Internet banking in Pakistan: an extended technology acceptance perspective

Int. J. Business Information Systems, Vol. 27, No. 3, 2018 383 Internet banking in Pakistan: an extended technology acceptance perspective Sahar Afshan Dadabhoy Institute of Higher Education, Karachi, Pakistan Email: sahar_khan09@hotmail.com Arshian Sharif* Department of Management Sciences, Iqra University, Karachi, Pakistan Email: arshian.aslam@gmail.com *Corresponding author Nazneen Waseem Karachi University Business School, Karachi, Pakistan Email: nazneen.waseem@yahoo.com Reema Frooghi KASB Institute of Technology, Karachi, Pakistan Email: reemafrooghi@yahoo.com Abstract: The growing attractiveness of internet banking is contributing well in the success and growth process of banking sector around the world (Xu et al., 2009). This study examines the integrative framework of internet banking (IB) in Pakistan using technology acceptance model (TAM) with the integration of several risk dimensions and initial trust model. The techniques of both exploratory and confirmatory factor analyses are employed to assess the reliability and validity of the measurement model. The structural equation modelling method was also applied to investigate the hypothetical framework with the help of literature’s recommended goodness-of-fit measurements. The findings of the study found significant contribution of personal propensity to trust, structural assurance, and familiarity with bank in influencing initial trust of people to accept IB. The findings are beneficial for banks that are pursuing IB in formulating strategies of enhancing user’s acceptance of internet banking in Pakistan. Keywords: internet banking; technology acceptance model; TAM; risk dimensions; initial trust; Pakistan. Copyright © 2018 Inderscience Enterprises Ltd. 384 S. Afshan et al. Reference to this paper should be made as follows: Afshan, S., Sharif, A., Waseem, N. and Frooghi, R. (2018) ‘Internet banking in Pakistan: an extended technology acceptance perspective’, Int. J. Business Information Systems, Vol. 27, No. 3, pp.383–410. Biographical notes: Sahar Afshan is an Assistant Professor in Department of Management Sciences at Dadabhoy Institute of Higher Education. She received her MBA/MS in Economics and Finance from Iqra University. She teaches economic analysis, advance research methods, financial statement analysis and strategic management. She has published more than nine articles in selected peer reviewed journals. Her interest in research lies in the areas of economics, mobile banking and consumer buying behaviour. Arshian Sharif is a Lecturer and Manager Research and Publication in Department of Management Sciences at Iqra University. He received his MBA/MS in Economics and Finance from Iqra University. Currently, he is pursuing his PhD in Management Science from Mohammad Ali Jinnah University. He teaches quantitative technique in analysis and research methods. He has published more than nine articles in selected peer reviewed journals. His interest in research lies in the areas of economics, banking, e-commerce, and education. Nazneen Waseem is an Assistant Professor in Department of Management Sciences at Karachi University Business School. She received her MPhil in Management from Hamdard University. She teaches principles of management, human resource management and strategic management. Reema Farooghi is an Educationist, with MS in Management Sciences. She has more than eight years of experience in quality assurance and also serving in imparting education at undergraduate and graduate level. She is pursuing her PhD degree. Her area of interest includes; marketing, consumer behaviour, management and human resource. 1 Introduction Internet banking which is growing faster than other e-commerce sectors has resulted to be an evolution in the banking sector; as it helps in provision of interactivity, convenience, low cost, time saving and high degree of personalisation (Lin et al., 2015; Al-Ajam and Md Nor, 2015). The recent trend in the success of today’s business is technology. This acknowledgement has not only encouraged the industry competitions but also forced businesses to adopt similar tactics in performing business operations (Chau and Lai, 2003; Afshan and Sharif, 2016; Arif et al., 2016; Raza and Hanif, 2013; Ali and Raza, 2015). The increasing use of technology in banking sector allows banks to enhance customer satisfaction, boost retention and augment earnings (Manzano et al., 2009; Durkin and O’Donnell, 2005; Kim and Prabhaker, 2004). Internet banking (IB) is among the most efficient and vital form of online businesses. Kesseven et al. (2008) describes internet banking as a system which offers people to execute banking activities at home, via the internet. It allows individuals and organisations to access bank services without the mere dependence over the banks physical location or premises. The growth of online Internet banking in Pakistan 385 purchases will help in provision of immense business opportunities, particularly in internet banking (Rawashdeh, 2015). The emergent popularity of IB has played a major role in the success and growth process of banking sector around the world (Xu et al., 2009). The advancements in banking sector not only boosts the financial institutions of a country but also strengthen the economy. IB is now used as the key term for the new era of banking system (Lee and Allaway, 2002). Internet banking offers various services that benefit the end users. Some of the vital advantages of utilising IB involves online payment, balance inquiry, payments of utility bills, 24/7 online funds transfer, ticket booking, online shopping, prepaid mobile recharge etc (Daniel, 1999; Sathye, 1999; Mols, 1998). The success of internet banking not only benefits the banks in attaining higher efficiency and improved productivity but also benefits the end users in providing rapid access to information and enhancing customer comfort. Internet banking provides the banks reduced operating cost through fewer staff and physical branches (Rawashdeh, 2015). The precursor for the modern online banking services were the electronic media services started in early 1980s. It was when the innovations in online banking have begun to assist people in managing their money. By the end of 90s several banks have started to view web-based banking as a strategic imperative (Cronin, 1998). This adoption of new financial technology in modern banking like computerisation of bank branches and automated teller machines with new financial products like credit cards and car financing schemes etc has brought various opportunities in banking and finance sector of countries around the globe. 1.1 Internet banking in Pakistan Internet banking in Pakistan is in its early stages. There are 38 scheduled banks in the country regulated by State Bank of Pakistan (SBP). The ownership of these banks is characterised as public sector banks, private sector banks and specialised banks. Overall 24 banks of the country offer internet banking. These banks comprise of 4% volume share of total e-banking transactions of the banking sector in Pakistan. Even though the country hardly has any infrastructure to support internet enabled electronic banking, the situation has improved significantly in last few years. SBP’s payment systems review for the quarter ended October–December 2013 has reported 3.9 million transactions by over a million registered users of internet banking. This shows the volume growth of 5.7% compared to last quarter. This usage amounts Rs 161 billion with value growth of 2.7%. Banking and finance sector of the country is also progressing to be the prominent investors of technology of the country (Mohsin, 2009). In late 90s liberalisation in financial policies of the country with regards to superior electronic framework and improved IT policies supported by e-commerce activities have created opportunities for banking organisations to adopt innovations (Abbas, 2007). The adoption of internet banking (AIB) has brought healthy competition among the banking sector of Pakistan. It has brought both the opportunities and challenges to the modern banking. IB has greatly assisted banks in getting cost effectiveness and greater exposures. However, in order to get competitive edge, banks are required to continuously upgrade their technologies by providing improvement in service qualities and innovation in its product offerings (Polatoglu and Ekin, 2001; Howcroft et al., 2007; Khan, 2007–2009). 386 S. Afshan et al. In developed countries, a lot of research has done to study the magnitude and role of internet banking however, for emerging countries it is relatively a new concept. The notion of internet banking in emerging countries is confined only to operational requirement. This lack of strategic importance of internet banking is reflected also through limited research in the field. In most of the developing countries, internet access is limited by the network efficiency and low speed. Banks in the country are averse to risks with high concerns of security threats. Such deficiencies confine the progress of e-growth of many emerging countries. Trust has been considered among the myriad of factors that impacts customers intention s towards internet banking (Yu et al., 2015). There is a shortage of methodical awareness and understanding of the role of internet banking of the country (Ahmad et al., 2010; Aslam et al., 2011; Chandio, 2011). The existing internet banking literature utilised the simplistic approach in examining IB solutions (Aslam et al., 2011; Chandio, 2011; Ahmad et al., 2010) and thus could not shed greater sights to the field. The uniqueness of our study lies in its aim to analyse behavioural and environmental aspects of internet banking of the country. The present study utilised an inclusive approach to examine internet banking of the country by integrating the literature’s well accepted technology acceptance framework with initial trust model (ITM). The study also analysed the role of several risk dimensions in influencing users’ likelihood of accepting internet banking to perform their banking activities. The results will guide policymakers in making sound and reliable policies instead of anecdotal verification and speculation for the progress of internet banking in Pakistan. The present research is arranged in 7 sections. Section 2 sets light on the literature reviews of internet banking mentioning theoretical background, empirical studies and hypotheses. Section 3 comprises of the research methodology; Section 4 signifies the data analysis and results in detail; Section 5 presents discussions following by managerial implications in Section 6 and finally Section 7 presents limitation and future implications of the present research. 