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Article

How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model

by
Suqin Liao
*,
Qianying Hu
and
Jingjing Wei
School of Management, Zhejiang University of Technology, Hangzhou 310000, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(5), 3948; https://doi.org/10.3390/su15053948
Submission received: 14 October 2022 / Revised: 28 January 2023 / Accepted: 2 February 2023 / Published: 21 February 2023
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Abstract

:
Building upon the knowledge-based dynamic capabilities view, this study seeks to examine how big data analytic capabilities can be leveraged to improve innovation ambidexterity by developing a mediated moderation framework. Survey data were collected from 199 Chinese big data companies to test our model. The results indicate that the dynamic decision-making capability mediates the relationship between big data analytic capabilities and innovation ambidexterity, and this mediating relationship is conditional on the moderator variable of cross-functional integration. This study enriches the literature about big data analytic capabilities and innovation ambidexterity by clarifying how big data analytic capabilities are positively related to innovation ambidexterity and uncovering the driver for pursuing innovation ambidexterity in a digital context. It also contributes to this line of research by revealing contingent factors to leverage big data analytic capabilities from the knowledge-based dynamic capabilities perspective.

1. Introduction

In the current data-driven digital economy, digital technologies have changed the way that firms develop capabilities for value acquisition and creation [1,2]. Firms in emerging economies are now increasingly focusing their attention on harnessing big data (BD) power and leveraging big data analytics (BDA) to make better-developed decisions suitable to sustain their competitive advantage [3]. BDA refers to the tools for data analysis and the visualization of results to support decision-making [4], which can revamp the level of resource arrangement and operation networking [5]. For example, Procter & Gamble consistently ranked among the top suppliers in Gartner’s supply chain rankings by leveraging decision science and optimization to determine supply chain location and procurement. As a result, many scholars have highlighted the importance of examining the influence of BDA [1,6]. However, firms need to develop the relevant capabilities needed to exploit the power of big data [7].
Big data analytics capabilities (BDACs) refer to the firm’s abilities to deploy technology and talent to exploit BD to generate insight, which are important capabilities for developing competitive advantages [3]. While most existing research focuses on the effect of BDACs on organizational economic performance [8,9], some scholars have called for research on the relationship between BDACs and strategic choice [1,10]. Innovation ambidexterity refers to a special strategy that simultaneously pursues exploitation and the exploration of innovation [11]. The best firms are believed to be those that can balance explorative innovation with exploitative innovation in an ambidextrous fashion [12]. Despite some studies examining the relationship between BDAC and ambidexterity [13,14], limited empirical works analyzing mechanisms through which BDACs can facilitate innovation ambidexterity can be found within the strategic management literature. Hence, understanding how to effectively adopt BDACs to successfully pursue innovation ambidexterity is salient to theoretical and practical implications. This study intends to fill this gap and seeks to answer the research question: Through what mechanism of mediating organizational capabilities is innovation ambidexterity achieved?
Dynamic capabilities view (DCV) suggests that structured adoption can be seen as a driver of the underlying processes that comprise a firm’s dynamic capability and results in competitive performance gains [3,8]. Leveraging on the literature that argues the possibility of maximizing decision-making qualities through the deployment of BDA [15], we consider the importance of the dynamic decision-making capability (DMC) in the linkage between BDAC and innovation ambidexterity. DMC refers to a firm’s decision-making ability to continuously and proactively transform to shifts in the environment [16], which helps firms to simultaneously exploit new combinations of existing knowledge and opportunities and explore new knowledge and innovative solutions. In addition, the knowledge-based view (KBV) highlights the role of internal knowledge sharing and collaboration in the innovation process [17]. Cross-functional integration is known as the extent of intra-firm cross-functional information sharing and collaboration through synchronized processes, and it may help reduce the conflict among different functions in pursuing ambidextrous innovation [18].
Combining the DCV and KBV, we draw on the knowledge-based dynamic capabilities view (KBDCs) to address the above-mentioned questions by positing the DMC as an essential mediator and a cross-functional integrator as the moderator to link the BDACs and innovation ambidexterity. This study makes several contributions. First, this study adds knowledge to KBDCs view of the firm by discussing how to capture the value of the BDACs in the context of the emerging economy. Second, we provide empirical evidence that the utilization of BD knowledge resource, in particular, BDACs successfully pursue innovation ambidexterity by enhancing the DMC. In doing so, we advance research on BD and innovation ambidexterity that seeks to uncover the mechanism of how BD capabilities can be used to facilitate innovation. Third, we investigate the cross-functional integration as a boundary condition between BDACs and innovation ambidexterity. We offer new insights into how the intra-firm cross-functional information sharing and collaboration may exert important indirect moderating effects on the relationship between BDACs and ambidexterity.

