The Impact of Artificial Intelligence on Organizational Performance: Insights from VFD Micro Finance Bank (VBank), Lagos State Nigeria

The Impact of Artificial Intelligence on Organizational Performance: Insights from VFD Micro Finance Bank (VBank), Lagos State Nigeria

Annabel I. Amadi^1*, Rolland O. Emuobor², Tolulope O. Mosue³, Salisu A. Abdullahi⁴

¹Federal University of Technology Owerri, Nigeria

^2,3Northumbria University, United Kingdom

⁴Teesside University United Kingdom

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.814MG0044

Received: 06 December 2024; Accepted: 11 December 2024; Published: 09 January 2025

ABSTRACT

This study examined “The Impact of artificial intelligence on organizational performance: Insights from VFD micro finance bank”. Methodology: Relevant data were drawn from selected one hundred (100) staff of VFD micro finance bank in Lagos state, using a well-structured questionnaire. The findings of the study revealed that there is an impact of artificial intelligence on organizational performance. Study conclusion and policy recommendations: The study concluded that in the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in business. The study recommends that businesses must take proactive measures to address the obstacles to AI adoption if they want to optimize the technology’s beneficial effects on organizational performance. It is advised that businesses concentrate on making investments in the training of a knowledgeable workforce by providing courses that give staff members the skills they need to work with AI. In addition to ensuring that employees can efficiently manage and fully utilize AI technologies, this will assist close the talent gap. Additionally, in order to stay up with the rapid advancement of AI technologies and approaches, organizations need to cultivate a culture for continuous learning.

Keywords: Artificial Intelligence, Micro Finance Banks, Organizational Performance, Financial Institutions, Technological Advancement.

INTRODUCTION

Artificial intelligence (AI) has emerged as a significant research topic in the twenty-first century in almost every discipline, including engineering, science, education, medical, business, accounting, finance, marketing, economics, manufacturing, the stock market, and law, to name a few (Lohr, 2017). The subject of artificial intelligence has expanded so much that it is now challenging to keep track of the number of studies being conducted (Shabbir & Anwer, 2015). According to Gurpartap (2017), In contrast to the natural intelligence (NI) exhibited by humans and other animals, artificial intelligence (AI), also referred to as machinery intelligence (MI), is intelligence displayed by machines. It is defined as any device that senses its surroundings and acts in a way that maximizes its chances of accomplishing its objectives. Gurpartap (2017) referred to the intellectual capacity of machines to comprehend, interpret, or rationalize and react to external stimuli similarly to live beings is known as artificial intelligence. It is a relatively new area of automation and computing that builds machines that can do tasks that previously required human abilities. Artificial intelligence is capable of mimicking human intelligence in a variety of tasks that call for learning and analytical thinking, problem-solving, and decision-making (Shabbir & Anwer, 2015). AI has a reputation for effectively completing cognitive activities, but it also significantly increases people’s dependence on the technology. Large data sets can be processed by artificial intelligence (AI) systems, which can also collect and analyze data at supersonic speed. Shabbir and Anwer (2015), developed to the point where artificial intelligence is mirrored in the form of an artificial brain model that attempts to replicate the learning process in order to imitate the capabilities of the human brain.

Lohr (2017) posits that Artificial intelligence (AI) technology is being adopted by many companies in an effort to save operating costs, boost productivity, boost sales, and enhance customer satisfaction. Businesses consider integrating the entire spectrum of smart technologies—such as robots, machine learning, data mining, the Internet of Things (IoT), and natural language processing—into their operations and goods in order to reap the biggest benefits. Businesses that are fresh to AI can still benefit greatly. Businesses can save time and money by automating repetitive tasks and processes; increase productivity and improve operational effectiveness; make quicker decisions based on cognitive technology outputs; avoid mistakes and “human error” if smart systems are properly configured; use insight to predict consumer needs and provide a better, more individualized experience; mine vast amounts of data to generate quality leads and expand their customer base; reduce costs by optimizing the business, workforce, or products; boost revenue by identifying and maximizing sales opportunities; and develop expertise by enabling analysis and providing intelligent advice and support. By automating repetitive work, allocating resources optimally, and increasing overall efficiency, AI also improves organizational performance. According to PwC (2023), implementing AI can boost productivity by up to 40%, emphasizing its substantial impact on operational performance. This is especially important for Nigerian businesses that must innovate quickly to remain competitive in the global market and deal with issues like scarce resources and inadequate training. Deloitte (2022) highlights that by improving strategic skills and optimizing procedures, Nigerian businesses using AI are better able to overcome these obstacles.AI has completely changed how businesses exchange and manage knowledge. Businesses may improve their knowledge-sharing capabilities and make information more accessible and actionable by utilizing AI technologies. Better productivity, efficiency, data analysis, accuracy, round-the-clock availability, better decision-making, cost savings, innovative solutions, and safety are the outcomes of this. AI also facilitates individualized solutions, promotes diversity, and assists complex problem-solving. Artificial intelligence (AI) systems analyze vast amounts of data, derive insightful information, and enable real-time information sharing, all of which support ongoing learning and innovation. AI also improves communication and information exchange between teams and departments, which strengthens collaboration. It is to this the study centers the impact of artificial intelligence on organizational performance using VFD Microfinance bank, Nigeria as a case study.

