerned with extrinsic rewards. Moreover, they contended that males and females produced different outcomes when using information systems.

The hypotheses are adapted from the original UTAUT (Venkatesh et al., 2003) and Sobti (2019):

H₈. The impact of EE, PE, FC, SI, trust and PR on BIU will be stronger for males than the females.

H₉. The impact of trust on PR will be stronger for males.

H₁₀. The impact of EE, PE, FC, SI, trust and PR on BIU will be stronger for the younger than the alders.

H₁₁. The impact of trust on PR will be stronger for the young.

H₁₂. The impact of EE, PE, FC, SI, trust and PR on BIU will be stronger for the higher educated.

H₁₃. The impact of trust on PR will be stronger for the higher educated.

The framework that is expected to be tested in this research is highlighted in Figure 1.

Figure 1.

Source: Author’s proposed model.

Methods

Methodology

The PLS-SEM method was used instead of covariance-based SEM because it is more flexible and robust to normality assumptions and sample size requirements. According to Hair et al. (2019), the PLS-SEM integrated both explanation and prediction, which serves as the foundation for improving managerial implications. Moreover, PLS-SEM has been widely applied in many social science disciplines (Hair et al., 2019). Furthermore, many studies have used it (e.g. Abebe & Lemma, 2020; Gao & Waechter, 2017; Goyal et al., 2020) to measure the intent to use m-payment services.

Smart PLS 2.0 (Ringle et al., 2005), Google Sheets and SPSS version 23 were used. The analysis was done in two stages (Hair et al., 2019), in which the first stage comprised of the measurement model assessment as follows:

The indicator loadings were assessed, the acceptable levels should be higher than 0.70; the construction reliability testing, by computing Cronbach’s alpha and composite reliability (CR) to verify reliability for each variable examined (Fornell & Larcker, 1981), and both should be 0.70 or higher. However, it has been argued that a reliability coefficient as low as 0.5 is moderate reliability (Perry et al., 2004) and should not seriously attenuate validity. The average variance extracted (AVE) was computed to verify each construct measure’s convergent validity in which AVE should exceed 0.50. In addition, discriminant validity was verified by computing each construct’s AVE square root values (Fornell & Larcker, 1981). All constructs should have an AVE above the correlation between this construct and other constructs.

To address the potential common method bias variance inflation factor (VIF) was used. VIF values ≥ 3–5 indicate possible collinearity issues, but these are rarely important enough to warrant attention (Kock, 2015).

In the second stage, the structural model was assessed by computing the coefficient of determination R². According to Hair et al. (2019), the R² is considered the sample predictive power and serves as an indicator of the model’s explanatory power. However, R² values of 0.75, 0.50 and 0.25 are considered substantial, moderate and weak, respectively. R² values of 0.90 and higher are typically indicative of over fitting. Furthermore, calculating the Q² was computed using the blindfolding technique. As a rule of thumb, all values should be larger than zero.

The ultimate step was to assess the statistical significance and relevance of the path coefficients. The bootstrap resampling approach with 5,000 resamples (Ringle et al., 2005) was employed to establish the relevance of direct pathways and estimate standard errors. Furthermore, the moderating effects of age, gender and education qualification on the influence of the latent constructs on BIU are examined along with their significance levels.

Measures

To ensure the validity of the content, the survey employed in this study has been adapted with some changes and the necessary wording changes and validation in the context of m-payment usage from the original UTAUT (Venkatesh et al., 2003) and other literature (see Appendix II). All survey items were translated into Arabic. The back-translation method (Behr, 2017) was used to ensure that the English and Arabic versions did not contradict each other.

The questionnaire was structured into two sections: the first section deals with a nominal scale to identify respondents’ demographic information; the second section elicited information about UTAUT constructs, trust, PR and BIU m-payment Appendix II. These constructs were measured by following the five-point Likert scale: 1 = strongly disagree, 5 = strongly agree.

Sample

In this study, Egyptian retailers are the targeted population to examine the BIU m-payment. Since the number of retailers that use m-payment is unknown, the study employed the convenience sampling technique to identify respondents. The sample size is calculated using the PLS-SEM analysis’ prerequisites, which necessitates a ten times larger sample than the total number of structural paths leading to construction (Hair et al., 2017). In order to achieve greater accuracy and avoid any problems that arise during the data analysis stage due to the small sample size, Cochran’s unknown population formula was used, and the result shows that the minimum sample size of 385 was required (Cochran, 1977).

