Food Quality and Preference 17 (2006) 166–178 www.elsevier.com/locate/foodqual
Using simulations from discrete choice experiments to measure consumer sensitivity to brand, region, price, and awards in wine choice Larry Lockshin a
a,*
, Wade Jarvis a, Franc¸ois dÕHauteville b, Jean-Philippe Perrouty
b
Wine Marketing Group, University of South Australia, GPO Box 2471, Adelaide, South Australia 5000, Australia b UMR Moı¨sa Agro-Montpellier, 2 place Pierre Viala, 34060 Montpellier cedex, France Received 25 September 2004; received in revised form 23 February 2005; accepted 8 March 2005 Available online 10 May 2005
Abstract The complexity of the wine category has forced researchers to try different means to understand how consumers choose wines. This research uses a discrete choice experiment approach to understand how key extrinsic cues are used by different consumer groups when choosing wine. We extend common practice by using a simulation algorithm to show how relative purchase rate changes as brand, region, price, and award are changed. The results show that low involvement consumers use price and award to a greater degree than high involvement consumers. A gold medal increases the choice probability the most, but mainly at the lower and middle price points, and a well known region amplifies the desirability of small brands more than large brands. The results are complex across the four factors and two levels of involvement, but provide a realistic appraisal of how consumers use extrinsic cues in combination when choosing wines. The strong differences in choice behavior between low and high involvement consumers show this to be a viable segmentation strategy and one that other researchers should consider utilizing. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Discrete choice analysis; Wine choice behaviour; Product involvement
1. Introduction Understanding how consumers choose wine continues to be a complex problem for researchers and practitioners alike. Wine is a difficult and confusing product for consumers to choose due to number of cues on the label, such as brand name, region, grape variety. Unlike most food products, the taste of the wine can vary due to the specific vintage, even if the brand and other extrinsic information remains the same. We know that in other grocery categories consumers easily switch between different brands in accordance
*
Corresponding author. Tel.: +618 8302 0261; fax: +618 8302 0442. E-mail address:
[email protected] (L. Lockshin).
0950-3293/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.foodqual.2005.03.009
with market share and are not very concerned with taste or quality differences behind the different labels (Ehrenberg, 2000). Brands on the shelf are seen as easily substitutable for one another. Wine provides a very different product category. Wine producers and sensory scientists focus for obvious reasons on the taste of wine as their key criterion for wine choice by consumers. Due to the number of products available and vintage variation, consumers may not be able to predict how the wine will taste before they buy. We agree that understanding consumer tastes and being able to create wines to specific taste profiles is an important goal for wine companies and researchers. However, when consumers are shopping for wine, they face a bewildering array of products bearing a wide range of information. The consumers cannot usually taste the wine before purchase, so they
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must make their decision based on the available information on the label and bottle.
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2. Literature review 2.1. Region of origin
1.1. Key background and findings This paper explores how consumers use label information to make their purchase decision for wine. Discrete choice analysis (Louviere, Hensher, & Swait, 2000; Louviere & Woodworth, 1983; McFadden, 1973) has been used to determine the utilities of product or service attributes in combinations, and is often referred to as Ôchoice-based conjointÕ. The methods are relatively well known and offer insights into how consumers use tradeoffs among product attributes in making purchase decisions. We take the concept to the next stage to look at how the different combinations of attributes affect consumer wine choice. Using a market share simulator with a randomised first choice algorithm, we systematically vary one attribute at a time to understand the way consumers use major cues in wine purchase. The cues chosen for this experiment were brand name, region of origin, price, and award (gold medal or not). We used a design incorporating high and low market share brands and well-known/less well-known wine regions to allow us to simulate the effects of growing a brand or making a region better known. We also used four price points and gold, bronze or no medal. We tested different segmentation schemes and show that using product involvement is superior to using consumption frequency, age or gender in explaining choice. Product involvement is a consumer characteristic of interest in a product category, and is explained in detail in the literature review. We provide results for large and small brands, well-known and less well-known wine regions, at four price levels, with and without a gold medal for both high and low involvement consumers. We show that the award has the greatest effect for low involvement consumers. Brand size and how well known the region of origin is add important effects, which vary at different prices. Price sensitivity also varies between low and high involvement consumers. We develop our paper in the following way. First, we review the literature on wine choice behaviour, including sections on overall choice, country and region of origin, and product involvement. We then briefly discuss the literature on discrete choice. We provide an outline of the experimental design and data collection with detail on the mechanics of the simulations. We then provide the results in a series of graphs, which allow the comparison of the effects across different choice conditions. Finally, we discuss the usefulness of the results for both wine marketers and for future development of more sophisticated choice models incorporating revealed choice (panel data of actual transactions) with stated (discrete) choice experiments.
