2.1. Air Service Distribution

The COVID-19 pandemic has brought about a profound transformation and disruption across various sectors, resulting in substantial societal, economic, and industrial changes. This significant shift has led to the division of time into the 'pre-COVID' and 'post-COVID' eras, underscoring the far-reaching impact of this global crisis. Notably, the aviation industry has experienced considerable damage and is poised for fundamental transformations and systemic shifts in the future. To proactively prepare for external shocks and threats, the aviation distribution industry must pivot towards adopting a crisis management system and developing strategies that dynamically respond to market shocks and losses while simultaneously building value.

The post-COVID-19 landscape has witnessed alterations in overseas tourism and travel patterns, with a decline in group package travel and a notable increase in individual travel, commonly referred to as FIT (Free Independent Travel). Additionally, there has been a surge in non-face-to-face distribution networks, leveraging web-based online travel agencies (OTAs) and meta search engines for travel product purchase. In response to these shifts in consumer travel patterns and purchasing behavior, airlines must be agile in adjusting their ticket distribution and sales strategies.

As we navigate the post-COVID-19 era, it is imperative for airlines to proactively seek strategies that align with the paradigm shift in the air distribution industry. Detecting changes in future air distribution trends will allow them to stay ahead of the curve in adapting to evolving market dynamics."

2.2. Job Stress

The concept of job stress within the service distribution industry, often referred to as the distribution sector, is multifaceted and has gained substantial attention in recent years. Scholars have proposed various definitions to capture the essence of job stress in this sector. As defined by Gill et al. (2006), job stress in the context of service distribution pertains to the tension and anxiety resulting from the work environment. It represents the harmful physical and emotional responses that emerge when work demands do not align with an employee's abilities, resources, or needs (Rode, 2004). This definition holds particular relevance in the service distribution industry.

2.3. Job Satisfaction and Service Quality

Job satisfaction within the service distribution industry has been defined as the positive emotional state derived from one's occupation, contributing to the comfortable and pleasant experiences individuals feel while working (Locke, 1976). Research indicates that job satisfaction significantly enhances workers' service quality (Tharikh et al., 2016). Employees who are satisfied with their work duties tend to be more customer-oriented, resulting in increased levels of customer satisfaction (Scanlan & Hazelton, 2019). Job satisfaction has consistently been shown to impact customer-oriented behaviors (Hoffman & Ingram, 1992), with happy employees being more motivated to provide exceptional service to customers. This connection between job satisfaction and service quality is particularly pertinent in the service distribution industry.

H9: Job satisfaction significantly affects service quality.

2.4. Job Engagement and Service Quality

Job engagement within the service distribution industry refers to the belief that individuals and their organization are strongly connected (Morrow, 2011).

A committed employee within the context of service distribution is inclined to employ extensive exertion on behalf of the organization and has a strong aspiration to maintain membership in the organization (Mowday et al., 1979).

H10: Job engagement has a significant effect on service quality.

2.5. Service Quality

An employee's customer orientation can be defined as "a state-like variable regarding their attitude toward satisfying customers and their needs" (Mathe et al., 2016). Companies in the service distribution sector typically prioritize their customers' needs, as employees play a pivotal role in the service product (Tajeddini, 2010). Customers expect high levels of customer service from employees, emphasizing the importance of valuing customers. Service quality in the service distribution industry not only leads to business success but also results in desirable employee outcomes (Hennig‐Thurau, 2004; Jaworski & Kohli, 1993). Moreover, customer orientation positively influences an employee's job satisfaction and commitment (Donavan et al., 2004), highlighting the significant role of service quality in service distribution. This evidence demonstrates that service quality contributes significantly to an entity's competitiveness, promoting and maintaining long-term customer-supplier relationships (Kelley, 1992).

3.1. Survey Design

According to Saunders (Saunders et al., 2009), 42 survey items were developed for a quantitative approach based on the research model shown in Figure 1. Table 1 shows the results of revising and complementing items from previous studies on stress, job satisfaction, job engagement, and service quality. For this study, all questionnaires were revised to use a 5-point Likert scale. Based on Tarhini's guidelines, a convenient sampling method (Hair et al., 2006), based on the non-probabilistic, self-participation sampling method, was used for the study (Tarhini et al., 2016). Fourteen volunteers with current cabin crews in the Korean airline industry were asked to participate in three pilot tests. Questionnaires were modified to suit the topic of this study.

