Big data analytics capability as a major antecedent of firm innovation performance

Abstract

Purpose: This study aimed to investigate the big data analytics capabilities (BDAC) model using resource-based theory (RBT) and dimensions of big data analytics (management, technological, and talent) that influenced the firm innovation performance. Design/methodology/approach: The research uses quantitative research design where 548 respondents were selected for the survey from Pakistan electronic media regulatory authority (PEMRA), national database and registration authority (NADRA), and cellular companies. Only 394 useable responses were received from the respondents. Findings: The findings revealed that BDAC has a statistically positive impact on firm innovation performance. All of the proposed hypotheses were approved in this study. Research limitations/implications: The study gives future direction to the researchers and practitioners to implement this model in other industries. Practical implications: The research makes important theoretical and methodological contributions to the business and society's nexus in developing country firms that are under economic pressure. Originality/value: The paper is new in the context of the developing firm's innovation.

Keywords

Big data analytics capabilities Firm innovation performance.

1. Introduction

Big Data has emerged as an exciting frontier of productivity and opportunity in recent years, and is posing a growing obstacle to business firms (Grover et al., 2018). The big data analytics capability (BDAC) is generally regarded as having the potential to change the way businesses operate (Albergaria and Jabbour, 2020; Ciampi et al., 2021). According to recent literature, BDAC has the ‘potential to transform management theory and practice’ (George et al., 2014: 235), is the ‘next big thing of innovation’ (Marjanovic et al., 2019); and is ‘the fourth paradigm of science' (Strawn, 2012: 34), or is the next ‘management revolution’ (McAfee et al., 2012).

In the current competitive environment, businesses seek to maintain a competitive edge, global based on the BDAC (Ciampi et al., 2021; Mikalef et al., 2020a; Mikalef et al., 2020b). The BDAC likely to alter management practice and theory, which is the upcoming resolution for the innovation (Mikalef et al., 2019b; Yang et al., 2017). This research views the human and technical base, a mixture of technology, management, and talent of human resources (Kim and Lee, 2012). The survey of 2016 explained that about 48% of the firms capitalized on big data applications (Dery et al., 2017). Therefore, the objective of the current study is to observe the impact of each big data dimensions (i.e. management, technical, talent) on firm innovation performance. Which is lacking in the context of the developing country and especially in the context of Pakistan. In this study, we focus on the innovation process through the influence of BDPA and its sub-dimensions which need attention in Pakistan electronic media regulatory authority (PEMRA) and national database and registration authority (NADRA) sectors.

Moreover, the importance of process innovation in firm achievement has been reinforced widely in the literature (Slater et al., 2014; Vicente-Saez et al., 2020). Enlightening firms’ achievement is the key motive that firms capitalize on big data (Akter and Wamba, 2016; Ghasemaghaei and Calic, 2019; Hallikainen et al., 2020). Recent studies show that big data has likely to improve a firm's performance up to a 5.9% level (S. Gupta et al., 2020; Mikalef et al., 2019a; Müller et al., 2018). The above arguments show that firms’ performance can be increased by using the latest digital approach (Vicente-Saez et al., 2020; Wamba et al., 2017).

Based on the literature mentioned above, BDAC had further divided into three dimensions these are (management, technology, talent), which highlight the link between them to make efficacy in functioning level and organization performance in a supportable way and get a competitive advantage over others. The current study focuses on the association between BDAC and firm innovation performance. Regardless of the key perception, and empirical evidence of BDAC subsidizes the performance is lacking (Bauer et al., 2016). Thus, on the academic basis of resource-based theory (RBT) and informational technology ability, the study will answer the following questions.

a. what are the importance of the BDAC and its key dimensions? b. How the BDAC and its dimension improve the firm ability? c. How BDAC can help to boost up the firm innovation performance?

To report the above mentions questions, we distributed a questionnaire to 548 respondents and only 394 useable responses were received for the proposed study model. This paper makes numerous vital contributions to the literature. The findings of the current study show the requirement to planning and operationally differentiate among the main features of big data, relatively treating big data as a holistic concept. Especially, the results specify that big data management capabilities enhance overall planning, control, and strategy development regarding process innovation and quick response. Another finding of the current paper is BDA technical capabilities improve the organization's innovation performance through connectivity with the goal, compatibility, and building effective models according to the actual situation to overcome the hurdles. And BDA talent capabilities influenced the firm innovation performance in several ways, from recruiting to training and development and retaining the competent workforce to overcome the challenges. In calculation, this study contributes to the RBT literature by analyzing whether big data helps firms generate new ideas positively and proficiently, leading to enhance the firm's overall performance. The study also has interesting results for the significance of innovation efficiency and efficacy on firm performance. Overall, this study's results propose beneficial guidelines to help firms realize the important role of each main characteristic of big data in improving their outcomes.

