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Article

Analysis of Consumer Attitudes as an Important Tool for the Segmentation and Development of the Game Market in the Czech Republic

by
Martin Němec
,
Marcel Riedl
*,
Vilém Jarský
and
Roman Dudík
Department of Forestry and Wood Economics, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, Suchdol, 165 00 Prague 6, Czech Republic
*
Author to whom correspondence should be addressed.
Forests 2023, 14(3), 450; https://doi.org/10.3390/f14030450
Submission received: 20 January 2023 / Revised: 17 February 2023 / Accepted: 19 February 2023 / Published: 22 February 2023
(This article belongs to the Section Forest Economics, Policy, and Social Science)
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Abstract

:
The demand for the multipurpose use of forestry accentuates the development of non-timber forest products and services and the search for other sources of financial benefits for forest owners and users. One of the essential market items of non-timber forest production is game production, a very high-quality local food source. Currently, in the Czech Republic, as well as in neighbouring countries, the amount of game being offered is increasing, while the purchase prices of game have stagnated at very low levels for a long time, despite the fact that consumer prices of meat have generally increased. A prerequisite for the development of the market for game products and the effective use of marketing tools is the analysis of important factors influencing the consumer demand for game. Therefore, the possible limiting factors on the customer side, the removal of which can change the demand for game meat and increase the consumption of game production, were investigated. Igor Ansoff’s approach is used to analyse growth opportunities in the game market from the point of view of marketing strategies. The preconditions for using Porter’s STP process to design a pull strategy on the consumer market are verified. Based on the Parfitt–Collins model, the research mainly focuses on a sample of active consumers who are a subgroup of a representative panel of 1000 respondents. The demographic characteristics and buying behaviour of the subgroup of 523 active consumers who regularly consume game meat were analysed. Significant factors and potential segmentation criteria were identified. On the basis of these research results, the article discusses, in detail, the marketing aspects related to the development of the game market, and marketing procedures are also proposed that can significantly support consumer demand in the game market using a mix of marketing tools. The increased demand for game meat will help to reduce the overpopulation of cloven-hoofed game in the forests of the Czech Republic, which will positively impact the reduction of animal damage to the forest environment, especially in areas newly forested after the bark beetle calamity.

1. Introduction

The gradually changing face of human society brings new challenges for the environment and forest management in developed countries. Forest production is consistently being translated into a stream of services provided for society [1]. In the last two decades, three major classifications of ecosystem services have evolved, namely MEA [2], The Economics of Ecosystems and Biodiversity (TEEB) [3], and The Common International Classification of Ecosystem Services (CICES) [4].
The systematics, in general, divide tangible forest provisioning services into two main categories of interest. The first one relates to the production of timber, and the second one relates to the remaining products and services of the forest except for timber production.
Non-timber forest products (NTFPs), in this respect, are a widely discussed topic outlined by the definition that encompasses all biological materials other than timber that are extracted from forests for human use [5].
Although forest management is slowly transforming from serving a timber production function to having a multipurpose use [6], which includes NTFP products and services [7], this area is new and unexplored for many owners [8]. For this reason, there is a demand for information that helps owners transform forest management into having a multipurpose use [9].
The growing importance of NTFPs has been followed up by numerous studies focusing on the financial inflow into forestry and its evaluation, for example [10,11,12], among others. The degree of importance of individual NTFPs varies greatly geographically. In Central Europe, fall forest fruits, mushrooms, medicinal plants, decorative fruits, and game [13,14] are among the most frequently collected NTFP products. Some of those products are available to the public free of charge (with limits depending on the country); some are subject to market exchange as alternatives to agricultural products [15].
Game is characterised by its nutritional value and specific taste [16]. In terms of nutrition, reference [17] stipulates that game is characterized by its lower fat content compared to beef, pork, and lamb. Moreover, the nutritional value is considered high due to its high protein content and high amounts of minerals, vitamins, trace elements, and unsaturated fatty acids.
This fact is also confirmed by other studies, e.g., [18,19,20], evidencing the versatility of the overall health-related benefits of game meat in detail. Game is a natural product that, in addition to its nutritional value and naturally organic origin, also represents a culturally conditioned approach to its consumption. Multiple social factors directly impact its consumption, such as health concerns, animal welfare, and a hunting culture [21]; conversely, hunting for game production could be understood as a part of one’s cultural heritage [22]. Several studies have dealt with income based on game production [22,23,24], emphasising the fact that game hunting provides numerous benefits to local economies and communities as it provides additional sources of income to forest owners. It has the potential to create sustainable value chains and become beneficial for rural areas with limited employment opportunities. Creating a regular source of financial income from market game production is a challenge for forestry. Game marketing and production are the limiting factors that follow the nature of this product [25,26,27]. Specifically, these factors are the format of the game product, its availability (in time and volume), product quality, value-chain price differentiation, and distribution channels [28].
A significant proportion of game is purchased through social ties; however, studies documenting the movement of game across society are not available. Understanding the key factors on the market demand side provides an opportunity to master sustainable access to natural resources, including the game itself [29].
The development of the game market is in the interest of both the forestry and agricultural sectors due to the protection of forest stands and agricultural products from any damage caused by wild animals [30], and, from the point of view of increasing the quality of the population’s nutrition [17,27,31,32], relevant studies have highlighted that game meat can be an excellent addition to a population’s nutrition. It is rich in quality proteins and essential vitamins and minerals, and sourcing local game meat can help support local producers and the environment. In addition, for these reasons, this article focuses on an analysis of the factors that would contribute to the development of the game market in the example of the Czech Republic, especially in the form of an increase in consumer demand for game. The analysis of this primary (consumer) demand is key, because the demand of processors and distributors from the producers (hunters in this case) throughout the entire value chain is derived from consumer demand (the so-called derived demand) [33,34].This consumer demand is the most significant factor that determines the dynamics of the market and its further development [35]. If a combination of different forms of communication is used to increase consumer demand, it is a so-called pull strategy [36].
Analysing growth opportunities in the game market from the point of view of marketing strategies, Igor Ansoff’s approach is often used [37,38]. The Ansoff matrix breaks down market growth options in relation to new products and markets, as well as existing products and markets. It is a square table that has two axes: a horizontal one with products that are divided into “existing” and “new” ones; and a vertical one with markets that are also divided into “existing” and “new” ones. These two axes of the Ansoff matrix create four quadrants that include all the possible combinations of “existing” and “new” products and markets corresponding to one of four possible marketing strategies:
  • Market penetration strategy (“existing product/existing market”).
  • Market development strategy (“existing product/new market”).
  • Product development strategy (“new product/existing market”).
  • Diversification strategy (“new product/new market”).
Given that the markets in the neighbouring countries of the Czech Republic (CZ) are supplied with a growing supply of game from local sources [24], Ansoff’s growth strategy of penetrating the domestic market in combination with partial product innovation comes into consideration.
As the game market is part of the meat market, the effort to increase the domestic game market means, from a marketing point of view, increasing the market share of game in the overall domestic meat market. From a conceptual point of view and for estimating a possible increase in the market share, it is advantageous to use the classic Parfitt–Collins approach [39,40] that decomposes the market share into three essential, meaningful variables related to consumer demand that can explain what is causing changes in the market share of an analysed product or commodity. The Parfitt and Collins model, further explained in the Materials and Methods section, is a relatively simple formula. It follows from the formula that increasing the market share of game meat consumption is a direct consequence of increasing the volume of the consumer demand for game. Therefore, the basic prerequisite for Ansoff’s penetration and an increase in the market size is an increase in demand in the consumer market [41,42].
Suppose Michael Porter’s approach [43,44] to create a long-term competitive strategy is used. His generic strategies are low cost, differentiation, and focus [45,46]. In the case of the game market, strategy differentiation with a focus on specific parts of the market comes into consideration. Philip Kotler proposed the segmentation, targeting, and positioning (STP) strategy as a marketing strategy in the consumer market [47,48,49]. It is a systematic approach to creating a marketing strategy, including three stages: segmenting (i.e., market segmentation), targeting (selection of target segments), and positioning (choice of positioning on selected segments). Many successful companies use the STP strategy in the consumer market, viz. [50,51,52]. An analysis of the possibilities of using the potential of the STP strategy and the possibilities of differentiation for the development of the game market is also the starting point for the formulation of the research questions listed below. These questions focus on verifying the existence of factors that could be used for differentiation based on consumer characteristics and adequate combinations of marketing mix tools.
Research Questions
  • Are there significant differences in game consumers given their demographic characteristics?
  • Are there limiting factors on the customer side, the removal of which can more fundamentally change the demand for game on the consumer market side?
  • What marketing tools can significantly influence consumer demand in the game market?
Game in the Czech Republic
The research area covers the Czech Republic (CZ), a Central European country with 10 million inhabitants. Hunting is regulated in the Czech Republic by the Hunting Act no. 449/2001 and several ministerial decrees, such as the specification of hunting seasons regulation of the Ministry of Agriculture 245/2002 Sb., the detailed instructions regulation of the Ministry of Agriculture 244/2002 Sb., and some others.
The central authority for hunting control and game management is the Ministry of Agriculture, except for the land in the national parks, which is subordinated to the Ministry of the Environment. The local and regional authorities execute the power of the state’s administration of the game management on the territory of the administrative regions.
The Ministry of Agriculture and the Czech Statistical Office collected the main statistics.
In 2020, the country registered 5786 hunting grounds, where 751 were managed by the owner and 5035 were rented [53,54].
CZ forestland covers 2,671,700 ha, i.e., 33.9% of the total area of the CZ. There is 6,887,798 ha of hunting land in the Czech Republic (38% forest land, 57% agricultural land, 6% water areas and other land). In 2021, 16.7 thousand tonnes of cloven-hoofed game were produced in the Czech Republic, of which 69% was wild boar (Sus scrofa) [53,54]. The total production of game in the Czech Republic is around 18,000 tonnes/year and has been increasing for a long time [55].
As shown in Table 1, the selected ungulates in the Czech Republic represent more than 90% of the total game production by volume. It is clear from the development of the historical time series that there has been a consistent increase in the spring conditions of game and hunting.
According to the Ministry of Agriculture, the high numbers of forest animals and the need to restore Czech forests with varied tree compositions and with an increased proportion of deciduous trees (which are particularly sensitive to animal damage) are the reasons for supporting the decision to significantly decrease game numbers [54]. Deciduous trees also include pioneer tree species. As a result, these factors have increased the supply of game not only in the Czech market but also in neighbouring countries [24].
Meat consumption in the Czech Republic has long been around 80 kg/year/capita (Figure 1) [56]. Of this, game consumption represents around 1 kg, i.e., a very low share at around 1.2% of the total meat consumption [57]. This consumption is comparable to the share of consumption in neighbouring countries, as can be seen, for example, from [26].
Game production is also significant in neighbouring countries, which, in some cases, greatly exceeds the production in the Czech Republic, such as in Poland and Germany (Table 2) [24,58,59]. However, the calculated number of pieces caught per 1000 people in the Czech Republic is comparable to and, for some species, even higher than in the neighbouring countries (Table 3).
Despite the high inflation, the purchase price of the game offered by the processing and distribution channels in the Czech Republic is lower than in previous years or is stagnant.
For example, the purchasing price for 1 kg of wild boar was around 40 CZK in 2000. In 2022, the purchasing price was around 35 CZK [60], while the selling price of game to end customers offered by the shops and distribution channels was 175–380 CZK per kg [61]. Hunting, thus, instead of contributing to forestry income, has become an unprofitable activity. This is a long-term trend that can be documented, for example, in the development of prices and the decreasing number of people interested in hunting [55,62].

