Artificial Intelligence (AI) Power and Entrepreneurial Success

The rapid advancement of Artificial Intelligence (AI) has transformed business operations across the globe, with increasing relevance for Small and Medium Enterprises (SMEs) in emerging economies like India. This study investigates the impact of AI implementation on the performance, productivity, and competitiveness of Indian SMEs. By analyzing primary data collected through surveys and interviews with SME owners and managers across various sectors, and secondary data from industry reports, the research identifies key areas where AI technologies—such as machine learning, predictive analytics, and intelligent automation—are being adopted. The study reveals that AI adoption enhances operational efficiency, customer engagement, and decision-making processes in SMEs. However, it also highlights several challenges including high implementation costs, lack of technical expertise, and resistance to change. The findings underscore the critical role of government support, digital infrastructure, and upskilling initiatives in facilitating effective AI integration within SMEs. This research contributes to the understanding of AI's transformative potential for small businesses in developing nations and offers strategic recommendations for stakeholders to foster an inclusive and innovation-driven SME ecosystem in India..

Purpose: This study aims to examine the impact of customer orientation, competitor orientation, and brand commitment on the business performance of Micro, Small, and Medium Enterprises (MSMEs), focusing on the moderating role of artificial intelligence (AI) in enhancing strategic responsiveness and competitiveness in the digital transformation era.Method: A quantitative explanatory approach was used, involving structured questionnaires distributed to 373 MSME owners and managers in Lowokwaru District, Malang City. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS software to assess both direct and moderating effects. Reliability and validity tests were performed through composite reliability and average variance extracted (AVE) analysis.Result: We found that customer orientation, competitor orientation, and brand commitment significantly and positively influenced MSME performance. However, AI did not significantly moderate these relationships, indicating that AI adoption among MSMEs is still in the early stages and has not yet shown measurable effects. Despite this, AI adopters exhibited improved operational efficiency and decision-making accuracy.Practical Implications for Economic Growth and Development: The study highlights the need for MSMEs to strengthen customer focus, competitor awareness, and brand commitment to enhance business performance. Although AI did not show a moderating effect, gradual adoption of AI technologies could improve efficiency, innovation, and competitiveness, fostering regional economic growth.Originality/Value: This research contributes to the literature by integrating AI as a moderating variable in the relationship between marketing orientation and business performance, offering empirical evidence that extends AI-based strategic capability models within the MSME context.

In many developed countries, the usage of artificial intelligence (AI) and machine learning (ML) has become important in paving the future path in how data is managed and secured in the small and medium enterprises (SMEs) sector. SMEs in these developed countries have created their own cyber regimes around AI and ML. This knowledge is tested daily in how these countries’ SMEs run their businesses and identify threats and attacks, based on the support structure of the individual country. Based on recent changes to the UK General Data Protection Regulation (GDPR), Brexit, and ISO standards requirements, machine learning cybersecurity (MLCS) adoption in the UK SME market has become prevalent and a good example to lean on, amongst other developed nations. Whilst MLCS has been successfully applied in many applications, including network intrusion detection systems (NIDs) worldwide, there is still a gap in the rate of adoption of MLCS techniques for UK SMEs. Other developed countries such as Spain and Australia also fall into this category, and similarities and differences to MLCS adoptions are discussed. Applications of how MLCS is applied within these SME industries are also explored. The paper investigates, using quantitative and qualitative methods, the challenges to adopting MLCS in the SME ecosystem, and how operations are managed to promote business growth. Much like security guards and policing in the real world, the virtual world is now calling on MLCS techniques to be embedded like secret service covert operations to protect data being distributed by the millions into cyberspace. This paper will use existing global research from multiple disciplines to identify gaps and opportunities for UK SME small business cyber security. This paper will also highlight barriers and reasons for low adoption rates of MLCS in SMEs and compare success stories of larger companies implementing MLCS. The methodology uses structured quantitative and qualitative survey questionnaires, distributed across an extensive participation pool directed to the SMEs’ management and technical and non-technical professionals using stratify methods. Based on the analysis and findings, this study reveals that from the primary data obtained, SMEs have the appropriate cybersecurity packages in place but are not fully aware of their potential. Secondary data collection was run in parallel to better understand how these barriers and challenges emerged, and why the rate of adoption of MLCS was very low. The paper draws the conclusion that help through government policies and processes coupled together with collaboration could minimize cyber threats in combatting hackers and malicious actors in trying to stay ahead of the game. These aspirations can be reached by ensuring that those involved have been well trained and understand the importance of communication when applying appropriate safety processes and procedures. This paper also highlights important funding gaps that could help raise cyber security awareness in the form of grants, subsidies, and financial assistance through various public sector policies and training. Lastly, SMEs’ lack of understanding of risks and impacts of cybercrime could lead to conflicting messages between cross-company IT and cybersecurity rules. Trying to find the right balance between this risk and impact, versus productivity impact and costs, could lead to UK SMES getting over these hurdles in this cyberspace in the quest for promoting the usage of MLCS. UK and Wales governments can use the research conducted in this paper to inform and adapt their policies to help UK SMEs become more secure from cyber-attacks and compare them to other developed countries also on the same future path.

