Past Research on Restaurant Attributes

Cousins et al. (2002) pointed out that restaurant consumers base their decision for eating out on the type of experience that is sought. It can be argued that this experience has transcended the mere necessity to eat as discussed below. Macht et al. (2005) conducted a study on the hedonic pleasures of eating and concluded that indeed those pleasures are beyond food and nutrition and are shaped by features of the environment, social factors, and emotions. According to Mittal et al. (1998), the components of a service are evaluated by consumers separately. Several attempts have been made to establish what these aspects are within the restaurant setting. Campbell-Smith (1967) developed the concept of the meal experience with five different components that were later refined by Cousins et al. (2002). These components are food and drink, level of service, cleanliness-hygiene, value-for-money and ambiance. This model, as asserted by Wood (1994), has been very influential. Wood commented that the model has had a considerable effect on education in the hospitality industry, and also that it has initiated the application of practical marketing concepts in that industry.

The pursuit of leisure can arguably imply trying to maximise consumer utility. For this reason, it is maintained that consumers pursue the maximisation of utility for attributes that satisfy their needs and expectations. The different models of the meal experience attempt to offer an explanation of those factors which consumers may evaluate prior to making the decision to choose a particular restaurant. Indeed, research conducted on the meal experience has attempted to ascertain the relative importance of factors as considered by consumers since these may influence their decision. Several studies of these factors have brought about varied results. It should be noted that these investigations have conveyed different aims and objectives and have adopted different approaches. Whilst there are some similarities between attributes in the fast-food restaurant sector, there are also very important differences. To illustrate this point, in a study of fast-food restaurants, Mamalis (2009) found that the key critical success factors are adaptation to locality, food quality, service, facilities, ‘Place to be’ and sales incentive programme. Adaptation to locality may be critically important when restaurant chains have to cater to local tastes. Food quality, service and facilities are also elements of the meal experience in a fine dining restaurant, but key performance objectives of the operation such as speed are obviously more important in fast-food operations. Place to be refers to elements of ambiance, and includes elements of safety, which have not been mentioned in less casual dining environments. The last factor, the sales incentive programme, is particularly important for price-sensitive segments, such as the ones who regularly patronise fast-food restaurants. The implications of Mamalis’s study may also affect sectors other than the fast-food industry, particularly for the increasing trend of globalisation of restaurant chains and the targeting of a customer who is brand conscious, even for dining out occasions. Another aspect is that of ethnic restaurants, those which portray a particular type of cuisine: Italian, Chinese, Greek, etc. Authenticity has been referred to as an attribute that is specifically linked to ethnic restaurants. Authenticity relates to both food and the environment and the degree to which it reflects the genuine taste and culture of the ethnic origin (Jang et al., 2011). Liu and Jang (2009) found that authenticity affects customer satisfaction, particularly when it relates to the perception of the food being authentic. Sukalakamala and Boyce (2007) extended the number of attributes that affect perceptions of authenticity to aspects such as interior decor, music, staff clothing, greetings, tableware/silverware and menu design.

