**1. Introduction**

Innovation is one of the main economic activities that lead the company to organizational success and high results, independently of its size and the sector in which it operates [1]. It brings a positive change within the enterprise and it is led by many factors such as, for example, competition and customer demand. For this reason, every company must adapt its behavior to external demands, in order to maintain or raise the level of its performance [2]. Over time, innovation has also gained interest in the agri-food sector, where the open type of innovation is very much appreciated in the recent decades [3,4]. Open innovation is an e ffective driving force to promote innovation performance [5,6]. It is based on obtaining technical resources and market information, to increase the company's internal resources, thus improving the original level [7]. It is possible to distinguish four types of open innovation [8]: product innovation (which concerns a good or a service); process innovation (which involves a new production method); marketing innovation (which refers to a new marketing method, such as changes in product packaging, product promotion or prices); and organizational innovation (which involves improvements in the organization of work or in the company's external relations).

Among the main advantages of open innovation, we can list the improvement of business efficiency [9], which makes late companies keep up with the technological development of the reference market [10]. On the other hand, open innovation can lead the company to a reduction in marginal returns, caused by the time spent on search [11] and by collaborative activities with other entrants or companies [12], which require significant coordination e fforts [13].

The openness assumes that firms construct several ties with business, science and professional partners in order to create bi- and multilateral connections for acquiring innovation ideas, making development progress, as well as promoting and marketing new products and services [3,14]. Indeed, companies that want to innovate, can turn to external sources of information for innovation, in order to seek specific knowledge useful for their purpose [15]. In particular, four specific sources of external knowledge sources have been identified in the literature [16], including suppliers [17], customers [18], competitors [19] and universities [20]. Through these relations the in- and outflow of information related to innovation can more e fficiently and smoothly be managed [21]. The e ffectiveness of open innovation activities, as well as creating links with the external environment is now consolidated [22]. It is clear that sourcing needs resources (financial, managerial and specific knowledge) and that each of them competes with other possible uses. This rivalry of resources for recruiting outside information should be explored, as, net of our knowledge, the topic has received scant attention in the existing literature. In addition, using too many information sources can lead to managemen<sup>t</sup> problems [23]. Consequently, our research question focuses on the information acquiring strategy of the firm. We are interested, from which directions it is appropriate that information arrives to the company and how much information is really needed. Our assumption is that this strategy di ffers based on the type of innovation. Therefore, the present study positions a double research question: 1. What is the chance that a specific source of information is selected in relation to the type (product, process, organization and market) of the innovation? and, 2. By grouping the sources into three di fferent ones (business, science and professional), how many of them are selected in di fferent types of innovation? In order to answer the two research questions, we use the Community Innovation Survey—2012 Hungary data [24] filtering for the Nace. Rev 10-12 categories (food, beverages and tobacco industries—more precise breakdown is not possible within this database). We apply probit and OLS regression for exploring our answer. Hungary is an interesting case from an innovation point of view, because according to the European Innovation Scoreboard (2017) report [25], Hungary's summarized innovation score is 67.4 against the EU28 average of 102. This implies that the Hungarian economy has go<sup>t</sup> rather serious disadvantages in the EU community. This statement is more pronounced in case of the food industry. From an innovation point of view, food industry is seen as a slow one, which is lagging behind the technology pushed possibilities, but sometimes behind the customers' desires and requirements as well. One possible way of boosting the food economy is, therefore, to speed up the innovation.

The remaining part of this paper is structured as follows: first we shed light on some basic theoretical concepts and empirical findings in the related fields. Next, we introduce our data and methodology. After that, we comprise the results. At the end, we discuss, conclude and draw the limitations of our findings.

### **2. Theoretical Considerations and Empirical Evidences**

Open innovation can be defined as "the use of inflows and outflows of knowledge that improve internal innovation and at the same time widen the markets for the external use of innovation" [26]. It involves the use of multiple internal and external sources, integrating this activity with company resources and exploiting these opportunities through multiple channels [27]. Indeed, based on the theories of inter-organizational knowledge flows and organizational learning, many authors [28–33] have stated that the use of a limited number of external channels facilitates the performance of the

innovative company. This approach refers to the depth of the research strategy [34], according to which the term "depth of open research" indicates from how many intense channels the company gets ideas for innovation.

The incremental nature of innovation is a realistic hypothesis in the case of the food industry, because the fundamental attributes of the food we eat today are only slightly different from what humanity ate a hundred years ago. For this reason, previous researches e.g., [21,32,35] have shown that organizations that do not use current external knowledge, do not have the means to be effective competitors. Therefore, companies often establish collaborations with other actors in the supply chain, such as suppliers, customers in the public and private sector, competitors, universities, professional and sector associations for self-improvement [1]. Suppliers and industry associations are an important source of knowledge, and collaboration is usually an opportunity to ge<sup>t</sup> more information about the competition. At the same time, consumers and universities are valuable sources of knowledge as they know the product better than the manufacturer [36].

However, the increase in external collaborations entails higher costs for the company, while the advantages of this open innovation system may only be observable in the long term [37], connecting this scheme to strategic thinking. The costs of selecting suitable partners are also likely to increase, leading to the need for supplementary resources. In addition, companies must pay attention to balancing external and internal research activities as otherwise, they will have negative consequences for their innovative performance [11] and their costs of coordination, managemen<sup>t</sup> and control of partner activities involved will increase [38]. Furthermore, in transition to an open research and development system, the company's internal research and development structure requires a fundamental transformation, as its role shifts "from the generation of discovery as a primary activity to the design and integration of systems as a function key" [26].

