Why do guests stay at Airbnb versus hotels? An empirical analysis of necessary and sufficient conditions

Sánchez-Franco, Manuel J.; Aramendia-Muneta, Maria Elena Article Why do guests stay at Airbnb versus hotels? An empirical analysis of necessary and sufficient conditions Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Sánchez-Franco, Manuel J.; Aramendia-Muneta, Maria Elena (2023) : Why do guests stay at Airbnb versus hotels? An empirical analysis of necessary and sufficient conditions, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 8, Iss. 3, pp. 1-16, https://doi.org/10.1016/j.jik.2023.100380 This Version is available at: https://hdl.handle.net/10419/327289 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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S anchez-Franco a, *, Maria Elena Aramendia-Muneta b a Faculty of Economics and Business Administration, Universidad de Sevilla, Avda. Ramon y Cajal, n1, 41018 Sevilla, Spain b Faculty of Economics and Business Administration, Universidad P ublica de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain ARTICLE INFO Article History: Received 5 July 2022 Accepted 4 May 2023 Available online 11 May 2023 ABSTRACT Our study explores the differences in necessary and sufficient conditions for producing (dis)satisfactory guest experiences between Airbnb and hotels, intending to develop competitive strategies for the hospitality industry. Using advanced Natural Language Processing techniques, we analysed user-generated content from both platforms in the Andalusian market, utilising Contextualised Topic Modelling and Necessary Condition Analysis to identify the main topics and relationships that impact guests’experiences. We also employed XGBoost to assess sufficient conditions for customer satisfaction, providing insights that can enhance the quality of lodging stays and improve marketing strategies. Overall, our findings show that both types of accommodation share similar necessary conditions for (dis)satisfaction, but differ in the order of importance. Proximity to tourist attractions and staff recommendations are important for Airbnb guest satisfaction, while hotel guests prioritise facilities and staff professionalism. Both types of accommodation share similar themes that contribute to guest dissatisfaction, including noise complaints, value for money, and staff professionalism. Airbnb offers unique and personalised experiences, while hotels prioritise efficient and appropriate interactions between staff and guests. Identifying and prioritising factors influencing guest satisfaction and dissatisfaction is essential for remaining competitive in the hospitality sector. To sum up, our research contributes significantly to the literature on hospitality services, with methodological implications for future studies. © 2023 Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: Airbnb Hotel Satisfaction User-generated content Contextualised topic modelling Necessary condition analysis XGBoost the Andalusian region Introduction The sharing economy (or collaborative economy) is (a) rooted in the desire for sustainability, enjoyment and economic profits, and (b) grounded in co-creation through networks in online environments (Hamari et al., 2016;cf. also Allee, 2003; Prahalad and Ramaswamy, 2004;Reuschl et al., 2022). In particular, tourist areas have witnessed the explosive growth of the sharing economy, that here refers to creating value from underused assets -e.g., coordinating rent-outs for a short period (Huarng & Yu, 2019). However, despite its growing interest amongst scholars and managers, travellers engage with peer-to-peer accommodation rental services (from now on, P2P accommodation) for motives (or conditions) that are not yet completely clear (Lalicic & Weismayer, 2018;Sainaghi & Baggio, 2020). Furthermore, attention paid to the critical differences between P2P accommodation and hotels has been even scarcer (e.g., Gao et al., 2022;Mao & Lyu, 2017;S anchez-Franco & Alonso-Dos-Santos, 2021; Tussyadiah & Zach, 2015;Tussyadiah, 2016;Varma et al., 2016, amongst others). 1 Given its position as the world’s largest P2P accommodation service provider, our study initially focuses on Airbnb (an accommodation rental business and a trusted third party) (Winkler et al., 2020). Airbnb has gained increasing attention for its efficient marketing strategies for multiple segments and for improving financial performance by reducing search and payment transaction costs (e.g.,Dolnicar, 2021;S anchez-Franco & Rey-Moreno, 2022;Strømmen-Bakhtiar & Vinogradov, 2019). In addition, Airbnb has a near zero-marginal cost structure, thus representing a considerable advantage over traditional hotel chains (Strømmen-Bakhtiar & Vinogradov, 2019). As a result, Airbnb has become one of the most successful sharing economy models in the hospitality sector and the hottest trend for hospitality (Sainaghi & Baggio, 2020). Airbnb leads academics to analyse its potential threat to the traditional hotel sector or conclude that it creates new demand, providing opportunities for guests who * Corresponding author. E-mail addresses: [email protected] (M.J. S anchez-Franco), elena. [email protected] (M.E. Aramendia-Muneta). 1 The authors are grateful to the Junta de Andalucía for funding the research (Project I+D+i FEDER Andalucía 2014-2020, US-1380960). Likewise, the authors thank Federico Bianchi (Bocconi University) for his expertise and assistance throughout all aspects of Contextualised Topic Modelling and for his help in understanding its functionality. https://doi.org/10.1016/j.jik.2023.100380 2444-569X/© 2023 Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Journal of Innovation & Knowledge 8 (2023) 100380 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge otherwise would not have been able to travel. Nevertheless, previous research has provided controversial findings from a logic of sufficiency, frustrating hosts and hotel managers from forming a higher understanding of hospitality services to develop their competitive advantages and strategies. For instance, Strømmen-Bakhtiar and Vinogradov (2019, p.1) -analysing a mixture of small