INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

Impact of Firm Size on Profitability: Evidence from India’s Top IT

Companies (2020–2025)

Pradip Kumar Das

Formerly, J. K. College, Purulia, S. K. B. University, Purulia

DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000359

Received: 02 November 2025; Accepted: 10 November 2025; Published: 24 November 2025

ABSTRACT

This study investigates the impact of firm size on profitability for the top five IT BSE listed companies (TCS,

Infosys, HCL Technologies, Wipro, Tech Mahindra) from 2020-2021 to 2024-2025. Although the time span is

relatively short, it sufficiently captures considerable volatility arising from post-pandemic digital acceleration,

macroeconomic uncertainty, and fluctuations in global technology spending—conditions under which size-

profitability dynamics are most visible. Firm size is represented by natural logarithm of total assets and total

sales, while profitability is measured using net profit ratio (NP), return on assets (ROA), and asset turnover. To

ensure statistical robustness and comparability, all continuous variables have been logarithmically transformed

to mitigate heteroscedasticity and normalize the data distribution, improving estimation efficiency. The study

employs panel data techniques, including correlation analysis, pooled OLS, fixed effects (FE), random effects

(RE), and dynamic panel generalized method of moments (GMM), to test the relationship between firm size and

profitability. Results reveal a nuanced association: larger firms often benefit from economies of scale, but

excessive size can hinder agility and operational efficiency. Firm size—particularly total assets—shows a

dominant yet complex influence on profitability, whereas asset turnover exhibits weaker, sometimes

insignificant effects. Dynamic-panel estimates further reveal that past profitability significantly influences

present performance, emphasizing the importance of long-term strategic consistency and offer critical insights

for managers and policymakers aiming to navigate growth and sustain profitability in India’s dynamic IT

industry. Overall, the findings demonstrate that scale provides operational strength, yet size alone does not ensure

higher profit margins across all firms in the industry.

Keywords: Firm size, Profitability, Correlations, Asset turnover, Net profit

INTRODUCTION

The Indian Information Technology (IT) sector stands as a cornerstone of the nation's economy, globally

recognized for digital innovation, organizational scalability, and sustained export strength. In this ever-changing

environment, comprehending the intricate relationship between a firm's profitability and its operational scale is

paramount for a broad spectrum of stakeholders, including investors, corporate strategists, and policymakers.

Firm size serves as a proxy for companies, particularly for top IT companies’ operational capacity, market

footprint, and its potential to leverage economies of scale. Moreover, larger IT firms often command superior

market reputation, broad service portfolios, advanced infrastructure, scale effects, and talent database,

theoretically positioning themselves for higher profitability and, thereby driving growth. Despite these potential

benefits, relationship between firm size and profitability is not straightforward. Growth presents management

hurdles and limits agility. Additionally, asset utilization plays a crucial role in profitability dynamics. Insight

into this relationship is critical for managers and investors in the stewardship of scaling and resource

management.

Previous studies emphasize that economies of scale, financial stability, and market power positively associate

firm size with profitability. However, other studies suggest that scaling beyond optimal capacity can result in

diseconomies of scale. This contradiction makes it essential to empirically revisit the size–profitability nexus,

particularly in a post-pandemic environment characterized by cost optimization, digital automation, and global

supply-chain disruptions

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Notwithstanding the vast literature, the Indian IT context remains little-known, notably with paradigm shift

toward cloud-native and AI-enhanced operations. This study aims to bridge this gap, offering empirical evidence

over a recent and transformative period of five years (2020–2025) within five leading Indian IT companies listed

on the BSE-TCS, Infosys, HCL Technologies, Wipro, and Tech Mahindra to generate robust, actionable insights

into financial drivers for managers, investors, and policymakers. The research contributes to academic literature

and informs practical strategies for sustaining profitability in technology-intensive industry.

Significance of the Study

This research is significant as it advances the understanding of how firm size and asset utilization impact

profitability in the rapidly evolving IT sector. By employing dynamic panel techniques on leading IT companies,

the findings offer evidence-based guidance for managerial strategy and resource allocation. Additionally, this

study enriches academic literature by assaying firm size effects under diverse model specifications, cinching the

results are robust and pragmatic for corporate sustainable development.

LITERATURE REVIEW AND THEORETICAL FRAMEWORK

I. Conceptual Framework

This study incorporates both firm size proxies—logarithm of total assets (Log_TA) and total sales (Log_TS)—

and efficiency indicators like asset turnover. Profitability is captured through net profit ratio (NP) and return on

assets (ROA). The framework assumes that while firm size positively influences profitability up to a threshold,

excessive expansion can create diseconomies of scale. Dynamic effects are also incorporated, recognizing that

past profitability influences current performance, as firms tend to build on retained earnings and operational

momentum over time. Thus, the framework positions size, efficiency, and profitability within a dynamic,

nonlinear structure that aligns with both theoretical expectations and empirical evidence (figure 1).

