Knowledge,innovation,and outcomes in craft beer: Theoretical framework and fuzzy-set qualitative comparative analysis

Abstract

This paper explores the relationship between knowledge, innovation, and profit-making in the craft beer industry in Baja California, Mexico. The research highlights the cultural nature of this industry, in which the depth of culture and tradition bolster the capacity for innovation. One source of interest in the study of cultural industries is the importance of businesses in regional development, as is happening in the Baja California region with craft beer. At its core, this study draws on the SECI model as a reference to highlight the different ways in which knowledge and learning combine to produce new forms of processes or products or break into new market segments. This empirical study is based on the fuzzy-set qualitative comparative analysis method (FsQCA), which serves to identify sequences or combinations of knowledge and learning that lead to innovation and profit.

Keywords

Craft beer industry tacit knowledge explicit knowledge formal learning fuzzy-set qualitative comparative analysis (FsQCA)

1 Introduction

In many present-day societies, where income levels enable consumption beyond simply meeting basic needs, there has been an increase in products that appeal to consumers on the basis of their symbolic nature. In many cases, this gives rise to industries that bring together culture and creativity to offer new goods and services. These are products that integrate traditional craftwork and modern technology, and goods and services that contribute to new dimensions of knowledge, esthetic appreciation, and leisure, while generating wealth through their production.

This economic dimension, which mobilizes resources and results in new forms of wealth, is produced when cultural industries form a new fabric for industries or services in different parts of the world, as is the case with the craft beer industry in Mexico, the phenomenon studied in this paper.

Firstly, this study presents a general reflection on culture (section 2.1), which highlights how the depth of culture and tradition strengthens the capacity for innovation. Then, section 2.2 outlines the significance of the beer industry in Mexico and craft beer in the state of Baja California, Mexico, its relationship to GDP, and certain legislative changes that have contributed to the sector’s growth. Thirdly, section 3 of the paper presents the theoretical framework, which is intended as a discussion of concepts associated with knowledge creation in these companies and how this knowledge relates to innovation and generates profit. On the basis of this discussion, five hypotheses can be formulated, which this empirical study will endeavor to corroborate.

Section 4 uses the fuzzy-set qualitative comparative analysis methodology to process statistics relating to the forty companies surveyed (21 in Ensenada, 10 in Tijuana, and 9 in Mexicali), thereby identifying, within the sample, statistically significant paths or sequences that establish a relationship between tacit knowledge and formal learning. In turn, these reveal a significant relationship with the development of product technology, innovation in processes and products, and the company’s entry into new market segments. This corroborates two of the hypotheses formulated. Section 5 (the discussion of results) pays particular attention to the unconfirmed hypotheses.

Lastly, section 6 sets out the study’s conclusions and limitations, and offers suggestions for future research.

2 The craft beer sector

As mentioned in the introduction, industrial and commercial activity involving the production, distribution, and sale of craft beer is considered an industrial sector within the cultural and creative industries. With this in mind, this section is divided into two parts. The first is a brief outline of the key aspects that define a cultural and creative industry, while the second presents the characteristics of the craft beer sector that is the object of this study.

2.1 Cultural and creative industries

Peris-Ortiz et al. [14] cite Baudrillard [3] in their assertion that “all products and services provided by industries possess value in a specific cultural context and, accordingly, they are cultural products.” However, the authors add, “this undeniable affirmation, which is founded on the scenario that everything is impregnated and conditioned by culture, obscures the fact that in certain products, consumers seek functional utility while in other products, the appeal for consumption resides in their symbolic nature.”

Indeed, as stated in the previous paragraph, one distinctive feature of cultural industries is the importance placed by consumers on products’ symbolic nature. Among other industries, this occurs in visual media (films, TV, radio, and photography) and the arts (museums, galleries and music), in addition to gastronomy, whereas industries further removed from culture appeal to consumers on the basis of their functional utility or their capacity to solve different problems or needs.

One matter worth addressing, however, in this brief outline of cultural industries is their status as cultural and creative industries, or industries characterized by knowledge and innovation. The fact that culture is the basis for knowledge explains the capacity for innovation, and the two are able to evolve without coming into conflict [6]. Other authors [10], on the other hand, stress that culture is strongly linked to tradition and continuity in processes and products, whereas creativity tends toward change and innovation and therefore, they argue, the greater the influence of culture, the less change and innovation there will be.

