Understanding heterogeneous preferences of hotel choice attributes: Do customer segments matter?

Kim, Do-Hee; Hong, Sukhwa; Park, Byung-Jin; Kim, In-Young

doi:10.1016/j.jhtm.2020.08.014

Cited by 25 publications

(35 citation statements)

References 28 publications

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“…In the limited literature on benefit segmentation in the accommodation sector, various approaches to enumerating benefits have been applied for consumer segmentation-interviews with customers and experts (Kim et al 2020), factor analysis of customers' surveys based on literature review (Chung et al 2004;Guttentag et al 2018), or data mining of textual eWOM (Ahani et al 2019). In this study, in contrast, a predetermined set of attribute ratings present on the online travel agent's website has been adopted as potential clients are assumed to base their decisions on a limited set of the most readily accessible information (Simon 1979;Keeney et al 1993).…”

Section: Discussionmentioning

confidence: 99%

“…Benefit studies in the accommodation market are relatively more limited (Guttentag et al 2018). Usually, they are based on motives derived from interviews with customers and experts (Kim et al 2020) or factor analysis of customers' surveys regarding multiple hotel selection criteria or satisfaction attributes and limited to a special subgroup of tourists: business travellers in luxury hotels (Chung et al 2004), female travellers (Khoo-Lattimore and Prayag 2015), or Airbnb users (Guttentag et al 2018). Also the study by Ahani et al (2019) limits the investigation to the guests of spa hotels.…”

Section: Benefit Segmentationmentioning

confidence: 99%

“…Since then, the number and details of the selection attributes identified by researchers have grown immensely (Chu and Choi 2000;Dolnicar and Otter 2003;Shah and Trupp 2020). Besides location and price, literature has shown, inter alia, the importance of room quality, service quality, cleanliness, safety and security, hotel reputation, or atmosphere (Kim et al 2020). Moreover, these determinants have been proved to be heavily influenced by traveller type and trip purpose (Wang et al 2020), gender (Hao and Har 2014;Kim et al 2018), destination nature and origin of tourists (Ying et al 2020).…”

Section: Determinants Of Tourist Accommodation Selectionmentioning

confidence: 99%

“…Price of an accommodation is habitually the basic decision criterion (Song et al 2011), especially for leisure tourists (Lewis 1985;Chow et al 1995;Parasuraman et al 1998) For the business tourists price doesn't seem less important than for the leisure ones (Lewis 1985;Kim et al 2020), but it is found to be of lesser significance than location, quality, and comfort (Wong and Chi-Yung 2002;Kim and Park 2017). Moreover, price is more important for those travelling less frequently and for shorter periods, as well as for those travelling with their families or friends (Wong and Chi-Yung 2002), deciding to stay at a lower standard hotel (Rhee and Yang 2015b), or for women than men (McCleary et al 1994).…”

Section: Determinants Of Tourist Accommodation Selectionmentioning

confidence: 99%

“…However, the limits of this approach to segmentation (Crawford-Welch 1990) have led researches to shift focus on to product and service benefits sought by customers. Consequently, for some scholars (Bowen 1998;Kim et al 2020), the benefit segmentation (grouping consumers desiring the same sets of benefits) offers the best understanding of consumer behaviour in different market segments.…”

Section: Introductionmentioning

confidence: 99%

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Benefit segmentation in the tourist accommodation market based on eWOM attribute ratings

Nessel

Kościółek

Wszendybył-Skulska

et al. 2021

Inf Technol Tourism

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Given the increasing importance of electronic word-of-mouth (eWOM) in the global tourism market, the purpose of the study was to estimate weights customers assign to main attributes of tourist accommodations embodied in easily observed eWOM numerical ratings and subsequently to determine segments of customers with homogenous preferences. To this goal, the preferences tourists attach to price and seven other accommodation attributes rated by Internet users on Booking.com were revealed with the analytical hierarchy process (AHP). Next, a two-stage clustering procedure based on these preferences was undertaken followed by profiling of the clusters in terms of their socio-demographics and travel characteristics. The results show that even if the ranking of the attributes is roughly the same for all the segments (with cleanliness, value for money, and location always in top four), all eight attributes effectively segment tourists into three clusters: “quality-seekers” (45% of the market), “bargain-seekers” (35%), and “cleanliness-seekers” (20%). The segments differ in terms of tourists’ income and expenditures, type of accommodation, actual payer for accommodation, and trip purpose. In contrast, socio-demographics, and most tourists stay variables are alike across the segments. The proposed method of benefit segmentation provides a new perspective for an exploitation of eWOM data by accommodation providers in their marketing strategy.

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Section: Discussionmentioning

confidence: 99%

Section: Benefit Segmentationmentioning

confidence: 99%

Section: Determinants Of Tourist Accommodation Selectionmentioning

confidence: 99%

Section: Determinants Of Tourist Accommodation Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Benefit segmentation in the tourist accommodation market based on eWOM attribute ratings

Nessel

Kościółek

Wszendybył-Skulska

et al. 2021

Inf Technol Tourism

View full text Add to dashboard Cite

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Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach

Kar

Kumar

Ilavarasan

2021

Glob J Flex Syst Manag

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Among services, the immense growth of Indian tourism in the last years has attracted the interest of practitioners, researchers, and governments. Service experiences at the point of encounter can impact the consumption of these tourism services extensively. However, measuring the service experience at the point of service encounter becomes a bit difficult. The tourists who visit India often share their experiences immediately regarding their service encounter in social media. These tweets often have high sentiments and emotional content. In this study, we attempt to identify factors which impact customer service experience, at the point of service encounter, by mining social media discussions. After removing spurious tweets, 7,91,804 tweets were identified and analysed in this study. Factors such as accessibility, accommodation, assurance, cultural attraction, Jugaadu service flexibility, cleanliness, hospitality, price, restaurant, and security were identified using topic modelling, topic association mining, and sentiment analysis. We attempt to model these experiences and their drivers across five zones of India, namely North, South, East, West, and North-East India. Our inferential analysis highlights that the importance and impact of these factors differ significantly zone wise across India, which indicates high location specificity of factors which impact the customer service experience. The study elaborates implications for theory and practice based on our findings.

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A motivation-based study to explain accommodation choice of senior tourists: Hotel or Airbnb

Nicolau,

Rodríguez-Sánchez,

Ruiz-Moreno

2024

International Journal of Hospitality Management

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Understanding heterogeneous preferences of hotel choice attributes: Do customer segments matter?

Cited by 25 publications

References 28 publications

Benefit segmentation in the tourist accommodation market based on eWOM attribute ratings

Benefit segmentation in the tourist accommodation market based on eWOM attribute ratings

Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach

A motivation-based study to explain accommodation choice of senior tourists: Hotel or Airbnb

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