Antonio Palmiro Volpentesta
email: antonio.volpentesta@unical.it

Alberto Michele Felicetti
email: alberto.felicetti@unical.it

Nicola Frega
email: nicola.frega@unical.it

Collaboration Networks for Information Empowerment of Food Consumers

Keywords: food information, food consumer's empowerment, Internet-of-food, collective awareness, consumer's perceived value

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Today's food consumers need more reliable food information to be aware of the consequences of choices that they make along their food consumption activities.

Food information is traditionally provided by food producers and/or distributors through mass media and labels on-package. This way of food information provision is producer-centered since it tends to satisfy companies marketing-related objective, rather than consumers' information needs.

On the other hand, food consumers are increasingly demanding high-quality, safe and healthy food [START_REF] Volpentesta | Research Investigation on Food Information User's Behaviour[END_REF], as they are more and more engaged in food related discussions in social networks with other consumers. Moreover, they interact with food related business (food producers, distributors, third parties) in loose, open and flexible ways, continuously searching for food information transparency along food supply chains.

Recently, the convergence between "Social Networking Platforms" and "Internet of things" opened the way for a new generation of context-aware systems [START_REF] Atzori | The social internet of things (siot)-when social networks meet the internet of things: Concept, architecture and network characterization[END_REF]. The increasing availability of sensors and mobile devices represent the technological layer of a cyber-physical system that is able to provide context-based services to people in a smart environment [START_REF] Ammirato | Bank branches as smart environments: introducing a cognitive protection system to manage security and safety[END_REF].

New smart food applications and devices (e.g. "food scanner" or "food sniffer" for food analysis) suggest that the time is ripe to conceive new approaches to food information management that would empower consumers and be more responsive to their information needs.

The main challenge is to exploit the potential of collaboration through ICT networks to create a collective intelligence [START_REF] Volpentesta | Critical thinking and concept design generation in a collaborative network[END_REF]. The basic assumption is that "a large number of individuals tied in a social network can provide more accurate answers to complex problems than a single individual or a small group" [START_REF] Surowiecki | The Wisdom of Crowds[END_REF]. Collective knowledge represents an interesting issue that has been addressed in many research fields [START_REF] Ammirato | A methodology to support the adoption of IoT innovation and its application to the Italian bank branch security context[END_REF] [START_REF] Yang | Swarm intelligence and bio-inspired computation: an overview[END_REF]. The food sector represents another promising application field. In fact, technological advances in the so-called Internet of Food (IoF), make possible the development of a new generation of intelligent food services [START_REF] Volpentesta | Intelligent food information provision to consumers in an internet of food era[END_REF] able to provide context-based food information to consumers. In particular, IoF can be viewed as a network of food smart object augmented with sensing, computing (e.g. timetemperature indicators, sensors to detect food spoilage or bacterial infection, and so on), allowing a fast analysis of food items.

The consumer's opportunity to access to more food information and the capability to exchange opinions and information with other consumers, are gradually shifting the balance of the competition. The chance to collect more information makes consumers more powerful, giving them the opportunity to be aware of their food-related choices.

In particular, consumers' information empowerment, i.e. the consumers' improved capability to access, process and share food information [START_REF] Volpentesta | Intelligent food information provision to consumers in an internet of food era[END_REF], is gaining more and more importance. In fact, the possibility for consumers of "being in control" and "of being smarter" is crucial to carry out better food-related decisions during their food consumption activities.

This "consumer-centric" perspective of food information management (FIM) opens new ways in offering value to food consumers, driving some tech companies to enter the food information market at full steam. These companies offer the opportunity to exploit benefits new technologies, providing consumers to manage food information more responsive to their requests for information. In particular, these emerging trends make possible to design new collaboration networks where consumers and stakeholders of the food supply chain interact to create and share valuable and reliable food information.

This work aims to propose a model of a collaborative network where members manage food information in a collective and distributed way (in terms of information generation, validation and delivery), leveraging on open food data, IoF-based devices, and cloud/app-based solution. The model is a tool for researchers and practitioners, to explore a pathway towards collective food knowledge and information empowerment for a food consumer community.

Moreover, we propose an analysis of the outcome value of this new collaborative process of FIM under a consumer perspective that considers an evaluation of benefits and costs a consumer perceives as the result of the collaborative process when compared with other available FIM processes.

Modeling Food Information Empowerment Network

In a consumer-centric perspective of food information management, we envisage a new form of collaboration as letting consumers to be more aware during their food consumption activities. This collaboration can leverage on both potentialities of new smart-food technologies in determining food properties (from a physical, biochemical, and microbiological point of view), and a cooperative process in fostering collective food knowledge awareness. The rationale is to let a consumers' community have the opportunity to create and share reliable information about food.

In the emerging network, called Food Information Empowerment Network (FIEN), empowered food consumers collectively manage (generate, verify/validate, and distribute) information about several aspects (e.g. safety, quality) of food products and processes. In what follows, we view a FIEN as a collaboration network and we model it by describing its main endogenous dimensions, as suggested in [START_REF] Camarinha-Matos | A modeling framework for collaborative networked organizations[END_REF].

