Commoditization in the U.S. Lodging Industry

Abstract

A comparison of four elements that constitute commoditization as viewed by U.S. hotel industry executives and by customers from a national sample found that the two groups ascribe different weights to the four factors in the commoditization model. In a test of these four factors, the seventy industry executives in the survey thought that product homogeneity contributed the most to commoditization, followed by industry stability, switching costs, and price sensitivity. However, from the customers’ point of view, price sensitivity was far and away the key element of commoditization, with product homogeneity having a much smaller effect. Stability and switching costs were not considered as part of the consumers’ perception of commoditization. Although the study is based on the views of hotel industry executives and customers, it makes no attempt to gauge the level of industry commoditization, because its goal is to establish the nature of commoditization as framed in the hotel industry.

Keywords

commoditization hotels differentiation brand strategy partial least squares

Commoditization has been defined as a market condition that occurs when competitors in a comparatively stable industry offer increasingly homogeneous products to price-sensitive customers who incur relatively low costs in changing suppliers (Reimann, Schilke, and Thomas 2010, 188). Commoditization has been a continuing concern in the U.S. lodging industry for some time now (Cai and Hobson 2004; Carroll and Siguaw 2003; Connolly, Olsen, and Moore 1998; Gilmore and Pine 2002; Olsen and Connolly 1999). The industry’s segmentation, wide assortment of brands (many of them having a similar description), and a fast-evolving distribution system increasingly driven by third-party intermediaries have together painted a picture of an industry that offers a large number of similar products with different names. With price reductions and a dilution of individual brand identity, the industry is also concerned about the commoditizing effects of intermediaries.

Before one discusses the commoditization of the hotel industry—or any industry—it’s essential to determine the underlying elements of commoditization. Moreover, we believe that key stakeholder groups have different views of the factors that support commoditization—in this case, industry executives and customers. Perceptions are important in this context because they explain the underlying thought processes through which executives and customers make decisions in the marketplace. Knowing how these key actors think about the dimensions underlying commoditization, especially differences in their perceptions, paves the way for a richer understanding of an evolving phenomenon, both in academic and practical terms. The goal of this analysis of commoditization is to assist industry executives in understanding the elements of commoditization and in developing appropriate strategies to counteract the forces of commoditization. While the study described here does not seek to assess the levels of commoditization in the U.S. hotel industry, it provides a window into managers’ and customers’ views of the elements of commoditization.

In developing a definition of commoditization, Reimann, Schilke, and Thomas (2010) conceptualize four dimensions manifested as first-order reflective constructs that lead to commoditization level as a second-order formative construct. The first-order constructs proposed by Reimann and colleagues, which we apply in our study, are product homogeneity, price sensitivity, switching costs, and industry stability. In this view, high levels of product homogeneity, price sensitivity, and industry stability, combined with low switching costs, indicate high commoditization levels in a product category. Although we draw on Reimann, Schilke, and Thomas’s (2010) conceptualization and model as outlined, we again caution that our study does not assess a level of commoditization in the hotel industry. Instead, our goal is to establish the reliability and validity of first-order dimensions underlying the phenomenon from both industry and customer angles. In particular, we want to assess perceptual gaps between industry practitioners and consumers by examining the relative weight of the dimensions of commoditization as viewed by each subsample. By inference, an understanding of the elements of commoditization provides actionable information that industry can use to develop strategies and initiatives.

In this context, we sought to answer the following questions pertaining to commoditization:

Research Question 1: What are the relative effects of individual dimensions constituting commoditization as perceived by industry executives?

Research Question 2: What are the relative effects of individual dimensions constituting commoditization as perceived by customers?

Research Question 3: Are there differences in the relative magnitudes of dimensions constituting commoditization between the industry and customer samples?

