1 Introduction

A collaborative business culture (CBC) is where cooperation, teamwork, and shared goals are central to the values and operations of organisations [59]. It is essential for any business that wants to succeed in today’s fast-paced and ever-changing marketplace [65]. CBC involves openness, transparency, and information sharing, which provides firms and their employees with the most significant opportunities for innovation [72]. It can be essential for driving business innovation. Successful business innovations are critical for companies to stay ahead of the competition and remain relevant in today’s cut-throat business. Companies that embrace innovation are more likely to succeed in the long run and create value for their customers and stakeholders. For sustainable innovation, it is essential to consider big data analytical capability [54].

Gartner [33] explained Big data analytical capability (BDAC) as “Big data is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation” (p. 10). Many of the world’s top companies have prioritised organisation growth, focusing on leveraging advancements in artificial intelligence (AI) technology to drive revenue growth [61]. As a result of AI, big data analytical capability (BDAC) can develop rapidly and is driving the transition to Industry 4.0 [67]. For instance, powerful BDAC has been implemented by firms like Samsung, Apple Inc., and Dell to improve business and create opportunities for new business.

There has been a shift in the global economy from an industry-based economy to a digital-based economy in the past decade [63]. A rapidly globalised and digital economy has made businesses operate in an environment of extreme uncertainty, volatility, and competition. The development of BDAC is necessary for companies to remain in the competition, survive in the market, and sustain growth [49]. With big data dominating the environment, traditional analytical methods are ineffective in extracting data critical for business decision-making. There is a growing consensus in academia and business that big data analytics is a breakthrough technology [52]. Companies are focusing on acquiring BDAC to enable managers to make better decisions.

Although there are extensive recent studies on collaboration and innovation, however, it cannot be regarded as exhaustive (see [1, 10, 29, 73, 74]). Despite this, there are still conflicting views regarding this relationship in the literature [76]. Some studies have shown that collaborative innovation is positively associated with firms’ innovation performance (e.g., [42, 75]). Still, other studies suggest a negative relationship between collaborative and innovation performance [50, 78]. Therefore, there is still much to be discovered, and this study could potentially hold the answers needed to reconcile the conflicting findings. More so, moderating and mediating influence on collaboration and innovation remains underexplored. There is a need for a more in-depth understanding of why, when, and how questions are necessary to influence practice [46]. Furthermore, despite the hype surrounding BDAC, the issue of examining why, when, and how it affects collaboration and innovation relationships remains undetermined [15]. Most studies on BDAC primarily focused on adoption, implementation, competitive situations, and analytics tools [19, 21], while other related issues on BDAC, such as collaborative culture and business innovation remain under-researched [15, 44]. Furthermore, most existing BDAC studies were conducted in developed countries. However, its impact on organisations in developing countries is unknown [58]. Therefore, in general, despite the surge of companies investing in big data, empirical research on its value is still at a rudimentary stage [53]. To fill this substantial gap and add knowledge to the literature, the objectives of this paper are as follows:

  1. a)

    What is the essence of BDAC toward CBC and business innovation?

  2. b)

    To acquire insight into the impact of CBC on business innovation

  3. c)

    To what extent does CBC mediate the relationship between BDAC and business innovation

  4. d)

    To what extent does BDAC moderate the relationship between CBC and business innovation

This current study examines the relationship between BDAC, CBC, and business innovation, focusing on mediation and moderation effects and offering valuable insights for academia and industry. Specifically,

  • The findings of this study could provide practical guidance for organisations looking to optimise their innovation strategies. By identifying the conditions under which data analytics and collaboration have the most significant impact

  • The findings could also help organisations leverage their data assets more effectively, foster collaborative environments that drive innovation, and ultimately enhance competitiveness and adaptability in today’s data-driven business landscape.

  • Insights from this study could inform strategic decision-making in organisations. Management could use the findings to allocate resources, develop training programs, and shape their organisational culture to better align with innovation goals.

  • Understanding the mediation and moderation impact of BDAC and CBC on business innovation can provide organisations with a competitive advantage. This knowledge can lead to more targeted and effective innovation strategies, setting organisations apart in their respective industries.

