The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance: evidence from MENA region commercial banks

Mahboub, Rasha; Ghanem, Mohamed Gaber Article The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance: evidence from MENA region commercial banks Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Mahboub, Rasha; Ghanem, Mohamed Gaber (2024) : The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance: evidence from MENA region commercial banks, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-28, https://doi.org/10.1080/23311975.2024.2404484 This Version is available at: https://hdl.handle.net/10419/326576 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance: evidence from MENA region commercial banks Rasha Mahboub & Mohamed Gaber Ghanem To cite this article: Rasha Mahboub & Mohamed Gaber Ghanem (2024) The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance: evidence from MENA region commercial banks, Cogent Business & Management, 11:1, 2404484, DOI: 10.1080/23311975.2024.2404484 To link to this article: https://doi.org/10.1080/23311975.2024.2404484 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 19 Sep 2024. Submit your article to this journal Article views: 3729 View related articles View Crossmark data Citing articles: 3 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 Accounting, corporAte governAnce & Business ethics | reseArch Article Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2404484 The mediating role of knowledge management practices and balanced scorecard in the association between artificial intelligence and organization performance: evidence from MENA region commercial banks rasha Mahbouba and Mohamed gaber ghanemb aFaculty of Business administration, Department of accounting, Beirut arab university, Lebanon; bFaculty of Commerce, Department of accounting, alexandria university, egypt ABSTRACT recently, several research practitioners were recommended that artificial intelligence (Ai), knowledge management practices (KMp) and balanced scorecard (Bsc) should be taken into consideration collectively to more accurately predicts the consequences that they can have in terms of organizational performance (op). consequently, this research aims to answer these calls by providing and empirically testing a conceptual model that simultaneously take into account Ai, KMp and Bsc with regard to their interactions with and effects on op. the main aim of this research is to assess the association between Ai and op; and whether this association is mediated through the bank’s KMp and through Bsc implementation. to achieve this aim, nine hypotheses have been formulated and tested through a web-based survey that was distributed to 594 employees working in commercial banks operated in MenA region. the data have been analyzed using the partial least squares structural equation modeling (pls-seM) technique. the findings demonstrate the positive impact of the adoption of Ai on KMp, Bsc and op along with the impact of KMp on Bsc and op. the findings also reveal that KMp act as a mediator through which Ai influences Bsc and op. Further, the findings show that Bsc has a significant positive effect on op and act as a mediator through which Ai influences op. hence, this research demonstrates that banks interested in using Ai to improve performance must be very careful when taking KMp into account as a mediator mechanism. 1. Introduction 1.1. Background of the research the coviD-19 crisis has increased and accelerated the digitalization tendency that had been developing before the pandemic (oecD, 2020). consequently, the information technology (it) implementation in the various sectors including banking sector has spread internationally and been embraced by every country (ris et al., 2020). Among the numerous it innovations of latest years, the development in Ai is predominantly notable (Kaya etal., 2019). hence, the coviD-19 epidemic has altered how businesses behave in accordance with new societal standards and increased the significance of Ai applications (ho etal., 2022). ‘Although the term Ai was not coined until 1956, the roots of the field go back to at least the 1940s, and the idea of Ai was crystalized in Alan turing’s famous 1950 paper, ‘computing Machinery and intelligence’. turing’s paper posed the question: ‘can machines think?’ it also proposed a test for answering that question, and raised the possibility that a machine might be programmed to learn from experience much as a young child does’ (holdren & smith, 2016, p. 5). hence, Ai is defined as ‘machine © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group CONTACT Rasha Mahboub r[email protected] Faculty of Business administration, Department of accounting, Beirut arab university, Lebanon https://doi.org/10.1080/23311975.2024.2404484 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY received 26 April 2024 revised 20 August 2024 Accepted 10 september 2024 JEL CLASSIFICATION M41 KEYWORDS Artificial intelligence; knowledge management practices; balanced scorecard; organizational performance; commercial banks; MenA region SUBJECTS Business, Management and Accounting; information technology; Finance 2 r. MAhBouB AnD M. g. ghAneM intelligence’ or ‘intelligence demonstrated by machines’, in contradiction of the ‘natural intelligence displayed by humans’ (Zhao et al., 2019). Ai is frequently used to illustrate machines that impersonate human intellectual functions such as understanding, learning, reasoning or problem solving (russell & norvig, 2016). Disciplines that contribute to Ai comprise economics, linguistics, psychology, computer science, statistics, mathematics, neuroscience and evolutionary biology among others (ghosh etal., 2018). thus, Ai is present in each industry, and the banking sector is no exception (singh, 2022). According to Anderson et al. (2021), banking sector was the largest financier on Ai services. Banks and fin tech firms are under pressure to embrace new technologies to stay competitive (Feyen