Since the focus of the present research was on the end users of the technology offered by banks, i.e., customers, a decision to consider representative sample of the banking customers in India had been taken. Accordingly, multistage stratified sampling approach has been applied, wherein, all the twenty-nine states of the Indian Union had been ranked based on per capita net state domestic product (PCNSDP) at factor cost (constant prices) for the year 2012–13 (published by Central Statistical Office, GOI). Thereafter, all the states had been categorized into four categories with seven states each in the first three categories and eight states in the fourth category. From each category, three states had been chosen randomly out of which the city with highest gross domestic product, from each of the selected state, was chosen. Accordingly, 110 responses from each of the twelve selected cities, namely, Mumbai, Hyderabad, Lucknow, Delhi, Gurgaon, Bhopal, Jammu, Shimla, Bhubaneswar, Agartala, Imphal and Itanagar had been collected. The selection of the customers and banks was based on convenience sampling approach and the collection of responses was self-administered and personal interviews were conducted by one of the authors (i.e. corresponding author). While collecting responses, due diligence has been made to the fact that the responses of the customers, having their own active bank accounts for a period not less than six months should only be taken. Therefore, the questionnaire designed to serve the purpose of the present study has explicitly asked whether the respondent himself or herself is holding bank account and if yes, since how long the bank account is being operated by the respondent.

Accordingly, a total of 1320 number of responses of the customers had been collected during the period November 2013 to May 2014. Primarily, the entire data of 1320 respondents had been scrutinized against outliers, missing responses and incomplete responses, thereby, reducing the size of the useable data to 1201 responses.

Further, an outlook to the socio-economic characteristics of the sampled respondents has indicated that majority of the respondents were males with an average age of 34 years and average family income (monthly) equals to ₹ 1,43,273 (Indian Currency). Further, maximum number of the respondents was found to be either graduates or under-graduates and was related to the employees class.

The present primary data based study has been conducted through the development of a well-structured pretested questionnaire. The preliminary draft of questionnaire was pilot tested on 40 banking customers holding bank accounts in commercial banks (namely, State Bank of India, Axis Bank, HDFC Bank and Punjab National Bank) in Jammu and Delhi districts of India. On the basis of the responses, final draft of the questionnaire was prepared.

All the aforesaid constructs, namely, CV, PD, PU, PEOU, FC, Att and BI have been scrutinized against the normality assumptions. Accordingly, an outlook to the values of standard deviation, skewness and kurtosis have been found to establish data normality as all the values fall within the prescribed limit as suggested by Hair, Black, Babin, Anderson, and Tatham (2012), Curran, West, and Finch (1996) and West, Finch, and Curran (1995).

Further, internal consistency of all the aforementioned constructs has been assessed employing Cronbach's α statistics. Accordingly, the measured values of Cronbach's α are reported to fall in range between 0.860 to 0.972 for the constructs, namely, CV (i.e., FV, EV, EmV and SV), PD (optimism, innovativeness, risk taking propensity, habit, psychological resilience and social influence), PU, PEOU and BI which is above the prescribed limit of 0.70 as suggested by Hair et al. (2012), Tavakol and Dennick (2011) and Donio, Massari, and Passinate (2006). While, the constructs of self-efficacy, Att and FC have been purified on the basis of ‘if item deleted criterion’ and the resultant values are found to be above the prescribed norms. Also, the composite values for the construct, namely, PD and CV are found to be above 0.70, thereby, reinforcing internal consistency of these two constructs.

