Addicted to cruises? Key drivers of cruise ship loyalty behavior through an e-WOM approach

Addicted to cruises? Key drivers of cruise ship loyalty behavior through an e-WOM approach José Ignacio Castillo-Manzano,Mercedes Castro-Nuño and Rafael Pozo-Barajas Applied Economic and Management Research Group, Universidad de Sevilla, Seville, Spain Abstract Purpose –This paper aims to identify the drivers that explain loyalty behavior in cruise tourism with the aim of achieving a better understanding of repeat cruisers’intentions to sail on the same ship or on another ship belonging to the same cruise line or cruise corporation. Design/methodology/approach –Based on over 150,000 online reviews about their satisfaction and experience posted by cruisers using so-called electronic Word of Mouth (e-WOM), the authors apply both a graphic and an econometric technique through input-output circular plots and discrete choice models. Findings –The main results show that cruisers’behavior is influenced by multiple onboard attributes, such as the service crew, entertainment options, type of cabin, some characteristics of the ship (age, capacity) and the cruise line (Premium-Luxury versus mainstream), and, specifically, the quality and variety of the gastronomic experience. Practical implications –The results highlight that repeats cruisers are predominantly linked to a cruise company or a cruise corporation rather than a particular ship. This result provides information on the moderators that can influence the customers’repetition behavior, which might be useful for planning revenue management and extending knowledge on hospitality loyalty in general and in the cruise industry in particular, specifically under the current uncertainty due to the COVID-19 pandemic. Originality/value –The previous literature has essentially examined revisit intentions in the cruise market from a qualitative approach and the authors have found no study to date that has simultaneously addressed this issue in three dimensions, namely, ship, cruise line and cruise corporation. The research fills this gap by determining the reasons why passengers would repeat a cruise either on the same ship, with the same cruise line or the same cruise corporation based on previous experience. Keywords Hospitality industry, Tourist loyalty, Online customer reviews, e-WOM, Repeat tourists, Cruiser satisfaction Paper type Research paper 1. Introduction For two decades before the COVID-19 crisis, the cruise segment was one of the fastestgrowing hospitality industry activities in the world, with more than 30 million passengers in 2019 according to CLIA (2020). By said year, the leisure cruise business had become a regular vacation choice and one of the most dynamic and lucrative sectors in the tourism industry (Hung et al., 2019). However, everything changed in 2020, the year of the pandemic, when some of the most serious negative impacts of SARS-CoV-2 (COVID-19 pandemic) on tourism affected cruise ships (Castaldo et al.,2021). The example of the Diamond Princess showed that cruise vessels are a perfect habitat for the virus to spread, with thousands of passengers living together in a confined space where innumerable social activities take place throughout the day (Castillo-Manzano et al.,2015,2018), i.e. the nemesis of social distancing. Key drivers of cruise ship loyalty behavior 361 Received 23 May 2021 Revised 12 August 2021 7 October 2021 Accepted 18 October 2021 International Journal of Contemporary Hospitality Management Vol. 34 No. 1, 2022 pp. 361-381 © Emerald Publishing Limited 0959-6119 DOI 10.1108/IJCHM-05-2021-0642 The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/0959-6119.htm In addition, cruise vessels were also seen to be the optimum vehicle for transmitting the virus from one country to another, in this case, from China to Japan. Dozens of incidents on cruise ships occurred during the first quarter of 2020. To be precise, by the end of April 2020, the Washington Post had detected infections on 55 vessels and at least 65 deaths (www.washingtonpost.com/graphics/2020/politics/cruise-ships- coronavirus/). This, in turn, led to the collapse of cruise companies on the stock market, with falls in March of around 70%–80% from their January 2020 highs according to Klynveld Peat Marwick Goerdeler (https://home.kpmg/xx/en/blogs/home/posts/2020/07/covid-19- impacts-on-global-cruise-industry.html). In other words, the financial markets doubted the survival of the cruise business model. However, in the opinion of scholars such as Sun et al. (2018), the cruise sector had already entered the maturity stage before the COVID-19 crisis as the emphasis was on cruise companies to not only attract new travelers but also to use a variety of marketing strategies to encourage existing customers to make a repeat purchase. One of these strategies is loyalty programs, which reward consumers