A framework for managing spreadsheet-based end user applications

Abstract

As spreadsheets are a ubiquitous tool used in most organisations, often for management support as well, managing them is more than indispensable but very often inadequate. The aim of the study is therefore to improve the management of spreadsheet-based end user applications. Starting from the existing frameworks for building and managing spreadsheets developed by end users, and guidelines of good practices, an initial version of spreadsheet framework for managing spreadsheet-based end user applications was developed (consisting of three parts: steps, skill categories and user categories). Fifteen spreadsheet experts were subsequently identified, who overviewed the framework, and the latter was modified and improved based on their suggestions. The final version of the framework was evaluated positively by four (additional) experts, based on which it can be concluded that the set framework can be applied in organisations using spreadsheet programs and, at the same time, reduce the possibility of occurrence of errors and other spreadsheet risks.

Earth Observation and Deep Learning for Sustainable Development: New Approaches To Facilitate Data-Informed Decision-Making

“…The system does not provide recommendations, as the Financial Service Providers were reluctant to share data on the location of their services and agents. Instead, the functions and the interface allow the providers to design a recommendation engine tailored to their needs by using the provided functionality as part of an End-User Developed Application (Raković, 2019).…”

“…At the FinTech workshops in Ghana and Tanzania, the Financial Service Providers specifically requested the addition of a recommendation engine for placing new financial services but were unwilling to share data on the location of their offered services (Laufer, 2022;NIRAS, 2020). As such, the implementation of recommendations is left to the providers themselves, using spreadsheets to create end-user applications to produce recommendation engines tailored to their use case (Raković, 2019).…”