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The primary purpose of this study is to reveal how the local food consumption intentions of international tourists are influenced by core variables of the theory of planned behavior (TPB). The secondary purpose is to explore the moderating role of responsible tourist behavior in these relationships.

The present research was conducted on international tourists visiting Antalya, Turkey. The research data, collected from a total of 457 respondents, were tested and analyzed via partial least squares-structural equation modeling (PLS-SEM).

Attitudes toward local food and perceived behavioral control have a positive and significant impact on local food consumption intention. Also, responsible tourist behavior has a moderating effect on both the relationship between attitude toward local food and local food consumption intention and the relationship between subjective norms and local food consumption intention.

In their advertising campaigns, destination management organizations (DMOs) and local restaurants should prepare more sentimental advertising content by further focusing on nonrenewable limited resources of the destination and addressing tourists’ role in consuming the limited resources in question. Thus, tourists’ sense of responsibility toward destinations may be increased, possibly stimulating enhancement in their local food consumption intentions.

This study explores the role of responsible tourist behavior in the construct of local food consumption through the lens of TPB. Said construct was included in the local food consumption intention model. This endeavor has paved the way for unraveling the interaction between responsible tourist behavior, attitudes and subjective norms, all of which are fundamental elements of the TPB toward increasing local food consumption intentions. This constitutes a remarkable contribution in understanding the local food consumption behavior of tourists. Moreover, because local food will be consumed in local restaurants located in the destinations, understanding the local food consumption intentions of tourists will enable local restaurants to develop innovative products by focusing on local food.

This paper aims to explore financial anxiety and generalized anxiety as the serial mediators linking perceived organizational support (POS) to career commitment.

Data were collected from 388 managerial and nonmanagerial employees in diverse service areas, such as restaurants, airlines and hotels in Turkey. The direct and mediating effects were tested via the PROCESS macro.

Financial anxiety partly mediates the impact of POS on career commitment. The findings further reveal that financial anxiety and generalized anxiety serially mediate the effect of POS on career commitment.

Management should work with mentors to provide employees with psychosocial support during the COVID-19 pandemic. When employees perceive that the firm really cares about them and values their contribution during these challenging days, they display lower anxiety and higher career commitment. Management should also retain employees who are high on career commitment because such employees possess a sense of calling and are unlikely to quit. These implications may not be considered new. However, management would need such employees concerning the firm’s performance recovery after COVID-19.

Workers in the service industries suffer from financial and generalized anxieties and display reduced career commitment during COVID-19. However, little is known about the antecedents and outcomes of financial anxiety among hospitality and tourism workers. More importantly, no empirical piece has tested these anxiety variables as the mediators linking POS to career commitment in the pertinent literature so far.

It is quite obvious that the negative consequences of climate change will be one of the most important agendas in the near future. Many societies are developing various policies and trying to raise awareness in order to be less affected by these negativities. One of the most important keys to social awareness is undoubtedly teachers. Teachers' knowledge and awareness levels about climate change can affect the attitudes of future generations toward the causes and consequences of climate change. For this reason, in this chapter, I discuss climate change and its possible problems, the effects of climate change in Turkey and the policies carried out, public awareness of climate change, climate change awareness of teachers in Turkey and teacher education in the context of climate change. I believe that the discussions in this chapter can shed light on education policies.

Government policy, which comprises laws, rules, regulations, standards, and incentives, is an important driver of system change. Many climate change programs, from the local to the global level, fail owing to a lack of “political will and coordination of subdimensions of public policy.” Rethinking public policy is more than a requirement; it should be examined beyond the established method of government. The literature on the function of public policy is extensive; yet, the application of complexity theory (CT) to examine climate change is limited. In this regard, this chapter seeks to examine how public policy might become more effective in combating climate change by applying CT in two dimensions: reforming national policies and renewing cosmopolitanism on a local to global scale.

Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.

This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.

Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.

This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

The study examined the antecedents of lecturers' online teaching anxiety and analyzed their relationships with variables such as gender, age, education, teaching experience and faculty.

Using a mixed-methods design, the study involved 115 university lecturers through surveys and 15 through semi-structured interviews, with data analyzed via mixed-methods approaches.

Personal matters and digital literacy caused the lowest anxiety, teaching methods and classroom management triggered moderate anxiety, and teaching context and health factors provoked the highest. Female lecturers experienced greater health-related anxiety due to prolonged screen time. Education lecturers reported lower anxiety than science lecturers, whose laboratory-based content was harder to adapt to online teaching.

Lecturers' online teaching anxiety stemmed from six factors: digital literacy, teaching context, teaching methods, class management, health and personal matters. Institutional and social support have been identified as crucial for improving well-being and reducing anxiety.

This study is aimed to develop a novel intuitionistic fuzzy P-graph with Gaussian membership function to help decision-makers deal with complex process network systems.

Two fuzzy P-graph case studies of the cogeneration system were selected, and relevant data were collected, including the structure and flow sequence of the system, and the rate of material and product transitions between the operating units. Gaussian function membership was set according to the restriction of fuzzy upper and lower bounds. Then the α-cut was used to obtain different upper and lower bound restrictions of each membership degree. After finding the optimal and suboptimal solutions for different membership degrees, the results of non-membership and hesitation were calculated.

The proposed method will help the decision maker consider the risk and provide more feasible solutions to choose the optimal and suboptimal solutions based on their own or through experience. The proposed model in this study has more flexibility in operation and decision making.

This study is the first to propose a novel intuitive fuzzy P-graph and demonstrates the effectiveness and flexibility of the method by two case studies of the cogeneration system. However, the addition of hesitation can increase the error tolerance of the system. Even for the solutions with a high degree of membership, optimal and suboptimal solutions still exist for the decision maker to select. Since decision makers expect the higher achievement of the target requirements; thus, it is important to have more feasible solutions with a high degree of membership.

This study proposes a theoretical model to explain user intention to continue engaging with Social Commerce (SC) from a habit perspective. The research uses social impact theory, user personal traits, and SC quality to explain how user habits are formed in SC, leading to continued usage.

The study collected data from 868 Indonesian respondents using a cross-sectional field design. SEM analysis confirmed the proposed theoretical model, calculating direct, indirect, and moderating effects.

The results showed that the social aspect is the dominant construct influencing users’ habit of using SC. Further, the outcome indicates that habit significantly predicts Continuity usage. Profoundly, subjective norms are the most significant predictors of habit, followed by self-efficacy, content quality, and herd behavior. Meanwhile, Trust and Social Support only indirectly affect Habit through self-efficacy and content quality, respectively, as mediators. Additionally, the moderating effect analysis revealed that age and gender play a role in habit formation.

This study specifically explores the factors affecting the development of habits in SC usage, leading to repeated behaviors. This area has not been thoroughly examined in previous research. Therefore, this study seeks to address this gap by investigating how habits are formed through social interactions on SC platforms. Understanding habit formation provides an alternative way of comprehending the continued use of SC, as it is considered a significant factor that leads to continued intention.