etal., 2021). By investing in ‘machine learning solutions’, financial firms can attain a competitive advantage over their competitors as Ai can help banks increase efficiency and accurateness automate processes, cut costs as a result of reduced human participation, and enhance customer satisfaction because of quicker resolution times (Belhaj & hachaïchi, 2021). consequently, Ai technology plays a vital role in banking sector transformation, which has noteworthy influence on the op of banks (le et al., 2021). on the other hand, KM has continuously been essential, mainly for organizations with a vision for achievement and success. the significance of KM is well recognized, and numerous prosperous companies are already skillful at it (Anshari etal., 2023). it was defined by Davenport (1994:119) as ‘a systematic approach of capturing, distributing and effectively using the knowledge’. every firm needs KM to speed up the process and attain access to information (Yu et al., 2022); improve the decision-making process (hajric, 2018); support innovation and cultural transformation (Al-Qershi et al., 2021); increase the efficiency of a company’s operational divisions and business operations; and boost customer satisfaction (Anshari etal., 2023). the development of it has shown a greater influence on banking sector. currently, managing knowledge is more significant in the banking sector in comparison to any other firm (Belaid & steven, 2006). it thus becomes more and more important for all banks to have KM in place to shorten delivery of time and provide the relevant information with a view to full exploiting its potential (Monteiro & Bhoui, 2020). it was apparent that one of the most essential focuses of Ai is knowledge, which can be signified and used with sufficient technology means. Associations between KM and Ai are very strong and there is an influence on developing both of them. KM needs technology for carrying out conventional functions belonging to the process. Most of these functions can be directly attended by Ai technology. conversely, any processes concerned with knowledge are subjects of Ai; hence, KM became the innovative challenge and chief enough in the Ai community (Jakubczyc & owoc, 1998). therefore, through KM and application of Ai to business processes; firms and banks can enhance op and generate competitive advantage (patrisia et al., 2022). According to Mohammadpoor and torabi (2020, p. 321), op refers to ‘how well an entity is achieving its specified goals using the resources available at its disposal’. hence, it is necessary to measure performance systematically in order to know how well planned objectives are met. one of the noticeable frameworks on performance measures that place less emphasis on the excessive concentration on financial performance (Fp) is the Bsc (iwuanyanwu, 2021). it is an op management system introduced by Kaplan and norton in (1992), which views a firm from numerous diverse perspectives to provide a more complete image of performance. the Bsc framework addressed the concern that firms were assigning excessively prominence on the results of Fp, and insufficient focus on the aspects of the business that lead to Fp – such as ‘customer satisfaction’, ‘business operations’ and ‘learning and growth’ (Kaplan & norton, 1992, 1996a). consequently, it becomes imperative for firms deploying Ai and using KMp to evaluate performance from these perspectives, as doing so will validate the value relevance of transferring from a manual operated method to a computerized system powered by Ai (iwuanyanwu, 2021). in fact, several studies have provided evidence that the success and survival of banks are linked to their capability to embrace innovative digital technologies, such as Ai (singh, 2022); implement KMp proficient at pinpointing the required knowledge and sharing it effectively across the organization (Anshari et al., 2023) and measure performance systematically through Bsc (Mohammadpoor & torabi, 2020). indeed, when examined separately, these three constructs (Ai, KMp, and Bsc) have a beneficial impact on the overall performance of the company (Abrokwah-larbi & Awuku-larbi, 2023; Abueid etal., 2023; Alharbi & Aloud, 2024). however, it is important to highlight that this effect becomes even more apparent when these constructs are examined in combination. cogent Business & MAnAgeMent 3 indeed, these three constructs (Ai, KMp and Bsc) – separately considered – positively influence bank performance. For example, leo etal. (2019) and Dzhaparov (2020) have demonstrated that the adoption of Ai has a positive effect on bank performance. Further, Al-sohaim et al. (2016) and Abuaddous et al. (2018) have revealed that KM impact positively all aspects of op. Moreover, Khatoon (2016) and cignitas etal. (2022) have found that the adoption of the Bsc by firms can be a tactic to enhance op. however, even though the impact that these constructs can have on op, there are no previous research that examine these same constructs in an integrated way. previous studies offers a partial view of these construct effects on op. hence, there is a research gap in the literature regarding investigating these same constructss in a holistic and integrated way with respect to their reciprocal influences on op. thus, these constructs require further investigation. As recently recommended by practitioners, it is important to consider Ai, KMp and Bsc together in order to accurately assess the positive impact they may have on op. 1.2. Objectives of the research Motivated by calls for more research on the effect of these constructs in an integrated way on op; the objectives of this research is to investigate the effect of Ai on KMp; the effect of Ai on Bsc implementation; the effect of Ai on op; the effect of KMp on Bsc implementation; the effect of KMp on op; the effect of Bsc implementation on op; the mediating role of KMp in the association between Ai and Bsc implementation; the mediating role of KMp in the association between Ai and op; and the mediating role of Bsc implementation in the association between Ai and op of the banking sector which is the most technologically advanced in MenA region. 