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International Journal of Information Management 59 (2021) 102344

Available online 11 March 2021 0268-4012/© 2021 Elsevier Ltd. All rights reserved.

Research Article

Assimilation of business intelligence: The effect of external pressures and top leaders commitment during pandemic crisis

Akriti Chaubey a,*, Chandan Kumar Sahoo b

a School of Management, National Institute of Technology Rourkela, Rourkela, 769008, India b Human Resource Management, School of Management, National Institute of Technology Rourkela, Rourkela, 769008, India

A R T I C L E I N F O

Keywords: Business intelligence Institutional theory Business intelligence assimilation Leadership COVID-19

A B S T R A C T

The business intelligence (BI) has been often touted as a game-changer especially during the pandemic crisis. Although most managers are familiar with BI and agree that, it should be operationalized across their organi- zations. The BI is not well assimilated throughout adopting organizations. Rooted in institutional and upper echelon theories, this study proposes a theoretical model aimed toward explaining BI assimilation. We surveyed 174 respondents occupying leadership positions from174 auto-components manufacturing firms in India to gather data. The findings suggest that normative and mimetic (but not coercive) factors significantly influence top leader’s commitment to the BI initiatives. We found that the commitment of the top leaders influences the assimilation of BI via acceptance and routinization. Our study is an attempt to address the previous research calls related to BI assimilation. The findings of the study inform the information management scholars via theory- based research on phenomena related to post-adoption BI diffusion during a pandemic crisis. Practitioners can utilize the results of our study to design their policies that help assimilate BI such that forecasted benefits can be fully realized during an uncertain time.

1. Introduction

“Necessity has been the mother of invention in the response to the COVID- 19 pandemic, triggering many an innovation, often without the luxury of time to test these makeshift solutions to pressing problems. But there is much to be learned from times of crisis for times of plenty” (Harris, Bhatti, Buckley, & Sharma, 2020, p. 814)

The pandemic due to COVID-19 has seriously affected the small and medium enterprises (Dwivedi et al., 2020; Ivanov & Dolgui, 2020; Papadopoulos, Baltas, & Balta, 2020; Remko, 2020). Many organisations have significantly exploited the business intelligence (BI) capability to stay afloat in this unprecedented time (Kummitha, 2020; Queiroz, Tal- lon, Sharma, & Coltman, 2018; Ranjan & Foropon, 2021). It is well understood that BI plays an important role in improving business per- formance (Dwivedi et al., 2021; Koh & Gunasekaran, 2006; Pramanik, Mondal, & Haldar, 2020). In a recent report published by Sisence (The State of BI and Business Analytics Report, 2020) has highlighted sig- nificant rise in the use of BI and analytics in response to COVID-19 crisis (Queiroz, Ivanov, Dolgui, & Wamba, 2020). Although there are numerous BI success stories reported in the academic literature (Olszak,

2016), there remain many skeptics who often criticize the role and impact of BI (see, Božič & Dimovski, 2019) during pandemic crisis (Lee & Trimi, 2020). Although, the failure stories of the BI has gathered significant attention from the academic community (Tian et al., 2015) and in many instances, predicted benefits of BI are not realized (Aud- zeyeva & Hudson, 2016). Furthermore, BI is often inconsistently oper- ationalized across different contexts (see, Chen & Lin, 2020) and is often implemented based on prescriptive and not participative assumptions. Despite of rich body of literature on BI, the existing literature has largely remained silent on how BI is assimilated across the organisation (Elba- shir, Collier, & Davern, 2008; Fosso Wamba & Queiroz, 2020).

While there is a rich body of literature on factors influencing the success of BI implementation (Ramakrishnan, Jones, & Sidorova, 2012; Wang, 2014; López-Robles et al., 2019), studies aimed toward explain- ing BI assimilation are limited (Ahmad & Hossain, 2018; Shao, 2019). The previous studies have noted that the adoption and implementation, are often considered as the foundation of the diffusion of any techno- logical innovation. In any organization (Dubey et al., 2018; Hazen, Overstreet, & Cegielski, 2012), and the full benefits may not be well realized by the organization until and unless the technological

* Corresponding author. E-mail addresses: [email protected] (A. Chaubey), [email protected] (C.K. Sahoo).

Contents lists available at ScienceDirect

International Journal of Information Management

journal homepage: www.elsevier.com/locate/ijinfomgt

https://doi.org/10.1016/j.ijinfomgt.2021.102344 Received 30 November 2020; Received in revised form 5 March 2021; Accepted 5 March 2021

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innovation is fully assimilated (Dubey et al., 2018; Dwivedi, Rana, Jeyaraj, Clement, & Williams, 2019; Hazen et al., 2012; Williams, Dwivedi, Lal, & Schwarz, 2009). Based on Purvis, Sambamurthy, and Zmud (2001) and Hazen et al. (2012) definitions, we define BI assimi- lation as the extent to which BI philosophy diffuses across organizational processes and activities. Hence, the key objective of BI post-implementation activities is to assimilate the philosophy and practices across business routines such that organization achieve maximum benefits of BI implementation (Nam, Lee, & Lee, 2019). Moreover, how organization assimilate during pandemic crisis is not well understood. The purpose of this study is to investigate the means through which BI is assimilated throughout organizations during pandemic crisis. To address our research objective, we posit two guiding research questions as:

RQ1: What are the antecedents of BI assimilation? RQ2: How can firms assimilate BI across their organizations during

pandemic crisis? Kar and Dwivedi (2020) argued in favour of building theory that may

help organization to understand how the use of big data analytics and business intelligence capability may enhance performance during un- certain environment. Drawing on institutional theory (DiMaggio & Powell, 1983) and upper echelon theory (Hambrick & Mason, 1984), we develop a theoretical model to explain how the external institutional forces and the top leader’s commitment influence BI assimilation within an organization. Extending the findings of Liang, Saraf, Hu, and Xue (2007) and Nam et al. (2019), we submit that top leader’s commitment plays a pivotal role in channelizing the external institutional pressures into BI assimilation. Furthermore, we extend the work of Wang (2014) and Ain, Vaia, DeLone, and Waheed (2019) by studying assimilation instead of adoption or implementation. Hazen et al. (2012) have attempted to explain the journey from adoption to assimilation using two intermediary stages, namely acceptance and routinization.

