|
Sreelakshmi C. C. Research Scholar School of Management Studies Cochin University of Science & Technology |
Dr. Sangeetha K. Prathap Assistant Professor School of Management Studies Cochin University of Science & Technology |
Mobile banking is considered as the most convenient e-banking delivery platform by customers as well as the bankers. We developed a research framework in the mobile banking contextstudying the continuance intention of customers by drawing literature from valence theory, diffusion theory and trust-based consumer decision making model. The study empirically examined the impact of positive and negative valences on continuance intention to use mobile banking services by Indian consumers. We also investigated the direct and indirect effect of trust on the consumer intention to use mobile banking. The results of structural equation modelling analysis revealed that trust has a significant impact on positive valence and negative valence factors and it further positively influences the continuance intention. In addition, the positive valence factors including perceived benefits and compatibility has significant positive impact on continuance intention and the negative valence factors consisting of perceived risk and complexity negatively influence the continuance intention to use mobile banking services.
Keywords: -Mobile banking, trust, positive valence, negative valenceTechnology based banking enables the consumers to exercise all the traditional banking services through his mobile handset. In comparison with other automated channels for banking services, mobile banking is considered as the most convenient e-banking delivery platform by customers as well as the bankers. It enables anytime- anywhere facility to the consumers to access banking services and hence it has the potential to improve the consumers quality of life (Malaquias & Hwang, 2016) . Indian banking system has wide spectrum of opportunities in widening mobile banking coverage across the country with the policy embracing faster transition into digital economy. Smart phone users in the country are registering an exponential rise indicating increase in data driven population. Total volume of wire-less data increased at the rate of 131 per cent in the year 2018 compared to the previous year. It is estimated that the total data usage will be increased by 200 per cent by the year 2020 (TRAI, 2019) indicating that more people will be digitally included implying switch to payment options using mobile phones. However, adoption of mobile banking happens only when the consumer is convinced about the positive features and is free from apprehensions. The main challenge for bankers is to convince the consumers about the positive features and minimize the perception of negative aspects of mobile banking compared to the traditional banking system as well as other digital payment services of fin-tech companies. Hence it was found relevant to study the positive valences and negative valences which can trigger and improve the mobile banking usage by Indian consumers. Given the importance of targeting the Indian consumers to expand the banks business opportunities, the study will be useful in extracting information to expand the digital banking push.
Prior researchers have studied mobile banking adoption using various technology adoption theories including Technology Acceptance Model (Davis, 1989), Theory of Planned Behavior (Ajzen, 1991), Unified Theory of Acceptance and Use of Technology (Venkatesh, Morris, Davis, & Davis, 2003) and Theory of Diffusion of Innovation (Rogers, 1983). Priya, et al., (2018) probed mobile banking adoption by young Indian consumers and proved that usefulness, perceived ease of use, perceived credibility and structural assurance are the determinants of behavioral intention to use the mobile banking service. Singh & Srivastava, (2018)also supported this finding and they proved that security, perceived ease of use, perceived financial cost and computer self-efficacy affect customers’ intention to adopt mobile banking. Glavee-Geo et al., (2017) combined TAM and TPB to predict the antecedents of mobile banking services and the study revealed perceived usefulness and perceived risk as predictors of behavioral intention to use mobile banking in Pakistan. Saxena, (2017)combined TAM, UTAUT and TPB to study the mobile government services adoption in India and found the significant impact of perceived ease of use, perceived usefulness and trust on the adoption of m-government services. Few researches have also probed technology adoption like online banking and mobile payments adoption using valence theory put forwarded by (Peter & Tarpey, 1975). The authors theorized that consumers try to diminish the adverse features (negative valence) of the product or service and increase the favorable features (positive valence) and balance the utilities out to reach a net positive valence. Though valence theory is used extensively in validating consumer behavior researches, the use ofit in explaining technology adoption is limited, especially with respect to mobile banking adoption. The valence theory considers only perceivedrisk as the