Using Dissatisfied Customers as a Source for ...

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Using Dissatisfied Customers as a Source for Innovative Service Ideas Philippe Duverger Journal of Hospitality & Tourism Research 2012 36: 537 originally published online 18 July 2011 DOI: 10.1177/1096348011413591 The online version of this article can be found at: http://jht.sagepub.com/content/36/4/537

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USING DISSATISFIED CUSTOMERS AS A SOURCE FOR INNOVATIVE SERVICE IDEAS Philippe Duverger Towson University When service firms are challenged by their aging brands to become “innovation oriented,” they depend in part on key organizational competencies, such as employee competency and market orientation. These competencies are at the core of innovation in a service firm such as a hotel or a restaurant franchise. An innovation-oriented service franchise knows how to listen to its employees and customers; however, choosing which customer or employee to listen to might be paramount to finding radical innovative ideas. Learning from the past, this article investigates sources of innovation in the hospitality industry and tests the lead-user method borrowed from the manufacturing industry to extract innovative service ideas from the market before they materialize as competing market offerings. Using a downtown hotel as an empirical example, this study shows that not only are innovative service ideas present in the marketplace, but also that the best ideas most likely exist within the minds of current and past dissatisfied clients, the latter of which is often referred to as “service defectors.” KEYWORDS:  service innovation; innovation orientation; innovative users; defectors “It is the customer who determines what a business is, what it produces, and whether it will prosper.” —Drucker (1954, p. 37) INTRODUCTION

With services now dominating the global economy (Berry, Shankar, Parish, Cadwallader, & Dotzel, 2006), competition is fierce and differentiation through innovation is paramount (Ottenbacher, 2007). Incremental innovations no longer suffice to acquire new customers, but radical, or breakthrough service innovations are now needed. Despite its economic importance (Berry et al., 2006; Menor, Tatikonda, & Sampson, 2002), the service sector remains underresearched by analysts of innovation (Ottenbacher & Gnoth, 2005; Tether, 2003). Services are seen as innovation “laggards” (Miles, 1993), with weak intellectual protection (Howells, 2000), and R&D spending growing only in technology intensive service companies. Innovative ideas capable of creating new markets (Berry et al., 2006), come much more sporadically once the company is in the growth phase Journal of Hospitality & Tourism Research, Vol. 36, No. 4, November 2012, 537-563 DOI: 10.1177/1096348011413591 © 2012 International Council on Hotel, Restaurant and Institutional Education Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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(Price, 1997). Past the initial innovative creation phase, the service firm, seeking controlled rapid growth, imposes on each unit of its chain a set of standard operating procedures that allows for high client satisfaction but stifles the creativity needed to generate radical ideas. Service firms that adapt well to the dynamics of the market are more likely to have an orientation toward innovation. The challenge of becoming “innovation oriented” depends in part on key organizational characteristics such as employee competency and market orientation (Siguaw, Simpson, & Enz, 2006). Employee competency seems to be required at high levels of the organization (i.e., CEO), as evidenced by examples of innovation in hospitality that seem to be significantly affected by individual outstanding “champions,” but disappear once the champion moves on to a new job (Enz & Siguaw, 2003). A case can be made that once the CEO leaves, although the innovation stays (e.g., Heavenly beds at Westin, Filet-O-Fish at McDonald’s), the innovation orientation may be weakened. It is suggested that market orientation (Narver & Slater, 1990), for its part, may be an antecedent to a firm’s innovative output (Siguaw et al., 2006). Firms that listen to their customers using voice-of-the-customer techniques such as surveys and focus groups, and that routinely scan the market for intelligence, are better at being innovative (Urban & Hauser, 2004). This article argues that listening to the customer can be of strategic importance for the future of hospitality firms as well as service firms in general. A large majority of innovation has come from entrepreneurs who were dissatisfied as customers, not industry experts. These customers-entrepreneurs reside at the forefront of innovation and often create disruptive services that become market changers. Learning from the past, this article investigates the sources of innovation in the hospitality industry and tests a method borrowed from the manufacturing industry to extract innovative ideas from the market before they materialize as competing market offerings. We show that not only are innovative service ideas present in the marketplace, but also that the best ideas most likely exist within the minds of current and past dissatisfied clients, the latter of which is often referred to as “service defectors” (Reichheld, 1993, 1996; Reichheld & Sasser, 1990). SOURCES OF INNOVATION IN THE HOSPITALITY INDUSTRY

