Identifying Hotel Revenue Management Implementation Drivers

33 IJISEBC, 7(2), 2020 International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) Identifying hotel revenue management implementation drivers Identificación de los impulsores de la implementación de la gestión de ingresos del hotel domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com recibido: 30-10-2020 / revisado: 05-12-2020 aceptado: 05-12-2020 / publicado: 15-12-2020 aBStraCt. this research means to globally analyse which variables drive the implementation of revenue management and the specific variables related with its application that have been boosted. we selected the size of the establishment, belonging to a hotel chain, the implementation of revenue management and the use of specific revenue management software as driving forces for its application, as well as possible relations between them. the model verified the repercussion of these aspects on 13 variables related with demand and pricing and 13 with capacity management linked to its implementation. variance-based Structural Equation modelling (plS-SEm) has been used to both build and test the model. we carried out measurements at two moments with a 10- year difference. revenue management has recently been more advanced and sophisticated. Belonging to a chain and the implementation of a revenue management system have been identified as driving forces which positively and significantly influence revenue management application. rESumEn. Con esta investigación se pretende analizar de forma global qué variables impulsan la implementación de revenue management y las variables concretas que han sido impulsadas por estas. Se seleccionan la dimensión del establecimiento, la pertenencia a cadena, la implantación de revenue management y la utilización de un software de revenue management como fuerzas impulsoras de su aplicación, así como la relación entre estas. El modelo confirma la influencia de estos aspectos para 13 de las variables relacionadas con la gestión de demanda y precios, y 13 relacionadas con la gestión de la capacidad, todas ellas vinculadas con su implantación. Se utiliza un modelo de ecuaciones estructurales (plS-SEm) tanto para la construcción como para la prueba del modelo. la medición de variables se realiza en dos momentos diferentes con una diferencia de 10 años entre la primera y la segunda medición. los resultados confirman que revenue management ha avanzado y es más sofisticado en la actualidad. la pertenencia a cadena y la implantación de un sistema de revenue management (software) se identifican como fuerzas impulsoras que ejercen una influencia positiva y significativa sobre la aplicación de revenue management. KEywordS: revenue management, Hotel implementation, model, drivers, partial least squares. palaBraS ClavE: revenue management, Implantación en hoteles, modelo, Impulsores, mínimos cuadrados parciales. miguel a. domingo-Carrillo1, rosario gonzález-rodríguez1, Esther Chávez-miranda1 1university of Seville, Spain [email protected] , [email protected] , [email protected] 34 IJISEBC, 7(2), 2020 1. Introduction revenue management has its origin in the airline industry more than sixty years ago (anderson & Xie, 2010, p. 53). It is the basis of the booking systems which currently operate and that enable the increase of revenues via the optimisation of joint management of the perishable resources (inventory) of the firm (for example, hotel rooms per night, places of a training centre per day or event), the demand and the prices. given the benefits and advantages which its implementation yields, it has been extended to numerous sectors. plenty of success cases have been noted, such as american airlines (driver of its development), British airways, marriott International, Hilton, Holiday Inns, Sheraton, disney, upS, fedEx and ford motor Company, among others (Cross, Higbie, & Cross, 2010). the innovative character of this management philosophy has been highlighted since its beginnings and currently by numerous authors. Belobaba & wilson (1997) underscore the novelty of revenue management to determine the inventory of seats (referring to airlines), by allowing an increase of future revenues via establishing limits about advance booking. other authors coincide in asserting that the revising of classical pricing management concepts has been required and that their optimisation means innovation at both the strategic and the tactical level (Cross, Higbie, & Cross, 2010; lieberman, 2010; post and Spann, 2012; pekgün, menich, achatya, finch, dschamps, mallery, Sistine, Christianson, & fuller, 2013). despite pricing or other elements of revenue management standing out, it also provides major upgrades in the use of perishable resources through its capacity management. Its implementation can minimise the sum of oversales and spoilage (Kasilingam, 1996), enables obtaining productivity improvements (Elimam & dodin, 2001; Kimes & thompson, 2004), a higher utilisation of perishable inventories (Elimam & dodin, 2001) and improved capacity/scheduling linkage (pinder, 2005). for this reason, we can therefore state that revenue management has signified important advances, mainly in the administering of the processes geared to capacity management, on the one hand, and demand and pricing management, on