and large regionsconclude that hotels face no pressure to reduce prices in areas with high Airbnb activity. On the other hand, Heo et al. (2019) note that Airbnb and the hotel industry “are not in direct competition and that their relationship might be more complex”(p. 87). Furthermore, Benítez-Aurioles (2019, p.3) concluded that “the expansion of the peer-to-peer market for tourist accommodation has negatively affected hotel occupancy and economic returns in Barcelona, independently of the hotel category”. Mat e-S anchez-Val (2020, p.610) notes that “Airbnb’s private rooms and the concentration of the Airbnb market in fewer hosts are the main threats to traditional accommodation providers”. Travellers could regard Airbnb as an alternative (more or less perfect) to a hotel, e.g., mid (or low)-price hotels (Guttentag & Smith, 2017) or those not specialising in business guests (Zervas et al., 2021). Airbnb would also compete with the hotel domain regarding guest experience (Mody et al., 2017) and create “a more diversified experience than conventional hotel accommodation”(Wang & Nicolau, 2017, p.120). The human relationship is sometimes the primary shared asset (Dolnicar, 2019;Zach et al., 2020). In sum, previous studies on Airbnb show no conclusive evidence of its effects on the hotel industry. Therefore, with the new challenges that Airbnb represents for hotels, it is still necessary to compare both types of accommodation to set side by side and test the differences between Airbnb and traditional hotels. “Understanding customer preferences is important for Airbnb accommodation hosts and hotel managers because they compete with and complement each other” (Gao et al., 2022, p.119; see also Cheng & Jin, 2019). The higher the overlap (or similarity) of the preferences (or conditions) that promote satisfaction and the revisit intention of using Airbnb or hotels, the greater the potential threat of substitution. In this study, we aim to contribute to the existing body of literature on the hospitality industry by addressing the gap in knowledge regarding the vital features of Airbnb experiences and hotels as perceived by guests and how to improve guest (dis)satisfaction. While previous research has explored the relationship between hospitality features and guest satisfaction, they have not thoroughly examined it in the context of necessity and sufficiency. Our study aims to fill the gap in the literature by providing a comprehensive assessment of the relationship between the vital features of Airbnb experiences and hotels as perceived by guests and guest (dis)satisfaction. To achieve our research objectives, we employ advanced Natural Language Processing (NLP) techniques to analyse a large volume of natural and non-structured user-generated content (UGC) from Airbnb and hotels in the Andalusian market (Spain). By applying Contextualized Topic modelling (CTM), we can identify the main topics and semantic structures that affect guests’experiences. Our findings are supported by the work of S anchez-Franco and Alonso-Dos-Santos (2021) and S anchez-Franco and Rey-Moreno (2022), who have also used CTM to analyse UGC and explore consumer preferences. Our study employs Necessary Condition Analysis (NCA) to identify the essential prerequisites that must be met in order to achieve the desired outcome of guest satisfaction. This rigorous approach enables us to uncover the intricate interrelationships between key factors and the guest experience, effectively highlighting the conditions that must be fulfilled by managers to attain their desired objectives. Prior research conducted by Dul (2016) and Dul et al. (2010) also utilises NCA to analyse complex systems and isolate the necessary conditions for achieving the desired outcomes. In addition, our study employs the XGBoost algorithm for classification tasks and regression analysis to reveal which topics are sufficient conditions for customer (dis)satisfaction. This approach enables us to identify the topics most influential in shaping guest (dis)satisfaction, providing insights that can be used to improve marketing strategies and enhance the quality of lodging stays. Our findings align with previous research by Birinci et al. (2018), who have also used regression analysis to explore the relationship between hospitality features and customer satisfaction. Overall, our study contributes to the ongoing debate regarding the competition between Airbnb and hotels in the same domain. By providing a comprehensive assessment of the necessary and sufficient conditions for achieving guest satisfaction, our findings enable managers to improve their marketing strategies and overcome issues related to lodging stays. In addition, our study validates the effectiveness of advanced NLP analysis and expands the literature on hospitality accommodation features, providing a reliable and accurate assessment of consumer preferences. Theoretical framework Airbnb focuses on obtaining, giving, or sharing access to accommodations amongst individuals at competitive prices (S anchezFranco & Alonso-Dos-Santos, 2021). It is a business model where “customised content of a large variety of users is integrated into a network that creates value”(Reuschl et al., 2022, p. 101). Although Airbnb has become a low-cost option to earn additional income in a flexible manner (Guttentag, 2015;Guttentag et al., 2018;Liang et al., 2018), the growing sharing supply creates severe competition and forces (in conjunction with the reputational capital of the hosts; Ikkala & Lampinen, 2014)“to set the standards high and adjust the prices”(Lalicic & Weismayer, 2018, p.88). For instance, Airbnb provides a range of differentiated accommodations to experience wellbeing in (fashionable and warm) environments and satisfy guests’ curiosity and novelty seeking. As a result, Airbnb becomes a social option that allows exploring the destination authentically - allocating resources more efficiently. On the one hand, assuming that the essential services could be comparable to a hotel’s(Tussyadiah & Zach, 2015), research on Airbnb -from the demand sideidentifies “a multiplicity of relevant motives, including