Furthermore, the survey was carried out via a Google Form, and respondents were invited to respond via social media platforms such as Facebook Groups within 40 days, from 29 August 2020, to 7 October 2020. A total of 700 questionnaires were received. However, 682 responses remained for statistical analysis after data screening, indicating an overall response rate of about 97.4%.

Table 1 shows the demographic profile of the respondents. There are five demographic characteristics. The proportion of male (52.3%) and female (47.7%) respondents was almost equal. The majority of respondents were in the range of 41 to 50 years old, with almost (78.30%). Moreover, (83.3%) of the respondents had bachelor’s degrees. Most of the respondents (95.5%) operate their business through a physical headquarters. Respondents’ monthly profit was mostly less than 20,000 pounds (40.5%).

Table 1.

Respondent’s Profile (N = 682).

Variable	Classification	Frequency	Percent
Gender	Male	357	52.3
	Female	325	47.7
	Total	682	100.0
Age	Under 20	85	12.5
	21–30	28	4.1
	31–40	30	4.4
	41–50	534	78.3
	51 or older	5	0.7
	Total	682	100.0
Educational level	Uneducated	27	4.0
	Diploma	22	3.2
	High school graduate	65	9.5
	Bachelor	568	83.3
	Total	682	100.0
Monthly profit	Less than 20,000	276	40.5
	21,000–40,000	151	22.1
	41,000–60,000	93	13.6
	61,000–80,000	47	6.9
	81,000–10,0000	115	16.9
	Total	682	100.0
Nature of activity	Directly	638	93.5
	Online	44	6.5
	Total	682	100.0
Business domain	Food	140	20.5
	Furniture	68	10.0
	Cosmetics	83	12.2
	Digital appliances	98	14.4
	Grocery	178	26.1
	Etc.	115	16.9
	Total	682	100.0

Source: Author’s model.

Results

Measurement Model

As shown in Table 2, all indicators loading and the measurement of CR exceeded the acceptable levels. Moreover, the Cronbach’s alpha value for all constructs has reached acceptable levels (greater than 0.55) as recommended by (Tabachnick & Fidell, 2007), indicating that the measurement model is consistent and reliable. Finally, The AVE value for each construct is more than 0.5. As a result, the measurement model demonstrates strong convergent validity.

Table 2.

Measurement Model Results.

Constructs	Factor Loading	Alpha	(AVE)	CR
BIU		0.812	0.640	0.877
BIU1	0.746
BIU2	0.829
BIU3	0.834
BIU4	0.789
EE		0.817	0.576	0.871
EE1	0.790
EE2	0.812
EE3	0.636
EE4	0.738
EE5	0.804
FC		0.558	0.519	0.761
FC1	0.579
FC2	0.808
FC3	0.754
PE		0.743	0.568	0.839
PE1	0.778
PE2	0.798
PE3	0.820
PE4	0.599
PR		0.558	0.529	0.770
PR1	0.651
PR2	0.757
PR3	0.769
SI		0.699	0.768	0.869
SI1	0.890
SI2	0.863
TRUST		0.818	0.579	0.872
Trust1	0.790
Trust2	0.643
Trust3	0.747
Trust4	0.830
Trust5	0.783

Source: Author’s model.

Table 3 shows that the AVE values are greater than any correlation. Therefore, the measuring model demonstrates strong discriminant validity.

Table 3.

Fornell–Larcker Criterion.

	BIU	EE	FC	PE	PR	SI	TRUST
BIU	0.800
EE	0.759	0.759
FC	0.388	0.375	0.721
PE	0.721	0.783	0.361	0.754
PR	0.524	0.430	0.355	0.349	0.727
SI	0.661	0.598	0.489	0.489	0.406	0.877
TRUST	0.791	0.751	0.361	0.760	0.473	0.594	0.761

Source: Author’s model.

Notes: The diagonal values (bold) represent the AVE square root.

The Structural Model

Table 5 shows the VIF results indicating that collinearity is not an issue in the structural model in this study. Moreover, Figure 2 shows that the R² value indicated that the model constructs explained 74% of BIU variance. According to Table 4, the t test values (t values ≥ 1.96; P < 0.001; P < 0.05) indicate that BIU was significantly positively influenced by PE, EE, SI, PR, FC and trust, thereby H1, H2, H4 and H5 were supported, but H3 and H7 were rejected since the relationship was not in the predicted direction. Although trust significantly affects PR, the relationship was not in the predicted direction, thereby not supporting H6. As for Figure 2 and Table 4, it is also observable that the highest (SRW) paths are the hypothesized paths between trust and PR (0.473), followed by the paths between trust and BIU (0.318) and SI and BIU (0.220).

