Insurance Price Quality

Pricing strategy & practice

Consumer use of the price-quality cue in financial services Hooman Estelami Graduate School of Business, Fordham University, New York, New York, USA Abstract Purpose – The purpose of this paper is to examine the extent of use of the price-quality cue in financial services, and to uncover some of its drivers. The drives studied are: advertising exposure, product complexity, and consumer price knowledge. The use of price as an indicator of quality has been a welldocumented phenomenon in consumer goods markets. However, the existence of this relationship has not been tested in services, and in particular in financial services markets. Design/methodology/approach – A consumer survey of over 200 individuals contacted through intercept interviews was conducted. The use of the price-quality cue and its drivers were measured using multi-item scales, for six financial services categories: checking accounts, financial advisory services, automobile insurance, home insurance, life insurance, and tax accounting. Findings – Significant variations in the use of price as an indicator of quality across financial services categories are identified. Furthermore, it is found that both consumer price knowledge and advertising exposure increase the use of the price-quality cue, while product complexity was found to have no significant impact on price-quality cue utilization for financial services. Research limitations/implications – Future research could expand the array of variables which drive consumers’ use of the price-quality cue. In addition, a wider range of financial services categories could be studied. Practical implications – Knowing the extent by which consumers depend on the price-quality cue in their decisions is critical to optimal positioning of a financial brand. This paper provides specific managerial recommendations on how to approach the pricing and marketing of each of the six financial services categories studied. In addition public policymakers may find the findings of interest due to quality perception biases that may result from financial services providers’ pricing tactics Originality/value – While previous research studies in price-quality cue utilization have primarily focused on manufactured goods, this paper is the only study that has examined the dynamics of price-quality cue utilization by consumers in financial services. This is an important inquiry due to the large volume of consumer expenditures in financial services categories, and the significant impact that these categories have on the financial stability and well-being of the public. Keywords Financial services, Pricing, Quality, Customer service management, Customer relations Paper type Research paper

Introduction

better quality, and the phenomenon is commonly referred to as “price-quality cue utilization” (Miyazaki et al., 2005; Kardes et al., 2004; Monroe, 2002). From a strategic marketing perspective, knowing the extent by which consumers use the price-quality cue in their decisions is critical to optimal positioning of the brand. If in a given category consumers tend to utilize the price-quality cue, then higher prices would signal superior levels of quality than comparable products or services which are priced lower. The strategic implication of this phenomenon is that consumers could gravitate towards higher priced products and may shift away from lower priced alternatives which they may consider to be inferior. This behavior not only helps favor higher priced brands within a category, but it also represents significant profit opportunities for suppliers through price increases, while at the same time reducing the potential for price wars in a given category. Consumer use of the price-quality cue is not only significant to price-setters, but also to public policymakers and consumer advocates who may be concerned about potential consumer misperceptions of quality resulting form the assumption that

Consumer research over the past three decades has documented the persistent impact that price has on consumer perceptions of a product (Dodds et al., 1991; Gabor and Granger, 1961; Janakiraman et al., 2006; Monroe, 2002; Nagle and Holden, 2003; Vanhuele et al., 2006). While higher prices are often associated with lower consumer demand, research has also found that in particular categories of consumer goods, higher prices may result in increased consumer interest in the product (Rao and Monroe, 1989; Scitovsky, 1944). This pattern of behavior is often a result of consumers’ assumption that higher priced goods must be of The current issue and full text archive of this journal is available at www.emeraldinsight.com/1061-0421.htm

Journal of Product & Brand Management 17/3 (2008) 197– 208 q Emerald Group Publishing Limited [ISSN 1061-0421] [DOI 10.1108/10610420810875115]

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Consumer use of the price-quality cue in financial services

Journal of Product & Brand Management

Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

higher priced offers are of superior quality. This issue becomes more pronounced in contexts where significant variations in prices and quality exist, and objective assessment of quality is difficult for consumers to carry out. Financial services provide such a context, and in this paper, the results of a survey regarding consumer use of the price-quality cue are reported. The strength of consumers’ belief that price is an indicator of quality is examined across six commonly used financial services. In addition, factors which may influence consumers in their use of the price-quality cue for a financial service, such as their level of knowledge of service prices, the extent of their exposure to mass media advertising, and their perceptions of the complexity of the service were measured. The study results indicate significant differences in the use of the price-quality cue across financial services categories, and identify specific financial services with a high degree of utilization of the price-quality cue combined with a low level of price knowledge, pointing to potential consumer vulnerabilities. The paper concludes with a discussion of marketing and public policy implications for the financial services categories studied.

