The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

The intermediating role of organizational culture and internal analytical knowledge between the capability of big data analytics and a firm’s performance Parijat Upadhyaya, Anup Kumarb,* a b

TMT Nagpur (India), India IMT Nagpur (India), India

ARTICLE INFO

ABSTRACT

Keywords: Big data analytics Organizational culture Firm performance Internal analytical knowledge

Firms are increasingly relying on business insights obtained by deploying data analytics. Analytics-driven business decisions have thus taken a strategic imperative role for the competitive advantage of a firm to endure. The extent and effectiveness through which business firms can actually derive benefits by deploying big databased practices requires deep analysis and calls for extensive research. This study extends the big data analytics capability (BDAC) model by examining the mediatory effects of organizational culture (CL) between internal analytical knowledge (KN) and BDAC, as well as the mediating effects of BDAC between CL and firm performance. The findings bring into focus that CL plays the role of complementary mediation between BDAC and KN to positively impact firm performance (FP); BDAC also plays a similar mediatory role between CL and the performance of a firm.

1. Introduction The digital transformation of business processes has a massive role in generating unstructured data, in terms of volume, vareity and varacity. This leads to a new age of business where the firm performance will be purely dependent on the capability of data-driven decision-making. The rise of digital transformation has rendered a new field for data-driven culture for organizations to discover and develop a competitive advantage (George, Haas, & Pentland, 2014). There is a significant need to have analytical capabilities to use large bulks of unstructured data in various formats (Gandomi & Haider, 2015). Business organizations are investing in big data analytics capability (BDAC) to augment their swiftness and performance in terms of the prediction of customer behaviour (Ashrafi, Ravasan, Trkman, & Afshari, 2019; Shirazi & Mohammadi, 2019). Business organizations of all sizes have realized the importance of big data not only in driving their profitability but also in ensuring sustainability. Firms as per their technical and financial capabilities are intensively investing in it to leverage competitiveness (Altindag, Zehir, & Acar, 2011; Galavotti, 2019; Nakamura, 2011). Are all the firms who are joining the bandwagon of big data analytics (BDA) deriving the desired return? Firms need to pay adequate attention to issues such as organizational culture (CL) and the alignment of the business process among others, to expect

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a positive outcome. Gupta and George (2016) touted that focusing only on BDAC is ineffective until the firm has a supportive culture to leverage the prospects as determined through BDAC. The knowledge integrating capability of firms and the role of business processes have also emerged as significant drivers for leveraging BDAC. Côrte-Real, Ruivo, Oliveira and Popovič (2019), in their study, have attempted to rank various drivers influencing the value of BDAC and its effects on a firm’s performance. Dozens of organizational theories have been used to study the effect of BDA on firm performance (FP); the popular theories are actor-network theory, agency theory, contingency theory, diffusion of innovation theory, dynamic capabilities view, ecological modernization, game theory, institutional theory, knowledge-based view, knowledge management theory, organizational information processing view, resource dependence theory, resource-based view (RBV), social capital theory, social exchange theory, sociomaterialism theory, stakeholder theory, technology acceptance model, and transaction cost theory (de Camargo Fiorini, Seles, Jabbour, Mariano, & de Sousa Jabbour, 2018). We have extended the dynamic capabilities view (resource-based theories) and the sociomaterialism theory to investigate the role of CL on FP in the presence of BDAC. The primary objective of BDAC in an organization is to create a competitive advantage from the action ideas derived from BDA (Mikalef, Pappas, Krogstie, & Giannakos, 2018; Mishra, Luo, &

Corresponding author. E-mail addresses: [email protected] (P. Upadhyay), [email protected], [email protected] (A. Kumar).

https://doi.org/10.1016/j.ijinfomgt.2020.102100 Received 4 November 2019; Received in revised form 15 February 2020; Accepted 15 February 2020 0268-4012/ © 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Parijat Upadhyay and Anup Kumar, International Journal of Information Management, https://doi.org/10.1016/j.ijinfomgt.2020.102100

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Hazen, 2018). The emergence of BDAC has created a blue ocean for researchers in terms of management theories and practices, innovation and next management revolution (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016; McAfee, Brynjolfsson, Davenport, Patil, & Barton, 2012). Wamba et al. (2017) reported that business firms can enhance their competitive advantage by using BDAC when the organization is ready to adopt BDA facilitating tools. BDAC has been reported to support data-driven management decision mechanism to improve overall FP (Campbell & Freeman, 1991; Xia & He, 2017). Studies in the area of information and communication technology have reported contradictory outcomes regarding the consequence of information technology (IT) investment on FP (Rahman, 2017). Some scholars have reported that the investment on it does not guarantee improvement in efficiency or effectiveness of the firm (Akter et al., 2016; Irani, 2010), while some other researchers have the opposite view (Barua, Kriebel, & Mukhopadhyay, 1991; Barua, Kriebel, & Mukhopadhyay, 1995). BDAC includes IS(Information System) infrastructure and a skilled analyst. A BDAC measurement scale has been developed by Gupta and George (2016) to gauge the BDAC; furthermore, the scale is used to test the effect of BDA on FP. Wamba et al. (2017) have further refined the idea of BDA by addressing the influence of process-oriented dynamic capability (PODC) on FP. In this study, a hybrid methodology has been used to illustrate that data, technology, people, processes, and the organization have a significant role in business performance (Mikalef et al., 2018). We refine and extend the BDAC model to further probe the examination of the following questions:

study to measure BDAC. BDAC has three dimensions, namely (1) explicit knowledge (data, technology), (2) tacit knowledge (culture, organizational learning), and (3) human knowledge (managerial skills, technical skills) (Gupta & George, 2016). The BDAC model proposed by Wamba et al. (2017) defines the measurement items which include second-order measurement constructs such as BDA infrastructure flexibility, management capability, and personal expertise capabilities. FP is measured in two dimensions: financial performance and market performance (Mithas, Ramasubbu, & Sambamurthy, 2011; Tippins & Sohi, 2003). In this study, we have also included a third dimension (innovation performance) to measure FP. BDAC is broadly defined as the competence to provide business insights using data management, infrastructure (technology), and talent (personal) capabilities to transform the business into a competitive force (Kiron et al., 2013). Similarly, constructs such as BDA infrastructure capability, big data management capability, and PODCs are adapted from Kim et al. (2011), Kim et al., 2012. The items that measure BDAC are shown in Table A1a in the Appendix A. 2.2. Organizational culture According to Gupta and George (2016), BDA capability has three dimensions, as discussed in Section 2. However, CL is tacit knowledge, which is measured in terms of BDAC using the second-order construct in the BDAC model. We intend to measure it separately and test its mediating effect on BDAC and Internal Analytical Knowledge. CL is defined as the organizational norms and expectations regarding how people behave and how things are done in an organization. This includes implicit norms, values, shared behavioural expectations, and assumptions that guide the behaviours of the members of a work unit. CL refers to the core values of an organization, its services, or products, as well as how individuals and groups within the organization treat and interact with each other (Schein, 2010). The ability to build CL is a key driver in executing the learned capabilities (Zheng, 2005). Good culture in the organization supports better utilization of learned capabilities (Alavi & Leidner, 2001; Campbell & Freeman, 1991; Ke & Wei, 2008). It is necessary to embed the BDAC into the CL to leverage the capabilities for better FP (Massey & Montoya-Weiss, 2006). The instruments that are available to represent the CL have been formulated with a focus on various dimensions of organization culture, for example, the competing values framework (Campbell & Freeman, 1991; Gerowitz, Lemieux-Charles, Heginbothan, & Johnson, 1996; Hau, Kim, Lee, & Kim, 2013) focuses on staff climate, leadership style, bonding systems, and prioritization of goals. Walker, Symon and Davies (1996) focused on four major areas: performance, human resources, decision-making, and relationships. Håvold and Håvold (2019) identified thirteen dimensions: overall satisfaction, understanding of expectations, access to required resources, appropriate use of skills, recognition and praise for achievements, relationship with supervisors, encouragement for self-development, perceptions of worth, engagement with the organizational mission, commitment to all employees, friendships, appraisal, and opportunities for career progression. Law and Geng (2020) focus on three values: the need for security, the importance of work, and the need for authority. Within these, there are six factors relating to practice issues: process vs. outcome, employee vs. task, parochial vs. professional, open vs. closed system, loose vs. tight control, and normative vs. pragmatic. An Organizational culture survey by Glaser, Zamanou and Hacker (1987) focused on six empirical factors: teamwork and conflict, climate and morale, information flow, involvement, supervision, and meetings. Mackenzie (1995) focuses on employee commitment, attitudes toward and belief about innovation, attitudes to change, style of conflict resolution, management style, confidence in leadership, openness and trust, teamwork and cooperation, action orientation, human resource orientation, consumer

