Driving organizational performance: the impact of business intelligence, agility and creativity

Business intelligence (BI) is a widely used concept referring to analytical decision-making in management. It has become an essential strategy for organizations seeking to remain competitive (Bouaoula et al., 2019; Tirno, 2024). Recently, BI’s popularity has surged due to its incorporation of analytics, big data, and artificial intelligence (AI), all of which are crucial components of digital transformation, a key focus for business executives (Vugec et al., 2020). BI builds on big data analytics, capturing processes, practices, and technologies that search for and extract important business data, generating fresh understandings about the business context (Chen et al., 2012; Gupta and George, 2016).

BI integrates firms’ available capabilities, current operating conditions, patterns or anticipated market trends, technologies, and regulatory and competitive environments (Chen et al., 2012). Essentially, BI implements processes and technologies that collect, store, retrieve, and interpret data from various internal and external resources to facilitate informed decision-making across business activities (Aruldoss et al., 2014; Watson, 2009).

Organizational agility (OA) reflects how well a company can notice and react to changes by deliberately adjusting the range of products or services it offers, and how quickly it can implement these changes compared to its competitors (Singh et al., 2013). This ability is becoming increasingly important as organizations face escalating challenges from diverse internal and external threats within and beyond their operational boundaries (Annarelli and Nonino, 2016).

It should be noted at this point how agility differs from resilience. Organizational resilience refers to an organization's capacity to address disruptive activities proactively through strategic planning and the integrated management of internal and external operational challenges (Annarelli and Nonino, 2016). OA is shaped by the establishment of internal structures and processes that facilitate the development of skills among its members, enabling them to navigate environmental changes effectively (Awan et al., 2022). A recent literature review (Motwani and Katatria, 2024) explicitly explored OA and its critical relevance in dynamic business environments. The review findings highlighted the dependence of agility on dynamic capabilities (DC), particularly the ability to sense and seize opportunities and transform operations accordingly.

In the current era of rapid change, creativity plays a crucial role in empowering organizations to compete effectively in today's competitive conditions (Ameen et al., 2024; Eidizadeh et al., 2017). Organizational creativity (OC), as defined by Woodman et al. (1993), involves doing something entirely new or creating new knowledge within a complex social context. This represents a dramatic change that is crucial for understanding organizational effectiveness and survival (Ribeiro et al., 2018). Thus, creativity encompasses the development of innovative ideas, inventions, marketing strategies, valuable products and services, and practical concepts (Hanaysha et al., 2022). OC reflects the ability of individuals and organizations to generate creative ideas that align with business needs and priorities (Awan et al., 2022).

Organizations are encountering significant challenges in today's competitive landscape. Additionally, businesses focus on maintaining profitability and outperforming competitors amidst various internal and external challenges. Externally, they navigate opportunities and risks posed by increased competition, rising predictions, and fast technological advancements. Internally, there is pressure to cut operational expenses, enhance efficiency, improve customer service delivery, and create greater customer value simultaneously (Eidizadeh et al., 2017). Therefore, the ultimate objective for any organization is to enhance its performance in both financial and strategic terms, which aligns with the resource-based view (RBV) and DC theories.

While vast research identified positive associations between BI and performance, several gaps remain in the literature (Popovič et al., 2019). In particular, there is limited understanding of the mechanisms through which BI influences performance. There is also a need for deeper insight into how BI impacts firm performance (Ahlijah, 2020; Vugec et al., 2020) and a lack of empirical evidence on the factors that affect effective BI implementation and its link to competitive advantage (Eidizadeh et al., 2017).

Although previous research has explored the relationships between BI, OA, and performance, most studies have treated agility or creativity as independent mediators rather than as serial mechanisms through which BI drives performance. Moreover, limited attention has been given to how BI-enabled agility can foster creativity, creating a DC that enhances overall organizational success. This study addresses these gaps by developing and empirically testing a serial mediation model, demonstrating how BI strengthens agility, which in turn stimulates creativity, ultimately leading to improved performance.

Studying a serial mediation model advances the understanding of stepwise, sequential effects (Gupta et al., 2025; Pazetto et al., 2024). In the conceptual framework model of this study, BI first acts as a structural input that facilitates agility (the first mediator) by fostering swift changes and flexibility. Second, agility acts as a structural input to creativity (the second mediator), manifesting innovative ideas and novel strategies. This model demonstrates how BI can trigger certain DC that mediate its effect on organizational performance. Furthermore, this study is important because, using the lenses of the RBV and DC theories it shows how BI’s tangible resources, such as data integration, and intangible resources, like analytical capability, work together with OA and OC to improve overall performance. It highlights that BI is more than just a technological tool; it is a strategic resource that fosters knowledge-driven capabilities and innovation, helping organizations gain a competitive edge.