Following previous arguments we assume the role of external pres- sures (Liang et al., 2007) and top leader’s (internal human agents) play significant roles in the acceptance, routinization and assimilation of BI, we submit that the role of contextual assimilation factors remains largely unexplored. We therefore propose a BI assimilation framework for pandemic crisis, grounded in organizational theories, that offers two unique contributions to the literature (Pan & Zhang, 2020). Firstly, we examine BI assimilation using two organizational theories (i.e. institu- tional theory and upper echelon theory). Secondly, we investigate to what extent top leader’s commitment mediates the relationship between institutional pressures and BI acceptance. This research thus provides a new perspective on BI assimilation.

The remainder of the article is organized as follows. In the next section, we discuss the theoretical framework and research hypotheses. Second section focuses on the development of our research model and hypotheses. Third section focuses on the research method. In this sec- tion, we discuss our questionnaire development, sampling design and data collection strategy. In the fourth section, we present our data analysis and results. In the fifth section, we present our discussion sec- tion based on our research findings. In this section, we have further discussed our contributions to the theory. In the same section, we further discuss our findings in context to the practice. We further outlined our limitations of our study and further noted future research directions. Finally, we concluded our study.

2. Research model and hypotheses

Our research model is grounded in extant literature. The foundation of the model is comprised of two elements, namely, institutional theory and upper echelon theory. Kauppi (2013) suggests that “…operations management (OM) researchers and practitioners tend to view their work in terms of the logic of rational efficiency, which has been questioned by organizational theorists arguing that rational action is always embedded in a social context…” (p. 1318). Hence, institutional theory may provide an

alternative perspective to examine the complexity of BI assimilation (BI-ASM).

Liang et al. (2007) developed a model to explain the assimilation of ERP using institutional theory. Our model attempts to extend Liang et al. (2007) work by examining BI assimilation. Furthermore, consistent with the work of Dubey et al. (2018), top leaders commitment is proposed to translate external forces (institutional pressures) into desired assimila- tion of BI. In our study, we draw from the extensive literature on insti- tutional theory (see, Oliver, 1997; Delmas & Toffel, 2004; Colwell & Joshi, 2013; Greenwood, Hinings, & Whetten, 2014; Dubey, Gunase- karan, Childe, & Papadopoulos, 2019) to develop a research model that identifies the antecedents of BI assimilation. In doing so, we seek to address our guiding research questions. Our research model (Fig. 1) is grounded in the proposition that institutional forces affect organiza- tional behaviour after being mediated by the leaders. Based on previous arguments, we have presented our research hypotheses. In the next subsections, we further discuss these hypotheses.

2.1. Institutional theory and BI assimilation

Zhu, Kraemer, and Xu (2006) advocate for innovation assimilation, noting that regulatory environment plays an important role. Liang et al. (2007) further found that institutional pressures significantly affect assimilation of ERP. Chinese firms comprise the setting of the study conducted by Liang and colleagues, suggesting that the role of legiti- macy in developing countries can help explain assimilation. To this end, Li et al. (2008) argued that ERP implementation can be successful if it is preceded by a BI focus. Drawing on these studies, we adapt the assimi- lation concept for the BI literature.

Dubey et al. (2018) research sought to explain TQM assimilation using three institutional factors and top management commitment. In our current study, we attempt to examine BI assimilation using institu- tional pressures to offer deeper insight into post-adoption processes. Singh, Power, and Chuong (2011) suggest that theory-based explanation enhances understanding and appreciation for standards, and provides clarity on how standards benefits organizations In comparison to other organization theories such as resource dependence theory (Singh et al., 2011) and contingency theory (Sila, 2007). Dubey et al. (2018, p.2992) argue that “the institutional theory posits that structural and behavioural changes in the organization are driven less by competition and the desire for efficiency, but more by the need of organizational legitimacy” (c.f DiMaggio & Powell, 1983). DiMaggio and Powell (1983) argue that the desire of the organization to align their business strategies in the line of the stakeholder’s expectations (i.e. legitimacy), the organization often embrace institutional logic. We can also refer the process of seeking legitimacy via embracing institutional logic as ‘institutional isomorphism’ (see, DiMaggio & Powell, 1983; Liang et al., 2007; Kauppi, 2013; Lin, Luo, & Luo, 2020; Zuo, Ma, & Yu, 2020). The institutional isomorphism occurs via three stages: coercive pressure, which refers to the external pressures resulting from government or any regulatory bodies or ex- pectations from cultural expectations of the community or any profes- sional associations (Liang et al., 2007). In an attempt to negate the pressures arising from external agencies or bodies, organization develop “coercive isomorphism” (Dubey et al., 2018). Normative pressures arise from professionalization, which is defined by DiMaggio and Powell (1983) as “…the collective struggle of members of an occupation to define the conditions and methods of their work, to control the production of the future member professionals, and to establish a cognitive base and legitimisation for their occupational autonomy.” Organizational researchers have noted that employee sharing similar traits (and hence normative isomorphism) which, is often developed via professional education and training (see DiMaggio & Powell, 1983; Liang et al., 2007; Heugens & Lander, 2009; Dubey et al., 2018; Dubey, Gunasekaran, Childe, Blome, & Papado- poulos, 2019; Zuo et al., 2020). Mimetic pressures refer to mimicking actions of organizations with respect to their competitors. This is often done because of environmental uncertainty, such as when new

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technology is not well understood, organizations have struggled to explain any degree of uncertainties or there is poor alignment of vision, mission and goals in organizations. In such cases, organizations develop mimetic isomorphism (DiMaggio & Powell, 1983). In sum, institutional theory can offer interesting perspective to understand BI assimilation.

2.1.1. Coercive pressures (CP) Liu, Ke, Wei, Gu, and Chen (2010) argued that institutional pressures

is considered as an important driver particularly in context to the adoption of innovation. Management scholars have increasingly argued that the pressures resulting from the government and other bodies, are transmitted via operational channels, affect the organization predispo- sition towards adoption of technology (Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019; Liang et al., 2007; Liu et al., 2010; Zhu et al., 2006). It has been shown that coercive pressures have significant influence on the adoption of ERP (Liang et al., 2007) and TQM (Dubey et al., 2018). Following previous studies we believe that coercive pressures plays a significant role in the assimilation process (see, Liang et al., 2007; Dubey et al., 2018). The existing studies on assimilation suggest that coercive pres- sures often arises from local government policies or regulatory author- ities or expectations from local bodies and community, may have direct or indirect impact on assimilation (see, Liang et al., 2007; Dubey et al., 2018). Extending this argument to BI assimilation, we hypothesise as follows:

H1. The coercive pressure has positive and significant effect on top leader’s commitment.

