English

The effects of Internet usage, financial development and trade openness on economic growth in South Africa: A time series analysis

This study examines the short run and long relationship between agricultural expenditure, health expenditure and economic growth in Nigeria. This study was motivated due to the lack of sufficient studies regarding this subject matter in recent times. Consequently, Data were collected from CBN Statistical Bulletin from 1981 to 2016. Relevant pre-estimation tests such as unit root and Bound tests were carried out because all the study variables were integrated of order zero and one. Estimated results from ARDL and ECM models established the existence of a short-run and long-run relationship between the variables of interest in Nigeria. While the error correction model reveals that about 19 percent of total disequilibrium due to external shock in the previous year is corrected in the current year. Therefore, it will take about five (5) years for the system to adjust back to its long-run equilibrium path. Results further showed that there is a significant positive relationship between agricultural expenditure and economic growth in Nigeria. However, there is a significant negative relationship between health expenditure and economic growth in the long run. Finally, policymakers in Nigeria should allocate substantial budget towards health and agricultural sectors in Nigeria.

Purpose: This Study aims to investigate the impact of foreign debt on the economic growth of Pakistan by categorizing the foreign debt into two different factors: IMF debt and non-IMF debt.
 Design/Methodology/Approach: The secondary data was taken quarterly from the State Bank of Pakistan for the period 2010 to 2021, and Johansen cointegration analysis and the Vector error correction model were used to study the relationship.
 Findings: The study's findings revealed that the IMF debt is significantly positive in the short run, while non-IMF foreign debt has an insignificant relationship with economic growth. Moreover, short-run results also found an insignificant relationship between foreign debt servicing and inflation rate and economic growth. On the other hand, in the long run, the relationship between IMF debt, foreign debt servicing, and the inflation rate were found to be significantly negative with the economic growth, whereas a significant positive relationship was identified between the non-IMF foreign debt and the economic growth.
 Implications/Originality/Value: The study concluded that the authorities should avoid foreign debt from IMF and rely upon non-IMF foreign debt if ever financing through foreign debt is needed. Even though the non-IMF foreign debt has a positive impact on the economic growth in the long, this positive effect is counter-negated by the cost of foreign debt, which is the foreign debt servicing, due to its significant negative impact on the economic growth of Pakistan.

This study examined at tax evasion also related trade-based money laundering mitigation strategies in Nigeria in the age of cyber security. Three objectives and research questions were designed to guide the study using a sample of 120 respondents. A survey research methodology was used for this study. The instrument used was a set of pre-coded, independently developed structured questionnaires. The study's analytical approaches included frequency distribution tables with percentages, bars, and charts, and logit regression was used to assess the objective. Since the majority of respondents (100%) are of working age based on the descriptive age group conclusion, they are qualified to understand how tax fraud and laundering of money affect economy growth. about educational achievement, it is implied that most of respondents has higher education standard than what the Cultural Organization, United Nations Educational and Scientific recommends for secondary school, which qualified them to provide an appropriate
 
 response. This shows that, in terms of working experience, most of respondents have the fact and expertise necessary to comprehend the importance of laundering of money and tax evasion and in terms of economy growth. The findings indicated that among the duties of banks in recognizing cyber-scam were the development of the Bank Verification Number (BVN) and Single Treasury Account System,) and the instruction of collector of tax. In Nigeria's age of the cyber security, numerous measures have also been implemented to curtail and trade-oriented and tax evasion money laundering. According to logit regression, both laundering of money and tax evasion & were significant has indirect impact on economy growth. It follows, it is implied that both government revenue and economic growth are decreased as a result of large organizations' or individuals' tax avoidance. At a 5% level of significance, the logit regression outcome shows that tax collection (TAXC) was significant & positively connected with economy growth (ECOA). This means that 99.9% of the economic growth that the government funds through taxes is directly facilitated by the provision of infrastructure. For the amount of businesses that were registered, which was 5% and not statistical significant. This implied that the number of registered enterprises in the study area did not contribute enough to tax receipts to support economy growth. Tax evasion, its fact that it impacted on economy growth that was detrimental. Also, the findings indicated that Nigeria's economy growth would slow down as money laundering increased (MOLA). The marginal impact showed that laundering of money (MLA) was sensitive to economy growth at 99.9 percent and that it was statistically significant at 5 percent.