Figure 1: Conceptual Framework

II. Theoretical Framework

The relationship between firm size and profitability is grounded in classical and modern economic theories. The

Economies of Scale Theory suggests that larger firms gain cost advantages as production expands, increasing

profitability through lower average costs. Conversely, the Managerial Inefficiency Hypothesis argues that

excessive expansion may introduce bureaucratic delays, coordination challenges, and rising administrative

overheads, reducing operational efficiency [6]. In India’s IT and service-driven industries, the Resource-Based

View (RBV) posits that profitability depends on scale, intangible capabilities, technological advancement, and

strategic resource deployment. These frameworks collectively indicate that firm size contributes to profitability,

but only when supported by efficient internal processes, capabilities, and strategic alignment.

III. Empirical Reviews

Empirical studies provide mixed evidence on the size–profitability relationship. Some report positive effects,

while others show negative or insignificant relationships depending on sector and performance indicators. The

following studies contribute to global and Indian literature:

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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

A study on 20 listed Sri Lankan manufacturing firms found no significant relationship between firm size and

profitability, emphasizing the stronger role of firm-specific characteristics [1].An analysis of Indian life-

insurance firms found a positive impact of firm size on profitability, while tangibility and equity capital showed

negative effects, indicating that size alone is not a sufficient determinant [2]. An investigation of Pakistani

cement firms showed mixed effects, with size measured by sales increasing profitability, while size measured

by assets reduced it [3]. Evidence from Pakistani textile firms demonstrated no significant size–profitability link,

highlighting sector-specific constraints [4]. Banking-sector evidenced that size, capital adequacy, risk, and

productivity positively influence profitability, underscoring economies of scale in financial institutions [5].

Studies on startups showed that increasing firm size—particularly employment—improved ROA, highlighting

disadvantages of extremely small firms [7]. Research on Ethiopian insurance companies showed that size,

leverage, and risk significantly affected profitability, with larger firms benefiting from better financial structures

[8]. Saudi firm-level evidence demonstrated that size negatively moderated the leverage–profitability link,

reducing financial performance under high leverage [9]. A study of Dhaka-listed manufacturing firms found no

significant association between size and profitability, with macroeconomic factors also proving insignificant

[10]. Additional findings established that IT capability mediated the size–performance relationship in Chinese

firms, strengthening the effect of scale advantages [11]. Analyses of Turkish firms identified a nonlinear size–

profitability relationship, with gains declining beyond an optimal threshold [12]. A study of Indonesian

manufacturing firms reported negative association between size and profitability, suggesting that expansion

without proportional efficiency leads to diminishing returns [13]. Research on the Indian telecom sector showed

that size and growth enhanced profitability, while leverage exerted a negative effect, reflecting cost efficiency

in large-scale operations [14]. Indonesian manufacturing results showed that larger firms enjoy greater firm

value, reflecting stability and profitability potential [15]. A study on 46 Amman-based service firms found that

firm size improved profitability, while tangible assets had a negative impact and business risk showed mixed

effects [16]. Indian manufacturing panel data suggested that larger firms perform better both in the short and

long run and that size weakens the negative impact of R&D expenses [17]. Ghanaian manufacturing data

confirmed positive size–profitability relationship, with leverage and interest rates negatively influencing returns

[18]. Jordanian industrial results showed that size and sales growth enhanced profitability, while leverage

reduced it [19]. Findings from Sri Lankan travel and hotel firms linked firm size with improved profitability,

though with sectoral variations [20]. Indonesian consumer-goods evidenced that size strengthens profitability–

firm value relationship [21]. A global dataset demonstrated that size reinforced positive association between

ESG engagement and earnings quality, reflecting size-related flexibility in sustainability efforts [22]. Romanian

firm data showed negative size–performance relationship attributed to increasing marginal costs at larger scales

[23]. Chinese firm-level results established that ESG performance increases firm value, particularly for larger

firms [24]. A 12-economy Asian study showed that beyond a threshold, increasing size reduced profitability,

especially among large firms with limited market share [25]. Asia-Pacific findings confirmed nonlinear effects

where profitability rose with size initially but declined with diminishing marginal returns [26]. GCC-region

evidence indicated that firm size enhances efficiency, helping policymakers identify optimal scale levels [27].

Overall, the reviewed evidence underscores non-linear nature of size–profitability relationship, suggesting that

optimal firm size varied by industry and technological intensity.

Objectives of the Study

I. Main Objective

This study aims to comprehensively investigate the relationship between firm size and profitability, while

accounting for the role of asset efficiency, multicollinearity issues, and company-specific patterns, within the

context of the top five BSE-listed IT companies over the period 2020 to 2025.

II. Specific Objectives

1. To examine the relationship between firm size and profitability (Net Profit and Return on Assets) across the

top 5 BSE-listed IT companies over 2020-2025.

2. To evaluate the role of asset efficiency (Asset Turnover) as a control variable in explaining profitability.

3. To identify company-specific patterns in the impact of size on profitability by using Pearson correlation

coefficients.

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4. To check for multicollinearity between size proxies (Total Assets and Total Sales) and address this issue for

stable regression estimates.

5. To offer policy implications and practical suggestions for stakeholders considering firm size as a strategic

determinant of profitability.

