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Cited by 10Corporate Governance and Financial ManagementGovernance, Compliance, and SustainabilityCorporate Finance and Governance

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Advances in Corporate Governance and Performance Accountability in Global Energy Enterprises

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Agu, Michael Uzoma, Akomolafe, Olawole·2020·International Journal of Multidisciplinary Research and Growth Evaluation
DOI 10.54660/.ijmrge.2020.1.5.375-384
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Abstract

Global energy enterprises operate within increasingly complex governance environments shaped by rising climate risks, geopolitical uncertainty, digital transformation, and intensified stakeholder scrutiny. The expanding scope of global energy value chains encompassing hydrocarbons, electricity, renewables, and emerging clean-energy technologies has heightened expectations for transparency, ethical conduct, and rigorous performance accountability. Drawing on scholarship published up to 2019, this paper reviews advances in corporate governance structures, mechanisms of accountability, regulatory frameworks, and performance-monitoring systems applied across global energy enterprises, including state-owned utilities, multinational oil and gas firms, and vertically integrated renewable energy corporations. The review highlights improvements in board governance, audit oversight, risk-management practices, disclosure standards, ESG integration, and strategic alignment. It also identifies persistent gaps arising from political interference, weak regulatory enforcement, corruption exposure, cyber-security vulnerabilities, and operational complexities associated with global energy supply chains. The study contributes a structured synthesis of governance developments prior to 2020 and outlines implications for strengthening accountability and performance discipline in the global energy sector.

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  1. 10.1108/CG-11-2017-0271 · DOI
  2. Zuckweiler KM, Rosacker KM, Hayes SK. Business students' perceptions of corporate governance best practices. Corp Gov. 2016;16(2):361-76. doi: 10.1108/CG-08-2015-0117 · DOI
  3. Khongmalai O, Tang JCS, Siengthai S. Empirical evidence of corporate governance in Thai state-owned enterprises. Corp Gov. 2010;10(5):617-34. doi: 10.1108/14720701011085580 · DOI
  4. Roohani S, Furusho Y, Koizumi M. XBRL: improving transparency and monitoring functions of corporate governance. Int J Discl Gov. 2009;6(4):355-69. doi: 10.1057/jdg.2009.17 · DOI
  5. Fooladi M, Farhadi M. Corporate governance and detrimental related party transactions: evidence from Malaysia. Asian Rev Account. 2019;27(2):196-227. doi: 10.1108/ARA-02-2018-0029 · DOI
  6. Khongmalai O, Distanont A. Corporate governance model in Thai state-owned enterprises: structural equation modelling approach. Corp Gov. 2017;17(4):613-28. doi: 10.1108/CG-01-2016-0021 · DOI
  7. Richards G, Yeoh W, Chong AYL, Popovič A. Business intelligence effectiveness and corporate performance management: an empirical analysis. J Comput Inf Syst. 2019;59(2):188-96. doi: 10.1080/08874417.2017.1334244 · DOI
  8. Verma R, Verma P. Behavioral biases and retirement assets allocation of corporate pension plans. Rev Behav Finance. 2018;10(4):353-69. doi: 10.1108/RBF-01- 2017-0009 · DOI
  9. Elhelaly M. Related party transactions, corporate governance and accounting quality in Greece [dissertation]. Glasgow: University of Strathclyde; 2014.
