Introduction

The popularity of big data as a trend is increasing as modern technology advances (Sena et al. 2019). Successful companies recognize that BDA is a critical and essential aspect that drives performance differences and enhances development (Shabbir and Gardezi, 2020). The global BDA technology and the services market were valued at $90 billion in 2021 and are projected to double by 2026 (FutureScape IDC, 2018). A growing number of firms recognize the significance and application of BDA due to its ability to create value from financial and some non-financial perspectives (Aydiner et al. 2019). However, despite its potential, many firms struggle to grasp the benefits of big data, with some even encountering negative consequences (Popovič et al. 2018). This discrepancy suggests the influence of additional variables mediating the link between BDA and SME performance.

Big data analytics involves systematically examining data to acquire valuable information and knowledge for superior decision-making and strategic choices (Ferraris et al. 2019). Several scholars asserted that Organizations use big data analytics to support their marketing operations, enhance the quality of customer service and marketing operations, and engage in interactions that contribute to revenue (Ji-fan Ren et al. 2017; Ghasemaghaei et al, 2018). Moreover, managers can enhance their understanding of organizational dynamics, enabling informed decision-making that improves performance by effectively utilizing big data. However, a robust method for managing the knowledge generated through BDA is essential (Gupta and George, 2016; Ferraris et al. 2019). Furthermore, a significant challenge identified by executives is the talent gap; over 60 percent acknowledge the necessity of recruiting skilled individuals who can adeptly navigate the big data landscape to bolster business performance through BDA (Hazen et al. 2018).

Several scholars demonstrate a direct association between big data analytics capability and various dimensions of firm performance, including marketing and financial performance (Su et al. 2021; Yasmin et al. 2020). Furthermore, literature has demonstrated a significant correlation between BDAC and marketing performance indicators, specifically in sales growth, market share, product development, sales volume, and overall share growth (Olabode et al. 2022). Some scholars asserted that BDAC can improve the firm’s financial performance, including cost reduction, increased return on investment, and enhanced free cash flow (Mikalef et al. 2020). However, fragmented research exists on the link between BDAC and organizational performance. This research addresses this missing link in the literature by proposing a more comprehensive review of how BDAC enhances organizational financial and marketing performance.

One primary reason for big data is to improve firm performance by transforming data and information into knowledge, enabling decision-makers to make effective decisions. Hence, a systematic and comprehensive approach is necessary to manage the knowledge generated by the BDA effectively. This is particularly crucial when combining it with company information and utilizing knowledge to maximize its value (Ferraris et al. 2019). Otherwise, the data remains mere data, and the integration and enhancement of knowledge cannot be achieved.

Research suggests that when a firm has a clear and efficient strategy for managing and refining existing information with the help of new knowledge, it can enhance the impact of BDA on SME’s performance (Khan and Vorley, 2017). Despite the growing importance and interest in this research area, there is a notable scarcity of empirical studies examining the relationship between big data analytics and organizational knowledge management practices (Kaivo-Oja et al. 2015; Sumbal et al. 2017). To address this research gap, empirically investigating the relationship between BDAC and Knowledge management practices is essential.

In this research study, the authors propose a framework grounded in the dynamic capability view that elucidates how the interplay among organizational elements, including data analytical tools and KM practices, creates value, boosts effective marketing campaigns, and ultimately impacts organizational financial and marketing performance. Organizations can achieve a competitive edge by maximizing resource utilization (Shabbir and Gardezi, 2020), but not all resources hold equal value.

The firm’s intangible resources include skilled (talented) individuals with more substantial technical, technological, and managerial competencies. Firms with exceptional data expertise have a greater chance of fully leveraging the advantages of big data. (Dubey et al. 2019). This study is an endeavor to understand the relationship between successful strategies and resource utilization (tangible and intangible) for better financial and marketing performance.

Only a few studies discuss the connection between the BDAC and the two dimensions of firm performance, i.e., marketing performance and financial performance, respectively, and there is a lack of extensive and specific analysis of how the relationship between KM and BDAC influences the two dimensions of firm performance. To get a further understanding of big data analytical capability and business performance framework, this study is an attempt to explore the answers to the following research questions (RQ):

RQ1: How do big data analytical capability and Knowledge Management influence an SME’s performance in financial and marketing terms?