Open innovation concept has sparked the interest of both academics and practitioners, as illustrated by the multiple studies on this topic. In this vein, many debates have developed in managerial literature and several studies have investigated the innovating company's methods of accessing knowledge from external channels. Although these empirics and theories touch and sometimes describe the different ways of information acquisition for certain types of innovation, they do not develop applicable information search strategies. For illustrating this shortcoming, we summarize the main findings of several papers from the last one and a half decade.

In 2006, Cassiman and Veugelers [39] analyzed complementarity between internal research, development and external knowledge acquisition, suggesting that they are complementary innovation activities, but the degree of complementarity is sensitive to other elements of the firm's strategic environment. In the same year, Emden and colleagues [40] developed the process theory of partner selection for collaboration, using a theory development approach. Laursen and Salter [11] studied the effect of open research strategies with other companies that rely on the product life cycle theory. They used data from the UK's Innovation Survey and found that the more important the innovation is, the deeper the influence of external research on the company's innovative performance will be.

In 2007, Perkmann and Walsh [41] analyzed links between university and industry and they have emphasized how important the collaboration is between companies and the scientific sector. Subsequently, Knudsen [36] analyzed the employment of inter-organizational relationships in product innovation by European manufacturing in the food sector. It appeared that all the companies interviewed had collaborated with at least one other organization in order to increase their production. He also has found that these companies preferred to collaborate with customers, suppliers and competitors rather than with public/private research organizations or consultants, preferably in the phase of initial research rather than during the development of the innovations acquired.

Gumusluoglu and Ilsev [42] found that transformational leadership positively affects organizational innovation in small businesses.

In 2010, Dahlander and Gann [43] studied the advantages and disadvantages of innovation in the procurement and acquisition processes, creating a guideline for the development of the research agenda. In the same year, Zhou and Wu [44] supported the argumen<sup>t</sup> that technological capability has an inverted U-shaped relationship with exploration. That is, a high level of technological capability prevents exploratory innovation. Capitanio and co-workers [45] stressed that the ability to build relationships on product markets is a key factor in successfully developing and introducing product innovation.

In 2013, Xiaobao and co-authors [21] analyzed the e ffect the size of a company has on innovation, using data from a survey of 420 innovative SMEs in China from the point of view of social networks. Garcia Martinez and collaborators [34] studied the impact of companies' open behavior on their performance, considering the breadth and depth of collaboration. Subsequently, Bayona-Saez and colleagues [46] wanted to extend our knowledge on the relationship between open innovation and the company's innovative performance. In particular, the authors aimed to determine whether the benefits of open innovation practices are di fferent for food businesses than for other industries.

Ferraris, Santoro and Dezi [47] verified the positivity of using moderate external knowledge. This means that branches with superior Knowledge Management are more capable of managing external information, improving their innovative performance. Giacosa, Ferraris and Monge [48] in their study concerning an Italian company, stated that the company's competitiveness is the result of a balanced managemen<sup>t</sup> of innovation and tradition.

In 2019, Török, Tóth and Balogh [49] studied how external impulses and internal knowledge resources influenced the development of innovation in the Hungarian agri-food sector, finding that tacit knowledge is more important than explicit knowledge.

Apparently, there are many studies that take into consideration the di fferent channels of information acquisition and their methods of attainment. Although in the field of open innovation there are di fferent research findings and empirical results, we could ge<sup>t</sup> convinced that there were no investigations which linked the type of innovation with the search strategy.

Understanding these dynamics is therefore essential for the development of specific programs for the promotion of each type of innovation.

Table 1 comprises all the studies mentioned in the section.





### **3. Data and Empirical Strategy**

The empirical analysis in this paper is based on data from Community Innovation Survey (CIS-2012) [24], filtering for the Nace. Rev 10-12 categories (food, beverages and tobacco industries—more detailed breakdown is not possible within this database). We use the openness of firms to European and global markets and continuous innovation activity as control variables. This is because the European food companies are mainly SMEs, and they usually do not have enough resources for doing their own serious R&D activities. However, the openness and past innovation activities force them to be innovative in the present.

This survey covers 6317 Hungarian firms that are distributed across all major sectors of economic activity. Out of them, there are 440 companies which belong to food, beverage and tobacco industries. The questionnaire includes three main sections: general information about the enterprise, type of innovation and source of information.

In particular, we have twelve types of innovation, which are divided into four groups according to the questionnaire (Table 2).



In addition, we considered ten sources supporting the innovation activities which, by factor analysis, are being grouped into three major sets: business, science and profession (Table 3).


### **Table 3.** Innovation activities sources.

In order to understand what is the possibility of choosing a specific source of information regarding the type of innovation, and then, to verify how much of a source is selected in di fferent types of innovation, the data collected through the questionnaire were processed in three distinct phases, using the STATA 16.0 integrated statistical software. In the first phase, the descriptive analysis of the data were conducted in order to define the socio-demographic characteristics of the sample; in the second

phase, a probit regression was made between source of information, type of innovation and two control variables (ongoing innovation and openness to European and world markets); in the final part, after doing a Factor Analysis in order to group information sources into three large groups, the three new variables were used as dependent variables for an OLS regression with type of innovation and control variables.