can result in deceptive inferences (Olson and Dover, 1978). When price is used as a basis of making quality inferences, the phenomenon is commonly referred to as “price-quality cue utilization” (Kardes et al., 2004; Peterson, 1970; Rao, 2005). The price-quality link was first examined in by Scitovsky (1944) who suggested that price can serve dual roles in the consumers’ decision process. Scitovsky noted that while a higher price may serve as a deterrent to consumer purchase as suggested by neo-classical economic thinking, it may also help motivate consumer purchase of the product through consumers’ inference formation processes, whereby higher prices are interpreted by consumers as indicative of higher quality levels. Subsequent studies have examined how the relationship between price and inferred quality might be influenced by additional variables in the consumer environment. For example, Gardner (1970) showed that price-quality cue utilization will vary by the product category, with some categories showing high use of the price-quality cue while other categories exhibit little use of it by the consumer. It has also been shown that the direct relationship between price and objective quality becomes stronger as the product category matures. Therefore, as new products are introduced to the market and become more widely accepted in the marketplace, consumers’ ability to utilize the price-quality cue with greater confidence increases (Curry and Riesz, 1988). Consumer researchers typically quantify the extent of pricequality cue utilization by asking consumers how much they use this judgment strategy (e.g. Huber and McCann, 1982; Rao, 2005; Wheatley and Chiu, 1977). Samples of consumers are recruited and their beliefs on the strength of the relationship between price and quality are measured using rating scales. These studies have found considerable variation across categories of goods, and variations attributed to research methodology have also been observed. In a review of three dozen previous studies of the link between price and inferred quality, Rao and Monroe (1989) found that the type of research methodology used to assess the strength of this relationship may influence the findings. Shiv and Ariley (2005) demonstrate that products at higher prices can be perceived by consumers to perform at higher levels, though their actual performance may be objectively equivalent to lower-priced items. Research has also shown that consumers’ use of the price-quality cue may significantly vary from one consumer to the next and that some consumers may find price to be indicative of quality more so than other consumers would (Grewal et al., 1998; Richardson et al., 1996).

The price-quality link In evaluating the quality of an offer in the marketplace consumers integrate the available set of information presented to them, in order to form an overall judgment. However, often the information being presented to the consumer may be incomplete or overwhelming (Rao and Monroe, 1989; Scitovsky, 1944). For example, a product’s true quality is often unmeasured and rarely advertised or stated explicitly at the time of sale. As a result consumers may have to rely on other pieces of information available at the time of purchase (e.g. brand name, country of manufacture, price) to form their judgment of the quality of the offered product (Grewal et al., 1998; Kardes et al., 2004). Research has shown that marketers may have an incentive to provide only a subset of the information related to product attributes in their communications with consumers (Nelson, 1978). As a result of the information acquisition costs that consumers would have to incur in order to become fully informed about the product attributes not disclosed in consumer communications, they may have to rely on the available information to make inferences about the missing attributes (Nelson, 1970) and to arrive at an overall assessment of the offer (Franke et al., 2004; Suri and Monroe, 2003) Product attributes and characteristics used as a basis for judging missing attribute information are often referred to as “cues” (Bettman, 1979; Ofir and Lynch, 1984; Olson, 1978; Troutman and Shanteau, 1976). In most consumer purchase contexts, objective product quality is often a missing attribute as consumers typically do not have access to detailed technical information about the product, or may not have personally experienced the use of the product in the past. Therefore, reliance on other attributes such as the country of manufacture, price, brand name and third-party endorsements as indicators of quality becomes a common consumer decision making strategy (Olson and Jacoby, 1972). Research has shown that cue-utilization may be affected by the consumer task characteristics (Olson, 1977) and that it

Price-quality cue utilization in financial services As noted above, research on the price-quality link dates back several decades. While considerable research in recent years has been dedicated to quantifying consumer perceptions of service quality through instruments such as SERVQUAL and SERVPREF (Cronin and Taylor, 1994; Parasuraman et al., 1988), measuring consumer price knowledge (Estelami and Lehmann, 2001; Vanhuele et al., 2006), and examining the use of the price-quality cues (Lichtenstein and Burton, 1989; Olson and Jacoby, 1972), no study to date has examined the extent of price-quality cue utilization in services, especially in the context of financial services. The use of the price-quality 198

Consumer use of the price-quality cue in financial services

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cue has significant importance in the practice of pricing financial services. Price-quality cue utilization would imply that consumers would find higher priced financial services to be of higher quality and may therefore tolerate higher prices more than what conventional marketing thinking would suggest. Examining the strength of the price-quality cue in a financial services context is also important since consumers’ processing of complex decisions such as those encountered in financial markets is highly dependent on simplifying decision strategies which would utilize only a sub-set of the available information (e.g. brand name, price, retail location) at the time of the decision (Estelami, 2007; Grewal et al., 1998; Huber and McCann, 1982). Several characteristics of financial services provide the specific conditions under which the price-quality cue tends to be used by consumers. Financial services are highly intangible. For example, the quality of an insurance product cannot be assessed unless one has to file claims or come into contact with customer service employees. Similarly, the performance of an investment product may not be evident for many years after the purchase of the product, since investment products are typically of a long-term nature. As a result the quality of a financial product or service may be unobservable. This would encourage consumers to focus on other cues such as price in order to assess quality (Bettman, 1979; Richardson et al., 1996): H1. Consumers will use price as a quality cue for financial services.