Table 4.

Results of Structural Model Path Coefficient.

Hypothesis	Relationship	β	SE	T Value	P Value	Decision
H1	EE ↓ BIU	0.197	0.015	13.220	0.000	Supported
H2	PE ↓ BIU	0.175	0.015	11.705	0.000	Supported
H3	FC ↓ BIU	0.023	0.009	2.540	0.011	Rejected
H4	SI ↓ BIU	0.220	0.012	19.076	0.000	Supported
H5	TRUST ↓ BIU	0.318	0.016	19.799	0.000	Supported
H6	TRUST ↓ PR	0.473	0.015	32.357	0.000	Rejected
H7	PR ↓ BIU	0.146	0.011	13.726	0.000	Rejected

Source: Author’s model.

Notes: ***P <0.001, **P <0.05.

Figure 2.

Source: Author’s model.

Finally, Table 5 shows the value of Q²; each value was greater than zero, confirming the framework’s out-of-sample predictive relevance.

Table 5.

VIF and Stone–Geisser or Q².

	VIF	Q² (1 – SSE/SSO)
BIU	3.851	0.640
EE	1.000	0.579
FC	1.000	0.518
PE	1.000	0.583
PR	1.288	0.529
SI	1.000	0.289
TRUST	1.000	0.579

Source: Author’s model.

The Moderating Effects Analysis

The results in Table 6 revealed that gender, age and educational qualification do not act as a moderating variable in the influence of the latent constructs on BIU.

Table 6.

Gender, Age and Educational Qualification as Moderating in Structural Models.

	Moderating Effect of Gender			Moderating Effect of Age			Moderating Effect of Education
Relationship	β	P value	D	β	P value	D	β	P value	D
EE -> BIU	0.402	0.701	NS	0.336	0.696	NS	0.276	0.810	NS
FC -> BIU	0.051	0.742	NS	0.076	0.839	NS	0.031	0.776	NS
PE -> BIU	0.371	0.424	NS	0.329	0.870	NS	0.381	0.936	NS
PR -> BIU	0.281	0.974	NS	0.362	0.554	NS	0.073	0.569	NS
SI -> BIU	0.454	0.610	NS	0.378	0.611	NS	0.287	0.660	NS
TRUST -> BIU	0.603	0.370	NS	0.717	0.604	NS	0.957	0.549	NS
TRUST -> PR	0.947	0.734	NS	0.986	0.813	NS	0.847	0.816	NS

Source: Author’s model.

Notes: D = Decision.*P <0.001, P <0.05.

Discussion and Conclusions

Based on the data obtained, it was demonstrated that the extended UTAUT model in this study might improve understanding of the roles of trust, PR, PP, PE, FC and SI in m-payment adoption among Egyptian retailers.

According to prior research, PE is an essential factor that positively affects the retailers’ BIU m-payment (Agarwal, 2020; Ariffin et al., 2020). This study estimated the impact of PE on retailers’ BIU, and the finding led us to believe that retailers will adopt m-payment if they see it as beneficial to achieving their goals in a more organized manner. Therefore, m-payment service providers should focus on designing applications able to augment the retailers’ carrying out of transactions. Moreover, EE primarily denotes the anticipation that m-payment will be free of labour. When retailers believe that using m-payment is simple, its applicability grows as they use it and the greater their desire to utilize it. Previous research has found that EE positively affects retailers’ BIU m-payment services (Agarwal, 2020; Ariffin et al., 2020). Similarly, EE was recognized as an essential determinant of customer behaviour because it influences behaviour toward using m-payment (Tang et al., 2021; Widyanto et al., 2021). Thus, m-payment service providers should create user-friendly, simple applications to maintain retailers’ positive intentions.

Moreover, the data proved that FC had a statistically significant and negative effect on the retailers’ BIU m-payment contradicts the study by Agarwal (2020) as he found that FC is insignificant for retailers’ BIU, and Ariffin et al. (2020) found that FC is significant for retailers’ BIU. This contradiction in the finding can be explained as FC is an essential determinant of actual use and not BIU, as Venkatesh et al. (2003) explained. As a result, The Egyptian government should use more aggressive communication channels to encourage m-payment service providers to sustainably improve technical support to retailers, raise retailers’ awareness about m-payment availability and use, and develop applications to improve retailers’ transaction performance. Furthermore, the government should create a suitable ecosystem for ubiquitous internet access.