negative valence and perceived benefit as the positive valence, but these two constructs are not sufficient to predict the digital payment transactions (Lu et al., 2011).Researchers like Huang, Li, & Li, (2014); Lu et al., (2011); Ozturk et al.,(2017); and Yang et al.,(2012) have validated and extended the valence framework in various contexts like tourism, e-business and mobile payment contexts. Further Kim et al, (2008) extended the valence theory by incorporating the impact of trust on consumer decision making process in the e-commerce context and the model is known as ‘Trust-based Consumer Decision Making Model’. The model conceptualizes that trust makes direct effect on consumer purchase intention and an indirect effect on purchase intention through perceived risk. In the trust-based model also, perceived risk remains as the single negative valence and perceived benefits represents the single positive valence construct. The Diffusion of Innovation (DOI) theory of Rogers, (1983) identified five major constructs, namely relative advantage, compatibility, complexity, trialability and observability, which prompts people to adopt innovative technologies. Studies by Al-Ajam & Md Nor, (2015); Dash et al., (2014); Ramavhona & Mokwena, (2016) identified relative advantage and compatibility as good predictors of mobile banking adoption. The relative advantage and compatibility act as positive valences which motivate the consumers to adopt technology while complexity is a negative valence which reduces the consumer preference towards new technologies. In the TAM model, perceived ease of use and perceived usefulness is considered as the determinants of technology adoption. The relative advantage construct of DOI and perceived usefulness of TAM can be conceptualized as perceived benefits which reveal similar concepts. Hence, the present study is an attempt to develop a research framework by drawing literature from valence theory, diffusion theory and trust-based consumer decision making model. After examining literature on technology adoption and consumer decision making process, the study considers two adoption drivers viz., perceived benefit and compatibility which creates a positive impact on continuous intention to use mobile banking and two negative valences viz., perceived risk and complexity which may exert negative impact on continuous intention to use mobile banking services by the consumers. Due to perception of increased risk and uncertainty associated with mobile banking transactions, consumer trust may significantly influence the intention to continue the mobile banking usage. Chiu et al., (2017); Gu et al., (2009); Kim et al., (2009); and Liu et al., (2005) confirmed the significant positive effect of trust on internet banking and mobile banking adoption. Hence the consumer trust is also included in the conceptual frame work of the study. Since the architecture of mobile payment transactions are constantly improving, the banking companies have to maximize the positive valences and minimize the negative valences in order to increase the net utilitarian value of mobile banking, so that they will be able to survive in the highly competitive digital payment industry. In this competitive environment, consumers demand constant improvement of positive valences and reduction of negative valences to stick to continuous usage. The study intends to integrate the Valence Theory framework of Peter & Tarpey, (1975), trust-based consumer decision making model of Kim et al., (2008) and Diffusion of Innovation Theory Rogers, (1983) to investigate the positive and negative attributes leading to continuance intention to use mobile banking services.
We conceptualize perceived benefit and compatibility as the positive valence which exerts significant positive impact on continuous intention to use mobile banking. Perceived benefit can be defined as ‘the consumer’s belief about the extent to which he or she will become better-off from the banking transactions with a certain mobile banking application’ and the definition was modified from Kim et al., (2008) defined in the electronic commerce context. This construct has conceptual resemblance to the perceived usefulness of TAM and relative advantage of diffusion of innovation theory and they proved its significant influence on continuous intention to use mobile banking. Further Dash et al., (2014); and Islam et al.,(2013) also hypothesized the positive association of benefits and adoption intention of various technologies. Compatibility is defined as ‘the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters’ (Rogers, 1983). Lin, (2011); and Lu et al., (2011) conceptualized and proved the positive relationship of compatibility and adoption/ continuance intention to use mobile banking mobile payment services. Further, Gerrard & Barton Cunningham, (2003) and Tan & Teo, (2000) confirmed this positive relationship in the internet banking context. Hence the study proposes the first two hypotheses as:
H1: Perceived benefits have a positive impact on continuance intention to use mobile banking. H2: Compatibility has a positive impact on continuance intention to use mobile banking.
