Performance Analysis of the Indian IT & ITeS Sector: An Application of Additive-DEA and G2SLS

Authors

DOI:

https://doi.org/10.17015/ejbe.2024.034.04

Keywords:

Additive-DEA; BSC-DEA; IT&ITeS; India; Efficiency.

Abstract

This study uses an integrated balanced scorecard-based Additive-DEA framework to identify proxy variables for the inputs and outputs for a sample of firms in India’s information technology and information technology-enabled services sector to identify and analyse these firms’ inefficiencies. The additive-DEA model is used because it is invariant to data translation, in addition to being non-radial and nonoriented, and hence can deal with negative values of variables that are critical to analyse in(efficiency). This is the first such study in the Indian context that focuses on dealing with negative values for earnings as one of the output variables. The results show that high-performing firms, as calculated by the Additive-DEA method, have higher financial gains in terms of revenue, earnings, and return on equity. Further, the study also attempts to explain the factors influencing the firms’ performance
using a regression framework for which a generalised two-stage least square method is used. The regression results show that firm characteristics like age, industry specialisation, and business type have no influence on firm performance, while factors like exports, exchange rate changes, and market focus impact its performance. These results have critical policy implications for this sector to reduce inefficiency by controlling costs and increasing spending on research and development.

References

Asosheh, A., Nalchigar, S., & Pour, M. J. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37(8), 5931-5938. https://doi.org/10.1016/j.eswa.2010.02.012.

Babakus, E., Bienstock, C. C., & Van Scotter, J. R. (2004). Linking perceived quality and customer satisfaction to store traffic and revenue growth. Decision Sciences, 35(4), 713-737. https://doi.org/10.1111/j.1540-5915.2004.02671.x

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078

Baumgarten, D., Bonenkamp, U., & Homburg, C. (2010). The Information Content of the SG&A Ratio. Journal of Management Accounting Research, 22(1), 1-22. https://doi.org/10.2308/jmar.2010.22.1.1

Bhat, N. A., & Kaur, S. (2019). Decomposition of Total Factor Productivity (TFP) of Indian software industry. Indian Journal of Economics and Development, 7(2), 1-9.

Binh, Q. M., & Tung, L. T. (2020). The Effect of R&D Expenditure on Firm Output: Empirical Evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 7(6), 379–385. https://doi.org/10.13106/JAFEB.2020.VOL7.NO6.379

Bisgaard, S., & Freiesleben, J. (2004). Six Sigma and the bottom line. Quality Progress, 37(9), 57.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8

Cooper, W. W., Seiford, L. M., & Tonne, K. (2007). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. (2/e ed.). New York: Springer. https://doi.org/10.1007/978-0-387-45283-8

Das, P. (2017). An evaluation of the Determinants of Total Factor Productivity Growth in Indian Information Technology Industry: An Application of DEA-based Malmquist Index. Central European Review of Economics and Management, 1(4), 175-224. https://doi.org/10.29015/cerem.566

Das, P., & Datta, A. (2017). Performance Evaluation of Indian Information Technology-enabled Services (ITeS) Industry: An Application of Two-Stage Data Envelopment Analysis. International Journal of Advances in Management and Economics, 6 (2), 52-70.

Davidson, R., & MacKinnon, J. (1993). Estimation and inference in econometrics. New York: Oxford.

DPIIT (2022). FDI Statistics, Department for Promotion of Industry and Internal Trade, Government of India October – December 2022, https://dpiit.gov.in/publications/fdi-statistics

Edvardsson, B., Johnson, M. D., Gustafsson, A., & Strandvik, T. (2000). The effects of satisfaction and loyalty on profits and growth: Products versus services. Total Quality Management, 11(7), 917-927. https://doi.org/10.1080/09544120050135461

Emrouznejad, A., & Yang, G.-l. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978 - 2016. Socio-Economic Planning Sciences, 61, 4-8. https://doi.org/10.1016/j.seps.2017.01.008

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A (General), 120(3), 253-281. https://doi.org/10.2307/2343100

Harry, M. J. (1998). Six Sigma: A breakthrough strategy for profitability. Quality Progress, 31(5), 60-64.

