Factors Affecting Going Public Decision in Sri Lanka: Structural Equation Modelling Approach

Abstract

This study aims to identify the factors that influence the decision of companies to go public in Sri Lanka, where many qualified companies remain private despite meeting the listing requirements. The data are collected through a survey, and the analysis is conducted using exploratory factor analysis, binary logistic regression and structural equation modelling. The findings reveal that financing for future growth at a lower cost of capital, corporate image and liquidity, ownership and control rights, market establishments, company size, age and sector impact the decisions, while information and compliance costs and market efficiency do not.

Executive Summary

Listing companies on the stock exchange offers numerous benefits for the companies, investors and the economy. However, few companies have been listed on the Colombo Stock Exchange (CSE), a frontier market—specifically, only 285 of 109,810 registered firms, constituting a 0.26% listing rate that relegates Sri Lanka to frontier-market classification despite regulatory accommodations comparable to regional benchmarks. This study aims to identify the factors that influence the decision of companies to go public in Sri Lanka, where many qualified companies remain private despite meeting the listing requirements. The data are collected through a survey of higher authorities who influence the decision of relevant companies, and the analysis is conducted using exploratory factor analysis, binary logistic regression and structural equation modelling. The findings reveal that financing for future growth at a lower cost of capital, corporate image and liquidity, ownership and control rights, market establishments, company size, age and sector impact the decisions, while information and compliance costs and market efficiency do not. These results establish boundary conditions for canonical initial public offering theories developed in mature markets, demonstrating that frontier-market dynamics systematically diverge from developed-economy patterns. Policymakers should attract more local and foreign investors, enhance trading platforms, increase international recognition and liquidity and educate potential companies about the benefits of listing. These efforts may help dispel misunderstandings and fears surrounding the loss of ownership and control rights. This study builds upon the limitations of previous research, which relied solely on descriptive statistics or binary logistic regression, and moves beyond them. Instead, it utilizes method triangulation, employing both binary logistic regression and partial least squares structural equation modelling, to analyse the complex causal relationships among variables and provide a deeper assessment of the decision to go public.

Keywords

Binary Logistic Regression Going Public Listing Decision Sri Lanka Structural Equation Modelling Survey

It is well-documented that an efficient and developed capital market is essential for a country’s economic growth and development. It provides a platform for excess and deficit units of funds to meet and facilitates capital formation within an economy. Corporations frequently need funds for investment projects during the growth phase of a business’s life cycle. Although private financing options exist, they might not be feasible or adequate for large-scale expansion projects. Relying largely on debt as a financing source may be risky due to high-interest rates and the potential for default. As a result, the next most viable option is to raise capital from the market by selling some ownership interest to the general public, known as the going public decision (GPD). This decision is crucial for privately owned companies as it dilutes their ownership. Existing theories and empirical evidence suggest that cost and benefit considerations guide the decision to go public.

The benefit–cost trade-off theory (Pagano et al., 1998) and the lifecycle and market timing theory (Ritter & Welch, 2002) have been put forward to explain why companies go public. These theories suggest that going public through an initial public offering (IPO) benefits companies, providing the most effective option to raise finance (Larrain et al., 2025; Meluzin & Zinecker, 2014; Ritter & Welch, 2002; Signori, 2018), among many other advantages. Going public can help firms overcome credit and borrowing constraints from banks and other capital markets (Bancel & Mittoo, 2009; Brau, 2012; Brau & Fawcett, 2006; Pagano et al., 1998). Additionally, going public can increase liquidity for its shares (Black & Gilson, 1998; Chemmanur et al., 2023; Maug, 1998; Mayur & Kumar, 2013), reduce the cost of capital (Becker & Pollet, 2008; Booth & Chua, 1996; Maug, 1998; Pagano et al., 1998), facilitate risk diversification of initial investors (Acquaah, 2015; Chemmanur & Fulghieri, 1999; Croci et al., 2022; Maghyereh & Awartani, 2018; Mayur & Kumar, 2013) and increase the visibility and reputation of firms (Burton et al., 2006; Ravasi & Marchisio, 2001; Stoughton et al., 2001). Despite these benefits, companies must comply with certain rules, regulations and statutory requirements of the stock exchange once they are listed, which cause additional burdens and loss of strategic information (Aghamolla & Thakor, 2022; Pagano et al., 1998; Ritter, 1987; Titman & Grinblatt, 2002). Furthermore, non-compliance or failure to meet the requirements leads to disciplinary actions against them (Mayur & Kumar, 2013). Additionally, owners may lose control of the company after going public (Acquaah, 2015).

Although going public has provided considerable benefits, many qualified companies remain reluctant to go public. There are 109,810 registered companies in Sri Lanka as of 2021 (Department of the Registrar of Companies, 2021). However, despite the presence of many qualified companies, only 285 companies, representing 6.5% of the public companies in Sri Lanka, are listed on the Colombo Stock Exchange (CSE). As a result, MSCI has designated the CSE as a frontier market, characterized by reduced levels of liquidity and transparency. The Securities and Exchange Commission of Sri Lanka (SEC) and CSE also have taken considerable measures to incentivize companies to list on the exchange, aiming to foster a stable and efficient stock market. However, the number of new listings was zero in 2017, one in 2020 and 2022, two in 2018 and three in 2016. In addition, over the past two decades, numerous publicly listed companies across various global markets (Doidge et al., 2017), including those in Sri Lanka, have demonstrated a discernible inclination towards delisting from the stock exchanges. Ten companies were delisted from the CSE in 2019 (Colombo Stock Exchange, 2021). The low number of listings on the CSE presents a significant challenge to the policymakers overseeing stock market development in Sri Lanka, despite continuous efforts by the stock exchange and capital market regulatory bodies to attract new listings and keep them on the list. Unfortunately, these efforts have not resulted in the desired outcomes.

Studies on the GPD are relatively limited in corporate finance literature due to the non-availability of data (Brau, 2012; Fidanza et al., 2018). Previous studies find the factors and/or motives for GPD in a few markets, including the study by Pagano et al. (1998) in Italy, Brau and Fawcett (2006), Chemmanur et al. (2010) and Kim and Sung (2005) in the USA, Boehmer and Ljungqvist (2004) in Germany, Adamúz and Rivas (2018) in Mexico, Mayur and Kumar (2013) and Das (2025) in India, Khan et al. (2018) in Pakistan, Meluzin et al. (2016) and Meluzin and Zinecker (2014) in Poland and the Czech Republic, Bancel and Mittoo (2009) and Larrain et al. (2021) in Europe, Smith and Chun (2003) in Korea, Maghyereh and Awartani (2018) in Abu Dhabi, Cheng et al. (2024) in China and Acquaah (2015) in Ghana. However, these studies’ findings are mostly inconsistent and contradict each other (Chemmanur et al., 2023). It reveals that the results are subject to the context in which the studies were undertaken. Therefore, the findings cannot be generalizable to another context (Cheng et al., 2024; Das, 2025; Liu, 2017; Sottile, 2005).