1 How does CL affect BDAC and the firm’s performance? 2 How does CL mediate the influence of internal analytical knowledge (KN) on BDAC and FP? 3 How does KN mediates the association between CL and the firm’s BDAC? This article is presented as follows: Section 2 discusses the theoretical foundation of the research framework used in this article. Section 3 presents the research hypothesis and the proposed model. It is followed by Section 4, where the research methodology is presented. Section 5 presents the results obtained from the data analysis. Section 6 highlights the theoretical contributions to the existing theoretical base and the managerial implications of the findings along with data synthesis. The limitations of this study and scope for future research are presented in Section 7. 2. Review of literature The review of the literature begins with details of the BDAC model and its effect on organizational performance. Furthermore, the review formerly covers the other three significant constructs of the study, namely CL, FP, and KN. 2.1. BDAC model The BDAC model was proposed by Wamba et al. (2017). The model theory is rooted in the RBV (Gunasekaran et al., 2017, Hazen et al., 2012, Zhao et al.,2010 and Grant, 1991) and relational sociomaterialism (Kim, Shin, Kim, & Lee, 2011; Kim, Shin, & Kwon, 2012; Orlikowski, 2007; Orlikowski & Scott, 2008). The BDAC model is also based on PODCs, and the emerging literature on BDA (Davenport, Barth, & Bean, 2012; Kiron, Ferguson, & Prentice, 2013). The model investigates the effect of BDAC on FP. The findings of the study confirm that FP is affected by the organizational capability to leverage BDAC and its PODCs to render the knowledge. The BDAC model is based on BDA capabilities, IT capabilities, and sociomaterialism theories (Ji-fan Ren, Fosso Wamba, Akter, Dubey, & Childe, 2017). The original constructs of the BDAC model (Wamba et al., 2017) have been used in this 2

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orientation, and organizational direction. The abovementioned instruments and various other instruments are available to measure organizational culture, which are mainly categorized into two categories (Scott, Mannion, Davies, & Marshall, 2001):

knowledge embedded in the human mind through experience and jobs, personal wisdom, and experience. It is context-specific and is difficult to extract (Haldin-Herrgard, 2000). Conversely, explicit knowledge has been referred to as knowing-that, and also as knowledge codified and digitized in books, documents, reports, memos, etc. (Boiral, 2002). Employees’ internal knowledge is one of the key performance indexes to improve FP (Wang, Sharma, & Cao, 2016). KN is the knowledge that is the combination of tacit knowledge and explicit knowledge. Extending the theories of tacit and explicit knowledge, KN is the composite of technical knowledge (TK), technological management knowledge (TMK), business knowledge (BK), and relational knowledge (RK) (Wamba et al., 2017). KN is a resource that may be acquired over a period of time with the help of organisational culture. KN is measured using the scale provided by Wamba et al. (2017). The details of the items and their loadings are shown in Table A3 (see Appendix A). The review of the literature clearly indicates that BDAC is measured using the combined effect of tacit and explicit knowledge. The published literature does not specify how tacit knowledge is transformed into its explicit form, thereby contributing toward a firm’s KN and organizational culture. This study attempts to gauge the interplay between various forms of knowledge and their effect on the firm’s performance through the lens of CL and KN leading to effective BDAC.

1 Typological approaches that deal with the competing values framework, Harrison’s organizational ideology questionnaire, and quality improvement implementation survey. 2 Dimensional approaches which include CL inventory, hospital culture questionnaire, nursing unit culture assessment, tool practice culture questionnaire, MacKenzie’s culture questionnaire survey of organizational culture, corporate culture questionnaire, core employee opinion questionnaire, Hofstede’s organizational culture questionnaire, and organizational culture survey. Between these categories, the choice of instrument is based on the topic of this research, which is to examine the intermediating role of CL and KN between the capability of BDA and FP. Therefore, the scales that replicate important traits such as innovative culture, data-driven culture, collaboration, meetings, motivation, communication, and the use of new technologies and good relationships are chosen. We have used the scale given by Glaser et al. (1987), which can adequately show the required traits. The instrument has 31 items which are listed in the Appendix. The original scale had six components of CL teamwork—conflict, climate morale, information flow, involvement, supervision, and meetings (Glaser et al., 1987). We have included an additional component—data-driven decision-making—to fulfil our objective, which increases the number of questionnaire items from 31 to 35. The details of items and their loadings are listed in Tables A2 and A2.1 (see Appendix A).

3. Theoretical background and hypotheses development Prior research in the area of big data capability have used theories such as the RBV, diffusion of innovation, unified theory technology acceptance, and network theory, which interplay to explain the benefits of the implementation of information systems in an organization (Alalwan, Dwivedi, & Rana, 2017; Dwivedi, Rana, Jeyaraj, Clement, & Williams, 2019). However, the role of CL and internal analytical knowledge, which includes tacit and explicit knowledge, is still unexplored in the context of BDA. A considerate study may be useful to explain the role of CL and KN to develop a conceptual framework based on the integration of knowledge management, the sociometrilism theory, the RBV, and the dynamic capability theory (DCT).

2.3. Firm performance (FP) The measurement of performance is vital due to the fact that an organization’s innovation performance decides the life of the organization (Covin & Slevin, 1990; Laursen & Foss, 2003; Laursen & Salter, 2006). Innovation and design thinking are the key to operate in a global business environment (Darroch, 2005; Kotler & Alexander Rath, 1984). Innovation is the buzzword that is equally supported by a good knowledge management system, and consequently that knowledge management system is to be leveraged with BDAC. The major reasons for product innovations are demanding market, rapid change in technology, and intense international competition (Alegre, Lapiedra, & Chiva, 2006; Bisbe & Otley, 2004). To accomplish the objective of our study, we study the effect of BDAC on a firm’s innovation performance controlled by the CL and select an appropriate scale for the measurement of FP. FP is measured in this article through a composite score of the firm’s financial performance, the firm’s marketing performance, and the firm’s innovation performance (Wamba et al., 2017), measuring items are shown in Table A3a. FP is demarcated as the firm’s ability to gain and retain customers, and to improve sales, profitability, and ROI; it has been adapted from Mithas et al. (2011) and Tippins and Sohi (2003). We extend Wamba et al. (2017) model to check the moderating effect of tacit knowledge and CL on a firm’s performance. BDAC is a composite of connectivity (CN), compatibility (CP), modularity (MOD), planning (PLAN), decision-making (DM), and control (COL); the items and code are shown in Table A1. BDAC is measured using the scale provided by Wamba et al. (2017). The details of items and their loadings are shown in Table A2 (see Appendix A).