Little is known about how the deployment of AI impacts these important outcomes, even if the goal of applying AI is to improve overall performance, productivity, and effectiveness within organizations. Even though scholars have examined a number of aspects of AI adoption (Smith and Johnson, 2018), The direct effects of AI adoption on these important organizational outcomes are largely unknown. This knowledge gap needs to be filled in order to ensure that large investments in AI provide noticeable and measurable results for businesses. AI is becoming a more competitive tool for enterprises (Kinkel et al., 2022). It is to this the study centers the impact of artificial intelligence on organizational performance using VFD Micro Finance bank, Nigeria as a case study.

METHODOLOGY

The research used descriptive survey design as the strategy or plan of action regarding events which upon implementation will enable the researcher to investigate the problem of this study. The design is suitable for this study because data was collected from respondents using structured questionnaires as research instrument to give an assessment on the impact of Artificial Intelligence on organizational performance. The study was conducted in VFD Micro Finance Bank in Lagos State, Nigeria, and the study population consists of selected staff of VFD Microfinance bank in Lagos. The Simple Random sampling technique was used for the selection of One hundred (100) staff of VFD micro finance bank in Lagos state which formed the researcher’s respondents from the entire population. The quantitative data obtained from the field were processed and analyzed using descriptive statistics such as the use of simple percentages, tables and frequency distribution. The data obtained from respondents through the administration of questionnaires was collated and analyzed using Statistical Package for Social Sciences (SPSS Version 20.0).  All One hundred (100) questionnaires that were administered within the study area, were fully completed and returned by the respondents.

DATA PRESENTATION, ANALYSIS AND INTERPRETATION

Table 1: Responses on Artificial Intelligence as a potential to significantly enhance operational efficiency

Source: Field Survey, 2024.

Table 1 shows the responses of respondents that Artificial Intelligence has the potential to significantly enhance operational efficiency. 50 respondents representing 50.0 percent strongly agree that Artificial Intelligence has the potential to significantly enhance operational efficiency. 25 respondents representing 25.0 percent agree that Artificial Intelligence has the potential to significantly enhance operational efficiency. 5 respondents representing 5.0 percent were undecided. 10 respondents representing 10.0 percent disagree that Artificial Intelligence has the potential to significantly enhance operational efficiency 10 of the respondents representing 10.0 percent strongly disagrees that Artificial Intelligence has the potential to significantly enhance operational efficiency.

Table 2: Opinions of Respondents on Artificial Intelligence as a tool that facilitates the personalization of products and services, creating more tailored customer experiences to enhance efficiency

Source: Field Survey, 2024.

Table 2 shows the responses of respondents that Artificial Intelligence also facilitates the personalization of products and services, creating more tailored customer experiences to enhance efficiency. 40 respondents representing 40.0 percent strongly agree that Artificial Intelligence also facilitates the personalization of products and services, creating more tailored customer experiences to enhance efficiency. 50 respondents representing 50.0 percent agree that Artificial Intelligence also facilitates the personalization of products and services, creating more tailored customer experiences to enhance efficiency. 2 percent were undecided. 3 respondents representing 3.0 percent disagrees that Artificial Intelligence also facilitates the personalization of products and services, creating more tailored customer experiences to enhance efficiency while the remaining 5 of the respondents representing 5 percent strongly disagrees that Artificial Intelligence also facilitates the personalization of products and services, creating more tailored customer experiences to enhance efficiency.

Table 3: Opinion of Respondents on Artificial Intelligence contributes to cost reduction and resource optimization, which are essential for maintaining profitability

Source: Field Survey, 2024.

Table 3 shows the responses of respondents that Artificial Intelligence contributes to cost reduction and resource optimization, which are essential for maintaining profitability. 40 respondents representing 40.0 percent strongly agree that Artificial Intelligence contributes to cost reduction and resource optimization, which are essential for maintaining profitability.  50 respondents representing 50.0 percent agree that Artificial Intelligence contributes to cost reduction and resource optimization, which are essential for maintaining profitability. 2 respondents representing 2 percent were undecided. 5 respondents representing 5.0 percent disagrees that Artificial Intelligence contributes to cost reduction and resource optimization, which are essential for maintaining profitability while the remaining 3 of the respondents representing 3 percent strongly disagree that Artificial Intelligence contributes to cost reduction and resource optimization, which are essential for maintaining profitability.

Table 4: Responses on Artificial Respondents has an impact on operational process in business organization.

Source: Field Survey, 2024.

Table 4 shows the responses of respondents that Artificial Intelligence has an impact on operational process in business organization. 50 respondents representing 50.0 percent strongly agree that Artificial Intelligence has an impact on operational process in business organization. 30 respondents representing 30.0 percent agree that Artificial Intelligence has an impact on operational process in business organization. 5 respondents representing 5 percent were undecided. 10 respondents representing 10.0 percent disagrees that Artificial Intelligence has an impact on operational process in business organization while the remaining 5 of the respondents representing 5 percent strongly disagrees that Artificial Intelligence has an impact on operational process in business organization.

Table 5: Responses on Artificial Intelligence has proven to be a catalyst for growth and innovation.

Source: Field Survey, 2024.

Table 5 shows the responses of respondents that Artificial intelligence has proven to be a catalyst for growth and innovation. 40 respondents representing 40.0 percent strongly agree that Artificial intelligence has proven to be a catalyst for growth and innovation. 30 respondents representing 30.0 percent agree that Artificial intelligence has proven to be a catalyst for growth and innovation. 15 respondents representing 15.0 percent were undecided. 10 respondents representing 10.0 percent disagrees that Artificial intelligence has proven to be a catalyst for growth and innovation while the remaining 5 of the respondents representing 5.0 percent strongly disagrees that Artificial intelligence has proven to be a catalyst for growth and innovation.