economic, sustainability-related, and social aspects”(Dann et al.,2019, p.450). &Although Cheng and Jin (2019) propose that Airbnb users do not care more about value, competitive prices (cf. Balck & Cracau, 2015) and physical space in accommodation (cf. Dolnicar, 2019) are traditional motives for selecting P2P accommodation instead of hotels (see also Quinby, 2014). As Strømmen-Bakhtiar and Vinogradov (2019, p.101) note, “Airbnb customers are different from traditional hotel guests, i.e., they [an extended family of over five people] possibly would not have visited the place had the prices not been suitable”. &Beyond economic benefits, social or physical distance from others (guests) could also be a relevant reason for selecting P2P accommodation, particularly entire apartments (Bresciani et al.,2021). &Mody et al. (2017) conclude that Airbnb outperforms hotels in communities and localness, e.g., staying in more residential neighbourhoods and offering real people and a home atmosphere (cf. Ert et al., 2016;Guttentag, 2015;Tussyadiah, 2015). Quattrone et al. (2016) also propose that P2P accommodation extends into residential areas and grows in areas with few or no hotels. Furthermore, Airbnb properties provide a more authentic interactive experience than hotels (Birinci et al., 2018) that could even lead to loyalty (cf. Grayson & Martinec, 2004, for their authenticity conceptualisation). Airbnb users would thus value the authentic settings around the accommodation. &In this regard, Guttentag et al. (2018) demonstrate that interaction and home benefits belong to the core of the Airbnb experience (i.e., M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 2 authenticity based on enjoyment, escape and novelty seeking, cf. Lalicic & Weismayer, 2018)−above the perceived risk. Although Airbnb guests might not get what they expect from amateur hosts −unpleasant personal treatment, a lack of cleanness, or the noise could create unsatisfactory assessments during the Airbnb stay (cf. Gao et al., 2022) - authenticity becomes an essential element of hospitality experiences and guest satisfaction. &Gao et al. (2022) conclude that the host is the second most common topic and the most critical feature that separates Airbnb from hotels. In addition, previous research proposes cultural exchange and social interaction with their hosts (Balck & Cracau, 2015;Guttentag, 2015;Wang & Nicolau, 2017). “There is [indeed] a(...) higher trust between guests and hosts”(Bresciani et al., 2021, p. 2). &The accommodation amenities in P2P are identified as one of the critical factors influencing Airbnb guests’satisfaction (Tussyadiah, 2016;Tussyadiah & Zach, 2015) and enhance guests’decisionmaking processes and service experience (S anchez-Franco & Alonso-Dos-Santos, 2021). Amenities attract guests who (a) prefer the feeling of being at home rather than staying at a conventional hotel, and (b) enjoy multiple experiences, finding cost-savings to be their main driver in conjunction with location and household amenities (cf. Guttentag et al., 2018; Paulauskaite et al., 2017). For instance, “males are focused on the importance of hygiene, quality or room amenities serving as surrogates for more comprehensive processing”(p.2497). Therefore, the issues associated with a sense of being at home are essential amongst Airbnb guests (cf. Ju et al., 2019). &Finally, Young et al. (2017) confirm that leisure travel, party size, and trip length could be moderating drivers in selecting P2P accommodation, evidencing the relevance of leisure customers. Airbnb is also preferred when travelling with friends (Poon & Huang, 2017). For instance, Airbnb facilitates everything from day trips to stays of several months, and in this sense, travellers tend to stay longer (Tussyadiah, 2016), except business travellers (Varma et al., 2016). On the other hand, although research comparing the determinants in hotels that create customer satisfaction and dissatisfaction is scarce, its main conclusions can be summarised as follows. Hotels are preferred for shorter stays and family trips (Poon & Huang, 2017). Guests emphasise drivers such as location and accessibility (e.g., walking distance from major attractions such as city centre, transportation hub, or beach), lower risks - through standardisation, regulations, and reputationand property features such as open escapes, higher safety and security, car parking or gym services. BenítezAurioles (2019, p.3) proposes that “hotels that cannot compete on price or location may have to differentiate their services to travellers to ensure their survival”. Hotels, therefore, cultivate a traditional delivery-focused paradigm that emphasises service quality and facilities beyond guest characteristics (e.g., age, nationality, gender, or trip purpose). For instance, amenities (such as Internet access, mini-bar, TV streaming services, hair dryers, and coffee makers) play an essential role in guests’satisfaction (Radojevic et al., 2015). As Yu et al. (2020, p.3168) note, “research indicates that hotel amenities play a significant role in guests’decision-making processes and service experience [...], including satisfaction”. Chu and Choi (2000) highlight the room, reception, and security. Kandampully and Suhartanto (2000) demonstrate a positive correlation between cleanliness, reception, food & beverage characteristics and price and customer satisfaction. In addition, hotels offer housekeeping services, guest loyalty programmes (related to money-saving) and facilities to make them less vulnerable to competition (cf., Festila & M€ uller, 2017;Young et al., 2017; see also Dann et al.,2019). As a result, hotel guests have quality expectations according to their ratings. Furthermore, hotels offer staff contact and facilitate indirect (and engaging) communication (vs social or physical distance) with other guests (Osman et al.,2019). Indeed, Parasuraman et al. (1988) highlight the quality of guests’interaction with employees. To sum up, previous research provides limited recommendations concerning the main relevant (necessary or sufficient) conditions in the context of the sharing economy vs the conventional hospitality