Without a doubt, in the history of technology and business, tradition has prevented or hampered progress in many cases. However, in the craft beer industry, along with other sectors such as haute cuisine, the prevailing line of thought is clearly that of [6]: the depth of culture and tradition strengthens the capacity for innovation.

2.2 The cultural and creative craft beer sector

In recent years, the craft beer industry has experienced significant growth in the region of Baja California, Mexico, as a result of various factors including the region’s wine-growing and gastronomical vocation. Such has been the growth of the beer industry that the state of Baja California, Mexico, decided to amend the Law on the Sale, Storage and Public Consumption of Alcoholic Beverages (State Congress of Baja California, 2016) to grant business licenses at a reduced rate for micro-breweries, tasting rooms, and craft beer boutiques.

Currently, industrial production of standard (or non-craft) beer in Mexico is carried out by 55 companies that make up 1.2% of total gross manufacturing output. Craft beer production, on the other hand, represents just 0.5% of the market total. In other words, Mexican craft beer remains a small-scale industry, but this does not subtract from its importance in the Baja California region.

Lastly, it should be noted that the boom in craft beer, which began in 2008, picked up further speed from 2013 with a resolution by the Federal Economic Competition Commission (COFECE) on the non-exclusivity of major beer distribution companies.

Therefore, the craft beer sector in the Baja California region in Mexico is seemingly of great relevance, due to the tradition present in forms of production that have learned to combine hops and other ingredients to produce beers with a unique flavor, color, and foam consistency, among other characteristics. These forms of product innovation are grounded in culture and tradition [4] and enrich a given geographical area and population.

3 Theoretical framework and hypothesis

Cultural depth, as previously mentioned in connection with craft breweries in Baja California, Mexico, is the basis for their capacity to acquire and apply new knowledge and put into practice what literature calls dynamic capabilities (DCV), meaning the transformation of the company’s bundle of resources into a source of continuous improvement in technology, production processes, products, and the capacity to access new market segments.

For this to take place, [16, 17] remark that if the bundle of company resources is very – or too – finely tuned, its idiosyncrasy will ensure that the company’s workings cannot be imitated by others [1, 2], but at the same time, will make change and innovation difficult. In such a case, the bundle of resources will be subject to its own inertia.

While there is broad consensus on this matter [7–9 , 20], such studies tend to focus on large companies with routine practices that play a significant role in their operation. In the case of small businesses, such as those that produce craft beer, there is less risk that an excessive tuning of resources will hamper the innovation dynamic. This is what is to be expected from the flexibility of these companies, as underscored by Mintzberg [11].

Another significant issue related to the small scale of the companies studied here is the fact that their informal structure makes it difficult to distinguish between the different phases of knowledge creation. To draw on the SECI model by [13] as one of the clearest representations of the knowledge creation process in the literature [21], it is impossible to identify, in these companies, the four phases of socialization, externalization, combination and internalization of knowledge. Perhaps the two largest businesses in the sample – which employ 30 and 40 workers – come close to the SECI model, but the remaining companies employ close to 20 workers at most (29 have no more than 15) and their study calls for a more intuitive and synthetic approach, either that illustrated by [19]’s work on organizational learning, or an approach that simplifies the SECI model.

This last option, chosen on account of the clear and comprehensive nature of the SECI model, suggests that tacit knowledge is, as proposed by [12] and [13], the phase at which experience is accumulated and shared, and which is at the origin of knowledge. Later, this knowledge matures and becomes explicit, is disseminated, and then applied (the externalization, combination, and internalization phases in the SECI model), having been converted into forms of learning that are formal and substantiated by a capacity for innovation and new practices.

If this is the case, in the companies studied, it should be possible to identify a set of significant relationships pertaining to (1) tacit knowledge, formal learning, development of production technology; (2) tacit knowledge, formal learning, process innovation; (3) tacit knowledge, formal learning, product innovation. Consequently, a significant positive relationship is to be expected between development of production technology, process innovation, product innovation, and profit or performance.