the other hand. these have driven the economic progress of the firms in which it has been implemented. However, in previous research it was not proposed to go deeply into the factors that can boost and influence this process or to identify the specific variables that drive the most relevant changes stemming from its implementation. this research’s main aim is to identify the variables which stand out when revenue management is adopted by hotels. to do so, we set out from the identification of the variables that can affect the implementation of revenue management, and those which enable us to measure and analyse demand-pricing and capacity management. In order to identify the variables in which this philosophy is reflected, we practise a measurement of them at two moments in time with a difference of 10 years between the first and second series of data. to study the global relation between variables, we specify and test a model for each study case. the comparison of the two models allows us to both analyse the evolution in the revenue management implementation and to know the variables which have driven a greater change and, therefore, to identify the driving forces. 2. literature review the revenue management implementation is influenced by different factors. the study of previous works has allowed us to identify the dimension of the hotel (number of rooms), belonging to a hotel chain, confirmation of its implementation and application of a revenue management system as the main ones. these are the ones which we will consider as drivers of its application. on the other hand, so that the improvement in process management provided by the revenue management implementation can be noted, we turn to works centred on its management in the hotel sector and which carry out a discussion about the data and policies which its application means. to complete this latter analysis, the variables are grouped into two dimensions to distinguish the aspects related with the demand and pricing management and those with capacity management. the selection of the variables included in the model are now justified. domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com 35 IJISEBC, 7(2), 2020 different behaviours have traditionally been detected depending on the dimension of the hotel and its belonging to hotel chains. Jarvis et al. (1998) identify the number of rooms and the ownership regime as variables which influence the revenue management application in hotel establishments. also, aware of this reality, diverse publications, in the light of an empirical study, analyse what happens in the business reality, differentiating between small and medium-sized hotels (European Commission, 1997; luciani, 1999), due to their belonging to hotel chains (Bradley & Ingold, 1993; weatherford & Kimes, 2003; rohlfs & Kimes, 2007; noone & Hultberg, 2011; wang, 2012; Chen & Xie, 2013), and even analyzing the influence of various of these factors at the same time (Chávez-miranda, 2005; talón, 2010). the first works which tackle implementation of revenue management in hotels date from the end of the 1980s (orkin, 1988; relihan, 1989; Kimes, 1989a, 1989b). In later articles the knowledge of this aspect is broadened. Examples of this are the formulation of the seven steps to attain success with revenue management by Jones and Hamilton (1992), the study of the key variables which justify it (Jarvis et al., 1998), the proposal of a model which describes the implementation process (Jones, 1999) and the four steps for its implementation (Huyton & thomas, 2000). these works have allowed us to select some of the variables in which revenue management application is reflected, although in those investigations, unlike in this research, the possible influence on its application is not verified. In later studies in Spain (Chávez-miranda, 2005; talón, gonzález, & figueroa, 2014), the partial influence of some of these dimensions is considered but the individual or joint interaction among them is not proposed, nor is the identification of factors which contribute to process innovation related with revenue management application. on the other hand, the complexity and variety of decisions to be adopted has determined the need to count on revenue management Systems, as various authors have recognised since its origins (Kimes, 1989a and 1989b; Jones & Hamilton, 1992). In this sense, numerous authors have acknowledged that the availability of a revenue management System enables obtaining additional improvements to those achieved with the revenue management implementation, such as increases in the revenues or profits of 2-5% along with the possibility of obtaining competitive advantages (Belobaba & wilson, 1997; Kimes & wagner, 2001; Emeksiz, gursoy, & Icoz, 2006). In more recent research, the advantages of using software for the application of revenue management continue being highlighted (aziz, Saleh, rasmy, & ElShishiny, 2011; Bayoumi, Saleh, atiya, & aziz, 2013; talón et al., 2014; domingo-Carrillo, Chávez-miranda, & Escobar-pérez., 2016). with respect to the identification of grouped variables, such as management of demand-pricing and capacity, given that the first field work was developed in 2005, we set out from the classic works dedicated to the implementation of revenue