Integration of unified theory of acceptance and use of technology in internet banking adoption setting: Evidence from Pakistan

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Contents lists available atScienceDirect

Technology in Society

journal homepage:www.elsevier.com/locate/techsoc

Integration of unified theory of acceptance and use of technology in internet banking adoption setting: Evidence from Pakistan

Samar Rahi

^a,∗

, Mazuri Abd.Ghani

^b

, Abdul Hafaz Ngah

^c

aAssistant Professor at Hailey College of Banking & Finance, University of the Punjab, Lahore, Pakistan

bAssociate Professor at Universiti Sultan Zainal Abidin (UniSZA), Terengganu, Malaysia

cSenior Lecturer at Universiti Malaysia Terengganu (UMT), Terengganu, Malaysia

A R T I C L E I N F O Keywords:

Unified theory of acceptance and use of technology (UTAUT)

E-Service quality (E-SQ) Internet banking adoption Integration

Structural equation modeling (SEM)

A B S T R A C T

The banking sector has evolved in information technology for their internal and external business operations. In effect, user acceptance of internet banking is considered as one of the most fundamental issue in banking sector.

In order to identify which factors affect user intention to adopt internet banking, this study develops an amalgamated model based on technology and social psychological literature. The research model was empirically tested using 398 responses from customers of commercial banks. Data was analyzed using structural equation modeling (SEM). The results of this study provided theoretical and empirical support for newly developed integrated model. Importance performance matrix analysis (IPMA) revealed that assurance is the most influential factor among all others to determine user's intention to adopt internet banking. These findings provide valuable insight to marketers and managers to understand customer behavior towards adoption of technology, especially in emerging e-payment domain. To the best of our knowledge, this is the first study that investigates internet banking adoption issues with integrated technology model (UTAUT & E-SQ) in South Asia.

Finally the study calls for researchers to use current integrated model in other e-commerce domains such as online shopping websites to establish the external validity of the model.

1. Introduction

The advent of internet and sophisticated technologies not only sti- mulated the new industries but also changed the business model in- cluding the banking sector. The banking sector has adopted internet banking as a delivery channel for their services. Internet banking is a banking channel that allows consumers to do a wide range of financial and nonfinancial services through a bank website [1,2]. Several banks have deployed internet banking system in an attempt to reduce cost while improving customer services (A. A. [3,4]. Internet banking has also appeared as one of the most profitable e-commerce application [5].

Despite the potential benefits that internet banking offers to consumers, the adoption of internet banking has been limited and in many cases fallen short of expectations [6,7]. There are still a large group of customers who refuse to adopt internet banking services due to uncertainty and security concerns [8–11]. Therefore, it is important to understand the determinants that influence the user intention to adopt internet banking.

There are numerous studies conducted to unleash technology adoption issues in banking sector [12–15]. Therefore, the current study

integrates the unified theory of acceptance and use of technology and e- service quality to investigate the user behavior towards adoption of internet banking. According to Oliveira et al. [15] an integrative model reinforces the significance and predictability of the results. Previous studies have claimed that performance expectancy and effort expectancy are the most important determinants for accepting or rejecting the internet banking. Therefore, little has been discussed about the antecedents of performance expectancy and effort expectancy. Our study revealed that website design and customer service are the key factors that enhance users performance expectancy and effort expectancy towards use of internet banking technology. Therefore, the current study enriches the service quality and information system literature in internet banking context. The purposes of this study are as follows:

•

To evaluate whether the integration of UTAUT model (UTAUT+ESQ) affects user intention to adopt internet banking.

•

To clarify which factor drives user intention towards adoption of internet banking.

•

To investigate whether e-service quality factors (customer service,

https://doi.org/10.1016/j.techsoc.2019.03.003

Received 4 February 2018; Received in revised form 16 February 2019; Accepted 2 March 2019

∗Corresponding author.

E-mail addresses:[email protected](S. Rahi),[email protected](M. Abd.Ghani),[email protected](A. Hafaz Ngah).

Available online 07 March 2019

T

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website design, assurance, and reliability) affects user intention to adopt internet banking.

The remainder of this paper is structured as follows: section two introduces adoption factors underlying UTAUT and E-SQ and the theoretical foundation. In third section research model and hypotheses are presented while section four outlines the research methodology. The fifth section shows the data analysis and hypotheses testing. Section six discusses research findings, and section seventh explains implications, and finally this paper presents research conclusion and future research directions.

