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This study mainly aimed to investigate the impact of seasonal changes on media advertising revenues (AR) in R environment through a visual-print media company example. So, the study primarily aimed to examine whether visual, print and internet ARs were affected by seasonal change. The study also aimed to indicate the trends of visual/ print/ and internet ARs over time in the light of seasonality. Furthermore, it was also aimed to make AR estimates for the next year with the best performing methods covering ARIMA and ETS. Methodologically, R programming language and its libraries were used together with ARIMA and ETS time series models. The data set consisted of a monthly ARs series consisting of 180 rows covering the period between January/2007-December/2021 and having a sufficient number of observations for the application of the models. Data were analyzed with R and next year’s forecasts were done with ARIMA and ETS. The study revealed that all AR types were affected by seasonal changes in the specified period and included seasonality, i.e. specially in the summer months.  Additionally, it was observed that AR types have different trends despite containing seasonality. The study found that visual ARs denoted a slight downward trend including seasonality during the period. It was also indicated that print ARs reflected a very rapid and drastic downward trend covering seasonality. The study also revealed that internet ARs showed a very rapid and sharp upward trend compared to the other two AR types despite having seasonality. Furthermore, taking into account the calculated error metrics, it was found that the ARIMA model showed the best estimation performance in both i.e. visual and internet ARs. On the other hand, the ETS model indicated the best estimation performance in print ARs. Accordingly, estimations of the visual/print and internet ARs for the next year were made with the best performing methods. It was pointed out that estimated ARs included seasonality in the summer months and revealed similar results to the previous trends based on historical data.

This study explores the influence of digital marketing strategies on brand loyalty among young airline passengers, specifically targeting Millennials and Gen Z individuals aged 18-35 years old. With the increasing reliance on digital channels such as social media, email marketing, and search engine marketing, it is crucial to understand how these strategies impact the customer-brand relationship, brand trust, and ultimately, brand loyalty. The research employs a quantitative approach, gathering data from 230 participants who are frequent users of digital platforms and have recent travel experiences with airlines. The findings reveal that social media marketing has the most significant impact on the customer-brand relationship, followed by email and search engine marketing. Additionally, the study confirms that brand trust acts as a critical mediator between digital marketing activities and brand loyalty. These results highlight the importance of tailored digital marketing strategies for airlines aiming to engage and retain young passengers in a highly competitive industry.

Industry 4.0 technologies have optimized efficiency and effectiveness through the manufacture of sophisticated products and services, which result in enhancement of operations, improvement in safety, time, and cost-saving, and have revolutionized operations in almost all industries and have tremendously transformed the aviation sector. This study aims to examine the passenger travel experience with the implementation of e-services before, during, and after trips, from ticket bookings, self-check-in, smart boarding, in-flight services to baggage tracking after flight. Furthermore, the study seeks to identify the modern technologies with which air passengers are familiar or actively use, and to analyze the factors that influence their acceptance of these e-services.  A research model was developed based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and modified to suit the objectives of this study. The study objectives were achieved through a survey, a self-administered online questionnaire from 253 air passengers was obtained, and data was analyzed using SPSS 25 software. The results reveal that performance expectancy, price-value, hedonic motivations, perceived benefits and habit have a positive significant influence on behavioral intention, while social influence, perceived challenges, facilitating conditions and effort expectancy were not supported. The results of the study give insights into how different passengers, different age groups, different geographical locations, and different technological maturity perceive such implementations. This will provide policymakers ideas on how to better manage and enhance e-services.

This paper examines the impact of selected variables on the value creation of tourism enterprises in Western Austria (Salzburg, Tyrol, and Vorarlberg) with the objective of developing an insolvency prediction model for Alpine tourism businesses. This study specifically highlights practical applications for business owners, policymakers, and financial institutions, providing strategies to mitigate bankruptcy risks in the tourism sector. The objective of this study is to address existing gaps in the literature regarding the early detection of insolvency in the tourism sector, particularly in German-speaking regions. To this end, a hybrid analytical method is employed, combining quantitative statistical approaches with sector-specific contextual analysis. A dataset comprising tourism firms was divided into two groups, namely solvent and insolvent, with a view to identifying significant explanatory variables influencing insolvency risks. The primary findings indicate that enterprise size, company age, and debt levels (as indicated by the equity ratio) are significant risk factors for insolvency. Furthermore, the results suggest that small and medium-sized enterprises (SMEs) are particularly vulnerable to financial crises. Finally, the findings demonstrate that macroeconomic structures and sectoral effects significantly impact insolvency probabilities. These findings emphasise the necessity for the development of early warning systems that are tailored to the specific requirements of SMEs in Alpine tourism.This research is particularly pertinent in light of the rising insolvency rates in the tourism industry and the lack of comprehensive official statistics, which impede effective crisis management. By addressing these challenges, the study contributes to the development of targeted risk management and intervention strategies to enhance the resilience of tourism enterprises in the region.