2. Theoretical Foundation and Hypothesis Development

2.1. Knowledge-Based Dynamic Capability View

The KBDCs view that emerged as a theoretical integration of the DCV and the KBV is an appropriate lens to examine the effect of BDACs. The main argument of KBV is that the knowledge, whether it be tangible (flexibility infrastructure) or intangible (personal skills, management knowledge), is the main strategic resource for developing competencies [19]. As such, organizations were regarded by firms as institutions for developing and integrating value-creating knowledge [20]. However, the KBV alone may not be very effective in turbulent and dynamic environments, and the managers must develop and apply dynamic capabilities to capture, interpret, and deploy knowledge resources [21]. DCs can develop over time and help firms create distinctive strategic competencies to remain successful in the long-term within rapidly changing environments [22]. Although the KBV and DCV have evolved from two different perspectives, they are complementary to each other because capabilities are attained through the utilization of resources and knowledge [23]. The integration of DCV and KBV leads to the KBDCs view, which refers to the ability that exploits knowledge resources to address the dynamic environment [23,24]. KBDCs allow firms to develop distinctive strategic routines and competencies to gain a competitive advantage in this dynamic environment [23]. However, despite their importance, little research has examined KBDCs view on the utilization of BDACs in an ambidexterity context.
KBDCs are suggested to deliver rents from new combinations of knowledge and produce outcomes that are capable of shaping the marketplace, such as entrepreneurship and innovation [25]. Therefore, the definition of KBDCs specifies that they can create value indirectly by changing a firm’s way of conducting business. Previous studies suggest that there are two levels of DCs: lower-order and higher-order organizational capabilities [26]. Lower-order capabilities can be leveraged and integrated to form higher-order capabilities [27]. DCs view focuses on updating existing organizational capabilities as a means of firm’s competitive survival [28]. Kaur and Kaur [29] suggest that KBDCs in contexts that demand organizational agility contributes to the innovation management through the development of higher-order DCs. Accordingly, KBDCs can be used to extend, or create, higher-order DCs and then facilitate changes in the way the firm operates and competes [29]. In addition, KBDCs view suggests that the effectiveness of DCs is contingent on internal knowledge integration [30]. A firm with DCs to strengthen innovation ambidexterity is more likely to benefit from knowledge integration mechanism. The KBDCs, thus, represents a suitable framework for investigating how BDAC can be leveraged to facilitate innovation ambidexterity by exploring the mediation of dynamic decision-making and the internal conditions of cross-functional integration. Figure 1 present the research model that connect the study variables based on the aforementioned theories.

2.2. Big Data Analytic Capabilities

BD is considered a value-creating resource of knowledge creation that has become especially significant for firms that can revolutionize information processing technology and capabilities [8,14]. To stay competitive over time, firms need to continuously update their BD systems to explore new multi-faceted data, which results in the development of organization-wide BDACs [1]. Prior research on IT capabilities defined IT capabilities as the “firm’s abilities to mobilize and deploy IT-based resources in combination or co-present with other resources and capabilities” [31]. IT capabilities can be regarded as the source of competitive advantage through the deployment of the value and heterogeneity of IT resources [32]. Drawing upon the notion of IT capability, BDACs are conceptualized as the firm’s distinctive abilities to effectively deploy technology and talent to capture and analyze data to obtain strategic insights [15,33].
Building BDACs requires the integration of tangible resources (e.g., physical and financial resources), intangible resources (e.g., organizational culture), and human skills (e.g., employee skills) [34]. The infrastructure quality, business expertise, and learning intensity of firms effectively affect the degree of competitive advantage [32]. Along these lines, this study proposes BDA management capabilities, BDA infrastructure flexibility capabilities, and BDA personnel expertise capabilities as three important aspects of a firm’s BDACs [13]. The infrastructures flexibility capability of a BDA is the fundamental capability to ensure that technologies can handle different data streams and formats in any situation [33]. Despite the importance of BDA infrastructures, management is needed to choose the appropriate technical solution suit for the organization. BDA managerial capabilities are essential for selecting and implementing the correct BDA infrastructure and identifying the key information from massive amounts of data [33]. The person should also be skilled in BDA to improve the efficiency of the BDA infrastructure and to analyze the right data. Thus, these capabilities may help firms to acquire a competitive advantage. Existing studies agree that BDACs could influence an organization’s different outcomes, including performance [33], agility [6], and innovative corporate strategies [3]. For the firms’ strategy choices, some scholars have begun to understand the complex relationships between the development of BDACs and ambidexterity [14,35]. However, quantitative research on BDA capabilities and innovation ambidexterity is still in its infancy [36]. There is a clear need to explore through which mechanisms BDACs can affect ambidextrous innovation. There is also a need to identify the boundary conditions that influence the successful implementation of BDACs.
BDACs rely heavily on knowledge and play a vital role in ensuring the integration of big data into business processes [37], which in turn enables dynamic capabilities such as innovation [15]. A strong BDAC can overcome organizational inertia by facilitating a firm’s DC [38], and a strengthened capacity to innovate is achieved [3]. Combining these theoretical views and in line with past studies [1,3], we consider BDACs as KBDCs that need to be renovated continuously to seize market opportunities and maintain competencies in dynamic environments. BDACs can change the business logic for a firm both directly and through the development of higher-order DCs [1]. The firm’s ability to integrate different organization capabilities and higher-order DCs in a reinforcing manner leads to a competitive advantage. Thus, we argue that a firm’s BDACs have an indirect effect on agility innovation activities, mediated by higher-order DCs.

2.3. BDACs and Innovation Ambidexterity

Innovation ambidexterity refers to engaging in both exploratory and exploitative innovation, which is associated with higher levels of performance [11,39]. Exploitative innovation encompasses activities that improve the existing market to pursue short efficiency, while exploratory innovation concentrates on exploring new opportunities and markets for acquiring long-term performance [40]. Both exploration and exploitation are related to knowledge resource acquisition and utilization [41]. An ambidextrous firm has the capability to both compete in a mature market and explore new products and services in an emerging market, through which it can acquire sustainable competitive advantage [42]. However, most firms have difficulty pursuing innovation ambidexterity because of the inherent tensions between explorative and exploitative processes [43]. The competition for scarce resources often leads to conflicts, contradictions, and inconsistencies [44]. Existing research has found that BD resource sensing and reconfiguration can provide new potential alternatives for solving the conflict of ambidexterity [13,45]. However, the role of BDACs in untangling the ambidexterity dilemma remains sparse. Our research aims to add to this body of knowledge by investigating the relationship between BDACs and innovation ambidexterity.
Innovation ambidexterity can be described as a complex innovation activity that encompasses the routines and processes that ambidextrous organizations rely on to allocate, coordinate, and integrate various contradictory innovative efforts [46]. Following this approach, innovation ambidexterity represents the natural outputs of KBDCs, which facilitate resource and capability configurations and is associated with sustained competitive advantage [41]. We argue that BDACs may play a positive role in pursuing innovation ambidexterity due to the following reasons. First, BDACs improve the connectivity of IT components, which can help the firm consolidate knowledge flow within the whole organization using an integrated technological interface [33]. This consolidation enables the firm to have a smooth flow of knowledge concerning existing and emerging products and markets across the organization to decrease the knowledge and resource conflicts between exploration and exploitation [47]. Second, BDACs can facilitate knowledge management using advanced technology management to support inter-organizational communication [48]. As such, managers can gain a deeper insight into the needs of different units and try to allocate the resource to satisfy disparate demands to support both exploratory and exploitative activities [49,50]. Third, BDACs enable firms to use the proper IT solutions, which may enhance the speed of knowledge exploration and exploitation from individuals to organizational members [51]. Thus, we propose the following:
Hypothesis 1. 
A firm’s BDACs positively relate to its innovation ambidexterity.