1.3. Significance of the research this research contributes to the previous literature on the effect of Ai by providing practical visions into whether the adoption of Ai could enhance op of banks. the novelty of this research is obvious from its enlargement of existing literature on Ai, KMp, Bsc and op of banks; as very little is recognized of the nature of the association between these constructs. As indicated, Ai, KMp and op are urgently important to the liveliness of the banking sector. in addition, the research provides commercial bank managers with an indication of the importance of KMp, which are reinforced by Ai, in terms of op and Bsc, by indicating that KMp operate as a mediation mechanism through which Ai benefits Bsc and op. this research is distinctive as it tries to investigate the effect of Ai, KMp and Bsc on op in MenA banking sector. After this introduction, section two emphasizes the evolution and prevailing literature related to Ai, KMp, Bsc and op, introduces the proposed research model and development of hypotheses; section three introduces the research methodology; section four is dedicated to findings and discussions; while section five demonstrates the conclusions, research limitations and future research avenues. 2. Literature review 2.1. Theoretical review two theories imperative to Ai within the banking industry are the ‘technology-organization-environment’ (toe) framework and the ‘resource-Based view’ (rBv) theory. the toe framework gives a comprehensive system for understanding the selection and utilization of technology in organizations (Awa et al., 2017). this framework considers three primary components: technological components as Ai, organizational components as organizational structure, culture, and assets, and environmental components as competition, market dynamics and regulatory requirements (tornatzky et al., 1990). on the other hand, the rBv theory centers on the assets and capabilities of firms as sources of competitive advantage. Firms that successfully manage their ability, create talented representatives, and cultivate a culture of development and innovation are more likely to use Ai technologies successfully (Barney et al., 1991). this theory emphasizes the importance of human capital as an important asset for driving the utilization of Ai 4 r. MAhBouB AnD M. g. ghAneM technologies (Kraaijenbrink etal., 2010). together, these speculations contribute to a holistic understanding of the complex elements of Ai usage within the banking industry. 2.2. Artificial Intelligence Ai has been progressively emerging as a leading technology in the year 2020 (Krulický et al., 2020), the technology has advanced considerably due to progress in big data and computing capabilities, leading to a phase of rapid development and widespread application (horák & turková, 2023). Ai is leading a transformation in the business sector, the economy, and society overall by altering the dynamics and connections among stakeholders and individuals (Bharadiyaet al., 2023). Ai refers to the capacity of a system to replicate human intelligence, enabling machines or systems to interact with individuals or organizational customers autonomously. this entails the development of intelligent machines and systems capable of executing tasks in a manner similar to humans, achieving high levels of precision and quality throughout the various stages of task completion, all without requiring human intervention (Khawan, 2023). Machine learning (Ml) is a widely used form of Ai that is currently being developed for implementation in various business applications. the main objective of Ml is to efficiently analyze large amounts of data within a short timeframe (Bharadiyaet al., 2023). A diverse range of Ml techniques are available, including linear regression, underfitting and overfitting in polynomial estimations, generalizations, and others. the choice of method depends on the specific learning requirements and the objectives of the Ml process (svenson, 2022). Ai complements human intelligence and creativity rather than replacing it. Although Ai is adept at processing and analyzing large volumes of data at a rapid pace, it struggles with basic tasks. consequently, Ai software may provide synthesized recommendations to humans. this enables Ai to aid in predicting the outcomes of different actions and simplifying the decision-making process (Bharadiyaet al., 2023). 2.3. Knowledge Management in the contemporary business landscape, organizations are actively exploring diverse management strategies in order to gain a competitive edge in the market. As a result, there has been a noticeable shift towards placing greater emphasis on intangible assets. currently, knowledge serves as a crucial resource for every company, as it represents a distinct domain of operation that, when utilized effectively, becomes the foundation of its achievements (stachera-Włodarczyk, 2019). According to rBv and ‘Knowledge-Based view’ (KBv) theories, knowledge serves as a crucial resource for the survival, stability, and advancement of organizations (saqib et al., 2017). consequently, since 1990s, the prosperity of businesses has been intricately tied to the effective management of knowledge (liao & Wu, 2009). KM is defined as’ a set of systematic, organized, thoughtful and flexible activities undertaken and performed with the intention of achieving the objectives of the organization efficiently and effectively’ (Figurska et al., 2014:57). KM has the potential to deliver tangible advantages to both individuals and the organization. For individuals, it facilitates more efficient job performance and timesaving by enhancing decision-making and problem-solving skills. Additionally, it fosters a sense of unity within the organization, enables individuals to stay current, and presents them with