Show simple item record

dc.contributor.advisorMiddelberg, S.L.en_US
dc.contributor.advisorBuys, P.W.en_US
dc.contributor.authorMasuke, Reubenen_US
dc.date.accessioned2021-09-16T05:48:06Z
dc.date.available2021-09-16T05:48:06Z
dc.date.issued2021en_US
dc.identifier.urihttps://orcid.org/0000-0003-1080-913Xen_US
dc.identifier.urihttp://hdl.handle.net/10394/37424
dc.descriptionMCom (Management Accountancy), North-West University, Potchefstroom Campus
dc.description.abstractThe global business environment is constantly changing due to the advancement in technology. Currently, business organisations are experiencing a shift in their competitive strategy, operational structure and production methods due to the advancement in technology such as robotic processing systems, blockchain, artificial intelligence (AI) and the associated big data phenomenon. The shift in the business environment creates a change in informational demand by managers, which consequentially changes the management accounting practice in an organisation over time. Big data, AI and robotics processing systems have gained acceptance as part of the fourth industrial revolution (4IR). This phenomenon is associated with the term ‘disruptive technologies’, meaning that the way we do things is bound to change, and the management accounting practice is no exception. The arrival of big data has created new big data technologies which assist in collecting and analysing big data for decision-making. The practice of management accountancy is to collect, analyse and communicate information for decision-making. This creates a linkage between the practice of management accounting and the function fulfilled by the big data technologies. Entities that will adopt big data technologies will have a competitive advantage to those that will not. Management accountants should adopt new big data technologies in order to aid management in decision-making, as well as in creating a competitive advantage for the organisations they work for. This study explored the use of big data analytics by management accountants in decision-making. In doing so, the study assessed how management accountants practically interact with the real world of big data. Thus, within its context and from the participant’s standpoint, Action Design Research (ADR) as a research approach was followed. ADR is a research method that seeks a solution to prescribe, invent, build or evaluate and improve an artefact through a combination of the researcher’s theory and stakeholders’ ongoing practical use of the artefact in an organisational setting. As this was an exploratory study, the focus was on the diagnosis stage of the ADR process, where management accountants conceptualise the problem as a solution to the research objective. An induction approach was used to solve the research question and as a result, the research was nested in an interpretivist philosophy, through the application of a qualitative research process. The institutional framework for management accounting change was applied as the foundational framework for the research study. The study concluded that big data analytics, if used by management accountants, assist management in decision-making in a big data-driven business environment. It assists organisations in gaining competitive advantage. It was also found that a big data-driven management accountancy technology should be cloud-based, of a dashboard format, with the ability to handle large data sets and a capability to collect both internal (from the ledger) and external data from which, if a query is run, it is able to produce a robust report that can be exported into other formats. Further, management accountants need to develop big data skills to fully utilise the big data technology capabilities.
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa)en_US
dc.subjectbig data
dc.subjectbig data analytics
dc.subjectdisruptive technologies
dc.subjectmanagement accountant
dc.subjectmanagement accounting change
dc.titleExploring the use of big data analytics by management accountants in decision-makingen_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID10779221 - Middelberg, Susanna Levina (Supervisor)en_US
dc.contributor.researchID10127100 - Buys, Pieter Willem (Supervisor)en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record