2.4. Mediating Role of Dynamic-Decision Making

A DMC is defined as a firm’s ability to proactively utilize knowledge resources to make valuable decisions to address rapid environments [16]. DMCs distinguish from the decisions that exist at the operational or product-level, which are more appropriately deemed ordinary decision-making capabilities. DMCs consist of planning decision-making, creative decision-making, and spontaneous decision-making. Planning refers to a firm’s ability to collect and evaluate internal and external information to inform its strategic alternatives [52]. Decision-making planning makes the strategic goal achievement process more focused and strengthens the organizational capacity to make the right decision. Creative decision-making refers to an ability to produce and try new solutions and ideas during the process of decision-making, which can encourage the generation of new ideas and information openness [53]. Spontaneous decision-making indicates that decisions can often be made by responding timely within the moment [54]. DMCs gain strength from KBDCs to dynamically make decisions towards the transformations of organizational processes that are necessary for acquiring sustainable firm performances [16]. Thus, DMCs reflect a high-level routine that can be considered a high-order DC that meets the KBDCs requirements to integrate and reconfigure internal and external knowledge and resources to respond to rapidly changing business environments [55].
The planning and novelty of decision-making requires infrastructural, managerial, and organizational capabilities to control and orchestrate the data resources that permit the dynamic making of quality decisions [54,56]. In particular, BDACs facilitate the exploitation of potentially valuable insight and knowledge extracted from mass data regarding customers, markets, and competitors [57], which helps make creative and planned decisions in a rapid environment. Scholars have also indicated that the information extracted by BDA infrastructures, managers, and personnel with strong BDA skills can help firms make spontaneous and quicker decisions, which may influence an organizational ability to make the decision dynamically [38]. Following the previously established theoretical framework—whereby researchers have established that BDACs (KBDCs) are strong predictor of higher-order DCs [8]—BDACs are critical to achieving DMCs. Thus, a strong BDAC will have a positive effect on a DMC.
A recent review of the mechanisms and outcomes of DCs highlights that innovation is a main outcome of DCs, and DCs can lead to different forms of new products, services, and processes [58]. Previous studies have verified that DCs are regarded as a facilitator of both explorative and exploitative innovation strategies [11]. The value from a DMC is a result of improved decision-making and repositioning pertaining to the emerging and existing needs and opportunities [16]. The planning of DMCs enables firms to determine whether there is a good fit between their intended innovation strategies and competencies [59], which can help firms to pursue exploration and exploitation simultaneously. In addition, the creativity of DMCs allows variation to be introduced into the decision-making process and focuses managers’ attention on new knowledge about existing and emerging market demands [16]. A firm with a strong DMC can select complex strategic options, such as supporting the development of new competencies and deepening existing ones [60], thereby facilitating a balanced allocation of resources between exploratory and exploitative processes by making comprehensive decisions. Hence, we propose the following:
Hypothesis 2a. 
A firm’s BDACs positively relate to its DMCs.
Hypothesis 2b. 
A firm’s DMC positively relates to its innovation ambidexterity.
Taken together, H1, H2a, and H2b imply that the relationship between BDACs and innovation ambidexterity may be both direct and indirect and that DMCs may mediate it. While a KBDC can generate a competitive advantage in its own right, it is suggested that one of its mechanisms of action is through enabling a higher-order DC. Wamba et al. [38] argue that high levels of BDACs can have an indirect impact on a firm’s innovation capabilities by strengthening dynamic capabilities. DMC appears to represent the dynamic mediator between BDAC and innovation ambidexterity because it is related to the higher-order DCs that can address the tension between exploration and exploitation to facilitate novel processes and systems. This mediation model is under the KBDCs framework that KBDCs can acquire and combine knowledge resources to change the way the firm operates and competes through the development of higher-order DCs. Thus, we propose the following:
Hypothesis 3. 
A firm’s DMC mediates the relationship between BDACs and innovation ambidexterity.