2. Background and related literature

2.1. Technology adoption models

In recent years, internet banking has received colossal attention in technology adoption research. The banking sector has been using IS not only for internal business activities, but also to provide core services to their customers [10,11]. The most well-known theoretical models that have sought to investigate the relationship between user beliefs, atti- tude and intention includeMotivational Model(MM) byDavis, Bagozzi, and Warshaw [16];Theory of Planned Behavior(TPB), a hybrid model that combine constructs of TAM and TPB (C-TAM-TPB) by Taylor and Todd [17],Model of PC Utilization(MPCU) by Thompson, Higgins, and Howell [18];Innovation Diffusion Theory(IDT) by Moore and Benbasat [19]; andSocial Cognitive Theory(SCT) by D. R. Compeau and Higgins [20]. As the objective of this study is to identify the internet banking adoption issues in technology perspective, for this purpose researcher has undertaken the unified theory of acceptance and use of technology.

2.2. Unified theory of acceptance and use of technology

Venkatesh, Morris, Davis, and Davis [21] tested eight models in longitudinal field studies of four different organizations. Based on the comparison between eight models it was found that performance expectancy, effort expectancy, social influence and facilitating conditions have significant influence on user intention to adopt technology. Fol- lowing is the detail of the factors underlying UTAUT.

According to Rahi, Ghani, Alnaser, and Ngah [22,23]; performance expectancy in internet banking context is the degree to which an individual believes that using internet banking will help him/her to attain gains in performing banking tasks. A. Alalwan, Dwivedi, and Williams [24] postulated that performance expectancy is considered as term of utility that encounter during use of internet banking. Effort expectancy is similar to perceived ease of use (TAM) and complexity (DOI, MPCU).

Zhou, Lu, and Wang [25] demonstrated that the user feeling that internet banking is easy to use and does not require much effort leads to high chances in adopting the internet banking.

Similarly, “social influence is defined as the degree where an individual perceives that best known persons believe he/she should use the new system” [21,88]. While facilitating conditions is defined as “the degree to which an individual believes that an organizational and technical infrastructure exists to support and use the system” [21].

Facilitation condition was similar as perceived behavioral control and compatibility. S Rahi et al. [22,23] stated that usage of a system requires particular skills and resources and the facilities such as internet and computer are usually not freely approachable in consumer context.

2.3. E-service quality in banking sector

Parasuraman, Zeithaml, and Malhotra [26] stated that e-service quality issues should cover entire online process that must include information searching, online ordering, delivery of the products, online payment, and post customer services. Several researchers agreed that four core dimensions of e-service quality are required in an online

environment that includes: customer service, website design, assurance, and reliability [26–32,89]. These dimensions are defined as follows:

2.3.1. Website design

The pervasiveness of the internet technology in all business domains demands efficient design of the website [27]. It is argued that previous studies do not include entire purchase process such as information phase to after-sale-phase [33]. Holloway and Beatty [33] postulated that attributes related to websites have major importance during early stage of online buying process. In order to investigate website design performance this study considered three main dimensions that include:

content layout, content updating, and user friendliness adopted by research work of [34–37]. Hence, to provide quality information about products on website is essential.

2.3.2. Reliability

Reliability or fulfillment of order refers to “the online store ability to ensure that customers receive what they thought they ordered” [27].

According to Bauer, Falk, and Hammerschmidt [38]; this stage is re- levant to post-sale issues where customers already placed their order and expect to receive same what they ordered. Thus, accuracy of order fulfillment, order timing and condition of the delivered products are the main attributes of fulfillment dimension [38,39]. Thus, it is assumed that during online shopping delivery timeliness, order accuracy and delivery condition are the most important attributes for order fulfillment dimensions of e-service quality [27].

2.3.3. Assurance

In services marketing literature security/privacy refers to customer's concern regarding potential security/privacy lapses [27]. Similar to this, Holloway and Beatty [33] argued that customers have great concern in their private information for instance their name, address, and profession. Research indicates that online service providers should provide protection to customers from fraud, theft, phishing and junk email after having an online transaction [33]. This dimension of e- service quality is essential in an online environment and it has been used in different online perspective studies [28,32,40,41].

2.3.4. Customer service

Customer service refers to “online support prior to, during, or after the online order has been replaced” [38]. It is stated that, websites should fulfill the expectation of the end users [32]. According to Bauer et al. [38] general services and return handling policies are the main attributes of customer service dimension. Agreeing with, Blut [27]

postulated that customer service contributes to their judgments towards online website. Online customer service is sensitive, what consumer demands quality services like correct and timely transaction, personal attention towards their emails and queries [30,31,42].

3. Research model and hypothesis development 3.1. Theories integration rational

The proposed research model comprises the key factors of UTAUT model and e-service quality for understanding the internet banking adoption trend in Pakistan. According to Shen, Huang, Chu, and Hsu [43] consumer acceptance of new technology is a complicated phe- nomenon that requires more than a single model. Similarly, Jackson, Mun, and Park [44] postulated that an integrative perspective model provides a more complete account of the causal mechanisms underlying the relationships as well as unique insights that cannot be obtained with a single theory driven model. After reviewing above arguments, it can be assumed that, the integration of UTAUT model with e-service quality will provide broad picture of technology adoption issues in banking sector of Pakistan. The following sections shed light on scientific linkage between factors proposed for the new research model.