influenced by individual consumer characteristics as well as the market environment. One of these influencing variables is the extent of the consumer’s exposure to media advertising for the financial service. Advertising may serve as a mechanism for consumer education (Chandran et al., 1979; Smith, 1996; Zhang and Buda, 1999). Therefore consumers that are more exposed to media advertising for financial products may learn more about the product category. In addition, advertising exposure may help reduce consumers’ price sensitivity, as evident from prior research (Bijmolt et al., 2005; Eskin and Baron, 1977; Monroe and Della Bitta, 1978), and therefore encourage consumers to focus on non-price attributes such as image and company name. The shifting of the focus away from price would then result in lower levels of price learning, and encourage consumers to use price as a means for predicting financial services quality. This effect is especially a likely possibility in the context of financial services advertising since financial services ads often do not include price information, due to consumer heterogeneity and marketing regulations which often prohibit or limit the extent of price advertising for financial services (Benn, 1986; Harrison, 2000; Poiesz and Robben, 1994). It is therefore expected that advertising exposure will increase consumers’ reliance on the use of the price-quality cue for financial services: H3. Consumer use of the price-quality cue for financial services will be positively influenced by advertising exposure.

In addition, some financial products tend to provoke little or no consumer involvement. The technical and numeric nature of certain categories of financial services can make them daunting for most consumers, and as a result the level of consumer involvement and knowledge is likely to be low, depending on the financial services category (Estelami, 2007; Harrison, 2000). This fact is further reinforced by the low purchase frequency associated with financial products. Financial transactions, such as the purchase of an insurance policy, investing in a mutual fund, or securing a mortgage occur with far lower frequencies than purchases of other services or goods. The infrequent nature of these financial services transactions can help reduce consumer engagement and interest, and is a contributing factor to consumers’ overall lack of knowledge on certain financial matters, which has been well-documented in consumer research studies (Estelami, 2005; Fox and Lee, 2005; Kinsey and McAlister, 1980). It is therefore expected that consumers would rely on the use of the price-quality cue in financial services decisions to varying degrees. As a result, use of the price-quality cue may be prevalent in financial services, and may also considerably vary in strength from one financial services category to another: H2. Consumer use of the price-quality cue will vary across financial services categories.

The impact of product complexity on use of the price-quality cue Research has shown that consumer reliance on the pricequality cue can increase in decision contexts where the alternatives being examined are complex (Kardes et al., 2004; Rao, 2005; Troutman and Shanteau, 1976). This is because the complexity of the individual alternatives and the decision environment resulting from it can force consumers to simplify their decisions by focusing on the most transparent and seemingly diagnostic elements of information – price being one of them. Given that financial services are often multidimensional and most consumers do not fully comprehend their attributes and functions (Fox and Lee, 2005; Kinsey and McAlister, 1980), the use of the price-quality cue may be a dominant decision strategy for many consumers. The dominance of this strategy may however vary from one consumer to the next and from one financial category to another, due to variations in the degree of perceived product complexity. Financial services which may be considered by a consumer to be difficult to understand or evaluate can lead to higher degrees of consumer confusion with respect to the underlying benefits and merits of the offer. In such cases, the consumer may be more likely to use price as an indicator of quality, than for financial services categories that are easier to comprehend. This pattern of behavior would then be reflected in a positive relationship between perceived product complexity of a financial service and the use of the pricequality cue: H4. Consumer use of the price-quality cue for financial services will be positively influenced by the perceived of the financial service.

The impact of advertising exposure on use of the price-quality cue While the use of the price-quality cue may be evident across various categories of financial services, its use may be 199

Consumer use of the price-quality cue in financial services

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Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

The impact of consumer price knowledge The use of the price-quality cue by consumers may be further related to the level of knowledge they possess with respect to financial services prices (Kardes et al., 2004). This is because a consumer’s prior knowledge of prices may reflect an informed state of mind with respect to the category of interest of interest (Estelami and Lehmann, 2001; Vanhuele et al., 2006). Such knowledge may have been developed due to the diagnostic value of price in assessing the quality of the product. Given the complexity of financial services, consumers may have to rely on price as a diagnostic indicator of quality. Therefore, in order to make good decisions, consumers would have to educate themselves on prices, which are subsequently utilized to establish financial services quality in a decision context. This perspective would therefore suggest a positive relationship between price-quality cue utilization and consumer knowledge of prices for a financial service. Price-quality cue utilization for a financial service may therefore be closely associated with higher levels of consumer price knowledge: H5. Consumer use of the price-quality cue will be positively related to their level of price knowledge for the financial service.

