Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 Financial Implications of Competitive Pricing Strategies: Evidence from the Jordanian Hotel Industry Firas J. Al-Shakhsheer1, Ma’moun A. Habiballah2, Mukhles M. Al-Ababneh3 & Samer M. Al-Sabi4 Abstract The present study aimed to investigate the differing effects of competitive pricing strategies on the performance of hotels operating in bad regional political conditions. To this end, two complementary analyses were carried out based on a three-year dataset (2014-2016) collected through a questionnaire from 120 star-rated hotels in Jordan. The results of the one-way multivariate analysis of variance revealed statistically significant differences in the key performance indicators between hotels adopting a discount pricing strategy and those implementing a premium pricing strategy. The results showed that hotels adopting a discount pricing strategy enjoy a relatively higher market penetration index, but lag behind their competitors in terms of revenue generation index. On the contrary, hotels implementing a premium pricing strategy have a relatively lower market penetration index, but outperform their competitors in terms of revenue generation index. The results of the detailed descriptive analysis further demonstrated that although lower-priced hotels compared to their competitors have relatively higher occupancy rates, they experience relatively lower RevPARs. By contrast, hotels with higher prices compared to their competitors lose some occupancy, but achieve relatively higher RevPARs. Overall, the results suggested that adopting a premium pricing strategy, as opposed to a discount pricing strategy, is the most convenient way for improving hotel long-term financial performance, especially when hotel demand is relatively inelastic due to poor regional political conditions. Key words: Competitive pricing, Discount pricing, Premium pricing, Hotel pricing strategies. Available online www.bmdynamics.com ISSN: 2047-7031 INTRODUCTION Demand for hotel rooms shrunk remarkably during the past six years in Jordan. This was due to the regional political turmoil that accompanied the Arab Spring revolutions and the resulting increase in the fears of tourists wishing to visit the Middle East. The issue was further complicated by the unplanned expansion of more hotel construction due to the liberalization of local markets and the subsequent entry of many international hotels into the Jordanian hotel market. Between 2010 and 2016, the number of available rooms raised from 8.717 to 10.278 million rooms, achieving an average annual growth rate of nearly 2.8%, an increase of 244,000 rooms annually. By contrast, the number of occupied rooms steadily dropped from 3.222 to 2.635 million, recording an average annual decline in hotel demand of 2.63%, a decrease of 84,740 rooms per year (based on our analysis of statistics issued by the Jordanian Ministry of Tourism and Antiquities in 2017). The imbalance in supply and demand led to increased competition among hotels, which negatively affected hotel occupancy and room rates. This raised many questions about the competitive pricing strategies of Jordanian hotels and how they affect the financial performance of these hotels in the long term. Identifying the financial impacts of competitive pricing strategies is critical to ensuring long-term success of hotels, especially when the operating environment of hotels is economically and politically unfavorable. Nevertheless, empirical research on this issue is still limited in the context of Middle East hotels and almost non-existent for Jordanian hotels. With the exception of a few studies on Asian hotels * Al-Hussein Bin Talal University, Petra College for Tourism and Archaeology, Department of Hotel and Tourism Services Management. 1 E-mail: alshakhsheer.f.j@gmail.com 2 E-mail: mamounh@yahoo.com 3 E-mail: mukhles.ababneh@gmail.com 4 E-mail: sa.alsabi@gmail.com ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 (e.g. Canina and Enz, 2008; Chen, 2010), most previous empirical studies on this subject focused exclusively on U.S. and European hotels (e.g. Enz et al., 2004 & 2009; Enz and Canina, 2010; Noone et al., 2013; Enz et al., 2015). Therefore, the present study aimed to expand and validate previous empirical research by examining the competitive pricing strategies of Jordanian hotels during the period 2014-2016, using the one-way multivariate analysis of variance supported by a detailed descriptive analysis. Specifically, the study sought to: 1) examine the differences in the market penetration index vs. the revenue generation index between hotels applying a discount pricing strategy and those implementing a premium pricing strategy; 2) explore the impacts of discount pricing vs. premium pricing on the hotel’s relative occupancy and RevPAR; 3) identify the most appropriate competitive pricing strategy to improve the hotel’s financial