RQ2: How does big data analytics’ talent capability influence an SME’s performance in financial and marketing terms?

The objective of this work is manifold. First, this study uses the dynamic capability view to empirically test if BDAC capabilities positively impact a firm’s FP and MP; second, our study aims to develop an understanding of KM’s mediating role between BDAC and different dimensions of firm performance. Third, this study offers a comprehensive insight into the moderating role of BDATC, strengthening the association between KM and firm performance. Hence, it provides numerous intuitions and visions when coordinated with valuable intangible resources of SMEs like knowledge and talent management. Fourth, the important contribution of this research is its focus on the Chinese context and its examination of how knowledge management and human-related factors in SMEs. Lastly, our study offers suggestions and guidance to practitioners on managing knowledge and human resources, thereby facilitating the enhancement of the BDAC and, consequently, the firm’s performance dimensions.

Literature review

Big data and big data analytics

Hasan et al. (2024) define big data as “vast amounts of data” facilitating various decision-making processes. Big data is believed to enhance business profitability by five to six percent, and many firms recognize the significance of big data (Akter et al. 2016; Niu et al. 2021).

However, it is commonly agreed that simple big data lacks practical significance and only depicts the fluctuating nature of data. Therefore, BDAC is introduced as a novel technique and framework designed to extract practical benefits and value from large datasets (Mikalef et al. 2019). There is a strong demand for analytical capabilities to manage vast volumes of unstructured information in different formats (Gandomi and Haider, 2015). As a result, businesses are investing in BDAC to enhance their agility and performance.

Saggi and Jain (2018) define BDA as a collection of predictions, semantic analysis, algorithms, statistical analytic techniques, and technologies utilized to investigate actionable perceptions from BD. Literature suggests that BDA should be able to identify complex and ever-changing business environments due to numerous factors, not just technology (Jha et al. 2020). In light of these conditions, scholars have proposed the term “big data analytics capability” to encompass a range of facets about “management,” “technology,” “human resources,” and “strategy” (Yasmin et al. 2020).

Big data analytics capability and firm financial performance

BDAC refers to the capability of a company to efficiently utilize technology and talent to gather, mine, and analyze data to engender intuitions” (Mikalef et al. 2020; Upadhyay and Kumar, 2020). Additionally, it is widely acknowledged that BDAC significantly enhances firm performance (FPER). Financial Performance refers to an organization’s ability to attract and hold customers while enhancing sales, profitability, and return on investment (Upadhyay and Kumar, 2020).

Companies can enhance their financial strategies, effective decision-making, sales improvement, expand market share, profitability, and return on investment by utilizing high-level BDAC and professional expertise (Giacosa et al. 2018). Research has demonstrated that organizations with more robust BDAC invariably exhibit superior financial performance (Lutfi et al. 2023). In a more complex and continuously changing business environment, effective information processing helps to decrease uncertainty. Consequently, these perceptions create opportunities to expand business operations (Srinivasan and Swink, 2018). Enterprises can create higher revenue by enhancing operational competence, providing value-added services, and leveraging BDAC to reduce costs (Côrte-Real et al. 2020). The firm possessing superior BDAC can enhance its overall capabilities and achieve superior financial performance. Therefore, we propose:

H1. Big data analytics capability (BDAC) positively impacts firm financial performance.

Big data analytics capability and firm market performance

Previous research studies have demonstrated that BDA enhances company profitability, operational efficiency, and market performance (Shen et al. 2019). Consequently, many organizations prioritize substantial investments in BDA to facilitate prompt decision-making and enhance market performance, particularly in examining customer behavior (Shirazi and Mohammadi, 2019). Customer knowledge plays a crucial role in marketing performance, and it is the foundation of a positive cycle driven by big data (Kar and Dwivedi, 2020). It encompasses the information and experiences generated and required during customer interactions/transactions with the company (Gebert et al. 2002; Upadhyay and Kumar, 2020).

BDA will likely be an excellent tool for gathering timely and high-quality big data to give firms a competitive advantage regarding market share, customer preferences, spending patterns, buying or purchasing behavior, and organizational capabilities (Bharadiya, 2023). Integrating big data technology and advanced analytics is reconfiguring marketing strategies to provide deep customer insights and enhance customer responsiveness (Gupta et al. 2021). Thus, the authors propose the following:

H2. Big data analytics capability (BDAC) positively impacts a firm’s market performance.