2.5. Moderating Role of Cross-Functional Integration

Cross-functional integration is known as internal integration, and reflects the degree of interaction, coordination, and the extent of joint involvement across functions [61]. It involves knowledge and information sharing among different functions [62], which can help reduce conflicts among different functions within firms. Previous studies suggest that information integration in the cross-functional structure enhances consistency among decisions made throughout the operation process, which are considered critical for flexibility and success [63]. Furthermore, the KBDCs view theorizes on the conditions under which organizations effectively leverage the KBDC for acquiring an organizational competitive advantage [64]. Knowledge integration mechanisms allow firms to reduce conflict between areas and increase product novelties that can help to develop a lasting competitive advantage [65]. Thus, we propose that cross-functional integration provides support that facilitates efficient leverage of DMCs for innovation ambidexterity.
We argue that cross-functional integration can help firms materialize the benefits of DMCs in enabling conflict-reducing and consistent decision on various strategic choices, thereby improving innovation ambidexterity. High levels of cross-functional integration enable different functions to develop shared understandings and insights into the knowledge integration of the decision process [66]. Thus, cross-functional integration will encourage different functions to engage in conflict resolution regarding exploratory and exploitative activities rather than locking such decisions into one unit [18], which may facilitate innovation ambidexterity. In other words, cross-functional integration helps realize this potential for conflict reduction in DMCs by weakening the locus of the tensions between exploration and exploitation. In addition, the information-processing demands, relating to the utilization of knowledge for decision-making, require higher levels of coordinated integration across functions to align different knowledge to successfully pursue various innovation strategies [67]. Through the coordinating mechanism, different functional units can better utilize their diversity of capabilities to enhance the comprehensiveness of decision-making [68], which, then, facilitate innovation ambidexterity. Thus, we propose the following:
Hypothesis 4. 
The relationship between DMC and innovation ambidexterity will be stronger when firms have a higher rather than lower level of cross-functional integration.
From the KBDCs perspective, knowledge-integration mechanism functions as the architecture that moderates the effects of accumulated knowledge on the development of DCs [24]. When knowledge integration mechanisms are leveraged to facilitate higher-order DCs, the KBDCs have a greater potential to lead to the change in firms’ business management and then generate competitive advantage [69]. Cross-functional integrations represent important knowledge and information-integration mechanisms in an organization’s network for DCs and innovations [65]. Integrating the logic of H3 and H4 forms a theoretical framework in which DMCs play a mediating role between BDACs and innovation ambidexterity. Cross-functional integration moderates the DMC–innovation ambidexterity relationship. While DMCs can explain the linkage between BDACs and innovation ambidexterity (H3), since the linkage between DMC–innovation ambidexterity is predicted to be stronger when cross-functional integration is higher (H4), we predict that the mediated relationship captured by Hypothesis 3 is stronger when cross-functional integration is higher. With cross-functional integration acting as a mechanism to enhance the potential positive impact of BDACs and DMCs, the incidental consequence of DMCs on the connection between BDACs and innovation ambidexterity is postulated to become more robust when cross-functional integration is high. Thus, we propose the following:
Hypothesis 5. 
Cross-functional integration will moderate the mediated effect of BDACs on innovation ambidexterity via DMC such that the indirect relationship will be stronger when there is higher rather than a lower level of cross-functional integration.

3. Methodology

3.1. Sample and Data Collection

This study adopted a questionnaire-based survey method, and a structured questionnaire was used to collect data. The sample population for this study consisted of Chinese big data firms. With the assistance of the Provincial Government, we developed a dataset of Chinese big data firms that actively engaged in big data activities by collecting the Chinese firms that participated in the “China international big data industry Expo” in 2015, 2016, 2017, and 2018. The dataset comprised 1262 firms. Recent research highlights the engagement of firms in emerging economies in big data activities [70]. China is paying special attention to create value from big data, and appeared as one of the most digital economies [15]. Therefore, China provided important context to test the proposition, which was important for extending the research in various contexts. Understanding the big data analytic capabilities and innovation ambidexterity in China can provide more general insight for economies at similar development stages in the region.
Data was gathered by two research teams comprising doctoral students beginning in November 2020 and ending in May 2021. The members of the research team sent invitation letters through WeChat, email, and telephone to the senior managers, chief information officers, and IT managers of the sample firms in the list, introducing the aims, contents, and procedures of the survey, and the importance of truthful answers and confidentiality. To ensure a collective response, we assessed respondents’ self-report knowledge of the firm’s big data analytic capabilities, innovation ambidexterity, and innovation-related activities. We adopted on-site surveys over the online survey platform Wenjuanxing and sent the link through WeChat or email. The senior managers clicked the link and completed the survey, which included BDACs, cognitive variables (for scale validation), DMCs, and innovation ambidexterity. A total of 218 firms started to complete this survey, with 199 firms providing a complete response.
The survey items adopted in this questionnaire were based on previous studies and modified according to Chinese conditions. All constructs and their respective items were operationalized on a 5-point Likert scale. To ensure validity, we conducted a pilot study with 10 executives who were part-time EMBA students in China to verify the content, clarity, and wording of the items [71]. Some minor modifications were made after the pre-testing procedure. To ensure consistency, the questionnaire contained a basic definition of big data capabilities, dynamic decision-making capabilities, and innovation ambidexterity.
To determine whether this study had any non-response bias, we compared the early and late respondents in terms of the age, size, and capital. Chi-square analysis indicated that there was no systematic response bias [72]. In addition to a single response bias, we test the intra-correlation coefficients (ICC) for each variable. The results showed that the ICC scores ranged from 0.81 to 0.93, indicating single managers provided reliable information. To control the common method bias, we employed several procedural methods. Ex ante, we randomized the order of the different variables and ensured the anonymity of the respondents. Ex post, Harman’s one-factor analysis was conducted on the items included in the regressions. The result indicated that the presence of multiple factors explained 52.44% of the total variance and the variance was evenly dispersed among the factors. Hence, a common method bias makes no sense for the outcome.

3.2. Measures and Validation of Constructs

The multi-item constructs were adopted from previous studies and operationalized by the mean value of all items. The Appendix A provides a summary of the measurement items used to operationalize the constructs.
Dependent variable: To capture innovation ambidexterity, we used a measure developed by Jansen and Van Den Bosch [73], which combines organizational exploitation and exploration. Each scale consists of six items. This scale is particularly recommended when predicting the effects or drivers of innovation ambidexterity.
Independent variable: Using the foundations of the exploratory study conducted by Rialti et al. [33], we developed the items suited to measure big data analytic capabilities in an emerging economy. The BDAC was conceptualized and developed as a second-order formative construct. These items gauged the extent to which the organization engaged in or supported the three first-order variables, namely, the flexibility of the BDA Infrastructure Flexibility, BDA Management Capabilities, and BDA Personnel Expertise.
Moderated variable: Following Yang and Tsai [66], we measured cross-functional integration as the extent to which a firm’s functional departments worked together to accomplish innovation planning and execution. The measure of cross-functional integration consisted of five items adapted from extant research [74].
Mediating variable: We used the DMC scale developed and validated by Hughes et al. [16]. The measurement was adopted to assess the extent to which a firm engaged in efforts to make planned, spontaneous, and creative decisions. The measure for DMCs consisted of five items adapted from extant research [75].
Control variables: Several variables may affect both big data analytic capabilities, DMC, and strategy innovation. Therefore, firm-level and industrial or environment-level control variables were controlled for this research. Firm age and firm size were controlled as firm-level variables. Firm age was calculated as the number of years since the firm’s foundation, and firm size was measured based on a firm’s total number of employees (ranging from 1 for firms that have fewer than 10 employees to 5 firms that have 100 or more employees). Two industrial- or environmental-level variables were controlled: industry sectors and environmental turbulence. Industrial sectors generally influence a broad spectrum of firms’ strategic activities, and the influence of big data analytic capabilities and innovation ambidexterity could be different for different sectors. We coded four industry dummy variables (1 pertaining to this industry; 0 otherwise) to represent different sectors. IC1 was defined as biotechnology and pharmaceuticals, IC2 as manufacturing, IC3 as service, and IC4 as the financial and consumer goods, using Computers and IT as the baseline. Environment turbulence was measured by the scale adapted form Jansen et al. [73].

