Marko Mäki
email: marko.maki@haaga-helia.fi

Ari Alamäki
email: ari.alamaki@haaga-helia.fi

Data Privacy Concerns Throughout the Customer Journey and Different Service Industries

Keywords: data, privacy concerns, customer journey, service industries

Data privacy concerns play a significant role during collaboration with customers in digital networks. An important element in collaboration between business partners is the utilization of customer data in common offering development, marketing, and service operations. The aim of this paper was to focus on data privacy in the context of the customer journey and in different service industries. Based on empirical survey data (n = 306), our findings highlighted the importance of recognizing phases in the customer journey and have drawn attention to information sensitivity in different service industries. Findings showed that customers' willingness to share data with different industries varied. Moreover, consumers' attitudes towards the use of their personal data varied at different phases of the customer journey. Our findings have emphasized the importance of information sensitivity in different phases of the customer journey, especially when designing a means for effective data utilization in collaborative networks.

Introduction

Creating a superior customer experience has become a dominant aim and stream of value chains and ecosystems. One important element in developing new digital services is the utilization of customer data in personalization, online service performance monitoring, and sales and marketing operations improvement [START_REF] Alamäki | Business Roles in Creating Value from Data in Collaborative Networks[END_REF]. While companies have created multichannel designs for customers and have become more data driven in their daily activities, customers, as the most important actors of business ecosystems, have become increasingly concerned about their privacy. Privacy concerns are prevalent, especially in online environments [START_REF] Ackerman | Privacy in e-commerce: Examining user scenarios and privacy preferences[END_REF][START_REF] Martin | The role of data privacy in marketing[END_REF] where customers and service providers interact in collaborative networks.

Many companies have highlighted the importance of customer experience [START_REF] Walter | Drivers of customers' service experiences: A study in the restaurant industry[END_REF]. Customer experience has become an important part of marketing and service development, and it is the basis of the emerging experience economy [see 4]. Successful attempts to build connection with customers and to ensure customer satisfaction and superior customer experience require the use of customer data. There are growing concerns among consumers about revealing personal information, and many are dissatisfied with the way in which some organizations collect and use their information. This may pose a major problem, hampering growth in some areas, such as e-commerce [START_REF] Malhotra | Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model[END_REF]. In general, digital servicescapes and markets pose unique information privacy threats due to two-way communications possibilities [see 5]. Moreover, new General Data Protection Regulation (GDPR) regulations pinpointed data privacy issues when the legislation was enforced within the EU in spring 2018. New regulations have underlined the importance of customer experience in a service context The GDPR regulations has two major aspects related to use of data, the need for permission for the customer data utilization [START_REF] Kolah | Unlocking the power of data under the new EU general data protection regulation[END_REF] and the more general need for companies to become more customer driven when utilizing data [see 7]. There is an identified research gap concerning to customer preference and choice related to organizational use of their information [START_REF] Martin | The role of data privacy in marketing[END_REF].

The aim of this paper was to focus on data privacy in the context of the customer journey and in different service industries. In this study, data privacy refers to the claim of individuals to determine when, how, and to what extent information about them is communicated to others [see 3]. The research questions were as follows: 1) is there a difference in privacy concerns throughout the phases of the customer journey, and 2) how do different service industries differ in terms of privacy concerns? We also discuss about related data analytics theme. This paper consists of four sections. After this introduction, customer privacy concerns in the context of the customer journey have been discussed. Then, the methodology and survey sample have been defined. In the last two parts of the study, conclusions and implications have been discussed.

Data Privacy Throughout the Customer Journey

Digital service environments and digital tools have become widespread in the service industry in recent years. Although these practices have allowed the target group to participate in a branded e-marketplace, consumer advocates have expressed growing concern about the invasion of privacy caused by e-marketers' information collection practices [START_REF] Youn | Determinants of online privacy concern and its influence on privacy protection behaviors among young adolescents[END_REF]. Customer interaction, which may be one of the most important elements in service delivery [see 4], looks quite different in digital servicescapes than in traditional personal interactions between services clerks and customers. Moreover, the development of marketing and service practices using consumer data and analytics has advanced rapidly in recent years, which has raised data privacy concerns among consumers.

Privacy Concerns

Previous research [START_REF] Chellappa | Personalization versus privacy: An empirical examination of the online consumer's dilemma[END_REF][START_REF] Bleier | The importance of trust for personalized online advertising[END_REF][START_REF] Wilkowska | Privacy and data security in E-health: Requirements from the user's perspective[END_REF] has shown that privacy concerns affect online customer behavior during the use of digital services. Customers may become concerned if they feel that their privacy has been invaded or threatened in some way [START_REF] Ackerman | Privacy in e-commerce: Examining user scenarios and privacy preferences[END_REF]. News items about the vulnerability of digital services and stolen private data promote privacy concerns among customers. The vulnerability of digital services also creates potential business risks for companies [START_REF] Martin | Data privacy: Effects on customer and firm performance[END_REF]. Privacy concerns are also directly connected to customers' purchase behavior, as online stores require customers to provide personal information while shopping online [START_REF] Ackerman | Privacy in e-commerce: Examining user scenarios and privacy preferences[END_REF][START_REF] Walter | Drivers of customers' service experiences: A study in the restaurant industry[END_REF]. Customers are mindful of privacy concerns while shopping online. However, this may create a competitive advantage for companies who are good at creating online trust between customers and service providers [START_REF] Bart | Are the drivers and role of online trust the same for all web sites and consumers? A large-scale exploratory empirical study[END_REF]. Previous research has shown that customers felt fewer privacy concerns when they interacted with trusted retailers [START_REF] Chellappa | Personalization versus privacy: An empirical examination of the online consumer's dilemma[END_REF][START_REF] Bleier | The importance of trust for personalized online advertising[END_REF]. Additionally, good control over customers' shared personal information and visible data collection processes have been shown to improve trust [START_REF] Culnan | Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation[END_REF][START_REF] Phelps | Privacy concerns and consumer willingness to provide personal information[END_REF].

Many factors, including information type, situational factors, the features of service providers, and the individual characteristics of customers have been shown to affect customer's privacy concerns [START_REF] Bleier | The importance of trust for personalized online advertising[END_REF][START_REF] Wilkowska | Privacy and data security in E-health: Requirements from the user's perspective[END_REF]. However, there are differences between individual customers [START_REF] Sjöberg | Factors in risk perception[END_REF], as each observes the potential risks of online shopping for products and services differently [START_REF] Nordin | Solutions offerings: A critical review and reconceptualisation[END_REF]. Gender differences and the health of individuals may also influence privacy concerns [START_REF] Wilkowska | Privacy and data security in E-health: Requirements from the user's perspective[END_REF].