Big data analytics capability and knowledge management

A critical feature of BDAC is its skill in uncovering hidden insights and generating new knowledge. This process enhances KM, knowledge attainment, transformation, and application through practical data analytics. If appropriately examined, the organized, semi-organized, and unorganized data collected from multiple origins can assist the business in producing practical knowledge for better decision-making within the organization (Piai and Claps, 2013; Roosan et al. 2016). This link highlights the relationship between the firm’s BDAC and knowledge management. Consequently, the knowledge gained from analyzing big data enhances decision-making effectiveness and efficiency, demonstrating a link between knowledge management practices and advanced big data analytics capability (BDAC).

H3: Big data analytics capability (BDAC) positively impacts Knowledge management.

Knowledge management and firm financial and marketing performance

Knowledge, as defined by Bolisani et al. (2018) and Farooq (2019), is a set of logical opinions that can be organized and maintained through effective actions to improve an organization’s performance. Three primary processes are recognized: Knowledge acquisition, conversion, and use (Alavi et al. 2006; Gasik, 2011). Knowledge acquisition denotes generating innovative knowledge from figures and information. In contrast, knowledge conversion focuses on translating that accumulated knowledge valuable for the organization, such as organizing it or converting tacit knowledge into the organization’s more explicit knowledge (Cairó Battistutti and Bork, 2017). Knowledge application entails utilizing this knowledge to carry out tasks.

KM encompasses the processes through which a firm acquires new knowledge, transforms it into a usable and accessible form, and applies it within the organization, thereby influencing overall performance (King, 2018). Knowledge takes a meaningful shape only when codified, categorized, or stored in a convenient format. there are greater chances that only then can this knowledge be utilized by the appropriate person, at the right moment, and in the proper manner (Zaim et al. 2019), ultimately enhancing customer satisfaction, generating more prudent and effective marketing strategies, increasing market share, and improving financial outcomes. Thus, the authors propose that:

H4: KM positively impacts Financial Performance.

H5: KM positively impacts Marketing Performance.

BDA talent capability (BDATLC) as a moderator

BDATLC refers to an “analytics professional’s capacity” to do specific jobs in big data settings, including expertise in analytics and possessing the necessary skills and knowledge (Wu et al. 2024). BDA professionals should be able to transform raw data into valuable business acumen and effectively communicate these insights to domain specialists (Qaffas et al. 2022). Moreover, researchers propose that successful KM relies on effectively managing organizational talents who possess critical knowledge, encompassing talent allocation, performance assessment, succession planning, and so on (Whelan and Carcary, 2011; Qaffas et al. 2022). In fact, managers and employees are expected to make hundreds of decisions daily in today’s highly competitive landscape. These decisions are based on information and knowledge derived from big data analysis or intuition (Daradkeh, 2019).

From the Dynamic capability lens, internal capabilities, including skilled talent, within the firm, positively impact financial performance (Lutfi et al. 2023). Creating value from a dataset relies not only on the data’s quality and reliability but also on the ability of skilled talent to give it meaning, assimilate it, and use it effectively (Janssen et al. 2017).

The current study emphasizes that talent capabilities in handling big data (BDATLC) are crucial for businesses to establish a competitive edge in the current big data era. Prior studies have demonstrated that BDAC can enhance firm performance by maximizing profit and increasing sales growth, share in the market, and investment returns (Saputra et al, 2022). Research indicates that users of big data analytics can make accurate decisions five times faster than their competitors and have twice the probability of achieving financial success in the top quartile (Huang and Wang, 2017). Companies with skilled big-data employees may achieve higher returns than their competitors (Dubey et al. 2019). A company’s financial performance is a dependent variable reflecting its competitive advantage (Kaufman, 2015). Research indicates that retailers utilizing big data analytics have experienced a 15 to 20% increase in ROI (Wamba et al. 2017). In brief, implementing efficient talent management capable of employing big data can maximize the effectiveness and competitiveness of a company’s decisions, leading to improved financial performance. Therefore, it is proposed:

H6. BDATLC strengthens the impact of BDAC on firm financial performance.