industry. Despite this paradigm shift in the industry, as Mody et al. (2017, p.2379) concluded, “experience-related research remains underrepresented in the hospitality and tourism literature (Jiang et al., 2015;Ritchie et al., 2011)”. More recently, the gap remains. Sainaghi and Baggio (2020, p.2) note that scarce research explores the moderating effect of accommodation type on guests’perception, i.e., "the central question about the competitive threat generated by Airbnb toward hotels remains largely a black box". Research questions Our study analyses necessary features that allow an outcome to exist (Dul, 2016). Our first research question is: Are features related to hospitality services necessary (but not automatically sufficient) for the outcome (satisfaction or dissatisfaction) to exist? Second, based on the study of Tussyadiah and Zach (2015), our research addresses predicting a type of accommodation (classification approach). Our second research question is thus: What (identified) key accommodation characteristics provide the most accurate and best separation of the two classes of accommodation (i.e., Airbnb and hotels)? Third, once the best classifying features have been identified, our third research question focuses on predicting a quantity (regression approach). It seeks to compare its impact on guest satisfaction or dissatisfaction. As Gao et al. (2022, p.119) note, “existing studies only extract the topic proportions and top words of each document from Airbnb or hotel reviews, but fail to identify and analyse the associated emotions”. For all kinds of accommodation, our study thus proposes the following question: Are the characteristics (detected and) related to hospitality services sufficient for the model outcome (e.g., satisfaction or dissatisfaction) to be produced? See Fig. 1. Materials Data collection This study analyses data from the Sevilla and Malaga destinations in southern Spain. Both cities could be conceptualised as strongly leisure-orientated destinations. On the one hand, Airbnb data are obtained from the Inside Airbnb website http://insideairbnb.com/ (available on 25 September 2021). InsideAirbnb is a third-party open-access site containing Airbnb listings’count data and is not associated with or endorsed by Airbnb or its competitors. Our study preserves the amateur character of the host and selects only hosts with a single listing. Moreover, Airbnb listings are restricted to entire apartments to compare offers easily. The entire apartments represent 84−88% of the Airbnb offer in Seville and Malaga, respectively (AirDNA, 2022). In addition, Airbnb listings are considered outliers if their price and the number of beds fall outside the interval formed by the 5th and 95th percentiles. Our research removes listings with a price lower than 22 US dollars (not including cleaning fees or additional guests) and higher than 216 US dollars. Our study filters out all lodgings with more than six beds. On the other hand, for strictly academic purposes, our study extracts (from TripAdvisor.com) the anonymous reviews of hotels, with initially more than 1000 reviews per hotel available on 25 September 2021, similar to Airbnb apartments in price. TripAdvisor reviews widely serve as a data source for previous studies (Ding et al., 2022;Gao et al., 2022;Liu et al., 2020, amongst others). The number of full-service hotels analysed M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 3 here is 38 (13 and 25 hotels in Seville and Malaga, respectively). The reviews are obtained using a Python crawler. The final dataset contains 34,917 (English) reviews from 2017 until 25 September 2021, precisely 23,259 Airbnb reviews and 11,658 hotel reviews. As a result, the volume of sentences is slightly unbalanced between the two types of accommodation. Data cleansing process Our text mining approach is divided into the following steps: (a) retrieval of documents and conversion from markup languages into (unstructured) plain text; (b) text preprocessing, i.e., segmentation into sentences and text normalisation; and (c) text analysis and extraction of knowledge from the corpus by identifying topics. Then: &The text is split into logical constitutes (here, sentences) through the period (“.”), question mark (“?”), and exclamation mark (“!”) -implemented here with a customised set of rules (in Python language) that capture particular issues. &Our approach becomes sentences in ASCII. It removes non-alphabets and any other kind of characters which might not be a part of the language. In addition, it discards extra whitespaces. &It checks the spelling of sentences and fixes contractions and compound terms. &It eliminates a list of common stop words to filter out overly common terms (not contributing to information content). Stop words are terms that are used frequently. Filtering them enables a reduction of the search space without losing the semantic meaning. &Our study lemmatises the words so they can be analysed as a single item to reduce the variability of the language and avoid redundancy in the document vocabulary. Data mining Our study proposes identifying guests’latent semantic structures (or topics) by analysing a bulk set of UGC through CTM-based data mining processing. In particular, our research applies a topic model with contextualised BERT embeddings to enhance the quality of topics; that is,overall coherence and interpretation (CombinedTM, CTM 2.2.0 in Python; cf. Bianchi et al., 2021a). Likewise, CTM fosters higher competitive topic diversity than classical topic modelling (e.g., Latent Dirichlet Allocation). As Bianchi et al. (2021a, p.760; cf. Bianchi et al., 2021b) conclude, “topic models [could] benefit from latent contextual information, which is missing in Bag-of-Word (BoW) representations”. CTM thus allows obtaining contextualised representations of documents. In this sense, &The CTM here uses preprocessed sentences (derived from the cleansing process) for BoW. Our preprocessing reduces the variability of the language and avoids redundancy in the narrative vocabulary. &BoW represents the input in an inherently incoherent manner, however. Accordingly, CTM combines