Customer Journey

A customer journey can be defined as 'a description of customer experience where different touchpoints characterize customers interaction with a brand, product, or service of interest' [START_REF] Wolny | Mapping customer journeys in multichannel decision-making[END_REF]. Følstad and Kvale [START_REF] Følstad | Customer journeys: A systematic literature review[END_REF] highlight the processual and experiential aspects of services as seen from the customer's viewpoint. It is described as the repeated interactions between a service provider and the customer within a customer journey. Customers interact with service providers via different digital touchpoints throughout their customer journey [START_REF] Hallikainen | Individual preferences of digital touchpoints: A latent class analysis[END_REF]. In the omni-channel environment, customer interactions often follow a non-linear structure. Additionally, the critical interaction is with the brand, not with the channel [START_REF] Elizabeth | Omni-channel marketing, integrated marketing communications, and consumer engagement[END_REF]. This scenario is typical in digital platform interactions in general and in the service industry. Customers may use multiple devices in different phases of their customer journey. During the early stages of a customer journey, when contacting a potential customer, search engines, Search Engine Marketing (SEO), Search Engine Marketing (SEM), and display ads play a critical role. For example, search engine results pages present a convenient way for consumers to gather information about service brands [START_REF] Cheng | Service online search ads: From a consumer journey view[END_REF]. Behavioral targeting connects consumers with brands based on data relating to their past online behaviors. These data could comprise web pages they have visited, the time they spent on each page, and the things they put in their shopping baskets or purchased online [START_REF] Yu | Targeting the luxury consumer[END_REF]. All these actions, which are based on the use of customer data, take place in conversions during a customer journey.

Service Industries

Service industries differ across many attributes. Early service industry categorization and conceptualization focused on distinguishing the characteristics of service delivery from the sale of physical products [see [START_REF] Brown | The development and emergence of services marketing thought[END_REF][START_REF] Grönroos | Service Management and Marketing: A Customer Relationship Management Approach[END_REF]. Service industry classification has focused on service delivery or encounter-based activities or solutions, including social interaction and perceived risk [START_REF] Johnston | Service Operations Management[END_REF] and service script-based classifications [START_REF] Shoemaker | Scripts: Precursor of consumer expectations[END_REF]. It has also involved examining the level of emotional attachment or investment a customer has in the customer experience process, for example, a customer is likely to have a lower level of emotional attachment to purchasing fast food compared to having medical treatment [START_REF] Bolton | Small details that make big differences: A radical approach to consumption experience as a firm's differentiating strategy[END_REF]. Cheng, Anderson, Zhu, and Choi [START_REF] Cheng | Service online search ads: From a consumer journey view[END_REF] have enriched the existing literature on service categorization with their explanation of the complex online service environment. In this study, we have analyzed four different industires; Municipal health stations, Private health stations, Insurance companies, Hobby clubs and Grocery stores. The last two industries represent a low level of perceived risk and purchase effort where consumers are thought to have little involvement, while both health station alternatives represent high involvement service consumption situation. In this classification, we followed Stell & Donoho [START_REF] Stell | Classifying services from a consumer perspective[END_REF] line of thinking. In general, customer involvement varies from passive participation of customer, such as simple physical presence, to a very high degree of user integration as a part of the service process [START_REF] Dadfar | Customer involvement in service production, delivery and quality: The challenges and opportunities[END_REF].

In general, categorization and classification models and concepts help management understand and learn about service operations in different industries [START_REF] Payane | The Essence of Services Marketing[END_REF]. While service industry characteristics modify service encounters and operations in general, it is likely that customers in different service sectors have different levels of concern related to their data privacy when interacting with a company.

Methods

Data were collected from undergraduate students in Finland, who represented customers of healthcare companies. Student samples are often used in academic research. Yavas [START_REF] Yavas | Research note: students as subjects in advertising and marketing research[END_REF] states that students are appropriate surrogates in studies where e.g. attitude-behavior relationships or orders of attitudes are researched. University students also represent young digital customers in the omni-channel environments. There were 309 responses, of which 306 were valid and were used in this research. Descriptive statistics revealed that the sample was female-dominant; 67% (n = 205) of respondents were female, and 33% (n = 101) were male.

Participants were sent an email that included a web-link to the questionnaire. All participants who answered the questionnaire were invited to participate in a lottery that gave them the chance to win minor gifts. We used the questionnaire to measure privacy concerns using four items adopted from Martin et al. [START_REF] Martin | The role of data privacy in marketing[END_REF]. Tradeoff value questionnaire items were adopted from Martin et al. [START_REF] Martin | The role of data privacy in marketing[END_REF] and information sensitive items from [START_REF] Dinev | Information privacy and correlates: An empirical attempt to bridge and distinguish privacy-related concepts[END_REF]. In the questionnaire, respondents were asked to rate value using a 5-point Likert scale ranging from totally agree (5) to totally disagree [START_REF] Alamäki | Business Roles in Creating Value from Data in Collaborative Networks[END_REF]. Results were analyzed using Statistical Package for the Social Sciences (SPSS) version 25.0. In Figure 1, a conceptual model of the study has been shown. This study examines how privacy concern vary throughout different phases of the customer journey and in different service industries. The prior research [START_REF] Chellappa | Personalization versus privacy: An empirical examination of the online consumer's dilemma[END_REF][START_REF] Bleier | The importance of trust for personalized online advertising[END_REF][START_REF] Wilkowska | Privacy and data security in E-health: Requirements from the user's perspective[END_REF] reveals that privacy concerns affect online customer behaviors. Additionally, information type, situational factors, the features of service providers have been shown to affect customer's privacy concerns [START_REF] Bleier | The importance of trust for personalized online advertising[END_REF][START_REF] Wilkowska | Privacy and data security in E-health: Requirements from the user's perspective[END_REF]. The individual characteristics of customers also affect to privacy concerns indicating that there are differences between individuals [START_REF] Sjöberg | Factors in risk perception[END_REF]. Furthermore, differences between the phases of customer journey and service industries may be related to information sensitivity, as context and situation affect the behavior of customers. From an interactional psychology perspective, situational factors influence customer behavior and vice versa [START_REF] Terborg | Interactional psychology and research on human behavior in organizations[END_REF]. Thus, a customer may more easily give their consent to use his or her personal data during different phases of the customer journey and in the context of different service industries. This has significant implications for the digitalization projects of companies and for collaborations with customers in different business ecosystems.

Results

Data privacy throughout the customer journey

We analyzed respondents' willingness to share information at different phases of the customer journey. The journey process consisted of information search, alternative evaluation, purchase, after sales contact, and the sharing of information, such as customer experiences and reviews. The model was adopted from Solomon et al.'s [START_REF] Solomon | Consumer Behaviour -A European Perspective[END_REF] customer journey process model. Table 1. Respondents' willingness to share their data at different phases of the customer journey.

Findings indicated that respondents were more willing to share their data when they were "in buying mode" and actually in the process of buying a product or service. On the other hand, respondents had data privacy concerns at the beginning of their customer journeys. These results challenged current marketing practices, such as re-targeting, which is typically done in a digital environment. We further analyzed the sample according to gender, and there were some indications that female respondents were more willing to interact with the company during the after-purchase phase, but differences were not statistically significant.

Data privacy within different service industries

We asked respondents how willing they would be to disclose personal information in five different service industry cases.