The rest of this study reports the theoretical position and hypotheses development. Then, instruments and methods are described. The following section highlights the results of the findings with a discussion. Knowledge, managerial and practice Implications are presented. The paper ends with directions for future research.

1.1 Theoretical position

This study’s theoretical foundation is the Theory of Collaborative Advantage (TCA) and Resource-Based View (RBV). TCA is a practice-based theory about the collaboration of management focusing on the possibility of collaborative benefit (Huxham and Vangen 2005) arising out of joint development structures where shared decisions, resources, and mutual accountability are exercised. The theory assumes that all partners must expressly acknowledge the collaboration goals for it to succeed (Agranoff and McGuire 2001; Ansell and Gash 2008). Therefore, CBC has become increasingly important in today’s fast-paced and ever-evolving business world. By working together and sharing ideas, companies can tap into the collective expertise of their employees and maximise their potential (Table 1).

Table 1 Summary explanation of research model constructs
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CBC is a work environment that promotes collaboration by taking advantage of every team member’s unique skills [77]. As a result of this approach, innovation, creativity, and productivity are fostered. The TCA supports this idea, stating that collaborating companies are more likely to outperform those that do not. In a CBC, an individual voice is heard and valued, regardless of position or title [69]. Thus, the CBC could lead to a more inclusive and diverse working environment where ideas are debated and refined, and everyone is committed to the common goal of success. The second theory, which is the Resource-Based View (RBV), has since been developed and expanded upon by several scholars and researchers in the field of strategic management [13, 28, 70]. It has become a fundamental framework for understanding how firms gain and sustain competitive advantage by leveraging their unique and valuable resources and capabilities. RBV suggests that a firm’s resources and capabilities drive its competitive advantage. Big Data Analytics Capability can be considered a strategic resource, allowing organisations to extract valuable insights from data. Collaborative Business Culture complements this by enabling organisations to jointly pool their resources, including data and analytical expertise, to develop innovative solutions. Therefore, RBV is deemed to be a suitable theoretical framework [53] when examining BDAC and business innovation.

1.2 Hypotheses development

1.2.1 The impact of BDAC on CBC and business innovation

BDAC is described as the capacity of businesses to derive strategic insights from big data [4, 17]. A business innovation process introduces new ideas, workflows, methodologies, products, or services [5]. Supporting study has demonstrated the importance of technology in accelerating business innovation [56]. Significantly, business model innovation is influenced by BDAC [18]. More so, according to Munir et al. [57], big data analytics in this era of AI enables the achievement of innovation performance. More so, big data analytics capabilities improve an organisation’s exploration activities (Rialti et al. 2019). As a result, the author argued that BDAC is crucial for promoting innovation in organisations. Thus, businesses can remain competitive and drive innovation with data-driven insights, optimised processes, and responsiveness to changing market dynamics. Therefore big data analytics can lead to business value [52].

Collaborative cultures are environments where individuals and teams work together to accomplish common goals, share ideas, leverage strengths, and resolve complex problems [62]. To make the best decisions, a CBC shares insights from big data and best practices across the organisation where collaboration performance and BDAC are positively correlated [25]. Further, communication and cooperation among departments can be improved through the collaboration of internal company information [31]. Using big data effectively and generating insights from big data allows companies to grasp market demands and establish an effective collaborative relationship with internal and external partners, developing new and unique knowledge [15]. Firms’ use of information technology is correlated positively with business environment changes [11] and with collaboration at the workplace [44]. From the discussions, BDAC could significantly improve innovation and collaborative business culture by providing data-driven insights, fostering cross-functional collaboration, and promoting a culture of adaptability and learning. Therefore, it suggested that:

H1: Big data analytics capabilities have a positive impact on business innovation

H2: Big data analytics capabilities have a positive impact on collaborative business culture

1.3 The impact of collaborative business culture on business innovation

In established companies like Google, program managers are known to collaborate in their business structure, which brings more innovation. Collaboration intensifies the chances of association between ideas, speeds up the necessary iterations, provides energy, results in more connections, and finally helps ideas reach implementation, which results in innovation [22]. Dong and Yang [24] noted that collaboration within a business can foster innovation. However, it is crucial to keep in mind that there may be other factors that can impact this relationship. Effective collaboration and innovation depend on making informed decisions based on data analysis [2, 3, 48]. By harnessing the power of data, organisations can create a more effective and efficient collaborative environment that drives business success. The author argues that big data analytical capability can strengthen the relationship between a collaborative business culture by providing insights, optimising processes, and fostering a data-driven approach to collaboration. Based on the discussion above, the following hypotheses are proposed:

