Innovation and Technological Management Model in the Tequila Sector in Mexico

Abstract

Creativity, ideas, and an entrepreneurial attitude are needed to innovate. However, it is also necessary to have practical instruments that allow innovations to be reflected in the company. One of those tools is technology. This research aims to analyze innovation and technology in the tequila industry through Bayesian networks with machine learning techniques. Likewise, an innovation and technology management model will be developed to make better decisions, which will allow the company to innovate to generate competitive advantages in a mature low-tech industry. A model is made in which the critical factors that influence management innovation and technology optimally to generate value translate into competitive advantages. The evidence shows that the optimal or non-optimal management of knowledge management and its various factors, through the causality of the variables, allow the interrelation to be more adequately captured to manage it. The results show that the most relevant factors for adequate management of innovation and technology are knowledge management, sales and marketing, organizational and technological architecture, national and international markets, cultivation of raw materials, agave, and management, use of waste, and not research and development.

Full text

Citation: Terán-Bustamante, A.;
Martínez-Velasco, A.;
Castillo-Girón, V.M.;
Ayala-Ramírez, S. Innovation and
Technological Management Model
in the Tequila Sector in Mexico.
Sustainability 2022,14, 7450.
https://doi.org/10.3390/su14127450
Academic Editor:
Mariarosaria Lombardi
Received: 18 April 2022
Accepted: 13 June 2022
Published: 18 June 2022
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sustainability
Article
Innovation and Technological Management Model in the
Tequila Sector in Mexico
Antonia Terán-Bustamante 1,* , Antonieta Martínez-Velasco 2, Víctor Manuel Castillo-Girón3
and Suhey Ayala-Ramírez 4
1Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana,
Ciudad de México 03920, Mexico
2Facultad de Ingeniería, Universidad Panamericana, Ciudad de México 03920, Mexico;
amartinezv@up.edu.mx
3Departamento de Ciencias Económico Administrativas, Centro Universitario de los Valles,
University of Guadalajara, Ameca 46600, Mexico; victor.cgiron@academicos.udg.mx
4Departamento de Ciencias Sociales y Humanidades, Centro Universitario de los Valles,
University of Guadalajara, Ameca 46600, Mexico; suhey.ayala@academicos.udg.mx
*Correspondence: ateran@up.edu.mx
Abstract:
Creativity, ideas, and an entrepreneurial attitude are needed to innovate. However, it is also
necessary to have practical instruments that allow innovations to be reflected in the company. One
of those tools is technology. This research aims to analyze innovation and technology in the tequila
industry through Bayesian networks with machine learning techniques. Likewise, an innovation and
technology management model will be developed to make better decisions, which will allow the
company to innovate to generate competitive advantages in a mature low-tech industry. A model is
made in which the critical factors that influence management innovation and technology optimally
to generate value translate into competitive advantages. The evidence shows that the optimal or
non-optimal management of knowledge management and its various factors, through the causality
of the variables, allow the interrelation to be more adequately captured to manage it. The results
show that the most relevant factors for adequate management of innovation and technology are
knowledge management, sales and marketing, organizational and technological architecture, national
and international markets, cultivation of raw materials, agave, and management, use of waste, and
not research and development.
Keywords:
innovation; Bayesian networks; machine learning; Mexico; technology model; technological
innovation; tequila industry
1. Introduction
Innovation is increasingly important, particularly in economic crises like the one we
are currently experiencing caused by the COVID-19 pandemic. However, to innovate,
one must consider two key elements within the firm: the management of innovation
and technology. These are dynamic processes that need to be considered strategically to
generate value.
Innovation is a crucial factor in economic development for companies. Currently, in the
knowledge economy, more accelerated innovation processes are developed that generate
new products, production processes, and distinctive forms of marketing, commercialization,
and organization, which contribute to raising competitiveness, the economic growth of the
firm, and at the same time impact the well-being and quality of life of people [1,2].
The company will look for tools that allow it to innovate, improve its productivity,
and have competitive advantages. One of those tools that allow the company to innovate
is technology management (GT) because the company’s activities related to innovation
have a more significant impact to the extent that they are adequately managed, their
Sustainability 2022,14, 7450. https://doi.org/10.3390/su14127450 https://www.mdpi.com/journal/sustainability

Sustainability 2022,14, 7450 2 of 23
processes are well defined, they are carried out systematically, and they have an area that
coordinates them [3].
COVID-19 affected almost all sectors of the economy; however, the tequila sector
in 2021 grew. Production increased by 41.2% compared to 2020, reaching 487.3 million
liters produced. On the other hand, exports also registered their highest level in history,
reaching 310.5 million liters. This figure represents 17.6% compared to the same period in
2020. It is worth mentioning that 70% of the tequila exported is bottled from the origin,
which translates into a more significant economic benefit for the territory protected by the
designation of origin tequila (DOT) [4].
Along with production, the consumption of agave grew, presenting an increase
of 43.5%, reaching 1 million 866 thousand tons. That is also a historical high for
tequila agribusiness [4].
Two other figures that are added to the previous records in the year 2022 of 2021 are the
number of registered trademarks. At the end of 2021, the CRT registered 1913 trademarks,
while in the year 1995, there were only 516 trademarks; for their part, the registered agave
growers at the end of 2021 reached a critical number of 25 thousand agave producers;
in 2014, there were barely 3000 agave growers. Therefore, the above figures represent
new records for this production chain [
4
]. The tequila industry has established itself as
one of the leading agribusinesses in Mexico by producing one of the most emblematic
alcoholic beverages worldwide [
5
] (Gallardo, 2019); however, to innovate and adopt new
technologies throughout the sector, it has to focus on addressing the problems that it
presents integrally.
The main problems faced by the tequila industry are the following:
I. Many medium and small companies compete with large companies.
II.
Availability and supply of raw material, which suffers from cyclical crises that
fluctuate over the years and have a considerable impact on both production
levels, costs, and all the points mentioned for the production of tequila and the
different distillates [6].
III. The adulterations.
IV.
Sustainability, since its treatment processes for some types of waste, such as stillage,
are not yet fully efficient.
V.
Dissimilar use and development of technology between large and smaller compa-
nies, which continue to use old processes.
In addition to the above, it is necessary to consider that the tequila industry is a mature
low-tech industry, where there is low intensity in research and development (R&D).
Derived from the previous problem, the questions that guide the research are: What
are the main factors to innovate in a mature low-tech sector like tequila? What are the
critical factors in the optimal management of innovation and technology in the tequila
industry? Using an innovation and technology management model, how can a company in
the tequila sector make better decisions to innovate new products/services, processes, sales
and marketing, and the organization? What are the best correlations between the various
factors in the innovation management model in the tequila industry?
This research aims to analyze innovation and technology in a mature low-tech sector
such as the tequila industry. For this purpose, a predictive decision-making model is
designed through Bayesian networks with machine learning techniques.
This research is structured into three sections. The first section addresses the theoretical
framework, specifically the management of innovation and technology and its relationship
with knowledge. Characterization of the tequila sector in Mexico is also made. The second
section presents the methodology of analysis and construction of the tequila model based
on Bayesian networks (BNs). Finally, the third section presents the results, discussion,
and conclusions.