Table 2. Respondents' willingness to disclose personal information in different service industries (n=306)

"I do not feel comfortable with the type of information that (service x) requests from me" (totally agree = 1, …, totally disagree = 5) Both private and municipal health stations were examples of services that respondents access with high levels of emotional attachment and cognitive processing, while a grocery store was a service environment in which the role of service encounters was smaller.

Findings indicated that respondents were more willing to disclose their personal data in service settings where they were more involved. Here, involvement referred to consumers' motivation to process product related information at a given point in time [ see 35]. The definition also consisted of a person's perceptions of the relevance of an object. We assumed that municipal and private health stations represented service situations where more cognitive processing is likely to take place during a customer journey; hence, the relevance of these settings was higher than in other examples.

We tested our sample with a t-test to explore gender differences in data privacy concerns in different industries.In all industries, female respondents were more willing to share their private information with a company than male respondents. The t-test for equality of means indicated that this difference was statistically significant in insurance company services (p = 0.02) and private healthcare stations (p = 0.04).

Discussion

The first research question in this study concerned differences in privacy concerns related to the phases of the customer journey. Findings revealed that respondents (72%) were most willing to share personal data with service providers after their purchase. Of the total sample, 59% were willing to share their personal data while completing their purchase. Respondents were least willing to share their personal data during the information search (34%) and evaluation (35%) phases. Respondents were more willing to share personal data when they were already customers of service providers or when they were purchasing products and services. In these cases, they would have built a relationship with service providers. Respondents had privacy concerns at the beginning of their customer journey, when they would not have developed a formal relationship with service providers.

The second research question concerned differences in privacy concerns across service industries. Results suggested that healthcare information was more sensitive than information concerning purchases from grocery stores, hobby organizations, or insurance companies. Information sensitivity varied across service sectors. Different value chains and business networks deal with different types of information, which may affect the same customer differently. Moreover, our findings indicated that female customers were more willing to interact with companies than male customers.

There is little previous research on privacy concerns throughout the customer journey and in different service industries. This study filled this research gap by exploring personal data disclosure throughout the customer journey and in different service industries. Respondents seemed to focus constantly on information sensitivity and the value that they would receive if they provided personal information. We concluded that at the heart of most privacy concerns is a trade-off between the costs and benefits of the disclosure of personal information. This aligned with prior research suggesting that customers evaluate the pros and cons of a behavior before acting (see, for example, Lanier & Saini [START_REF] Lanier | Understanding consumer privacy: A review and future directions[END_REF].

The current study has contributed to data privacy and service marketing practices in different service industries. Our study supported the personalization-privacy paradox [START_REF] Aguirre | The personalization-privacy paradox: Implications for new media[END_REF], which suggests that personalization can both enhance and diminish customer engagement with the firm. That is, targeted offerings generated by customer data analysis may enhance the customer experience, but they may also raise privacy concerns among customers. The critical issue here appears to be the phase of customer journey. Our findings indicated that the best times for businesses to use customized communication is close to when the consumer actually makes a purchase and right after the purchase. This raises concerns over cookie remarketing activities that are typically conducted according to potential customers' browsing behavior. If personalized messages and actions trigger customers' privacy concerns about how the firm is using their personal information, then these may lead to decreased engagement [37 p. 98]. Privacy concerns among customers may increase skepticism toward and avoidance of advertising and can prompt negative actions. All these reactions may reduce overall marketing effectiveness [37 p.99] and harm customer experience. In general, customer experience may be good (enjoyable) or bad (unenjoyable), leading to positive or negative customer response with regard to value, image, quality, patronage, loyalty, satisfaction, recommendations, and purchase intentions [38, p. 655.].

Today's companies operate in collaborative networks that utilize computer networks [START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0. In: Collaboration in a Data-Rich World[END_REF]. Collaborative networks are also becoming data-rich environments [START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0. In: Collaboration in a Data-Rich World[END_REF][START_REF] Bruno | A reference ontology to support product lifecycle management[END_REF] and it raises issues such as the privacy concern of customers. Service ecosystems based on collaborative networks include new platform ecosystems who compete in service offering without ownership to a subject [see 41]. They collect and manage data to operate their business ecosystem and several companies participate value creation in different phases of value chains Thus, data sources that are needed to serve and build customer relationships and operative processes are located in collaborative networks of customers, partners, and service providers. From the customer's viewpoint, privacy issues are related to all phases of a customer journey, as each phase is often served and operated by different service providers. For example, search history and marketing relate to marketers, such as Google and advertisement agents, while purchase and payment data are collected by platform owners and payment operators, and service providers deal with usage and customer satisfaction data. Hence, throughout their customer journey, customers interact with many service providers, providing or withholding consent for each to collect their personal data. Data privacy is not a single point event but is a key question in the operations of collaborative networks.

The findings provide new knowledge for marketers about the respondents' information sensitivity concerning different industry segments and privacy concern relating to the phases of customer journey. Privacy concern in different phases of the customer journey often reflects to the different roles of service providers in the value chain of service ecosystems. Marketers should pay attention to these differences in planning data collection and motivating customers to provide consent for the use of their private data in managing omni-channel operations. From the perspective of collaborative networks literature, the findings contribute to the processes of collaborative networks [START_REF] Barile | Systems, networks, and ecosystems in service research[END_REF], especially to the management and governance of omni-channel service ecosystems.

Conclusions

This study showed that companies and business networks should consider privacy concerns when planning and designing new digital services. Privacy concerns are closely related to data analytics, which are crucial in digital service delivery. Findings suggested that businesses should focus customer engagement actions close to the actual buying phase and that information sensitivity across different service sectors varied. During the early stages of the customer journey, companies should focus on offering and brand development and possibly SEO activities, which may draw a customer's attention without provoking data privacy concerns. Additionally, companies should give special attention to the visibility of data usage at the beginning of a buying journey.

The basic limitation of this study is its quite small target group consisting of student sample. It limits the generalizability of the results, and this study increases only understanding about the factors that affect the respondents' online behavior. Finally, this study suggested that respecting customers and their data privacy is an investment in relationship building. The results of this study have suggested that researchers should explore privacy concern empirically, especially in the context of value chains and networks, as customers deal with several actors throughout their journey. Additionally, the different types of information in different customer journeys merit further research.

Figure 1 .

 1 Figure 1. The relationship of customer journey and industry segments to privacy concern.

Acknowledgments. The authors would like to thank the Big Data-Big Business (BIG) Project, all parties behind the project, and Business Finland (the Finnish innovation funding, trade, investment, and travel promotion organization) for their support of this study.


David Pérez Perales

María-José Verdecho
email: mverdecho@omp.upv.es

Faustino Alarcón-Valero

Enhancing the Sustainability Performance of Agri-Food Supply Chains by Implementing Industry 4.0

Keywords: sustainability, performance, agri-food supply chains, Industry 4.0

In order to enhance the sustainability in the supply chain, its members should define and pursue common objectives in the three dimensions of the sustainability (economic, environmental and social). The Agri-Food Supply Chain (AFSC) is a network of different members such as farmers (producers), processors and distributors (wholesales, retailers…), etc. In order to achieve the performance objectives of the AFSC, Industry 4.0 technologies can be implemented. The aim of this paper is to present a classification of these technologies according to two criteria: objective to be achieved (environmental or social) specified in the main issues to be covered in each objective and member of the AFSC supply chain where it is implemented. In this work, we focus on technologies that deal with environmental and social sustainability because economic sustainability will depend on the specific characteristics of the business (a supply chain using a specific Industry 4.0 technology may be profitable while others do not).

