Artificial intelligence (AI) has significant potential to transform small- and medium-sized enterprises (SMEs), yet its adoption is often hindered by challenges such as limited financial and human resources. This study addresses this issue by investigating the core AI technologies adopted by SMEs, their broad range of applications across business functions, and the strategies required for successful implementation. Through a systematic literature review of 50 studies published between 2016 and 2025, we identify prominent AI technologies, including machine learning, natural language processing, and generative AI, and their applications in enhancing efficiency, decision-making, and innovation across sales and marketing, operations and logistics, finance and other business functions. The findings emphasize the importance of workforce training, robust technological infrastructure, data-driven cultures, and strategic partnerships for SMEs. Furthermore, the review highlights methods for measuring and optimizing AI’s value, such as tracking key performance indicators and improving customer satisfaction. While acknowledging challenges like financial constraints and ethical considerations, this research provides practical guidance for SMEs to effectively leverage AI for sustainable growth and provides a foundation for future studies to explore customized AI strategies for diverse SME contexts.

Analysing the nexus between board diversity, CEO power, state holding, and corporate social responsibility disclosure in an emerging country: Vietnam, where some listed firms are held significantly by the State, is the fundamental objective of this study. In order to achieve this goal, we employed regression analysis using panel data. While board diversity consists of board gender diversification and board independence and CEO (executive) power, consisting of executive duality, executive holding (ownership), and deputy CEO, and state ownership are explanatory variables, and CSR disclosure is a dependent variable. The sample contains of 166 Vietnamese listed firms at the Hanoi Stock Exchange (HNX) for 2014−2016. After performing regression analysis, the result revealed that the proportion of female directors, deputy CEO, and state holding had a significant correlation with CSR publication. In contrast, the proportion of independent directors, CEO duality, and CEO ownership was found to be insignificant. Our research adds to the research on firm governance and CSR in several approaches. First, the paper adds to the study on the advancement of research toward corporate social responsibility and firm governance and CEO features impress on it. Second, our research expands CSR literature in developing countries, which has not been treated in detail. Fourth, this research advances and adds literature to some theories, including agency theory and resource-based view theory.

Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS?” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.

Despite the abundance of literature on university performance (UP), few studies have examined the impacts of knowledge management (KM) enablers and KM processes on UP. This study introduces a model explaining the variance in UP, including KM enablers, which include organisational enablers (OEs) and personal enablers (PEs) as indirect determinants, and KM processes as direct determinants. Data from 296 Vietnamese university lecturers and managerial staff indicated that KM processes mediate the link between KM and UP. However, OEs and PEs had unequal impacts on KM processes. Of the three OE components, organisational rewards and culture had a direct impact on KM, while organisational leadership had an indirect impact through the two other processes. Of the two PE components, knowledge self-efficacy had a significant influence on KM. The findings indicate ways for Vietnamese universities to enhance performance and thus develop sustainably.

This paper analyzes the relationship between working capital management and profitability from operations of 121 firms listed on the two stock exchanges, i.e. Hanoi Stock Exchange (HNX) and Ho Chi Minh Stock Exchange (HOSE) in the period 2008-2012. The research result has indicated that Receivables Collection Period (RCP), Ratio of debt to total assets (LEV), Current Ratio (CR) has negative correlation; whereas Growth of Sales (GOS) and Size of Companies (SIZE) have positive with the firm’s return on equity (ROE). The research findings of researches provide experimental evidence to confirm the important role of working capital management that has direct effect on profitability of firms. Particularly in financial crisis, the managers should accord a particular attention on solutions concerning Receivables Collection Period; by justifying the Leverage and Current Ratio at reasonable level; by enhancing measures for increasing the growth of sale in order to stabilize the firm

Purpose: This paper examines whether ESG-related media signals affect the volatility of corporate earnings among Vietnamese listed firms. It focuses on the second moment of financial performance, earnings stability rather than profitability level and investigates how environmental, social and governance signals exert distinct, dimension-specific effects on earnings risk and whether state ownership moderates these dynamics. Design/methodology/approach: Using the Covalence EthicalQuote database, which aggregates global media, NGO publications and institutional communications into firm-level ESG reputation scores, the study constructs a panel of 62 Vietnamese listed firms over 2012–2023 (709 firm-year observations). Panel fixed-effects models are estimated across four earnings volatility specifications with ESG variables lagged one year. Robustness is assessed through winsorization, COVID exclusion, alternative lag structures, placebo tests and first-difference specifications. Findings: Social media signals are positively associated with short-term earnings volatility, consistent with the transitional costs of active stakeholder engagement and this effect attenuates within two years. Governance signals are negatively associated with earnings volatility, particularly downside risk, reflecting the contemporaneous earnings-stabilizing function of internal monitoring. Environmental signals predict higher future earnings volatility but are uncorrelated with current volatility, consistent with an investment channel in which upfront environmental expenditure precedes efficiency gains. All three effects are concentrated among non-state-owned enterprises; majority state-owned firms exhibit no significant ESG-risk relationship, consistent with the soft budget constraint hypothesis. A conditional profitability analysis confirms that the risk and profitability channels of ESG operate largely independently. Research limitations/implications: The sample is restricted to the 62 Vietnamese firms covered by the Covalence database, an internationally visible subset that may not represent the broader listed population. Identification relies on lagged fixed-effects specifications rather than quasi-experimental variation, and residual endogeneity from unobserved time-varying characteristics cannot be fully excluded. Future research should employ natural experiments or regulatory shocks to strengthen causal identification, particularly for the social dimension. Practical implications: For investors and trade partners assessing Vietnamese firms, ESG sub-component analysis is more informative than composite scoring, as environmental and social signals carry distinct and partially opposing risk implications that aggregate indices obscure. For policymakers, the findings indicate that market-based ESG accountability mechanisms will have the greatest impact in the private corporate sector; extending ESG discipline to state-owned enterprises requires direct regulatory mandates and integration of ESG metrics into state shareholder performance frameworks. Originality/value: This study provides the first systematic evidence from a Southeast Asian emerging market that ESG dimensions have distinct and opposing effects on earnings stability. It extends the soft budget constraint argument from profitability to risk management, demonstrating that ESG-based discipline mechanisms are absent in state-owned segments. The use of a media-based, externally constructed ESG measure, rather than self-reported disclosure scores, is particularly suited to the Vietnamese context, where mandatory ESG reporting remains limited.