Marketing performance is defined as an organization’s ability to move into new business markets earlier than its rivals, frequently develop novel goods and services, achieve a higher success rate with these offerings, and capture a substantial market share. While primarily focusing on enhancing a firm’s competitive position (Mithas et al, 2011), researchers suggest that incorporating big data analytical talent into strategic marketing and new product development can help improve market share (Wamba et al. 2017). Firms need to implement highly efficient talent management strategies to completely utilize the advantages of big data and digital technologies (Shamim et al. 2019). The influence of big data analysis on business acumen and developing infrastructure is significant since it helps the development of marketing intelligence, which is crucial for marketing performance (Sun et al. 2018).

Keeping the dynamic capability view in mind, organizations can enhance marketing effectiveness by employing systems such as “data mining,” “predictive analysis,” and “machine learning” to extract valuable insights from various sources, including customer preferences, purchasing behavior data, customer characteristics, and market survey data (Surendro, 2019). Using BDAC can enhance business intelligence, leading to enhanced marketing intelligence. This, in turn, enables the acquisition of more comprehensive and detailed information regarding customer preferences, purchasing behavior, and other relevant factors, consequently enhancing a firm’s market performance. Therefore, we hypothesize the following:

H7. BDATLC strengthens the impact of BDAC on firm marketing performance.

Knowledge management- the mediating role

Prior research has suggested three primary KM processes that are widely accepted: “knowledge acquisition,” “knowledge transformation,” and “knowledge utilization” (Gold et al. 2001). The big data analytics system can mine and refine new information and knowledge from the large volume of data; thus, the three processes of KM can be accomplished and improved via big data analytics (Li et al. 2023). The knowledge-based view (KBV) posits that BDAC has the potential to enhance the breadth and depth of collaboration by facilitating information assimilation and search (Hensen and Dong, 2020), integration of the value chain activities (Jaouadi, 2022), and knowledge sharing practices (Černe et al. 2013).

The dynamic capability view posits that tangible resources contribute to competitive advantage only when combined with specific knowledge and are difficult to replicate (Ferreira et al. 2020). However, true competitive advantage stems from a firm’s capacity to successfully utilize prior and new knowledge to develop new products and processes (Mahdi et al. 2019). Consequently, knowledge management identifies and leverages knowledge to improve the firm’s innovation processes and returns (Ferraris et al. 2019).

Research findings suggest that the ability to analyze big data can significantly enhance the value of knowledge management, showing the interconnections between BDAC and KM (Manyika et al. 2011; Ferraris et al. 2019). An understanding that the BDAC can enhance knowledge reserve and knowledge utilization through its extensive data and efficient analysis ultimately positively influences firm performance.

Employing effective KM techniques can improve the utilization of knowledge not only inside an organization but also enhance team members’ understanding of existing issues, resulting in a more thorough analysis of relevant information and promoting active learning, resulting in the development of higher-order cognitive processes (Sung and Choi, 2012).

A firm’s efforts to expand and utilize its knowledge base can enhance its financial performance in the long run. Engaging in KM activities enables the firm to discover unique approaches, processes, and offerings that better cater to customer needs, subsequently affecting the firm’s financial performance (Griffith and Sawyer, 2010; Nabi et al. 2022). Firms that successfully implement KM methods are more likely to outperform their competitors (Farooq et al. 2022). Therefore, we propose:

H8. KM mediates the relationship between a firm’s BDAC and the firm’s financial performance

A firm’s capability to build an effective BDAC can lead to better marketing performance (Wamba et al. 2017). The premise underlying this view is that BDAC provides firms with an alternative perspective on the market (Anfer and Wamba, 2019). This is particularly critical owing to the ever-increasing size of datasets currently accessible to businesses, which enables them to create new insights from a diverse range of information to know the market, satisfy consumer demands, and surpass their expectations. Consequently, the talent to access large volumes of consumer datasets, utilize various data types, and analyze information rapidly can result in significant competitive advantages in the marketplace.

The dynamic capability view asserts that intangible resources of the organization (KM) are used to convert current knowledge into important information and superior experiences for different management tasks that are difficult to imitate, such as decision-making, problem-solving, diverse learning, and strategic planning and provide an organization with an edge over its competitors (Mciver et al. 2013). Thus, within an organizational context, acquiring and enhancing knowledge through various sources provides a competitive advantage to the business and enhances its marketing operations (Salloum et al. 2019). Thus, it is proposed that:

H9. KM mediates the relationship between a firm’s BDAC and the firm’s marketing performance

Figure 1 shows the overall proposed hypothesis of the conceptual framework proposed by the authors

Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
Full size image

Theoretical model.