2.1.2. Normative pressures (NP) Following institutional logic, we argue that the institutional envi-

ronment shape the working behaviour of the individuals and the orga- nizations (DiMaggio & Powell, 1983; Liu et al., 2010; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019). Following Liu et al. (2010, p. 374), Normative pressures (NP) refer to the “pressures that stem from collective expectations within particular organizational contexts of what constitutes appropriate, and thus legitimate, behaviour”. NP penetrate via channels of professional affiliations as well as the popularity generated by confer- ences hosted by professional bodies (Liang et al., 2007). Lowry, Zhang, Zhou, and Fu (2010) argue that normative isomorphism play an important role in the diffusion of new technology innovation in any organization. These arguments are consistent with the Zhu et al. (2006) findings. Moreover, Dubey et al. (2018) in one of their studies have found that normative pressures play a significant role in case TQM assimilation. Hence, we believe that normative pressures have signifi- cant influence during assimilation stage. Thus, we hypothesize it as:

H2. The normative pressure has positive and significant effect on top leader’s commitment.

2.1.3. Mimetic pressures (MP) DiMaggio and Powell (1983, p. 151) argue, “not all institutional

isomorphism, however, derives from coercive authority. Uncertainty is also a powerful force that encourages imitation. When organizational technologies are poorly understood, when goals are ambiguous, or when the environment creates symbolic uncertainty, organizations may model themselves on other organizations”. When organization faces highly uncertain and ambiguous, the role of leadership is highly critical in shaping organizational strategies (Schoemaker, Heaton, & Teece, 2018; Yang, Huang, & Wu, 2019). Schoemaker et al. (2018) argue that in highly uncertain environment, the leaders instead of focusing on executing the plan, the leaders prepare the organization to quickly adapt to the rapid changes with the help of technology. Prior studies have found that mimetic pressures influence management commitment (Liang et al., 2007; Zuo et al., 2020). The MP arise from the tendency of organizations to mimic other organizations. Previous studies (see, Liang et al., 2007; Dubey et al., 2018, 2019a; Lin et al., 2020; Zuo et al., 2020) have noted that lack of clarity regarding the outcomes of the programs whether it is referring to ERP adoption or adoption of TQM related practices, organizations mimic other organizations within the similar industry. The COVID-19 crisis has caused significant disruption and the continuous lockdown has create high degree of uncertainties (Bryce, Ring, Ashby, & Wardman, 2020; Pan, Cui, & Qian, 2020). Hence, we believe that mimetic pressures have significant influence during assim- ilation stage. Thus, we hypothesize it as:

H3. The mimetic pressure has positive and significant effect on top leader’s commitment.

2.2. Top leaders commitment and BI assimilation (BI-ASM)

Institutional theory predicts institutional isomorphism but in reality, organizations exhibit diversity with respect to benefits from BI imple- mentation or manifest different levels of BI assimilation under similar institutional environments. The organizational theorist have often argued that institutional logic; often fail to explain the mechanism of the translation of the external pressures in shaping organizational internal policies (Colwell & Joshi, 2013; Kostova & Roth, 2002). Hence, following Colwell and Joshi (2013) and Liang et al. (2007) arguments we, argue that role of human agent help address the limitations of the institutional theory. Thus, we propose that top leaders commitment towards BI may help translate external pressures into BI assimilation. Top leaders are expected to share the vision and mission of their orga- nization with their employees (Dubey, Gunasekaran, Bryde, Dwivedi, & Papadopoulos, 2020; Dubey et al., 2018; Dubey, Bryde et al., 2020). They not only motivate their team members but also provide adequate resources that may help assimilate BI (Dubey et al., 2018; Liang et al., 2007), thereby creating a BI culture which is conductive for employees’ involvement. Overstreet, Hazen, Skipper, and Hanna (2014) that servant leadership theory can assist in providing deeper insights into organiza- tional commitment, which indirectly leads to business performance result. Hence, we hypothesize based on previous arguments as:

H4. The top leader’s commitment has positive and significant effect on the BI acceptance;

Fig. 1. BI Assimilation Model.

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Hazen et al. (2012) have suggested ‘acceptance’ and ‘routinization’ as two preceding activities that help assimilation. Acceptance has attracted significant attention from management scholars (see, Davis, 1989; Zhu et al., 2006; Hazen et al., 2012; Ahmad & Hossain, 2018). Acceptance (ACP) in context to BI can be defined as how well organi- zations constituents receive BI. Hazen et al. (2012) have argued that once organizational constituents have accepted an innovation like BI as a guiding philosophy, then it begins the process of being routinized within organizations. Based on Dubey et al. (2018, p. 2993) arguments we define “BI routinization as the permanent adjustment of the organiza- tions’ governance systems to account for BI”. Based on previous studies on information management and technology innovation (see, Jarvenpaa & Ives, 1991; Purvis et al., 2001; Liang et al., 2007) we posit that the top leader’s commitment may contribute to the BI assimilation via accep- tance. Thus, we hypothesize:

H5. The BI acceptance has positive and significant effect on BI routinization;

Following Zhu et al. (2006), we argue that the routinization is one of the stages involved between adoption of the BI and the assimilation of the BI. Following Zmud and Apple (1992, p. 149) we understand routinization as “the permanent adjustment of an organization’s governance system to account for the incorporation of a technology”. Routinization refers to the organizational ability to put a procedure in the place that evaluates the equipment turnover procedures to assure that the orga- nization is prepared for the dynamic changes in the environment. This is consistent with the innovation literature. Zhu et al. (2006) have found that the routinization has a significant effect on the assimilation of the technology. Ahmad and Hossain (2018) arguments is in consistent with the Zhu et al. (2006) and the Hazen et al. (2012). In pretext of pandemic crisis, the technology is quickly evolving to keep the pace with the rapid changing environments. In such case the effective routinization pro- cedure in the organization play a significant role in the assimilation of the BI (Laato, Islam, Islam, & Whelan, 2020). Based on the preceding discussions, we hypothesize it as:

H6. The BI routinization has positive and significant effect on BI assimilation.

In our studies, we have controlled the size of the organization and time since the BI has been adopted in their organization (Brown & Kaewkitipong, 2009; Dubey et al., 2018; Liang et al., 2007). Liang et al. (2007) argue that large organizations are more resilient towards hur- dles, which tend to slow down the assimilation process. Furthermore, decision-making is quite faster in smaller in comparison to large size organizations. Hence, we believe that size of the organizations may have significant influence on findings. Time since adoption has been noted in several diffusion studies (see, Liang et al., 2007; Dubey et al., 2018) as an important variable. Liang et al. (2007) argue that time is a significant predictor of diffusion-related phenomenon like assimilation. Thus, this variable reflects the assimilation learning curve (see, Fichman, 2001).