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3.2. Research model

The research model as presented inFig. 1depicts the role of UTAUT and E-Service Quality to investigate the internet banking adoption trend among customers of commercial banks in Pakistan. The model posits that behavioral intention to adopt internet banking is jointly determined by performance expectancy, effort expectancy, social influence, facilitation condition, assurance, reliability, customer service and website design. The individual components and related hypotheses are discussed in the following section.

3.3. Hypothesis development

There are several factors that can influence on customer intention to adopt internet banking. Therefore, a detail literature review has nailed down these into eight core factors that could influence on customer intention. These factors are summarized as follows:

3.3.1. Performance expectancy

A study conducted by Morosan and DeFranco [45] revealed that performance expectancy has significant effect on behavioral intention to adopt online banking. Several researchers have provided evidence of significant influence of performance expectancy on behavioral intention to adopt internet banking [6,15,46,47]; [22,23,48]. In the light of reported evidence from prior research the following hypothesis about the impact of performance expectancy on customer intention to adopt internet banking is proposed:

H1. Performance Expectancy has positive influence on customer intention to adopt internet banking.

3.3.2. Effort expectancy

Miltgen, Popovič, and Oliveira [49] demonstrated that effort expectancy contributes to a precise prediction of intention to adopt a new technology. According to Zhou et al. [25]; when a user feels that internet banking is easy to use and does not require much effort, they would have higher chances to adopt internet banking. Furthermore, it has a significant influence on behavioral intention to adopt internet banking [6,50]. Individual who believes that online banking is effort- less is likely to adopt it [51]. In view of above arguments, effort expectancy is hypothesized as:

H2.Effort Expectancy has positive influence on customer's intention to adopt internet banking.

The relationship between effort expectancy and behavioral intention is widely debated. It is said that, perceived ease of use has provided contradictory results in examining of complexity by using TAM, IDT and MPCU models [18,52]. Previous studies have found that effort expectancy has significant influence on performance expectancy [12,15]. In light of above arguments, following hypothesis about the effects of effort expectancy on performance expectancy is suggested:

H3. Effort Expectancy has positive influence on performance expectancy of internet banking users.

3.3.3. Social influence

According to Martins et al. [6]al influence will effect user intention to adopt internet banking services. Similar to this, Chaouali et al. [51]

have postulated that the use of new product or services by prominent people is likely to hugely affect the usage of products or technology services by others. In information technology adoption studies, social influence represents as social pressure exerted on individual to adopt new technology [6,51,53,54]. Based on above arguments, social influence can be deemed as having a positive influence on behavioral intention of consumers to adopt internet banking in Pakistan. Thus, social influence is hypothesized as:

H4.Social Influence has positive influence on customer intention to adopt internet banking.

3.3.4. Facilitating conditions

According to Oliveira et al. [15] if an operational infrastructure exists and support the use of online payment, the behavioral intention to use that technology will be increased. Hong, Thong, Moon, and Tam [55] argued that if users would not have necessary operational skills, they would have lower intention to adopt the technology. Hence, facilitation condition has major impact on user intention to adopt internet banking [6,15]. In light of the above arguments, facilitating condition is hypothesized as:

H5.Facilitating condition has positive influence on customer intention to adopt internet banking.

Fig. 1.The proposed research model.

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3.3.5. Assurance

Customer assurance is essential in an online environment and it has been used in different online perspective studies [28,32,40,41]. Website security and privacy are important attributes of online transaction and it affects overall assessment of an online buying process [27]. By following above arguments, it is assumed that assurance has significant influence in acceptance of an online technology. Thus, assurance is hypothesized as:

H6.Assurance has positive influence on customer intention to adopt internet banking.

3.3.6. Reliability

Reliability refers to the possibility of the modifying or postponing the purchase process at any given moment with no obligation and ob- taining information on product availability at the moment of purchase [28,56]. Blut [27] argued that customer expects the product description on the website to be accurate and should be delivered on right place at the right price in good condition. By following above arguments, it can be assumed that during internet banking usage transfer of money on- time and accuracy in transactions are the main attribute of reliability.

Thus, consistent with prior research reliability is hypothesized as follows:

H7.Reliability has positive influence on customer's intention to adopt internet banking.

3.3.7. Customer service

According to Ho and Lin [29]; customer service is the most favor- able dimension in developing of e-banking service quality scale. Blut [27] postulated that customer service contributes to overall quality assessment of customers and their judgments towards online website.