Method

Research design and sample

The authors designed the research model built on the view of dynamic capability, created the survey, and validated the hypothesized relationships using structural equation modeling(SEM) as the estimation method, depicted in Fig. 1. All firms in the study sample meet the criteria for SMEs as defined by OECD (2005). This study used a questionnaire for data collection and examined the research hypotheses from Chinese SMEs who have incorporated BDA within their firms. Recently, Big data has become increasingly popular among Chinese firms in the products and service industries. First, we designed a questionnaire that clearly explains the purpose of the study. Our team of proficient Chinese speakers translated the complete questionnaire from English into Chinese to guarantee its quality. Second, we selected a target sample by contacting various firms in both the service and product industries. We briefed the CEOs of all selected firms about the survey’s objectives and the expected arrival time of the questionnaire, requesting that they direct it to the most knowledgeable person within their organization to ensure accurate responses.

Data was collected through a reliable data collection platform, i.e., Credamo. A total of 379 samples were collected. The questionnaire used a “7-point Likert scale”, ranging from “extremely low” to “extremely high”.

Study measurement

Firm performance (Financial and Marketing)

This study’s dependent variable is firm performance (including financial and market performance). We used 9 items to measure the firm financial and marketing performance adapted from Tippins and Sohi (2003) and Wang et al. (2012).

Big data analytics capability (BDAC)

To measure the independent variable “big data analytics capability (BDAC)”, this research used the scale adapted from Akter et al. (2016). It adopts two dimensions, i.e., “BDA management” and “BDA technology”. To measure BDA management, 6 items were used. The scale for BDA technology was adapted from Byrd (2000) in this study. A total number of 6 items were used.

Knowledge management (KM)

This study inspects the knowledge management’s mediating role, using three components of KM: “knowledge acquisition,” “knowledge diffusion,” and “responsiveness related to knowledge,” adopted from the scale developed by Ferraris et al. (2019) for each component. For “knowledge acquisition”, we used 6 items. For knowledge diffusion, we used 5 items. Lastly, the “responsiveness related to knowledge” scale was measured by 5 items. Overall, 16 items were used.

Big data analytics talent capability (BDATLC)

To assess the big data analytics talent capabilities (BDATLC), the scale was adapted from Akter et al. (2016), which comprises 16 items organized into four components: “technical knowledge,” “technology management knowledge,” “business knowledge,” and “relational knowledge.” Specifically, the measurement for technical knowledge comprises of four items, while technology management knowledge is also assessed using four items. Similarly, business knowledge is evaluated through four items, and relational knowledge is measured with an additional four items.

Data analysis

Cronbach’s alpha (Cronbach, 1951) was used in this study to assess the current study’s reliability. A minimum alpha value of 0.70 suggests acceptable reliability (Nunnally, 1978) as a fundamental benchmark. The questions were validated through factor analysis, Promax rotation, and principle component analysis.

The loadings were evaluated to identify a strong correlation between items within the same construct and assess convergent validity. Cook and Campbell (1979) examined discriminant validity by analyzing factor loadings to determine whether the questions are strongly associated with their expected measures rather than with alternative measures. As stated by (Comrey, 1973), factor loadings are considered sound within the range of 0.25 to 0.54, while those between the range of 0.55 and 0.62 are considered good. Loadings between the range of 0.63 and 0.70 are classified as very good, while loadings over 0.71 are considered outstanding. All the factor loadings of the construct in this research study exceed 0.61. Table 1 shows the factor analysis results, revealing that the composite reliability (CR) values exceed the 0.70 benchmark, demonstrating sufficient reliability (Nunnally and Bernstein, 1994). Table 1 shows a factor analysis of all the measures above 0.6 showing good factor loading.

Table 1 Factor analysis.
Full size table

Hierarchical multiple regression

The hierarchical multiple regression analysis of the proposed model is presented in Table 2. This analysis allows for examining the independent variables’ contributions and their impact on the FPER. Table 2 presents the incremental validity as determined by R2 changes. The results indicate that control variables, i.e., gender, education, position, firm size, industry, and BDAC experience in models 1–6, have a negative impact on the FPER.