3.3. The Construct Reliability and Validation

The reliability of all the constructs was examined through composite reliability (CR) and Cronbach’s alpha. The result in the Appendix A showed that the values of the composite reliability (CR) and Cronbach alpha (CA) for each construct was above the threshold of 0.60, which reflected good reliability and acceptable internal consistency [76]. The factor loadings for each item were all higher than 0.60, indicating all items have good construct validity. All average variance extracted (AVE) values were higher than 0.50, meeting the criteria for convergent validity. In addition, discriminant validity required that the AVE of all constructs were higher than the squared correlation among the constructs. The results showed that the AVE of each construct satisfied this criterion, which offered evidence for discriminant validity [77]. We further conducted an alternative model strategy. Table 1 shows that four-factor model (baseline model) provided a significantly better fit than other models (χ2/df < 3, RMSEA < 0.08, CFI > 0.9, TLI > 0.9, SRMR < 0.06), which confirms the discriminant validity.

4. Analyses and Results

4.1. Descriptive Analysis

Table 2 provided the characteristics of the firms and informants in the sample. In terms of firm size, 31.1% of firms had more than 500 employees and 68.9% had less. In terms of the characteristics of respondents, 22.6% were vice presidents or above and 77.4% were middle managers or below. Table 3 presents the means, standard deviations, and pairwise correlations between the main variables. The correlations among the main variables were generally low, with a maximum absolute value of 0.647. Using the 0.6 benchmark for the strength of the correlations [78], none of the variables were highly correlated. To alleviate the concerns about multicollinearity, all constructs were zero-centered before regression [79]. The variance inflation factors (VIF) values on all predictor variables were calculated, and the results showed that the maximum VIF assigned to one of the main constructs was 2.876, well below the cut-off point of 10 [80]. As a result, the multicollinearity problem could not be a threat to our results. Hence, all of the variables were included in the moderated mediation analysis.

4.2. Hypotheses Testing

A regression approach was adopted to assess the explanatory power of each set of variables, and PROCESS was adopted for the mediation and the moderated mediation analysis. Specially, the SPSS 21 software was used for the statistical and quantitative analyses of data. Table 4 presents an overview of the regression results for the proposed research model. The significance of estimates (t-statistics) was obtained by performing a bootstrap analysis with 5000 resamples. Model 1 and Model 3 only entered control variables, which established a baseline against other models. Hypothesis 1 proposed a positive relationship between BDAC and innovation ambidexterity. The result of Model 4 showed that BDAC is positively related to the innovation ambidexterity (β = 0.365, p < 0.001), thus, supporting H1. In Hypothesis 2a, we posited that the BDACs positively affect DMCs. Model 2 shows that the effect of BDACs on DMCs is significant (β = 0.379, p < 0.001), indicating the H2a is supported by our data. Hypothesis 2b proposed a positive relationship between DMCs and innovation ambidexterity. Model 5’s result showed that DMC exerted a statistically significant influence on innovation ambidexterity (β = 0.265; p < 0.01), thus, Hypothesis 2b is supported.
Hypothesis 3 predicted the mediating role of DMCs in the relationship between BDACs and innovation ambidexterity. The direct effects required for mediation were all confirmed (The significant betas of Model 2, Model 4 and Model 5). In addition, as shown in Model 5, when a DMC was entered, the effect of BDACs on innovation ambidexterity decreased while remaining significant (β = 0.298; p < 0.01). This case is a necessary, but not sufficient, condition for an indirect effect of BDACs on innovation ambidexterity existing through a realized DMC [81,82]. Model 4 of Preacher and Hayes was further applied to test the mediating relationship in SPSS via an extension of Process v3.3 [81]. Process produced a bias-corrected 95% bootstrap CI for the indirect effect. The tests demonstrated there was a significant indirect effect of BDACs on innovation ambidexterity via the DMC (β = 0.101, SE = 0.049, 95% CI = 0.014–0.242), and, thus, Hypothesis H3 is supported. In sum, the results supported Hypothesis 3 (DMC has a partial mediating effect on the relationship between BDACs and innovation ambidexterity). This indicated that the use of big data analytics will bring good outcomes to firms through organizational capabilities (DMC).
With regards to the moderating variable, the two-way interaction terms exerted a statistically significant and positive influence on innovation ambidexterity (β = 0.109, p < 0.05), which provided clear evidence for the moderating effect of cross-functional integration. We then plotted this interaction term in Figure 2 to better understand the moderating effect. The panel shows that a high level of DMC coupled with high level of cross-functional integration gave rise to higher level of innovation ambidexterity than lower levels of cross-functional integration. Thus, Hypothesis 4 is supported.
To obtain a deeper insight into how the indirect effect differs according to the change of cross-functional integration, Model 14 of Preacher and Hayes was applied and a process bootstrapping procedure was conducted to quantify the indirect effect at a low level (−1 SD), mean, and high level (+1 SD) cross-functional integration [81]. The results showed the indirect effect of these values on cross-functional integration and provided 95% confidence level intervals for the effect. The indirect effect of BDACs on innovation ambidexterity via DMC was significantly moderated by cross-functional integration (Δγ = 0.06, p < 0.01 while CI = 0.117–0.162 does not cross zero). In addition, we also found that the indirect effect of BDACs on innovation ambidexterity was stronger at a high level (γ = 0.07, p < 0.01) than at low level (γ = 0.01) cross-functional integration. Consequently, Hypothesis 5 was supported.