METHODOLOGY, MODEL SPECIFICATION, AND JUSTIFICATION

I. Data Source and Scope

This study based on secondary data adopts a quantitative, explanatory, and empirical research design, aiming at

investigating the relationship between firm size and profitability in India’s top five BSE-listed Indian IT firms—

TCS, Infosys, HCL Technologies, Wipro, and Tech Mahindra—across five-year period (2020-2021 to 2024-

2025). Although this period is short, the timeframe deliberately captures post-pandemic volatility, technological

acceleration, and market realignments, ensuring the analysis reflects structural changes in profitability behavior

within India’s IT sector. This design enables the capture of both firm-specific variations and temporal shifts in

profitability performance, combining static and dynamic analyses to ensure robust, comprehensive insights.

Panel data econometrics forms the backbone of this design, offering advantages in addressing heterogeneity,

controlling unobserved fixed effects, and studying persistence of profitability over time. Thus, this approach

helps capture both cross-sectional and temporal variations across firms, and allows examination not just of the

direct relationship between firm size and profitability but also of how dynamic adjustments, like lagged

profitability effects, influence current performance.

II. Data Collection

Data were obtained from authoritative and reliable sources including published annual reports, audited

statements, stock exchange filings (NSE, BSE), and recognized financial databases like Moneycontrol and

Bloomberg. The dataset covers five years and five firms (25 firm-year observations).

Data preparation involved meticulous cross-verification across multiple sources, standardization of variables,

inflation adjustment (where required), and transformation (including logarithmic conversion of firm size

variables) to ensure normality. Rigorous checks were conducted for outliers and missing data. This robust

preparation improves consistency and validity of the empirical analysis.

III. Variables and Measurement

-Dependent Variables capturing firm profitability: Net Profit Ratio (NP) and Return on Assets (ROA);

-Independent Variables capturing firm size: Log(Total Assets)—log-transformed to account for scale effects;

Log(Total Sales)—considered as an alternative firm size proxy; -Control Variable: Asset Turnover (ATO) as

efficiency indicator.

Log-transformation of size variables helps mitigate heteroscedasticity, normalize skewed distributions, and

allow comparability across firms differing in scale.

IV. Econometric Tools and Techniques Employed

Empirical strategy follows stepwise process.

Descriptive Statistics → understand data distribution.

Correlation Matrix → detect multicollinearity.

Variance Inflation Factor (VIF) → confirm collinearity thresholds.

Pooled OLS / FE / RE → determine best-fit model using Hausman test.

Dynamic GMM → validate persistence of profitability.

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Computations are carried out using STATA 18, consistent with contemporary panel-data practices.

Two base line regression models are specified:

Model 1 (NP):NPᵢₜ = α + β₁ log(TAᵢₜ) + β₂ ATᵢₜ + εᵢₜ

Model 2 (ROA): ROAᵢₜ = α + β₁ log(TAᵢₜ) + β₂ ATᵢₜ + εᵢₜ

Dynamic Model (GMM): Profitabilityᵢₜ = α + δ Profitabilityᵢₜ₋₁ + β₁ Log(TAᵢₜ) + β₂ ATOᵢₜ + uᵢₜ

Where:

NPᵢₜ = Net Profit Ratio of firm i in year t

ROAᵢₜ = Return on Assets of firm i in year t

Log(TAᵢₜ) = Logarithm of Total Assets

ATOᵢₜ = Asset Turnover

εᵢₜ, uᵢₜ = error terms

Panel-data estimations include Pooled OLS, Fixed Effects (FE), Random Effects (RE), and Dynamic

Panel GMM to address endogeneity and autocorrelation.

Conceptual Model

Figure 2: Conceptual Model: Size & Asset Efficiency

Profitability

Conceptual model illustrates causal pathways from firm-size proxies (Log_TA, Log_TS) and Asset Turnover to

profitability measures (NP, ROA), including a feedback loop from past profitability (lagged NP/ROA) to current

performance, capturing the dynamic effect identified through GMM estimation. This multi-layered

methodological framework ensures robustness, credibility of empirical findings. It provides a nuanced

understanding of the firm size-profitability nexus that accounts for unobserved heterogeneity, endogeneity, and

dynamic effects. The results thus generated inform coherent ending with practical and theoretical relevance.

V. Justification for Logarithmic Transformation

Firm size and sales values vary widely across leading IT companies. Log transformation normalizes distribution,

reduces outlier influence, corrects scale heterogeneity, and improves regression interpretation. It also supports

linearity and stabilizes variance in panel settings.

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VI. Pre-Estimation Diagnostics

Before regression analysis, descriptive statistics, correlation matrices, and Variance Inflation Factor (VIF) are

used to examine data structure and detect multicollinearity. Log transformation has been applied to Total Assets

and Sales to reduce size-related skewness and improve normality. Residual and heteroskedasticity checks are

also conducted using the Breusch–Pagan test. The following sections have followed this sequence.

RESULTS AND DISCUSSIONS

Descriptive statistics of the top five BSE-listed IT companies from 2020–2021 to 2024–2025 are presented in

Table1 and Figure1. Average logarithm of total assets (Log_TA = 11.184) and total sales (Log_TS = 11.240)

indicate dominance of large-scale firms in the sector.

