Positioning big data analytics capabilities towards financial service agility Abeeku Sam Edu Operations and Management Information Systems, University of Ghana Business School, Accra, Ghana Abstract Purpose –Enterprises are increasingly taking actionable steps to transform existing businessmodels through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility. Design/methodology/approach – The study relied on survey data from 485 banking professionals’ perspectiveswith BDAusage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed. Findings – Based on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources. Practical implications – The study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering andmarket observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products. Originality/value – This study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations. Keywords Big data capability, Financial service agility, IT capability view, PLS-SEM Paper type Research paper 1. Introduction Digital technology innovations have gradually changed the phase of doing business globally and subsequently influenced organisations performance. In their view, Cai et al. (2019) observed that the business ecosystems are evolvingmainly because of advanced innovations in digital technologies. Therefore, many enterprises such as financial institutions have taken actionable steps to transform existing business models through digital technologies for service transformation. Consequently, financial institutions are steadily heading towards digital banking for a number of reasons (Chanias et al., 2019; Pramanik et al., 2019). However, the most imperative reason is informed by deploying innovative information technologies to improve financial service agility (FSA) driven by data and information (Werth et al., 2020). There is, therefore, a demand for an orientation towards enterprise agility as a new paradigm within the financial service ecosystems. Diverse studies have stressed the antecedents or consequences of enterprise agility (Meli�an-Alzola et al., 2020). Keenly, antecedents for most enterprise agility has been ascribed to digital technology innovations, which stimulates strategic decision-making, operational efficiency and stability (Harraf et al., 2015; Tallon et al., 2019). Although Financial institutions have been deploying unique information technology BDA capabilities towards FSA 569 Funding: This research was funded by the University of Ghana Business School (Award number: 03221821). The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2050-3806.htm Received 20 August 2021 Revised 18 November 2021 13 December 2021 Accepted 18 December 2021 Aslib Journal of Information Management Vol. 74 No. 4, 2022 pp. 569-588 Emerald Publishing Limited 2050-3806 DOI 10.1108/AJIM-08-2021-0240 https://doi.org/10.1108/AJIM-08-2021-0240 (IT) strategies to shift from traditional banking into the era of digital banking, digital disruptions are on the ascendency (Manser et al., 2021; Morkunas et al., 2019). This has presented an opportunity for financial institutions to deploy emerging digital technologies with their accompanying capabilities to build FSA to differentiate products or services valuable to customers and ultimately improve revenues. Seemingly, continuous advances and rapid availability of huge financial data calls for banks to deploy digital technologies and applications to improve data acquisition, data processing, data and information storage, knowledge creation and decision-making process in real-time (Labun, 2016; Mavlutova et al., 2020). In recent times, billions of intelligent devices connect to create intelligent systems to interact through data sharing on the cloud. Therefore, financial institutions have opined that knowledge derived from financial data through the use of technologies and financial resources is the new greatest asset for banks (Skyrius et al., 2018). This has immensely offered emerging digital technologies such as big data analytics (BDA) capabilities to comprehensively analyse customers’ financial data and other related financial data to enhance banking operations. BDA capabilities offer financial institutions to source financial data, analysis of data, insight and store such data and information on collaborative platforms for a quick decision-making process in real-time. Financial data acquisition, processing and storage, have become pivotal for financial institutions (Roumeliotis, 2019). While current studies on BDA deployment generally suggest an enhancement in enterprise operation, there is still missing discourse on its implications on financial service operations, hence, linkage to BDA capabilities is an important gap to explore. Based on this concept, the objective of this study is to reveal the connection between BDA capabilities implications on financial services agility. As such, the role of BDA capability usage relies on the overall IT capability development by a firm to promote enterprise agility. Additionally, based on Tallon et al. (2019) recommendation for future study to investigate the impact of IT on enterprise agility, this study proposes BDA capability (IT) topology usage as a factor to improve FSA. Moreover, this insight is useful for shaping the discourse in IS literature towards BDA evolution for financial services. The usage of BDA tools has been ascribed to be a catalyst to promote firms’ preparedness to collect and analyse large sizes of data for intelligence decisions. Thus, the study proposes a holistic view of BDA capabilities usage in the financial service context. Hence the study analysed the extent to which BDA capability uses can stimulate FSA through the mediating role of the IT capability framework. The remaining section of the study presents the literature review and the theoretical framework. The next section presents the research methodology followed by data and, and the final section presents the discussions of findings, implications and conclusions. 