Literature Review

Commoditization has both sociological and economic underpinnings. The sociological view, offered by Kopytoff (1986), is that commoditization is a process where products become more exchangeable in a system characterized by the presence of other products that are seemingly different. These products or commodities have “use value and exchange value” (Kopytoff 1986, 64). The opposite of commoditization is singularization, which is the process of making something conspicuous or giving it a distinctive identity. Commoditization is a dynamic process during which brands can move either way along the commoditization spectrum, toward singularization (also called de-commoditization) or toward commoditization. Lodging brands resist commoditization by, among other strategies, providing personal services to customers and by applying the principles of hospitality. Despite the noncommercial original of the term, hospitality has come to mean providing “home away from home” experiences to generate revenue. In this way, the link between the sociological and economic nature of hospitality becomes inextricably intertwined.

In the economic approach, commoditization is indicated by the lack of differentiation among brands in a product category. As we indicated above, the Reimann, Schilke, and Thomas (2010, 188) study on which our paper is based describes commoditization as a four-dimensional construct of evolving marketing competition characterized by increasing homogeneity of products, higher price sensitivity among customers, lower switching costs, and greater industry stability.” But this also is a dynamic situation, in which individual brands can seek to de-commoditize by using strategic levers, counteract the forces of commoditization, and subsequently enhance distinction in the market. Note that while both realms (sociological and economic) describe it in different ways, the loss of identity is a common thread in commoditization.

Sources of Commoditization in the U.S. Lodging Industry

The dimensions underpinning commoditization can vary between markets and product categories, as highlighted in D’Aveni’s (2009) typology of commodity traps, which uses price-product benefit analysis. In the deterioration trap, both prices and benefits in a product category diminish due to the dominance of a low-cost leader. While that situation does not describe the hotel industry, the industry does appear to be in D’Aveni’s proliferation trap, which happens due to intense competition by too many players and brands. In this case, prices and benefits can rise or fall depending on other forces. Between 1991 and 2000, for example, the U.S. lodging industry saw a 34 percent increase in the number of brands, from 140 to 188 (Smith Travel Research [STR] 2001). Just ten years later, in 2011, STR identified 226 hotel brands operating in the United States. The industry continues to consolidate, and a substantial number of hotel brands are managed by a relatively small group of hotel conglomerates. The jury is still out on the last trap identified by D’Aveni (2009), which is the escalation trap. This is characterized by a market scenario where prices go down and benefits go up, typically due to rapidly evolving technology. In some ways, this seems to describe the hospitality industry.

Dimensions of Commoditization: Industry and Customer Perspectives

Product homogeneity

Let’s look more deeply at Reimann, Schilke, and Thomas’s (2010) four underlying variables for commoditization, beginning with product homogeneity. Typically, a by-product of market saturation and intense competition, this occurs when a product or service is perceived as being similar in quality and performance to those of its competitors. As a consequence, customers tend to perceive that offerings in a product category can be substituted easily, given the similarities in features and attributes. Also known as brand parity, product homogeneity occurs when customers’ overall perception is that differences between major brand alternatives in a product category are small (Iyer and Muncy 2005, 222). Such a phenomenon may apply to hotels when there are no substantial differences in the services offered by firms within the same competitive set or market segment (Bowie and Buttle 2004). Thus, greater brand parity or product homogeneity signifies greater commoditization of a product offering (Ramirez and Goldsmith 2009).

Price sensitivity

Reimann et al. define the next factor, price sensitivity, as the result of customers searching for the “best price for a standard product [and service] on the assumption that the products and services are equivalent in quality and function” (p. 190). Customers’ knowledge of substitute products further influences their price sensitivity (Heil and Helsen 2001). In this view, price sensitivity is an individual reaction to price variation, which in turn contributes significantly to purchase behavior (Ramirez and Goldsmith 2009). Deal proneness (Lichtenstein, Netemeyer, and Burton 1995) and coupon proneness (Bawa, Srinivasan, and Srivastava 1997) are alternative marketing phenomena that have been studied under the umbrella of price sensitivity.

At the product level, cross-price disparity, which is the propensity of a customer to switch to an alternative brand when the preferred brand’s price increases, also tends to increase price sensitivity (Choi et al. 2009). This alludes to the concept of rate parity, which is the maintenance of a uniform rate across multiple channels for a unit of inventory (Simonich 2011). In a complex distribution landscape with a multitude of distribution models and channels, maintaining rate parity is onerous (Buhalis and Licata 2002).