Furthermore, the results reveal that SI positively impacts retailers’ BIU m-payment. The greater the influence of others on retailers, the more likely they will continue to adopt m-payment services. That is consistent with Ariffin et al. (2020) and Li and Li (2020) findings and contradicts the study by Agarwal (2020) as he found SI insignificant for retailers’ BIU who use m-payment. This contradiction in the finding indicates that the social impact differs from one context to another. The path analysis between these two constructs also had the second-highest direct effect in this study. That suggests SI as an essential factor in explaining retailers’ BIU m-payment. Overall, in Egypt, impactful peers and peer pressure positively impact the retailers’ BIU.

In line with previous research (Singh & Sinha, 2020), trust was a significant predictor of retailers’ BIU. Interestingly, the path relationship between these two constructs has the highest effect in the current study. In other words, retailers are more likely to continue using an m-payment if they develop an unyielding trust in the service providers, supporting the critical importance of trust for retailers in developing BIU m-payment services. As a result, to maintain the positive BIU, service providers in Egypt must nevertheless instil trust in the applications of m-payment that are being used.

Moreover, surprisingly, the findings show that PR has a statistically significant and positive influence on the retailers’ BIU m-payment; another unexpected result is that trust has a statistically significant and positive effect on PR. The literature review revealed that a relative majority of empirical studies leading towards the direction of both relations are negative (Goyal et al., 2020; Widyanto et al., 2021). Nevertheless, according to Pelaez et al. (2019), some studies have found the relationship between PR and intention insignificant or even positive. Goyal et al. (2020) discovered that trust has a negligible positive effect on risk. Kassim and Ramayah (2015) discovered that some risk dimensions have a positive effect on attitude. Ling et al. (2011) discovered a positive correlation between PR and online trust. Initially, Chin et al. (2018) and Goyal et al. (2020) highlighted the effect of motivational avoidance on the relationships between trust and PR, as well as the relationship between PR and intention. The researchers state that motivational avoidance was a contributory cause and that individuals relied solely on institutional dependence.

Goyal et al. (2020) have cited the argument of invoking cognitive dissonance theory as follows ‘to the extent that people increasingly trust or justify the legitimacy of an authority to cope with their dependence on it, they should be motivated to avoid information that could potentially rupture this trust.’ Moreover, on the one hand, when an individual is confronted with an unfamiliar and complex subject, he/she increasingly relies on the institutional authority that governs the system, ignoring the need to seek additional information, which increases his confidence in the systems used. On the other hand, people ignore any new or complex information that could shake their confidence in the organization or push them out of their sense of safety. The lack of prior exposure to the occurrence of risk leads the individual to ignore the existence of that risk (Chin et al., 2018). Therefore, results can be interpreted as retailers neglecting risks because they ignore the possibility of their occurrence, in addition to their confidence in service providers and government support. That is to say, retailers are more concerned about trust and will ignore risk if they trust the service providers. Consequently, retailers should be alert to such services’ risks to their transactions. Further investigation into this issue is strongly encouraged.

Finally, the results suggest that gender, age and educational qualification do not act as a moderating variable in the influence of the latent constructs on BIU. This insignificant may be due to the small size of the sample, and the sample is somewhat skewed, with ages ranging from 41 to 50 (78.30%) and retailers with a bachelor’s degree (83.3%).

Implications of the Study

Theoretical Implications

Using existing literature to identify factors influencing retailers’ intentions to use m-payment in Egypt, the UTAUT model was successfully extended and implemented. Two important constructs were incorporated based on retailers’ privacy, namely trust and PR, which have not been thoroughly explored in previous studies from retailers’ perspectives.

Furthermore, the model explains 74% of the retailers’ BIU m-payment variance. Taken all together, the results suggest that the model provides a better understanding of the retail sector in Egypt in the context of the intention to use m-payment.

Managerial Implications

First, the Egyptian government and m-payment service providers should pay attention to some variables in this study, which were discovered to directly impact retailers’ BIU m-payment. Specifically, trust and SI were discovered to be significantly higher.

Moreover, this would help policymakers better understand the role of these constructs in developing policies to increase trust and decrease PR within the retailer and devise ways and means to improve the cashless movement of funds within the economy. Additionally, the results presented here would broaden the understanding of m-payment providers by incorporating these constructs into developing their services and applications. Furthermore, our findings support previous literature indicating that retailers’ BIU m-payment is subject to societal norms. As a result, service providers could stimulate retailers’ subjective norms by mass marketing strategies at their respective levels.