Customer service has proved a key element for achieving good website performance results in an online shop [5,12,28,29,57]. By following above arguments it can be assumed that customer service will influence on performance expectancy of internet banking users. Hence, customer service is hypothesized as:

H8.Customer service has positive influence on Performance expectancy of internet banking users.

Customer service has been found to significantly influence the user's effort expectancy towards use of internet banking [5,12,58,87]. By following above arguments it can be assumed that customer service will influence on user's effort expectancy to use internet banking. Thus, customer service is hypothesized as;

H9.Customer service has positive influence on Effort Expectancy.

In internet banking context Ho and Lin [29] stated that customer expects to be able to complete transaction correctly, receives persona- lized attention, product delivered on time and have their emails answered quickly. Customer service has found significant influence on user's intention to adopt internet banking [26–29,57–59]. Thus, customer service is hypothesized as:

H10. Customer service has positive influence on intention to adopt internet banking.

3.3.8. Website design

Holloway and Beatty [33] postulated that attributes related to websites bring easiness and would increase the performance expectancy of the customers during online buying process. Website design has profound impact on performance expectancy [10–12]. By following above arguments, it is assumed that, website design has significant influence on performance expectancy of the internet banking users.

Thus, website design is hypothesized as:

H11.Website design has positive influence on Performance expectancy.

It is found that attributes related to website design brought easiness for users on websites [12,33,60]. By following above arguments, it is assumed that website design has significant influence on effort expectancy. Thus, website design is hypothesized as:

H12.Website design has positive influence on Effort expectancy.

In an online environment website design has played an important role [38]. Quality information on website always motivates users to buy a product on internet [12,28,60–63]. By following above arguments, it can be assumed that, website design has significant impact on customer's intention to adopt internet banking. Thus, website design is hypothesized as:

H13. Website design has positive influence on intention to adopt internet banking.

4. Research methods

4.1. Instrument development

The instrument was developed based on nine latent constructs mentioned in research model. All the construct items were adopted from previous research work. There were four items of Intention to adopt internet banking adopted from R. Samar et al. [10,11]; while four items of each performance expectancy, effort expectancy, social influence and facilitating condition were adopted with a slight modification from Venkatesh et al. [21] and S Rahi et al. [22,23]. Next to this six items of customer service adopted from Refs. [10,11], three items each for website design and assurance were adopted from Ho and Lin [29].

Finally, three items of reliability were adopted from Wolfinbarger and Gilly [32].

4.2. Data collection

In order to refine the questionnaire a pilot survey was conducted with 100 respondents. The most important change was in items of customer service wherein the loading of item CS5 was less than 0.5 and was excluded in order to achieve AVE. The main survey data collection process was started by contacting bank managers of five different commercial banks located in Lahore and Islamabad, the major cities of Pakistan. The respondents engaged in this study were sampled by convenience sampling. Hulland, Baumgartner, and Smith [64] stated that when the aim of research is to test the veracity of the proposed theoretical effects, the use of convenience sampling may suffice. The researcher first got the consent of the managers and then dropped bundles of questionnaires at selected branches.

The participation was voluntary and the survey was conducted over the period of one month from 09 September 2017 to 10 October 2017.

The questionnaires were distributed uniformly in five banks of each three cities. The total number of questionnaires distributed was 750 in five banks in which 415 returned with a response rate of 55%. Among the 415 responses, 17 were discarded based on two criteria: 1) the respondents did not fill all the questions (2) the questionnaire contained non-serious answers. For instance, although respondents answered all the questions, if the answer indicated only same answer from beginning to end, the questionnaire was considered as non-serious answer [65,66]. Thus, finally 398 valid questionnaires with a response rate of 53% were used for further data analysis.

4.3. Common method bias

To test response bias, Harman's single factor test was incorporated Podsakoff, MacKenzie, Lee, and Podsakoff [67]. No significant common method bias was found in the data. The maximum co-variance

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explained by single factor was only 29.62% which is less than 50%

indicating that common method bias is not a likely issue in this study [10,11,68].

5. Data analysis and results

For data analysis, PLS-SEM approach was used instead of CB-SEM due to the fact that the objective of the current research is to predict factors that influence on user's intention to adopt internet banking.

Another reason was the data normality issue. The data was not nor- mally distributed thus, variance based technique using partial least square was suitable for this study. SmartPLS 3.0 software Ringle, Wende, and Becker [69] was used to analyze the structural equation model.

5.1. Measurement model

The steps involved in assessing the measurement model include testing of convergent and discriminant validity. Convergent validity is measured by examining factor loading, average variance extracted and composite reliability [10,11,70].Table 1depicted composite reliability, degree where the construct indicator represent the latent construct, values exceeded 0.7 as recommended by Hair et al. [70]. Similarly, the values of average variance extracted were higher than 0.5 as suggested by Fornell and Larcker [71]. Finally, factor loading values were greater than 0.6 as recommended by Chin [72]. Thus, study achieved

convergent validity of the measure.