performance in the long run. It is hoped that the results of this study will provide a better understanding of the various competitive pricing strategies and their differing implications for hotel long-term performance. LITERATURE REVIEW Competitive pricing strategies are closely related to the issue of strategic price positioning- that is, where a hotel positions itself in the long run compared to its direct competitors - above or below or on par with the competition (Noone et al., 2013). The present study is primarily concerned with the financial implications of discount pricing strategy (ie, pricing below the competition) versus premium pricing strategy (ie, pricing above the competition) in the hotel industry. It focuses specifically on the differing impacts of these two strategies on the hotel’s key performance indicators, including relative occupancy, relative RevPAR, market penetration index and revenue generation index. Previous empirical evidence suggests that relative occupancy and RevPar are influenced differently by the competitive pricing strategies used, depending on the degree of demand elasticity. Although adopting discount pricing strategy enables hotels to obtain relatively higher occupancy rates than those of their competitors, it does not allow them to achieve relatively higher RevPARs (e.g. Enz et al., 2004 & 2009; Canina and Enz, 2008; Chen, 2010; Enz and Canina, 2010; Noone et al., 2013; Enz et al., 2015). The demand for hotel rooms is relatively inelastic (Canina and Carvell, 2005); meaning that discounting room rates may not stimulate sufficient demand to permit higher revenues (Enz et al., 2009). For instance, Canina and Enz (2008) examined pricing strategies for competitive hotels in 14 different Asian and Pacific countries over the period 2001-2006. The authors found that hotels that offer lower prices than competitors do not experience significant increases in their relative occupancies. On the contrary, they witness marked declines in their relative RevPARs. Specifically, they found that Asian hotels that cut their average room rates by 10% to 15% compared to the competition gained occupancies 3.35% above those of their competitors, but recorded annual RevPARs 9.08% below those of competitors. Likewise, Enz et al. (2009) analyzed the dynamic relationship among the average room rate, occupancy and RevPAR in the U.S. hotel industry during the period 2001-2007. They found, for example in 2003, that hotels that reduced their average room rates by 20 to 30% compared to the competition recorded annual RevPARs 12% below those of competitors, even though they gained occupancies 15.54% above those of competitors. Applying and modifying Nash’s decision rule (1975), Van der Rest and Harris (2008) identified conditions that could make room rate discounting more profitable for hotels. To be financially beneficial, discounting should be accompanied by increased demand elasticity and/or reduced variable costs. Assuming that hotel demand is relatively inelastic, this implies that hotels should not use the price discount unless they are able to cut their variable costs (Van der Rest and Harris, 2008). Using a longitudinal case study (1999–2007), Murphy et al. (2013) found that discounting room rates below their premium rates during low demand seasons does not result in a significant increase in in-house restaurant sales. Based on an econometric case study analysis, Croes and Semrad (2012) demonstrated that discounting works both in the short run and the long run only if the discount rate is serially correlated. The authors concluded that discounting room rates below their premium rates can be an effective shortterm strategy to compensate for periods of disequilibria. This strategy can be regarded as a rational business response in a monopolistic competitive market. Semrad (2016) further asserted that discounting ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 room rates below their premium rates can have a long-term positive effect on hotel financial performance. However, she cautioned that the use of short-term discounting as a long-term pricing strategy may not yield similar results. On the other hand, most empirical evidence suggests that hotels achieve relatively higher RevPARs by adopting a premium pricing strategy. This is supported by the findings of Enz et al. (2004), which revealed based on a descriptive analysis of US hotels between 2001 and 2003 that pricing above the competition allows for increased relative RevPAR. Although doing so would cause hotels to lose some of their relative occupancies, it would ultimately improve their relative RevPARs. Similarly, Canina and Enz (2008) found that premium pricing helps Asian-Pacific hotels to improve their relative RevPARs, without causing significant losses in their relative occupancies. Enz et al. (2009) also provided additional descriptive evidence to support the use of premium pricing in the