Limitations and Future Research

As with all studies, this study has several limitations. First, despite the relative evidence for our framework, future studies can refine it by using a bidirectional perspective and including trust and PR antecedents. Second, while the sample size is adequate for the statistical techniques used in this study, the results cannot be generalizable. One possible future research direction would be to conduct this study with a much larger sample size. Third, our sample is somewhat skewed, with ages ranging from 41 to 50 and retailers with a bachelor’s degree; the findings may not apply to those who are significantly younger or those who are illiterate. Future research could look into different age groups and uneducated retailers. Fourth, because this study used non-probability sampling (i.e. convenience sampling), the potential bias caused by such sampling cannot be estimated. As a result, when generalizing the findings of our study, extra care must be taken.

Data accessibility

The datasets that support the findings of this study are available upon reasonable request from the corresponding author.

Appendix I.

Papers Surveyed Studying Factors Impacting the Use of M-payment.

Paper	Technology Application	Model	Participants	PE	EE	SI	FC	T-BIU	PR-BIU	T-PR	Factors Examined
(Ariffin et al., 2020)	M- payment	UTAUT	53 retailers. Malaysia	S	S	S	S	-	-	-	Building on the UTAUT, privacy and perceived security
(Agarwal, 2020)	Digital payment	UTAUT	203 merchants. India.	S	S	R	R	R	-	-	Building on the UTAUT, habit and trust.
(Li & Li, 2020)	M-payment		Merchant	-	-	S	-	-	-	-	Three different ensemble learning algorithms, Ad boost model, random forest and XGBoost model,
(Abebe & Lemma, 2020)	M-payment	TAM	363 merchants, Ethiopia.	-	-	-	-	-	-	-	Building on the TAM, relative advantage, compatibility, cost, trust and risk.
(Singh & Sinha, 2020)	M-wallet		315 Indian merchants	-	-	-	-	S	-	-	Perceived compatibility, P cost, awareness, trust, PEOU and P customer value.
(Yeboah et al., 2020)	M- payment	TAMTTM	Two merchants health industrialGhana	-	-	-	-	-	-	-	Building on the TAM, P- opportunism, environment risk and P structural assurance.
(Altounjy et al., 2020)	M- payment	TAM	125 F&B providers, Malaysia.	-	-	-	-	-	R	-	Building on the TAM, compatibility and risk
(Możdżyński, 2018)	Payment systems	TAM, UTAUT		-	-	-	-	-	-	-	EP and usefulness, the PEOU and EE, perceived cost and price, PR and confidence. Moderators sales channels, scope and range, experience
(Liébana-Cabanillas & Lara-Rubio, 2017)	M-payment		MerchantsSpain	-	-	-	-	S	-	-	logistic regression modelling and neural network analysisUser’s experience, socio-economic sector and behavioural variables
(Abubakar & Ahmad, 2014)	E-payment	UTAUT	Retail industryNigerian	-	-	-	-	-	-	-	UTAUTconcern for customers

Appendix II.

Measurement Variable, Items and Their Sources.

Factors	Measurement Items	Source
PE	PE1. Using the MPS would improve transaction.	(Venkatesh et al., 2003)
	PE2. Using the MPS would allow me to accomplish transaction more quickly
	PE3. I think MPS makes transaction more effective
	PE4. I found MPS is useful for transaction.
EE	EE1. Learning how to use MPS is easy	(Venkatesh et al., 2012)
	EE2. My interaction and navigation with MPS is clear and understandable
	EE3. Overall I found that MPS is easy to use
	EE4. It is easy for me to become skilful at using MPS.
	EE5. I would find it easy to get the MPS to do what I want it to do.
SI	SI1. The important people (family/relatives/ friends) recommend m-paymentSI2. The important people (family/relatives/friends) use m-payment systemFC	(Gholami et al., 2010; Venkatesh et al., 2003)
FC1. I have the resources necessary to use MPS.FC2. I have the knowledge necessary to use MPS.FC3. I can get help from others when I have difficulties using MPS.	(Venkatesh et al., 2012)	Trust
PT1. I trust each participant, such as seller and buyer, involved in EPSPT2. I trust the security mechanisms of EPSPT3. I trust EPS servicesPT4. I trust the information provided during the EPS process	(Kim et al., 2010)	PR
PR1. I would not feel secure sending sensitive information across the mobile payment system.PR2. Using MPS would involve more technical risk when compared with more traditional ways of payment.PR3. Using MPS would involve more financial risk when compared with more traditional ways of payment.	(Kim et al., 2008)	BIU
BIU1. Assuming I had access to the MPS, I intend to use it.	(Venkatesh et al., 2003, 2012)
BIU2. Given that I had access to the MPS, I predict that I would use it.
BIU3. I plan to use the MPS in the future.
BIU4. I recommend MPS to my colleagues.