challenges and chances to make valuable contributions (Figurska etal., 2014). on the organizational level, KM aids in strategic direction, rapid problem resolution, dissemination of best practices, enhancement of knowledge within products and services, generation of new ideas, promotion of innovation, attainment of a stronger competitive edge, and establishment of organizational memory (Mohammed et al., 2023; stachera-Włodarczyk, 2019). historically, the emphasis in KM has been on the storage and retrieval of information throughout an organization; however, there is now a trend towards the adoption of more dynamic and interactive systems (Davenport & prusak, 1998). Kayworth and leidner (2004) recommend four main processes in which KM can be categorized: cogent Business & MAnAgeMent 5 Knowledge creation: an organization’s capability to create novel and valuable ideas and solutions extends from product development to managerial practices (Kianto & Andreeva, 2011). Knowledge storage: involves the retention of pre-existing, obtained, and freshly generated knowledge across interconnected storage systems (ranjbarfard et al., 2014). Knowledge transfer/sharing: the process of transferring knowledge to locations where it is needed and can be efficiently applied (pirkkalainen & pawlowski, 2013). Knowledge application: this process incorporates the practical application of acquired knowledge (Davenport & prusak, 1998). 2.4. Balanced Scorecard the Bsc is widely recognized as a highly important advancement in the field of management accounting (cooper et al., 2017). the concept has rapidly disseminated across global business and consulting communities (schneiderman, 1999). the concept has garnered significant attention from various organizations and has been implemented by numerous organizations globally (othman etal., 2006). it has rapidly gained recognition as a crucial tool in management that has the capacity to enhance op (gupta & salter, 2018). this innovative tool takes into account not only the financial metrics but also the non-financial indicators as equally important in assessing op (sinha, 2006). in fact, robert Kaplan and David norton are credited with the rise and widespread adoption of the Bsc, which they introduced through a collection of articles published in the ‘harvard Business review’ (kaplan & norton, 1992). the fundamental concept driving its implementation was the recognition that relying solely on conventional financial metrics is insufficient in establishing a precise and thorough performance objective or directing focus towards all essential aspects of the organization that have a substantial influence on its sustained viability, expansion, and progress. instead, a well-rounded approach incorporating both financial and operational indicators is necessary (ghosh & Mukherjee, 2006). its objective is to provide management with a brief overview of the critical success factors of a company, and to enable the harmonization of business activities with the overarching strategy (Mooraj et al., 1999). it was initially developed mainly as a novel performance measurement system in response to critiques regarding the one-sided evaluation of a company’s performance (išoraitė, 2008). it serves as a platform for organizations to define their vision, mission, and strategy, and to effectively implement them (Kaplan & norton, 1996a). Moreover, it functions as a means of disseminating strategic information and educating numerous managers on strategy execution (singh & Kumar, 2007). essentially, it functions as a performance evaluation tool, strategic management framework, and communication instrument (chen et al., 2006). its name indicates a desire to track a collection of items that uphold equilibrium among short and long-term goals, financial and non-financial metrics, lagging and leading indicators, as well as internal and external performance perspectives (kaplan and norton, 1996b). Across different organizations, the relevant components of the Bsc may differ based on the institution’s unique objectives and situations. nevertheless, there is a consensus that a standard Bsc typically consists of four components in some capacity (von-Bergen & Benco-Daniel, 2004). these components involve the development of metrics, as well as the collection and analysis of data pertaining to four distinct perspectives (papenhausen & einstein, 2006): 1. ‘the financial perspective – (how do we look to shareholders?)’ 2. ‘the customer perspective – (how do customers see us?)’ 3. ‘the internal process perspective – (what must we excel at?)’ 4. ‘the learning and growth perspective – (how can we continue to improve and create value?)’ inherent in this system is the idea that ‘gains in the learning and growth perspective lead to improvements in internal business processes, which in turn lead to higher customer satisfaction and market share, and finally to superior financial performance’ (horngren etal., 2000, p. 467). 6 r. MAhBouB AnD M. g. ghAneM 2.5. Organizational Performance the op is recognized as a crucial representation of the organization, illustrating the extent to which its processes or results achieve a specific goal (pitt & tucker, 2008). it is characterized as the evaluation of progress towards predetermined goals (Amaratunga & Baldry, 2003). it pertains to the ability of the organization to achieve its stated goals by implementing robust corporate governance practices, effective management strategies, and a steadfast dedication to delivering outcomes (richard et al., 2009). griffin (2003) expanded the definition of op to encompass the organization’s ability to fulfill the requirements of both its stakeholders and its own necessities for continued existence. this expansion moved the concept of op beyond purely market-oriented indicators like ‘profit margin’, ‘market share’, or ‘product quality’, all of which are crucial for specific stakeholders and the overall survival of the organization, to encompass various other non-monetary