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

The link between inflation and economic growth is one of the most important controversies in the economic literature. This paper investigates the short-run and the long-run relationship between the economic growth and the inflation of three Asian courtiers over the period 1980-2010. The methodology used in the study is cointegration and causality test. Johansen’s cointegration test and bound test approach were performed on the variables which have been tested for the stationary property using Augmented Dickey-Fuller and Phillips and Perron tests in order to examine the cointegration between the economic growth and the inflation. Vector error correction and Granger Causality test were further performed to discover the short run dynamics of the relationship between the variables and identify the direction of causality. The results reveal that there is a long run negative and significant relationship between the economic growth and inflation in Sri Lanka. Whereas no statistically significant relationships were found between the variables in China and in India, a negative and significant short run relationship was found for China. The causality results reveal that there is a unidirectional causality that runs from the economic growth to the inflation in China. The paper discusses the important policy implications of the results.

Money laundering is a hot debate discussion among policymakers, as money laundering usually arises due to theft of money or other illegal activity. Such criminal activities damage every stakeholder of the economic cycle, whether it is trade, productivity, or contribution of the financial sector itself. Due to the fact money laundering makes the industrial growth process very slow and undercuts economic activities, which are essential for the development. This chapter explores the nexus between money laundering as a threat to a sustainable development goal from different angles. The discussion reveals that money laundering negatively impacts economic growth, and the fundamental pillar of sustainable development is economic growth. So can we achieve sustainable economic growth and development without controlling money laundering? The authors conclude it is not possible.

Economic growth measures growth in economic activities. Economic growth is important for countries, because it often brings better economic and social standards. Stock exchange performance is often a good indicator of the economy since it shows the performance of some of the important companies in the economy. The empirical analysis in this article aims to investigate the relationship between economic growth and stock exchange performance. Percentage change in GDP of Turkey and BIST100 are used as variables for this purpose. Two different data sets; 2003-2022 and 2013-2022 are available in the research. The analysis include error correction model, vector auto regression, Granger causality, impulse response, and variance decomposition. For the period 2013-2022, cointegration long run relationship is determined but there is no short run relationship as explained by error correction model. 30% of changes in economic growth can be explained by stock exchange performance and only 5% of changes in stock exchange performance can be explained by economic growth. BIST100 Granger causes economic growth. For the period 2003-2022, both short run and long run relationships are detected. Shocks in the long run disappear by 24%. The results indicate that the stock exchange performance can be used as an indicator of economic growth. Works in the literature also show significant relationship between stock markets and GDP both in local and international studies. The findings have important academic, policy and practical implications.

In Bangladesh the garment sector is one of the major industrial sector which occupied its foremost presence in the economy of the country in terms of foreign exchange earnings, contribution to GDP, and employment generation and as well. The garment sector plays significant roles for socio-economic development and poverty alleviation in Bangladesh. The export-oriented readymade garments (RMG) sector in Bangladesh, started its journey in late 1970s as a small non-traditional sector of export. The industry began its journey from 1978 and formally started its journey in the 1980s, after that RMG sector of Bangladesh started growing, capture the market globally and providing valuable performance. In 1979 the garment sector exported only 0.672 million $ with operating 12 garment factories by 0.39 million workers but in 2020 within a span of two decays export values have gone up to 31456.73 million USD with its 4764 export oriented garment industries including 4.23 million workers. Thus an important question is usually raised by the researchers, is there a long-run cointegration relationship between garment sector development and economic growth in Bangladesh? To give the answer of this question this paper has tried to find out whether the long-run cointegration relationship exists between garment sector development and economic growth in Bangladesh economy. The study is used the times series data from 1976-2020 for garment sector development and economic growth. The existence of long-run cointegration relationship is found between garment sector development and economic growth in Bangladesh economy. From the estimated results of the vector error correction (VEC) model it is found that there exists short run bidirectional causality between garment sector development and economic growth. The significance and negative sign of test statistic of ECM(-1) denotes the existence of long run causality between garment sector development and economic growth. Therefore, it can be presumed that there is a long-run equilibrium connection between garment sector development and economic expansion in Bangladesh economy. From the estimated values of response function it can be said that with respect to one standard deviation the variable garment sector development responds positively for the next fifteen years in the variable economic growth in Bangladesh.