H3: Collaborative business culture is positively associated with business innovation

H4: Big data analytic capabilities moderate the relationship between Collaborative business culture and business innovation

1.4 The mediating effect of CBC on the BDAC-business innovation relationship

An organisation’s BDAC could not promise enhanced performance because other collaboration may also be required [51]. In support of this assertion, a study has revealed that collaborative culture motivates knowledge-sharing behaviour among employees, consequently leading to organisational innovation capabilities. [77]. This indicates that BDAC cannot increase business innovation solely but may require a collaborative culture. Therefore, the author argues that business innovation is expected to be enhanced by BDAC when there is mediation, such as a collaborative business culture. Based on these discussions, the following hypothesis is.

H5: Collaborative business culture mediates the relationship between big data analytics capabilities and business innovation.

1.5 Instrument and methods

The study aims to investigate the impact of BDAC and CBC on Business innovation regarding the mediation moderation effect. Consistent with previous research [16, 23, 32]. This current study employed a cross-sectional survey design. Data were collected from the respondents using a survey questionnaire. Before the actual study, approval from the management of the selected organisations was requested to have permission to administer questionnaires. A significant effort was made to reach out to as many potential participants as possible with the help of research.

1.6 Pilot survey and instrumental design

Before the final survey questionnaire was administered, a pilot study was conducted. The piloting questionnaire was administered to 50 employees with different managerial levels from different samples. The feedback was used to modify the questionnaire to develop the final survey questionnaire. The questionnaire included two sections. The first section involved the participants’ demographic, while the second had the variables (BDAC, CBC, and business innovation).

1.7 Sampling technique

When conducting research, selecting the appropriate sample size is crucial [7]. While researchers may have differing opinions on the best approach, making the sample size as large as possible is generally advised. Alreck and Settle (1985) recommended that a sample size of between 200 and 1000 participants in a particular study applies to a population of 10,000 or above. In agreement, Gorsuch [35] and Kline [45] suggested that the sample size should be at least 100, while Guilford [36] claimed that at least the sample size should be 200. Moreover, Cattell [12] called for a desirable minimum sample size of 250 for factor analysis. More so, 10 times rule of thumb was applied, the sample size should be greater than 10 times the maximum number of inner or outer model links pointing at any latent variable in the model [34]. To determine the minimum sample size for PLS-SEM, the following numbers must be used: (a) ten times the number of formative indicators used in a single construct, (b) ten times the number of structural paths used in a structural model that targets a particular latent construct [37]. Therefore, a 577 sample size was selected for the study which is more than the minimum sample size. This was done to ensure more robust results, especially in complex models or small effect sizes. The data was collected from January to May 2023. The study applies the following conditions to select participants:

  • The participant must be a full-time employee working in the selected organisation.

  • They must work for not less than two years

  • They must be ready and available for the study

1.8 Measures

Instruments used in relevant previous studies were employed to measure the participants’ responses. Out of three (3) items used to measure BDAC [9, 66], one item (BDAC3) was dropped since its items loadings were not up to 0.5 (Hair et al. 2010). BDAC had a Cronbach’s Alpha value of 0.935. Five items were used to measure Collaboration Business Culture [43, 59], with one item (CBC2) dropped due to low factor loading. The CBC had the value of Cronbach’s Alpha of 0.930. Further, the measurement of Business Innovation was done using five items [8], with one item (BI2) dropped because of low factor loading. The Cronbach’s Alpha value for business innovation was 0.951. A five-point Likert scale was used for the observed items: 1 (strongly disagree)–5 (strongly agree). The values of Cronbach’s Alpha ranged from 0.930 to 0.9521, suggesting good internal consistency of the respondent data.

