The analysis used data on 2,484 firm-level observations covering 2016–2022. Researchers applied ordinary least squares regression to examine the relationship between TSG and corporate performance. They also conducted robustness checks and endogeneity tests, which the abstract describes as supporting the validity of the results. Resource-based and institutional theories were integrated to explain how TSG may create value for firms.

The available summary does not provide the regression models, effect sizes, confidence intervals, performance measures, construction of the TSG variable, treatment of missing data or detailed results of the endogeneity analyses. Because this report is based only on the abstract, the magnitude and precision of the associations cannot be independently assessed from the supplied evidence.

The findings are limited to Chinese listed companies and the 2016–2022 study period, so they should not be generalized to smaller firms, other countries or other periods without further study. The abstract presents practical implications for managers, including strategically aligning TSG initiatives with digital transformation and external communication, but does not establish that either strategy alone causes improved performance. The article was shared as an accepted, open-access version subject to further edits before the final Version of Record.

Subject of Research: Empirical analysis of technology for social good and corporate performance in Chinese A-share listed firms

Article Title: The impact of corporate technology for social good on corporate performance: evidence from Chinese listed companies

Article References: Ruan, R., & Chen, W. (2026). The impact of corporate technology for social good on corporate performance: evidence from Chinese listed companies. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09470-4

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09470-4

Keywords: technology for social good, corporate performance, Chinese listed firms, A-share companies, digital transformation, media attention, financial performance, innovation performance, endogeneity tests, resource-based theory, impact, corporate

The research, which forms part of Pham’s doctoral work at National Economics University in Hanoi, analyzes a balanced panel of 135 Vietnamese listed industrial firms covering the period from 2016 to 2024. That window is analytically valuable because it spans years of robust pre-pandemic expansion, the sharp disruption of COVID-19, and the uneven recovery that followed. By holding the sample constant across all nine years, the authors ensure that every firm contributes equally to the analysis, avoiding the selection biases that can creep in when companies enter or exit a dataset mid-study. The result is a comprehensive portrait of how the financial anatomy of Vietnam’s industrial listed sector has evolved through one of the most turbulent stretches in modern economic history.

What sets the study apart methodologically is its three-stage analytical framework, which begins with a technique rarely applied to corporate finance in emerging markets: compositional data analysis. Financial statements are inherently compositional in nature, because the components of a balance sheet or income statement are constrained to sum to a fixed total. Treating raw ratios without accounting for this constraint can produce misleading statistical artifacts, a problem long recognized in fields like geology and ecology but often ignored in financial research. By applying compositional data methods, the authors respect the true geometry of financial statement data, ensuring that the relative structure of assets, liabilities, and equity is analyzed in a mathematically coherent way.

Building on that foundation, the researchers employed Ward’s hierarchical clustering to sort firm-year observations into recurring financial profiles. Ward’s method is a clustering algorithm that merges observations in a way that minimizes the increase in within-group variance at each step, producing compact and internally coherent groups. Rather than forcing firms into arbitrary categories defined by regulators or rating agencies, the clustering lets the data speak for itself, revealing natural groupings based on the relative composition of each firm’s financial statements. The analysis identified four distinct financial profiles, ranging from financially robust firms to the most vulnerable companies whose balance sheets signal elevated structural risk.

The second stage of the framework tackles a persistent problem in performance research: the proliferation of accounting ratios. Analysts can compute dozens of measures covering profitability, leverage, liquidity, efficiency, and growth, but these indicators overlap heavily and often move together in ways that obscure rather than clarify a firm’s true condition. Pham and Dao addressed this by applying principal component analysis to eleven accounting ratios, distilling them into three composite indices that capture the dominant dimensions of variation across the sample. The first index summarizes overall Performance, the second captures Financial Risk, and the third measures Management Efficiency. Principal component analysis works by identifying linear combinations of the original variables that explain the maximum possible variance, effectively compressing a crowded dashboard of ratios into a small set of orthogonal summary scores that retain most of the informational content.

With firms classified into profiles and composite indices constructed, the third stage estimated the relationships between financial risk, management efficiency, and performance using interactive two-way fixed-effects regression models. Fixed-effects models control for time-invariant characteristics of each firm and for shocks common to all firms in a given year, which helps isolate the genuine association between the explanatory indices and performance from confounding factors such as managerial quality or industry-wide cycles. The models included interaction terms between the indices and profile membership, allowing the strength of each relationship to differ across the four financial profiles. Standard errors were clustered at the firm level to account for serial correlation in the residuals of individual companies over time.