2. Materials and Methods

The work can be methodically divided into four phases:
  • Selection of the target segment for research;
  • Survey organisation;
  • Statistical analysis;
  • Interpretation of the data obtained by the research.

2.1. Selection of the Target Segment for Research

As mentioned in the introductory section, from a conceptual point of view, it is suitable to use the classic Parfitt–Collins formula [39,40], because it enables one to systematically analyse the basic factors influencing the size of consumer demand (see Formula 1). Because the Parfitt–Collins formula is a product of three factors, it is clear that increasing the market share of meat consumption and increasing the volume of consumer demand for game can be achieved by increasing any indicator P, R, or B. To choose the target segment for the analysis, parameter P is crucial: cumulative penetration, which expresses, in our case, the share of customers who have ever bought and tasted game, as the content of this research is focused on the analysis of active customers who are part of cumulative penetration. The possibilities of increasing the cumulative penetration indicator P will be the subject of further research, which can also be very challenging in the case of game, because the reasons that have led a potential customer to have never tasted game before can be very diverse. Those reasons can often be related to strong attitudes or prejudices that marketing tools cannot change, for example, if the person in question is a vegetarian or has a strong attitude towards hunting, worries about forest animals, or experienced a different upbringing and was raised in a different culinary practice [63].
The advantage of the mentioned procedure is that it also enables one to conceptually differentiate and analyse the demand potential for game among those customers who are part of the cumulative penetration and whose demand depends on the frequency and intensity of their purchasing from the demand potential for game of those who have not yet become customers.
Parfitt–Collins Formula:
Market Share = P ∗ R ∗ B
where P = Cumulative product penetration (percentage of consumers who have purchased venison at least once); R = Repeat purchasing rates (average percentage of repurchases made by consumers of the product); and B = Buying Index or the heaviness of buying.
The research, therefore, focuses on a more detailed analysis of the segment of customers who actively purchase and consume meat and meat dishes with the aim of obtaining information on how to use marketing mix tools to increase the R (repeat purchasing rates) or B (the heaviness of buying). On the basis of the achieved results, follow-up research can be planned regarding the refinement of the segmentation criteria and the increase in cumulative penetration, the results of which will be the content of another professional article.

2.2. Survey Organisation

The research was carried out by a professional research agency using Computer-Assisted Web Interviewing (CAWI) (see, e.g., [64]) from 24 August to 30 August 2021. The respondents were part of the Czech National Panel, a joint project of the research agencies NMS Market Research, Nielsen Admosphere, and STEM/MARK. From a sample of 1000 respondents, who were representative in terms of their gender, age, education, region, and the size of their place of residence, the target group of respondents was selected. The target group consists of respondents who consume meat, actively shop or visit restaurants, have mostly tasted game, and are over 20 years old (a prerequisite for independent shopping and decision-making). The structure of the sample is shown in Table 4. The research agency guarantees the data representativeness following the European Society for Opinion and Marketing Research (ESOMAR) Standards.

