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Tourism SME’s Adoption of Social Media Analytics Tools and Technology
Abstract
The study analysed the current state of the adoption of social media analytics (SMA) technology and tools to tourism small and medium enterprises (SME) in Tanzania and to understand the factors which influence the adoption of SMA tools to tourism SMEs. SMA became the basic technology in understanding and getting insight into social media data. Tourism SMEs are among the users of social media platforms. The researcher carried out the survey in Arusha and Kilimanjaro regions to determine the current state of the adoption of SMA to tourism SMEs. The technology, organization and environment (TOE) framework has been used to determine the factors that contribute to the adoption of SMA. The study finding shows that many tourism SMEs are using social media in their business activities however the adoption of SMA is very low, majority of the SMA are not adopting and using SMA. Technology, organization and environmental factors seem to allow the adoption of SMA to tourism SMEs. The study contributes to the research body of knowledge on the current status of adoption of SMA to tourism SMEs and managerial contribution on the factors to determine when tourism SMEs wants to adopt SMA.
... Like the other content, content creators require an ideation process [8] in creating text-based content because the ideation process is trying to choose a crucial relevant topic [9], [10]. In the topic selection process, content creators are trying to search, collect, and create insights by analyzing social media, which is accommodated by a tool or system called social media analytics (SMA) [11], [12]. ...
This study aims to create Social Media Analytics (SMA) tools to help Digital Marketers or Content Creators create content topics for creating text-based Instagram content and support digital marketing strategy. Since no SMA tools can provide topic discovery for text-based Instagram content, this research aims to make an SMA tool. The data requirements to make an SMA tool include content text, content caption text, likes, comments, upload time, and content category obtained through the Instascrapper. The method used in this study is the Topic Modelling method using the Latent Dirichlet Allocation (LDA) approach to find the most dominant topic in the content. Optical Character Recognition (OCR) performs an image transformation process to extract text from text-based Instagram content images. The results of SMA tool creation are tested on three expert users, which shows that 93% of test participants could use the SMA to find topic references, and 85% can still be used by users even though they find it difficult. Since the test result shows that SMA tools still need development, for further research, SMA tools can focus on developing the user experience to increase the value of user acceptance by paying attention to the ease of the SMA tools. Also, SMA tools can focus on target users such as Data Analysts, Business Intelligence Analysts, or others within a company to support decision-making for the marketing department.
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