Switching costs

Switching costs are barriers resulting from the expenses incurred or the benefits forgone when a customer switches from one brand to another. The reality of the hotel industry is that switching costs are relatively low. In an effort to increase switching costs (Klemperer 1995), hotel firms have developed rewards-based loyalty programs, so that the risk associated with switching will “outweigh the perceived benefits” of utilizing competing offerings (Wang 2010, 255).

Switching costs can be attributed to both monetary and non-monetary factors (Klemperer 1995; Wang 2010). In the Reimann et al. framework, monetized switching costs, which are analogous to calculative commitment, are typically associated with “economic risk,” evaluation time, learning, setup, benefit loss, and monetary loss. In contrast, non-monetary risks, related to affective commitment, “involve personal relationship loss and brand relationship loss” (p. 190; see also Bendapudi and Berry 1997; Fullerton 2003; Garbarino and Johnson 1999; Morgan and Hunt 1994). Customers who hold a calculative commitment operate on a transactional basis, in which they evaluate their collection of points for future stays. In contrast, those who feel affective commitment value trust, reciprocity, and personal interactions. Affective commitment has been found to predict customer retention better than calculative commitment (Mattila 2006; Verhoef 2003).

Industry stability

The moderating influence of industry conditions such as dynamism, technological turbulence, or competitive intensity on market orientation has been controversial (Kohli and Jaworski 1990; Slater and Narver 1994). Empirical studies have not found an impact of this type (Slater and Narver 1994), but conceptually, one would anticipate that highly commoditized competitive environments are characterized by high industry stability (Pelham 1997). Stability of this type refers to predictable market demand, a consistent competitive structure, and little change in the set of customers in an industry that involves an unchanging group of competitors in a highly competitive environment (Day and Wensley 1988). Therefore, the argument is that commoditization exists where the market’s competitiveness level remains stable.

Despite a profusion of brands, the lodging industry is a stable one. The industry’s largest fifty hotel brands (based on the number of rooms) are controlled by no more than twenty companies. As of 2002, nearly 70 percent of all hotel room supply was affiliated with brands or franchises, according to one source, and another reference in 2008 alludes to 65 percent of U.S. hotels being franchised (Turkel 2008).

Summarizing this discussion, Exhibit 1 outlines the conceptual framework of the study from both industry and customer perspectives, based on the Reimann, Schilke, and Thomas (2010) framework. From an industry perspective, the dimensions are product homogeneity, switching costs, price sensitivity, and industry stability. On the customer side, switching costs and industry stability were left out because we believe they lack direct relevance with customers, leaving product homogeneity and price sensitivity, the measure for which was altered to fit the purposes of this study.

Exhibit 1:

Research Model: Dimensions of Commoditization (Industry Executives and Customers).

Data and Measures

We conducted two surveys, one of U.S.-based active members of the American Hotel and Lodging Association (AH&LA), as of December 2009, and the other of U.S. lodging customers. For the hoteliers, we e-mailed a link to our online survey to 692 AH&LA e-mail addresses, requesting participation in March 2010. We received a total of ninety responses, plus forty three return e-mails that the intended participant was either not a member of the firm or that the address was not valid. Of the ninety responses, twenty were deemed incomplete or unreliable, yielding a final sample comprising seventy respondents (a 10.7 percent response).

For the survey of lodging customers, we sent an invitation and survey link to 5,000 active U.S. lodging customers drawn randomly from a marketing research company database. We received an initial response of 690, of which 435 were deemed usable, for a final response rate of 8.7 percent.

Low response rates raise concerns of non-response bias (Diem 2002; Moore and Tarnai 2002; Vehovar et al. 2002). Given the nature of sampling in this study, we followed the standard approach of comparing early and late respondents in both samples, and found no significant differences in perceptions based on when responses were received. In addition, in the industry sample, we conducted Chi-Square analysis and found that only gender differences were present, with females responding later. However, given the lack of differences across all other characteristics, we felt this demonstrated a lack of non-response bias.