U.S. hotel industry. Their results showed that although U.S. hotels that offer higher average daily rates than those of their competitors have relatively lower occupancy rates, they achieve relatively higher RevPARs. Similar results were reached by a study conducted by Enz and Canina (2010) in the context of European hotels, with some variation in the findings concerning Spanish and Italian hotels. Using a case study design and descriptive analysis, Chen (2010) found that premium pricing strategy is a viable option to improve the hotel’s RevPAR performance, thanks to the inelastic demand for hotel rooms in Singapore. Nevertheless, Chen (2010) cautioned that there is a price limit to a premium pricing, beyond which the growth in RevPAR stagnates, because customers become willing to look for other alternatives in the market. Noone et al. (2013) found that the hotel’s RevPAR performance is related positively with relative price positioning and negatively with relative price fluctuation, indicating that pricing above the competition and maintaining a consistent relative price position lead to a relatively higher RevPAR. Similarly, Enz et al. (2015) found that European hotels that position with average daily rates above those of their competitors gain higher relative RevPARs, albeit they experience lower relative occupancies. However, the authors found that the hotel’s RevPAR performance is modestly related with relative price positioning and marginally with relative price fluctuation. Similar results were found in the entertainment industry by Enz and Canina (2017). It was found that that higher-priced golf courses gain higher revenue per available tee time round and achieve higher daily course utilization. Given the inelasticity of demand in the golf market, price cuts will not have a significant positive impact on demand and revenue. So, rather than lowering prices, golf course owners can maximize revenue by charging higher prices than their competitors (Enz and Canina, 2017). It should be noted here that the premium pricing hypothesis is similar in spirit to the price leverage hypothesis. Based on an analysis of the average economies of 2,463 companies in Compustat aggregate, it was shown that a 1% price increase could result in an 11.1% increase in operating profits, provided there was no drop in sales volume. On the other hand, a 1% increase in sales volume could lead to a 3.3% increase in operating profit, provided that the price was not discounted (see Marn and Rosiello, 1992: 84). Similar conclusions were reached by Mckinsey’s analysis of the average income statement of an S&P 1500 company. It was shown that a price increase of 1%, if sales volume remained constant, could generate an 8% increase in operating profits; an impact nearly 50% bigger than that of a 1% drop in variable costs and more than three times bigger than the impact of a 1% increase in sales volume (see Marn et al., 2003). Hence, pricing above the competition can improve hotel financial performance provided that hotel demand is relatively inelastic and that premium services are provided to customers. As foreign tourists currently visiting Jordan may be more sensitive to security and safety issues than cost and price considerations, there is good reason to believe that the demand for Jordanian hotel rooms is relatively price-inelastic. Accordingly, adopting a premium pricing strategy, as opposed to a discount pricing strategy, is potentially the most appropriate way for maximizing hotel revenues from the current limited demand. Under this assumption, two hypotheses can be proposed for the present study: H1: There are statistically significant differences in the key performance indicators between hotels applying a premium pricing strategy and those implementing a discount pricing strategy. ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 H2: Hotels adopting a premium pricing strategy enjoy a relatively higher revenue generation index (higher relative RevPAR) but experience a relatively lower market penetration index (lower relative occupancy). By contrast, hotels embracing a discount pricing strategy enjoy a relatively higher market penetration index, but experience a relatively lower revenue generation index. DATA COLLECTION AND SAMPLING Data collection was the most challenging part of this study due to the lack of sufficient data on the performance of the Jordanian hotel industry. Unfortunately, there is no database that collects and compiles performance data for hotels in Jordan, including average room rates, demand, supply and revenue. This is in part because a large number of Jordanian hotels are independently owned, and therefore are not formally demanded or voluntarily interested to publish detailed data on their performance to the public. Therefore, a researcher who is wishing to examine the performance of Jordanian hotels has no choice but to obtain this data directly from their