With possibly the exception of a few, such as E. M. Statler,1 most innovators seem to originate from outside the hospitality industry, having diverse backgrounds (e.g., bankers, architects, realtors, home builders, and sales people). One common characteristic unites these innovators: they were all customers who became so dissatisfied by their experience that they decided to create their own hotel or restaurant to satisfy their unmet needs. Some of the most innovative ideas in well-established franchises, such as Starbucks’s Frappuccino or McDonald’s Egg McMuffin, came from customers, employees, or franchisees Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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rather than the corporate R&D department (Kroc & Anderson, 1977; Schultz & Yang, 1997). Examples of hotel or restaurant customers turning entrepreneurs are prevalent in the last 100-year history of hospitality (American Hotel & Lodging Association, 2009). In fact, 60% of the top 10 hotel corporations in the world, representing 65% of the world industry, have been created by entrepreneurs who did not receive any formal training in hotel management. Similarly, 80% of the top 10 restaurant concepts were brought to market by “non-restaurateurs” (see Table 1). These astonishing numbers are also fairly consistent with innovative service practices as evidenced by the Siguaw, Enz, Kimes, Verma, and Walsh’s (2009, 2010) two-part report: “Cases in Innovative Practices in Hospitality and Related Service.” More than 47% of these current innovative practices have been created by entrepreneurs outside of the hospitality industry who were more often than not disappointed by their previous experiences. These innovators fit Eric von Hippel’s (2005) description of a particular category of customers called lead-users. We will first review the characteristics of lead-users and argue that industry examples cited above fit the profile depicted by von Hippel; then we will argue, in light of the current literature, that potential lead-users can be identified in the service industry by seeking dissatisfied customers, service defectors, and frequent service switchers with high level of industry knowledge. LEAD-USERS Lead-Users Characteristics

Lead-Users have four specific characteristics and motives (Lüthje, 2004, von Hippel, 1986). First, they have a strong personal need that is ahead of the marketplace; and will become a general market need, months or years later. Second, they are positioned to personally benefit significantly from their ideas, hence motivating them to create a solution to their problem. Third, they are willing to share their ideas freely. Finally, they have sufficient knowledge of the industry, or alternatively of an analogous industry, to be able to technically produce their innovation. Lead-users also exhibit characteristics such as opinion leadership and innovativeness (Rogers, 2003). In that respect, lead-users are ahead of the diffusion curve. Numerous studies of major innovations created by users have been identified in industries as diverse as scientific instruments (von Hippel, 1986), semiconductors (Urban & von Hippel, 1988), windsurfing (Franke & Shah, 2003), software development (Weber, 2004), and sporting goods (Piller & Walcher, 2006). Between 9.0% and 37.8% of innovations in these industries are attributed to lead-users (von Hippel, 2005). Thus, we hypothesize that potential lead-users will be found within the group of dissatisfied customers, left with unmet needs they seek to fulfill and are willing to share with the service provider. Hypothesis 1: Dissatisfied customers will produce better innovative service ideas than satisfied customers. Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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Table 1 Analysis of the Sources of Innovation in the Top Service Concepts Top 10 Hotel Corporations InterContinental Hotels Wyndham Worldwide

Original Brand

No. of Rooms

Creator, Industry

Holiday Inn

3,606

Kemmons Wilson, entrepreneur

Ramada, Days Inn Marriott Hilton Quality Inns

6,344

Accor

Novotel Motel 6

4,065

Best Western International Starwood Hotels & Resorts Worldwide

Best Western Sheraton Westin

4,195

Marion Isbell, restaurateur, Cecil B. Day, real estate J. Willard Marriott, fast food owner Conrad Hilton, retail J. F. Paterson and A. J. Mckay, hoteliers Paul Dubrule and Gerard Pelisson, salesman and IBM engineer; Paul Greene home builder M. K. Guertin, hotelier