Henseler, Ringle, and Sinkovics [73] postulated that when deciding to eliminate an indicator, it makes sense to eliminate only when the indicator's reliability is low and elimination of the indicator increase the composite reliability of the indicator. Thus, customer service item CS2 was deleted in order to achieve AVE. Based on the results all other items used for this study have demonstrated satisfactory indicator reliability. Thus, the convergent validity of the measure is confirmed.

The discriminant validity was tested by following guideline of Fornell and Larcker [71]. According to D. Compeau, Higgins, and Huff (1999) “the average variance shared between each construct and its measure should be greater than the variance shared between the constructs and other constructs”. Refereeing toTable 2square root of the AVE (as showed in bold values on the diagonals) was greater than the corresponding row and column values that showed the measure is discriminant.

Discriminant validity can be measured by examining the cross loading of the indicators [74]. It is measured by comparing outer loadings with associated constructs and it must be greater than other construct loading [10,11]. The results of the cross loading indicated that all construct loadings were higher than other constructs which confirmed the discriminant validity of the measure. Additionally, discriminant validity was also checked method suggested by Henseler, Ringle, and Sarstedt [75] through multitrait and multimethod matrix, namely the Heterotrait-Monotrait Ratio (HTMT). HTMT values were lower than the required threshold value of HTMT.85 by Kline [76] and Table 1

Results of measurement model.

Constructs/Items Loading CR AVE

Assurance (ASS) 0.979 0.941

ASS1-Transactions by internet banking website are reliable. 0.973

ASS2-My transaction data are protected by internet banking website. 0.961

ASS3-I feel relieved to transact through internet banking website. 0.976

Customer Service (CS) 0.818 0.534

CS1- The online transaction process was accurate. 0.787

CS3- Web page loaded quickly on internet banking website. 0.847

CS4- Internet banking website performs the service correctly at the first time. 0.624 CS6- When problems occur, the internet banking system guides me to solve them. 0.639

Effort Expectancy (EE) 0.944 0.808

EE1-My interaction with internet banking would be clear and understandable. 0.800 EE2-It would be easy for me to become skillful by using internet banking. 0.957

EE3-I would find internet banking easy to use. 0.939

EE4-I think that learning to operate internet banking would be easy for me. 0.891

Facilitating Condition (FC) 0.864 0.615

FC1- I have the resources necessary to use the internet banking. 0.791

FC2- I have the knowledge necessary to use the internet banking. 0.776

FC3-Internet banking is compatible with other technologies I use. 0.798

FC4-A specific person is available for assistance of internet banking difficulties. 0.771

Intention to Adopt (INT) 0.914 0.780

INT1- I intend to use internet banking in the next months. 0.864

INT2- I predict I would use internet banking in the next months. 0.891

INT3- I plan to use internet banking in the next months. 0.894

Performance Expectancy (PE) 0.929 0.767

PE1- Internet banking is useful to carry out my tasks. 0.903

PE2- I think that using internet banking would enable me to conduct tasks more quickly. 0.867 PE3- I think that using internet banking would increase my productivity. 0.874 PE4- I think that using internet banking would improve my performance. 0.858

Reliability (REL) 0.910 0.771

REL1- You get what you order from bank website. 0.832

REL2- The transaction that is done processed accurately by the bank website. 0.894 REL3- The transaction is done by the time promised by the bank website. 0.906

Social Influence (SI) 0.852 0.591

SI1- People who influence my behavior think that I should use internet banking. 0.839 SI2- People who are important to me think that I should use internet banking. 0.786 SI3-People in my environment who use internet banking services have a high profile. 0.767 SI4- Having internet banking services is a status of symbol in my environment. 0.674

Website Design (WD) 0.927 0.760

WD1- I can complete online transactions easily. 0.920

WD2- I can sign up on internet banking website easily. 0.834

WD3-It is easy to understand which button should be clicked for the next step. 0.947 WD4-Internet banking website enables me to complete a transaction quickly. 0.776

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HTMT 0.90 by Gold and Arvind Malhotra [77]; confirmed the discriminant validity of the measure.

5.2. Structural model 5.2.1. Multicollinearity

The structural model assesses the multicolllinearity issues with VIF.

It is argued that lateral collineraity or predictor-criterion collineraity may mislead the study findings [78]. No multicollineraity issue was found among exogenous variables as all values were lower than 3.3 as suggested by Diamantopoulos and Siguaw (2006). These findings indicate that lateral multicollinearity is not a concern in this study [79].

The results of VIF values can be seen inTable 3.

5.2.2. Hypotheses testing

In order to assess the significance and relevance of the structural model relationship bootstrapping procedure was used. The results in Table 4depicts path coefficient of respective constructs with its level of significance, t-statistics and confidence interval values.

