MSMEs Competency: Parallel to Risk Management Practices and Holistic Approaches

This paper aims to investigate the extent to which Malaysian small- and medium-sized enterprises (SMEs) practised enterprise risk management (ERM) framework and their effects on sales performance. The components of ERM examined includes: risk appetite, control environment, assessing the risk management framework and control activities. The paper is based on a questionnaire survey study among 214 SMEs that consist of 77.6% (166) from services related enterprises; and 22.4% (48) of manufacturing related enterprises. The data analysis indicates that 80% of the respondents are clear about the importance of risk management to achieve organisational long term sustainability; the senior management is committed to cultivate good culture and have policies to support ERM. More than 20% of the respondents failed to appoint an independent director to chair risk management committee and to obtain external stakeholders’ view when developing risk appetite. Regression analysis also indicates that control environment has a significant and positive impact on sales. Hence, it is important for SMEs to establish a structured system of internal control, to appoint oversight functions and independent assurance providers to manage risks. Risk management is vital in SMEs to reduce exposure to business loss. It is recommended that standards and professional bodies need to develop ERM principles applicable to SMEs. This principle should balance between preferences, requirements and resources within SMEs. This paper contributes to the empirical literatures on the extent ofERM practices and their effects on SMEs’ sales in emerging markets. Keywords: enterprise risk management, small and medium-sized enterprises, emerging markets, Malaysia

Small and Medium Enterprises (SMEs) in both developing and developed economies play crucial roles in economic development through wealth creation, employment opportunities and poverty alleviation. However, the uncertainties surrounding businesses require SMEs to effectively manage their risk exposures. This study examined the influence of risk management practices on the performance of SMEs in Osun state, south-western Nigeria. A total of 340 SME operators in the state were chosen for this study and primary data were collected through a structured questionnaire which was administered to managers/operators of the selected SMEs. A total of 330 copies of the questionnaire were returned having been correctly filled and deemed suitable for this research. A linear regression model, through the Ordinary Least Squares technique was used for the data analysis and test for hypothesis, with the aid of the E-view statistical software. The result from the analysis revealed a significant relationship between risk management and the performance of SMEs (t-value=23.47; p<0.05). The result further showed that attitude to risk positively affects risk management practices and that there is a strong positive correlation between risk identification and risk management (r=0.92). The study therefore concluded that risk management practices have significant and positive influence on the performance of SMEs in Osun state. It was recommended that SME operators in Nigeria should put in place cost-effective measures for timely risk identification and effective risk mitigation in order to ensure that their financial performance is not negatively impacted.

In Iran, approximately 94 percent of the country’s economic enterprises are small and medium-sized enterprises (SMEs). SMEs are limited-scale economic entities that, through the accumulation of micro-financial resources, entrepreneurship, and linkages with industries, create opportunities for economic development. These enterprises also play a vital role in economic growth, employment, wealth creation, and poverty reduction in countries. Despite the significant role of SMEs in economic growth, in most developing countries these companies have not been able to perform effectively in the economic growth process. One of the major reasons for the lack of growth of SMEs in most developing countries is the lack of access to financing and severe constraints in accessing liquidity. Considering the importance of this issue, this study, based on global experience and adopting a systematic review approach, analyzes SME financing by the banking sector. In this study, 215 research works were identified, and after screening, 31 relevant and high-quality studies published between 2010 and 2024 were collected. Based on the findings of this research, it can be argued that company size, company age, corruption, company location, business plan, trust level, income and profitability level, length of banking relationships, number of company owners, field of activity, managerial personality, credit history, high liquidity and repayment period, and SMEs’ adaptation to technology are among the determining factors in improving SMEs’ access to bank credits and facilities.

PurposeDemographics are perceived to influence risk management practices (RMPs) in construction. However, empirical evidence supporting this perception is lacking in the South African Construction Industry (SACI), especially within small and medium enterprises (SMEs). This study, therefore, aims to investigate the influence of demographic characteristics on RMPs.Design/methodology/approachThis study followed a positivist research philosophy, using a questionnaire survey for data collection. In total, 225 questionnaires were distributed, of which 187 were returned from conveniently sampled respondents, which included owners and senior managers of construction SMEs organizations in Gauteng province in South Africa (SA). In total, 181 questionnaires were usable, yielding a response rate of 80.44%. Data from the survey were analyzed using descriptive and inferential statistics i.e. multiple regression analysis (MRA). The outputs were represented using percent and regression coefficients values, respectively.FindingsIn total, 42 practices were established from the literature review and grouped into 9 major RMPs. The data analysis suggested that understanding the organizational environment, defining objectives, resource requirements, risk measurement, risk identification, risk assessment, risk response and action planning, communication and monitoring and review are reliable and valid practices. Findings from MRA established that demographic characteristics i.e. experience in the construction industry (CI), education level and the number of employees in the organization are not good predictors to determine the use of RMPs.Research limitations/implicationsThe study was limited to the data acquired from the SACI and to a lesser extent, construction SMEs in Gauteng. Therefore, the findings cannot be generalized to all SMEs in SA. Though neighboring and developing countries can use the RMPs identified in this study, the results cannot be directly used in developed countries without adequate substantiation.Practical implicationsThe current study provides useful information to assist construction organizations to pay more attention to risk management implementation. The RMPs established in this paper are reliable and valid in projects undertaken by SMEs, and therefore, may be used by top management and/or the risk task team of these enterprises for effective project risk management.Originality/valueThe study presents findings of an investigation of the influence of demographics on RMPs from the perspectives of construction SMEs, an area less explored. This work advances knowledge of RMPs in the SME sector. It, therefore, adds value to researchers and industry practitioners on the theme where no agreement has been attained relating to key factors and practices that should constitute a risk management model.