2 Literature review 2.1 Theoretical background Numerous researchers and economists studied the user’s AIB with diverse approaches. Due to a lack of grounded theory in the field of information technology, researchers have turned to models that have been developed in other areas for their research (Nor and Pearson, 2008). In order to predict the individual’s intention to adopt IT, literature of internet banking linked intention models from social psychology as the foundation of their research (Harrison et al., 1997). These theories provided different models to foresee the dynamics of AIB in various environments. These theories included: the technology acceptance model (TAM) (Davis, 1989), the theory of reasoned action (Fishbein and Ajzen, 1975), the theory of planned behaviour (Ajzen, 1991), the theory of innovation diffusion (Rogers, 1983) and decomposed theory of planned behaviour (Taylor and Todd, 1995). However, in the presence of complexity of behaviour research and the limitation of the researchers themselves, there is no single framework that outshines all or even majority of the factors. Therefore, efforts were made to integrate the theories in order to Internet banking in Pakistan 387 reduce their level of limitations. The examination of modern IS research proposes to centre their attention on coupling various theoretical models in predicting IT acceptance suggesting that a broad view in the context is needed (Williams et al., 2009; Oliveira et al., 2014). Complying with this fondness, the present research combines the framework of literature’s famous theory of TAM along with ITM and risk dimensions to explain the concept of acceptance of internet banking in Pakistan. 2.1.1 Technology acceptance model A widely used model in the field of Information technology is TAM. TAM is one of the most popular theories that are used widely to explain information system usage. In TAM, Davis (1989) recommended that perceived usefulness and perceived ease are the primary factors that explain why users opt to use or not use information technology. TAM is a mature model and has been tested numerous times in previous studies. This highlights the acceptance of the framework in the literature. The addition of other factors in TAM assists researchers in gaining greater insights of the technology by reducing its unpredictability in the process of acceptance. Even in recent studies, the model is applied for different technologies and is refined and validated by the researchers. For example, Park et al. (2014) tested extended TAM in mobile network games; Wallace and Sheetz (2014) studies software adoption using the same model; Pai and Huang (2011) also studied healthcare adoption using TAM framework. Considering the massive acceptance of the model in the literature, the current study utilised TAM framework with the extended approach to investigate its role in the acceptance of emerging internet banking industry of Pakistan. To the best of our knowledge, none of the studies combine ITM with TAM along with research dimensions. The present study fills this gap by applying extended TAM in the newly emerging internet banking of the country. 2.1.2 Initial trust model (ITM) ITM has gained separate attention in electronic commerce literature due to a high level of ambiguity and risks associated with the domain. Initial trust reflects “the willingness of an individual to take risks in order to fulfill a need without prior experience, or credible, meaningful information” (Kim and Prabhaker, 2004; McKnight and Chervany, 2001). Trust has already analysed to be linked with various service sectors and found significant to contribute in its acceptance (Howard Chen and Corkindale, 2008; Pavlou, 2003; Flavián et al., 2005; Connolly and Bannister, 2007; Fisher et al., 2008; Lu and Yu-Jen Su, 2009), however, initial trust underlies greater uncertainty due to the lack of past experience of the usage (Kim and Prabhaker, 2004; Oliveira et al., 2014). Branchless banking especially m-commerce relies on the indirect relationship of the concerned parties. In such circumstances, the presence of knowledge-based trust that commonly emerged as a result of personal interaction, is not present (McKnight et al., 2002; Koufaris and Hampton-Sosa, 2004). For that reason it is assumed that initial trust relying on individual’s certain perspectives will be crucial in accepting the use of technology. ITM sets the basis of initial trust on numerous environmental and structural factors. These includes of structural assurance, personal propensity to trust, firm size, familiarity with organisation, situational normality and trustworthiness (Gefen et al., 2000; McKnight et al., 2002, 2004; Kim et al., 2009; Gu et al., 2009). The model is being preferred in various studies. For example, Lee and Turban (2001), Lowry et al. (2008), 388 S. Afshan et al. Ratnasingham (1998) studied ITM in the field of internet shopping. Also for mobile commerce the model is widely accepted in the research of Mallat (2007), Gu et al. (2009) and Zhou (2011, 2012). 2.1.3 Perceived risk Perceived risk is also considered as a crucial factor in predicting technology adoption. Many studies recognised risk as the fundamental barrier of branchless banking (Ho and Ng, 1994; Lockett and Littler, 1997; Park et al., 2004; Kesharwani and Tripathy, 2012). In consumers decision of adopting a technology, perceived risk is found to play significant role (Laforet and Li, 2005; Pavlou, 2003). It is also believed that consumer’s acceptance of internet banking is impeded by the security concerns and the likelihood of hackers accessing consumers’ phones remotely. In the perspective of online transactions, perceived risk is usually described as a perception about implicit risk in utilising internet infrastructure to exchange private information. Considering the substantial impact of internet banking in modern business and presence of its potential growth in Pakistani market, the current study reaches out to provide the additional insights into the literature of internet banking by providing an inclusive framework that seeks to explore: a the degree to which various dimensions of risk effect users intention of technology adoption b the domains of personal trust in internet banking solutions c how critical is the role of perceptions are in shaping the intentions of internet banking customer. The awareness resulting after such wider approach will assist banks not only in targeting bottlenecks that hinder user acceptance but also aids in finding the decision factors to perk up their services. 2.2 Empirical evidences and hypotheses In our study we integrated the variables of TAM framework [perceived usefulness and perceived ease of use (PEOU)], ITM [propensity to trust (PTT), structural assurance and familiarity with bank] and risk dimensions (financial risk, privacy risk, time risk and security risk) to predict behavioural intention of internet banking (IBI) usage. Following section explains the separate empirical evidences for each of these variables with IB intention adoption. 2.2.1 Perceived usefulness Perceived usefulness defines as a degree to which a technology will be helpful in enhancing its performance (Davis, 1989). It reflects one’s evaluation of the extrinsic advantages received when technology is adopted or used. Bhattacherjee (2002) performed a study identifying the influence of cognitive beliefs and one’s intention to use internet banking. The study hypothesised and confirmed the influence of perceived usefulness in predicting intention of continue using internet banking by the end users. Wang et al. (2003) in their study also found significant effects of perceived usefulness on Internet banking in Pakistan 389 behavioural intention to use the IB. In US, Rimenschneider et al. (2002) also studied the role of usefulness in the sample size of 156 respondents. The results confirmed the significant contribution of usefulness in IB adoption. Similar results were found in the studies of Suh and Han (2002), Sohail and Shanmugham (2003) etc. In this study, we hypothesised the influence of perceived usefulness on IB as the following: Hypothesis 1 IB intention is positively influenced by perceived usefulness. 