Holistic and consumer-centric assessment of beer: A multi-measurement approach
Graphical abstract
Introduction
Product developers and marketers seek ways for their food/beverage innovations to stand out from the crowd. Hedonic optimization, which for many years was a cornerstone activity in this regard, is no longer sufficient (Meiselman, 2013). Characterising products beyond their sensory and hedonic properties facilitates insightful and nuanced product differentiation that is aligned with product performance as experienced, and more importantly valued, by consumers (Thomson, 2010, van Trijp and van Kleef, 2008).
A prescriptive method to deliver such nuanced product characterisation and differentiation does not, and probably cannot exist. However, the necessary foundations for a methodological framework whereby these insights can be gained exist and are rooted in the knowledge that food-related consumer behaviour is dynamic and complex, influenced by intrinsic and extrinsic factors, and not fully captured through a univariate approach (e.g., Jaeger, 2006, Rozin and Tuorila, 1993, Thomson, 2010). When translated with a view to empirical implementation it implies a tailored, product-focused and multi-variate approach that obtains holistic (i.e., integrated and more comprehensive) product understandings from consumers. A focused and pragmatic orientation is often called for since the amount of research required to acquire a fully comprehensive product understanding would likely be prohibitive in typical product development applications. Considering that central location/laboratory tests remain dominant in consumer research (Jaeger & Porcherot, 2017), working within the confines of these test settings while retaining a multi-variate approach will likely be required.
The purpose of the present research was to implement holistic and consumer-centric product characterisation and illustrate the nuanced product differentiation that is gained. Beer was chosen as the product category of interest as it has a long history of expanding/contracting degrees of product differentiation and its product landscape is currently undergoing many changes (see Section 1.2). Three types of product responses were obtained: attitudes/perceptions, situational appropriateness of consumption (IBU: item-by-use) and emotional associations. Each type of response adds value to product innovation efforts (e.g., Lundahl, 2012, Schutz and Jaeger, 2010) and is derived from well-established and well-known methods (see Section 2.3). Interested readers can refer to Cardello et al. (2016) for background knowledge regarding these three types of product responses and their relevance in multi-variate product research.
Additional product responses could have been chosen, some of which are considered in the Discussion section. This would have made the study more comprehensive, but their inclusion was not paramount to illustrate the holistic approach. The high degree of flexibility and ease of implementation in a central location setting additionally supported the choice of attitudinal/perceptual statements, situational appropriateness and emotional associations as the dependent measures in the present research. For example, Likert statements, one of the most frequently used metric approaches in attitudinal research, can be developed to capture any aspect of product attitudes/perceptions that is relevant to an investigation including health/wellbeing, sustainability, branding/packaging, value for money, food safety, etc. This is also true for statements about situational appropriateness, which, as illustrated by Schutz and Ortega (1974) in the case of wine, can cover time of day, occasion served, where served, how served, persons served, as well as physiological, psychological and physical uses. It was also a determining factor that the three sets of measures can be worded to tap into the same constructs. For example, if there was particular interest in understanding whether one or more beers were differentiated on their appeal to/relevance for men, it would be straightforward to include both Likert statements (e.g., “This beer is more suitable for men than women”) and IBU statements (e.g., “For men”, “For women”). The same principle can be applied to attitudinal and emotional product associations (e.g., “Beers like this one bore me” and “Dull/Blue”). The value of this strategy lies in the increased confidence that a result is robust if two or more samples are similarly discriminated by multiple variables (i.e., principle of triangulation in research).
At its core the present research is similar to other previous research on beer where multiple dimensions of product characteristics have been measured/uncovered in search of integrated and more comprehensive insights (e.g., Cardello et al., 2016, Giacalone et al., 2013, Gómez-Corona et al., 2016, Jaeger et al., 2017, Schouteten et al., 2015, Sester et al., 2013, Silva et al., 2017), and the term ‘holistic’ is not unique to this research (e.g., Aoun and Tournois, 2015, Doets and Kremer, 2016, Kim et al., 2015, Lundahl, 2012). We recognise and value the fact that the multi-variate and holistic perspective is becoming more common as a means for capturing product representations in consumers' minds and allowing consumption and behaviour practices to be better understood.