This study examines the role of Strategic Management Accounting (SMA) in enhancing innovation and infrastructure development for Micro, Small, and Medium Enterprises (MSMEs) located in Disadvantaged, Frontier, and Outermost Regions. The objective is to support the achievement of Sustainable Development Goals (SDGs), specifically SDG 8 on decent work and economic growth and SDG 9 on industry, innovation, and infrastructure. Using a systematic literature review of 13 journal articles published between 2020 and 2025, the findings show that SMA contributes significantly to strengthening financial literacy, resource utilization, and managerial capabilities of MSMEs. SMA also promotes digital transformation and collaboration among stakeholders, enabling broader market access and the formation of an inclusive business ecosystem. From the Resource-Based View (RBV), SMA acts as a strategic approach to managing unique and hard-to-imitate internal resources, thus fostering sustainable competitive advantage. Overall, SMA supports innovative, competitive, and resilient local economic development. Keywords: Strategic Management Accounting; MSMEs; SDG 8; SDG 9; Disadvantaged; Frontier; Outermost Regions; Sustainable Development Goals

Micro, Small, and Medium Enterprises (MSMEs) are the main drivers of Indonesia's economic growth, but many face challenges in adopting digital technology. This study aims to develop and evaluate the impact of the BERKEMBANG.ID digital platform in enhancing the capacity of MSMEs in Depok City, in line with achieving Sustainable Development Goal (SDG) 8. Using a mixed-methods approach, the study involves 100 MSMEs with a focus on three main indicators: digital knowledge (human resources), operational efficiency, and sales performance. The results show a significant improvement in MSMEs' capacity after using the BERKEMBANG.ID platform. Digital literacy among MSMEs increased from 40% to 75%, operational efficiency rose from 45% to 80%, and sales performance increased from 50% to 85%. These improvements were supported by the platform’s features, such as digital training modules, analytical dashboards, and marketing tools, which effectively addressed key challenges faced by MSMEs, including limited digital skills and market access. This study highlights the transformative role of digital platforms in empowering MSMEs and the importance of tailored digital solutions to meet their specific needs. By enhancing digital technology adoption, BERKEMBANG.ID not only improves MSME performance but also supports inclusive and sustainable economic growth. Future research could focus on replicating this platform in other regions and integrating advanced analytics to optimize its impact.

Risk management in SMEs: A systematic literature review and future directions

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in economic development, employment generation, and sustainable growth. However, their adoption of Sustainable Development Goals (SDGs) faces several financial, technological, regulatory, and market-related challenges. This study aims to identify and analyze the major barriers faced by MSMEs in achieving SDGs. A structured questionnaire was administered to 120 MSME respondents in Haryana, capturing perceptions on 15 key sustainability challenges. Data were analyzed using descriptive statistics and one-way ANOVA to examine variations in perceptions across age demographics. Results indicate that financial constraints, high cost of green technologies, lack of technical knowledge, and limited awareness of SDGs are perceived as the most critical challenges. ANOVA results reveal that age does not significantly influence the perception of these challenges, suggesting that barriers are systemic rather than age-dependent. The findings highlight the need for policy interventions, financial support, capacity building, and market incentives to facilitate SDG adoption among MSMEs. The study contributes to the literature by providing empirical evidence on the challenges of sustainable practices in MSMEs and offers practical implications for policymakers and business support agencies.

Companies in the 21st century face a complex business environment, full of opportunities, but pitted with the risks, in which they should make effective business decisions, improve interpersonal relations, meet societal obligations with the right strategy and manage risks. To overcome those challanges, the company needs management tools that can help the company to achieve its objective. Balanced Scorecard (BSC) dan COSO Enterprise Risk Management (ERM) Framework are management tools that are well -recognized and accepted among academicians and more importantly business leaders. They link the high level corporate strategies as determined by top level management with the day-to-day activities of the employees within the organization. Surprisingly, most of companies view BSC and COSO ERM as separate, unrelated management initiatives. In this article, we studies the feasibility of integrating BSC with the COSO ERM Framework, the framework for state-of-the art risk management, similiar to the way the BSC can be integrated with a variety of business management frameworks and methods. The integration of these two management tools (BSC and COSO ERM Framework) is a natural step in the evolution of corporate management . Keywords : Balanced Scorecard, COSO Enterprise Risk Management Framework, Strategic Management System, Risk Management, Key Performance Indicators, Key Risk Indicators.