Tabulated values inTable 4depicted that customer's Intention to use internet banking is jointly predicted by performance expectancy (PE - > INT, β 0.120 = , t-value, 2.785, significance at p < 0.01), effort expectancy (EE - > INT, β 0.107 = , t-value, 3.718, significance at p < 0.01), social influence (SI - > INT, β 0.073 = , t-value, 3.197, significance at p < 0.01), facilitating condition (FC - > INT, β 0.094 = , t-value, 3.744, significance at p < 0.01), assurance (AS - > INT, β 0.444 = , t-value, 7.702, significance at p < 0.01), reliability (RL - > INT, β 0.268 = , t-value, 5.557, significance at p < 0.01), customer service (CS - > INT, β 0.076 = , t-value, 1.851, significance at p < 0.05) and website design (WD - > INT, β 0.114 = , t-value, 3.761, significance at p < 0.01) and these variables together explained 80.2% of the variance in intention to adopt internet banking ( R² = 0.802, coefficient of determination). As a result, hypotheses 1,2,4,5, 6,7,10, and 13 were accepted.

Performance expectancy was predicted by effort expectancy (EE - > PE, β 0.207 = , t-value, 3.830, significance at p < 0.01), customer service (CS - > PE, β 0.290 = , t-value, 5.343, significance at

p < 0.01), website design (WD - > PE, β 0.302 = , t-value, 6.218, significance at p < 0.01). These findings indicated support forH3,H8 andH11 and together these variables explained 33.9% of the total variance in performance expectancy. Accordingly, effort expectancy was predicted by customer service (CS - > EE, β 0.200 = , t-value, 3.844, significance at p < 0.01), and website design (WD- > EE, β 0.217 = , t-value, 3.833, significance at p < 0.01). These findings validatedH9andH12 and explained 11.4% of the total variance in effort expectancy.

5.2.3. Evaluating effect sizes( )f²

The effect size( )f² used to assess the relative impact of a predictor construct on an endogenous construct. It is said that p value can show you that effect exist however, it does not disclose the size of the effect.

Thus, researcher assessed the effect size( )f² . According to Cohen [80]

the acceptable effect sizes( )f² values of 0.35, 0.15 and 0.02 are considered substantial, medium and small effect sizes respectively.

Table 5depicted, there is a difference for the effect size analysis results for intention to adopt internet banking, performance expectancy and effort expectancy. For intention to adopt internet banking assurance has a substantial effect size (0.597). Therefore, reliability has shown a medium effect size (0.215), whereby all other variables have small effect size. This indicates that for intention to adopt internet banking assurance is the most important variable. For endogenous variable performance expectancy all exogenous variables website design, customer service and effort expectancy have small effect size. Si- milarly, for effort expectancy both customer service and website design have small effect size.

5.2.4. Predictive relevanceQ²

Researcher used blindfolding procedure in order to assess the Table 2

Discriminant validity using Fornell and Larcker criterion.

ASS CS EE FC INT PE RL SI WD

Assurance 0.970

Customer Service 0.460 0.731

Effort Expectancy 0.271 0.267 0.899

Facilitating Condition 0.363 0.312 0.140 0.784

Intention 0.776 0.554 0.430 0.438 0.883

Performance Expectancy 0.521 0.438 0.369 0.296 0.657 0.876

Reliability 0.506 0.458 0.317 0.279 0.692 0.548 0.878

Social Influence 0.042 0.083 0.119 0.102 0.168 0.091 0.089 0.769

Website Design 0.287 0.306 0.279 0.234 0.454 0.448 0.283 0.116 0.872

Note: Diagonal represents the square root of the AVE while off-diagonal represent the correlations.

Table 3

Results of lateral Collinearity assessment.

Effort Expectancy

VIF Intention VIF Performance

Expectancy VIF

Assurance 1.67

Customer Service 1.103 1.478 1.148

Effort Expectancy 1.219 1.128

Facilitating Condition 1.217

Intention NA

Performance

Expectancy 1.89

Reliability 1.683

Social Influence 1.030

Website Design 1.103 1.312 1.157

Table 4

Hypotheses testing.

Hypothesis Relationship Direct

effect (β) SE t-statistic Interval estimate (CI)

LL UL

H1 PE - > INT 0.120 0.043 2.785** 0.054 0.196

H2 EE - > INT 0.107 0.029 3.718** 0.062 0.156

H3 EE - > PE 0.207 0.054 3.830** 0.117 0.295

H4 SI - > INT 0.073 0.023 3.197** 0.037 0.111

H5 FC - > INT 0.094 0.025 3.744** 0.054 0.136

H6 ASS - > INT 0.444 0.058 7.702** 0.351 0.536

H7 RL - > INT 0.268 0.048 5.557** 0.193 0.351

H8 CS - > PE 0.290 0.054 5.343** 0.197 0.379

H9 CS - > EE 0.200 0.052 3.844** 0.110 0.282

H10 CS - > INT 0.076 0.041 1.851* 0.011 0.145

H11 WD - > PE 0.302 0.049 6.218** 0.220 0.381

H12 WD - > EE 0.217 0.057 3.833** 0.119 0.306

H13 WD - > INT 0.114 0.030 3.761** 0.067 0.165

Note: LL, lower limit; UL, upper limit at 95 per cent confidence interval.