owners/managers using a welldesigned questionnaire. In keeping with this, a draft questionnaire was designed at the beginning of February, 2017, which was then discussed with a group of academics and practitioners to validate its content. The focus was on downtown star-rated hotels that already had competitive pricing strategies during the period 2014-2016. Most of these hotels were located in the Great Amman Province (the capital of Jordan), Ma’an province (that embraces the historical city of Petra) and Aqaba province. At the beginning of 2017, there were 255 classified hotels in Jordan, with a supply capacity of 19,732 rooms (36,815 beds). Out of these hotels, 219 properties were located in the above-mentioned provinces, with a supply capacity of 16,770 rooms. All these hotels were reached and surveyed over a five-month period, from February through June, 2017. Of these hotels, only 120 hotels provided sufficient data and confirmed they actually compete on a price basis. The sample hotels contained about 13,081 rooms, representing approximately 66% of the stock of star-rated hotel rooms and 46% of the total number of hotel rooms in Jordan. Table 1 below summarizes the distribution of the sample hotels by their star rating. Table 1 : Distribution of the population & sample hotels by their star rating Class Subpopulation Subsample % Five-stars 33 22 67% Four-stars 31 23 74% Three-stars 57 41 71% Two-stars 68 22 32% One-stars 66 12 18% Total 255 120 ---Source: The Ministry of Tourism and Antiquities of Jordan, 2017 MEASURES One of the main pillars of this study was to identify hotels competing based on pricing and their competitive pricing strategies. To achieve this, hotel owners/managers (key respondents) were asked to indicate whether they were taking their competitors' room rates into account when setting or adjusting their hotels’ room rates. Respondents were also asked to indicate whether they were always trying to maintain long-term price positions different from those of their competitors. The long-term price position was conceptualized as the average of different room rates offered by the hotel to all segments of customers over time (see Kimes, 2010; Noone et al., 2013). Moreover, to identify the content of the competitive pricing strategy used by each hotel in the sample, each respondent was asked to answer two interrelated questions: 1) "In this hotel, the room rates are set so that the average of these room rates remains relatively below the average room rates charged by competitors?". If the respondent’s response to the preceding question was "NO", then he was asked to answer the following question: 2) " In this hotel, the room rates are set so that the average of these room rates remains relatively above the average ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 room rates charged by competitors?”. The answers to these questions allowed the creation of a dichotomous variable classifying the sample hotels based on their competitive pricing strategies into two independent groups: (a) the discounted hotel group, which contained the hotels that set their average room rates comparatively below those of their competitors; and (b) the premium hotel group, which included the hotels that set their average room rates comparatively above those of their competitors. The second issue in this study was to identify the competitive set to which each hotel in the sample belongs. The competitive set of each hotel was identified by asking each respondent to name at least four hotels that are directly competing with his hotel according to the following definition: the competitive set is a group of hotels that are operating in the same geographical spot (e.g. within a three-mile distance); offering comparable rooms and services; serving the same segments of customers; and, competing on a price basis. This definition was formulated in view of Smith Travel Research's (STR) guidelines and the definitions used in previous studies (see Enz and Canina, 2005; Canina and Enz, 2008; Enz et al., 2009). The third issue in this study was to measure different aspects of hotel long-term performance. Therefore, several performance metrics were used based on the purpose of each of the two complementary analyses performed. For one-way multivariate analysis of variance and hypothesis testing, the market penetration index was used as a measure of the hotel’s comparative occupancy performance, while the revenue generation index was employed as a measure of the hotel’s comparative financial performance. On the other hand, the descriptive analysis was performed using both the relative occupancy indicator and the relative RevPAR indicator. In order to construct these measures, each respondent was asked to report the annual average daily rate (ADR) and annual average daily occupancy rate (ADO) achieved by his hotel in each year during the period 2014-2016. Each respondent was informed that ADR