Structural Dimension

This dimension deals with the composition of a FIEN in terms of its constituting elements and roles performed by these elements. We identify the following actors and roles in a FIEN:

• Simple Consumer (SC): a community member requesting for information about a specific food item performance [START_REF] Peri | The universe of food quality[END_REF]. • Empowered consumer (EC): a community member who provides (in an implicit or explicit way) some measurements of food item characteristics by means of a smart food thing (i.e. a device able to catch some signals from food, like infrared emission, volatile compounds, etc.) and other descriptive data about a food item (e.g., date and place of production, batch number) • Information Broker (IB): an intermediate agent that processes requests from SCs, receives and controls data acquired by ECs and provide SCs with understandable (i.e. human-readable) food information. • Food Analyzer (FA): an agent able to perform a diagnosis on a food item. It could be assisted by a software tool applying some intelligent methods (e.g. statistical methods, machine-learning based techniques) to determine food item characteristics. • Food Ledger Manager (FLM): a food database manager that receives and organizes data that comes from the Food Analyzer. Moreover, it provides results to query formulated by a Collective Challenge Solver. • Collective Challenge Solver (CCS): an agent playing the fundamental role in the collective process to generate reliable food information. It leverages on a food knowledge base and collectively reliable criteria to find the value of the food performance p shared by all food items that possess the same identity properties i. • Network Authority (NA): an entity that is in charge to manage the governance of the FIEN. Referring to the collaborative process, it sets and manages the criteria adopted by the Collective Challenge Solver to generate food information. These criteria are based on a collective interpretation of food item characteristics, in order to determine to what extent information on food performances are reliable.

Functional Dimension

This dimension addresses the flows of operations (procedures, processes and methods) dealing with the operational phase of a FIEN. In particular, we focus on a collaborative process that allows the consumer community of a FIEN to create and share information on food performances related to some food items that belong to the same food class. The assumption that underpins the process model is that the reliability of food performance information can be derived from a collective interpretation of food characteristics information coming from measurements performed by smart food things. The process consists of the following activities : A.0.1 Collective Criteria Definition: NA defines the rules (e.g. methods, threshold values) for collective interpretation of food items' characteristics.

A.1.1 Request formulation: SC needs for reliable information about a specific performance p of a food item. SC makes a request r(i, p) to IB, where i is referred to a set of identity property values (food item descriptive data), while p represents a specific a performance SC wants to know (e.g. safety). SC transmits request data to IB through his/her own handheld device.

A. 1.2 Request Acquisition: IB verifies if the request can be instantly satisfied by querying a database containing data on challenges already solved. Otherwise, IB send a new challenge to the CCS.

A.1.3 Challenge formulation: Do food items with the same identity value i have the same value of performance p? CCS identifies food characteristics that are needed to determine p, by leveraging on a a food knowledge base. CCS formulates the query q(i, c) to FLM in order to retrieve values related to food items sharing the same identity value i.

A.1.4 Ledger Answer: FLM also provides results to the query q(i, c) formulated by CCS. Query results consist in a set of values of characteristics c for food items sharing the same identity value i;

A.1.5 Challenge Solution: CCS analyzes data provided by FLM and verifies whether the value of p can be calculated by leveraging on collectively reliable criteria established by the NA. If so, CCS determines p. Ther result is sent to FIB that is in charge to set-up the solution in a format understandable for R. Moreover, the result is stored in the solved challenge database. Otherwise, it notifies to IB that the challenge could not be resolved.

A.1.6 Results provision: IB receives challenge results from the CCS and provides results to SC in a human-readable form.

A.1.7 Results acquisition: SC receives Food information. A.2.1 Food Data Acquisition: EC scans a food item by using his/her smart devices in order to acquire food properties data . In addition to these data, EB also provides descriptive data on food identity (id) A.2.2 Food Data Validation: IB acquires and verifies food data as well as other interaction context data catched through environmental sensors. It passes the whole data to FA.

A.2.3 Food Data Analysis: FA performs a diagnosis of the food item in order to determine the value c of some food characteristics. In particular, it applies some intelligent methods (e.g statistical methods and machine learning-based approaches) able to deduce food characteristics. The pair (id, c) is sent to FLM.

A.2.4 Food Data Storaging: FLM collects and organizes data, namely the pair (id, c), in a database.

In figure 1, we provide a BPMN representation of the collaborative process, highlighting the contact points between consumers and the back-end process.

Componential Dimension

Intangible and tangible resources of a FIEN (e.g. information, knowledge, software, hardware) are taken into account by this dimension. In [START_REF] Volpentesta | Organizational and Technological Aspects of a Platform for Collective Food Awareness[END_REF] a three-tier conceptual architecture for the FIEN has been proposed. This architecture consists of: 1) an interface layer that enables the user to entry, fetch and process food data; 2) a logic tier for food data computation and analysis; and 3) a storage layer concerning with food information storage and retrieval. More details about a FIEN technological are provided in [START_REF] Volpentesta | Organizational and Technological Aspects of a Platform for Collective Food Awareness[END_REF].