Table 2 Hieratical regression.
Full size table

Model 7 (Table 2) incorporates control variables and BDAC to assess their influence on FPER. However, BDAC has a substantial impact on FPER at 0.001. Model 8 consists of control variables, BDAC, and BDATLC. The correlation between BDAC, BDATLC, and FPER is significant at 0.001. The model 9 incorporated control variables, BDAC, BDATLC, and KM. The findings indicate a positive correlation among BDAC, BDATLC, and KM at the 0.01 level. The R2 increases steadily from 0.001 for model 1 to 0.742 for model 9. Model 10 shows the effect of the moderate variable and other proposed variables on the firm’s performance. Thus, the study suggests that BDAC, BDATLC, and KM are significant predictors of the FPER; therefore, these independent variables must be considered when researching the FPER.

Model fitness

Table 3 displays the overall model fit for the proposed variables. The findings indicate a chi-square to degrees of freedom ratio (χ2/df) of 1.8, which meets the recommended threshold of less than 5. Additionally, the Comparative Fit Index (CFI), Normed Fit Index (NFI), Incremental Fit Index (IFI), and Goodness of Fit Index (GFI) all meet the standard of ≥0.9, with values of 0.92, 0.94, 0.93, and 0.91, respectively, indicating a good fit (Bentler, 1983, 1988; Bollen, 1989b; Browne and Cudeck, 1993). The Root Mean Square Error of Approximation (RMSEA) should be ≤0.08, and the model achieves an RMSEA of 0.05, while the Adjusted Goodness of Fit Index (AGFI) should be ≥0.8, with the model showing an AGFI of 0.82. These statistically significant values support the proposed conceptual model (Dudgeon, 2004; Jöreskog and Sörbom, 1993).

Table 3 Model Fitness.
Full size table

Common method bias

In this study, we gathered information via a questionnaire and self-reported the results to provide empirical evidence. Business researchers used statistical approaches of four post-hoc to assess the common method bias (CMB) or, more precisely, common method variance (CMV). A significant risk of CMB and/or CMV may occur in self-reported data. CMV arises when a single data source is scaled using the same approach. CMV bias may arise if the purported technique, as a causative factor, considerably affects causal results (Fuller et al. 2016). However, if CMV exists, it may not affect the significance level, change it slightly, or modify it insignificantly. As a result, an addressing report of CMV is somewhat of partial utility.

Prior research asserted that CMV biases data by introducing significant and substantial discrepancies between real and observed relationships (Ostroff et al. 2002). When CMV occurs, it raises questions about reported results and leads to attenuated trustworthiness (Babin and Zikmund, 2016), an error of measurement that could compromise the validity of the findings. These errors are linked to random and some systematic components (Nunnally, 1978; Bagozzi and Yi, 1991; Spector, 1987). Thus, constructs hinge on the hypothesis of the conceptual model (Podsakoff et al. 2003) to provide explanations related to correlations between measures.

In this study, the authors employed an approach proposed by Podsakoff et al. (1986) by running a one-factor test developed by Harmon. This approach ensures that CMB is not a potential threat in this study. The results show that the percentage of variance is 34%, which is less than 50%, and there is no CMB threat. Additionally, the authors performed CFA to ensure that all of the items are together loaded in one factor, a one-factor model. Studies (Cheng et al. 2014; Handley and Benton, 2012), suggest that if the one-factor model has poor model fit, there is no CMV. The results show a poor model fitness from the data (χ2 = 767.02, df = 34, GFI = 0.542, AGFI = 0.444, CFI = 0.520, NFI = 0.497, RMSEA = 0.51, confirming no problem of CMB.

Hypothesis testing

Table 4 demonstrates the relations proposed in the hypothesis. The first hypothesis the authors predict is that BDAC significantly influences FP. The results β = 0.721 and p < 0.000 demonstrate a significant correlation between BDAC and FP (H1). β = 0.845 and p < 0.000 showing a significant relation between BDAC and MP (H2). Subsequently, the authors claim that BDAC has a positive impact on KM. The results disclose β = 0.826 and p < 0.000, demonstrating a positive relationship among the variables (H3). Furthermore, the authors predict that KM directly relates to FP and MP. The results show a positive relation of this variable as β = 0.722 and β = 0.876 at p < 0.000, respectively (H4,5). As predicted earlier, all the hypotheses in the results are supported using AMOS 24.0. For the mediating and moderating hypotheses of the study, the authors employ the Hayes model in the following sections to provide empirical evidence for H6, H7, H8 6,7,8 and H9.