Further, the dimensionality of all the aforesaid constructs utilized in the development of ITA model has been examined employing Exploratory Factor Analysis (EFA) approach. Accordingly, the values of KMO statistics (ranging from 0.627 to 0.959) and Bartlett's test of sphericity (all values are statistically significant at 1 per cent level of significance) are found to meet the norms prescribed by Hair et al. (2012), Williams, Brown, and Onsman (2012) and Beaumont (2012), thereby, substantiating the statistical fitness of the data for employing EFA approach. Also, the values of communality statistics and factor loadings are found to meet the prescribed norms as suggested by Hair et al. (2012), i.e., value of each of the measures assessing the constructs, namely, PD, PU, PEOU, Att and BI are reported to be above 0.50. While in case of FC, value of communality statistics of a measure is found to be below the prescribed aforesaid limit, thereby, calling for refinement in FC inventory. Consequently, the said measure has been dropped from the inventory of FC. Thereafter again, EFA has been applied to the four-measure FC construct and all the four values of communality statistics are found to be above the prescribed norm of 0.50. Adding more, the application of EFA with principal component analysis and varimax rotation has yielded single factor solution in case of all the constructs, namely, PD, PU, PEOU, FC, Att and BI and the percentage of variance is found to fall in the range between 64.50 per cent to 86.84 per cent in case of all the aforesaid constructs.

Besides, all the constructs used in the present composition have also been scrutinized for analyzing their structural validity by employing Confirmatory Factor Analysis (CFA) approach. Accordingly, the results of construct validity and reliability are found to be in compliance with the prescribed norms of 0.50 and 0.70, respectively as suggested by Hair et al. (2012), Hu and Bentler (1999), and Lei and Wu (2007). Also, the results of model fit indices have confirmed structural validity of the constructs (refer Appendix II). Further, the correlation statistics among all the aforesaid constructs have also been examined in order to detect the problem of multicollinearity. Accordingly, all the correlation coefficients (refer Table 1) are found (ranging from 0.002 to 0.833) below the threshold limit of 0.85 as suggested by Zainudin (2015) and Lei and Wu (2007). Thus, no problem of multicollinearity has been expected among the constructs. Adding to the point, discriminant validity of all aforesaid constructs has also been examined. The same can be done in two ways. In this regards, Hammer et al. (2011) and Saade and Bahli (2005) have suggested that the values of squared inter-construct correlation should be compared with the values of Average Variance Extracted (AVE) and the latter values should be greater than the former values for establishing Discriminant Validity (DV) of the constructs. Also, Fornell and Larcker (1981) have suggested that the square root of AVE should be higher than the values of correlation coefficient for establishing discriminant validity of the constructs and the same method has been used to analyze discriminant validity in the present study. As shown in Table 1, all the values of square root of AVE (diagonal elements) are found to be greater than that of correlation coefficients (off-diagonal elements), thereby, establishing discriminant validity of the constructs.

The review of literature has depicted that the value perception of the customers depends on their technology adoption decision (reflected through BI and Att) which, in turn, depends on three categories of facets, namely, PD, technology attributes and FC. Based on the existing review of literature, ITA model has been theoretically framed and the same has been depicted in Fig. 1. Further, the theoretical ITA model has been empirically analyzed by employing structural equation modelling (SEM) approach, since this technique has been considered as one of the effective techniques for analyzing web of relationships between different variables simultaneously by drawing casual inferences (Hair et al., 2012; Lei & Wu, 2007). For same, $Z$-scores (where mean=1 and SD=1) of the aggregate of each of the constructs, namely, CV, PD, PU, PEOU, FC, Att and BI have been taken. Further, the socio-economic characteristics, namely, family income (FI) (monthly) and age have been assessed through $Z$-scores of the absolute values. While, educational qualification (Qual) of the customers has been assessed through dummy variable approach, wherein, 1 has been assigned to the category of the customer possessing qualification above graduation and 0 otherwise (i.e., qualification≤graduation).

Fig. 1.

Theoretical integrated technology adoption model.

(0.12MB).

