Purpose This paper aims to shed light on differences in the patterns of online rating behavior that Chinese- and English-speaking travelers adopt when making hotel reviews on TripAdvisor. Design/methodology/approach A dual analysis was conducted on 800 online reviews for eight hotel brands. The brands, which are currently operating in Bangkok City, are considered to be representative of their categories. The hotels were selected based on their abilities to meet the quantitative and qualitative requirements of the text mining strategy adopted in this study. Findings The results reveal that, with respect to all of the hotel service attributes (i.e. Service, Cleanliness, Room, Sleep Quality, Location, Value and Overall), the ratings offered by the English-speaking guests were higher than the Chinese-speaking travelers. Based on the qualitative analysis, the ten service attributes which had the most impact on traveler satisfaction were distinguished. These attributes were then classified into three broadly themed categories including intangible service, tangible service and staying experience. The results from the word frequency analysis also helps to clarify which features are drawing attention from travelers from different backgrounds. Originality/value This study contributes to tourism and hospitality literature by confirming certain differences in the online rating behavior of Chinese- and English-speaking hotel guests.
Organic products have been gaining popularity among consumers worldwide due to the environmental and health benefits they are associated with. As a result of this trend, organic industries have been flourishing and have been able to expand into a variety of consumer product/service categories. Looking to explore purchasing behavior related organic coconut cosmetic products, this study attempted to apply the theory of planned behavior (TPB), which is a method of predicting consumer behavior that has been used extensively in a variety of research areas in recent years. Based upon the literature review, an extended TPB model that incorporates health concerns and health knowledge, in addition to attitude, subjective norms and perceived behavior control was examined in this study. For the data collection, an online survey was issued to residents of Bangkok, Thailand; with a total of 613 respondents retuning the questionnaires. Structural equation modeling (SEM) was employed to analyze the data using SPSS AMOS 24. The results showed that attitude, subjective norms, perceived behavior control and health concerns positively affect purchase intention; however, health knowledge did not influence purchase intentions related to the organic coconut cosmetic products. Similar to the findings in most extant literature, attitude was found to exert the most influence on the purchase behavior in this study.
Introduction: COVID-19 has severely impacted industries and individual lives globally. Due to travel restrictions and social distancing to reduce the spread of the disease, it has seriously affected the travel and tourism industry in Thailand, especially its community-based tourism. The impact of travel bans has magnified employment and income loss to most local families and their communities, negatively impacting the development of local tourism economies. Purpose: The main objective of this study is to investigate the impacts of COVID-19 on community-based tourism as well as its adaptation solutions using a case study from a specific region in Thailand. Design/methodology/approach: Using a phenomenological approach, a series of qualitative face-to-face, semi-structured interviews were undertaken with 42 stakeholders involved in community-based tourism. Findings: The study results indicate that job relocation, temporary business closures, reducing fixed costs, and increasing different sale channels represent the most critical factors impacting CBT operational activities directly affected by the COVID-19 crisis and requiring immediate action. Other supplementary actions involve government financial support, business compensation, early vaccination, reduction of agricultural debt, and increasing agricultural product value. Social implications: These study findings offer direction for Thai governmental policy makers and CBT leaders for the establishment of actionable practices designed to respond rapidly and appropriately to local communities and entrepreneurs during crises such as the COVID-19 pandemic. Originality: The originality of this research was obtained from local stakeholders’ insights on the impacts of COVID-19 upon community-based tourism in the northeast region of Thailand, where tourism represents significant economic value in terms of salaries, wages, and employment generation.
Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.
The main purpose of this study was to analyze and compare the online complaining behavior of Asian and non-Asian hotels guests who have posted negative hotel reviews on TripAdvisor to voice their dissatisfaction towards a select set of hotel service attributes. A qualitative content analysis of texts which relied on manual coding was used while examining 2020 online complaining reviews directed at 353 UK hotels and posted by visitors originating from 63 countries. The results from the word frequency analysis reveal that both Asian and non-Asian travelers tend to put more emphasis on Booking and Reviews when posting complaints online. Based on a manual qualitative content analysis, 11 different major online complaint categories and 65 sub-categories were identified. Among its important findings, results of this study show that non-Asian guests frequently make complaints which are longer and more detailed than Asian customers. Managerial implications and opportunities for future studies are also discussed.Sustainability 2020, 12, 1838 2 of 37 booking intentions [7,8], consumer decision-making [9], consumer attitudes [10,11], and perception of trust [12]. Research on online consumer behavior has demonstrated that negative reviews hold more sway than positive reviews; therefore, consumers will give more weight to negative information when making judgments or performing decision-making tasks [13]. An example of this can be seen in a study by Casaló, Flavián, Guinalíu, and Ekinci (2015) on travelers' perceived usefulness of online reviews. The results show that the travelers find negative online reviews more useful than positive ones. Nevertheless, although the significance and the influence of online reviews is well recognized, the important question of how to understand online complaining behavior within the context of different cultural backgrounds still remains, with only a few researchers focusing on the relevant factors thus far.Customer dissatisfaction and consumer complaining behavior were quick to draw attention from the researchers of and service providers in the hospitality industry. Although consumer complaints do give hoteliers the opportunity to improve their marketing programs in ways that can enhance customer satisfaction and hotel profitability, they can also impact the hotel's reputations and potentially do great damage to the company [14]. Thus, we duly recognize the importance that must be attached to proper and applicable understandings of consumer complaint behavior. Literature has argued that complaint behavior may manifest itself differently on account of the distinct norms that are inherent to varying cultures [15]. For example, Ngai, Heung, Wong, and Chan (2007) examined the different attitudes Asian and non-Asian hotel guests held towards complaining behavior. The study found out that Asian guests were less likely to complain directly to the hotels for fear of "losing face", preferring rather to take non-direct action, such as through negative word-of-mouth. Yuksel, Kilinc, and Yuksel (2006) ...
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