Table 6: Opinion of Respondents on Artificial Intelligence has enabled companies to become more agile, innovative and competitive.

Source: Field Survey, 2024.

Table 6 shows the responses of respondents that Artificial intelligence has enabled companies to become more agile, innovative, and competitive. 40 respondents representing 40.0 percent strongly agree that Artificial intelligence has enabled companies to become more agile, innovative, and competitive.  50 respondents representing 50.0 percent agree that Artificial intelligence has enabled companies to become more agile, innovative, and competitive. 2 respondents representing 2 percent were undecided. 5 respondents representing 5.0 percent disagrees that Artificial intelligence has enabled companies to become more agile, innovative, and competitive while the remaining 3 of the respondents representing 3 percent strongly disagree that Artificial intelligence has enabled companies to become more agile, innovative, and competitive.

Table 7: Responses on Artificial Intelligence helps to make business operations easier.

Source: Field Survey, 2024.

Table 7 shows the responses of respondents that Artificial intelligence helps to make business operations easier. 50 respondents representing 50.0 percent strongly agree that Artificial intelligence helps to make business operations easier. 25 respondents representing 25.0 percent agree that Artificial intelligence helps to make business operations easier. 5 respondents representing 5.0 percent were undecided. 10 respondents representing 10.0 percent disagree that Artificial intelligence helps to make business operations easier. 10 of the respondents representing 10.0 percent strongly disagrees that Artificial intelligence helps to make business operations easier.

DISCUSSION OF FINDINGS

From the study above, majority of the respondents agrees with the fact that there is an impact of artificial intelligence on organizational efficiency in organizations in Lagos state. This can be traced to the empirical studies of Levin (2019) that examined the impact of artificial intelligence and block chain technology on entrepreneurship performance and success in Nigeria. This study adopted a survey research method and primary and secondary sources of data. A total of 70 employee of Kassy Block chain and technology agency, Lagos, Nigeria were chosen for the study using purposive sampling with 60 returned questionnaires administered. The data was analyzed using least square to test the formulated hypothesis in line with the objective. The findings showed that, there is significant relationship between artificial intelligence and block chain technology on entrepreneurship performance and success in Nigeria. Also, the findings above show that there is an impact of adoption of artificial intelligence on organizational performance of business organizations in Lagos state. This is in line with the of study conducted by Cheung & Messom (2018) on the impact of adoption of artificial intelligence on organizational performance of business organizations in Lagos state. The objective of the study was to empirically investigate how Artificial Intelligence relates with organizational performance of Money Deposit Banks in Rivers State in terms of customer satisfaction, economic performance and effective decision making. The study revealed that Artificial Intelligence has a very strong positive correlation with customer satisfaction of Money Deposit Banks in Rivers State, Artificial Intelligence has a strong positive correlation with economic performance of Money Deposit Banks in Rivers State, and Artificial Intelligence has a very strong positive correlation with effective decision making of Money Deposit Banks in Rivers State.

CONCLUSION

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding. AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in business through capable artificial beings appeared as storytelling devices in antiquity, and have been common in fiction, as in Mary Shelley’s Frankenstein or Karel Čapek’s R.U.R. (Rossum’s Universal Robots). In order to boost their performance in today’s competitive market, a growing number of manufacturing companies, particularly in developed countries are turning to artificial intelligence (AI) to help transform their operations and services. Modern information technologies and the advent of machines powered by artificial intelligence (AI) have already influenced the world of work in this 21st century. Computers, algorithms and software simplify the work process and everyday tasks and have given a facelift to our business operations.

RECOMMENDATION

After careful observation from the data collected and analyzed based on the objectives of the study, the study wishes to recommend the following:

1. Businesses must take proactive measures to address the obstacles to AI adoption if they want to optimize the technology’s beneficial effects on organizational performance. It is advised that businesses concentrate on making investments in the training of a knowledgeable workforce by providing courses that give staff members the skills they need to work with AI. In addition to ensuring that employees can efficiently manage and fully utilize AI technologies, this will assist close the talent gap. Additionally, in order to stay up with the rapid advancement of AI technologies and approaches, organizations need cultivate a culture of continuous learning.
2. In terms of infrastructure, businesses should prioritize upgrading their technological capabilities, ensuring that they have the necessary tools and resources to support AI integration. This may include investing in cloud computing, data storage, and advanced cybersecurity measures to protect sensitive information. AI solutions should be chosen carefully, with a focus on those that align with the specific needs of the organization and industry.
3. Organizations should address data privacy and security concerns by implementing robust data protection frameworks that comply with relevant regulations. Ensuring transparency and accountability in AI systems will help build trust with customers and stakeholders. To this end, businesses should adopt ethical AI practices, ensuring that AI systems are free from biases and operate in a way that is fair and just for all users.