Burton, 1989), general knowledge of financial services prices (Le Boutillier et al., 1994), advertising exposure levels (Estelami, 2005), and perceptions of complexity of the financial service (Lawson et al., 1995). The multi-item scales were then subjected to exploratory factor analysis and scale refinement in order to develop a final set of measurement scales for field administration, reported below. Survey method Respondents were recruited in two shopping areas and provided with an incentive of $2 and a souvenir key chain to engage in the study. They were told that the survey was being conducted with the intention of understanding their knowledge and perceptions of a given type of financial service. They were then told that they would be asked specific questions related to that service. The participant was first provided with the description of the financial service and presented with a sequence of questions measuring his/her use of the price-quality cue, general knowledge of prices, advertising exposure levels, and perceptions of complexity of the service. These items were measured on 1-to-7 rating scales ranging from “strongly disagree” (1) to “strongly agree” (7). The series of questions related to each of the above items are listed in the following sections. The design of the questionnaire items used to measure these variables was based on existing studies on price and service perceptions (Lawson et al., 1995; Le Boutillier et al., 1994; Lichtenstein and Burton, 1989). Most respondents completed the survey in less than ten minutes. Given that there were six different financial services categories being examined (checking accounts, financial advisory services, automobile insurance, home insurance, life insurance, and tax accounting) and a large number of questions were to be asked, it was practically impossible to request each study participant to examine all six categories. Therefore, each study participant responded to questions related to only a single category. Participants were randomly assigned to one of the six categories, and cell sizes ranged from 33 to 37 subjects. In total, 223 participants were administered surveys, of which 13 provided unusable responses, resulting in a final sample size of 210. The demographics of the final sample closely resemble the underlying population of the county where the study was conducted, and the utilized approach to data collection was consistent with prior studies (Dickson and Sawyer, 1990; Lawson et al., 1995; Le Boutillier et al., 1994; Lichtenstein and Burton, 1989; Turley and Cabannis, 1995).

Empirical study Overall study design In order to establish the drivers of consumers’ use of the price-quality cue across an array of financial services, a consumer survey was conducted. The survey was administered through intercept interviews in two shopping areas in northeastern USA with similar shopper demographics. The survey was designed to measure the extent by which consumers associated higher prices with higher levels of quality for an array of financial services, utilizing a series of structured questions. Additional questions were also used to measure the level of consumer exposure to advertising, their overall knowledge of prices, as well as their perceptions of the complexity of the financial services category. The survey was completed by asking respondents to provide general demographic and background information. Prior to field administration, in order to ensure that the research instrument is valid, two rounds of pretests were conducted. In the first round 18 consumers recruited through a convenience sampling approach were asked to examine a list of 12 financial services and to rate the services on a 1-to-7 Likert scale based on the extent by which they believe these services would be needed by them in the next five years. This helped identify financial services with most relevance. The six financial services with the highest relevance ratings were identified as: checking accounts, financial advisory services, automobile insurance, home insurance, life insurance, and tax accounting. The reduced number of financial services categories allowed for a more efficient administration of the final research instrument by reducing the sample size requirements. In order to refine the measurement scales for the independent and dependent variables, a second pretest was conducted, using 31 graduate business students. Multiitem scales were adopted from existing research to measure the extent of price-quality cue utilization (Lichtenstein and

Results To examine the variations in consumers’ use of price as an indicator of service quality for financial services, two questionnaire items specifically focusing on this issue were analyzed. The two questions were: “The higher the price of this type of product, the higher the quality,” and “For this type of product, one can use price to judge how good the product is.” For each of the six categories, average consumer ratings on the 1-to-7 response scale were computed. These are shown in Table I. 200

Consumer use of the price-quality cue in financial services

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Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

Table I Consumers’ use of the price-quality cue across financial services categories

Category Accountant Advisory services Auto insurance Checking account Home insurance Life insurance

“For this type of product, one can use price to judge how good the product is”

“The higher the price of this type of product, the higher the quality”

Combined scale average

% of respondents above the median of the combined scale

3.26 3.65 3.64 2.44 3.44 3.52

3.23 3.62 3.03 2.25 3.12 3.27

3.24 3.63 3.34 2.35 3.28 3.39

14.3 26.5 13.5 0.0 14.3 22.6

use of the price-quality cue for financial services (H1), and the existence of cross-category variations in the use of this cue (H2). In order to assess the variations in price knowledge across financial services categories, price knowledge was quantified for each respondent using two questions. The questions were: “My knowledge of prices in this type of service is quite good,” “I am very confident in my price estimates for this service,” and “I’m good at guessing prices of this type of service.” Each of the questions was rated on a 1-to-7 Likert scale ranging from “Strongly Disagree” (1) to “Strongly Agree” (7). The coefficient alpha among the three items was 0.9, and the average of the three items was used to quantify price knowledge. A plot of this measure for each of the six financial services categories is shown in Figure 1, and the resulting contrast tests are reported in Appendix 1.