2.3. Statistical Analysis

The demographical data characterising the respondents were collected as a part of the survey.
Because of this fact, the data were able to be further examined using methods of categorical data analysis and contingency tables [65]. The analysed categories were created using the following statistical variables: age of respondent, gender, completed education, size of the community, and region of the CZ. The variables corresponding to those categories are a standard part of the survey, and the research agencies guarantee that the sample is representative of these demographic variables. These categories were the subject of a statistical examination using R software [66] to test the p values corresponding to the hypothesis of independence using a chi-squared distribution. The p value is a random variable derived from the distribution of the test statistics used to analyse a data set and test the null hypothesis [67].
In this article, in addition to traditional tables, the mosaic displays designed in [68] and extended in [69] are used for greater clarity. The use of this advanced graphical method when analysing the dependence of several categorical variables simplifies data interpretation, as this method of visualisation helps to understand the relationships between the individual factors better than traditional graphs [70]. The counts in the contingency table are represented by tiles whose size is proportional to the cell frequency. The hypothesized model is based on the independence of the variables, which means the cells in the same row should have the same height regardless of the explaining variable. The differences in the sizes then clearly signal mutual conditionality and dependence, the degree of statistical significance of which is verified using the above-mentioned method. The following variables were used in the mosaic displays: frequency of game consumption, type of game purchased, age and gender of the respondent, and his/her size of residence.

3. Results and Discussion

The results of the research point to significant consumer experience of those interviewed with the consumption of game, where only 21% of the respondents indicated that they do not consume game at all, and approximately 50% of those interviewed consume game both at home and in a restaurant.
Table 5 shows the differences between men and women, who consume game only in restaurants or do not consume at all. This difference is also statistically significant. The corresponding p value of the test is 0.00018 (chi-square value = 19.88, degrees of freedom = 3, sample size = 523). Out of 257 women, 25% do not consume game at all, while 17% of the 266 men in the group do not consume game, thus showing a significant proportion of women who do not consume game compared to men. The share of women that do not consume game or only consume it in restaurants is significantly higher than that of men. This finding is in line with other research studies, which have stated a generally higher level of game consumption in men compared to women [71,72].
The individual categories related to purchase frequency are relatively evenly distributed and do not significantly differ even for the individual types of purchased commodities as apparent in Table 6.
The above data also show the high potential for an increase in the overall consumer demand of over 20% if marketing activities can be used to increase the frequency of the categories with a lower shopping frequency, i.e., “at least once every 3 months”, “at least once a year”, and “less often”, which represent 21.9% + 16.7% + 12.2% = 50.8% of the respondents, moving one category to the left.
If a more detailed segmentation of purchasing behaviour by age category is analysed, see Figure 2, a statistically significant dependence of the frequency of game purchases based on age group can be identified. The chi-square test has a p value = 4 × 10−6 (chi-square value = 47.26, degrees of freedom = 12).
The analysis shows a very strong group of shoppers in the 20–29 (at least once a week) and 50 and over (at least once every three months) age groups. Most respondents who do not buy game are in the 30–39 age group. Studies confirm that a frequent reason for the low consumption of game is concern about the specific taste [26,73,74]. To overcome this concern, it is advisable to maximise the consumer experience through tastings to increase the cumulative penetration of venison into the consumer market, which is confirmed by [75].
In Figure 3, one can compare the two mosaic displays analysing the relationship between the categorical variables: the type of purchased game and the age of the female and male respondents. In both cases, this dependence is statistically significant (men p value = 0.016, women p value = 0.265, chi-square value = 29.03/17.96, degrees of freedom = 15). Regarding the type of game, men consume more wild boar, while women consume a significantly larger share of game birds than men (Figure 3). As for game prepared at home, across the age groups, the most frequently prepared game was wild boar meat, followed by venison. In the age group of 30–39 years, roe deer equalled fallow deer in preparation volume. The differences visible in the mosaic display in Figure 4 are statistically significant (p-value = 0.029, chi-square value = 27.00, degrees of freedom = 15).
The dependence of the age group on the demand for a specific type of game turned out to be statistically significant (p value = 0.029). Thus, the demand for a particular type of meat depends on the age of the consumer. For example, previous studies [76,77] also mention this.
On the basis of the research results, it can be stated that 84% of the respondents rather or completely consider game to be a healthy organic food. However, almost 60% of the answers are of the “rather agree” type, which is apparently not a strong enough perception of organic quality for them to be willing to pay a premium price for game. Furthermore, 75% of the respondents rather or completely consider game to be an ecologically friendly food. At the same time, 58% of the respondents rather or completely agree with the opinion that the consumption of game contributes to the development of forestry (see Table 7).
The survey (see Table 8) also shows a significant demand for fresh, portioned venison n = 433 (68%), while the least interest is in frozen, portioned venison (9%). In addition, 11% of consumers expressed an interest in unprocessed meat, while 12% expressed an indifferent attitude.
The dependence of product processing and the size of the respondent’s residence turned out to be statistically very high, with a p value = 0.00105 (chi-square value = 22.34, degrees of freedom = 6). In order to increase purchase intensity, it is necessary to make game commonly available in various forms for consumption (chilled, frozen, or semi-finished products) for the daily supply of households. This can increase the frequency of consumption and the related intensity of purchasing and consumption, which is confirmed by, for example, [25].
A detailed analysis of the dependence of the place of purchase on the size of the final consumer’s residence points to a strong dependence between the choice of sales channel and the size of the respondent’s residence (chi-square test: p-value = 0.0059; chi-square value = 24.71; degrees of freedom = 10). Our analysis shows that local hunters are mainly sought after by consumers from smaller settlements, while the supermarket/hypermarket is significantly chosen by residents of large cities (over 100,000 inhabitants).
The large share of local hunters on the supply side reflects the easiest way that hunters sell game meat. According to Czech legislation, there are more relaxed hygienic rules for users of the hunting grounds. Under the regulation, users of the hunting grounds can, in small quantities, sell directly to consumers. The condition for this simplified consumer distribution is that the place of sale must be located in the territory of the region in which the game was caught or in the neighbouring regions.
This study did not consider game meat being exchanged “for free” within the community or undeclared as a sale (grey market).
The population segments from [78] represent households according to socioeconomic status, from households with the highest socioeconomic status (A) to the poorest households (E), where the average income per person is presented as segment (C) (Table 9).
This research shows that for segments A and B, price is not a determining factor for not consuming game in a restaurant. This segment mainly mentions other personal preferences when choosing food in a restaurant. In segments D and E, there is a noticeable concern about the quality of the game offered in the restaurant for consumption. For this segment (low-income groups of the population), it can be hypothesised that this concern may be related to visiting lower-level restaurants and the resulting concerns regarding the origin and quality of the game processing. Because the test yielded a p value = 0.1433 (chi-square value = 12.18, degrees of freedom = 8), the hypothesis that the dependence between the stated arguments and the income segments of the population is random and cannot be rejected at a sufficient level of significance.
Roughly half of the population believes that game should be priced like other types of meat. If we analyse the differences between men and women, this difference is statistically very significant (p value = 0.00018, chi-square value = 17.26, degrees of freedom = 2). Over 43% of women believe that game should cost more than other types of meat (Table 10). From the above results, it is clear that there are significant differences between groups of game consumers due to their demographic and socioeconomic characteristics, which can be a starting point for effective market segmentation, as evidenced in the answer to the first research question.
From the point of view of marketing strategy and the optimal choice of marketing mix, it is also necessary to propose the positioning for the selected target segments. The positioning of a product relates to its characteristics, the benefits it fulfils, and how it is differentiated from competitive products, which have a direct effect on customer repurchase intention and loyalty [80].
The positioning study [74] elaborates that rational motives influence game meat consumers’ choice more than emotional factors, while the most critical motives are connected with healthcare issues, like in study [81]. The study [79] states that Central European consumers, especially younger ones, are more concerned with game price and sensory characteristics. In general, the taste, overall quality, and odour were the most important sensory characteristics of game meat appreciated by the consumers. The taste, nutritional value, and low-fat content are the essential attributes for buying decisions. The study [82] states that despite the high nutritional value and health benefits, the younger generation is reluctant to consume game.
Another study [83] states that for middle-segment meat products, the combination of the price level and level of attention to animal welfare is of key importance. This information needs to be visible on the packaged product. The study [84] states that the country of origin and quality labelling pertaining mainly to game’s health appeal have great potential for differentiation in meat. In order to realise higher added value in the consumer market, it is, therefore, appropriate to present game with a certification of origin and meat processing. These brands will help customers orient themselves while increasing the value for consumers by reducing the uncertainty or the risk associated with a purchase. This is stated, for example, in [23,85].
Several studies have emphasised the cultural role and contribution to biodiversity of game [27,86] and have mentioned the monetary aspect of game production only as a secondary consequence. The above examples show several possible approaches to positioning, including the possibility of introducing brands.
In order to evaluate the relevance for the Czech Republic, further detailed research will be required that includes not only a more detailed analysis of the segmentation criteria but also an analysis of the attractiveness of the customer segments created, the evaluation and the selection of target segments, and the subsequent appropriate positioning for the selected target segments (see, e.g., [87,88]). However, the above results already show a relatively large demand potential among the segment of customers from larger cities in the case of packaged (frozen) venison with information on the organic quality and domestic origin of the meat using a brand that would become a guarantor of quality.