There is extensive research on restaurant attributes in different geographical areas and with different customer segments. Some of these studies deserve attention, albeit briefly. Amongst these, Dulen (1999) and Susskind and Chan (2000) suggested that food, atmosphere and service are three major components of the restaurant experience. Ribeiro-Soriano (2002) studied four main attributes: food, service, cost and place (a combination of ambiance, location, facilities such as car parking and cleanliness-hygiene). The study confirms that food was the highest ranked with consumers over 60 years of age, ranking it significantly higher than any other attribute. Law et al. (2008) conducted a study of attributes for tourists in China and used the following classification of attributes and sub-attributes: food and beverage (portions, variety, quality, presentation); service (operating hours, diversity, speed and servers attitude); value for money; and environment (atmosphere, cleanliness, comfort, location and decoration), and they included an additional attribute, namely attraction (image, novelty, word-of-mouth, advertising). There was little elaboration in that research paper about the last attribute and it is arguable whether it is a restaurant attribute at all, as attraction may be considered to be a consequence of other restaurant attributes. Meng and Elliott (2008) referred to relationship marketing and communication as predictors of retention of loyal and satisfied customers based on the model of Kim et al. (2006). The latter authors proposed a measurement model of predicting relationship quality for luxury restaurants in South Korea and used the following classification for attribute dimensions: physical environment, customer orientation (service), communication, relationship benefits, price fairness and relationship quality. Narine and Badrine (2007) researched consumers eating out in Trinidad, West Indies, and they found similar aspects of the meal experience. In the study, food choices were influenced by health/nutritional benefits (60.8%), safety/sanitation (60.0%) and price of menu (55-8%). The celebration of a special occasion (60.8%) was the most popular reason for ‘eating out’. In a research of attributes in Quick Service Restaurants (QSR), Harrington et al. (2013) supported other studies that indicate the general importance of the following restaurant attributes: (a) food safety, (b) cleanliness, (c) food quality, (d) speed of service, (e) perceived value of the food and drink items, (f) quality of service, (g) staff friendliness, (h) price, (i) variety of menu and (j) close travel distance. On the other hand, in a study of online customer reviews on restaurants (from the UK, USA, India, Germany, Italy, Norway, the Netherlands, Sweden, Switzerland and Spain), Pantelidis (2010) found that the most mentioned factors for customer satisfaction were food (96%), service (92%), ambiance (51%), price (29%), menu (27%) and design/decor (10%). An aspect worthy of investigation is the combined effects of restaurant attributes. So far, only Wall and Berry (2007) have addressed this topic in a study of the combined effects of the physical environment and employee behaviour, and found that the human element was significantly more important, as to an extent these ‘humanistic clues’ can make up for deficiencies in what they call ‘mechanistic clues’. Andaleeb and Conway (2006) suggested that to satisfy customer expectations, restaurateurs ought to focus their efforts on service quality, price and food quality, in that order. Nonetheless, these authors acknowledged that this order is partly induced by the design of their methodology which is heavily focused on service quality. In another study, Namkung and Jang (2008) also ranked food first, followed by the physical environment and service. However, ‘their study failed to consider price — an unfortunate omission - in the midst of an economic recession, given the likelihood that restaurant guests would have greater price sensitivity’ (Pantelidis, 2010; p. 485).

From this discussion, it can be noted that the number of studies about restaurant attributes is overwhelming. For the constraints of space referred to, only an excerpt of the most influential - and/or cited - work regarding restaurant attributes have been discussed.

Ascertaining Attribute Importance

Challenges for Ascertaining Importance

The main limitations in ascertaining importance is that in the first place respondents are normally given a list rather than having the possibility of choosing what attributes they actually consider important; secondly, ascertaining importance isolated from the actual decision of selecting a restaurant seems like an unnatural task; thirdly, the description of an attribute does not offer enough information about what the attribute is about (this is defined more clearly when levels of the attribute are presented — see research design), and finally attribute importance ascertained by listing does not take into account the fact that price may influence attribute importance and that there may be a trade-off between price and the relative importance of an attribute. This means that for some consumers an attribute changes its relative importance if it attracts a higher price.

For all of the exposed above, it seems like it is about time that another approach is taken for ascertaining restaurant attribute importance, an approach that deals with those critical limitations. A set of methodologies, namely conjoint analysis, appears to offer a reasonable alternative as it looks as the natural trade-offs that consumers make.

Conjoint Analysis or Discrete Choice Analysis

Conjoint analysis refers to techniques used to estimate attribute utilities based on subjects’ responses to combinations of multiple decision attributes (Louviere, 1988). Basically, conjoint analysis could also be called trade-off analysis because that is basically what the techniques are about. However, as highlighted by Louviere (1988), it must be clearly understood that conjoint analysis is not a single tool but a set of techniques that share some commonalities but also important differences. Orme (2010) distinguished between traditional conjoint analysis, developed in the 1970s, and conjoint analysis after the development of commercial software in the 1990s.