2. Literature review 2.1 An overview of technology applications in financial services A cursory review of technology applications and platforms deployed by financial institutions for retail banking and other services over the period ranges from stationed to virtual technology platforms. Again, organisations that develop technology and some start-ups whose activities do not guarantee obtaining financial services licences are gradually expanding to collaborate with banks in various aspects of financial trading. These trends contribute to driving financial institutions to refocus attention to financial technology (FinTech) for operations. FinTech is therefore defined as “technologically enabled innovations that could result in new business models, applications, process, or products with an associated effect on financial markets and institutions and the provision of financial services” (Gun, 2020). Therefore, financial technologies are driving the extent to which AJIM 74,4 570 financial institutions are meeting customers’ expectations intending to improve payments, transfers, deposits and other business-related concerns (Boraty�nska, 2019; Makeeva et al., 2021). The evolution of digital technology started with the introduction of automated teller machines (ATMs), and gradually due to widespread use of the Internet, most banks started Internet banking. Increasingly, the improvement of analogue phones to smartphones also changes the face of banking activities. Currently, due to the cost of banking operations, customer data management, customised products, and services, improving remittances and payments, ATM to ATM interactivity, scalability of digital resources and improving customers services, many banks globally are focussing on integrating emerging technologies like the Internet of Things (IoTs), BDA applications, cloud computing and blockchain to enhance financial services. More importantly, financial institutions are largely faced with conceivable volumes of financial data, and customers’ financial transactions require emerging technologies to facilitate insight and knowledge creation from these data. Similarly, the financial industry is the most data-driven compared to other industries leading to a far-reaching limit with their legacy systems. Big data technologies have therefore advanced the analysis of a range of data that provide financial institutionswith the capabilities and tools to obtain, classify, integrate and analyse structured and unstructured financial data from BoTs and cloud services. This, in turn, promotes retail banking the opportunity to aggregate available information about customers to provide better service comprehensively. In a nutshell, big data technologies have enabled financial institutions to fully project customer- centric outcomes, operational optimisation, financial management, creating new financial products and employee collaboration(Hajiheydari et al., 2021). The usefulness of BDA for banking operations includes customer data scalability, wide network accessibility of data, regularising reporting, real-time customer focus, sentiment analysis, control anti-money laundering and customer segmentation (Ali et al., 2020; Sun et al., 2019; Latif et al., 2018). 2.2 IT capability viewpoint The contention for most firms is the level at which distinctive capabilities of organisations resources are developed to support business needs. From the understanding of resource- based view, championing unique resources within a firm alone will not yield the needed business value from IT and other resources available to an organisation (Barney et al., 2001). It is, therefore, fundamental to further focus on how the capabilities of IT resources and different outlooks support firms’ processes. Diverse IT capabilities have been cogitated to comprise “managerial IT skills, technical IT skills, IT infrastructure, IT-enabled processes, relationship IT infrastructure, IT business experience and IT based-assets” (Garrison et al., 2015; Fink, 2011; Bhatt and Grover, 2005). Substantial findings from extant studies have advocated that deploying the applications of these capabilities alongside other aligning resources support an enterprise’s performance (Mikalef and Pateli, 2017; Zhou et al., 2015; Wang et al., 2012). Perception from the Capability framework suggests a definite advantage for an enterprise using the distinct capabilities of resources to improve efficiency. Therefore, the capability view is explained as the “ability of firms to build unique competencies that can leverage their resources” (Karimi et al., 2007). The preceding argument is that, whereas enterprise resources can be uniquely used to create an advantage, identifying and harnessing the capabilities from a strategic position or at a functional level eventually drives business success (Makadok, 2001). More recently, continuous innovation in IT development and the ubiquitous nature of digital applications and platforms by Chae et al. (2018) and Ravichandran et al. (2014) suggest enterprises develop capabilities through IT assets and configure these capabilities with other resources to promote decision-making, operational excellence and quick customer feedback. Also, Garrison et al. (2015) suggested that capabilities are identified as “firms-specific and BDA capabilities towards FSA 571 non-transferable”, and their unique deployment promotes business success. IT capabilities require an organisation to harness all IT infrastructure, digital platforms and human IT capabilities to steer performance. There is a need to holistically integrate all organisation’s digital applications and platforms to support functional and operational activities. More importantly, for firms that rely on enormous data to steer operational activities, there is the view to identify and deploy IT resources with capabilities and other resources that are central to transforming service delivery. Holistically, within the context of data management, data transformation, data storage, and data transferability deploying emerging digital technologies such as BDA is essential to promote the hierarchy of “Data to Information to Knowledge and Wisdom” (Ardolino et al., 2018). Based on extant studies from the opportunities BDA provides to various enterprises, this study proposes integrating BDA capabilities as pivotal to service transformation in financial services. Therefore, a framework is proposed to elucidate the capabilities of these IT resources to improve data management, data analytics, data transferability and knowledge sharing. 