This research was conducted in the United Arab Emirates (UAE) where digital transformation is a strategic priority for governmental and business entities. In its increasingly dynamic, complex and volatile context, the country is adopting agile approaches, such as the AI Strategy 2031, to boost competitiveness (Al Jabri et al., 2024). The UAE is experiencing significant growth in BI software adoption, fueled by the national digital transformation strategy and the increasing importance of data-driven decision-making. Market forecasts estimate that BI software revenues in the UAE will reach US$80.03 million by 2025, with a projected compound annual growth rate of 4.84% from 2025 to 2030. This would yield a market volume of approximately US$101.35 million by the end of the forecast period (Statista, 2024). This reflects the rising investment in analytics tools to support OP and strategic agility. In this line of thought, this study formulates the following research questions:

This study draws upon two foundational theories, the RBV and DC, to propose a comprehensive model aimed at enhancing organizational creative performance. The RBV emphasizes the importance of developing capabilities that enable effective resource utilization for gaining a competitive advantage (Barney, 1991). DC theory focuses on a company's ability to coordinate, learn, and adapt its internal and external resources, allowing it to respond swiftly to environmental changes and disruptions (Teece, 2007). Eisenhardt and Martin (2000) further refined the concept of DC, describing it as “the firm’s processes that use resources–specifically the processes to integrate, reconfigure, gain, and release resources–to match and even create market change” (p. 1107).

Consistent with prior research (Singh et al., 2013; Motwani and Katatria, 2024; Teece, 2022), this study conceptualized OA as a key BI capability that emerges through the implementation of DC and knowledge creation processes. Within a supply chain (SC) framework, agility is regarded as a higher-order capability that enables firms to sense opportunities and threats in the marketplace, seize opportunities and transform assets and structures (Aslam et al., 2018). This allows the organizations to adapt, develop and grow in response to a changing environment (Tripathi et al., 2025). Nevertheless, literature on agility lags behind overarching DC studies (Cristofaro et al., 2025).

Drawing on DC theory, this study composes a framework with three main constructs – BI, agility and creativity – reflecting the “sense-seize-transform” sequence, as depicted in Figure 1. This figure illustrates how intelligent and agile organizations can sense opportunities and threats, generating insights based on real-time data and analytics. Agile organizations respond swiftly to environmental changes with flexible decision-making and speedy resource allocation (Motwani and Katatria, 2024; Teece, 2022). OC enables novel solutions, new business models and experimental approaches to adaptation. This allows organizations to identify and seize emerging opportunities and shaping transformations (Chaubey and Sahoo, 2022). The relationships of this framework are discussed in the following paragraphs, yielding research hypotheses.

BI leverages organizational capabilities, including functionality, scalability, and reliability, to construct increasingly larger data warehouses, and improve business analytics, which are acknowledged as ground-breaking technologies and cutting-edge solutions for advancing enterprise growth (Rumman et al., 2024). It involves activities such as data mining, warehousing, and visualization, facilitating competition and performance analysis (Olszak, 2016; Vercelis, 2009). Therefore, BI has transformed into a strategic initiative, acknowledged by business leaders for its pivotal role in enhancing business effectiveness and fostering innovation (Watson and Wixom, 2007).

When examining the impact of BI on OP, it is essential to consider the role of AI algorithms in enhancing BI capabilities and improve performance through process autonomation and enhanced data-driven insights (Mikalef et al., 2023). AI and big data analytics can optimize decision-support functions by making accurate predictions, detecting patterns in large datasets, and providing real-time actionable insights (Fosso Wamba et al., 2024a). Research indicates that AI-driven BI systems improve forecasting accuracy, reduce manual data processing, and enhance strategic decision-making, thereby strengthening OA and responsiveness (Chebrolu, 2025). By integrating AI into BI, organizations are able to make faster and more informed decisions, ultimately contributing to improved overall performance and sustainable competitive advantage (Fosso Wamba et al., 2024a).