for repeat business with a supplier/group of suppliers or by a brand offering discounts for points obtained with purchases (Petrick et al.,2006; Vega-Muñoz et al., 2020). There is a strong rivalry between companies to retain cruisers and several cruise loyalty programs can be found that segment customers based on third-degree price discrimination (Namin et al.,2020). Programs of this type offer the opportunity to join loyalty clubs with different tiers (i.e. Silver, Gold, Platinum, Diamond [...]) associated with different membership benefits e.g. suite upgrades, exclusive dining options, butler services, and additional overnight stays (Ahn and Back, 2019) depending on, for example, the number of cruises and types of staterooms booked. Cruise corporations have various tactical strategies to achieve customer repetition. These include acknowledging customers who reach the maximum number of points possible on one of a corporation’s cruise lines by automatically extending the benefits to other lines in the group. Such is the case of Royal Caribbean Cruises Ltd., for example, as stated on their website www.celebritycruises.com/captains-club/exclusive-offers/loyalty-reciprocity- program. Many cruise lines also have a “next-cruise onboard sales program”. Programs of this type offer even first-time passengers perks, such as exclusive onboard savings and/or extras, credits and discounts [1] on their next cruise, providing that they make the reservation on the same line before their current cruise ends. It would, therefore, seem that cruise companies are likely to depend more and more on their most loyal repeat customers to progressively revive their business activity after the current pandemic. This justifies the pertinence of studies such as the present paper, which, as Figure 1 shows, addresses the factors that determine passengers’decisions to repeat purchase a cruise. The starting point that we take for this is cruise customers’satisfaction with some specific determinants of their cruise experience, as expressed in online reviews. We adopt the theoretical approach followed by previous scholars, such as Hosany and Witham (2010) and Li and Kwortnik (2017), which is based on the idea that travel determinants are internal/external stimuli that influence memorable experiences and satisfaction and trigger behavioral intentions to repeat cruise bookings and give recommendations as “emotional responses”. Considering this argument, the purpose of our paper is to understand the cruise loyalty phenomenon. We try to fill the gaps and overcome the limitations of the prior research, which has been predominantly based on qualitative data obtained from small samples. In a novel approach, our study investigates the factors that drive cruise loyalty at all levels to explain why passengers, based on their previous experience, repeat a cruise either on the same ship or another ship belonging to the same cruise line or, at the very least, on a ship of IJCHM 34,1 362 a different line that belongs to the same cruise corporation. From a practical point of view, we derive some implications about repeat cruiser behavior to advance research in this area and offer interesting insights for revenue management, operations and marketing in an industry that is currently striving to recover from the aftereffects of the COVID-19 pandemic. For the first time in hospitality management, in general, and cruise studies, in particular, we explore cruisers’online evaluations of their experiences to create a graphic model for the visualization of interactive data, complemented by discrete choice models. We construct a database of over 150,000 trip reviews posted on the “Cruiseline.com”platform. This specific cruise-travel online review website is used to capture cruisers’value perception and satisfaction for repeat purchase intention (following Mohd-Any et al., 2015). In general terms, online customer reviews based on the so-called electronic Word of Mouth (e-WOM) phenomenon are now widely accepted by the hospitality industry and the literature as a predictor of loyalty (Sun et al.,2021;Tao and Kim, 2019;Zhang et al.,2015). In the case of the cruise industry, previous authors such as Papathanassis (2017) have highlighted the considerable potential that online customer reviews offer for empirical cruise research. The opportunities offered by online data mining in this field have been examined by scholars, such as Park et al. (2016),Penco et al. (2019) and Tiago et al. (2018), who analyzed data on cruise experiences on social media networks, such as Facebook and Twitter; Bahja et al. (2019),Chen et al. (2020) and Zhang et al. (2015), who conducted studies to analyze the use of e-platforms and websites to express passenger satisfaction, and Buzova et al. (2020) and Gonzales Santiago and Correia (2019), who examined the use of blogs. Considering all the above, this paper is structured as follows: after this Introduction, Section 2 presents a literature review of cruiser behavior and cruise tourism loyalty. Section 3 describes the empirical context of the research and the methodology. Section 4 presents our Results. Section 5 offers the discussion and conclusions. Finally, a list of References is given. 