Introduction

As [START_REF] Camarinha-Matos | Collaborative networks as a core enabler of Industry 4.0. In: Collaboration in a Data-Rich World[END_REF] point out, Industry 4.0 has become an "integration factor" for various new technologies towards a new generation of more efficient, agile, and sustainable industrial systems where collaboration issues are at the heart of most challenges of this movement. Collaborative networks community is a field for the analysis and development of Industry 4.0 knowledge [START_REF] Camarinha-Matos | Collaborative networks as a core enabler of Industry 4.0. In: Collaboration in a Data-Rich World[END_REF][START_REF] Stich | Need and Solution to Transform the Manufacturing Industry in the Age of Industry 4.0 -A Capability Maturity Index Approach[END_REF]. The term "Industry 4.0" comprises several technologies as Internet of Things (IoT), Big Data, Artificial Intelligence, Virtual Reality, 3D Printing, Cyber Security, etc. [START_REF] Flores | Awareness Towards Industry 4.0: Key Enablers and Applications for Internet of Things and Big Data[END_REF].

On the other hand, Sustainable supply chain management is defined by [START_REF] Seuring | From a literature review to a conceptual framework for sustainable supply chain management[END_REF] as 'the management of material, information and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of sustainable development, i.e., economic, environmental and social into account which are derived from customer and stakeholder requirements'. Thus, the supply chain members should define and pursue common objectives in the three dimensions of the sustainability. The sustainability performance of the supply chain will be monitored by the achievement of all these objectives. The Agri-Food Supply Chain (AFSC), as any other supply chain, is a network of different actors working together in different processes and activities in order to bring products and services to the market, with the purpose of satisfying customers' demands [START_REF] Prima | Collaboration and sustainable agri-food supply chain: a literature review[END_REF]. Different actors can be considered depending of the AFSC type. The most complex AFSC would include farmers (producers), processors and different tiers of distributors (wholesales, retailers…). Simpler AFSC would substitute the processors by different types of traders. These actors perform different activities, such as growing, harvesting, storaging, processing, washing, sorting, grading, packaging, labeling and distributing, etc. However, AFSCs hold some relevant characteristics that characterize them [START_REF] Pérez | Towards a Sustainable Agri-food Supply Chain Model. The Case of LEAF[END_REF]: limited shelf-life, high levels of uncertainty and increasing awareness in environmental and social aspects.

In order to achieve the performance objectives of the AFSC, Industry 4.0 technologies can be implemented. The aim of this paper is to present a classification of these technologies according to two criteria: objective to be achieved (environmental or social) specified in the main issues to be covered in each objective and member of the AFSC supply chain where it is implemented.

The paper is structured as follows. Section 2 presents the background. Section 3 presents the main issues of environmental sustainability in AFSC and Industry 4.0 main technologies to enhance its performance. Section 4 presents the main issues of social sustainability in AFSC and Industry 4.0 main technologies to enhance its performance. Finally, in the last section, conclusions are exposed.

Background

Industry 4.0 is a field of growing interest at both practitioner and academic levels but is still in its initial stage of development and implementation. Previous literature reviews propose different classification frameworks. [START_REF] Savastano | Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review[END_REF] expose a literature review on Industry 4.0 technologies for manufacturing processes to identify theoretical and methodological approaches. Some recent literature reviews link Industry 4.0 and sustainability concepts. [START_REF] Varela | Evaluation of the Relation between Lean Manufacturing, Industry 4.0, and Sustainability[END_REF] present a review of works that assess the relation between Industry 4.0 and specific objectives of the three dimensions of sustainability (economic, environmental or social). [START_REF] Bonilla | Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges[END_REF] analyze the environmental impact and challenges of Industry 4.0 from four different scenarios: deployment, operation and technologies, integration and compliance with the sustainable development goals, and long-run scenario. [START_REF] Bányai | Optimization of Municipal Waste Collection Routing: Impact of Industry 4.0 Technologies on Environmental Awareness and Sustainability[END_REF] develop a review of works to show the impact of Industry 4.0 technologies on environmental sustainability but limited to waste collection issue. Other related works are [START_REF] Lin | A Cross-Strait Comparison of Innovation Policy under Industry 4.0 and Sustainability Development Transition[END_REF][START_REF] Kamble | Sustainable Industry 4.0 Framework: A Systematic Literature Review Identifying the Current Trends and Future Perspectives[END_REF][START_REF] Franciosi | Maintenance for Sustainability in the Industry 4.0 context: a Scoping Literature Review[END_REF]. In these works, it is observed that there is a lack of studies focusing on providing a framework addressing the intersection of the following components in AFSC: AFSC members, environmental and social sustainability issues, and Industry 4.0-based technologies. Therefore, the aim of our work is to focus on how these technologies can enhance AFSC environmental and social issues, respectively, depending on the AFSC main actors (farmers, processors and distributors). Due to space limitations, social issues will be more extensively deployed than the environmental ones.

Industry 4.0-based Technologies for Environmental Sustainability

Environmental sustainability performance is approached in various ways in the literature. Some authors distinguish between input and output oriented indicators.

Others classify them into different categories such as procurement, internal operation and product development. Using the works [START_REF] Bocken | A literature and practice review to develop sustainable business model archetypes[END_REF][START_REF] Bourlakis | Firm size and sustainable performance in food supply chains: Insights from Greek SMEs[END_REF][START_REF] Garbie | An analytical technique to model and assess sustainable development index in manufacturing enterprises[END_REF][START_REF] Beier | Sustainability aspects of a digitalized industry -A comparative study from China and Germany[END_REF], we consider as the most relevant environmental sustainability issues: soil management, crop protection, water management, animal welfare, energy efficiency, waste control and pollution control. Table 1 shows how Industry 4.0-related technologies can enhance soil and waste management issues. Table 1 is an adaptation from [START_REF] Pérez | Industry 4.0 for the development of more sustainable Decision Support Tools for Agri-food Supply Chain Management[END_REF]. Table 1. Industry 4.0-based technologies for environmental sustainability of soil and waste management (adapted from [START_REF] Pérez | Industry 4.0 for the development of more sustainable Decision Support Tools for Agri-food Supply Chain Management[END_REF]) Soil Management REF Farmers [START_REF] Xiaolin | Procedia Engineering County Soil Fertility Information Management System Based on Embedded GIS[END_REF][START_REF] Satyanarayana | Wireless Sensor Based Remote Monitoring System for Agriculture Using ZigBee and GPS[END_REF][START_REF] Phillips | Integrated sensing of soil moisture at the field-scale : Measuring, modeling and sharing for improved agricultural decision support[END_REF] 1) Aerial drones to map weeds, yield and soil variation; 2) Performance maps that allow to know the performance of the soil in a certain area thanks to georeferenced images of the soil; 3) Robots capable of microdot application of fertilizer; 4) Smart tractors GPS controlled steering and optimized route planning to diminish soil erosion and saving fuel costs.