Kadarova, J., Durkáčová, M., Teplická, K., & Kádár, G. (2015). The Proposal of an Innovative Integrated BSC – DEA Model. Procedia Economics and Finance, 23, 1503-1508. https://doi.org/10.1016/S2212-5671(15)00375-5

Kaplan, R., & Norton, D. (1996). The Balanced Scorecard: Translating Strategy into Action. Boston, Harvard Business Review Press. https://doi.org/10.2307/41165876

Khezrimotlagh, D., & Chen, Y. (2018). Decision Making and Performance Evaluation Using Data Envelopment Analysis. International Series in Operations Research & Management Science, Volume 269. Springer International Publishing AG, Switzerland. Kočišová, K., Hass-Symotiuk, M., & Kludacz-Alessandri, M. (2018). Use of the DEA method to verify the performance model for hospitals. E+M Ekonomie a Management. 21. 125-140. https://doi.org/10.15240/tul/001/2018-4-009.

Kumar, S., & Arora, N. (2012). Evaluation of Technical Efficiency in Indian Sugar Industry: An Application of Full Cumulative Data Envelopment Analysis. Eurasian Journal of Business and Economics, 5(9), 57-78. https://ejbe.org/index.php/EJBE/article/view/63

Kumar, S., & Gulati, R. (2019). An Examination of Technical, Pure Technical, and Scale Efficiencies in Indian Public Sector Banks using Data Envelopment Analysis. Eurasian Journal of Business and Economics, 1(2), 33-69. https://ejbe.org/index.php/EJBE/article/view/11

Mahanti, R., & Antony, J. (2009). Six Sigma in the Indian software industry: some observations and results from a pilot survey. The TQM Journal, 21(6), 549-564. https://doi.org/10.1108/17542730910995837

Mathur, S. (2007). Indian IT and ICT industry: A performance analysis using data envelopment analysis and Malmquist index. Global Economy Journal, 7(2). https://doi.org/10.2202/1524-5861.1259

Mehta, H. (2022). Maverick Effect: The Inside Story of India’s IT Revolution. Harper Business, HarperCollins Publishers; Gurugram, Haryana, India.

Melitz, M. (2000). Firm Productivity Estimation in Differentiated Product Industries. Working Paper 14404. Harvard University.

Murthy, N. R. (2011). The Indian Software Industry: Past, Present and Future. In M. Pai, & R. K. Shyamasundar (Eds), Homi Bhabha and the Computer Revolution. New Delhi: Oxford University Press.

Oberholzer, M. (2014). A model to estimate firms accounting-based performance: a data envelopment approach. International Business & Economics Research Journal, 13(6), 1301-1314. https://doi.org/10.19030/iber.v13i6.8921

Öztürk, E., & Zeren, F. (2015). The impact of R&D expenditure on firm performance in manufacturing industry: further evidence from Turkey. International Journal of Economics and Research, 6(2), 32-36.

Pandit, S., Wasley, C. E., & Zach, T. (2011). The Effect of Research and Development (R&D) Inputs and Outputs on the Relation between the Uncertainty of Future Operating Performance and R&D Expenditures. Journal of Accounting, Auditing & Finance, 26(1), 121–144. https://doi.org/10.1177/0148558X11400583

Sabnavis, M., & Unwalla, V. M. (2020). IT-BPM Industry Update. Care Ratings. https://www.careratings.com/uploads/newsfiles/IT-BPM%20Industry%20Update.pdf

Sahoo, B. K., & Nauriyal, D. K. (2013). Technical Efficiency and Total Factor Productivity of the Software Industry in India: An Empirical Analysis. The Indian Economic Journal, 61(2), 227--254. https://doi.org/10.1177/0019466220130205

Statista. (2022). Share of Information technology/business process management sector in the GDP of India from financial year 2009 to 2022. https://www.statista.com/statistics/320776/contribution-of-indian-it-industry-to-india-s-gdp/

Statista. (2023). Share of Indian IT industry in global IT spend from financial year 2001 to 2020, with an estimate for 2021. https://www.statista.com/statistics/1188848/india-share-of-domestic-it-industry-in-global-it-spend/

Ullah, S., Zaefarian, G., & Ullah, F. (2021). How to use instrumental variables in addressing endogeneity? A step-by-step procedure for non-specialists. Industrial Marketing Management, 96, A1-A6. https://doi.org/10.1016/j.indmarman.2020.03.006

Published

30-11-2024

How to Cite

MUKHERJEE, S., AJAZ, T., & GHOSH, T. P. . (2024). Performance Analysis of the Indian IT & ITeS Sector: An Application of Additive-DEA and G2SLS. Eurasian Journal of Business and Economics, 17(34), 65-92. https://doi.org/10.17015/ejbe.2024.034.04

Issue

Section

Articles