Likewise, in the customer sample, we found no significant differences based on the socio-demographic variables of age, education, and income. We also examined whether the customer sample had differences based on the level of survey completion. Although incomplete surveys were not included in the final analysis, we compared those incomplete responses with the completed surveys. In both comparisons, we found no statistically significant differences, and thus deemed the samples to fairly represent the target population with no evidence of non-response bias.

Exhibits 2 and 3 provide the descriptive results of the sample participants for the industry executives and hotel customers. As shown in Exhibit 2, nearly 16 percent of the industry respondents were at the CEO or president level, and 28 percent were at the VP level, while the remaining 56.7 percent were GMs and directors.

Exhibit 2:

Industry Sample Descriptive Profile (N = 70).

	%
Gender
Female	21.9
Male	78.1
Age
18-25	3.1
26-33	9.4
34-41	21.9
42-49	23.4
50-57	21.9
≥58	20.3
Hotel type
Independent	36.2
Franchised	62.3
Education
High school or less	10.9
Bachelor degree	64.1
Post graduate degree	25.0
Role/designation
President/CEO	15.0
Vice president	28.3
GM/Sr. managers/directors	56.7
Hotel representation (direct employer)
Owner	61.0
Management company	33.0
Management company/franchisor	6.0

Exhibit 3:

Customer Sample Descriptive Profile (N = 435).

	%
Gender
Female	65.5
Male	34.5
Age
18-25	3.1
26-33	9.4
34-41	21.9
42-49	23.4
50-57	21.9
≥58	20.3
Education
High school or less	22.9
Some college no degree	36.3
Associate’s degree	26.0
Bachelor’s degree	19.3
Post graduate degree	14.7
Occupation
Student (not employed)	7.2
Homemaker	17.6
Not employed	20.5
Self employed	26.4
Employed	28.3
Income
<$25,000	27.5
$25,000-$50,000	37.0
$50,001-$75,000	16.6
$75,001-$100,000	15.0
>$100,000	4.4

Measures

As discussed above, questions pertaining to commoditi-zation dimensions as perceived by respondents in both groups were adapted from Reimann, Schilke, and Thomas (2010) to apply to the lodging industry (see the online appendix). All items pertinent to commoditization dimensions were measured using a 7-point Likert-type scale ranging from 1 = strongly disagree to 7 = strongly agree. Exhibit 3 outlines the individual items and the psychometric properties of the constructs.

We used partial least squares (PLS) analysis using SmartPLS 2.0.M3 software (Ringle, Wende and Will 2005) to model commoditization as a second-order formative construct with both samples. PLS was chosen over the common covariance-based technique (e.g., LISREL), given that it places fewer restrictions on sample sizes, data distribution, and normality (Chin, Marcolin, and Newsted 2003; Hensler, Ringle, and Sinkovics 2009; Hulland 1999). As outlined by Reimann, Schilke, and Thomas (2010), the first-order dimensions were modeled as reflective constructs for commoditization as the second-order construct. Exhibit 4 shows the visual representations of these models of commoditization from both industry and customer perspectives.

Exhibit 4:

Second-Order Commoditization Model—Industry and Customer Perspectives.

Findings and Discussion

The dimensions we tested proved to be constructs of commoditization, as judged by both the industry and consumer groups. Exhibit 5 highlights the items listed under each construct and provides the individual means and standard deviations, along with composite reliability and average variance extracted (AVE) scores. Reliability is assessed using composite reliability of first-order constructs, which in turn reflect a larger second-order construct in commoditization. All first-order constructs demonstrated composite reliability scores above the recommended .70 (Bagozzi and Yi 1988; Hair et al. 2012). Finally, a .50 cutoff was applied to loading scores of indicators on measures.

Exhibit 5:

Reflective First-Order Constructs.