4. Conclusions

The main goal of this research was to identify the main marketing factors that influence the demand for game among the existing game consumers and, thus, obtain a starting point for further detailed research, proposals aimed at increasing demand through the more effective use of marketing mix tools, and a more detailed segmentation that would facilitate a more accurate targeting of marketing activities. To fulfil this purpose, research questions were formulated, the answers to which are summarized in the conclusion.
Our research results show that there are significant differences in game consumers given their demographic characteristics. Such differences we discovered between the behaviour of women and men, and, for example, between customers who prefer home preparation compared to consumption in a restaurant (see Table 5 and Table 8). Differences were also found in the relationship between education and place of game consumption, as well as between rural and urban residents. Apart from identifying the limiting factors on the customer side, the removal of which can more fundamentally change the demand for game on the consumer market side, our research has proven the existence of factors that reduce the intensity of consumer demand. Only a quarter of the respondents strongly agree with the statement (see Table 7) that game is a healthy organic food, while 60 % of the population rather agrees with this statement. Although the perception of the healthiness of venison is not likely the only or a sufficient argument for its purchase, there is an excellent opportunity to significantly support purchasing behaviours regarding game meat through appropriate and effective communication. The number of respondents who definitely agree with the statement that game is an ecologically friendly food, the consumption of which also helps Czech forests, is even smaller. In addition to communication aimed at overcoming prejudices and explaining some facts and contexts, there are other tools of the marketing mix, which are listed in the next question. Here, we summarise the marketing tools (product, price, distribution, and communication) that can significantly influence consumer demand in the game market. As for product, it is evident that residents of larger cities, in particular, would rather welcome butchered, portioned, or frozen meat (see Table 8). For price, if the game is not a direct sale from the hunter, the final price of the game cannot be influenced at the level of the forestry sector. The reason is that there is a huge difference between the purchase price of venison and the selling price that is ten times higher for the final product paid for by the consumer. Such a situation is not unique but can also be found in other countries (e.g., in Italy). The high share of game purchases (see Figure 5) indicates that a significant part of the game trade is based on personal ties, and for a large part of the population, game, in a processed form, is hardly available in the store. Therefore, increasing the intensity of the distribution and availability of game throughout the year is tied to the frozen form of the product. Such a situation creates an opportunity for communication through packaging and branding and conveying additional information to the consumer. Based on Table 5, Table 6, and Table 7, the following thematic areas focused on strengthening the positive image and refuting some experienced ideas are proposed for application as communication tools. Game is a healthy, safe organic food with an emphasis on the favourable ratio of quality versus price. Hunting is an activity necessary for the restoration of forests that also includes the care of game and observing ethical standards. The preparation of game is comparable to that of other commonly available meats, and a number of relatively simple recipes can liven up your daily menu.
Although some customers perceive game as healthy organic food, customers are not yet ready to pay for this quality. At the same time, customers are generally willing to pay extra for organic and regional foods for households and in the gastro segment [89,90,91]. More than two-thirds of customers prefer to buy fresh and cut game. If the sale of game is to increase, either the volume of the meat offered in this format must be increased, or the customer’s preferences must be changed, for example, by explaining why game is easier to process in the frozen state.
At the same time, the demand for game is also influenced by its being offered in the retail network; almost half of the customers stated that they buy it in supermarkets/hypermarkets or from butchers. The customer rarely experiences a real impulse to try game. The solution may be a long-term campaign to promote consumption, which will slowly change food preferences and include game to a greater extent in the household diet.
Due to the high costs of similar types of campaigns, it is advisable to use the form of collective advertising, i.e., the joint advertising of entities, in our case, game producers with possible state support (justifying social interest, the connection with forest restoration, the use and evaluation of resources, and support for sustainability).

Author Contributions

Conceptualisation, M.N., M.R. and V.J.; validation, R.D. and V.J.; statistical analysis, M.R.; investigation and visualisation, M.N.; resources, MR.; data curation, M.N.; writing—original draft preparation, M.N.; writing–review and editing, M.R.; supervision, V.J.; funding acquisition, R.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the IGA 2022 (Internal Grant Agency–Project ID A_33_22) of the Czech University of Life Sciences Prague and by the National Agency for Agricultural Research of the Ministry of Agriculture of the Czech Republic (project No. QK22020008: Comprehensive assessment of wood-producing and non-wood-producing functions of pioneer tree species stands).

Data Availability Statement

Data are available upon e-mail request made to corresponding author.