3.1. Dimensions of knowledge The research on knowledge management largely categorizes two comprehensive kinds of knowledge: explicit and tacit. Explicit knowledge is the knowledge that could be put in safekeeping and could be understood (Seppänen, Pässilä, & Kianto, 2019). Explicit knowledge is basically the coded and written knowledge available with many business firms and knowledge-intensive organizations. Another form of knowledge is tacit knowledge, which has proved to be equally significant for business firms. Tacit knowledge is embedded or hidden in the process or the culture, and therefore, it is hard to assign a streamlined definition of tacit knowledge; it is imbibed in the business processes of the firm and employees’ internal analytical knowledge. Kikoski and Kikoski (2004) opined that it is the internal analytical know-how amongst the employees of a business firm that produces insights which are necessary for achieving a competitive advantage. It is a management challenge to transform tacit knowledge into explicit knowledge (Kikoski & Kikoski, 2004). 3.2. Theoretical definition of the constructs The RBV lacks an explanation to why the organizations that are rich in resources failed in a turbulent environment. Therefore, the DCT, which is an extension of the RBV, was introduced to explain this phenomenon, and that organizations can build dynamic capabilities by integrating various internal and external knowledge management resources. Therefore, it is essential to integrate the RBV and the DCT to leverage the competitive advantage of BDAC. Based on the discussion presented in the previous sections, we

2.4. Internal analytical knowledge (KN) KN is a composite of tacit and explicit knowledge that an organization has at a particular time in terms of their employee resources. Tacit knowledge has been defined in several ways, like knowing-how, 3

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Fig. 1. The research model. Table 1 Constructs and definitions of the proposed model. Construct (latent variables)

Constructs and definition

Source

Big data analytics capability (BDAC)

BDAC is broadly defined as the competence to provide business insights using data management, infrastructure (technology), and talent (personal) capabilities to transform the business into a competitive force. CL is described as the beliefs and values shared within the organization, which help in forming the patterns of behaviour of employees.

Adapted from Kiron et al. (2013), Kim et al. (2012, p. 335, 336), and Wamba et al. (2017)

Organizational culture (CL) Internal analytical knowledge (KN) Firm performance (FP)

Indicator variables to measure BDAC Connectivity (CN) Compatibility (CP) Modularity (MOD) Planning (PLAN) Decision-making (DM) Coordination (COD) Control (COL) Indicator variables to measure organizational culture Teamwork and conflict Climate and morale Information flow Higher management involvement Supervision Meetings Indicator variables to measure internal analytical knowledge Technical knowledge (TK) Technological management knowledge (TMK) Business knowledge (BK) Relational knowledge (RK) Indicator variables to measure firm performance (FP) Financial performance (FP) Marketing Performance (MP) Firm innovation performance (FIP)

KN is the capacity of organizational members to use their personal experiences, values, beliefs, and discretion to analyse their organizational environment and enhance performance. FP includes both financial and non-financial performance; the former refers to tangible or monetary benefits such as the return of investment, revenue, and profit margins, while the latter refers to customer satisfaction, growth, and other intangible benefits like innovation performance. Definition It is defined as a resource-based capability which is called BDA infrastructure.

Adapted from Glaser et al. (1987), Claver-Cortés et al. (2015), Abualoush et al. (2018), and Oyemomi et al. (2019) Adapted from Ibidunni, Ibidunni, Oke, Ayeni and Olokundun (2018), Olaisen and Revang (2018) Adapted from Muthuveloo et al. (2017)

Source Adapted from Gupta and George (2016), Wamba et al. (2017), Akter et al. (2016)

It is the big data management capability.

Definition It is a firm’s It is a firm’s It is a firm’s It is a firm’s making. It is a firm’s It is a firm’s Definition

Source capability to build cohesive teams and manage conflicts. capability to motivate employees. capability to communicate properly across the hierarchy. top management’s involvement in motivation and decisionability in democratic supervision. ability to have feedback meetings and follow-up.

Adapted from Glaser et al. (1987), Oyemomi et al. (2019)

Source

BDA personnel capability refers to the BDA staff's professional ability (e.g., skills or knowledge) to undertake assigned tasks.

Wamba et al. (2017)

Definition

Source

It is a firm’s profitability, and ROI. It is a firm’s ability to gain and retain customers, and to improve sales. It is a firm’s ability to produce patents and new knowledge in terms of intellectual property.

Adapted from Tippins and Sohi (2003), Mithas et al. (2011)

propose the following model (shown in Fig. 1) for our study. In the proposed model, constructs such as BDAC, CL, FP, and KN are measured using composite (formative) indicators, because it does not assume causality we used the linear combinations of indicator variables to

measure these constructs (latent variables). Bollen and Bauldry (2011) opined that composite (formative) indicators form exact linear combinations of variables that need not share a concept. Therefore, BDAC as examined in this study has been construed as a composite of the 4

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Fig. 2. The effect of culture in the synchronization of various tacit knowledge.

as leadership, people motivation, flexible work culture, and proper organizational communication; all these variables eventually become part of organizational tacit and explicit culture (Bamel & Bamel, 2018; Naqshbandi & Tabche, 2018). Agility and resilience are also part of organizational dynamic capabilities that are moderated by organizational tacit and explicit knowledge (Altay, Gunasekaran, Dubey, & Childe, 2018). Knowledge dissemination within the organization and the innovation capabilities of organizations are also affected by CL (Çakar & Ertürk, 2010; Jones, Jimmieson, & Griffiths, 2005; Kim & Lee, 2006). Although BDA literature has given more importance to the alignment of ‘BDAC and process-oriented dynamic capabilities,’ a little deliberation has been done to investigate the role of CL on BDAC and FP. Previous studies demonstrated that process-oriented dynamic capabilities have a significant role in leveraging the BDAC to enhance firm performance, but they lack in exploring the role of CL on process-oriented capabilities. Therefore, in the context of big data dynamic capabilities, we hypothesize that:

following variables: connectivity (CN), compatibility (CP), coordination (COD), control (COL), and decision-making (DM). Similarly, we construe CL as a composite of the following variables: teamwork and conflict, climate and morale, information flow, involvement, supervision, meetings, and data-driven decisions. The theoretical origin and definitions of all the constructs and indicators variables are appended in Table 1. Apropos to the proposed theoretical framework shown in Fig. 1, two main hypotheses and two subhypotheses were formed for analysis. Organizational culture, which had been embedded in BDAC in the research work of Wamba et al. (2017), is now assumed as a separate amalgamation of tacit and explicit knowledge, which denotes the learning and comprehension of BDA-related practical knowledge learned formally or informally on the job in an organization. The theoretical platform of tacit knowledge and its relation to CL is evident and can be found in many pieces of literature (Al-Qdah & Salim, 2013; Boiral, 2002; Zaim, Gürcan, Tarım, Zaim, & Alpkan, 2015). Therefore, we intend to test whether CL influences FP and BDAC. Although Hung, Yang, Lien, McLean and Kuo (2010) have shown that CL significantly affects FP, its influence was mediated by dynamic capability. In contrast, we make a case that the effect of dynamic capabilities on FP is mediated by organizational culture. The direct and indirect effects of big data dynamic capabilities have been explored to test the hypothesis. We also argue that CL plays a significant role in achieving strategic dynamic capabilities and therefore, both direct and indirect effects are investigated in this study. The transformation of tacit knowledge into explicit knowledge, which contributes to assets and income is a complex process. Individual employees’ tacit knowledge shared through a supportive culture makes the shared knowledge explicit, converts explicit knowledge into resources, and enhances organizational performance. Internal analytical tacit knowledge is converted into explicit knowledge through a process known as the process of socialization, externalization, organization, and internalization (Jin-Feng, Ming-Yan, Li-Jie, & Jun-Ju, 2017). The conversion process is effective only when CL supports socialization, externalization, organization, and internalization. Therefore, we propose the following hypothesis:

H1a. BDAC mediates the effect of CL on FP. We argue that KN is a mixture of tacit and explicit knowledge of the employees of the firm which has a significant role in FP (Jin-Feng et al., 2017; Muthuveloo, Shanmugam, & Teoh, 2017). There is logical reasoning that KN builds the BDAC that in turn helps improve FP, although we hypothesize that KN has a significant effect on FP. To support our reasoning, we were also interested to test whether KN has a significant effect on BDAC, which is mediated by a positive effect on analytical knowledge between BDAC and organizational culture. The focus of this research is to explore the complexity of the conversion of tacit knowledge to explicit knowledge and in turn, to develop the capability to enhance performance. It is also important to note that merely having tacit technical knowledge about BDA does not assure enhanced performance; a similar knowledge management process is explained by Al-Karaghouli, Ghoneim, Sharif and Dwivedi (2013), which shows that having technical knowledge about medicine does not assure a satisfied consumer if not supported by intellectual culture and medical knowledge in a healthcare supply chain. Therefore, we also intend to test whether KN directly influences a firm’s performance.

H1. CL positively influences a firm’s performance.

H2. KN has a significant effect on FP.

Organizational dynamic capabilities are a function of variables such 5

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Fig. 2 shows that tacit knowledge about BDA, management, relational, and business knowledge, are important constructs of internal analytical knowledge. Management knowledge is related to the management skills of the employee, business knowledge is the know-how of the trade, and relational knowledge relates to the employees’ skills of working in a team. This knowledge is effectively used only when the intellectual and professional gap of individual employees is overcome by a supportive culture. Any new technology suffers the inherent challenge of adaptation and uncertainty about technical knowledge implementation, given that many organizations are able to build their dynamic capabilities by leveraging internal knowledge management processes (Marsh & Stock, 2006; Wang & Ahmed, 2007). KN is related to the intellectual capital of the organization which includes tacit and explicit knowledge. The effect of intellectual capital on FP is mediated by a firm’s dynamic capabilities (Han & Li, 2015; Hsu & Wang, 2012). The dynamic capability can be accentuated by employees’ internal knowledge, experience, and actions (Laaksonen & Peltoniemi, 2018). We therefore propose the following hypothesis:

different companies. The survey agency had surveyed many premium institutes in India. Using simple random sampling, the company circulated the questionnaire to 2000 relevant respondents. It got 500 responses within 2 weeks. The company repeated the circulation to 300 new respondents. In this process, the firm received a total of 600 responses. After obtaining the responses, we conducted data screening and got 400 valid responses. A total of 800 valid responses were obtained from the two sources. The non-response bias was controlled using the standard procedure proposed by Fulton (2018). 4.2. Data description We collected data from the companies which work in the area of BDA and which operate in India. India is one of the major source of IT manpower of the world’s major IT companies. Out of 800 respondents, 120 were female and 680 were male, the average years of service for female respondents was 9 years whereas for male respondents it was 9.5 years. The respondents are from pan-India locations; the demographics of the respondents is shown in Table A3b (Appendix A).

H2a. Organizational Culture mediates the influence of KN on BDAC.

5. Data analysis

4. Research methodology

We first used exploratory factor analysis (EFA) to assure adequate factors without directly opting for confirmatory factor analysis. We observed that the second-order confirmatory factor analysis used by Wamba et al. to measure BDAC had low discriminant power because they used ‘infrastructure flexibility, BDA management capability, and BDA personal expertise capability’ as latent constructs to measure BDAC. These latent constructs had very high correlations, and hence, the fit measures were low. The EFA showed that latent variables’ infrastructure flexibility and BDA management capability together represent a single factor which we name as BDAC, while BDA personal expertise capability by itself represents a single factor, which we name as analytical knowledge (KN). We also performed EFA on the CL items; the EFA showed that the items represent a single factor, and that FP is a composite of firm innovation performance, firm financial performance, and firm marketing performance. This is because the items represent a single factor and were measured accordingly.

Most of the hypotheses presented in the previous section were based on existing literature; hence, the most appropriate methodological approach is a positivist and deductive approach (Alzubaidi, Slade, & Dwivedi, 2020). The positivist approach has been used in this study. This approach extends the objective and social reality of the study. Gupta and George et al. (2016) have used the survey method to explore the BDACs, and Wamba et al. (2017) further expanded the capabilities of the BDAC model. To address the research question in this study, we started exploring the relationship between CL and BDAC and also explored the impact on the overall performance of a firm with the innovation in place and looking at the moderating role of CL between BDAC and FP. The research model has been conceptualized based on the RBV and the sociomaterialism theory. To validate the relationship hypothesized in the model, we used structural equation modelling. 4.1. Data collection and sampling

5.1. Structural equation modelling

A questionnaire has been used as a research instrument in this study. The questions have been borrowed from published research articles (Wamba et al., 2017). A 5-point Likert scale (strongly disagree to strongly agree) has been used throughout the questionnaire. To measure the construct in this model, a cross-sectional survey was done. The survey was conducted in a way such that the common method bias due to survey structure is mitigated; this was done by following the procedures suggested in seminal literature by Podsakoff, MacKenzie, Lee and Podsakoff (2003). To avoid the biases due to place and source, we have collected data from different places and different sources (Executive MBA students and IT Professionals). While doing so, the reliability of the data was checked (MacKenzie, Podsakoff, & Podsakoff, 2011; Malhotra, Kim, & Patil, 2006; Podsakoff et al., 2003). Data were collected using three steps. A pilot study was done to assess the validity of the measures and the internal consistency of the data. Data were collected from two sources. First, data was collected from Executive MBA students of premium B-schools of India. A majority of the students came from an IT background with over 3 years of experience. Data were collected from those students who had more than 6 years of relevant work experience related to BDA and had worked in a IT company which deals with BDA. We distributed the questionnaires to 1000 students and got 400 valid responses. Second, we hired a survey agency that had a large database of IT and business analytics firms. We chose it because it had a large (more than 10,000) number of IT managers and business analysts from

5.1.1. Measurement model The objectives of this article are to find the relationship between BDAC, CL, KN, and FP. All the latent constructs are defined in previous sections. Before applying the structural equation modelling to test the hypothesis, we performed measurement analysis to check the construct’s reliability, uni-dimensionality, convergence validity, and discriminant validity as well as the goodness-of-fit indices. A few items were deleted as they showed poor loadings. We use EFA to measure BDAC, the factor loadings, average variance extracted, and other fit indices which are shown in the tables. The Bartlett’s test shows that the variance is homogeneous for each treatment. The Kaiser-Maeyer-Olkin test reveals that the samples are adequate for factor analysis (> 0.9). Cronbach’s Alpha is 0.98, which shows that the data are reliable (Tables A1 and 2). The average variance extracted is 0.80 (Table A1). KN is a composite of TK, TMK, BK, and RK. The items and codes are shown in Table A1 (Appendix A). We use EFA to measure KN, the factor loadings, average variance extracted, and other fit indices which are shown in the tables. The Bartlett’s test shows that the variance is homogeneous for each treatment. The Kaiser-Maeyer-Olkin test reveals that the samples are adequate for factor analysis (> 0.9). Cronbach’s Alpha is 0.97 and the average variance extracted is 0.81 (Table A4 in the Appendix A), which shows that the data are reliable. The standardized root-mean-square error of approximation is 0.06, Tucker-Lewis Index is > 0.9, and the 6