2.3 Linking BDA capabilities for financial services operations Review from research findings and industry outlook has highlighted how intelligent devices and systems connectivity alongside cloud computing and data predictive and descriptive are anticipated to disrupt a firm’s IT strategies and operational execution (Coreynen et al., 2017; Bughin andVanZeebroeck, 2017). Supporting this view, Porter andHeppelmann (2015) explore how smart device connectivity supports BDA to transform an industry for a competitive advantage. Specifically, the adoption of the IoTs, now being referred to as “Bank of Things”, has helped commercial banks use ATM kiosks to directly interact with customers’ mobile phones to easily withdraw money without using their debit cards or master cards. The fundamental value derived from IoTs connected devices is the transmission of customers’ financial transactions that allow financial institutions to collect, exchange and create insight from such customers information. As cited by Monteagudo (2020), “Bank of Things (BoTs) is the material infrastructure that facilitates the billions of data transfers that take place every day”. Subsequently, commercial and retail banks are leveraging on BoTs data to improve the use of ATMs services by increasing or decreasing the installation of ATMs at specific zones based on the volumes of transactions. Also, the interconnectivity of technologies has improved digital payments and transfers with seamless procedures. An additional essential feature of IoTs that banks use is leveraging sensor devices embedded on vehicles in transit to dispense cash to ATMs machines and, similarly, sensor devices embedded in auto loan vehicles. These added IoT actuators support banks to monitor or easily track all vehicles in transit to specific ATMs to prevent diversions or wrongful locations. The sensors on the auto loan vehicles also provide data on customers’ vehicle availability. As such, this move is helping the banks to curtail theft and assist fast recovery auto loan cars. To sum up, banks are leveraging on IoTs to envisage potential fraud with master cards or credit cards transactions. Depending on the location, master cards are used or swiped, and banks can verify the account holder’s device or mobile and the location of the transaction. Most banks have acknowledged that the level of data been generated has enormously increased due to different sources of collecting data with BoTs. As such, data have now become the most vital assets banks relied upon to effect changes for financial services operations. Largely, the focus for banks is the ability to create value, insight and leverage from data assets. Most banks have therefore construed big data into “a greater scope of information, new kinds of data and analysis, real-time information, data influx from new technologies, modern media, large volumes of data, the latest buss word and data from social media” (Forest et al., 2014). Applications and platforms from new digital technologies have further classified data into volumes, variety, velocity, veracity and value. Personal data and AJIM 74,4 572 data from daily financial transactions from customers have been optimised using BDA capability tools to create new financial business models, collaborations amongst employees, detecting fraud, optimising financial operations and customer-focussed services (Hasan et al., 2020). BDA tools such as data mining, query and reporting, data visualisation tools, streaming analytics, etcetera have been deployed by commercial and retail banks to analyse data to drive specific business models to benefit customers and operational improvements. Besides, banks also imitated that the move to invest in big data infrastructure improves data processing, data storage, data management and data analytics. Seemingly, through "weblog data” from banks connected channels and “geospatial data” frommobile phones applications alongside data from core banks operations are quickly retrieved for analysis using BDA intelligence tools. The scope of investigating money fraud-related transactions has largely been improved through a large sample of financial transaction data generated by banks. Clearly, big data and accompanying analytical tools have extended the scope of conceivable means to perform analysis from a large spectrum of available data. BDA capabilities significantly explore the ability to transform an enormous amount of disparate data to maximise enterprise value with speed. BDA capabilities have been categorised into “aspirational, experienced and transformed” (Lavalle et al., 2011). The aspirational and experienced level of capabilities emphasises exploiting big data technologies to accomplish cost reduction and optimising operations, whereas transformed capabilities aim to drive customer profitability and “market targeted investments inniche analytics” (Wangand Hajli, 2017). From the view of financial data-information lifecyclemanagement, BDA capability could be viewed as the capacity to “generate, store, organise, process, analyse, evaluate the sizable amount of financial data in copious forms towards delivering meaningful insight to financial analysts in a timely fashion”. This study further categorises BDA capabilities into analytical capabilities, traceability capabilities, predictive capabilities, prescriptive capabilities, decision-making capabilities and visualisation capabilities. Significantly, developing BDA capabilities impacts enterprise agility (Gupta et al., 2020; Stylos et al., 2021). 