can be simply calculated by dividing the total room revenue for a given year by the total number of rooms sold during the same year. Likewise, each respondent was told to compute ADO by dividing the total number of rooms sold in a given year by the total number of rooms available for sale during that year. Based on ADR and ADO figures, the various performance metrics referred to above were constructed in the following manner. First, the total room revenue over the three-year period was computed for each hotel in the sample and for each hotel’s competitive set. The total room revenue for each hotel in each year during the period 2014-2016 was calculated using the following formula: annual room revenue = ADR x ADO x number of guest rooms x 365 days. This was due to the refusal of many hotel owners/managers to report their annual room revenues, since they consider this data to be very sensitive and confidential (about 83 hotels). Second, RevPAR for each hotel over the three-year period was calculated by dividing the hotel’s total room revenue over the three-year period by the total number of rooms available for sale during that period. The same procedure was applied to compute ADR and ADO for each hotel over the three-year period. Third, RevPAR for each hotel's competitive set over the threeyear period was calculated by dividing the total room revenues achieved by all member hotels in the competitive set over the three-year period by the total number of rooms available for sale by these hotels during that period. ADR and ADO for each hotel’s competitive set over the three-year period were computed similarly. Fourth, both the market penetration index and the revenue generation index over the period 2014-2016 were calculated, considering the manner used by Smith Travel Research (STR) to compute these indices. The market penetration index for each hotel in the sample was computed by dividing ADO of the hotel by ADO of its competitive set. In the same token, the revenue generation index for each hotel was computed by dividing the hotel’s RevPAR by its competitive set’s RevPAR. Finally, both the relative occupancy indicator and the relative RevPAR indicator for each hotel were calculated in the same way as in previous studies (e.g. Enz et al., 2004 & 2009; Canina and Enz, 2008; Enz and Canina, 2010; Chen, 2010). The relative occupancy indicator was calculated as the percentage difference between ADO of the hotel and ADO of its competitive set. Likewise, the relative RevPAR indicator was calculated as the percentage difference between RevPAR of the hotel and RevPAR of its competitive set. ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 DATA ANALYSIS As mentioned previously, the current study aimed to: 1) examine the differences in the market penetration index vs. the revenue generation index between hotels applying a discount pricing strategy and those implementing a premium pricing strategy; 2) explore the impact of discount pricing vs. premium pricing on the hotel’s relative occupancy and RevPAR; 3) identify the most appropriate competitive pricing strategy to improve the hotel's long-term financial performance. The first and second objectives were achieved by testing the two main hypotheses of this study, using the one-way multivariate analysis of variance in SPSS Statistics 21. As in previous studies, the third objective of this study was achieved through the preparation of a graphical representation of the relationship between different ranges of price discounts/premiums and their corresponding relative occupancies and RevPARs (e.g. Enz et al., 2004 & 2009; Canina and Enz, 2008; Enz and Canina, 2010; Chen, 2010). The results of these two complementary analyses are discussed in detail in subsequent sections. Multıvarıate Analysıs The one-way multivariate analysis of variance (MANOVA) was conducted to locate the differing impacts of each competitive pricing strategy on the hotel's key performance indicators, including the market penetration index (MPI) and the revenue generation index (RGI). Both MPI and RGI were employed as dependent variables in the analysis. The independent variable, on the other hand, was the competitive pricing strategy, by which the sample hotels were separated into two groups: the discounted hotel group and the premium hotel group. Prior to performing MANOVA test, several steps of inspection were taken. In order to check whether the dependent variables in each hotel group follow normal distribution, data were examined using the Shapiro-Wilk W test for univariate normality, where p > 0.05 for each of the dependent variables suggested that both variables did not deviate from normal distribution. Likewise, Box's M Test of Homogeneity of Covariance Matrices was performed on the data, where p= 0.113 indicated that the covariance matrices of dependent variables were equal in the two hotel groups. On the other hand, Bartlett's Test of Sphericity was