  10. Sarens G, Abdolmohammadi MJ, Lenz R. Factors associated with the internal audit function's role in corporate governance. J Appl Account Res. 2012;13(2):191-204. doi: 10.1108/09675421211254876 · DOI
  11. Mantere S, Pajunen K, Lamberg JA. Vices and virtues of corporate political activity: the challenge of international business. Bus Soc. 2009;48(1):105-32. doi: 10.1177/0007650307303388 · DOI
  12. La Torre M, Valentinetti D, Dumay J, Rea MA. Improving corporate disclosure through XBRL: an evidence-based taxonomy structure for integrated reporting. J Intellect Cap. 2018;19(2):338-66. doi: 10.1108/JIC-03-2016-0030 · DOI
  13. Cheah ET, Chan WL, Chieng CLL. The corporate social responsibility of pharmaceutical product recalls: an empirical examination of U.S. and U.K. markets. J Bus Ethics. 2007;76(4):427-49. doi: 10.1007/s10551-006- 9292-1 · DOI
  14. Chesbrough H, Rosenbloom RS. The role of the business model in capturing value from innovation: evidence from Xerox Corporation's technology spin-off companies. Ind Corp Change. 2002;11(3):529-55. doi: 10.1093/icc/11.3.529 · DOI
  15. Dunning JH, Lundan SM. The institutional origins of dynamic capabilities in multinational enterprises. Ind Corp Change. 2010;19(4):1225-46. doi: 10.1093/icc/dtq029 · DOI
  16. Gans JS, Stern S. Is there a market for ideas? Ind Corp Change. 2010;19(3):805-37. doi: 10.1093/icc/dtq023 · DOI · Paper page
  17. Pitelis CN, Teece DJ. Cross-border market co-creation, dynamic capabilities and the entrepreneurial theory of the multinational enterprise. Ind Corp Change. 2010;19(4):1247-70. doi: 10.1093/icc/dtq030 · DOI
  18. Nonaka I, Toyama R. Strategic management as distributed practical wisdom (phronesis). Ind Corp Change. 2007;16(3):371-94. doi: 10.1093/icc/dtm014 · DOI
  19. Sturgeon TJ. Modular production networks: a new American model of industrial organization. Ind Corp Change. 2002;11(3):451-96. doi: 10.1093/icc/11.3.451 · DOI
  20. Abratt R, Kleyn N. Corporate identity, corporate branding and corporate reputations: reconciliation and integration. Eur J Mark. 2012;46(7/8):1048-63. doi: 10.1108/03090561211230197 · DOI
  21. Carson SJ, Ghosh M. An integrated power and efficiency model of contractual channel governance: theory and empirical evidence. J Mark. 2019;83(4):101-20. doi: 10.1177/0022242919843914 · DOI
  22. Carson SJ, Madhok A, Tao W. Uncertainty, opportunism, and governance: the effects of volatility and ambiguity on formal and relational contracting. Acad Manag J. 2006;49(5):1058-77. doi: 10.5465/amj.2006.22798187 · DOI
  23. Wilkinson N, Coetzee P. Internal audit assurance or consulting services rendered on governance: how does one decide? J Gov Regul. 2015;4(1):186-200. doi: 10.22495/jgr_v4_i1_c2_p3 · DOI
  24. Khatri V, Brown CV. Designing data governance. Commun ACM. 2010;53(1):148-52. doi: 10.1145/1629175.1629210 · DOI · Paper page
  25. Evans J, McKemmish S, Rolan G. Participatory information governance: transforming recordkeeping for childhood out-of-home care. Rec Manag J. 2019;29(1- 2):178-93. doi: 10.1108/RMJ-09-2018-0041 · DOI
  26. Thomas M. Evaluating electronic health records interoperability symbiotic relationship to information management governance security risks [dissertation]. Minneapolis (MN): Walden University; 2019. Available from: https://search.proquest.com/openview/19b1d289dc52f8 ed896a62bdf4e568b7/1
  27. Hope KR. Capacity development for good governance in developing countries: some lessons from the field. Int J Public Adm. 2009;32(8):728-40. doi: 10.1080/01900690902908562 · DOI
  28. El-Helaly M. Related-party transactions: a review of the regulation, governance and auditing literature. Manag Audit J. 2018;33(8-9):779-806. doi: 10.1108/MAJ-07- 2017-1602 · DOI
  29. Holeman I, Cookson TP, Pagliari C. Digital technology for health sector governance in low and middle income countries: a scoping review. J Glob Health. 2016;6(2):020408. doi: 10.7189/jogh.06.020408 · DOI · Paper page
  30. Lawanson MAT. Land governance and megacity projects in Lagos, Nigeria: the case of Lekki Free Trade Zone. Area Dev Policy. 2018;3(1):114-31.