PurposeThe purpose of this paper is twofold: first to add to the debate on good governance and ethics of enterprise risk management (ERM) and second to describe an ethical maturity scale based on duty and responsibility for practical implementation to ensure better governance.Design/methodology/approachThe methodology has centred on risk governance as a way for many organisations to improve their risk management (RM) practices from an ethical perspective based on responsibility and on fulfilling one's duty within the organisation.FindingsWhile companies in Australia, for example, are more mature than those in Russia in terms of governance systems life cycle, there are a number of common international challenges in risk governance implementation. These relate to a link between risk framework, enterprise value model and strategic planning; to a definition of risk appetite, the embodiment of RM in organisational culture, internal audit and ERM function, the evolving role of a chief risk officer (CRO) and senior management buy‐in and sponsorship of the integrated ethical RM from a chief executive officer.Practical implicationsERM – a way for many organisations to improve their RM practices – is a key component of the applied ethics of corporate governance. It has developed into a philosophy to assist organisations with the process of protecting shareholders' value while also increasing the bottom‐line profitability. Effective ERM is based on ethical risk governance. Internal audit needs to be involved in the process of integrating RM and compliance. It should maintain a degree of independence when assisting with ERM establishment. CRO is most effective when reporting to the board.Originality/valueGlobal companies are becoming more accountable to multiple stakeholders. It is the adoption of an ethical code to arrest the lack of clarity of roles ascribed to the audit committee and risk committee and management's accountability or lack thereof that remains the challenge across different jurisdictions. In attempting to implement good governance and meet the challenges, the paper introduces an ethical maturity scale as an internal measure that could be embedded in an organisation's strategy.

Purpose This study examines the integration of data analytics and sustainability within Malaysian accounting education grounded by experiential learning theory (ELT) and constructivist learning theory. It aims to bridge the gap between student competencies, industry expectations and institutional curricula, offering a structured framework for comparable international business education contexts. The study aligns accounting programmes with the UN Sustainable Development Goals (SDGs), the Malaysian Institute of Accountants (MIA) Competency Framework and the Association to Advance Collegiate Schools of Business (AACSB) accreditation standards. Design/methodology/approach Adopting a mixed-methods exploratory sequential design, this study combined a survey of 350 undergraduate accounting students across UniSZA, UMT and UMK with responses from 100 industry stakeholders. Quantitative data were analysed using descriptive statistics, independent-samples t-tests and regression analysis, while qualitative data, including interviews, focus groups and document analysis, were thematically coded and triangulated. Findings Results reveal strong student proficiency in Excel but significant weaknesses in advanced analytics (Power BI, Tableau and Python) and sustainability literacy. Employers emphasised the importance of environmental, social and governance (ESG) reporting, anomaly detection and carbon accounting, identifying substantial gaps in graduate readiness. Regression analysis confirmed that digital competency significantly predicts sustainability literacy and employability readiness. Qualitative findings highlighted institutional barriers, including curriculum overload, limited faculty training and insufficient academic−industry collaboration. Research limitations/implications This study was limited to three East Coast Malaysian universities and relied on cross-sectional, self-reported data. Longitudinal and cross-country studies are recommended to enhance generalisability and test the framework’s applicability across diverse contexts. Practical implications The framework provides curriculum designers and educators with a roadmap to embed analytics within existing accounting courses, thereby addressing curriculum overload without increasing credit hours. For policymakers, it aligns with Malaysia’s Digital Economy Blueprint and the Education for Sustainable Development (ESD) agenda. For the industry, it supports the cultivation of future accountants capable of leveraging analytics to enhance ESG assurance, sustainable finance and integrated reporting. Social implications By equipping graduates with both digital and sustainability competencies, the framework contributes to achieving SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure) and SDG 13 (Climate Action), while fostering partnerships for sustainability (SDG 17) through stronger university−industry collaboration. Originality/value This study provides one of the first empirical examinations of how Malaysian accounting curricula can integrate analytics and sustainability to achieve SDG-aligned outcomes. Offering a locally grounded yet globally relevant three-tier framework advances the sustainability literature in higher education while providing actionable insights for universities, regulators and industry stakeholders.

Imagine a world where businesses thrive by regenerating resources, fostering equity, and prioritizing innovation to protect the planet. This vision is made possible by the UN Sustainable Development Goals (SDGs), a framework of 17 global targets addressing challenges like poverty, inequality, and climate change. The SDGs urge businesses to move beyond profit as a sole measure of success, embracing environmental stewardship, social inclusion, and ethical governance. This study examines two key aspects: how businesses are transforming their ecosystems by adopting sustainable models aligned with SDG 8 (Decent Work), SDG 9 (Innovation & Infrastructure), SDG 12 (Responsible Consumption), and SDG 13 (Climate Action); and the role of innovation and collaboration in these efforts. These goals guide businesses to balance profitability with positive global impact, demonstrating that sustainability is not a cost but an investment in long-term success for businesses, communities, and the planet.

