Customization

The motivation of this paper is to examine empirically the effect of social media marketing on value equity, relationship equity and brand equity on fashion apparel brand among Universiti Malaysia Kelantan (UMK) students who have high attributes of internet savvy’s and take delight in undertaking social media marketing. This study is backed by the use and gratification theory that creates desire for amusement and enhancement of information while people use social media marketing.  Data was collected by means of Google Form online survey from a total of 361 respondents. The simple random sampling approach was utilized to collect data from the respondents and data analyzed using the SPSS. The findings indicated that social media marketing activities, which are entertainment, interaction, trendiness, and customization had a positive effect on customers’ equity among students. The results confirm that social media marketing is one of the key success factors in enhancing customer equity. Further, the results showed that interaction is the first concern among the respondents. In other words, the communication between the apparel fashion brands industry with customers is important and helps to create relationships so that customers can get more information about them.

Big-tech corporations like Google, Meta, Microsoft etc. extensively utilize customization to collect and analyze user data, a practice integral to their business models. Google leverages user data to personalize services across its platforms, notably in its search engine and YouTube, to enhance user experience and bolster its targeted advertising strategies. Similarly, Meta uses algorithmic content curation on Facebook and Instagram, tailoring user feeds to individual preferences and behaviors, thereby generating detailed user profiles for marketing purposes. Microsoft's approach, particularly with Office 365 and LinkedIn, focuses on productivity enhancements while also gathering user data for feature refinement and targeted advertising. These practices, I argue, while improving user engagement, raise significant privacy concerns. The extensive data collection often occurs without full transparency or user consent, leading to debates about ethical implications, digital surveillance, and societal impacts. In response, there is a growing demand for stricter data governance and privacy regulations, as seen in initiatives like the GDPR and CCPA, aiming to balance the benefits of personalization with the rights and privacy of users.

Background: Network slicing has turned out to be one of the key enablers in the 5G networks due to the ability to support the diverse applications such as ultra reliable and low latency communications for the self-driving cars or IoT-like massive machine type communications. Prior expeditions lacked integrated tools for the dynamic assignment and allocation of resources and no possibility for maintaining constant QoS. Objective: In this article, the primary aim is to synthesis and test a reinforcement learning–driven slicing framework in order to orchestrate the resources of the three types of slices – URLLC, mMTC, and eMBB. This is to improve the performance of the sliced resource, ensure high availability, and minimize competition of the resources in multi-tenant scenarios in 5G networks. Methods: The proposed study design includes a focus on the key stakeholders and their needs for requirements gathering and an experimental field for actual implementation. Resource distribution is guided by the reinforcement learning algorithms by trying to minimize a cost function which incorporates the relation between the latency, isolation, throughput and energy expended. Using a number of runs, quality of performance is monitored to enable assessment of stability as well as response rates. Results: Experimental results show that the proposed framework achieves a lower level of latency violations and capacity oversubscription compared to heuristic methods. Furthermore, it consistently achieves nearly 2.5X better throughput for telemedicine slices and guarantees less than 5 ms latency for time-sensitive services during dynamic traffic conditions. Conclusion: The study shows how reinforcement learning can be effective and applied for end-to-end 5G network slicing. This sort of adaptive orchestration can increase service dependability while optimising overhead and herald instantly climbable multi-tenant networks compatible with various industries