2. Literature review The relationship between quality perception, satisfaction and prediction of behavioral intentions has been explored from both a cognitive and an attitudinal perspective by some scholars in tourism, in general (Ali et al., 2016;Yolal et al., 2017), and the field of cruises, in particular (Calza et al.,2020;Chiou et al.,2021;Chua et al.,2017;Forgas-Coll et al.,2014;Tao and Kim, 2019). The predominant conclusion is that the behavioral intentions that lead to loyalty are explained by perceived quality (an evaluation of the attributes of the service) and Figure 1. Research framework Source: Prepared by authors Key drivers of cruise ship loyalty behavior 363 satisfaction. In the specific case of a cruise, passengers judge the quality of the physical environment and services provided according to their subjective perceptions of food, accommodation, entertainment, onboard facilities and staff attention. If their satisfaction is positive, they will possibly become repeat customers (Wang et al., 2018). A small number of empirical studies in the hospitality and tourism literature (Petrick, 2004a;Su et al.,2020) also consider the study of first-time and repeat tourists. Previous analyzes suggest behavioral antecedents that can serve as a crucial source for repeat travel decision-making (Hultman et al., 2015;Suhartanto et al.,2020), including motivation and preferences (Lim et al., 2016); quality and perceived value (Dedeo glu, 2019), and satisfaction with previous experiences (Eid, 2015). At the same time, loyalty is an emergent field in the tourism academic literature. Most studies focused on the conduct of repeat tourists are based on the analysis of land-based destinations and activities (Chi, 2012). There is also a large body of loyalty research related to certain topics as follows: destinations (Camp on-Cerro et al.,2017); casinos (Shi et al.,2014); transportation issues, such as infrastructure (Bezerra and Gomes, 2019) and modes such as airlines (Koklic et al., 2017); hotels and accommodation (Koo et al.,2020;Mao and Lyu, 2017;Xie and Chen, 2014) as all-inclusive resorts (Yolal et al., 2017); restaurants (Hyun, 2010), and travel agents and tour-operators (Albayrak et al.,2020). The cruise product, which is this paper’s object of study, is complex and differs from other travel and hospitality market segments. Despite the relevance of determining the differences between first-time and repeat tourists, only a limited number of studies have been conducted into the different behaviors of these two types in the cruise industry and the way that previous experiences influence cruise customers’behavior (Li and Petrick, 2008; Sun et al.,2018). The previous studies on cruise passenger retention highlight the importance of this issue as possibly one of the major challenges for cruise companies (Han and Hyun, 2019) and conclude that motivations, values, image, desire and relationship investment play a key role in generating repeat cruising intentions (Hwang and Han, 2014). From a methodological point of view, several studies have demonstrated that repeat cruisers usually display higher levels of satisfaction than new cruisers, as well as a strong brand loyalty (see, for example, Chua et al., 2017;Papathanassis, 2017). And yet, despite the economic relevance of cruise passenger retention, the earlier, limited investigation based on surveys, qualitative methodologies (Jones, 2011;Li and Petrick, 2008;Petrick, 2004b,2004c, 2005;Petrick and Sirakaya, 2003;Wang et al., 2018) and exploratory techniques (Sun et al., 2018) has only addressed the differences between new and repeat cruisers considering individual cruise lines and small samples. The conclusion was that loyal cruisers are influenced by different personal attributes, such as age, information sources, cruise characteristics, price sensitivities and motivations. Motivations to repeat may often include some hedonic and social values related to onboard cultural activities, the appeal of destinations and itineraries, the ship’s level of luxury and the opportunity to meet new people (Hosany and Witham, 2010). Also, more specifically, Chua et al. (2017),Forgas-Coll et al. (2014) and Shahijan et al. (2018) agree that the relevance of perceived variables such as quality of service (e.g. ship facilities, staff efficiency and professionalism, personalized customer service) raise the levels of satisfaction and, consequently, strengthen cruisers’ revisit intentions. Repeat purchase intentions also seem to be influenced by the perceived quality-price ratio (Chua