Product differentiation in beer has a long and evolving history that can be traced back over 5000 years, where a variety of sweet, heavy, fragrant beers were made by the Sumerians (Kiefer, 2001). Much later, European monks and Colonial American farmers contributed innovations and product diversification (Bostwick, 2014), but it was not until the Industrial Revolution that beers of consistent quality became available. With this development also came a loss of product differentiation, which continued to modern times. In the closing years of the 20th Century and accelerating during the first years of the 21st Century, beer consumers began to eschew mass-produced beer and amidst this revolt, small “micro-breweries” began to evolve. They were driven by a desire for better tasting beer that was enjoyable to drink, seeing an opportunity to profit from consumers' growing dissatisfaction (Acitelli, 2013). Recent estimates put the total percentage of such craft beers at 6% of the global beer market (Ascher, 2012) and growing rapidly.
With the current growth in the number of styles, types, and flavours of beer has come the problem for brewers of identifying beers and beer flavours that are new and different. The aim of the present research is to showcase how multi-variate/holistic product characterisation can assist in this effort and help to harness the growing consumer interest in exploring new beer styles and/or flavours and their accompanying willingness to pay higher prices (Ascher, 2012, Brager and Greco, 2011). To deepen the analysis and the insights gained from the multi-variate approach, consumer segmentation is performed. This is motivated by masking of heterogeneity in a consumer sample when performing aggregated analyses, and a possible negative on impact for innovation and understanding of food-related consumer behaviour. Segmentation is encouraged (Meiselman, 2013) and aligns with a central dogma in sensory and consumer research: the “average consumer” does not exist (Köster, 2009). Segments can be defined a priori and distinguish consumers on the basis of age, gender, income, household size, product involvement etc. (e.g., Ares and Gámbaro, 2007, Lockshin et al., 2001, Onwezen and Bartels, 2011) or be data-driven (e.g., Mueller and Rungie, 2009, Helgesen et al., 1997, Thybo et al., 2004). Both approaches are common, but with the latter segments based on differences in hedonic responses to samples can be identified. This is pertinent in the present research where product-specific insights are sought and with it an understanding of how the multi-variate approach extends “beyond liking.”
Section snippets
Participants
One hundred and twenty eight (128) beer consumers (convenience sample) were recruited by a professional recruitment company from the greater Auckland (New Zealand) region. Consumers were 38% female, ranged in age from 20 to 66 years old, predominantly Caucasian (80%), and had diverse socio-economic backgrounds. Directed by the aims of the research, consumers who were eligible for participation: i) liked beer and consumed it at least once a fortnight, ii) were able to list at least three beer
Attitudinal and perceptual responses to beer samples
Table 2 and Fig. 1 contain the results from analysis of attitudinal responses. For all 13 Likert statements, significant differences between beer samples were established, although with varying magnitude as shown by F-values. Discrimination was largest for “This is a simple beer without much depth”, “This beer has complex flavours”, “This is a good example of an ordinary beer” and “I would describe this beer as “easy-to-drink”. The samples divided into two groups (Beers 1–4 and Beers 5–9),
Insights gained through holistic and consumer-centric assessment of beers
The present research showcased holistic product characterisation as a means for obtaining more nuanced product insights, whereby information about sample similarities and differences obtained from the collated set of consumer-centric measures would be more informative than the information these measures provided in isolation. In the present research, use of either one of the three types of product responses – attitudinal/perceptual, situational appropriateness or emotional associations - would
Conclusions
In the case of beer, where product landscapes are currently evolving rapidly, the present research presented a holistic and consumer-centric approach for obtaining nuanced insights to better understand the underlying motivations for consumers' consumption behaviours. This multi-method approach, implemented in a CLT setting, identified a complex web of interrelated attitudinal/perceptual, situational appropriateness and emotional associations, which when considered jointly exposed product
Author contributions
AVC and SRJ planned the study, wrote the paper and oversaw data analysis, which was performed by DIH. All other authors contributed to data collection.
Acknowledements
Financial support was received from The New Zealand Ministry for Business, Innovation & Employment and The New Zealand Institute for Plant & Food Research Ltd. Staff at Plant & Food Research are thanked for help in collection of data. Dave Andersen from Plant & Food Research (Motueka Site) and Mark Goldman (Lion Breweries, Auckland) are thanked for performing chemical analysis on the beers.
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