The headline findings are both intuitive and consequential. Financial risk was negatively associated with performance across all four profiles, confirming that firms carrying heavier structural financial burdens tend to underperform regardless of their position on the risk spectrum. Crucially, however, the strength of this adverse relationship was not uniform. The most financially vulnerable profile exhibited the strongest negative association, suggesting that risk is not merely a drag on performance but an increasingly punishing one as a firm’s balance sheet deteriorates. This nonlinearity carries a clear implication for investors and policymakers: the marginal cost of additional leverage or liquidity strain is highest precisely where firms can least afford it.

Management efficiency told a different story. The composite index of efficiency was positively associated with performance in several of the identified profiles, and the relationship was significantly stronger in the largest profile of firms. In other words, operational discipline, asset utilization, and cost control appear to translate into superior performance most powerfully for the broad middle of Vietnam’s industrial listed sector, where competitive pressures reward managerial skill most directly. For the smallest and most vulnerable groups, the payoff to efficiency gains appears more muted, possibly because structural financial fragility overwhelms the benefits of good management, or because such firms face constraints that prevent efficient practices from being fully exploited.

Robustness testing is where the study earns particular credibility. The authors re-estimated their models using Driscoll-Kraay inference, a technique designed to handle cross-sectional dependence in panel data, where shocks to one firm may spill over to others through supply chains, credit markets, or common exposure to macroeconomic conditions. They also considered lagged profile membership, addressing the concern that a firm’s current performance might influence its profile classification rather than the other way around. The main patterns remained broadly consistent under both checks, indicating that the core conclusions are not artifacts of a particular statistical assumption or of simultaneity between classification and outcomes.

The broader significance of the research extends beyond Vietnam’s borders. Emerging markets often lack the deep institutional infrastructure of developed economies, making it harder for investors, creditors, and regulators to distinguish genuinely resilient firms from those that merely look healthy on headline metrics. A profile-based approach offers a complementary lens: instead of asking whether a firm beats a uniform sector-wide benchmark, analysts can ask whether a firm’s financial structure, risk exposure, and management efficiency combine in ways characteristic of resilient or fragile archetypes. As Vietnam continues to attract foreign investment and its industrial firms take on longer-term sustainability-oriented projects, the capacity to match assessment methods to financial profiles may prove essential for channeling capital toward the companies best positioned to sustain growth through the next decade of global economic uncertainty.

Subject of Research: Financial performance assessment of Vietnamese listed industrial firms using compositional data analysis and principal component analysis

Article Title: Evaluating the financial performance of Vietnamese listed industrial firms using compositional data analysis and PCA indexing

Article References: Pham, V. H., & Dao, T. T. B. (2026). Evaluating the financial performance of Vietnamese listed industrial firms using compositional data analysis and PCA indexing. Discover Sustainability. https://doi.org/10.1007/s43621-026-04976-y

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04976-y

Keywords: Vietnam, financial performance, compositional data analysis, principal component analysis, financial risk, management efficiency, hierarchical clustering, panel data, listed firms, industrial sector, corporate financial resilience, financial econometrics

The research, led by Hua Han of Baiyin Hope Vocational and Technical College in China together with Ali Ehsani, Nader Naghshbandi, Karlo Abnoosian, Jinlu Liu, and Mahdi Darbendi, was published as an open access article on 5 October 2026. Its central premise is deceptively simple: sustainability in the financial sector means adopting strategies and practices that benefit both the environment and society while still ensuring profitability and financial growth. Companies that genuinely adhere to sustainability principles, the authors argue, manage their resources more efficiently and prove more resilient to environmental and economic shocks. But proving that premise rigorously requires a statistical apparatus capable of handling the tangled web of variables that influence corporate performance.

That is where partial least squares comes in. Partial least squares is a family of statistical methods that sits somewhere between multiple regression and principal component analysis. Rather than asking which individual variables predict an outcome, it constructs new composite dimensions, called latent components, that capture the maximum shared variance between a block of predictor variables and a block of response variables. This makes it especially well suited to situations where predictors are numerous, highly correlated, and potentially outnumber the observations available, conditions that routinely arise in financial and accounting research where economic indicators, regulatory measures, and technology metrics tend to move together.

The Smart Partial Least Squares model proposed in the study extends this classical machinery with an explicitly intelligent, data-driven orientation designed for the big data environment. The authors describe it as a model that considers both technology-based changes and managerial perspectives when evaluating the key performance drivers of green finance firms. In practical terms, the framework organizes the determinants of company performance into internal and external categories. Internal factors comprise technological variables and management variables, reflecting the firm’s own capabilities in information and communication technology and the quality of its managerial practices. External factors comprise economic variables and legal and regulatory variables, capturing the market conditions and policy environment in which green finance companies operate.