3. Research method

3.1. Construct operationalization and measurement

In our study, we have followed Churchill (1979) suggestions to improve the reliability and validity of our study via following two-stage process. Firstly, we have undertaken an extensive review of literature to draw our construct and their measurement. Secondly, we have inter- viewed twelve managers who have extensive years of experience in the BI assimilation. We used qualitative content analysis to validate our multi-item constructs. In response to the previous calls of management scholars (Flynn, Huo, & Zhao, 2010; Malhotra & Grover, 1998; Rossiter, 2008; Mithas, Ramasubbu, & Sambamurthy, 2011; Schryen, 2013; Fawcett et al., 2014; Schilke, 2014; Dubey, Gunasekaran et al., 2020; Dubey, Bryde et al., 2020), we argue that qualitative content analysis is a

useful method to validate the borrowed multi-item construct with real-life practices. We further gathered secondary data related to our samples selected for our study. In a way we attempted to overcome the limitations of our literature review and secondly, we have further tried to reduce the negative effects of the common method bias resulting from single source of data (see, Chin, Thatcher, & Wright, 2012; Fawcett et al., 2014; Iyengar, Sweeney, & Montealegre, 2015).

We finally arrived to the list of items for each construct based on our review of the literature and on a pre-test exercise, which we carried out with the panel of eighteen identified experts to avoid any ambiguity of the measurement items (see Appendix A). The measures listed in the Appendix A were measured using Likert scale with the anchors ranging between: 1 (strongly disagree) to 5 (strongly agree).

3.2. Sample and data collection

We chose auto component manufacturing sector in India due to two main reasons: firstly, Indian auto component- manufacturing sector has experienced significant dip in the operating margin in comparison to the last year performance due to the pandemic crisis. Secondly, the Indian auto component-manufacturing sector has made significant investment in the BI capability to improve the competitiveness (McKinsey & Com- pany, 2020). Hence, we believe that the data gathered from Indian auto-component manufacturing sector using survey based tool will be highly useful for testing our research hypotheses.

We distributed our questionnaire via e-mail to the 532 auto- components manufacturing firms located in the western and the southern regions. We gathered the organisations detail from the data- base of the The Automotive Components Manufacturers Association of India (ACMA) and further validated the details via Dun & Bradstreet (see, Dubey et al., 2018). Following previous studies using survey based approach (see, Dwivedi, Kapoor, Williams, & Williams, 2013; Chen, Preston, & Swink, 2015; Dubey et al., 2018; Srinivasan & Swink, 2018; Dubey, Gunasekaran, Childe, Blome et al., 2019), was adopted following Dillman’s (2011) suggestions. We followed up with the respondents after one weeks to seek whether they have received an e-mail containing the introduction letter and the questionnaire. Firstly, we received questionnaire from 110 respondents. After several follow up calls, we again sent packages to non-respondents after two weeks and 64 ques- tionnaires were subsequently returned for an overall response rate of 32.71 %. In sum, 174 completed questionnaires were received (see Table 1). We acknowledge that our association with the ACMA has played an important role in gathering quality data.

3.3. Non-response bias

We note that data gathered using survey-based instrument at one point of time might suffer from non-response bias (Fawcett et al., 2014; Dubey et al., 2018, 2019a). In this study, we have used two approaches to examine non-response bias in our collected data. Firstly, we adopted traditional method [i.e., wave-analysis (Armstrong & Overton, 1977)] to test non-response bias. The comparisons between early and late re- sponses showed no statistical differences at p < 0.05, indicating that non-response bias is not a potential issue in our study. Secondly, following Wagner and Kemmerling (2010) arguments we have

Table 1 Demographic Profile of the Respondents.

Title Number %

CEO 9 5.17 CIO 33 18.97 Finance & Accounting Manager 15 8.62 Supply Chain Manager 67 38.51 Human Resource Manager 12 6.90 Customer Relationship Manager 23 13.22 Sales Manager 15 8.62

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compared the demographics of the respondents to demographic infor- mation from non-respondents via Dun & Bradstreet and observed no inconsistencies.

4. Data analyses and results

Before deciding on our modelling technique, we first performed an assumptions test on our indicators (see, Fawcett et al., 2014, p.13). Based on Eckstein, Goellner, Blome, and Henke (2015), we tested as- sumptions related to constant variance, outliers, and normality. We examined residual plots, rankits plot of residuals, and measures of skewness and kurtosis. Based on Cohen (2008), we used Mahalanobis distance to detect outliers. The maximum absolute values of skewness and kurtosis were found to be 1.26 and 2.29, respectively (see Appendix B). Based on Curran, West, and Finch (1996), we these values are well within recommended limits (univariate skewness < 2, kurtosis < 7). In sum, we found that the typical assumptions required for inference-based statistics were met.

4.1. Measurement model

We have performed confirmatory factor analysis (CFA) following Fornell and Larcker (1981) to examine the: (a) construct validity [indi- vidual factor loadings, scale composite reliability (SCR) and average vari- ance extracted (AVE)]. In our study we found that the individual factor loadings were greater than 0.5, the SCRs were calculated to be greater than 0.7, and the AVE for each construct was greater than 0.5 (Chin, 1998) (see, Table 2). (b) Next we have performed discriminant validity test (see, Table 3). The Table 3 represent a matrix that contain the correlations between paired constructs, and the leading diagonal of the matrix shows the square root of the AVE of each construct. All measures indicate adequate discriminant validity (Fornell & Larcker, 1981). Model fit indices indicated acceptable fit of the data to the measurement model [Normed Chi-Square = 1.9, which is less than 2 as recommended by Carmines, McIver, Bohrnstedt, and Borgatta (1981) and Hu and Bentler (1999) pointed that the threshold value of normed chi-square is 0.09].