The following section examines the relevant literature on RBT theory and big data. Based on the previous research studies, we are developing our research model and hypotheses. Following that, we discuss our sample and processes. We discuss our research model using CFA and path co-efficient in the results section. Since innovation, firm performance, and big data are multivariate paradigms, we analyze the impact of each big data characteristics on firm innovation performance in the post hoc study. We address the theoretical and practical consequences of our results in the following section. Our analysis suggests avenues for future studies, which we will also explore in the Discussion section. The paper begins with a recapitulation of our key objectives and a rundown of our results.

This study has the theoretical and practical contribution to the body of knowledge of the firm innovation performance.

2. Literature review

There are two main conventions of the RBT regarding firm resources and why some firms give a better result to improve the performance. Business operating firms own a mixture and variety of resources (Peteraf and Barney, 2003; Shan et al., 2019; Zareravasan and Ashrafi, 2019).

This assumption about different resources specifies the in some firms to complete the purposes. Secondly, ‘variances properties enabled by the difficulty of exchanging resources crosswise firms.’ This assumption indicates ‘resource immobility,’ which highpoints the detail synergistic assistance from several resources continual over time (Barney and Hesterly, 2012). Initially, the valuable measurement of resources allows an organization to boost net returns and decrease expenses (Zhu, 2004), which supports businesses take advantage of an occasion and minimalize a risk (Barney and Hesterly, 2012). The second dimension specifies that a smaller number of businesses-controlled resources to take competitive age over others, third is an imitable dimension, recommends that the organization not straight copy the resources since they are not imitating and expensive. The investigation about resource complementarity between the firm's resources is complex that the rival business cannot replace (Morgan and Payne, 2009). According to Porter and Millar (1985), the firm fundamental ambiguity, path reliance, and social complexity make long-term advantages that are firm innovative capability. Ali et al. (2020) investigated that BDAC is a vital factor of firm capabilities, which is not easily copied able; hence the firm innovative performance became a superior source. Firm innovative performance is the creation of some extra values compared to an ordinary competitor that cannot have the ability to produce these values in the industry (Peteraf and Barney, 2003). All these above discussions explain that resource-based theory and big data predictive analytics has a strong connection with each other (Mishra et al., 2019).

2.1 Big data analytics capability

The investigations and taking action to huge data for making the future direction of the firm is known as big data (Allam and Dhunny, 2019; Davenport, 2014; Gupta and George, 2016). Based on RBT, BDAC is defined as a firm's unique ability to investigate the quality problem, given the best suitable price, identify and retain customers in the huge data environment (Davenport and Harris, 2007). There are two main features of big data: big data analytics (BDA), the technological and computational infrastructure aspects, which are known as challenges in data analysis, and technical challenges (Yang et al., 2018). Another is a big data analytics capability (BDAC), which is concerned with organizational processes, like a combination of big data and other management processes and challenges (M. Gupta and George, 2016). The empirical and theoretical growths of the BDA rotate among the basic source of data, gathering, storing, handling, and data analysis. The researcher had investigated the seven characteristics and concepts of BDA (Chen et al., 2014; Mikalef et al., 2018; Sivarajah et al., 2017). The first quality denotes the size of the data, acquisition, data storage challenges, and process, which is required technological investment, this aspect of BDA is called volume (Barnaghi et al., 2013; George et al., 2014; Mittal, 2020). The second quality of the BDC is multiplicity, which is linked to the different types of data like text, images, video, and audio (Bhimani, 2015; Chen et al., 2014). The third characteristic of the BDA is velocity; the movement of data is shaped, in some cases the just in time analysis is required, and challenges for data analysis through new techniques (Carillo, 2017; Wang et al., 2019). In the fourth stage, the researcher placed the accuracy of the data related to the value, which concerns the correctness of data and mainly its sources. The fifth attribute is visualization, which denotes the presentation of the data in a meaningful way (Seddon and Currie, 2017). Sixth value attribute, which takes out from the big data for the end-user, influences the performance (Carillo, 2017). Lastly, the feature of BDA is variability, which refers to changes in interpretation and meaning of data (Carillo, 2017; Plageras et al., 2018; Seddon and Currie, 2017).