REFERENCES

1. Abudi, G. (2016). The Five Stages of Team Development: A Case Study. Project Management, Project Smart
2. Abuzid, H. F. T., & Abbas, M. (2017). Impact of Teamwork Effectiveness on Organizational Performance Vis-a-Vis Role of Organizational Support and Team Leader’s Readiness: A Study of Saudi Arabian Government Departments Work Teams. International Business Management, 100(3), 683-691.
3. Agarwal S and Adjirackor T (2016) Impact of Teamwork on Organizational Productivity in some selected basic Schools in the Accra Metropolitan Assembly. European Journal of Business, Economics and Accountancy 4: 40-52.
4. Agarwal, S., & Adjirackor, T. (2016). Impact of teamwork on organizational productivity in some selected basic schools in the Accra metropolitan assembly. European Journal of Business, Economics and Accountancy, 4(6), 40-52.
5. Agwu, M.O. (2015). Teamwork and employee performance in Bonny Nigeria Liquified Natural Gas Plant. Strategic Management Quarterly, 3(4): 39-60.
6. Ahmad, S. and R. Schroeder (2019). ‘The impact of human resource management practices on operational performance: recognizing country and industry differences’, Journal of Operations Management, 21, pp. 19–43.
7. Ahmad, S. and R. Schroeder (2019). ‘The impact of human resource management practices on operational performance: recognizing country and industry differences’, Journal of Operations Management, 21, pp. 19–43.
8. Al Mansoori, S., Salloum, S. A., & Shaalan, K. (2020). The impact of artificial Intelligence and information technologies on the efficiency of knowledge management at modern organizations: a systematic review. Recent advances in intelligent systems and smart applications, 163-182. https://www.academia.edu/download/64016732/The%20Impact%20of%20Artificial%20Intelligen ce.pdf
9. Ali, B., J.A. Omar, W., A.W. & Bakar, R. (2016). Accounting Information System (Ais) Andorganizational Performance: Moderating Effect of Organizational Culture. International Journal of Economics, Commerce And Management, 4 (4),58-75.
10. Alie, R, Beam, H and Carey,  A. (2015). The use of teams in an under graduate management program. Journal of Management Education, 22(6), 707- 19
11. Alrfai, M. M., Alqudah, H., Lutfi, A., Al-Kofahi, M., Alrawad, M., & Almaiah, M. A. (2023). The influence of artificial Intelligence on the AISs efficiency: The moderating effect of the cyber security. Cogent Social Sciences, 9(2), 2243719.https://www.tandfonline.com/doi/pdf/10.1080/23311886.2023.2243719
12. Alsheibani, S., Cheung, Y., & Messom, C. (2018). Association for Information Systems AIS Electronic Library (AISeL) Artificial Intelligence Adoption: AI-readiness at Firm-Level. https://core.ac.uk/download/pdf/301376079.pdf
13. Ayan A. O., (2018). Impact of teamwork on organizational performance in Kenya with special focus to Kenya Livestock Marketing Council (KLMC)-Nairobi. Master of Human Resource Management, University of Nairobi.
14. Bartlett, K.R. (2017). The relationship between training and organizational commitment: a study in the health care field. Human Resource Development Quarterly. 12(4): 335-352.
15. Becker, B.. and Huselid, .A. (2015). High performance work systems and firm’s performance: a synthesis of research and managerial implications. In G.R. Ferris (Ed.), Research in Personnel and human resource. Stannford, CT: JAI.
16. Belhadi, A., Mani, V., Kamble, S. S., Rehman, A., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research. https://doi.org/10.1007/s10479-021-03956-x
17. Bender, D. H. (2018). Financial impact of information processing. Journal of Management Information Systems, 3(2), 22–32.
18. Black, W.C. (2017). Invited reaction: the influence of individual characteristics and the work environment on varying levels of training outcomes. Human Resource Development Quarterly, 12(1): 25-31.
19. Bontis, N., M. M. Crossan and J. Hulland (2018). ‘Managing an organizational learning system by aligning stocks and flows’, Journal of Management Studies, 39, pp. 437–469.
20. Bradley, S. W., J. S. McMullen, K. Artz and E. M. Simiyu (2016). ‘Capital is not enough: innovation in developing economies’, Journal of Management Studies, 49, pp. 684–717
21. Braganza, A., Chen, W.-F., Ana Isabel Canhoto, & Sap, S. (2021). Productive employment and decent work: The impact of AI adoption on psychological contracts, job engagement and employee trust. 131, 485–494. https://doi.org/10.1016/j.jbusres.2020.08.018
22. Brewer, G. A., & Coleman Selden, S. (2019). Why elephants gallop: Assessing and predicting organizational performance in federal agencies. Journal of Public Administration Research and Theory, 10(4), 685–712.
23. Brougham, D., & Haar, J. (2020). Technological disruption and employment: The influence on job insecurity and turnover intentions: A multi-country study. Technological Forecasting and Social Change, 161, 120276–120276. https://doi.org/10.1016/j.techfore.2020.120276 15
24. Brynjolfsson, E., & McAfee, A. (2017). The Business of Artificial Intelligence. Harvard Business Review. https://hbr.org/2017/07/the-business-of-artificial-intelligence
25. Cameron, K. (2018). Effectiveness as paradox: Consensus and conflict in conceptions of organizational effectiveness. Management Science, 32(5), 539–553
26. Cameron, K. S., Quinn, R. E., DeGraff, J., &, & Thakor, A. V. (2014). Competing values leadership. Edward Elgar Publishing.
27. Campbell, J.P.(2015). Modelling the performance prediction problem in industrial and organizational psychology in Dunnete,M.D & Hough.L.M. (Eds), Handbook of Industrial and Organizational Psychology. Consulting Psychologists Press. Palo Alto, 687-732