It is crucial to comprehend the factors influencing companies’ GPD to increase CSE listings. Empirical studies investigating the Sri Lankan context are necessary to develop effective policy strategies for stock market development that attract new listings. However, the lack of such studies presents challenges in formulating effective strategies. This study addresses this gap by investigating the factors that influence companies’ GPD in Sri Lanka. This study provides a valuable contribution to understanding GPD. Firstly, it addresses a critical literature gap, as no prior studies have examined this decision within the Sri Lankan context, enhancing our knowledge on this topic. Secondly, the questionnaire encompasses all prevailing GPD theories, leading to a comprehensive investigation. Lastly, this study is the first to employ structural equation modelling to investigate factors impacting GPD, adding to existing knowledge.

LITERATURE REVIEW

Research on firms’ decisions to go public uses various theoretical models and empirical frameworks. A foundational model is the benefit–cost trade-off framework (Pagano et al., 1998), where firms weigh IPO costs against benefits. This framework has been developed further through dynamic analysis (Chemmanur et al., 2023) and regulatory disclosure theory (Aghamolla & Thakor, 2022)ÿ. Ritter and Welch (2002) proposed life cycle and market timing theories, suggesting that firms go public at specific development stages or during favourable market conditions. Recent advances incorporate regime-dependent analysis (Das, 2025) and human capital in timing (Lakkis, 2022). Empirical studies primarily use two approaches: survey-based studies with advanced mixed methods (Riyath & Dayaratne, 2025) and quantitative analysis of secondary financial data, offering complementary insights.

Several survey-based studies shed light on why firms decide to go public. Marchisio and Ravasi (2001) found that family-owned companies in Italy go public primarily to increase visibility and gain a competitive advantage rather than raise finance. Brau et al. (2006), in a survey of US CFOs, found that the primary reason is facilitating acquisitions and preserving control. Burton et al. (2006) demonstrated that companies listed on the London Stock Exchange prioritize increased visibility and reputation, with financing needs influencing IPO timing. Bancel and Mittoo (2009) surveyed across 12 European countries and revealed enhanced visibility and finance for expansion as key advantages, although motivations varied across companies and legal frameworks. Meluzín and Zinecker (2014) found that GDP and industrial production growth rates impact IPO numbers in Czech and Polish markets, with financing growth potential as the primary motive. Acquaah (2015) found that understanding stock market dynamics and institutional support encourage listings in Ghana, while Maghyereh and Awartani (2018) showed knowledgeable managers are more inclined to take firms public in competitive sectors.

In studies based on secondary data, various researchers have explored factors influencing private firms’ decisions to go public. Pástor et al. (2009) found that firms benefit most when predicted profitability is high but likely to decline post-IPO. Chemmanur et al. (2010) identified total factor productivity, firm size, sales growth, market share, industry competitiveness and capital intensity as significant factors. External drivers like capital market size and liquidity influence IPO trends (Meluzín & Zinecker, 2015), while firm characteristics such as R&D investment, capital investment, less working capital and more long-term debt affect funding amounts raised (Jin et al., 2017). Country-specific studies show that larger, younger, less leveraged companies are more likely to go public in Mexico (Adamúz & Rivas, 2018), while size, growth, profitability and sector are crucial factors generally (Rasheed & Sohail, 2019). Recent research finds positive associations with company size and industry market-to-book ratios, negative correlations with leverage and age (Lee et al., 2020), innovation benefits for non-US firms (Cai & Zhu, 2020), higher initial risks for newly listed European firms (Meles et al., 2021) and positive effects on bank profitability in emerging markets (Le et al., 2021). Cross-country factors like financial development and cultural orientation also influence IPO decisions (Salerno, 2021).

While firm-level factors drive IPO decisions, macroeconomic conditions provide the contextual backdrop that influences the timing and feasibility of IPOs. Studies across various markets demonstrate that macroeconomic variables such as interest rates, GDP growth, stock market performance and economic stability affect IPO volume and activity patterns (Ameer, 2012; Angelini & Foglia, 2018; Mehmood et al., 2020; Tran & Jeon, 2011). In the Sri Lankan context, Riyath et al. (2024) examined the relationship between macroeconomic variables and IPO activities, finding significant associations between macroeconomic factors and IPO timing decisions. In emerging markets, macroeconomic volatility can influence how firms evaluate the costs and benefits of going public (Kovandová & Zinecker, 2015; Thanh, 2020). However, the relative importance of macroeconomic versus firm-specific factors may vary across different market development stages and institutional environments (Acquaah, 2015; Maghyereh & Awartani, 2018).

Regulatory frameworks provide the structural environment for IPO decisions. A comparative analysis of South Asian markets (Table 1) shows that Sri Lanka’s regulatory framework is neither the most restrictive nor the most permissive regionally, yet it exhibits disproportionately low IPO activity. While Sri Lanka’s capital requirements are higher than some peers, the country offers flexible profitability pathways (revenue-based and cash flow-based alternatives) and a graduated public float structure that should theoretically facilitate more listings. Despite these regulatory accommodations and favourable macroeconomic periods, Sri Lanka’s IPO activity remains significantly below regional benchmarks. This dual paradox, where both macroeconomic timing opportunities and regulatory accessibility fail to translate into robust listing activity, indicates that firm-level factors likely play the most decisive role in IPO decisions, making company-specific decision drivers critical for completing the IPO decision framework.

Table 1:

Comparison of Main Board Listing Requirements Across South Asian Markets.

Country	Minimum Capital Requirement	Profitability Criteria	Public Float/Shareholder Requirements	Governance Standards	Disclosure Requirements
Sri Lanka (CSE – Main Board)	LKR 500 million (1.65 M USD) stated capital	One of the following: 3-year net profit + 2-year positive net assets (no market cap required) Aggregate revenue ≥ LKR 3 bn (9.91 M USD) (3 years) + LKR 5 bn (16.51 M USD) market cap Positive operating cash flow (2 years) + LKR 5 bn (16.51 M USD) market cap	Float-adjusted based on market cap: ≥ LKR 10 bn (33 M USD): 500 shareholders (no %) ≥ 7.5 bn (24.77 M USD): 5% ≥ 5 bn (16.55 M USD): 7.5% ≥ 2.5 bn (8.26 M USD): 10% < 2.5 bn (8.26 M USD): 20% All options require ≥ 500 shareholders	Independent audit; compliance with SEC Corporate Governance Code (2023); board resolutions and listing undertakings	Prospectus; quarterly and annual reports; XBRL reporting implementation planned: interim statements (2026), annual reports (2027), sustainability reports (2028)
India (BSE/NSE – Main Board)	INR 10 crore (1.14 M USD) post-issue paid-up capital	Two main routes: Profit route: Avg. operating profit ≥ INR 15 crore (1.7 M USD) (3 years) QIB route: ≥ 75% allocation to qualified institutional buyers (no profitability test)	Standard: ≥ 25% public float Min. 1,000 public shareholders No exceptions for large-cap companies	SEBI Listing Obligations and Disclosure Requirements, depository agreements, 20% minimum promoter holding	Draft Red Herring Prospectus; quarterly and annual financials; immediate price-sensitive disclosures
Pakistan (PSX – Main Board)	PKR 200 million (0.71 M USD) paid-up capital	3-year operational history 2 years of net profit Loss-making firms allowed with 51% sponsor lock-in until profitable	Graduated free-float by company size: Up to PKR 2.5 bn (8.84 M USD): 10% → 25% (3 years) PKR 2.5 -5 bn (8.84 - 17.67 M USD): 10% → 15% (3 years) PKR 5-10 bn (17.67 – 35.34 M USD): 10% maintained >PKR 10 bn (35.34 M USD): 5% maintained ≥ 500 shareholders + min 5 million tradable shares	Minimum 7 directors (2 independent); dematerialized shares via CDC; QCR-rated audit firm	SECP-approved prospectus (English and Urdu); PRIDE portal for reporting; periodic financials
Bangladesh (DSE/CSE – Main Board)	Fixed-price method: BDT 15 crore (1.23 M USD) existing + min BDT 30 crore (2.47 M USD) issue Book-building method: BDT 15 crore (1.23 M USD) existing + min BDT 75 crore (6.16 M USD) issue	Fixed-price method: 3 years operations + 2 years profit/cash flow Book-building method: Same + credit rating + higher capital requirements	Both methods: ≥ 10% public float Fixed-price distribution: 50% general public, 30% eligible investors Book-building distribution: 40% general public, 50% eligible investors	BSEC Corporate Governance Code: ≥ 30% sponsor holding ≥ 2% per individual director Independent directors required	IFRS-compliant financials; audited by BSEC-approved firm; cost audits if applicable; clean audit opinions