According to the RBV, BI is a pivotal organizational resource that encompasses both tangible and intangible elements, which drive performance (Barney, 1991). A wealth of research underscores the positive link between BI and OP (Abusweilem and Abualoush, 2019; Ahmad et al., 2023; Aydiner et al., 2019; Bhatiasevi and Naglis, 2020; Chen and Lin, 2021). For example, Ahmad et al. (2023) found that BI positively and substantially impacts OP in a study of 269 Jordanian telecommunication firms. Both the RBV and DC theories emphasize BI as a system that integrates crucial capabilities, highlighting its role in bolstering firm performance (Chen and Lin, 2021). Performance appraisal and big data analytics capabilities rely on available information to improve business processes and meet organizational goals (Mikalef et al., 2020). In this context, knowledge management is a critical success factor in effective business decision-making (Cheng et al., 2020). Using data mining and other techniques BI can collect and disseminate data and information, generate and manage knowledge, and thus effectively monitor both the internal and the external business environment, increasing competitiveness and performance (Bouaoula et al., 2019). Building on these insights, the first hypothesis is generated:

Agility refers to a firm's capability to efficiently and effectively allocate or reallocate its resources towards business actions that generate, capture, and protect originality, in response to internal and external circumstances as needed (Teece, 2022). Thus, the fast-paced global business landscape, along with significant technological advancements, compels firms to adopt a more innovative and agile approach to understanding and meeting their customers' changing needs and preferences (Aydiner et al., 2019). BI now incorporates big data analysis, utilizing advanced techniques such as data mining, statistical analysis, and predictive analysis to process large datasets efficiently (Hsu et al., 2021). As industry 4.0 applications, like accurate sensors and big data analytics, have progressed in the last years, data-driven approaches, such as data mining and predictive model strategies based on machine and deep learning, provided significant advantages in decision-making and operational performance (Hu et al., 2025; Zhang et al., 2023, 2024).

Consequently, BI provides organizations with the necessary tools and insights to adapt, innovate, and thrive in the face of challenges, thereby enhancing their agility in an ever-changing business landscape. The fundamental elements of DC theory include learning and transformational capabilities, as well as achieving a competitive edge (Aldabbas and Oberholzer, 2024; Kovilage et al., 2024). Previous studies have stressed that BI infrastructure and reliability affect performance by facilitating decisions for innovative business activities, hence generating a sustainable competitive advantage and improving market position (Satar et al., 2025). Furthermore, recent research has recognized technological innovation and knowledge management as significant antecedents of organizational agility and competitiveness (Satar et al., 2025). In other words, it is critical for the organization to use BI to gain a thorough understanding of the new market needs and respond quickly and effectively. Based on the above, a second hypothesis is generated as follows:

Creativity can be understood at different levels of analysis. On the individual level, it reflects an employee’s ability to generate new and useful ideas (Aldabbas et al., 2025; Ismail et al., 2019). At the organizational level of analysis, creativity reflects the shared capacity of the organization to generate innovative outcomes through systems, structures, and processes that encourage idea generation (Woodman et al., 1993). Amabile and Pratt (2016) revised the componential model of individual or small-group creativity, and organizational innovation (Anderson et al., 2014), and adopted the openness and collectiveness of the model of Woodman et al. (1993). Therefore, they included the contextual influences in their conceptualization of the work environment (Amabile and Pratt, 2016). Most importantly, they went beyond the initial multi-level interpretation of creativity and adopted the understanding of collectiveness, highlighting that “group creativity depends on, but is not a simple aggregation of, the creativity of the individuals in the group” (Woodman et al., 1993). They even distinguished the “collaborative process among multiple individuals” toward a so-called “collective creativity” (Hargadon and Bechky, 2006).

This study focuses on organizational-level creativity, following the conceptualization of Woodman et al. (1993), Amabile and Pratt (2016), and Mikalef and Gupta (2021). Specifically, it explores how company-wide factors shape a firm’s overall ability to perform creatively. Many studies report positive association between creativity and performance (De Vasconcellos et al., 2024; Ismail et al., 2019; Mikalef and Gupta, 2021; Rumanti et al., 2023). By fostering a creative environment for their employees and offering opportunities for innovative thinking and action, organizations can surpass their competitors (Ismail et al., 2019). Earlier studies have recognized that when coupled with firm capabilities, creativity exerts a significant influence on performance (Awan et al., 2022). While creativity often enhances performance and success at the individual level (Tse et al., 2018), relevant research at the organizational level is still scarce (Aldabbas et al., 2025). Building on the above, the following hypothesis is generated:

Previous studies evidenced that BI influences OA significantly and positively (Al Aqasrawi and Alafi, 2022; Cheng et al., 2020; Jafari et al., 2023). BI enhances decision-making through rapid information analysis capabilities within minimal time, thereby fostering agility (Eidizadeh et al., 2017). Although BI has the potential to enhance agility and subsequently boost performance, the mechanisms that drive BI and agility to elevate performance remain unclear. OC represents a potential avenue for generating new ideas and solutions that are adaptable and responsive to complex environments, fostering growth and performance (Awan et al., 2022). To connect organizational agility with creativity, it is important to emphasize that organizations should generate numerous novel and valuable ideas, products, or services at fact pace. Prior studies examining different aspects of organizational agility have also highlighted the role of OC (Awan et al., 2022).