management (Jomes & Hamilton, 1992; Jarvis, lindh, & Jones, 1998; Jones, 1999; European Commission, 1997; luciani, 1999 and upchurch, Ellis, & Seo. 2002 y 2003). nevertheless, numerous references have been consulted which corroborate the relevance and the suitability of the variables used. table 1 and table 2 gather the works of the different authors who have tackled the topic, classified based on the variables used in this study. domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com 36 IJISEBC, 7(2), 2020 3. methods for the data gathering interviews were carried out in 40 hotels in 2005 (year 1) and 37 hotels in 2015 (year 2) in the city of Seville and its province, all 4 and 5-stars, both belonging to chains and independent. the destination is among the first 5 tourist points in Spain according to diverse sources (Instituto nacional de Estadística, 2013). Information was compiled of 4 variables considered to be influential, 13 of demand and pricing management, and 13 of capacity management. the selection of the variables included in the model are justified by their use in previous works, as has been described in the previous section. as well as those domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com table 1. references in which the variables of demand and pricing used in the study are identified. Source: Self-made. table 2. references in which the variables of capacity used in the study are identified. Source: Self-made. 37 IJISEBC, 7(2), 2020 incorporated in previous studies, in year 1 the questionnaire was validated by experts which meant incorporating additional variables, in which the chain Sol meliá took part. we have used the version 24 of the IBm SpSS Statistics packet for the data of descriptive statistics and tests of normality and SmartplS 3.2. for the model proposal, the evaluation of the measurement model and the evaluation of the structural model. to measure the variables of demand-pricing and capacity management a 5- point likert scale has been used (1 = totally disagree; 5 = totally agree), including the option (does not know/does not answer). table 3 shows the main descriptive values that in the model are considered as influential in the application of revenue management by the establishment for years 1 and 2. the results show a slight increase in the number of rooms and the number of hotels belonging to a chain from year 1 to 2. the most recent values indicate that, as stated by the respondents, all the establishments have implemented revenue management. this means a significant advance with respect to the previous values and enables us to answer the proposal of our research. a growth is also noted in relation with the use of software specifically assigned to the administering of revenue management in the hotel. table 4 brings together the minimum, maximum and mean values of different items which allow measuring the revenue management application in its facet of demand and pricing and at the two moments in time analysed (variables d1 to d13 in the table). In general, a positive evolution of the values obtained is noted from the first year (global average of 4.6) to the second (average of 4.8), being over 4 in this latter measure. on the other hand, after having carried out the tests of normality of these variables, it is confirmed that in all the cases the variables do not behave normally. domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com table 3. descriptors of the unidimensional variables of the models. Source: Self-made. table 4. descriptors of the variables of demand and pricing management. Source: Self-made. 38 IJISEBC, 7(2), 2020 table 5 shows the summary of some descriptors in relation with the items of capacity management (variables C1 to C13 in the table). In this case, a greater variability in the results is noted, so it is to be expected that there will have been a greater advance in the aspects related with demand-pricing than in those of capacity. therefore, although in general terms the variables show an increase of the mean values over time (3.7 in year 1 and 3.9 in year 2), and unlike the aspects of demand, not all the mean scores are over 4. It is also noted that four variables show lower current values. as with the previous case, the tests of normality show lower current values. the same as in the previous case, the tests of normality indicate that the variables are not normal. to verify the hypotheses, we have used Structured Equation modelling (SEm) for each year of the study. these models are part of the second-generation statistical techniques which enable researchers, especially in social sciences, to incorporate variables which are not directly observable, although they are indirectly measured through indicators (Hair, Hult ringle and Sarstedt, 2014). In our research we have decided to use a variance-based structural equation modelling technique, partial least Squares SEm (plS-SEm), mainly because there is little knowledge a priori of the model’s causal relations. on the other hand, it has been observed that the variables do not have a normal distribution. moreover, the sample size is small for the two years of the study – year 1 (n=40) and year 2 (n=37). for the first model, the size required for a verification power of 0.8, alpha=0.5 and four predictor variables (green, 1991) would be n=599, n=84 and n=39 to capture small, medium and large effects, respectively, of the predictor variables concerning the endogenous constructs. for year 2 and given that the structure equations model has 3 predictor variables, the size needed would be n=547, n=76 and n=35 to capture small, medium and large effects, respectively, of the predictor variables concerning the endogenous constructs. the sample size for years 1 (n=40) and 2 (n=37) are adapted to the minimum size required to detect only those relations which are sufficiently important. therefore, these characteristics of the research justify the use of variancebased structure equation modelling. 