*p < 0.05; **p < 0.01 (one-tailed).

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predictive relevance of the research model. Blindfolding procedure should only be applied to endogenous constructs that have a reflective measurement. According to Hair Jr et al. [74] blindfolding procedure is applied on endogenous constructs.Table 6presented the results ofQ²_. As shown inTable 6theQ² values for intention to adopt internet banking (0.578), performance expectancy (0.235) and effort expectancy (0.085) are greater than 0, indicating that the model has

sufficient predictive relevance.Table 6also shows the results of small q

( )² effect size. Customer service, reliability, website design, performance expectancy, effort expectancy and social influence have small effect size( )q² on intention to adopt internet banking. However, assurance exhibits medium( )q² effect size on intention to adopt internet banking. For the second endogenous construct, which is performance expectancy,( )q² indicates that customer service, effort expectancy and website design have small effect size. Similarly,( )q² effect size of customer service and website design on effort expectancy is also noted small.

5.3. Importance performance matrix analysis (IPMA)

As an extension to the results of the study, researchers ran a post- hoc importance performance matrix analysis (IPMA) using intention to adopt internet banking as target construct [81].Fig. 2IPMA map depicts the index values and total effect scores. It can be seen that assurance is the most important factor to determine intention in adopting internet banking due to high importance value as compared to other latent variables. Similarly, assurance is the second highest in performance index. In contrast, social influence has the lowest importance but the highest performance. Though social influence has high performance it has little relevance in influencing customer intention to adopt internet banking. The second important factor in determining intention to adopt internet banking is reliability with a medium level of performance values. The results of IPMP map revealed that customer service, website design, performance expectancy and effort expectancy have medium size of importance and performance. Finally, facilitation condition has little importance but third highest value on performance index.

6. Discussion

Several insightful results could be summarized from our research framework. Findings indicate support for newly developed integrated model. It can be seen that the explanatory power of research modelR² 80.2% for customer's intention to adopt internet banking, andR²for performance expectancy 33.9%, finally,R²for effort expectancy 11.4%

that ensured the significant integration of UTAUT and E-SQ factors.

Compared with previous investigation by S Rahi et al. [22,23] our study presented a stronger predictive power for users intention to adopt internet banking in Pakistan. Similarly, Martins et al. [6] integrated the UTAUT model with perceived risk and explained 59.6% variance in users intention to adopt of internet banking.

Findings revealed that the effect of assurance on intention was substantial, meaning that internet banking users care about assurance provided by relative bank website. These results are consistent with previous studies that examine assurance influence towards customer's intention to adopt internet banking [27,28,32,33,82]. The effect of reliability was found medium and in line with previous studies Bauer et al. [38]; Bauer, Hammerschmidt, and Falk [83]; Blut [27]; Ho and Lin [29]; Parasuraman et al. [26]; Rahi [84]. While performance expectancy, effort expectancy, social influence, facilitating condition, customer service and website design have significant positive effect on user intention to adopt internet banking, these results are consistent with previous studies (Foon and Fah [47]; Martins et al. [6]; Mazuri, Samar, Norjaya, and Feras [85]; Morosan and DeFranco [45]; Oliveira et al. [15]; S. Samar et al. [11]; Venkatesh et al. [21]. These findings suggested that our respondents are concerned with factors underpinned in research model and these factors propel users to adopt internet banking.

On the other hand customer service and website design showed positive and significant influence on performance expectancy and these findings are consistent with Al-Qeisi et al. [12]; Udo et al. [60];

meaning that higher level of website design, customer service and effort expectancy influence on individual user performance expectancy Table 5

Effect size analysis.

Intention to adopt internet banking

Construct R² ( )f² Decision

Intention 0.802

Assurance 0.595 Substantial

Customer Service 0.020 Small

Effort Expectancy 0.047 Small

Facilitating Condition 0.036 Small

Performance Expectancy 0.039 Small

Reliability 0.215 Medium

Social Influence 0.026 Small

Website Design 0.050 Small

Performance Expectancy

Performance Expectancy 0.339

Effort Expectancy

Effort Expectancy 0.114

Table 6

Predictive relevanceQ²and( )q² analysis.