Citation (2023), "Index", Tyagi, P., Grima, S., Sood, K., Balamurugan, B., Özen, E. and Eleftherios, T. (Ed.) Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (Contemporary Studies in Economic and Financial Analysis, Vol. 110B), Emerald Publishing Limited, Bingley, pp. 265-276. https://doi.org/10.1108/S1569-37592023000110B018 Publisher: Emerald Publishing Limited Copyright © 2023 Pallavi Tyagi, Simon Grima, Kiran Sood, B. Balamurugan, Ercan Özen, and Thalassinos Eleftherios INDEX Accounting, 146 Actual economic activity, 175 Administrative security controls in SME, current state of, 35–37 Africa, effect of COVID-19 debt accumulation, 165 Agriculture, 133 Alternative finance models, 102 Amazon (technology company), 98 Annual appraisals, 252 Anti-black Money Act (2015), 126 Anti-money laundering (AML), 60, 172 Apple (technology company), 98 Artificial intelligence (AI), 23, 85–86, 172, 217, 244, 252–253 ability of AI and SA to improve PM systems, 257 application of AI and SA in business processes, 255 automating PM Systems, 259–261 in business processes, 255–256 classification models to predict employee performance, 257–259 classification of AI, based on capabilities, 253–254 classification of AI, based on functionality, 254 competition, 219 demographics of customers, 219 drivers of digitisation and AI in retail, 218 in HR management, 259 limitations of AI and SA in PM systems, 261–262 for mapping cybersecurity controls for SME, 46–48 in PM systems, 257 retail customers, 218–219 retail digitisation using, 219–220 super, 254 technology and, 218 types, 253 Aspirational Districts Programme, The, 88 Asset management segment, 99 Augmented reality, 23 Authors’ network analysis, citation as per, 68–69 Autonomous robotics, 23 Balance sheet lending (BSL), 100 Bank 4.00, 102 Bank of Baroda, 7, 11–12 Bank of Hindustan, 2 Banking, 2, 98, 206 co-operative banks, 2 commercial banks, 2 industry, 205 payments banks, 2 small finance banks, 2 Banking, financial services, and insurance (BFSI), 29 Banking Regulation Act (1949), 2 Banks, 205 Bibexcel, 135 Bibliometrics, 57, 60 analysis, 141 of entrepreneurial universities, 57 R Package, 135 research, 141 Bibliometrix, 178 Biblioshiny, 178 Big data, 23, 85, 255 analytics, 84 BigTechs, 101 Billion Prices Project, 87 Black box model, 85 Black money, 58–59 Blending data sciences in economic and social issues, 86–87 Blockchain, 132 content analysis, 137–141 data analysis, 135–137 directions for future research, 141–142 implications, 141 methodology, 133–134 technology, 132 Bombay Stock Exchange (BSE), 75–76 Bootstrapping methods, 227 Bring your own device policy (BYOD policy), 40 Business domain-wise mission critical asset security (Business DMCAS), 43–44 Business to-Person lending models (B2P lending models), 101 Business-to-Business lending models (B2B lending models), 101 Businesses, 230 AI in, 255–256 changing priorities of CIA triad based on business domain, 30–31 SA in, 256–257 Buy Now Pay Later (BNPL), 101 C4.5 decision tree, 258 Canara Bank, 7, 9–10, 18 Capability maturity model (CMM), 26 Cashkumar (P2P lending platform), 107 version 2. 0, 108 Cashkumar Peer-to-Peer Lending Platform (Cashkumar P2P Lending Platform), 107–108 digital lending, 100–107 emotional analysis, 116–117 FinTech, 98–100, 108–109 literature review, 108 negative reviews, 119–120 P2P lending, 109–111 positive reviews, 118–119 research methodology, 112 sentiment analysis perspective, 111–112 theoretical background, 107 Castle approach, 28 Casual conversations, 250 Central Bank, 193 Central bank digital currency (CBDC), 190 Central Bank of Nigeria, 190 CIA triad assess priority of CIA triad for DMCA, 43 changing priorities of CIA triad based on business domain, 30–31 CIA trinity, 27 Citation network analysis, 67–68 citation as per authors’ network analysis, 68–69 Cloud computing, 23, 86 Co-authorship analysis, 60 Co-authorship network analysis as authors on money laundering, 62–66 as countries, 64–66 Co-occurrences analysis, 60 of keywords analysis on money laundering, 66–69 network analysis on money laundering, 66 Co-operative banks, 2, 4 Commercial banking, 2 Commercial banks, 2 foreign banks, 3 private sector banks, 3 public sector banks, 3 RRBs, 3 Communications service providers, 101 Companies Act, 105 Competitive pressure, 229 Conceptual model constructs and items measured, 221–223 development of, 221 Conceptual research model, 221 Confidence interval (CI), 227 Consumers, 216 Consumption of goods and services, 22 Content analysis, 137, 179 digitisation of agriculture, 140 supply chain management, 140 sustainable economic development, 140–141 Cooperative savings, 59 Cooperative Societies Act (1912), 4 Core cybersecurity concepts, 26–31 changing priorities of CIA triad based on business domain, 30–31 CIA Triad, 27 Defence in Depth, 28–29 DMCA, 29–30 Coronavirus, 162 Corporation threat agents, 24 Corruption, 56, 124 Cost-benefit analysis, 84 COVID 19, 5, 218 Africa, 165 effect of COVID-19 debt accumulation, 164 debt crisis, 164 debt statistics, 162–164 Europe, 164–165 global debt crisis, 161 heavily indebted countries, 167–168 and Indian Banking, 5 literature review, 161–162 methodology, 161 multilateral organisations, 166–167 pandemic, 160, 234–235 rich countries, 165–166 solution to overcome, 165 Cross industry standard process for data mining (CRISP-DM), 257 Crowdfunding, 99–100 Cryptocurrencies, 98 Customer relationship management (CRM), 253 Customers, 229 demographics of, 219 screening process, 58 Cutting-edge technology, 58 Cyber assets, 27 Cyber risk, 23 Cyberattacks faced by SMEs, experience of, 39–40 Cybercrime, 56 Cybercriminals, 37, 40 Cybersecurity, 23 calculate SME’s minimum overall cybersecurity controls implementation level, 44–45 implement prioritised cybersecurity controls for Business DMCAS, 43–44 responsible AI for mapping cybersecurity controls for SME, 46–48 Cybersecurity framework (CSF), 25 Cyberspace, 23 Data analysis, 224 measurement model, 224–226 structural model, 226–229 test, 