Acknowledgments

The authors would like to express their special thanks to Andrea Skřivánková for her cooperation and support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Maes, J.; Teller, A.; Erhard, M.; Grizzetti, B.; Barredo, J.I.; Paracchini, M.L.; Condé, S.; Somma, F.; Orgiazzi, A.; Jones, A.; et al. Mapping and Assessment of Ecosystems and Their Services: An Analytical Framework for Ecosystem Condition; Publications Office: Luxembourg, 2015. [Google Scholar]
  2. Duraiappah, A.K.; Naeem, S.; Agardy, T.; Ash, N.J.; Cooper, H.D.; Diaz, S.; Faith, D.P.; Mace, G.; McNeely, J.A.; Mooney, H.A.; et al. Ecosystems and human well-being: Biodiversity synthesis. In A Report of the Millennium Ecosystem Assessment; World Resources Institute: Washinghton, DC, USA, 2005. [Google Scholar]
  3. Sukhdev, P.; Wittmer, H.; Schröter-Schlaack, C.; Nesshöver, C.; Bishop, B.; ten Brink, P.; Gundimeda, H.; Kumar, P.; Simmons, B. The Economics of Ecosystems and Biodiversity: Mainstreaming the Economics of Nature: A Synthesis of the Approach, Conclusions and Recommendations of TEEB; TEEB: Geneva, Switzerland, 2010. [Google Scholar]
  4. CICES. Available online: https://cices.eu/ (accessed on 2 November 2022).
  5. de Beer, J.H.; Mcdermott, M.J. The Economic Value of Non-Timber Forest Products in Southeast Asia: With Emphasis on Indonesia, Malaysia and Thailand; Netherlands Committee for IUCN: Amsterdam, The Netherlands, 1989. [Google Scholar]
  6. Farrell, E.P.; Führer, E.; Ryan, D.; Andersson, F.; Hüttl, R.; Piussi, P. European forest ecosystems: Building the future on the legacy of the past. For. Ecol. Manag. 2000, 132, 5–20. [Google Scholar] [CrossRef]
  7. Sheppard, J.P.; Chamberlain, J.; Agúndez, D.; Bhattacharya, P.; Chirwa, P.V.; Gontcharov, A.; Sagona, W.C.J.; Shen, H.; Tadesse, W.; Mutke, S. Sustainable Forest Management Beyond the Timber-Oriented Status Quo: Transitioning to Co-production of Timber and Non-wood Forest Products-a Global Perspective. Curr. For. Rep. 2020, 6, 26–40. [Google Scholar] [CrossRef] [Green Version]
  8. Sardeshpande, M.; Shackleton, C. Wild Edible Fruits: A Systematic Review of an Under-Researched Multifunctional NTFP (Non-Timber Forest Product). Forests 2019, 10, 467. [Google Scholar] [CrossRef] [Green Version]
  9. Muttilainen, H.; Hallikainen, V.; Miina, J. Forest Owners’ Perspectives Concerning Non-Timber Forest Products, Everyman’s Rights, and Organic Certification of Forests in Eastern Finland. Small-Scale For. 2022, 1–33. [Google Scholar] [CrossRef]
  10. Kilchling, P.; Hansmann, R.; Seeland, K. Demand for non-timber forest products: Surveys of urban consumers and sellers in Switzerland. For. Policy Econ. 2009, 11, 294–300. [Google Scholar] [CrossRef]
  11. Croitoru, L. Valuing the non-timber forest products in the Mediterranean region. Ecol. Econ. 2007, 63, 768–775. [Google Scholar] [CrossRef]
  12. Kovalcík, M. Value of forest berries and mushrooms picking in Slovakia as an ecosystem service of mountain forests. Beskydy 2014, 7, 39–46. [Google Scholar] [CrossRef] [Green Version]
  13. Lovric’, M.; Da Re, R.; Vidale, E.; Prokofieva, I.; Wong, J.; Pettenella, D.; Verkerk, P.J.; Mavsar, R. Non-wood forest products in Europe–A quantitative overview. For. Policy Econ. 2020, 116, 102175. [Google Scholar] [CrossRef]
  14. Maier, C.; Hebermehl, W.; Grossmann, C.; Loft, l.; Mann, C.; Hernández-Morcillo, M. Innovations for securing forest ecosystem service provision in Europe–A systematic literature review. Ecosyst. Serv. 2021, 52, 101374. [Google Scholar] [CrossRef]
  15. Šišák, L.; Riedl, M.; Dudík, R. Non-market non-timber forest products in the Czech Republic-Their socio-economic effects and trends in forest land use. Land Use Policy 2016, 50, 390–398. [Google Scholar] [CrossRef]
  16. Kudrnáčová, E.; Bartoň, L.; Bureš, D.; Hoffman, L. Carcass and meat characteristics from farm-raised and wild fallow deer (Dama dama) and red deer (Cervus elaphus): A review. Meat Sci. 2018, 141, 9–27. [Google Scholar] [CrossRef]
  17. Deutz, A. Wildbrethygiene Heute: Beurteilung|Versorgung|Rechtslage, 1st ed.; BLV, ein Imprint von GRÄFE UND UNZER Verlag GmbH: München, Germany, 2012. [Google Scholar]
  18. Soriano, A.; Sánchez-García, C. Nutritional composition of game meat from wild species harvested in Europe. In Meat and Nutrition; Ranabhat, C.L., Ed.; IntechOpen: London, UK, 2021. [Google Scholar]
  19. Strazdina, V.; Jemeljanovs, A.; Sterna, V.; Ikauniece, D. Nutritional characteristics of wild boar meat hunted in Latvia. In Proceedings of the FoodBalt 2014: 9th Baltic Conference on Food Science and Technology, Jelgava, Latvia, 8–9 May 2014; pp. 32–36. [Google Scholar]
  20. Wyness, L. The role of red meat in the diet: Nutrition and health benefits. Proc. Nutr. Soc. 2016, 75, 227–232. [Google Scholar] [CrossRef] [Green Version]
  21. Corradini, A.; Marescotti, M.E.; Demartini, E.; Gaviglio, A. Consumers’ perceptions and attitudes toward hunted wild game meat in the modern world: A literature review. Meat Sci. 2022, 194, 108955. [Google Scholar] [CrossRef]
  22. Olaussen, J.; Mysterud, A. Red deer hunting—Commercialising versus availability. Eur. J. Wildl. Res. 2012, 58, 597–607. [Google Scholar] [CrossRef]
  23. Gaviglio, A.; Marescotti, M.E.; Demartini, E. The local value chain of hunted red deer meat:scenario analysis based on a northern Italian case study. Resources 2018, 7, 34. [Google Scholar] [CrossRef] [Green Version]
  24. Game Meat Production and Trade in the UNECE Region. Available online: https://unece.org/fileadmin/DAM/timber/meetings/201489/2201meat-draft-2018–03.pdf (accessed on 15 November 2022).
  25. Proskina, L.; Cerina, S.; Viksne, D. 6th International Scientific Conference on Rural Development-Innovations and Sustainability. Rural. Dev. 2013, 6, 289–293. [Google Scholar]
  26. Czarniecka-Skubina, E.; Stasiak, D.M.; Owczarek, T.; Hamulka, J. Consumers’ Perception and Preference for the Consumption of Wild Game Meat among Adults in Poland. Foods 2022, 11, 830. [Google Scholar] [CrossRef]
  27. Lupp, G.; Tangerding, S.; Kantelberg, V. Venison from the Bavarian forests. In Routledge Handbook of Landscape and Food; Zeunert, J., Waterman, T., Eds.; Routledge: London, UK, 2018; pp. 81–91. [Google Scholar]
  28. Komarek, L.; Tóth, S. The Issues of the Relations of Hungarian Game-Meat Production and Selling. Quaestus 2019, 14, 359–371. [Google Scholar]
  29. Ljung, P.E.; Riley, S.; Ericsson, G. Game Meat Consumption Feeds Urban Support of Traditional Use of Natural Resources. Soc. Nat. Resour. 2015, 28, 657–669. [Google Scholar] [CrossRef]