The knowledge externalization, combination and internalization stages exist beneath these relationships, but in these small companies they overlap and are indistinguishable in the transition from tacit to explicit knowledge. The companies’ small size, while precluding the advantages of economies of scale [5], does however offer each company its own idiosyncratic bundle of resources and, as a result, its own form of differentiation [2], without this leading to the excessive rigidity that [16, 17] caution against.

On the basis of the previous paragraphs, the following hypotheses can be put forward:

H1: If tacit knowledge, or a significant part thereof, is transformed into learning that becomes formal and applied in nature, this will lead to the development of production technology within the company.

H2: If tacit knowledge, or a significant part thereof, is transformed into learning that becomes formal and applied in nature, this will lead to innovations in company processes.

H3: If tacit knowledge, or a significant part thereof, is transformed into learning that becomes formal and applied in nature, this will lead to innovations in the company’s products.

H4: Improvements in the company’s production technology, processes, and products will provide access to new market segments.

H5: Improvements in the company’s production technology, processes, and products will be positively associated with profit-making.

An empirical study was then conducted using a fuzzy-set qualitative comparative analysis methodology.

4 Method and results

4.1 Sample and data

In order to obtain the data necessary to test the hypotheses, 40 beer producers were interviewed (see Appendix). The respondents were managers of craft beer companies in Mexico. Face-to-face interviews are a useful data collection method to ensure a full understanding and more objective assessment of the variables considered in the study. The conditions (independent variables) and outcomes (dependent variables) were assessed mostly by means of dichotomous variables. Table 1 shows the conditions and outcomes measured with their mean, standard deviation and correlations.

4.2 Analysis and results

This study uses fuzzy-set qualitative comparative analysis (FsQCA) to explore the importance of knowledge in process and product innovation and the effect on profit. FsQCA is a qualitative method that makes it possible to introduce quantitative analysis into case-oriented research.

First, the need to combine explicit and tacit knowledge to achieve innovation was analyzed. These innovations may concern products or processes and can affect firms’ profitability. Different configurations of innovation efforts can produce different economic results. The results of the correlation between conditions and outcomes (Table 1) show complex relationships that only exhibit significant correlations between product innovation and no profit, and between process innovation and product innovation. These correlations are negative and positive respectively.

Table 1
Correlations between conditions and outcomes

Conditions and outcomes Mean S.D. 1 2 3 4 5 6 7

1. Explicit knowledge. Formal learning (formal knowledge) 0.60 0.496

2. Explicit knowledge. Self-learning (books, online information 0.58 0.50 – 0.19

and courses)

3. Tacit knowledge (experience, learning process inside the company) 0.95 0.22 – 0.19 – 0.20

4. Development of production technology (min = 0; max = 4) 2.05 0.99 – 0.12 – 0.27 0.01

5. Process innovation (min = 0; max = 3) 1.78 0.89 – 0.21 – 0.05 – 0.06 0.01

6. Product innovation 0.75 0.44 – 0.12 – 0.03 0.13 0.21 0.38*

7. New market segments 0.43 0.50 0.08 0.02 0.20 – 0.04 – 0.18 0.03

8. No profit 0.05 0.22 – 0.05 0.20 0.05 – 0.01 – 0.07 – 0.40* – 0.20

Conditions and outcomes	Mean	S.D.	1	2	3	4	5	6	7
1. Explicit knowledge. Formal learning (formal knowledge)	0.60	0.496
2. Explicit knowledge. Self-learning (books, online information	0.58	0.50	– 0.19
and courses)
3. Tacit knowledge (experience, learning process inside the company)	0.95	0.22	– 0.19	– 0.20
4. Development of production technology (min = 0; max = 4)	2.05	0.99	– 0.12	– 0.27	0.01
5. Process innovation (min = 0; max = 3)	1.78	0.89	– 0.21	– 0.05	– 0.06	0.01
6. Product innovation	0.75	0.44	– 0.12	– 0.03	0.13	0.21	0.38*
7. New market segments	0.43	0.50	0.08	0.02	0.20	– 0.04	– 0.18	0.03
8. No profit	0.05	0.22	– 0.05	0.20	0.05	– 0.01	– 0.07	– 0.40*	– 0.20

*p < 0.05; **p < 0.01 (two-tailed). All variables are dichotomous except 4 and 5.