BoW with contextualised embeddings from the not-preprocessed (only lemmatised) corpus to significantly increase topic coherence and competitive topic diversity. Here, the sentence encoding model is the pre-trained ‘all-mpnet-base-v2’that maps sentences to a 768-dimensional dense vector space. &Assuming there is no single correct path to select an optimal number of topics, our study tries different topics (10, 20, 30, 40 and 50). Our analysis of the coherence metric and a handy number of topics finally sets 30 topics (100 epochs and 50 samples). In addition, it estimates the document-topic distribution matrix to which a logarithmic transformation is applied due to its significant right skewness. In short, the CTM is trained with narrative input representations that consider word order and contextual information and bypass one of the main limitations of BOW models (Bianchi et al., 2020). Results Extracting topics In Table 1, our study displays the most relevant terms per topic. The topics are numbered from 0 to 29. The extracted topics are easily interpretable and offer a coherent impression. Our study initially outlines the topics related to a general assessment of the service received. Topics 22 and 25 relate to guest dissatisfaction triggered by a negative service encounter that brings about complaints. That is to say, the service experience offered would not correspond with, for example, those reflected in the published photographs, consequently damaging the trust between the two sides involved. Therefore, service failures (and negative experiences) are determinants of the brand-hate users express through UGC. On the contrary, topics 11, 14 and 19 are associated with a positive overall experience (guests’satisfaction). Finally, topic 27 relates to behavioural intentions indicating whether guests remain in or defect from a hospitality firm. Second, our study describes the topics related to the stay’s features (or conditions) that could determine the levels of satisfaction or dissatisfaction. Below, our analysis summarises the semantic content of each topic: ˗Core services are associated with accommodation quality, i.e., topics 2 (accommodation size and type), 16 (functional space and decoration and cleanliness regarding core services), 21 (noise complaints derived from out-accommodation activities) and 28 (amenities such as water machine, refrigerator, drying machine, and bathrooms). Topic 20 also relates to feeling “at home”or Fig. 1. Steps for transforming free-form text into a structured form and main research questions. M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 4 hedonic comfort while staying in a hospitality accommodation. Finally, topic 7 refers to comfortable, branded accommodation with nice facilities. In other words, core services refer to the essential benefits that guests seek (e.g., a cosy place to live). ˗Relational drivers are inherent in hospitality services: responding to inquiries, keeping guests well informed, or simply making guests feel welcome. They are related to customer service performance (e.g., communication about directions, tips or advice, accommodation rules, Wi-Fi instructions, or check-in/out, amongst others), for instance, how staff deliver the service to make a hospitality experience satisfactory or dissatisfactory. Topics 9, 18, 24 and 26 focus on staff interactions (here, the term staff replaced the term host or the proper nouns used) and professionalism (e.g., friendliness and helpfulness, amongst others) associated with booking, checking guests in and out, or maintenance services. Topic 17 is related to additional services, e.g., seamless, effortless transaction and booking tasks. Relational topics are thus associated with a feeling of welcome or personal interaction with the staff, e.g., its reliability (proper performance), responsiveness (knowledge and courtesy) and empathy (ability to care). ˗Site-specific characteristics (convenience and access) are related to the location, i.e., easy accessibility to the hotel or other hotspots, attracting many visitors. Site-specific factors include distance to major attractions (topics 1 and 8) and pedestrianorientated infrastructure encouraging people to walk quietly and peacefully from accommodation to hotspots (topic 29). Additionally, topic 12 focuses on public transportation hubs (trains or buses) and accessibility to tourist destinations by providing, for instance, shuttles. Finally, topic 15 concerns restaurants and groceries (neighbourhood amenities) that offer more high-quality experiences. ˗Property characteristics are related to services such as a terrace overlooking the city that enhances customers’delight (topic 6), a swimming pool for sunbathing (topic 13), or incidences such as long waiting times in crowded car parks (topic 3) -also related to location. ˗Topics 4 and 23 relate to staying with friends or family and its duration. Likewise, topic 0 is associated with an experiential value proposition for the different guest profilesan evolving feeling of communion with family, friends, or other peopleduring hospitality experiences. Table 1 Topics represented by the top eight lemmatised terms from CTM - ordered from highest to lowest prevalence on the topic. experiential value proposition to the different guest profiles Topic 0 people enough one cool bite long little use closeness to the main attractions of the city; Topic 1 major site real alcazar plaza tourist short distance accommodation size, and type Topic 2 suite large huge big upgrade live double superior waiting times in crowded car parks Topic 3 park car find drive difficult rental underground garage staying with friends/family and its duration Topic 4 base family couple kid explore ideal perfect place value for money Topic 5 appoint maintain money value equip good stock furnish customers’delight Topic 6 top lovely roof view terrace excellent nice great comfortable, brand accommodation with lovely facilities Topic 7 luxury cosy accommodation lovely heart charm nice situate location related to old town Topic 8 historic part heart far explore situate old city staff recommendations, advice, and suggestions Topic 9 give