	M	SD	Factor Loading	Composite Reliability	AVE
Commoditization dimensions (industry perspectives)
Product homogeneity
Most hotel brands have no intrinsic differences	3.39	2.08	0.82	0.92	0.73
Hotel product offerings are highly standardized	4.54	1.77	0.84
Hotel service standards are quite identical (homogenous)	3.74	1.97	0.92
Most hotel brands are identical in quality and performance	3.20	1.92	0.92
Price sensitivity
Most guests check prices even when it comes to lower tier hotel brands	6.01	1.23	0.66	0.81	0.59
Guests buy the lowest priced hotel stays that will suit their needs	4.84	1.62	0.88
Guests rely heavily on price when it comes to choosing a hotel to stay	5.12	1.53	0.79
Switching costs
The guests’ costs for switching to another hotel brand are low	4.95	1.97	0.81	0.81	0.68
The process of switching to a new hotel brand is quick and easy for the guest	5.14	1.65	0.82	0.81	0.68
Industry Stability
There are no frequent changes in guest preferences	4.21	1.51	0.83	0.78	0.56
There are no frequent changes in service levels by hotel brands	4.08	1.71	0.85
Brand obsolescence is low, meaning that brands do not die off quickly^a	3.65	1.54	0.27
Technological changes are fast and unpredictable	4.81	1.46	0.48^a
Commoditization dimensions (customer perspectives)
Product homogeneity
Most hotel brands in the same category/segment have no perceivable differences	4.37	1.65	0.88	0.93	0.81
Hotel service standards of brands in the same category/segment are quite identical	4.30	1.61	0.93
Most hotel brands in the same category/segment are identical in quality and performance	4.28	1.66	0.90
Price sensitivity
I put extra efforts to find lower hotel prices^a	5.74	1.54		0.91	0.67
I shop around more than one hotel brand in search for lower prices	5.83	1.49	0.89
I consider the money saved by finding lower hotel prices to be worth the effort	5.87	1.44	0.91
I commit to buying a hotel stay in a particular segment (Economy, Mid- Scale or Luxury) prior to knowing the actual hotel brand	4.64	1.81	0.91
I shop around more than one travel website in search for lower prices	5.42	1.76	0.57

Note. AVE = average variance extracted.

Items discarded—insufficient loading (< 0.50).

The first-order constructs in both models demonstrate convergent validity by exceeding the AVE values recommended by Hair et al. (2012), with values ranging between .55 and .81. As shown in Exhibit 6, discriminant validity was achieved with both industry and customer samples (Fornell and Larcker 1981). This result underlines the reliability and validity of the measurement of commoditization along the dimensions specified.

Exhibit 6:

Discriminant Validity Results (Fornell and Larcker Test 1981).

	Industry Sample				Customer Sample
	Product Homogeneity	Price Sensitivity	Switching Costs	Industry Stability	Product Homogeneity	Price Sensitivity
Product homogeneity	0.73				0.81
Price sensitivity	0.01	0.59			0.03	0.67
Switching costs	0.05	0.02	0.68
Industry stability	0.16	0.08	0.24	0.56

Note. AVE values on the diagonal, and squared correlations in remaining cells. AVE = average variance extracted.

Finally, we found that the dimensions were significantly related to commoditization using a bootstrapping procedure in SmartPLS. Bootstrapping is a technique where samples are created by randomly drawing cases with replacement from the original sample, where the significance of estimates is ascertained (Hensler, Ringle, and Sinkovics 2009). The procedure provides standardized coefficients supplemented with t-statistics for each path relationship through which one can compare the relative weights of first-order constructs in explaining the variance in the second-order construct. In formative constructs, the items or measures leading to the constructs are deemed as weights represented by path coefficients as opposed to loadings, which are used in reflective measurement. In a formative construct, the causal relationship is from the first-order measures to the second-order construct (Bollen and Lennox 1991), and standardized coefficients can be compared descriptively. We, therefore, draw on comparisons and insights only when one coefficient is more than twice or half the size of another, with the results shown in Exhibit 7.

Exhibit 7:

Commoditization—Formative Measurement Model Results.

	Commoditization—Industry Perspective			Commoditization—Customer Perspective
Constructs	VIF	Std β	SE	VIF	Std β	SE
Product homogeneity	1.16	.70***	0.03	1.03	.32***	0.13
Price sensitivity	1.13	.14*	0.03	1.03	.90***	0.07
Switching costs	1.28	.19***	0.02	NA
Industry stability	1.51	.33***	0.02	NA

p < .1. **p < .05. ***p < .01. VIF = Variance Inflation Factor.