significant at p < 0.0005, which suggested sufficient correlation between the dependent variables. Furthermore, Levene’s test of equality of variances was non-significant for the two dependent variables, indicating that the assumption of homogeneity of variance was met (for more information about MANOVA test see Field, 2009; Meyers et al., 2013). Table 2: Multivariate Test Strategy Effect Value FValue Hypothesis d.f. Error d.f. Sig. Partial Eta2 Observed Power Wilks' Lambda 0.345 111.158 2.000 117.000 .000 0.655 1.000 Hotelling's Trace 1.900 111.158 2.000 117.000 .000 0.655 1.000 Roy's Largest Root 1.900 Computed using alpha = 0.05 111.158 2.000 117.000 .000 0.655 1.000 Table 2 above shows some results derived from MANOVA test. The overall F test (the first row in the table) indicates that there are significant differences in both MPI and RGI between the discounted hotel group and the premium hotel group, F (2, 117) = 111.158, p < 0.0005; Wilk's Λ = 0.345. The multivariate partial eta squared based on Wilks’s lambda is also high at 0.655, indicating that 65.5% of variance in MPI and RGI is attributed to the difference in competitive pricing strategies adopted by the two hotel groups (only 34.5% of the variance is unexplained). Moreover, power to detect the difference is 100%, suggesting that the first hypothesis of the current study is confirmed; that is to say: there are statistically significant differences in the key performance indicators between hotels applying a premium pricing strategy and those implementing a discount pricing strategy. These results corroborate that hotel comparative ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 occupancy and RevPAR performance are influenced differently by the competitive pricing strategy used, whether it is a premium pricing strategy or a discount pricing strategy. To further investigate whether the competitive pricing strategy has a significant impact on MPI and RGI separately, the univariate main effects were examined using univariate analysis of variance. Given that the analysis involved conducting two separate F-tests, the significance level was set at p < 0.025. Results derived from the two tests are displayed in table 3 below. Table 3: Univariate ANOVA Tests Strategy Effect Market Penetration Index Revenue Generation Index Computed using alpha = 0.025 Type III DF Mean Sum of Square Squares 1.159 1 1.159 0.521 1 0.521 F Value Sig. Partial Eta Observed Squared Power 89.973 36.060 0.000 0.433 0.000 0.234 1.000 1.000 As is clear from the table above, the competitive pricing strategy has a statistically significant effect on MPI, F (1, 1.159) = 89.973, p < 0.0005, partial eta squared = 0.433, power = 100%; as well as on RGI, F (1, 0.521) = 36.060 p < 0.005, partial eta squared = 0.234, power = 100%. These results indicate that both the premium hotel group and the discount hotel group differ significantly in terms of both MPI and RGI, evidence that different competitive pricing strategies have divergent effects on the hotel’s key performance indicators. In order to investigate how each competitive pricing strategy affects MPI and RGI, separately (ie, the second hypothesis of this study), the marginal mean values of these indices in each hotel group were estimated and compared to each other. Results of these comparisons are summarized in table 4 below. Table 4: Descriptive Statistics (Estimated Marginal Means) Competitive Strategies Performance Measures Market Penetration Index Revenue Generation Index Discount strategy Mean Value Std. Error Premium strategy 1.103 0.906 0.015 0.014 Discount strategy Premium strategy 0.929 1.061 0.016 0.015 95% Confidence Interval Lower Upper Bound Bound 1.073 1.133 0.878 0.934 0.898 1.032 0.961 1.091 As can be seen from table 4 above, there are significant differences in the mean value of MPI between the discount hotel group (M = 1.103) and the premium hotel group (M = 0.906). In a similar vein, significant differences in the mean value of RGI can be observed between the discount hotel group (M= 0.929) and the premium hotel group (M= 1.061). These results can be interpreted as indicating that, while a premium pricing strategy has a positive impact on the hotel's comparative financial performance, a discount pricing strategy has an adverse effect. Conversely, while a discount pricing strategy has a positive impact on the hotel's comparative occupancy performance, a premium pricing strategy has the opposite effect. It appears, therefore, that adopting a premium pricing strategy, as opposed to a discount pricing strategy, is the most convenient way for improving hotel long-term financial performance, especially when regional political conditions are unfavorable. ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 Descriptive Analysis The third issue of interest to this study was to explore the impact of discount pricing vs. premium pricing on the hotel’s relative occupancy and RevPAR. To this end, a graphical representation was prepared to depict the relationship between different ranges of price discounts/premiums and their corresponding relative occupancies and RevPARs. Similar to previous studies (e.g. Enz et al., 2004 & 2009; Canina and Enz, 2008; Enz and Canina, 2010; Chen, 2010), the sample hotels in this study were divided into 14 hotel groups according to different ranges of price discounts/premiums. The price discount/premium was calculated as the percentage difference between the annual average daily rate of a hotel and the annual average daily rate of its competitive set. The average relative occupancy and RevPAR for each of the 14 hotel groups were then calculated. Figure 1 below shows the 14 different ranges of price discounts/premiums and their corresponding relative occupancies and RevPARs. As shown, there appears to be a positive relationship between premium pricing and the hotel’s relative RevPAR. The sample hotels seem to achieve relatively higher RevPARs when pricing their rooms above the competition. This is reflected in the gradual increase in relative RevPAR across different ranges of price premiums. This is particularly true for hotels that raise their average room rates by more than 10% relative to the average room rates of their competitors. On the contrary, raising average room rates by more than 2% above the competition leads to a gradual decrease in relative occupancy. It is interesting to note that price increases (ie, price premiums) are associated with smaller decreases in relative occupancies, resulting in higher relative RevPARs. For example, as shown in the figure below, when hotels raise their average room rates by 10 to 15% compared to competitors, their relative occupancies drop by 10%, while their relative RevPARs rise by 7%. And, when they raise their average room rates by more than 30% compared to competitors, their relative occupancies drop by 15%, while their relative RevPARs increase by 13%. These results can be interpreted as indicating that the average room rate rises at a relatively higher rate than the decrease in average occupancy, which means there may be a relatively inelastic demand for hotel rooms in Jordan. Therefore, the use of fairly premium pricing would be financially beneficial for hotels, as it could lead to a decrease in average occupancy and a relatively larger increase in the average room rate, resulting in higher room revenues and hence RevPAR. Figure 1 : Different ranges of price discounts/premiums and their corresponding relative occupancies and RevPARs Price Discounts 25.00 Price Premiums 20.00 15.00 Price Ranges 10.00 5.00 .00 -5.00 -10.00 -15.00 Relative Occupancy Relative RevPAR -20.00 -25.00 Price Ranges > - -2030% 30% Rel. 20% 17% Occupancy Rel. RevPAR -21% -13% -1520% -1015% -510% -25% -20% 025102% 5% 10% 15% 12% 10% 8% 3% 7% -1% -4% -9% -12% -6% -1% 1% -1% 1% 0% -2% 1520% 2030% > 30% -10% -11% -12% -15% 7% 9% 10% 13% ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 On the other hand, the use of discount pricing appears to cause a relatively larger decrease in the average room rate than the increase in average occupancy, resulting in lower revenues and thus RevPAR. For example, as shown in the figure above, when hotels reduce their average room rates by 10-15% compared to competitors, their relative occupancies rise to 10%, up to 20% at a discount level of more than 30% compared to competitors. However, the improvement in relative occupancy does not appear to be sufficient to compensate for the price discount, which results in a lower relative RevPAR. For example, although reducing the average room rate by more than 30% leads to a 20% increase in relative occupancy, at the same time it causes a 21% reduction in relative RevPAR. In summary, the results above show once again that adopting a premium pricing strategy, rather than a discount pricing strategy, is the most appropriate way to improve the hotel’s comparative financial performance in the long term, especially when regional conditions are politically unfavorable. These results confirm the results obtained from MANOVA test. They are also consistent with the results of studies conducted in different political and economic contexts, for example, by Enz et al., 2004 & 2009; Canina and Enz, 2008; Enz and Canina, 2010; Chen, 2010, among others. IMPLICATIONS The present study provided convincing empirical evidence from the Jordanian hotel industry supporting the premium pricing hypothesis. The results showed that the adoption of a premium pricing strategy helps improve the hotel’s comparative financial performance in the long term. This is particularly true when hotel demand is relatively inelastic for a long period of time due to poor regional political conditions. This is so because foreign tourists in such circumstances will be more sensitive to security and safety issues than cost and price considerations. Therefore, the