Waste Management

REF

Farmers

Processors Distributors [START_REF] Liopa-Tsakalidi | Application of Mobile Technologies through an Integrated Management System for Agricultural Production[END_REF][START_REF] Yerpude | Impact of Internet of Things ( IoT ) Data on Demand Forecasting[END_REF][START_REF] Wolfert | Big Data in Smart Farming -A review[END_REF] 1) ICT to the deployment of "production on demand" business models; 2) Precision agriculture leveraging technologies for decisions related to planting and harvesting time.

1) Intelligent equipment enables quality detections in the process, reducing the number of failures and material consumption; 2)

Cloud computing platform to share data with suppliers to synchronize orders and shipments and reducing stock.

1) Artificial intelligence applied to consumers trends to reduce waste; 2) Automatic control of temperature to reduce product spoilage; 3)

Point of sale applications that collect and transmit information at the point of sale in real time by reading their respective barcodes.

Industry 4.0-based Technologies for Social Sustainability

Social sustainability performance is also approached in various ways in the literature.

Although social standards such as ISO 260000 and Social Accountability SA8000 are well-known among companies, there is a wide spectrum about the different issues that must be taken into consideration to manage and measure this social performance, mainly due to their intangible nature. Using the works ( [START_REF] Pérez | Towards a Sustainable Agri-food Supply Chain Model. The Case of LEAF[END_REF], [START_REF] Castka | ISO 26000 and supply chains-On the diffusion of the social responsibility standard[END_REF][START_REF] Stock | Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential[END_REF][START_REF] Verdecho | Proposal of a Customer-Oriented Sustainable Balanced Scorecard for Agri-food Supply Chains[END_REF]) we use as the most relevant social sustainability issues: employment, work conditions, safety, health, nutrition, traceability and community engagement. Table 2 shows how Industry 4.0related technologies can enhance each one of the previous issues, affecting the overall social sustainability performance of a generic AFSC. It has to be noted that the literature regarding social sustainability technologies so far is not so extent as the literature dealing with environmental technologies.

Conclusions

Sustainability in the supply chain is a topic of high interest in business and academic literature. Sustainability is composed of three dimensions: economic, environmental and social sustainability. Supply chains pursuing sustainability must define common objectives (in these three dimensions) to be reached by the different members.

The accomplishment of these objectives in AFSCs can be supported by the introduction of Industry 4.0 technologies. Specifically, different Industry 4.0 technologies can be used to support specific issues within sustainability dimensions. In this work, on the one hand, we have selected as environmental issues: soil management, crop protection, water management, animal welfare, energy efficiency, waste control and pollution control. On the other hand, we have defined as social issues: employment, work conditions, safety, health, nutrition, traceability and community engagement. It has to be noted that in this work, we focus on technologies that deal with environmental and social sustainability because economic sustainability will depend on the specific characteristics of the business. Once the environmental and social issues have been defined, the different Industry 4.0 technologies have been associated to each issue and to the different members (farmers, processors and distributors) of the supply chain.

In the literature, there are less Industry 4.0-related technologies explicitly reported for dealing with social sustainability issues than in the environmental case, probably due to the different nature. There is still a need to develop further both environmental and, especially, social technologies that improve the sustainability performance of AFSCs in order to improve efficiency and competitiveness.

Table 2 .

 2 Industry 4.0-based technologies for social sustainability Employment

	REF		Farmers		Processors	Distributors
		1) Cooperatively used
		farm-monitoring
	[28-29]	technology of agriculture might influence employment opportunities and job profiles of farmers	1) Digital technologies to automate some tasks where labor is difficult to hire.	1) ICTs for better employee education, integration and inclusion, work enrichment, and better work-life balance.
		and	farming	related
		professionals.
	Work Conditions
	REF		Farmers		Processors	Distributors
		1) ICT tools to lower	1) Intelligent assistance
	[30]	administrative workload, enhance regulatory laws, allow "hands-free" reporting automation and make work/life balance for farmers easier; 2) "Farmer-friendly" technologies designed taking into account the	systems can simplify some tasks providing some previously unknown information (f.e. augmented reality glasses); 2) Intelligent assistance systems, such as Learn instruments, can make work more ergonomic	1) ICT application in non-automated transportation activities can provide digital information that can help to make more ergonomic and safer labour conditions by optimizing loading-related tasks.
		final client.		simplifying it.
	Safety
	REF		Farmers		Processors	Distributors
	[31-33]	1) Smart Devices implemented in tractors that send alerts when they detect immediate overturning risks; 2). Drones used in the fields, enabling farmers to control growth in a comfortable and safety manner.	1) Intelligent Manufacturing equipment in processes controlled by ICT to reduce pollutants and noise.	1) Intelligent tachograph that allows the driver to book parking, cancel telematics tolls in advance and helps reduce unnecessary stops by traffic agencies; 2) GPS that allows trucks drivers remote access to data (speed, distance…) on work time, avoiding delays and helping to meet the delivery time.
	Health
	REF		Farmers		Processors	Distributors
	[34-35]	1) ICT tools to make farmers more aware about	1) Automated machines to perform dangerous, dirty	1) Intelligent packaging that has sensors in each package for

Acknowledgements. This work has been funded by the Project GV/2017/065 "Development of a decision support tool for the management and improvement of sustainability in supply chains" funded by the Regional Government of Valencia. Authors also acknowledge the Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems.


Ricardo Santos
email: ricardosimoessantos84@ua.pt

Antonio Abreu

Vitor Anes

Developing a Green Product-based in an Open Innovation Environment. Case Study: Electrical Vehicle

Keywords: Business Sustainability, Open Innovation, New Product Development, Collaborative Networks

In order to respond to new market challenges, companies have attempted to develop open innovation processes with other organizations such as research centers and higher-education institutions. However, it is also frequently mentioned by several companies that the lack of models that support open innovation in a sustainable way, involving higher-education institutions or research centers and companies in the context of a collaborative environment, is an obstacle for a wider acceptance of this way of promoting innovation processes. Starting with some discussion about innovation models in a collaborative context, this paper discusses the developed of an electric vehicle based on an open-innovation approach among several companies and a Portuguese university.

Introduction

Currently, there are even more electric vehicles in the market, mainly due to the competitiveness levels achieved, as well as the increasingly affordable acquisition costs, coupled in turn, with a greater autonomy and energy efficiency, placing therefore this vehicle, in a position, even more closed by the internal combustion vehicles.

The increasingly need of competitiveness by the companies, as well as the need to expand their competences, know-how and create substantial competitive advantage, lead them, to form alliances, partnerships and collaborative networks with outsiders in order to overcome potential capacity limitations, knowledge gaps, promoting therefore, innovative and dynamic relationships, useful for future projects for instance ( [START_REF] Pisano | Which Kind of Collaboration Is Right for You?[END_REF][START_REF] Camarinha Matos | Targeting major new trends[END_REF][START_REF] Urze | Collaborative Systems for Smart Networked Environments[END_REF]).