Our analysis confirms commoditization as a second-order formative construct, although we must address the concern of multicollinearity among the dimensions (Hair, Ringle, and Sarstedt 2011). In this instance, the variance inflation factor values were all below 2.5 as recommended to ascertain convergent validity (Exhibit 7). The bootstrapping procedure administered on both samples shows the standardized coefficients. All dimensions in both models were significant at the p < .05 significance level or lesser (required t-value for this level is 1.96); all t-statistics are above 2.56, which is the value to exceed to demonstrate statistical significance at p < .01.

As shown in Exhibit 4, all four dimensions in the industry sample, as represented by first-order constructs, were significant. Of the four dimensions, product homogeneity had the highest path coefficient at β = .70 (t = 7.51, p < .001), while the path coefficients of the remaining three dimensions were less than half in magnitude: industry stability at β = .33 (t = 5.61, p < .001), switching costs at β = .19 (t = 3.40, p < .01), and price sensitivity at β = .14 (t = 1.94, p < .10). In the customer sample, by contrast, price sensitivity had the largest effect with β = .90 (t = 9.47, p < .01), while the effect of product homogeneity was less than half of that, at β = .32 (t = 3.29, p < .01).

Discussion

Industry practitioners saw product homogeneity as having the largest effect on commoditization, compared with the other three characteristics, while they viewed price sensitivity as having the least effect and lowest significance. They also saw switching costs as a minimal issue, when compared with product homogeneity. In short, the industry leaders focused heavily on product homogeneity as perpetuating commoditization, far ahead of industry stability, price sensitivity, and low switching costs. These findings address our first research question on the relative effects of the individual dimensions on commoditization from an industry perspective.

In contrast, customers weighted price sensitivity far more heavily than product homogeneity. This finding ties in directly with the second research question pertaining to relative differences in effects of individual dimensions contributing to customers’ view of commoditization.

In answer to our third research question, we see a clear divergence between the industry practitioners and consumers when it comes to what has greater weight in the phenomenon of commoditization: product homogeneity (from an industry perspective) as opposed to price sensitivity (from the customer perspective). One caution here is that industry executives could have factored in their beliefs about their own customers while responding about the market in general. But commoditization is a broader market-level phenomenon where lodging firms dip into the larger market to fill capacity, and few have the option of not addressing price sensitivity.

Summary and Limitations

Our study affirmed that industry executives see all four dimensions in our study as constituting commoditization, but they put the greatest weight on product homogeneity. For customers, price sensitivity outweighed homogeneity, and we did not ask them about two of the dimensions. We have no way to explain the perceptual gap between the two groups, but we must note that industry leaders should take notice of their customers’ view.

Limitations

Despite the significance of some our findings, the study has its share of limitations. To begin with, the study is based only on one framework for defining commoditization, albeit a well-established one. It is also true that consumers were asked about only two of the four dimensions in that framework. Both industry and customer perspectives are generic in context and may not be applied to any single lodging brand or firm. Also, the findings can be generalized only to 2010, a time when the impacts of the great recession were significant. The economic downturn may have had an impact on customers’ view of price sensitivity in relation to product homogeneity. To ascertain the impact of the state of the economy on the study’s findings, future research should be conducted when the economy is relatively upbeat, which in turn will provide more insights into commoditization.

The industry sample size was relatively small at seventy, although the sample’s profile was rich. Although we believe that the high level of the sample compensates to some extent for the limitation of the sample size, future research should validate this study’s findings using a broader sample of industry executives and customers. Finally, the customer sample was drawn from the general population. Future research again can examine the model with a sample of typical lodging customers.

Recommendations and Future Research

As we said at the outset, our study makes no judgment about the extent of commoditization in the hotel industry, and the major finding is the differing views of the elements of commoditization between industry practitioners and consumers, under the Reimann et al. framework. Given that perceptual gap and given that the customers’ view of commoditization is critical, it’s worth expanding on that view to find ways to improve industry differentiation, as outlined below.