Data consists of raw facts or observations generated from business events or transactions within an organization, lacking context, and serves as input for data warehouses. Information, on the other hand, is data with context and meaning, considered both as input and as the outcome in a BI application. Following this interpretation, adopting business analytics has a proven positive impact on business process and performance (Aydiner et al., 2019). Furthermore, BI supports and speeds up comprehensive decision-making, which in turn boosts firm performance (Alzghoul et al., 2022; Khaddam et al., 2023). This relationship between BI and performance can be mediated by factors such as business process management (Vugec et al. (2020). From a broader perspective, BI, SC integration, and agility are antecedents of SC performance (Jafari et al., 2023).

DC play a vital role in creating a favorable environment for OA, strengthening the capacity for ongoing improvement, and encouraging OC (Almheiri et al., 2024). Agile organizations can dismantle barriers of uncertainty, involving and empowering their employees effectively. Considering the significance of creativity for long-term viability and achievement in dynamic and competitive settings, businesses should nurture the capacity to address shifts in an adaptive mode (Darvishmotevali et al., 2020). Recent research suggests that integrating AI capability with strategic agility directly supports the enhancement of product and service creativity (Ameen et al., 2024). Based on the aforementioned arguments, a fourth hypothesis is generated:

All research hypotheses are illustrated in Figure 2.

The researchers conducted a cross-sectional survey to collect the data and test the research hypotheses. They used their personal networks to contact senior managers working in public and private organizations in the UAE. 250 invitations were administered via email, WhatsApp, and other social media platforms. A total of 195 responses were returned, resulting in a 78% response rate. Data collection spanned the first two months of 2024.

All constructs in the model were measured using seven-point validated Likert scales. BI was measured using two subscales on data integration, and analytical capability of Cheng et al. (2020). Reliability analysis of the BI construct yielded a Cronbach’s alpha coefficient of 0.906. The six OA items were taken from Cegarra-Navarro et al. (2016). Cronbach’s alpha for OA was 0.889. A five-item scale, validated by Scheibe and Gupta (2017), was used to measure OC. Reliability analysis for OC yielded a Cronbach’s alpha value of 0.868. The five-item OP scale was drawn from Lee and Choi (2003). The Cronbach’s alpha value for OP was 0.845.

Three demographic variables were considered for analysis. The first is firm age, measured by the number of years a firm has been in operation. Firm age can influence the explored relationships, as older firms may have entrenched practices, while newer firms might be more agile; both cases can influence firms’ ability to innovate (Zabel and O'Brien, 2024). The second variable is firm size, which is proxied by the number of employees, in line with relevant research (e.g. Mikalef et al., 2020; Fosso Wamba et al., 2024b). Size reflects the extent of organizational resources and capabilities, which can impact innovation dynamics (Anderson et al., 2014). The third variable is ownership type (either private or public). These three characteristics (organizational age, size, and ownership type) are widely recognized as key contextual factors that may influence the adoption and effectiveness of BI practices.

The study sample comprises 195 organizations. In terms of organizational age, the largest segment (43.1%) has been operating for 5–10 years. Regarding size, most organizations (59.0%) employ 250 to 500 employees. Private organizations represent 62.1% of the sample, with notable industry presence in real estate and construction (10.3%), IT (9.2%), and hospitality and tourism (7.7%). In the public sector (37.9%), government entities form the largest subgroup (17.9%), followed by semi-government companies (14.4%). Full demographic details are reported in Table 1.

An exploratory factor analysis (EFA) was firstly conducted on collected data. Next, regression analysis and serial mediation modeling tested the research hypotheses.

