The relationship between sustainability and customer satisfaction in hospitality: An explorative investigation using eWOM as a data source

Gerdt, Sven-Olaf; Wagner, Elisa; Schewe, Gerhard

doi:10.1016/j.tourman.2019.02.010

Cited by 128 publications

(100 citation statements)

References 31 publications

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“…Future research examining cultural differences could explore this further by comparing review data produced in the languages representing the country or culture the guests originate from. Second, to ensure credibility, TripAdvisor has established complex procedures to investigate whether reviews are manipulated or not [71]; however, this study still cannot be certain that the data selection did not contain fake reviews which were not identified by TripAdvisor [56]. Third, since service experience has been identified as the dominant category of complaints in this research, future studies may consider concentrating on coding service-related comments only, perhaps with reference to the SERVQUAL constructs, in order to gain insights on which specific service dimension(s) may need more attention [21].…”

Section: Discussionmentioning

confidence: 99%

“…This study opted to assign codes manually in order to capture the idiosyncrasies of the reviews and account for nuances that would otherwise be neglected [56]. Moreover, by adopting a manual approach, the researchers benefited from the strong interpretational power that is derived from a pre-built framework [57].…”

Section: Data Preparation and Codingmentioning

confidence: 99%

“…It was thus very difficult to conduct a pre-analysis to delineate a set of words or expressions that should receive specific attention or weighting. Therefore, the researchers decided instead to manually code the dataset in order to avoid the shortfalls that analytic tools would inevitably create while working to identify each aspect of the online complaining behaviors [56].…”

Section: Data Preparation and Codingmentioning

confidence: 99%

“…Finally, to ensure reliability and overcome the possible biases of a single coder, this study followed the approach of Lombard, Snyder-Duch, and Bracken (2002) [61] and Gerdt et al (2019), making use of two independent coders. Inter-coder reliability was verified by calculating the percentage of agreement, namely: "the percentage of all coding decisions made by pairs of coders on which the coders agree with the values of 0.00 (no agreement) to 1.00 (perfect agreement)" [61] (p. 590).…”

Section: Data Preparation and Codingmentioning

confidence: 99%

“…This finding is similar to that of Mey et al (2006), who found that that Asian travelers appeared to place a greater emphasis on value for money. With higher price expectations for quality increases [56], if the expectations are not met, the customer is left with a lower level of satisfaction. Another reason might relate to personal bias.…”

Section: Differences Between the Online Complaining Behavior Of Asianmentioning

confidence: 99%

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Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians

2020

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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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Section: Discussionmentioning

confidence: 99%

Section: Data Preparation and Codingmentioning

confidence: 99%

Section: Data Preparation and Codingmentioning

confidence: 99%

Section: Data Preparation and Codingmentioning

confidence: 99%

Section: Differences Between the Online Complaining Behavior Of Asianmentioning

confidence: 99%

See 3 more Smart Citations

Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians

2020

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Changes and evolution in the intellectual structure of consumer dissatisfaction

Nebreda

Martín

González

2020

J of Consumer Behaviour

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Technological progress has encouraged dissatisfied consumers hostility to companies. This has notably caught the attention of researchers, which have developed numerous advances about dissatisfaction causes and consequences. A large volume of scientific knowledge has been produced, making it difficult to process the multiple lines of research generated. The aim of this work is to provide an integrated perspective of the scientific domain's research traditions on consumer dissatisfaction, showing the development, trends, and future lines of research. Using bibliometric methodology, based on co‐cites, we map the intellectual structure of current scholarship in this field. Results point out the major areas of research, the evolution, path of dissemination, and transformative discoveries. This study contributes to the development of the customer dissatisfaction literature by providing an integrated view of the field, facilitating quick understanding of their structure and identifying future lines of research.

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Exploring the role of the Amazon effect on customer expectations: An analysis of user‐generated content in consumer electronics retailing

Vollero

Sardanelli

Siano

2021

J of Consumer Behaviour

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While Amazon's disruption of the retail market has been associated with significant changes in consumer behavior, empirical studies on how interacting with Amazon has changed customers' expectations toward other offline/online retailers remain scarce. Such Amazon‐driven perceptions of service attributes are sometimes referred to as the ‘Amazon effect’. After clarifying the meaning of the Amazon effect and reviewing the studies on consumer complaints online, this paper aims to identify key triggers for the Amazon effect from consumer comments on social media. Based on natural language processing techniques, a content and sentiment analysis of users' comments drawn from the Facebook pages of three leading consumer electronics retailers in Italy over a two‐year span (2016–2018) was used to evaluate the dissatisfaction toward these retailers associated to Amazon‐related service attributes. The findings show that there is a wide diffusion of consumer comments and service complaints related to the Amazon effect on consumer electronics retailers, especially regarding price, customer service, in‐store staff, and post‐purchase support. Compared with corresponding evaluations on the Italian Amazon website, the negative sentiments revealed in consumers' comments on Facebook suggest that the Amazon's service standards have raised consumer expectations and have made consumers less satisfied when they interact with other retailers. We argue the need for further research to better clarify Amazonification in terms of customer impatience and dissatisfaction in general, also going beyond price and logistics issues, which are usually considered as the main constitutive factors.

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The relationship between sustainability and customer satisfaction in hospitality: An explorative investigation using eWOM as a data source

Cited by 128 publications

References 31 publications

Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians

Does Culture of Origin Have an Impact on Online Complaining Behaviors? The Perceptions of Asians and Non-Asians

Changes and evolution in the intellectual structure of consumer dissatisfaction

Exploring the role of the Amazon effect on customer expectations: An analysis of user‐generated content in consumer electronics retailing

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