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Table 2 Model fit indices. Fit indices’ analysis of research model 2

χ /df Goodness-of-fit index Adjusted goodness-of-fit index Normed fit index Bentler-Bonnet non-normed fit index Tucker-Lewis Index Comparative fit index Standardized root mean square error of approximation

Model fit

Reference index

Reference literature

2.8 0.91 0.912 0.899 0.90 0.92 0.91 0.06

<3 > 0.9 > 0.9 > 0.9 > 0.9 > 0.9 > 0.9 < 0.08

McIver and Carmines (1981) Hooper, Coughlan and Mullen (2008). Hooper et al. (2008) Hooper et al. (2008) Hooper et al. (2008) Wamba et al. (2017) Wamba et al. (2017) Hooper et al. (2008)

Fig. 3. Path model.

company, place, and time. The third source of common method bias may be the items and the structure of the survey itself (MacKenzie et al., 2011; Podsakoff et al., 2003). To avoid the personal and social biases of the respondents and to reduce the common method variance, data collection is done over two different sources and at different points of time. The properties of these possible sources are explained in the data collection section (Podsakoff et al., 2003). Also, the Herman’s single factor test was conducted on the data and the test results show that the common variance of common factor is 1.52 percent; the Marker variable test revealed that the correlation between the factors was not an issue, which was within the standard limit and therefore, the common method bias is not an issue for this research (Malhotra et al., 2006). A major source of endogeneity is the common method variance itself, as this research does not have a meaningful effect of common method variance; there is less chance that error terms in the path model have an endogeneity bias (Ullah, Akhtar, & Zaefarian, 2018). The diagnostics measures for multicollinearity, and collinearity for constructs were also taken care of to avoid any effects of multicollinearity. The analysis shows that the collinearity indicator (variance inflation factor) falls below the acceptable cut-off point (VIF < 5) (Hair, Tatham, Anderson, & Black, 2006). The average variance extracted of each construct is > 0.50 (Tables in the Appendix), which adequately reflects unidimensionality (Fornell & Larcker, 1981). This indicates that the observed items explain more variance than the error terms. Finally, unidimensionality was supported by the composite reliability of each construct, which exceeds the 0.80 cut-off value (Hair, Hult, Ringle, & Sarstedt, 2013; Segers, 1997).

comparative fit index is > 0.9 The items and codes are shown in (Tables A4 and A4.1 (Appendix A). As discussed earlier, FP is a composite construct of a firm’s innovation performance, its financial performance, and its marketing performance. The items and the codes for the measurement of FP are shown in Table A3 (Appendix). We use EFA to measure FP, the factor loadings, average variance extracted, and other fit indices which are shown in the tables. The Bartlett’s test shows that the variance is homogeneous for each treatment. The Kaiser-Maeyer-Olkin test reveals that the samples are adequate for factor analysis (> 0.9). Cronbach’s Alpha is 0.9, the average variance extracted is 0.75 (Table A3, Appendix A), which shows that the data are reliable (Table 2). The standardized root-mean-square error of approximation is 0.07, the Tucker-Lewis Index is > 0.9, and the comparative fit index is > 0.9. We use EFA to measure CL, the factor loadings, average variance extracted, and other fit indices which are shown in the tables. The Bartlett’s test shows that the variance is homogeneous for each treatment. The Kaiser-Maeyer-Olkin test reveals that the samples are adequate for factor analysis (> 0.9). Cronbach’s Alpha is 0.89, the average variance extracted is 0.78 (Table A2 in the Appendix A), which shows that the data are reliable (Table A2 in the Appendix A). The TuckerLewis Index is 0.91, the standardized root-mean-square error is 0.05, and the comparative fit index is > 9. There are a few sources of the common method variances in this research. The first source may be because the dependent and the independent variables were measured by same person. The second source may be the personal bias of the respondent towards a particular 7

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KN and subsequently examined in this study. This article, through its findings, highlights that to build the essential organizational big data capabilities, it is essential to overcome the professional and intellectual clash (Al-Karaghouli et al., 2013) to leverage the four significant components of KN (as shown in Fig. 2). We suggest that a firm’s internal resources, such as its employees, must develop and absorb analytical capabilities to ensure competitive and sustainable FP. Much of this capability building exercise depends on the commitments made by strategic management of the organization by adopting an inclusive and conducive organizational culture that ensures returns from investments made in big data initiatives (Acharya, Singh, Pereira, & Singh, 2018; Braganza, Brooks, Nepelski, Ali, & Moro, 2017). A framework comprising three stages, namely big data acceptance, routinization, and assimilation has been proposed by researchers (Dubey et al., 2017; Wang, Kung, & Byrd, 2018; Wang, Kung, Wang, & Cegielski, 2018) to achieve the desired objective. The first stage, acceptance, has to be championed by the top management to recognize the significance of BDAC (Dubey et al., 2017). Once acceptance is ensured, a firm must work on routinization to enable the internalization of the acquired capabilities within the business and governance processes as prescribed by Zmud and Apple (1992) and recently, by other researchers as well (Gunasekaran et al., 2017; Wang, Kung, & Byrd, 2018; Wang, Kung, Wang et al., 2018). The third stage, that is, assimilation, should ensure the proper integration of technologies of business analytics with that of the firms’ business processes throughout the organization so that the firm can reap the benefits (Hazen et al., 2012). A significant issue which this study highlights is the role of the analytical absorption capability of the firm’s internal stakeholders in seamlessly completing the above stages. This research has added a new angle to look at BDAC research. Previous research has focused less on the mediating role of CL in BDAC’s influence on FP. This study examined the mediating role of CL on BDAC and FP using data collected from Indian firms. The research findings added that an organization’s internal culture plays an important role in leveraging BDAC and internal knowledge. This research is first of its kind, which test the mediation effect of CL between BDAC and FP. Previous literature has ignored this important construct which has a significant role in organizational performance. The result of hypothesis H2 is significant in the sense that KN is useless until it is supported by CL. The role of CL in FP emerges clearly from this research. This study also combines BDAC and CL in an integrated model. Also, the combined effects of CL and BDAC have also been studied as shown in Fig. 2. This research gives a holistic understanding of the BDAC model, and it explains that the invested resources in BDAC will work effectively when the CL is in support of internal knowledge.

Table 3 Path model statistics. Regressions FP∼ BDAC CL KN BDAC∼ CL KN CL∼ KN

Estimate

Std. err.

z-value

p (> |z|)

0.431 0.688 ―0.183

0.065 0.057 0.084

6.603 12.085 ―2.174

0.000 0.000 0.030

0.223 0.744

0.055 0.076

4.014 9.792

0.000 0.000

1.174

0.052

22.521

0.000

Table 4 Hypothesis testing. Hypothesis H1 H1a H2 H2a

Results CL positively influences firm’s performance. BDAC mediates the effect of CL on FP. KN has a significant effect on FP. CL mediates the influence of KN on BDAC.