The Kaiser-Meyer-Olkin (KMO) measure yielded a value of 0.908, confirming dataset adequacy, as KMO values close to one suggest strong relationships between items and their respective factors, with each variable being perfectly predicted without error by the other variables (Hair et al., 2019: p. 136). Additionally, Bartlett's test of sphericity was found significant at 0.001 level, indicating that sufficient correlations exist among the variables to proceed variables (Hair et al., 2019: p. 137). Regarding item loadings, a cut-off value of 0.50 resulted in retaining all 22 items without any deletion for further analysis, confirming the structure of this study's key constructs. The average loading for BI, OA, creativity, and performance (OP) was 0.760, 0.746, 0.720, and 0.743, respectively. The detailed factor loadings for all items derived from the EFA are reported in Table 2.

The average variance extracted (AVE) values for the constructs are 0.587 for BI, 0.559 for OA, 0.521 for OC, and 0.555 for OP, all exceeding the 0.50 threshold (Hair et al., 2019: p. 676). Similarly, the composite reliability values for these constructs are 0.893 for BI, 0.883 for OA, 0.844 for OC, and 0.861 for OP, all surpassing the 0.70 criterion (Hair et al., 2019: p. 760). These findings confirm the convergent validity of all constructs. Furthermore, the square root of the AVE values exceeds the correlations with other variables (i.e. the AVE for BI is 0.766; for OA, 0.747; for OC, 0.722; and for OP, 0.745). Therefore, the discriminant validity of the constructs has been confirmed according to the Fornell-Larcker criterion (Hair et al., 2019: pp. 761, 776). Additionally, heterotrait-monotrait (HTMT) values were found to be lower than the recommended threshold of 0.85 (Franke and Sarstedt, 2019), indicating that the constructs are sufficiently distinct. Table 3 lists the HTMT ratio values.

A challenging issue in behavioral research is common method bias (CMB) or variance, which is the variance caused by the measurement method rather than the latent constructs. Given that there was a single source of data, CMB was of concern (Podsakoff et al., 2003). Therefore, certain measures were taken in this study to address CMB before and after data collection. The questionnaire was specifically designed to minimize the impact of CMB. Firstly, it contained only 22 questions aiming to avoid boredom and fatigue of respondents (Lindell and Whitney, 2001). Secondly, the demographic questions were placed at the end of the questionnaire, as they require minimal cognitive processing (Lindell and Whitney, 2001). Lastly, the anonymity and confidentiality of the respondents were assured, and the wording of the items was consistent and simple (Tehseen et al., 2017). Furthermore, the survey targeted senior employees, who are well-informed participants. According to Rindfleisch et al. (2008), when having theoretically well-grounded variables, using multiple measurement scales, and targeting educated respondents, cross-sectional data are most appropriate. Harman's single-factor test was used to test for CMB after data collection. This test measures whether the variance explained by a single factor is below 50%. In this study, the Harman’s test yielded four distinct factors, with the first factor accounting for 40.214%, indicating that CMB is not a significant issue.

Table 4 provides an overview of the mean, standard deviation, skewness, kurtosis, and correlation for the key variables. The correlation analysis shows that all four primary variables in this study exhibit significant positive correlations at the 0.01 level. The strongest correlation is found between OA and OC (Cor. = 0.553, p < 0.01), followed by the correlation between BI and OA (Cor. = 0.524, p < 0.01). The lowest correlation is between BI and OP (Cor. = 0.398, p < 0.01).

A regression analysis was performed using the PROCESS macro add-on to test the research hypotheses. Serial mediation model number 6 with 2 mediators (Hayes, 2022: p. 623) was applied because it fit the conceptual framework of this study. The regression coefficients (B), standard errors (SE), t-values, p-values, and 95% bootstrap confidence intervals are reported in Table 5. The results indicate a positive direct effect of BI on performance (B = 0.119, t = 2.479, p < 0.05), which supports H1. A substantial positive correlation was estimated between BI and agility (B = 0.426, t = 8.553, p < 0.001), thereby validating H2. Additionally, a significant positive association was found between OC and performance (B = 0.404, t = 8.802, p < 0.001), confirming H3. Furthermore, the positive effect of BI on performance through agility and creativity (B = 0.065, bootstrapping SE = 0.017, p < 0.05) confirmed the hypothesized serial mediation (H4). The direct effect of BI on OP decreased from (B = 0.142, bootstrapping SE = 0.034, p < 0.05) to (B = 0.119, t = 0.048, p < 0.05) when accounting for these mediators. Thus, regression analysis substantiated the indirect effect of BI on performance, mediated by organizational agility and creativity. Figure 3 illustrates the key statistical findings.

