PurposeThis paper examines the impacts of technical efficiency and intellectual capital efficiency (ICE) on bank performance in India after controlling other bank-, industry-specific and macroeconomic variables.Design/methodology/approachThe authors use secondary data on listed Indian commercial banks for the period 2005–2018. The authors use data envelopment analysis (DEA) technique-based Malmquist index (MI) to obtain technical efficiency and value-added intellectual coefficient (VAIC) model for computing ICE. System generalized method of moments (GMM) (SGMM) model in a dynamic framework is used to estimate the parameters, which takes into consideration issues of endogeneity, heterogeneity and persistence of bank performance. Further, the authors use quantile regression model to examine whether the impacts of covariates are homogeneous at different locations of the conditional distribution of bank performance.FindingsThe authors find positive impact of technical efficiency and negative influence of market concentration on bank performance. The results of the study support the efficient structure (ES) hypothesis (ESH). The authors observe positive influence of intellectual capital (IC) on bank performance, which indicates the relevance of intellectual resources in enhancing banks' value. Further, the results of quantile regression indicate that the impacts of technical efficiency and ICE are more pronounced at higher quantiles of the conditional distribution of bank performance.Originality/valueThis paper in the Indian context examines the influences of technical efficiency and ICE after controlling bank-, industry-specific and macroeconomic factors.

This study aims at measuring the technical efficiency of banks in India and examining its determinants. Efficiency is said to be achieved if a bank is able to maximise its output subject to limited inputs. To obtain technical efficiency score, input-oriented Malmquist Data Envelopment Analysis is applied on two outputs and three input variables, based on a VRS (variable returns to scale) assumption. Three foreign banks—namely, A B Bank Ltd, Bank of Ceylon, and Citibank N A—and two Indian banks—namely, HDFC Bank and State Bank of India—are found to be most efficient during the study period. The efficiency scores when subsequently used as the dependent variable along with independent variables—bank size, capitalisation, liquidity risk, returns on assets, interest rate, credit risk, market concentration and gross domestic product (GDP)—in a panel regression analysis found the fixed effect model to be more appropriate in explaining the determinants. The results reveal that liquidity risk, returns on assets, credit risk, market concentration and GDP have a significant effect on the technical efficiency, while banks size, interest rate and level of capitalisation are found to be insignificant variables. JEL Classification: G21, C13, C60

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This study examines whether the impact of annual report readability on agency cost varies with firm size among listed Indian non-financial firms. For this study, we have selected two cross-sectional samples comprising 360 non-financial firms listed on National Stock Exchange (NSE)—divided into 183 small firms and 177 large firms—for the financial year 2019–2020 and 2020–2021. The classification between large and small firms is drawn based on the respective quartile values of total assets and market capitalization taken together. The proxy for agency cost is the natural logarithm of selling and distribution expenses. We have developed an index using the count of characters, words, lines, and pages of the annual report to measure readability. To investigate the association between the annual report readability, agency cost, and firm size, we use the OLS methodology. In our model, we control for fixed assets, leverage, ownership concentration, institutional ownership, the board size, and board independence. Our findings reflect an inverse relationship between readability and agency cost. Additionally, we find that the effect of annual report readability on agency cost is higher for large firms than for smaller firms. We further checked our findings’ robustness using a logistic regression model and found similar results.