factors. there was no consensus regarding the various aspects of overall op are or appropriate methods for assessing them (stannack, 1996). however, carton (2004) identifies the following five main classifications of op metrics: Accounting Measures: ‘rely upon financial information reported in income statements, balance sheets, and statements of cash flows’. Operational Measures: ‘include variables that represent how the organization is performing on non-financial issues’. Market-Based Measures: ‘include ratios or rates of change that incorporate the market value of the organization’. Survival Measures: ‘address the chances of organization remaining in business over a specific period of time’. Economic Value Measures: ‘are adjusted accounting measures that take into consideration the cost of capital and some of the influences of external financial reporting rules’. every category possesses its own set of benefits and drawbacks, offering a distinct viewpoint on the broader topic of assessing op (stannack, 1996). 2.6. Conceptual framework A conceptual model based on which Ai, KMp and Bsc are immediately taken into account in terms of their mutual relationships and influence on op has been proposed. therefore, the following sections are dedicated to drawing up a set of hypotheses based on an investigation that has already been developed. Figure 1 provides an indication of the possible associations between these constructs. 2.3. Empirical literature and hypotheses development 2.3.1. Impact of Artificial Intelligence on knowledge management Practices in a changing business environment, the development of KM is of critical importance for the adaptability and competitiveness of companies (Bencsik, 2022). consequently, numerous prominent firms around the world have embraced various KMp to ensure that they persist a head of their competing firms in nowadays ‘competitive business world’ and they persist to seek for means to enhance these KMp (Al-Mansoori et al., 2021). in the 20th century, Ai has been seen as a key element of KMp, since it has developed knowledge gathering, sharing, and efficient use of information within companies. (Al-hashmi etal., 2019). hence, the intertwining of KM and Ai is no longer a surprising fact today (Bencsik, 2022). however, there is a lack of research on how these can be linked to provide benefits to an organization (Jallow et al., 2020). For instance, Al-Mansoori et al. (2021) revealed that Ai techniques used to enhance KMp by enhancing the organization’s ability to access, create, organize and disseminate relevant knowledge and information. rhem (2021) found that the impact of Ai on KMp is that it allows knowledge to be transmitted rapidly, accurately and personally. pai etal. (2022) and Jallow etal. (2020) emphasized that Ai can be built and used in order to support with the KMp that the companies have already implemented cogent Business & MAnAgeMent 7 them. leoni et al. (2022) demonstrated that the use of Ai in the manufacturing sector has a positive impact on improving KMp effectiveness. Bencsik (2022) proved that there is a close relationship between Ai and KMp. KMp makes the understanding of knowledge possible, while Ai has the potential to create new knowledge in ways that have not been possible before, by expanding and exploiting existing knowledge. Al-Qahtani et al. (2022) verified that Ai can play a role in KMp to enhance the exchange of information between participants and to make it more convenient to seek out information about a number of search programmes. Jarrahi et al. (2023) evidenced that Ai has a role in promoting the basic dimensions of KM: creation, preservation and retrieval, knowledge sharing and use. Mittal et al. (2023) demonstrated that the most important factor contributing to KM was knowledge distribution, followed by amplifying efficiency, real-time engagement and collaboration, and Ai as an artificial neural network. Yang (2024) suggested that the successful implementation of Ai in KM can significantly bolster the adaptability of the organization, fortify its competitive advantage, and enhance its operational effectiveness. Al-Qahtani (2024) concluded that the utilization of Ai can greatly enhance the capabilities of the public sector in the Kingdom of saudi Arabia across various domains. Ai has the capability to optimize data management, enable well-informed decision-making, automate repetitive tasks, and extract valuable insights from extensive datasets. Ai has a beneficial influence on the distribution of knowledge, enhancing accessibility and customization of information for the general public. Furthermore, chatbots and virtual assistants powered by Ai have the capability to offer continuous support and interaction. indradevi et al. (2024) found that the integration of Ai tools enhances KMp and performance. Ai facilitates the expansion and effective utilization of knowledge, ensuring that information is accessible at any time and from any location. consequently, KM leads to improved proficiency, heightened sensitivity, increased innovation, and greater efficacy, all of which significantly contribute to the overall effectiveness of KMp. Based on this discussion, the following hypothesis was derived: H1. Artificial intelligence has a positive effect on knowledge management practices. 2.3.2. Impact of Artificial Intelligence on Balanced Scorecard implementation numerous studies have been carried out to examine the effect of Ai on op using diverse measures and settings (Mehralian etal., 2018; przegalinska etal., 2019). nonetheless, there is a lack of empirical studies that explores the relationship between Ai and op (Mikalef & gupta, 2021). the Bsc framework enables firms to assess multiple aspects of Fp and non-Fp. its unique structure can facilitate the implementation Figure 1. Proposed Conceptual Model (Developed by the Researchers). 14 r. MAhBouB AnD M. g. ghAneM appropriately filled in and suitable for further investigation. table 2 display the characteristics of the respondents. 