Sustainability 2022,14, 7450 3 of 23
2. Theoretical Framework
2.1. Innovation and Technology Management: Key Elements
Management contemplates the activities from which the human being reflects his
creativity and ways of adapting to the context in which he participates. It is the form from
which it uses the available knowledge, both theoretical and practical, and the forms in which
it manifests itself (artifacts, experience, processes, systems, and products/services) [7].
Innovation and its management are an important issue for high-tech industries
and lower technological industries and companies because it has a significant impact
on competitiveness.
Technology is applied knowledge (scientific, technical, and traditional). It is a set
of knowledge about techniques that can encompass both knowledge and the tangible
materialization of that knowledge in a production process, in a system, machinery,
or equipment [7].
It is action-oriented practical knowledge; it involves the systematic application of
scientific knowledge or other knowledge organized into practical tasks. It is a knowledge
whose application is oriented to a specific end to solve action problems, and its object is
not simply to know but to act. That is, a knowledge that one has not only in terms of
knowledge but also knowing how to do [8,9] and why to do it [10].
Technology integrates components associated with innovation. Innovation is a com-
plex concept, which according to Schumpeter [
11
], is related to that process of creative
destruction in which you have to rebuild to innovate. This process is both a means and
an end for companies to respond faster to customer needs and achieve advantages in
a global economy. It is about managing a process that always seeks improvements in itself.
Nevertheless, it also delivers new products and services to customers efficiently, effectively,
and faster than the competition or improves the delivery of existing products and services
by improving processes [3,10].
Currently, innovation is a constant process in companies, in which changes are con-
tinuously introduced in their products and processes, new organizational methods are
applied, new business methods are implemented, essential changes are implemented in the
company, and new knowledge is acquired, among others [1,3].
Therefore, innovation has become a core factor in the company that allows it to
generate value for its implementation and achieve benefits that are reflected in profitability
due to the risks assumed [3,9].
Therefore, the company will look for tools that allow it to innovate, improve its pro-
ductivity, and have competitive advantages. One of these tools that allow the company to
innovate is the management of technology (GT) because the company’s activities related
to innovation have a more significant impact to the extent that they are properly man-
aged, have well-defined processes, are carried out systematically, and have an area that
coordinates them [2,3,10,12,13].
Technological management should be seen as the process that allows acquiring knowl-
edge necessary to make technological innovations; that is, value is created for the company
since the efficiency of operations is increased [14].
The company must be able to develop technological capabilities through its manage-
ment that allow it to identify, adopt, use, dominate, modify, and/or create technologies and
make use of new or existing knowledge for the development of new products and improve-
ment in products, processes [
15
] and the company itself, which enable its sustainability
over time [2,3,7,9,10].
The Cotec Foundation [
8
] conceptualizes technological management as the organi-
zation and direction of human and economic resources to increase the creation of new
knowledge. The generation of technical ideas allows obtaining new products, processes,
and services or improving existing ones; the development of these ideas in prototypes and
their transfer to the manufacturing, marketing, and use phases.
For Solleiro and Castañon [
16
], technology management is the set of tools and tech-
niques that allow an organization to properly take advantage of its resources (people,

Sustainability 2022,14, 7450 4 of 23
money, machines, and information, among others) through the elaboration and execution
of innovation plans.
For its part, the National Technology and Innovation Award includes five functions
to manage innovation: (i) monitor, (ii) plan, (iii) enable, (iv) protect, and (v) implement [
17
].
For the Mexican Standard NMX-GT-003-IMNC-2008 on Technology Management [
18
],
adopting a technology management system is a strategic decision of the organiza-
tion. Therefore, its design and implementation in an organization are influenced by its
different needs [3,18].
Barletta et al. [
19
] argue that innovation is the result of multiple factors that go beyond
the activities carried out in R&D laboratories and include a combination of routines and
solutions to problems both inside and outside the R&D laboratory.
According to the above, to understand the explanatory factors of these activities, it is
necessary to understand innovative behavior as a complex phenomenon that is part of the
firm’s competencies.
2.1.1. Management of Innovation, Technology, and Knowledge
Innovation management requires capturing the continuously created knowledge,
which is integrated into work routines when defining long-range strategies or complex
problems that demand more structured decision-making. In all cases, knowledge is re-
quired. Therefore, it is necessary to understand how it is valued economically, in the
organization, and socially and created, used, disseminated, managed, and changed [7].
Innovation implies applying new knowledge; for this reason, companies follow par-
ticular learning processes and have defined trajectories that influence their development.
Through these learning processes, companies build competencies.
Learning is a process that involves repetition and experimentation, which makes it
possible to perform tasks better and faster and identify new production opportunities [
3
,
20
].
According to the above, managing knowledge does not necessarily mean innovating;
the relevant thing is knowing and understanding how to manage it to influence company
improvements. That is, innovations are made in any of its possible forms. That is, to
know the dynamics of planning, organizing, controlling, and directing the process so that
when used, it has a productive result so that it becomes a conscious and deliberate act of
creation, access, use, and protection. Technological learning refers to the dynamic process
of acquiring technological capabilities.
Companies learn over time, accumulate knowledge and technological capabilities, pro-
gressively undertake new activities, and acquire new technological capabilities. Studies tend
to analyze technological learning processes and capacity-building processes together [21].
External sources include strategic alliances and licensing agreements; independent
suppliers of materials, components, and equipment; technical analysis of competitors’ prod-
ucts; research institutes and universities; experts and consulting firms; patents; conferences;
professional and scientific journals; fairs and exhibitions, among others.
Therefore, the learning process is fundamental in the construction of innovation
capabilities. The ability to learn and accumulate knowledge over time is the essence of the
innovation process, and this is built through its management.
Therefore, knowledge and the development of learning processes become critical
elements in generating competitive advantages.
Innovation is a process that enables value to be created and captured from ideas.
Successful innovation management routines are not easy to acquire. Because they
represent what a particular firm has learned over time, through a process of trial and error,
they tend to be very firm specific.
From a general perspective, knowledge is relevant for low-tech firms and can be con-
ceived as practical knowledge, where it cannot always be distinguished from scientifically
generated knowledge. In other words, the adaptation of the process is constructed in an
application-oriented form—by doing—are based on the accumulated practical knowledge
of the company and the tacit knowledge of people [22].

Sustainability 2022,14, 7450 5 of 23
2.1.2. Innovation in Low-Tech Industries or Non-Research-Intensive
According to the Organization for Economic Cooperation and Development (OECD) [
23
],
a firm’s technological level is related to its intensity and expenditure on R&D. Thus, in the
classification, there are companies and sectors of high-tech, medium-tech, and low-tech,
measured by the results and impact of R&D.
For Tidd and Bessant [
24
], innovation and competitive success are not simply about
high-technology companies, innovating is about learning. In recent decades, literature on
innovative activities in sectors and firms that do not carry out formal R&D activities has
become widespread, mainly in Germany and Spain [19,25–34] among others.
However, derived from specific studies, especially in the manufacturing industry,
these researchers agree that studies of innovation are reduced to the analysis of the intensity
of formal R&D. For this reason, it is only possible to explain the behavior of a part of the
productive structure, generally based on knowledge-intensive activities.
However, a significant proportion of studies confirm a strong influence of other factors
on the rest of the productive structure. In other words, different types of resources and skills
are mobilized that can compensate for the absence of formal efforts in internal R&D [
22
,
31
].
In these companies, the primary sources of knowledge to innovate come from external
laboratories, customers, and suppliers. Bender and Laestadius [
26
] and Rammer, Czarnitzki,
and Spielkamp [
27
] argue that, especially in the case of small and medium-sized companies,
R&D laboratories are often replaced by the development of social technologies, which
are associated with human resource management, work organization, and the search for
external sources of innovation.
Bender and Laestadius [
26
] also suggest that formal R&D is not an essential asset that
firms have to generate innovation processes, which are their skills to transform codified
knowledge into specific knowledge contextualized in each process organization. More-
over, tacit knowledge allows them to utilize dispersed organizational knowledge to put it
together creatively. To generate the organizational skills to innovate, in low-tech sectors,
innovation is the result of a particular configuration of tacit and codified resources that
firms build throughout their lives rather than strategies based on R&D [19].
According to Potters [
29
], R&D as an input for innovation (output) plays a minor role
in low-tech sectors, as they are mature industries where many (small) producers operate at
marginal cost. However, these sectors play an essential role in the economy.
For Som and Kirner [
30
,
31
], despite the debate and criticism of innovation with a high-
tech focus, low manufacturing tech (LMT) research shows that non-research-intensive
industries are surprisingly innovative and play an essential role in the development of mod-
ern economies. The LMT sector bases its innovation on modifying available technologies
and existing knowledge to combine them in a hybrid concept with high-tech components.
According to Zouaghi [
33
] and Law [
34
], low-tech companies—in contrast with the
complexity of high-tech companies—are characterized by focusing on innovation by pro-
cess, organization, and marketing. For this reason, these companies have low internal
innovation capabilities and are dependent on external knowledge acquisitions. In addition
to the above low-tech firms, to improve their innovation performance, look for R&D human
capital, while high-tech firms invest in R&D expenditures. However, due to low R&D
investment, low-tech firms usually lack absorptive capacity [33].
Thomä [32] asserts that innovation at the firm level occurs with or without R&D, but
rarely without acquired skills such as by doing, using, and interacting. These competencies
are given through an informal learning process and know-how based on experience. Thus,
the excessive emphasis on formal, internal R&D processes ignores the fact that competencies
of this type are a prerequisite for successful innovation.
Knowledge management practices (KMP) are a critical factor that directly affects
innovation activities [34]. Thus, knowledge is an essential and complex input.

