This paper introduces AutoChi, a novel statistical software for automated hypothesis testing and statistical analysis using non-parametric chi-square and pairwise post-hoc z-tests. The first novelty of AutoChi is exhaustive and automated hypothesis testing and reporting, comparing all groups and pairwise comparisons. Although several software packages and libraries are available for statistical analysis, including R packages, Python libraries, and ready-to-use software packages with graphical user interfaces (GUI), only a small percentage of these software library tools implement end-to-end automated statistical analysis through a user-friendly graphical user interface. Furthermore, no software tool was found to carry out end-to-end automated statistical analysis for testing differences in proportions, followed by post-hoc statistical tests and visual analysis. AutoChi addresses both gaps for chi-square testing, namely the gap with respect to a fully integrated automated workflow and the gap with respect to post-hoc statistical and visual analysis. The novelty of AutoChi is the application of graph analytics through dominance graphs and arc diagrams to visualize the results of all the conducted tests. Overall, AutoChi can accelerate data analysis and hypothesis testing through automation and enable rapid generation of insights through post-hoc visual analysis that are currently not conveniently possible in existing statistical software.

The rapid emergence and growth of social media platforms across the globe and the GCC (Gulf Cooperation Council) countries had a significant impact on commerce and business. While extensive research was carried out on social media usage in the GCC, the varying presence across platforms has so far not been investigated. The presented research, based on data from a commercial data provider, focuses on seven major social media platforms, namely Instagram, TikTok, Facebook, Twitter, LinkedIn, YouTube, and Pinterest and the presence of e-commerce companies in the GCC countries under these platforms. The Chi-Square test of independence was employed for proportional statistics, whereas Bonferroni correction was employed for post-hoc testing of social media platform presence. The analysis results identified Instagram, Facebook and Twitter as the most common social media platforms overall across the GCC region, supported with statistical significance. However, there was strong heterogeneity across countries with respect to the presence of social media platforms. The projected outcomes can be employed in digital marketing planning, resource allocation, consumer engagement, business development, and service quality assessment in e-commerce in the GCC while the methodology can be adopted to any select benchmark group of countries.

Standard retail shelf configurations impose unequal attention patterns, with eye-level products consistently attracting disproportionate gaze, while items on upper and lower shelves remain perceptually disadvantaged. This study examines whether physical shelf partitioning can reduce this positional bias by assigning each product to a bounded display compartment hence, redistributing fixation behavior uniformly across shelf levels. A controlled laboratory experiment using replica retail shelves and head-mounted eye-tracking compared fixation counts and durations across standard and partitioned configurations of a small home appliance display, while counterbalanced presentation and product rotation were used to isolate structural effects. The standard configuration produced a clear attention hierarchy, with statistically significant differences across shelf levels. In contrast, the partitioned design yielded statistically balanced fixation behavior across levels, with the bottom shelf showing the largest absolute gain. The findings suggest that structural partitioning enhances visual balance through common-region cues, offering practical implications for retail merchandising and shelf-space management.

This study introduces the NUD-GESI platform, a customized social media simulation to encourage Gender Equality and Social Inclusion (GESI) in the sociocultural context in Qatar. Built on top of the Gorilla Text Builder, it is used to manipulate text stimuli in real-time in environments similar to popular ones, including support for the Arabic language and culturally appropriate filters. Grounded in behavioral science, the platform incorporates co-designed digital nudges such as framing effects and social proof to promote prosocial, inclusive interactions between Arab youth. Its has its scalable, cloud-based infrastructure to support large-scale controlled experiments, pushing the research on digital behaviors and inclusion in society. By bringing together technology and behavioral insights, NUD-GESI provides policymakers, researchers and social media platforms with a powerful tool to harness the power of digital interventions in order to achieve positive social outcomes across different cultural contexts.