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Variations in customer expectations pose a challenge to service quality improvement in the airline industry. Understanding airline customers' expectations and satisfaction help service providers improve their offerings. The extant literature examines airline passengers' expectations in isolation, neglecting the overall impact of online reviews on service quality improvement. This paper systematically evaluates the airline industry's passengers' expectations and satisfaction using expectation confirmation theory (ECT) and the SERVQUAL framework. The paper analyzes online reviews to examine the relationship between airline service quality attributes and passengers' satisfaction.

The SERVQUAL framework was employed to examine the effects of customer culture, the reason for traveling, and seat type on customer's expectations and satisfaction across a large sample of airline customers.

A total of 17,726 observations were gathered from the Skytrax review website. The lowest satisfaction ratings were from passengers from the USA, Canada and India. Factors that affect perceived service performance include customer service, delays and baggage management. Empathy and reliability have the biggest impact on the perceived satisfaction of passengers.

This research increases understanding of the consumer expectations through analysis of passengers' online reviews. Results are limited to a small sample of airline industries.

This study provides airlines with valuable information to improve customer service by analyzing online reviews.

This study provides the opportunity for airline customers to gain better services when airline companies utilize the findings.

This paper offers insights into passengers' expectations and their perceived value for money in relation to seat types. Previous studies have not investigated value for money as a construct for passengers' expectations and satisfaction relative to service quality dimensions. This paper addresses this need.

This paper aims to examine the role of social media communication, tourist satisfaction and destination brand equity components in enhancing destination brand equity based on the Stimulus – Organism – Response (S-O-R) theory.

The conceptual model and research hypotheses were assessed using covariance-based structural equation modeling (SEM). An online survey was used to collect data from 369 domestic tourists who had traveled to Danang and knew about content related to Danang generated by either DMOs or other users on social media.

Except for the effect of DMO-generated social media communication on tourist satisfaction and the impact of destination brand awareness on destination brand loyalty, the findings confirmed the sequential causal relationships between research concepts based on the S-O-R model.

Future research should explore the proposed model based on comparisons of different nationalities to better understand the impact of cultural factors.

DMOs should associate social media with their marketing strategies to enhance destination brand equity, using cutting-edge technologies to create content and update information in a significant way to make communications by DMOs more effective. The findings especially suggest that UGC plays a vital role in improving brand equity dimensions, so DMOs could exploit UGC to engage existing customers and build relationships with potential customers. This research provides guidance for DMOs to improve their brand equity based on social media.

This study has contributed to the destination marketing literature by applying the S-O-R theory to propose a pathway for effectively increasing destination brand equity and highlight the importance of social media communication as a driver to achieve a hierarchical relationship between destination brand equity components and tourist satisfaction from stimulus to organism (e.g. cognition to affect).

This study aims to assess the efficacy of thermal analysis of concrete slabs by including different insulation materials using ANSYS. Regression equations were proposed to predict the thermal conductivity using concrete density. As these simulation and regression analyses are essential tools in designing the thermal insulation concretes with various densities, they sequentially reduce the associated time, effort and cost.

Two grades of concretes were taken for thermal analysis. They were designed by replacing the natural fine aggregates with thermal insulation aggregates: expanded polystyrene, exfoliated vermiculite and light expanded clay. Density, temperature difference, specific heat capacity, thermal conductivity and time were measured by conducting experiments. This data was used to simulate concrete slabs in ANSYS. Regression analysis was performed to obtain the relation between density and thermal conductivity. Finally, the quality of the predicted regression equations was assessed using root mean square error (RMSE), mean absolute error (MAE), integral absolute error (IAE) and normal efficiency (NE).

ANSYS analysis on concrete slabs accurately estimates the thermal behavior of concrete, with lesser error value ranges between 0.19 and 7.92%. Further, the developed regression equations proved accurate with lower values of RMSE (0.013 to 0.089), MAE (0.009 to 0.088); IAE (0.216 to 5.828%) and higher values of NE (94.16 to 99.97%).

The thermal analysis accurately simulates the experimental transfer of heat across the concrete slab. Obtained regression equations proved helpful while designing the thermal insulation concrete.