28. Camps, J. and R. Luna-Arocas (2016). ‘A matter of learning: how human resources affect organizational performance’, British Journal of Management, 23, pp. 1–21.
29. Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, pp. 97, 205–219.https://fardapaper.ir/mohavaha/uploads/2021/10/Fardapaper-The-effect-of-AI-based-CRM-onorganization-performance-and-competitive-advantage-An-empirical-analysis-in-the-B2Bcontext.pdf
30. Chege FW (2017) Effect of Teamwork on Productivity in Sales and Marketing Departments: A Case Study of Nairobi Bottlers limited. A Published Research Thesis of the School of Management and Leadership in the Management University of Africa
31. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188
32. Chen, J., Wu, S., & Zhang, D. (2019). Application of artificial intelligence technology in the manufacturing industry.
33. Chen, L., Yang, G., & Cheng, F. (2019). The Impact of Artificial Intelligence on Productivity: Evidence from the Healthcare Sector. International Journal of Environmental Research and Public Health, 16(10), 1847. https://doi.org/10.3390/ijerph16101847
34. Cohen, S.G., and Bailey, D.E. (2020). What makes teams work: group effectiveness research from the shop floor to the executive suite. Journal of Management, 23(3), 239-90.
35. Collings, D., and Scullion, H. (2018) Talent management: Progress and prospects, Human Resource Management Review, Vol. 25, Pages 233-235.
36. Collins, C. and K. Clark (2019). ‘Strategic human resource practices, top management team social networks, and firm performance: the role of human resource practices in creating organizational competitive advantage’, Academy of Management Journal, 46, pp. 740–751.
37. Collins, C. J. and K. G. Smith (2018). ‘Knowledge exchange and combination: the role of human resource practices in the performance of high-technology firms’, Academy of Management Journal, 49, pp. 544–560
38. Conti, B., and Kleiner, B. (2019). How to increase teamwork in organizations. Journal of Quality, 5(1), 26-29.
39. Crook JR, Bratton VK, Street VL, K. D. (2014). Has strategic management shed the normal science straight jacket? J Manag
40. Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial Intelligence on labor productivity. Eurasian Business Review, 11, 1-25https://link.springer.com/article/10.1007/s40821-020-00172-8
41. Darwish, T. and S. Singh (2016). ‘Does strategic human resource involvement and devolvement enhance organisational performance? Evidence from Jordan’, International Journal of Manpower, 34, pp. 674–692.
42. Darwish, T., S. Singh and A. F. Mohamed (2016). ‘The role of strategic HR practices in organisational effectiveness: an investigation in the country of Jordan’, International Journal of Human Resource Management, 24, pp. 3343–3362.
43. Delarue, A., Van Hootegem, G., Procter, S., & Burridge, M. (2020). Teamworking and organizational performance: A review of survey‐based research. International Journal of Management Reviews, 10(2), 127-148.
44. Delery, J. E. and D. H. Doty (2014). ‘Modes of theorizing in strategic human resource management: tests of universalistic, contingency and configurational performance predictions’, Academy of Management Journal, 39, pp. 802–835.
45. Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., … & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School Technology & Operations Mgt. Unit Working Paper, (24–013). https://www.iab.cl/wpcontent/uploads/2023/11/SSRN-id4573321.pdf Enholm, I. M.,
46. Duncan, E. & Elliott, G. (2012). Customer service quality and financial performance among Australian retail financial institutions. Journal of Financial Services Marketing, 7(1), 25–41.
47. Dyler, L., & Reeves, T.(2019). Human resource strategies and organization performance: what do we know and where do we need to go?. International Journal of HRM, 6(3), 656-671
48. Earley, P. C. & Erez, M. (2014). The transplanted executive. New York: Oxford University Press.
49. Edmondson, A. C. (2020). Psychological safety and learning behavior in work teams. Admin. Sci. Quart. 44(2) 350-383.
50. Erdem, Ferda, Ozen and Janset. (2019). Cognitive and Affective Dimensions of Trust in Developing Team Performance. Team Performance Management: An International Journal, 9(5.6) 131-135.
51. Fitzgerald, L., Johnson, R., Brignall, S., Silvestro, R., & Voss, C. (2016). Performance measurement in service business. London: CIMA.
52. Flapper, S.D.P., Fortuin, L., & Stoop, P.P.M (2014). Towards consistent performance measurement systems. International Journal of Operations and Production Management, 16(7), 27-37
53. Froebel, P., and Marchington, M. (2018). Teamwork structures and worker perception: a cross national study in pharmaceuticals, International Journal of Human Resource Management, 16(2), 256-276.
54. Georgopoulos, B. S. and A. S. T. (2016). A study of organizational effectiveness. American Sociological Review, 22(5), 534–540.
55. Gowan, M., Seymour, J., Ibarreche, S., & Lackey, C. (2001). Service quality in public agency: same expectations but different perceptions by employees, managers and customers. Journal of Quality Management, 6, 275-291
56. Guest, D. E. (2011). ‘Human resource management and performance: still searching for some answers’, Human Resource Management Journal, 22, pp. 3–13.
57. Hartenian, L.S. (2019), Team member acquisition of team knowledge, skills, and abilities. Journal of Team Performance Management, 9(1/2), 23-30.
58. Hunnes, A., Kvaløy, O., & Mohn, K. (2020). Performance Appraisal and Career Opportunities.A Case study. Norway: Norway School of Economics and Business..