224 tool, 88 Data collection, 223–224 Data extraction, 134 Data lags, 91–92 Data localisation problem, 92 Data mining, 85 techniques, 90 Data privacy and security, 91 Data science, 86 techniques, 85 Data-driven policy, India’s stance on, 87–89 Debt crisis, 161, 164 Debt restructuring, 162 Debt Service Suspension Initiative (DSSI), 166 Debt statistics, 162 external debt stocks, 162–163 massive spending by richer countries, 163–164 sovereign default, 164 Decision Tree, 257 Decision-making management, 173 Defence in depth (DiD), 28 Delay payments, 204 Democratisation of opinions, 236 Descriptive analysis, 60 Desktops, 40 Digital circuits, 22 Digital government, 87 Digital India campaigns, 89 Digital lending, 100 alternative finance models, 102 ecosystem, 100–102 functional business models in India, 105–107 funds transfer mechanism as per RBI guidelines, 105 global scenario, 101 governing enactments, 104–105 India, 102 landscape, 102–104 market share, 102 Digital sentiments, 236 Digital technologies, 109 Digital transformation, 216 in agriculture, 141 Digitalisation process of banking industry, 6 Digitisation intelligence competition, 219 demographics of customers, 219 drivers of digitisation and AI in retail, 218 retail customers, 218–219 technology and AI, 218 Digitisation of agriculture, 133, 140 Digitisation transforms retailing exchanges, 220 Directorate of Enforcement (ED), 126 Dirty money, 124 Document-term matrix (DTM), 178 Domain-wise Least Cybersecurity Implementation Framework (DLCI Framework), 41 assess priority of CIA triad for DMCA, 43 calculate SME’s DLCI Level, 45–46 calculate SME’s DMCAS Level, 44 calculate SME’s minimum overall cybersecurity controls implementation level, 44–45 identify DMCA, 41–43 implement prioritised cybersecurity controls for business DMCAS, 43–44 responsible AI for mapping cybersecurity controls for SME, 46–48 WCCI for SME, 44 Domain-wise Mission Critical Asset (DMCA), 29–30 Domestic lending by banks, 205 Drug trafficking, 56 e-CNY, 192 E-commerce platforms, 101 sites, 216 Earnings management, 146 conceptual framework, 147 income-targeting approach, 150–151 literature review, 148–150 priority theory of sustainable finance, 147–148 surplus income model, 152–155 surplus income model for sustainability, 151–152 Econometric models, 84 Economic and financial performance in topic modelling framework, relationship between money laundering and, 184–185 Economic growth (EG), 125, 128, 161, 173, 201 Electric vehicles infrastructure, investment target for, 202 Electronic word of mouth (e-WOM), 235 Embed quality assurance, 47 Emotion Lexicon (EmoLEX), 111 Emotional analysis, 116–117 Emotions, 116 Employees, 245, 258 in India, 175 narrow assessment of, 252 performance, 245 threat agents, 24 eNaira, 190, 193–195 CBDC, 190 money, 190 performs, 190 speed wallet, 196 Environmental, social and governance (ESG), 76, 79–81 ESG-based socially responsible investments, 75 Indian investors looking for, 75–76 Environmental accounting, 149 Environmental reporting, 146 Europe, effect of COVID-19 debt accumulation, 164–165 European Central Bank (ECB), 162 External debt stocks, 162–163 External incentives, 154 External stakeholders, 248 Facebook (technology company), 98 Federal Bank, 7, 13–14, 18 Finance models, 102 Financial Action Task Force (FATF), 58, 125 Financial crimes action plan for resolving financial crimes using SRI–ESG model, 81 channel for, 77–78 effects of, 79 terrorism financing devastating part of, 78–79 impact of unethical investments on, 79 Financial crisis (2008), 98 Financial industry, 205 Financial institutions, 205 in achieving sustainable economic goals, 206–207 through priority sector lending, 208–210 Financial markets, 101, 104 Financial performance Bank of Baroda, 11–12 Canara Bank, 9–10 co-operative banks, 4 COVID 19 and Indian Banking, 5 COVID 19, 5 data analysis, 8 data collection, 7 Federal Bank, 13–14 findings, 17–18 foreign banks, 3 HDFC Bank, 14–15 hypothesis testing for net profit, 16–17 hypothesis testing for NPA, 17 ICICI Bank, 12–13 Indian Bank, 10–11 limitation of study, 8 literature review, 5–7 non-performing assets, 4 non-scheduled banks, 4 payments banks, 4 private sector banks, 3 public sector banks, 3 research methodology, 7 research methodology, 8 research objectives, 7 RRB, 3 sample frame, 7 schedule banks, 4 small finance banks, 3–4 in topic modelling framework, relationship between money laundering and economic and, 184–185 Union Bank, 10 Yes Bank, 15–16 Financial services, 98 Financial study, 58 Financial Technology (FinTech), 98 branches of FinTech Industry, 99–100 classified into the following types, 98–99 credit, 102 landscape, 99 lenders, 101 start-ups, 109 Financing segment, 99 Firms, 147 Five-year planning process, 87 Forecasting methods, 84 Foreign Banks, 3 Formal process in PM systems, 249–250 Fugitive Economic Offenders Act (2018), 126 Functional business models in India, 105–107 Funds transfer mechanism as per RBI guidelines, 105 Global economy, 160 Global reporting initiative (GRI), 149 Globalisation of economies, 124 Gold medal, 238 Goods and Service Tax, 126 Google (Private companies), 91, 98 reviews, 114 Governance in India, 87 reporting, 146 Grameen Foundation, The, 87 Green accounting, 149 Green banking assessing funding needed by international agency, 205 investment target for electric vehicles infrastructure, 202 investment target for renewable energy generation, 201–202 investment target to set up smart cities, 203–204 methodology, 206 results, 206 role of financial institutions in achieving sustainable economic goals, 206–207 role of financial institutions through priority sector lending, 208–210 Green development, 210 Green energy, 210 Green growth, 206 Gross domestic product (GDP), 160, 174, 205 HDFC Bank, 7, 14–15, 18 Human bias, 252 Human resources (HR), 172, 255 AI in HR management, 259 Human-based PM systems, 252 Hume’s Aesthetic Theory, 108 Hypothesis testing for net profit, 16–17 for NPA, 17 ICICI Bank, 7, 12–13, 18 ID3 