et al.,2015;Matzler et al.,2019;Petrick, 2005). As a result, the promotion efforts of major cruise line operators, such as Disney, Norwegian, Carnival and Celebrity include the offer of member loyalty programs (Han and Hyun, 2019). Taking into account all the above, we study the key factors and drivers of cruise loyalty at all levels (ship, line, corporation) according to cruisers’previous experiences and subjective value perception. We consider cruisers in different years and at different locations IJCHM 34,1 364 and destinations to increase the generalizability of the findings and the knowledge on tourism loyalty. As our theoretical foundation, we take the idea that cruise passengers’ loyalty to cruise vessels, lines and corporations is based on their memorable experiences of internal/external factors that could influence their satisfaction, repeat purchase intention and e-WOM recommendations (Figure 1). 3. Data and method Our database has been constructed from information on the “Cruiseline.com”cruise online review portal. Although other review portals exist, “Cruiseline.com”was selected for its high number of reviews (over 150,000), among other things. Data were taken from all available reviews from the platform’s launch in 2006 to September 2018. The “Cruiseline.com” platform also enabled us to construct the maximum number of variables justified by the previous literature, which, in turn, allowed us to maximize the number of hypotheses that we could test. For example, it was the only portal that enabled us to construct the variable Group_size. As is logical, a database cannot be constructed by merging online reviews from different websites as a cruiser could use different nicknames on each, which would result in duplicates. We compiled and extracted the data from a total of 150,183 reviews posted by 135,400 different authors and covering trips from July 1987 to September 2018, along with another 1,283 undated reviews. Once the database had been constructed, an exhaustive procedure was carried out to identify errors, omissions and duplicates. This kept the loss of original reviews down to under 1% during the extraction process. Authorswereidentified by their usernames registered on the website. As the objective was to study repeat cruiser behavior, we only retained the 24,746 reviews corresponding to travelers with multiple reviews. Further reviews were discarded from this total when it was impossible to identify the order in which the cruises had been taken as they had been uploaded to “Cruiseline.com”on the same date. This left 22,019 reviews listed by author and sailing date. Subsequently, R software (RCoreTeam, 2018) was used to create a table to link authors’first reviews to their following reviews. All authors’last reviews were discarded (as there was no indication that they had any upcoming trips pending), leaving a final total of 13,011 reviews. Figure 2 presents the information extracted from our database and shows repeat cruiser flows between the different corporations. This is the first time that this graphic model has been applied to cruise hospitality management, following the methodology proposed by Abel and Sander (2014). An interactive version can also be accessed at https://aem.us.es/Graphics/CruisePassengersFlows/, which enables an intuitive analysis of the flows between the cruise lines represented by the lines in the graphic. Clicking on a sector shows the ships that belong to the cruise line in question, enabling an analysis of the flow of passengers from each specific ship to the same or a different line. Figure 2 shows that a significant percentage of passengers repeat on the same cruise line, which justifies our subsequent more detailed analysis of the factors that explain this. In the figure, each external arc of the circumference represents a cruise line with over 200 sailing logs. These 8 lines represent 92.05% of logs, i.e. with 11,976 movements between ships, 206 vessels of origin and 212 of destination. The curved lines that connect one sector with another represent the number of cruisers who decide to take their next cruise on a ship from another cruise line (for example, changing from a Norwegian Cruise Line ship to a Royal Caribbean Cruises Ltd. ship). The end of the line closest to the cruise line name marks the origin of the flow. So, the lines that begin and end in the same sector are passengers who Key drivers of cruise ship loyalty behavior 365 repeat on the same vessel or a different vessel belonging to the same cruise line (for example, the first cruise aboard the Norwegian Jade and the second on the Norwegian Gem). The size of each sector is determined by the number of repeat passengers who use each of the cruise lines. The size of the Celebrity Cruise flows in Figure 3 cannot be explained by the number of the cruise line’s vessels of destination alone, as their size is not proportional