Generally, define BDAC is a skill that gives organization infrastructure, data management, human resource talent, convert the business into an inexpensive force (Kiron et al., 2014). Winter et al. (2013) argued that BDAC is the strategic analytics that gives value and sustainable development to the firm (Sivarajah et al., 2017). The researcher investigated that BDAC is the capacity to practice big data for making a decision, which is joint to the strategies of the organization (LaValle et al., 2011), the firm gets an advantage over other (Kiron et al. (2014); (Schroeck et al., 2012) highlights regarding capability and technology and talent to attain competitive compensations.

2.2 Resource-based theory (RBT)

There are two main conventions of the RBT regarding firm resources and the reasons why some firms give a better result to improve the performance. Business operating firms own a mixture and variety of resources (Barny & Peteraf, 2003).

This assumption about different resources specifies the in some firms to complete the purposes. Secondly, ‘variances properties enabled by the difficulty of exchanging resources crosswise firms.’ This assumption indicates ‘resource immobility’ which highpoints the detail synergistic assistance from several resources continual over time (Barney and Hesterly, 2012). Initially, the valuable measurement of resources allows an organization to boost net returns and decrease expenses (Arikan & Barney, 2001), which in other words supports businesses take advantage of an occasion and minimalize a risk (Barney and Hesterly, 2012). The second dimension specifies that a smaller number of businesses-controlled resources to take competitive age over other, third is imitable dimension recommends that organization cannot straight copy the resources since they are not imitating and expensive. The investigation about resource complementarity between resources of the firm as complex that the rival business cannot a replacement (Morgan and Payne, 2009). According to Millar & Porter (1985) the firm fundamental ambiguity, path reliance, and social complexity make long-term advantages that are firm innovative capability. Davenport (2006) investigated that BDAC is a vital factor of firm capabilities, which is not easily copied able; hence the firm innovative performance became a superior source. Firm innovative performance is the creation of some extra values as compared to an ordinary competitor that cannot have the ability to produce these values in an industry (Barney & Peteraf, 2003).

Following RBV logic, resources are ordered as physical capital, human capital, and organizational capital (Barney, 1991), and have been drawn-out to include other resources, for instance financial capital, technological capital, and reputational capital (Grant, 1991). Größler and Grübner (2006) argue that resources are something a firm possesses or has access to. They may be tangible, such as infrastructure, or intangible, such as information or knowledge sharing (Größler and Grübner, 2006). The literature on the RBV has argued that organizational capabilities are defined as a higher-order construct, which relies on the bundling of resources (Wu et al., 2006). Grant (1991) further argues that when resources are combined and utilized together, they create capabilities. In BDAC studies, scholars argue that BDAC is an organizational capability, which explains how organizations can leverage BDAC to achieve better organizational performance (Wang et al., 2016; Gupta and George, 2016). The BDPA capability can be created by combining strategic resources such as data connectivity and information sharing (Wang et al., 2016), and human skills and big data culture (Gupta and George, 2016), which can enhance operational performance (Srinivasan and Swink, 2018).

2.2.1 BDA management capability and firm innovation performance

BDAC heavily depends on the BDAMAC that makes important business decisions; some essential observations are related to management capabilities towards big data analytics, forecasting, investment, direction, and control system (Saide and Sheng, 2020; Sivarajah et al., 2017). BDA management capability starts with the appropriate planning procedure of BDA, which recognizes business opportunities and makes strategies for how big data models can boost the organization's performance (Barton and Court, 2012; Zareravasan and Ashrafi, 2019). On the other hand, BDA decisions for investment are crucial aspects for BDAMAC because it involves cost-benefit analyses. The firm with a massive amount of big data investments is in competitive advantages and gets surplus returns (Ramaswamy et al., 2013). By adding BDA coordination in the discussion, which has gained maximum consideration in the big data atmosphere, it is the form that represents the daily activities that correlate with the capabilities of the firm (Kiron et al., 2014).

Finally, the BDA controlling system is achieved by properly unitizing. According to (Schroeck et al., 2012), the Amazon act in BDA plans and evaluates the activities through a continuous performance monitoring system. The big data management capability allows the firm to collaborate effectively with business partners and the general public, which allows the company to achieve value creation in the business process (Dachyar et al., 2019). The firm's innovation is based on a combination of multiple actors, for example, collaboration with external and internal in an effective way (Lee et al., 2012). Based on the above literature, we can hypothesize as follow;

H1. Big data management capability positively influenced the firm innovation performance.