4. results and discussion Having estimated the structural equations models, we evaluate the quality of the results obtained via: (1) the evaluation of the measurement model for year 1 and year 2 and (2) the evaluation of the structural model for each of these years. domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com table 5. descriptors of the variables of capacity management. Source: Self-made. 39 IJISEBC, 7(2), 2020 4.1. Evaluation of the measurement models for the evaluation of the measurement model, year 1 and year 2 have been specified as reflective measurement models, given that the indicators are manifestations of the construct considered (demand-pricing and capacity). that is to say, the application of revenue management is reflected in the different values which the variables present grouped into the dimensions of demand-pricing and capacity. for the evaluation of the reflective measurement models (demand-pricing and capacity), we analyse the individual reliability of the items, the composite reliability (internal consistency), and the average variance Extracted (avE) to evaluate the convergent validity. furthermore, we use the fornell-larcker criterion and the Htmt coefficient (Heterotrait-monotrait ratio of Correlations) to measure the discriminant validity of the factorial cross-loadings (Henser, ringle and Sarstedt, 2015). the results obtained for each test and in both models are shown in table 6 and table 7. the variables which do not appear in table 6 have been removed because, according to the values obtained, they do not enable an explanation of the behaviour of the demand-pricing and capacity, due to very similar answers being obtained from the respondents. In table 6 it is observed that the reflective constructs for the models of year 1 and year 2 have an internal consistency with a composite reliability over 0.7. the demand construct in both models has a convergent validity with an avE over 0.5. the capacity construct in both models has an avE close to 0.5, hence fulfilling the requirements. to evaluate the discriminant validity, we have used the Htmt criterion (Henser, ringle and Sarstedt, 2015). the results obtained are shown in table 7. given that the Htmt criterion is less than 0.90 for the constructs demand and capacity in the two years considered, it can be said that there is discriminant validity. when applying the sample procedure (bootstrapping n=5000 subsamples) to verify the hypotheses (H0: Htmt≥1), the confidence interval obtained for each of the years does not contain the value 1. this indicates that there is discriminant validity between the two constructs. domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com table 6. measurement model. Internal consistency and convergent validity. Source: Self-made. table 7. discriminant validity according to the Htmt criterion. Source: Self-made. It stands out from the evaluation of the measurement model in both years that the reflective items which allow the measuring of the constructs demand-pricing and capacity vary in the two years considered (figure 1 and figure 2). this leads us to think that in a period of 10 years there has been an evolution in the applicability of revenue management which has affected the definition of the constructs demand-pricing and capacity. regarding demand and pricing variables, it is observed that in year 2 two indicators of year 1 are maintained, application of different prices to the different segments (d2) and repeated guest’s data is available (d13). additional indicators are also included in year 2 indicating that there is a greater knowledge of the demand and pricing: discounts by segments has been established (d7), application of constraints to apply promotions (d8), pricing policies depend on seasons (d9), pricing policies depend on events (d10) and historical data is available (d11). these results highlight that the hotels work with a greater number of segments and apply more rates, therefore a greater complexity and breadth in the application of revenue management in the hotel is appreciated. on the other hand, a greater change in the measurement of the variable capacity is observed. In year 2 the variables denials data is available. number of requests not accepted for the hotels due to constraints applied (C6), walk-in’s data is available (C10) and overstays data is available (C12), which were reflected in the year 1 model, disappear. this fact means that in the more recent periods they are not reflected in the aspects of capacity management. In year 2 only the indicators fully booked data is available (C7) and application of overbooking