Behavioral Dimension

Along this dimension, the focus is onh policies, principles and governance rules influencing the behavior of the FIEN members. The NA has a crucial role in managing network members' behavior. In particular, the NA plays a dual role as:

• technological intermediary, whose role is to provide the ICT platform including, front-end and back-end software, security and communication; • guarantee authority, that is responsible for the "ethical code" and "behavioural rules" for FIEN members. Moreover, it has an important role in attracting and selecting new members of the FIEN. It provides a comprehensive governance role, ensuring behavioural correctness of members' interactions in the FIEN. In a FIEN, food information trustworthiness depends on the consumers' reliability on instrumental measurement, food data analysis techniques and security of information flows. The NA takes on the burden of building consumers' trust by defining the following aspects:

• cooperation agreements: the NA must guarantee the effectiveness and correctness of the measuring instruments adopted by the ECs. Therefore it should make collaboration agreements with smart things producers in order to define a set of certified and guaranteed FIEN platform tools. • obligatory behavior: define rules and principles that are mandatory to be followed inside the FIEN. This comprises the definition of the collectively reliable criteria that are applicable to generate new food information, the definition of authorization profiles within the platform as well the definition of operational and managerial processes within the FIEN. • constraints and conditions: ensuring transparency throughout the whole process of generation and sharing of food information and define the degree of restriction on the use of intellectual property of FIEN.

Consumers' Value of FIM

The multifaceted nature of food consumption makes the outcome of a FIM process extremely valuable to a consumer [START_REF] Volpentesta | Research Investigation on Food Information User's Behaviour[END_REF]. From a consumer perspective, the FIM outcome value is the ultimate trade-off between benefits and costs a consumer perceives as the result of his/her interaction with the FIM process and the involved community. Benefit/cost assessment varies from a consumer to another as it is affected by consumer's attributes (knowledge, food related values, experiences, attitudes). In what follows, we present a FIM outcome value framework consisting of the following factors and components: Utility benefits. They refer to the overall utility of the information that a consumer acquires when interacting with a FIM process. Here, we refer to this utility as a measure of the impact on consumer's food-related decision-making, brought about by the variation that occurs in consumer's assessment of a food product/service, because the interaction with the FIM process. Two entities play a crucial role in determining utility benefits: a) Content: a set of information 'cues' that are exchanged between the consumer and the FIM process during the interaction. They may be declarative, as they describe and specify food quality attributes, or procedural, as they concern with food consumption activities. The main value factor is the content relevance that establishes how much impact the is exchanged has on a consumer's food decision process. Content relevance sub factors are topicality, accuracy, depth, scope, clarity, organization, and format of the information exchanged [START_REF] Nocella | Consumers' attitudes, trust and willingness to pay for food information[END_REF]. b) External Context: a set of characteristics of the situation (food presence, place, time, food consumption activity, social relations, etc.) in which the consumer interacts with the FIM process. The main value factor is situation relevance that establishes how much impact the "how", "when", and "where" of the interaction has on a consumer's food decision process [START_REF] Volpentesta | Intelligent food information provision to consumers in an internet of food era[END_REF]. It may be defined as the extent to which information content is required to be specified to practical matters of the current situation. Sociocultural benefits. They concern with the satisfaction of consumer's sociocultural needs through the consumer participation at the FIM community formed by other consumers and food chain stakeholders. Participation at the community brings a wealth of food cultural knowledge (including beliefs or practices), social norms, food literacy, as well as challenges that occur in consumer daily lives, e.g. credibility of food information sources. Main value factors are: a) Sociability of the FIM community. It affects satisfaction of consumer needs of personal connectedness and relationships with other consumers and food chain stakeholders; b) Trustworthiness of information sources in the FIM community. It affects consumer assessment of the reliability of the information content that FIM provides; c) Transparency of the FIM process. It affects satisfaction of consumer needs of information empowerment and information asymmetry reduction [START_REF] Verbeke | Impact of communication on consumers' food choices[END_REF]. Costs. They refer to consumer's physical and cognitive efforts that a consumer needs to interact with the FIM process or community, such as time, inconvenience and comprehension of food information. These efforts may be of two types: • Personal burden. It refers to the amount of efforts a consumer has to put into interacting with a FIM process to get food information for personal use; • Collaborative burden. It is the overload of efforts due to the participation to a collaborative FIM process. In what follows, we apply the above framework to highlight main factors of the outcome value of FIM processes belonging to three broad classes: conventional FIM, social FIM, and collaborative FIM based on a FIEN. In conventional FIM, consumers obtain food information through traditional channels like labels, radio, newspapers and television. The information content provided is:

massive and generic, i.e., unable to meet specific consumer needs, -limited, in terms of information amount and time-space availability, -biased, as it is often directly provided by manufacturers or sellers who might emphasize some food properties, due to business oriented purposed. As a result, consumers are sceptical, and they often ignore the information provided or perceive it as misleading. Moreover, they express concern about the information truthfulness and do not perceive any social benefits [START_REF] Fenko | Overcoming consumer scepticism toward food labels: The role of multisensory experience[END_REF].