As can be seen from the table, financial services exhibit high levels of association between price and quality. Since the two questions are highly correlated (r ¼ 0:69), in order to facilitate a simpler analysis of the numeric results, an average of the ratings for the two questionnaire items was produced and is also reported in Table I. Overall, across all six financial services studied, the average rating on the combined price-quality cue-utilization scale was 3.26 (standard deviation ¼ 1:36), exceeding the mid-point of the 1-to-7 scale. Furthermore, only 13 percent of the respondents scored below 2 (“strongly disagree” or “disagree”) on the response scale indicating, that the majority of consumers utilize the price-quality cue in financial services. Furthermore, certain categories of financial services exhibit high levels of association between price and quality. The highest degree of such an association can be observed for financial advisory services, with an average price-quality cue utilization rating of 3.63. The second highest degree of use of the price-quality cue can be seen for life insurance. In these categories, consumers tend to believe that higher prices reflect higher levels of quality. In contrast, the association between price and quality is perceived by consumers to be weakest for checking accounts, with an average rating of 2.35. The remaining financial services (accounting, automobile insurance, home insurance) lie between these two extremes and reflect moderate levels of the usage of the price-quality cue. The differences in the averages for the combined scale observed in Table I, assessed using ANOVA, are found to be significant at the p , 0.01 level (F5;203 ¼ 3:69). To further analyze the data the percentage of respondents who show a combined price-quality utilization rating beyond the mid-point of the scale for each category were computed and is also reported in Table I. A similar pattern as observed earlier for the averages is evident with respect to this measure, across the various financial services categories. Financial advisory services exhibit the highest percentage, followed by life insurance. Similar to the earlier results, checking accounts exhibit a weak pattern of price-quality use, with no respondent expressing responses beyond the mid-point of the combined scale. The statistical significance of the differences in price-quality cue utilization percentages reported in Table I across the financial services categories was tested using Chi-square analysis and found to be significant (x25 ¼ 11:08; F ¼ 0:23). The results of Table I demonstrate existence of the price-quality cue for financial services and specific cross-category variations. These results support the first two hypotheses concerning the consumers’

Figure 1 Average price knowledge by category

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Consumer use of the price-quality cue in financial services

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As can be seen from Figure 1, across the various categories, significant variations in consumer price knowledge exist. For example, for checking accounts and automobile insurance, which are a highly commoditized, frequently purchased and widely used financial services, consumer price knowledge is the greatest. In contrast, home insurance, and financial advisory services, which are financial services with relatively low transaction frequencies, exhibit lower levels of consumer price knowledge. The differences in average price knowledge levels across financial services categories were tested using ANOVA and found to be statistically significant at the p , 0.01 level (F5;204 ¼ 7:48). A similar analysis was done to examine the cross-category variations in perceived product complexity across financial services categories. Perceived product complexity was quantified using the following two questions, rated on 1-to-7 Likert scales: “This product category is complex,” and “This is a very simple type of product” (reversed). The average of the two measures was used to quantify perceived product complexity, and plot of this measure for all six financial services categories can be found in Figure 2. As can be seen in Figure 2, variations in perceived product complexity exist across the various financial services categories. For example, while checking accounts – a highly commoditized and standardized financial service – exhibit the lowest level of perceived product complexity, professional services such as those offered by financial advisors and accountants exhibit the highest levels of perceived complexity. The cross-category differences in perceived complexity, tested

using ANOVA, were found to be significant (F5;204 ¼ 9:57; p , 0.01), and the associated contrast tests are reported in Appendix 2. To further explore differences in advertising exposure across financial services categories, two other survey items in the questionnaire were used. The first item asked the respondents to respond to the question: “I recall seeing and advertisement for this type of service”, and the second item asked them to respond to: “The media is full of ads for this type of service”. The correlation between these two measures was 0.72, and the averages for consumers’ responses to these two items were then computed for each respondent as a measure of advertising exposure. These averages are shown in Figure 2. Differences across financial services categories can be observed, with commonly used financial services such as checking accounts and automobile insurance showing the highest levels of advertising exposure, and specialized categories such as financial advisory services and accounting showing the lowest levels. The cross-category differences in advertising exposure, tested using ANOVA were found to be significant at the p , 0.01 level (F5;204 ¼ 3:50), and the associated contrast tests are reported in Appendix 3. To examine the combined impact of price knowledge, product complexity and advertising exposure on price-quality cue utilization a regression analysis was conducted, the results of which are shown in Table II. Dummy variable coding was used in order to isolate category-based differences in pricequality cue utilization. By doing this, the relative impact of the financial category on price-quality utilization can be isolated from other intervening variables. The overall regression is significant at the p , 0.01 level (F8;200 ¼ 4:54). As can be seen from the regression results, the dummy variable coefficient for checking accounts (intercept) is negative and significant, indicating that consumers tend not to utilize the price-quality cue in this category. In contrast the coefficient for financial advisory services is positive and the largest among the six categories, indicating a higher propensity by consumers to use the price quality cue in this category – a result that is consistent with those observed in Table I, and H2. The results show that the impact of advertising exposure on the use of the price-quality cue is positive ( p , 0.01). This result supports the perspective that advertising can change consumers’ price perceptions by encouraging them to consider price as an indicator of financial services quality, as hypothesized in H3. However, the coefficient for product complexity is not significant (p ¼ 0:44), indicating no relationship between price-quality cue utilization and perceived product category complexity. As a result, H4 is not supported. This effect may, however, be a result of the fact that financial products are generally perceived to be highly complex and cross-category variations may not be very pronounced, resulting in no notable influence on price-related responses such as the use of the price-quality cue in decision making. The results also indicate a positive coefficient for consumer price knowledge, significant at the p , 0.05 level. This result indicates that there is a positive relationship between the use of the price-quality cue and consumer price knowledge, as hypothesized in H5, and confirms the view that increased use