Proved Proved Not significant Proved

5.1.2. Structural model The structural model is evaluated using R version 3.5.2 (Lavan Package). For the current structural model (Fig. 3), The result suggest an adequate model fit. The model fit is shown in Table 2. The path analysis of the estimated model is shown in Table 3. Based on the above statistics, the stated hypothesis is analyzed in Table 4. 5.1.3. Mediation effect of CL and BDAC To understand the role of mediators in this study is important as we assume that the research is based on the positivist approach. Our hypothesis is to test the mediator role of BDAC and CL. The results show that BDAC partially mediates the effect on CL on FP, and CL also partially mediates the effect of KN on BDAC. Fig. 3 shows the path model which is supported by the statistics in Table 2. It is clear that CL plays a partial mediation or complementary mediation role between KN, BDAC, and CL to support FP as the indirect effect and direct effect have the same sign and total effect of mediation: (1.174*0.223)+0.744 = 1.005802 (Zhao et al., 2010). Without top management commitments and tacit knowledge of the organization (CL), BDAC and KN are not very effective in improving FP. It is also important to note that CL positively influences BDAC and FP. The direct effect of CL on FP is 0.668, while the total effect of CL on FP mediated by BDAC is: (0.223 * 0.443)+0.668 = 0.766. This indicates that KN has a significant effect on BDAC, which is mediated by CL.

6.1. Theoretical contributions In today’s world, there has been a proliferation of cellular devices leading to extensive generation of data. Such devices are being extensively used by users in every age group (from 6 to 80 years) to access platform-based businesses (Kamboj, Sarmah, Gupta, & Dwivedi, 2018) leading to the generation of big data. Business firms should be aware of their own existing capabilities to deal with the deluge of data. A proper firm-level framework to effectively manage big data can help a firm gain competitive edge. Big-data-based analytical models can significantly impact a firm’s performance by enabling knowledge creation. Knowledge is produced in several forms, be it structured or unstructured in nature. Business firms should be able to leverage any form of data and use it for knowledge management purposes. Big data, with its inherent analytical capabilities, plays a central role in knowledge formation facilitated by proper organization-wide policies and absorption capabilities of its internal stakeholders. This helps the firm gain a sustainable competitive edge by improving internal business processes. Therefore, a very important issue to explore in this context is to make

6. Discussion: data synthesis and implications Big data, along with its inherent capabilities, has the potential to not only transform business but also to be a significant factor in ensuring competitive advantage for the business. However, this necessitates a proper understanding of the implications of adopting a big data capability model like BDAC, with respect to cultural alignment and preparedness to meet the dynamics of the conversion from tacit to explicit knowledge in business operations (Seles et al., 2018). However, to use the tacit knowledge base, the orthodox means of data transformation and subsequent analysis may prove inadequate (Hampton et al., 2013; Seles et al., 2018). Business firms must devise big-data-based capabilities and formulate a framework that facilitates the transformation from tacit to explicit knowledge thereby aiding better FP (de Camargo Fiorini et al., 2018; Tan, Zhan, Ji, Ye, & Chang, 2015; Wamba et al., 2017). In this context, a firm’s internal capabilities (in the form of ‘competencies’ and ‘internal processes’) have been measured in terms of 8

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effective use of knowledge to develop sustainable competitive advantage. However, the challenge before business firms lies in aligning their resources to formulate an inclusive knowledge management culture. Tacit knowledge in an organization is exhibited in the form of socialization, externalization, combination, or internalization. All these forms of tacit knowledge play a significant role in defining a business firm’s performance as has been opined by previous researchers as well (Muthuveloo et al., 2017; Naqshbandi & Tabche, 2018). Socialization with respect to an organization can be effectuated through social interactions, thereby generating tacit knowledge, which can later be internalized by certain structured knowledge management processes. Through externalization, a business firm can attempt to codify knowledge, thereby generating explicit knowledge. Several popular methods based on visualization, metaphors, and physical learning tools (Karim, Jalaldeen Mohamed Razi, & Mohamed, 2012) may be used to obtain the desired objectives. The combination process is the synthesizing of the concepts of knowledge wherein the employees of the firm start communicating through a systematic communication process, thereby developing an internal knowledge culture. Wang, Kung, and Byrd (2018) highlighted that BDAC assists not only in the sharing/exchange of common and specialized knowledge which generates business intelligence, but also in creating a CL conducive to the development of a firm’s internal analytical knowledge. Business intelligence systems are increasingly being deployed by firms to support decision-making and to gain a competitive advantage. Researchers have tried to explore the possible linkages between IT, BI, and knowledge transformations with respect to firms of various sizes so that firms may formulate a proper knowledge management framework to derive optimal benefits (Wang, Kung, & Byrd, 2018; Wang, Kung, Wang et al., 2018). Over the past few years, there have been few topics which have received as much attention from researchers as big data and BDA. Practitioners across business domains seem to agree that BDAC will help a business achieve a competitive edge. However, the extent and the form of benefit to a business organization is still evolving, as different sectors have different organizational dynamics, and hence it is not prudent on the part of any firm to adopt a ‘one size fits all’ kind of an approach when it comes to the implementation of data-based analytical capability for their organization. Emergent topics like BDAC provide opportunities for researchers to contribute to challenging the existing business practices by providing insights on how an organization should adopt BDAC and gain competitive advantage. In this article, we opine that it is imperative for any business firm today to transform any form of knowledge (tacit or explicit) into effective knowledge to gain a competitive edge and hence enhance FP. Several research studies have focused on the aspect of integrating tacit and explicit knowledge into building a conducive CL with a focus on KN (Abualoush, Masa’deh, Bataineh, & Alrowwad, 2018; Claver-Cortés, Zaragoza-Sáez, Molina-Manchón, & Úbeda-García, 2015; Oyemomi, Liu, Neaga, Chen, & Nakpodia, 2019). Business firms can ensure significant improvements in their performance if a team’s capability can be developed (Olaisen & Revang, 2018). Organizations also need to adequately focus on national and organizational culture as reported by previous researchers (Liu, Moizer, Megicks, Kasturiratne, & Jayawickrama, 2014), in enhancing knowledge management practices. The role of CL in enhancing better adoption of the analytical capability of a business firm has not been extensively researched while analysing the impact of BDAC in a firm. We argue that the national and industry perspective is important while building big-data-based analytical capability; it is essential to focus and leverage existing CL and KN. This aspect has been scantly reported in BDAC literature which has been found to be significant in this study.