Table 4 Hypothesis testing.
Full size table

Indirect effect

KM plays a partial mediating role between Sobel tests (Spector, 1987) and the test of bootstrapping mediation (Preacher et al. 2007), BDAC and FPER (FP and MP). The authors tracked the endorsements of prior studies (Zhao et al. 2010). The first mediation between BDAC and FP, KM, plays a positive mediating role, and the Sobel test indicates (t-stat = 18.657, p < 0.001). The second mediation between BDAC and MP is through KM. The Sobel test indicates (t-stat = 17.346, p < 0.001), showing the positive mediation role of KM. To further validate the mediating role of KM, the authors used the Hayes PROCESS procedure for SPSS V25 (Hayes, 2013).

To check the mediating effect of KM on the link between BDAC and FPER, we employed a bias-corrected bootstrapping method, Model 4. The findings from the mediation analyses demonstrate that BDAC is indirectly related to the FPER through its relationship with KM. The first model includes BDAC, KM, and FP, showing the positive mediating role of KM (β = 0.4143, t = 6.6878, R2 = 7482, р = 0.000, LLCI = 0.2925, ULCI = 0.5361). The second model includes BDAC, KM, and MP. The results show that (β = 0.4348, t = 5.3656, R2 = 5729, р = 0.000, LLCI = 0.2754, ULCI = 0.5941). Furthermore, a 95% bias-corrected confidence interval derived from 5000 bootstrap samples indicated that the indirect effect of BDAC on FP and MP via KM does not include 0 (i.e., −1,0, +1). Suppose LLCI and ULCI do not include 0. Thus, the mediation effect is significant enough (Efron and Tibshirani, 1985), so there is no 0 value in LLCI and ULCI in any path.

Moderating role of BDATC

To investigate BDATC as a moderating variable, this study utilizes the Preacher and Hayes (2007) PROCESS model. In this study, BDATC moderates KM and Financial and marketing performance. Results for the first relation between KM and FP, BDATC playing a positive moderating role (β = 0.0648, t-value = 4.9426, R2 = 0.8412, р = 0.000, LLCI = 0.0161, ULCI = 0.0457). In the second relation, the results indicate the positive role of BDATC between KM and MP (β = 0.0780, t = 3.1268, R2 = 0.5957, р = 0.001, LLCI = 0.0290, ULCI = 0.1271). The moderating role played by BDATC between KM and SME performance (i.e., financial and marketing performance) is shown in Fig. 2.

Fig. 2
Fig. 2The alternative text for this image may have been generated using AI.
Full size image

Moderating effect of BDATLC.

Discussion

Big data unlocks a new domain of dependable predictive analytics. Accurate predictions can be made by closely examining and analyzing the relationships reflected in big data. firms increasingly look to big data to improve their financial, market, and innovation performance. This study demonstrates that to capitalize on the advantages of BDA effectively, SMEs must possess big data analytics capabilities, a strategic focus on knowledge management, and a pool of highly skilled talents in big data analysis. These factors may result in improved decision-making. Decisions made with big data in mind often lead to better results. Enterprises should consciously cultivate employees’ BDA-specific capabilities or recruit big data analysis talents. In today’s data age, firms that pool their domain proficiency with data science will be more competitive than their rivals. However, this study highlights how developing knowledge management capabilities and big data talent can augment these positive effects, resulting in better financial and marketing performance. The main aim of this research study is to emphasize the significance and usefulness of BDAC, KM, and BDATLC on FPER. This study’s contributions are beneficial not only in theory but also in practice.