59. Ingram, H. (2019). Linking teamwork with performance. Journal of Team Performance Management, 2(4): 5-10.
60. Jashapara, A. (2019). ‘Cognition, culture and competition: an empirical test of the learning organization’, The Learning Organization, 10, pp. 31–50
61. Jetha, A., Shamaee, A., Bonaccio, S., Gignac, M. A. M., Tucker, L. B., Tompa, E., Bültmann, U., Norman, C. D., Banks, C. G., & Smith, P. M. (2021). Fragmentation in the future of work: A horizon scan examining the impact of the changing nature of work on workers experiencing vulnerability. American Journal of Industrial Medicine (Print), 64(8), 649–666. https://doi.org/10.1002/ajim.23262
62. Johnson, J. L., Adkins, D., & Chauvin, S. (2020). A Review of the Quality Indicators of Rigor in Qualitative Research. American Journal of Pharmaceutical Education, 84(1), 7120–7120. https://doi.org/10.5688/ajpe7120
63. Johnson, L. (2019). “AI Implementation Strategies: A Survey of Organizational Practices.” AI & Society, 34(3), 521-536.
64. Jon, M., & Randy, L.D. (2017). Human Resource Development. 5th Edt. South Western: USA
65. Jones, A., Richard, B., Paul, D., Sloana, K. and Peter, F. (2015). Effectiveness of team building in the organization. Journal of Management, 5(3): 35-37.
66. Joshi, D. A., & Masih, D. J. (2023). Enhancing employee efficiency and performance in industry 5.0 organizations through artificial intelligence integration. European Economic Letters (EEL), 13(4), 300-315. https://eelet.org.uk/index.php/journal/article/download/589/838
67. Julian, & Green, S. (2008). Cochrane Handbook for Systematic Reviews of Interventions. Wiley EBooks. https://doi.org/10.1002/9780470712184 ‘
68. Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 working group. Forschungsunion, Acatech.
69. Kaplan, R. S. and D. P Norton (2018). ‘The balanced scorecard – measures that drive performance’, Harvard Business Review, pp. 71–79
70. Kaplan, R.S., & Norton, D.P. (2018). The balanced scorecard: Translating strategy into action. Boston, MA: Harvard Business School Press
71. Karanja, E. W., Muraguri, C., & Kinyua, G. (2018). Effects of Teamwork on Performance of the Water Service Regulatory Board. The Strategic Journal of Business & Change Management, Vol. 5(3), Page 1-6.
72. Katou, A.A. (2020). Measuring the impact of HRM on organizational performance. Journal of Industrial Engineering and Management, 1(2), 119-142.
73. Khanderkar, A., & Sharna, A. (2018). Organizational Learning and performance: Understanding Indian scenario in present global context, 48(8/9), 682-692
74. King, A. (2017). Cooperation between corporations and environmental groups: a transaction cost perspective. Academy of Management Review, 32(3), 889-900
75. Kinkel, S., Baumgartner, M., & Cherubini, E. (2022). Prerequisites for the adoption of AI technologies in manufacturing–Evidence from a worldwide sample of manufacturing companies. Technovation, 110, 102375. https://www.sciencedirect.com/science/article/pii/S0166497221001565
76. Kleinberg, J., Himabindu Lakkaraju, Leskovec, J., Ludwig, J., & Sendhil Mullainathan. (2017). Human Decisions and Machine Predictions*. The Quarterly Journal of Economics. https://doi.org/10.1093/qje/qjx032
77. Kositanurit, B., Ngwenyama, O. & Osei-Bryson, K.M. (2014). An exploration of factors that impact individual performance in an ERP environment: an analysis using multiple analytical techniques. European Journal of Information Systems, 15(6), 556–568
78. Kunze, F., S. Boehm and H. Bruch (2016). ‘Organizational performance consequences of age diversity: inspecting the role of diversity-friendly HR policies and top managers’ negative age stereotypes’, Journal of Management Studies, 50, pp. 413–442.
79. Kyrgidou, L. P. and S. Spyropoulou (2016). ‘Drivers and performance outcomes of innovativeness: an empirical study’, British Journal of Management, 24, pp. 281–298.
80. Laitinen, E. (2018). A dynamic performance measurement system: Evidence from small Finnish technology companies., Scandinavian Journal, 18, 65-69
81. Lebas, M. (2015). Oui, il faut définir la performance [Yes, One Must Define Performance]. French Accounting Review, 269(JulyNovember).
82. Levin, D. (2019).Automation As Part of The Solution, Journal of Management Inquiry,28,316
83. Li, X., Cui, L., & Huang, L. (2020). Artificial Intelligence and organizational productivity: A metaanalysis. International Journal of Production Economics, p. 227, 107643. https://doi.org/10.1016/j.ijpe.2019.107643
84. Lipnack, J., & Stamps, J. (2014). Reaching across space, time, and organizations with technology
85. Lopez, S.P., Peon, J.M.M. and Ordas, C.J.V. (2018). Human resource practices, organizational learning and business performance. Human Resource Development International, 8(2): 147-164.
86. Lu, Y., Zhou, L., & Li, J. J. (2020). Research on the application of artificial Intelligence in innovative entrepreneurship. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 71.
87. Lumpkin, G. T. and G. G. Dess (2014). ‘Clarifying the entrepreneurial orientation construct and linking it to performance’, Academy of Management Review, 21, pp. 135– 172
88. Lusthaus, C., Adrien, M.-H., Anderson, G., Carden, F. and Montalván, G. . (2012). Organizational Assessment: A Framework for Improving Performance. International Development Research Centre and Inter-American Development Bank.
89. Lynch, R., & Cross, K. (2016). Measure up—the essential guide to measuring business performance. London: Mandarin.
90. Mankins, M.C., & Steele, R. (2020). Turning great strategy into performance. Harvard Business Review: 65-72, July –November.