The corn industry has increasingly emerged as a new source of wealth and income for Malaysia. However, the country’s dependence on corn imports remains high due to insufficient local production to meet domestic demand. Therefore, this study was conducted to assess the long-term and short-term effects of corn imports and domestic consumption on Malaysia’s economic growth. This empirical study employs secondary time-series data for the period 1986–2019 obtained from IndexMundi and the World Bank. The analysis was carried out using the Autoregressive Distributed Lag (ARDL) approach, the Johansen Cointegration test, the Error Correction Model (ECM), and the Granger Causality Test in EVIEWS-10. The findings reveal that both corn imports and domestic consumption have long-run and short-run relationships with Malaysia’s economic growth. The ARDL model and cointegration tests confirm the existence of integrated relationships among the variables, while the ECM results indicate a significant adjustment towards long-run equilibrium. The Granger Causality Test further demonstrates one-way causality from domestic consumption to GDP, from imports to GDP, and from domestic consumption to imports. This indicates that changes in domestic consumption and corn imports have direct implications for the nation’s economic growth. In conclusion, this study contributes to a deeper understanding of the strategic role of the corn industry in Malaysia’s economic development. Additionally, the findings can serve as a reference for policymakers and relevant agencies in formulating strategies to strengthen the corn supply chain, enhance domestic consumption, and optimize import activities in support of national economic growth.

PurposeThe purpose of this study is to investigate long-run and short-run relationships between trade diversification, financial system development, capital formation and economic growth.Design/methodology/approachARDL estimation approach is applied to analyze long-run and short-run relationships between the financial system development, capital formation, economic growth and trade diversification in case of the Sultanate of Oman over the period 39 years starting from 1979 till 2017.FindingsThe results show that financial system development and economic growth has a positive impact on trade diversification in the short-run and long-run. However, capital formation has a negative impact on trade diversification in the short run and long run. The negative relationship between trade diversification and capital formation implies that over the period of study, the investment in capital goods was made to enhance the production capacity of the oil sector to maximize revenue.Research limitations/implicationsThis research is limited to analyze long-run and short-run relationship between the financial system development, capital formation and economic growth and trade diversification in case of Sultanate of Oman.Practical implicationsTo achieve the diversification goal, the policymakers need to formulate policies to strengthen the financial system and invest in infrastructure development to promote the non-oil sector. The research findings of this study will provide insights to the policymakers to formulate an effective diversification policy.Originality/valueThis research contributes to the existing literature by providing empirical evidence of the short-run and long-run analysis of the selected variables in the context of an oil-dependent country.