2.3 BDA technology capability (BDATEC) and firm innovation performance

Big data technology capability gives a stage to the BDA (connectivity of data, compatibility, and model building) that quickly gives data analytes a strategy and supports the firm innovation process (C. Lin et al., 2012). There are three main components of big data technological capability, connectivity, compatibility, and modularity. These are so important for a business to track the direction, competition, and behaviours of consumers and make parallel resources with short and long-term business strategies. The organization connects with various sources and areas and points out directly by remote, creating data-sharing channels to various functional areas, and addressing the channel need through models. Thus, according to Ren et al. (2017), the firm's flexibility regarding big data technical ability depends on connectivity and compatibility. The connectivity between different business areas and analyzing a wide range of data. T. H. Davenport and Harris (2007) argued that Comprehensibility allows the fluent flows of information to make on-the-spot decisions. According to Lu et al. (2011), there is a significant positive relationship between big data-innovation field surveys.

Similarly, Chen et al. (2014) found that big data technical capability allows a firm to innovative performance. The results showed that technical capabilities and human capital investment directly contributes to the overall value creation process (Lin, 2007). Big data can affect the efficiency of the firm and improve the features of the product according to the requirements of customers, leading to improved innovation performance (Ghasemaghaei and Calic, 2019). According to Kim and Lee (2012), BDATECA directly affects firm performance. According to the above arguments, we proposed our hypothesis as;

H2: Big data technical capability positively influences the firm innovation performance.

2.4 BDA talent capability (BDATLC) and firm innovation performance

The corporate data denotes considerate numerous settings, and different professional functions develop their impression for customers understanding, personal knowledge about the ability to communicate among the work with team and business departments overall the proficiency and developed skills through proper coaching, training, and managing the project can boost the knowledge and infrastructure (Luo et al., 2019; Zhang et al., 2020). Communication is the only way to exchange and create knowledge and uses various tasks relating to the firm innovation cycle, such as evaluation, collaborative idea suggestion, and expansion of business innovation activities (Ahmadi-Abhari et al., 2017; Teece, 2017). According to Del Vecchio et al. (2018), the big data talent capability shares a complementary and essential feature with its innovation; its management generates and improves superior value and maintains competitive advantages. Cabrilo et al. (2020) argued that organizations need to develop a collaborative work culture, which facilitates new knowledge, building a fostering the joint knowledge to enhance the firm innovation performance (Chatterjee et al., 2020). The big data talent capability allows a firm to create and built new ideas to facilitate the collection, evaluation, and refinement of ideas to determine potential investment projects, which leads to firm innovation (Konya-Baumbach et al., 2019; Rizzi et al., 2019). see (Figure 1). Considering the above discussion, we proposed the following hypotheses

H3: Big data talent capability have positively and directly influenced the firm innovation performance

Figure 1.

Study proposed model.

3. Research methodology

3.1 Participants and procedure

The quantitative (survey-based) methodology is used in this study. We collect the data from three different sectors of Pakistan that include PEMRA, NADRA, and cellular companies. The reason for taking these sectors was that they are using big data and their work heavily depends on big data across the country. The data were collected by the researcher in November and December 2019 by using random sampling techniques. The respondent of the survey was the in-charge/head of the unit/branch, head of the planning unit, the person in-charge of the R&D department, business analyst. 548 questioners were distributed among participants. We received 394 responses for further analysis, which were filled by the respondent. Thus, the given response rate of almost 72%. Table 1 shows the demographic information of the respondent with industry and firm information. According to Table 1, 37.1% of the respondent are 50 + years old. Table 1 shows that 69% of the respondent were male. For the level of education, 36.5% of the respondent having a master's or professional degree. In terms of several years of employees working with an organization, 32.8% of respondents had spent between 6–10 years serving in a firm. Table 1 illustrated that 31.4% of the respondents working in NADRA and passport offices, 33.2%, worked in media houses and PEMRA, and 35.2% of respondents working in the telecommunication sector. For the firm size, the number of employees working in a business setting 29.8% is 251–500 workers. Most firms are aged between 10–15 years working in the same industry.

Table 1.
Demographic information of respondent and firm information.