2.2.2 Perceived ease of use Another widely accepted factor of predicting IB adoption is PEOU. PEOU explains “the degree to which a person believes that using a particular system would be free of physical and mental effort” [Davis, (1989), p.320]. In Malaysia, Sohail and Shanmugham (2003) reported the significant role of ease of use in internet banking adoption of retail users of the country. Chau and Lai (2003) in their research of user’s acceptance of internet banking recognised the efficient contribution of ease of use in IB adoption. Likewise, Adams et al. (1992) also established the linkage of ease of use with information technology adoption by employing TAM. Similar empirical evidences are found in the studies of Davis (1989), Davis et al. (1989), Sathye (1999) Karahanna et al. (1999), White and Nteli (2004), Goette (1995), Wang et al. (2003), Hendrickson et al. (1993), Mathieson (1991) etc. Rawashdeh (2015) in his research conducted analysed the relationship between usefulness and privacy for adoption of internet technology. The study accepts the TAM and confirms the robustness of behavioural intentions for adoption of internet technology. We therefore hypothesised the following: Hypothesis 2 IB intention is positively influenced by PEOU. 2.2.3 Perceived risk Cox and Rich (1964) define perceived risk as the level of uncertainty perceived by the end users in a specific purchase situation. Perceived risk for IB users refers to the anticipation of loss in search of a preferred result from utilising e-banking services (Yousafzai et al., 2003). Many studies identified perceived risk as the crucial barrier of IB adoption. It is recognised as a significant obstacle that deters e-transactions considering the possibility of information stolen or lost of information by using internet (Park et al., 2004). Security concerns on the internet generally refer to any factor affecting the perceived risk (Grewal and Dharwadkar, 2002). White and Nteli (2004) in their study found that security is the most significant attribute among the users in the domain of IB in British. Sathye (1999) also confirmed the influence of security in the adoption of IB. Similarly, studies of Kesharwani and Tripathy (2012), Ho and Ng (1994) and Lockett and Littler (1997) also empirically confirmed the contribution of risk in the use of electronic banking. Laforet and Li (2005) examined the association between consumer attitudes towards mobile banking and their adoption of mobile banking in China. The study concludes that attitudes towards security and perception of risk are the most critical factors discouraging adoption. Pavlou (2003) also analysed the role of risk in the acceptance of electronic commerce. The findings of the study established the significant contribution of two risks dimensions i.e. reputation and satisfaction with past transaction in influencing intention of online banking transactions. 390 S. Afshan et al. Perceived risk however is not a uni-dimensional concept. The literature has highlighted several dimensions of perceived risk. Pavlou (2003) and Littler and Melanthiou (2006) hypothesised risk in five dimensions of financial, performance, time, psychological, and security risks. Akturan and Tezcan (2012) measured perceived risk in six dimensions of financial, time privacy, social, security and performance risk. Aldas-Manzano et al. (2011) use two dimensions of measuring perceived risks i.e. privacy and security risks. We have hypothesised four dimensions of perceived risk in the present study. These dimensions include financial risk, privacy risk, time risk and security risk. Table 1 presented the definitions for the studied dimensions of perceived risk utilised in this study. Table 1 Risk dimension definitions Risk dimension Definition Time risk The possibility of losing time to learn how to use the product Financial risk The probable financial expenses which are related with the preliminary purchase price and maintenance cost of the product Privacy risk The possibility of losing the personal information while using the product Security risk The possibility of losing the control over a transaction and also the financial information Source: Akturan and Tezcan (2012) Al-Ajam and Md Nor (2015) in their paper have discussed the factors that influence a user to adopt internet banking technology. The findings show a great impact of attitude, subjective norm and perceived risk on the attitude of an individual. We therefore hypothesised the following: Hypothesis 3 IB intention is negatively influenced by financial risk. Hypothesis 4 IB intention is negatively influenced by privacy risk. Hypothesis 5 IB intention is negatively influenced by time risk. Hypothesis 6 IB intention is negatively influenced by security risk. 2.2.4 Initial trust Initial trust is defined as “the willingness of an individual to take risks in order to fulfill a need without prior experience, or credible, meaningful information” (Kim and Prabhaker, 2004; McKnight and Chervany, 2001). The role of trust has been identified in many studies in processing banking and e-commerce. Connolly and Bannister (2007) examined consumer trust in internet shopping in Ireland. The study measures the effects on trust through trustworthiness of internet vendors and external environmental variables. The results concluded significant contribution of these variables in enhancing users trust in the field of internet shopping. Similar results are discussed in the studies of Lee and Turban (2001), Lowry et al. (2008), Ratnasingham (1998). For mobile banking, Kim et al. (2009) also examined the association of initial trust in determining intention to use the service. The study utilised four dimensions to reflect initial trust i.e. structural assurances, relative benefits, personal propensity to trust and firm reputation. The outcomes of the study revealed that relative benefits, PTT and structural assurances are 391 Internet banking in Pakistan significant factors in influencing initial trust in mobile banking. Similar results are found in the studies of Mallat (2007), Zhou (2011), Gefen et al. (2003), Yang and Minjoon (2002), Sohail and Shanmugham (2003) and Pavlou (2003). Yu et al. (2015) in their paper have discussed the relationship between trust and customers intention to continue the use of internet banking services. They found trust to have a mediating effect between internet banking use and trustworthiness. We therefore hypothesised the following: Hypothesis 7 Figure 1 Initial trust positively affects the behavioral IBI. The hypothesised model Risk Dimensions Privacy Risk Security Risk Financial Risk Time Risk TAM Perceived Usefulness Intention Adoption Perceived Ease of Use ITM Initial Trust Structural Assurance Familiarity Propensity to Trust 392 S. Afshan et al. ITM sets the basis of initial trust on contributing situational and environmental factors. In our study we analysed three constructs of initial trust i.e. PTT, familiarity with bank and structural assurance. PTT is defined as the user’s natural tendency to trust other people (McKnight et al., 2002). The environmental factor like familiarity also influences trust and exhibits the knowledge of the vendor and understanding of its relevant procedures and technology (Gefen et al., 2000). Likewise, structural assurance reflects individual’s trust regarding safety nets such as legal resources, guarantees, and regulations existed in a specific context (Gefen et al., 2003; McKnight et al., 1998; Shapiro, 1987). Zhou (2011) studied the role of PTT and structural assurance in influencing initial trust. The study established significant contribution of structural assurance and PTT in determining trust. Similarly, Oliveira et al. (2014) also studied both the variables but failed to find the role of propensity in influencing trust. In the study of Afshan and Sharif (2016), the results confirmed significance of structural assurance and familiarity with bank in determining initial trust. Therefore, in our study we hypothesised the following: Hypothesis 8 Initial trust is positively influenced by PTT. Hypothesis 9 Initial trust is positively influenced by familiarity with bank. Hypothesis 10 Initial trust is positively influenced by structural assurance. The final objective of present research is to decide whether the intention to adopt internet banking leads to a decision for its adoption. Therefore, we hypothesised the following: Hypothesis 11 Behavioural intention to use internet banking positively influences user adoption. Displayed in Figure 1 is the hypothesised model of the study. 3 Methodology 3.1 Sample and data collection The present study uses online questionnaire as the instrument for the research. Data was collected during the period of August 2015 to October 2015. The study utilised convenience sampling method. The method used is consistent with the approach adopted in many previous studies of technology adoption (e.g. Chen, 2008; Featherman and Pavlou, 2003; Luarn and Lin, 2005; Wu and Wang, 2005). A total of 428 responses were collected that further examined for missing values and replaced with mean. Later on, univariate and multivariate outliers are examined that resulted in 89 cases being dropped. The final count for this study was 339 cases. 3.2 Measures In order to test the hypothesised model displayed in Figure 1, a survey instrument was prepared by using prior published studies. The attributes of studied variables are designed using a five point Likert scale from 1 (strongly disagree) to 5 (strongly agree). The scale items measuring the constructs of perceived usefulness (PU) and PEOU are adapted from Davis (1989), Davis et al. (1989) and Teo et al. (1999). The scale items of initial trust (IT), PTT, familiarity with bank (FB), structural assurance (SA) and mobile banking Internet banking in Pakistan 393 intention (MBI) and adoption (MBA) are adapted from Kim et al. (2009) and Gu et al. (2009). However, the item and the scales for the constructs of financial risk (FR), security risk (SR), privacy risk (PR) and time risk (TR) are derived from Cruz et al. (2010), Akturan and Tezcan (2012) and Chen (2013). 