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

Abstract−Technology plays an important role in economic development. Covid-19 has changed people's lifestyles and the behavioral patterns of business actors have transformed from traditional to digital. Even though Covid-19 has had a significant impact in the form of a decline in the global economy, including on Micro, Small and Medium Enterprises (MSMEs), it has also provided lessons in the form of adaptation to innovation and digital transformation in the form of the application of the latest technology and artificial intelligence. In line with the industrial revolution 4.0 and digital transformation, it can provide opportunities and strengths to build sustainable businesses. Along with changes in people's consumption patterns, MSMEs have also implemented digital adoption in the form of switching to e-commerce platform sites, food delivery applications and online business models. However, the adoption of artificial intelligence is still not widely used by MSMEs. However, several MSMEs have adopted artificial intelligence technology such as chatbot applications for communication services. Apart from that, support and providing programs from the government and the private sector are needed for MSMEs so that they can develop and adapt their business models to the latest technological adaptations so that MSMEs are also able to survive and even grow rapidly in the future.Keywords: Industrial Revolution 4.0, Digital Transformation, Artificial Intelligence, MSMEs

SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices

Researchers increasingly recognise Artificial Intelligence (AI) as a transformative force in organisational management, yet Small and Medium Enterprises (SMEs) face unique challenges in adopting these technologies. This study examines the administrative challenges and strategic pathways involved in adopting AI in SMEs. The research team used a qualitative design and collected data through semi-structured interviews, focus group discussions, and document reviews with SME administrators across multiple sectors.Findings reveal four significant administrative challenges: financial constraints, skills deficits, organisational resistance to change, and policy uncertainty. These barriers restrict SMEs' ability to integrate AI technologies effectively. However, SMEs demonstrate resilience through strategic pathways, including incremental adoption of AI solutions, staff training and capacity building, partnerships with technology providers and research institutions, and collective advocacy for policy support. The study highlights that AI adoption is not merely a technical process but a complex administrative and institutional phenomenon shaped by leadership readiness, organisational culture, and external policy environments.The study concludes that effective AI adoption requires a dual approach: strengthening internal organisational strategies while ensuring supportive external institutional frameworks.

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in supporting national economic growth and expanding employment opportunities in Indonesia. However, many MSMEs still face significant challenges in improving production efficiency due to limited human resources, low levels of technology adoption, and suboptimal operational management. To address these issues, this study developed an innovative digital technology platform designed to help MSMEs enhance productivity, reduce material waste, and accelerate the production process. The platform integrates Internet of Things (IoT), Artificial Intelligence (AI), and simplified Enterprise Resource Planning (ERP) technologies tailored for small business operations. Through IoT, production processes can be monitored in real time; AI is applied to analyze sales data and predict material requirements and production schedules; while the ERP system automates inventory, transaction, and financial reporting processes. Trials conducted across MSMEs in the food, handicraft, and textile sectors demonstrated a 30% improvement in production efficiency and a 20% reduction in operational costs. The results indicate that the implementation of innovative technological platforms can significantly enhance efficiency, accuracy, and competitiveness among MSMEs. Digital transformation not only increases production efficiency but also enables broader business integration into the global market through cloud-based systems. Government and institutional support are essential to expand the adoption of such technologies, ensuring that Indonesian MSMEs become more adaptive, productive, and sustainable in the era of Industry 4.0.