Nevertheless, most relationships are not simple, and their significance remains low. If the theoretical framework posits strong effects from conditions on outcomes (learning on innovations, and innovations on profit), the absence of such effects in the correlations between variables may be due to complex causality and asymmetrical relationships. FsQCA makes it possible to identify combinations of variables (configurations) that are necessary or sufficient to cause an outcome [22] and avoid relying solely on single variables.

To incorporate non-dichotomous conditions and outcomes (i.e. development of production technology, process innovation) into the analysis, the use of FsQCA is mandatory. The first step in FsQCA is to calibrate the conditions and outcomes. The general rule applied was to take the mean of the variable as the crossover point between “more in” and “more out” of the set in question [15]; 90% of the highest value was considered full membership and 10% full non-membership.

The necessary conditions for the presence and absence of open innovation are shown in Table 2.

Table 2

Analysis of necessary conditions

	Outcomes
	Development of production technology		Process innovation		Product innovation		New market segments
Conditions	Consistency	Coverage	Consistency	Coverage	Consistency	Coverage	Consistency	Coverage
Formal learning	0.62	0.78	0.64	0.83	0.57	0.71	0.65	0.46
Self-learning	0.62	0.82	0.61	0.88	0.57	0.74	0.59	0.43
Tacit knowledge	0.95	0.76	0.58	0.87	0.97	0.76	1.0	0.45

The consistency values for the conditions fall below the minimum threshold of 0.9 [18] in all outcomes, with the exception of tacit knowledge, which only fails to meet the threshold in process innovation (0.58). Tacit knowledge (individual and shared experience) is therefore a necessary condition for innovation.

Tables 3 6 present truth tables with all possible combinations with types of learning to improve intermediate outcomes after applying the Quine–McCluskey algorithm to reduce the number of rows. The coverage shows the empirical relevance of each solution. The consistency reflects the degree to which cases with the same configuration of conditions share the same outcome [15].

Table 3

Antecedent configurations leading to the development of production technology (DPT)

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	Tacit Knowledge * Formal learning * ⟶ DPT	0.31	0.31	0.94

Table 4

Antecedent configurations leading to process innovation (ProcI)

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	Tacit Knowledge * Self-learning ⟶ ProcI	0.53	0.27	0.88
2	Tacit Knowledge * Formal learning ⟶ ProcI	0.57	0.31	0.89

Table 5

Antecedent configurations leading to product innovation (ProdI)

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	Tacit Knowledge * Formal learning ⟶ ProdI	0.43	0.43	0.81

Solution coverage: 0.43; Solution consistency: 0.81.

Table 6

Antecedent configurations leading to new market segments (NMS)

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	Tacit Knowledge * Formal learning ⟶ NMS	0.64	0.64	0.50

Solution coverage: 0.64; Solution consistency: 0.50.

The truth table with all possible combinations of causal conditions (configurations) presents several consistent configurations. Of the four outcomes considered (development of production technology, process innovation, product innovation, and new market segments), the most difficult to achieve is development of production technology (Table 3), which requires a combination of the three types of knowledge analyzed (tacit knowledge, formal learning, and tacit knowledge, path in Table 3). Indeed, only 31% of the firms studied (raw coverage, Table 3) have been able to develop production technology. For process innovation (outcome of Table 4), tacit know-ledge can be combined with both self-learning and formal learning to produce a satisfactory outcome (paths 1 and 2 respectively of Table 4). The development of product innovations (outcome of Table 5) and associated entry into new market segments (outcome of Table 6) is a combination in both cases of formal learning and tacit knowledge (path solutions of Tables 5 and 6). According to these results, process innovation has more paths to success than product innovations and new market segments, and successful paths to achieve process innovation cover 84% of the cases analyzed (total coverage of model in Table 4, data not shown in table), whereas successful paths leading to product innovations and new market segments represent only 43% and 64% of cases respectively (Tables 5 and 6).

Table 7 shows the combinations of development of production technology, process innovation, and new market segments in profit-making. This is an interesting result as it shows that firms that develop their own technology and have the capacity to innovate in processes are able to compete with product innovation or develop new segments in the market with a high chance of success. These two combinations cover 58% of cases.