Source: All data sourced from official exchange/regulatory documents. For granular details (e.g., lock-in periods and audit clauses), refer to the primary sources.

Notes: All conversions use spot rates as of 15 October 2025: 1 USD = LKR 302.84, INR 88.00, PKR 282.98, BDT 121.71. Minor rounding applied.

HYPOTHESIS

Financing for Future Growth at a Lower Cost of Capital

Companies often choose to go public to raise funds for future growth at a lower cost of capital. The decision to incorporate debt capital into a company’s capital structure can provide tax shields and help reduce its capital cost (Modigliani & Miller, 1963). However, adding too much debt can increase financial risk and ultimately increase the overall cost of capital. To avoid this, companies with a higher cost of capital may choose to go public and issue equity shares to reduce their overall cost. Going public also allows companies to raise external finance at a lower cost without much intervention from financial intermediaries (Holmström & Tirole, 1993). Once listed in the stock market, the company becomes visible to many investors, attracting them to buy its shares at reasonable prices (Booth & Chua, 1996). The benefits of going public are many. It broadens the investor base and strengthens bargaining power with banks and credit organizations (Merton, 1987; Rajan, 1992). It also allows companies to exploit valuation (Ritter, 1991), benefit from favourable opinions and recommendations from analysts (Bradley et al., 2003) and gain recognition and publicity (Brau et al., 2003). Moreover, being a listed company provides diversified sources of financing, further strengthening the company’s bargaining power with banks and credit institutions. Pagano et al. (1998), Rajan (1992) and Riyath and Dayaratne (2025) note that listed companies fund themselves at a lower cost because they have access to public information, and banks do not have informational advantages concerning other remittances.

Research hypothesis: The need for financing for future growth at a lower cost of capital positively influences the decision to go public.

Corporate Image, Increasing Liquidity and Risk Diversification

Going public is a strategic decision firms make to achieve various objectives. Primary reasons include enhancing corporate image and increasing share liquidity (Black & Gilson, 1998; Mayur & Kumar, 2013). Public listing increases a company’s visibility to investors, improving its market reputation and attracting a broader investor base that boosts share liquidity. Improved liquidity enables easier buying and selling, granting investors enhanced flexibility while facilitating risk diversification for owners. Companies with high-risk investments often seek external finance through public markets to avoid additional debt burdens (Huyghebaert & Van Hulle, 2006). Going public provides access to funds for riskier projects while spreading risk across larger shareholder bases. Smaller companies with limited shareholders can mitigate business risk exposure through expanded ownership. Initial owners can liquidate investments through IPOs, while venture capitalists convert stakes to cash, enabling exit from risky projects (Black & Gilson, 1998). This risk diversification strategy reduces the concentration of risk for initial owners, creating more diversified and less risky investment portfolios (Riyath & Dayaratne, 2025).

Research hypothesis: The need for enhancing corporate image, increasing liquidity and risk diversification positively influences the decision to go public.

Ownership and Control Rights

The loss of ownership is a significant factor contributing to entrepreneurs’ hesitancy to go public. According to Pagano and Röell (1998), public firms have less concentrated ownership, with managers owning relatively less than their counterparts in private firms, leading to misaligned interests with shareholders. Additionally, floating a large proportion of shares when going public can increase the risk of a hostile takeover by other investors (Zingales, 1995). Family businesses often value control due to its non-pecuniary benefits, such as reputational advantages (Burkart et al., 2003; Zingales, 1995), which may lead to reluctance to offer shares to external parties. Losing control is a major concern for founders who have invested considerable time and resources into building their companies. Hostile takeovers resulting from underwriters diluting stocks too much can lead to a loss of control for founders (Alavi et al., 2008; Kroll et al., 2007; Meluzín et al., 2018). The fear of losing control has made some entrepreneurs hesitant to go public, even if they meet listing requirements (Meluzín et al., 2018).

Research hypothesis: The fear of losing ownership and control rights over the firm negatively influences the decision to go public.

Information and Compliance Costs

Going public presents companies with significant costs, both direct and indirect. Direct costs include legal services, underwriting, printing of the prospectus and accounting fees. Indirect costs encompass capital dilution leading to control loss, time and effort in preparation and expenses for financial disclosure and shareholder communication (Mayur & Kumar, 2013). These costs pressure companies, particularly smaller ones, potentially dissuading them from going public. Compliance costs further influence company reluctance (Acquaah, 2015). Public entities are required to regularly disclose financial information, maintain effective investor relations and comply with corporate governance regulations. Listed companies must meet specific IPO conditions for share liquidity, which contributes to recurring costs, including annual report printing, stock exchange listing subscriptions and accounting/auditing expenses. Despite having the necessary infrastructure, potential compliance costs may deter private firms from public offerings (Mayur & Kumar, 2013). Larger companies are able to cope better with going public costs compared to smaller ones (Ritter, 1987), with GPDs more prevalent among larger companies (Pagano & Röell, 1998). This reflects the greater financial capacity of larger firms to address initial and recurring financial burdens, including compliance costs.

Research hypothesis: The fear of the loss of information and compliance costs negatively influences the decision to go public.

Efficient Stock Market

An efficient market infrastructure serves as a compelling platform for companies considering an IPO. Robust trading platforms promote seamless trading and enhance liquidity for newly listed companies. Effective market regulations and enforcement mechanisms foster transparency and investor protection, strengthening market credibility and confidence (Shi, 2008). Well-regulated markets prove instrumental for IPO-planning companies, creating stable trading environments that provide assurance and solid foundations (Krishnamurti et al., 2003). This reliability empowers companies to navigate public market complexities with enhanced confidence. Robust market infrastructure provides access to diverse investors, including institutional players, enabling heightened IPO demand and enhancing pricing and attractiveness (Dicle & Levendis, 2013). By facilitating broad investor access, market infrastructure cultivates environments conducive to thriving IPO ecosystems, setting the stage for success and long-term growth.

Research hypothesis: An efficient stock market positively influences the decision to go public.