3.4. Ethical considerations the study was approved by ethics committee of faculty of Business Administration, Beirut Arab university, lebanon. the study followed ethical procedures, which involved acquiring introductory letter from the ethics committee and securing permission to gather data. involved participants were provided with a written consent form that clearly outlined the research’s primary objectives and underscored the significance of their voluntary participation in contributing to the study’s success. the written consent form explicitly stated participants’ autonomy to withdraw from the study at any point, ensuring their involvement remained voluntary. 3.5. Data analysis and results this research was based on the PLS-SEM to test the ‘research models’ (hair etal., 2021). it has been considered suitable for this research due to its flexibility and the research models are complex and composite structures (Benitez et al., 2020). it is built on two phases: ‘measurement model assessment’ and ‘structural model evaluation’ (hair et al., 2019). 3.5.1. Measurement model the findings of the pls analysis are illustrated in Figure 2. initially, the suitability of each ‘multi item scale’ was assessed. there were 91 observed variables in the initial model. this research measures ‘internal consistency’ (ic), ‘reliability’, ‘convergent validity’, and ‘discriminant validity’ (Dv). the minimum threshold for each item was set at 0.6 (hulland’s, 1999). Accordingly, little items were removed and the adjusted model with ‘86 items’ was tested using ‘smart pls 2.0 M3’. the results indicated that all items surpassing threshold 0.6. the results confirm that each item is adequate for representing its structure. to assess the ic of the constructs, ‘cronbach’s alpha’ (cA), ‘composite scale reliability’ (cr) and ‘average variance extracted’ (Ave) were computed (chin, 1998). table 3 represents that cA for all constructs Table 2. Characteristics of the respondents. Count % nationality egyptian 125 21% Lebanese 85 14% Jordanian 72 12% saudi arabian 77 13% Qatar 56 9% algerian 42 7% Palestinian 36 6% Qatari 30 5% Libyan 27 5% tunisian 24 4% emirati 20 3% experience 0–5 177 30% 6–10 192 32% 11–15 114 19% 16–20 53 9% 21–25 36 6% 26–30 22 4% Department teller 95 16% Customer service 49 8% accounting 140 24% Bank Manager 30 5% internal auditing 23 4% Credit 23 4% information technology 115 19% Human Resources 56 9% Marketing 29 5% Risk Management 34 6% Source: sPss output (p. 25). cogent Business & MAnAgeMent 15 surpassed the threshold value demonstrating higher ic. the cr and Ave exceeded the threshold value (0.7 and 0.5), indicating acceptable reliability of the constructs. valuation of the Dv of the constructs was the second step to ‘measurement validation’. table 4 represents Dv of Ai, KMp, Bsc and op and Ave’s square root surpasses ‘the inter-correlations of the constructs with the other constructs in the model’ (henseler et al., 2009). Moreover, cross loadings of the items were investigated to observe further support for Dv (chin, 2010). hence, it can be determined that the results displayed adequate Dv of the Ai-op model. 3.5.2. Assessment of the structural model the findings of the seM, specifying the ‘path coefficients’ and ‘t-statistics’ are demonstrated in table 5. A ‘bootstrapping procedure’ was utilized to ‘test the significance of the model and the assumed relationships’ (hair et al., 2011). the findings of the seM revealed that all hypotheses are supported. the results indicate that Ai improves KMp (β = 0.780, t = 30.278, p < 0.000) and Bsc (β = 0.610, t = 18.754, p < 0.000) as well as op (β = 0.113, t = 2.758, p < 0.006) in support of h1, h2, and h3. Moreover, the results display the direct impact of KMp on Bsc (β = 0.835, t = 36.987, p < 0.000), and on op (β = 0.114, t = 2.802, p < 0.005), and are significant and support h4 and h5. Also, the effect of Bsc on op (β = 0.114, t = 2.802, p < 0.005) is significant and support h6. on the other hand, the mediation effect of KMp on the link between Ai and Bsc (β = 0.651, t = 22.851, p < 0.000) and on the link between Ai and op (β = 0.089, t = 2.369, p < 0.001) are significant and support h7 and h8. Besides, the mediation effect of Bsc on the link between Ai and op (β = 0.069, t = 2.746, p < 0.006) is significant and support h9. 4. Results and discussion 4.1. Results the results indicated that Ai technologies have the capability to enhance KM through the facilitation of advanced analytics and the enhancement of information retrieval processes. Ml algorithms have the capability to detect patterns and extract valuable insights from vast datasets, ultimately enhancing the significance and practicality of knowledge. chabot and virtual assistants play a crucial role in enabling immediate knowledge exchange, providing quick assistance and resolutions for both customers and staff members. the advancement of Ai holds the promise of transforming the way in which organizations obtain and apply knowledge, leading to enhanced decision-making processes. Additionally, the results Figure 2. Results of PLs analysis. 