2.4 Financial service agility Continuous advances in market trends have largely affected the way enterprises operate. In response, enterprises have developed management models to address these complexities and changes ref essentially. Therefore, a combination of extant studies focusses on enterprise agility to respond to uncertainties within the global market space (Mandal et al., 2017; Tallon et al., 2019). Enterprise agility must relate to paradigms that foster business adaptability, flexibility and speed in a timely fashion within an ecosystem (Sherehiy et al., 2007). In this regard, financial institutions awareness is to adapt to the developments in the financial industry and respond to customer demand. Although generally financial institutions score low on adaptiveness, speed and stability, research has observed that enterprise agility significantly contributes to financial optimisation. Amongst other things, IT-enabled platforms facilitate strategic FSA for competitive advantage and sustainability. Therefore, FSA is recognised as a financial institution’s ability to identify appropriate digital technologies to improve financial processes such as customisation, creating new financial products, employee collaboration, financial data management and knowledge from financial operation data ref. Ostensibly, BDA technologies have been agued to empower agility for “dynamic, volatile and time-sensitive services industries” such as the financial industry (Stylos et al., 2021; The ASEAN Post, 2020). 2.5 Conceptual framework From a holistic review and perspective of BDA capability intelligence and IT capability viewpoint, this study proposed a conceptual framework in Figure 1 to conceptualise IT BDA capabilities towards FSA 573 capability as the useful channel for creating managerial and technical competencies for enterprise agility. Thus, the IT capability view embraces the quality of BDA technology resources and the ability to use them to promote FSA. Furthermore, the framework hinges on previous studies position on promoting information systems capabilities to enhance digital technology usage for value creation. Therefore, deploying and incorporating BDA capabilities intelligence through the IT capability framework as an enabler will enhance FSA (Panda and Rath, 2016). Cepeda and Arias-P�erez (2019) further observed that IT capability aid as an enabler for promoting the linkage between investment in digital technology and overall service agility. Additionally, the success of BDA usage depends not only on the accompanying technological resources BDA possesses but also on the deployment of a competent management approach to leverage technology usage. In this regard, this study advocates that IT capability dimensions such as managerial and technical capabilities to integrate the BDA intelligence positively affect FSA. This perspective is further opined that a firm’s ability to integrate and manage IT resources together, apart from their ownership, creates sustainable strategic and competitive value (Buhalis and Volchek, 2021; Gonzalez et al., 2019; Tallon et al., 2019). Beyond the challenges, firms encounter in the BDA deployment (Latif et al., 2019), adequate development of BDA capabilities intelligence supports firms sustainability (Raguseo and Vitari, 2018; Ali et al., 2020; Zhu and Yang, 2021). For example, Ali et al. (2020) argued that strategic BDA capabilities intelligence promotes financial services efficiency and sustainability. Also, Zhu and Yang (2021) posited that there is clarity with navigating BDA capability intelligence for transforming product to customer- centric significantly affects FSA for survival. In that regard, H1 is proposed to identify the role of IT capability strategy to direct effective use of BDA to support FSA. Hence, there is a positive relationship between BDA usage and IT capability strategy. H1. BDA capabilities usage has a positive and direct influence on IT capability dimensions. Additionally, based on a cursory review of literature, H2 is proposed to identify the relationship between BDA usage and FSA. For example, Vinodh et al. (2012) estimated that amongst other drivers to agility, deploying and incorporating IT applications and tools promotes enterprise agility. More specifically, incorporating emerging technologies such as BDA intelligence applications with other digital platforms have a positive impact on enterprise agility (Gupta et al., 2019, 2020). Recent empirical evidence also advocates that BDA implementation successfully impacts banking and financial services operations (Hajiheydari et al., 2021; Mohini, 2021; ZareRavasan, 2021). Based on this viewpoint, hypothesis 2 is proposed to explain the positive impact of BDA capabilities on banking and financial service operations. H2. Using BDA capabilities enhances FSA. Figure 1. Research framework AJIM 74,4 574 Lastly, hypothesis 3 as shown in Figure 1, advocates that strategic management of IT capability dimensions significantly supports BDA incorporation with other resources to enhanceFSA . Considerably, Gao et al. (2020) posited that managing technical IT capability effectively can differentially enhance firms’ agility. However, in their review Mao et al. (2016) highlight that a comprehensive synergy amongst technical IT capability, managerial capability and operational capabilities must incorporate variabilities in emerging technologies to leverage on FSA. Significantly, firms that are minded with emerging digital technology with appropriate IT capability strategy succeed with market agility (Li et al., 2021; Felipe et al., 2020). H3. IT capability has a mediating effect on the relationship between BDA usage andFSA . 