  31. Agbaje GI, Bello IE, Ojo AG. Empirical assessment of the role of geospatial technology in delivering governance and strengthening democracy in Nigeria. GeoJournal. 2018;83(4):743-56. doi: 10.1007/s10708- 017-9797-4 · DOI
  32. Rusu L, Tenga RP. IT governance in the healthcare sector: a case study of a public and private hospital in Tanzania. Int J Inf Syst Change Manag. 2010;4(4):314- 37. doi: 10.1504/IJISCM.2010.036915 · DOI
  33. Agbaje GIO. Empirical assessment of the role of geospatial technology in delivering governance and strengthening democracy in Nigeria. GeoJournal. 2018;83(4):743-56.
  34. Allen C, Des Jardins TR, Heider A, Kanger CR, Lobach DF, McCormack J, et al. Data governance and data sharing agreements for community-wide health information exchange: lessons from the Beacon communities. EGEMS (Wash DC). 2014;2(1):1057. doi: 10.13063/2327-9214.1057 · DOI
  35. Savory C. Patient and public involvement in translative healthcare research. Clin Gov. 2010;15(3):191-9. doi: 10.1108/14777271011063823 · DOI
  36. Katuu S. Healthcare systems: typologies, framework models, and South Africa's health sector. Int J Health Gov. 2018;23(2):134-48. doi: 10.1108/IJHG-10-2017- 0054 · DOI
  37. Díez-Montero R, Solimeno A, Uggetti E, García-Galán MJ, García J. Feasibility assessment of energy-neutral microalgae-based wastewater treatment plants under Spanish climatic conditions. Process Saf Environ Prot. 2018;119:242-52. doi: 10.1016/j.psep.2018.08.008 · DOI
  38. Erturk E, Lopez D, Yu WY. Benefits and risks of using blockchain in smart energy: a literature review. Contemp Manag Res. 2019;15(3):205-25. doi: 10.7903/cmr.19650 · DOI
  39. Baccarelli E, Cordeschi N, Mei A, Panella M, Shojafar M, Stefa J. Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study. IEEE Netw. 2016;30(2):54-61.
  40. Onar SC, Oztaysi B, Otay I, Kahraman C. Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy. 2015;90:274-85. doi: 10.1016/j.energy.2015.06.086 · DOI
  41. Jain S, Lindskog E, Andersson J, Johansson B. A hierarchical approach for evaluating energy trade-offs in supply chains. Int J Prod Econ. 2013;146(2):411-22. doi: 10.1016/j.ijpe.2013.03.015 · DOI
  42. Shafique MN, Rashid A, Bajwa IS, Kazmi R, Khurshid MM, Tahir WA. Effect of IoT capabilities and energy consumption behavior on green supply chain integration. Appl Sci (Switzerland). 2018;8(12):2481. doi: 10.3390/app8122481 · DOI
  43. Amankwah-Amoah J. Solar energy in sub-Saharan Africa: the challenges and opportunities of technological leapfrogging. Thunderbird Int Bus Rev. 2015;57(1):15- 31. doi: 10.1002/tie.21677 · DOI
  44. Benyeogor O, Jambol D, Amah O, Obiga D, Awe S, Erinle A. Pressure relief management philosophy for MPD operations on surface stack HPHT exploration wells. SPE Nigeria Annual International Conference and Exhibition; 2019 Aug 5-7; Lagos, Nigeria. SPE-198828- MS. doi: 10.2118/198828-MS 47. Etim ED, Essien IA, Ajayi JO, Erigha ED, Obuse E. AI- augmented intrusion detection: advancements in real- time cyber threat recognition. IRE Journals. 2019;3(3):225-30. · DOI
  45. Ayanbode N, Cadet E, Etim ED, Essien IA, Ajayi JO. Deep learning approaches for malware detection in large-scale networks. IRE Journals. 2019;3(1):483-502.