  30. Nagy, J.; Bokor, J. The Management of Enclosed and Domesticated Deer: International Husbandry Systems and Diseases; Springer Nature: Berlin, Germany, 2022; pp. 193–227. [Google Scholar]
  31. Okuskhanova, E.; Assenova, B.; Rebezov, M.; Amirkhanov, K.; Yessimbekov, Z.; Smolnikova, F.; Nurgazezova, A.; Nurymkhan, G.; Stuart, M. Study of morphology, chemical, and aminoacid composition of red deer meat. Vet. World 2017, 10, 623. [Google Scholar] [CrossRef]
  32. Apollonio, M.; Andersen, R.; Putman, R. European Ungulates and Their Management in the 21st Century, 1st ed.; Cambridge University Press: Cambridge, UK, 2010. [Google Scholar]
  33. Asche, F.; Flaaten, O.; Isaksen, J.; Vassdal, T. Derived demand and relationships between prices at different levels in the value chain: A note. J. Agric. Econ. 2002, 53, 101–107. [Google Scholar] [CrossRef]
  34. Govindan, K.; Shaw, M.; Majumdar, A. Social sustainability tensions in multi-tier supply chain: A systematic literature review towards conceptual framework development. J. Clean. Prod. 2021, 279, 123075. [Google Scholar] [CrossRef]
  35. Timsit, J.-P.; Castiaux, A.; Truong, Y.; Athaide, G.A.; Klink, R.R. The effect of market-pull vs. resource-push orientation on performance when entering new markets. J. Bus. Res. 2015, 68, 2005–2014. [Google Scholar] [CrossRef]
  36. Kotler, P.; Keller, K.L. Marketing Management, 15th ed.; Pearson: Boston, MA, USA, 2016. [Google Scholar]
  37. Ansoff, H.I.; McDonnell, E.J. The New Corporate Strategy, 1st ed.; J. Wiley: New York, NY, USA, 1988. [Google Scholar]
  38. Kukartsev, V.V.; Fedorova, N.V.; Tynchenko, V.; Danilchenko, Y.; Eremeev, D.; Boyko, A. The analysis of methods for developing the marketing strategies in agribusiness. In IOP Conference Series: Earth and Environmental Science; IOP Publishing: Bristol, UK, 2019; p. 315. [Google Scholar]
  39. Grünig, R.; Kühn, R.; Clark, A.; O’Dea, C.; Montani, M. Solving Complex Decision Problems: A Heuristic Process, 4th ed.; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
  40. Parfitt, J.; Collins, B. Use of Consumer Panels for Brand-Share Prediction. J. Mark. Res. 1968, 5, 131–145. [Google Scholar] [CrossRef]
  41. Keiningham, T.; Aksoy, L.; Bruce, H.; Cadet, F.; Clennell, N.; Hodgkinson, I.; Kearney, T. Customer experience driven business model innovation. J. Bus. Res. 2020, 116, 431–440. [Google Scholar] [CrossRef]
  42. Arora, N.; Allenby, G.M.; Ginter, J.L. A hierarchical Bayes model of primary and secondary demand. Martketing Sci. 1998, 17, 29–44. [Google Scholar] [CrossRef] [Green Version]
  43. Porter, M.E. Competitive Advantage-Creating and Sustaining Superior Performance, 40th ed.; The Free Press: New York, NY, USA, 1998; p. 523. [Google Scholar]
  44. Porter, M.E. The Five Competitive Forces That Shape Strategy. Harv. Bus. Rev. 2008, 86, 79–93. [Google Scholar]
  45. Islami, X.; Mustafa, N.; Topuzovska, M. Linking Porter’s generic strategies to firm performance. Future Bus. J. 2020, 6, 3. [Google Scholar] [CrossRef] [Green Version]
  46. Helmond, M. Marketing Management as Part of the Corporate Strategy. In Performance Excellence in Marketing, Sales and Pricing; Springer: Cham, Germany, 2022; pp. 13–34. [Google Scholar]
  47. Kotler, P. Kotler on Marketing; Simon and Schuster: New York, NY, USA, 2012. [Google Scholar]
  48. Thomas, M.; George, G. Segmenting, Targeting, and Positioning (STP) of Generational Cohorts Y, Z and Alpha. IIMS J. Manag. Sci. 2021, 12, 115–129. [Google Scholar] [CrossRef]
  49. Rudawska, E. From Sustainable Market Orientation to Sustainability Marketing. In The Sustainable Marketing Concept in European SMEs; Emerald Publishing Limited: Bingley, UK, 2018. [Google Scholar] [CrossRef]
  50. Kalam, K. Market Segmentation, Targeting and Positioning Strategy Adaptation for the Global Business of Vodafone Telecommunication Company. Int. J. Res. Innov. Soc. Sci. 2020, 4, 427–430. [Google Scholar]
  51. Ming, L.; Ke, T. Product Positioning Research of P & G Camay. In Information Engineering and Applications; Rongbo, Z., Yan, M., Eds.; Springer: London, UK, 2012; pp. 65–71. [Google Scholar]
  52. Vigneron, F.; Nazarian, D. The Seven Habits of Luxury Brands. J. Int. Mark. Strategy 2017, 5, 1–16. [Google Scholar]
  53. Czech Statistical Office–Hunting Statistics 2021–2022. Available online: https://www.czso.cz/csu/czso/zakladni-udaje-o-honitbach-stavu-a-lovu-zvere-od-1-4-2021-do-31-3-2022 (accessed on 8 October 2022).
  54. Ministry of Agriculture, Report on the State of Forests and Forestry in the Czech Republic in 2020. 2021. Available online: https://eagri.cz/public/web/file/688968/Zprava_o_stavu_lesa_2020_web.pdf (accessed on 5 November 2022).
  55. Czech Statistical Office–Hunting Statistics 2011–2020. Available online: https://www.czso.cz/documents/10180/142813413/1000052514306.p58c5-4d46-834b-abcf6f2221c5?version=1.3 (accessed on 8 June 2022).
  56. Czech Statistical Office–Food Consumption-2020. Available online: https://www.czso.cz/documents/10180/143060175/2701392105145.pdf08ea-47c0-92b2-e5db7c812165?version=1.1 (accessed on 29 September 2022).
  57. Agrarian Chamber of the Czech Republic–Food Consumption-2020. Available online: https://www.akcr.cz/data_ak/21/k/Stat/Po5t4r7avin (accessed on 3 October 2022).
  58. Jahresjagdstrecke Bundesrepublik Deutchland. Available online: https://www.jagdverband.de/sites/default/files/2022-54902/2022-01_Infografik_Jahresjagdstrecke_Bundesrepublik_Deutschland_2020_2021_0.jpg (accessed on 14 November 2022).
  59. Statistical Yearbook of Forestry. Available online: https://stat.gov.pl/en/topics/statistical-yearbooks/statistical-yearbooks/statistical-yearbook-of-forestry-2020,12,3.html (accessed on 18 November 2022).
  60. Výkupní Ceník Zvěřiny v Kůži Pro Rok 2022. Available online: https://www.vilemktis.cz/wp-content/uploads/2022/05/Vy%CC%81kupni%CC%81-553ceny-od-1.5.2022.pdf (accessed on 10 November 2022).
  61. E-Shop, Prase Divoké. Available online: https://prodej-zveriny.cz/product-category/prase-divoce/ (accessed on 10 November 2022).
  62. Myslivecká Statistika 2020/2021. Available online: https://www.myslivost.cz/Casopis-Myslivost/MYSLIVOST-Straz-myslivosti/2021/Rijen-2021/MYSLIVECKA-STATISTIKA-2020-2021 (accessed on 10 November 2022).
  63. Font-i-Furnols, M.; Guerrero, L. Consumer preference, behavior and perception about meat and meat products: An overview. Meat Sci. 2014, 98, 361–371. [Google Scholar] [CrossRef]