3. Methodology 3.1 Research design and data collection The study employed a mix of qualitative and quantitative research approaches. These approaches provided an in-depth insight into the literature review, design of the questionnaire, data collection and empirical analysis of data collected. Crewel (2014) observed that combining qualitative and quantitative approaches reveals a sufficient understanding of a research problem. The study, therefore, relied on a survey approach for data collection through questionnaire administering. The empirical study was based on three stages. Firstly, from an in-depth review of literature, the questionnaire was designed and structured into two parts purposely to respond to the objectives of the study. Secondly, data were collected through an online survey and face-to-face administering of the questionnaire. In order to ensure the validity and reliability of the constructs with their respective set of questions, a pilot test was initially conducted with 50 respondents. Their perspective and suggestions improved the final version of the questionnaire administered. Thirdly, the survey questionnaire was administered amongst IT workers, bank branch managers and operations staff from the financial institutions. Respondents were mainly from the IT department, operations department and bank managers because these categories of bank staff are involved in deploying and using digital technology innovations such as “Bank of Things”, BDA applications, and cloud computing services. Data collected comprised attributes of respondents and their understanding of BDA technology and applications. Additionally, perceptual data was collected to access and evaluate the level of respondents’ agreement to test the proposed model regarding the use of BDA technology capabilities and its significant impact on FSA. A total of 555 questionnaires were collected and retrieved between 2020 and 2021. However, 485 questionnaires were deemed useful for the analysis representing 87% response rate. 3.2 Measurement of variables and indicators This study used three variables as elucidated from the conceptual framework to propose the financial sector can leverage with emerging digital technology. The measurement scale of the constructs was developed and adopted after the literature review. A five-point Likert scale was used to rate the dimension ranging from 1 (strongly disagree) to 5 (strongly agree) with respect to three constructs (Dwivedi et al., 2017; Hair et al., 2015; Gupta et al., 2020). The three constructs (BDA usage, IT capability and financial service agility) were made up of a set of specific dimensions from literature. Extensively based on empirical contributions from literature, BDA usage was centred on 11 measurement items covering financial data BDA capabilities towards FSA 575 aggregation, insight creation, decision support analysis, risk analysis, fraud detection, financial knowledge repository database and internal data and information traceability (Gupta et al., 2020; Edu et al., 2020; Hasan et al., 2020; Hassani et al., 2018; Srivastava and Gopalkrishnan, 2015). The BDA capability intelligence provides high-frequency financial analysis to ensure safety with financial activities and to protect customers’ privacy with available data (Sun et al., 2019). Hasan et al. (2020) and Rabhi et al. (2019) further suggested that the opportunities with big data capabilities deployment and developing useful IT capability strategy promote financial operation sustainability. Additionally, proponents of the IT capability viewpoint considers BDAs as intelligent applications that facilitate firms to create enterprise agility and sustainable competition (Barlette and Barlette, 2020; Côrte- Real et al., 2017; Wang et al., 2017, 2019). This study relied on the proposed dimensions as espoused in literature to measure the mediating role of IT capability for FSA. Mainly, the dimensions include the deployment of relevant digital technologies for financial services, developing operational capability, managerial capability and strategic use of digital technology resources (Gupta et al., 2020; Gao et al., 2020; Wang et al., 2019; Hosseini et al., 2017). Respectively, FSA measurement items reflect financial service delivery sustainability, financial service adaptability, scaling up customer service delivery, leveraging the integration of BDA technology, incorporating IT applications and tools and flexible operation systems (Vinodh et al., 2012; Forest et al., 2014; Srivastava and Gopalkrishnan, 2015; Hajli et al., 2020). The FSA measurement indicators used, relate to a firm’s propensity to be agile to adapt within the ecosystem they operate. This study, therefore, relied on the concept of enterprise agility, which explains firms’ resilience to proactively respond swiftly within an ecosystem they operate (Pereira et al., 2021; Clauss et al., 2021). Therefore the indicators used are related attributes as espoused from extant studies to measure the use of BDA in the financial sector (Hajli et al., 2020; Hajiheydari et al., 2021). Data collected were analysed using structural equation model (SEM) with the aid of the PLS 3 analytical tool. SEM is a technique that can model latent variables and statistically test the entire model for accounting for any measurement errors accompanying a “plethora” of research hypotheses (Henseler et al., 2015). Essentially, because the study’s objective is exploratory in nature, the partial least square method is appropriate to measure the causal relationship between the BDA usage and financial service agility grounded on the IT capability viewpoint. 