This study investigates the impact of Artificial Intelligence (AI) and Financial Technology (FinTech) on business sustainability among Micro, Small, and Medium Enterprises (MSMEs) in Medan City. Employing an associative research approach, the study utilizes quantitative data derived from both primary and secondary sources. The analysis methods include descriptive statistical analysis and Structural Equation Modeling (SEM). The study's population consists of 100 MSMEs in Medan City, selected through accidental sampling. The findings demonstrate that the adoption of AI and FinTech positively and significantly influences business sustainability. The integration of these technologies enhances operational efficiency, supports faster growth, and ultimately strengthens the sustainability of MSMEs. This research highlights the critical role of digital innovation in driving the development and resilience of small businesses, emphasizing the need for broader adoption of AI and FinTech solutions in the MSME sector to foster long-term growth and competitiveness.

The rapid evolution of artificial intelligence (AI) and cloud-based accounting systems (CBAS) presents transformative opportunities for small and medium enterprises (SMEs), yet adoption remains inconsistent in developing economies due to resource constraints, infrastructural gaps, and cultural skepticism. This study examines how AI practices influence intentions to adopt CBAS among SME accountants and owners in Southern districts Khyber Pakhtunkhwa (KP) Province, Pakistan, with a focus on the moderating role of trust in AI. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and trust theory, the research proposes that AI practices (e.g., automation efficiency, predictive analytics) positively drive adoption intentions (H1), while trust in AI amplifies this relationship (H2). A cross-sectional survey of 401 SME professionals was conducted, utilizing validated scales to measure AI practices, trust in AI, and CBAS adoption intentions. Data analysis revealed a small but significant direct effect of AI practices on adoption intentions. Critically, trust in AI emerged as a significant moderator, strengthening the positive impact of AI practices on adoption decisions. This underscores trust’s role in mitigating perceived risks (e.g., data breaches, ethical concerns) and bridging the gap between technological utility and socio-cultural reservations in KP’s context. The findings extend UTAUT by integrating trust as a contextual moderator, offering a localized framework for AI adoption in high-stakes, resource-constrained environments. Practically, the study highlights the need for AI implementation strategies that prioritize transparency, ethical governance, and capacity-building initiatives tailored to SMEs’ socio-technical realities. Keywords: Artificial intelligence, cloud-based accounting, trust, SME adoption, UTAUT, Pakistan.

The Micro, Small, and Medium Enterprises (MSME) sector serves as a cornerstone of India's economy, playing a crucial role in contributing to GDP, generating exports, and creating employment opportunities.Despite its economic significance, the sector faces several challenges, including limited access to finance, intense competition, inefficiencies in operations, and slow adoption of advanced technologies.This chapter explores how technology and innovation can address these challenges and foster the sustainability and competitiveness of MSMEs in India.Emerging technologies such as digital payments, cloud computing, artificial intelligence (AI), and block chain are reshaping MSME operations by improving financial inclusion, streamlining processes, and expanding market access.Digital payment platforms, including UPI and fin tech solutions, have eased access to credit, while cloud technologies provide scalable and cost-efficient infrastructure.AI-powered tools and data analytics equip MSMEs with valuable insights into market trends and consumer behaviour, enabling more informed business decisions.Furthermore, block chain enhances supply chain transparency and security by minimizing fraud risks and building trust among stakeholders.The chapter also examines government initiatives, such as the Digital MSME Scheme and MSME Databank, which aim to accelerate technology adoption within the sector.However, several barriers remain, such as low digital literacy, the high upfront cost of technology investment, and concerns about data security.Overcoming these hurdles is critical to building a resilient MSME ecosystem that can adapt to economic fluctuations and compete effectively on a global scale.Through case studies and real-world examples, this chapter highlights the transformative role of technology in the MSME sector, offering practical insights for policymakers, business leaders, and entrepreneurs committed to driving sustainable growth.