Carlson Hospitality Global Hyatt Corp

Radisson Hyatt

922 731

Marriott International Hilton Hotels Corp Choice Hotels Corp

Total As a % of the top 300

2,741 2,817 5,897

845

32,163 65%

Top 10 Restaurant Chains

No. of Units

McDonald’s KFC

30,766 13,731

Burger King

11,141

Pizza Hut Subway Wendy’s Starbucks Taco Bell Domino’s Pizza

12,572 24,810 6,746 10,241 6,090 8,079

Applebee’s Grill & Bar

1,804 Total As a % of the top 400

Ernest Henderson and Robert Moore, hoteliers; Severt W. Thurston and Frank Dupar hoteliers Curt Carlson, trading stamps Hyatt von Dehn and Jack D. Crouch, entrepreneurs; Jay Arthur Pritzker, lawyer

Creator, Industry Ray Kroc, salesman Colonel Sanders, gas station owner James McLamore and David R. Edgerton, fast food owners Dan and Frank Carney, students Fred De Luca, student Dave Thomas, KFC executive Howard Schultz, salesman Glen Bell, Marine Corps Tom Monaghan, student architecture Bill and T.J. Palmer, Burger King manager

125,980 50%

Source: Hotels Top 300 Corporations, Restaurants, & Institutions Top 400 Restaurant Chains. Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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Identifying Potential Lead-Users

Given the unique service context of intangibility and co-creation, it is typically not be possible to detect lead-users in the process of creating service prototypes before they actually become implemented, more likely by competitors. At this point, it might be too late for the incumbent service provider to capture the innovative idea, develop it, and market it. Hence, capturing the ideas in the mind of a creative customer needs to be timely and needs to use a specific process of identification. This article proposes that the probability of detecting potential lead-users or innovative customers is greater when tapping into the service provider’s defectors as a primary source. Why Would the Most Innovative Customers Be Defectors?

Customers’ innovative service ideas are triggered either by their unmet needs and/or by their knowledge of an analogous service at an alternative provider that was most likely acquired via switching behavior and motivated by variety seeking tendencies. Although ordinary users have been shown to have innovative ideas (Magnusson, 2009), they often lack the technical knowledge to implement them. Capraro, Broniarczyk, and Srivastava (2003) observe that the level of knowledge about alternatives advances the understanding of (lack of) defection. For instance, it helps to explain why dissatisfied customers sometimes do not defect. If they simply have a lower level of knowledge about alternatives, this would explain why dissatisfied customers may not defect. Research has shown that consumer knowledge is intimately linked to search behavior (Moorman, Diehl, Brinberg, & Kidwell, 2004) and switching behavior. Switching Behavior