  64. Kagerbauer, M.; Manz, W.; Zumkeller, D. Analysis of PAPI, CATI, and CAWI Methods for a Multiday Household Travel Survey. In Transport Survey Methods; Zmud, J., Lee-Gosselin, M., Munizaga, M., Carrasco, J.A., Eds.; Emerald Group Publishing Limited: Bingley, UK, 2013; pp. 289–304. [Google Scholar]
  65. Agresti, A. Categorical Data Analysis, 3rd ed.; Wiley & Sons: Hoboken, NJ, USA, 2013. [Google Scholar]
  66. R-Core-Team. Available online: https://www.r-project.org/ (accessed on 14 August 2022).
  67. Hung, H.; O’Neill, R.; Bauer, P.; Kohne, K. The behavior of the P-value when the alternative hypothesis is true. Biometrics 1997, 53, 11–22. [Google Scholar] [CrossRef] [Green Version]
  68. Hartigan, J.; Kleiner, B. Mosaics for contingency tables. Computer Science and Statistics. In Proceedings of the 13th Symposium on the Interface Country, Pittsburgh, PA, USA, 12–13 March 1981; pp. 268–273. [Google Scholar]
  69. Friendly, M. Mosaic displays for multi-way contingency tables. J. Am. Stat. Assoc. 1994, 89, 190–200. [Google Scholar] [CrossRef]
  70. Wainer, H.; Velleman, P. Statistical graphics: Mapping the pathways of science. Annu. Rev. Psychol. 2001, 52, 305–335. [Google Scholar] [CrossRef]
  71. Burger, J. Gender differences in meal patterns: Role of self-caught fish and wild game in meat and fish diets. Environ. Res. 2000, 83, 140–149. [Google Scholar] [CrossRef]
  72. Kubberød, E.; Ueland, Ø.; Rødbotten, M.; Westad, F.; Risvik, E. Gender specific preferences and attitudes towards meat. Food Qual. Prefer. 2002, 13, 285–294. [Google Scholar] [CrossRef]
  73. Goguen, A.D.; Riley, S.J. Consumption of Wild-Harvested Meat in Society. Wildl. Soc. Bull. 2020, 44, 553–563. [Google Scholar] [CrossRef]
  74. Niewiadomska, K.; Kosicka-Gebska, M.; Gebski, J.; Gutkowska, K.; Jeżewska-Zychowicz, M.; Sułek, M. Game Meat Consumption—Conscious Choice or Just a Game? Foods 2020, 9, 1357. [Google Scholar] [CrossRef]
  75. Radder, L.; le Roux, R. Factors affecting food choice in relation to venison: A South African example. Meat Sci. 2005, 71, 582–589. [Google Scholar] [CrossRef]
  76. Schupp, A.; Gillespie, J.M.; Reed, D. Consumer Choice Among Alternative Red Meats. J. Food Distrib. Res. 1998, 29, 35–43. [Google Scholar] [CrossRef]
  77. Vanhonacker, F.; Van Loo, E.; Gellynck, X.; Verbeke, W. Flemish consumer attitudes towards more sustainable food choices. Appetite 2013, 62, 7–16. [Google Scholar] [CrossRef]
  78. NIELSEN. Available online: https://uploads-ssl.webflow.com/611241de77a0a2bf4c87dd55/61c47cb5f8220d6dc7c4428c_Nielsen%20Admosphere%20A58B8CD%20specifikace%202022.pdf (accessed on 24 August 2022).
  79. Tomasevic, I.; Novakovic, S.; Solowiej, B.; Zdolec, N.; Skunca, D.; Krocko, M.; Nedomova, S.; Kolaj, R.; Aleksiev, G.; Djekic, I. Consumers’ perceptions, attitudes and perceived quality of game meat in ten European countries. Meat Sci. 2018, 142, 5–13. [Google Scholar] [CrossRef]
  80. Lin, C.-H.; Sher, P.; Shih, H.-Y. Past progress and future directions in conceptualising customer perceived value. Int. J. Serv. Ind. Manag. 2005, 16, 318–336. [Google Scholar] [CrossRef]
  81. Proskina, L. Consumer behaviour on the venison market in Latvia. Econ. Sci. Rural Dev. 2013, 32, 68–75. [Google Scholar]
  82. Xie, X.; Huang, L.; Li, J.; Zhu, H. Generational Differences in Perceptions of Food Health/Risk and Attitudes toward Or- 597 ganic Food and Game Meat: The Case of the COVID-19 Crisis in China. Int. J. Environ. Res. Public Health 2020, 17, 3148. [Google Scholar] [CrossRef]
  83. de Jonge, J.; van Trijp, H.C.M. Meeting Heterogeneity in Consumer Demand for Animal Welfare: A Reflection on Existing Knowledge and Implications for the Meat Sector. J. Agric. Environ. Ethics 2013, 26, 629–661. [Google Scholar] [CrossRef]
  84. Verbeke, W.; Roosen, J. Market differentiation potential of country-of-origin, quality and traceability labeling. Estey Cent. J. Int. Law Trade Policy 2009, 10, 20–35. [Google Scholar]
  85. Beverland, M. Can cooperatives brand? Exploring the interplay between cooperative structure and sustained brand marketing success. Food Policy 2007, 32, 480–495. [Google Scholar] [CrossRef]
  86. Gordon, I.; Hester, A.J.; Festa-Bianchet, M. The management of wild large herbivores to meet economic, conservation and environmental objectives. J. Appl. Ecol. 2004, 41, 480–495. [Google Scholar] [CrossRef]
  87. Payne, A.; Frow, P.; Eggert, A. The customer value proposition: Evolution, development, and application in marketing. J. Acad. Mark. Sci. 2017, 45, 467–489. [Google Scholar] [CrossRef]
  88. Barnes, C.; Blake, H.; Pinder, D. Creating & Delivering Your Value Proposition: Managing Customer Experience for Profit, 1st ed.; Kogan Page: London, UK, 2009. [Google Scholar]
  89. Morone, P.; Caferra, R.; D’Adamo, I.; Marcello, P.; Imbert, F.; Imbert, E.; Morone, A. Consumer willingness to pay for bio-based products: Do certifications matter? Int. J. Prod. Econ. 2021, 240, 108248. [Google Scholar] [CrossRef]
  90. Velčovská, Š.; Del Chiappa, G. The food quality labels: Awareness and willingness to pay in the context of the Czech Republic. Acta Univ. Agric. Et Silvic. Mendel. Brun. 2015, 63, 647–658. Available online: https://www.academia.edu/download/72724737/9d6126c2ef (accessed on 20 January 2023). [CrossRef] [Green Version]
  91. Bryła, P. The Impact of Consumer Schwartz Values and Regulatory Focus on the Willingness to Pay a Price Premium for Domestic Food Products: Gender Differences. Energies 2021, 14, 6198. [Google Scholar] [CrossRef]
Forests 14 00450 g001 550
Figure 1. Meat consumption in the Czech Republic per capita [56].
Figure 1. Meat consumption in the Czech Republic per capita [56].
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Forests 14 00450 g002 550
Figure 2. The mosaic display of the relationship between the respondent’s age and consumption frequency (n = 445).
Figure 2. The mosaic display of the relationship between the respondent’s age and consumption frequency (n = 445).
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Figure 3. The mosaic displays the relationship between the respondent’s age and the type of game cooked at home (n1 = 168 Men, n2 = 155 Women).
Figure 3. The mosaic displays the relationship between the respondent’s age and the type of game cooked at home (n1 = 168 Men, n2 = 155 Women).
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Figure 4. The mosaic displays the relationship between the respondent’s age and the type of game cooked at home (n = 323).