1.9 Common method bias

Participants’ involvement in the study was voluntary to reduce the possibility of Common Method Bias (CMB) [64]. Harman’s one-factor test was also conducted [39] to examine CMB. It was reported that 46.4% of the variance was accounted for by the first factor, a value below the 50% limit. Therefore, according to Fuller et al. [30], CMB does not affect the responded data. Also, to solve the issue of CMB, a t-test was conducted to find whether there is a significant difference between the age and size of the first responses and later responses of the organisation firms [6]. It was revealed that no statistically significant difference exists between the results (p-values = 0.512 for the first response and p = 0.392 for the second responses of the participants).

2 Results

2.1 Data analysis technique

Data analysis using structural equation modelling (SEM) was conducted (Purwanto et al. 2021). PLS-SEM, also known as Partial Least Squares SEM, has been increasingly used in recent articles. PLS-SEM is widely used in many social science fields, including organisational management [71]. It highlights relationships that are indeed present in the population more likely than when they are not [68]. Therefore, the current study used PLS-SEM for data analysis after considering the above discussion. SmartPLS v4 software was used in this study to apply the PLS-SEM. The present study also used 5000 bootstrap samples to compute standard errors.

2.2 Descriptive statistics

Table 2 presents the results of the respondents’ demographic characteristics. The respondents are 48.0% (n = 277) males and 52.0% (n = 300) females. Most respondents (51.0%) are in the age group of 31–50 years. 46.1% and 2.9% are in the 18–30 and above 50 groups, respectively. Among the respondents, 51.6% had more than eight years of working experience in their organisation. While 6.8% work for at least two years. Most participants were selected from the manufacturing (51.5%) and Bank and Financial (17.7) sectors. Further, 35.5% of respondents were production workers and supervisors, and 26.7% were operational managers.

Table 2 Summary of participants’ demography statistics
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2.3 Reliability and validity test

To ensure the reliability of study instruments, construct reliability and composite reliability (CR) were examined [14, 38]. Table 3 reports that the values of Cronbach Alpha ranged from 0.930 to 0.951, higher than the recommended threshold value of 0.70 [60]. Convergent validity was tested using Average Variance Extracted (AVE) values. The values of AVE for all the constructs were above the recommended threshold of 0.50 [41].

Table 3 Summary results of exploratory factor analysis
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Tables 4 and 5 report the Fornell Larcker and HTMT ratios used to assess discriminant validity. Other studies have recommended and applied these two methods [20, 41]. To attain discriminant validity, the square root of the AVE for each construct must be greater than the correlation between the constructs in the study [27], and HTMT values should not exceed 0.85 [41]. Table 4 indicates acceptable discriminant validity. 0.721 is the highest value of HTMT, suggesting that the constructs of the study have discriminant validity, as indicated in Table 5.

Table 4 Discriminant validity (Fornell & Larcker)
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Table 5 Discriminant validity (HTMT)
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2.4 Structural model

The standardised root mean square residual (SRMR) saturated and estimated values were used to investigate the model’s fitness of 0.08 as a recommended value [41]. The model’s saturated and estimated values of SRMR were 0.016 and 0.044, respectively, suggesting the model’s overall fitness. Further, the geodesic discrepancy (d_G) sat < est and unweighted least squares discrepancy (d_ULS)_ sat < est, which satisfied the model fit as recommended by Henseler [40] as indicated in Table 5. In addition, as presented in Fig. 1, the R2 for business innovation is 0.598, which confirms the 59.8% model explanation of the variance in BI (business innovation). Since the R2 should be above 10%, the R2 found in the is satisfactory [26]. More so, Normed fit index (NFI) values above 0.9 usually represent acceptable fit. From Table 6, NFI values for both the saturated model and the estimated model were above 0.90, suggesting good model fitness.

Fig. 1
figure 1

Path analysis

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Table 6 Model fit
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As reported in Table 7 and Fig. 1, all the proposed hypotheses in the study are supported. Hypothesis H1 was supported. Big data analytical capability (β = 0.201, t = 4.454, p < 0.000) significantly and positively impacts business innovation, suggesting support for H1. More so, big data analytical capability (β = 0.347, t = 6.896, p < 0.000) positively influences collaborative business culture, proving support for H2. While collaborative business culture (β = 0.585, t = 11.289, p < 0.000) has a significant positive effect on business innovation, H3 is supported.