Figure 2 Average perceived product complexity and advertising exposure by category

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Table II Regression results Predictor Dummy variable (Accountant; Intercept) Dummy variable (Advisory services) Dummy variable (Auto insurance) Dummy variable (Checking account) Dummy variable (Home insurance) Dummy variable (Life insurance) Price knowledge Product complexity Advertising exposure

Coefficient

t-value

Significance

1.89 0.41 20.15 21.09 0.19 0.14 0.14 0.06 0.15

4.12 1.22 20.47 23.30 0.59 0.45 2.26 0.77 2.61

p , 0.01 p ¼ 0:22 p ¼ 0:64 p , 0.01 p ¼ 0:55 p ¼ 0:66 p ,0.05 p ¼ 0:44 p , 0.01

Notes: F8;200 ¼ 4:54; R2 ¼ 0:15

tendency for customers to stay with their existing insurance provider over the years, without actively shopping around for competing prices (Harrison, 2000). Moreover, similar to life insurance products, the purchase decision may be highly centralized and delegated to the head of the household, thereby reducing the likelihood that the general population would be aware of prices. Unlike life insurance, the category is generally not heavily advertised and is not considered to be highly complex by consumers. Consumer use of the pricequality cue in this category suggests that for some consumers preference could be given to competitors at the higher ranges of the price spectrum. The automotive insurance category does not exhibit the same intensity of use for the price-quality cue by consumers as most of the other categories studied here. Furthermore, consumers seem to believe that they are quite knowledgeable about prices in this category. This may be a result of the higher frequency of purchase associated with the category, the low level of brand loyalty in the marketplace, or the high levels of general mass media advertising which has trained consumers to shop around for automobile insurance prices. As a result of the above category characteristics, the opportunities for differentiation-based pricing strategies which may capitalize on the price-quality cue may be somewhat limited. This is not to say that consumers will always opt for lower-priced competitors, but that the likelihood of such a behavior taking place is more likely in automobile insurance than in, say, life or homeowners insurance categories. In contrast, the financial advisory service category tends to heavily experience consumer use of the price-quality cue. Furthermore, the study results indicate that consumers tend to be unaware of prices in this category. This unique combination seems to promote a perceived-value pricing approach by financial advisors. The fact that consumers consider the service to be highly complex and that similar to insurance products the ultimate quality of the service may not be clear for many years (e.g. the return on one’s investment portfolio configured by a financial advisor) supports the use of perceived value and image-based pricing strategies in this category. The results of this study clearly show that consumers do not associate higher prices with higher levels of quality for checking accounts. The standardized nature of this category and the fact that service quality can be easily observed by the

of the price-quality cue may trigger consumers to pay closer attention to prices and therefore can be associated with higher levels of consumer price knowledge.

Managerial implications Based on the results of this survey several marketing implications can be drawn for the various financial services categories studied. It is important to note, however, that these implications need to be considered in light of the specific competitive environment in which the marketer is operating (Estelami and Lehmann, 2001; Estelami et al., 2002; Harrison, 2000). Therefore no definitive recommendations can be made in the absence of specific company information and without careful consideration of the competition, customers, regulations, and potential reactive strategies within an industry. However, the use of the price-quality cue by consumers in evaluating financial services offers points to significant vulnerabilities that exist in consumer decisions and highlights the need for careful examination of consumer protection issues. The following discussion provides some general perspectives on pricing and marketing practices for the various financial service categories studied. The life insurance category exhibits a high level of use of the price-quality cue by consumers. In this category, higher prices seem to suggest higher levels of quality. Given the fact that for life insurance validation of service quality and service delivery (e.g. payout of claim amount in case of a policyholder’s death) is limited to the image of the company and perceived customer service (i.e. the policyholder personally does not experience the claims process with the exception of policies with living benefits), price may very well serve as a confidence-building attribute in consumer decisions. This observation, combined with the low level of consumer price awareness within the category, enables increased reliance on image-driven and perceived value pricing methods. Furthermore, the level of mass media advertising in this category highlights the importance of maintaining a consistent level of advertising intensity and focusing on a unique message to counter competitive advertising efforts. The homeowner insurance category exhibits patterns similar to life insurance as far as consumers’ use of the price-quality cue and the low level of consumer knowledge about prices. The low level of price knowledge in the category may be a result of infrequent consumer exposure to purchase scenarios and the 203