Well-established Stock Market

A good market framework serves as an attractive entry point for companies that are willing to become a publicly traded entity. Effective and efficient trading platforms facilitate easy trading and improved liquidity for newly listed companies. Market regulations and their enforcement mechanisms are effective in creating transparency and protection of investors in the market, which enhances market credibility and confidence in the market (Shi, 2008). The presence of well-regulated markets is beneficial to IPO-planning companies since this forms a stable and predictable trading environment that provides assurance and solid foundations for share trading (Krishnamurti et al., 2003). This dependability gives businesses the strength to go through the intricacies of the public market with greater confidence. A well-developed market infrastructure offers a wide range of investors, including institutional investors, which allows an increased IPO demand and contributes to the pricing and attractiveness of the offering (Dicle & Levendis, 2013). By ensuring an expansive reach to investors, market infrastructure helps create an environment favourable to flourishing IPO ecosystems, which provides the potential for success and long-term growth.

The research hypothesis: A well-established stock market positively influences the decision to go public.

METHODOLOGY

Prior research on this topic has employed two primary data collection methods: secondary data derived from published financial statements of public and private companies (Albornoz & Pope, 2004; Boehmer & Ljungqvist, 2004; Chemmanur et al., 2010; Chorruk & Worthington, 2010; Mayur & Kumar, 2013; Pagano et al., 1998) and primary data collected through surveys of individuals who influence the decision-making process of the companies under investigation. In the Sri Lankan context, private companies seldom publish their financial statements publicly, rendering data collection challenging. Consequently, this study adopts a survey strategy to collect primary data through questionnaires for the investigation. Previous survey-based studies have mainly used descriptive statistics, particularly frequency and mean of responses, to identify primary reasons and constraints (Bancel & Mittoo, 2009; Brau, 2012; Chandriotis, 2013; Meluzin et al., 2016). Acquaah (2015) introduced a questionnaire and applied exploratory factor analysis (EFA) and binary logistic regression (BLR) for the first time in the literature to examine the GDP in Ghana. Maghyereh and Awartani (2018) adapted Acquaah’s questionnaire and employed similar methodologies for a comprehensive investigation in Abu Dhabi. In line with these prior studies, we adapted both of their questionnaires to match the Sri Lankan context to collect data for this study. Meanwhile, we have noted that these prior studies have commonly used the company’s current state (public/private) as the dependent variable, often measured using single-dichotomous questions. However, relying on such a question to assess the company’s GPD may not be the most appropriate approach. Because the GPD is subjective and abstract, it cannot be accurately measured with a single-dichotomous question. Therefore, we have developed a new multidimensional scale to capture the company’s decision reasonably. We have validated the new scale through expert panels and item response theory analysis, as recommended by DeVellis (2016) and Koskey et al. (2018). Ultimately, this approach enables us to measure a company’s GPD more accurately and gain insights into factors that may influence the decision.

The first section of the questionnaire focuses on gathering basic information about the companies, including their current state and GPD. The subsequent sections measure the different aspects that influence the GPD. The responses measured on the Likert scale range from strongly disagree (1) to strongly agree (7). Finally, the questionnaire was converted into electronic form for the convenience of issuing it to and receiving responses from target respondents. The study population comprises publicly quoted (public) companies and unquoted (private) companies that meet the listing requirements of the CSE. The survey targeted individuals who could influence the GPD among the population. The questionnaire was distributed via e-mail. The contact details were taken from the Central Depository System (CDS) and the membership directories of leading accounting professional bodies. The questionnaire included a few questions to ensure the respondents represent the correct target population. Therefore, it helped minimize the risk of selecting inappropriate responses in the sample. Lastly, the survey collected 283 responses out of 5,137 emails. The basic information about the survey respondents is presented in Table 2.

Table 2:

Descriptive Statistics.

		Count	ColumnN %
Respondents’ current designation	Chief executive officer (CEO)	18	6.4%
	Chief financial officer (CFO)/accountant/finance manager	166	58.7%
	General manager (GM)/director	61	21.6%
	Other	38	13.4%
Corporate structure	Independent company	172	60.8%
Corporate structure	Member of group of companies	111	39.2%
Significant control	Family controlled	64	22.6%
	A large number of dispersed shareholders	67	23.7%
	Few numbers of large individual shareholders	108	38.2%
	Institutional investors	25	8.8%
	Venture capital	19	6.7%
Company age	Below 15 years	50	17.7%
	Between 16 and 30 years	119	42.0%
	Between 31 and 50 years	89	31.4%
	Above 51 years	25	8.8%
Sector	Retailing/consumer services	68	24.0%
Sector	Other	215	76.0%
Total assets	Below 5,000 million rupees	36	12.7%
	5,001–10,000 million rupees	75	26.5%
	10,001–20, 000 million rupees	117	41.3%
	Above 20, 000 million rupees	55	19.4%
Total equity capital in million rupees	101–500 million rupees	32	11.3%
	501–2,500 million rupees	81	28.6%
	2,501–5,000 million rupees	132	46.6%
	Above 5,000 million rupees	38	13.4%
The number of employees	Below 500	91	32.2%
	501–1,000	118	41.7%
	1,001–5,000	51	18.0%
	Above 5,000	23	8.1%
Market competition	Low competitive	30	10.6%
	Moderate competitive	120	42.4%
	Highly competitive	133	47.0%
Nature of products	Traditional	99	35.0%
Nature of products	Innovative/technology intense	184	65.0%
Company current status	Listed company	43	15.2%
	Unlisted but willing to list	105	37.1%
	Unlisted and not willing to list	135	47.7%

Furthermore, the BLR has limitations, is often insufficient to capture complex relationships and is difficult to quantify the effects of predictor variables (Uanhoro et al., 2021). Meanwhile, structural equation modelling (SEM) is highly beneficial in analysing complex causal relationships among variables and accounting for latent variables requiring consideration (Vashist et al., 2021). Partial least squares SEM (PLS-SEM), a type of SEM gaining popularity in various disciplines, is suitable for causal model and theory construction, can handle reflective and formative indicators, and has high statistical power even with small samples (Islam & Shamsuddoha, 2023; Ogbeibu & Gaskin, 2023; Rigdon, 2014). Therefore, PLS-SEM is more suitable than BLR for investigating the phenomenon and providing more profound insights into the literature. This approach considers multiple factors and provides a more comprehensive understanding of the decision, allowing for a more detailed assessment of the GPD. Despite this, this study uses BLR and SEM to enhance reliability by triangulating the findings (Baker et al., 2019; Dewasiri & Abeysekera, 2022). BLR is employed for the binary-dependent variable, while SEM is employed for the new multidimensional scale. This approach ensures a more comprehensive understanding of the phenomena, enables a deeper interpretation of the results and increases its trustworthiness (Vivek & Nanthagopan, 2021). Furthermore, it balances the limitations of one method with the strengths of the other, thereby strengthening the study’s validity.

FINDINGS AND DISCUSSION

Factor Analysis

The EFA outputs are presented in Table 3, with a Kaiser–Meyer–Olkin (KMO) value of 0.927, exceeding Kaiser’s minimum threshold, and Bartlett’s test of sphericity is significant, indicating that the sample dataset and correlation structure are suitable for factor analysis. Using the principal axis factoring extraction method and Promax rotation, the factor analysis with an eigenvalue of 1 rule was conducted on 40 items, resulting in a seven-factor solution that accounted for 63.616% of the variance in the sample dataset. After applying a minimum factor loading of 0.40 for item retention, the response items for the seven identified factors were assigned. Table 4 presents the labels for the seven factors (including one factor for the dependent variable), the respective response items, factor loadings and Cronbach’s α. To assess reliability, Cronbach’s α was used to evaluate the internal consistency of the factors. After conducting the reliability analysis, the factor score for BLR was obtained as the mean value of all response items included in a respective factor.