Intention to adopt Internet Banking

Construct R² Q² ( )q² Decision

Intention 0.802 0.578

Assurance 0.180 Medium

Facilitating Condition 0.009 Small

Performance Expectancy 0.012 Small

Reliability 0.073 Small

Social Influence 0.007 Small

Performance Expectancy

Performance Expectancy 0.339 0.235

Effort Expectancy

Effort Expectancy 0.114 0.085

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towards adoption of internet banking. Concerning the effort expectancy, researcher found that website design and customer service have significant influence on user effort expectancy towards adoption of internet banking and these findings are consistent with e.g. Al-Qeisi et al. [12]; Holloway and Beatty [33]; Oliveira et al. [15]; Udo et al.

[60].

7. Implications

7.1. Theoretical implications

From theoretical perspective several implications can be derived from the findings of the research. First, this research used UTAUT model in a new context which is internet banking acceptance. Findings revealed that UTAUT model has a substantial explanatory and predictive power to explain user intention to adopt internet banking. Thus, the finding enriches the UTAUT literature in internet banking adoption context. Second, previously researcher has explained e-service quality as a single factor. Therefore, current study explored the e-service quality dimensions and confirmed that website design, customer service, assurance and reliability are the core dimensions of e-service quality in online setting.

This research applied an integrated model UTAUT+ E-SQ in internet banking adoption context and the success is evident from the results. The results suggested that the proposed integrated model de- monstrates a substantial explanatory and predictive power. Thus, the integration of website design, customer service, assurance and reliability with UTAUT model is both theoretically and empirically significant. Additionally, the integrated model can be employed for other technology acceptance studies such as online shopping.

This study has identified factors prompting adoption behavior based on information system and social psychological literature. Thus, the newly integrated model might be used in various domains either linked to technology or psychological behaviors. The current study investigates the effects of website design and customer service on performance expectancy and effort expectancy, which in turn increase the performance expectancy and effort expectancy of internet banking users. Thus, the identification of antecedents of effort expectancy and performance expectancy augmented the information system literature.

7.2. Managerial implications

From managerial perspective, there are several important implications which can be derived from the findings of this research for different stake holders such as bank managers and web-designers. For instance, the integrated model UTAUT+ E-SQ has showed valuable insight to banks to better understand the internet banking system

according to user's need in Pakistan. Thus, managers and designers are suggested to take into consideration the factors underlying integrated model to develop effective internet banking website in Pakistan.

Second, this study has identified that assurance is the most influential factor in newly integrated model. Thus, banks should develop online banking information systems by offering assurance (trust, statement of guarantee) to users. This in turn, will boost user's confidence and would speed up the acceptance of internet banking.

Thirdly, this study suggested that website design within the integrated model is the third important determinant of internet banking adoption.

Thus managers and designers should take into consideration the aspects of website design.

Lastly, this study emphasized to provide appropriate customer services to perform internet banking on bank website. By providing suitable customer services on banking website managers can enhance the effort expectancy and performance expectancy of internet banking users. Thus, in order to increase internet banking adoption managers and designers should develop information systems according to customer services requirements.

8. Conclusion and future research

The current study proposed an integrated model UTAUT & E-SQ to investigate user behaviors towards adoption of internet banking. In line with study objectives, the proposed integrated model has direct and positive impact on user intention. This study identified determinants of user beliefs in internet banking adoption context such as website design, assurance customer service, reliability, performance expectancy, effort expectancy, social influence, and facilitating condition. The results of the structural equation modeling revealed that both website design and customer service have significant influence on performance expectancy and effort expectancy. Previous studies have claimed that performance expectancy and effort expectancy are the most important determinants to accept internet banking. Therefore, a little has been discussed about the antecedents of performance expectancy and effort expectancy. This study has revealed that website design and customer service are the key factors that enhance users performance expectancy and effort expectancy towards use of internet banking technology.

These findings demonstrated the success of the proposed integrated model in achieving the objectives of the current study.

The findings emanating from current research suggested that there is the need for future research. First, this study integrates UTAUT model with e-service quality to understand user intention towards adoption of internet banking. Therefore, several beneficial areas remain to be explored in other online technology acceptance to investigate customer behavior in online shopping. Second, this study has used intention to adopt as dependent variable to measure the acceptance of internet Fig. 2.Importance-performance map.

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banking, consistent with prior research Chaouali et al. [51]; Morosan and DeFranco [45]. Therefore, future research may be conducted with actual usage of internet banking instead of intention to adopt. Fur- thermore, prospect exists for future studies to examine how the newly integrated (UTAUT+E-SQ) model affect the relationship of the constructs put across in this study in other cultural settings. Thus, applying this model to other Asian countries might be interesting.

Appendix A. Supplementary data

Supplementary data to this article can be found online athttps://

doi.org/10.1016/j.techsoc.2019.03.003.

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