When a product has a high proportion of attributes that can only be assessed during consumption (experience attributes) as with wine (Chaney, 2000), then the ability of consumers to assess quality prior to purchase is severely impaired, and consumers will fall back on extrinsic cues in the assessment of quality (Speed, 1998). Chaney (2000) found there is very little external search effort undertaken prior to entering the store to purchase wine, with the two highest ranked information sources in her study being point of sale material and labels, but these were found to rate at only the somewhat important level. Lockshin, Rasmussen, and Cleary (2000) highlight the fact that the brand name acts a surrogate for a number of attributes including quality and acts as a short cut in dealing with risk and providing product cues. Gluckman (1990) postulates that consumers tend to infer the same status to general cues––grape variety and region––as they do to brand names. Consumers are shown to develop a small brand repertoire, which may well be a collection of brands and generic types, such as grape varieties or regions of origin. In Australia there are over 16,000 different labels emanating from over 1600 different wineries, while Europe may have over 100,000 different labels (Lockshin, 2001). The brand name by itself is not a strong enough cue for the purchase decision, as much more information is available on the label. Batt and Dean (2000) found that the origin of the wine was the third most important variable influencing consumersÕ decision to purchase wine in Australia. In Europe research by Dean (2002), Koewn and Casey (1995), Gluckman (1990), and Skuras and Vakrou (2002) suggest that country of origin is a primary and implicit consideration of consumers in their decision to purchase wine. Recent research by Tustin and Lockshin (2001) in Australia confirmed region to have a major impact on wine purchase. Other authors have studied the region of origin as both a cue to wine purchasing (Angulo, Gil, Gracia, & Sanchez, 2000; Gil & Sa´nchez, 1997; Quester & Smart, 1998; Rasmussen & Lockshin, 1999), and as a cue for which consumers are willing to pay (Combris, Lecocq, & Visser, 1997; Gergaud, 1998; Skuras & Vakrou, 2002). Wine regions have been shown to have differences in salience (Lockshin, Romaniuk, & Tustin, 2003), which confers recognition and can drive the wine choice process. Salience is a measure of the number of attributes that come to mind associated with a product. Greater salience leads to a greater likelihood of retrieving the cue in a purchase situation (Romaniuk & Sharp, 2002), so wine regions with greater salience are more likely to be chosen.