to said number. For example, 20% and 10% of passengers who had previously sailed with Celebrity switched to Norwegian and Princess, respectively, despite Princess having 13.33% more ships than Norwegian (note that the number of ships can be seen by clicking on the corporations’arcs in the interactive diagrams). Multiple examples of this can be found at https://aem.us.es/Graphics/CruisePassengersFlows/. This is further justification for the need to develop our complimentary econometric analysis below. Figure 2. Flows of travelers between cruise lines Source: Taken from https://aem.us.es/Graphics/CruisePassengersFlows/. Prepared by authors IJCHM 34,1 366 In Figure 4, the flows between Carnival vessels can be observed to be the same as the total number of flows to vessels of other cruise lines, which indicates a strong loyalty to this corporation irrespective of the Carnival vessel considered. Note that the effect can be seen in the interactive diagram by hovering the cursor over the arc corresponding to the ship. The size and variety of the flows that link any Carnival ship to another ship of the same line seem to indicate that passengers recognize different Carnival ships as near substitutes for any future cruises that they might take. In other words, Carnival’s ships are homogenized and highly standardized passenger products and the line’s name alone is sufficient to guarantee cruisers a similar experience on any of the line’s ships. The above graphic analysis is complemented by a set of estimates derived from a binary discrete demand model, which enables an analysis of the specific factors that explain why cruise passengers decide to take their next cruise on the same ship, with the same cruise line Figure 3. Flows of travelers between Celebrity Cruises and other cruise lines Source: Taken from https://aem.us.es/Graphics/CruisePassengersFlows/. Prepared by authors Key drivers of cruise ship loyalty behavior 367 or the same cruise corporation. Binary logit models have been preferred to probit models as they maximize the verosimilarity pseudo logarithm and the exogenous variables joint significance Wald Test. Equation (1) shows the conditional probability that a cruiser i might take a repeat cruise on the same ship, line or corporation in both binary outcome models as follows: PiPr yi¼1jx ðÞ ¼Fx 0 i b  ¼ex0 i b 1þex0 i b ðÞ logit ¼ ð x0 b 1 f z ðÞ dzprobit (1) where: y is the binary observed variable to explain (i.e. the cruiser’s choice to repeat or not on the same ship, cruise line, cruise corporation); x i is a vector of independent variables and b is a vector of coefficients to estimate. Figure 4. Flows of travelers from Carnival Cruise Line vessels Source: Taken from https://aem.us.es/Graphics/CruisePassengersFlows/. Prepared by authors IJCHM 34,1 368 Table 1 presents the definitions of the variables used and the descriptive statistics. It shows three endogenous loyalty variables and three different groups of exogenous variables based on the prior literature that could be motivations for the cruising decision. These the following three categories of covariates are: (1) Factors related to ship characteristics: the size of the vessel and its launch year (e.g. some cruisers might prefer new ships with fewer customers, as in Chua et al.,2017; Whyte et al.,2018;Xie et al.,2012;Zhang et al.,2015); the presence and/or quality of Table 1. Variables used in the analysis and descriptive statistics Variable Sum Mean P50 SD 1. Endogenous variables RepShip: 1 if the passenger repeats on the same ship on his/her next trip, 0 otherwise 1,419 0.109 0 0.312 RepLine: 1 if the passenger repeats with the same cruise company on his/her next trip, 0 otherwise 6,909 0.531 1 0.499 RepCorp: 1 if the passenger repeats on a cruise company belonging to the same cruise corporation as on his/her previous trip, 0 otherwise 7,983 0.614 1 0.487 2. Explanatory variables 2.1 Ship characteristics Capacity: Maximum number of passengers allowed on the ship –2,880.295 2,758 963.520 CrewPerPass: Number of crew members/Number of cruise passengers –2.475 2.485 0.310 Premium: 1 if the company can be classified in the Premium or Luxury segment, 0 otherwise 2,964 0.003 0 0.053 Age: Age of the vessel at the time of the trip, in years –10.032 9 6.380 2.2 Trip and cruise passenger characteristics CabinType: Type of cabin: 1 (Inside), 2 (Oceanview), 3 (Balcony), 4 (Suite) –2.418 3 0.979 Date_cruise: Months since initial voyage to time of data mining, December 2018 –54.316 42.115 39.794 Group_size: Composition of the cruise passenger’s travel group: 1 (Singles/Friends), 2 (Couple), 3 (Family with older children), 4 (Family with young children), 5 (Large Group) –2.124 2 0.638 Wish_for_improvement:Cruiseline.com valuation of second ship minus valuation of first ship –0.021 0 0.503 Caribbean: 1 if the first trip was in the Caribbean, 0 otherwise 7,954 0.611 1 0.487 Mediterranean: 