Background: High-tech's rapid growth and prioritization of expansion over profitability can lead to vulnerability in economic downturns. The SaaS market, a part of the high-tech industry, offers affordable and flexible software solutions but is also susceptible to market volatility. To succeed, SaaS startups must strike a balance between growth and profitability. Stochastic frontier analysis can measure technical efficiency and productivity in the SaaS market, offering insights into resource and labor utilization. We present an empirical study that explores factors that influence a firm's profitability, aiming to inform decision-making for SaaS companies.

Purpose: Our academic work is centered around gaining a comprehensive understanding of the Software-as-a-Service (SaaS) market and the role of labor and research and development expenses toexplore these factors and their influence on a firm's profitability. This study seeks to address this gap in knowledge by conducting an empirical analysis to examine the technical efficiency distribution among SaaS firms, with the aim of gaining insights into resource and labor utilization. By analyzing technical efficiency distribution among SaaS firms, the study will provide insights into resource and labor utilization and its effect on profitability. The research questions will focus on the relationship between technical efficiency, labor utilization, and production functions on profitability.

Methodology: We utilized Model I - Cobb Douglas Panel Data Regression with Fixed Effects, Model II - Cobb Douglas Panel Data Stochastic Frontier Analysis using the Kumbhakar and Lovell (1990), and Model III - Transcendental Logarithmic Panel Data Cobb Douglas Stochastic Frontier Analysis using the Kumbhakar and Lovell (1990). These models allowed us to measure the technical efficiency of SaaS firms and examine the interplay between various variables, such as employee count and R&D expenseswith liabilities and assets as control variables.

Results and analysis: The three models revealed that labor, assets, and R&D expenses positively and significantly affect profitability in SaaS firms. The SaaS industry also exhibits decreasing returns to scale in two models, suggesting that increasing all inputs proportionally leads to a less-than-proportional increase in output with the third model exhibiting an increasing return to scale. Also, top performers in technical efficiency tend to have higher marginal product of labor (MPL) values than bottom performers.Conclusions: Technical efficiency is positively correlated with profitability, indicating that more efficient SaaS firms achieve higher profitability levels. The relationship between technical efficiency and profitability is stronger when using the Translog model compared to the Cobb-Douglas model. The study also found that the factors contributing most to profitability in SaaS firms are the number of employees and assets, followed by research and development expenses. 

Recommendations for future research: Further studies could explore the extent to which factors such as the quality of the workforce, technology, and business processes impact MPL and technical efficiency in SaaS firms. Additionally, future research could investigate the effects of market competition, firm size, and industry regulation on profitability in the SaaS industry. Finally, research could investigate the potential benefits of diversifying investment portfolios to include SaaS stocks, given the significant impact of labor, assets, and R&D expenses on profitability.