Negative valences are the undesirable features of the product or service(Peter & Tarpey, 1975). Perceived risk in mobile banking is defined as ‘the degree of uncertainty about the outcome of mobile banking’ (Gerrard & Barton Cunningham, 2003). The perceived risk is a major factor in determining the adoption intention. The security information like passwords, Personnel Identification Number, user identities and loss of mobile phone are the possible risk involved in mobile banking. A meta-analysis study of mobile payments conducted by Liu et al.,(2019) found that more than 20 papers found the significant negative impact of perceived risk on continuous intention of mobile payments usage. Al-Ajam & Md Nor, (2015) supported the negative impact of perceived risk on attitude towards mobile banking adoption. Luo et al., (2010) found that user perception of risk directly determines technology acceptance. Further, Kazemi et al.,(2013) confirmed the negative relationship between perceived risk and mobile banking adoption. Complexity is yet another negative valence which can reduce technology adoption and continuous usage. Rogers, (1983) defined complexity as ‘the degree to which an innovation is perceived as relatively difficult to understand and use’(Rogers, 1983). Prior researchers validated mixed results, for example, Olatokun & Igbinedion, (2009)found significant impact for complexity on attitude towards ATM usage, while Folorunso, (2010)stated that complexity of use of social networking sites does not have significant impact on user’s attitude. Hence the study posits the following hypotheses:
H3: Perceived risk has significant negative impact on the continuance intention to use mobile banking. H4: Complexity has significant negative impact on continuance intention to use mobile banking.
Masrek et al., (2012) defined trust in mobile banking as the individual belief that leads to vulnerability to mobile technology, telecommunication provider and banks, given the technology contains banks and telecommunication providers characteristics. Chiu et al., (2017); Gefen & Straub, (2003); Gu et al., (2009); and Liu et al., (2005) found that intention to use internet banking and mobile banking is significantly influenced by the trust. Kim et al., (2008) extended the valence frame work by incorporating consumer trust and its antecedents and confirmed its direct effect on purchase intention and indirect effect through perceived risk on purchase intention. Further Huang et al., (2014); and Lu et al., (2011) extended the valence theory with trust and examined its direct and indirect effect on consumer intention through both perceived risk and perceived benefits in online tourism and mobile payment context respectively. Chandra et al., (2010); and Lee, (2010) also supported the positive influence of trust on perceived benefits. Hence the study postulates the following hypotheses:
H5: Consumers trust has significant positive impact on continuance intention of mobile banking usage H6: Consumer trust in mobile banking has significant negative impact on consumer’s perceived risk H7: Consumer trust in mobile banking has a significant positive impact on perceived benefits of consumers towards mobile banking H8: Perceived risk and perceived benefits partially mediate the effect of consumer trust on continuance intention to use mobile banking
The subjects of the study constitute existing users of mobile banking. Data was collected through structured questionnaires. The questionnaire was mailed to 500 mobile banking users in India, out of which 415 responded. Out of the filled in questionnaires, 317were found usable. As recommended by Hair et al.,(2014), the sample size should be greater than 10 times of total items in the questionnaire to perform structural equation modelling and hence the study ensured appropriate sampling adequacy.
The constructs of the study were adapted from existing literature. The research model consists of six constructs and to measure the responses of items of constructs, a seven-point Likert scale with anchors starting from strongly agree to strongly disagree has been employed. Trust was measured using a six item scale adapted from (Gefen & Straub, 2003). Perceived risk was assessed based on five items taken from (Kim et al., 2008). Complexity was measured with three items developed by (Tan & Teo, 2000). Perceived benefits were assessed with four items adapted from (Kim et al., 2008). The compatibility was measured with three items taken from (Lin, 2011). The endogenous variable continuance intention was based on three items taken from (Bhattacherjee, 2001).
In order to empirically validate the conceptual model and prove the hypotheses, the study employed Structural Equation Modelling using AMOS-23 software and mediation analysis with Haye’s Process. The structural equation modelling comprises measurement model and structural model. The measurement model explains the relationship between latent constructs and the observed variables whereas the structural model establishes the causal relationship between the latent constructs. Initially, the CFA has been carried out to ensure the validity, reliability and model fitness followed structural model to estimate the path analysis.