deploying data analytics capabilities. Analytics-driven business decisions have thus taken a strategic imperative role for the firm to sustain its competitive advantage. The extent to which business firms can actually derive a competitive advantage by deploying a big-data-based analytical model requires deep analysis and deliberation. A business firm may have the necessary resources to deploy a big-data-based analytical model to enhance the FP, but to leverage the benefits, it should ensure proper synchronization between its CL and its KN absorption capability. While business firms are proactively considering deploying BDAC to leverage the business insights obtained thereof, there are trade reports and popular accounts depicting failed implementations of technologydriven decision models without proper assessment of the firm’s culture. The introduction and adoption of any sort of strategic intervention in general are quite challenging. It is thus not surprising that BDAC-based system applications would be equally difficult, if not more difficult, due to their complexity. In fact, implementing any strategic framework like BDAC is not as much a technological exercise but an organizational revolution. Extensive deliberation and firm-level internal preparations before the actual deployment may ensure a positive outcome. This study extends the BDAC model by investigating the moderation role of CL in firm’s performance, in the presence of BDAC. This study analyses the responses from 800 IT consultants having data analytics experience from an emerging economy. The findings affirm that the effect of BDAC on FP is controlled by CL. Industrial reviewers (Arpaci, 2017) for any type of economy advocate that organizations need to improve their efficiency not only to gain competitive advantage but also to ensure business sustainability. Thus, a business organization must commit its resources to build a conducive knowledge-based culture to increase efficiency (Chen, 2010). It is the culture coupled with knowledge management that will enhance stability (Liu et al., 2014) and guide the organization in adopting the innovative strategy. In addition to factors which have already been reported such as leadership style, the organizational culture and the BDAC adaptive capacity of their employees are also significant for firms to maintain a competitive edge. Business firms will not be able to derive the benefits of big data and its analytical insights if they fail to nurture a conducive culture among their employees to effectively adapt to BDAC capabilities. Thus, investment in establishing a structured knowledge management framework wherein both tacit and explicit knowledge management practices are given equal importance, and translating them into effective learning for the employees, help the organization in long run. Nelson (2011) advocated an organizational culture model for the external adoption of knowledge and integral integration with new members to build a strong culture. The engagement of the top management in implementing BDAC learning and enhancing adoption capability as well as monitoring will help the firm build a learning culture. 7. Conclusion In this study, we investigate the role of culture—which consists of people and processes—in the utilization of big data capability to improve FP. From the research, it is concluded that CL has a significant role in mediating the effect of BDAC on FP. The model proposed in this study is based on well-developed organizational theories. The findings of a few studies (Arpaci, 2017; Chen, 2010) have shown that the adoption of new technology has been efficient only if the culture of the organization is in sync with the technology requirements; this is reinforced by the findings of this study as well. The finding that the performance of business firms is directly related to efficiency calls for better supportive and an enlightened culture for efficient firms. We reinforce the fact that a well-synchronized CL delivers a sense of distinctiveness for the organizational members and enhances the sustainability of the organization, as has also been reported by a few researchers as well (Liu et al., 2014; Nelson, 2011).

6.2. Implications for practice Business firms are increasingly relying on business insights by 9

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Internal analytics knowledge which is a form of tacit knowledge in an organization requires systemmatic treatment in order to gain sustainable competitive advantage. Our findings corroborate the hypothesis that internal analytics knowledge transforms into explicit knowledge in a conducive organizational culture.

performance measures to validate our hypothesis. However, we suggest that future researchers could use secondary data (if available) to measure the FP to validate the hypotheses used in this study. It is worth pointing out that the data used in this study have been obtained from users associated with the IT industry, and hence, there is scope for generalization of the model. Future researchers may like to validate that the model presented in this study with respect to other sectors’ reliance on BDA has been quite pervasive across sectors without ascertaining the tangible benefits. Finally, we have not examined the influences of externalities on a firm’s performance in the presence of BDAC.

7.1. Limitations and scope for future research This study has attempted to highlight the significant role played by CL and internal knowledge in providing a competitive edge to business firms relying on BDAC. In this study, we have used perceptual Appendix A

Table A1 Factor loading statistics BDAC (α = 0.98; CR: 0.95; AVE: 0.80). Items

Item statistics Item loading on BDAC

Mean

SD

CN1 CN2 CN3 CN4 CP1 CP2 CP3 MOD1 MOD2 MOD3 MOD4 PLAN1 PLAN2 PLAN3 PLAN4 DM1 DM2 DM3 DM4 DM5 COD1 COD2 COD3 COD4 COL1 COL2 COL3 COL4 COL5 COL6 COL7

0.88 0.86 0.52 0.8 0.7 0.76 0.73 0.71 0.74 0.58 0.63 0.78 0.71 0.81 0.74 0.59 0.7 0.74 0.71 0.82 0.78 0.82 0.83 0.87 0.84 0.71 0.79 0.78 0.71 0.79 0.8

4.5 4.1 4 4.1 4.3 3.9 4 4.2 4.2 3.8 3.9 4 4 4.1 4 3.9 4 4 4 4.1 4.1 4.1 4.2 4.4 4 4.1 4.1 4.1 3.8 4 4.2

0.96 0.93 0.92 0.91 0.91 0.93 0.92 0.95 0.92 1.01 0.96 0.93 0.93 0.93 0.96 0.93 0.97 0.89 0.97 0.97 0.94 0.95 0.94 0.93 0.89 0.98 0.96 0.91 0.97 0.92 0.96

Table A1a Items of BDAC. Big Data Analytics Capability (BDAC)

Item code

Connectivity Compared to rivals within our industry, our organization has the foremost available analytics systems. All other (e.g., remote, branch, and mobile) offices are connected to the central office for sharing analytics insights. Our organization utilizes open systems network mechanisms to boost analytics connectivity There are no identifiable communications bottlenecks within our organization for sharing analytics insights. Compatibility Software applications can be easily used across multiple analytics platforms. Our user interfaces provide transparent access to all platforms. Information is shared seamlessly across our organization, regardless of the location. Modularity (MOD) Reusable software modules are widely used in new system development. End users utilize object-oriented tools to create their own applications. Analytics personnel utilizes object-oriented technologies to minimize the development time for new applications.

CN1 CN2 CN3 CN4 CP1 CP2 CP3 MOD1 MOD2 MOD3

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Table A1a (continued) Big Data Analytics Capability (BDAC)

Item code

The legacy system within our organization restricts the development of new applications. Planning (PLAN) We continuously examine innovative opportunities for the strategic use of business analytics. We enforce adequate plans for the utilization of business analytics. We perform business analytics planning processes in systematic ways. We frequently adjust business analytics plans to better adapt to changing conditions. Decision-Making (DM) When we make business analytics investment decisions, we estimate the effect they will have on the productivity of the employees' work. When we make business analytics investment decisions, we project how much these options will help end-users make quicker decisions. When we make business analytics investment decisions, we estimate whether they will consolidate or eliminate jobs. When we make business analytics investment decisions, we estimate the cost of training that end users will need. When we make business analytics investment decisions, we estimate the time managers will need to spend overseeing the change. Coordination (COD) In our organization, business analysts and line people meet regularly to discuss important issues In our organization, business analysts and line people from various departments regularly attend cross-functional meetings. In our organization, business analysts and line people coordinate their efforts harmoniously. In our organization, information is widely shared between business analysts and line people so that those who make decisions or perform jobs have access to all available know-how. Control (COL) In our organization, the responsibility for analytics development is clear. We are confident that analytics project proposals are properly appraised. We constantly monitor the performance of the analytics function. Our analytics department is clear about its performance criteria. Our company is better than competitors in connecting (e.g., communication and information sharing) parties within a business process. Our company is better than competitors in reducing costs within a business process. Our company is better than competitors in bringing complex analytical methods to bear on a business process.