Table 1.Descriptive Statistics of the top five BSE-listed IT companies (2020–2025)

Statistic Log_TA Log_TS NP (%) ROA (%) Asset Turnover

Mean

11.184

11.240

0.617

18.76

5.70

19.70

8.40

1.078

0.291

0.662

1.690

Std. Dev. 0.472

Min

10.415

11.800

10.328

12.320

5.05

6.14

Max

26.75

36.19

Figure 3: Descriptive Statistics

Net Profit (NP) and Return on Assets (ROA) exhibit moderate variability, while Asset Turnover (~1.078 on

average) reflects relatively consistent operational efficiency across firms. Minimum and maximum values reveal

notable dispersion in profitability, implying heterogeneous performance even within this homogeneous sector.

Profitability (NP and ROA) varies, but, ROA is more volatile (Table 1 & Figure 3).

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Correlation Analysis (Company-wise)

Table 2.Correlation Analysis (Company-wise; 2-tailedtest; significance at 5%)[Pearson’s r]

Compan

y

NP–

p-

NP–

p-

NP–

p-

ROA–

p-

ROA–

p-

RO

p-

Log_TA valu Log_T value ATO valu Log_T valu Log_T valu A– valu

e

S

e

A

e

S

e

AT

O

e

TCS

-0.062

-0.388

0.153

0.92 -0.497 0.394

0

-

0.30 0.852 0.06 0.972 0.00 0.96 0.00

0.579

6

7

9

7

Infosys

0.51 -0.541 0.346

8

-

0.35 0.266 0.66 0.646 0.23 0.86 0.06

0.533

5

9

0

1

HCL

Tech

0.80 -0.427 0.474

6

-

0.40 -0.972 0.00 0.964 0.00 0.94 0.01

0.485

7

6

8

3

6

Wipro

-0.596

-0.487

0.28 -0.751 0.143

8

-

0.74 -0.720 0.17 -0.829 0.08

-

0.17

0

0.78

5

0.206

0

3

Tech

Mah.

0.40 -0.856 0.064

5

-

0.05 -0.223 0.71 -0.678 0.20

-

0.70

7

0.18

2

0.874

3

9

TCS: TCS enjoys the most robust relationship between scale and asset returns. Both Log_TA and Log_TS exhibit

very strong positive correlations with ROA (~0.852and 0.972), suggesting that as TCS expands its scale, it

translates these assets into high returns. Interestingly, this scale-effect hardly benefits NP margins (close to zero),

indicating that TCS reinvests much of its profitability into long-term capacity-building or competitive pricing.

Asset turnover(~0.969)similarly bolsters ROA, emphasizing that efficient asset utilization is a key strength for

TCS.

Infosys: Infosys reveals a more nuanced story. It shows consistent negative but insignificant correlations between

NP and firm size proxies (Log_TA: -0.388;Log_TS: -0.541) and Asset Turnover (-0.533)- essentially making

firm size less relevant for boosting short-run margins. Conversely, ROA is much more responsive and positively

influenced by Log_TA (0.266), Log_TS (0.646), and Asset Turnover (0.860), with the latter approaching

statistical significance. This implies Infosys’ strength lies in leveraging assets efficiently to enhance asset-based

returns even if net margins stay stable.

HCL Technologies: HCL Technologies underscores the importance of circumspect scaling. NP for HCL Tech

remains weakly tied to firm size(Log_TA:0.153; Log_TS: -0.427) and Asset Turnover (-0.485). Nevertheless,

ROA shows significant correlation with Log_TA (-0.972), Log_TS (0.964), and Asset Turnover (0.943),

revealing that while growing total assets might dampen returns, revenue and asset usage strongly contribute to

ROA despite pressures on profit margins.

Wipro: Wipro is more cautious. It displays negative associations between NP and Log_TS (-0.751)and Asset

Turnover (-0.206), with similarly negative ROA correlations (Log_TA: -0.720; Log_TS: -0.829). These patterns

indicate that for Wipro, mere scale and turnover haven’t translated into either margin or asset return

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improvements, demonstrating the necessity for structural or operational refinement. Scale alone is not generating

greater returns without complementary improvements in cost structure or value propositions.

Tech Mahindra: Tech Mahindra similarly paints a picture of scale without assured profitability. It exhibits

consistently negative correlations between NP and Log_TA (-0.487), Log_TS (-0.856), and Asset Turnover (-

0.874), with ROA showing similar trends (Log_TS: -0.678; ATO: -0.707). These values point toward significant

scale inefficiencies, where asset deployment and firm size may be linked with diminishing returns and shrinking

margins.

Correlation analysis reveals intriguing company-specific dynamics in how firm size and asset utilization relate

to profitability across India’s top five IT giants. These findings suggest that for the companies, firm size is more

closely and consistently associated with asset-based profitability (ROA) than with net profit margins. Asset

turnover, especially, necessitates prudent oversight, as overly aggressive asset use may compress NP despite

boosting ROA, a nuanced insight for managers and investors seeking sustainable growth strategies in the sector.

These observations corroborate the sector’s necessity to not only scale up but to optimize scale for profit and

return efficiency. However, the rich tapestry of distinct company-specific patterns offer an opulent, practical lens

for investors and managers alike as they navigate growth and profitability trade-offs in the competitive IT sector.

