4. Results and Discussion
The general characteristics pertaining to explanatory variables for the banks under study one-year prior to their adoption the social media are presented in Table 2. As expected, the average value of the most of the explanatory variables seems to be different for banks with Facebook adopted and not adopted.
4.1 Survival analysis- Results
Kaplan-Meier Nonparametric-Survival Model:
In the survival analysis our failure event is the adoption of social media i.e., joining Facebook. As on March 2016, 31 Indian banks has joined Facebook. Six public sector bank are yet to join the Facebook. The Kaplan-Meier median survival time is estimated to be 39 months (95% CI: 28 – 52 months) to join the Facebook by the bank. The …show more content…
It measures how well the estimated model fits the data. β is the coefficient of independent variables. S.E. is the standard error of the β.
By using the Cox’s proportional hazard regression model, empirical findings suggest that the size of the bank is negatively affect joining of the Facebook by the bank which means that medium/small size banks tends to adopt more quickly than large banks. This indicates, medium size banks are faster than large size banks in adopting social media, as they are not keen on investment in expensive physical infrastructure, rather they will rely on investment in relevant financial innovations (Corrocher, 2006). The coefficient of noninterest income is significant and positive, suggests that banks with high share of noninterest income are inclined to join Facebook. Further, the coefficient of intermediation cost is significant and positive, which confirms that efficient banks tends to invest in new technological innovation even though it may increase operating cost in the short term. In general, efficient banks have the relatively higher productivity and they can offer to invent in new technological …show more content…
The noninterest income and intermediation cost are positively correlated to joining the Facebook by the bank. Across the model I to V, nonperforming assets (NPA) is significantly and negatively associated with joining Facebook by the banks. In the present macroeconomic environment, generally, public sector banks are having higher NPAs and that might forcing them to not to invest in new technological innovations. However, in multivariate model size of the bank and profitability are not associated with the adoption of new innovations by the
The general characteristics pertaining to explanatory variables for the banks under study one-year prior to their adoption the social media are presented in Table 2. As expected, the average value of the most of the explanatory variables seems to be different for banks with Facebook adopted and not adopted.
4.1 Survival analysis- Results
Kaplan-Meier Nonparametric-Survival Model:
In the survival analysis our failure event is the adoption of social media i.e., joining Facebook. As on March 2016, 31 Indian banks has joined Facebook. Six public sector bank are yet to join the Facebook. The Kaplan-Meier median survival time is estimated to be 39 months (95% CI: 28 – 52 months) to join the Facebook by the bank. The …show more content…
It measures how well the estimated model fits the data. β is the coefficient of independent variables. S.E. is the standard error of the β.
By using the Cox’s proportional hazard regression model, empirical findings suggest that the size of the bank is negatively affect joining of the Facebook by the bank which means that medium/small size banks tends to adopt more quickly than large banks. This indicates, medium size banks are faster than large size banks in adopting social media, as they are not keen on investment in expensive physical infrastructure, rather they will rely on investment in relevant financial innovations (Corrocher, 2006). The coefficient of noninterest income is significant and positive, suggests that banks with high share of noninterest income are inclined to join Facebook. Further, the coefficient of intermediation cost is significant and positive, which confirms that efficient banks tends to invest in new technological innovation even though it may increase operating cost in the short term. In general, efficient banks have the relatively higher productivity and they can offer to invent in new technological …show more content…
The noninterest income and intermediation cost are positively correlated to joining the Facebook by the bank. Across the model I to V, nonperforming assets (NPA) is significantly and negatively associated with joining Facebook by the banks. In the present macroeconomic environment, generally, public sector banks are having higher NPAs and that might forcing them to not to invest in new technological innovations. However, in multivariate model size of the bank and profitability are not associated with the adoption of new innovations by the