decision tree, 258 Illicit drug trafficking, 59 IMF Fact Sheet, 125 Income smoothing, 151 Income-targeting approach, 150–151 Incremental approach, 84 India current market trend of, 74–75 digital lending, 102 functional business models in, 105–107 measurements to control money laundering terror funding in, 125–126 retail scenario in, 217–218 SRI in, 75 stance on data-driven policy, 87–89 Indian Bank, 7, 10–11 Indian banking industry, 4 sector, 2 Indian Economy future implication of study, 128 impacts of money laundering on, 126–127 impacts of terror financing on, 127–128 measurements to control money laundering terror funding in India, 125–126 nexus of money laundering and terrorism financing, 125 Indian government, 90 Indian investors looking for ESG Investments, 75–76 Industry 1.0, 22 Industry 2.0, 22 Industry 4.0 digitisation, 23 Inferential research approach, 223 Informal economy, 174 Informal process in PM systems, 250 Information and communications technologies (ICTs), 23, 88, 217 assets, 27 Information security, 27 Information security management system (ISMS), 24 Information technology, 124 Institute for Energy Economics and Financial Analysis (IEEFA), 201 Insurance, 98 Intangible assets, 27 Integration of economies, 124 Interest-bearing eNaira, 194–195 Internal stakeholders, 248 International agency, assessing funding needed by, 205 International Energy Agency (IEA), 205 International Monetary Fund (IMF), 163, 175 International Standards Organization, 260 Internet of Things (IoT), 23, 84, 86, 255 Investment, 98 management, 175 target for electric vehicles infrastructure, 202 target for renewable energy generation, 201–202 target to set up smart cities, 203–204 IP Act (2000), 91 Keynesian model, 193 Know Your Customer (KYC), 106 Knowledge management cybersecurity, 40 Laptops, 40 Latent Dirichlet allocation (LDA), 179 Lender’s Club, 110 Lending policies, 205 Lending practices, 206 Link strength, 137 Logical controls, 35 Machine learning (ML), 86, 257 Malware assaults, 39 Management accounting, 148 Managerial discretion, 147 Managers and employees, 245–246 Market share, 102 Marketers, 218 Marketplace lending (MPL), 100 Massive data analysis approach, 59 Measurement model of data analysis, 224–226 Medal, 238 Mendeley database, 133 Mentor–Protege relationship, 250 Micro, small and medium enterprises (MSMEs), 208 Microsoft (Private companies), 91 Minerals, 30 Mission critical assets, 29 MIT Sloan School of Management, 87 Modern banking, 2 Money, 56 Money laundering, 60, 79, 124, 172–173 analysis of word cloud, 181 annual trend of publication, 61 citation network analysis, 67–69 co-authorship network analysis, 62 co-authorship network analysis as authors on, 62 co-authorship network analysis as countries, 64–66 co-occurrences network analysis on, 66–67 countries analysis, 62 data and methodology, 175–179 descriptive analysis, 179–180 historical background of, 58–60 impacts of money laundering on Indian economy, 126–127 investigating TF-IDF, 182–184 link between words, 181 literature review, 173–175 measurements to control money laundering terror funding in India, 125–126 nexus of, 125 relationship between money laundering and economic and financial performance in topic modelling framework, 184–185 research methodology and data, 60 results, 61 sources analysis, 61–62 Money lending, old concept of, 100 Mordor Intelligence, 89 Naïve Bayes classifiers, 258 Narcotic Drugs and Psychotropic Substances Act (NDPS), 59 NASSCOM, 89 National Association of Securities Dealer Automated Quotations, The, 98 National Strategy for Artificial Intelligence by NITI Aayog, 88 Natural catastrophes, 24, 34 Natural disasters, 34 Neo banks, 101 Net profit, hypothesis testing for, 16–17 Netnograhphy, 236 Nigeria CBDC, 190 current features of, 193–194 interest-bearing eNaira, 194–195 literature review, 191–193 no transaction costs, 195 redesigning, 194 security, 195–196 NIRAMAI, 88 NIST, 25 NITI Aayog, 88 Non-banking financial corporations (NBFCs), 100, 206 Non-performing Assets, 4 Non-performing loans (NPL), 162 Non-scheduled banks, 2, 4 Normalisation, 178 NPA, hypothesis testing for, 17 Offline P2P lending, 100 Olympic Games Tokyo (2020), 237 Olympics, 240 destination, 234 players, 239 Online P2P lending platforms, 100, 109–110 Online platforms, 40 Opaque system, 252 Open Government Data (OGD), 87 Opensource Stack software, 87 Oracle (Private companies), 91 Organisation-owned devices, 40 Organisational goals, 246 Organisations, 244 Organised retailers, 230 Organised retailing, 221, 223 Palantir Technologies (American software company), 87 Pandemic, 160 Parallel economy, 124 Partial least square (PLS), 224 Payment, 191 Payments Banks, 2, 4 Performance appraisal (PA), 244 Performance assessments, 249 Performance improvement, notifications of, 249–250 Performance management systems, 244–245 AI and SA, 252–257 AI and SA in PM systems, 257–262 components of, 245–246 formal processes, 249–250 informal processes, 250 inputs to PM systems, 250–251 issues in current PM systems, 251–252 monitoring, 246–247 outputs from PM systems, 251 planning, 246 principles of PM systems, 248–249 processes in PM systems, 249 reviewing, 247 rewarding, 247 stakeholders in PM systems, 247–248 tasks in PM Systems, 246 Person-to-Business lending models (P2B lending models), 101 Person-to-Person lending models (P2P lending models), 101, 109–111 crediting, 109 elements, 100 lending platforms, 100–101, 106 lending services, 104 Physical retailers, 216 Physical security current state of physical security controls in SME, 34 measures, 34 Physical stores, 217 Planning–programming–budgeting system, 84 Platform lending, 100 Policy-making process, 84 in India, 87 Predictive modelling techniques, 85 Prevention of Money Laundering Act (PMLA), 59, 126 Priority sector lending role of financial institutions through, 208–210 Priority theory of sustainable finance, 147–148 PRISMA 2020 Flow Diagram, 133 Private Banks, 8, 18 Private companies, 91 Private giants, 89 Private