From a dynamic capability perspective, this study’s results for BDAC and FPER (i.e., financial and marketing performance) have been supported by empirical evidence such as β = 0.721 and β = 0.845, respectively, approving H1&2. Similarly, the results (β = 0.826) support the H3 and demonstrate a positive relation between BDAC and KM. The results also show that knowledge and knowledge management capability are important to firm performance in the big data era, which adds evidence for the KM view. This is evident from the results where the first model includes BDAC, KM, and FP, showing the positive mediating role of KM (β = 0.4143, t = 6.6878, R2 = 7482, р = 0.000, LLCI = 0.2925, ULCI = 0.5361). The second model includes BDAC, KM, and MP. The results show that (β = 0.4348, t = 5.3656, R2 = 5729, р = 0.000, LLCI = 0.2754, ULCI = 0.5941). These results support a positive mediating role for KM. In addition, from a real-world or managerial viewpoint, this study offers practical suggestions from a firm’s standpoint on what it can do regarding human resources, knowledge management, and the usage of big data to expand the firm’s performance. This study innovatively proposes the moderating role of BDATLC between BDAC and FPER, discussing how individual capabilities can enhance how overall BDAC impacts the FPER. Results indicate that BDATLC plays a positive moderating role in the link between KM and FP. The results (β = 0.0780 & β = 0.0648) indicate the positive moderating role of BDATC between KM and financial and marketing performance. It also specifies the SME’s financial and marketing performance to investigate the influences and clarify the relationship. Figure 3 demonstrates the overall relationships between the proposed variables.

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
Full size image

Overall relationships between variables.

Conclusion

The results from the current study underscore an increasing imperative for an SME to access diverse data types and to cultivate the capabilities necessary for analyzing and interpreting predictive trends within that data to enhance strategic decision-making. Consistent with the dynamic capability framework, the study demonstrates that big data analytics capabilities (BDACs) empower SMEs to derive insights that can improve market and financial performance compared to their competitors. The current research study observes the importance of human resources to big data capabilities and addresses the research gap concerning big data talent capabilities. The positive impact on corporate performance can be enhanced by hiring personnel with proficient technical skills, effective communication abilities, and pertinent knowledge of big data. This research confirms that implementing excellent Business Data Analytics Talent Capability (BDATLC) and efficient talent management can enhance the effectiveness and competitiveness of firms’ decision-making processes, leading to improved performance.

SMEs with superior big data analysis capabilities and professional expertise can enhance their financial performance by adjusting financial strategies, making effective decisions, improving sales, expanding market share, and increasing profitability, subsequently leading to overall financial improvement. This study’s conclusions validated that big data and advanced analytics reshape marketing approaches, allowing companies to gain deep customer insights, enhance customer responsiveness, and ultimately improve market performance. Consequently, big data analytics talent capability (BDATC) emerges as a crucial factor in leveraging intuitions from big data analytics to enhance performance effects for SMEs, particularly in emerging and volatile markets characterized by heightened competitive activity.

While some scholars have explored the importance of big data analytics, this research contributes by proposing a multidimensional model that illustrates the comprehensive impact of BDATC on firm performance through knowledge management capabilities.

This study also adds to the literature about KM by investigating the positive outcome for firms in developing knowledge management capabilities and strategies. Through organized and dynamic management of company-related knowledge, these capabilities can enhance BDA’s positive influence on firm performance while reducing the threats linked with big data management. Empirical data from 379 Chinese SMEs indicate a positive and significant relationship within the proposed model. These findings suggest that managers and industry practitioners should prioritize the development of big data analytics talent capabilities alongside effective knowledge management practices, as mere data collection, without the ability to derive actionable insights, is insufficient for driving organizational success.

Limitations and future research directions

This study admits numerous limitations. Firstly, it is exclusively focused on Chinese firms; therefore, future research studies could employ longitudinal data or alternative samples from various geographic contexts to yield broader insights and generalize the results. Second, our investigation is limited to the specific domain of big data analytics (BDA) within a singular context. It is important to note that BDAC may interact synergistically with many other organizational capabilities. Specifically, leveraging big data to create acumens for fast-changing consumer wants and demands could be more advantageous than solely analyzing historical data; thus, integrating marketing capabilities may serve as a valuable complementary asset.

Future research might incorporate organizational information processing theory to explore how BDAC relates to an organization’s resilience, defined as the capacity to absorb and rapidly pull through from shocks to influence overall firm performance. Additionally, examining the role of a few other critical moderators, such as managerial commitment, could provide another lens to yield valuable new insights in this domain.