Big social data analysis for impact of food quality on travelers’ satisfaction in eco-friendly hotels

Nilashi, Mehrbakhsh; Abumalloh, Rabab Ali; Almulihi, Ahmed; Alrizq, Mesfer; Alghamdi, Abdullah A.; Ismail, Muhammed Yousoof; Bashar, Abul; Zogaan, Waleed Abdu; Asadi, Shahla

doi:10.1016/j.icte.2021.11.006

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…The results of the PLS-SEM analysis indicate that eco-friendly hotels' service quality significantly influences visitors' satisfaction. Therefore, maintaining service quality in accordance with customer expectations is crucial to increase satisfaction with eco-friendly hotels, as supported by Nilashi et al [54]. Additionally, the study confirms that green practices in eco-friendly hotels generate a higher satisfaction among visitors.…”

Section: Discussionsupporting

confidence: 73%

“…This suggests that meeting or exceeding customers' expectations regarding eco-friendly services plays a crucial role in fostering favorable intentions towards eco-friendly accommodations. Nilashi et al [54] also showed that the green service quality of hotels influences customer satisfaction and their future hotel selection. Therefore, this study can hypothesize the following: H1.…”

Section: Service Quality (Sq)mentioning

confidence: 99%

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Assessing Green Practices on Eco-Friendly Hotel Customer Loyalty: A Partial Least Squares Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Hybrid Approach

Chang,

Chanda,

Vafaei-Zadeh

et al. 2024

Sustainability

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With a global focus on environmental sustainability, hotels worldwide are actively transitioning their services from conventional to eco-friendly practices. This study aims to comprehensively understand the factors that contribute to visitors’ satisfaction in eco-friendly hotels and how this satisfaction influences customers’ future reactions towards such environmentally conscious establishments. Employing the Stimulus-Organism-Response theory, this study collected data from 277 respondents using a robust quantitative research strategy. A combined approach of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) was employed, to uncover deep insights into visitors’ satisfaction and their reactions towards eco-friendly hotels. The PLS-SEM results reveal significant associations between customers’ satisfaction towards eco-friendly hotel services and service quality, green practices, perceived value, and environmental sensitivity. Moreover, this study highlights a positive correlation between satisfaction and crucial outcomes like revisit intention (RVI), willingness to pay a premium (WTPP), and word-of-mouth intention (WOMI). Complementing these findings, the fsQCA analysis uncovers intricate causal relationships among antecedents that influence customer satisfaction in eco-friendly hotels. By offering critical marketing insights, this study provides guidance for hotels, the tourism industry, and policymakers on attracting customers to eco-friendly hotels, to meet the increasing demands for environmental sustainability.

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Section: Discussionsupporting

confidence: 73%

Section: Service Quality (Sq)mentioning

confidence: 99%

Assessing Green Practices on Eco-Friendly Hotel Customer Loyalty: A Partial Least Squares Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Hybrid Approach

Chang,

Chanda,

Vafaei-Zadeh

et al. 2024

Sustainability

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“…El análisis de datos se encuentra ligado al uso tecnológico en empresas de hotelería no solamente para apoyar en las decisiones, sino para optimizar y maximizar los beneficios (Pimentel et al, 2019) ya que la información generada desde la gestión constituye una serie de datos que se gesta desde una estructura compleja (Hernández et al, 2021). Algunos de los temas centrales en el manejo de cifras en la industria de la hospitalidad son los aspectos financieros (Ozdemir et al, 2021), la calidad de servicios (Nilashi et al, 2021) y el uso de los recursos (Iranmanesh et al, 2022). Sin embargo el análisis de datos toma relevancia significativa para revelar tendencias de comportamiento y con ello mejor la generación de fuentes de información confiables que direccionan la planeación de acciones en el servicio (Pan et al, 2012).…”

Section: Revisión De La Bibliografíaunclassified

Modelo de tendencias para series complejas: caso de estudio tarifas hoteleras

Juárez,

Castro

2023

risti

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El análisis riguroso de datos en turismo es cada vez más común a raíz de las afectaciones sufridas por el covid-19, lo que ha contribuido a identificar trayectorias a partir de datos históricos. En este artículo, se propone una combinación de la teoría de la complejidad y la suavización exponencial para conocer los patrones que ayuden a estimar la estacionalidad y tendencia en las tarifas hoteleras. Para ello, se fragmenta la serie en particiones para explorar la similitud y se aplican cinco métodos de trazado. Los resultados muestran que el gráfico de longitud R, es el único que despliega anti-persistencia, con una saturación en el tiempo n=8, este dato se usa como ciclo de longitud del modelo aditivo de Holt-Winters cuyos parámetros (a=0.36, b=0.78, y=0.67), minimizan el error presentado. Se concluye que la combinación de métodos de análisis numérico permite el ajuste estacional en los datos tarifarios.

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“…In 2021, Nilashi et al [25] proposed a two-stage approach framework to study the correlation between customer satisfaction and eco-friendly hotel performance. They collected data from TripAdvisor, considering reviews and complaints about local cuisine quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Unlocking the power and unveiling the challenges of big data management and analysis: insights from diverse domains

2022

TIS

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Massive data is being generated daily from various sources, including social data, machine data, and transactional data, and is characterized by the three Vs: velocity, volume, and variety [13]. Understanding data quality is crucial in dataintensive domains, as data and processing techniques impact the reliability of data sources and analytics [4,5]. Technologies such as the Internet of Things, geolocation systems, cameras, smart devices, and social media contribute to the generation of diverse data types used for decision-making, often employing computational intelligence methods like deep learning and artificial intelligence [68]. The management of big data has become more complex with the introduction of new strategies like data lakes, affecting industries across the board [9, 10].The motivation behind this research stems from the increasing importance of managing and analyzing big data, which is essential in various domains, including healthcare, where patient data and medical information are abundant [11,12]. Deep learning and artificial intelligence have found applications in the medical field, but scalability and privacy remain key concerns. *Author for correspondenceThe contribution of this research lies in its exploration of various aspects of big data management, including its application in healthcare, IoT security, and consumer goods industries. It offers

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Big social data analysis for impact of food quality on travelers’ satisfaction in eco-friendly hotels

Cited by 13 publications

References 16 publications

Assessing Green Practices on Eco-Friendly Hotel Customer Loyalty: A Partial Least Squares Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Hybrid Approach

Assessing Green Practices on Eco-Friendly Hotel Customer Loyalty: A Partial Least Squares Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Hybrid Approach

Modelo de tendencias para series complejas: caso de estudio tarifas hoteleras

Unlocking the power and unveiling the challenges of big data management and analysis: insights from diverse domains

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