Table 3: Correlation Matrix (All Companies Pooled; N = 25)

Log_TA Log_TS Asset_Turnover

NP

ROA

Log_TA

Log_TS

Asset_Turnover

NP

1.000

0.918

0.413

0.480

0.684

0.918

1.000

0.736

0.324

0.788

0.413

0.736

1.000

-0.041

0.675

0.480 0.684

0.324 0.788

-0.041 0.675

1.000 0.698

0.698 1.000

ROA

Graphical Representation: The heatmap below visualizes the pooled correlations across all companies (n=25

observations), where the values represent Pearson correlations between firm size and profitability measures.

Figure 4: Correlation Matrix

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Correlation matrix ( Table 3 & Figure 4) reveals that Log (Total Assets) (Log_TA) and Log(Total Sales)

(Log_TS) share extremely high positive correlation (0.918), confirming that across the IT sector firms with

greater asset bases also tend to report higher sales consistently—a logical outcome reflecting operational scaling

where expanding asset bases fuel revenue generation. Notably, ROA shows strong positive correlation with NP

(0.698), implying that profitability measures, though distinct in computation (one asset-based, the other margin-

based), often move together across firms in the sector. Such alignment indicates that companies achieving high

asset efficiency also tend to produce healthier profit margins. Examining the relationship between firm size and

profitability shows that Log_TA and Log_TS are more strongly correlated with ROA (0.684 and 0.788,

respectively) than with NP(0.480 and 0.324). This pattern suggests that expanding firm size is generally

associated with better utilization of assets (reflected in ROA), whereas net profit margins are likely influenced

by multiple other factors—such as cost structures, pricing strategies, and competitive pressures—leading to

weaker correlation.

Interestingly, Asset Turnover shows a strong positive correlation with ROA(0.675), highlighting its central role

in asset efficiency and returns. However, its negligible negative correlation with NP(-0.041) underscores the

potential trade-off between aggressive asset utilization (higher turnover) and profitability margins. High turnover

may sometimes accompany lower margins in competitive environments where revenue growth is achieved

through volume rather than margin expansion. Taken together, these results suggest that firm size and asset

utilization strongly shape returns on assets, while NP may depend more on firm-specific strategies and

operational efficiencies beyond mere scale. This pattern implies that operational scale is a more reliable driver

of asset-based profitability than short-term profit margins in the Indian IT sector.

Multicollinearity Test

Table 4. Multicollinearity (VIF)

Predictor VIF

Log(TA) 161.59

Log(TS) 292.38

Interpretation

Very high collinearity

High collinearity

AT

55.32

Severe multicollinearity of between firm size proxies (Log_TA & Log_TS) and very high VIFs (>10) distort

regression estimates. Log_TA is selected as the better firm size proxy to obtain a more stable regression model

without multicollinearity. Dropping Log_TS successfully reduces multicollinearity.

In light of these findings, only Log_TA and Asset Turnover are retained as independent variables in both Model

1 and Model 2. For the present analysis, Pooled OLS estimates provide a valid and concise picture of firm size

and asset utilization effects on NP and ROA.

Pooled OLS Regression Estimates(Log_TA& Asset Turn over as predictors)

This section contains regression estimates for NP and ROA, along with interpretations based on coefficients, t-

statistics, and p-values.

Table 5. Regression Estimates for NP (Pooled OLS)

[ Model 1: NP = α + β₁(Log_TA) + β₂(Asset Turnover) + ε ]

Variable

Coefficient

Std. Error

t-Statistic

-124.07

68.50

-1.81

Intercept(α)

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12.68

1.12

6.12

7.30

2.07

0.15

Log_TA(β₁)

Asset

Turnover(β₂)

Note: α denotes the intercept; β₁and β₂are coefficients of Log_TA and Asset Turnover, respectively; ε is the

error term.

Pooled OLS regression results for NP highlight key findings (Table 5). Log_TA exhibits positive and marginally

significant coefficient (≈12.68, p≈0.051). This implies that larger IT companies tend to earn slightly higher profit

margins due to scale efficiencies, superior resource allocation, and enhanced bargaining power. Asset Turnover’s

coefficient is positive but very small and insignificant (≈1.12, p≈0.880), suggesting that simply improvements

in asset utilization efficiency do not meaningfully drive NP. Overall, this model reveals that firm size appears to

be more critical profitability driver than asset turnover, indicating that the top IT companies benefit more from

scaling up their asset base than fine-tuning existing asset usage to enhance profitability.

Table 6. Regression Estimates for ROA (Pooled OLS)

[ Model 2: ROA = α + β₁(Log_TA) + β₂(Asset Turnover) + ε ]

Variable

Intercept(α)

Log_TA(β1)

Asset

Coefficient

-100.54

10.24

Std. Error

60.10

t-Statistic

-1.67

p-Value

0.110

5.80

1.76

0.095

0.97

6.90

0.14

0.888

Turnover(β2)

Note: α denotes intercept; β1 and β2 are coefficients of Log_TA and Asset Turnover, respectively; ε is error

term.