3.2. Data Collection and Analysis

Using a Google survey program, the main survey format was assembled and released to FSCs (full-service carriers; Korean Air, Asiana), LCCs (low-cost carriers), and foreign airlines operating from/to Korea. The survey was conducted over two weeks from May 11, 2021 and 312 responses were collected. A variety of analyses were conducted using SPSS version 25, including descriptive statistics and internal reliability (Cronbach, 1951), as well as AMOS 23 for convergent reliability, discriminant reliability, model fit analysis, SEM path analysis, as well as validation of hypotheses (Hairet al., 2011; Leontitsis & Pagge, 2007).

4.1. Demographic Profile

As shown in the demographic profile in Table 2, 312 individuals 62.8% were female and 37.2% were male. In terms of age, 51.6% of respondents were in the 31-40 range, which is the largest age group. There were 34.0% in the 41-50 range, 11.2% in the 20-30 range, and 3.2% in the 51-60 range. Junior cabin crew members make up 46.2%, followed by 25.3% being assistant pursers. There was also an average income of million won per month for the highest income category approximately 31.7% with a range of 3.1 million to 4.0 million won. Considering employment status, paid leave was the largest with 34.9%, followed by paid + unpaid leave at 33.9% and 31.7% for unpaid leave.

There are some unusual phenomena that can be identified based on the data. According to the question about service years, 37.2% of respondents have worked for more than 12 years, and their age range is also in their 30s and 50s. It is more important to survey women in this study since the gender ratio of cabin crew in Korea is still much higher for women than for men.

4.2. Confirmative Factor Analysis

Confirmatory factory analysis method is used to verify the factor structure among the observed variables in table 2. To analyze the relationship between observed variables and latent variables, squared multiple correlation is used to know the proportion of variance accounted for in the latent variables. Squared multiple correlations have a minimum value of 0.441 and the maximum value of 0.897. In addition, Cronbach’s alpha is used to assess the reliability, internal consistency of observed variables. All the Cronbach values are proven to be acceptable (Cronbach, 1951; Santos, 1999). To measure the amount of variance captured by a construct in relation to the amount of variance due to measurement error, average variance extracted (AVE) and composite reliability (CR) are calculated in table 3. All the AVEs are proven to be higher than minimum threshold of 0.5 (Geldhof et al., 2014). It is also presented in table 4 the analysis of discriminant validity assessment.

In table 5, the measurement of model fit indices is provided to establish whether, overall, the model is acceptable. According to the model fit result, the model appears to be a good fit to the data. The χ² is 597.564, CMIN/DF is 1.191, RMR is 0.042, GFI is 0.909, AGFI is 0.885, and RMSEA is 0.025. Thus, the structural equation model has an acceptable fit to the data.

4.3. Structural Equation Modeling Analysis

Path analysis, in figure 2 and table 6, shows the main results of the structural equation model providing coefficient estimates, construct reliability and p-value etc. First, the hypothesis connecting Unstable Employment to Job Satisfaction (H1), with the values of β = 0.140 was not supported. Secondly, the hypothesis connecting Unstable Employment to Job Engagement (H2), with the values of β = 0.066 was not supported. Thirdly, the hypothesis connecting Working Environment to Job Satisfaction (H5), with the values of β =- 0.002 was not supported. Fourthly, working Environment to Job Engagement (H6), with the values of β =- 0.034 was also not supported. Finally, the hypothesis connecting Job Satisfaction to Service Quality (H9), with the values of β =- 0.022 was not supported.

The hypothesis connecting Performance Evaluation to Job satisfaction(H3), with values of β = -0.637; the hypothesis connecting Performance Evaluation to Job Engagement(H4), with values of β = -0.594; the hypothesis connecting Job Responsibility to Job Satisfaction(H7), with values of β = -0.630; the hypothesis connecting Job Responsibility to Job Engagement(H8), with values of β = -0.553 and the hypothesis connecting Job Engagement to Service Quality(H10), with values of β = 0.685 are supported. According the figure 2, both job satisfaction and job engagement are not directly explained by either unstable employment or working environment. In addition, job satisfaction has no significantly positive effect on service quality.