4 Data analysis and results 4.1 Descriptive statistics and testing the assumptions of multivariate analysis The data analysis was carried out through SPSS 21 and AMOS 21 software with sample size of N = 339. The research is carried out after checking the basic data analysis assumptions of; sample size, outliers, scales of the variables and multicollinearity (Hair et al., 2005; Fotopoulos and Psomas, 2009). Convenience sampling has been used for data collection purpose; in this form of sampling the researcher selects readily available respondents regardless of their characteristics until the required sample size is achieved (Tansey, 2007). The sample size of 300 or above is considered good, in our case the sample size in 339 exhibiting the sufficiency of sample size (Hair et al; 2005). The univariate outliers were omitted utilising box plot identification method. Multivariate outliers however are extracted using Mahalanobas D2 criteria and were excluded from the studied sample data set. 4.2 Common method biasness Common method variance is defined as the “variance that is attributed to the measurement method rather than to the construct of interest” (Bagozzi and Yi, 1991). Many studies stressed on dealing with the issue of common method variance prior to testing studied hypotheses (Podsakoff et al., 2003; Woszczynski and Whitman, 2004, Ashkanasy, 2008; Richardson et al., 2009; Williams et al., 2009; Craighead et al., 2011; Waseem et al., 2013). The analysis of common method biasness in the present study is performed through Harman’s one factor model (Podsakoff et al., 2003). Applying principal axis factoring framework, promax rotation and fixing the number of factors to 1, the results indicated that total number of variance explained is 15.88%. Since the value is less than threshold of 50%, it is concluded that the present study has no issue of common method biasness. 4.3 Exploratory factor analysis The study utilised literatures highly preferred principal components method (Guadagnoli and Velicer, 1988; Velicer and Jackson, 1990; Schonemann, 1990; Steiger, 1990) in order to reduce its 50 questionnaire Likert-based items into 12 best manageably proposed factors. To measure the adequacy of the sample, Kaiser-Meyer-Olkin exhibits the value of 0.806 which is above 0.7 and rejecting the stance that there exist sufficient items to predict each component. In other words, the sample is sufficient enough to run factor analysis (Barkus et al., 2006; Leech et al., 2005). Bartlett’s test of sphericity (approx. chi-square = 9361.31, df = 1275, p < .000) describes that the correlation matrix is significantly dissimilar from the identity matrix and correlation among variables is not zero (Leech et al., 2005). PEOU TR FR PU SR PR PTT FB 2.77 2.49 2.42 2.34 2.12 1.63 1.58 1.39 1.25 % Variance 17.34 8.51 6.75 5.90 5.30 5.15 4.98 4.51 3.47 3.36 2.95 2.65 Cumm. % 7.34 14.21 20.94 27.44 33.53 39.25 44.93 50.49 55.82 61.00 66.06 70.88 SA4 .885 SA2 .885 SA3 .874 SA1 .809 IBA2 .861 IBA3 .852 IBA4 .836 IBA1 .808 IBI5 .766 IBI2 .758 IBI3 .721 IBI1 .706 IBI4 .698 IT3 .823 IT4 .790 IT2 .784 IT1 .757 PEOU3 .880 PEOU4 .877 PEOU2 .837 PEOU1 .716 TR2 .850 TR4 .808 Notes: Extraction method: principal component analysis; Rotation method: Varimax with Kaiser normalization; Rotation converged in six iterations S. Afshan et al. IT 3.17 394 IBI 4.00 Rotated component matrix IBA 8.15 Table 2 SA Eigen IBI IT PEOU TR FR PU SR PR PTT FB 4.00 3.17 2.77 2.49 2.42 2.34 2.12 1.63 1.58 1.39 1.25 % Variance 17.34 8.51 6.75 5.90 5.30 5.15 4.98 4.51 3.47 3.36 2.95 2.65 Cumm. % 7.34 14.21 20.94 27.44 33.53 39.25 44.93 50.49 55.82 61.00 66.06 70.88 .788 TR3 .773 FR3 .812 FR2 .807 FR1 .804 FR4 .790 PU1 .839 PU3 .816 PU4 .770 PU2 .765 SR4 .800 SR1 .784 SR3 .778 SR2 .728 Rotated component matrix (continued) TR1 PR1 .777 PR2 .776 PR3 .734 PR4 .704 PTT3 .879 PTT2 .840 PTT1 .735 FB1 .810 FB2 .790 FB3 .787 395 Notes: Extraction method: principal component analysis; Rotation method: Varimax with Kaiser normalization; Rotation converged in six iterations Internet banking in Pakistan IBA 8.15 Table 2 SA Eigen 396 S. Afshan et al. Following the criteria for factors extraction explained in Hair et al. (2009), the methods of Latent root, percentage of variance explained and scree test together leads to the conclusion of retaining 12 factors for the analysis. These 12 components depict 70.88% of the total variance explained with Eigen values above 1. The rotated component matrix consists of final 46 items with factor loadings above 0.70. According to general rule of thumb, the factor loadings above 0.55 are considered good (Tabachnick and Fidell, 2007). The resulting solution shown in Table 2 does not show any cross loading among the items indicating no issue of discriminant validity. After exploratory factor analysis, the reliability through estimating Cronbach alpha of each of the factor was computed. The overall reliability of 46 loaded items after exploratory factor analysis was found to be 0.73. In contrast with Cronbach alpha, Fornell and Larcker (1981) and Lin and Lee (2004) suggested that composite reliability (CR) is considered an improved measure of ensuring construct validity since it measures the overall reliability of a collection of heterogeneous but similar items. We analysed both measures to ensure construct validity. Table 3 presents the results of construct and convergent validity including Cronbach alpha (Cα), CR of scale and average variance explained (AVE). The values of Cα and CR for all 12 constructs are above the threshold level of 0.7 representing above fair construct validity. Similarly values of AVE are also exhibiting good validity of constructs, except for PR (0.46) and FR (0.47) whose values are slightly below the threshold of 0.5. Overall the results indicate an appropriate measurement model (Molina et al., 2007). 4.4 Confirmatory factor analysis (CFA): (measurement model) The study further performed confirmatory factor analysis (CFA) with 46 final loaded items that represent 12 factors namely, behavioural IBI, IBA, perceived usefulness (PU), PEOU, time risk (TR), financial risk (FR), security risk (SR), privacy risk (PR), initial trust (IT), PTT, familiarity with bank (FB) and structural assurance (SA). The CFA measurement model projects the associations between the observed and unobserved variables (Byrne, 2010). Measurement model relies on the assessment of its model fitness. According to McDonald and Ho (2002) the most frequently stated model fitness indices are comparative fit index (CFI), goodness of fit index (GFI), normed fit index (NFI) and the non-normed fit index (NNFI). However, in reporting which index to include, one should not emphasis on common practices since some of the indices (for instance GFI) repeatedly are considered for historical reasons instead of their sophistication (Hooperet al., 2008). Various indices are important individually since all represent different aspects of model fitness (Crowley and Fan 1997). Thus there exists no consensus in literature for any single index of measuring model fitness. This leads to the necessity of reporting several combinations of indices. Kline (2005) strongly suggested the combination of chi-square test, the root mean square error of approximation (RMSEA), the CFI and the standardised root mean square residual (SRMR). These indices are preferred over other indices since they are most insensitive to sample size, misleading and parameter estimates. Following Kline (2005) recommendation, Table 4 presents the goodness of fit indices for our final hypothesised model. Perceived ease of use Security risk Financial risk Time risk PEOU2 I find it easy to use internet banking to accomplish my banking tasks. PEOU3 Overall, I believe internet banking is easy to use. PEOU4 I think it is easy to remember how to use internet banking. IBA1 I use internet banking. IBA2 I use internet banking to manage my accounts. IBA3 I use internet banking to make transfers. IBA4 I subscribe financial products that are exclusive to internet banking. PR1 Using internet banking financially is not secure. PR2 I do not trust in the ability of internet banking to protect my privacy. PR3 A hacker may hack into my private information when using IB service. PR4 Personal information when using mobile banking services may be stolen by others. SR1 I do not trust in internet banking as a bank. SR2 I may worried about the security of internet banking. SR3 Matters of security have influence on using internet banking. SR4 Going to the branch and for making any transaction make me feel more safe as compare to my own internet banking account. FR1 I think my account information may hacked and I may lose my money. I think IB is financially risky, I would have to waste a lot of time fixing. FR3 When using IB, I am afraid I will lose money due to careless mistakes. FR4 I think that there would be problems with my financial transactions while using mobile banking. TR1 I think I would spend too much time learning how to use internet banking. TR2 It takes time to use internet banking services. TR3 I think I would use IB if someone give me time and train me how to use it. TR4 Using a internet banking service would lead to a loss of convenience for me because I would have to waste a lot of time fixing payment errors. Cα, CR, AVE .86, .87, .64 Kim et al. (2009) .92, .92, .75 Akturan and Tezcan (2012), Chen (2013) .76, .77, .46 Akturan and Tezcan (2012), Cruz et al. (2010) .74, .74, .50 Akturan and Tezcan (2012) .76,.76, .47 Akturan and Tezcan (2012), Chen (2013) .83, .84, .57 397 FR2 Adapted from Davis (1989), Davis et al. (1989), Teo et al. (1999) Internet banking in Pakistan Privacy risk Items Learning to use internet banking is easy for me. Measured items, Cronbach alpha (Cα), CR and AVE Intention to adopt internet banking Labels PEOU1 Table 3 Variables Familiarity with bank Initial trust Perceived usefulness IBI2 I am curious about internet banking. IBI3 I have the intention of managing my accounts using internet banking. IBI4 I have the intention of making a