Examining the Relationship Between Enterprise Resource Planning (ERP) Implementation: The Role ofBig Data Analytics Capabilities and Firm Performance

Enterprise Resource Planning (ERP) implementation continues to hold attraction from information systems enthusiasts. Perhaps due to the rising budget dedicated to the implementation in many an organization in recent times. However, understanding the critical role that ERP implementation plays in Big Data Analytics Capabilities and firm performance is lacking sufficient treatment in the literature. By applying quantitative research techniques in a case study research orientation through the use of resource-based view theoretical insights, the study takes on three key hypotheses: That ERP implementation has a positive relationship with organizational big data analytics capabilities; Big data analytics capability has a positive effect on firm performance and ERP implementation is positively related to organizational performance. Using Partial Least Squared Structural Equation Model (PLS-SEM)data analysis techniques the study established a direct link between big data analytics capabilities and firm performance, and that ERP has a direct positive and significant effect on big data analytics capabilities. Lastly, it is the claim of this study that big data analytics capabilities have a direct positive and significant effect on firm performance. Part of the implications of the study highlights the need for a qualitative or even mixed method research undertakings to broaden the frontiers of our understanding in terms of ERP implementation and big data analytics capabilities in similar organizational contexts

Digital Transformation Powered by Big Data Analytics: The Case of Retail Grocery Business

Companies are investing in big data analytics capabilities as they look for ways to understand and innovate their business models by leveraging digital transformation. We explore this phenomenon from the perspective of retail grocery business where evolving consumer attitudes and behaviors, rapid technological advances, new competitive pressures, laser thin margins, and the COVID-19 pandemic have accelerated the pace of digital transformation. We specifically analyze the role of big data analytics capabilities of the top five grocery companies in the United States in light of their digital transformation initiatives. We find that retailers are making major investments in big data analytics capabilities to power all aspects of their digital ecosystem—the online shopping experience for the digital consumer, digital store operations, pickup and delivery mechanisms—to enhance shopping experience, customer loyalty, revenue, and ultimately profit

Business Analytics Capabilities for Organisational Resilience

Nowadays, organizations are facing unique challenges created by different disruptions, including natural disasters, new technologies, regulatory changes, and more recently, a global pandemic. Consequently, the need to build, sustain, and continuously enhance Organizational Resilience (OR) is greater than ever. An ongoing process of building OR requires high-quality data and business analytics (BA) capabilities. In this paper we aim to investigate the yet-to-be explored link between BA and OR. We achieve this aim by conducting a multidisciplinary literature review on OR and BA, focusing on BA capabilities for OR. Based on our findings, we then propose a conceptual framework of BA capabilities for OR. In doing so, we also bring a well-established area of OR to the attention of BA researchers, as a critically important area for further BA research and practice

Why HR is set to fail the big data challenge

Few organisations have made much progress in developing HR analytics capabilities, write Andy Charlwood, Mark Stuart, Ian Kirkpatrick and Mark T Lawrenc

IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility

BIG DATA ANALYTICS CAPABILITIES FOR IFRS 9 SUCCESS

In the aftermath of the global financial crisis, financial reporting standards have proven inadequate in providing sound governance. With financial data being heavily dependent on information systems, a new standard, IFRS 9, is being adopted. IFRS 9 could leverage recent advancements in big data ana-lytics capabilities to improve financial compliance and assurance. While such potential is widely acknowledged, big data analytics capabilities have not yet been adequately identified and validated in the context of financial reporting compliance. In addressing such discrepancy, this study attempts to explore the relationship between a firm’s capability to conduct big data analytics and their perception of IT applications leveraged for compliance with the standard. This study identifies four constituent capabilities and provides empirical validation for their interrelation with a holistic big data analytics construct. It addresses the link between capabilities and perceived IFRS 9 benefits by a range of insti-tutional stakeholders. The findings suggest that analytics governance, analytics personnel capabilities, and Big Data characteristics have a significant influence on big data analytics capabilities. The latter was found to have a significant relationship with perceived benefits of IFRS 9. These findings hold im-portant implications to theory and practice given the impending mass adoption of IFRS 9

