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The purpose of this study is to address a critical gap in the existing literature on business excellence implementation. While various studies have examined different aspects of business excellence, there is still a lack of comprehensive research on the optimal organizational excellence architecture (OEA) for an award-winning business excellence journey. The absence of a unified framework has led to inconsistent practices across organizations. The aim of this research based on data collected from 50 organizations across 17 countries is to address this gap and present a refined OEA model.

This study employed quantitative and qualitative data collection methods. Member organizations from the Global Excellence Model (GEM) Council participated through an online survey administered via the Qualtrics software platform. Quantitative data were analyzed using the SPSS and Microsoft Excel software tools, while content analysis techniques were applied to the qualitative data to gain detailed insights.

The findings refine and extend the OEA model. These contributions lead to a refined OEA definition and open avenues for future research, enriching the current understanding of BE implementation.

This study introduces a novel concept of OEA to the literature on business excellence implementation. The refined OEA model provides a fresh perspective on the critical components necessary for an award-winning business excellence journey. This research offers significant value to both practitioners and academics by presenting a unified approach to BE implementation, effectively addressing the previously identified gap in BE research.

This study aims to analyze memes as valuable engagement and marketing communication tools in promoting over-the-top (OTT) platforms by monitoring users' sentiments and offering insightful information about their opinions by drawing themes from viral memes.

Content analysis of 1,230 user interactions was conducted using NVivo software on Instagram and Twitter pages from May 2022 to July 2023. Data were collected for sentiment analysis (consumer responses), and relevant themes (consumer interactions) were drawn which created the virality of memes.

Research findings reveal relevant themes, such as relatable, informative and interest-generating, that make memes go viral over social media. The sentiment analysis results showed that the intensity and strength of the positive comments were more substantial, contributing more to the virality of memes.

These findings provide themes for engaging content for OTT advertisers to boost brand recognition and engagement by strategically creating meme content and implementing better marketing communication.

This study uses factual data to offer new perspectives on viral meme propagation. It provides evidence that OTT marketers boost brand value and customer engagement through innovative customer-centric social media analysis.

The main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).

This study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).

The most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.

The originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.