An examination of the service-switching literature (including the literature subsumed under the umbrellas of “loyalty,” “customer retention,” “service quality,” and “satisfaction” research) reveals that the most commonly studied predictors of service provider switching include “transactional” variables such as quality, satisfaction, switching costs and alternative attractiveness, and “relationship” variables such as customer commitment and trust. Satisfaction and the conceptually close construct of service quality have received the most attention in this research. As Jones, Mothersbaugh, and Beatty (2000) point out, the primary empirical and conceptual focus in customer retention research has been on satisfaction, yet the predictive abilities of this variable have shown considerable variability across studies. There is abundant evidence that quality, the consumer’s overall impression of the relative superiority of the service (Taylor & Baker, 1994), and satisfaction, “the summary psychological state resulting when the emotion surrounding disconfirmed expectations is coupled with the consumer’s prior feelings about the consumption experience” (Oliver, 1981, p. 27), is related to switching intentions (e.g., Cronin & Taylor, 1992; Taylor & Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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Baker, 1994). The higher the perceived quality and the more satisfied the customer, the less likely he or she is to switch service providers. Two other predictors that have received considerable attention are switching costs and alternative attractiveness. Switching costs are “the perceived economic and psychological costs associated with changing from one alternative to another” (Jones et al., 2002, p. 441). Burnham, Frels, and Mahajan (2003) found evidence of eight types of switching costs: economic risk costs, evaluation costs, learning costs, set-up costs, benefit–loss costs, monetary loss costs, personal relationship loss costs, and brand relationship loss costs. As switching costs increase, the likelihood that customers will switch service providers decreases (Bansal, Taylor, & St. James, 2005; Burnham et al., 2003; Ping, 1993). The attractiveness of alternatives to a customer’s current service provider may also influence his/her intentions to switch service providers. (Bansal et al., 2005; Ping, 1993). The more attractive the alternative service providers, the more likely customers are to switch. Recently, researchers have begun to include “relationship variables” in their service switching models; the most prominent being customer commitment and trust. Customer commitment is defined as a “force that binds the consumer to the service-provider out of desire” (Bansal, Irving, & Taylor, 2004, p. 238) or an individual’s “psychological bond” (Gruen, Summers, & Acito, 2000, p. 37) with a service provider. Trust refers to the consumer’s feeling that the seller will fulfill promises (Morgan & Hunt, 1994). There is growing evidence that commitment and trust drive switching intentions and behaviors (e.g., Bansal et al., 2004; Gruen et al., 2000). Although much of the extant research would suggest that evaluative responses (service quality and satisfaction) are likely the primary drivers of customer defection (e.g., Cronin, Brady, & Hult, 2000; Jones et al., 2000; Keaveney, 1995; Susskind, 2005), there is growing evidence that this may not always be the case. Capraro et al. (2003) argue that “satisfaction explains a relatively small proportion of variance (less than 8%) in repurchasing behaviors” (p. 164). Recently, several researchers (Bansal et al., 2005; Burnham et al., 2003) found that variables that facilitate the decision to switch (such as switching costs) play a more important role than quality and satisfaction do in customer defection. In particular, Burnham et al. (2003) have conceptualized the experience with a particular product domain in the study of switching cost perception. These authors have shown that domain expertise is an antecedent to consumer switching behavior when the expertise is high and leads to higher mental structures, making encoding and retrieval easier and faster, while reducing the perception of uniqueness of an existing provider, hence weakening the strength of the current relationship. Studies have found that a vast majority of consumers fail to act through a complaint (Chebat, Davidow, & Codjovi, 2005). Switching cost perception is suggested as a reason for consumers to take no action (complaints or switching) when dissatisfaction occurs (Day & Landon, 1977; Singh, 1988). However, Chebat et al. (2005) argue, in light of the cognitive–emotive theory (Lazarus & Folkman, 1984), that some consumers with low levels of assertiveness will not seek redress via complaint behavior because they might perceive the cause of service failure to be outside of the control of the provider. Thus, these consumers seem to Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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rationalize that it is not worth mobilizing their energy for a confrontation with the firm. Therefore, it is possible for a customer to defect to a new service provider because he or she is dissatisfied not with the current level of performance of the service provider, but rather with the existing gap between the current provider and the best provider he or she is aware of in the marketplace. In some cases, we argue that the customer switches in hope of finding a better provider that will satisfy his or her unmet needs, knowing that the possibility of not finding a better service is entirely possible. This behavior would be consistent with variety seeking, a personality trait often associated with innovators and early adopters also identified as a cause for switching (Keaveney, 1995; Keaveney & Parthasarathy, 2001). Variety-Seeking Behavior

