Literature Review
State Transport Undertakings
Since independence, the Indian bus industry has grown exponentially and dominated the transport industry. State Transport Undertakings (STUs), which are groups of companies owned by the public, dominate the bus transport industry. The rapid growth of the bust industry is evident as the number of buses has increased at the growth rate of 6.8% from 56,800 in 1961 to approximately 1.887 million in 2014 (Singh & Jha, 2017). STUs has dominated the bus industry and remained more competitive due to its organisation when compared to disjointed private companies. The analysis of performance indicates that STUs owns 137,000 buses and employs 700,000 workers, which have generated 15 billion bus-kilometres, 500 billion passenger-kilometres and transported 23 billion passengers (Singh & Jha, 2017). These figures demonstrate that STUs play a significant role in the bus transport industry in India. However, despite its dominance and competitiveness in the bust transport industry, STUs face numerous challenges that have made it decrease in efficiency, effectiveness and profitability.
Profitability Factor
Profitability is a key performance gage in various organisations, including companies in the transport industry. In a study to determine factors that influence profitability in the transport industry, Saripalle (2018) established that age, market share, liquidity and debt-equity ratio are marked predictors of profitability. Organisations that have existed for decades with large market share, high level of liquidity and low debt-equity ratio tend to perform better than recent companies. The analysis of the performance of STUs in eight cities in India revealed that swelling costs of inputs and declining productivity are two significant factors that have contributed to persistence occurrence of losses (Fatima & Kumar, 2014). The costs of labour, fuel and materials have increased considerably in the past 30 decades, resulting in diminished profits of STUs. Given that costs determine the profitability of transport companies, the application of an appropriate costing mechanism is critical. The use of activity-based costing method in the urban mass transport is relevant because it provides an accurate determination of unit costs, establishes resource allocation method and eliminates haphazard estimation of overheads (Popesko, Zamecnik, & Kolkova, 2016; Dwivedi & Chakraborty, 2016). The increasing costs of labour, fuel and other materials need accurate costing to enable STUs to manage their operations profitably and sustainably.
Efficiency and Effectiveness Factors
Inefficiencies and ineffectiveness have made STUs incur huge financial losses. Current status shows that STUs do not meet the increasing demand, offer diminishing quality of services and experience decline in market share (Singh, 2017; Siddiquei, 2018). Efficiency and effectiveness of STUs vary from one city to another, depending on the management strategies and extraneous forces. In a comparative study of performance across Indian cities, Fatima and Kumar (2014) report that inefficiency and ineffectiveness have decreased productivities of bus, labour and fuel in the public transport system. A further comparative study by Singh and Jha (2017) found that STUs operating in Andrah Pradesh are more efficient and effective than those operating in West Bengal. Characteristics of operations and management strategies determine the effectiveness and efficiency of organisations in performing their functions. In a critical analysis, Singh (2017) noted that lack of promotions based on merit, the existence of a bureaucratic process in cash utilisation, the use of a complex system of accounting and slow procurement process are some of the factors that reduce effectiveness and efficiency of STUs.
Data Envelopment Analysis
The theoretical model of data envelopment analysis (DEA) has proved to be the most reliable method of assessing the efficiency and effectiveness of decision-making units (DMUs). DEA utilises a linear programming approach that allows the determination of the influence of several inputs on numerous outputs (Mehdiloozad, Mirdehghan, Sahoo, & Roshdi, 2015; Park, Simar, & Zelenyuk, 2015). In the analysis of performance, DEA rates efficiency and effectiveness of DMUs for organisations to make optimal decisions regarding their operations. Since its inception, DEA has undergone numerous improvements to fit into complex systems in different organisations and industries (Mariz, Almeida, & Aloise, 2015). Therefore, the application of DEA in the assessment of the effectiveness and efficiency of STUs generates accurate and valid findings.
Methodology
Research Design
The study will employ a survey research design because it seeks to use primary and secondary data from STUs. According to Saunders, Lewis and Thornhill (2015), a survey research design is valuable because it promotes representation of the population, limits researchers’ bias, eases data collection and increases the validity of findings. The study will obtain secondary data from the databook from the Central Institute of Road Transport (CIRT) named “State Transport Undertaking Profile and Performance” and primary data from sampled employees of STUs.