4.2. Common method bias

The data gathered using single respondent questionnaire might suf- fer from common-method bias (CMB) that may affect our statistical re- sults (Podsakoff & Organ, 1986). To reduce the negative effects of the CMB, we performed Harman’s one factor test (see, Podsakoff, MacK- enzie, Lee, & Podsakoff, 2003) to examine whether a single latent factor would account for all the theoretical constructs (Dubey et al., 2018). The exploratory factor analysis yielded seven factors parsimonious structure. The single factor does not explain more than 13.95 % percent of total 51.03 percent of total variance. Hence common method bias is likely not a significant threat to the findings. Dubey, Gunasekaran, Childe, Blome et al. (2019), Dubey, Gunasekaran, Childe, Papadopoulos et al. (2019) noted that the Harman’s one factor test is a traditional approach and may not be sufficient to test CMB. We followed Malhotra, Kim, and Patil (2006) suggestions via performing CFA loading all items on a single factor, and further examine the fit indices. The single factor in this case is equivalent to “one factor”, which indicates the existence of bias resulting from data collection from a single source (Srinivasan & Swink, 2018). The fit for the one factor model is not adequate [RMSEA = 0.313; NNFI = 0.094; CFI = 0.231 and SRMR = 0.513] and chi-square change with respect to the hypothesized model is highly significant (p < 0.000). Finally, we tested for CMB using the correlation marker technique (Lindell & Whitney, 2001). We assumed an unrelated variable to delineate out the correlations caused by CMB. In addition, we computed the significances of the correlations based on Lindell and Whitney (2001) suggestions (Srinivasan & Swink, 2018; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019). We observed minimal differences between adjusted and unadjusted correlations. Following the results based on three methods, we argue that the potential effects of the CMB is not significant. How- ever, we caution readers that in future an attempt should be made to collect data using instrument designed for multi-respondents (see, Ketokivi & Schroeder, 2004).

4.3. Endogeneity test

Following Guide and Ketokivi (2015) arguments, we adopted some measures to correct the endogeneity (see, Liu, Wei, Ke, Wei, & Hua, 2016). According to Guide and Ketokivi (2015, p. v), “when arguing that the variance of X gives rise to the variance of Y (causally or otherwise), we expect to see a plausible argument that the direction is indeed from X to Y, not vice versa, or perhaps caused by an omitted variable. Measurement error can also cause an endogeneity problem: if X and Y have a common measurement error source, X will unavoidably correlate with the error term of Y. Finally, sample selection bias may lead to problems very similar to that of endoge- neity”. Hence, we understand that we cannot eliminate the endogeneity problem due to our research design. However, we adopted some mea- sures to correct it in our model that may lead to inconsistent and biased outcomes (Liu et al., 2016). We performed two-stage least squares regression analysis with the questionnaire variables (Bellamy, Ghosh, & Hora, 2014; Liu et al., 2016). To conduct two-stage least squares regression analysis, we identified size of the organization as the poten- tial instrumental variable as it does not have significant effect on the BI assimilation. Following Bellamy et al. (2014) we regressed TLC on all instrumental variables and the control variables at the first stage. We observed that the R2 value increased significantly in comparison to the model with only control variables. This indicates that the organization size can be effectively assumed as an instrumental value for TLC in our study. Next, we conducted Durbin-Wu-Hausman post-estimation test for endogeneity (Davidson & MacKinnon, 1993). In this test we performed augmented test on the TLC by adding the error term that we obtained in the first stage while performing two-stage least squares regression test. The path coefficients of the error term of TLC were insignificantly related to the BI assimilation. This establishes that the endogeneity associated with the TLC is insignificant in our study. Thus, we can argue

Table 2 Loadings of Indicator Variables (Scale Composite Reliability and Average Vari- ance Extracted).

Constructs Measures Factor Loading Variance Error SCR AVE

CP CP-BI1 0.73 0.54 0.46 0.76 0.51 CP-BI2 0.70 0.49 0.51 CP-BI3 0.72 0.52 0.48

NP NP-BI1 0.87 0.76 0.24 0.85 0.65 NP-BI2 0.78 0.60 0.40 NP-BI3 0.76 0.58 0.42

MP

MP-BI1 0.83 0.69 0.31 0.89 0.67 MP-BI2 0.82 0.67 0.33 MP-BI3 0.82 0.67 0.33 MP-BI4 0.81 0.65 0.35

TLC

TLC2 0.79 0.62 0.38 0.83 0.50 TLC3 0.68 0.46 0.54 TLC4 0.53 0.28 0.72 TLC5 0.75 0.56 0.44 TLC6 0.75 0.57 0.43

BI-ASM ASM-BI1 0.69 0.48 0.52 0.75 0.50 ASM-BI2 0.61 0.37 0.63 ASM-BI3 0.81 0.65 0.35

ACP ACP-BI2 0.80 0.64 0.36 0.84 0.72 ACP-BI3 0.89 0.79 0.21

RO

RO-BI1 0.80 0.64 0.36 0.94 0.74 RO-BI2 0.79 0.62 0.38 RO-BI4 0.86 0.74 0.26 RO-BI5 0.90 0.81 0.19 RO-BI6 0.91 0.83 0.17 RO-BI7 0.89 0.79 0.21

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that the TLC is an exogeneous variable and not the endogeneous. Similarly, we examined in case of CP, NP and MP we found the path coefficients of the error term of CP, NP and MP were insignificantly related to the TLC.

4.4. Hypotheses testing

Although we considered structural equation modelling approaches (Wendorf, 2002), we concluded that the hierarchical regression modelling is the favourable approach in this study in consideration of both the estimation function and the parsimony of presentation. Test results are presented in Table 4. Our hypothesis H2 (NP→TLC) is sup- ported (β = 0.17, p = 0.01). This result of our study is consistent with the previous studies (Dubey et al., 2018; Liang et al., 2007). Next, we found support for H3 (MP→TLC) (β = 0.71; p = 0.00). We can argue that the MP has a positive and significant effect on the TLC. These findings of our study are consistent with the previous studies in context to ERP assim- ilation (Liang et al., 2007) and the TQM assimilation (Dubey et al., 2018). Similarly, we found support for H4 (TLC→ACP) (β = 0.11; p = 0.02), H5 (ACP→RO) (β = 0.56; p = 0.00) and H6 (RO→BI-ASM) (β = 0.25; p = 0.00). These results are consistent with the Zhu et al. (2006) and Dubey et al. (2018) findings. We observed that the size of the or- ganization has no significant effect on the BI-ASM. We conclude that during pandemic resulting from COVID-19, the importance of the BI has been realized by all kind of organizations irrespective of their size. Although, we assumed that larger organization have access to more resources. However, the COVID-19 crisis has played an important role in bridging the wide gaps that existed pre-COVID-19 crisis.