Sustainability 2022,14, 7450 6 of 23
2.2. Characterization of the Tequila Industry in Mexico
Tequila is one of the most famous drinks nationally and worldwide. It is obtained from
the Agave Tequilana Weber Blue plant, better known as Agave Azul, which is produced in
181 municipalities in five states, where there is a denomination of origin for such purposes:
Jalisco, its 125 municipalities; Michoacán, 30 municipalities; Nayarit, 8 municipalities;
Tamaulipas, 11 municipalities; Guanajuato, 7 municipalities [35].
Tequila is a product that has been associated with the values of regional and national
identity, popular culture, literature, and Mexican cinema [
36
]. Currently, its production
constitutes one of the usual activities of Mexico and is significant in the agricultural and
industrial development of various states, especially Jalisco [
37
,
38
]. Its distinction and
importance come from its historical roots; its production and commercialization have
reached great cultural and economic relevance [39].
The consolidation of the tequila industry in Mexico has been evident since the last
century and now continues. However, in recent years, the popularity of the drink has
been increasing, not only nationally but also internationally [
40
]. This is reflected in the
increase in beverage production. From 1995 to 2008, production tripled, going from 104.3
to 312.1 million liters [
4
]. In terms of value, in 2019, the exports made by 162 companies
represented USD 1874.00 (millions of dollars). In 2021, production reaches the highest
point in its history, producing about 374 million liters of tequila, the highest volume
recorded since 2000, representing, in the last two decades, an increase of 106% [
4
]. Germany,
Spain, France, and Canada are the leading destinations for these exports. However, the
United States is positioned as the leading destination country for this beverage [41,42].
2.2.1. Cultivation and Production of The Agave Tequilana Weber Blue Variety
Agave is a plant native to Mexico, there are more than 200 varieties, and only one can
be used to produce tequila, the Agave Tequilana Weber Blue variety, which gives the drink
its unique organoleptic characteristics and its denomination of origin (DO) [43].
According to the General Declaration of Protection of the Denomination of Origin
Tequila (DO) and the NOM of Tequila, for the Weber Blue variety to be used in the manufac-
ture of tequila, it must meet the following two requirements: 1. The Agave Tequilana Weber
Blue must be grown within a geographical area delimited by the general declaration of the
denomination of origin, and 2. Must be registered with the Tequila Regulatory Council
(CRT), a tequila certification body [4].
The Agave Tequilana Weber Blue variety does not exist in wild form, and its vegetative
propagation is carried out using the offspring produced by the mother plants, whose ideal
planting time is just before the rainy season to favor the beginning of the development of the
plant. The average time required by the plant to reach maturity ranges from seven to nine
years, a period in which the maximum accumulation of carbohydrates is reached [
6
,
44
,
45
].
The main carbohydrate is inulin, a high molecular weight polymer of approximately
43 fructose monomers whose ends constitute a glucose molecule. For the harvest or Jima of
the agaves, all the leaves are cut, and then the plant with a pineapple appearance is cut,
which weighs between 20 and 90 kg [6,46].
Being the raw material for the elaboration of tequila, some companies also take
advantage of it to elaborate functional foods such as inulin and fructose; the availability
of agave is one of the strategic elements of this industry. Furthermore, given its long life
cycle and that during its development it is exposed to various climatic factors such as
frost/snowfall, droughts, fires, pests, and diseases, the planning of the crop is essential
and must be aligned with the market expectations of tequila. Thus, one of the priorities
of the actors involved, particularly the Tequila Regulatory Council, is the inventory of the
number of agaves planted according to their age, the place of planting, their owner, their
phytosanitary status, their link with a tequila company, among other aspects [4,47,48].

Sustainability 2022,14, 7450 7 of 23
2.2.2. The Process of Elaboration and Types of Tequila
Tequila is made through a process that, although it varies depending on the tools, arti-
facts, practical types, and techniques that are used, in general, is integrated into six phases:
the cultivation and Jima of the agave, cooking, grinding, fermentation, distillation, and
packaging for sale and marketing (Figure 1).
Sustainability 2022, 14, x FOR PEER REVIEW 7 of 24
frost/snowfall, droughts, fires, pests, and diseases, the planning of the crop is essential
and must be aligned with the market expectations of tequila. Thus, one of the priorities of
the actors involved, particularly the Tequila Regulatory Council, is the inventory of the
number of agaves planted according to their age, the place of planting, their owner, their
phytosanitary status, their link with a tequila company, among other aspects [4,47,48].
2.2.2. The Process of Elaboration and Types of Tequila
Tequila is made through a process that, although it varies depending on the tools,
artifacts, practical types, and techniques that are used, in general, is integrated into six
phases: the cultivation and Jima of the agave, cooking, grinding, fermentation, distillation,
and packaging for sale and marketing (Figure 1).
Figure 1. The tequila production process. Source: own elaboration [49,50].
According to SAGARPA [44], although agave can be grown on land with a particular
slope, the ideal is to do it on flat land whose preparation before planting must include
proper subsoiling and fallowing, and be tracked, whitewashed, and quartered or marked.
The harvest or Jima of agave usually occurs between 7 and 9 years after planting and
translates into obtaining the pineapples or heads of the agave that, when transferred to
the factory, undergo a cooking process.
The cooking aims to perform the hydrolysis of fructans to generate simple sugars,
fructose, and glucose in an approximate ratio of 90/10, soften the agave for subsequent
grinding and promote the generation of essential compounds for the aromatic profile of
the final distillate [48].
The grinding consists of tearing cooked heads or pineapples to extract the sugars
from the agave. Fermentation is the biochemical breakdown that results in transforming
agave sugars into alcohol. Distillation is divided into three phases. In the first, the vinasses
are separated from other components such as aldehydes and ketones, obtaining a low
alcohol content. In the second, known as rectification, ethyl alcohol is concentrated and
purified from other alcohols obtaining what is known as heads and tails. In economic
terms, distillation consumes about 50% of the energy required in the production process,
and, therefore, it is the phase where costs could be reduced through strategies that reduce
energy consumption. [50]. With these operations, a tequila of 45° to 50° GL is obtained
that can be immediately packaged as white tequila or move to a third phase, known as the
aging process, whose objective is to give the tequila the color and the bouquet (scent and
flavor) depositing it in barrels for a specific time which gives rise to different types of
tequila [51].
The packaging of tequila differs according to the type of tequila. For example, in the
case of mixed tequila and the sugar of the blue weber tequila, agave contains other types
Figure 1. The tequila production process. Source: own elaboration [49,50].
According to SAGARPA [
44
], although agave can be grown on land with a particular
slope, the ideal is to do it on flat land whose preparation before planting must include
proper subsoiling and fallowing, and be tracked, whitewashed, and quartered or marked.
The harvest or Jima of agave usually occurs between 7 and 9 years after planting and
translates into obtaining the pineapples or heads of the agave that, when transferred to the
factory, undergo a cooking process.
The cooking aims to perform the hydrolysis of fructans to generate simple sugars,
fructose, and glucose in an approximate ratio of 90/10, soften the agave for subsequent
grinding and promote the generation of essential compounds for the aromatic profile of the
final distillate [48].
The grinding consists of tearing cooked heads or pineapples to extract the sugars
from the agave. Fermentation is the biochemical breakdown that results in transforming
agave sugars into alcohol. Distillation is divided into three phases. In the first, the vinasses
are separated from other components such as aldehydes and ketones, obtaining a low
alcohol content. In the second, known as rectification, ethyl alcohol is concentrated and
purified from other alcohols obtaining what is known as heads and tails. In economic
terms, distillation consumes about 50% of the energy required in the production process,
and, therefore, it is the phase where costs could be reduced through strategies that reduce
energy consumption. [
50
]. With these operations, a tequila of 45
◦
to 50
◦
GL is obtained that
can be immediately packaged as white tequila or move to a third phase, known as the aging
process, whose objective is to give the tequila the color and the bouquet (scent and flavor)
depositing it in barrels for a specific time which gives rise to different types of tequila [
51
].
The packaging of tequila differs according to the type of tequila. For example, in the
case of mixed tequila and the sugar of the blue weber tequila, agave contains other types of
sugars; its distribution can be in bulk or without packaging or packaging. On the contrary,
100% Agave Tequila needs to be packaged, usually in new glass containers and with its
due labeling to show that the production of agave and the elaboration of tequila occurred
within the denomination of origin zone [51].