4. Results 4.1 Sample profiling The demography details the sample profiles of the respondents to ensure adequate representation of diverse views from their answers. Respondents were generally from different banking institutions comprising bank managers, IT and operation staff. From the analysis, respondents’ professional positions comprise IT and operations, data analyst, systems analyst, application and security, data processing officer, research and operations, bank managers and customer service operations. The results show that 8% of the represents are IT and operational professionals, 4% are customer services operations staff, 10% are application and security professionals and 22% are data analysts. Additionally, bank branch managers, research and operations officers and system analysts represent 47% in total. In terms of the various digital applications technologies deployed for financial services concerning BDA, IoTs and cloud computing, the results indicate that BDA and cloud computing applications are largely shaping the level of financial service delivery. AJIM 74,4 576 4.2 Reliability and validity test To ensure indicator reliability for constructs proposed in the research model, this study set factor loading values at the threshold 0.70 and above as recommended by Hulland (1999) and Hair et al. (2014). In addition, the study used the Cronbach coefficients, composite reliability (CR) and average variance extracted (AVE) to examine internal consistency, convergent validity and reliability. Hair et al. (2017) observed that the threshold for Cronbach values should be 0.70 or above, and all CR values as a confirmatory feature for all constructs indicators should be≥ 0.70 (Bagozzi and Yi, 1988; Vinzi et al., 2010). The acceptable value for AVE values for measuring convergent validity must be ≥ 0.50 as indicated by Hair et al. (2017). Accordingly, the results from Table 1 demonstrate that indicators for internal consistency, convergent validity and reliability were within the acceptable threshold for the framework proposed in Figure 1. Discriminant validity metrics were further used to assess the relationship between constructs substantially. Hence, the Heterotrait-Monotrait Ratio (HTMT) recommended by Hair et al. (2018) was used. Table 2 shows that the HTMT values were all below the threshold of 0.80. The phenomenon of common method bias (CMB) was further considered using the collinearity diagnostic test as recommended by Kock (2015) (see Table 3). A collinearity test was therefore performed for all the constructs and indicators as shown in Tables 3 and 4. Overall, the results demonstrate that all variance inflation factor (VIF) values are less than 3.3, indicating that the model proposed has no issue with CMB (Kock, 2015). Significantly, the results show that CMB poses no problem with results shown. Construct Indicators Factor loadings Ca CR AVE Big data analytic usage BDA 1 0.723 0.929 0.939 0.584 BDA 2 0.756 BDA 3 0.741 BDA 4 0.765 BDA 5 0.722 BDA 6 0.796 BDA 7 0.821 BDA 8 0.811 BDA 9 0.742 BDA 10 0.765 BDA 11 0.758 IT capability IT CAP 1 0.811 0.849 0.888 0.570 IT CAP 2 0.812 IT CAP 3 0.817 IT CAP 4 0.820 Financial service agility FSA 1 0.755 0.832 0.888 0.664 FSA 2 0.754 FSA 3 0.766 FSA 4 0.760 FSA 5 0.761 FSA 6 0.742 BDA usage Financial service agility IT capability BDA usage Financial service agility 0.789 IT capability 0.776 0.739 Table 1. Validity and reliability test Table 2. Heterotrait-Monotrait Ratio (HTMT) BDA capabilities towards FSA 577 4.3 Model fit and hypothesis test Consistency with PLS-SEMmodel estimation, the overall fit of the proposed model was tested using the recommended model fit indices. The standardised root mean square residual for covariance matrix (SRMR), normed fit index (NFI) and RMS-theta were considered (Hair et al., 2017). The acceptable threshold for each model fit index and the results obtained from the analysis is presented in Table 5. The fit index values show that the proposed model for BDA usage moderated through IT capability to enhance FSA is substantially fit. The overall significance of the relationship between BDA usage and FSA is tested at a 0.05 significance level. The results shown in Table 6 indicate that BDA capabilities usage and effective IT capability dimensions are positively related. Hypothesis 1 also shows a significant impact between BDA capability and effective IT capability dimensionsðβ ¼ 0:687; t ¼ 24:283Þ. Similarly, BDA capabilities usage is positively associated and statistically significant with FSAðβ ¼ 0:532; t ¼ 12:652Þas proposed inH2. Hypothesis 3 further indicates that the role of IT capability dimensions has a positive and significant impact onFSAðβ ¼ 0:264; t ¼ 5:743Þ. Construct Indicators VIF Big data analytic usage BDA 1 1.805 BDA 2 2.181 BDA 3 2.136 BDA 4 2.210 BDA 5 1.916 BDA 6 2.924 BDA 7 2.052 BDA 8 2.567 BDA 9 2.754 BDA 10 2.972 BDA 11 2.193 IT capability IT CAP 1 1.751 IT CAP 2 1.756 IT CAP 3 1.797 IT CAP 4 1.821 Financial service agility FSA 1 1.831 FSA 2 1.757 FSA 3 1.734 FSA 4 1.957 FSA 5 2.090 FSA 6 1.688 IT capability Financial service agility BDA usage 1.00 1.892 IT capability – 1.892 Indices Threshold Estimated model SRMS <0.08 0.066 NFI Closer to 1 0.828 Rms-theta <0.12 0.128 Table 3. Collinearity test – variance inflation factor (VIF) Table 4. Collinearity test – variance inflation factor (VIF) Table 5. Model fit AJIM 74,4 578 In addition, Figure 2 shows that BDA usage through IT capability alignment positively improves FSA. The overall value for R2 indicates that 54.44% of improvement from FSA can be attributed to developing the capability of BDA usage enabled through IT capability strategies. 