Sector Banks, 3 Production-linked Incentive Scheme (PLI Scheme), 207 Profitability, 5 of Indian banks, 7 Programmable integrated circuits (PLCs), 22 Public policies, 84 Public Sector Banks, 3, 8, 18 Python application, 112, 114 Qualitative research techniques, 85 Quality assessment, 134 Quasi-legal enterprises, 56 Real-time resource monitoring techniques, 85 Refugee Olympic, 237 Regional Rural Banks (RRB), 2–3 Relative frequency, 183 Renewable energy, 22, 201, 207 investment target for renewable energy generation, 201–202 Research methodology, 85 Reserve Bank of India (RBI), 2, 6, 99, 207 funds transfer mechanism as per RBI guidelines, 105 Reserve Bank of India Act (1934), 2, 4 Resource allocation, 76 Retail business analytics, 219 Retail customers, 218–219 Retail digital promotion, 220–221 Retail industry, 221 Retail scenario in India, 217–218 Retail stores, 216 Retailers, 216–217, 229 Retailing, 220 data analysis, 224–229 data collection, 223–224 development of conceptual model, 221–223 drivers of digitisation and AI in retail, 218–219 implications of study, 229–230 limitation, scope for further research, 230 literature review and hypothesis development, 217 research methodology, 223–223 research objectives, 217 retail digital promotion, 220–221 retail digitisation using AI, 219–220 retail scenario in India, 217–218 Reward system, 247 Rio Olympic (2016), 236 Scalability, 252 Schedule Banks, 4 Scheduled banks, 2 Search strategy, 133 Security awareness training for employees in SMEs, frequency of, 37–38 Security controls in SME, current state of, 34 Security objectives, 27, 29 Selection criterion, 133–134 Sentiment analysis, 238 method, 114 perspective, 111–112 Sentiments, 236 Sin stocks, 80 Small and Medium Enterprises (SME), 22, 31 age of, 32 analysis of research interview results, 41 analysis of research survey results, 32 biggest problems faced by SMEs implementing or deciding/planning to implement cybersecurity controls, 38–39 core cybersecurity concepts, 26–31 current state of administrative security controls in, 35–37 current state of implemented standards or framework in, 32–33 current state of physical security controls in, 34–35 current state of security controls in, 34 current state of technical security controls in, 35 DLCI framework, 41–48 DLCI Level, 45–46 DMCAS Level, 44 experience of cyberattacks faced by, 39–40 frequency of security awareness training for employees in, 37–38 literature review, 24–26 methodology, 31–32 minimum overall cybersecurity controls implementation level, calculate, 44–45 responsible AI for mapping cybersecurity controls for, 46–48 WCCI for, 44 Small and medium-sized businesses (SMBs), 23, 101 Small business owners, 109 Small finance banks, 2–4 Smart Analytics, 252, 254–255 ability of AI and SA to improve PM systems, 257 AI in HR management, 259 application of AI and SA in business processes, 255 automating PM Systems, 259–261 benefits of, 255 classification models to predict employee performance, 257–259 limitations of AI and SA in PM systems, 261–262 in PM systems, 257 SA in business processes, 256–257 Smart cities, investment target to set up, 203–204 Social accounting, 148 Social media, 219, 235 Social reporting, 146 Socially responsible investments (SRI), 74–76, 79–81 action plan for resolving financial crimes using SRI–ESG Model, 81 current market trend of SRI in India, 74–75 effects of financial crimes, 79 ESG and SRI, 79–81 findings of study, 77 heterogeneity, 76 in India, 75 investors, limitations of study, 77 research 77 research methodology, 77 review of socially responsible 75–76 76 terrorism financing devastating part of financial 78–79 impact of unethical investments on financial crimes, 79 unethical investors, 77–78 41 energy generation, 201 234 234 239 239 149 in PM Systems, 247–248 107 Bank of India 2 Data Analysis, 224 theory 235 modelling 224 model of data analysis, 226–229 178 chain management, 140 income model, implication of model, implications for or for sustainability, 151–152 profit, 206 149 Sustainable development, 206 Sustainable development Sustainable economic development, 140–141 Sustainable economic goals, role of financial institutions in 206–207 Sustainable 147 Sustainable growth in India, 206 security 26 18 249 (Private 89 98 controls, 35 security controls in SME, current state of, 35 132 and AI, 221 companies, 98 Technology and and model Technology governance data sciences in economic and social issues, 86–87 India’s stance on data-driven policy, 87–89 research and methodology, findings and 22 frequency 179 investigating TF-IDF, 182–184 financing on Indian economy, impacts of, 127–128 59 financing, 79 devastating part of financial 78–79 nexus of, 125 threat agents, 24 analysis, 178 data methods, 178 agents, 24 Tokyo Olympic (2020), 234 data analysis and findings, literature review, research methodology, 236 Tokyo Olympic 234 179 relationship between money laundering and economic and financial performance in, 184–185 profit, action 236 PM systems, 252 149 253 234 content analysis, 236 data, 236 sentiments, 235 laundering system, 59 investments on financial crimes, impact of, 79 investors, 77–78 payments 6 Union Bank, 7, 10 56 National Security 28 224 analysis, 60 60, 135 23 87 40 of 173 Cybersecurity Implementation 44 22 23 Yes Bank, 7, 18 25 COVID 19 and Financial Performance of Banks in and of Least Cybersecurity for Small and Medium Enterprises for Global Digitalised Economy Money of from to Socially for Financial Technology and of Governance and Policy-making for the the Sentiment Analysis of Cashkumar Peer-to-Peer Lending on Google of Money Laundering and Financing on the Indian Economy the on Implementation of in Earnings Management for Income of Sustainable COVID-19 Global Debt to the Analysis of Money Laundering Research the Central Bank Digital for Payments and Green Banking to Sustainable Economic Digitisation and Artificial Intelligence in Sector on Sentiment Analysis in 2020 Smart and AI for Modern Performance Management