Variety seeking is defined as “the tendency of individuals to seek diversity in their choices of services and goods” (Kahn, 1995, p. 139). It has been linked to the underlining reason for varied consumption (Givon 1984; Menon & Kahn, 1995) and identified as a cause for switching (Keaveney, 1995; Keaveney & Parthasarathy, 2001). Variety seeking is a cognitive need for stimulation (Ratner, Kahn, & Kahneman, 1999) or curiosity (Raju 1980). Huber and Reibstein (1978) suggest that consumers seek variety because they are not able to find a single option that contains all of the attributes of an ideal product. The literature focusing on the explanation of variety-seeking behavior can be summarized into two streams of research: variety-seeking behavior is the result of some other motivation (derived) or is a motivation in and of itself (direct). If derived, varied behavior is either an artifact of multiple needs (multiple use of the same purchase, multiple users within the same household, multiple situations leading to varied behaviors) or of changes in the choice problem (changes in constraints or taste, changes in alternative availability). More pertinent to our discussion, if variety-seeking behavior is direct, then it is inherently rewarding (variety seeking leads to an optimum level of stimulation). Inherent rewards have been hypothesized to be both intrapersonal and interpersonal (McAlister & Pessemier, 1982). Three components seem to be the basis of intrapersonal direct motives for varied behavior: the desire of the unfamiliar, the desire for information, and the desire for alternation among familiar alternatives (McAlister & Pessemier, 1982; Raju, 1980). Among the interpersonal motives for variety seeking, authors have outlined the desire for group affiliation or individual identity. The obvious link between the desire for social distinctiveness and a tendency to buy “new products” was affirmed by Szybillo (1973). Fromkin (1976) suggests that innovators are expressing the desire to see themselves as different from their peers in a socially acceptable way (McAlister & Pessemier, 1982). Whether the consumer’s motives are intrapersonal or interpersonal, the varietyseeking behaviors will likely result in the early adoption of a new product/service. This is consistent with previous research (Franke & Shah, 2003; Morrison, Roberts, & Midgley, 2004; Urban & von Hippel, 1988). Variety seeking can, therefore, be a characteristic shared between early adopters/innovators and innovative users. Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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Users with high levels of consumer knowledge and variety-seeking tendencies will resort to frequent switches to search for a satisfactory provider. At the same time, the most creative of these users, faced with unmet needs, will more likely be in a better position to create solutions to their problems and become true lead-users. In conclusion, unmet needs might, in the most settled cases, trigger a decrease in expectations with no expressed dissatisfaction, or in the most severe cases, despite the service provider’s best efforts (i.e., the service was “perfect” from the firm’s perspective), trigger a switching behavior that might or might not be preceded by expressed dissatisfaction. It is, therefore, not surprising that customer satisfaction models have always some unexplained reason for dissatisfaction and switching behavior, implying that no matter what the firm does to address the situation, the needs of these customers are not met. These customers have a high probability to carry unmet needs and should be the object of systematic recruitment into an idea generation processes, as they potentially have the most innovative ideas. On the basis on this argument the following hypothesis is proposed: Hypothesis 2: Defectors will produce better innovative service ideas than current customers.

These hypotheses are consistent with past research. For instance, Reichheld and Sasser (1990) assert that “customers who defect to the competition can tell you [the firm] exactly what parts of the business you must improve” (p. 107). USING THE LEAD-USER METHOD IN THE HOSPITALITY INDUSTRY Inviting Customers and Collecting Innovative Ideas

This study uses an electronic brainstorming (EBS) mechanism for extracting ideas from a random sample of customers. The EBS consists of a web-based bulletin board, where invited customers participate freely in idea generation for the host firm. Customers are invited by the firm they patronize (or have patronized in the recent past) and their participation is voluntary and anonymous. Previous research has shown that the most creative customers tend to self-select in such idea forums (von Hippel, 2005). Their motivation ranges from material (i.e., gift certificate), to brand attachment, to altruism (Piller & Walcher, 2006). Several studies have shown the positive effect of incentives on the creativity and participation of creative users (Toubia, 2006). Sometimes the mere recognition gained via the process and the exposure is sufficient motivation (Hars & Ou, 2002; Hertel, Niedner, & Herrmann, 2003; Lakhani & Wolf, 2005). Additionally, several methods have been tested to unleash more ideas by addressing blocking mechanisms such as: production blocking, or fear of evaluation, and uncreative strategies such as cheating or free-riding. EBS is such a method, allowing participants to stay anonymous, while giving ideas at their own pace (in an asynchronous session); hence, reducing production blocking and fear of evaluation (Nunamaker, Applegate, & Konsynski, 1987; Toubia, 2006). Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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A brief creative direction is given allowing the customer to “think-outsidethe-box” and not let their domain knowledge limit their creativity. Examples are given such as the following: •• Think of the hotel as a hospital. What should the hotel do to better serve you? •• Think of the hotel as a private mansion, and you are a millionaire, what would exclusive service look like? Evaluation of Ideas

Creations are often evaluated by a panel of experts (Amabile, 1982, 1996). The present research evaluates the ideas on the basis of the consensual assessment technique based on the following criteria: 1. Originality of the idea 2. Benefit of the idea to the customers 3. General appeal of the idea