The conceptual model was empirically tested using Structural Equation Modelling (SEM). As recommended by Anderson & Gerbing, (1988), the major assumptions of structural equation modeling like normality and absence of common method bias were tested and ensured the data is fit for performing structural equation modelling. Then the reliability and validity of the measurement model was tested using Confirmatory Factor Analysis (CFA) followed by structural model to estimate the empirical relationships hypothesized in the conceptual model.
Three hundred and seventeen valid questionnaires were completed by Indian mobile banking consumers. Of the total sample size, 52 percent of the respondents are male and 48 percent are female. Majority of the respondents belonged to the age group of 30-40 (35 percent) followed by the group 20- 30 years (33 percent). With reference to the educational qualification, 48 percent of the respondents are graduates and 38 percent are postgraduates. With regard to internet knowledge and experience, cent percent were observed to have sufficient knowledge.
To test the normality of data, the skewness-kurtosis approach was used (Anderson & Gerbing, 1988) and the statistical values of all items were calculated and found that they are within their respective boundaries. All values of skewness lie below the cut-off point of three and all the kurtosis values were less than seven (Kline, 2011).
To ensure that the data set is free from common method bias, Harman’s single factor(Podsakoff et al., 2003) with six constructs (trust, perceived risk, complexity, perceived benefits, compatibility and continuous intention) and 24 scale items was conducted. Six components emerged out of the principal component analysis. No single component exceeded more than 50 per cent the total variance as recommended by (Podsakoff et al., 2003). The first variable explained a variance of 32.05 per cent.
To test the overall goodness of model fit, the fit indices namely ꭕ2 value (CMIN/ D.F.), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Comparative Fit Index (CFI), Tucker-Lewis Fit Index (TLI), Normed Fit Index (NFI) and Root Mean Square Error of Approximation (RMSEA) were used. A good model should have the fit index values viz., GFI should be greater than 0.8(Etezadi-Amoli & Farhoomand, 1996), AGFI should exceed 0.8 (Anderson & Gerbing, 1988), CFI, NFI and TLI must be greater than 0.9 (Anderson & Gerbing, 1988; Hair, 2014) and finally the value of RMSEA and CMIN/D.F should be less than 0.08 and 5.0 respectively as recommended by (Anderson & Gerbing, 1988; Hair, 2014). All the fit indices values of the conceptual model are within their respective boundaries (GFI =0.897; AGFI = 0.869; CFI =0.977; NFI= 0.952; TLI = 0.974, RMSEA = 0.052; and CMIN/D.F. =1.85). To test the construct reliability of the Model, Cronbach’sAlfa (α), Average Variance Extracted (AVE) and Composite Reliability (CR)were used. The values of Cronbach’s alfa, AVE and CR of all latent constructs are above the cut-off level of 0.7, 0.5 and 0.7 respectively as recommended by (Anderson & Gerbing, 1988; Hair, 2014; Hu & Bentler, 1999). The maximum Cronbach’s alfa was found for the construct perceived benefits (0.967) and the compatibility scored the least α (0.924).The AVE score was highest for benefits and compatibility (0.81) and perceived risk exhibited the minimum AVE (0.72). Similarly, CR values of constructs are ranging from 0.89 for continuous intention to 0.95 for trust.
|
Latent Variable |
Cronbach’s Alfa (α) |
AVE |
CR |
|
|
1 |
2 |
3 |
4 |
5 |
6 |
|
Trust |
0.959 |
0.76 |
0.95 |
|
|
0.871 |
|
|
|
|
|
|
Perceived benefits |
0.967 |
0.81 |
0.95 |
|
|
0.322 |
0.897 |
|
|
|
|
|
Compatibility |
0.924 |
0.81 |
0.93 |
|
|
0.233 |
0.486 |
0.90 |
|
|
|
|
Perceived risk |
0.951 |
0.72 |
0.91 |
|
|
-0.507 |
-0.425 |
-0.213 |
0.848 |
|
|
|
Complexity |
0.945 |
0.76 |
0.91 |
|
|
-0.372 |
-0.433 |
-0.255 |
0.502 |
0.872 |
|
|
Continuous intention |
0.955 |
0.73 |
0.89 |
|
|
0.539 |
0.486 |
0.401 |
-0.595 |
-0.563 |
0.850 |
Note: Diagonal values in bold are the square roots of AVE and off-diagonal values are inter-correlation between latent constructs
To test the convergent validity, the standardized regression coefficients of observed variables with their latent construct has been estimated and all the values are above the recommended threshold limit of 0.50 and are significant(Anderson & Gerbing, 1988; Hair, 2014). While analyzing the correlation estimates of latent constructs, the inter-constructs correlation coefficients are less than the square root of AVE and hence discriminant validity is established (Anderson & Gerbing, 1988; Hair, 2014).