Variable Frequency Percentage

Age

20–25 years 25 6.3

26–30 39 9.9

31–40 84 21.4

41–50 100 25.3

50 + 146 37.1

Gender

Male 272 69

Female 122 31

Education

Primary education 20 5.3

Secondary education 76 19.2

Under graduation 81 20.5

Master degree 140 35.5

PhD degree 77 19.5

Experience

0–5 years 101 25.6

6–10 129 32.8

11–15 73 18.5

16–20 57 14.5

20 + 34 8.6

Firm size

2–50 workers 54 13.7

51–100 50 12.5

101–250 69 17.4

251–500 117 29.8

500 + 104 26.2

Firm age

Less than 5 years 28 7.1

6–10 years 49 12.4

11–15 161 41

16–20 87 22.1

20 + 69 17.4

Industry

Media houses and PEMRA 131 33.2

NADRA and passport offices 124 31.4

Telecommunication 139 35.2

Respondent position

Head of unit/branch 64 16.2

Head of R&D 91 23.1

Head of the planning department 107 27

Business analyst 77 19.5

IT professionals 55 14.2

Variable	Frequency	Percentage
Age
20–25 years	25	6.3
26–30	39	9.9
31–40	84	21.4
41–50	100	25.3
50 +	146	37.1
Gender
Male	272	69
Female	122	31
Education
Primary education	20	5.3
Secondary education	76	19.2
Under graduation	81	20.5
Master degree	140	35.5
PhD degree	77	19.5
Experience
0–5 years	101	25.6
6–10	129	32.8
11–15	73	18.5
16–20	57	14.5
20 +	34	8.6
Firm size
2–50 workers	54	13.7
51–100	50	12.5
101–250	69	17.4
251–500	117	29.8
500 +	104	26.2
Firm age
Less than 5 years	28	7.1
6–10 years	49	12.4
11–15	161	41
16–20	87	22.1
20 +	69	17.4
Industry
Media houses and PEMRA	131	33.2
NADRA and passport offices	124	31.4
Telecommunication	139	35.2
Respondent position
Head of unit/branch	64	16.2
Head of R&D	91	23.1
Head of the planning department	107	27
Business analyst	77	19.5
IT professionals	55	14.2

3.2 Measures

3.2.1 Big data analytics management capabilities BDAMC

Overall, we measure BDMC by using 16 items on a 5-point Likert scale. We further divide BDMC into (planning, investment decision making, coordination, and control (Karimi, 2014; Kim and Lee, 2012).

3.2.2 Big data analytics technical capabilities (BDATEC)

Big data analytics technical capabilities were measured by using 12 items. BDATEC is further divided into three factors that are, connectivity, compatibility, and modularity. These all have four items at a 5-point Likert scale which is previously used (Duncan, 1995; Kim and Lee, 2012; Terry Anthony Byrd, 2000).

3.2.3 Big data analytics talent capabilities (BDATLC)

Big data analytics talent capabilities were measured using 16 items. The big data analytics talent capabilities are further divided into technical knowledge, technical management knowledge, business knowledge, and relational knowledge. To measure big data analytics talent capabilities, we used the four-item for all the sub-dimensions of BDATLC, which is previously used by (Kim and Lee, 2012; Terry Anthony Byrd, 2000).

3.2.4 Firm innovation performance

The firm innovation performance was previously measured by (Garmaki et al., 2016; Tippins and Sohi, 2003) using nine items. We adopted the same scale as they used to measure the firm innovation performance.

3.3. Control variable

The respondent's age, gender, education level, and experience in a firm are measured as control variables, and the firm's size, age of the firm, and industry by which the firm operates. The position of the respondent that takes part in the survey was also investigated. Wamba et al. (2018) used the same measurement scale by using a 5-point Likert scale.

4. Results

4.1 Measurement model

Table 2 confirms convergent discriminant rationality by using CFA; all the factor loading exceeded the minimum limit of 0.5 and significant at 0.001. All the factor loading is greater than 0.70, which provides greater convergence, composite reliability (CR) all scales exceed 0.70 and AVE more than 0.50 (Aguirre-Urreta et al., 2013). We examine common method bias by conducting Harman's single factor test (Podsakoff et al., 2003), recommended by Sun (2012); Zhang et al. (2018). Every factor shows less than 50% variance at an unrotated solution. If one factor accounts for most of the covariance in variables or a single factor appears from an unrotated solution, maybe common method bias exists. Still, the finding showed nothing happens about these issues in this study. We also conducted a full collinearity test to measure VIF values for all the research model variables. The maximum value of VIF of the data (2.46) indicates that in this research model, VIF values were below the threshold of 3.3 (Kock and Lynn, 2012). Based on the findings, both common method bias and multicollinearity were not an issue in this study. Hair et al. (2010) and Hu and Bentler (1999) suggested model fitness indices as threshold parameters for the final measurement model's fitness. All of the results are within ranges (IFI = 0.965, NFI = 0.962, CFI = 0.967, TLI = 0.965, GFI = 0.903, RMSEA = .056, SRMR = 0.042, CMIN/DF = 278.76/145 = 1.931). So that model is fit for the further analysis process.