16 r. MAhBouB AnD M. g. ghAneM Table 3. Measurement items and validity assessment. Construct item Factor Loading CR Cronbach’s alpha aVe ai a1 0.799 0.925 0.923 0.580 a2 0.78 a3 0.831 a4 0.693 a5 0.731 a6 0.65 a7 0.754 a8 0.781 a9 0.819 KM KM1 0.703 0.977 0.981 0.557 KM2 0.694 KM3 0.728 KM4 0.711 KM5 0.744 KM6 0.698 KM7 0.638 KM8 0.702 KM9 0.836 KM10 0.819 KM11 0.656 KM12 0.733 KM13 0.754 KM14 0.697 KM15 0.822 KM16 0.78 KM17 0.698 KM18 0.741 KM19 0.729 KM20 0.669 KM21 0.75 KM22 0.817 KM23 0.778 KM24 0.779 KM25 0.721 KM26 0.754 KM27 0.772 KM28 0.743 KM29 0.799 KM30 0.812 KM31 0.837 KM32 0.829 KM33 0.78 KM34 0.671 KM35 0.672 BsC BsC1 0.607 0.983 0.985 0.652 BsC2 0.665 BsC3 0.774 BsC4 0.886 BsC5 0.862 BsC6 0.841 BsC7 0.879 BsC8 0.897 BsC9 0.873 BsC10 0.728 BsC11 0.67 BsC12 0.9 BsC13 0.847 BsC14 0.857 BsC15 0.843 BsC16 0.693 BsC17 0.768 BsC18 0.868 BsC19 0.686 BsC20 0.846 BsC21 0.809 BsC22 0.921 BsC23 0.898 BsC24 0.859 BsC25 0.867 BsC26 0.698 BsC27 0.731 BsC28 0.888 BsC29 0.771 BsC30 0.712 BsC31 0.772 BsC32 0.797 (Continued) cogent Business & MAnAgeMent 17 observed that Ai-driven features have the potential to improve the implementation of Bsc approach through the utilization of automated data collection and analysis, predictive modeling, real-time performance monitoring, and personalized recommendations. By automating the collection and analysis of data from diverse sources, Ai simplifies the process of gathering and processing performance metrics for the Bsc. Furthermore, Ai can employ predictive modeling techniques to forecast future performance by analyzing historical data and external factors. real-time monitoring facilitated by Ai enables instant updates on key performance indicators, thereby enabling timely decision-making. Moreover, Ai can provide personalized recommendations for enhancing performance based on individual goals and targets. the results also confirmed that Ai technologies and KMp have a huge potential to improve op. By leveraging Ai-powered datasets and KMp, banks are able to achieve high profit margins; increase revenues; raise return on investment; retain customers; enter new markets more quickly; introduce new products or services, growth in market share, improvement in the overall financial performance and enhance the speed, accuracy, effectiveness and consistency of their decision-making processes. Moreover, the results pointed out that the effect of KMp on the four Bsc perspectives is meaningful. Kaplan and norton proposed a hypothesis regarding the sequence of cause and effect that ultimately leads to strategic success. this hypothesis holds significant importance in comprehending the metrics of KM in a manner consistent with the principles advocated by the Bsc. upon examining the Kaplan and norton implementation framework, it becomes evident that KM is situated within the learning and growth perspective of the Bsc. thus, according to this assertion, the outcomes of KM will have a significant influence on various organizational processes. this is mainly due to the optimal strategy for effectively implementing KM involves adopting a human-centric perspective. While machinery continues to play a significant role in a knowledge-driven economy and technology is crucial, the primary driver of productivity remains the human mind. Knowledge is a product of individual minds. While money can influence decisions and streamline processes, it cannot replace human thought. Machinery, on the other hand, is capable of performing tasks but lacks the ability to innovate. the results also revealed that enhanced staff training Construct item Factor Loading CR Cronbach’s alpha aVe oP oP1 0.788 0.969 0.968 0.778 oP2 0.872 oP3 0.936 oP4 0.916 oP5 0.916 oP6 0.924 oP7 0.871 oP8 0.839 oP9 0.868 Source: Based on Researchers’ Calculations. Table 3. Continued. Table 4. Discriminant validity. ai KM BsC oP ai 0.761 KM 0.74 0.746 BsC 0.61 0.735 0.807 oP 0.113 0.114 0.114 0.882 Source: Based on researchers’ calculations. Table 5. Hypotheses testing results. Hypothesis Relationship Coefficient (β) t-value p-Value Result H1ai → KMP 0.780 30.278 0.000 supported H2ai → BsC 0.610 18.754 0.000 supported H3ai → oP 0.113 2.758 0.006 supported H4KMP → BsC 0.835 36.987 0.000 supported H5KMP → oP 0.114 2.802 0.005 supported H6BsC → oP 0.114 2.802 0.005 supported H7ai → KMP→ BsC 0.651 22.851 0.000 supported H8ai → KMP→ oP 0.089 2.369 0.001 supported H9ai → BsC → oP 0.069 2.746 0.006 supported Source: sPss output (p. 25). 18 r. MAhBouB AnD M. g. ghAneM contributes to enhanced business operations, subsequently yielding superior service or product standards, thereby boosting customer satisfaction levels. elevated customer satisfaction fosters loyalty, attracts new customers, drives higher sales figures, boosts revenues, and consequently enhances financial outcomes. improved Fp, in turn, bolsters op in terms of business development, expansion, reputation, creditworthiness, and overall confidence. With respect to the mediation effect, the results indicated a positive and significant effect for the relationship Ai- KMp-op and Ai- KMp-Bsc. the findings indicate a significant effect resulting from the mediation of KMp. therefore, utilizing of KMp and integrating Ai can have a positive and significant impact on op and Bsc. this indicated that without appropriate KMp, the use of Ai tools alone is ineffective. thus, the adoption of Ai should be supported by structured KMp. in the same vein, the results designated a positive and significant effect for the relationship Ai- Bsc-op. the results show a significant effect resulting from the mediation of Bsc. therefore, with the mobilization of Bsc and Ai adoption can have a positive and significant impact on op. this implied that Ai-driven features have the potential to improve the implementation of Bsc and better op is attained upon using Bsc. hence, the synergy between Ai and the Bsc creates new prospects for strategic management and performance evaluation. embracing this fusion can enable banks to enhance their agility, responsiveness, and overall success in strategic endeavors. 