  46. Abba A, Zakuan N, Uba Ado AS, Bichi A, Shettima U, Ali Saif M, et al. Supply chain sustainability practices of oil servicing firms in the downstream sector of Nigeria's oil and gas industry. J Econ Info. 2019;6(4):11-4. doi: 10.31580/JEI.V6I4.1031 · DOI
  47. Pavão LV, Costa CBB, Ravagnani MASS. A new framework for work and heat exchange network synthesis and optimization. Energy Convers Manag. 2019;183:617-32. doi: 10.1016/j.enconman.2019.01.018 · DOI
  48. Mohamed N, Al-Jaroodi J, Lazarova-Molnar S. Leveraging the capabilities of Industry 4.0 for improving energy efficiency in smart factories. IEEE Access. 2019;7:18008-20. doi: 10.1109/ACCESS.2019.2897045 · DOI · Paper page
  49. Ammar Y, Joyce S, Norman R, Wang Y, Roskilly AP. Low grade thermal energy sources and uses from the process industry in the UK. Appl Energy. 2012;89(1):3- 20. doi: 10.1016/j.apenergy.2011.06.003 · DOI
  50. Koperski L. Why the renewable energy credit market needs standardization. Wash J Law Technol Arts. 2017;13:65-88.
  51. Wu P, Xia B, Wang X. The contribution of ISO 14067 to the evolution of global greenhouse gas standards -a review. Renew Sustain Energy Rev. 2015;47:142-50. doi: 10.1016/j.rser.2015.02.055 · DOI
  52. Jebaraj S, Iniyan S, Goic R. Forecasting of coal consumption using an artificial neural network and comparison with various forecasting techniques. Energy Sources Part A. 2011;33(14):1305-16. doi: 10.1080/15567030903397859 · DOI
  53. Heo E, Kim J, Cho S. Selecting hydrogen production methods using fuzzy analytic hierarchy process with opportunities, costs, and risks. Int J Hydrogen Energy. 2012;37(23):17655-62. doi: 10.1016/j.ijhydene.2012.09.055 · DOI
  54. Rana M, Koprinska I, Agelidis VG. Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers Manag. 2016;121:380-90. doi: 10.1016/j.enconman.2016.05.025 · DOI
  55. Shuen A, Feiler PF, Teece DJ. Dynamic capabilities in the upstream oil and gas sector: managing next generation competition. Energy Strategy Rev. 2014;3:5- 13. doi: 10.1016/j.esr.2014.05.002 · DOI
  56. Feiler P, Teece D. Case study, dynamic capabilities and upstream strategy: Supermajor EXP. Energy Strategy Rev. 2014;3:14-20. doi: 10.1016/j.esr.2014.05.003 · DOI
  57. Babawarun T, Ho WH, Ngwangwa H. Stress validation of finite element model of a small-scale wind turbine blade. Wind Eng. 2019;30(2):87-97.
  58. Blaga R, Sabadus A, Stefu N, Dughir C, Paulescu M, Badescu V. A current perspective on the accuracy of incoming solar energy forecasting. Prog Energy Combust Sci. 2019;70:119-44. doi: 10.1016/j.pecs.2018.10.003 · DOI
  59. Baležentis T, Štreimikiene D. Sustainability in the electricity sector through advanced technologies: energy mix transition and smart grid technology in China. Energies (Basel). 2019;12(6):1142. doi: 10.3390/en12061142 · DOI
  60. Stavrou V, Bardaki C, Papakyriakopoulos D, Pramatari K. An ensemble filter for indoor positioning in a retail store using bluetooth low energy beacons. Sensors (Switzerland). 2019;19(20):4550. doi: 10.3390/s19204550 · DOI
  61. Tao M, Zhenpeng L, Jiaxin Z. Photovoltaic panel integrated with phase change materials (PV-PCM): technology overview and materials selection. Renew Sustain Energy Rev. 2019;116:109406. doi: 10.1016/j.rser.2019.109406 · DOI