Sustainability 2022,14, 7450 8 of 23
As shown in Table 1, by the type of sugars used, tequila can be 100% tequila, mixed
tequila, and due to the rest time, it is classified into white, young, and rested [49].
Table 1. Classification of tequila by the sugars of origin and the time of rest.
Factor Type of Tequila Characteristics
By type of sugar
Tequila 100% Made with 100% Weber Azul tequilana agave, sugar.
Tequila
Made with 51% sugar from the blue Weber tequilana agave and the rest (49%) of
total reducers, expressed in mass units, come from other sources (except other
agave species).
By the rest time
Mixed tequila Clear and transparent in color, it is bottled immediately after being distilled.
White tequila This tequila is softened with colorants and flavors, such as caramel.
Young tequila
White tequila that is left to rest in oak barrels for more than two months and up
to a year. It maintains its blue agave flavor.
Rested tequila Aged in oak barrels for over a year. It has an amber color and a woody flavor.
Source: own elaboration [49,50].
2.2.3. A Technical But Also Traditional Industry
One of the differentiating elements between companies in the tequila industry is the level
of equipment and automation of the different phases of the production process [
3
,
49
,
50
,
52
].
Although it is not a rule, the most technical companies are those with the largest size
and production capacity [
53
]. At the same time, the rest of the producers experience
a hybridization of their processes, and even in many cases, small companies continue to
use the artisanal method, as explained in Table 2.
According to the above, the composition of tequila is very complex, it is affected
by each of the stages of its production process. Therefore, its scent and flavor—sensory
characteristics—depend on the amount and type of volatile compounds present [50].
Table 2. Technification of production processes in tequila producing companies.
Type of Process Phases of Production
Cultivating Baking or Cooking Grinding Fermenting Distilling
Traditional
Basic, rudimentary
agricultural tools,
i.e., machete and
tillage stick (coa).
Underground ovens
are covered with
stones on the inside.
The oven consists of
a circular cavity
4.60 m in diameter
and 0.50 m deep.
Wood fuel.
A rolling stone
pulled by oxen
and/or mules
grinds the baked
agave cores.
Wooden vats with
a capacity of
approximately
1500 L. The
saccharimeter, an
instrument locally
called pesamiel,
“weighs” the sugar
concentration of
the juices.
Metal pots
containing must are
placed on the fire.
They are heated
until the alcohol
reaches the point
of evaporation.
From 10 to
12 years. Four days. From two to
three days.
From 12 days in
summer up to
18 days in winter.
From one to
two days.
Hybrid
Basic agricultural
tools, i.e., machete
and coa,
are prioritized.
Masonry oven with
a thermometer on
thedoor.
Fuel from diesel, tar,
and pig manure.
An agave core
shredder.
Grinding machines
were introduced in
the 1950s.
Stainless steel tubs
with a capacity of
up to 20,000 L.
Steam-based
copper or
steel stills.
From 7 to 10 years. From 36 to 38 h. From one to
two days. 72 h. From one to
two days.

Sustainability 2022,14, 7450 9 of 23
Table 2. Cont.
Type of Process Phases of Production
Cultivating Baking or Cooking Grinding Fermenting Distilling
Technicalized
New machinery is
designed internally
for planting
and harvesting.
Autoclaves bake or
cook the agave at 90
to 110 degrees
Celsius temperatures.
An extruder grinds
the agave. The raw
agave enters on
one end, and must,
mezcal juices, and
fiber come out on
the opposite end.
This machine has
a high rate of ex-
traction efficiency.
The fermentation
process is
automated.
Column
distillation; allows
a continuous
process and
reduces costs and
distillation times.
Maturity
mechanisms
reduce the cycle to
5.5 years.
18 h. 15 h.
The fermentation
time is consider-
ably reduced.
The distillation
time is consider-
ably reduced.
Source: own elaboration [49,50].
2.2.4. Waste and Environmental Sustainability in the Tequila Industry
During the tequila production process, various wastes are generated that, as a whole,
place the tequila industry as one of the most polluting [
54
] and, therefore, this constitutes
one of the significant challenges for the sector. The residues with the greatest impact are
the leaves of the agave rosette, bagasse, and vinasses [55].
The leaves of the agave rose window represent about 14% of the usable portion of the
plant that, despite its biotechnological potential, are left in the field when harvesting the
pineapples or heads, constituting a residue that has no uses so far [45,56].
Bagasse is the fiber of the agave that is obtained once the pineapples have been cooked
and ground to generate sugars that, during fermentation, will produce alcohols and other
chemical compounds, which will be separated in distillation. In the production of one liter
of tequila, between seven and eight kilograms of agave are required, which are converted
into five kilograms of bagasse/wet base (residue) once the juice is extracted [45].
According to Moreno et al. [
45
], bagasse residues can become a raw material source
for obtaining various by-products. For example, bagasse as a material to produce organic
fertilizer stands out through composting incorporated into the crop fields as fertilizer. The
potential of agave bagasse for the production of biofertilizers is very significant due to the
recirculation of nutrients in the agave crops themselves. Some tequila companies are trying
to change the bagasse composting process to a vermicomposting process to reduce time
and improve agave waste management [45,48].
Bagasse can also be used as fuel in boilers, filling furniture and mattresses, fodder
for birds and livestock, and brick and adobe making. Another alternative for its use is the
one made by obtaining some enzymes with high commercial value that can be used in the
development of new products such as biofuels such as bioethanol, mainly due to its hexose
content that makes fermentation processes less complicated [45,48].
Vinasses are liquid residues composed of non-volatile substances generated and remain
at the bottom of the still after distillation of fermented agave must [
45
]. In the vinasses
remain the agave fibrils that were not retained in the juice filtration stage, depleted yeast
cells, residual sugars, acids, esters, higher alcohols, and substances that give caramel color.
Although these effluents are not classified as hazardous waste, they are considered complex
wastewater [
57
,
58
] because they have a chemical oxygen demand more significant than
38,215 mg/L, total solids (ST) greater than 21,883 mg/L, and have a low pH of 3.5–3.9 [
59
,
60
].
Vinasses also contain high concentrations of phenolic compounds that give them
a characteristic dark brown color [
61
]. Although they have some use as biofuels, their
characteristics and the high volumes generated make vinasses a tremendous environmen-