Table 7 Path coefficients
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2.5 Testing mediating effect

The BDAC and business innovation relationship (β = 0.203, t = 2.450, p < 0.05) is partially mediated by collaborative business culture, supporting H4. Table 8 reports the value of Variance Accounted For (VAF) value. Since SmartPLS 4 software cannot automatically generate the value of VAF, the VAF was manually computed using values from the PLS algorithm. Table 7 shows the detailed calculation. If the value of VAF exceeds 80%, it suggests a full mediation. However, if the value of VAF is between 20 and 80%, it shows partial mediation. Also, if the value of VAF is less than 20%, there is no mediation [37]. 50.2% was found to be the value of VAF for this present study, suggesting a partial mediation effect.

Table 8 Mediation effects
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2.6 Testing moderating effects

Table 9 and Fig. 2 present the results of moderating effect. The relationship between collaborative business culture and innovation in business (β = 0.210, t = 4.491, p < 0.05) is moderated by big data analytical capability, supporting H5. Figure 4 indicates a simple slopes approach to test for the moderation effect as recommended (Aiken and West 1991). The moderation plot suggests that the relationship between collaborative Business culture and business innovation is strengthened when big data analytical capacity is high. The contrary applies when big data analytical capacity is low; the relationship between collaborative Business culture and business innovation is weakened.

Table 9 Moderating effects
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Fig. 2
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Results of the analysis of the simple slope plotting

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A simple slope plot (see Fig. 2) is a graphical representation employed in the perspective of moderation analysis in statistics. Moderation analysis aims to explore the conditions under which the relationship between CBC and BI changes. In simple terms, it helps to understand whether the strength or direction of the relationship between a dependent variable (BI) and an independent variable (CBC) varies under different conditions. The x-axis represents the CBC, the y-axis represents the BI, and there are separate lines or slopes for each level of the moderator variable. In analysing the slopes (lines), if the slopes are different, it suggests that the BDAC as moderator variable has a significant impact on the relationship between the CBC and BI.

3 Discussion

The finding of this study revealed that business innovation is influenced by big data analytical capability, which agrees with the results of a previous study [47], which concluded that it is possible to support the innovation management process with big data analysis. Similarly, Mikalef, van de Wetering and Krogstie (2021) found that big data analytics could be used to enhance a firm’s dynamic capabilities. BDA supports collaborative business culture, in line with the study conducted by Dubey et al. [25], where they indicated BDAC has a positive and significant effect on collective performance in countries across South America, Asia, Europe, North America, and Africa. The finding of this study reported that collaborative business culture influences business innovation. Close inter-cultural collaboration is critical for developing innovative businesses [55]. The author also found that the relationship between big data analytical capability and business innovation relationship is partially mediated by collaborative business culture. To the best of the researcher’s knowledge, these findings are unique.

These findings might add value across different industries or geographical regions. It is therefore possible for different industries to invest differently in technology and data-related capabilities. The recognition of the positive impact of big data analytics on innovation would help industries across different geographical areas to allocate resources more effectively, thereby prioritizing investments in areas that enhance innovation. The industries that harness big data analytics effectively are likely to gain a competitive advantage. When organizations recognize the impact of BDAC on innovation, they can position themselves as innovators in their industries, attracting customers, partners, and employees. More so, different industries may have unique innovation dynamics. For instance, technology companies might focus on technological advances that disrupt industries, while healthcare companies may prioritize collaborative research and development. By understanding the positive impacts of collaborative culture, innovation strategies can be tailored to meet the needs of specific industries. Moreover, different industries have distinct characteristics, regulatory environments, and business processes. Understanding how big data analytics and collaborative business culture impact innovation in specific sectors can provide tailored insights.

3.1 Theoretical implications

According to the Theory of Collaborative Advantage, organizations can leverage collective intelligence in a collaborative environment. Applied to big data analytics, this could suggest that a collaborative decision-making process, informed by unique perspectives and skills, boosts innovation. By combining advanced analytics with collaborative work, teams may be able to generate more nuanced insights that lead to innovative solutions. Also, a collaborative advantage often occurs when individuals from different functional areas combine their skills and expertise. Using big data analytics as a business intelligence tool, cross-functional collaboration can lead to synergies between domain knowledge and analytical capabilities, leading to more innovative solutions.