Big data technologies are not merely the backdrop of the study; they are one of its substantive subjects. The authors emphasize that analyzing large data sets through big data technologies enables companies to collect extensive environmental, social, and economic information, which in turn supports more informed decisions about sustainable practices. Green finance itself is characterized in the paper as a relatively new concept that offers financing options to individuals, corporate entities, and governments willing to fund green activities or low-carbon initiatives. The use of big data in this domain facilitates a more accurate assessment of the investment opportunities and risks associated with green and low-carbon projects, a task that traditional financial analysis has historically struggled to perform because environmental performance data are heterogeneous, unstructured, and voluminous.

Machine learning and artificial intelligence enter the picture as the analytical engines that can convert this data abundance into insight. The study notes that big data technologies such as machine learning and artificial intelligence can be leveraged to gain a deeper understanding of how the identified drivers affect green finance and accounting firms. This matters because the relationship between, say, the intensity of ICT adoption and firm growth is unlikely to be linear or uniform across firms. Intelligent methods can detect interactions, threshold effects, and nonlinear patterns that conventional regression approaches may miss, and embedding them within a partial least squares structure allows researchers to retain the interpretability of latent-variable modeling while exploiting the pattern-recognition power of modern algorithms.

The results of the analysis deliver a clear and consequential message: both internal factors and external factors significantly contribute to the growth and efficiency of green finance firms. Technological variables and management variables on the inside, and economic variables together with legal and regulatory variables on the outside, all register as significant determinants of performance. Perhaps more striking is the finding about how these categories interact. The research shows that the interaction between internal and external factors is effective in creating a positive cycle between sustainability and green financial performance, a feedback loop that can lead to sustainable, long-term growth for companies operating in competitive markets. In other words, good environmental practice and good financial performance are not competing objectives to be traded off; under the right technological and regulatory conditions, they reinforce one another.

For practitioners, the most immediately actionable conclusion concerns information and communication technology. The authors state plainly that managers should note that any improvement in ICT application may contribute meaningfully to the overall performance of green finance and accounting firms. That recommendation carries weight in an industry where the ability to ingest, clean, and analyze environmental data streams increasingly determines which firms can credibly price green bonds, evaluate the climate risk of loan portfolios, or verify the sustainability claims of corporate clients. Firms that underinvest in their data infrastructure may find themselves not only operationally slower but also less trusted by investors who demand rigorous, data-backed evidence of environmental impact.

For policymakers, the significance of the external variables is equally pointed. Because legal and regulatory variables emerge as significant contributors to firm performance alongside economic conditions, the study suggests that the regulatory architecture surrounding green finance is not a neutral backdrop but an active ingredient in the sector’s success. Clear rules, credible standards, and supportive economic conditions appear to work in concert with firms’ internal technological and managerial capabilities, and the positive sustainability-performance cycle identified by the model depends on that cooperation. Policymakers seeking to accelerate the flow of capital toward low-carbon projects therefore have a statistical argument for strengthening both the economic incentives and the legal frameworks that green finance companies navigate daily.

The study also makes a methodological contribution to the academic literature. By integrating technological, managerial, economic, and legal-regulatory variables into a single smart partial least squares framework, and by explicitly incorporating the role of big data in optimizing green finance strategies, the authors offer a template that other researchers can adapt to related questions in sustainable finance, financial econometrics, and fintech innovation. The work arrives at a moment when the volume of environmental, social, and governance data is growing faster than the analytical tools available to interpret it, and it demonstrates that hybrid approaches, combining classical latent-variable statistics with the intelligence of machine learning, can extract decision-relevant signals from that flood. As green practices continue to gain traction across the financial industry, identifying the factors that explain the success of green finance will remain crucial, and this study provides both a conceptual map of those factors and a technical instrument for measuring them. The research received no external funding, and the authors declare no competing interests.

Subject of Research: A smart partial least squares model identifying factors driving green finance and accounting company performance using big data

Article Title: A novel smart partial least squares-based model for analyzing key factors driving green finance and accounting companies’ performance in big data era

Article References: Han, H., Ehsani, A., Naghshbandi, N., Abnoosian, K., Liu, J., & Darbendi, M. (2026). A novel smart partial least squares-based model for analyzing key factors driving green finance and accounting companies’ performance in big data era. Journal of Big Data. https://doi.org/10.1186/s40537-026-01575-6

Image Credits: AI Generated

DOI: 10.1186/s40537-026-01575-6

Keywords: green finance, big data, partial least squares, sustainability, machine learning, artificial intelligence, accounting firms, ICT adoption, regulatory variables, financial performance, low-carbon investment, Journal of Big Data