Social FIM uses internet-based technologies (the so-called web 2.0, including mobile apps and social media) for sharing information and overcoming some limitations of conventional channels [START_REF] Wendel | Situation variation in consumers' media channel consideration[END_REF] [START_REF] Corvello | Factors affecting the utilization of knowledge acquired by researchers from scientific social networks: An empirical analysis[END_REF]. In social FIM, consumers group together in communities around a collective goal and contribute to the creation and distribution of food information, but they rely on third parties (e.g., forum moderators, food bloggers, recommender systems) that control the FIM process [START_REF] Bialkova | Standing out in the crowd: The effect of information clutter on consumer attention for front-of-pack nutrition labels[END_REF]. Consumers may access:

a larger amount of information; -tailored information, according with consumer's profile and use; -in-time and in-place information. However, beyond these advantages, utility and sociocultural benefits remain limited by the lack of a verification and validation of the food information shared by consumers [START_REF] Saracevic | Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance[END_REF].

In collaborative FIEN-based FIM, social and IoF technologies enable a cooperative process focused on promoting collective food knowledge and awareness. Through such a process, food consumers may share food information originated from scientific instrument measurement of food properties. This type of FIM is still to come and it has been envisaged in this paper.

Even if it results in collaborative costs, as it engages a consumer in providing or validating information, collaborative FIEN-based FIM provides significant utility and social benefits. Food information based on scientific data, coming from in-context smart food things, could assure higher accuracy and depth, more correct scope, and specified for in-context food items. In addition to that, the collective validation process could enhance consumer trust in food information sources.

Table 1 summarizes significant characteristics affecting the consumer's value of the three types of FIM above discussed.

Conclusions

Conventional ways to provide food information have proved to be inadequate to satisfy today's food consumers' needs. In this direction, we have introduced a collaborative approach that offers to food consumers the opportunity to be more food aware and to carry-out more informed food-related decisions. This approach relies on a collaboration network where consumers manage food information in a collective and distributed way. The resulting collective food awareness would contribute to make vanish many "problems" linked-up with information asymmetries, driving consumers towards a greater consciousness about environmental, social and healthrelated issues. Moreover, we highlighted the consumer's value of this new way of food information management by making a comparison with current ways of food information provision to consumers.

Figure 1 .

 1 Figure 1. A BPMN representation of a collaborative process in a FIEN.

Table 1 .

 1 FIM valuable components to assess the consumer's value of three types of FIM.

	Valuable	Conventional	Social-based	Collaborative-based
	Components	FIM	FIM	FIM
	Content information	-static, limited in amount, massive, and generic	-consumer tailored	-consumer tailored -based on scientific data
				-in-time and in-place
	Contextualized provision	-no contextualization	-in-time and in-place	-tailored on consumer's food activity -specific for in-context
				food items.
	Community engagement	-consumer's information understanding and contextualizing	-seeking and evaluating channels and sources	-IoF device interaction -reduced cognitive effort for consumer's tailored


Paula Urze

João Rosas
email: jrosas@uninova.pt

Alexandra Tenera

Luis M Camarinha-Matos

Open Innovation Practitioners Mindset on Risk

Keywords: Open Innovation, Risk Mindset, Collaboration

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Open innovation (OI) is generally seen as a key collaborative strategy that organizations can use to evolve and keep up with the (emergent) market shifts and disruptive technological development. According to several studies, the advantages of OI over lesser open models of innovation have been proven [START_REF] Chesbrough | Managing Open Innovation[END_REF][START_REF] Felin | Closed or open innovation? Problem solving and the governance choice[END_REF]. Despite the diversity of theoretical and empirical research on OI, there seems to exist, in the prevailing discourse of scholars and practitioners, the tendency to exclude the less successful experiences that occur during the innovation process. But such (in) visible side of OI must also be researched, as it is crucial to integrate into the interpretation of OI, not only the advances, successes, and advantages but also the setbacks, failures, and constraints. Such setbacks can be seen as the unintended consequences of the innovation process [START_REF] Godin | Introduction: innovation -from the forbidden to a cliché in Benoît[END_REF]. But OI requires learning both from successes as well as from failures [START_REF] Godin | Introduction: innovation -from the forbidden to a cliché in Benoît[END_REF][START_REF] Sveiby | Unattended consequences of innovation n Benoît[END_REF].