Figure 4. The mosaic displays the relationship between the respondent’s age and the type of game cooked at home (n = 323).
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Figure 5. The mosaic display of the relationship between the size of the residence and the place the meat was purchased (n = 472) *.* A specialised e-shop is an e-shop that specialises in the sale of game meat only, whereas an e-shop is meant as a general grocery shop in our research.
Figure 5. The mosaic display of the relationship between the size of the residence and the place the meat was purchased (n = 472) *.* A specialised e-shop is an e-shop that specialises in the sale of game meat only, whereas an e-shop is meant as a general grocery shop in our research.
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Table
Table 1. Selected ungulates in Czech Republic hunting statistics (in pieces/whole carcasses) [55].
Table 1. Selected ungulates in Czech Republic hunting statistics (in pieces/whole carcasses) [55].
Item201120132015201720192021
Red deer (Cervus elaphus)Spring count30,83826,61828,22329,78929,77331,916
Hunt20,95823,57823,97827,87829,01730,792
Fallow deer (Dama dama)Spring count26,61127,77431,09933,73437,79941,663
Hunt13,13116,40418,96823,06928,97833,250
European mouflon (Ovis musimon)Spring count21,29419,43520,47121,70720,94922,730
Hunt814692229495940010,10510,019
Roe deer (Capreolus capreolus)Spring count302,206290,661291,241298,852291,070293,565
Hunt113,913105,68099,828103,455103,018107,433
Wild boar (Sus scrofa)Spring count59,29559,17560,96658,74660,86362,676
Hunt109,383152,250185,496229,182239,818230,905
The spring state of the ungulates is determined with direct observation in the months of January and February.
Table
Table 2. Selected ungulates in Czech Republic hunting statistics (in pieces).
Table 2. Selected ungulates in Czech Republic hunting statistics (in pieces).
2019 Harvest in PcsCzech RepublicPolandGermany
Red deer (Cervus elaphus)29,86398,87776,897
Fallow deer (Dama dama)31,057866468,211
Roe deer (Capreolus capreolus)105,665192,8501,226,169
Wild boar (Sus scrofa)161,699331 886882,231
Table
Table 3. Selected ungulates harvested per 1000 capita (comparison-adjusted per 1000 capita).
Table 3. Selected ungulates harvested per 1000 capita (comparison-adjusted per 1000 capita).
2019 Harvest in PcsCzech RepublicPolandGermany
Red deer (Cervus elaphus)2.82.60.9
Fallow deer (Dama dama)2.90.20.8
Roe deer (Capreolus capreolus)9.95.114.7
Wild boar (Sus scrofa)15.18.810.6
Table
Table 4. Structure of the sample (number of respondents n = 523).
Table 4. Structure of the sample (number of respondents n = 523).
GenderMale266
Female257
Age category20–29 years old84
30–39 years old137
40–49 years old136
50 or more years166
Highest completed educationElementary school/secondary school without high school diploma95
Secondary school with high school diploma261
University/Higher vocational school167
Region of the Czech RepublicPrague and the Central Bohemian Region169
Bohemia162
Moravia192
Place of residenceVillage153
Small and medium-sized cities199
Big cities171
Table
Table 5. Do you eat game-based foods (including pâté, sausages, etc.) (n = 523)?
Table 5. Do you eat game-based foods (including pâté, sausages, etc.) (n = 523)?
GenderYes, I Consume at Home and in RestaurantsYes, I Only Consume at HomeYes, I Only Consume in RestaurantsI Do Not Consume
Male49.9%23.7%9.5%16.9%
Female49.1%10.8%15.2%24.8%
Table
Table 6. What kind of game or game-based food do you buy (whether for yourself or members of your household) (n = 523)?
Table 6. What kind of game or game-based food do you buy (whether for yourself or members of your household) (n = 523)?
ProductAt Least Once a WeekAt Least Once a MonthAt Least Once Every 3 MonthsAt Least Once a YearLess OftenNever
Meat17.1%15.0%21.9%16.7%12.2%17.1%
Cold meats (sausages, salami, etc.)18.7%19.5%20.2%11.6%15.1%14.8%
Pies10.3%25.8%20.9%13.2%13.5%16.4%
Other6.3%7.8%8.4%8.8%26.3%42.3%
Table
Table 7. Which of the following statements about game do you agree with (n = 523)?
Table 7. Which of the following statements about game do you agree with (n = 523)?
PreferenceIt Is a Healthy Food in Organic QualityIt Is an Ecologically Friendly FoodBy Consuming Game I Help the Czech Forests
I definitely agree24.0%16.6%9.9%
I rather agree59.9%58.7%48.2%
I rather disagree11.9%18.8%30.5%
I definitely disagree4.2%5.9%11.4%
Table
Table 8. When buying meat-game, you prefer (n = 433).
Table 8. When buying meat-game, you prefer (n = 433).
Place of ResidenceFresh Meat, PortionedFresh Meat, Unportioned (I Obtain and Portion it Myself)Frozen Meat, PortionedIt Does Not Matter to Me
Village69.2%17.9%4.8%8.1%
Small and medium-sized cities70.3%9.4%9.3%11.0%
Big cities65.3 %7.3%10.9%16.6%
Table
Table 9. What are the reasons why you do not eat game in a restaurant? (Base: 200 respondents either eat game food only at home or do not, multiple answers possible).
Table 9. What are the reasons why you do not eat game in a restaurant? (Base: 200 respondents either eat game food only at home or do not, multiple answers possible).
[79] Offer It
A,B–Highest24.6%15.0%61.8%23.4%10.8%
C34.2%17.7%55.1%23.6%10.5%
D,E–Lowest33.7%30.8%38.0%35.8%2.3%
Total30.6%19.5%54.0%26.1%8.9%
Table
Table 10. How do you perceive the price of game compared to other types of meat (n = 523)?
Table 10. How do you perceive the price of game compared to other types of meat (n = 523)?
GenderIt Should Cost More than Other Types of MeatIt Should Cost the Same as Other Types of MeatIt Should Cost Less than Other Types of Meat
Male26.3%64.3%9.5%
Female43.2%51.3%5.5%
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Němec, M.; Riedl, M.; Jarský, V.; Dudík, R. Analysis of Consumer Attitudes as an Important Tool for the Segmentation and Development of the Game Market in the Czech Republic. Forests 2023, 14, 450. https://doi.org/10.3390/f14030450

AMA Style

Němec M, Riedl M, Jarský V, Dudík R. Analysis of Consumer Attitudes as an Important Tool for the Segmentation and Development of the Game Market in the Czech Republic. Forests. 2023; 14(3):450. https://doi.org/10.3390/f14030450

Chicago/Turabian Style

Němec, Martin, Marcel Riedl, Vilém Jarský, and Roman Dudík. 2023. "Analysis of Consumer Attitudes as an Important Tool for the Segmentation and Development of the Game Market in the Czech Republic" Forests 14, no. 3: 450. https://doi.org/10.3390/f14030450

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