|
Fit indices |
Cut-off level |
Initial Model |
|
CMIN/ DF |
≤3.00 |
1.85 |
|
GFI |
≥0.80 |
0.897 |
|
AGFI |
≥0.80 |
0.869 |
|
NFI |
≥0.90 |
0.952 |
|
CFI |
≥0.90 |
0.977 |
|
TLI |
≥0.90 |
0.974 |
|
IFI |
≥0.90 |
0.974 |
|
RMSEA |
≤0.08 |
0.052 |
The structural model was empirically tested and all the fit index values are within the recommended cut-off levels. The calculated values are; GFI = 0.871; CMIN/ D.F.= 2.285; AGFI = 0.841; CFI = 0.965; NFI = 0.940; IFI = 0.965; TLI = 0.960, and RMSEA = 0.064 and all the values are within their threshold limits recommended by (Etezadi-Amoli & Farhoomand, 1996; Hair, 2014).
Table 3 Fit indices of structural model
|
Fit Indices |
Cut-off Level |
Value |
|
CMIN/ DF |
≤3.00 |
2.285 |
|
GFI |
≥0.80 |
0.871 |
|
AGFI |
≥0.80 |
0.841 |
|
NFI |
≥0.90 |
0.940 |
|
CFI |
≥0.90 |
0.965 |
|
TLI |
≥0.90 |
0.960 |
|
IFI |
≥0.90 |
0.965 |
|
RMSEA |
≤0.08 |
0.064 |
Once the model fitness is assessed, the path-coefficient analysis has been done to measure the associationship between exogeneous and endogenous variables and the R2 was used to identify the explanative power of exogeneous variables on the endogenous variable. The path analysis implies that the positive valence constructs viz., perceived benefits (β= 0.12, P = 0.007) and compatibility (β= 0.234, P = 0.000) have significant positive impact on continuous intention and hence the hypothesis H1and H2 are validated. The components of negative valence, viz., perceived risk (β= -0.269, P = 0.000) and complexity (β= -0.243, P = 0.000) are having significant negative influence on continuous intention to use mobile payments and thus the hypotheses, H3 and H4 were supported. The study found that trust has a significant positive impact on continuous intention to use mobile banking (β= 0.246, P = 0.000). Further, trust has a strong negative impact on perceived risk (β= -0.623, P = 0.000) and a strong positive impact on perceived benefits (β= 0.405, P = 0.000) and hence validating H5, H6 and H7. The R-square value extracted for continuous intention, perceived risk and perceived benefits were 55.6 per cent, 28.5 per cent and 12 per cent respectively, reflecting that adequate explanation of variance in perceived risk, perceived benefits and continuous intention to use mobile banking.
|
Hypotheses |
Path |
Estimate (β) |
SE |
CR (t-value) |
P-value |
Result |
|
H1 |
Trust→ Perceived Risk |
-0.633 |
0.061 |
-10.354 |
0.000 |
Accepted |
|
H2 |
Trust→ Perceived benefits |
0.405 |
0.065 |
6.255 |
0.000 |
Accepted |
|
H3 |
Trust→ Continuous Intention |
0.246 |
0.056 |
4.361 |
0.000 |
Accepted |
|
H4 |
Perceived Risk→ Continuous Intention |
-0.252 |
0.043 |
-5.910 |
0.000 |
Accepted |
|
H5 |
Complexity→ Continuous Intention |
-0.226 |
0.043 |
-5.08 |
0.000 |
Accepted |
|
H6 |
Benefits→ Continuous Intention |
0.118 |
0.038 |
3.119 |
0.002 |
Accepted |
|
H7 |
Compatibility→ Continuous Intention |
0.219 |
0.043 |
5.116 |
0.000 |
Accepted |
To understand the mediating effects of perceived risk and perceived benefits on the relationship between trust and continuous intention to use mobile banking, parallel mediation analysis has been done as recommended by (Baron & Kenny, 1986; Hayes, 2013). The results are presented table 6 and the relationship between trust and continuous intention to use mobile payment services was found to be partially mediated by perceived risk as well as perceived benefits.