The mediation analysis revealed that BI exerts both direct and indirect effects on OP through agility and creativity. The indirect effect of BI on performance through OA alone was negligible (B = −0.008, not significant), indicating that agility by itself does not significantly translate BI capabilities into performance gains. However, the indirect effect through OC was moderate (B = 0.085), suggesting that BI enhances creativity, which subsequently drives performance improvements. Furthermore, the sequential pathway BI → OA → OC → OP showed a moderate indirect effect (B = 0.065), implying that agility fosters creativity, which then contributes to performance outcomes. The total indirect effect (B = 0.142) was of medium magnitude, while the total effect (B = 0.260) reflected a large overall influence. These results indicate that the impact of BI on performance is primarily realized through the complementary roles of agility and creativity, with creativity emerging as a stronger mediating mechanism in the relationship. Therefore, it is clear that the total effect of BI is largely explained by the indirect pathways, particularly the one involving creativity.

For the first hypothesis, which examined the association between BI and OP, this study uncovered a positive and significant association between BI and performance, indicating that organizations with robust BI capabilities tend to experience higher levels of performance across various metrics. This finding highlights the strategic importance of BI as a driver of firm’s success, as it enables informed decision-making, data-driven insights, and improved operational efficiency. The results indicate that BI significantly boosts performance through timely and precise information delivery, market trend identification, resource allocation optimization, and support for strategic planning. Additionally, the findings of this study reinforce the growing body of evidence linking BI to enhanced OP. A recent study of Cepeda-Cardona and Arias-Pérez (2025) reported that digital orientation has a positive impact on performance, which aligns with the results of this study showing that BI significantly contributes to improved organizational outcomes.

For the second hypothesis, which investigated the association between BI and OA, the results of this study identified a positive and significant relationship between the two concepts. This finding is consistent with the results of recent studies (Al Aqasrawi and Alafi, 2022; Aljawarneh, 2024; Cheng et al., 2020).Therefore, this study suggests that OA acts as a link between BI and a company's agility. The effect of OA on a company's decision-making depends on the efficiency of its BI (Cheng et al., 2020). This implies that leveraging BI tools strategically can enhance an organization's adaptability, decision-making, and responsiveness to market dynamics.

The third hypothesis examined the impact of creativity on performance. Creativity enables a company to develop innovative products and services that attract customers, leading to strategic and financial improvements in performance. This finding is consistent with previous studies that highlight a significant association between creativity and performance (Awan et al., 2022; Ismail et al., 2019; Mikalef and Gupta, 2021). Consequently, better differentiation and increased product and service performance contribute to a larger market share and overall organizational success. Additionally, OC plays a pivotal role in today's business environment, significantly enhancing a company's performance (Rumanti et al., 2023).

The outcome of the fourth hypothesis demonstrates a significant and meaningful relationship between organizational agility, creativity, and performance, confirming that higher levels of both agility and creativity correspond to improved performance outcomes. Since OA is achieved by fostering strong connections with subordinates and encouraging them to prioritize organizational goals, enhancing efficiency in a dynamic and uncertain work environment (Ramadan et al., 2023). This emphasizes the significance of nurturing creative thinking and innovative strategies within organizations, as they directly enhance problem-solving abilities and overall effectiveness. The finding suggests that creative organizations in the given sample in the UAE are more likely to generate novel ideas, adapt to changing environments, and excel in complex tasks, leading to tangible benefits for organizations (e.g. performance). These findings are consistent with the research on the link between creativity and performance (Ismail et al., 2019; Mikalef and Gupta, 2021).

The findings reveal that organizational agility and creativity play a sequential mediating role in the relationship between BI and OP. First, BI strengthens OA by providing timely access to information, predictive insights, and faster decision-making. These capabilities allow firms to sense market changes and respond proactively. Greater agility then fosters an open and flexible environment that supports experimentation, collaboration, and the exchange of ideas (Williams and Shaw, 2025). Such conditions can naturally stimulate OC. This creative capacity enables organizations to develop innovative products, services, and processes, ultimately enhancing their overall performance.

Viewed through the lens of the DC framework, BI acts as a sensing and seizing capability that drives agility, while creativity represents the reconfiguring capability that fuels innovation and renewal. The sequential mediation highlights how agility equips organizations with the structural readiness to adapt, while creativity provides the cognitive readiness to innovate. Together, they transform BI-driven insights into tangible outcomes that strengthen efficiency, adaptability, and competitiveness.