Footnotes

Declaration of Conflicting interest

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding Statement

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ahmed Taher Esawe

References

Abebe

, & Lemma

(2020). Factors affecting mobile payment adoption by merchants in Ethiopia. The 6th African Cdonference on Information Systems and Technology (ACIST2020) (pp. 1–11). Kennesaw State University.

Abubakar

, & Ahmad

(2014). Determinants of behavioural intention to use e-payment system in Nigerian retail industry: A conceptual extension of UTAUT with concern for customers. International Journal of Contemporary Business Management , 1(1), 87–93.

Agarwal

R. N.

(2020). The role of effective factors in UTAUT model on behavioral intention. Business Excellence and Management , 10(3), 5–23.

Alkhowaiter

W. A.

(2020). Digital payment and banking adoption research in Gulf countries: A systematic literature review. International Journal of Information Management , 53, 102102.

Al-Saedi

, & Al-Emran

(2021). A systematic review of mobile payment studies from the lens of the UTAUT model. In Al-Emran

& Shaalan

(Eds.), Recent advances in technology acceptance models and theories . Studies in systems, decision and control (Vol. 335, pp. 79–106). Springer.

Altounjy

, Omar

, Hafezali

I. H.

, & Kot

(2020). Moving from bricks to clicks: Merchants’ acceptance of the mobile payment in Malaysia. International Journal of eBusiness and eGovernment Studies , 12(2), 136–150.

Ariffin

, Ahmad

, & Haneef

U. M.

(2020). Acceptance of mobile payments by retailers using UTAUT model. Indonesian Journal of Electrical Engineering and Computer Science , 19(1), 149–155.

Behr

(2017). Assessing the use of back translation: The shortcomings of back translation as a quality testing method. International Journal of Social Research Methodology , 20(6), 573–584.

Boateng

, & Sarpong

M. Y.

(2019). A literature review of mobile payments in sub-Saharan Africa. International Working Conference on Transfer and Diffusion of IT (pp. 128–146). Springer.

10.

Central Bank of Egypt. (2020, May 21). cbe.org.eg. https://www.cbe.org.eg/en/Pages/HighlightsPages/Circular-dated-21-May-2020-regarding-the-Central-Bank-of-Egypt-initiative-for-electronic-payments.aspx

11.

Chai

J. C.

, & Dibb

(2014). How consumer acculturation influences interpersonal trust. Journal of Marketing Management , 30(1–2), 60–89.

12.

Chin

A. G.

, Harris

M. A.

, & Brookshire

(2018). A bidirectional perspective of trust and risk in determining factors that influence mobile app installation. International Journal of Information Management , 39, 49–59.

13.

Cochran

W. G.

(1977). Sampling techniques (4th Ed.). John Wiley & Sons.

14.

crci.sci.eg. (2015). http://www.crci.sci.eg/wp-content/uploads/2015/06/Egypt_2030.pdf

15.

Dahlberg

, Guo

, & Ondrus

(2015). A critical review of mobile payment research. Electronic Commerce Research and Applications , 14(5), 265–284.

16.

Esawe

, & Elwkeel

(2020). Managing the digital transformation, strategic management, and tactical actions to implement GFMIS: An Egyptian case study. Journal of Management Research , 1–29.

17.

Fornell

, & Larcker

D. F.

(1981). Structural equation modeling and regression: Guidelines for research practice. Journal of Marketing Research , 18(1), 39–50.

18.

Gao

, & Waechter

K. A.

(2017). Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers , 19(3), 525–548.

19.

Gholami

, Ogun

, Koh

, & Lim

(2010). Factors affecting e-payment adoption in Nigeria. Journal of Electronic Commerce in Organizations (JECO) , 8(4), 51–67.

20.

Goyal

C. A.

, Harris

M. A.

, & Brookshire

(2020). An empirical investigation of intent to adopt mobile payment systems using a trust-based extended valence framework. Information Systems Frontiers , 1–19.

21.

Hair

J. F.

, Risher

J. J.

, Sarstedt

, & Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review , 31(1), 2–24.