From an epistemological perspective, more than a final result, it is the trajectory that must be understood to accurately know the OI concept and the elements that systemically combine to reach it. There are certainly contingency factors in the process, but there are also structural factors that underlie the social construction of OI. What paths have been taken? What mishaps have been overcome? What choices have been made? Therefore, much more research analysis on this topic is needed at the level of academia. The main goal in this paper is to analyse the mindset of OI practitioners regarding risk and other factors, e.g. trust, incorporating both positive and negative elements of OI in our research. For this, we aim at understanding the narratives and practices of OI actors regarding these elements. Despite recent research efforts, risk in OI lacks a deeper and more systematic analysis [START_REF] Rosas | Risk assessment in open innovation networks[END_REF][START_REF] Rosas | Exploratory study on risk management in open innovation[END_REF].

We hypothesize that the apparent lack of risk assessment research in literature might be associated with the way the OI concept is usually portrayed, with more favourable narratives in which failures and setbacks are less likely to arise. For this purpose, we proceed to an exploratory analysis of the elements underlying the mindset of OI practitioners, starting with literature analysis. For this, Text Mining and Sentiment Analysis were applied to web documents containing interviews addressing risk in OI, which were available online during the year 2018. Afterwards, semi-directive interviews were conducted with OI practitioners (at the level of company managers) and we finish with results analysis and synthesis.

Background

Considering the scope and depth of the topics involved in open innovation, in this work we concentrate efforts focusing on the aspects that are mostly relevant to the defined research goals. In this way, we start by revisiting the concept of OI, then moving on to a perspective around the mindset that characterize the practitioners. We conclude this section with a synthesis concerning risk in OI.

Revisiting the Concept of Open Innovation

Open innovation is already a recurring strategy used by organizations to help them keep up with market changes and technological development. According to Chesbrough [START_REF] Chesbrough | Everything You Need to Know About Open Innovation[END_REF], OI is seen as a more profitable form of innovation, because it can reduce costs, accelerate time-to-market, increase product variability on the market "and create new revenue streams for the company"

In addition to the initial concept of OI as proposed by Chesbrough, OI also appears in the discourse of several scientific areas, generally taking an even further positive tone. The term acquired its more positive view due to its instrumentality from the social, political and material perspectives [START_REF] Godin | Introduction: innovation -from the forbidden to a cliché in Benoît[END_REF]. This is what we can commonly refer to as the bright side of OI. Although tendentially less visible, while not less relevant, it starts becoming increasingly recognized that the less bright side is also a part of the innovation dynamics, i.e., the other side of the same coin [START_REF] Vinck | Learning thanks to innovation failure in Benoît[END_REF][START_REF] Martin | Innovation Studies: An Emerging Agenda[END_REF].

In this sense, organizations, in general, tend to highlight their success cases and hide the mishaps, setbacks and failures during their OI projects. Whenever these are reported, they are shown as sporadic episodes, without meaningful relevance in the innovation dynamics. But, to adequately tackle the OI concept, it is crucial to incorporate those less publicizable elements. Such critical aspects are of paramount importance to innovation in general, and to OI in particular, as difficulties, and often the failures, frequently allow reaching increased achievements in knowledge and results. Then, we need to focus also at the Innovation practitioners. To walk the path of OI, practitioners need to aggregate several OI compatible attitudes, allowing them to be able to benefit from successes and learn from failures.

The Open Innovation Mindset

When Chesbrough colloquially placed the question "What is open innovation?", he answered that it mainly means having a much more open mindset and much more engaged with both the inside and outside world [START_REF]A growth strategy for the digital age: the key is Open Innovation[END_REF].

Furthermore, as mentioned by Björling in [START_REF] Björling | 5 key steps to creating an innovation mindset[END_REF], participating in OI requires having some important attitudes, namely, that we must be "open to change", because the world is constantly changing. We need to "embrace creativity", recognizing that instead of "management processes" and "organizational structures," the starting point of OI is creativity, which requires a certain type of culture and organizations to make it possible. Another important facet is to have the ability and courage to "think big" and proceed beyond current norms and truths in the marketplace. This implies extending beyond normal/ordinary thinking and analysis. The author also states that OI implies to "show courage" to constantly rethink how things can be done. Finally, he argues that in OI, we must "think and act quickly" since innovations typically comprise agile processes.

In this regard, mindset can be understood as having a way of thinking or having psychological predispositions, which are tied to corresponding attitudes, ideas, beliefs and patterns of behavior. As such, it is scientifically important and strategically interesting to determine and accurately characterize the OI practitioners' mindset.

A Risk Perspective in Open Innovation

To properly establish an OI risk perspective, it is important to start by contrasting the two innovation models, internal and open.

Within company boundaries, innovation projects include practitioners who are subjected to management and supervision processes, in which risk control mechanisms can be used, allowing to deterministically anticipate and circumscribe contingencies and failures. Therefore, the "internal" innovation, typically supported by Research and Development Innovation (RDI) management systems and projects, has been already consolidated [12,[START_REF]Development and Innovation (RDI): Requisitos de um projeto de gestão IDI / Requirements for RDI project[END_REF] as well as the internationally consolidated risk assessment methods and tools for risk evaluation [START_REF]Risk management -Risk assessment techniques[END_REF] within a company. In short, despite its intrinsic uncertainty and significant rate of products failures, internal innovation can be handled within management practices.