Regarding the direct relationships of BI with OC and OA with OP, the finding that OA has no significant direct effect on performance is both surprising and intriguing. The absence of a direct relationship between OA and performance supports the serial mediation model, suggesting that agility's primary value lies in enabling creativity rather than directly driving performance. Investigating the reasons behind this result could provide valuable insights into how agility operates in different industries. One possible explanation is that the impact of agility varies depending on the nature and pace of the industry. In fast-changing sectors such as information technology, logistics, and aviation, agility may already be a fundamental capability that every firm must possess to stay competitive, making it less likely to stand out as a unique driver of performance. In more stable or highly regulated industries, including real estate, education, and financial services, agility alone may not immediately translate into measurable performance outcomes. Furthermore, a review covers 249 empirical studies up to early 2024, and finds that while agility often correlates with performance, there are many contextual factors (industry volatility, firm size, external environment) which moderate or alter this relationship (Nguyen et al., 2025).

From a theoretical standpoint, this finding suggests that agility may influence performance indirectly—primarily by enabling creativity and innovation rather than through direct operational effects. This comes in line with recent findings of Hasan et al. (2025) in the healthcare sector, where BI adoption positively affected ambidextrous innovation through highly agile, responsive systems. This interpretation supports the sequential mediation results, where agility generates the conditions that allow creativity to flourish enabling creativity to drive performance improvements. Future research could examine this relationship in more depth by testing moderating factors such as environmental turbulence, firm size, or the degree of digital capability to better understand when and how agility contributes most effectively to OP.

This work focused on Arab states in the Middle East and North Africa (MENA) region, grouping them according to their economic and digital maturity. The Gulf Cooperation Council countries, including the UAE, are highly developed, boasting advanced digital infrastructure and widespread internet access. Jordan and Lebanon are relatively well-connected and urbanized, while North African countries such as Algeria, Egypt, Morocco, and Tunisia have lower Gross Domestic Product (GDP) per capita and more limited digital access. Conflict-affected and least developed countries, including Yemen, Iraq, and Somalia, face significant infrastructural and connectivity challenges. These variations indicate that insights drawn from the UAE may not fully apply to countries with lower income levels or less digital development.

The findings of this study yield valuable recommendations for organizations operating in the UAE and other business settings. First, managers should invest in BI to identify market trends, customer behavior, and operational efficiencies. This knowledge supports accurate strategic decision-making and improves OP. Second, BI systems streamline data collection, analysis, and reporting processes, improving day-to-day operational efficiency. Integrating OA and OC with BI outputs enables workflow optimization, opportunities and threats identification, and implementation of proactive measures for continuous improvement. This results in cost savings, resource optimization, and overall operational excellence, contributing to enhanced OP. Thus, BI is considered a critical factor in achieving a competitive advantage, requiring senior management's focused attention and dedication to implement actions based on BI insights (Abu-AlSondos, 2023).

Third, organization managers should focus on customer perspective. For instance, BI-driven insights into customer preferences, market demand, and competitive landscapes facilitate the development of creative products and services. Agility and creativity play pivotal roles in translating BI findings into actionable strategies for product differentiation, market positioning, and customer satisfaction. Therefore, organizations that effectively leverage BI alongside OA and OC are better positioned to drive creative and capture new market opportunities, thereby boosting performance metrics such as revenue growth and market share.

Fourth, organization managers should encourage and spread a data-driven culture by emphasizing the importance of BI, OA, and OC in fostering a culture of analytics within organizations (Fosso Wamba et al., 2024a; b). In detail, employees at all levels are encouraged to use data insights, embrace agility in decision-making, and apply creative thinking to drive innovation and performance. This cultural shift promotes collaboration, knowledge sharing, and continuous learning, leading to a more agile and resilient organization capable of navigating complex business landscapes and achieving sustained success (de Waal et al., 2025).

In addition to the four key managerial implications, this study offers important academic contributions and insights into performance management. Specifically, to enhance OP, it is recommended that organizations:

1. Invest in BI tools to support data-driven decision-making and gain competitive market insights.

2. Integrate cross-departmental data to improve consistency and support advanced analytics.

3. Use BI dashboards to track agility and performance metrics in real time.

4. Foster a culture that values creativity as a strategic asset linked to business goals.

5. Invest in digital and agility-enabling technologies to adapt rapidly to market and operational changes.

6. Leverage BI to benchmark performance against key competitors for continuous improvement.

This study contributes to the RBV, DC, and the Knowledge-Based View theories by demonstrating how BI capabilities serve as strategic resources that enhance organizational agility, creativity, and performance. Together, these theories underscore the dual role of BI as both a technological tool and a source of competitive advantage grounded in knowledge and adaptive capacity. Future research could build on these findings by employing longitudinal designs or by targeting employees at all levels to capture more nuanced relationships between BI adoption and performance outcomes, mediated by DC such as agility and creativity. The research scope could be narrowed to small and medium-sized enterprises, where resource constraints and varying levels of digital maturity generate different challenges and opportunities for leveraging BI.

