- Title
- A social media analytics framework for decision-making in citizen relationship management
- Creator
- Yakobi, Khulekani
- Subject
- Social Media Analytics -- South Africa
- Subject
- Decision making --Mathematical models
- Subject
- Service delivery
- Date Issued
- 2022-12
- Date
- 2022-12
- Type
- Doctoral theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/60048
- Identifier
- vital:62815
- Description
- Globally social media has shown unprecedented levels of adoption and Social Media Analytics (SMA) is a rapidly growing topic. For governments, SMA holds the promise of providing tools and frameworks to collect, monitor, analyse and visualise social media data, usually driven by specific requirements from a target application. However, social media data is noisy and unstructured, and organisations struggle to extract knowledge from this data, and convert it into actual intelligence. This study argues that SMA can support intelligent decision-making for Citizen Relationship Management (CzRM). CzRM is a growing effort of governments around the world to strive to respond rapidly to their citizens by fostering a closer relationship thereby creating more effective and efficient service delivery. However, there is a little evidence in literature on empirical studies of any existing decision-making framework for CzRM and SMA adoption. In particular, there is a gap with regards incorporating SMA into decision-making for CzRM of governments, particularly in developing countries like South Africa. The aim of this study was to develop a framework that provides guidelines, including methods and tools, incorporating SMA into decision-making for CzRM in the Gauteng Provincial Government (GPG) and the Free State Provincial Government (FSPG) of South Africa. A Systematic Literature Review (SLR) and conceptual analysis method was conducted to design the Social Media Analytics Framework for Decision-making in the context of CzRM (the SMAF). The findings from the literature review revealed several benefits and challenges with SMA, in particular the shortage of skills, guidelines, methods and tools for SMA. These challenges were used to draft guidelines that were included in the framework, which consists of five components that can be used to derive intelligent information from SMA. The pragmatic philosophy and a case study design was used to generate an in-depth, multifaceted understanding of the underlying problems in the case of the GPG and the FSPG. The German North-West Metropolitan region was used as a third case study to provide a more global perspective and a case of a developed country in terms of Gross Domestic Product. The scope of the study was limited to social media posts by provincial citizens related to CzRM and service delivery. Both formative and summative evaluations of the proposed theoretical framework were conducted. The formative evaluation was conducted v | Page as an Expert Review to receive feedback of the framework from the experts in the field of Computer Science and Information Systems. The findings validated the framework and some minor improvements were made based on the experts’ recommendations. Focus Group Discussions (FGDs) with participants from government managers and decision makers in the three cases were conducted. Case documents for the three cases were collected and reviewed. All collected data was analysed using the Qualitative Content Analysis (QCA) method and common categories and themes were identified. Summative evaluations were conducted in the form of a Field Study, which consisted of an analysis of Twitter data from the three cases, and a closing FGD with Business Intelligence (BI) experts at the primary case of the e-Government department of the GPG. The findings revealed that SMA has been adopted in all three cases; however, while their strategies are comprehensive their implementations are very much in their early stages. The findings also highlighted the status of SMA in government and some potential gaps and areas for implementing the framework.
- Description
- Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2022
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (243 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Yakobi, K Dec 2022 (1).pdf | 8 MB | Adobe Acrobat PDF | View Details Download |