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2.2. Price Accumulated theoretical and empirical evidence suggests that wine prices depend on quality, reputation and objective characteristics (Oczkowski, 2001). Koewn and Casey (1995) found that pricing was extremely important to all respondents in a study of wine purchasing influences. Similarly, in a study conducted by Jenster and Jenster (1993), price was an overriding criterion in making the purchase decision among European wine consumers. Generally, price is an important cue to quality when there are few other cues available, when the product cannot be evaluated before purchase, and when there is some degree of risk of making a wrong choice (Cox & Rich, 1967; Dodds & Monroe, 1985; Monroe & Krishnan, 1985; Zeithaml, 1988). As such price is often a primary cue, which is utilised to indicate wine quality (Olson, 1977; Szybillo & Jacoby, 1974). Johnson, Ringham, and Jurd (1991) used price combined as a criteria in a cluster analysis segmentation of Australian wine consumers. In the purchase of wine, price is also used to overcome perceived risk (Spawton, 1991). 2.3. Quality and reputation It has been found that the reputation of the producer and objective wine trait measures such as the wineÕs year of vintage, region from which the grapes were sourced and the grape variety are significantly related to price (Combris et al., 1997, Combris, Lecocq, & Visser, 2000; Landon & Smith, 1997; Oczkowski, 2001). It was also found that when overall sensory quality scores are employed with objective characteristic traits, a significant relationship with price occurs (Angulo et al., 2000; Combris et al., 2000; Golan & Shalit, 1993; Landon & Smith, 1997; Ling & Lockshin, 2003; Oczkowski, 1994, 2001; Schamel, Gabbert, & von Witzke, 1998; Wade, 1999). Landon and Smith (1997) suggest that given the incomplete information on quality, consumers rely heavily on both individual firm reputation based on the past quality of the firmÕs output and collective or group reputation indicators. These characteristics allow consumers to segment firms into groups with differing average qualities to predict current product quality. To help deal with that uncertainty, quality-conscious consumers process various perceived signals of quality, mainly of an extrinsic nature, such as price, producer, brand, vintage, region, awards, ratings and recommendations (Lockshin et al., 2000). One would assume that taste is the most important factor in driving wine purchase. Koewn and Casey (1995) found that the taste of the wine was a dominating factor for wine consumers. Thompson and Vourvachis (1995) found that taste was the most highly correlated
attribute relating to wine choice. The taste of the wine represents one of the major perceived risks presented by Mitchell and Greatorex (1988). They found that the taste of the wine was the risk that concerned consumers most. However, almost all purchase situations do not include the opportunity to taste the wine before purchase. New research used descriptions of wine tastes, such as Ôfruity sweet whiteÕ, or Ôtannic and full-flavoured redÕ, to test consumersÕ preferences (Hughson, Ashman, de la Huerga, & Moskowitz, 2003), but the link to purchase or purchase intentions was not established. We do know that most wine purchases are made using information from the label as proxies or indications of what lies inside the bottle. The key finding from Hall and Lockshin (2000) was that the above factors themselves are related to the situation where the consumer intends to drink the wine. For example, high price was important when a consumer was purchasing wine in order to impress a business associate or to celebrate a special anniversary. Low price was important, when the situation was to relax at home by oneself, or for entertaining at an informal party or BBQ. Different consumption situations amplified or muted the importance of different wine attributes. 2.4. Product involvement We must add to these external factors the consumer characteristic of product involvement. Involvement has been used in a variety of marketing studies since Sherif and Cantril (1947) first presented the concept. Involvement is a motivational and goal-directed emotional state that determines the personal relevance of a purchase decision to a buyer (Rothschild, 1984). Involvement is thought to exert a considerable influence over consumersÕ decision processes (Laurent & Kapferer, 1985; Quester & Smart, 1998). Researchers have typically analysed the influence of product involvement on consumersÕ attitudes, brand preferences, and perceptions (Brisoux & Cheron, 1990; Celsi & Olson, 1988; Quester & Smart, 1998) using a single factor scale applied post hoc to consumer surveys. Involvement has been conceptualised as the interest, enthusiasm, and excitement that consumers manifest towards a product category (Bloch, 1986; Goldsmith, dÕHauteville, & Flynn, 1998). Involvement has been linked to wine purchase (Lockshin, Spawton, & Macintosh, 1997; Quester & Smart, 1998), where high and low involvement wine buyers have been shown to behave differently. Lockshin, Quester, and Spawton (2001) showed involvement was a better predictor of wine choice behaviour than the nationality of the wine consumers, using a French and Australian comparison. The use of key attributes, such as price, region and grape variety was also influenced by involvement level (Zaichkowsky, 1985; Quester & Smart, 1998). Other differences
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Taking all the factors cited in the literature review shows how complex the wine purchase decision can be. Other externalities, such as the mood of the consumer, displays and the packaging of the wine (Areni, Duhan, & Klecker, 1999) will certainly have some effect on an individual purchase. However, marketers should be less concerned with predicting an individual purchase than with understanding how the cues interact to predict or understand overall purchase rates for groups or segments of consumers. Conjoint analysis (Green & Srinivasan, 1978) has been used to model the decision rules consumers use when making product choices using multiple attributes. These early models have been broadened to utilise simulated choices rather than rankings or ratings. Discrete choice analysis (often called choice-based conjoint analysis) asks the consumer to make a choice from a set of product concepts and these choices are converted to ÔutilitiesÕ or part-worths for each of the levels of the individual attributes using multinomial logit (Louviere et al., 2000; Louviere & Woodworth, 1983).