On the other hand, OI is characterized by its practitioners' autonomy in an environment which favors creativity, knowledge sharing, collaboration, and trustworthiness. As such innovation process are much more complex and much more unpredictable. Furthermore, due to partners' self-interests, relatively frequent conflicting goals and lack of preparedness, there is a growing risk of failures. On each failure, both resources and time are lost. For instance, intellectual property can be taken away and used by other actors for their benefit. In this sense, risk assessment is considered far more challenging in OI contexts. Furthermore, as mentioned in [START_REF] Castellion | Perspective: new product failure rates: influence of argumentum ad populum and self-interest[END_REF], the failure rate of a new product's development is around 35%, and this just considering the products that could reach the market phase. Other sources [START_REF] Simoons | The 80% rule of business partnerships[END_REF][START_REF] Fisher | Why most innovations are great big failures[END_REF][START_REF] Martinez | Open innovation in the food and beverage industry[END_REF] report that failure rate can go up to 80% if the failures in the preliminary stages are also considered. Therefore, although risk models are difficult to apply in OI, their effective use could have significant impacts on OI outcomes.

Exploratory Study

In this chapter, we describe the methodological approach applied to characterize the mindset of practitioners in OI. The approach includes two distinct methods.

As illustrated in Fig. 1, the first method involves Text Mining and Sentiment Analysis performed on interviews recorded in on-line documents provided throughout the year 2018. The second method consisted on semi-directive interviews conducted CEOs and CTOs, who are OI practitioners. Sentiment Analysis is a widely used method particularly in the Social and Behavioral Sciences, as well as in Web-based services for characterization of online information and users from Online Social Networks [START_REF] Liu | Sentiment Analysis and Subjectivity[END_REF][START_REF] Farhadloo | Fundamentals of sentiment analysis and its applications[END_REF][START_REF] Kaushik | A Study on Sentiment Analysis: Methods and Tools[END_REF]. The semi-directive interview can be seen as a methodologically adequate technique to more deeply enquired entrepreneurs and managers regarding OI, as it allows collecting information of qualitative nature, through interaction with interviewees, in which the discourses, feelings, emotions, and contexts intersect together. Therefore, sentiment analysis of online interview documents (extensive analysis) combined with face-to-face interviews results in a methodological approach that enhances the interpretation framework.

Exploratory Analysis of Online Documents

Research Method Description

The research method followed in this phase involved several steps which are represented in Fig. 2. Firstly, we searched documents containing interviews regarding OI. Then these documents were downloaded to a local repository to be further checked and analyzed, and in the final steps subject to Text Mining and Sentiment Analysis. The interviews were collected from online documents made available during the year 2018, with the search engine, "Google Search". We searched for documents of that year that contained the terms "open innovation" and "interview." This search performed in step (a) resulted in 18,093 URL links of potential interview documents.

In the next step (b), each site was visited, and its content downloaded and recorded in a local file. For this part, we developed a small Java application integrating the JSoup library [START_REF]jsoup: Java HTML Parser[END_REF] for extracting the documents' contents. However, after inspecting the content of some documents, we found that many of them did not correspond to interviews. Thus, it was also developed a filter for identifying only valid interview documents. This filter is composed of a regular expression which tracks sentences ending with a question mark. The rationale is that if each document arose as a result of a search with the keyword "interview," the odds of being a document with an interview is higher. If the document contains questions, then it must be an interview. After inspecting a sample of the downloaded documents, we verified that documents with at least five questions were interviews. To reduce false positive documents, we collected documents with at least ten questions inside. The next step included the elimination of "stop words" and the stemming of the documents' terms (step (c)) [START_REF] Aggarwal | A survey of text clustering algorithms[END_REF]. During the stemming transformation, if each yielded word did not belong to the dictionary, then we considered the original word, as we needed valid words for the sentiment analysis part. The remaining steps are synthesized in Table 1.

In steps (d) and (e), we relied on the "R" environment [START_REF]R: A Language and Environment for Statistical Computing[END_REF], for its good features on Text mining, including Sentiment Analysis. With R, we proceed to the determination of the document-term matrix, but its transformed form, using the "inverse document frequency" formula (Tf-idf), which reduces the importance of terms that are too common across the documents. Phase (e), corresponding to the sentiment analysis, was also performed within the R environment, using the scripts illustrated and described in [START_REF] Silge | Text mining with R: A tidy approach[END_REF]. Application of sentiment analysis. [START_REF] Rosas | Risk assessment in open innovation networks[END_REF] Plot sentiment analysis chart.