Table 7

Antecedent configurations leading to profit-making

Sol.	Path	Raw coverage	Unique coverage	Consistency
1	DPT * ProcI* ProdI ⟶ Profits	0.51	0.24	1.00
2	DPT * ProcI* NMS ⟶ Profits	0.53	0.08	1.00

Solution coverage: 0.58; Solution consistency: 1.00. Development of production technology (DPT); process innovation (ProcI); product innovation (ProdI); new market segments (NMS).

It is noteworthy that the results show that direct relationships between knowledge and profit are inconsistent, whereas combining knowledge with different forms of formally established and applied learning does produce the expected results. At the same time, profit-making requires complex combinations of the types of innovation considered, which always include some kind of innovation associated with the final product or market segment.

5 Discussion of results

This empirical study has enabled the corroboration of two of the five hypotheses put forward (hypotheses H3 and H5), and the findings surpassed the initial study objectives, particularly regarding the rejection of hypotheses H1 and H2. Hypothesis H1 is not met since the relationship between tacit knowledge, formal learning, and development of production technology goes beyond the initial assumption in the hypothesis. Indeed, the statistically significant path for this relationship combines tacit knowledge*formal learning*tacit knowledge and results in the development of production technology. In other words, the sequence in the relationship corresponding to this hypothesis exhibits a complex relationship between tacit knowledge, formal learning that brings about changes in product technology, and new tacit knowledge that opens up a dynamic of continuous improvement and technical change. This is far more comprehensive and richer than the original proposal in hypothesis H1, and therefore, although it does not strictly meet the hypothesis as it was worded, it does confirm, more generally, the perspective underpinning this study on knowledge and innovation in the craft beer industry.

As much can be said for hypothesis H2. In this case, the tacit knowledge-formal learning relationship requires another parallel path comprising tacit knowledge and self-learning. Again this fails to meet the hypothesis literally, but does lend weight to the theoretical framework of this research: complex forms of acquisition of experience and tacit knowledge that, when combined with formally established and applied learning, lead to process innovations.

On the other hand, hypotheses H3 and H5 are met exactly as they were formulated. As far as hypothesis H4 is concerned, the statistical method used (FsQCA) identified no significant path establishing a link between, on the one hand, improvements or innovations in products, processes and production technology, and on the other, entry into new market segments. The rejection of this hypothesis contradicts a powerful intuitive logic, but it could not be corroborated with the data from this study.

6 Conclusions

The use of hops and other additives that give craft beer its color and flavor, as mentioned in section 2.2., opens the door to continuous improvement, innovation, and differentiation processes within this sector, and explain the interest in identifying significant relationships between knowledge, innovation, and profit-making in the market. Moreover, these beer-making companies are part of a cultural and creative industry, and therefore of particular interest due to the positive economic impact they have on the areas they operate in. In this case, the symbolic nature of the products also serves to increase the importance of innovation and the capacity for differentiation, such that although efficiency in processes is a prerequisite, as in any competitive industry, performance will be largely dependent on the capacity for differentiation [14].

The results from this group of companies make it possible to draw the following conclusions. Firstly, innovation processes require the interaction of tacit and explicit knowledge to be successful. In-depth knowledge of production processes and production technology is unattainable without understanding the combination of practical experience and formal learning, both of which are a consequence of the relationships between the human, physical, and technical phenomena involved in different improvements. This forms the basis of product innovations that enable to company to expand its current market and access new ones.

Despite the simplification of the SECI model mentioned earlier, these empirical results support [13]’s general model, which considers that overlapping between different types of knowledge is essential to produce innovation. The authors address knowledge management from a fully dynamic perspective in which knowledge creation depends on individuals’ and groups’ tacit knowledge, acquired through experience and developed through interaction, learning, and communication between different agents within the organization. All this will enable explicit knowledge [12] and the formalization and application of learning.

It should be stressed that to achieve economic success, as stated above, it is necessary to control production technology and processes. In addition, however, managerial staff should have a fairly deep connection to the culture of the area where the company is located in order to align the symbolic nature of the products accordingly. This means that competitive advantages are derived primarily from improvements or changes in products that expand one’s market or provide access to new markets. This can only be achieved with an in-depth knowledge of manufacturing technology and production processes that makes innovation possible.