1 if the first trip was in the European Mediterranean or Portugal, 0 otherwise 446 0.000 0 0.021 Europe: 1 if the first trip was in non-Mediterranean western Europe, 0 otherwise 329 0.000 0 0.018 Social_networking: Author’s contribution level to the website according to score given by Cruiseline, from 1 to 9 –3.086 3 1.681 Cruiser_experience: Cruiser’s trip experience: 1 (1 cruise), 2 (2–3 cruises), 3 (4–6 cruises), 4 (7þcruises) –3.269 4 0.968 2.3 Valuation of the experience, from 0 (min.) to 5 (max.) Entertainment_value: Onboard entertainment, such as music and comedians –4.019 5 1.298 Cabin_value: Cabin –4.283 5 1.259 Ship_quality: General opinion of the ship –4.359 5 1.115 Service&Staff_value: Crew members and service –4.502 5 1.064 Ports of call_value: Average opinion of ports visited –4.055 4.250 0.933 Food&Dining_value: Food and restaurants –4.149 5 1.254 Key drivers of cruise ship loyalty behavior 369 the same cruise corporation. These programs could be based on, e.g. the application of specific perks and benefits to loyalty club members with no distinction between the lines in the same cruise corporation (as stated in the Introduction section). In addition to prevent the group size-related fall in loyalty indicated by our results, cruise lines could explore measures such as allowing passengers who are members of loyalty programs to invite their traveling companions in other staterooms to attend the Loyalty Club receptions that only they are eligible for. Moreover, given the clear positive correlation found between repeat behavior and the most active cruisers on the internet, it seems evident that an incentive exists for cruise lines to make their websites more interactive, as many of the large hotel chains have already done. They could include e-WOM evaluations of their vessels, for example, either given by their own customers or imported from specialized portals such as “Cruiseline.com”. Considering the importance of the onboard gastronomic experience for cruiser retention that our results reveal, loyalty programs could be diversified and the perks could be more closely linked to food quality and variety than other onboard activities such as premium shows. This result would validate the sound strategy developed by some cruise lines to enhance their culinary excellence by, for example hiring illustrious chefs as consultants for their menus, especially in the case of Premium or Luxury cruise lines that have already signed up internationally renowned chefs, such as Oceania’s culinary personality Jacques Pépin and Princess’master chocolatier Norman Love (Castillo-Manzano et al., 2018). 5.4 Limitations and future research One of the main strengths of this research, the size of the database, also determines the research limitations, as in the case of other studies that handle broad databases with such high numbers of data. The hypotheses tested in this paper are to a large extent limited to the variables that can be constructed from the database extracted from the website used, in this case, “Cruiseline. com”. In contrast, traditional fieldwork based on direct interviews with cruisers, in which the researchers can develop their own ad hoc questionnaires, offer greater flexibility and enable the inclusion of other variables that we were not able to include in this case, such as the cruiser’s ageorgender.However,thehigheconomiccostofconstructingafieldwork-based database equivalent to the database used in this paper means that this cannot be considered a viable alternative. Notwithstanding, we have endeavored to overcome these limitations in this paper by creating some explanatory variables that could not be explicitly found on “Cruiseline.com”, such as those relating to Ship Characteristics (Table 1, Point 2.1), i.e. capacity and age of the vessel, crew to passenger ratio and premium category. Concerning future research lines, the first, for tourism in general, is related to the database. Despite the perhaps undeniable advantages in terms of costs and benefits of working with immense databases from online reviews (as is shown by the proliferation of such works in the hospitality field using portals, such as TripAdvisor.com and Booking. com; see e.g. Filieri et al.,2020;Mariani and Borghi, 2021), their limitations with respect to defining variables will continue to justify the need for fieldwork with small samples and longer questionnaires, the results of which could serve as a robustness check for findings. Another second, complementary future research line concerning cruise research specifically could be the use of the same e-WOM database to analyze how the ongoing complicated sailing restrictions imposed by the COVID-19 pandemic, which often turn a cruise vessel into a “big floating social bubble”, are influencing general satisfaction and cruisers’loyalty behavior. 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