Open Innovation (OI), allows to distribute innovation across organizational boundaries, by using collaborative networks and a wide range of external actors and sources to help them to achieve and sustain innovation and competitiveness as well [START_REF] Urze | Circulation of Knowledge in a Co-innovation Network[END_REF][START_REF] Abreu | An Approach to Measure Social Capital in Collaborative Networks[END_REF].

However, there seems to be a lack of evidence of successfully applied models, to support the way that the organizations, uses OI, to develop new products or services in the collaborative network's context, especially regarding the electrical vehicle's development.

In this work it's presented an Open Innovation model to support the decision makers of the collaborative network, by planning the development of a green product such an electric vehicle in an open innovation context.

The proposed approach combines collaborative networks, regarding different partnerships (suppliers, government, university, etc.) with a model of innovation management, regarding New Products Development (NPD) concept, in order to accelerate the development of an electrical vehicle, used as a case study here.

Evidences of perceived benefits in this case study, and regarding OI context, will also be presented in this work.

Therefore, and based on what was referred before, the research question is: How to support the green product development in an open innovation context?

Literature Review

Various literature streams are synthetized here, in order to highlight the multidisciplinary approaches regarding open innovation (OI), including OI models. The OI, can be defined as "the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively" ( [START_REF] Huizingh | Open innovation: State of the art and future perspectives[END_REF]).

On the center of this definition is the triangular relationship among outflow (insideout), inflow (outside-in) and coupled (both directions) open innovation process ( [START_REF] Linstone | Comment on "Is open innovation a field of study or a communication barrier to theory development[END_REF]). This concept has received wide acceptance on literature ( [START_REF] Trott | Why Open Innovation is old wine in new bottles International[END_REF]), as well as some criticism, by some authors like [START_REF] Slowinski | Good practices in open innovation[END_REF].

Authors, like [START_REF] Adler | Social Capital: prospect for a new concept[END_REF], defines OI as "a model using a wide range of external actors and sources to help them to achieve and sustain innovation".

OI is also based on collaborative relationships-organizational alliances and partnerships ( [START_REF] Urze | Knowledge Transfer Assessment in a Co-innovation Network[END_REF]) who are willing to work together by sharing ideas, know-how, experiences and knowledge to generate value, by achieving innovative outcomes ( [START_REF] Urze | Mapping Patterns of Co-innovation Networks[END_REF]).

In line with this concept, authors such as [START_REF] Abreu | System thinking shaping innovation ecosystems[END_REF] perceive three levels of collaborative innovation, namely: management of interorganizational collaboration process, management of the overall innovation process and creation of a new collaborative knowledge.

To expand their competences, their know-how and achieve substantial competitive advantage, organizations share the need to form alliances, partnerships and collaborative networks with outsiders to overcome potential capacity limitations, knowledge gaps, develop the ability to jointly work into new projects and promote innovative and dynamic relationships ( [START_REF] Pisano | Which Kind of Collaboration Is Right for You?[END_REF]).

There are several open innovation models that can be found on literature [16][START_REF] Galeano | Open innovation models, a literature review with a focus on SMEs[END_REF].

One of the OI models most used, is the InnoCentive Model ( [START_REF] Slowinski | Good practices in open innovation[END_REF]). Created in 2001, InnoCentive is a model of OI, supported in a web platform that bears the same name ([18]) This model consists of six steps that begins with the identification of problems and ideas, the formulation of a challenge, the specification of intellectual property agreements, the publication of the challenge, the evaluation of solutions and an award to finalize with the transfer of intellectual property [START_REF] Proctor | Connect and Development[END_REF][START_REF] Kline | An overview of innovation[END_REF].

Other model found on literature, more recent and widely used, is created by Procter & Gamble P & G, which is defined as "Connect + Develop"( [START_REF] Galeano | Open innovation models, a literature review with a focus on SMEs[END_REF]).

This model works in both directions inwards and outward, and ranges from registered trademarks to packaging, from marketing models to engineering, and from commercial services to design ( [START_REF] Micaëlli | How to improve Kline and Rosenberg's chainlinked model of innovation: building blocks and diagram-based languages[END_REF]).

The chain-linked model (CLM), developed by Kline and Rosenberg in 1985 ( [START_REF] Kline | An overview of innovation[END_REF]) is an attempt to describe complexities in the innovation process.

This model is based on a concept called "systemism" [START_REF] Micaëlli | How to improve Kline and Rosenberg's chainlinked model of innovation: building blocks and diagram-based languages[END_REF], which considers innovation as a system. Systemism, allows the possibility to imagine different scenarios; high versus low research intensity innovation, routine versus breakthrough innovation, methodic versus empirical innovation, local versus global multinational level innovation, making CLM an important innovation model widely used today [START_REF] Galeano | Open innovation models, a literature review with a focus on SMEs[END_REF].

Although, the existence of an academic consensus, regarding the value of this model ( [17][21]), some authors propose that such model, can be improved to be more contextualized with other contexts, such as open innovation ( [START_REF] Forrester | Principles of Systems[END_REF]).

Proposed Open Innovation Model

The present open innovation model, proposed here, was designed with the aim to serve companies of any size and business, in the transition to an Open Innovation (OI) context.

The purpose of this approach is to contribute with a tool, to support the innovation management and the information associated.

The proposed model, was based on four assumptions, as follows:

• Generalize the classical and very influential model of chain linking from Kline and Rosenberg model ( [START_REF] Kline | An overview of innovation[END_REF]); • Accommodate the concepts of the 4 th Edition of the Oslo Manual ( [START_REF]Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation, 4th Edition, The Measurement of Scientific, Technological and Innovation Activities[END_REF]); • Consider innovation in both industry assets (tangible and intangible) and regarding low-tech companies as well as high-tech ones; • Innovation results from a chain of interactions between the knowledge from OI's network and the organizations, regarding its external environment;

The proposed model, besides incorporating the assumptions referred above, also intends to structure the process of developing a product /service in a collaborative context, which is supported by a set of knowledge, necessary for its development.

This type of knowledge, needed to reach product/service innovation, is shared with a set of Open Innovation Network (OIN) partners (Pn), which makes the collaboration network, necessary for the development of the product / service in question.

In general, the model can be divided into four different groups of components; Stages of product development (Sn), Interfaces (In), Knowledge Interface groups (KIn) and Process groups (Pr).

These components, can be briefly described, as follows:

• Stages of product development (Sn) -Consists in a set of monitor indicators, regarding the product/service development, to support the project management;

• Interfaces (In) -Divided into 3 groups, works as channels in which the transfer of knowledge, takes place between the OI's network and the external environment (EE) of the open innovation network, through the interaction between the different partners (Pn) and the corresponding External Collaborators (ECn).

