- Title
- Factors influencing consumers’ adoption of chatbot assisted marketing activities in the South African banking industry
- Creator
- Rusike, Christabel
- Subject
- Banks and banking -- South Africa
- Subject
- Consumer movements
- Date Issued
- 2023-04
- Date
- 2023-04
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/62380
- Identifier
- vital:72643
- Description
- In a world where technology is evolving at an alarming rate there have been so many advancements and developments in the marketing field and how consumers engage in accessing products and services. In general, a great body of literature on information technology shows evidence that areas such as mobile banking and organisational technology adoption have been explored. However, limited attention has been dedicated to consumer adoption or acceptance stages of technology, particularly chatbots in the South African context. Apart from that, during the peak of the Covid 19 pandemic, consumers had to adjust to mainly doing transactions online as there was a restriction in accessing banking halls. Given this backdrop, the aim of this study is to address this particular research gap through investigating factors influencing consumers’ adoption of chatbot assisted marketing activities in the South African banking industry. The research was inspired and constructed upon three research theories, namely Technology Acceptance Model (TAM), The Diffusion of Innovation Theory (DoIT) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Based on these theories, a hypothesised model was formulated with eight independent variables, namely Perceived Usefulness, Perceived Ease of Use, Facilitating Conditions, Price Value, Hedonic Motivation, Social Influence, Perceived Compatibility and Relative Advantage. The dependent variable was put forward as Chatbot Adoption. Descriptive and explanatory research designs were selected for this study, utilising a quantitative research methodology. In addressing the objectives of the study, secondary data was collected through the internet, magazines, newspapers, articles, journals and books to aid in completing the literature chapters and construction of the measuring instrument. Primary data was also collected through a self-administered questionnaire which was created on QuestionPro and the link was distributed to the respondents. The population under study were consumers of the banking industry products and services in South Africa. The target respondents consisted of consumers who hold valid bank account and have used or experienced online activities within the banking sector. A non-probability vii sampling method through convenience and snowball sampling was adopted to recruit the respondents. Data were obtained from 151 usable survey questionnaires. The data collected from the respondents was coded and captured on a Microsoft excel spreadsheet which was then followed by analysing of data using IBM SPSS version 16. From the analysed results, all the suggested independent variables were retained as the respondents confirm in varying degrees the influence on behaviour that the factors have. The study found that the eight independent factors have practical and statistically significant correlation with consumer adoption of chatbot assisted marketing activities within the South African banking industry. In addition, the inferential ranking of the factors indicates that Relative Advantage, Perceived Usefulness and Price Value fall under one group of significant factors perceived by consumers in their decision to adopt chatbot assisted marketing activities. It can therefore be concluded that it is useful for the banking industry to implement the identified factors and recommendations offered to enhance the use of chatbots in consumers’ online banking activities as the responses obtained are in general favourable. The study thus contributes theoretically and practically to the body of knowledge particularly digital marketing through chatbots in the banking sector. Therefore, the findings can be useful for financial marketing, digital banking and the suggested model can help the marketing and artificial intelligence departments in the banking industry in the decision-making process.
- Description
- Thesis (Ma) -- Faculty of Faculty of Business and Economic Sciences, 2023
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (xvii, 184 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Faculty of Business and Economic Sciences
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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Collections
NMU Business School
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | RUSIKE, C.pdf | 2 MB | Adobe Acrobat PDF | View Details Download |