hotel can fairly increase room rates above the competition without incurring a significant loss of occupancy, provided that it offers premium services to tourists. Even though premium pricing will result in some occupancy losses, gains from price increases will be more than enough to compensate for these losses and thus increase RevPAR. Therefore, in such circumstances, the hotel should focus on improving prices by providing a high level of service, especially with respect to safety, security and comfort standards. On the other hand, the results do not lend support to the discount pricing hypothesis, which states that pricing below the competition increases room occupancy, which improves RevPAR. The results showed that although adopting a discount pricing strategy helps improve room occupancy, it is detrimental to the hotel’s comparative financial performance in the long term. When the regional conditions surrounding a particular tourist destination are politically unstable, reducing room rates will not entice more tourists to travel to that destination and increase demand for hotels. Therefore, the additional demand resulting from the reduction in room rates will not be sufficient to compensate those hotels for the price discounts offered to tourists. Moreover, discounting room rates by a large number of hotels can ignite an industrywide price war, the biggest beneficiaries of which will be current tourists, who will receive cash savings from price discounts, while hotels will lose consumer surplus and incur large revenue losses. However, this does not mean avoiding price discounts at all times and occasions, but rather adopting more flexible short-term prices that do not significantly affect the hotel’s long-term price position (see Noone et al., 2013). Also, it is important to cut variable costs to increase profit margins (Van der Rest and Harris, 2008), especially if increased occupancy is associated with lower room rates. CONCLUSION The present study examined the divergent effects of competitive pricing strategies on the long-term financial performance of hotels. The study was conducted in the context of the Jordanian hotel industry, which experiences weak demand and lack of price elasticity due to the prevailing political conditions in the Middle East. Two complementary data analyses were performed, using the one-way multivariate analysis of variance supported by a detailed descriptive analysis. The results of the multivariate analysis of variance revealed statistically significant differences in the key performance indicators between hotels that adopt a premium pricing strategy and those implementing a discount pricing strategy. While the adoption of a premium pricing strategy improves the comparative financial performance of hotels in the ©Society for Business and Management Dynamics Business Management Dynamics Vol.7, No.05, Nov 2017, pp.16-26 long term, the use of a discount pricing strategy boosts their comparative occupancy performance. More specifically, the results showed that although hotels that maintain their prices above the competition experience a relatively lower market penetration index, they have the upper hand on their competitors in terms of revenue generation index. By contrast, hotels that keep their prices below the competition enjoy a relatively higher market penetration index, but lag behind their competitors in terms of revenue generation. The results of the descriptive analysis further indicated that although hotels with higher prices compared to their competitors achieve relatively lower occupancy rates, they gain relatively higher RevPARs. By contrast, lower-priced hotels compared to their competitors have relatively higher occupancy rates, but experience relatively lower RevPARs. Overall, the results suggested that adopting a premium pricing strategy is the most convenient way for improving hotel long-term financial performance, especially when regional conditions are politically unfavorable. It should be noted that the above results were obtained from a small sample of 120 hotels whose competitive pricing strategies were examined over a three-year period from 2014 through 2016. Therefore, it is recommended that future research examines larger samples of hotels over longer periods of time. There is also an urgent need for more in-depth empirical studies on the dynamic pricing issue in the Jordanian hotel industry, taking into account the time changes in demand and the differences in customers' willingness and ability to pay. It is also important that future studies examine the impacts of using dynamic pricing on the hotel's long-term price position and financial performance. REFERENCES Canina, L. and Carvell, S.A. (2005). Lodging Demand for Urban Hotels in Major Metropolitan Markets. Journal of Hospitality and Tourism Research, 29(3), 291-311. Canina, L. and Enz, C.A. (2008). 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