policies (C9) were kept. and new indicators were included with respect to year 1, no shows data is available (C2), application of constraints (C4), application of upselling policies (C5), cancellations data is available (C8) and booking evolution data is available (C13). there has therefore been a greater change in the knowledge of the capacity in the ten-year period. as happened with regards to the values of the demandpricing, the results indicate that the revenue management is more advanced in year 2, without doubt stemming from an evolution of the information systems used, as to carry out these operations the prior registering of the data is required, which usually depends on the performance of the software, be it a property management System and/or tools or specific revenue management software. 4.2. Evaluation of the structural models Having evaluated the measurement model, the evaluation of the structural model is assessed. this step entails analysing the significance of the relations existing in the model. for this investigation we deal with: (i) analysis of collinearity (table 8). to detect problems of multicollinearity the tolerance index and the variance inflation factor (vIf) are used. It is considered that levels of tolerance under 0.20 (vIf >5) in the predictive variables indicate collinearity; (ii) the significance of the parameters using the bootstrapping technique (table 9); (iii) the coefficient of determination (r2) which, associated with each dependent variable, represents the proportion of variance explained by the set of predictive variables; (iv) the f2 statistic, which enables us to analyse the effect that omitting a relevant exogenous construct has on the variance explained of an endogenous construct. In general, f2 values of 0.002, 0.15 and 0.35 represent small, medium and large effects, respectively, of the exogenous variable on the endogenous variable (Cohen, 1998) (table 10); (v) and the measurement of the model’s goodness of fit, Srmr (root mean Square residual). this has recently been proposed as an indicator of goodness of fit in plS-SEm in the work of Henseler et al. (2014). a value equal to or less than 0.08 is recommended, although there is not yet a consensus in about this threshold in the literature. 40 IJISEBC, 7(2), 2020 domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com table 8. Evaluation of collinearity. Source: Self-made. as is noted in table 8, the variance inflation factors (vIf) for each endogenous construct are less than 5, so the models for year 1 and year 2 do not present problems of multicollinearity between the exogenous variables of each endogenous variable. there not being collinearity in the models thus enables the estimating of the coefficient paths and the analysing of their significance. let us recall that in the structural model of year 2 the causal relations which involved the Implementation of revenue management variable were removed, as in this year all of the hotels surveyed had implemented revenue management. the causal relations between year 1 and year 2 indicate that the knowledge of demand-pricing and capacity in a period of ten years has varied. table 9 enables the observing of which causal relations were significant for each of the years considered in the study. for year 1 it is noted that the implementation of revenue management has a direct and very significant influence on the implementation of specific revenue management software. on the other hand, it is seen that the size of the hotel has a positive and significant influence on capacity. for year 2, a positive and significant influence of the implementation of software and capacity, and a positive influence of belonging to a chain is observed, which is very significant for capacity and significant for demand-pricing. as the sample size in the years considered is very small and only allows the capturing of those path relations that are quite important, it is interesting to observe the relative contributions of the predictive variables to the endogenous variables, as is gathered in the following figures (figure 1 and figure 2). relations with a thick line represent the greater relative contributions of the predictive valuables to the endogenous construct. 41 IJISEBC, 7(2), 2020 domingo-Carrillo, m. a.; gonzález-rodríguez, r.; Chávez-miranda, E. (2020). Identifying hotel revenue management implementation drivers. International Journal of Information Systems and Software Engineering for Big Companies (IJISEBC) , 7(2), 33-48. www.ijisebc.com table 9. Structural Estimation model. Source: Self-made. figure 1. model estimated for year 1. Source: Self-made. talluri, K. t.; van ryzin, g. (2005). the theory and practice of revenue management. ny, uSa: Springer-verlag. talón, p. (2010). revenue yield management en los hoteles de madrid: análisis empírico de su aplicación y resultados. unpublished phd thesis, universidad rey Juan Carlos. madrid. Spain. talón, p.; gonzález, l.; Segovia, m. (2012). yield revenue management en el sector hotelero: Estrategias e implantación. delta, publicaciones universitarias. 1ª Edición. madrid. talon, p.; gonzález, l.; figueroa, C. (2014). a model for evaluating revenue management implementation (mErmI) in the hotel industry. 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