22.

Hair

, Hollingsworth

C. L.

, Randolph

A. B.

, & Chong

A. Y.

(2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems , 117(3), 442–458.

23.

Harris

, Chin

, & Beasley

(2019). Mobile payment adoption: An empirical review and opportunities for future research. Southern Association of Information Systems (SAIS) , 8.

24.

Hasan

, & Gupta

S. K.

(2020). Exploring tourists’ behavioral intentions towards the use of select mobile wallets for digital payments. Paradigm , 24(2), 177–194.

25.

Karsen

, Chandra

Y. U.

, & Juwitasary

(2019). Technological factors of mobile payment: A systematic literature review. Procedia Computer Science , 157, 489–498.

26.

Kassim

, & Ramayah

(2015). Perceived risk factors influence on intention to continue using internet banking among Malaysians. Global Business Review , 16(3), 393–414.

27.

Ketokivi

, & Mahoney

J. T.

(2017). Transaction cost economics as a theory of the firm, management, and governance. Oxford Research Encyclopedia of Business and Management.

28.

Khan

A. N.

, & Khan

M. N.

(2021). Determining mobile payment adoption: A systematic literature search and bibliometric analysis. Cogent Business & Management , 8(1), 1893245.

29.

Kim

, Tao

, Shin

, & Kim

K.-S.

(2010). An empirical study of customers’ perceptions of security and trust in e-payment systems. Electronic Commerce Research and Applications , 9, 84–95.

30.

Kim

D. J.

, Ferrin

D. L.

, & Rao

H. R.

(2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems , 44(2), 544–564.

31.

Kock

(2015). Common method bias in PLS–SEM collinearity assessment approach. International Journal of e-Collaboration , 11(4), 1–10.

32.

Lee

, Ryu

, & Lee

(2019). A study on the reciprocal relationship between user perception and retailer perception on platform-based mobile payment service. Journal of Retailing and Consumer Services , 7–15.

33.

, & Li

(2020). Study of merchant adoption in mobile payment system based on ensemble learning. American Journal of Industrial and Business Management , 10(5), 861–875.

34.

Liébana-Cabanillas

, & Lara-Rubio

(2017). Predictive and explanatory modeling regarding adoption of mobile payment systems. Technological Forecasting and Social Change , 120, 32–40.

35.

Ling

K. C.

, Bin Daud

, Piew

, Keoy

, & Hassan

(2011). Perceived risk, perceived technology, online trust for the online purchase intention in Malaysia. International Journal of Business and Management , 6(6).

36.

, Yang

, Chau

P. Y.

, & Cao

(2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management , 48(8), 393–403.

37.

Magsamen-Conrad

, Wang

, Tetteh

, & Lee

Y.-I.

(2020). Using technology adoption theory and a lifespan approach to develop a theoretical framework for ehealth literacy: Extending UTAUT. Health Communication , 35(12), 1435–1446.

38.

Mallat

(2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems , 16(4), 413–432.

39.

mcit. (2021). Egypt financial services content guide Covid-19 recovery roadmap. Financial Regulatory Authority. Oxford Business Group.

40.

Mehta

, Singla

, Saha

, & Tyagi

(2021). A pathway to technology integration: Eliciting consumer’s behavioural intention to use paytm services. Paradigm , 25(1), 7–24.

41.

Moghavvemi

, Mei

T. X.

, Phoong

S. W.

, & Phoong

S. Y.

(2021). Drivers and barriers of mobile payment adoption: Malaysian merchants' perspective. Journal of Retailing and Consumer Services , 59, 102364.

42.

Mohamad

S. A.

, & Kassim

(2017). An overview of e-payment adoption among Muslim micro–entrepreneurs in Malaysia. International Journal of Accounting , 2(5), 49–59.

43.

Możdżyński

(2018). Acceptance of payment systems from the perspective of merchants. Information Systems in Management , 7(1), 26–34.

44.

Pelaez

, Chen

C.-W.

, & Chen

(2019). Effects of perceived risk on intention to purchase: A meta-analysis. Journal of Computer Information Systems , 59(1), 73–84.

45.

Perry

R. H.

, Charlotte

, Isabella

, & Bob

C. S.

(2004). SPSS explained, 1st ed.. Routledge Taylor & Francis Group.

46.

Pisani

, & Moormann

(2018). The role of competition in the adoption of mobile payment among merchants. 31st Bled eConference Digital Transformation, pp. 507–514. Slowenien.