For each interface, are assigned a set of Partners (Pn), which are responsible to establish and control the interactions between Pn and ECn;

• Knowledge interface groups (KIn) -The knowledge changed between Pn and ECn, are organized into 3 types of knowledge (Kin), regarding the correspondent interface (In) (see Table 1). The mechanism of such transfer, is exemplified on Fig. 1;

• Process groups (Pr) -Is where the innovations will be applied to develop the product and it results from the transfer and development of different knowledge types (Kn), occurred between the collaborative network partners (Pn), and also between Pn and the External Collaborators (ECn), i.e. from the outside of OI's network (Fig. 1).

On Fig. 1, are presented the four components referred before. The ideas, with technological and economic viability, are then selected and give rise to innovation. The invention, product design or service design, are the first step of the project (Pr2), followed by the detailed design (Pr3), in order to better specify the product/service.

After the Redesign Demonstration or Test and Production process group (Pr4), the process of innovation continues, until the commercialization or implementation of its final result is achieved, which can be related to a product, process, marketing or even organizational (Pr4).

Interactions may occur between the different processes of the chain, since that the innovation does not follow a linear path.

The knowledge regarding each process (KnPrn), needed to develop innovation projects, may be internally available through the different partners (Pn) of the open innovation network, or be obtained abroad through the External Environment (EE) between Pn and the EE's External Collaborators (ECn).

Regarding the knowledge transfer mechanisms, there are 2 types used in this model. In Fig. 2, are presented two examples of how these two types of mechanisms occur. According to Fig. 3, each knowledge transferred (KnPrn) between the Partners (Pn), is a contribution to the Process Group (Prn), which is one of the 5 Process Groups, that composes the linked chain, necessary to develop the product or service, which follows a sequence order. This last concept is based on the work of Kline and Rosenberg [START_REF] Proctor | Connect and Development[END_REF].

The whole development product/service's process is composed by a set of Process Groups, that composes the chain, namely: Pr1, Pr2, Pr3, Pr4 and ending in Pr5.

For product/service's continues improving, this chain can continue to Process Group 1 (Pr1), from Pr5 directly, until the product/service objectives are accomplished.

The knowledge required and obtainable by the external collaborators (ECn), will have to be transferred between the Partners (Pn) and the correspondent External Collaborators (EC.n) involved in the process.

The knowledge to be transferred on this context (Kin.(n)), will be transferred from the respective Interface (In), which will be managed by the Partners (Pn) of the network (isolated or in group) assigned for his function.

Regarding the three interfaces referred above, they don't have to exist simultaneously, and they don't have to constitute disjoint entities as well.

In this sense, the product development process, occurs in a collaboration context, by incorporating the different innovations, resulted from the knowledge transferred, associated within the various network partners and external collaborators as well, resulting thus, into a final product/service.

Application to a Case Study: Electric Vehicle

A Portuguese company of electric vehicles, intended to manufacture an electric passenger vehicle (2 seats), energy efficient, safer, and with a greater autonomy, in order to be available in the market segment of two-seat green passenger vehicles.

The insufficient know-how for its production, have leaded the company to produce the vehicle in collaboration with several entities, including industries, universities, Research & Development (R&D) centers, and consultancy companies (Fig. 4).

Fig. 4. Diversity of partners and respective competencies involved

It was created 14 partnerships, involved industries from different areas, as well as two R & D centers (from two Portuguese universities) and a Portuguese military institution) with the aim to develop and test the new components of the car. It was also intended to manufacture a vehicle that would exploit the concept of reverse trike, i.e. a vehicle having 3 wheels on its constitution-base.

The partnerships, established with the two R & D centers, have consisted on a interdisciplinary involvement of fellow students and other researchers from several areas of engineering (Electrical, Mechanics, Computers and Electronics), where there was an intervention in several disciplinary areas, regarding the vehicle's development (Fig. 4).

The existing competences, together with those resulting from the interaction with the different partners, have allowed the development of a set of other competences necessary for the development of the vehicle.

Developing a Green Product-based in an Open Innovation Environment 121

Still in the scope of the development of the vehicle, there were concerns with the communication abroad, namely with the marketing and sales areas, with all support being assumed by two consulting companies in Marketing and Advertising (Fig. 4).

The model application is presented on Table 3.

Table 3. Model application (through Table 1)

Knowledge (K) KIn(n) Actors Involved K1 KI1 (1)
P7, P11

Through Table 3, it can be seen, the relationship between the knowledge transferred and each process group (Prn).

Based on the model proposed before, the partners involved in the project, are presented in Fig. 5, as well as the knowledge transfers preformed.

Fig. 5. Model application

The partners (Pn) were also mapped in this methodology, which has helped the innovation managers, regarding I1, I2 and I3, to identify the necessities of in terms of knowledge, associated to each process group (Prn).

The performed tests, regarding the development of the vehicle, have allowed feedback, regarding the components developed by the partner companies, in order to improve them later. One of these tests have involved the aerodynamics of the vehicle, carried out in partnership with a military institution, to optimize the aerodynamic coefficient of the vehicle.

Other tests carried out, was related to the vehicle's battery system, whose performance allowed adjustments in the thermal behavior of the batteries with the load management system developed between the partner companies of the area and the partner universities. These partnerships, have proved to be crucial to the good performance of the vehicle, allowing both companies to gain insight into the development of LiFePO4 (lithium phosphate) batteries for other applications.

Benefits from collaboration

The benefits of collaboration can be better understood, by presenting 2 scenarios, regarding the development of the electrical vehicle, through the company which have promoted the project, namely; 1, Isolated context and 2, Collaborative context.

On first scenario (isolated context), there is a lack of knowledge, needed by the company as well as other assets, to develop the product, resulting therefore in additional costs, including time. This scenario would delay the development of some specific systems, such as the car battery system for example, which was design specifically for that vehicle, given its specificities.

Regarding the second scenario (collaborative context), the company had for example, the possibility to learn from the development of the chassis of the car, as well as the other company and the R&D center involved, which have allowed, to produce new knowledge for both partners, and at the same time, accelerate the chassis process design, given the proximity between both partners, to better discuss the project.

In this sense, and given the knowledge transferred between the different partners of the network, as well as the knowledge transferred from the outside to inside of the network, there was additional competences achieved by all the partners involved, which would be harder, or even impossible in the "isolated context" scenario.

Additionally, and given the proximity between the collaboration partners, the product's time-to-market, was also reduced, regarding the collaboration context, which has allowed the developed product to be more competitive into the segment market of twoseat green passenger vehicles, given the pioneer's strategic advantage.

Conclusions and Further Work

In this work, it was presented an approach to manage innovation in collaborative context (Open Innovation). A case study was used for its application, by presenting a collaboration network, originally created by a company to produce a green product: an electrical vehicle. The advantages in recurring to Open Innovation, are showed in this work. One of them was cost reduction, regarding the competences, needed to the product developed. Given the collaboration context, the product time-to-market, was also reduced, allowing the company (promoter) to be more competitive in this market segment. The proposal model, proposed and applied in this work, was based on a model existed in literature, although adapted to the collaborative context.

The approach presented here, has allowed to map the partners, involved in this project, as well as the competences needed to develop the final product, by distinguish the competences associated to the internal partners, from the competences associated to the external ones. Based on these last ones, and regarding the collaborative context, it was possible to developed additional competences for every partner of the network.