Obtained Results

Fig. 3 presents the chart obtained during sentiment analysis. An initial general interpretation suggests that there is a generally positive attitude towards OI. On the positive side, such words as "capability", "patient", "award", "sustainability", "flexibility", "talent", "master" and "skill" match with the more frequent sentiments detected, followed by the term "trust", which corresponds to an important sentiment in collaboration. The other terms appearing on the positive side, namely "skills", "agility", "competitive", "intelligence" and "creativity" allow us to, in principle, confirm the kinds of attitudes, or mindset, related to open innovation, as portrayed in section 2.2.

On the negative side, the sentiment analysis highlighted several terms that also meet the topics addressed in this work. The term "risk" is the most important element appearing on the negative sentiment analysis, which raises a question by itself. Why is risk the most cited element from the interviews? Considering that Sentiment Analysis of these interviews yields a more positive than negative tone, perhaps OI participants feel that in spite of the dangers, OI is a strategy that is worth to take. This result supports the argument that risk is an intrinsically major concern of OI. It also might mean that risk in OI deserves much more attention by researchers. Other terms that may also be tied to risk are "disruption" and "conflict", which are concerns that are known to increase the risk of Open Innovation partnerships failure.

The term disruption is interesting in this regard. Costumers benefit from "disruptive technology", which drive the creation of innovative products, so disruptiveness is seen a positive element. Researchers of innovation, and similarly in other areas, like to conceive innovation as a "disruptive phenomenon", so disruptiveness is seen in a more positive mood. However, companies may perceive "disruptive" events with more cautious eyes. We would say that "disruptive technologies" or similar situations may frequently cause both industrial and market shifts that companies may find threatening and detrimental for their business models. Therefore, from the companies point of view "disruptive" cases might be seen as negative.

Interviews with OI Practitioners

Methodological Steps

In the scope of empirical research, we complementarily conducted semi-directive interviews with three CEOs and two managers from four information technology (IT) companies. The interviews were carried out during March and April 2019. We defined several questions oriented to OI practices in companies' contexts (interview script). The collection of qualitative information from the interviewees' discourses allowed to complement the sentimental analysis and the construction of an interpretative framework based on the experience of OI practitioners. The way to reach companies was based on the technique of "snowball", taking as a starting point a company, whose practice of OI is already a case of widespread scientific dissemination [START_REF] Urze | Knowledge transfer assessment in a co-innovation network[END_REF].

Our goal was to obtain more in depth, rather than extensive, information regarding OI. Qualitative questions constitute a reflexive process that gives density to the analysis and allows one to gauge the "microscopic" details [START_REF] Geertz | The interpretation of cultures: Selected essays[END_REF] from the understanding of OI practitioners.

Obtained Results

Based on the testimonies collected from the selected OI practitioners, we prepared a content analysis grid for the interviews, covering a set of categories considered relevant for our research aims, namely, practices, risk, trust, contracts, and partnerships. These categories were empirically chosen, as they were recurring elements in the interviewed practitioners' discourses. In each of these categories, we included the recording units of the various interviews [START_REF] Bardin | Análise de conteúdo[END_REF], which we interpreted as negative or positive discourses, and we converted them into enumeration units based on the occurrence of the records in the respective categories (Table 2). From weighting them, we may conclude a more positive than negative tone considering OI.

Overall Sentiment Positive

In the remaining of this section, we present the more in-depth and richer insights provided by the interviewed OI practitioners.

Practices and Partnerships

From the analysis of the interviews, we obtained relevant information that allows deepening the aspect regarding the mindset of the participants in open innovation. One of the interviewees mentions that "OI is a virtuous concept, enabler of various businesses, but that from concept to practice goes a great distance." He added that "we need to have a mindset for open innovation". There is a clear idea that practicing OI requires a specific form of thinking and a particular attitude. It means accepting the risk, vulnerability, and uncertainty. OI is, in general, seen from a positive perspective, "several initiatives fit into open innovation. We have an area dedicated to identifying possible partners, whether national or international. The fact that we open and share with other companies we all win. We are not going from scratch; we start from a more advanced level". But it is also said that "they share little of the failures, which is inevitable and integral and essential for us to reach the part of success. It is a very great wealth. There are situations of contingency success".

Nevertheless, the experiences are successful; there are paths with advances and retreats until the final result. As the interviewee stresses, both negative and positive experiences are important and should be shared as they allow for joint learning to deal with the complexity of problems and solutions.

Risk, Trust, and Contracts

The OI practitioners' discourse points to different practices taking into account the structure of the company and the type of business. There is, however, a coincident narrative in the risk perspective. More risk requires more trust. And the idea that OI is risky is consensual. As the interviewee adds "there is no innovation without risk".

The words of the CEOs make us confirm that the relationship with the partners is essential to the existence of trust, as a way to minimize the risk. It is crucial that the development of the business takes place within a calculated risk framework, as one of the interviewees indicates, which allows for the establishment of commitments and guaranteeing the delivery of products/services by the expectations of the clients. "When we go with other companies, we have more risk, in principle. We have a very close relationship, but we have no control. It is important to look at the risk to do the math and say is between 40 and 50."