Table 3:

Factor Analysis.

Factor	Initial Eigenvalues			Extraction Sums of Squared Loadings
Factor	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	12.179	30.447	30.447	11.865	29.663	29.663
2	4.453	11.133	41.580	4.164	10.410	40.073
3	3.153	7.883	49.463	2.834	7.085	47.157
4	2.435	6.087	55.550	2.145	5.362	52.519
5	2.073	5.183	60.732	1.695	4.238	56.757
6	1.785	4.462	65.194	1.449	3.622	60.379
7	1.634	4.086	69.281	1.295	3.237	63.616
Kaiser–Meyer–Olkin measure of sampling adequacy.						0.927
Bartlett’s test of sphericity χ²						7,998.501

Table 4:

Factor Label and Response Items.

Response Items		Loading
Factor 1: Market efficiency = 0.929
	The stock prices on the CSE accurately reflect the available information about the value and activities of companies*.	0.896
	The stock prices of companies that are listed on the CSE change reasonably*.	0.700
	Listing on the CSE has a potential incremental impact on the company’s growth and performance^#.	0.859
	The number of companies’ shares that are bought and sold in the CSE is sufficient enough*.	0.79
	The transparency and disclosure rules in the CSE are stringent*.	0.895
	The level of participation of investors in the CSE is reasonable.	0.877
Factor 2: Information and compliance cost = 0.919
	Listing on the CSE requires the company to disclose a lot of information on the value of the company and insiders^#.	0.762
	Listing on the CSE exposes the company’s business activities to its competitors because of the extensive disclosure requirements*.	0.857
	Listing on the CSE requires the company to follow stringent legal rules regarding reporting business activities*.	0.906
	Listing on the CSE involves many accountings, legal and selling costs because of the extensive financial disclosure and independent audits of financial statements^#.	0.809
	CSE has fair listing requirements and procedures for listing stocks	0.883
Factor 3: Corporate image and liquidity = 0.914
	A listing on the stock exchange will allow companies’ managers to be monitored by their owners/stockholders more efficiently*.	0.833
	A benefit of the IPO is that it allowed our company to increase options to change control of the company^#.	0.876
	A listing on the stock exchange will increase the liquidity of companies’ stocks to be bought and sold quickly^#.	0.857
	A benefit of the IPO is that it allowed for the original owner’s retirement*.	0.828
	A listing on a stock exchange would enhance companies’ prestige, image and credibility*.	0.913
	A listing on the stock exchange will create more incentive compensation schemes for managers*.	0.455
Factor 4: Market establishments = 0.918
	Regulatory control and supervision that prevent withholding relevant information, misrepresenting information, forgery, and fraud are strong in the CSE*.	0.891
	Listing costs and administrative expenses in the CSE are reasonable*.	0.808
	CSE has an efficient centralized depository system (CDS).	0.870
	Stock brokers are reliable and efficient enough to provide better services.	0.899
	Underwriters are industry experts and have connections.	0.688
Factor 5: Intention to go public = 0.883
	Listing stocks in CSE will increase/increased opportunities to add more value to our company.	0.786
	Our company observers/observed the capital market to find a better time to sell equity shares.	0.767
	Our company thinks/thought about an IPO underwriter to list in the capital market.	0.826
	Our company considers/considered the listing procedure and regulations of CSE.	0.789
	Our company considers/considered restructuring of business operations of the company.	0.823
	Our company is/was upgrading its corporate governance standards.	0.500
Factor 6: Financing for future growth at a lower cost of capital = 0.859
	A benefit of the IPO is that it allowed our company to gain additional financing for future growth^#.	0.787
	Listing on the stock exchange will allow companies to finance cheaply*.	0.786
	A benefit of the IPO is that it allowed our company to reduce its bank loan and other debt^#.	0.630
	A listing on the stock exchange will increase the bargaining power with banks and decrease the cost of credit*.	0.909
	A listing on a stock exchange will create public shares (opportunities) for future mergers and acquisitions*.	0.502
	A company processes IPO when the market is strong and gets higher prices.	0.696
Factor 7: Ownership and control rights 0.878
	Listing on the CSE may threaten the founders’ control (big shareholders) in the company’s management*.	0.762
	The company’s founders should let family members control and manage rather than allow outsiders to own part of the company^#.	0.762
	Listing on the CSE will allow the company’s managers to be controlled by their owners/stockholders^#.	0.900
	Listing on the CSE will make the company much more vulnerable to hostile takeovers#.	0.660

Note: *Maghyereh and Awartani (2018). ^#Acquaah (2015).

Control Variables

When analysing a company’s decision to go public, it is crucial to consider and control other factors to draw accurate inferences. The life cycle theory and studies by Pagano et al. (1998), Black and Gilson (1998) and Mayur and Kumar (2013) suggest that a company’s size and age can influence its GPD. Additionally, Chemmanur and He (2011) suggest that market competition for a company’s product may also play a role, and Spiegel and Tookes (2008) postulate that product innovativeness may also play a role. Further, Mayur and Kumar (2013) indicate that industry characteristics, such as those in the retail sector, may also factor in a company’s GPD. By considering and controlling all these factors, the analysis provides a more comprehensive understanding of a company’s GPD.

Model Development

This study employs three models to investigate the phenomenon. Model 1 includes six factors obtained from EFA: financing for future growth at a lower cost of capital (FIN), corporate image and liquidity (IMG), ownership and control rights (OWN), information and compliance cost (COS), market establishment (EST) and market efficiency (EFF). Model 2 adds firms’ age and size as categorical control variables, and model 3 extends model 2 by introducing three additional categorical control variables: product market competition (COMP), sector (SECT) and product type (PROD). Firm size is categorized as small (SIZE(0)), medium (SIZE(1)), large (SIZE(2)) and very large (SIZE(3)). Firm age is categorized as very young (AGE(0)), young (AGE(1)), maturated (AGE(2)) and older (AGE(3)). The product market competition is categorized as low (COMP((0)), moderate (COMP((1) and high (COMP((1)). The sector is categorized as (retailing/consumer services (SECT((0)), other sectors (SECT((1)). The product type is categorized as (traditional (PROD((0)), innovative/technology intense (PROD((1)). The reference categories used for the control variables are small for firm size (SIZE(0)), very young (AGE(0)) for firm age, low for product market competition (COMP(0)), other sectors for product market sector (SECT(0)) and traditional (PROD(0)) for product innovativeness.

Construct Validity and Reliability

An analysis of the factor loadings of indicators is conducted to assess the reliability of constructs for PLS-SEM, where a threshold of 0.70 is considered acceptable. The measurement model of model 1, depicted in Figure 1, shows that all reflective indicators of the constructs exceeded this threshold. The reliability and validity indices are presented in Table 5. Composite reliability and Cronbach’s α are used to evaluate internal consistency, where values exceeding 0.8 indicate high consistency. The average variance extracted (AVE) is calculated to assess convergent validity, which should exceed 0.5. Discriminant validity is evaluated using the Fornell and Larcker (1981) criterion, which requires the square root of the AVE of each construct to be greater than its factor loading. All constructs in this study meet these criteria, confirming that the constructs are reliable and valid in SEM.