5. Discussion and Contribution

While the previous studies on BD highlighted the opportunity for firms to leverage digitalization to implement innovation to achieve competitive advantages [3,83], limited empirical studies have so far investigated how BDACs affect the capacity of a firm to achieve innovation ambidexterity. The goal of the present research is to understand through which mechanisms can BDACs facilitate innovation ambidexterity. We leveraged on the KBDCs to investigate how BDACs are related to organizational innovation ambidexterity through the capabilities of dynamic decision-making. We also examined how cross-functional integration moderates the mediational relationship between BDACs, DMCs, and innovation ambidexterity.
The quantitative results first indicated that high levels of BDACs positively affect innovation ambidexterity. This finding could be attributable to the fact that BD utilization can support inter-organizational communication and help managers to allocate resources to satisfy disparate demands to support both exploratory and exploitative activities. Thus, this finding empirically reiterated the importance of the BDACs and is consistent with previous studies [84] that argued that BDACs help organizations make better use of their knowledge assets to facilitate innovation strategies and higher performance.
Second, we found evidence for a mediating role of DMC between BDACs and innovation ambidexterity. Most previous studies argue that BDACs can help firms efficiently deal with environmental changes or the seizing of emerging opportunities [8] and, thus, enhance decision-making quality [85]. However, some have asserted that the BDACs of some firms are associated with no, or reduced, decision-making qualities [86]. Thus, the results provided additional evidence for firms using BDACs to enhance DMCs. Furthermore, the value of DMCs for innovation ambidexterity is consistent with the views of Mihalache et al. [49], who asserted that a balanced allocation of resources between exploratory and exploitative activities requires comprehensive and dynamic decision making.
Third, the findings suggest that cross-functional integration moderates the mediating effect of DMCs. This result indicates that it may be more feasible for firms with BDACs to make comprehensive decisions for stimulating organizational ambidexterity because of the mutual alignment of cross-functional interdependencies. Consistent with Strese et al. [87], we support the scholars with a view point that cross-functional integrations can overcome communication barriers and resolve conflicts.

5.1. Theoretical Implications

Our findings contribute to the literature on BDACs and innovation ambidexterity in the following ways. First, this study extends the literature on the KBDCs view of the firms by discussing how BDACs affect innovation ambidexterity in this framework. Most previous studies of ambidexterity have been based on a single perspective, such as a knowledge-based view [88] and DCs view [46]. Research on KBDCs has yet to receive full attention in organizational ambidexterity and emerging economies [14]. Shamim et al. [14] found that big data management capabilities lead to employee ambidexterity through the mediation of big data creation, based on KBDCs in Chinese enterprises. Different from Shamim et al. [14], this study focused on organizational ambidexterity and explored the mediating effect of higher-order DCs and the moderating effect of internal knowledge integration mechanisms in the relationship between BDACs and organizational ambidexterity. Therefore, this study extends the applicability of KBDCs in the research of organizational ambidexterity in emerging economies.
Second, despite the growing interest in exploring BD and competitive advantages [9,33,89], less is known about the intermediated mechanisms that can translate BDACs into an enabler for innovation ambidexterity. There is evidence indicating that using BDA to facilitate firms’ innovation is not always simple [3]. We found that BDACs not only had a positive effect on innovation ambidexterity, but, also, positively affected DMCs and, in turn, facilitate innovation ambidexterity. This finding empirically supported the KBDCs view in that BDACs (a KBDC) acquire and combine knowledge resources to sense and address the dynamic environment for value creation through the development of DMCs (i.e., higher-order DCs) [69]. Furthermore, this finding also answers recent calls of investigating how BDACs affect strategic outcomes [33], such as innovation ambidexterity [83]. This study, therefore, enriches management studies of BDACs and ambidexterity by providing a better understanding of how BD can enable innovation ambidexterity via enhancing DMCs.
Third, this research further extends the model to cross-functional integration. Most previous studies of cross-functional integration pay more attention on the role of coordination between specified functional areas [18,66] and limited attention to how integration might affect innovation ambidexterity, especially in the context of data-driven digital economies with a high degree of BD resource exploitation, in which coordination requires various areas to be addressed. The finding that DMCs induced by BDACs resulted in low-level innovation ambidexterity when internal cross-functional integration was low accentuated the importance of coordinated integration context as a boundary condition. This finding suggests that DCs created through an attentive orchestration of BD knowledge resources can facilitate innovation ambidexterity, especially at a high level of internal integration. In this vein, our results offer new insights into how intra-firm cross-functional information sharing and collaboration may exert important indirect moderating effects on the relationship between BDACs and ambidexterity. Examining the influence of such internal integration characteristics and situational factors can significantly advance the understanding of boundary conditions under which DMCs can link BDACs to innovation ambidexterity. We also extended previous studies by responding to those calling for examinations of how the coordinated integration of different functional resources operate as boundary conditions in the BDACs strategic outcome relationships [38,48].

5.2. Managerial Implications

This study also has several implications for practitioners. In particular, our findings can help managers better understand the value of BDACs and the routines to balance the exploration and exploitation. Firstly, the findings show that BDACs play an important role in facilitating organizational innovation ambidexterity, and that firms should not only harness the power of big data, but also nurture organizational BDACs by hiring people with big data skills, developing data-driven culture, and adopting organizational practices. Top managers are advised to engage in the development of data-related capability fostering practices, such as making decisions to improve the firm’s IT infrastructure or organizing the skill training.
Secondly, our study highlights how BDACs affect innovation ambidexterity through the influence of the mediation of DMCs. In other words, while firms may be generating data-driven insights as a result of BDACs, practices are required to utilize it. Firms are required to develop flexible operations and deploy organizational capabilities rapidly to acquire competitive advantages. Hence, top managers should not limit their efforts to collect and analyze big data, but, also, they should fully capitalize on it to gain the extra profit (e.g., decision making). For example, organizations should encourage managers to make dynamic decisions based on big data, enhance the comprehensiveness of decision-making, and reduce the risk for balance exploration and exploitation.
Lastly, moving to the moderate impact of cross-functional integration on the BDACs–innovation ambidexterity relationship, we recommend that business managers encourage different functions to communicate and collaborate with shared goals and establish formal organizational arrangements to improve the level of cross-functional integration.