This chapter explores the implementation of artificial intelligence (AI) for knowledge management (KM) in small and medium enterprises (SMEs). Compared with larger corporations, SMEs frequently encounter limits to their capacity to adopt comprehensive KM strategies due to limited access to resources. The chapter argues that the deficit in KM is an obstacle to operational efficiency and SMEs’ sustainability. It thus proposes a comprehensive framework for understanding, implementing and optimising the integration of AI in SMEs from the lens of an effective KM system. A desk review approach was adopted to scan the extant literature on AI applications for KM in SMEs, including the analysis of selected secondary case studies. The chapter concluded that AI applications for KM are fundamental for knowledge sharing capabilities in SMEs. Therefore, a continuous learning and adaptability culture should be cultivated to promote a collaborative learning environment.

<p>Small and medium enterprises small and medium enterprises (SMEs) play a crucial role in nations’ economy, through job creations, reducing unemployment rate as well as increase the overall productivity and gross domestic product (GDP) of a country. However, most SMEs are often lagging in technology adoption which could be a game changer for their success. SMEs could adopt new technologies to improve their business operations and profitability. They are also useful in supporting SMEs to penetrate international market. This research suggests that implementation of the artificial intelligence (AI) through digital marketing (DM) and financial technology (Fintech) would assist SMEs to be competitive, current in leveraging on technology and increase their overall profitability. Based on secondary data analysis, this paper presents a conceptual framework of determining factors in adoption of AI through digital marketing and Fintech. It contributes to the academic knowledge of AI, DM and Fintech for small businesses, and presents a testable framework that can be replicated and adapted for future empirical study. </p>

In an era of rapid digital transformation, small and medium-sized enterprises (SMEs) are increasingly leveraging artificial intelligence (AI) and predictive analytics to enhance operational efficiency, optimize supply chain management, and improve overall business performance. This study explores the integration of AI-driven solutions in SMEs, focusing on their impact on demand forecasting, logistics optimization, and predictive maintenance. Using a mixed-methods approach, both quantitative and qualitative data were collected from 250 SMEs across various industries, supplemented by in-depth interviews with key decision-makers. The findings reveal that AI implementation leads to significant improvements in cost reduction (30% in logistics), operational efficiency (20% in inventory turnover), and predictive accuracy (15% improvement in maintenance planning). Despite these benefits, SMEs face challenges such as high initial investment costs, lack of AI expertise, and data management complexities. The study identifies best practices for overcoming these barriers, including phased AI adoption, strategic partnerships, and employee training programs. Additionally, the research highlights sector-specific AI applications, demonstrating its effectiveness in retail, e-commerce, and manufacturing industries. This study contributes to the growing discourse on AI adoption in SMEs by providing empirical evidence of its role in business optimization. It underscores the necessity of integrating AI into SME operations to remain competitive in a data-driven economy. The findings offer valuable insights for business leaders, policymakers, and technology providers, advocating for the development of AI frameworks that align with SME scalability and resource constraints. Future research should explore AI’s long-term impact on financial performance and sustainability, as well as its potential applications in emerging markets

Technology management plays a crucial role in enhancing the competitive advantage of Micro, Small, and Medium Enterprises (MSMEs), particularly in the evolving digital economy. In Davao Oriental, where MSMEs account for 99.67% of registered businesses, effective technology adoption is essential for ensuring business sustainability and growth. This paper explores the role of technology management in improving MSME competitiveness by examining key areas such as technological innovation, adoption strategies, market positioning, and challenges in implementation. This highlights the significance of government initiatives, including the Shared Service Facility (SSF) and the Small Enterprise Technology Upgrading Program (SETUP), in promoting technology adoption among MSMEs. It discusses technology-driven strategies that enable MSMEs to enhance operational efficiency, reduce costs, and expand market reach. Additionally, it identifies financial constraints, digital literacy gaps, and organizational resistance as barriers to technology adoption. Emerging trends, such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain technology, present new opportunities for MSMEs to strengthen their market positioning. This concludes that overcoming technological barriers through strategic planning, capacity-building programs, and policy support can significantly enhance MSME resilience and sustainability. By leveraging digital transformation and innovation, MSMEs in Davao Oriental can remain competitive and contribute to long-term economic growth.