Figure 1:

Measurement Model.

Table 5:

Reliability and Validity.

	Cronbach’s α	Composite Reliability	AVE	Discriminant Validity
	Cronbach’s α	Composite Reliability	AVE	GPD	FIN	IMG	OWN	COST	EST	EFF
GPD	0.911	0.934	0.738	0.859
FIN	0.890	0.920	0.698	0.497	0.835
IMG	0.936	0.952	0.797	0.531	0.526	0.893
OWN	0.881	0.918	0.737	−0.449	−0.362	−0.334	0.859
COST	0.920	0.940	0.757	−0.432	−0.399	−0.336	0.506	0.870
EST	0.921	0.941	0.760	0.310	0.234	0.207	−0.187	−0.141	0.872
EFF	0.933	0.948	0.752	0.348	0.347	0.257	−0.298	−0.196	0.430	0.867

Note: FIN: Financing for future growth at a lower cost of capital; IMG: Corporate image and liquidity; OWN: Ownership and control rights; COS: Information and compliance cost; EST: market establishment; EFE: market efficiency.

Hypothesis Tests

Each model is tested in BLR and PLS-SEM against the proposed hypotheses. Table 6 displays BLR output. Panel A shows the unstandardized regression slope coefficients (B), odds ratios (Exp(B)) and significance levels (Sig.) for each predictor variable across three BLR models. Meanwhile, Table 7 displays PLS-SEM analysis output. Panel A shows the proposed paths, path coefficients and t values for the three models of the SEM. The path coefficients measure the degree of influence of the factors on the GPD. The significance of the path coefficient is determined using a bootstrap procedure with 5,000 subsamples.

Table 6:

BLR Analysis.

Panel A: Variables in the Equation.
		Model 1			Model 2			Model 3
Factor/Variable		B	Exp(B)	Sig.	B	Exp(B)	Sig.	B	Exp(B)	Sig.
FIN		0.418	1.520	0.033	0.512	1.669	0.038	0.506	1.658	0.043
IMA		0.743	2.102	0.000	0.825	2.282	0.000	0.858	2.358	0.000
COS		−0.229	0.795	0.149	−0.209	0.811	0.461	−0.091	0.913	0.763
OWN		−0.586	0.557	0.001	−1.106	0.331	0.000	−1.059	0.347	0.000
EST		−0.032	0.969	0.853	0.503	1.654	0.008	0.523	1.687	0.006
EFF		0.268	1.307	0.057	−0.234	0.791	0.274	−0.195	0.823	0.371
AGE(0)							0.035			0.020
AGE(1)					−0.160	0.852	0.807	−0.192	0.825	0.773
AGE(2)					1.177	3.244	0.174	1.349	3.855	0.130
AGE(3)					2.253	9.515	0.092	2.501	12.193	0.072
SIZE(0)							0.003			0.007
SIZE(1)					0.963	2.620	0.178	0.841	2.319	0.250
SIZE(2)					2.265	9.631	0.006	2.128	8.397	0.011
SIZE(3)					3.857	47.330	0.001	3.560	35.152	0.002
COMP(0)										0.788
COMP(1)								0.400	1.492	0.546
COMP(2)								0.466	1.593	0.491
PROD(1)								0.583	1.791	0.236
SECT(1)								-0.403	0.668	0.364
Constant		−2.919	0.054	0.077	−4.180	0.015	0.046	−5.947	0.003	0.020
Panel B: Model Fit.w
		Model 1			Model 2			Model 3
−2 Log likelihood		276.228			195.574			193.09
Cox–Snell R²		0.335			0.500			0.504
NagelkerkeR²		0.447			0.667			0.673
Panel C: Hosmer and Lemeshow test
χ²		7.681			7.853			13.736
Sig.		0.465			0.448			0.089
Panel C: Classification Table.
		Model 1			Model 2			Model 3
	Predicted: Public Company
	No	Yes	% Correct	No	Yes	% Correct	No	Yes	% Correct
Observed: Public Company	No	98	37	72.6	113	22	83.7	116	19	85.9
Observed: Public Company	Yes	26	122	82.4	22	126	85.1	16	132	89.2
Overall Percentage		77.70%			84.50%			87.63%
Accuracy		82.43%			85.14%			87.43%
Specificity		76.73%			84.14%			89.19%
Sensitivity		79.03%			83.70%			85.93%
Precision		77.74%			84.45%			87.63%

Note: FIN: Financing for future growth at lower cost of capital; IMA: Corporate image and liquidity; COST: Information and compliance costs; OWN: Ownership and control rights; EST: Market establishment; EFF: Market efficiency.

Table 7:

PLS-SEM Analysis.

Panel A: SEM Path Analysis
		Model 1		Model 2			Model 3
Path		Path Coefficient	t Value	Path Coefficient		t Value	Path Coefficient		t Value
FIN -> GPD		0.166*	2.881	0.155*		3.819	0.147*		3.59
IMG -> GPD		0.290*	5.923	0.217*		5.664	0.225*		5.699
OWN -> GPD		−0.169*	2.744	−0.238*		4.773	−0.233*		4.461
COST -> GPD		−0.149*	2.748	−0.072		1.238	−0.021		0.371
INFR -> GPD		0.123*	2.25	0.125*		3.079	0.124*		3.132
EFF -> GPD		0.084	1.475	0.02		0.493	0.036		0.877
AGE(1) -> GPD				0.062		1.078	0.059		1.062
AGE(2) -> GPD				0.197		2.885	0.199*		2.973
AGE(3) -> GPD				0.174		3.007	0.171*		2.949
SIZE(1) -> GPD				0.224*		3.447	0.228*		3.654
SIZE(2) -> GPD				0.445*		6.156	0.440*		6.301
SIZE(3) -> GPD				0.532		7.096	0.526*		7.111
COMP(1) -> GPD							0.098		1.475
COMP(2) -> GPD							0.132		1.884
PROD(1) -> GPD							0.052		1.347
SECT(1) -> GPD							−0.070*		2.138
Panel B: Effect Size
Factor	Model 1			Model 2			Model 3
FIN	0.031		Small	0.050	Small		0.047	Small
IMG	0.104		Small	0.109	Small		0.118	Small
OWN	0.035		Small	0.079	Small		0.075	Small
COST	0.027		Small	0.006	No		0.000	No
EST	0.022		Small	0.042	Small		0.042	Small
EFF	0.009		No	0.001	No		0.003	No
Small				0.061	Small		0.065	Small
Medium				0.155	Medium		0.155	Medium
Large				0.185	Medium		0.182	Medium
Young				0.005	No		0.004	No
Middle				0.030	Small		0.031	Small
Mature				0.033	Small		0.032	Small
Moderate							0.011	No
High							0.019	No
Sector							0.017	No
Product							0.006	No
Panel C: Model Fit.
	Model 1			Model 2			Model 3
R square	0.444			0.710			0.721
Adjusted R square	0.432			0.697			0.704
Q square	0.311			0.507			0.513
SRMR	0.040			0.037			0.036
NFI	0.905			0.899			0.891

*Significant at 5%.