This study explores the influence of digital marketing strategies on brand loyalty among young airline passengers, specifically targeting Millennials and Gen Z individuals aged 18-35 years old. With the increasing reliance on digital channels such as social media, email marketing, and search engine marketing, it is crucial to understand how these strategies impact the customer-brand relationship, brand trust, and ultimately, brand loyalty. The research employs a quantitative approach, gathering data from 230 participants who are frequent users of digital platforms and have recent travel experiences with airlines. The findings reveal that social media marketing has the most significant impact on the customer-brand relationship, followed by email and search engine marketing. Additionally, the study confirms that brand trust acts as a critical mediator between digital marketing activities and brand loyalty. These results highlight the importance of tailored digital marketing strategies for airlines aiming to engage and retain young passengers in a highly competitive industry.

This paper examines the impact of selected variables on the value creation of tourism enterprises in Western Austria (Salzburg, Tyrol, and Vorarlberg) with the objective of developing an insolvency prediction model for Alpine tourism businesses. This study specifically highlights practical applications for business owners, policymakers, and financial institutions, providing strategies to mitigate bankruptcy risks in the tourism sector. The objective of this study is to address existing gaps in the literature regarding the early detection of insolvency in the tourism sector, particularly in German-speaking regions. To this end, a hybrid analytical method is employed, combining quantitative statistical approaches with sector-specific contextual analysis. A dataset comprising tourism firms was divided into two groups, namely solvent and insolvent, with a view to identifying significant explanatory variables influencing insolvency risks. The primary findings indicate that enterprise size, company age, and debt levels (as indicated by the equity ratio) are significant risk factors for insolvency. Furthermore, the results suggest that small and medium-sized enterprises (SMEs) are particularly vulnerable to financial crises. Finally, the findings demonstrate that macroeconomic structures and sectoral effects significantly impact insolvency probabilities. These findings emphasise the necessity for the development of early warning systems that are tailored to the specific requirements of SMEs in Alpine tourism.This research is particularly pertinent in light of the rising insolvency rates in the tourism industry and the lack of comprehensive official statistics, which impede effective crisis management. By addressing these challenges, the study contributes to the development of targeted risk management and intervention strategies to enhance the resilience of tourism enterprises in the region.

Industry 4.0 technologies have optimized efficiency and effectiveness through the manufacture of sophisticated products and services, which result in enhancement of operations, improvement in safety, time, and cost-saving, and have revolutionized operations in almost all industries and have tremendously transformed the aviation sector. This study aims to examine the passenger travel experience with the implementation of e-services before, during, and after trips, from ticket bookings, self-check-in, smart boarding, in-flight services to baggage tracking after flight. Furthermore, the study seeks to identify the modern technologies with which air passengers are familiar or actively use, and to analyze the factors that influence their acceptance of these e-services.  A research model was developed based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and modified to suit the objectives of this study. The study objectives were achieved through a survey, a self-administered online questionnaire from 253 air passengers was obtained, and data was analyzed using SPSS 25 software. The results reveal that performance expectancy, price-value, hedonic motivations, perceived benefits and habit have a positive significant influence on behavioral intention, while social influence, perceived challenges, facilitating conditions and effort expectancy were not supported. The results of the study give insights into how different passengers, different age groups, different geographical locations, and different technological maturity perceive such implementations. This will provide policymakers ideas on how to better manage and enhance e-services.

This study mainly aimed to investigate the impact of seasonal changes on media advertising revenues (AR) in R environment through a visual-print media company example. So, the study primarily aimed to examine whether visual, print and internet ARs were affected by seasonal change. The study also aimed to indicate the trends of visual/ print/ and internet ARs over time in the light of seasonality. Furthermore, it was also aimed to make AR estimates for the next year with the best performing methods covering ARIMA and ETS. Methodologically, R programming language and its libraries were used together with ARIMA and ETS time series models. The data set consisted of a monthly ARs series consisting of 180 rows covering the period between January/2007-December/2021 and having a sufficient number of observations for the application of the models. Data were analyzed with R and next year’s forecasts were done with ARIMA and ETS. The study revealed that all AR types were affected by seasonal changes in the specified period and included seasonality, i.e. specially in the summer months.  Additionally, it was observed that AR types have different trends despite containing seasonality. The study found that visual ARs denoted a slight downward trend including seasonality during the period. It was also indicated that print ARs reflected a very rapid and drastic downward trend covering seasonality. The study also revealed that internet ARs showed a very rapid and sharp upward trend compared to the other two AR types despite having seasonality. Furthermore, taking into account the calculated error metrics, it was found that the ARIMA model showed the best estimation performance in both i.e. visual and internet ARs. On the other hand, the ETS model indicated the best estimation performance in print ARs. Accordingly, estimations of the visual/print and internet ARs for the next year were made with the best performing methods. It was pointed out that estimated ARs included seasonality in the summer months and revealed similar results to the previous trends based on historical data.