  62. Dayan E. Wind energy in buildings. Refocus. 2006;7(2):33-8. doi: 10.1016/S1471-0846(06)70545-5 · DOI
  63. Ajayi OO, Fagbenle RO, Katende J, Ndambuki JM, Omole DO, Badejo AA. Wind energy study and energy cost of wind electricity generation in Nigeria: past and recent results and a case study for South West Nigeria. Energies (Basel). 2014;7(12):8508-34. doi: 10.3390/en7128508 · DOI
  64. Bahaj AS, Myers L, James PAB. Urban energy generation: influence of micro-wind turbine output on electricity consumption in buildings. Energy Build. 2007;39(2):154-65. doi: 10.1016/j.enbuild.2006.06.001 · DOI
  65. Louhghalam A, Tootkaboni M, Ulm FJ. Roughness- induced vehicle energy dissipation: statistical analysis and scaling. J Eng Mech. 2015;141(9):04015023. doi: 10.1061/(asce)em.1943-7889.0000944 · DOI
  66. Sharma A, Kaur P. A multitenant data store using a column based NoSQL database. 2019 12th International Conference on Contemporary Computing (IC3); 2019 Aug 8-10; Noida, India. IEEE; 2019. p. 1-6. doi: 10.1109/IC3.2019.8844906 · DOI
  67. Alvez C, Miranda E, Etchart G, Ruiz S. Efficient iris recognition management in object-related databases. J Comput Sci Technol. 2018;18(02):e12. doi: 10.24215/16666038.18.e12 · DOI
  68. Troilo G, De Luca LM, Guenzi P. Linking data-rich environments with service innovation in incumbent firms: a conceptual framework and research propositions. J Prod Innov Manag. 2017;34(5):617-39. doi: 10.1111/jpim.12395 · DOI
  69. Zhang JWY. Data-driven modeling and scientific computing. Appl Mech Rev. 2016;68(5):050801.
  70. Collins F, Green A, Adeyemi O, Adebamowo C, Adebiyi E, Rotimi C, et al. A database on global health research in Africa. Lancet Glob Health. 2013;1(2):e64-5. doi: 10.1016/s2214-109x(13)70012-3 · DOI
  71. Eaton I, McNett M. Protecting the data: security and privacy. In: Data for nurses: understanding and using data to optimize care delivery in hospitals and health systems. London: Academic Press; 2019. p. 87-99.
  72. Hemerly J. Public policy considerations for data-driven innovation. Computer. 2013;46(6):25-31. doi: 10.1109/MC.2013.186 · DOI · Paper page
  73. Brunton SL, Kutz JN. Data-driven versus physics-based modeling. Annu Rev Fluid Mech. 2018;50:645-68.
  74. Kadlec P, Gabrys B, Strandt S. Data-driven soft sensors in the process industry. Comput Chem Eng. 2009;33(4):795-814. doi: 10.1016/j.compchemeng.2008.12.012 · DOI · Paper page
  75. Kumar Jain P, Mandli K, Hoteit I, Knio O, Dawson C. Dynamically adaptive data-driven simulation of extreme hydrological flows. Ocean Model (Oxf). 2018;122:85- 103. doi: 10.1016/j.ocemod.2017.12.004 · DOI
  76. Geissbuhler A, Safran C, Buchan I, Bellazzi R, Labkoff S, Eilenberg K, et al. Trustworthy reuse of health data: a transnational perspective. Int J Med Inform. 2013;82(1):1-9. doi: 10.1016/j.ijmedinf.2012.11.003 · DOI
  77. Hu XCYL. Optimization of FMCG supply chain by using data-driven methods. J Intell Manuf. 2019;30(1):81-92.
  78. Song Y, Routray R, Jain R, Tan CH. A data-driven storage recommendation service for multitenant storage management environments. Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management; 2015 May 11-15; Ottawa, Canada. IEEE; 2015. p. 1026-40.
  79. Wang TTL. Data-driven fast moving consumer goods supply chain model and application. Int J Control Autom. 2018;11(5):125-38.