Sustainability 2022,14, 7450 10 of 23
tal concern [
45
,
54
,
60
], since their incorrect disposal can cause significant environmental
impacts [
62
], in addition to the significant cost of treating vinasses through an evaporation-
oxidation process in large vats [63].
This is especially worrisome in regions with high rates of tequila production, such as
the state of Jalisco [64,65].
To find alternative solutions to this environmental problem, anaerobic biological pro-
cesses have been presented, which, in addition to reducing the concentration of pollutants,
make it possible to recover methane and hydrogen as potential energy sources within
the same tequila production process [
60
,
65
,
66
]. However, currently, this treatment only
occurs in large companies, while small and medium-sized companies do not have the
financial resources.
On the other hand, the high agricultural and agro-industrial activities of this industry
also lead to greater exploitation and contamination of water resources [67,68].
In this context, various eutrophic conditions have been identified in multiple bodies
of water caused by excessive and uncontrolled discharges of solid waste and wastewa-
ter by agro-industrial activities, which generates anoxic conditions and toxic blooms of
cyanobacteria, which can harm wildlife and even pose a threat to nearby settlements [
69
,
70
].
Thus, the reduction of this eutrophication demands better management practices for
both solid waste and wastewater to reduce the amounts of organic matter and nutrient
discharge that enter the hydrographic systems of Jalisco [
67
,
68
,
71
]. The foregoing is so
relevant that the government of Jalisco has set a goal for 2030 to reduce its current GHG
(greenhouse gas) emissions by 22%, of which 14% is attributed to waste management
and sewage [72,73].
3. Materials and Methods
This research is qualitative (descriptive) and quantitative, with a correlational scope
and based on information obtained through questionnaires and semi-structured interviews.
A focus group was carried out during the first two months of 2022. A total of four ques-
tionnaires were collected, five semi-structured script interviews with experts, cameras,
and consultants. The questionnaire was applied to the leading companies in the tequila
industry. These firms represent 68% [
4
] of the industry. These interviews were conducted
with managers and key personnel performing essential activities and processes, such as
production and administration. In addition, three interviews were conducted with con-
sultants of the company and two with researchers from the university. All interviewees
handle strategic information to generate knowledge and management of innovation and
technology within the company or in the study sector.
The focus groups were based on experts’ opinions. This model was made to identify
the main processes needed for technological innovation management in the tequila industry.
The model was developed through a Bayesian network. Once the model was created,
a database of 100 records was generated based on the patterns generated through the
Bayes network. The data generated represents the network in the sense that it comes
from a joint probability distribution modeled by the network. Several experiments were
done, generating 1000-, 300-, and 100-record databases. The archive with 100 records was
sufficient to obtain conclusions since there were no significant differences in the results in
the other two cases.
Once the dataset was available, a classification was made through machine learning
tools based on the objective variable management of innovation and technology. The most
relevant variables were selected to predict the management of innovation and technology
class or variable objective employing the RRelief metric. The central idea of the RRelief al-
gorithm is to estimate the quality of variables based on how well it can distinguish between
instances that are close to each other. RRelief calculates a variable score for each variable
that can then be applied to classify and select the highest-scoring characteristics to select the
most relevant variables to predict the management of innovation and technology variables.

Sustainability 2022,14, 7450 11 of 23
The fundamental challenge in data mining or modeling is to identify and characterize
the relationships between one or more data features and some target feature. The remaining
features, which are usually not distinguished a priori in real-world problems, are not
informative but contribute to the overall dimensionality of the problem space. This increases
the computational load for the modeling methods. Feature selection is defined as the
process of identifying relevant features and discarding irrelevant ones [
74
]. There are
several types of methods for variable selection. Among them, the filter method stands out.
Filtering methods use an indirect measure calculated from the general characteristics of
the training data as a pre-modeling step. Filters are generally fast and work independently
of the induction algorithm. Therefore, selected features can be passed to any modeling
algorithm. Filtering methods can be roughly classified according to the filtering measures
they employ. Those are information, distance, dependency, consistency, similarity, and
statistical measures.
Kira and Rendell [
75
] conceived the original Relief algorithm based on instance-based
learning. As an individual evaluation, Relief computes an indirect statistic for each feature
that can be used to estimate the feature’s relevance to the target variable. These feature
weights can range from
−
1 (worst) to +1 (best). Its strengths are that it does not depend
on heuristics, runs in low-order polynomial time, is noise tolerant, and resistant to feature
interactions, as well as being applicable for binary or continuous data.
ReliefF is an adaptation of the original Relief algorithm, which is based on many
neighbors user parameter k that specifies the use of k nearest hits and k nearest misses
in the score update for each. This change increased the reliability of the weight estimate.
It is also capable of handling missing data values. It also uses strategies to handle multi-
classing. During the evaluation of multiclass problems, ReliefF finds the k closest bugs
from each “other” class and averages the weight update based on the prior probability of
each class [76].
Next, a clustering process was carried out, that is, unsupervised classification. Clus-
tering is a set of machine learning techniques that select observation classes (clusters),
which contribute identical characteristics. Where it happens that the same class has similar
characteristics and the observation of separate groups shows a difference in features. It is
a non-supervised learning method, where the group tries to identify a relationship between
the data without being trained by the response variable (Figure 2).
Sustainability 2022, 14, x FOR PEER REVIEW 11 of 24
class or variable objective employing the RRelief metric. The central idea of the RRelief
algorithm is to estimate the quality of variables based on how well it can distinguish be-
tween instances that are close to each other. RRelief calculates a variable score for each
variable that can then be applied to classify and select the highest-scoring characteristics
to select the most relevant variables to predict the management of innovation and tech-
nology variables.
The fundamental challenge in data mining or modeling is to identify and characterize
the relationships between one or more data features and some target feature. The remain-
ing features, which are usually not distinguished a priori in real-world problems, are not
informative but contribute to the overall dimensionality of the problem space. This in-
creases the computational load for the modeling methods. Feature selection is defined as
the process of identifying relevant features and discarding irrelevant ones [74]. There are
several types of methods for variable selection. Among them, the filter method stands out.
Filtering methods use an indirect measure calculated from the general characteristics
of the training data as a pre-modeling step. Filters are generally fast and work inde-
pendently of the induction algorithm. Therefore, selected features can be passed to any
modeling algorithm. Filtering methods can be roughly classified according to the filtering
measures they employ. Those are information, distance, dependency, consistency, simi-
larity, and statistical measures.
Kira and Rendell [75] conceived the original Relief algorithm based on instance-based
learning. As an individual evaluation, Relief computes an indirect statistic for each feature
that can be used to estimate the feature’s relevance to the target variable. These feature
weights can range from −1 (worst) to +1 (best). Its strengths are that it does not depend on
heuristics, runs in low-order polynomial time, is noise tolerant, and resistant to feature
interactions, as well as being applicable for binary or continuous data.
ReliefF is an adaptation of the original Relief algorithm, which is based on many
neighbors user parameter k that specifies the use of k nearest hits and k nearest misses in
the score update for each. This change increased the reliability of the weight estimate. It
is also capable of handling missing data values. It also uses strategies to handle multiclass-
ing. During the evaluation of multiclass problems, ReliefF finds the k closest bugs from
each “other” class and averages the weight update based on the prior probability of each
class [76].
Next, a clustering process was carried out, that is, unsupervised classification. Clus-
tering is a set of machine learning techniques that select observation classes (clusters),
which contribute identical characteristics. Where it happens that the same class has similar
characteristics and the observation of separate groups shows a difference in features. It is
a non-supervised learning method, where the group tries to identify a relationship be-
tween the data without being trained by the response variable (Figure 2).
Figure 2. Methodology.
The K-means grouping is the most direct and frequently practiced group method to
categorize a data set in many classes [77].
Clustering was carried out using the K-means algorithm, where the number of clus-
ters was selected based on the Silhouette metric that compares the distance between the
elements in a cluster with the average distance between the elements of other clusters. It
Figure 2. Methodology.
The K-means grouping is the most direct and frequently practiced group method to
categorize a data set in many classes [77].
Clustering was carried out using the K-means algorithm, where the number of clusters
was selected based on the Silhouette metric that compares the distance between the elements
in a cluster with the average distance between the elements of other clusters. It will make
it possible to elucidate the factors that allow us to achieve the best results in terms of
innovation (Figure 2).
Next, the relationship between the variables was modeled through a Bayesian network
(BN). A BN is a probabilistic graphic model that represents a set of variables and their condi-
tional dependencies through a directed acyclic graph (DAG). Bayesian networks can represent
and solve decision problems under uncertainty; these are called influence diagrams [78].