The BLR indicates that ‘financing for future growth at a lower cost of capital’ and ‘corporate image and liquidity’ have positive slope coefficients, greater than one odds ratios and significant p values in all three models, indicating their positive influence on a company’s likelihood of GPD. The SEM analysis provides similar findings, with positive path coefficients and significant t values indicating that these two factors positively influence companies’ decision to go public. This provides robust evidence to reject the null hypothesis and confirm the positive impact of these two factors on the GPD. These results suggest that Sri Lankan companies consider financing future growth at a lower cost of capital and improving corporate image and liquidity as crucial factors when deciding to go public. This finding is consistent with Pagano et al. (1998), Subrahmanyam and Titman (1999), Boehmer and Ljungqvist (2004), Huyghebaert and Van Hulle (2006), Brau (2012), Meluzin and Zinecker (2014) and Riyath and Dayaratne (2025), which empirically find that companies go public to raise capital for growth and expansion. Further, Modigliani and Miller (1963), Scott (1977), Holmström and Tirole (1993), Booth and Chua (1996) and Mayur and Kumar (2013) find that companies go public to lower the cost of capital of firms. Furthermore, Booth and Chua (1996), Black and Gilson (1998), Pagano et al. (1998), Bolton and Von Thadden (1998), Brau (2012) and Mayur and Kumar (2013) argue that firms go public to increase the liquidity of their shares in capital markets. Subrahmanyam and Titman (1999), Stoughton et al. (2001), Mazzola and Marchisio (2002), Burton et al. (2006) and Chemmanur and He (2011) argue that films go public to enhance corporate visibility and media attention.

The BLR indicates that ‘Ownership and control rights’ has negative slope coefficients, odds ratios with less than one and significant p values in all three models, indicating its negative influence on a company’s likelihood of GPD. The SEM analysis provides similar findings, with negative path coefficients and significant t values indicating that ‘ownership and control rights’ negatively influence companies’ GPD. This provides robust evidence to reject the null hypothesis and confirm the negative impact of the factor on the GPD in Sri Lanka. This finding is consistent with Pagano (1993), Zingales (1995), Pagano et al. (1998), Mayur and Kumar (2013), Acquaah (2015), Maghyereh and Awartani (2018) and Riyath and Dayaratne (2025), who argue that due to their concern about losing ownership, control and being possible targets for hostile takeovers, companies are less inclined to become public by listing their shares on the stock market. Meanwhile, in the BLR, the ‘information and compliance cost’ has negative slope coefficients, odds ratios with less than one and insignificant p values in all three models, indicating that the information and compliance costs do not significantly affect companies’ likelihood of GPD. Further, in the SEM, the factor had a significant negative path coefficient in model 1. However, the significance disappeared after including the control variables in the model. The analysis does not provide sufficient evidence to reject the null hypothesis, suggesting that ‘information and compliance cost’ does not significantly affect the GPD of companies in Sri Lanka. This finding is consistent with Maghyereh and Awartani (2018), who find that disclosure requirements and costs do not influence the likelihood of GPD of companies in Abu Dhabi. However, many studies in other contexts argue that many potential companies are reluctant to go public due to the higher compliance cost and loss of competitive information (Acquaah, 2015; Maksimovic & Pichler, 2001; Tinic, 1988; Yosha, 1995). The insignificance of loss of information and compliance cost in the Sri Lankan context claim that compliance cost and loss of confidential information may be immaterial for Sri Lankan companies compared to other countries.

Results indicate that in BLR, the slope coefficient for ‘market establishment’ is significant in models 2 and 3 but insignificant in model 1, indicating that market establishment significantly affects companies’ likelihood of GPD in Sri Lanka. The SEM provides a more robust finding than BLR. The SEM path coefficient of the factor is significant in all three models. Therefore, it provides evidence to reject the null hypothesis and confirm that the market establishment has a positive impact on the decision to go public in Sri Lanka. This finding is consistent with Maghyereh and Awartani (2018), who state that the poor quality of the stock market discourages firms from listing companies. It emphasizes that the stock market’s development significantly affects capital accumulation, productivity and economic growth because it enables more effective resource allocation, fosters capital creation and leads to fast economic expansion (Caporale et al., 2004; Dicle & Levendis, 2013). Conversely, the slope coefficient for ‘market efficiency’ is insignificant across all three models in both BLR and SEM. The analysis does not provide sufficient evidence to reject the null hypothesis, suggesting that ‘market efficiency’ does not significantly affect the GPD of companies in Sri Lanka. This finding is consistent with Acquaah (2015), who also finds that market efficiency has an insignificant impact in Ghana. However, Maghyereh and Awartani (2018) emphasize that market inefficiency significantly influences the GPD of companies in Abu Dhabi.

The analysis also examines the impact of various firm-level characteristics, such as firm age, size, product innovativeness, product market competitiveness and sector, on the GPD in Sri Lanka. AGE(0), SIZE(0), COMP(0), PROD(0) and SECT(0) served as reference categories in BLR and SEM. The BLR coefficients for all three dummy AGE categories are insignificant compared to their reference category in both models. It suggests that according to the BLR, firm age does not significantly affect the likelihood of companies’ GPD in Sri Lanka. However, the SEM path coefficients for older age categories, such as AGE(2) and AGE(3), are significant. The finding suggests that the firm age of companies significantly impacts the GPD of companies in Sri Lanka. This finding is consistent with the lifecycle theory of GPD. Many studies argue that older companies most likely to go public than young companies (Black & Gilson, 1998; Chemmanur & Fulghieri, 1999; Maghyereh & Awartani, 2018; Mayur & Kumar, 2013; Pagano, 1993; Yang-Pin & Wei, 2007; Zingales, 1995). However, Acquaah (2015) found that firm age does not affect the probability of GPD in Ghana. The BLR coefficients for firm size categories such as SIZE(2) and SIZE(3) are significant. However, the SEM path coefficients for all size categories are significant. Indicating that firm size significantly impacts the GPD in Sri Lanka. This finding is consistent with Black and Gilson (1998), Pagano et al. (1998), Mayur and Kumar (2013), Acquaah (2015) and Maghyereh and Awartani (2018). They suggest that larger companies are more likely to go public than small companies.

Regarding the influence of product market competition, the BLR and SEM coefficients of COMP(1) and COMP(2) are insignificant, suggesting that the levels of the product competitive environment have no significant impact. It reveals that companies’ product market competitive environment has no significant impact on their GPD. This finding is inconsistent with Chemmanur and He (2011), Acquaah (2015) and Maghyereh and Awartani (2018), who find that product market conditions significantly affect GPD of companies. Similarly, the BLR and SEM coefficients of PROD(1) are insignificant, indicating that product innovativeness does not significantly impact the decision to go public. It reveals that the product innovativeness of companies has no significant impact on the GPD of companies. This finding is consistent with Maghyereh and Awartani (2018).

Finally, analysis indicates that although the BLR coefficient for SECT(1) appears insignificant, a SEM analysis revealed it to be significant. This suggests that a firm’s sector is critical in its decision to go public in Sri Lanka. Particularly, the study suggests that companies in the retailing/consumer services sector are less inclined to pursue initial public offerings than those in other sectors. This finding is consistent with Acquaah (2015) and Maghyereh and Awartani (2018). However, Mayur and Kumar (2013) find that companies that fall in the retail sector are more likely to go public than others.