Initially, the results of SEM have indicated statistically insignificant critical ratios in three cases, i.e., impact of PU on Att; impact of FC on Att and impact of FC on BI. This depicts that there exists no statistically significant path in these cases, thereby, indicating the need to modify the model. Further, model fit indices have also been examined in order to confirm the need for modifications in the model as recommended by Hair et al. (2012), Lei and Wu (2007), Hu and Bentler (1999) and Anderson and Gerbing (1984). The values of model fit indices, namely, GFI (0.963), AGFI (0.918) and CFI (0.962) are found to comply with the referential values. Whereas, the values of CMIN (χ2/df=10.377) and RMSEA (0.088) are reported to be above the minimum referential criterion values (Hair et al., 2012; Lei & Wu, 2007), thereby, substantiating the need for modifications in the model. Thus, the aforesaid three relationships have been dropped from the model. Thereafter again, results for the improved model have been estimated through SEM approach and critical ratios, parameter estimates & model fit indices have been examined. For the improved model, the values of critical ratios as well as parameter estimates are found to be statistically significant. But the values of model fit indices, i.e., CMIN and RMSEA are not registered to be in compliance with the referential criterion values. This signifies that the model still requires modifications for a good fit.

For the purpose of further modifications in the model, the criteria recommended by Hair et al. (2012), Lei and Wu (2007), Hu and Bentler (1999), and Anderson and Gerbing (1984) has been followed. The said criteria states that the theoretical background of the measures should be examined in order to explore the measures which may infer same meaning to the respondents and error terms of such measures should be co-varied if the same is supported by values of modification indices (in cases where the results of parameter estimates and CR are statistically significant). Accordingly, the error terms of age and FI have been found co-varied and again, SEM technique has been employed to analyze improvements in the model. After the said co-variation, the values of parameter estimates and critical ratios remain unaltered and unaffected. While the results of model fit indices indicate model fitness as all the values are found to meet referential criterion values, i.e., CMIN (χ2/df=4.54)<5; RMSEA (0.054)<0.06; GFI (0.979)≥0.85; AGFI (0.959)≥0.80 and CFI (0.984)≥0.90. Since all the above said values reflecting model fitness are found to meet the recommended criterion values, this further improved model has been considered as the statistically appropriate model and for this reason, it has been denoted as final ITA model in the present composition.

The results (refer Table 2) have depicted that value perception of the customers depends on PD, PU, PEOU and BI. Since all the probable facets (PD, PU, PEOU and BI) found to have significant impact on CV, H0.1 has been rejected and it has been inferred that CV has significant impact of the technology adoption behaviour of the customers. The significant positive coefficient values depict that increase in PD, PU, PEOU and BI would lead towards enhancing CV. Further, the value of squared multiple correlation, which explains total variation in the explained variable due to the explanatory variables, indicates that jointly, PD, PU, PEOU and BI accounts for 78 per cent variation in CV (refer Table 2). Indeed, the magnitude of relationships (values of direct estimates) has uncovered BI (β=0.5000; p<0.01) as the strongest predictor of CV followed by PD (β=0.2410; p<0.01), PU (β=0.1770; p<0.01) and PEOU (β=0.0890; p<0.01). These results implicate that the value perception of the customers, regarding the latest banking technologies, depends primarily on their behavioural intentions towards technology adoption followed by their personal disposition towards technology adoption. Further, belief of the customers regarding the usefulness of the latest banking technological developments in serving their banking needs and the effortlessness involved in operating the latest banking technologies are also found to affect value perception of the customers regarding the use of latest banking technological developments.

Not only direct, PD, PU and PEOU also exhibit statistically significant indirect and total effect on CV. The statistically significant values of the indirect path from PEOU to CV, i.e., PEOU>BI>CV (0.076); PEOU>PU>Att>BI>CV (0.00030); PEOU>PU>Att>CV (0.00022); PEOU>PU>BI>CV (0.01150) uncover PU, BI and Att as significant mediating factors affecting the said relationship. But the examination of the four indirect paths depicts the path, i.e., PEOU>BI>CV as the most effective path (as indicated by relatively higher value of indirect estimate=0.0760). Likewise, the relationship between PU and CV is found to be significantly mediated by Att and BI. Two indirect paths from PU to CV, i.e., PU>Att>BI>CV (0.00034); PU>BI>CV (0.13183) have been identified, wherein, the latter path divulges as more effective path (reflected from relatively higher value of indirect effect in the case of latter path=0.1318). This describes BI not only as significant direct determinant of CV but also a significant mediator that affects the relationship of CV with PU and PEOU. From these results, it can be inferred that the customers, perceiving the use of the technology beneficial and hassle free, develop favourable intentions towards technology adoption which ultimately shape their value perceptions regarding the latest banking technological developments.