Based on hypothesis testing, we found that in the case of BI assimi- lation, the top leader’s commitment may not be significantly influenced by coercive pressure, as the first research hypothesis is not supported (β= -0.11; p = 0.23). This result provides a clear insight into the particular situation that has forced the organizations to use innovative technologies to improve their business operations and decision-making abilities. The COVID-19 crisis has forced organizations to adapt to the new norms. Hence, during pandemic, the organizations have increas- ingly invested in innovative technologies to maintain their competitive advantage.

5. Discussion

In this study, we have posited two guiding research questions and five research hypotheses suggesting that the institutional pressures under the mediating effect of the top leader’s commitment influence the assimilation of the BI. More specifically, building on institutional theory and upper echelon theory, we developed our research model (see Fig. 1) to address our research questions (RQ1 and RQ2). By addressing RQ1, our study attempts to bridge, the existing research gaps. To date, the collective influence of institutional pressure on assimilation of BI has not been studied (Liang et al., 2007; Lin et al., 2020; Teo, Wei, & Benbasat, 2003; Zuo et al., 2020). Addressing this gap is important given that types of pressure (coercive, mimetic and normative) are ‘not always empirically distinct’ (DiMaggio & Powell, 1983, p. 150). Our findings therefore contributes to this literature in two ways. Firstly, we provide the liter- ature with survey based reflective measures of institutional pressures that capture coercive, mimetic and normative pressures. Secondly, the previous literature has noted the limitations of institutional theory in explaining the extent to which companies within the same institutional field (i.e. industry) actually adopt the technological innovations (Col- well & Joshi, 2013; Dubey, Gunasekaran, Childe, Blome et al., 2019; Dubey, Gunasekaran, Childe, Papadopoulos et al., 2019; Greenwood & Hinings, 1996; Liang et al., 2007; Oliver, 1997; Zuo et al., 2020). To address these limitations, we have incorporated the role of top leader’s commitment within the institutional theory framework (Greenwood & Hinings, 1996; Liang et al., 2007). To date, however, few studies have explored this new extension. Our study closes this gap.

In an attempt to address our RQ2, we develop and empirically test a theoretically grounded model that confirms the collective influence institutional pressures may have on top leadership commitment for the BI assimilation. Specifically, we show how top leader’s commitment to the BI assimilation can mediate the relationship between institutional pressures and BI assimilation. By doing so, we provide some of the first empirical evidence to support the inclusion of intraorganizational dy- namics (see, Greenwood & Hinings, 1996; Colwell & Joshi, 2013), within institutional theory, as an approach for understanding why or- ganizations demonstrate differential behaviour in context to the adop- tion of BI tools. Moreover our study building on previous arguments, attempted to explain BI assimilation using institutional theory and upper echelon theory and extend the BI work of Nam et al. (2019) during pandemic crisis. In this way our study, provides theoretical explanation to pandemic effect on organizational responsiveness towards BI tools assimilation (Pan & Zhang, 2020; Papadopoulos et al., 2020). Dubey et al. (2019) used institutional theory to explain the motives of organi- zations when adopting big data analytics, whereas Dubey et al. (2018) explained the impact of contextual factors on organizational perfor- mance using institutional theory. However, both studies did not focus on post-implementation phases, which was the focus of this study. Overall, we can argue that mimetic and normative pressures affect top man- agement commitment, which subsequently affects acceptance, routini- zation, and assimilation of BI. However, we found that coercive pressures were not positively related to top leader’s commitment (β= -0.11; p > 0.1). This result is in contrast to the existing literature

Table 3 Correlations among major constructs.

(Note: The leading diagonal of matrix represented in grey shade is square root of average variance extracted).

Table 4 Overview of Hypotheses Test.

Hypothesis βvalues Direction of β t- statistics

P value Conclusion

H1: CP→TLC − 0.11 reverse − 1.21 0.23 not- supported

H2:NP→TLC 0.17 positive 2.15 0.01 supported H3:MP→TLC 0.71 positive 14.9 0.00 supported H4: TLC→ACP 0.11 positive 2.31 0.02 supported H5: ACP→RO 0.56 positive 16.71 0.00 supported H6:RO→BI-

ASM 0.25 positive 3.86 0.00 supported

[Notes: CP, coercive pressures; MP, mimetic pressures; NP, normative pressures; TLC, top-leadership commitment; ACP, acceptance; RO, routinization; BI-ASM- business intelligence assimilation; BI, business intelligence].

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exploring the role of institutional pressures and the statistically signifi- cant positive influence of coercive pressures on top managers’ posture towards the adoption of activities related to the improvement of orga- nizational processes or recovery of the environment in which firms are embedded such as reverse logistics (Ye, Zhao, Prahinski, & Li, 2013), green supply chain management practices (Zhu, Sarkis, & Lai, 2013), and supply chain management systems (Liu et al., 2010; Zhang & Dhaliwal, 2009) and technologies (Bhakoo & Choi, 2013; Liu et al., 2010; Saldanha, Mello, Knemeyer, & Vijayaraghavan, 2015). In a way, we present that how our study extend and contradicts previous studies focusing on adoption or assimilation of the technology. Our study in comparison to other studies we found that in our case we note that CP has no significant influence on TLC which contradict the Liang et al. (2007) and Dubey et al. (2018) research findings. Moreover, our study extend the previous studies (see, Teo et al., 2003; Lin et al., 2020 and Zuo et al., 2020) by examining the role of TLC in translating institutional pressures to shape the technology assimilation strategy. Hence, we believe that our statistical results paint an interesting picture of asso- ciations and complementarities among the external pressures, top management leader’s and BI diffusion stages (i.e., acceptance, routini- zation and assimilation) in pandemic crisis resulted from COVID-19. Collectively, these results have implications for the practitioners, as well as provide some new research questions in this field of research.

