Purpose This paper aims to examine the determinants of the dividend policy of the construction companies in India. Design/methodology/approach Data from 2011 to 2016 (six years) of 45 listed construction companies in India are collected, and a strong balanced panel is created. Dividend per share is dependent variable, and profitability, unstable earnings, institutional holding, cash flow, tangibility, liquidity, growth opportunities, age of the firm, life cycle, leverage, size of firm and taxation are explanatory variables. The panel is tested for stationarity and finally fixed and random-effect panel regression model with robust estimation option is performed. Findings The random effect model is found fit with an R2 of 62 per cent, and profitability, life cycle and size of the firm show a significant positive effect on dividend payment. Cash flow shows a negative significant relationship, indicating the presence of agency problem. Rest of the variables indicated an insignificant relationship. Research limitations/implications The study is carried out on a small sample of 45 companies with data of only six years. Further, there may be behavioral and psychological factors that drive the decision to declare dividend. Those factors have not been considered in present study. Despite considerable efforts, the author could not find more studies specific to the construction sector. Hence, the variables identified in the present study are more generic, even though a few sector-specific studies have been included. Originality/value The dividend policy determinants for the construction sector in India are investigated, and a comprehensive model based on 12 explanatory variables is tested to find the drivers of dividend payout in Indian construction companies. From the investor’s point of view, the sector has immense potential in terms of dividend as well as capital appreciation. Therefore, the study can be useful to the investors to understand the drivers of dividend payout in the construction sector. It can also be crucial for companies to create an appropriate dividend policy so as to attract and retain investors. The study contributes significantly to the existing body of knowledge by recommending the salient drivers of dividend payout in the construction sector based on a comprehensive dataset and using robust methodology.

PurposeThis study aims to investigate whether intellectual capital (IC) and its subcomponents enhance value and improve the profitability of real estate (RE) and infrastructure (INF) firms in India. In this study, IC is measured through the value-added intellectual coefficient (VAIC) model. The study further extends the VAIC model by incorporating an additional component of social welfare efficiency (SWE).Design/methodology/approachThe study uses the panel data investigation based on the data of 63 firms (22 RE and 41 INF firms), for a period of 10 years (2008–2017). The dependent variables in the study are return on assets (ROA) and market price to book value ratio (PB), whereas the independent variables are VAIC and its components. The panel is tested for stationarity, heteroscedasticity and multicollinearity problems. Finally, to account for heteroscedasticity and endogeneity, Arellano and Bond's (1991) panel regression estimator with robust estimates are used.FindingsThe findings of the study suggest that IC has a significant influence on the profitability and value of infra firms, whereas capital-employed efficiency (CEE) positively affects the profitability of both RE and INF firms.Originality/valueThe study is an attempt to find the effect of IC and its components on profitability and value of RE and INF firms in India. The author has also extended the VAIC model, which was introduced by Pulic (2000), by adding an additional IC component, i.e. SWE. The study uses Arellano and Bond's (1991) panel regression estimator with robust estimates, which helps produce robust results.

Purpose This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms. Design/methodology/approach The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity. Findings The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC. Originality/value The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.

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PurposeThe paper aims to investigate the effect of firm age and size on profitability and productivity of construction firms in India. It also attempts to understand the indirect effect of firm age and size on profitability mediated through firm's productivity.Design/methodology/approachData of 64 construction firms, for a period of 12 years (2006–2017), were collected. In order to measure the direct and indirect effect of size and age on profitability and productivity, a structural equation model was developed. In the structural models, productivity is a latent variable measured through proxies of material productivity (MP), labor productivity (LP) and equipment productivity (EP). The profitability is measured using three financial ratios: return on asset (ROA), return on capital employed (ROCE) and return on net worth (RONW). Then the direct and indirect effect of age and size is measured on ROA, ROCE, RONW and productivity.FindingsThe findings of the study suggest that age has a direct negative effect on profitability; however, it has an indirect positive effect on profitability, which is mediated by firm's productivity. This positive indirect effect compensates the direct negative effect and leads to an overall positive effect of firm age on profitability. However, firm size shows no effect on profitability and productivity.Originality/valueTo the best of authors’ knowledge, the study is the first attempt to measure the indirect effect of age and size on profitability, mediated through productivity. The study also examines the interrelationship among firms’ profitability and productivity and bridges an important research gap. The study proposes an integrated theoretical framework with a clear view of the interrelationships among age, size, profitability and productivity for construction firms in India, which can be further tested and validated for generalization.