tip information recommendation suggestion show advice useful high-quality food, breakfast, or variety Topic 10 choice buffet option breakfast selection fresh offer delicious guests’satisfaction (a) Topic 11 awesome brilliant outstanding accommodation fault incredible fabulous describe public transportation hubs Topic 12 stop train station far line bus ride railway swimming pool for sunbathing Topic 13 top terrace relax roof sun sit swim outdoor guests’satisfaction (b) Topic 14 accommodation stay much need just location staff great location associated with facilities in the neighbourhood Topic 15 tapa market store many supermarkets corner shop surround functional space, decoration and cleanliness regarding core services Topic 16 tidy bright sparkle stylish functional cosy new necessary reservations or waiting times or ease of placing a reservation Topic 17 hour last go wait reservation tell time flight hospitable and communicative staff Topic 18 hospitable communicative smile personnel attentive polite gracious courteous guests’satisfaction (c) Topic 19 beat great central excellent value ideal money location perceived trustworthiness of the accommodation’s photos Topic 20 home picture feel house charm photo decor detail noise complaints Topic 21 noise noisy hear busy light side traffic sound guests’dissatisfaction (a) Topic 22 nothing expect comment high attitude bad negative rate staying with friends/family and its duration Topic 23 week three wife husband weekend wish four long staff professionalism -associated with easy check-in/out Topic 24 desk pleasant efficient reception professional member polite attentive guests’dissatisfaction (b) Topic 25 wrong advertise complaint post believe wow club avoid efficient and appropriate interactions between staff and guests. Topic 26 quick fast communication communicate hide clear check-out question behavioural intentions Topic 27 hope return hesitate future love forward doubt fall in-accommodation amenities Topic 28 water. machine facility towel tea coffee dry fridge pedestrian-orientated infrastructure Topic 29 visit hospitality walkway number blvd peace aside, tranquil 5 M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 ˗Topic 5 relates to value for money, i.e., allocating resources more efficiently. Value for money could be considered a relevant attribute for attracting guests (Nash et al.,2006), related to features such as comfort and stylish design. ˗Our analysis extracts topic 10 related to high-quality food, breakfast, or variety. However, as Airbnb rental apartments do not usually provide breakfast, our study does not consider this topic in subsequent approaches. Findings Analysing relationships in terms of necessity Our analysis employs the transformed sentence-topic distribution matrix (averaged by narrative) and focuses on necessary topics or critical conditions of (dis)satisfaction. Our study applies NCA (NCA 3.1.1 package in R, Dul, 2021) that identifies critical levels of these predictors (conditions) that must necessarily be present to achieve the desired result (Arenius et al., 2017;Dul, 2016). In other words, “performance will not improve by increasing the values of other determinants unless the bottleneck is taken away first (e.g.,by increasing the value of the critical determinant)”(Dul, 2016, p. 15). First, our analysis applies CR-FDH less sensitive to outliers. CRFDH is the default technique for parametric data. Second, our research analyses the potential presence of a significant empty zone in the upper left-hand corner that would illustrate the constraint of the necessary condition. See Fig. 2. This means the necessity of X (or high level of X) for the existence of Y (or high level), i.e., there should be no narratives (or at least very few) with a low proportion of topic i and a high output value. Third, our study calculates the size of the necessity effect (d) or the relative size of the empty zone, i.e., to what extent the condition is necessary for the outcome. dranges from 0 to 1. Our analysis considers only the conditions that are practically (here, d≥0.1) and statistically significant (p-value ≤0.01) and, as a result, rejects the effect size being the result of random chance (cf. Dul, 2016 and Dul et al.,2010, amongst others). Fourth, our study sets c-accuracy ≥0.99, i.e., the percentage of observations on or below the ceiling. Finally, to be even tighter in determining the preconditions of necessity, the topics here need to be observed at no less than 30% (Bottleneck column in Table 2.) to reach an appropriate outcome level in practical settings (50%). The top critical conditions of Airbnb dissatisfaction (in comparison to hotels) are the following: &Topic 17 relates to additional services, such as reservations, waiting times, or the ease of making a reservation. &Topic 9 is associated with staff recommendations, advice, and suggestions to enjoy life as a local or share local tips to give guests a unique experience. &Topic 1 relates to proximity to the main heritage attractions. &Topic 15 is composed of terms on facilities in the neighbourhood, for instance, restaurants and grocery markets that offer more high-quality experiences. &Topic 24 is associated with staff interactions and professionalism, such as easy check-in/out. In particular, to reach a 50% level of mention of dissatisfaction, topic 17 should occur at no less than 34.6%, topic 9 at no less than 30%, topic 1 at no less than 31.2%, topic 15 at no less than 35.6%, and topic 24 at no less than 31%. See the column’Bottlenecks-50%’in Table 2. Fig. 2. Upper-left-corner. Source: Dul (2021). 