Efficient Scalable Accurate Regression Queries in In-DBMS Analytics

Recent trends aim to incorporate advanced data analytics capabilities within DBMSs. Linear regression queries are fundamental to exploratory analytics and predictive modeling. However, computing their exact answers leaves a lot to be desired in terms of efficiency and scalability. We contribute a novel predictive analytics model and associated regression query processing algorithms, which are efficient, scalable and accurate. We focus on predicting the answers to two key query types that reveal dependencies between the values of different attributes: (i) mean-value queries and (ii) multivariate linear regression queries, both within specific data subspaces defined based on the values of other attributes. Our algorithms achieve many orders of magnitude improvement in query processing efficiency and nearperfect approximations of the underlying relationships among data attributes

Case study: the implementation of a data-driven industrial analytics methodology and platform for smart manufacturing

Integrated, real-time and open approaches relating to the development of industrial analytics capabilities are needed to support smart manufacturing. However, adopting industrial analytics can be challenging due to its multidisciplinary and cross-departmental (e.g. Operation and Information Technology) nature. These challenges stem from the significant effort needed to coordinate and manage teams and technologies in a connected enterprise. To address these challenges, this research presents a formal industrial analytics methodology that may be used to inform the development of industrial analytics capabilities. The methodology classifies operational teams that comprise the industrial analytics ecosystem, and presents a technology agnostic reference architecture to facilitate the industrial analytics lifecycle. Finally, the proposed methodology is demonstrated in a case study, where an industrial analytics platform is used to identify an operational issue in a largescale Air Handling Unit (AHU)

Reframing agile organization: do big data analytics capabilities matter?

The Agile Manifesto has become the mantra for software developers seeking to create innovative software. However, recently, the principles of Agile Manifesto have also emerged as guidelines for organizations continuously seeking to develop and adopt innovations. Hence, the expression Agile Organization arose. Notwithstanding this interest, Agile Organizations' traits still have to be conceptualized. Yet, the role of Big Data Analytics capable information systems has been neglected. In this perspective, the research aims to develop a framework assessing why Big Data analytics capabilities are fundamental for organizations aiming to follow Agile Organization's principles. Implications of the potential role of such information systems on innovation development and adoption, performance and organizational flexibility are also presented

Developing and Deriving Value from Big Data Analytics Capabilities

In this big data age, big data analytics (BDA) has come to occupy a large role in becoming a major competitive differentiator for companies with many companies significantly accelerating the pace of their investments in BDA (Abbasi et al., 2016). As companies increasingly bet on BDA as the next competitive frontier, there is an imminent need for business leaders to clearly understand and rationalize the economic value gained from costly BDA investments by measuring their impact on objective measures of firm performance (Mikalef et al., 2020). Borrowing from prior empirical literature on IT capabilities and economic value, some scholars have drawn a positive relationship between BDA capabilities, which are built by assembling an array of resources that include a mix of big data, technology, human, and organizational resources among others and firm performance while others have failed to capture commensurate value from BDA investments (Gupta & George, 2016; Wamba et al., 2017; Popovič et al., 2018;). More work is required to understand and articulate the value creation process from capability building to value realization (Grover et al., 2018). While the BDA literature has been very prolific in defining the ingredients that go into building a BDA capability, not much work has been done to highlight the contributions of the manager as a potential source of BDA value creation (Mikalef et al., 2020). The IT-Business value literature has previously demonstrated that resource synchronization and orchestration is a prerequisite to develop and leverage resources strategically (Cragg et al., 2011). Using the resource orchestration framework as a theoretical foundation, this paper addresses the following research questions – 1) How do managers contribute to firm performance by bundling resources to build superior BDA capabilities? 2) How do managers mobilize, coordinate, and deploy these capabilities in concert with firm strategy and market context, and how does that moderate the relationship between BDA capabilities and performance outcomes? 3) Can managerial ability explain the differential performance outcomes in firms with otherwise BDA capability parity? This study will employ a quantitative research approach using a survey targeting top, middle, and operational level analytics managers in publicly traded companies drawn from multiple industries to measure BDA and BDA Managerial Capability given various market contingencies. The survey data will draw measures of firm performance from the Compustat database. The study adds to the scholarly literature by explicating the importance of effective resource management and the contribution of managers to the resource exploitation aspects of value realization from capabilities. From a practical viewpoint, the study enables companies to understand the processes and activities required to create and deploy high-quality BDA capabilities along with the organizational context and strategies necessary to produce superior firm performance