Further, PD has also been estimated to demonstrate statistically significant indirect and total effect on the assessment of the customers regarding the utility obtained from using the latest banking technological developments. At disaggregate level, the results have highlighted seven indirect paths from PD to CV, i.e., PD>PU>Attitude>BI>CV (0.00002); PD>PU>Attitude>CV (0.00017); PD>PU>BI>CV (0.00092); PD>PEOU>PU>Att>BI>CV (0.00017); PD>PEOU>PU>BI>CV (0.06690); PD>PEOU>CV (0.13690); PD>PEOU>BI>CV (0.04413). Among these seven paths, the most effective path identified is PD>PEOU>CV (based on the relatively higher value of indirect effect in this case. i.e., 0.1369). It indicates that as PD enhances, customers start perceiving technology easy to use, which in turn, enhances their value perception regarding the latest banking technological developments. Besides, at aggregate level, i.e., considering total indirect effects (refer Table 2), the statistically significant results unveil PU, PEOU, Att and BI as significant mediators in the relationship between PD and CV.

Alongwith exploring value perception of the customers towards technology, the phenomenon of technology adoption decision of the customers has also been examined in the present study. While analyzing antecedents of BI, PD (0.397; p>0.05) has been highlighted as the strongest predictor of BI followed by PU, PEOU and Att. These results indicate that the customers with favourable personal disposition towards technology adoption and having belief that the technology is useful and easy to use, develop willingness towards technology adoption. Further, FC has been found to exhibit statistically significant indirect impact on BI enrouted through PU, PEOU and Att. Adding more, it has also been found that PD, PU, PEOU and Att accounts for 56 per cent variation in BI. Further, a glance at the model reflects PEOU (0.878; p>0.01) as stronger antecedent of PU followed by PD. The positive correlation coefficient values indicate that when customers perceive the technology is easy to use and develop personal disposition towards technology adoption, they tend to believe that the technology is useful in serving their banking needs. While analyzing antecedents of PEOU, both PD and FC are found to account for 33 per cent variation in PEOU with PD (β=0.5770) being unveiled as stronger predictor than FC (β=0.0520) and both the aforesaid antecedents exhibit statistically significant direct impacts on PEOU. The results depicted that the presence of FC, such as technical support, manual support, etc. facilitate customers in operating the technology owing to which the use of the technology will no longer remains difficult for the customers. Further, it has also been found that the customers with favourable PD tend to learn hard regarding the new and improved technologies which, in turn, make the use of the technology hassle free for them, thereby, explaining the rationale for the significant relationship between PEOU and PD.

Further, the ITA model has also highlighted statistically significant direct as well as indirect impact of PD and PEOU on Att. Indeed, PD (β=0.3110; p<0.01) has been divulged as stronger antecedent of Att in comparison with PD. It seems that the personal disposition of the customers towards technology adoption and the ease with which technology can be operated affect the belief of the customers regarding the technology. Moreover, PU and FC have shown statistically insignificant direct but statistically significant indirect and total effect on the attitude of the customers regarding technology and its outcome. Also, it has been found that PD and PEOU accounts for 14 per cent variation in Att. Furthermore, Age, FI and Qual have been found to exhibit statistically significant impact on PD. The negative coefficient value depicting relationship between PD and Age concludes that as the age of the customers increases their disposition towards technology adoption tends to decrease. Further, positive coefficient values depicting relationship of PD with FI and Qual confirm that PD enhances with advancement in the family income and qualification of the customers. Indeed, FI has been highlighted as the stronger predictor of PD (0.349; p>0.01) than Qual (0.077; p>0.01). Besides, it has been found that as the qualification of the customers enhance, their PD also upgrade. Further, age, FI and Qual have been found to account for 6 per cent variation in PD.