5. Discussions This section highlights on key findings of the study by validating the proposed framework. The objective of the study was to elucidate the deployment and usage of BDA capabilities for promoting FSA. The study suggested that the impact of BDA usage significantly affect FSA. The empirical data analysis further indicates that themediating role of IT capability strategy Path coefficient Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (jO/STDEVj) p- values Decision BDA usage→ IT CAP 0.687 0.688 0.028 24.283 0.000** H1 -supported BDA usage → FSA 0.532 0.534 0.042 12.652 0.000** H2 -supported IT CAP → FSA 0.264 0.261 0.046 5.743 0.000** H3 -supported Note(s): Significance level @ 0.05** Table 6. Hypothesis testing Figure 2. Results of research framework BDA capabilities towards FSA 579 could lead to improving FSA. Consistent with existing studies, the use of technological capabilities alongside other resource capabilities improve enterprise agility (Meli�an-Alzola et al., 2020; Bustinza et al., 2019). As such, the need to identify specific digital technologies for enterprise data management, data transferability, data analysis, data scalability and knowledge creation cannot be overemphasised (Wang et al., 2018). This brings to the perception of this study about BDA technologies as suggested by Sivarajah and Papadopoulos et al. (2017), to improve enterprise sustainability. Also, from their perspective, developing BDA capability and usage with other relevant resource capabilities stimulates competitive advantage and enterprise performance (Fiorini et al., 2018; Edu et al., 2020). The result from this study demonstrates the relationship between BDA usage and FSA through the IT capability viewpoint. Therefore, from the IT capability view, key technical skills, IT infrastructure and managerial skills must seek to distinct BDA technologies to create sustainable financial services. A cursory review of BDA impact on enterprise performance shows that the results from this study are consistent with findings from other empirical studies (Stylos et al., 2021; The ASEAN Post, 2020; Hassani et al., 2018; Hajli et al., 2020). Accordingly, this study posits the call for financial institutions to identify specific BDA capabilities that align with IT capability strategies and operations as suggested by Gupta et al. (2020) and Hajiheydari et al. (2021). Holistically, technological capabilities encompass the idea of selecting digital technology resources and integrating enterprise strategies with emerging technology prospects (Buhalis and Volchek, 2021; Gonzalez et al., 2019; Tallon et al., 2019). Therefore, collocate these capabilities supports the strategic application of digital technology resources for daily financial service deliveries. Seemingly, deploying and using emerging BDA tools with existing digital resources scale up financial service stability (Hajli et al., 2020). Therefore, emphasis on IT capability strategy is the differential factor for financial institutions in a volatile financial market (Hajiheydari et al., 2021). On that basis, the first hypothesis (H1) for this study establishes a positive relationship between IT capability management and BDA capability usage (Meli�an-Alzola et al., 2020). Therefore, hypothesis one (H1) is supported. Again, the role of IT capability for BDA deployment and significantly contributes to 0.554 (R2) variations in financial service administration as demonstrated in Figure 2 and supported by hypothesis 3 (H3). Although deploying BDA tools alone may enhance FSA, this result shows that developing the overall capabilities with other digital technologies contributes to improving financial service performance (Tallon et al., 2019; Zhu and Yang, 2021). Accordingly, Hajli et al. (2020) and Nejatian et al. (2019) observed that key FSAmust consider technology awareness, leveraging the integration of BDA technology, incorporating IT applications and tools and leadership in IT usage. Essentially, the need to promote BDA usage in the financial sector has been advocated despite significantly missing empirical findings (Ali et al., 2020). This study empirically validates the scaling up of BDA usage in the financial sector from developing financial institution context in Ghana which is missing in the literature. Accordingly, Hassani et al. (2018) observed that financial institutions could deploy BDA capability tools such as data mining techniques to provide insightful information from the conceivable volume of financial data, customer financial transactions to reach enhanced strategic decisions. Forest et al. (2014, p. 11) also identified that managing BDA capabilities effectively provides financial institutions with the ability to “digest physical and institutional channel interactions, customer data, graph data, and geo-location data”. Further studies that corroborate this study have suggested that BDA capability can be used to enhance enterprise agility, especially to predict customers’ transactional patterns for tailored services (Stylos et al., 2021). Song et al. (2021) also promoted the need for financial institutions to use digital platformswith BDA to potentially identify the risk of borrowers and the quality of customised services. AJIM 74,4 580 Generally, enterprise credibility in a volatile market such as the financial sector depends on the ability to harness functional resource capability such as BDA technologies to improve service agility. Findings from this study and preposition from the research framework distinctively support financial institutions to develop the capabilities with BDA technologies. Prepositions from existing literature in general highlight the role digital technologies and applications contributes to enterprise agility in diverse forms (Hughes and Chandy, 2021; Gao et al., 2020). For example, Gao et al. (2020) observed that effective IT capability for digital resources and other applications promotes firms’ agility such as intelligence insights with market analysis and strategic business modelling. Other studies also intimated that aligning BDA tools within the financial services can leverage banks to gain competitive advantage and more of customer focus as revealed in this study Song et al. (2021), Hassani et al. (2018), Hajiheydari et al. (2021). 