Over several decades, taxation has been taken as a veritable medium of engineering the growth or performance of an economy. However, empirical literature is not conclusive, as several studies have indicated mixed effects of tax on economic growth. Arising from this, the study investigated the cointegration relationship between tax revenue and Economic growth in Nigeria from 1980 to 2013. Various preliminary tests including descriptive statistics, trend analysis, and stationary tests using Augmented Dickey Fuller (ADF) test were conducted. The Engle-Granger Cointegration test was employed to determine whether a long run relationship existed between the variables. The Vector Error correction model was employed to confirm the long run relationship and determine the short run dynamics between the variables. Two post estimation diagnostics tests (autocorrelation, and Heteroscedasticity) were also conducted in order to confirm the robustness of the model. Findings indicated that a long run (but no short run) relationship existed between taxation and economic growth in Nigeria. The result also, revealed a significant positive relationship at 5% level of significance between Petroleum profit tax, Company Income tax and economic growth, but a negative relationship between economic growth and customs and Excise Duties. However, the tax components are jointly insignificant in impacting the Nigerian economic growth. This study recommends strong institutional reforms in the Department of Customs in order to plug the manifest leakages. The tax collection mechanism used by tax officials must be free from corruption and embezzlement. If this is not done, the revenue collected many not reach the desired point. The Federal Government, state governments and local governments should urgently modernize and automate all its tax system.

Having reviewed previous empirical studies on the relationship between foreign direct investment and economic growth, limited attention was given on the role of exchange rate on the relationship between foreign direct investment and economic growth. Therefore this study investigates the role of exchange rate on the relationship between foreign direct investment and economic growth over the period 1986 to 2018 using annual time series data sourced from the Central Bank of Nigeria Statistical Bulletin. Augmented Dicker-fuller Unit Root Test and ARDL model were used for the analyses. The ARDL Bounds test to cointegration revealed that economic growth, foreign direct investment, export, import and exchange rate do not have long run relationship over the period under study. The results showed that foreign direct investment has positive relationship with economic growth at maximum, average and minimum level of exchange rate but the relationship is only significant at maximum level over the period under study. This means that at maximum level of exchange rate, an increase in foreign direct investment will lead to a risein economic growth. The results also showed that export has significant positive relationship with economic growth meaning that an increase in export will lead to a rise in economic growth while import showed insignificant negative relationship with economic growth. Based on the results, the study recommended that further depreciation of Nigeria’s currency should be encourage so as to allow more inflow of foreign direct investment considering its positive impact on economic growth while the Nigerian Government is encouraged to design and implement policies that will spur export by eliminating stringent excise duties and discouraging import which exerts negative influence on economic growth.

The main objective of the study is to investigate the relationship between economic growth and income inequality in Ethiopia during the period 1980-2014. The linear regression model is applied in order to investigate the long-run and short run relationship between the dependent variable (real GDP) and included explanatory variable. The finding shows that there is a stable long run relationship between real GDP and included variables. And the empirical results reveal that income inequality measured by gini coefficient is found to have negative impact on economic growth. The study has also an important policy implication which implies that economic growth can be improved significantly when the income inequality among people reduced through different redistributive mechanisms. Keywords: - Economic growth, Income Inequality, Gini coefficient, Ethiopia DOI: 10.7176/JESD/13-11-04 Publication date: June 30 th 2022

Energy is critical to the survival and expansion of any economy. In Nigeria, energy consumption has been skewed towards household use, and below thresholds for sector-driven growth. The article updates, in time and methodology, those studies highlighting the significance of energy use for economic growth, using the Bound test and the Auto Regression Distributed Lag (ARDL) to establish the long- and short-run relationships between disaggregated energy consumption and economic growth in Nigeria from 1990 to 2016. The variables considered are real GDP, energy consumption decomposed into electricity and petroleum consumption, labour and capital. The findings show that, in the short and long run, petroleum consumption and labour have a significant positive relationship with GDP. Furthermore, the causality results show that feedback causation between economic growth and energy consumption as well as labour exists, while one-way causation runs from labour to economic growth. The study recommends diversification of the power-generation portfolio in the country, as this will improve energy consumption. Also, full deregulating policies in the energy sector would encourage industrialization and move energy demand towards increasingly productive uses. Finally, a strong institutional framework is needed to ensure energy policies achieve their objectives and targets. Keywords : Energy Consumption, Economic Growth, Industrialization, Error Correction.