  80. Toft HS, Svenningsen L, Sørensen JD, Moser W, Thøgersen ML. Uncertainty in wind climate parameters and their influence on wind turbine fatigue loads. Renew Energy. 2016;90:352-61. doi: 10.1016/j.renene.2016.01.010 · DOI
  81. Dimitrov N, Natarajan A, Mann J. Effects of normal and extreme turbulence spectral parameters on wind turbine loads. Renew Energy. 2017;101:1180-93. doi: 10.1016/j.renene.2016.10.001 · DOI
  82. Pourrajabian A, Amir N, Afshar N, Ahmadizadeh M, Wood D. Aero-structural design and optimization of a small wind turbine blade. Renew Energy. 2016;87:837- 48. doi: 10.1016/j.renene.2015.09.002 · DOI
  83. Monfared M, Madadi Kojabadi H, Rastegar H. Static and dynamic wind turbine simulator using a converter controlled dc motor. Renew Energy. 2008;33(5):906-13. doi: 10.1016/j.renene.2007.06.007 · DOI
  84. Hirahara H, Hossain MZ, Kawahashi M, Nonomura Y. Testing basic performance of a very small wind turbine designed for multi-purposes. Renew Energy. 2005;30(8):1279-97. doi: 10.1016/j.renene.2004.10.009 · DOI
  85. Ahlström A. Influence of wind turbine flexibility on loads and power production. Wind Energy. 2006;9(3):237-49. doi: 10.1002/we.167 · DOI
  86. Rosenbaum S. Data governance and stewardship: designing data stewardship entities and advancing data access. Health Serv Res. 2010;45(5 Pt 2):1442-55. doi: 10.1111/j.1475-6773.2010.01140.x · DOI
  87. Mumtaz R, Jadoon IA. Effect of explicit deposit insurance premium on the moral hazard of banks' risk- taking: around the globe. Int J Financ Eng. 2018;5(2):1850012. doi: 10.1142/S2424786318500123 · DOI
  88. Hui CH, Lo CF, Zheng XF, Fong T. Probabilistic approach to measuring early-warning signals of systemic contagion risk. Int J Financ Eng. 2018;5(2):1850010. doi: 10.1142/S242478631850010X · DOI
  89. Sabherwal R, Becerra-Fernandez I. Business intelligence: practices, technologies, and management. Hoboken (NJ): Wiley; 2013.
  90. Mishra S. Financial management and forecasting using business intelligence and big data analytic tools. Int J Financ Eng. 2018;5(2):1850011. doi: 10.1142/S2424786318500111 · DOI
  91. Lenz R, Hahn U. A synthesis of empirical internal audit effectiveness literature pointing to new research opportunities. Manag Audit J. 2015;30(1):5-33. doi: 10.1108/MAJ-08-2014-1072 · DOI
  92. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879-903. doi: 10.1037/0021- 9010.88.5.879 · DOI · Paper page
  93. Lenz R, Sarens G, Hoos F. Internal audit effectiveness: multiple case study research involving chief audit executives and senior management. EDPACS. 2017;55(1):1-17. doi: 10.1080/07366981.2017.1278980 · DOI
  94. Woods M. Linking risk management to strategic controls: a case study of Tesco plc. Int J Risk Assess Manag. 2007;7(8):1074-88. doi: 10.1504/IJRAM.2007.015295 · DOI
  95. Kock N. Common method bias in PLS-SEM: a full collinearity assessment approach. Int J e-Collab. 2015;11(4):1-10. doi: 10.4018/IJEC.2015100101 · DOI · Paper page
  96. Abidin NHZ. Factors influencing the implementation of risk-based auditing. Asian Rev Account. 2017;25(3):361-75. doi: 10.1108/ARA-10-2016-0118 100. · DOI
  97. Goodwin-Stewart J, Kent P. The use of internal audit by Australian companies. Manag Audit J. 2006;21(1):81- 101. doi: 10.1108/02686900610634775 101. · DOI
  98. Kabuye F, Nkundabanyanga SK, Opiso J, Nakabuye Z. Internal audit organisational status, competencies, activities and fraud management in the financial services sector. Manag Audit J. 2017;32(9):924-44. doi: 10.1108/MAJ-09-2016-1452 102. · DOI