|
Path |
Effect |
Boot se |
Boot LLCI |
Boot ULCI |
|
Total Indirect Effect |
0.2498*** |
0.0414 |
0.1689 |
0.3293 |
|
Ind 1 (Trust→ Risk→ Intention) |
0.1719*** |
0.323 |
0.1097 |
0.2355 |
|
Ind 2 (Trust→ Benefits →Intention) |
0.0779*** |
0.0221 |
0.0374 |
0.1235 |
|
Direct Effect (Trust→ Intention) |
0.2780*** |
0.0468 |
0.1860 |
0.3701 |
The study reveals several relevant findings. The consumer trust directly influences consumer’s intention to continue the usage of mobile banking. The indirect effect of trust on continuance intention through perceived benefits and perceived risks is also significant. The consumer trust exerts a significant positive impact on the consumer perception of benefits (β = 0.405, P = 0.000) which in turn increases the intention to continue the usage of mobile payment services (β = 0.118, P = 0.000). The results are in line with the findings of Lu et al., (2011) who found an indirect impact of trust on behavioral intention through the relative advantage perceived by the consumers of mobile payments. Gefen et al., (2003); and Lu et al., (2011) also came up with similar findings of direct and indirect effect of consumer trust on continuance intention of technology usage through its benefits. Similarly, a higher level of trust reduces the risk perception of consumers towards mobile banking (β = -0.633, P = 0.000). Then a lower risk perception increases the intention to continue the mobile banking usage (β = -0.252, P = 0.000). This finding corroborate the previous research findings of Kim et al., (2009); and Lu et al., (2011) in the online banking context and mobile payment context respectively. This finding implies the significance of trust in mobile banking adoption. The results of the structural model confirm that, both positive valences exert significant positive impact and negative valences exert significant negative impact on continuous intention to use mobile banking. Among the positive valences, compatibility (β = 0.219, P = 0.000) is the most significant predictor of continuous intention followed by perceived benefits (β = 0.118, P = 0.002). This confirms the theory of Diffusion of Innovation which states that relative advantage and compatibility are the strong predictors of technology adoption (Rogers, 1983). The finding are also in line with Yang et al., (2012) who found that compatibility and relative advantage are the most significant predictors of continuance intention of mobile payment services. Further, Lee, (2010) confirmed the positive impact of perceived benefits on online banking adoption and continuance intention. In contrast, the negative valences consisting of perceived risk (β = -0.252, P = 0.000) and complexity (β = -0.226, P = 0.000) exerts a significant negative effect on continuance intention to use mobile payment services. The results are in confirmation with the findings of Al-Ajam & Md Nor, (2015); Priya et al.,(2018); and Thakur & Srivastava, (2014)which confirmed the negative influence of risk on consumer intention to continue the usage of various online services including online banking, mobile banking and mobile payment services. Complexity is yet another negative valence and technologies with high complexity reduce consumer’s intention to continue its usage. The Diffusion Theory Rogers, (1983) underpinned that complexity creates a negative attitude among consumers to adopt new technologies. Folorunso, (2010); and Islam et al., (2013) observed that complexity is an inhibitor of social networking sites and mobile payment services. The study also examined the mediating role of perceived risk and perceived benefits on continuance intention. As hypothesized, trust directly and indirectly influences continuance intention through perceived risk and perceived benefits. Overall the results of the research model are in consistent with the valence theory of (Peter & Tarpey, 1975) validating that consumer decision making process depends on the positive and negative utilities and perceived risk was found to be the most potential predictor than perceived return. The empirical results are also in line with the Trust Based Consumer Decision Making Model of Kim et al., (2008) and the Model recognized that the trust, perceived risk and perceived benefits significantly influences consumer intention and further the trust also impacts usage intentions indirectly through the perceived risk. The present study further extended the trust-based consumer decision making model with a new causal path trust → perceived benefits → continuous intention and the effect was found significant. This is an intriguing finding that trust not only can directly impulse continuous intention of mobile banking usage, they can also indirectly enhance the continuous intention by enhancing perceived benefits and reducing the risk perception of consumers towards of mobile banking. The similar findings are also validated by prior researchers like (Lu et al., 2011; Yang et al., 2012)
By identifying the most important factors predicting thecontinuance intention to use mobile banking, the present study makes a substantial contribution to the existing literature. The current study extended the valence theory by incorporating additional positive valences (compatibility) and negative valence construct (complexity) form the Diffusion of Innovation theory. The empirical results of the study revealed that both positive valences and negative valences influence the continuance intention to use mobile banking services. The study further validated the trust-based consumer decision making model by assessing the direct effect of trust on continuance intention as well as its indirect effect through perceived risk and extended the Theory by incorporating a new causal path of trust → perceived benefits→ continuous intention to use mobile banking services.