Sustainability 2022,14, 7450 12 of 23
In this work, the definition of the variables—parent node to a child—of the model is
made up of 36 relevant factors to carry out the management of innovation and technology—
6 parent nodes and 30 child nodes—which are presented in Table 3.
Table 3. Definition of the variables and nodes.
# Variable Concept Dimension
1Competitive and
Technological Intelligence
The process of identifying, collecting, and analyzing
information about the environment and the activities
of an organization, as well as to time use of such
information for decision-making.
Optimum Regular Deficient
2 Strategic Planning It is the plan that presents the strategy planning,
defined for the organization, as the guiding thread. Yes/No
3 Technological Planning
It is the development of a strategic technology plan
that allows selecting lines of action in the development
of products/services according to the market’s needs
and that focus on developing competitive advantages.
Yes/No
4 Human Capital
People with their talent translate into valuing the
knowledge and skills that each worker has in
a company.
Qualified
Not Qualified
5Protection of
Intellectual property
Safeguard all intangible assets of the company. The
protection of innovation seeks to prevent the
unauthorized use of an organization’s developments.
It includes all the measures that the organization takes
to ensure the benefits of exploiting the innovation.
Yes
No
6
Quality and Risk Management
Coordinated activities to direct and control in an
organization the possible risks to guarantee quality. Optimum Deficient
7Customer Needs and
Opportunity Detection
Analysis of customer needs and opportunities of the
market. Who? What? How? Analytics. Optimum Regular Deficient
8 Mission, Vision, and Values
Entreprise philosophy. A firm’s statements are based
on the core ethical values of an organization and are
essential to its success because they give it direction.
Yes
No
9 Strategy Model
A plan that integrates and provides tools for
decision-making and action plans in the face of
a given scenario.
Yes/No
10 R + D * I Research, development, and innovation. Optimum Regular
Deficient
11 Best Production Practices The norms and standards of quality and best
manufacturing practices.
Optimum Regular
Deficient
12 Compliance with Legal and
Regulatory Material
Activities and processes to comply with legal and
regulatory standards. Yes/No
13 Organizational and
Technological Architecture
It integrates a complete vision of the company, its
processes, and the available resources. It describes the
strategy of products and services of the company, as
well as the organizational, functional, process, and
information aspects.
Optimum Deficient
14 Business Intelligence
Set of strategies, procedures, and activities whose
objective is to present relevant business data and its
environment for decision-making.
Optimum Regular Deficient
15
Universities and
Research Centers
Linkage/collaboration
Linkage is the set of relationships established between
universities and the productive sector, and its purpose
is to transfer knowledge and technology.
Yes/No

Sustainability 2022,14, 7450 13 of 23
Table 3. Cont.
# Variable Concept Dimension
16 Designation of Origin
The name of a geographical area that contains that
name, or another well-known denomination that
refers to the area mentioned above, serves to designate
a product as originating from the same, when the
quality or characteristics of the product are due
exclusively or essentially to the environment
geographical, including natural and human factors,
and which has given the product its reputation [79].
Yes/No
17 Trademarks and Patents
A trademark is a sign that makes it possible to
differentiate the products or services of one company
from those of another. Trademarks are intellectual
property rights (IP). A patent is an exclusive right
granted over an invention.
Yes/No
18 Sustainability and the
Environment
The harmonious coexistence of society and its
environment, where the current population can satisfy
its needs and improve its well-being using the
available natural resources, but without
compromising the quality of life of future generations.
Optimum Regular Deficient
19 Core Competences
It refers to the unique and differential knowledge or
skills that a company has and that give it
a competitive advantage.
Optimum
Deficient
20 Business Model Structure
Design process to create a widely new business model
in the market, which is accompanied by a value
proposition that generates or ensures a sustainable
competitive advantage.
Optimum Regular Deficient
21 Official Standard Certifications, norms, standards, and rules. Optimum Regular Deficient
22 Cultivation of Raw
Material Agave
Knowledge of the soil and planting of agave. It
includes all activities from planting to harvest. Optimum Regular Deficient
23 Tequila Manufacturing
-Knowledge of handling agave pineapples
-Pineapple crushing/cooking/grinding/agave
juice extraction
-Fermentation
-Distillation
Optimum Regular Deficient
24 Tequila Packaging
Tequila packaging according to official standards:
NOM-006-SCFI-2012, Alcoholic
Beverages-tequila-Specifications.
Optimum Regular Deficient
25 Commercialization
an Marketing (MKT) Capacity
Understand and satisfy customer needs. The process
by which companies create value for customers and
build strong relationships with them to capture their
value in return. Search, promote, serve, and adapt
markets.
Optimum Regular Deficient
26 Management and
Use of Waste
Waste collection and processing methods combine
management options that include reuse and
recycling efforts.
Optimum Regular Deficient
27 Ancestral
Technical Knowledge
Techniques applied to the production of tequila based
on ancient knowledge. Optimum Regular Deficient
28 Modern Technologies Modern techniques applied to tequila production
based on technology. Optimum Regular Deficient
29 Process Management It is the management model of all value
chain processes. Optimum Regular Deficient

Sustainability 2022,14, 7450 14 of 23
Table 3. Cont.
# Variable Concept Dimension
30 Customers II Experience
Whom? How?
Delivery and capture value proposal.
Here is the customer experience perceived by the
customer. The definition of the product or service I sell
to clients.
I and II are the most important detonators of
the model.
Optimum
Regular
Deficient
31 Value System Model
Structure of value proposition, value creation, value
and delivery, value capture, topology of value
chain partners.
The value propositions. What are we offering
to whom?
Target segment(s).
Product or service offering.
Revenue model.
Optimum
Deficient
32 Positioning
The product’s positioning is divided first in terms of
the product type: White, Young, Reposado, Añejo, or
Extra Añejo. Moreover, second to the market to which
it is directed: premium, superpremium, and
ultra-premium. The companies promote the
development of their brands through differentiated
and defined positioning and marketing strategies for
each product, trying to give a different brand essence
and avoid positioning coincidences, and try to give
value to the customer.
Optimum Regular Deficient
33 National and
International Markets
Set of real and potential buyers both nationally
and internationally. Optimum Regular Deficient
34 Commercialization
Capacity/MKT
Actions and procedures effectively introduce
products/services to the market to satisfy
customer needs.
Optimum Regular Deficient
35 Knowledge Management
Knowledge management is the company’s ability to
put the knowledge it has acquired at the service of
those who need it within the company to develop new
solutions and innovate. A systematic process of
dissemination, use, generation, documentation, and
enhancement of individual and
organizational knowledge.
It search like this knowledge is used methodically for
competitive advantages for the firm. [2,3,10,78,80].
Optimum Regular Deficient
36 Innovation
It is the implementation of significant changes in its
product, process, marketing, or organization.
Innovation is a product or process (or a combination
of both) that differs significantly from previous
products and processes; it adds value.
Yes/No
37 Management of Innovation
and Technology
The learning process accelerates the transformation of
ideas into innovations. It is the organization and
management of resources to increase the creation of
new knowledge and technical ideas toward making
new products, processes, and services or improving
existing ones [2,3,10,78,80].
Optimum Regular Deficient
In this manner, the conceptual model is made and the Bayesian network was built
from six parent nodes called competitive and technological intelligence, strategic planning,

