Investigation to determine the role of two demand conditions (travel risk perceptions and digital media usage) on the competitiveness of emerging destinations

The datasets are for a PhD (Marketing Management) thesis.The datasets show the following:Statistical Package for Social Sciences (SPSS28) outputs showing moderation tests done with travel risk perceptions as moderator. The files can be opened using the SPSS software.Hypotheses table showing tested on all relationships showing the hypothesised path, standardised regression weight, p-value and whether the hypothesis is supported or not.Excel outputs for South Africa and Zimbabwe showing output descriptives of all questions asked in the survey instrument.Excel outputs showing single regression results for both South Africa and Zimbabwe.Extracts from qualitative data on tourists' responses to questions on travel risk perceptions during COVID-19.Correlation matrix for South Africa Zimbabwe showing the correlation between 10 independent variables.The correlation matrix show the following:The correlation matrix for South Africa shows multicollinearity of the following 10 independent variables: Innovativeness & Optimism, Insecurity, Ease of use, Usefulness, digital media preferences, Hedonic and Utilitarian digital media usage, Affective image, Cognitive image 1 and Cognitive image 2. The correlation matrix for Zimbabwe shows multicollinearity of the following 10 independent variables: Innovativeness & Optimism, Insecurity, Ease of use, Usefulness, digital media preferences, Hedonic and Utilitarian digital media usage, Affective image 1 and Affective image 2, Cognitive image 1 and Cognitive image 2.A quantitative survey was employed as a means to collect data from the largest sample possible. The research was cross-sectional, and structured online questionnaires were used to expedite the data collection process. Data was collected from international leisure tourists (18 years and above) that have travelled to South Africa and Zimbabwe. A sample size of 251 was achieved, of which 124 had visited South Africa and 184 Zimbabwe, where 58 respondents had visited both countries. In order to reach this sample size, the survey instrument was issued to respondents over a 180-day period (i.e., six months). The Qualtrics software was used to build, distribute and analyse the survey. The link generated on Qualtrics was sent to ZIMPARKS and South African Tourism (SAT) for onward distribution to tourists in their databases on a convenience sampling, available cases basis.The researcher resorted to sending the links (i.e., South Africa and Zimbabwe) separately to the LinkedIn platform on the 18th of April 2021 in an effort to generate more responses, after encountering a slow and low response rate which reflected on the Qualtrics platform. Convenience sampling in the form of a snowball sampling technique was also used in the selection of sample elements from the tourist population group. This was to increase the response rate and allow for a sufficient sample size. Snowball sampling was followed by sharing the survey links with colleagues in the Tourism and Hospitality Department at the Midlands State University’s Faculty of Business Sciences, as well as colleagues in the tourism and hospitality sectors for distribution within their circles.

该数据集旨在为市场营销管理领域的博士学位论文提供支持。数据集展示了以下内容：
社会科学统计软件包（SPSS28）的输出结果，其中包括以旅游风险感知作为调节变量的调节效应检验。这些文件可以使用SPSS软件打开。
假设检验表格，展示了针对所有关系的检验结果，包括假设路径、标准回归系数、p值以及假设是否得到支持。
针对南非和津巴布韦的Excel输出，展示了调查工具中所有问题的描述性统计结果。
针对南非和津巴布韦的单变量回归分析结果。
关于游客在COVID-19期间对旅游风险感知问题的定性数据摘录。
南非与津巴布韦的相关矩阵，显示了10个独立变量之间的相关性。相关矩阵显示以下内容：
南非的相关矩阵揭示了以下10个独立变量之间的多重共线性：创新性与乐观主义、不安全感、易用性、有用性、数字媒体偏好、享乐性与功利性数字媒体使用、情感形象1、认知形象1和认知形象2。
津巴布韦的相关矩阵揭示了以下10个独立变量之间的多重共线性：创新性与乐观主义、不安全感、易用性、有用性、数字媒体偏好、享乐性与功利性数字媒体使用、情感形象1、情感形象2、认知形象1和认知形象2。
为了收集尽可能大的样本数据，本研究采用了定量调查方法。研究具有横断面特征，并使用结构化在线问卷以加速数据收集过程。数据收集对象为国际休闲旅游者（18岁及以上），这些旅游者曾访问南非和津巴布韦。最终获得251个样本，其中124人访问过南非，184人访问过津巴布韦，其中58位受访者访问过两国。为了达到这一样本量，调查工具在180天（即六个月）内发放给受访者。
Qualtrics软件被用于构建、分发和分析调查。
在Qualtrics上生成的链接被发送至ZIMPARKS和南非旅游局（SAT），以便在他们的数据库中向游客进行便利抽样分发。
在遭遇Qualtrics平台上的缓慢且低响应率后，研究者在2021年4月18日将链接（即南非和津巴布韦）分别发送至LinkedIn平台，以试图获得更多响应。
此外，还采用了便利抽样中的雪球抽样技术，从旅游者群体中选择样本元素，旨在提高响应率并确保样本量充足。雪球抽样是通过与 Midlands State University 商学院旅游与酒店管理系的同事以及旅游和酒店业界的同仁分享调查链接来实现的，以便在他们的圈子里进行分发。