The empirical results revealed that perceived risk is the most significant factor which influences the consumer intention to continue the usage of mobile banking services and the perceived risk in turn negatively influenced by trust. The trust also influences the continuance intention through the construct perceived benefits. Further trust exerts a direct significant impact on continuance intention and these finding reflects the significance of trust in continuation of mobile banking usage by consumers. By enhancing consumer trust towards mobile banking, the service providers can reduce the consumer risk perception and enhance the perception of benefits and there by augment the usage rate of mobile banking services. Perceived benefit is the other factor influencing the continuance intention to use mobile banking services. Nowadays new competitors are entering the digital payment market with product innovations. The service providers can survive in the market only through continuously enhancing product portfolio and diversification that would match consumer perception of benefits. Compatibility is yet another factor which impacts the continuous intention to use mobile banking. Since, in this era of technology revolutions, consumers are highly tech-friendly and the mobile usage rate is very high, the service providers should continue to constantly work on providing technology platforms compatible to the customers to capture the market. By increasing the benefits of mobile payments, the banking companies can widen their market in India.
Ajzen, I. (1991). The Theory of Planned Behavior. In P. Van Lange, A. Kruglanski, & E. Higgins, Handbook of Theories of Social Psychology: Volume 1 (pp. 438–459). Al-Ajam, A. S., & Md Nor, K. (2015). Challenges of adoption of internet banking service in Yemen. International Journal of Bank Marketing, 33(2), 178–194. Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423. Baron, R. M., & Kenny, D. A. (1986). The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology, 51(6), 10. Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. Chandra, S., Srivastava, S. C., & Theng, Y.-L. (2010). Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis. Communications of the Association for Information Systems, 27. Chiu, J. L., Bool, N. C., & Chiu, C. L. (2017). Challenges and factors influencing initial trust and behavioral intention to use mobile banking services in the Philippines. Asia Pacific Journal of Innovation and Entrepreneurship, 11(2), 246–278. Dash, M., Tech, M., & Bhusan, P. B. (2014). Determinants of Customers’ Adoption of Mobile Banking: An Empirical Study by Integrating Diffusion of Innovation with Attitude. 19(3), 22. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. Etezadi-Amoli, J., & Farhoomand, A. F. (1996). A structural model of end user computing satisfaction and user performance. Information & Management, 30(2), 65–73. Folorunso, O. (2010). Diffusion of Innovation in Social Networking Sites among University Students. International Journal of Computer Science and Security, 4(3), 361–372. Gefen, Karahanna, & Straub. (2003). Trust and TAM in Online Shopping: An Integrated Model. MIS Quarterly, 27(1), 51. Gefen, & Straub. (2003). Managing User Trust in B2C e-Services. E-Service Journal, 2(2), 7. Gerrard, P., & Barton Cunningham, J. (2003). The diffusion of Internet banking among Singapore consumers. International Journal of Bank Marketing, 21(1), 16–28. Glavee-Geo, R., Shaikh, A. A., & Karjaluoto, H. (2017). Mobile banking services adoption in Pakistan: Are there gender differences? International Journal of Bank Marketing, 35(7), Gu, J.-C., Lee, S.-C., & Suh, Y.-H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605–11616. Hair, J. F. (Ed.). (2014). Multivariate data analysis (7. ed., Pearson new internet. ed). Harlow: Pearson. Hayes, A. F. (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis. New York London: The Guilford Press. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. Huang, J., Li, Y., & Li, Y. (2014). Study on Factors to Adopt Mobile Payment for Tourism E-Business: Based on Valence Theory and Trust Transfer Theory. Information and Communication Technologies in Tourism, 747–759. Islam, Z., Kim Cheng Low, P., & Hasan, I. (2013). Intention to use advanced mobile phone services (AMPS). Management Decision, 51(4), 824–838. Kazemi, Dr. A., Nilipour, Dr. A., Kabiry, N., & Hoseini, M. M. (2013). Factors Affecting Isfahanian Mobile Banking Adoption Based on the Decomposed Theory of Planned Behavior. International Journal of Academic Research in Business and Social Sciences, 3(7), Pages 230-245. 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. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and Satisfaction, Two Stepping Stones for Successful E-Commerce Relationships: A Longitudinal Exploration. Information Systems Research, 20(2), 237–257. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed). New York: Guilford Press. Lee, M.-C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. Lin, H.-F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260. Liu, C., Marchewka, J. T., Lu, J., & Yu, C.-S. (2005). Beyond concern—A privacy-trust-behavioral intention model of electronic commerce. Information & Management, 42(2), 289–304. Liu, Z., Ben, S., & Zhang, R. (2019). Factors affecting consumers’ mobile payment behavior: A meta-analysis. Electronic Commerce Research, 19(3), 575–601. Lu, Y., Yang, S., Chau, P. Y. K., & Cao, Y. (2011). Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Information & Management, 48(8), 393–403. Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222–234. Malaquias, R. F., & Hwang, Y. (2016). An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 54, 453–461. Masrek, M. N., Uzir, N. A., & Khairuddin, I. I. (2012). Trust in Mobile Banking Adoption in Malaysia: A Conceptual Framework. Journal of Mobile Technologies, Knowledge & Society, 2012(2012), 12. Olatokun, M. W., & Igbinedion, J. L. (2009). The Adoption of Automatic Teller Machines in Nigeria: An Application of the Theory of Diffusion of Innovation. Issues in Informing Science and Information Technology, 6, 373–393. Ozturk, A. B., Bilghan, A., Salehi-Esfahani, S., & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory: A case of restaurant transactions. International Journal of Contemporary Hospitality Management, 29(8), 2027–2049. Peter, J. P., & Tarpey, L. X. (1975). A Comparative Analysis of Three Consumer Decision Strategies. Journal of Consumer Research, 2(1), 29. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. Priya, R., Gandhi, A. V., & Shaikh, A. (2018). Mobile banking adoption in an emerging economy: An empirical analysis of young Indian consumers. Benchmarking: An International Journal, 25(2), 743–762. Ramavhona, T. C., & Mokwena, S. (2016). Factors influencing Internet banking adoption in South African rural areas. SA Journal of Information Management, 18(2). Rogers, E. M. (1983). Diffusion of innovations (3rd ed). New York : London: Free Press ; Collier Macmillan. Saxena, S. (2017). Enhancing ICT infrastructure in public services: Factors influencing mobile government (m-government) adoption in India. The Bottom Line, 30(4), 279–296. Singh, S., & Srivastava, R. K. (2018). Predicting the intention to use mobile banking in India. International Journal of Bank Marketing, 36(2), 357–378. Tan, M., & Teo, T. (2000). Factors Influencing the Adoption of Internet Banking. Journal of the Association for Information Systems, 1(1), 1–44. Thakur, R., & Srivastava, M. (2014). Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India. Internet Research, 24(3), 369–392. TRAI [Telecom Regulatory Authority of India], 2019. Wireless Data Services in India; An Analytical Report. Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129–142.