Population and Sampling
The target population comprises of the employees of STUs. Out of 54 STUs in India, the study will use a stratified sampling technique to select 10 STUs. Moreover, the study will employ the purposive sampling method to select 30 employees from each STU to represent the target population. The purposive sampling method allows researchers to choose respondents with desired attributes (Bryman & Bell, 2015). In this case, the study will select managers, operators and commuters because they possess specific information. Table 1 below shows selected STUs and summarises the distribution of respondents sampled.
Table 1. Distribution of Samples.
Data Collection Methods
The study will collect data using a questionnaire created to target managers, operators and commuters of STUs. The questionnaire will have both open- and closed-ended questions, and its internal consistency will be tested using test-retest and Cronbach’s alpha with a threshold of 0.7 (Sekaran & Bougie, 2016). During data collection, researchers will seek permission from the management of STUs and administer written informed consent to individual participants. Researchers will use emails and online survey instruments in the distribution of questionnaires and collection of data.
Data Envelopment Analysis Modelling
The study will use DEA in the analysis of the collected data to determine the effectiveness, efficiency and profitability of STUs in India based on the selected 10 STUs and 300 respondents. Specifically, the study will apply a three-stage process of DEA to determine effectiveness, efficiency and profitability of STUs (Lotfi & Jahanbakhsh, 2015). The analysis of the three-stage process reveals that there are three sub-DMUs inputs and three sub-DMUs outputs for each of the 10 DMUs representing 10 STUs that operate in India. The three-stage model of DEA lacks intermediate phases of production and allows concurrent determination of efficiency, effectiveness and profitability (Liu, 2017; Lotfi & Jahanbakhsh, 2015). The first stage comprises of capital, fuel, buses and employees, forming the four inputs that determine the efficiency of STUs. The outputs of the first stage form inputs of the second stage that measure the effectiveness of STUs. The outputs of the second stage constitute inputs of the third stage, which assess the profitability of STUs, as shown in figure 1.
References
Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). New York, NY: Cambridge.
Dwivedi, R., & Chakraborty, S. (2016). Adoption of an activity-based costing model in an Indian steel plant. Business: Theory and Practice, 17(4), 289-298. Web.
Fatima, E., & Kumar, R. (2014). Introduction of public bus transit in Indian cities. International Journal of Sustainable Built Environment, 3(1), 27-34. Web.
Liu, H. (2017). Applying three-stage DEA on the operational performance of foreign banks in Taiwan. International Review of Applied Economics, 32(1), 104-118. Web.
Lotfi, F. H., & Jahanbakhsh, M. (2015). Assess the efficiency and effectiveness simultaneously in a three-stage process, by using a unified model. International Journal of Research in Industrial Engineering, 4(1-4), 15-23.
Mariz, F. B. A. R., Almeida, M., & Aloise, D. (2015). A review of Dynamic Data Envelopment Analysis: State of the art and applications. Journal of Productivity Analysis, 43(2), 199-214. Web.
Mehdiloozad, M., Mirdehghan, S. M., Sahoo, B. K., & Roshdi, I. (2015). On the identification of the global reference set in data envelopment analysis. European Journal of Operational Research, 245(3), 779-788. Web.
Park, B., Simar, L., & Zelenyuk, V. (2015). Categorical data in local maximum likelihood: Theory and applications to productivity analysis. Journal of Productivity Analysis, 43(2), 199-214. Web.
Popesko, B., Zamecnik, R., & Kolkova, A. (2016). Profitability analysis of urban mass transport lines using activity-based costing method: An evidence from the Czech Republic. Research and Design of Economy, 14(3), 335-344. Web.
Saripalle, M. (2018). Determinants of profitability in the Indian logistics industry. International Journal of Logistics Economics and Globalisation, 7(1), 13-27. Web.
Saunders, M., Lewis, P., & Thornhill, A. (2015). Research methods for business students. Harlow, England: Pearson Education.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Chichester, England: John Wiley & Sons.
Siddiquei, M. I. (2018). Service quality specification gap in public transport service: A case study of Uttar Pradesh State Road Transport Corporation. International Journal of Management Studies, 3(2), 1-9. Web.
Singh, S. K. (2017). State Transport Undertakings in India: Status and issues. International Journal of Applied Business and Economic Research, 15, 279-297.
Singh, S. K., & Jha, A. P. (2017). Efficiency and effectiveness of State Transport Undertakings in India: A DEA approach. Theoretical Economics Letters, 7(06), 1646-1659. Web.