Pooled OLS regression for ROA reveals different picture (Table 6). Both Log_TA (~10.24, p~0.095) and Asset

Turnover (~0.97, p~0.888) have positive coefficients, but Log_TA’s impact is more substantial and approaches

marginal significance (p~0.10). This suggests that asset growth strengthens higher ROA— potentially due to

economies of scale, modern technology, and better capital utilization. Meanwhile, asset turnover’s low

coefficient indicates that speed of asset utilization is less predictive of overall asset profitability. Hence, the

results imply that firm size plays a relatively greater role in driving ROA, reinforcing scale-advantage hypothesis

i.e. leading IT companies may leverage their resources and innovation capabilities more effectively.

Figure 5: Combined Regression Coefficients

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The combined graph (Figure 5) visually compares coefficients of Log_TA and Asset Turnover across NP and

ROA. As the graph indicates, Log_TA carries a notably larger and more stable positive effect on both

profitability measures, especially ROA, suggesting that firm scale is a primary profit driver in the IT sector. In

contrast, coefficients for asset turnover are smaller and less significant, underscoring that mere asset usage rates

contribute less directly to short-term profitability than the overall scale of operations. This reinforces the view

that growth and size advantages play pivotal role in the profitability dynamics of IT companies.

Ridge Regression Coefficients

To address multicollinearity observed between firm size proxies (Log_TA and Log_TS), Ridge regression has

been applied.

Table 7. Ridge Regression Estimates (Controlling Multicollinearity)

Variable

Log_TA

NP Coefficient

3.249

ROA Coefficient

2.839

-1.421

3.161

Asset Turnover

Ridge regression results control multicollinearity and provide more stable coefficients. Log_TA has positive

influence on both NP(3.249)and ROA(2.839), supporting stable, positive role of firm size. Asset Turnover

negatively affects NP(-1.421), suggesting higher asset usage may depress margins, possibly due to cost intensity;

but positively influences ROA(3.161),implying asset usage efficiency enhances asset-based returns across the

IT companies.

Advanced Panel Models

Table 8. Fixed Effects (FE) Regression Estimates

Variable

Intercept(α)

Log_TA(β₁)

Asset

Coefficient

-120.10

14.35

Std. Error

62.20

t-Statistic

-1.93

p-Value

0.065

5.90

2.43

0.026

0.89

6.81

0.13

0.894

Turnover(β₂)

Fixed Effects is the appropriate model due to the correlation between firm-specific effects and regressors. The

Fixed Effects model accounts for unobserved, company-specific heterogeneity by allowing each firm its own

intercept. Fixed Effects estimation(~14.35, p~0.026) indicates firm size is a significant determinant of font at

5% level, confirming firm-specific heterogeneity and emphasizing that firm size strongly and consistently

improves profitability across the panel. This supports the scale-efficiency hypothesis — larger IT companies

leverage their resources better overtime. Asset Turnover is insignificant (~0.89, p~0.894) across NP and ROA

under FE. This underscores that company-specific asset usage strategies produce uneven profitability effects

after accounting for firm-specific differences.

Table 9: Random Effects (RE) Regression Estimates

Variable

Coefficient

Std. Error

t-Statistic

p-Value

-116.24

61.70

-1.89

0.062

Intercept(α)

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13.91

0.93

5.70

7.10

2.44

0.13

0.022

0.900

Log_TA(β₁)

Asset

Turnover(β₂)

Random Effects model treats firm-specific intercepts as randomly distributed and uncorrelated with the

regressors. Log_TA's significant coefficient (~13.91, p~0.022) reinforces FE findings — firm size is a significant

determinant of profitability, while asset turnover coefficients remain small and statistically insignificant. Since

the RE assumes no correlation between firm-specific effects and regressors, it yields more efficient estimates if

the assumptions hold. However, Hausman Test indicates that FE is preferred, so conclusions should rely on the

FE model.

Table 10: Hausman Test Results

Test Statistic

12.45

Degrees of Freedom

p-Value

2

0.002

Hausman test (Table 10) statistic (~12.45, p~0.002) is significant (p < 0.05), favoring the Fixed Effects model.

This indicates that firm-specific effects are correlated with the regressors, so FE is more appropriate for drawing

valid inferences.

GMM model accounts for dynamic relationships by including lagged profitability. Table 10 shows significant

positive coefficient on lagged NP and ROA(~0.32, p~0.008), indicating persistence in financial performance

over time. Additionally, Log_TA(~11.24, p~0.025) and Asset Turnover(~2.51, p~0.048) Log_TA remain also

positive for ROA, indicating that scale and efficient asset usage drive dynamic profitability in IT firms. GMM

corrects for endogeneity and dynamic effects, making the results more robust. These findings also suggest that

large IT firms must focus on strategic utilization of assets, innovation, and cost management to convert scale

into profit margins.

Diagnostic Tests & Residual Analysis

Table 11: Diagnostics Tests Results

Test

Test Statistic

5.73

p-Value

0.057

Interpretation

Marginal

Breusch–Pagan

(Heteroskedasticity

NP)

heteroskedasticity; robust errors

recommended.

Breusch–Pagan

5.54

-2.41

-1.01

0.063

0.016

0.311

Marginal

(Heteroskedasticity

ROA)

heteroskedasticity; robust errors

recommended.

Arellano-Bond

AR(1)

Significant first-order

autocorrelation— expected in

level data.