91. Manz, C., and Neck, S. (2018). Teamthink: Beyond the group think syndrome in self-managing work teams. Journal of Team Performance Management, 3(1) 18-31.
92. McGrath, I.E. (2014). Groups: Interaction and performance. New Jersey: Prentice Hall
93. Mickan, S., & Rodger, S. (2019). The organisational context for teamwork: Comparing health care and business literature. Australian Health Review, 23(1), 179 – 192.
94. Mulika. (2016). The Impact of Teamwork on Employee Performance in Strategic Management and the Performance Improvement Department of Abu Dhabi Police, UAE.
95. Nazer, L. H., Razan Zatarah, Waldrip, S., Janny, Moukheiber, M., Khanna, A. K., Hicklen, R. S., Lama Moukheiber, Moukheiber, D., Ma, H., & Mathur, P. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6), e0000278–e0000278. https://doi.org/10.1371/journal.pdig.0000278
96. Ndofor, H. A. and R. Priem (2011). ‘Immigrant entrepreneurs, the ethnic enclave strategy, and venture performance’, Journal of Management, 37, pp. 790–818
97. Neha, S. Enakshi, S. Narotam, S. & Amita, K. (2018). Impact of AI on business. International Conference on digital innovation, transformation and society, held in New Delhi, India, 14th January.
98. Okima, C.K (2018). Concept of business growth. http://myinfojet.blogspot.com/2017/09/. Retrieved on 19th April, 2019.
99. Ooko, P., & Odundo, P. (2015). Impact of teamwork on the achievement of targets in organisations in Kenya. A case of SOS children„s villages ELDORET. European Journal of Business and Management, 7, 69-77.
100. Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734. https://link.springer.com/article/10.1007/s10796-021-10186-w Intelligence Beyond the Hype: Exploring the Organisation Adoption Factors. AIS Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Christoph Luetge, Madelin, R., Ugo Pagallo, Rossi, F., Schafer, B., Valcke, P., & Effy Vayena. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines (Dordrecht), 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 Gichoya, J. W., Jacoba, C. M. P., Celi, L. A., Lorch, A. L., Fickweiler, W., Sobrin, L., Aiello, L. P., & Silva, P. S. (2023). Bias and Non-Diversity of Big Data in Artificial Intelligence: Focus on Retinal Diseases. Seminars in Ophthalmology. https://www.tandfonline.com/doi/abs/10.1080/08820538.2023.2168486
101. Paris, C.R., Sallas E. & Cannon-Bowers J.A. (2019). “Teamwork in Multi-person Systems: A Review and Analysis”. Ergonomics. Vol. 43, No. 8, pp. 1052-1075
102. Paul, A.K, & Anantharaman, R.N.(2019). Impact of people management practices on organizational performance. Analysis of a casual,1
103. Peter,P (2018). Strategic Management and business performance. Retrieved December 03, 2011, from www.prevos.net/humanities/sociology/strategy.
104. Pfaff, E., and P. Huddleston. (2019). Does it matter if I hate teamwork? What impacts student attitudes toward teamwork. Journal of Marketing Education 25:37–45.
105. Pfeffer, J. (2015). ‘Seven practices of successful organizations’, California Management Review, 40, pp. 96–124.
106. Pfeiffer, J. W., & Jone, J. E. (2020). 1974 Annual Handbook for Group Facilitators. Pfeiffer & Co.
107. Pun, K. F. & White, A. S. (2020). A performance measurement paradigm for integrating strategy formulation: a review of systems and frameworks. International Journal of Management Reviews, 7(1), 49-71
108. Pycraft, M., Singh, H., Phihlela, K., Slack, N., Chambers, S., & Johnston, R. (2010). Operations management: Global and Southern African perspectives. 2nd ed. Cape Town: Pearson Education
109. Ramesh, S., & Das, S. (2022). Adoption of AI in Talent Acquisition: A Conceptual Framework. Lecture Notes in Networks and Systems, 12–20. https://doi.org/10.1007/978-3-031-01942-5_2 Rigor in Qualitative Research. The American Journal of Pharmaceutical Education, 84(1), 7120–7120. https://doi.org/10.5688/ajpe7120
110. Real, J. C., J. L. Roldan and A. Leal (2018). ‘From entrepreneurial orientation and learning orientation to business performance: analysing the mediating role of organizational learning and the moderating effects of organizational size’, British Journal of Management, 25, pp. 186–208.
111. Richard, P. J., T. M. Devinney, G. S. Yip and G. Johnson (2017). ‘Measuring organisational performance: towards methodological best practice’, Journal of Management, 35, pp. 718– 804
112. Roberts, G. E. (2019). Employee performance appraisal system participation: A technique that Works. Public Personnel Management, 32(1), 89–98
113. Rouse, P., & Putterill, M. (2019). An integral framework for performance measurement. Management Decision, 41(8), 791-805.
114. Rowe, W. G., J. L. Morrow and J. F. Finch (2019). ‘Accounting, market, and subjective measures of firm performance: three sides of the same coin?’. Paper presented at t
115. Rowold, J.(2011). Relationship between leadership behaviors and performance: The moderating role of a work team’s level of age, gender and culture heterogeneity. Leadership and Organization Development Journal. 32(6), 628- 647.
116. Sauer, P. C., & Seuring, S. (2023). How to conduct systematic literature reviews in management research: a guide in 6 steps and 14 decisions. Review of Managerial Science, 1-35. https://link.springer.com/article/10.1007/s11846-023-00668-3
117. Schermerhorn, J.R., Hunt, J.G., & Osborn, R.N. (2018). Organizational Behaviour (9th ed). United States: John Wiley & Sons.In
118. Selden, S.C. and Sowa, J. E. (2004). Testing a multi-dimensional model of organizational performance: prospect and problems. Journal of Public Administration Research and Theory, 14(3), 395–416.