RBV contends that firms gain an advantage by leveraging and possessing unique resources. In this context, a firm’s big data analytical capability is viewed as a strategic resource. The theoretical implication is that firms with superior analytical capabilities are better positioned to drive innovation in their business processes, products, and services. The RBV emphasizes the importance of resource heterogeneity, arguing that different businesses have varying levels of resources. It is theoretically easier for companies with advanced big data analytical capabilities to analyze large datasets, extract meaningful insights, and transform those insights into innovative business strategies. As a result of this resource heterogeneity, firms have different innovation outcomes.

3.2 Knowledge implications in literature

Knowledge implications have been made in the present study. First, the study contributes to previous literature on the importance of big data analytical capability and collaborative business culture for businesses and organisations. Second, the study has answered the calls for further research on business innovation driven by internal factors. These drivers involve big data analytical capability and collaborative business culture. Additionally, to the best of the researcher’s knowledge, it appears that previous studies have not examined mediating factors in the relationship between big data analytics capabilities and business innovation and at the same time, moderating the effect of big data analytical capability on the relationship between collaborative business culture and business innovation. This conceptual model has not been explored previously in Sub-Saharan Africa, including Ghana.

3.3 Managerial implication and practice implications

Exploring big data analytics to enhance business innovation could have insightful managerial implications. First, managers should distribute resources to build and sustain a robust data infrastructure that involves data storage, processing, and analysis capabilities to guarantee that data is collected, stored, and managed securely and efficiently. Secondly, for businesses to be innovative, organisations must hire, train, and retain skilled data analysts, data scientists, and data engineers who can analyse and derive insights from their data effectively. More so, this finding highlights the need for big data analytics to be incorporated into strategic planning processes. Managers should take advantage of data-driven insights to inform decisions, identify market trends, and respond proactively to emerging opportunities.

The finding that collaborative business culture mediates big data analytical capability and business innovation relationship suggests that strong big data capabilities alone may not directly lead to business innovation. Instead, management needs to foster a collaborative culture within the organisation as it helps translate data capabilities into innovative outcomes. More so, big data analytical capability’s impact on business innovation is partially explained by the presence of a collaborative culture. In other words, the organisation’s ability to improve innovation is not just about having access to big data and analytics tools. It rather depends on how effectively employees’ collaborations are practical and applied.

The moderation effect of big data analytical capability on collaborative business culture and business innovation relationship implies that the impact of a collaborative culture on innovation outcomes can be amplified or enhanced when an organisation is involved. This finding has several important managerial implications. First, managers should know the strategic significance of developing and maintaining strong BDAC by acquiring the necessary tools, technologies, and expertise to gather, process, and analyse data effectively. Second, managers should recognise that big data analytics and collaborative efforts require time and personnel. Therefore, managers should invest resources to support data analytics initiatives and collaborative activities promoting innovation. Organizations should also provide employee training and skill development programs to enhance their data analytics skills and collaborative abilities. This could empower employees to use big data analytics tools and work effectively in a collaborative environment to promote business innovation. Lastly, managers should promote a culture of data-driven decision-making where data analytics insights are accessible and understandable to all relevant teams of employees. In conclusion, as the relationship between BDAC and business innovation is partially mediated by a collaborative business culture, managers should strive to cultivate a collaborative environment that complements and enhances big data analytical capabilities’ impact on innovation within an organization. Managers should also recognise that today’s data-driven business landscape requires organisations to integrate data analytics with collaboration strategies to drive innovation and enhance competitiveness.

3.4 Limitations of the study

Despite the significant findings and empirical approach of this study, employees in Ghana were selected as the sample size which may limit the generalisation of the findings. However, the research design is robust, utilizing a large sample size and appropriate methodologies like PLS-SEM for data analysis. More so, the pilot survey and the use of established instruments for measurement add to the reliability and validity of the findings. More so, some respondents may tend to agree with statements regardless of their content which affects the findings. Nevertheless, strategies such as clear and unbiased question wording were implemented to minimise their impact.