The empirical context of this study offers valuable leadership and governance insights. The UAE’s government-led efforts toward digital transformation and data-driven decision-making, exemplified by the UAE Digital Strategy, align closely with o initiatives such as the European data strategy and the European Union (EU) Digital Compass (Hunady et al., 2022). Through sustained investment in BI and analytics, the UAE continues to strengthen its position as a regional leader in using technology to enhance both organizational and national competitiveness. Collaborative innovation schemes through public-private synergies have triggered business success in the Middle Eastern countries and the UAE in particular (Alameedy et al., 2025). According to the findings, a technology-intensive, rapidly developing environment calls for transformational leaders who foster DC such as agility and creativity in driving OP. Agile organizations in dynamic business environments rely heavily on proactive decision-making, drawing on real-time data and predictive analytics (Motwani and Kataria, 2025; Shahid et al., 2025).

From a wider societal perspective, as BI adoption becomes more widespread, new ethical and governance challenges arise. Issues such as data privacy, algorithmic bias, and surveillance risks call for careful attention. To address these concerns, organizations and policymakers should develop responsible data governance frameworks that promote transparency, accountability, and fair access to digital opportunities (Marcucci et al., 2023). Embedding these principles into practice will support broader sustainability and innovation governmental agendas, reinforcing the idea that technological progress must go hand in hand with ethical responsibility and public trust.

First limitation of this study is its lack of consideration for the cross-cultural aspect of BI. In other words, the generalizability of the findings is somewhat restricted as the study was conducted within the UAE business environment. Secondly, the proposed research model was tested using cross-sectional data, which makes it difficult to establish causal links between BI adoption and organizational agility, creativity, and performance. Future research could address this by using a longitudinal approach to track BI adoption over time, offering stronger evidence of causality and a clearer understanding of how BI shapes organizational outcomes. Furthermore, as AI becomes more integrated into BI, it is expected that replicating the study at a later time will improve the explored relationships. Historically, BI has faced many challenges, such as poor or incomplete data and inadequately structured solutions. Nowadays, the cognitive computing aspects of AI, including AI-enabled machine and deep learning algorithms, support augmented analytics, BI autonomy, and BI governance mechanisms and architectures, facilitating decision-making, and, ultimately, enhancing OP (Osunnaiye and Kucukaltan, 2025).

Thirdly, although a CMB test was conducted and the results indicate that the findings are not affected by bias, future studies could enhance the robustness of results by incorporating the perspectives of consumers or third parties, such as external consultants for measuring OC and performance. Fourthly, this research utilized perceptual performance metrics, which could be supplemented or substituted with objective measures to offer a more holistic insight into the correlation between BI and company performance. Furthermore, this study did not explore the impacts of senior management dedication and organizational culture on the BI-company performance nexus.

Fifthly, this study primarily examined certain consequents of BI, i.e. agility, creativity, and performance. Future research could delve into the impact of organizational structure on BI implementation as such studies are notably scarce in the BI literature. Finally, OP was assessed using subjective, self-reported data. While these measures provide valuable insights into employees’ perceptions of performance in relation to BI, OA, and OC, they may introduce bias. Future research could strengthen these findings by including objective performance indicators such as financial metrics like Return on Investment (ROI), revenue growth, or profitability, alongside subjective measures, offering a more complete and balanced view of organizational outcomes.

This study primarily aimed to assess the effect of BI on performance, with organizational agility and creativity serving as mediating factors. The findings indicate that BI influences OP through the serial mediation of agility and creativity. This research work contributes to the existing discourse by synthesizing key insights from literature and presenting BI, OA, and OC as key antecedents of OP. While the relationship between BI and OP has been extensively explored in previous research, the mechanisms through which BI impacts OP require further investigation. This study advances theoretical understanding of performance management by identifying key mechanisms through which BI capabilities translate into performance outcomes, thereby extending RBV and DC literature in dynamic, digitally transforming environments. The conclusions were drawn from a cross-sectional survey of 195 companies in the UAE. The UAE is a country where the significance of BI and agility is recognized and fostered by governmental policies and business practices. The framework of this study can be conveyed to other regions to boost performance in both private and public organizations.