6 M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 On the other hand, only three preconditions are revealed (topics 23, 20 and 26) as necessary conditions for the dissatisfaction of hotel guests during their stays. Topics 23 and 26 relate to the overall (positive or negative) experience with the family and trip length (topic 23) for both types of accommodation and, in addition, replies and scheduled messages to keep in touch with guests while staying in a hospitality accommodation (topic 26). Moreover, topic 20 relates to the perceived trustworthiness of the accommodation’s photos. Without the 32.3% level of topic 20, there is no possibility of reaching the 50% level of output value (Dul, 2016). Hotels should thus provide an accurate description to shape guests’expectations. Moreover, the main necessary conditions for satisfaction coincide in both types of accommodation, although the order of importance differs. For Airbnb guests, the top 3 topics are 17, 9 and 24. Regarding hotel guests, the top 3 topics are 23, 15 and 17. See also Table 2. Analysing relationships in terms of sufficiency One essential contribution of this section is to answer the following research question: What key accommodation characteristics provide the most accurate and best separation of the two classes of data (i.e., Airbnb accommodations and hotels)? To answer this question, our analysis applies Extreme Gradient Boosting decision trees (from now on, XGBoost) on the transformed document-topic distribution matrix (averaged by narrative). XGBoost is an exciting version of the gradient-boost decision tree model for classification and regression issues (Chen & Guestrin, 2016). It includes a series of optimisations and regularisation techniques that help reduce overfitting. To improve the interpretability of the XGBoost model, our analysis also combines XGBoost with SHAP values (SHAPley Additive exPlanations) for plotting models’insights (cf. Lundberg & Lee, 2016). SHAP values are model-agnostic, present local accuracy, missingness, and consistency properties, and do not provide causality. Classification model The topics’probability distributions per narrative (obtained from CTM), except satisfactionand dissatisfaction-based topics, and topics 27 and 10 are here used as input features of the classification model. Our research also splits the data set into two subsets to ensure the effectiveness of our approach (Climent et al., 2019): &80% of the data (train dataset) allows us to train the XGBoost model to find the best combination of parameters. Our research performs 10-fold cross-validation. &The remaining 20% (validation dataset) validates the best-fitted model’s performance and ensures that the model is generalisable. The grid search of the scikit toolkit is used to select the parameters to achieve the best area under the curve (AUC) classification metric. The XGBoost classification model -or XGBClassifier- (after parameter tuning, i.e., colsample_bytree: 0.5, eta: 0.5, gamma: 0.2, max_depth: 4, min_child_weight: 9, n_estimators: 600, subsample: 0.4) obtains a prediction accuracy of 90% on the testing dataset. The ROC curves for the XGBClassifier are presented in Fig. 3. The ideal point is the upper left corner of the plot; that is, false positives are zero, and true positives are one. Our analysis also estimates the Matthews correlation coefficient (0.77 >0.60). This coefficient generates a high score only if models can predict a high percentage of true positives and negatives. Once the tuned XGBoost model is fitted, the next step is to identify the importance of the features. In Fig. 4a the x-axis represents the average of the absolute SHAP value of each feature (i.e., magnitude) estimated from the testing data set. In Fig. 4b, our analysis plots the magnitude and directionality for Airbnb (class: 0) versus hotels (class: 1) to jointly visualise the features’importance and impact on the prediction. Fig. 4b shows how much each feature (or topic) contributes Table 2 Main NCA parameters (topics ordered by accommodation, outcome, and effect size). Topics Ceiling zone Effect size c-accuracy p-accuracy p-value Slope Outcome ineff. Bottlenecks-50% Dissatisfaction −Airbnb Topic 17 19.605 0.302 99.892 0.000 0.000 1.806 0.000 34.600 Topic 9 17.556 0.256 99.849 0.000 0.000 1.556 3.544 30.000 Topic 26 16.513 0.255 99.953 0.000 0.000 1.333 13.434 32.400 Topic 1 12.078 0.217 99.148 0.000 0.000 1.492 21.662 31.200 Topic 15 11.441 0.191 99.884 0.000 0.000 1.472 24.274 35.600 Topic 23 10.270 0.188 99.875 0.000 0.000 2.396 8.474 40.800 Topic 24 9.057 0.165 99.871 0.000 0.000 1.822 25.037 31.000 Dissatisfaction −Hotel Topic 23 25.858 0.347 99.151 0.000 0.000 1.267 0.000 34.700 Topic 20 23.214 0.323 99.494 0.000 0.000 1.371 0.000 32.300 Topic 26 21.750 0.310 99.605 0.000 0.000 1.207 6.273 30.800 Satisfaction −Airbnb Topic 17 9.773 0.198 99.901 0.000 0.000 0.711 36.047 34.600 Topic 9 10.148 0.195 99.974 0.000 0.000 0.717 34.551 30.000 Topic 24 7.562 0.181 99.966 0.000 0.000 1.194 27.078 31.000 Topic 1 7.306 0.172 99.961 0.000 0.000 1.542 18.565 31.200 Topic 26 7.375 0.150 99.983 0.001 0.001 0.739 43.347 32.400 Topic 15 6.312 0.138 99.983 0.000 0.000 0.893 42.378 35.600 Topic 23 4.239 0.102 99.970 0.001 0.005 0.715 57.769 40.800 Satisfaction −Hotel Topic 23 18.849 0.408 99.485 0.000 0.000 0.840 0.000 40.800 Topic 15 15.733 0.356 99.880 0.000 0.000 0.876 0.000 35.600 Topic 17 15.169 0.346 99.846 0.000 0.000 0.883 0.000 34.600 Topic 26 14.115 0.324 99.880 0.000 0.000 0.837 0.000 32.400 Topic 1 12.698 0.315 99.168 0.000 0.000 0.742 9.430 31.200 Topic 24 12.140 0.310 99.786 0.000 0.000 0.966 0.000 31.000 Topic 9 12.760 0.300 99.880 0.000 0.000 0.835 3.705 30.000 Main metrics: The ceiling zone is the empty space above the ceiling. The size of the ceiling zone represents the effect size compared to the size of the entire area that can have observations. c-accuracy is the percentage of observations that are on or below the ceiling. Finally, outcome inefficiency is the percentage of the range of the outcome where the condition is not necessary for the outcome. In other words, the outcome is not constrained by the condition. 