The effect size, which indicates the strength of the relationship between two variables, is an important statistical term. It measures the magnitude of an effect independently of the sample size. In PLS-SEM, Cohen’s f-square is the most widely used effect size measure (Cohen, 1992). According to Cohen’s criteria, an f-square value of ≥ 0.02 indicates a small effect size, ≥ 0.15 reflects a medium effect size and ≥ 0.35 indicates a large effect size. Table 7, Panel B, presents Cohen’s f-square for each independent variable used in predicting the GPD in PLS-SEM. The results show that company size categories, medium and large, have a medium effect size in predicting the GPD. The factors: ‘financing for future growth at lower cost of capital’, ‘corporate image and liquidity’, ‘ownership and control rights’ and ‘market establishment’, and company size categories: small, and company age categories: middle and mature have a small effect size in predicting the GPD. On the other hand, the factors: ‘information and compliance cost’, ‘market efficiency’ and the company age category: young, product market competition, product innovativeness and sector have no significant effect in predicting the GPD. According to Cohen’s f-square, company size is the most dominant factor in predicting the GPD, followed by ‘corporate image and liquidity’ and ‘loss of ownership’ compared to all other factors.

Model Validity and Performance

Table 6, Panel B, shows model fit indices for the three BLR models. The −2 log-likelihood values, which quantify the discrepancy between the predicted probabilities of the model and the actual binary outcomes of the data, suggest that the models fit the data better than the base model. The Cox–Snell and Nagelkerke statistics measure the quality of logistic regression. Accordingly, model 1 explains 44.7% of the variance, while models 2 and 3 explain 66.7% and 67.3%, respectively, indicating that the higher models are more accurate than the lower ones. The Hosmer–Lemeshow test results confirm that all three models fit the dataset satisfactorily, as evidenced by p values exceeding 0.05. Further, BLR models’ performance in predicting GPD is evaluated using various metrics, including the classification table (see Panel C), the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) (see Figure 2). Including control variables in the model improves accurate predictions. All three models exhibit strong predictive performance, with model 3 outperforming models 1 and 2. AUC values surpass 50%, indicating better-than-chance differentiation between the two categories. Logistic regression is an effective approach for predicting GPD, and including control variables can enhance prediction accuracy.

Figure 2:

ROC Curve and AUC.

The PLS-SEM model fit indices are presented in Table 7, Panel C. The Q-square metric is crucial to evaluate the predictive relevance of structural models, with values above 0.311 indicating significant predictive significance (Hair et al., 2014). All three models showed Q-square values above this threshold, indicating an acceptable level of predictive validity. Additionally, the standardized root mean squared residual (SRMR) metric measures the average difference between observed and predicted correlations, with a desirable range of 0 to 0.08. The SRMR value of 0.04 indicates a good fit. Furthermore, Bentler and Bonett’s normed fit index (NFI) metric, which compares a model’s χ² value to a benchmark, demonstrates that model 1 has an excellent fit (NFI = 0.905), and models 2 and 3 have good fits, although slightly below the threshold value of 0.90. PLS algorithm analysis of model 1 shows a good predictive capacity (R-squared value of 0.444). Further, models 2 and 3 exhibit significant improvements, explaining 71% and 72.1% of the variance, respectively.

CONCLUSION

The effective functioning of any financial system’s capital market depends on the number of companies and their share capital. Consequently, the decision of companies to go public plays a crucial role in this process. However, in Sri Lanka, many companies remain private despite meeting the listing requirements, resulting in the CSE’s categorization as a frontier market. To address this issue, this study aims to identify the factors that influence the decision of companies to go public in Sri Lanka. The data are collected by surveying higher authorities of relevant companies that influence the GPD. This study employs EFA, BLR and PLS-SEM to investigate the factors influencing the GPD in Sri Lanka. Financing for future growth, corporate image, liquidity, ownership and control rights and market establishments significantly impact the GPD. However, information, compliance costs and market efficiency do not exert a significant influence. Control variables such as company size, age and sector play a role, while product market competition and innovation do not significantly affect the decision.

The study addresses the lack of financial statement disclosure among private companies in Sri Lanka, utilizing a survey strategy to explore the subjective factors that influence the decision to go public. While this approach provides valuable insights into firm-level decision-making factors, it has limitations, including a limited analysis of objective factors (particularly financial performance), the exclusion of macroeconomic variables that may moderate these relationships, and a reliance on subjective respondent interpretations. Future research should incorporate both subjective and objective factors, macroeconomic variables, comparative regulatory analysis across emerging markets, larger sample sizes and longitudinal approaches to provide a more comprehensive understanding of IPO decisions in emerging markets.

The study’s results offer several policy implications for capital market policymakers in Sri Lanka. First, they should attract more local and foreign investors to the CSE to provide ample capital for private companies that opt to go public. Second, policymakers should focus on enhancing the stock market’s trading platforms, international recognition and liquidity to increase investor confidence and attract well-performing companies. Lastly, special campaigns and initiatives should educate potential companies and their controlling parties about the benefits of listing and their options. These efforts may help dispel misunderstandings and fears surrounding the loss of ownership and control rights. Our findings should be interpreted within the specific macroeconomic and regulatory context of Sri Lanka. The relative importance of factors such as financing needs and market establishment may vary with economic cycles and differ in markets with alternative regulatory frameworks.

Footnotes

DECLARATION OF CONFLICTING INTERESTS

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

FUNDING

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID ID

Mohamed Ismail Mohamed Riyath

Mohamed Ismail Mohamed Riyath is a Senior Lecturer at the South Eastern University of Sri Lanka. He holds a PhD in Finance from Sabaragamuwa University and MSc in Management (Finance) from the University of Sri Jayewardenepura. With multiple Scopus-indexed publications in Q1 journals, including the Journal of Financial Reporting and Accounting, he is a Fellow Member of ICMA (Sri Lanka) with IBM Data Analytics and Google Business Intelligence certifications. He serves as Editor-in-Chief of the Journal of Accounting and Finance and has received the Vice Chancellor’s Award 2025 for Outstanding Senior Researcher and Best Paper Awards in Sri Lanka, Qatar and India.

e-mail: riyath@seu.ac.lk

Debehaarage Athula Indunil Dayaratne is a Professor in Finance at the Department of Accountancy and Finance, Sabaragamuwa University of Sri Lanka. He holds a PhD in Economics from the University of Colombo and MSc in Management from the University of Sri Jayewardenepura. With over 25 years of service, he has served as Dean of the Faculty of Management Studies and chairs the Board of Study for Graduate Studies. He serves as Director of the University Business Linkage Cell and holds editorial positions, including the Sri Lanka Journal of Economics. His research specializes in capital markets, IPO dynamics and capital asset pricing models.

e-mail: indunil@mgt.sab.ac.lk

Athambawa Jahfer is a Professor and former Head of the Department of Accountancy and Finance at South Eastern University of Sri Lanka. He holds a PhD in Business Administration from Yokohama National University, Japan, under the prestigious Monbukagakusho Scholarship, and Japan Foundation post-doctoral Fellow. His research specializes in financial market development, capital structure and corporate finance, with publications in Scopus-indexed journals including International Journal of Managerial and Financial Accounting and Global Business Review. He previously served as Director of the Staff Development Centre and Centre for Quality Assurance, and contributes to national quality assurance as a University Grants Commission programme reviewer.

e-mail: jahfer@seu.ac.lk

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