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This study aims to examine the language choices of outdoor signs and menus in addition to the functions of outdoor signs in restaurants in a Saudi tourist city, Abha. The primary focus is on identifying the extent to which outdoor signs accurately represent the language choices of restaurant menus.

The study developed a conceptual framework for the linguistic landscape (LL) of restaurants. It employed a quantitative approach to collect outdoor signs and menus of 75 sampled restaurants in Abha using online photos and a smartphone camera. Then it analyzed the frequency and percentage of language choices on outdoor signs and menus as well as the extent to which language choices of outdoor signs represent menus.

The findings indicate that more than half (58.66%) of the restaurants employ bilingual signage in both Arabic and English. Other languages like Spanish, French, Chinese and Turkish are sporadically used, with multilingualism observed only in isolated instances. The study also reveals that bi/multilingualism on outdoor signs primarily serves informational purposes, where more than one-third (36%) of the outdoor signs use languages other than Arabic to serve a symbolic function. Regarding menus, Arabic and English dominate, while Turkish appears on one menu. Spanish, French, and Chinese are absent from restaurant menus, indicating linguistic mismatch in terms of language choices.

This study contributes to LL studies of restaurants in tourist cities by showing language choices and functions of outdoor signs and their alignment with menus.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.