Other aspects that may be significant in profit-making, such as a company’s brand image or CSR policies, have not been taken into account in this paper and constitute study limitations. Similarly, future research may explore other sectors of cultural industries to corroborate the relationships between knowledge, innovation, and performance, thus increasing understanding of cultural and creative businesses.

Footnotes

Appendix

The following survey seeks to gather information for the research project “Factors that impact competitiveness in a cultural and creative industry: the case of craft beer in Baja California”. This study was conducted with support from the National Council of Science and Technology (CONACYT) and CETYS Universidad. The answers provided will be used solely for academic purposes and we thank you for your cooperation.

a. Profile

Where is your brewery located? (You may select more than one option.)

Ensenada

Tijuana

Mexicali

Tecate

Rosarito

How many establishments do you have in your brewery?

tap room(s)

production plant(s)

Approximately how many employees currently work in your brewery?

What was your motivation for producing craft beer?

As a hobby

A lack of supply in the market

Witnessing the success of other breweries in the USA

Influence from friends or family members

Other:

b. Competitiveness

Put the following difficulties in establishing your brewery in order from 1 to 5 (with 5 being the greatest difficulty and 1 the least).

Funding

Laws/regulations

Competition

Knowledge of the business

Other: _______

How did you learn to make beer? (You may select more than one answer.)

With other brewers (state which company).

Courses and workshops (organized by...)

Digital media (say which).

Other: ________

Has any brewer shared information with you that has been significant for your company’s growth?

__ No ___ Yes

Who? (Company)

In which respect?

Do you believe you have shared information with other brewers that has been significant for their growth?

__ No ___ Yes

Who? (Company)

In which respect?

In terms of knowledge, what degree of specialization did you need to make beer?

High

Medium

Low

In terms of knowledge, how intensive was the learning process you needed to develop your business model (company)?

High

Medium

Low

In terms of knowledge, which aspect of your business model was the trickiest to develop?

The infrastructure and technology

The production process

Adapting to rules, regulations, and legislation

Administration and finance

Marketing and commercialization

Strategic planning

How did you acquire the technology you use to make beer (the machinery)?

I developed it myself

I bought it from a Mexican supplier

I bought it from a foreign supplier

I rent it or share it with other brewers (whom?)

c. Relationship networks

When you establish ties or collaborative relationships with other actors, you do so with the following in mind: (you may select more than one answer)

Developing new ventures

Learning new ways to improve my product

Learning new ways to manage my business

Gaining access to another market segment

Securing funding

Securing licenses and permits

Helping other brewers (by sharing knowledge, resources, etc.)

Helping companies from other sectors (providing spaces for their products, entering new markets, etc.)

Expanding my network of suppliers and increasing my bargaining power

Increasing my production capacity

I do not tend to collaborate

When you have established ties or collaborative relationships with other actors, you have benefited in the following ways: (you may select more than one answer)

I developed new ventures

I learned new ways to improve my product

I learned new ways to manage my business

I entered a new market segment

I secured funding

I secured licenses and permits

I helped other brewers (by sharing knowledge, resources, etc.)

I helped companies from other sectors (by providing spaces for their products, entering new markets, etc.)

I expanded my network of suppliers and increased my bargaining power

I increased my production capacity and/or knowledge

I did not obtain any benefit

d. Innovation

What aspect of your business model best reflects creativity and originality?

The way in which experience generates knowledge

Product development

The way the sales outlet is managed

The image/brand management

Other: _______

Which aspect of your business model do you believe is the most difficult to imitate?

The product

The image

The price

The distribution channels

The supply channels

The establishment (design/architecture)

Which aspect of your business model do you believe is most highly valued by your customers?

The product

The service

The image/presentation

The price

The establishment

Your company’s development has been the result of:

Short-term planning (1-2 years)

Medium-term planning (3–5 years)

Long-term planning (over 5 years)

An internal learning and knowledge generation process

Have you innovated or improved the production process in any way?

The method or way in which the product is developed

The technology or machinery

The supplies used

Other

e. Corporate social responsibility

Which of these practices are part of your business philosophy?

Strict compliance with laws, rules, and regulations

Tax compliance

Respecting intellectual property rights

Adopting fair competition practices

Acknowledgments

We thank the National Council of Science and Technology (CONACYT) for the support and funding granted to develop Project 1241 in the 2015 call for proposals in Addressing National Problems.

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