transfer by internet banking. IBI5 I want to know more about internet banking. PTT1 I do not use new technologies. PTT2 I avoid the use of new products like internet banking. PTT3 I avoid the use of non-classical means to transact money. PTT4 I am cautious with the financial transactions I execute. SA1 I do not incur in the risk of financial losses using internet banking services. SA2 My banks internet banking service has a client protection policy. SA3 My personal internet information is secure when I use the internet banking service. SA4 I do not incur in the risk of personal information theft using internet banking services. FB1 I am familiar with my bank through magazines, newspaper or TV. FB2 I am familiar with my bank through personal interactions. FB3 I am familiar with my bank through visiting its website and getting information about it. IT1 Internet banking seems dependable. IT2 Internet banking seems secure. IT3 Internet banking seems reliable. IT4 Internet banking was created to help the client. PU1 I can accomplish my banking tasks more quickly using internet banking. PU2 I can accomplish my banking tasks more easily using internet banking PU3 Internet banking enhances my effectiveness in utilising banking services. PU4 Internet banking enhances my efficiency in utilising banking services. Adapted from Cα, CR, AVE Kim et al. (2009) .84, .84, .51 Kim et al. (2009) .84, .85, .65 Kim et al. (2009) .93, .93, .78 Gu et al. (2009) .86, .86, .67 Kim et al. (2009) .88, .87, .64 Teo et al. (1999), Davis (1989) .81, .82, .53 S. Afshan et al. Structural assurance Items I have the intention of making a service payment by internet banking. Measured items, Cronbach alpha (Cα), CR and AVE (continued) Personal propensity to trust Labels IBI1 398 Behavioural intention Table 3 Variables 399 Internet banking in Pakistan Table 4 Model fit indices Indices Final measurement model χ2 (df) 1,231.72(914)-sig CMIN/df 1.35 CFI 0.96 RMSEA (P-close) 0.03(.99) SRMR 0.04 Source: Authors’ estimation Overall, the outcomes of our goodness of fit indices suggested that the studied 12 factor model fits the data very well. In our final measurement model, chi-square is significant indicating the difference between hypothesised and actual model. For models with 75 to 200 cases, the chi square test is a reasonable measure of fit but for models with more cases, the chi square is almost always statistically significant (Tanaka, 1987). Due to the sensitivity of chi-square with sample size, Kline (2004) suggested that the value of chi-square should be divided by the degrees of freedom in order to get goodness of fit. This phenomenon is also called normed chi-square (NC) or CMIN/DF value. Tabachnick and Fidell (2007) provided the threshold of less than 2 for CMIN/DF value. In our case it is 1.35 and fits the goodness of fit criterion. Furthermore in supporting minimum discrepancy result with other sophisticated fit indices, our CFI = 0.96 is above the excellent model fitness level of > .95 and higher than the traditional level of 0.90 (Hu and Bentler, 1999). The RMSEA = 0.03 is lower than the recommended level of 0.07 (Steiger 2007). Our SRMR = 0.04 is also considerably smaller than the 0.08 value (Hu and Bentler, 1999) that is considered favourable for indicating model fitness. These fit indices suggested that our data fit very well to our model. It should also be noted that our final model has included numerous correlated error terms within a factor. The error correlation in our measurement model is done in the way that is accepted by prior researchers and correlation of error is not performed among different factors (Byrne et al., 1989). Table 5 Summary of model comparisons χ2(df) NC or CMIN/df CFI Null model 9,002.67 (1,081) 8.33 0.00 0.15(0.000) 0.20 Single factor model 6,769.77 (1,034) 6.55 0.28 0.13(0.000) 0.13 Three factor model 5,366.74 (1,031) 5.20 0.45 0.11(0.000) 0.11 Hypothesised model 1,231.72 (914) 1.35 0.96 0.03(0.99) 0.04 RMSEA(P-close) (SRMR) Notes: N = 339 A single factor was composed of all 46 items A three factor model composed of factor 1(17 items of TAM framework), factor 2(15 items of risk framework), factor 3(14 items of initial trust model). Although the results reflect the fitness of our final model, prior studies asserted that good fitness models can also have misspecification. In order to deal with it, alternate models’ fitness should be considered to compare with the hypothesised model. We therefore compared our hypothesised model with three alternate models. First, the null model in which no variables are interrelated is compared with our hypothesised model. The results of this comparison displayed the superiority of our model (see Table 5). Second, we 400 S. Afshan et al. compared our final model with a single factor model in which all items are linked with one factor that can be named IB acceptance. The direct comparison of this model ∆χ2 [5,538.04(120), p < .001] (see Table 5) also verified the superiority of our studied model. Finally, our measurement model is compared with a three-factor model. We preferred to test this fit with items representing TAM framework as factor 1, perceived risk dimensions as factor 2 and ITM framework as factor 3. The comparison of this three-factor model also concluded that our final model fits the data better, ∆χ2 [4,135.02(117), p < .001] (see Table 5). With all fit indices, the hypothesised model of the research exceeds the threshold values demonstrated in Kline (2004, 2005). Based on the strict elucidation of those thresholds, we established that our final measurement model not only fits the data well but also fits the data better than other possible models. 4.5 The structural equation modelling The structural model indicates the association among unobserved variables (Byrne, 2010). Table 6 displayed the results of our structural model using SEM. The structural model also holds a good fit assessed by CMIN/DF = 1.33; CFI = 0.96; RMSEA (PCLOSE) = 0.03(0.99) and SRMR = 0.05. The reported fit indices exceeded their recommended threshold and exhibited good model fitness (Hu and Bentler, 1999; Kline, 2004; Steiger, 2007). Table 6 SEM model fit indices Indices Recommended level CMIN/df CFI RMSEA (P-close) SRMR Final SEM model < 2 (Tabachnick and Fidell, 2007) 1.33 < 0.90 (Hu and Bentler, 1999) 0.96 > 0.07 in-sig (Steiger, 2007) 0.03(.99) > 0.08 (Hu and Bentler, 1999) 0.05 Source: Authors’ estimation Table 7 SEM hypothesis testing Hypothesised path Path coefficient S.E C.R P-value Remarks H1 IBI ← PU 0.10 0.052 1.988 0.047 Supported H2 IBI ← PEOU 0.05 0.024 2.079 0.038 Supported H3 IBI ← FR 0.02 0.041 0.385 0.700 Not-supported Hypothesis H4 IBI ← PR –0.27 0.052 –5.184 0.000 Supported H5 IBI ← TR –0.12 0.038 –3.294 0.000 Supported H6 IBI ← SR 0.03 0.033 0.956 0.339 Not-supported H7 IBI ← IT 0.20 0.029 6.814 0.000 Supported H8 IT ← PTT 0.25 0.053 4.673 0.000 Supported H9 IT ← FB 0.39 0.064 6.013 0.000 Supported H10 IT ← SA 0.26 0.058 4.558 0.000 Supported H11 IBA ← IBI 1.31 0.155 8.458 0.000 Supported Source: Authors’ estimation 401 Internet banking in Pakistan The statistical significance of all determinants was projected to establish the validity of the hypothesised regression paths. Table 7 displayed the results of SEM regression paths, standardised regression weights, standard errors, critical ratios, probability values and remarks of the hypothesis. In our ITM, propensities to trust (PTT), familiarity with bank (FB) and structural assurance (SA) have shown significant effects on initial trust with p < 0.001. The results indicated that unit increase in the standard deviation of PTT, FB and SA will increase the initial trust of users of IB by 0.25, 0.39 and 0.26 units. Familiarity with bank is found to play the most critical role in influencing initial trust of IB users in the present study. Figure 2 The structural model results Risk Dimensions Privacy Risk Security Risk Time Risk 0.03 –0.27*** –0.12*** Financial Risk 0.02 TAM Perceived Usefulness 0.10** Intention Perceived Ease of Use 1.31*** Adoption 0.05** 0.20*** Initial Trust Structural Assurance 0.26*** ITM 0.39*** 0.25*** Propensity to Trust Notes: *p < 0.10; **p < 0.05; ***p < 0.01. Familiarity 402 S. Afshan et al. In behavioural intention determinants, the results suggested the positive significant impact of initial trust (IT), PEOU and perceived usefulness (PU) on the user’s intention to use internet banking (IBI). The outcome also indicated that unit increase in the standard deviation of IT, PEOU and PU will increase IBI by 0.20, 0.05 and 0.10 units of standard deviations respectively. On the other hand, privacy risk (PR) and time risk (TR) are found to impact negatively on user’s intention of using IB. This highlights that unit increase in the standard deviation of PR and TR will decrease the standard deviation of IBI by 0.27 and 0.12 units respectively. Privacy risk is found to play the most critical role in effecting behavioural intention of IB. Lastly, behavioural intention is found to have significant influence on the IBA. Concluding the facts of these outcomes, it is established that influence of initial trust, PEOU, perceived usefulness, privacy risk and time risk are significant to effect user’s intention to use IB which further effects the AIB of the country. 5 Discussion The present research studied the factors of user’s intention and AIB in Pakistan. By incorporating various theories, the study strives to examine the role of ITM, TAM and risk dimensions in the acceptance of internet banking of the Pakistan. 