Effective Online Merchandising

To counter the impact of pricing pressures, firms should augment booking engines by providing rich informational contexts such as images and innovative packages to improve singularity. For example, firms can translate reputation scores effectively into the booking process, by making those scores a distinguishable and tangible attribute. Several firms already make use of reviews in various ways. Creating synergy through packaged options that include complementary elements is another way hoteliers can add value and stem the tide of commoditization (Quelch 2007) at both the purchase and experiential stages of consumption. The integration of tangible cues can counteract the effect of customer price sensitivity at the purchase end.

Build Affetive Commitment

Dubious guest loyalty and a highly competitive marketplace have fostered the industry’s move toward relationship marketing (Skogland and Siguaw 2004). A key premise of relationship marketing is that customer retention is relatively more important than customer acquisition (Winer 2001). The hospitality industry is well positioned to build customer relationships and enhance retention, because true hospitality involves host–guest interactions where the service is deeply personal.

Firms can also innovate in the areas of service process delivery, design, and customization, which are interpersonal service enhancements (Quelch 2007) that in turn build affective commitment. Lodging firms have a distinctive advantage over intermediaries in the travel supply network because they are responsible for providing the actual experience. We understand the business realities of rate fences that result in room allocations and variable service options, all of which have immediate tangible business value. However, hotels should do a better job of familiarizing customers on pricing mechanisms, which in turn can improve perceptions of fairness (Taylor and Kimes 2010).

Commodification versus Commoditization

The hotel industry’s business proposition begins with commodification, in which hotels translate a social process into a market-based opportunity. As the study indicates, commoditization results when these attributes are homogenized, which eventually leads to price being the primary driver of purchase (Manno 2002; Rushkoff 2005). We believe that the tendency of lodging firms to translate every facet of the “home away from home” experience into revenue generation opportunities promotes customers’ price awareness. While enhancing revenue in a competitive environment is a business prerogative, our contention is that the commodification of component services in the hospitality industry leads to commoditization of the product category as a whole, given the customers’ view of price sensitivity contributing to commoditization. Instead, when lodging firms shield customers from fragmented pricing of individual services, they can charge incremental premiums and bundle services accordingly (Quelch 2007).

Rate Integrity and Rate Parity

As a final note, hotels should maintain their rate integrity. In this regard, we are using a definition of value for money, as proposed by Palamar and Edwards (2007, 11): “A pricing structure is said to have rate integrity when it properly recognizes the value of each segment and offers rates and products that meet the needs of that segment.” The key here is to educate the customer on the fairness of the price paid for a service (Taylor and Kimes 2010). To counteract the forces of commoditization, hotel firms should strive to do better on rate integrity by communicating the value proposition more clearly, in an effort to offset the pricing issues that underlie guests’ view of commoditization.

Future Research

Based on the findings of this study that validates the dimensions of commoditization from both industry and customer perspectives, we posit four key research threads for future inquiry into this phenomenon.

Since this study makes no assessment of the hotel industry’s commoditization level, hotel brands can use the dimensions outlined here to determine where they stand on the commoditization spectrum.

Future research should examine the impact of commoditization on customer retention and acquisition.

Future research can examine the extent to which the level of commodification affects commoditization perceptions of individual hotel brands.

Finally, future research can study the impact of rate integrity and rate parity on perceptions of commoditization at both the industry and individual brand levels.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, or publication of this article.

Author Biographies

Srikanth Beldona is an Associate Professor and Director of Hospitality Graduate Studies at the University of Delaware’s Alfred Lerner College of Business and Economics. His primary research interest is in consumer psychology as it relates to hospitality consumption.

Brian Miller is an Associate Professor at the University of Delaware’s Alfred Lerner College of Business and Economics. His research interests include the use of technology for education/training, and organizational strategies for higher education, hotels and food service organizations.

Tiffany Francis is a graduate of the M.S. in Hospitality Management Program at the University of Delaware and is now a Program Analyst for the US Federal Government. She is based in Washington D.C.

Hemant V. Kher is an Associate Professor of Operations Management in the Department of Business Administration at the University of Delaware’s Alfred Lerner College for Business and Economics. His areas of interest are in simulation and research methods.

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