119. Shabbir J. & Anwer T.A. (2015). Artificial intelligence and its role in near future. Journal of Latex Class Files, 14 (8), 36-43.
120. Shah, K., Egan, G., Huan, L. N., Kirkham, J., Reid, E., & Tejani, A. M. (2020). Outcome reporting bias in Cochrane systematic reviews: a cross-sectional analysis. BMJ Open, 10(3). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076244/
121. Shahzad, F., Luqman, R. A., Khan, A. R., & Shabbir, L. (2016). Impact of Organizational Culture on Organizational Performance : An Overvieë, 975–985
122. Shaikh, F., Afshan, G., Anwer, R. S., Abbas, Z., & Sahibzada, U. F. (2022). Artificial intelligence and employee productivity: two-way process through knowledge sharing and well-being. https://ir.iba.edu.pk/sbsic/2022/program/6/
123. Shirouyehzad H & Tavakoli M (2016) Ranking the branches of a private bank through the service quality gap approach and by using multi criteria decision making, International Journal of Productivity and Quality Management. Vol 12, Pg 327 – 344.
124. Shouvik S and Mohammed WH (2018) examined The Impact of Teamwork on Work Performance of Employees: A Study of Faculty Members in Dhofar University. IOSR Journal of Business and Management (IOSR-JBM) 20: 15-22.
125. Sims, H. P., & Manz, C. C. (2017). Business without bosses: How self-managing teams are building high-performing companies
126. Singh, S., T. Darwish and N. Anderson (2016). ‘Strategic intent, high performance HRM, and the role of the HR director: an investigation into attitudes and practices in Jordan’, International Journal of Human Resource Management, 23, pp. 3027– 3044.
127. Singh, S., T. Darwish, A. C. Costa and N. Anderson (2016). ‘Measuring HRM and organizational performance: concepts, issues, and framework’, Management Decision, 50, pp. 651– 667
128. Smith, A., & Johnson, B. (2018). Artificial Intelligence and productivity: Evidence from the firm level. Working Paper Series, Federal Reserve Bank of San Francisco. Retrieved from https://www.frbsf.org/economic-research/files/wp2019-01.pdf.
129. Snell, S. and M. Youndt (2019). ‘Human resource management and firm performance: testing a contingency model of executive controls, Journal of Management, 21, pp. 711–737.
130. Sriwan, T.(2015). Examining the factor which influence performance measurement and management in the Thai Banking Industry: An implication of the balance Scorecard Framework. Doctor of Philosophy thesis Murcdoch University.
131. Staniforth, D. (2019). Teamworking, or individual working in a teams. Journal of Team Performance Management, 2(3), 37-41.
132.  Sukumar, S. R., Natarajan, A., Ananthakrishnan, R., & Khamitkar, S. D. (2018). Artificial Intelligence and machine learning in health care: Opportunities and challenges. Frontiers in Health Informatics, 7,9.
133. Sulaiman Alsheibani, Messom, C., Cheung, Y., & Mazoon Alhosni. (2020). Artificial Tornartzky, L. G., & Fleischer, M. (2020). The processes of technological innovation: Tornatzky, Louis G.: Free Download, Borrow, and Streaming: Internet Archive. Internet Archive. https://archive.org/details/processesoftechn0000torn/page/n5/mode/2up
134. Trunk, A., Birkel, H., & Hartmann, E. (2020). On the current state of combining human and artificial Intelligence for strategic organizational decision-making. Business Research, 13(3), 875-919. https://link.springer.com/article/10.1007/s40685-020-00133-x
135. Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological bulletin, 63(6), 384.
136. Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of small-group development revisited. Group & Organization Studies, 2(4), 419-427.
137. Venkatraman, N. and Ramanujam, V. (2018). Measurement of business economic performance: an examination of method convergence. Journal of Management Development, 13(1)(109–22).
138. Walid, A.S. and Zubair, H. (2016). Impact of effective teamwork on employee performance. International Journal of Accounting, Business and Management, 4(1): 77-86.
139. Wall, T. D. and S. Wood (2018). ‘The romance of human resource management and business performance, and the case for big science’, Human Relations, 58, pp. 429–462.
140. Wall, T., J. Michie, M. Patterson, S. Wood, M. Sheehan, C. Clegg, M. West (2015). ‘On the validity of subjective measures of company performance’, Personnel Psychology, 57, pp. 95– 118
141. Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial Intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924.
142. Wang, Y., Kung, L., & Byrd, T. A. (2019). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, pp. 144, 357– 364.
143. West, D. M., & Allen, J. R. (2018). How artificial Intelligence is transforming the world. Brookings. https://www.brookings.edu/articles/how-artificial-intelligence-istransforming-theworld/ Wiesenthal, M. (2022). The ethical implications of AI-based mass surveillance tools. Hs-Ruhrwest.de. https://repositorium.hs-ruhrwest.de/files/770/Masterarbeit_Marvin_Wiesenthal_10008386.pdf
144. William, D. R, Swee-Lim, C., and Cesar M. (2018). Job Insecurity Spill over to Key Account Management: Negative Effects on Performance, Effectiveness, Adaptiveness, and Esprit De Corps, Journal of Business and Psychology, 19 (4), 483-503.
145. Wright, P. M., T. M. Gardner, L. M. Moynihan and M. R. Allen (2018). ‘The relationship between HR practices and firm performance: examining causal order’, Personnel Psychology, 58, pp. 409–446.
146. Xio, R. & Bueme, V. C. (2012). Artificial intelligence in business. Journal of Computer Science, 13 (4), 118-129.
147. Zhou, Y., Liao, S., & Li, Y. (2021). Artificial Intelligence, Big Data, and Innovation: An Empirical Investigation in Chinese high-tech Industries. Sustainability, 13(2), 696.