5.1 Technology acceptance model The constructs of TAM are found crucial to influence user’s IBI usage. PEOU (B = 0.05, p < 0.05) is found to have positive significant effects on IB acceptance. The findings further established the valid effects of perceived usefulness in IB intentions (B = 0.10, p < 0.01). The results also highlighted the vital contribution of usefulness as compared to ease of use (B = 0.10 and B = 0.05). This importance of usefulness in internet banking is a result of greater benefits associated with the domain. These benefits includes faster and convenient execution of monetary transactions, convenient online access to financial information, lower economic cost in terms of reduced commuting, checking and postage expenses and others. The convenience resulting from such advantages are important in enhancing IB acceptance. However, this should only be taken to the extent of PU preference in determining its value and not the abundance of other constructs. 5.2 Initial trust model As advocated by many researchers, initial trust (B = 0.20, p < 0.001) is found to effect significantly on user’s intention to adopt internet banking (Afshan and Sharif, 2016; Oliveira et al., 2014; Zhou, 2011). In our ITM, all three antecedents of initial trust are found significant to surge initial trust in internet banking. This seems true since the awareness with bank’s processing and confidence in firms legal and technological structures play critical part in building the required trust in customers. Among the constructs, familiarity with bank (B = 0.39, p < 0.001) has evidenced the greater effects on initial trust (IT). Seeing mobile banking as greatly a personalised service, the major hindrance in its adoption is the uncertainty associated with the initial usage. The banks should consider this aspect from the earlier stages and make sure that every customer trusts the business bureaus and ethics character of the bank. The results of our ITM are Internet banking in Pakistan 403 consistent with the similar studies of Gefen et al. (2003), Kim et al. (2009) and Oliveira et al. (2014) and Afshan and Sharif (2016). 5.3 Risk The results of privacy risk (B = –.27, p < 0.001) and time risk (B = –0.12, p < 0.001) have shown the negative significant association with user’s intention to accept IB. The results are consistent with the work of Stone and Grønhaug (1993) and Dholakia (1997). This suggests that banks should strive to reduce their level of uncertainty associated with its IB activities. The matter of losing personal information is of great concern in using internet banking. Banks in Pakistan are also defamed for their service and technology failures like low speed and power loss. This enhance the time of performing banking activities via internet. If the consumers believe that use of IB will enhance the possibility of losing time in learning how to perform banking task online, they will prefer traditional banking in contrast. 6 Practical and managerial implication The outcomes of the present study have resulted in an all-inclusive awareness regarding the decision factors that affect the acceptance of internet banking. The present study, instead of focusing on single theoretical framework, provides an all-inclusive approach by incorporating three concepts namely TAM, initial trust and risk dimensions. The existing literature of technology acceptance has emphasised on the perception aspects of technology and hardly studies the impact of the environmental aspects. The present study bridges this gap by combing perceptions aspect of internet banking along with the environmental variables of familiarity, PTT and structural assurance together with the risk dimensions. The importance of usefulness and ease of use in influencing internet banking calls for the need of banks to publicise the benefits of IB solutions. This will be helpful in initiating the positive attitude amongst banks’ customers towards internet banking. Also banks can include in their web page a quick guide on how to use internet banking. Given the improvement in web programming, banks can also provide step-by-step demonstration on how to use internet banking as if the individual is accessing his or her actual internet banking account. Internet banking is greatly a personalised service. The major obstacle in its acceptance is the safety and privacy concerns. The banks should consider this aspect from the earlier stages and make sure that every customer trusts the business bureaus and ethics character of the bank. This calls for the need of communicating client protection policies and statements of guarantees by banks and other’s financial institution in their earlier marketing campaigns to discourage privacy concerns of users and to stimulate initial trust in m-banking services. One of the drawbacks of internet banking lies in its vulnerability to information interception due to wireless network. Furthermore, the presence of numerous malwares in online operations enhances the confidentiality concerns and in many cases increases the time of performing banking activities. Also in the absence of customers’ direct experience, users opt to rely on banks credibility to make certain that their financial transactions are safe to process and are backed with legitimate rules and structures. This 404 S. Afshan et al. stresses on the importance structural assurance and familiarity with banks credibility. The banks should centre their focus on providing service guarantees, policies regarding confidentiality, endorsement and acknowledgment of security in performing branchless banking activities (McKnight et al., 2004).This will help to make certain that IB users have confidence in the bank which will enhance their initial trust and decrease the level of uncertainty in IB activities. Overall, the present study will assist banking industry in forming and developing strategies to enhance the acceptance of internet banking in Pakistan. The research highlighted the key constructs that hold high contribution in user’s adoption of technology. This will help banks to channel their focus on those aspects that can bring development e-banking industry of Pakistan. 7 Research limitation and future implication One of the limitations of this study is utilising random sample as subjects. We here put forward the replication of random subjects with old banking customers. This will provide larger population of banking users overcoming the issue of generalisability. Second, our study is conducted in Pakistan. The outcomes may not hold suitable to users of other countries since they possess diverse corporate practices, level of technology acceptance, customer’s exposure and infrastructures. The reason different countries face different issues; constructs significant in this study may not be critical to others. Lastly, a more detailed understanding of the constructs and dimensions of internet banking is required. The need of more researches with thorough analysis of the studied field would be highly fruitful since very little work is done in the field of internet banking in Pakistan. References Abbas, S.S. (2007) ‘The understanding of e-readiness in context of e-banking in Pakistan’, Computer Science, p.106, COMSATS Institute of Information Technology, Islamabad, Pakistan. Adams, D.A., Nelson, R.R. and Todd, P.A. (1992) ‘Perceived usefulness, ease of use, and usage of information technology: a replication’, MIS Quarterly, Vol. 16, No. 2, pp.227–248. Afshan, S. and Sharif, A. (2016) ‘Acceptance of mobile banking framework in Pakistan’, Telematics and Informatics, Vol. 33, No. 2, pp.370–387. Ahmad, A., Rehman, K.U., Saif, M.I. and Safwan, M.N. (2010) ‘An empirical investigation of Islamic banking in Pakistan based on perception of service quality’, African Journal of Business Management, Vol. 4, No. 6, pp.1185–1193. Ajzen, I. (1991) ‘The theory of planned behavior’, Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, pp.179–211. Akturan, U. and Tezcan, N. (2012) ‘Mobile banking adoption of the youth market: perceptions and intentions’, Marketing Intelligence & Planning, Vol. 30, No. 4, pp.444–459. Al-Ajam, A.S. and Md Nor, K. (2015) ‘Challenges of adoption of internet banking service in Yemen’, International Journal of Bank Marketing, Vol. 33, No. 2, pp.178–194. Aldas-Manzano, J., Ruiz-Mafe, C., Sanz-Blas, S. and Lassala-Navarré, C. (2011) ‘Internet banking loyalty: evaluating the role of trust, satisfaction, perceived risk and frequency of use’, The Service Industries Journal, Vol. 31, No. 7, pp.1165–1190. Internet banking in Pakistan 405 Ali, M. and Raza, S.A. (2015) ‘Service quality perception and customer satisfaction in Islamic banks of Pakistan: the modified SERVQUAL model’, Total Quality Management & Business Excellence, Vol. 28, Nos. 5–6, pp.1–19. Arif, I., Afshan, S. and Sharif, A. (2016) ‘Resistance to adopt mobile banking in a developing country: evidence from modified TAM model’, Journal of Finance and Economics Research, Vol. 1, No. 1, pp.23–38. Ashkanasy, N.M. (2008) ‘Editorial: submitting your manuscript’, Journal of Organizational Behavior, Vol. 29, No. 3, pp.263–264. Aslam, H.D., Khan, M., Tanveer, A. and Amber, T. (2011) ‘Perceived barriers towards adoption of internet banking among non-metropolitan internet users of Pakistan’, International Business & Economics Research Journal (IBER), Vol. 10, No. 4, pp.45–56. Bagozzi, R.P. and Yi, Y. (1991) ‘Multitrait-multimethod matrices in consumer research’, Journal of Consumer Research, Bol. 2, No. 2, pp.426–439. Barkus, E., Yavorsky, C. and Foster, J. (2006) ‘Understanding and using advanced statistics’, Faculty of Health & Behavioral Sciences-Papers, p.393 [online] http://ro.uow.edu.au/ hbspapers/393/. Bhattacherjee, A. (2002) ‘Individual trust in online firms: scale development and initial test’, Journal of Management Information Systems, Vol. 19, No. 1, pp.211–241. Byrne, B.M. (2010) Structural Equation Modeling Using AMOS: Basic Concepts, Applications, and Programming, Taylor and Francis Group, New York and London. Byrne, B.M., Shavelson, R.J. and