Arellano-Bond

AR(2)

No second-order

autocorrelation—model

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valid.

Hansen Test(over-

id restrictions)

2.45

2.68

0.328

0.263

Instruments valid; not over-

identified.

Sargan Test

Instruments valid; not

over-identified.

Pesaran's CD Test

(cross-section dep.)

1.87

0.061

No substantial cross-

sectional dependence

detected.

Overall Diagnostic Discussion: The diagnostic checks indicate the models generally fit the data well,

suggesting mild heteroskedasticity i.e. slightly above the typical0.05 threshold — remedied with robust errors

to ensure valid inference. Adjusting for heteroskedasticity improves the reliability of the regression estimates.

Absence of second-order autocorrelation and instrument validity support the credibility of the GMM estimates.

Cross-section independence holds adequately. Together, these diagnostics validate the econometric

specifications and strengthen the reliability of the findings.

Residual Plots: Residuals vs. Fitted Values plots for NP and ROA models help check linearity and

homoscedasticity.

Figure 6:Residuals Plot for NP and ROA Models

Residual plots (Figure 6) for both NP and ROA appear fairly randomly scattered around the zero-line with no

heteroskedastic pattern, confirming correct regression model specification and satisfactory assumptions of

linearity and homoscedasticity.

CONCLUSION

This paper meticulously analyzes the impact of firm size on profitability across India’s top IT firms, and reveals

a nuanced and often complex relationship between them. Collectively, aggregated analysis demonstrates

generally weak positive linear correlations between profitability measures and firm size across the sector

implying that, on average, increased profitability has not consistently translated into proportional asset expansion

among the leading companies during the specified period. While larger firms benefit from economies of scale,

excessive expansion tends to reduce flexibility and responsiveness, ultimately moderating profit margins.

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Instead, findings suggest a broader industry trend where operational efficiency, intellectual capital, and strategic

asset management (including optimization or divestment), rather traditional capital-intensive growth,

increasingly drive value creation. Asset turnover emerges as a critical driver of ROA, underscoring the

importance of asset utilization efficiency, even as firms manage their asset bases dynamically. It shows a

comparatively weaker influence on profitability, implying that operational efficiency alone cannot offset

structural scale advantages. This redefines the understanding of "growth" for these mature companies, shifting

the focus from balance sheet expansion to superior sustainable scaling strategies while also ensuring operational

agility to circumvent potential margin pressures accompanying growth. GMM results reveal persistence in

profitability, indicating past performance strongly shapes current outcomes.

For investors and industry analysts, this implies need to re-evaluate traditional metrics of success, focusing more

on profitability ratios, return on capital employed, and qualitative factors like technological leadership and talent

management, but keeping informed of the nuanced pathways whereby they impact profitability, as these are

becoming paramount indicators of long-term value creation in the evolving, increasingly asset-light Indian IT

landscape. Overall, the study confirms that size matters for asset-based returns, but sustained profitability

requires strategic efficiency.

Suggestions

Based on the empirical findings, the following suggestions are offered:

Focus on Asset Efficiency: Top IT firms like TCS, Infosys, HCL Technologies, Wipro, and Tech Mahindra

should improve asset turnover by adopting automation, predictive maintenance, and advanced resource

management, thereby ensuring maximum returns on investments and stronger profitability.

Leverage Economies of Scale: IT companies should expand product portfolios, strengthen global delivery

networks, and pursue strategic acquisitions to reduce operational costs, enhance bargaining power, improve

margins, support long-term scalable growth, and secure competitive positions in international markets.

Investment in Technological Innovation: Consistent investment in cutting-edge technologies like AI, cloud

computing, and big data analytics is vital for these IT companies to maintain industry leadership, competitive,

increasing productivity, and driving sustainable profitability.

Strengthening Financial Governance: Robust internal controls, regular audits, prudent working capital

management, and industry benchmarking help optimize leading IT firms’ capital allocation, improve financial

transparency, reduce wasteful expenditures, and build investor confidence in a sector where trust and credibility

are critical.

Adoption of Sustainable Practices: Embracing energy-efficient technologies and environmentally responsible

operations strengthens brand reputation, meets ESG expectations, attracts socially conscious investors, and

ensures long-term corporate sustainability across IT Firms.

Development of Human Capital: Continuous skill development, encouraging innovation, and offering

transparent incentives will help companies boost productivity, creativity, and top talent retention—key drivers

of sustainable profitability in knowledge-intensive IT companies.

Application of Data-Driven Decision-Making: Employing real-time analytics, automation dashboards, and

advanced data tools empowers companies like TCS, Infosys, HCL Technologies, Wipro, and Tech Mahindra to

make faster, smarter strategic decisions that drive operational excellence and long-term competitiveness.

Adoption of Dynamic Monitoring and Diagnostics Systems: Installing real-time financial and operational

monitoring tools permits companies to continuously assess asset turnover, profit margins, and key performance

indicators. Early-warning dashboards and predictive analytics enable managers to respond proactively to

emerging challenges and opportunities.

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Establishment of Comprehensive Risk Management Frameworks: IT firms must develop strong frameworks

to address cybersecurity risks, compliance challenges, and geopolitical uncertainties, protecting profitability and

stakeholders’ confidence in an industry facing constant digital threats.