4.2. Practical implications the aim of this research is to test a theoretical model that considers the interactions between Ai, Kp and Bsc as well as their impacts on op in simultaneously. the results support the nine hypotheses. Accordingly, the research provides numerous managerial implications. For instance, while Ai has positive effect on op, there is inadequate empirical evidence concerning the association between Ai, KMp, Bsc and op. several scholars contend that the association between Ai and op is still in dispute because ‘empirical evidence’ remains scarce. in this research, it has been shown that the relationship between Ai and op is mediated by a number of other intervening variables such as KMp and Bsc. Moreover, the findings suggest that Ai has an effect on the performance of banks. the use of Ai will help to improve KMp for better bank decisions. Further, in the literature there are links between Ai and Bsc. By improving customer satisfaction and decreasing employee turnover, Ai provides banks with a competitive advantage that increases operating profit. Additionally, the findings show that managers must comprehend the advantages of Ai and its effects on bank performance, together with additional potential mediators such as KMp and Bsc. Accordingly, banks should develop and practice Ai in cooperation with other accompanying variables that will mutually improve their op. Besides, the findings of that research will provide managers with insights, justifying the necessity to be pursuing Ai as a competitive advantage tool. Banks in the MenA region have the chance to carefully examine the effects and advantages of Ai as a strategic instrument that may enhance their long-term sustainability and enhance their decision-making. this will foster effective op. nevertheless, Ai can provide optimal utilization of resources in order to improve performance by identifying redundancies throughout the business process. 4.3. Theoretical implications Despite the significance of Ai’s impact on op, the mediating roles of KMp and Bsc between the Ai-op linkages have received scant empirical attention. hence, this research investigates the mediating role Ai, KMp and Bsc have on op to fill this gap in literature. this research verifies that Ai has a positive impact on op. Banks undertake Ai activities to facilitate and enhance KMp and using of Bsc. the Ai activities undertaken by banks aim at enhancing their operations to ensure ‘long-term sustainability’ of the bank. the results revel that Ai has a positive effect on op via KMp and Bsc, which ultimately supporting the mediation role of KMp, and Bsc. Managers of banks in MenA region should realise that Ai practices should be applied to increase the op. 5. Conclusions and recommendations the results of this research contributes to the development of a theoretical model which explains the relationship between Ai, KMp and Bsc as well as its impact on op. Moreover, the empirical investigation of the cogent Business & MAnAgeMent 19 relationships between Ai, KMp and Bsc in terms of their effects on op contributes to the literature. thus, this research responds to the call from practitioners for a joint examination of Ai, KMp and Bsc in terms of their reciprocal effects on op. specifically, the research has demonstrated that KMp acts as a mediator, which benefits Bsc and op through Ai. therefore, they play an important role for banks that are interested in the proper use of Ai tools to improve their performance. hence, this research provides some recommendations for increasing the effectiveness of Ai on op. for instance, identify the right use cases; build a high-quality data infrastructure; choose the right Ai technology; invest in talent; continuously monitor and improve performance; and ensure transparency and accountability. consequently, banks can use these recommendations to design and implement effective Ai initiatives that deliver measurable benefits. however, the results of this research should be clarified with carefulness due to numerous limitations. this research examines banks in some countries of MenA region. consequently, although it may be reasonable to believe that these banks might constitute a representative sample, of–at least– MenA banks, the enrichment of the selected sample could provide valuable additional insights. Furthermore, the ‘response rate’ could be increased by extending the period for collection of data. Additionally, although MenA banks are characterised by a high proportion of islamic banks, this specific group has not been investigated. indeed, the banks in the sample are commercial banks exclusively; therefore, the results reported in this research potentially tend to be more positive than the actual situation and further research may apply the same investigation exclusively to islamic banks to specifically understand their reality. likewise, although questionnaires can be used for the sole collection of data, future research could tie this approach to a variety of methods like ad hoc interviews in order to gain more detailed information. Besides, this research proposed a linear pattern, but future studies could look into the existence of circular relationships between investigated constructs. in conclusion, this research highlights the critical importance of the mediating role of KMp and Bsc when examining the relationship between Ai and op. Future research and bankers would have important implications from the views presented in this research. Author contributions i. conception and design: M. rasha & M. ghanem. ii. Analysis and interpretation: M. rasha & M. ghanem. iii. Drafting of paper: M. rasha & M. ghanem. iv. revising it critically for intellectual content: M. rasha & M. ghanem. v. Final approval of the version to be published: M. rasha & M. ghanem. vi. Both authors agree to be accountable for all aspects of the work. Disclosure statement no potential conflict of interest was reported by the author(s). About the authors Dr. Rasha Mahboub holds a doctorate in Accounting and is an associate professor at Beirut Arab university, lebanon. rasha has extensive experience in accounting, auditing, taxation, and corporate finance. she taught various undergraduate and graduate business classes, published two textbooks and several research papers in peer refereed international academic journals. her primary research interests are in the fields of voluntary disclosure, impression management, forwardlooking information disclosure, financial reporting quality, impact of information and communication technology and social media on performance of banking sector, outsourcing of accounting functions. Dr. Mohamed Gaber Ghanem holds a doctorate in Accounting and is a lecturer at Alexandria university, egypt. 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