Muthen, B. (1989) ‘Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance’, Psychological Bulletin, Vol. 105, No. 3, pp.456–466. Chandio, F.H. (2011) Evaluating User Acceptance of Online Banking Information Systems: An Empirical Case of Pakistan, Brunel University West London [online] https://pdfs.semanticscholar.org/976a/ef2da2336022117be8db9732cf597049425b.pdf. Chau, P.Y.K. and Lai, V.S.K. (2003) ‘An empirical investigation of the determinants of user acceptance of Internet banking’, Journal of Organizational Computing and Electronic Commerce, Vol. 13, No. 2, pp.123–145. Chen, C. (2013) ‘Perceived risk, usage frequency of mobile banking services’, Managing Service Quality: An International Journal, Vol. 23, No. 5, pp.410–436. Chen, C.H. (2008) ‘Why do teachers not practice what they believe regarding technology integration?’, The Journal of Educational Research, Vol. 102, No. 1, pp.65–75. Connolly, R. and Bannister, F. (2007) ‘Consumer trust in internet shopping in Ireland: towards the development of an improved model of the antecedents of trust’, Journal of Information Technology, Vol. 22, No. 2, pp.102–118. Cox, D.F., and Rich, S. (1964) ‘Perceived risk and consumer decision making’, Journal of Marketing Research, Vol. 1, No. 4, pp.32–39. Craighead, C., Ketchen, D., Dunn, K., and Hult, G. (2011) ‘Addressing common method variance: guidelines for survey research on information technology, operations, and supply chain management’, IEEE Transactions on Engineering Management, Vol. 58, No. 3, pp.578–588. Cronin, M.J. (1998) Defining Net Impact: The Realignment of Banking and Finance on the Web: Banking and Finance on the Internet, pp.1–18, John Willey & Sons, New York. Crowley, S.L. and Fan, X. (1997) ‘Structural equation modeling: basic concepts and applications in personality assessment research’, Journal of Personality Assessment, Vol. 68, No. 3, pp.508–531. Cruz, P., Barretto Filgueiras Neto, L., Muñoz-Gallego, P. and Laukkanen, T. (2010) ‘Mobile banking rollout in emerging markets: evidence from Brazil’, International Journal of Bank Marketing, Vol. 28, No. 5, pp.342–371. Daniel, E. (1999) ‘Provision of electronic banking in the UK and the Republic of Ireland’, International Journal of Bank Marketing, Vol. 17, No. 2, pp.72–82. 406 S. Afshan et al. Davis, F.D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’, MIS Quarterly, Vol. 13, No. 3, pp.319–340. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989) ‘User acceptance of computer technology: a comparison of two theoretical models’, Management Science, Vol. 35, No. 8, pp.982–1003. Dholakia, U.M. (1997) ‘An investigation of the relationship between perceived risk and product involvement’, Advances in Consumer Research, Vol. 24, pp.159–167. Durkin, M. and O’donnell, A. (2005) ‘Towards a model of adoption in internet banking: strategic communication challenges’, The Service Industries Journal, Vol. 25, No. 7, pp.861–878. Featherman, M.S. and Pavlou, P.A. (2003) ‘Predicting e-services adoption: a perceived risk facets perspective’, International Journal of Human-Computer Studies, Vol. 59, No. 4, pp.451–474. Fishbein, M. and Ajzen, I. (1975) Belief, Attitudes, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA. Fisher, J., Burstein, F., Lynch, K. and Lazarenko, K. (2008) ‘“Usability + usefulness = trust”: an exploratory study of Australian health web sites’, Internet Research, Vol. 18, No. 5, pp.477–498. Flavián, C., Guinaliu, M. and Torres, E. (2005) ‘The influence of corporate image on consumer trust: a comparative analysis in traditional versus internet banking’, Internet Research, Vol. 15, No. 4, pp.447–470. Fornell, C. and Larcker, D.F. (1981) ‘Evaluating structural equation models with unobservable variables and measurement error’, Journal of Marketing Research, Vol. 8, No. 1, pp.39–50, doi:10.2307/3151312. Fotopoulos, C.B. and Psomas, E.L. (2009) ‘The impact of soft and hard TQM elements on quality management results’, International Journal of Quality & Reliability Management, Vol. 26, No. 2, pp.150–63. Gefen, D., Karahanna, E. and Straub, D.W. (2003) ‘Trust and TAM in online shopping: an integrated model’, MIS Quarterly, Vol. 27, No. 1, pp.51–90. Gefen, D., Straub, D.W. and Boudreau, M.C. (2000) ‘Structural equation modelling and regression: guidelines for research practice’, Communications of the Association for information Systems, Vol. 4, pp.1–70. Goette, T. (1995) Determining Factors in the Successful Use of Adaptive Technology by Individuals with Disabilities: A Field Study, Unpublished Doctoral Dissertation, Georgia State University, Atlanta. Grewal, R. and Dharwadkar, R. (2002) ‘The role of the institutional environment in marketing channels’, Journal of Marketing, Vol. 66, No. 3, pp.82–98. Gu, J.C., Lee, S.C. and Suh, Y.H. (2009) ‘Determinants of behavioral intention to mobile banking’, Expert Systems with Applications, Vol. 36, No. 9, pp.11605–11616. Guadagnoli, E. and Velicer, W.F. (1988) ‘Relation of sample-size to the stability of component patterns’, Psychological Bulletin, Vol. 103, No. 2, pp.265–275. Hair Jr., J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2009) Multivariate Data Analysis, New York. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2005) Multivariate Data Analysis, 6th ed., Pearson Prentice Hall, Englewood Cliffs, NJ. Harrison, D.A., Mykytyn, P.P. and Riemenschneider, C.K. (1997) ‘Executive decisions about adoption of information technology in small businesses: theory and empirical test’, Information Systems Research, Vol. 8, No. 2, pp.171–195. Hendrickson, A.R., Massey, P.D. and Cronan, T.P. (1993) ‘On the test-retest reliability of perceived usefulness and perceived ease of use scales’, MIS Quarterly, Vol. 17, No. 2, pp.227–230. Ho, S. and Ng, V. (1994) ‘Customers’ risk perceptions of electronic payment system’, International Journal of Bank Marketing, Vol. 12, No. 8, pp.26–39. Internet banking in Pakistan 407 Hooper, D., Coughlan, J. and Mullen, M. (2008) ‘Structural equation modelling: guidelines for determining model fit’, Electronic Journal of Business Research Methods, Vol. 6, No. 1, pp.53–60. Howard Chen, Y.H. and Corkindale, D. (2008) ‘Towards an understanding of the behavioral intention to use online news services: an exploratory study’, Internet Research, Vol. 18, No. 3, pp.286–312. Howcroft, B., Hamilton, R. and Hewer, P. (2007) ‘Customer involvement and interaction in retail banking: an examination of risk and confidence in the purchase of financial products’, Journal of Services Marketing, Vol. 21, No. 7, pp.481–491. Hu, L.T. and Bentler, P.M. (1999) ‘Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives’, Structural Equation Modeling: A Multidisciplinary Journal, Vol. 6, No. 1, pp.1–55. Karahanna, E., Straub, D.W. and Chervany, N.L. (1999) ‘Information technology adoption across time: a cross-sectional comparison of pre-adoption and post adoption beliefs’, MIS Quarterly, Vol. 23, No. 2, pp.183–213. Kesharwani, A. and Tripathy, T. (2012) ‘Dimensionality of perceived risk and its impact on internet banking adoption: an empirical investigation’, Services Marketing Quarterly, Vol. 33, No. 2, pp.177–193. Kesseven, P., Sawkut, R. and Boopen, S. (2008) ‘Investigating into the factors that influence the adoption of internet banking in Mauritius’, Journal of Internet Business, Vol. 5, No. 1, pp.99–120. Khan, S. (2007–2009) Adoption Issues of Internet banking in Pakistani’s Firm, pp.1–100, Lulea Institue of Technology, Sweden [online] http://epubl.ltu.se/1653-0187/2007/009/LTU-PB-EX07009-SE.pdf (accessed 25 March 2016). Kim, G., Shin, B. and Lee, H.G. (2009) ‘Understanding dynamics between initial trust and usage intentions of mobile banking’, Information Systems Journal, Vol. 19, No. 3, pp.283–311. Kim, K.K. and Prabhaker, B. (2004) ‘Initial trust and the adoption of B2C e-commerce: the case of internet banking’, Database for Advances in Information Systems, Vol. 35, No. 2, pp.50–64. Kline, R. (2004) Principles and Practice of Structural Equation Modeling, 2nd ed., Guilford, New York. Kline, R.B. (2005) Principles and Practices of Structural Equation Modeling, Guilford, New York. Koufaris, M. and Hampton-Sosa, W. (2004) ‘The development of initial trust in an online company by new customers’, Information & Management, Vol. 41, No. 3, pp.377–397. Laforet, S. and Li, X. (2005) ‘Consumers’ attitudes towards online and mobile banking in China’, International Journal of Bank Marketing, Vol. 23, No. 5, pp.362–380. Lee, J. and Allaway, A. (2002) ‘Effects of personal control on adoption of self-service technology innovations’, Journal of Services Marketing, Vol. 16, No. 6, pp.553–572. Lee, M.K. and Turban, E. (2001) ‘A trust model for consumer internet shopping’, International Journal of Electronic Commerce, Vol. 6, No. 1, pp.75–91. Leech, N.L., Barrett, K.C. and Morgan, G.A. (2005) SPSS for Intermediate Statistics: Use and Interpretation, Psychology Press, New York and London. Lin, F.T., Wu, H.Y. and Tran, T.N.N. (2015) ‘Internet banking adoption in a developing country: an empirical study in Vietnam’, Information Systems and e-Business Management, Vol. 13, No. 2, pp.267–287. Lin, H-F. and Lee, G-G. (2004) ‘Perceptions of senior managers toward knowledge-sharing behaviour’, Management Decision, Vol. 42, No. 1, pp.108–125. Littler, D. and Melanthiou, D. (2006) ‘Consumer perceptions of risk and uncertainty and the implications for behaviour towards innovative retail services: the case of internet banking’, Journal of Retailing and Consumer Services, Vol. 13, No. 6, pp.431–443. Lockett, A. and Littler, D. (1997) ‘The adoption of direct banking services’, Journal of Marketing Management, Vol. 13, No. 8, pp.791–811. 