SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Reputational risk management in the social media age: an evaluation of reputation management in the service industry of an emerging economy(Southampton University, 2024-07-30) Alyahya, Maryam; Katsikopoulos, Konstantinos; Dawson, IanDrawing upon the existing literature on reputation and social media (SM) risk management, this research investigates reputational risk management in the age of SM by conducting three different empirical studies. The first study investigates how customers’ comments on Twitter impact service firms’ social media reputation (SMR). While the first study explores the reputational risk on Twitter based on customers’ comments, the second study is focused on SM risk management with an emphasis on reputational risk from the firms’ perspectives. Lastly, the third research ascertains the antecedents and consequences of corporate reputation (CR). The insurance industry in Saudi Arabia is chosen as a sample for all the studies. The findings show that customer comments on Twitter do not significantly impact firms’ SMR. In addition, the findings reveal that the firms’ SM risk management strategies can be divided into two groups: proactive and reactive. Proactive firms have a clear vision toward SM risk, resulting in organizing comprehensive SM governance, the employment of effective SM strategies, and investment in technology. In contrast, reactive firms tend to only manage risks that have already surfaced and then follow the central bank regulations to meet compliance requirements. Moreover, these two groups differ in terms of addressing digital complaints received from customers. Proactive firms have a precise mechanism for digital complaints management, such as utilising technology to increase the quality of how complaints are handled. However, reactive firms lack this mechanism, resulting in the loss of customers, market share, and missed opportunities. Moreover, SM radar is developed based on the study findings, where it is found that proactive firms apply it unconsciously. It is a mechanism used by some firms where they employ digital tools/strategies to seize opportunities and detect threats in the early stages before they escalate into a crisis. Finally, the findings show that chatbot effectiveness positively and significantly impacts customer satisfaction (CS). In addition, both CS and trust influence CR, with the latter having a positive and strong effect on purchase decisions (PDs). Concerning the moderating effects, while customer loyalty (CL) is found to have a positive and significant moderation effect on the relationship between CR and PDs, word of mouth (WOM) negatively and significantly moderates the relationship. The thesis offers several contributions to the theoretical landscape of reputational risk management in the SM age. Specifically, it identifies that customers’ Twitter comments do not significantly impact the firm’s SMR. Moreover, it confirms previous work that there is a relationship between the sentiment of customers’ comments on SM and the communication strategies that firms employ to respond to customers. Moreover, since the findings show that firms are divided into two groups in managing SM risk (proactive and reactive), this provides insights that can help firms manage SM risk by developing the SM radar concept. The thesis also combines fresh insights that emerged during the analysis stage to develop two conceptual models: SM risk management and digital complaints management. In addition, the results revealed a significant and positive relationship between CS and CR. Lastly, the findings offer several practical implications for service industry firms in general, insurance firms, and regulators in Saudi Arabia in particular, in the age of SM. This includes the development of a comprehensive framework for reputational risk management that considers technologies such as Chatbots.28 0Item Restricted Medical Screening Assistant: A Chatbot to Help Nurses(Saudi Digital Library, 2023-11-08) Al Rabeyah, Abdullah Saleh; Da Silva, Rogerio E; Goes, FabricioOver the last several years, Machine Learning has emerged as a key player in the healthcare industry. The use of chatbots is a notable application of artificial intelligence within the field of healthcare. The advent of the ChatGPT revolution represents a significant breakthrough in the realm of natural language processing, a fundamental aspect of chatbot programming. This development has simplified the implementation of GPT to engage in user communication and fulfill the objectives of the application. The objective of this project is to reduce the excessive workloads faced by healthcare professionals and enhance the efficiency of decision-making processes. This will be achieved via the development of an intelligent medical chatbot as a mobile application, specifically designed to support nurses in conducting early patient diagnoses by analyzing symptoms. The chatbot uses Swift programming language for the iOS front-end and Python with Flask for the backend. It incorporates the ChatGPT API and machine learning models to effectively comprehend and interpret user inquiries. This project uses a Kaggle dataset of 41 distinct diseases along with their corresponding symptoms. The model is trained using Logistic Regression to predict the prognosis. The responsibility of managing the dialogue between the user and the chatbot, leading up to the compilation of the definitive list of symptoms shown by the patient, lies with ChatGPT. The use of a Flask RESTful API facilitates direct interaction between the iOS application and the server-side infrastructure. Finally, the application will provide the nurse with the five most probable prognoses, along with the prediction confidence scores, depending on the symptoms supplied. Additionally, the application will offer a description of the disease and provide precautionary measures for the patient.18 0