raz wine to purchase to serve at home tonight for an informal dinner with family and some friends. Which of the following wines would you choose?’’ We added the option of ‘‘I would not choose any of these wines’’ to allow for the possibility that none of them met the purchase requirements. The Ôwould not choose anyÕ option is recommended (Louviere et al., 2000) to make the purchase situation more real, but the option is not used in the analysis. We developed the levels (list) of the brands and regions used in this experiment from available data on the market share of brands and awareness of wine regions (Lockshin et al., 2003) in the Australian wine market. We also utilised some of the same brands and regions from previous discrete choice wine experiments conducted in Europe (Perrouty, dÕHauteville, & Lockshin, 2004) to allow for eventual comparisons. Our goal was to have a range of brands and regions from the largest (most well known) to the smallest (unknown), so that we could make some generalisations about consumer choice when choosing among brands and regions that were either well known or completely unknown and a few in between. We used four prices all in Australian dollars, again providing a range from realistically low prices for commercial quality wines to high prices typical of boutique wines. The awards were gold, bronze and no award (left blank). These are typical awards in Australia and we wanted to have the highest award (gold) and a low level of award (bronze). We used Ôno awardÕ twice (as two identical levels) to make sure it appeared at least as often as gold or bronze in the design. The list of the levels of brands, regions, prices and awards is provided in Table 1.
3.1. Attributes used in the experiment
3.2. The choice experiment design and survey technique
As noted above, trying to model all the influences on wine purchase behaviour is complex and the total number of attributes could make the design of such an experiment much too large to be practical. The literature review highlighted that the most important label attributes were price, region of origin, and brand name. We chose to add whether or not the wine had won an award (gold or bronze medal), because there has been little research on this aspect, yet more and more bottles are appearing on the shelf with medals attached. Hall and Lockshin (2000) showed that the consumption situation altered the importance of different choice cues. We chose the most common purchase situation in Australia, buying wine in a wine shop to serve at home. We also chose the most popular grape variety in Australia, Shiraz. Both of these insured the choice would be relevant to the greatest number of consumers. We controlled for situation and for grape variety by stating for each choice task, ‘‘you are standing in front of a shelf of wines in a wine shop and are deciding on a bottle of Shi-
The participants were provided with four product concepts (black and white labels with the attributes) plus the ‘‘I would not choose any’’ on a single page and were asked to select the bottle of Shiraz wine they would choose to buy to have with friends and family tonight for an informal dinner. Each product concept had one level of each of the four attributes (see Fig. 1 for an example of a choice task). The levels of the attributes were randomised across the 300 surveys using Sawtooth SoftwareÕs Choice-Based Conjoint Software. An algorithm is used to insure each level of each attribute appears an equal number of times across all surveys, but does not repeat in the other product concepts in each choice task. This is done to make sure that the respondent does not see the same level, (e.g., the same price) across all the choices in one task. With complete randomisation there is always the chance that almost identical concepts might appear together. Each respondent was provided 20 different choice tasks, as if they made, 20 different purchase decisions. We also provided the
in wine choice behaviour related to product involvement were consumption situation (Quester & Smart, 1998), medals and ratings (Lockshin, 2001) and quantity consumed (Goldsmith et al., 1998). All of these papers categorised consumer involvement levels after the data was collected and analysed. In summary, higher involvement consumers utilise more information and are interested in learning more, while low involvement consumers tend to simplify their choices and use risk reduction strategies.