Sustainability 2022,14, 7450 15 of 23
technological planning, human capital, intellectual property protection, and quality and
risk management (Figure 3and Table 4).
Sustainability 2022, 14, x FOR PEER REVIEW 15 of 24
Figure 3. Management of innovation and technology model: variables and nodes.
Table 4. Main correlations between variables for the management of innovation and technology
model.
Variable 1 Variable 2 Strength
Protection of Intellectual property Designation of Origin 1.000
Strategic Planning Mission, Vision, Values 0.588
Value System Model Clients 0.404
Protection of Intellectual property Sales and MKT 0.402
Quality and Risk Management Legal Compliance 0.400
Business Model Structure Tequila Manufacturing 0.313
Quality and Risk Management Sustainability and Environment 0.311
Technological Planning Human Capital 0.302
Sales and MKT National and International Markets 0.280
Competitive and Technological Intelligence Customer Needs Detection 0.275
Process Management Management of Innovation and Technology 0.241
Value System Model National and international markets 0.224
Legal Compliance Official Rules 0.192
Value System Model Positioning 0.184
Business Model Structure Packing 0.167
Knowledge Management Management of Innovation and Technology 0.160
Sustainability and Environment Official Rules 0.151
Sustainability and Environment Management and Use of Waste 0.132
Brands and Patents Official Rules 0.119
Universities and Research Centers Linkage/col-
laboration Management and Use of Waste 0.118
Packing Value System Model 0.111
Figure 3. Management of innovation and technology model: variables and nodes.
Table 4. Main correlations between variables for the management of innovation and technology model.
Variable 1 Variable 2 Strength
Protection of Intellectual property Designation of Origin 1.000
Strategic Planning Mission, Vision, Values 0.588
Value System Model Clients 0.404
Protection of Intellectual property Sales and MKT 0.402
Quality and Risk Management Legal Compliance 0.400
Business Model Structure Tequila Manufacturing 0.313
Quality and Risk Management Sustainability and Environment 0.311
Technological Planning Human Capital 0.302
Sales and MKT National and International Markets 0.280
Competitive and Technological Intelligence Customer Needs Detection 0.275
Process Management Management of Innovation and Technology 0.241
Value System Model National and international markets 0.224
Legal Compliance Official Rules 0.192
Value System Model Positioning 0.184
Business Model Structure Packing 0.167
Knowledge Management Management of Innovation and Technology 0.160
Sustainability and Environment Official Rules 0.151
Sustainability and Environment Management and Use of Waste 0.132
Brands and Patents Official Rules 0.119
Universities and Research Centers Linkage/collaboration
Management and Use of Waste 0.118
Packing Value System Model 0.111
4. Results and Discussion
4.1. Management of Innovation and Technology Model Results
The management of innovation and technology model’s result was to develop the best
practices of the leading companies in the tequila industry in Mexico.

Sustainability 2022,14, 7450 16 of 23
Once the model was proposed using a Bayes network, multiple assignments were
made to the probabilities for each variable (node) based on the experts’ opinions until
reaching the optimal configuration (Figure 4).
The probabilistic or propagation reasoning of probabilities is to disseminate the effects
of evidence through the BN to know the subsequent probability of the other variables. That
is, values are assigned to some variables (evidence), and the subsequent probability for the
other variables is obtained, given the known variables.
In this way, the 97% probability of optimal management of innovation and technology
was reached with the probabilities indicated in the model. That allows the company to
generate value and competitive advantages.
It should be noted that the arcs that join the nodes have thickness and color intensity
according to the strength of the interactions between nodes (Table 4).
The strength of influence is calculated from the conditional probability of the child
node and expresses the distance between various conditional probability distributions over
the child node conditional on the states of the parent node.
The variables protection of intellectual property and designation of origin have the
most substantial interaction among all the variables.
The next step was the search for the most relevant variables for the classification, based
on the objective variable management of innovation and technology. The RRelief metric
indicates that the essential variables are knowledge management, MKT, organizational and
technological architecture, national and international markets, cultivation of raw materials,
agave, management, and use of waste (Table 5).
Next, the database clustering process was done. Two clusters were generated through
the K-means algorithm; this allowed to group the records based on their similarities. In
cluster 1 (C1) are grouped the registries with a denomination of origin and intellectual
property protection. These pair of variables are the ones that showed strong linkage with
each other. That is, the value of one of the influences that of the other (Figure 5).
Thus, C1 has the desirable characteristics to achieve innovation. This is shown by
the relationship between the most relevant variables. C1 shows that if the protection of
intellectual property variable has a “YES” value, the cultivation of raw material variable
obtains the optimal value in most cases (see Figure 6).
Similarly, it was found that if the knowledge management variables have optimum
value, the sales and MKT variable has optimum value in most cases (see Figure 7).
Table 5. Most relevant variables for classification.
Variable RelieF
Knowledge Management 0.082
MKT 0.066
Organizational and Technological Architecture 0.06
National and International Markets 0.052
Cultivation of Raw Materials Agave 0.046
Management and Use of Waste 0.046
Human Capital 0.044
Best Production Practices 0.042
Competitive and Technological Intelligence 0.03
Tequila Manufacturing 0.018
Business Intelligence 0.016
Official Rules 0.01
Value System Model 0.008
Process Management 0.006
Packing 0.004

Sustainability 2022,14, 7450 17 of 23
Sustainability 2022, 14, x FOR PEER REVIEW 16 of 24
4. Results and Discussion
4.1. Management of Innovation and Technology Model Results
The management of innovation and technology model’s result was to develop the
best practices of the leading companies in the tequila industry in Mexico.
Once the model was proposed using a Bayes network, multiple assignments were
made to the probabilities for each variable (node) based on the experts’ opinions until
reaching the optimal configuration (Figure 4).
Figure 4. Bayes Network for management of innovation and technology in the tequila sector in
Mexico.
Figure 4.
Bayes Network for management of innovation and technology in the tequila sector in Mexico.
Sustainability 2022, 14, x FOR PEER REVIEW 18 of 24
Figure 5. Scatter plot for protection of intellectual property and designation of origin grouped by
cluster.
Thus, C1 has the desirable characteristics to achieve innovation. This is shown by the
relationship between the most relevant variables. C1 shows that if the protection of intel-
lectual property variable has a “YES” value, the cultivation of raw material variable ob-
tains the optimal value in most cases (see Figure 6).
Figure 6. Scatter plot for cultivation of raw vs. protection of intellectual property grouped by cluster.
Figure 5.
Scatter plot for protection of intellectual property and designation of origin grouped
by cluster.