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customer through indicators such as the length of the tellers’ line, the appearance of the bank branch, or the speed by which transactions are processed, reduces the need for the customer to rely on any external information cues such as price in the determination of service quality. Furthermore, consumers exhibit high levels of price knowledge about the category and perceive it to be of little complexity. Therefore, the ability of retail banks to capitalize on premium pricing through perceived-value methods may be limited. Tax accounting services on the other hand exhibit moderate use of the price-quality cue, moderate levels of consumer price knowledge and moderate levels of advertising exposure. Therefore, while in the tax accounting category consumers are likely to exhibit some preference for perceived value pricing approaches, they are also likely to exhibit reasonable levels of price sensitivity.

phenomenon. Given the high amount of consumer spending associated with financial services, and the impact of such services on the well-being of the public, it is hoped that this paper has triggered interest for further inquiry by academicians, practitioners and consumer advocates, in this relatively unexplored area of pricing research.

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Conclusion The results of this study indicate clear differences across financial services categories in terms of consumers’ use of price as an indicator of quality. Knowledge of these differences is critical in the promotion, advertising and general market positioning of brands within each category. Categories where the price-quality cue is stronger provide greater opportunities for differentiation and variations in service offerings. It is therefore critical for financial services providers to not only understand the strength of price as a determinant of consumers’ quality inferences, but to also have a clear understanding of the positioning of their own brand of service with respect to their competitors. Gaining such an understanding is a critical aspect of the pricing practice for services where quality may be illusive and consumers may heavily rely on cues such as price to assess the quality of a service provider. This form of price-based quality assessment becomes even more critical in financial services, especially from a public policy perspective, due to the high levels of consumer spending in financial services categories, and the significant impact that financial services can have on the economic stability and well-being of the public. It is important to note that each competitor needs to have an accurate empirical estimate of consumers’ tolerance levels for prices and the price ranges which consumers might typically consider to be representative of higher levels of quality. While price tolerance was not measured in this study, one needs to accurately assess market demand and possible competitive reactions at various price points through formal market research in order to optimize the pricing of a given financial service. Future research in this field can expand the range of the predictor variables that may influence consumers’ use of the price-quality cue. Individual-based variables such as need-forcognition or category-based involvement can significantly influence the use of the price-quality cue in financial services, and can therefore provide new avenues for research in this area. Furthermore, future research can explore the use of the price-quality cue in a wider range of financial services categories. Alternative measures of variables influencing price-quality cue utilization, such as objective price knowledge, rather than self-reported price knowledge used in this study may also help deepen our understanding of this 204

Consumer use of the price-quality cue in financial services

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Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

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Further reading Estelami, H. (1999), “The computational effect of price endings in multi-dimensional price advertising”, Journal of Product & Brand Management, Vol. 8 No. 3, pp. 244-56. Nelson, P. (1974), “Advertising as information”, Journal of Political Economy, Vol. 82 No. 3, pp. 729-54. 205

Consumer use of the price-quality cue in financial services

Journal of Product & Brand Management

Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

Appendix 1 Table AI Bonferroni contrast tests for price knowledge Product comparison Checking Checking Checking Checking Auto insurance Auto insurance Auto insurance Auto insurance Auto insurance Accountant Accountant Accountant Accountant Accountant Life insurance Life insurance Life insurance Life insurance Life insurance Advice Advice Advice Advice Advice Home insurance Home insurance Home insurance Home insurance Home insurance

Difference between means

Auto insurance Life insurance Advice Home insurance Checking Accountant Life insurance Advice Home insurance Checking Auto insurance Life insurance Advice Home insurance Checking Auto insurance Accountant Advice Home insurance Checking Auto insurance Accountant Life insurance Home insurance Checking Auto insurance Accountant Life insurance Advice

0.6001 1.4865 1.6449 2.0037 20.6001 0.4988 0.8864 1.0448 1.4036 21.0989 20.4988 0.3876 0.5459 0.9048 21.4865 20.8864 20.3876 0.1583 0.5172 21.6449 21.0448 20.5459 20.1583 0.3588 22.0037 21.4036 20.9048 20.5172 20.3588

Note: *Indicates differences are significant at the p , 0.05 level

206

Simultaneous 95% limits Confidence Limits 20.5157 0.3379 0.5051 0.8723 21.7159 20.6250 20.2548 20.0875 0.2798 22.2304 21.6227 20.7689 20.6018 20.2346 22.6352 22.0276 21.5441 21.0064 20.6393 22.7847 22.1771 21.693 21.3231 20.7889 23.1351 22.5275 22.0441 21.6737 21.5065