5.1. Theoretical contributions

There is a rich body of literature focusing on the role of institutional forces on the adoption of technology (Dubey, Gunasekaran, Childe, Blome et al., 2019; Liang et al., 2007; Lin et al., 2020; Pennings & Harianto, 1992; Teo et al., 2003). Liang et al. (2007) in context to ERP assimilation attempted to explain the role of top management commit- ment in translating the external pressures into the ERP assimilation. In a way, Liang et al. (2007) attempted to address the limitations of the previous work (see, Pennings & Harianto, 1992; Teo et al., 2003) by including top management commitment as mediating construct. How- ever, Liang et al. (2007) remained silent on the stages involved in the assimilation. Dubey et al. (2018) in one of their studies have tried to explain three stages of assimilation in context to the TQM philosophy. However, in the case of technological innovation, the existing works have largely remained silent. Our study findings enriches BI-Assimilation research via examining the role of institutional factors effects on organization intention to adopt the BI and embrace it completely across all the functional departments of the organization. Here, we attempted to develop a theory that explain the assimilation of the BI in an organization during unprecedented time. The existing literature offers rich discussion on the factors that influences the adop- tion of the BI. However, most of the studies have examined using resource based perspective or organizational information processing perspective. However, the literature on BI has remained silent on how the external pressures drive the organization to assimilate the BI as their organizational philosophy. Although, in context to ERP and TQM assimilation, the scholars have offered explanation grounded in the institutional theory. This study, thus heeds calls for theory-focused data driven study that provides in depth understanding of how BI assimila- tion occurs during pandemic crisis. Hence, we argue that our study contribution to the BI literature is threefold. Firstly, we attempted to provide an operational definition of BI assimilation and develop and statistically validate a model that examines the effect of coercive, mimetic and normative pressures on top leader’s commitment to BI assimilation. Hence, we argue that our study contributes to the theo- retical boundaries of BI assimilation in uncertain time. Secondly, we integrate institutional theory and upper echelon theory to explain BI assimilation in terms of a three-stage post-implementation process (i.e. acceptance, routinization and assimilation). In a way argue that the TLC help translate the pressures resulting from the competitors and the ex- pectations of organization to provide better results during pandemic

crisis. Thirdly, we extend prior literature (see, Nam et al., 2019; Chen & Lin, 2020) by integrating Liang et al. (2007) and Hazen et al. (2012) studies to model BI assimilation during pandemic crisis. In a way we attempted to address research calls of some information management scholars in the wake of pandemic crisis (see, Papadopoulos et al., 2020; Dwivedi et al., 2020; Pan & Zhang, 2020). We can argue that our find- ings help understand how institutional theory and upper echelon theory provides a better understanding of the assimilation of BI during an un- certain time. These findings of our study answer the antagonists who often criticized the institutional theory and their inability to influence the organizational policies.

5.2. Managerial implications

The pandemic crisis resulting from COVID-19 has transformed the lives of citizens and organizations way of doing business. The pandemic has triggered the humanity to find innovative ways of doing business to keep the sinking economy afloat. Although, we often blame COVID-19 and pandemic for current crisis. However, the pandemic has offered significant insight into our hidden problems that has plagued our world economy. The pandemic has exposed our weakness and reflected our capabilities to deal with such health crisis. The power of emerging technology has been understood during the pandemic crisis to fight against the disruptions caused by the pandemic crisis (Dwivedi et al., 2020; Ivanov, 2020). The BI tools have not only to help organizations to reduce the spread of the virus; it has helped enhance the performance of the organizations. BI has changed the overall business strategies of the organization. In recent times, the BI tools have played a significant role in building trust and collaboration among the various stakeholders. The majority of these BI tools extensively rely on data analytics to promote better communication between organizational stakeholders. Despite, availability of BI tools the organizations have struggled to optimally utilize these BI tools in an effective and efficient way. Hence, our find- ings clearly suggest practitioners who consider investments in BI care- fully evaluate: (1) how organizational policies are aligned with the external pressures; (2) to what extent the top leaders of the organization are familiar with the effects of the external pressures on the diffusion of BI. For example, during pandemic resulting from the COVID-19, BI and data analytics initiatives are proving to be a boon in disguise for many organizations. It helps organizations to sense and adapt to the disrup- tions caused by the pandemic. It enables organizations to develop new products as well as help protect their own employees without affecting their business propositions. Hence, we can argue that our findings can be used as guidance to managers and consultants who are involved in BI implementation. The mediating role of TLC in BI assimilation clearly suggests that top leaders plays a critical role in the BI assimilation process. For instance, as the organization struggle to adapt to the un- precedented crisis resulting from the COVID-19, top leaders are encouraging their BI teams to develop new solutions at a faster rate and to make allowances for needs that are changing rapidly to a high degree of uncertainties. Moreover, via this study, we have realized that the most important lesson that organizations are learning about the BI during a pandemic is that it will have a little effect within organizations still insisting on top-down decision making. Instead, the organizations using BI to the greatest effect are those that have a culture of the delegation of authority, where employees are empowered to make data-driven de- cisions without the need to wait for their superiors to approve it. We found how the acceptance of BI among an organization’s constituents further helps to align organization’s governance systems to ultimately usher in BI assimilation. The finding that institutional forces (apart from coercive forces) influence BI assimilation is quite interesting for man- agers and consultants. Traditionally, managers focus on implementation more so than post-implementation phases. Thus, a large percentage of organizations typically report BI failures due to a lack of understanding of assimilation processes. Hence, the study findings can help managers to focus on each of the intermediary steps that lead to assimilation of BI.

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Attending to the acceptance and routinization aspects of BI is important for the eventual assimilation of BI. Further, we believe that institutional pressures (normative and mimetic), if properly translated by top man- agers who are committed towards BI assimilation, can be very useful for those companies that have reported losses due to failure to reap benefits from their investments in BI implementation and would like to further investigate the reasons behind this failure in order to re-launch or re-energize BI efforts. Finally, our study offers some useful tips to the managers who are unable to exploit BI to minimize the disruptions caused by the pandemic resulting from COVID-19 and may serve as a useful guidance for the managers to deal with future crisis. Despite the significant success, the remaining flaws of the organizations that prevent the organization from achieving maximum benefits from their BI ini- tiatives are becoming known. That offers a unique opportunity to fix them finally. With that in mind, here are the top three lessons that businesses are learning about BI amidst the pandemic.