Table 2.
Factor loading, composite reliability (CR), and AVE.

Variables and sub-dimensions Items Factor loading CR AVE Cronbach's alpha

Big data analytics management capabilities planning BDAMCP1 BDAMCP2 BDAMCP3 BDAMCP4 0.910 0.893 0.925 0.900 0.94 0.822 0.87

BDAMCIDM Big data analytics management capabilities investment decision making BDAIDM1 BDAIDM2 BDAIDM3 BDAIDM4 0.786 0.808 0.879 0.908 0.90 0.71 0.83

Big data analytics management capabilities coordination BDAMCoC1 BDAMC0C2 BDAMC0C3 BDAMCoC4 0.885 0.935 0.921 0.916 0.95 0.83 0.86

Big data analytics management capabilities control BDACL1 BDACL2 BDACL3 BDACL4 0.862 0.849 0.897 0.914 0.93 0.77 0.90

Big data analytics technical capabilities connectivity BDATC1 BDATC2 BDATC3 BDATC4 0.964 0.948 0.935 0.956 0.97 0.90 0.85

Big data analytics technical compatibility BDTC1 BDTC2 BDTC3 BDTC4 0.780 0.793 0.836 0.861 0.89 0.67 0.79

Big data analytics technical modularity TECMO1 TECMO2 TECMO3 TECMO4 0.89 0.83 0.87 0.782 0.90 0.71 0.89

Big data analytics talent capabilities Technical Management knowledge BDTK1 BDTK2 BDTK3 BDTK4 0.896 0.956 0.915 0.924 0.95 0.85 0.91

Big data analytics talent capabilities BDATK1 BDATK2 BDATK3 BDATK4 0.946 0.938 0.923 0.936 0.96 0.87 0.89

Big data analytics talent capabilities business knowledge BG1 BG2 BG3 BG4 0.957 0.946 0.928 0.909 0.96 0.87 0.88

Big data analytics talent capabilities rational knowledge RK1 RK2 RK3 RK4 0.702 0.767 0.793 0.784 0.85 0.58 0.78

Firm innovation performance FRIP1 FRIP2 FRIP3 FRIP4 FRIP5 FRIP6 FRIP7 FRIP8 FRIP9 0.826 0.886 0.894 0.783 0.849 0.915 0.937 0.908 0.873 0.96 0.76 0.92

Variables and sub-dimensions	Items	Factor loading	CR	AVE	Cronbach's alpha
Big data analytics management capabilities planning	BDAMCP1 BDAMCP2 BDAMCP3 BDAMCP4	0.910 0.893 0.925 0.900	0.94	0.822	0.87
BDAMCIDM Big data analytics management capabilities investment decision making	BDAIDM1 BDAIDM2 BDAIDM3 BDAIDM4	0.786 0.808 0.879 0.908	0.90	0.71	0.83
Big data analytics management capabilities coordination	BDAMCoC1 BDAMC0C2 BDAMC0C3 BDAMCoC4	0.885 0.935 0.921 0.916	0.95	0.83	0.86
Big data analytics management capabilities control	BDACL1 BDACL2 BDACL3 BDACL4	0.862 0.849 0.897 0.914	0.93	0.77	0.90
Big data analytics technical capabilities connectivity	BDATC1 BDATC2 BDATC3 BDATC4	0.964 0.948 0.935 0.956	0.97	0.90	0.85
Big data analytics technical compatibility	BDTC1 BDTC2 BDTC3 BDTC4	0.780 0.793 0.836 0.861	0.89	0.67	0.79
Big data analytics technical modularity	TECMO1 TECMO2 TECMO3 TECMO4	0.89 0.83 0.87 0.782	0.90	0.71	0.89
Big data analytics talent capabilities Technical Management knowledge	BDTK1 BDTK2 BDTK3 BDTK4	0.896 0.956 0.915 0.924	0.95	0.85	0.91
Big data analytics talent capabilities	BDATK1 BDATK2 BDATK3 BDATK4	0.946 0.938 0.923 0.936	0.96	0.87	0.89
Big data analytics talent capabilities business knowledge	BG1 BG2 BG3 BG4	0.957 0.946 0.928 0.909	0.96	0.87	0.88
Big data analytics talent capabilities rational knowledge	RK1 RK2 RK3 RK4	0.702 0.767 0.793 0.784	0.85	0.58	0.78
Firm innovation performance	FRIP1 FRIP2 FRIP3 FRIP4 FRIP5 FRIP6 FRIP7 FRIP8 FRIP9	0.826 0.886 0.894 0.783 0.849 0.915 0.937 0.908 0.873	0.96	0.76	0.92

