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- MakaleYalnız künye2025How do green hotel practices affect guests' behavioral intentions? A PLS-SEM approach
Genç, Gokhan; Zengin, Burhanettin
This study examines how guests' perceptions of green hotel practices (GHP) shape their behavioral intentions, drawing on the Theory of Planned Behavior. It explores the extent to which eco-friendly practices influence guests' loyalty and their likelihood of recommending hotels that prioritize sustainability. Data were collected from 642 hotel guests aged 18 and over who had previous experience with green hotel practices. An online survey was employed, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping techniques to assess the relationships among guests' perceptions of GHP, satisfaction, and behavioral outcomes. The findings indicate that favorable perceptions of GHP strongly predict guest loyalty and recommendation intentions, with satisfaction as a critical mediator. These results highlight the pivotal role of sustainable practices in fostering positive guest experiences and behaviors in the hospitality industry. The study offers practical insights for hotel managers, enabling them to design and implement effective sustainability strategies that enhance guest satisfaction and loyalty. Furthermore, it contributes to the literature on sustainable tourism by providing robust empirical evidence on the influence of GHP on guest behavior, thus addressing a key gap in hospitality management research.
- MakaleAçık Erişim2025How do green hotel practices affect guests’ behavioral intentions? A PLS-SEM approach; [Como as práticas hoteleiras verdes afetam as intenções comportamentais dos hóspedes? Uma abordagem PLS-SEM]
Genç, Gökhan; Zengin, Burhanettin
This study examines how guests’ perceptions of green hotel practices (GHP) shape their behavioral intentions, drawing on the Theory of Planned Behavior. It explores the extent to which eco-friendly practices influence guests’ loyalty and their likelihood of recommending hotels that prioritize sustainability. Data were collected from 642 hotel guests aged 18 and over who had previous experience with green hotel practices. An online survey was employed, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping techniques to assess the relationships among guests’ perceptions of GHP, satisfaction, and behavioral outcomes. The findings indicate that favorable perceptions of GHP strongly predict guest loyalty and recommendation intentions, with satisfaction as a critical mediator. These results highlight the pivotal role of sustainable practices in fostering positive guest experiences and behaviors in the hospitality industry. The study offers practical insights for hotel managers, enabling them to design and implement effective sustainability strategies that enhance guest satisfaction and loyalty. Furthermore, it contributes to the literature on sustainable tourism by providing robust empirical evidence on the influence of GHP on guest behavior, thus addressing a key gap in hospitality management research.
- MakaleAçık Erişim2024EVALUATION OF CONSUMERS’ USE OF SMART ROBOTIC VACUUM CLEANERS UNDER EXTENDED EXPECTATIONCONFIRMATION MODEL; [VREDNOVANJE PROVEDENO MEĐU POTROŠAČIMA PAMETNIH ROBOTSKIH USISAVAČA U OKVIRU PROŠIRENOGA MODELA POTVRDE OČEKIVANJA]
Avci, Ibrahim; Kocan, Mahmut; Kirmizibiber, Ahmet
Purpose – The present research study extends the Expectation-Confirmation Model (ECM) by focusing on satisfaction, continuance intention, and recommendation intention among smart robotic vacuum cleaner users. The impacts of battery life concern and perceived privacy factors on satisfaction and continuance intention of the smart robotic vacuum cleaner were investigated by adding the variables of battery life concern, perceived privacy, and recommendation intention in the ECM. Design/methodology/approach – The participants of this study consisted of smart robotic vacuum cleaner users in Turkey. The snowball sampling method, as one of the non-random sampling methods, was used to reach the participants; data was collected using an online survey created with Google Forms, asking the participants to share the survey link with people around them who have smart robotic vacuum cleaners. A total of 218 smart robotic vacuum cleaner users participated in the study between March 1 and May 1, 2023. Findings and implications – Structural equation modeling (SEM) was used as the data analysis technique in the study. While the ECM was confirmed as a result of the SEM analysis, an important finding was that consumers with battery life concerns intend to continue using the smart robotic vacuum cleaner despite a negative relationship between battery life concern and satisfaction. While the effect of perceived privacy on continuance intention was not significant, satisfaction was found to have a significant effect on continuance intention. Given that rtificial intelligence-related businesses face fierce competition, organizations that wish to prosper in this environment must excel in a variety of areas, ranging from product design to marketing and from sales policies to post-sales assistance. Considering the findings, determining the factors that affect the satisfaction and intention to continue using robot vacuum cleaners will be a guide for smart robot vacuum cleaner manufacturers and marketers. At the same time, the fact that the issue has been investigated within the framework of a model will also make significant contributions to the literature. Limitations – Different factors influence smart robotic vacuum cleaner users’ satisfaction and desire to continue using them, but only battery-life concerns and perceived privacy variables were incorporated into the ECM model in this research study. Another limitation is connected to the research sample. Because the snowball sampling method was employed to obtain data for the research, its findings cannot be generalized as they cover only smart robotic vacuum cleaner users who participated in the survey. Originality/value – The ECM employed in the study was expanded by including the variables of battery-life concern, perceived privacy, and recommendation intention. A general lack of information among potential consumers regarding artificial intelligence and smart robotic vacuum cleaners, which is highly fascinating and a source of curiosity, as well as the lack of research on this issue in the literature, underscore the significance of this research study. By integrating and expanding the ECM for application to consumers using smart robotic vacuum cleaners, this study adds a fresh viewpoint to the literature.