Sustainability 2022,14, 7450 18 of 23
Sustainability 2022, 14, x FOR PEER REVIEW 18 of 24
Figure 5. Scatter plot for protection of intellectual property and designation of origin grouped by
cluster.
Thus, C1 has the desirable characteristics to achieve innovation. This is shown by the
relationship between the most relevant variables. C1 shows that if the protection of intel-
lectual property variable has a “YES” value, the cultivation of raw material variable ob-
tains the optimal value in most cases (see Figure 6).
Figure 6. Scatter plot for cultivation of raw vs. protection of intellectual property grouped by cluster.
Figure 6.
Scatter plot for cultivation of raw vs. protection of intellectual property grouped by cluster.
Sustainability 2022, 14, x FOR PEER REVIEW 19 of 24
Similarly, it was found that if the knowledge management variables have optimum
value, the sales and MKT variable has optimum value in most cases (see Figure 7).
Figure 7. Scatter plot for knowledge management vs. sales and MKT grouped by cluster.
4.2. Innovation and Industrial Property in the Tequila Sector
The results show that in a low technology sector such as the tequila industry there is
not much focus and investment towards research and development (R&D). However,
there is innovation. Innovation has allowed this low-tech sector to improve agricultural
management processes, facilitating activities and reducing the risks to which workers are
exposed. Likewise, it has been possible to mechanize tasks that allow the establishment of
plantations in places where labor is scarce.
According to Cárdenas [81], over several decades, there have been significant tech-
nological advances in the tequila industry, which have been taken advantage of in all
stages of its production process: cooking, grinding, fermentation, and distillation. How-
ever, these advances have been exploited unevenly due to the abysmal differences be-
tween large, medium, and small companies.
In addition to the above, there is also innovation in its products, where premium and
superpremium artisan tequilas stand out and all tequila drinks.
On the other hand, the intensity of the innovation process can measure its results—
outputs—and the efforts made to innovate—inputs. One of these ways of measuring in-
novation has been the patents obtained as an indicator of the result.
In the case of patents, in 2012 the tequila sector had 92, and in 2021, it has 120, repre-
senting an increase of 30.4% [82]. Concerning trademarks, in the year 2022 there are 1590
registered trademarks of packaging at the national level with a denomination of origin
and 380 registered trademarks of packaging abroad with a denomination of origin. In ad-
dition, the number of trademarks registered as a tequila drink is 226 certified under the
NMX-049-NORMEX-2004 standard [4,79].
For the tequila industry, the primary protection element is the denomination of origin
certificate. The number of companies that produce tequila certified with a denomination
of origin amounts to 272 by 2022. In addition, there are currently 118 companies for mixed
tequila, while among 100% tequila companies there are 154 [4].
Figure 7. Scatter plot for knowledge management vs. sales and MKT grouped by cluster.
4.2. Innovation and Industrial Property in the Tequila Sector
The results show that in a low technology sector such as the tequila industry there
is not much focus and investment towards research and development (R&D). However,
there is innovation. Innovation has allowed this low-tech sector to improve agricultural
management processes, facilitating activities and reducing the risks to which workers are
exposed. Likewise, it has been possible to mechanize tasks that allow the establishment of
plantations in places where labor is scarce.
According to Cárdenas [
81
], over several decades, there have been significant techno-
logical advances in the tequila industry, which have been taken advantage of in all stages
of its production process: cooking, grinding, fermentation, and distillation. However, these
advances have been exploited unevenly due to the abysmal differences between large,
medium, and small companies.

Sustainability 2022,14, 7450 19 of 23
In addition to the above, there is also innovation in its products, where premium and
superpremium artisan tequilas stand out and all tequila drinks.
On the other hand, the intensity of the innovation process can measure its results—
outputs—and the efforts made to innovate—inputs. One of these ways of measuring
innovation has been the patents obtained as an indicator of the result.
In the case of patents, in 2012 the tequila sector had 92, and in 2021, it has 120,
representing an increase of 30.4% [
82
]. Concerning trademarks, in the year 2022 there are
1590 registered trademarks of packaging at the national level with a denomination of origin
and 380 registered trademarks of packaging abroad with a denomination of origin. In
addition, the number of trademarks registered as a tequila drink is 226 certified under the
NMX-049-NORMEX-2004 standard [4,79].
For the tequila industry, the primary protection element is the denomination of origin
certificate. The number of companies that produce tequila certified with a denomination of
origin amounts to 272 by 2022. In addition, there are currently 118 companies for mixed
tequila, while among 100% tequila companies there are 154 [4].
According to the above, a strong influence of other factors was confirmed to innovate
in the tequila industry. These factors are characterized by focusing on innovation by process,
organization, and marketing, which can compensate for the absence of formal efforts in
internal R&D [19,22,29,33,34].
In these sectors, the primary sources of knowledge to innovate come from social
technologies associated with human resource management, work organization, and the
search for external sources of innovation like customers and suppliers [19,26,27].
Therefore, R&D as an input for innovation (output) plays a minor in this mature
low-tech sector [
29
]. Instead, innovation results from a particular configuration of tacit
and codified resources that firms build throughout their lives rather than strategies based
on R&D [
19
,
29
]. Thus, knowledge management practices (KMP) are a critical factor that
directly affects innovation activities [32,34].
5. Conclusions
Innovation and technology management are crucial in creating value for companies by
allowing people and companies to use knowledge and existing resources more efficiently
to learn and innovate. Moreover, the ability to manage it for the benefit of the business,
aligned with the rest of its strategic functions considering a dynamic environment such
as the current one, will allow it to maximize its competitive advantages. In other words,
it is learning to identify or create opportunities, developing products with added value,
looking for new ways to serve different consumers, detecting where and how new markets
can be created and grown, rethinking services, among others, and seeking a social impact.
Implementing an innovation and technology management model allows companies
to make better decisions to innovate and have competitive advantages. The benefits of this
model are reflected in the new products or services, savings due to the development of new
processes; entering new markets, increased sales, and higher profitability.
Based on this, a model has been generated through a Bayes network based on expert
opinions in the tequila industry. A set of data was obtained based on the existing patterns
between the nodes (variables).
The analysis of the data generated through the model has shown that according to
their relevance for the classification, the essential variables for innovation are knowledge
management, marketing, organizational and technological architecture, national and in-
ternational markets, cultivation of raw materials, and management and use of waste,
among others.
Likewise, it is shown that the variables that are related to greater strength through
the arcs that join the nodes that represent them in the model are protection of intellectual
property and designation of origin, strategic planning and mission, vision, values, value
system model and clients, protection of intellectual property and sales, and MKT. These
relationships emphasize the aspects that need to be taken care of to consolidate innovation.

Sustainability 2022,14, 7450 20 of 23
Thus, it is essential to note that the variables that represent the concepts taken into
account by experts in the field should be monitored and improved, considering the synergy
suggested by the relationships between them and the relevance found through the analysis
of the data generated using the patterns found in the designed model.
On the other hand, innovation can be of vital importance for the performance of
low-tech companies. However, a large part of the public policies to stimulate innovation
is focused on R&D and in high-tech companies, which leaves out the low-tech sectors
that have a significant impact on the economic development of countries. Moreover,
the classification and use of the term low-tech sectors do not clarify the limits between
high- and low-tech industries, affecting public policy formulation that supports these
low-tech sectors.
In the case of the tequila sector, there are agricultural producers and small and medium-
sized companies that could benefit from public policies focused on the knowledge of
innovation and its application in their context. It would allow them to innovate to have
competitive advantages and be more profitable and sustainable.
In summary, considering only formal R&D activities in low-tech industries leaves out
a large part of the productive apparatus with different competencies to innovate and not
necessarily traditional R&D activities.
In future work, it is recommended to consider more factors that improve innovation
in the sustainability of the process. In this sense, relations with companies that participate
in the world market for alcoholic beverages, mergers between these companies, and legal
and informal national and international mechanisms regulate business networks and the
consumption of these drinks.
Author Contributions:
Conceptualization, A.T.-B., A.M.-V., V.M.C.-G. and S.A.-R.; methodology,
A.T.-B. and A.M.-V.; software, A.T.-B. and A.M.-V.; validation A.T.-B. and A.M.-V.; formal analysis,
A.T.-B. and A.M.-V.; investigation, A.T.-B., A.M.-V., V.M.C.-G. and S.A.-R.; resources, A.T.-B., A.M.-V.,
V.M.C.-G. and S.A.-R.; data curation, A.T.-B. and A.M.-V.; writing—original draft preparation, A.T.-B.,
A.M.-V., V.M.C.-G. and S.A.-R.; writing—review and editing, A.T.-B. and A.M.-V.; visualization,
A.T.-B., A.M.-V., V.M.C.-G. and S.A.-R.; supervision, A.T.-B., A.M.-V., V.M.C.-G. and S.A.-R.; project
administration, A.T.-B. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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