  99. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43(1):115-35. doi: 10.1007/s11747-014-0403-8 103. Nigrini MJ. Benford's law: applications for forensic accounting, auditing, and fraud detection. Hoboken (NJ): Wiley; 2012. 104. Fiksel J, Polyviou M, Croxton KL, Pettit TJ. Creating businesses that adapt and flourish in a changing world. MIT Sloan Manag Rev. 2015;56(2):79-86. 105. Christensen BJ, Kowalczyk C. Globalization: strategies and effects. Berlin: Springer; 2017. 106. Margolis SL, Hansen CD. Visions to guide performance: a typology of multiple future organizational images. Perform Improv Q. 2003;16(4):40-58. doi: 10.1111/j.1937-8327.2003.tb00293.x 107. Wang T, Libaers D, Jiao H. Opening the black box of upper echelons in China: TMT attributes and strategic flexibility. J Prod Innov Manag. 2015;32(5):685-703. doi: 10.1111/jpim.12152 108. · DOI
  100. Castanheira N, Rodrigues LL, Craig R. Factors associated with the adoption of risk-based internal auditing. Manag Audit J. 2010;25(1):79-98. doi: 10.1108/02686901011007315 109. · DOI
  101. Allegrini M, D'Onza G. Internal auditing and risk assessment in large Italian companies: an empirical survey. Int J Audit. 2003;7(3):191-208. doi: 10.1046/j.1099-1123.2003.00070.x 110. Endaya KA, Hanefah MM. Internal auditor characteristics, internal audit effectiveness, and moderating effect of senior management. J Econ Adm Sci. 2016;32(2):160-76. doi: 10.1108/JEAS-07-2015- 0023 111. · DOI
  102. de Zwaan L, Stewart J, Subramaniam N. Internal audit involvement in enterprise risk management. Manag Audit J. 2011;26(7):586-604. doi: 10.1108/02686901111151323 112. · DOI
  103. Alzeban A, Gwilliam D. Factors affecting the internal audit effectiveness: a survey of the Saudi public sector. J Int Account Audit Tax. 2014;23(2):74-86. doi: 10.1016/j.intaccaudtax.2014.06.001 113. · DOI
  104. Abdullatif M, Kawuq S. The role of internal auditing in risk management: evidence from banks in Jordan. J Econ Adm Sci. 2015;31(1):30-50. doi: 10.1108/JEAS-08- 2013-0025 114. · DOI
  105. Barabesi L, Cerasa A, Perrotta D, Cerioli A. Modeling international trade data with the Tweedie distribution for anti-fraud and policy support. Eur J Oper Res. 2016;248(3):1031-43. doi: 10.1016/j.ejor.2015.08.042 115. Marin A, Bell M. Technology spillovers from foreign direct investment (FDI): the active role of MNC subsidiaries in Argentina in the 1990s. J Dev Stud. 2006;42(4):678-97. doi: 10.1080/00220380600682298 116. · DOI
  106. Agosin MR, Machado R. Foreign investment in developing countries: does it crowd in domestic investment? Oxf Dev Stud. 2005;33(2):149-62. doi: 10.1080/13600810500137749 117. · DOI
  107. Iguchi C. Determinants of backward linkages: the case of TNC subsidiaries in Malaysia. Asian Bus Manag. 2008;7(1):53-73. doi: 10.1057/palgrave.abm.9200244 · DOI

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Metadata

Authors
Agu, M. U., & Akomolafe, O.
Venue
International Journal of Multidisciplinary Research and Growth Evaluation
Publisher
Anfo Publication House
Published
2020
Volume
1
Issue
5
Pages
375-384
ISSN
2582-7138 (ISSN-L)
DOI
10.54660/.ijmrge.2020.1.5.375-384
OpenAlex
W7109121632
Cited by
10
References
0
Citations by year
2026: 2 · 2025: 1 · 2023: 3 · 2022: 3 · 2021: 1
FWCI
1.74
Type
journal article
Language
en
Open access
Locations
International Journal of Multidisciplinary Research and Growth Evaluation (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Corporate Governance and Financial Management
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Governance, Compliance, and Sustainability
  • Social Sciences › Economics, Econometrics and Finance › Finance › Corporate Finance and Governance
Keywords
corporate governance, performance accountability, board governance, audit supervision, risk management, disclosure standards, ESG integration, state-owned utilities, oil and gas companies, energy supply chain, regulatory enforcement, corruption, cybersecurity, climate risk, stakeholder scrutiny

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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