1.7159 2.6352 * 2.7847 * 3.1351 * 0.5157 1.6227 2.0276 2.1771 2.5275 * 0.0325 0.6250 1.5441 1.6936 2.0441 20.3379 * 0.2548 0.7689 1.3231 1.6737 20.5051 * 0.0875 0.6018 1.0064 1.5065 20.8723 * 20.2798 * 0.2346 0.6393 0.7889

Consumer use of the price-quality cue in financial services

Journal of Product & Brand Management

Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

Appendix 2 Table AII Bonferroni contrast tests for product complexity Product comparison Advice Advice Advice Advice Advice Accountant Accountant Accountant Accountant Accountant Life insurance Life insurance Life insurance Life insurance Life insurance Auto insurance Auto insurance Auto insurance Auto insurance Auto insurance Home insurance Home insurance Home insurance Home insurance Home insurance Checking Checking Checking Checking Checking

Difference between means

Accountant Life insurance Auto insurance Home insurance Checking Advice Life insurance Auto insurance Home insurance Checking Advice Accountant Auto insurance Home insurance Checking Advice Accountant Life insurance Home insurance Checking Advice Accountant Life insurance Auto insurance Checking Advice Accountant Life insurance Auto insurance Home insurance

1.1634 1.3266 1.4233 1.4920 2.0539 21.1634 0.1632 0.2598 0.3286 0.8905 21.3266 20.1632 0.0966 0.1654 0.7273 21.4233 20.2598 20.0966 0.0687 0.6306 21.4920 20.3286 20.1654 20.0687 0.5619 22.0539 20.8905 20.7273 20.6306 20.5619

Note: *Indicates differences are significant at the p , 0.05 level

207

Simultaneous 95% limits Confidence Limits 0.2384 0.3879 0.5107 0.5670 1.1352 22.0885 20.7689 20.6460 20.5897 20.0214 22.2654 21.0953 20.8232 20.7668 20.1985 22.3359 21.1657 21.0165 20.8371 20.2687 22.4171 21.2469 21.0975 20.9745 20.3500 22.9726 21.8024 21.6531 21.5300 21.4738

2.0885 * 2.2654 * 2.3359 * 2.4171 * 2.9726 * 20.2384 * 1.0953 1.1657 1.2469 1.8024 20.3879 * 0.7689 1.0165 1.0975 1.6531 20.5107 * 0.6460 0.8232 0.9745 1.5300 20.5670 * 0.5897 0.7668 0.8371 1.4738 21.1352 * 0.0214 0.1985 0.2687 0.3500

Consumer use of the price-quality cue in financial services

Journal of Product & Brand Management

Hooman Estelami

Volume 17 · Number 3 · 2008 · 197 –208

Appendix 3 Table AIII Bonferroni contrast tests for advertising exposure Product comparison Auto insurance Auto insurance Auto insurance Auto insurance Auto insurance Checking Checking Checking Checking Checking Life insurance Life insurance Life insurance Life insurance Life insurance Home insurance Home insurance Home insurance Home insurance Home insurance Accountant Accountant Accountant Accountant Accountant Advice Advice Advice Advice Advice

Difference between means

Checking Life insurance Home insurance Accountant Advice Auto insurance Life insurance Home insurance Accountant Advice Auto insurance Checking Home insurance Accountant Advice Auto insurance Checking Life insurance Accountant Advice Auto insurance Checking Life insurance Home insurance Advice Auto insurance Checking Life insurance Home insurance Accountant

0.6712 0.8530 1.3093 1.3236 1.3820 20.6712 0.1818 0.6381 0.6524 0.7108 20.8530 20.1818 0.4563 0.4706 0.5290 21.3093 20.6381 20.4563 0.0143 0.0727 21.3236 20.6524 20.4706 20.0143 0.0584 21.3820 20.7108 20.5290 20.0727 20.0584

Note: *Indicates differences are significant at the p , 0.05 level

Corresponding author Hooman Estelami can be contacted at: estelami@fordham. edu

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208

Simultaneous 95% limits Confidence Limits 20.5264 20.3719 0.1030 0.1173 0.1666 21.8688 21.0511 20.5763 20.5620 20.5126 22.0779 21.4147 20.7850 20.7707 20.7211 22.5155 21.8525 21.6975 21.2086 21.1592 22.5298 21.8667 21.7118 21.2372 21.1734 22.5973 21.9342 21.7791 21.3045 21.2902

1.8688 2.0779 2.5155 * 2.5298 * 2.5973 * 0.5264 1.4147 1.8525 1.8667 1.9342 0.3719 1.0511 1.6975 1.7118 1.7791 20.1030 * 0.5763 0.7850 1.2372 1.3045 20.1173 * 0.5620 0.7707 1.2086 1.2902 20.1666 * 0.5126 0.7211 1.1592 1.1734