The data's reliability and validity were examined in this report. The factor loading values were higher than the 0.60 threshold value suggested by Fornell and Larcker (1981). To ensure convergent validity, Cronbach alpha, AVE, and CR were calculated. Table 2 displays the results of CR: values ranging from 0.85 to 0.97, higher than Nunnally (1978) threshold value of 0.70; and Cronbach alpha values ranging from 0.58 to 0.90, higher benchmark value of 0.50. The threshold value of AVE, according to (Bagozzi and Yi, 1988), is 0.50. Table 2 shows that all AVE values ranged from 0.78 to 0.91.

Fornell and Larcker (1981) guidelines for determining discriminant validity were used (Kanwal et al., 2019). Table 3shows that all of the variables’ intercorrelations are smaller than the square root of AVE. To check distinguish validity, they compare the intercorrelation between constructs and the square root of the construct's AVE. The square root value of AVE is greater than the intercorrelations between constructs, indicating that discriminant validity is fine.

Table 3.

Descriptive statistics and correlation.

Variables	Means	SD	1	2	3	4
Big data management capability	4.86	1.44	0.92*
Big data technological capability	4.32	1.36	0.36	0. 90*
Big data talent capability	4.14	1.15	0.32	0.36	0. 89*
Firm innovation performance	4.28	1.32	0.29	0.34	0.38	0.91*

The bold values are square roots of average.

4.2 Structural model

Data shows that BDA capabilities affect co-innovation performance. The BDAMC higher firm innovation performance with path-coefficients 0.38 (0.001). The big data analytics technical capabilities (BDATEC) enhanced the FRIP (firm innovation), path-coefficients 0.36 (0.001). Finally, BDATLC (talent capabilities) enhanced the firm innovation performance by path-coefficients of 0.36 (0.001). Thus, three hypotheses, H1 to H3, were supported as path-coefficients were significant at p = 0.001. Our study model's result also in line with (Cohen and Lee, 1988; Ghasemaghaei and Calic, 2019). see (Table 4).

Table 4.
Result of the structural model.

Hypotheses Path coefficients Standard error t-statistics P-value R2 Results

BDAMC-FRIP 0.381 0.102 2.587 0.001 0.625 H1 Accepted

BDATEC-FRIP 0.318 0.102 4.652 0.000 H2 Accepted

BDATLC-FRIP 0.363 0.892 1.987 0.002 H3 Accepted

Hypotheses	Path coefficients	Standard error	t-statistics	P-value	R2	Results
BDAMC-FRIP	0.381	0.102	2.587	0.001	0.625	H1 Accepted
BDATEC-FRIP	0.318	0.102	4.652	0.000		H2 Accepted
BDATLC-FRIP	0.363	0.892	1.987	0.002		H3 Accepted

5. Discussion

The purpose of the study was to explore and test a model of BDAC enhanced innovation performance. Big data analytic capabilities are further divided into three dimensions that are management capabilities, technical capabilities, and talent capabilities. The result indicates that big data analytics capabilities significantly and positively affected firm innovation performance. First of all, BDAMC positively enhanced firm innovation performance. The analytics capabilities of firm management promote firm performance, especially innovation performance (Kiron et al., 2014).

BDA technical capabilities are also vital for an organization, identifying the key factors of the innovation process. The technological capability tells the direction to the analytics, share information, idea, and knowledge to develop the strategies for competitive advantages of the firm and launch a reliable model (Ferraris et al., 2019).

BDA talent capabilities play a key role in the process because it promotes talent and effects the recruitment process (B. Sun and Liu, 2020). Overall, the BDAC positively influenced the firm innovation performance; the firm pays attention to all the dimensions of big data analytes. Every element plays its role in the firm innovation performance, and these are interlinking with each other.