5.1. Conclusion

The purpose of this study is to analyze how COVID-19-induced job stress factors affect the service quality of airline cabin crews within the context of the distribution service industry. To investigate the influence of pandemic-induced stress factors on the performance of airline cabin crews, we employed mediators such as job satisfaction and job engagement. These mediators are essential in understanding the relationships between pandemic-induced stressors and service quality within the airline industry during COVID-19. Through the mediating roles of job satisfaction and job engagement, we elucidate how COVID-19-induced job stress factors impact the service quality delivered by airline cabin crews.

This research has yielded key findings. Among the COVID-19-induced job stress factors, both performance evaluation and job responsibility had significant impact on job satisfaction and job engagement. In contrast, unstable employment factor and working environment factor during the pandemic had no significant affect job satisfaction and job engagement. The possible reasons for these results seem to be that the employment retention support provided by the Korean government to the airlines greatly affected by the pandemic has contributed to reducing stress arising from unstable employment, and this was resulted affecting on both job satisfaction and job engagement.

In addition, as the pandemic has persisted for an extended period, it seems that flight attendants have become somewhat accustomed to the given work environment during the pandemic, and it has not possibly influenced job satisfaction and job engagement.

Another finding was revealed that job satisfaction had no significant impact on service quality during COVID-19. The possible reason for this result seems to be that service quality is influenced by factors such as training and organizational culture, rather than individual job satisfaction. Another plausible explanation is that, given the potential transmission of the COVID-19 virus through saliva, interactions between flight attendants and passengers may have been limited during flights. Consequently, the unique environment during the pandemic is expected to have had some impact on the job satisfaction and service quality of cabin crew members.

5.2. Implication

Based on these findings, we can draw several implications for both academic research and managerial practices:

Academic Implications: This study offers a theoretical foundation for comprehending the influences between variables of potential stresses including unstable employment, performance evaluation, working environment and job responsibility faced by airline cabin crews during the prolonged pandemic and service quality through mediating variables of job satisfaction and engagement.

Unlike many prior studies that concentrated on job stress and anxiety among cabin crews before the pandemic, this research examined the impact of stress of cabin crew specific to the pandemic situation on service quality by adopting mediating variables with different angle. By considering unprecedented circumstances of the pandemic era, this research has contributed to fill the research gaps left by previous studies.

Managerial Implications: The findings of this study hold meaningful insights for managerial practices within the airline industry.

First, the airline industry, which now stands at the end of the pandemic tunnel that began in March 2020, is in an uncertain environment, unsure of when another form of pandemic might emerge. As an industry highly vulnerable to external factors, airlines must thoroughly prepare for any future pandemics based on the experience gained during the recent pandemic era.

Second, based on the lessons learned from the recent pandemic, it is crucial to analyze the performance of cabin crew in detail and to create opportunities for enhancing their performance during emergency situations, such as a pandemic. This is also essential to enable cabin crew to achieve good performance and, simultaneously, enhance job satisfaction, navigating through challenging environments that may arise again in the future.

Third, in a pandemic situation, wearing protective gear and exposing oneself to risk for the sake of passenger safety and a comfortable flight is not an easy task. Even in such challenging circumstances, acknowledging and encouraging the dedicated efforts of the crew members who have selflessly worked for their company is crucial. The efforts of these companies can serve as a driving force, not only strengthening the job satisfaction and engagement of the crew members but also positively influencing the improvement of the airline's service quality.

Finally, in an emergency situation like a pandemic, where a limited number of cabin crew members perform their duties in a restricted environment, the ability to effectively carry out various roles becomes crucial. To facilitate this, it is important for companies to implement diverse training programs that empower cabin crew members to fulfill a range of responsibilities.

5.3. Limitation and Future Research

Based Despite the valuable insights gained from this research, there are several limitations to consider. First, due to the limited availability of prior studies on COVID-19-induced job stress factors, this study primarily drew upon existing literature on traditional work stress factors. To address this limitation, future research should investigate new findings and explore different facets of job stress factors caused by COVID-19. Furthermore, the evolving nature of the COVID-19 situation, which has persisted beyond the scope of this study, necessitates future research to encompass both domestic and international cases related to COVID-19, in order to derive relevant job stress factors. This comprehensive approach will provide a more holistic understanding of the challenges faced by airline cabin crews and offer insights into effectively managing their well-being in the context of the service distribution industry.