The effectiveness of organizations and the satisfaction of customers are both significantly improved by providing outstanding quality. To better meet the needs of their consumers, an increasing number of firms are implementing quality improvement programs such as total quality management (TQM). The major objective of this research paper is to examine the effects of TQM components on the efficiency and effectiveness of UAE higher education institutions (HEIs). The association between TQM components and organization performance was investigated using a quantitative research technique based on a survey study design. The partial least square structural equation model (PLS-SEM) was used to quantify the reflectiveness of 99 data. The study found that TQM has a positive impact on employee satisfaction and service quality in HEIs. It was revealed that TQM has a significant impact on organizational performance, with service quality serving as a mediator. Furthermore, the results revealed that service quality and students’ satisfaction are fully mediating the relationship between TQM and organizational performance. The current study concludes that HEIs are implementing TQM to enhance market performance and comprehend the evolving business trends. The study has added to the body of information about the relationship between TQM elements and organization performance.

Purpose: Examining how predictive analytics may be used in smart tourism frameworks to anticipate and improve sports tourism visitor experiences is the aim of the research. The paper aims to examine four predictive models – logistic regression, support vector machine (SVM), artificial neural network (ANN) and random forest – to forecast visitors' intent to propose sports venues using the Cross-Industry Standard Process for Data Mining (CRISP-DM) technique. The research highlights logistic regression as the best model, finds important demographic and contextual variables that impact proposals and provides helpful recommendations for improving consumer satisfaction and strategic planning in sports tourism. Design/methodology/approach: The CRISP-DM approach is used in this research to operationalize predictive analytics in sports tourism. Four classification models – logistic regression, SVM, ANN and random forest – were used to examine a dataset that has visitor records. Demographic, behavioral, contextual and experience-based features were among the variables. Metrics like area under the curve (AUC), accuracy, precision, recall, F1 score and Matthews correlation coefficient (MCC) were used to assess the preprocessed data, which had been divided into 60% training and 40% testing sets. In a smart tourism framework, the most effective methodology was logistic regression, which correctly predicted travelers' intention to suggest sporting venues. Findings: AUC, accuracy, precision, recall, F1 score and MCC = 1.000 were all ideal scores for logistic regression, which the research showed to perform more accurately than other models. Tourists' intention to suggest was influenced by a number of important features, including gender, country, event type, ticket type and season of travel. The venue was less likely to be recommended by male tourists, general ticket holders and visitors of summer events. Conversely, those who attended tennis matches, had very-important-person tickets and traveled during the winter shown more desire to suggest. These findings demonstrate how predictive analytics may improve decision-making and customize sports tourist experiences. Originality/value: In a field with little empirical research, sports tourism, this study combines predictive analytics in an unconventional manner inside a smart tourism framework. The research provides a data-driven strategy for comprehending the recommendation behavior of tourists by using the CRISP-DM methodology and numerous machine learning models. For tourist planners and event organizers, the use of comparative model analysis and actual visitor data offers beneficial findings. The results not only pinpoint important factors that influence satisfaction but also direct focused tactics to enhance visitor experience and service delivery. By showing how predictive modeling may improve strategic choices in the ever-changing sports tourism industry, this research closes a significant gap.

When a service system is extremely busy, customers are forced to wait in line. In addition to decreasing consumer satisfaction, this issue results in financial losses for the organization. This research suggests a queue model of consumers' queuing behavior in order to account for consumer losses. Researchers are examining the best practices for queue formation and optimization in random service systems with the aim of reducing customer losses. The MG1, GM1, and GG1 queuing models were created to estimate the quality control of the service. We look into queueing systems that use customer behavior models to predict responses for service quality control. Although GM1 and GG1 findings are quite close to MG1 results, the investigation concluded that MG1 produces the highest quality service.

Social Media continues to impact the marketing campaign in a big way, and Indian brands are not an exception. One of the influential yet understudied tools for social media is a hashtag, which in recent times created a valuable and spellbound marketing impression. Still, less research has been done on how hashtag creates such a magnificent consumer engagement. The paper aims to answer this unfolded query by evaluating the use of the hashtag by Indian brands through sentiment analysis. There are two ways Indian brands use the hashtag – Campaign hashtag and Brand hashtag. This research is based on brand hashtags and infers quite an interesting outcome. As an experiment, we executed the hashtags for top Indian brands to identify the overall sentiment polarity of the hashtag. The outcomes were divided into two parts, wherein, in the first part, we evaluated sentiments polarities (divided into positive and negative) and computed Conversation to Engagement (C/E) Ratio and Engagement to Reach (E/R) Ratio to identify the prominence of each hashtag. In the second part of the analysis, we analyzed the responses based on demographic analysis including gender, age, interests, and occupations, which gives a clear idea of hashtag exploration. Overall, we collected 5611 texts and 154472 tokens between 01/08/2019 and 30/09/2019 from the selected brands..

The layout of the Smart Computational Grid is an integral part of our information services infrastructure. In this paper, an anticipation of the architectural model of Smart Computational Grid is derived, which provides petabytes storage, tera-byte processing speed, and Gigabyte data transmission, where many supercomputers forms node with parallel data transmission. Impulsive demands posed by unexpected flows of data from users for complex computations, however, is exceedingly challenging. The paper specifies the design and implementation of a Dynamic Allocation of Memory (DAM) algorithm. DAM algorithm identifies the design of memory at supercomputing node, for ergodic functioning and for ensuring that no data overflow, using the methodology of queuing theory. The overall performance of the DAM algorithm is shown using MM1 and GG1 queue models and results are compared accordingly.