6. Limitations and Future Research

Despite the contributions of the current study, it has serval limitations that represent opportunities for future research. First, we used a cross-section research design from a single set of respondents. Although several methods (i.e., randomizing items, employing statistics) were used to reduce the common method bias, we cannot fully deal with endogeneity issues. Future research should incorporate panel data, longitudinal data, or secondary data to increase the content validity of these findings. Second, although our dataset covered a broad range of big data firms representing a variety of industries, care should be exercised in generalizing the results. Further studies could extrapolate these findings to other settings, incorporating different countries and/or other periods. An additional avenue for future research relates to the drivers of BDACs. Although we investigated the value of BDACs on innovation ambidexterity, we did not factor in the antecedent conditions of BDACs. Future studies should seek to address the question of how to enhance the BDACs at both individual and organizational levels. The final avenues for future studies relate to the different effects of the different dimensions of BDACs. Future studies first could, therefore, explore how different types of BDACs (e.g., BDA Infrastructure flexibility, BDA Management capabilities) affect the results, and, then, compare the results with the firms in other countries.

Author Contributions

Conceptualization, S.L. and Q.H.; methodology, software, validation, formal analysis, investigation, writing—original draft preparation.; writing—review and editing, S.L., J.W. and Q.H.; supervision, funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Zhejiang Provincial Natural Science Foundation of China (No. LQ20G020018) and the National Natural Science Foundation of China (No. 72002203).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Measurement Scales as Used in Survey

Construct and Their MeasuresLoadingAVECRα
Big data analytics capabilities
BDA Infrastructure Flexibility 0.5370.8740.803
Compared to rivals within our industry, our organization has the foremost available analytics systems.0.656
Our organization utilizes open systems network
mechanisms to boost analytics connectivity.		0.726
Software applications can be easily used across multiple analytics platforms.0.785
Our user interfaces provide transparent access to all platforms.0.731
Reusable software modules are widely used in new system development.0.779
The legacy system within our organization restricts the development of new applications.0.713
BDA Management Capabilities 0.5650.9120.857
We continuously examine innovative opportunities for the strategic use of business analytics.0.766
We enforce adequate plans for the utilization of business analytics.0.689
When we make business analytics investment decisions, we estimate the effect they will have on the productivity of the employees’ work.0.705
When we make business analytics investment decisions, we project how much these options will help end users make quicker decisions.0.778
In our organization, business analysts and line people coordinate their efforts harmoniously.0.780
In our organization, business analysts and line people from various departments regularly attend cross-functional meetings.0.801
In our organization, the responsibility for analytics development is clear.0.763
We are confident that analytics project proposals are properly appraised.0.726
BDA Personnel Expertise 0.5240.8980.853
Our analytics personnel are very capable in terms of programming skills (e.g., structured programming, web-based application, etc.).0.737
Our analytics personnel are very capable in decision support systems (e.g., expert systems, artificial intelligence, data warehousing, mining, marts, etc.).0.751
Our analytics personnel show superior understanding of technological trends.0.733
Our analytics personnel show superior ability to learn new technologies.0.718
Our analytics personnel are very knowledgeable about the critical factors for the success of our organization.0.691
Our analytics personnel are very capable in interpreting business problems and developing appropriate solutions.0.759
Our analytics personnel work closely with customers and maintain productive user/client relationships.0.711
Our analytics personnel are very capable in terms of executing work in a collective environment.0.687
Dynamic Decision-making Capability 0.6140.8880.788
When we formulate an decision it is usually planned in detail.0.726
We make our decisions based on a systematic analysis of our business environment.0.795
We usually make decisions spontaneously.0.788
We often produce new ideas during the process of decision-making.0.816
We are very good at finding new solutions to address problems.0.789
Cross-functional integration 0.5080.8380.754
Functional departments within our company have a common prioritization of innovative tasks.0.728
Our company’s strategic decisions are based on plans agreed upon by all functional departments.0.694
We freely communicate information about our successful and unsuccessful experiences across all functional areas.0.719
All of our functional departments are tightly integrated in serving the needs of our target markets.0.700
All functional departments work hard to jointly solve problems of innovative tasks.0.722
Exploitation innovation 0.5600.8830.797
Our unit accepts demands that go beyond existing products and services.0.721
We improve our provision’s efficiency of products and services.0.718
Our unit expands services for existing clients. 0.755
We regularly implement small adaptations to existing products and services.0.699
We introduce improved, but existing products and services for our local market.0.779
Lowering costs of internal processes is an important objective.0.810
Exploration innovation 0.5830.8930.785
We commercialize products and services that are completely new to our unit.0.780
We frequently refine the provision of existing products and services.0.738
Our unit regularly uses new distribution channels.0.754
We regularly search for and approach new clients in new markets.0.801
We experiment with new products and services in our local market.0.698
We invent new products and services.0.805