5.3. Limitations and further research direction

Drawing on institutional theory and upper echelon theory, and literature on BI and elements of innovation diffusion, we developed and tested our theoretical model using data from quality managers at 174 auto-component manufacturers during pandemic crisis stage. Our study has some limitations that should be noted. Firstly, we have tested our theoretical model using data gathered from the auto-components manufacturing sector. However, there may be variations in terms of practices between manufacturing sectors. Hence, future research can examine this model across sectors. Secondly, to test our framework we used cross-sectional, single-source data. Future research can employ longitudinal methods to test for causality in the model. In addition, future research examining outcomes of assimilation is especially encouraged. Our study is based on a single country and single industry data, which may limit the generalizability of our study. Hence, in order to reduce the variability induced by the industry differences, we pur- posely chose the Indian auto-component manufacturing industry (see, Liu et al., 2010). To minimize the biases resulting from personal dif- ferences due to the background, we identified respondents of similar backgrounds who had obtained training from a similar kind of institu- tion. Although we believe that our data collection strategy may have helped the internal validity of our study, this may limit the external validity of the study. Moreover, this study has been conducted to capture the managers response in context to pandemic crisis resulted from the COVID-19.Thus, we believe that the findings of our study should be cautiously evaluated in context to other settings. Moreover, our comparative analysis of the results show that in context to China the role of CP is highly significant. Similarly, in the context Indian automotive industry, the role of CP is insignificant. These differential results can be better explained using national culture theory. For instance, Prakash and Majumdar (2021) investigated how national culture plays an important

role in content creation in the context of a social media platform. Similarly, George et al. (2018) and Gupta and Gupta (2019) have advocated in favor of the influence of national culture on shaping organizational strategies. We believe that our study could be extended by examining the moderating effect of national culture dimensions on the paths joining institutional pressures and TLC. We also encourage future research using multiple case study, ethnographic, and action research methods to build more comprehensive theory to explain the BI assimilation.

6. Conclusions

The study examines the role of external pressures and top leaders commitment in BI diffusion process. Informed by information manage- ment and organizational theories we have conceptualized a theoretical model. To validate our theoretical model and test our research hy- potheses, we have gathered data from Indian auto component manufacturing sector to understand how external pressures and the top leaders have played a significant role in BI assimilation during pandemic crisis, which has affected the business worldwide. Despite of the poor operating margin, the sector has learnt a new way to deal such with such unprecedented time via investing in BI and exploiting them in an appropriate way. We hope our findings and limitations of our study provide enough food for thought.

Authors comment

The first author (Mrs Akriti Chaubey, who is a Doctoral Scholar at School of Management, National Institute of Technology Rourkela) has contributed in the manuscript through the following ways:

1 Conceptualized the theoretical model via extensive literature review; 2 Formulated research hypotheses; 3 Developed a structured questionnaire; 4 Carried out data collection; 5 Performed Data Analysis 6 Drafted the manuscript

The second author (Dr Chandan Kumar Sahoo, who is Professor at the School of Management, National Institute of Technology Rourkela) has contributed in the manuscript through the following ways:

1 Provided in-depth inputs during theoretical framing; 2 Offered significant inputs related to data analyses and the selection

of appropriate statistical tools; 3 Helped during proof editing; 4 Helped during discussion section writing (i.e., contributions to

theory).

Appendix A. Constructs and Items

Construct Relevant Literature Items

Coercive Pressures (CP- BI)

Dubey et al. (2018) 1 The local authority want our organization to use BI during pandemic crisis (CP-BI1). 2 The professional associations expect our organization to use BI during pandemic crisis (CP-BI2). 3 The consumers of our organization expect our organization to adopt BI during pandemic crisis (CP-BI3).

Normative Pressures (NP-BI)

Dubey et al. (2018) 1 The extent to which your channel partners have adopted BI during pandemic crisis (NP-BI1). 2 The extent to which first and second tier suppliers of your organization have adopted BI during pandemic crisis (NP-

BI2). 3 The extent to which the professional societies promotion schemes have influenced your organization to adopt BI

during pandemic crisis (NP-BI3). Mimetic Pressures (MP-

BI) Dubey et al. (2018) 1 Our main business rival has gained significant business advantage with the adoption of BI during pandemic crisis (MP-

BI1) 2 The use of BI is well received by other competitors in our industry during pandemic crisis (MP-BI2). 3 The customers of our organization have appreciated the use of BI during pandemic crisis (MP-BI3).

(continued on next page)

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(continued )

Construct Relevant Literature Items

4 The suppliers of our organization have appreciated the use of BI during pandemic crisis (MP4). Top leader’s

Commitment (TLC) Liang et al. (2007); Dubey et al. (2018)

1 Our organization top leaders believe that the BI has potential to enhance the business performance of our organization during pandemic crisis (TLC1).

2 Our organization top leaders believe that the use of BI will enhance business opportunities during pandemic crisis (TLC2).

3 Our organization top leaders have formulated a strategy for the use of BI during pandemic crisis (TLC3). 4 Our organization top leaders share the BI vision with all stakeholders during pandemic crisis (including you) (TLC4). 5 Our organization top leaders established the performance metrics to monitor the BI project during pandemic crisis

(TLC5). 6 Our organization top leaders recognizes the contribution of the partners engaged in BI project during pandemic crisis

(TLC6). Acceptance (ACP-BI) Hazen et al. (2012); Dubey

et al. (2018) 1 1To what extent you believe that BI enhance my job performance during pandemic crisis (ACP-BI1). 2 To what extent you and your colleagues associate with the BI during pandemic crisis (ACP-BI2). 3 To what extent the infrastructure support the innovation during pandemic crisis (ACP-BI3).

Routinization (RO-BI) Hazen et al. (2012), Dubey et al. (2018)

1 To what extent in your organization procedures are defined for replacement of tangible resources necessary to support BI during pandemic crisis (RO-BI1).

2 To what extent in your organization a separate budget has been created to support BI during pandemic crisis (RO-BI2). 3 Our organization have a dedicated team to support BI during pandemic crisis (RO-BI3). 4 Our organization have defined organizational procedures for procurement of necessary items during pandemic crisis

(RO-BI4). 5 Our organization hire and retain qualified people to support BI during pandemic crisis (RO-BI5). 6 To what extent my organization offers opportunities for initial and /or recurring training regarding the BI during

pandemic crisis (RO-BI6). 7 To what extent in my organization a person familiar with the BI have been promoted into higher positions of greater

authority such that they support the innovation further especially during pandemic crisis (RO-BI7). BI Assimilation (ASM-BI) Liang et al. (2007); Dubey

et al. (2018) 1 To what extent your organization has exploited the BI tools in every department (%) during pandemic crisis (ASM-

BI1). 2 To what extent all the functional departments in your organization used BI tool during the pandemic crisis (ASM-BI2). 3 To what extent your organization use BI tools in each functional department as indicated by you:

a) Business operations b) Management practices c) Decision making (ASM-BI3).

Appendix B. Skewness (top) and exc. kurtosis (bottom) coefficients

CP NP MP TLC BI-ASM ACP RO OS

− 0.488 − 0.634 − 0.286 − 0.453 − 0.862 − 0.289 − 1.202 1.618 0.584 0.858 0.104 − 0.073 − 0.205 − 0.606 1.307 2.183

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