Risk arises with a double face, the challenge of the business itself, and the business in the framework of an OI paradigm, and the risk that must be safeguarded in the relationship with partners. One of the aspects that are repeatedly underlined is precisely risk acceptance. Nevertheless, there are nuances in terms of risk in the interviewees' discourse. Important in the study of trust is the issue of risk. The border of trust can be defined by mistrust. It is the development of trust that lays the foundation for trusting one partner more than another and deciding the level of risk to which it is prudent to incur.

There are referred forms of contracting that define rules of understanding between the partners and with the customers regarding the sharing of intellectual property. As one of the interviewees says "we sign agreements with all companies".

There is, however, the feeling that more important than these contracts are the behavior of partners and the building of relationships of trust. "We have a lot of memorandums of understanding (MoU) that are practically replicated from each other, but it's worth it." Another interviewee adds: "It is important to have trusted partners to embrace the projects." In this way, risk containment through formal means (contracts) can be used as a device not to eliminate its exposure to risk, but to reduce exposure. As mentioned before, there is no OI without risk. In conclusion, creativity and pro-activity are characteristics that, in one way or another, refer to: we must go further, search for new paths, new methodologies, new approaches, and not be afraid to embrace new projects, ideally with partners in which we have trust, but when it does not happen, the stance is to go to the market to look for new partners to respond to new business.

Discussion and Conclusions

Summary of the Developed Work

In this paper, we present a research approach for exploring the mindset for risk in OI The approach involved the application of two specific methods: the first one was based on the application of "text mining" and sentiment analysis performed on documents containing interviews, which were provided online during the year 2018. The second method consisted of semi-directive interviews addressed to OI practitioners.

Critical Analysis of the Application of the Two Methods

Based on the results of both research methods, it was emphasized that participants take a more positive than a negative stance towards OI, which is consistent with the theoretical and empirical manifestations mentioned in the literature.

From the analysis of interview documents available online, the positive of sentiment analysis includes the trust term, which represents a crucial attitude in collaboration and OI. The risk appeared as the term that most contributes to the negative side of the sentiment chart.

From the CEOs interviews, the main idea that stands out is a relatively strong will to engage in innovation, although with safeguards that each practitioner applies as a way to mitigate risk. But they embrace it, as one stated, "There is no innovation without risk".

These results give a better understanding of the main mindset, positive and negative perspectives, regarding OI, from which it was possible to determine that risk plays an important role. There is also an apparent dissonance between the importance of risk in OI and the emergence in their research by academics.

Therefore, the combination of both research approaches and the literature confirm the idea that entrepreneurs should display OI enhancer mindsets, which maximize the success, like being open, trust partners, taking risks, adapt to changes, challenge tradition, and collaborate.

As a final remark, despite the theoretical character associated with the OI, it is through the interpretation of social practices, incorporating positive and negative occurrences, that the very concept of open-innovation achieves greater conceptual maturity.

We identified potential lines of research for future work. One such line consists of exploring the trust versus risk dichotomy that was recurrent in the results provided from both research methods. Although a more challenging goal, we also aim to develop work to integrate the OI mindset concept in risk assessment and into management processes.

Fig. 1 .

 1 Fig. 1. Phases in our research method.

Fig. 2 .

 2 Fig. 2. Steps comprising the Text Mining and Sentiment analysis of interviews.

Fig. 3 .

 3 Fig. 3. Sentiment analysis plot

Table 1 .

 1 Description of Steps involved in text mining and sentiment analysis

	Inputs Steps	Description
	(0)	Keyword search: "open innovation" AND interview, for the year 2018
	(a)	Find documents with Google Search engine. Considering documents with
		at least ten questions.
	(1)	Set of URLs obtained with a SERP extractor.
	(b)	Small Java App with JSoup that visits each URL and saves its content in
		a local filesystem. Non-English documents are eliminated.
	(2)	Collection of documents in a folder named "Corpus".
	(c)	Elimination of Stop words and stemming. Stemming of each word is
		considered only if the transformed term is still a word in the dictionary.
	(3)	Collection of documents in a folder named "Corpus".
	(d)	Download corpus; DTM determination "tidytext" and "tm" libraries
		inside R environment.
	(4)	Matrix dtm_tfidf.
	(e)

Table 2 .

 2 Categories from the semi-directive interviews

	Open Innovation #Practices #Risks	#Trust #Contracts #Partnerships SCORE
	Positive opinions	8	8	4	5	5	30
	Negative
	opinions	4	5	2	1	2	14

Acknowledgments.

A special thanks to A-to-Be, Do iT Lean, Fordesi and MakeWise companies for the valuable contributions for the performed research. This work has been partially supported by Interuniversity Center for the History of Science and Technology (CIUHCT) (Portuguese FCT-PEST program UID/HIS/00286/2019) Center of Technology and Systems (CTS) -Uninova, (Portuguese FCT-PEST program UID/EEA/00066/2019), and UNIDEMI -Research and Development Unit for Mechanical and Industrial Engineering (Portuguese FCT-PEST program UID/EMS/00667/2019).