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Figure 1. The proposed conceptual model. The dotted lines represent the hypotheses on mediated moderation effects via absorptive capacity and cross-functional integration.
Figure 1. The proposed conceptual model. The dotted lines represent the hypotheses on mediated moderation effects via absorptive capacity and cross-functional integration.
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Figure 2. The moderation of BDACS–innovation ambidexterity relationship by cross function integration.
Figure 2. The moderation of BDACS–innovation ambidexterity relationship by cross function integration.
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Table
Table 1. Confirmatory factory analysis for discriminant validity.
Table 1. Confirmatory factory analysis for discriminant validity.
Modelχ2/dfRMSEACFITLISRMR
Baseline modelFour factors1.750.0530.9230.9210.057
Model 1Three factors2.140.0690.8920.8490.085
Model 2Two factors2.360.0840.8130.7730.089
Model 3One factor model3.510.0990.7540.7120.094
Table
Table 2. Descriptive analysis (N = 199).
Table 2. Descriptive analysis (N = 199).
Firm CharacteristicsFrequencyPercentage (%)
Firm size (of employees)
<50115.5
51–1003216.1
101–2003115.6
201–5006331.7
501–10003316.6
>10002914.5
Firm age (years)
<373.5
3–107939.7
10–208542.7
>202814.1
Respondent’s Position
Vice President of above4522.6
Middle manager12060.3
Senior Technical 3015.1
Directors42.0
Table
Table 3. Correlations among the constructs.
Table 3. Correlations among the constructs.
MeanSD.12345678910111213
1. Firm Size3.651.437
2. Firm age3.710.8540.515
3. IC10.1720.217−0.0790.124
4. IC20.1530.287−0.0980.178 *−0.121
5. IC30.0890.3150.0160.091−0.087−0.081
6. IC40.2120.1590.0310.212−0.067−0.023−0.021
7. ET3.8700.6530.078 *0.045−0.064−0.120−0.033−0.125
8. BDAI3.9290.6460.168 *0.102−0.175−0.184−0.1260.1330.326 *
9. BDAM3.9880.5860.1710.139−0.019−0.174−0.147−0.1190.1880.622 **
10. BDAP4.0890.5920.1330.113−0.062 *−0.207−0.211−0.0880.396 *0.619 **0.599 **
11. DMC4.1930.613−0.017−0.119−0.045−0.1260.011−0.1460.533 **0.511 **0.502 **0.436 **
12. CFC4.0840.5780.127 *−0.118−0.121−0.154−0.168−0.1730.419 **0.486 **0.443 **0.423 **0.390 **
13. ERI4.0190.434−0.0410.196−0.023−0.116−0.203−0.2020.488 **0.472 **0.416 **0.372 **0.406 ***0.487 ***
14. EII3.9860.598−0.0570.2130.018−0.134−0.165−0.1910.476 **0.455 **0.439 **0.381 **0.412 ***0.510 ***0.647 ***
Notes: ET: Environment turbulence RP: Respondent’s position BDAI: BDA Infrastructure Flexibility BDAM: BDA Management Capabilities; BDAP:BDA Personnel Expertise: DMC: DMC; CFC: Cross-functional Integration Capability, ERI: Exploratory innovation: EII: Exploitative innovation. *** p < 0.001 ** p < 0.01 and * p < 0.05 (two-sided test).
Table
Table 4. The regression predicting innovation ambidexterity (t-values).
Table 4. The regression predicting innovation ambidexterity (t-values).
Model 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8
Dynamic Decision-
Making Capability		Innovation Ambidexterity
Control variable
Firm Size0.113
(0.219)		0.076
(0.256)		0.077
(0.037)		0.069
(0.577)		0.070
(0.978)		0.062
(0.747)		0.059
(0.229)		0.055
(0.331)
Firm Age0.069
(0.764)		0.057
(0.669)		0.094
(1.259)		0.081
(1.233)		0.077
(1.369)		0.091
(1.225)		0.083
(1.617)		0.079
(1.585)
IC1−0.035
(−1.891)		−0.055
(1.071)		−0.024
(−0.354)		−0.017
(−0.722)		−0.021
(−1.214)		−0.027
(−0.539)		−0.020
(−0.878)		−0.022
(−0.819)
IC2−0.061
(−1.263)		−0.055
(1.071)		−0.033
(−0.817)		−0.026
(−0.157)		−0.031
(−1.552)		−0.029
(−0.337)		−0.019
(−0.518)		−0.016
(−0.421)
IC3−0.125 *
(−2.376)		−0.055
(1.071)		−0.151
(−1.055)		−0.075
(−0.418)		−0.067
(−0.689)		−0.069
(−0.782)		−0.062
(−0.653)		−0.066
(−0.562)
IC4−0.098
(−1.891)		−0.073
(1.139)		−0.089
(−0.257)		−0.077
(−0.775)		−0.081
(−1.306)		−0.079
(−0.504)		−0.068
(−0.512)		−0.066
(−0.489)
ET0.316 *
(2.322)		0.227
(6.345)		0.313 *
(2.424)		0.265
(1.387)		0.215
(1.214)		0.101
(1.087)		0.108
(1.452)		0.099
(1.365)
Independent variable
BDACS 0.379 ***
(4.409)		 0.365 ***
(5.216)		0.298 ***
(4.746)		0.302 ***
(5.172)		0.267 ***
(4.032)		0.216 ***
(4.442)
Mediator
DMC 0.265 **
(3.187)		 0.261 **
(3.837)		0.193 *
(2.721)
Moderator
CFC 0.231
(1.556)		0.211
(1.032)		0.176
(4.442)
DMC × CFC 0.109 *
(2.480)
R20.2550.4630.2870.3780.3950.3360.4010.412
Adjust R20.2480.4420.2650.3460.3610.3170.3880.391
F25.178 ***46.128 ***17.196 ***19.345 ***18.176 ***28.134 ***26.358 ***27.675 ***
Notes: ET: Environment turbulence BDACS: big data analytic capabilities; DMC: Dynamic Decision-making Capability; CFC: Cross-functional Integration Capability. All beta coefficients are standardized, with t-value in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed test; N = 180).
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Liao, S.; Hu, Q.; Wei, J. How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model. Sustainability 2023, 15, 3948. https://doi.org/10.3390/su15053948

AMA Style

Liao S, Hu Q, Wei J. How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model. Sustainability. 2023; 15(5):3948. https://doi.org/10.3390/su15053948

Chicago/Turabian Style

Liao, Suqin, Qianying Hu, and Jingjing Wei. 2023. "How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model" Sustainability 15, no. 5: 3948. https://doi.org/10.3390/su15053948

APA Style

Liao, S., Hu, Q., & Wei, J. (2023). How to Leverage Big Data Analytic Capabilities for Innovation Ambidexterity: A Mediated Moderation Model. Sustainability, 15(5), 3948. https://doi.org/10.3390/su15053948

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