In the last five years, since the adoption of the SDGs, India has been trying to improve in all aspects according to the set targets. Significant progress has already been made in India in recent years. India has taken several SDG initiatives in recent times. Most of the initiatives of the Government of India (GoI) are aligned with the SDG framework to steer development activities. At present, we have 17 UN Sustainable Development Goals (SDGs), and the specific targets for each goal are there. SDG – 8 for economic growth and decent work is one of them. The research question is how far India has achieved the targets of SDG–8 because this goal is directly related to economic growth. In this context, the paper attempts to analyse the performance of India in the achievement of SDG–8, presenting the trends of employment generation and labour force participation rate. These two indicators are analysed in this study with the help of the International Labour Organisation (ILO) database. It is possible to conclude from this analysis that the improvement in India's employment rate and labour force participation rate over the last decade has been insufficient.

Predatory digital lending poses significant challenges to the sustainability of micro, small, and medium enterprises (MSMEs) in Indonesia. This study explores the reasons, consequences, and impacts of predatory digital loans on MSMEs, focusing on how these practices hinder economic well-being and growth. The research employs a qualitative method, utilizing content analysis from secondary sources, including academic journals, reports, and government documents. Data were collected through an in-depth review of literature and case studies of MSMEs affected by predatory loans. The findings reveal that many MSMEs, particularly in rural areas, lack sufficient financial literacy, which increases their vulnerability to predatory lending. Legal frameworks, such as POJK 35/POJK.05/2018, are inadequate in regulating debt collection ethics, allowing illegal practices to flourish. Additionally, gender disparities affect loan approval rates, with female borrowers facing higher risks of harassment. The study concludes that improving financial literacy, strengthening government regulations, and fostering collaboration between MSMEs, government bodies, and financial institutions are essential for protecting MSMEs from predatory digital lending. By doing so, MSMEs can achieve sustainable growth and contribute more effectively to Indonesia's economic development and the achievement of the Sustainable Development Goals (SDGs).