6. Implication of study 6.1 Theoretical implication of the study The theoretical implication of this study is provided in two folds. First, the study attempts to underscore a new perspective with BDA capability and enterprise agility. Through the IT capability viewpoint, the study projects a topology that emphasises digital technology capabilities to attain overall financial service delivery. Holistically, the study builds on developing distinctive BDA capabilities that will respond to enterprise agility, thereby providing flexibility for enterprises when faced with critical aspects of strategic decisions. Secondly, the study analysed the relationship between BDA usage and IT capability and the impact on FSA. The empirical results revealed that digital technology capability plays a key mediating role for BDA and FSA. Therefore, developing specific or unique capabilities from BDA technologies enhances financial institutions capability to respond to changes in the financial market. This study also elucidates that digital technology usage, IT capability view and enterprise agility are positively related. Hence the theoretical foundation for aligning digital technology and resource-based view and IT capability view in the literature is supported from the empirical data analysis. The proposed methodology and the framework in this study address how emerging digital technologies can effectively sustain firm sustainability. The complexity of identifying the interrelationship of technology and firm agility is supported in this study (Tallon et al., 2019; Meli�an-Alzola et al., 2020; Gao et al., 2020; Hughes and Chandy, 2021). 6.2 Managerial implication of the study Bolstering various study BDA capability implications for the financial sector (Song et al., 2021; Galhau et al., 2016; Gupta and George, 2016), this study demonstrates the impact of BDA usage on FSA. The findings help financial institutions focus on nurturing technical capabilities of digital technology deployment and strategic managerial capabilities to utilise emerging technologies like BDA applications. The study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products. In achieving service agility in an emerging market, financial institutions must adopt BDA tools to develop robust and durable digital products to access changes in the financial market. In effect, financial institutions must ensure prioritisation to review existing IT strategies to identify appropriate BDA tools for specific operations (Latif et al., 2018). Beyond the possible benefits BDA capabilities towards FSA 581 to derive, Chanias et al. (2019) and Sun et al. (2019) observed that financial institutions must examine the types of BDA capabilities usage, identify what BDA tools are needed, what they will facilitate and what they contribute. Besides, the ultimate use of digital technology tools and applications is not solely on the investment undertaken, but more on the technological capability to improve enterprise agility (Queiroz et al., 2018; Ghasemaghaei, 2019; Mikalef et al., 2020). 6.3 Conclusion Despite contributions from existing studies with emerging technologies such as big data, this study mainly explores BDA technology usage and FSA. The study was built on the IT capability viewpoint (Garrison et al., 2015; Li et al., 2021; Felipe et al., 2020) from IS research perspective to examine the implication of building strategic BDA capabilities to respond to enterprise agility. The study relied on survey data from 485 banking staff from the IT department, operations department and bank managers on their perspectives with distinctive BDA usage, IT capability development and FSA. Data collected were analysed using PLS-SEM to investigate the underlying hypothetical relationship and the applicability of the research framework proposed. Based on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint suggest that FSA could be enhanced provided enterprises develop technical capabilities alongside other relevant resources (Mikalef et al., 2020). This study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to enterprises operations (Sun et al., 2019). The proposed methodology for this study addresses the dynamics of digital resources relationship with firms’ sustainability. From the perspective of bank managers, operations staff and IT workers for this study, IT capability does moderate the impact of BDA usage on FSA (Meli�an-Alzola et al., 2020). The emphasis for this study also reiterates that digital technologies applications and tools such as BDA enable firms to transform and survive with changing trends (Hughes and Chandy, 2021; Tallon et al., 2019). The perspective from this study also contributes to deepening the understanding of the implication of the IT capability viewpoint. The emphasis for IT capability dimensions must therefore focus on the strategic use of resources, acquisition of relevant digital technology resources and relevant use of digital resources for BDA usage. Additionally, this study offers opportunities for the future to explore BDA and enterprise agility in different contexts based on the limitations of this work. Finally, a fundamental limitation for this study is focussed on financial institutions in Ghana, and therefore the findings are within the context of a developing country. Therefore, that result represents perceptual views from banking professionals. 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Corresponding author Abeeku Sam Edu can be contacted at: asedu@ug.edu.gh For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com AJIM 74,4 588 mailto:asedu@ug.edu.gh Positioning big data analytics capabilities towards financial service agility Introduction Literature review An overview of technology applications in financial services IT capability viewpoint Linking BDA capabilities for financial services operations Financial service agility Conceptual framework Methodology Research design and data collection Measurement of variables and indicators Results Sample profiling Reliability and validity test Model fit and hypothesis test Discussions Implication of study Theoretical implication of the study Managerial implication of the study Conclusion References Further reading