7 M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 to the target variable (or accommodation type). Topics with high SHAP values (see x-axis) push thus towards class 1 (Hotels), while low SHAP values (see x-axis) push towards class 0 (Airbnb). In the following, our study presents the main results by accommodation type. Airbnb According to Airbnb stays the main features that predict the Airbnb narrative are the following. &Topic 26 focuses on replies and scheduled messages to keep in touch with guests. &Topic 4 is associated with staying with family, e.g., family as an essential component of relationships and interactions in hospitality staying. &Topic 16 relates to being tidy, bright, cosy, or functional. Airbnb travellers frequently mention the cleanliness of the accommodation or its excellent design. &Topic 9 relates to staff help, sharing local treats, and giving a unique experience. Hotel The main features that show a positive impact (x-axis) on the model output -that are most likely to be mentioned in hotel narrativesare the following topics: Topic 2 refers to accommodation size. Its long-tail trend reaches to the right (and not to the left), thus being highly predictive for some dataset instances but not for others. Fig. 3. ROC curves for XGBClassifier. Fig. 4. The importance of topics. 8 M.J. S anchez-Franco and M.E. Aramendia-Muneta Journal of Innovation & Knowledge 8 (2023) 100380 efficient technologies, modernising the design and decor of the rooms, or upgrading the fitness or conference centre. &Offer competitive pricing strategies that balance cost savings with high-quality service and personalised experiences. For example, on the one hand, offer discounted rates for more extended stays or repeat guests, or provide a referral programme that rewards guests for referring new customers. On the other hand, loyalty programmes can be provided that reward frequent guests with unique perks, such as room upgrades, late check-out, or free meals. &Ensure precise and responsive communication with guests throughout their stay to foster a welcoming environment and establish a strong relationship. On the one hand, respond promptly to guest inquiries or concerns via email, phone, or messaging platforms. Provide clear and detailed instructions for check-in and check-out, and offer a 24/7 support line for emergencies. On the other hand, provide guests with a concierge service that can help them with transportation, dining reservations, or entertainment options. Also, provide regular updates on hotel events or promotions via email or social media, and offer a dedicated hotline for customer service. By implementing these recommendations, Airbnb and hotels can attract more guests and differentiate themselves from competitors. Additionally, promoting high-quality service and personalised experiences can help to enhance guest satisfaction and revisit intention, which can lead to positive reviews and an increased reputation. By identifying key differences and features that impact guest (dis)satisfaction, the present study provides valuable insights into the potential substitution or complementarity between Airbnb and hotels in the tourist accommodation domain. Conclusion Our study contributes to the literature on hospitality services by providing insights into the factors influencing guest satisfaction and dissatisfaction in Airbnb and hotels. Our research also highlights the importance of distinguishing between statements of need and satisfaction and using novel techniques to interpret the semantic structures hidden in the data. By applying these approaches, researchers can better understand hospitality services and inform the design of more effective policies and practices in the hospitality industry. Overall, our study has revealed similarities and differences in the necessary conditions for producing (dis)satisfactory guest experiences between Airbnb and hotels. Although both accommodation types share common themes of guest dissatisfaction, such as noise complaints, value for money, and staff professionalism, they differ in prioritising essential topics. For example, guests of Airbnb emphasise the importance of proximity to tourist attractions and staff recommendations, whereas hotel guests prioritise facilities and staff professionalism. Furthermore, while Airbnb offers unique and personalised experiences, hotels prioritise efficient and appropriate interactions between staff and guests. Our findings have significant implications for hospitality providers seeking to enhance guest experiences and improve guest satisfaction and loyalty by identifying and prioritising the factors influencing guest (dis)satisfaction. Our study’sfindings can help hospitality managers design effective policies and strategies to enhance guest experiences and improve guest satisfaction and loyalty. Therefore, our study highlights the importance of offering guests a balance between affordability and personalised experiences to increase satisfaction and suggests that hotels may need to incorporate social and interpersonal aspects into their services to remain competitive. Our research thus has significant implications for hospitality professionals seeking to attract and retain guests in a dynamic and diverse industry. By focusing on high-quality and professional staff, offering personalised experiences and promoting social and environmental responsibility, hotels can compete with P2P accommodation platforms and offer guests a more holistic and satisfying experience. Finally, guest (dis)satisfaction is subjective and can vary significantly amongst guests, making it difficult to identify universal factors influencing (dis)satisfaction. Additionally, the rise of the sharing economy has led to concerns about the impact of short-term rentals on residential communities, and the legality of such rentals in some regions has become a contentious issue. Moreover, while P2P accommodation platforms like Airbnb may offer unique and personalised experiences for guests, there is a lack of standardisation and regulation compared to traditional hotels, which could lead to potential risks for guests. Therefore, it is crucial to ensure that guests are wellinformed and aware of any potential risks when choosing Airbnb accommodations. Data availability statement Not applicable. Declaration of Competing Interest Not applicable. References AirDND (2022, 10 January). MarketMinder. https://www.airdna.co/ Arenius, P., Engel, Y., & Klyver, K. (2017). No particular action needed? A necessary condition analysis of gestation activities and firm emergence. Journal of Business Venturing Insights, 8,87–92. doi:10.1016/j.jbvi.2017.07.004. 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