408 S. Afshan et al. Lowry, P.B., Vance, A., Moody, G., Beckman, B. and Read, A. (2008) ‘Explaining and predicting the impact of branding alliances and web site quality on initial consumer trust of e-commerce web sites’, Journal of Management Information Systems, Vol. 24, No. 4, pp.199–224. Lu, H.P. and Yu-Jen Su, P. (2009) ‘Factors affecting purchase intention on mobile shopping web sites’, Internet Research, Vol. 19, No. 4, pp.442–458. Luarn, P. and Lin, H.H. (2005) ‘Toward an understanding of the behavioral intention to use mobile banking’, Computers in Human Behavior, Vol. 21, No. 6, pp.873–891. Mallat, N. (2007) ‘Exploring consumer adoption of mobile payments – a qualitative study’, The Journal of Strategic Information Systems, Vol. 16, No. 4, pp.413–432. Manzano, J., Lassala-Navarre, C., Ruiz-Mafe, C. and Sanz-Blas, S. (2009) ‘The role of consumer innovativeness and perceived risk in online banking usage’, International Journal of Bank Marketing, Vol. 27, No. 1, pp.53–75. Mathieson, K. (1991) ‘Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior’, Information Systems Research, Vol. 2, No. 3, pp.173–191. McDonald, R.P. and Ho, M.H.R. (2002) ‘Principles and practice in reporting structural equation analyses’, Psychological Methods, Vol. 7, No. 1, p.64. McKnight, D.H. and Chervany, N.L. (2001) ‘Trust and distrust definitions: one bite at a time’, Trust in Cyber-Societies, pp.27–54, Springer, Berlin, Heidelberg. McKnight, D.H., Choudhury, V. and Kacmar, C. (2002) ‘Developing and validating trust measures for e-commerce: an integrative typology’, Information Systems Research, Vol. 13, No. 3, pp.334–359. McKnight, D.H., Cummings, L.L. and Chervany, N.L. (1998) ‘Initial trust formation in new organizational relationships’, Academy of Management Review, Vol. 23, No. 3, pp.473–490. McKnight, D.H., Kacmar, C.J. and Choudhury, V. (2004) ‘Dispositional trust and distrust distinctions in predicting high-and low-risk internet expert advice site perceptions’, E-Service, Vol. 3, No. 2, pp.35–58. Mohsin, M. (2009) Adoption of e-Banking in Context of a Developing Country, Pakistan, Unpublished PhD Dissertation [online] http://prr.hec.gov.pk/Thesis/1524S.pdf (accessed 25 March 2016). Molina, L.M., Lloréns-Montes, J. and Ruiz-Moreno, A. (2007) ‘Relationship between quality management practices and knowledge transfer’, Journal of Operations Management, Vol. 25, No. 3, pp.682–701. Mols, N. (1998) ‘The behavioral consequences of PC banking’, International Journal of Bank Marketing, Vol. 16, No. 5, pp.195–201. Nor, K.M. and Pearson, J.M. (2008) ‘An exploratory study into the adoption of internet banking in a developing country: Malaysia’, Journal of Internet Commerce, Vol. 7, No. 1, pp.29–73. Oliveira, T., Faria, M., Thomas, M.A. and Popovič, A. (2014) ‘Extending the understanding of mobile banking adoption: when UTAUT meets TTF and ITM’, International Journal of Information Management, Vol. 34, No. 5, pp.689–703. Pai, F.Y. and Huang, K.I. (2011) ‘Applying the technology acceptance model to the introduction of healthcare information systems’, Technological Forecasting and Social Change, Vol. 78, No. 4, pp.650–660. Park, E., Baek, S., Ohm, J. and Chang, H.J. (2014) ‘Determinants of player acceptance of mobile social network games: an application of extended technology acceptance model’, Telematics and Informatics, Vol. 31, No. 1, pp.3–15. Park, J., Lee, D., and Ahn, J. (2004) ‘Risk-focused e-commerce adoption model: a cross-country study’, Journal of Global Information Technology Management, Vol. 7, No. 2, pp.6–30. Pavlou, P. (2003), ‘Consumer acceptance of electronic commerce – integrating trust and risk in the technology acceptance model’, International Journal of Electronic Commerce, Spring 2003, Vol. 7, No. 3, pp.69–103. Internet banking in Pakistan 409 Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003) ‘Common method biases in behavioral research: a critical review of the literature and recommended remedies’, Journal of Applied Psychology, Vol. 88, No. 5, pp.879–903. Polatoglu, V. and Ekin, S. (2001) ‘An empirical investigation of the Turkish consumers’ acceptance of internet banking services’, International Journal of Bank Marketing, Vol. 19, No. 4, pp.156–165. Ratnasingham, P. (1998) ‘The importance of trust in electronic commerce’, Internet Research, Vol. 8, No. 4, pp.313–321. Rawashdeh, A. (2015) ‘Factors affecting adoption of internet banking in Jordan: chartered accountant’s perspective’, International Journal of Bank Marketing, Vol. 33, No. 4, pp.510–529. Raza, S.A. and Hanif, N. (2013) ‘Factors affecting internet banking adoption among internal and external customers: a case of Pakistan’, International Journal of Electronic Finance, Vol. 7, No. 1, pp.82–96. Richardson, H.A., Simmering, M.J. and Sturman, M.C. (2009) ‘A tale of three perspectives: examining post hoc statistical techniques for detection and correction of common method variance’, Organizational Research Methods, Vol. 12, No. 4, pp.762–800. Rimenschneider, C., Hardgrave, B.C. and Davis, F.D. (2002) ‘Explaining software developer acceptance of methodologies: a comparison of five theoretical models’, IEEE Transactions on Software Engineering, Vol. 28, No. 12, pp.1135–1145. Rogers, E.M. (1983) The Diffusion of Innovations, Free Press, New York. Sathye, M. (1999) ‘Adoption of internet banking by Australian consumers: an empirical investigation’, International Journal of Bank Marketing, Vol. 17, No. 7, pp.324–334. Schonemann, P.H. (1990) ‘Facts, fictions, and common-sense about factors and components’, Multivariate Behavioral Research, Vol. 25, No. 1, pp.47–51. Shapiro, S.P. (1987) ‘The social control of impersonal trust’, American Journal of Sociology, Vol. 93, No. 3, pp.623–658. Sharif, A.A. and Bukhari, S.W. (2014) ‘Determinants of brand equity of QMobile: a case study of Pakistan’, Journal of Management Sciences, Vol. 1, No. 1, pp.49–60. Sohail, S. and Shanmugham, B. (2003) ‘E-banking and customer preferences in Malaysia: an empirical investigation’, Information Sciences, Vol. 150, No. 3, pp.207–217. Steiger, J.H. (1990) ‘Structural model evaluation and modification: an interval estimation approach’, Multivariate Behavioral Research, Vol. 25, No. 2, pp.173–180. Steiger, J.H. (2007) ‘Understanding the limitations of global fit assessment in structural equation modeling’, Personality and Individual Differences, Vol. 42, No. 5, pp.893–898. Stone, R.N. and Grønhaug, K. (1993) ‘Perceived risk: further considerations for the marketing discipline’, European Journal of Marketing, Vol. 27, No. 3, pp.39–50. Suh, B. and Han, I. (2002) ‘Effect of trust on customer acceptance of internet banking’, Electronic Commerce Research and Applications, Vol. 1, No. 3, pp.247–263. Tabachnick, B.G. and Fidell, L.S. (2007) Using Multivariate Statistics, Harper and Row, New York. Tanaka, J.S. (1987) ‘How big is big enough?: Sample size and goodness of fit in structural equation models with latent variables’, Child Development, Vol. 58, No. 1, pp.134–146, doi:10.2307/1130296. Tansey, O. (2007) ‘Process tracing and elite interviewing: a case for non-probability sampling’, PS: Political Science & Politics, Vol. 40, No. 4, pp.765–772. Taylor, S. and Todd, P. (1995) ‘Understanding information technology usage: a test of competing models’, Information Systems Research, Vol. 6, No. 2, pp.144–176. Teo, T.S., Lim, V.K. and Lai, R.Y. (1999) ‘Intrinsic and extrinsic motivation in internet usage’, Omega, Vol. 27, No. 1, pp.25–37. 410 S. Afshan et al. Velicer, W.F. and Jackson, D.N. (1990) ‘Component analysis versus common factor-analysis some further observations’, Multivariate Behavioral Research, Vol. 25, No. 1, pp.97–114. Wallace, L.G. and Sheetz, S.D. (2014) ‘The adoption of software measures: a technology acceptance model (TAM) perspective’, Information & Management, Vol. 51, No. 2, pp.249–259. Wang, Y.S., Wang, Y.M., Lin, H.H. and Tang, T.I. (2003) ‘Determinants of user acceptance of internet banking: an empirical study’, International Journal of Service Industry Management, Vol. 14, No. 5, pp.501–519. Waseem, S.N., Frooghi, R. and Afshan, S. (2013) ‘Impact of human resource management practices on teachers’ performance: a mediating role of monitoring practices, Journal of Education and Social Sciences, Vol. 1, No. 2, pp.31–55. White, H. and Nteli, F. (2004) ‘Internet banking in the UK: why are there not more customers?’, Journal of Financial Services Marketing, Vol. 9, No. 1, pp.49–56. Williams, M.D., Dwivedi, Y.K., Lal, B. and Schwarz, A. (2009) ‘Contemporary trends and issues in IT adoption and diffusion research’, Journal of Information Technology, Vol. 24, No. 1, pp.1–10. Woszczynski, A.B. and Whitman, M.E. (2004) ‘The problem of common method variance in IS research’, in Woszczynski, A.B. and Whitman, M.E. (Eds.): The Handbook of Information Systems Research, pp.66–77, Hershey, Singapore. Wu, J.H. and Wang, S.C. (2005) ‘What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model’, Information & Management, Vol. 42, No. 5, pp.719–729. Xu, B., Shao, B., Lin, Z. and Shi, Y. (2009) ‘Enterprise adoption of internet banking in China’, Journal of Global Information Technology Management, Vol. 12, No. 3, pp.7–28. Yang, Z. and Minjoon, J. (2002) ‘Consumer perception of e-service quality: from internet purchaser and non-purchaser perspectives’, Journal of Business Strategies, Vol. 19, No. 1, pp.19–41. Yousafzai, S., Pallister, J. and Foxall, G. (2003) ‘A proposed model of e-trust for electronic banking’, Technovation, Vol. 23, No. 11, pp.847–860. Yu, P.L., Balaji, M.S. and Khong, K.W. (2015) ‘Building trust in internet banking: a trustworthiness perspective’, Industrial Management & Data Systems, Vol. 115, No. 2, pp.235–252. Zhou, T. (2011) ‘An empirical examination of initial trust in mobile banking’, Internet Research, Vol. 21, No. 5, pp.527–540. Zhou, T. (2012) ‘Understanding users’ initial trust in mobile banking: an elaboration likelihood perspective’, Computers in Human Behavior, Vol. 28, No. 4, pp.1518–1525.