Orme (2010; p. 29) summed up the inherent limitations of conjoint analysis:

Human decision making and the formation of preferences is complex, capricious and ephemeral. Traditional conjoint analysis makes some heroic assumptions... and that complex decision making can be explained using a limited number of dimensions. Despite the leaps of faith, conjoint analysis tends to work well in practice, and gives managers, engineers and marketers the insight they need to reduce uncertainty when facing important decisions. Conjoint Analysis is not perfect, but we do not need it to be.

Therefore, conjoint analysis allows for an approximation to reality, acknowledging that it is ‘imperfectly apprehended’ (Lincoln et ah, 2011; p. 98).

In conjoint analysis, respondents choose attributes, for example, the portion which could be (a) small, (b) moderate and (c) large. If all the attributes with the corresponding levels are presented, that is called full profile. Green and Srinivasan (1990) noted that if the full-profile approach is used, it is important to limit the number of attributes and levels. Furthermore if the full profile is used, respondents tend to use simplification tactics if the information is overwhelming (Denstadli and Lines, 2007; Orme, 2010), and several authors have found that respondents may focus on salient attributes to the detriment of the rest. Because of the shortcomings of traditional conjoint, it was deemed necessary to examine the most popular conjoint analysis methodology, also known as discrete choice analysis (DCA).

Authors like Verma et al. (2002) and Louviere et al. (2010) argued that DCA is close but not the same as conjoint analysis. Verma and Thompson (1996) established a number of comparisons and based that differentiation on the fact that traditional conjoint analysis data are obtained in the form of ratings or rankings and that, in contrast, DCA places the respondents in simulated choice-making situations, in which they select choices they do not rate or rank. That is, DCA involves a single decision maker choosing one alternative amongst a small well-defined set (Ben-Akiva et al., 1997). Louviere et al. (2010) focused on the historical foundations of conjoint analysis, which are indeed different from DCA. However, there are so many similarities that it is possible to see DCA as an evolution of traditional conjoint analysis. To start with, influential conjoint research scholars such as Green et al. (2001) use the term ‘Choice Based Conjoint’ for DCA. Secondly, statistical models - also called estimation methods, for example, multinomial logit (MNL) models, or nested logit models developed from a DCA study relate service attributes to consumer preferences (Victorino et al., 2005). Thirdly, conjoint analysis is deemed to be inspired by scientific experimental design (Mazzocchi, 2008), in which the researchers manipulate the variables, in this case the attributes and their levels. In DCA, the research design takes the form of discrete choice experiments (DCEs). For instance, the decision maker responds to experimentally designed profiles of possible alternatives, in which each alternative has a different set of attributes (Verma and Thompson, 1996). Finally, the most popular software development organisation for conjoint analysis, Sawtooth Software ©, has developed several conjoint analysis solutions, with the most popular being Choice Based Conjoint (CBC) based on DCA theory. It should be noted that one of the main problems with traditional conjoint was with full profiles, as respondents have the task of rating, whereas that problem is minimised with discrete choices as respondents just have to choose a natural human task.

Conjoint Analysis in Restaurant Attributes Research

Conjoint analysis is a widely used methodology in the service industry, including hospitality and tourism. It can be noted that the studies in the context of restaurants of Koo et al. (1999), Verma and Thompson (1996), Myung et al. (2008) conducted research on restaurant attributes using conjoint analysis. However, there are important differences. The first study focused on preferences of different customer segments, but with a small sample size and a narrow number of attributes; its prediction power is very limited indeed. On the other hand, it used traditional conjoint analysis, which appears to be less natural to respondents than DCA. The second study, although it used DCA, was restricted to pizza restaurants, with fewer attributes in a limited sample of restaurants and a small sample size. The third piece of research used DCA as well but just concentrated on set menus.

On the other hand, it can also be noticed that although many recent studies have used DCA, a few still use traditional conjoint techniques with older studies such as the one of Wind et al. (1989) being very influential for further use of conjoint analysis in commercial and academic research. Research for academic papers appears to have limited resources because of the reliance on small sample sizes and use of respondents who may raise questions about representativeness, such as the choice of university students in the sample of the study of Koo and Koo (2010). The author has found that the development of conjoint analysis can be largely attributed to the work of researchers of Sawtooth Software who have published working papers of outstanding quality and are supported by the main authors in the area of conjoint analysis.

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