Experts’ ratings are evaluated for level of agreement, and ratings can be averaged across raters (Rust & Cooil, 1994) once the reliability is equal to or greater than .70 (Boulding, Staelin, Zeithaml, & Kalra, 1993). Following the scoring by experts of each idea, the most innovative idea can be determined by looking at the distribution of the ideas’ scores and by using a cutoff point along the Gaussian curve (Piller & Walcher, 2006). Additionally, to separately evaluate the level of “radical-ness” of the idea (to the firm and to the industry), the panel will be asked to judge how radical they consider the idea to be. IDENTIFICATION OF LIKELY DEFECTORS

Noncontractual services such as hotel and restaurants have a major drawback in the study of switching behavior; one can never know for sure if a customer has defected or not. Therefore, for the purpose of the present study, defection will be calculated in a probabilistic manner following research methods suggested by Schmittlein and Morrison (1985) and used by Kumar and Reinartz (2006) and many others (Fader & Hardie, 2009). The formula used to classify customers is based on an underlying Poisson distribution of the population. Schmittlein and Morrison (1985) describe a simple and insightful test of the hypothesis that a customer is still alive: P = tn,

where P is the probability of being “alive” or still a client of the firm, t is the time of last purchase, and n is the number of purchases by that individual in the period of study. Thus, a small P(Alive) value strongly suggests that the customer is no longer “active,” that is, a defector. Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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Establishment of a Cutoff Threshold for Classification

The threshold of choice in the classification literature is .5 (Reinartz & Kumar, 2000; Sharma, 1996). If the customers’ P(Alive) is greater than .5, they are assigned the status alive; otherwise, they are assigned the status defector. Recognizing that a .5 threshold might be suboptimal, Reinartz and Kumar (2000) conducted a sensitivity analysis over three periods, testing several cutoffs (from .1 to .9) on existing databases. After comparing the predicted classification to the actual state, they concluded that the threshold of .5 produced the highest percentage of correct classifications for the three samples (p. 23). As a result, for the purpose of our analysis, we use .5 as the cutoff threshold. Using .5 as a threshold, as opposed to say .95 (as one would do under a strict hypothesis testing rule), allows us to missclassify (Type II error) some customers as defectors even if they have a high probability to be alive (for P > .5 and P < .95). For the purpose of the present study, the chances of finding alive customers in the defector group is less important than the small probability of having a defector in the alive group. Data Collection

E-mail addresses were made available from the customer base of a mid-scale 262 room independent hotel property located in the U.S. mid-Atlantic area. Two databases were combined to make the sampling frame. First, the past 12 months of unique records was extracted from the property management system resulting in 5,175 e-mail addresses. Because it is likely that customers failed to give complete contact information during the reservation process, or on check-in, the sampling frame was then augmented with a direct marketing list collected on the property website. As the general public has access to the property website, a cross-check between the two databases needed to be done. This avoided sending an invitation to customers who have yet to stay at the hotel (i.e., future reservations and simple information subscribers) and eliminated duplication of addresses between the lists. The final sampling frame resulted in 9,127 unique addresses. Thus, the sampling frame encompasses 21 months of the firm’s activity leading to a higher probability to include a representative sample of the hotel’s clientele. A letter of introduction signed by the general manager was electronically sent to the sampling frame outlining the “idea contest” and giving an immediate reward for the survey participants in the form of planting a tree on their behalf (using a third party: http://www.plantabillion.org) for each response. A “grand prize” of a $400 Southwest Airline gift certificate was also listed to motivate the customers to engage in the idea generation competition. A total of 273 completed surveys (a response rate of 4.43% of the 6,167 valid e-mail addresses) with participation in the idea generation were achieved. Participation in online surveys has gradually decreased over the years, and ranges from a low 9.4% to a high 31.4% (Deutskens, De Ruyter, Wetzels, & Oosterveld, 2004). The relatively low participation in the present experiment might be the resultant of several factors. First, hotel customers are not used to being involved in such a process. Some Downloaded from jht.sagepub.com at TOWSON UNIV on June 5, 2014

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Table 2 Age Distribution Age Groups of Respondents (Years)

Study Sample (%)