6. Theoretical contributions

The current study significantly contributes to the literature on big data capabilities and firm innovation performance. Firstly, the current study developed the three main dimensions of the BDAC and sub-divided the scale into different dimensions. The study results show that the management capabilities enhance overall planning, control, and strategy development regarding big data model building. This finding is consistent with (Kiron et al., 2014). Secondly, our research model's findings with all the dimensions of BDAC maximize the firm innovation performance and give the directions to the management to focus on core components and improve the weak area of the firm. Thirdly the study results show that the technical and talent abilities of the firm directly engaged in the innovation process of business, which helps the analytics to analyzed the firm capabilities, keeping RBT in mind, which helps assess the direction of the firm (Teece, 2014).

7. Managerial contributions

The current study has several practical contributions to the BDAC. Big data affects the firm innovation process in an organization, like the BDA management capabilities focus on planning and give direction for future investments, coordination, and control. BDA technical capabilities improve the organization's innovation performance with its sub-dimensions, connectivity with the goal, compatibility, and building effective models, modularity. BDA talent capabilities influenced the firm innovation performance in several ways, from recruiting to training and development and retaining the competent workforce. The model guides the management to focus on the firm's technological sources and human resource capabilities to make strategies and models regarding BDA (Kiron et al., 2014; Zou and Zhao, 2018).

The study offers practical implications to the various organizations, like PEMRA and media houses, telecommunication sector, and NADRA/ passport offices that used big data by using BDA capabilities to make an alliance and focus on the strategic development to foster the innovation performance of the firm (W. Sun et al., 2020).

8. Future directions and research limitations

The study used only BDAC and its sub-dimension direct relationship with the firm innovation performance. It will be a more interesting study if the mediation and moderating variables with firm innovation performance, like business processes will be used in future studies (Chan et al., 2016). Similarly, institutional environment and employees performance studies can also be conducted with big data as a moderator (Wang et al., 2021). The study includes only individual base responses to the questions. Future studies can focus on the maximum number of participants involved in a survey (Soto-Acosta et al., 2017; Wang et al., 2020). Like an interview, discourse analysis, documentation analysis approach can be used in a future avenue of the (Zareravasan and Ashrafi, 2019) study. Lastly, this study is only specific to the big data-related organization; in future, a study can be conducted in multinational companies and large firms.

9. Conclusion

This study proposes a conceptual framework and hypothesizes relationships between big data management capabilities, big data technological capabilities, big data talent capabilities, and firms innovation performance in PEMRA, media houses, NADRA, and passport offices and cellular companies. The empirical results argue that shared goals and trust reduce knowledge hiding behaviour. Based on the SET, this research provides empirical evidence to verify big data management capabilities, big data technological capabilities, big data talent capabilities tendency to increase the firm's innovation performance. Overall, the study provides a clearer understanding of the firm's capacity to strategy and strategy to innovation success, and it opens new doors for academic researchers, corporations, and business analytics. The results of this study can be of strong policy implications for PEMRA, media houses, NADRA and Passport offices authorities, managers, supervisors, and other related parties by providing practical guidelines to design tasks associated with big data management capabilities, big data technological capabilities, big data talent capabilities, and firms innovation performance.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Arshad Muhammad

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Big data analytics capability as a major antecedent of firm innovation performance

Abstract

Keywords

1. Introduction

2. Literature review

2.1 Big data analytics capability

2.2 Resource-based theory (RBT)

2.2.1 BDA management capability and firm innovation performance

2.3 BDA technology capability (BDATEC) and firm innovation performance

2.4 BDA talent capability (BDATLC) and firm innovation performance

3.1 Participants and procedure

3.2.1 Big data analytics management capabilities BDAMC

3.2.2 Big data analytics technical capabilities (BDATEC)

3.2.3 Big data analytics talent capabilities (BDATLC)

3.2.4 Firm innovation performance

3.3. Control variable

4. Results

4.1 Measurement model

Table 4. Result of the structural model. Hypotheses Path coefficients Standard error t-statistics P-value R2 Results BDAMC-FRIP 0.381 0.102 2.587 0.001 0.625 H1 Accepted BDATEC-FRIP 0.318 0.102 4.652 0.000 H2 Accepted BDATLC-FRIP 0.363 0.892 1.987 0.002 H3 Accepted

6. Theoretical contributions

7. Managerial contributions

8. Future directions and research limitations

9. Conclusion

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References

Table 4.
Result of the structural model.

Hypotheses Path coefficients Standard error t-statistics P-value R2 Results

BDAMC-FRIP 0.381 0.102 2.587 0.001 0.625 H1 Accepted

BDATEC-FRIP 0.318 0.102 4.652 0.000 H2 Accepted

BDATLC-FRIP 0.363 0.892 1.987 0.002 H3 Accepted