3. Method
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Table 1 Attribute levels used in the experiment (from highest to lowest) Brands (Market share)
Regions (Salience)
Prices (per 750 ml bottle)
Awards
JacobÕs Creek Nottage Hill LindemanÕs Rosemont Yalumba Taylors Mouton Cadet Cellier des Dauphins
Barossa Valley Coonawarra Yarra Valley Rutherglen Grampians Coˆtes du Rhone Coteaux du Layon Southeast Australia (not technically a region, but used in other research in export markets)
$22.99 $16.99 $11.99 $7.99
Gold medal Bronze medal . ..
Hardy's Nottage Hill
Cellier des Dauphins
Lindemans
Taylors
Yarra Valley
Coonawarra
Grampians
Barossa Valley Gold Medal
Bronze Medal
I would purchase none of these
…
…
$AUD16.99
$AUD11.99 $AUD7.99
$AUD22.99
2
1
3
4
5
You are standing in front of a shelf of wine. Which one of these four bottles of Shiraz would you choose to enjoy over dinner tonight with friends? Please note: If none of these wines suit, please choose the option "I would purchase none of these".
Fig. 1. Sample choice task with four product concepts.
exact same (fixed) task to each respondent. This was used to calibrate the market simulator (see below). Respondents were given a short survey along with the choice tasks. A seven point Likert-type scale with seven items was administered to test wine involvement (Lockshin et al., 1997). Demographics and frequency of wine consumption were also collected for use in segmentation. Surveys were handed out to every fifth shopper standing at the checkout in four different wine stores in Adelaide, Australia chosen to represent both fine wines and discount wines in order to sample a wide range of wine buyers. (Wine is sold only in specialty alcohol beverage stores and not in grocery stores across most of Australia.) Participants were qualified by having purchased a bottle of wine in the last 3 months. Surveys were taken on different days and at different times to in-
crease the diversity of the convenience sample. This method improves the sample to that approaching a random sample (Calder, Phillips, & Tybout, 1982). Trained interviewers handed out the self-completion surveys and answered any questions while the surveys were filled out. A total of 300 surveys were handed out and 250 usable ones form the data in this research. The sample characteristics are presented in Table 2. The sample represents a reasonable range of wine drinkers in Australia (Lockshin et al., 1997; Quester & Smart, 1998). The age is skewed towards older drinkers above 34 years old, with higher education and wine consumption that the average Australian (Stanford, 2000). It should be noted that this is not meant to be a reasonable sample of Australians, but of Australian wine consumers in order to understand how they choose wine. 3.3. Initial analytical and segmentation method The discrete choice data were analysed using the Sawtooth Software Choice-Based Conjoint multinomial logit program. The levels of the attributes are coded so that the utilities add up to zero in each attribute category. Research cited in the literature review showed that age (Gluckman, 1990), frequency of consumption (Perrouty et al., 2004), and involvement affected wine choice behaviour (Lockshin et al., 2001; Lockshin et al., 1997; Quester & Smart, 1998; Zaichkowsky, 1985). We added gender, because some authors have stated without empirical evidence that women purchase wine differently than men (Chaney, 2000). Discrete choice provides a straightforward method for determining whether
Table 2 Sample characteristics Gender
%
Age
%
Education
%
Frequency of consumption
%
Male Female Total
60 40 100
18–24 25–34 35–44 45–54 55–64 65+ Total
9 16 28 29 16 2 100