47.

Rahman

, Tazim

M. Z.

, Das

, & Islam

(2020). State of the art of mobile banking services and future prospects in developing countries. 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 145–149). IEEE.

48.

Ringle

C. M.

, Wende

, & Will

(2005). SmartPLS 2.0 (M3) Beta, Hamburg.

49.

Roy

, & Sinha

(2014). Determinants of customers’ acceptance of electronic payment system in Indian banking sector. International Journal of Scientific & Engineering Research , 5(1), 177–187.

50.

Sahi

A. M.

, Khalid

, Abbas

A. F.

, & Khatib

S. F.

(2021). The evolving research of customer adoption of digital payment: Learning from content and statistical analysis of the literature. Journal of Open Innovation: Technology, Market, and Complexity , 7(4), 203.

51.

Schmitt

(1996). Uses and abuses of coefficient alpha. Psychological Assessment , 8(4), 350–353.

52.

Simatele

(2021). E-payment instruments and welfare: The case of Zimbabwe. The Journal for Transdisciplinary Research in Southern Africa , 17(1), 1–11.

53.

Singh

, & Sinha

(2020). How perceived trust mediates merchant's intention to use a mobile wallet technology. Journal of Retailing and Consumer Services , 52, 101894.

54.

Sobti

(2019). Impact of demonetization on diffusion of mobile payment service in India: Antecedents of behavioral intention and adoption using extended UTAUT model. Journal of Advances in Management Research , 16(4), 472–797.

55.

Tabachnick

B. G.

, & Fidell

L. S.

(2007). Experimental designs using ANOVA. Thomson/Brooks/Cole.

56.

Talwar

, Dhir

, Khalil

, Mohan

, & Islam

A. N.

(2020). Point of adoption and beyond. Initial trust and mobile-payment continuation intention. Journal of Retailing and Consumer Services , 55(102086).

57.

Tan

G. W.-H.

, Chong

C.-K.

, Ooi

K.-B.

, & Chong

A. Y.-L

. (2010). The adoption of online banking in Malaysia: An empirical analysis. International Journal of Business and Management Science , 3(2), 169–193.

58.

Tang

K. L.

, Aik

N. C.

, & Choong

W. L.

(2021). A modified UTAUT in the context of m-payment usage intention in Malaysia. Journal of Applied Structural Equation Modeling , 5(1), 39–60.

59.

Venkatesh

, Morris

M. G.

, Davis

G. B.

, & Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly , 27(3), 425–478.

60.

Widyanto

H. A.

, Kusumawardani

K. A.

, & Yohanes

(2021). Safety first: Extending UTAUT to better predict mobile payment adoption by incorporating perceived security, perceived risk and trust. Journal of Science and Technology Policy Management . https://doi.org/10.1108/jstpm-03-2020-0058

61.

, Le

, Deitermann

, & Montague

(2014). How different types of users develop trust in technology: A qualitative analysis of the antecedents of active and passive user trust in a shared technology. Applied Ergonomics , 45(6), 1495–1503.

62.

Yeboah

, Boateng

, Owusu

, Afful-Dadzie

, & Ofori-Amanfo

(2020). Assessing the role of trust in merchant adoption of mobile payments in Ghana. Responsible Design, Implementation and Use of Information and Communication Technology , 12066, 204–215.

Exploring Retailers’ Behavioural Intentions Towards Using M-payment: Extending UTAUT with Perceived Risk and Trust

Abstract

Keywords

Introduction

Literature Review and Hypotheses Development

Research Model

Hypotheses Development BIU

Effort Expectancy (EE)

Performance Expectancy (PE)

Facilitating Conditions (FC)

Social Influence (SI)

Trust

PR

Moderating Effect

Methods

Methodology

Measures

Sample

Respondent’s Profile (N = 682).

Results

Measurement Model

Measurement Model Results.

Fornell–Larcker Criterion.

The Structural Model

Results of Structural Model Path Coefficient.

VIF and Stone–Geisser or Q2.

The Moderating Effects Analysis

Gender, Age and Educational Qualification as Moderating in Structural Models.

Discussion and Conclusions

Implications of the Study

Theoretical Implications

Managerial Implications

Limitations and Future Research

Data accessibility

Papers Surveyed Studying Factors Impacting the Use of M-payment.

Measurement Variable, Items and Their Sources.

Footnotes

Declaration of Conflicting interest

Funding Statement

ORCID iD

References

VIF and Stone–Geisser or Q².