MOD4

Table A2 Item statistics and factor loadings organizational culture (CL) (α = 0.89; CR: 0.90; AVE: 0.78). Item statistics Items

Item loading

Mean

SD

CL1 CL2 CL3 CL4 CL5 CL6 CL7 CL8 CL9 CL10 CL11 CL12 CL13 CL14 CL15 CL16 CL17 CL18 CL19 CL20 CL21 CL22 CL23 CL24 CL25 CL26 CL27 CL28 CL29

0.82 0.86 0.85 0.82 0.77 0.82 0.84 0.81 0.84 0.75 0.74 0.82 0.79 0.81 0.86 0.88 0.73 0.85 0.75 0.67 0.84 0.77 0.82 0.66 0.81 0.76 0.78 0.82 0.86

4.2 4.1 4.3 4 4 4.2 4.2 4.3 4.1 4.2 4.1 4.3 4 4.2 4.4 4 4.2 4.3 4 4 4 4 4.1 4.2 4 4 4.1 4.2 4.1

0.94 0.93 0.96 0.91 0.96 0.92 0.95 0.94 0.93 0.95 0.98 0.95 0.91 0.92 0.93 0.88 0.96 0.93 0.96 0.93 0.97 0.96 0.95 0.96 0.95 0.94 0.96 0.95 0.93

11

PLAN1 PLAN2 PLAN3 PLAN4 DM1 DM2 DM3 DM4 DM5 COD1 COD2 COD3 COD4 COL1 COL2 COL3 COL4 COL5 COL6 COL7

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Table A2a Organizational culture items. S. No

Dimensions

Items

Item Code

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Teamwork & conflict

I work with are direct and honest with each other I work with accept criticism without becoming defensive I work with Function as a team I work with constructively confront problems I work with good are good listeners Labor and Management have a productive working relationship This organization motivates me to put out my best efforts This organization respects its workers This organization treats people in a consistent and fair manner There is an atmosphere of trust in this organization This organization motivates people to be efficient and productive I get enough information to understand the big picture here When changes are made, the reasons why are made clear. I know what < s happening in work sections outside of my own. I get the information I need to do my job well. I have a say in decisions that affect my work I am asked to make suggestions about how to do my job better This organization values the ideas of workers at every level. My opinions count in this organization. Job requirements are made clear by my supervisor. When I do a good job, my supervisor greets me My supervisor takes criticism well. My supervisor delegates responsibility. My supervisor gives me criticism in a positive manner. My supervisor is a good listener. My supervisor tells me how I am doing. Decisions made at meetings get put into action. Everyone takes part in discussions at meetings Our discussions in meetings stay on track. Time in meetings is time well spent Meetings tap the creative potential of the people present there The organization has an analytics wing. Organizations’ have implemented ERP. we are well convergent with the ERP Our organization takes a decision based on the proper analysis of data.

CL1 CL2 CL3 CL4 CL5 CL6 CL7 CL8 CL9 CL10 CL11 CL12 CL13 CL14 CL15 CL16 CL17 CL18 CL19 CL20 CL21 CL22 CL23 CL24 CL25 CL26 CL27 CL28 CL29 CL30 CL31 CL32 CL33 CL34 CL35

Climate and Morale

Information Flow

Involvement

Supervision

Meetings

Data-driven decision

Table A3 Items statistics and factor loadings (FP) (α = 0.9; CR: 0.93; AVE: 0.75). Item statistics Items FP1 FP2 FP3 FP4 FP5 MP1 MP2 MP3 MP4 FIP1 FIP2 FIP3 FIP4 FIP5

Item loadings 0.8 0.76 0.8 0.77 0.81 0.75 0.77 0.72 0.82 0.72 0.71 0.83 0.68 0.79

Mean 4.2 4.2 4.2 4 4.1 4.1 4.1 4 4 4.2 4.3 4.1 4 4.1

SD 0.91 0.96 0.93 0.91 0.91 0.95 0.95 0.96 0.9 0.9 0.94 0.89 0.92 0.92

Table A3a items of firm performance. Firm Performance (FP)

Item Code

Financial performance (FP): Using analytics improved during the last 3 years relative to competitors: ________Customer retention _________Sales growth __________Profitability __________Return on investment

FP1 FP2 FP3 FP4

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Table A3a (continued) Firm Performance (FP)

Item Code

__________Overall financial performance Market performance : Using analytics improved during the last 3 years relative to competitors ______We have entered new markets more quickly than our competitors ______ We have introduced new products or services to the market faster than our competitors ______Our success rate of new products or services has been higher than our competitors. ______Our market share has exceeded that of our competitors. Firm Innovation Performance the number of new products the number of patent applications, and the proportion of new product sales in total sales. the rate of new product development the success rate of innovation projects

FP5 MP1 MP2 MP3 MP4 FIP1 FIP2 FIP3 FIP4 FIP5

Table A3b Demographic description of the respondents. Count of Number of Gender

City

Company Name

Avg. Age,

Records

Avg. Year of Service

Female

Bengaluru New Delhi Noida

ORACLE TCS Infosys KPIT Technologies Limited NIIT Technologies Mphasis Ltd. ORACLE Mphasis Ltd. Mphasis Ltd. ORACLE TCS HCL Technologies IBM Infosys KPIT Technologies Limited NIIT Technologies Tech Mahindra Wipro Mphasis Ltd.

35.00 37.25 30.75 30.00

8.00 32.00 32.00 8.00

10.00 13.00 6.25 6.00

31.75 38.00 33.40 36.00 37.50 37.25 38.17 36.50 33.30 36.00 30.56

32.00 8.00 40.00 16.00 16.00 32.00 48.00 80.00 80.00 48.00 72.00

7.50 14.00 8.80 10.50 11.00 12.75 13.67 10.10 8.80 11.33 5.67

34.50 35.10 35.80 33.40

48.00 80.00 80.00 40.00

8.50 8.50 8.80 7.60

Male

Pune Bengaluru Chennai Delhi HYDERABAD New Delhi Noida

Pune

Table A4 Items statistics and factor loadings (KN) (α = 0.97; CR: 0.95; AVE: 0.81). Item statistics Items TK1 TK2 TK3 TK4 TK5 TMK1 TMK2 TMK3 TMK4 BK1 BK2 BK3 RK1 RK2 RK3 RK4

Item loadings 0.7 0.77 0.84 0.79 0.75 0.79 0.84 0.83 0.79 0.8 0.82 0.79 0.81 0.86 0.87 0.88

Mean 3.8 4 4.2 4 4.1 4 4.1 4.2 3.9 4 4.2 4 4.2 4 4.4 4.2

13

SD 0.92 0.96 0.92 0.94 0.97 0.99 0.98 0.94 0.93 0.92 0.94 0.94 0.94 0.91 0.97 0.92

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Table A4a Internal Analytical Knowledge is a composite of Technical knowledge (TK), Technological management knowledge (TMK), Business knowledge (BK), and Relational knowledge (RK). The items and code are shown in Table. Items

Code

Technical knowledge (TK) Our analytics personnel are very capable in terms of programming skills (e.g., structured programming, web-based application, CASE tools, etc.). Our analytics personnel is very capable in terms of managing project life cycles. Our analytics personnel is very capable in the areas of data management and maintenance. Our analytics personnel is very capable in the areas of distributed computing. Our analytics personnel is very capable of decision support systems (e.g., expert systems, artificial intelligence, data warehousing, mining, marts, etc.). Technological management knowledge (TMK) Our analytics personnel shows a superior understanding of technological trends. Our analytics personnel show superior ability to learn new technologies. Our analytics personnel is very knowledgeable about the critical factors for the success of our organization. Our analytics personnel is very knowledgeable about the role of business analytics as a means, not an end. Business knowledge (BK) Our analytics personnel understands our organization's policies and plans at a very high level. Our analytics personnel is very capable of interpreting business problems and developing appropriate solutions. Our analytics personnel is very knowledgeable about business functions. Our analytics personnel is very knowledgeable about the business environment. Relational knowledge (RK) Our analytics personnel is very capable in terms of managing projects. Our analytics personnel is very capable in terms of executing work in a collective environment. Our analytics personnel is very capable in terms of teaching others. Our analytics personnel work closely with customers and maintain productive user/client relationships.

TK1 TK2 TK3 TK4 TK5 TMK1 TMK2 TMK3 TMK4 BK1 BK2 BK3 BK4 RK1 RK2 RK3 RK4

Appendix B. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ijinfomgt.2020.102100.

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