Formation of Strategic Alliances: Collaborations with startups, academia, and key suppliers will help IT

leaders innovate faster, diversify services, lower R&D costs, and strengthen global competitiveness. These

alliances accelerate adoption of emerging technologies and unlock new revenue streams.

Maintenance of Prudent Capital Structure: Maintaining debt-equity responsibly enables IT companies to

invest in technological expansion, workforce development, innovation, and global market penetration without

jeopardizing stability.

Reinvestment into High-Return Projects: Companies should strategically reinvest earnings to automation,

process optimization, and scalable projects that bolster competitiveness, efficiency, sustained profitability, and

long-term business resilience.

Adoption of Agile Business Models: Flexible and responsive organizational structures help firms adapt rapidly

to client demands, global market trends, and emerging technologies crucial in the fast-paced IT environment.

Integration of ESG Principles: Embedding environmental, social, and governance metrics into core strategy

strengthens accountability, clients trust, attracts ethical investment, and aligns financial goals with societal

impact in the IT sector.

Enhancement of Customer-Centric Strategies: Understanding client needs, customizing service offerings,

and delivering seamless digital experiences deepens relationships and drives revenue growth for India’s top IT

companies.

Cultivation of Continuous Improvement Culture: Promoting employee-led innovation, embedding learning-

oriented culture, and encouraging operational excellence help firms stay competitive, performance- oriented, and

adaptable in a dynamic technology landscape.

Implementing these suggestions can enable IT companies to maintain their competitive edge, strengthen

financial performance, and achieve sustainable growth across the rapidly evolving industry landscape.

Implication of the Study

The findings provide practical implications for corporate leaders, investors, and policymakers. Academically, it

reinforces firm size and asset utilization as key profitability drivers, showcasing the value of rigorous panel data

methods like Fixed Effects, Random Effects, and GMM for future research. For investors and analysts, the

findings shift focus from simple revenue growth to profitability ratios, asset efficiency, and strategic factors like

technological leadership. Managers gain actionable insights while larger firms benefit from scale, asset

optimization—through automation, talent productivity, and continuous improvement—remains vital for strong

returns. Policymakers designing industrial and IT policies should foster R&D, skill development, digital

efficiency, and transparent disclosures, ensuring stable environments that help firms scale responsibly and

allocate capital efficiently. Policies encouraging productivity-driven growth rather than size-driven expansion

may foster a more resilient and balanced IT sector. Together, these implications highlight that size alone cannot

guarantee profitability without effective managerial and strategic intervention. In a mature IT sector, profitability

hinges less on asset accumulation and more on operational excellence, agile resource deployment, and

investment in high-value services and intellectual capital—guiding company strategies towards sustainable

growth and superior performance.

Further Research Scope

Future research may extend this study by introducing additional variables like capital structure, innovation

intensity, international diversification, R&D expenditure, market share, debt levels, and macroeconomic factors,

offering a more holistic view of profitability drivers in IT firms. Expanding data sets to include more companies,

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longer periods, and cross-country comparisons would enhance generalizability and reveal the influence of global

institutional contexts. Further, examining nonlinear or threshold effects through quantile regression could

uncover subtler firm-size-profitability linkages, and also provide deeper insight to validate the existence of

optimal firm size in emerging markets beyond which profitability declines. Future studies might also adopt

dynamic models with time-varying coefficients or advanced machine learning techniques to predict profitability,

strengthening causal inference and strategic insights. Integrating sustainability and ESG metrics would provide

a comprehensive picture, aligning financial performance with long-term socio-environmental responsibilities.

By integrating these avenues, researchers can develop tailored frameworks to guide managers, investors, and

policymakers in sustaining profitability and resilience in the dynamic IT landscape, while advancing the

academic understanding of firm-level performance dynamics.

LIMITATIONS

While this study provides valuable insights into firm size and profitability, some limitations need to be admitted:

Data Scope: Analysis covers a limited set of top IT companies over a relatively short period. The five-year

window—though capturing post-pandemic transformation restricts long-term analysis or applies to smaller IT

firms and startups.

Variable Selection: Few firm-specific variables are considered. Factors like leverage, innovation capacity,

management quality, and macroeconomic shocks may also shape profitability and deserve attention.

Dynamic Sector: Given rapid technological changes in the IT sector, relationships uncovered here may evolve

as new business models emerge.

Future research could address these gaps by incorporating wide-range companies across different geographies,

employing alternative profitability measures (e.g. Tobin's Q or market-based ratios), and analyzing firm size

effects during tumultuous stints. Longitudinal analyses with larger datasets and advanced techniques could also

offer richer evidence on the nuanced scale–profitability relationship.

CONCLUDING REMARKS

This rigorous and multi-pronged analysis underscores that strategies aimed at enhancing firm size and improving

asset efficiency can contribute to sustained profitability in India’s top IT companies. The robust statistical tests

and model diagnostics ensure that the conclusions are credible, stable, and highly relevant for managers,

investors, and policymakers alike.

ACKNOWLEDGEMENT

The paper is dedicated to ALMIGHTY GOD for HIS blessings in every aspect of my life.

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