Given the benefits showed in this work, the approach proposed here, could be applied into other industries, although, and besides the adjustments to be made, there is a need of systematize the external knowledge, i.e., the knowledge group, from the outside of the network, that will be created by the external research, to be applied on network.

Fig. 1 .

 1 Fig. 1. Model components Based on what was referred before, and through Fig.1, the model's components, can better described, as it follows: • KI1 -Existing market knowledge (needs preferences values) • KI2 -Existing Organizational Knowledge • KI3 -Existing scientific and technological knowledge • Pr1 -Potential Market Assessment, Economic Viability Assessment, Selection of ideas Projects

Fig. 2 .

 2 Fig. 2. The two types of knowledge's transferred mechanisms used (Examples): A-Between Partners , B-Between Partners (Pn) and External Collaborators (EC.n)

Fig. 3 .

 3 Fig. 3. Proposed model, integrated in open innovation context Based on what was referred before, The knowledge required and not obtainable by the external collaborators (ECn), will have to be developed and/or transferred, between the Partners (Pn), of the open innovation network.According to Fig.3, each knowledge transferred (KnPrn) between the Partners (Pn), is a contribution to the Process Group (Prn), which is one of the 5 Process Groups, that composes the linked chain, necessary to develop the product or service, which follows a sequence order. This last concept is based on the work of Kline and Rosenberg[START_REF] Proctor | Connect and Development[END_REF].The whole development product/service's process is composed by a set of Process Groups, that composes the chain, namely: Pr1, Pr2, Pr3, Pr4 and ending in Pr5.For product/service's continues improving, this chain can continue to Process Group 1 (Pr1), from Pr5 directly, until the product/service objectives are accomplished.The knowledge required and obtainable by the external collaborators (ECn), will have to be transferred between the Partners (Pn) and the correspondent External Collaborators (EC.n) involved in the process.The knowledge to be transferred on this context (Kin.(n)), will be transferred from the respective Interface (In), which will be managed by the Partners (Pn) of the network (isolated or in group) assigned for his function.Regarding the three interfaces referred above, they don't have to exist simultaneously, and they don't have to constitute disjoint entities as well.In this sense, the product development process, occurs in a collaboration context, by incorporating the different innovations, resulted from the knowledge transferred,

Acknowledgments

This work was partially supported by FCT, through CTS, project UID/EEA/00066/2019.

Through Fig. 2, and more specifically, through Example 1 (Ex.1), it can be seen an interaction between 2 open innovation's partners, where the knowledge (K1Pr5), related to Process 5 (Pr5), is transferred between P1 and P2.

In the same figure, and regarding Example 2 (Ex.2), there are some differences, since between each knowledge transfer, there is an interface (In), which controls and monitories, each knowledge transfer, regarding to group Kn, which is related to In. This control occurs in order to manage the knowledge transfer, according to the project needs, promoting therefore efficiency.

On Ex.2, there are two interactions, that can be observed; one, between P5 and EC.9 (KI3(2)) and the other one, between P4 and EC.10 (KI3(1)).

In more detail, and based on Fig. 1, the model assumes the following components, listed on Table 1 1 allows to assign, each knowledge to be transferred, to a set of actors, involved in the correspondent process. The same, can be preform through Table 2, and regarding each models' interface (I1, I2, I3), where a set of partners, can assign the task (isolated or in group) of managing each interface, as innovation managers. Both tables allows to gain control over the process of product/service development, not only between the Partners (Pn) of Open Innovation Network (OIN), but also between Partners (Pn) from OIN and the External Collaborators (ECn), from the External Environment (EE), by identifying the actors/organizations, involved on each knowledge transfer.


John Paul Kasse
email: jkasse@bournemouth.ac.uk

Lai Xu

Paul Devrieze
email: pdvrieze@bournemouth.ac.uk

Yuewei Bai
email: ywbai@sspu.edu.cn

Verifying for Compliance to Data Constraints in Collaborative Business Processes

Keywords: Compliance, Collaborative Business Processes, Virtual Factory, Business Process Verification, Algorithm

Production processes are nowadays fragmented across different companies and organized in global collaborative networks. This is the result of the first wave of globalization that, among the various factors, was enabled by the diffusion of Internet-based Information and Communication Technologies (ICTs) at the beginning of the years 2000. The recent wave of new technologies possibly leading to the fourth industrial revolution -the so-called Industry 4.0is further multiplying opportunities. Accessing global customers opens great opportunities for organizations, including small and medium enterprises (SMEs), but it requires the ability to adapt to different requirements and conditions, volatile demand patterns and fast-changing technologies. Regardless of the industrial sector, the processes used in an organization must be compliant to rules, standards, laws and regulations. Non-compliance subjects enterprises to litigation and financial fines. Thus, compliance verification is a major concern, not only to keep pace with changing regulations but also to address the rising concerns of security, product and service quality and data privacy. The software, in particular process automation, used must be designed accordingly. In relation to process management, we propose a new way to pro-actively check the compliance of current running business processes using Descriptive Logic and Linear Temporal Logic to describe the constraints related to data. Related algorithms are presented to detect the potential violations.

Introduction

Compliance is about adherence to regulations, guidelines or predefined legal requirements like norms, laws and standards. Compliance verification in business process management is addressed at different levels of the life cycle i.e. deign time, runtime, post runtime. A hybrid approach addresses compliance verification for all levels [START_REF] Hashmi | Are we done with business process compliance: state of the art and challenges ahead[END_REF]. Moreover, existing research has made significant contribution to addressing verification of models for compliance with a range of requirements such as, activity ordering requirements [START_REF] Pesic | Constraint-based workflow management systems: shifting control to users[END_REF]- [START_REF] Elgammal | Formalizing and appling compliance patterns for business process compliance[END_REF], resource assignment constraints [START_REF] Cabanillas | Specification and automated design-time analysis of the business process human resource perspective[END_REF]- [START_REF] Nakatumba | Resource-aware business process management: analysis and support[END_REF], data requirements [START_REF] Knuplesch | On Enabling Data-Aware Compliance Checking of Business[END_REF], [START_REF] Borrego | Conformance checking and diagnosis for declarative business process models in data-aware scenarios[END_REF], security requirements [START_REF] Salnitri | Modeling and verifying security policies in business processes[END_REF]- [START_REF] Vijay | Security for workflow systems[END_REF] and privacy [START_REF] Mont | Privacy policy enforcement in enterprises with identity management solutions[END_REF], [START_REF] Mont | Privacy policy enforcement in enterprises with identity management solutions[END_REF]- [START_REF] Tan | The Consistency of Task-Based Authorization Constraints in Workflow Systems[END_REF], compliance between process variants [START_REF] Tealeb | Context-based variant generation of business process models[END_REF]- [START_REF] Groefsema | A Survey of Formal Business Process Verification: From Soundness to Variability[END_REF]. These works show the state of the art in business processes compliance management and verification. They have also resulted into various compliance approaches, frameworks, methods, languages and tools. However, more compliance challenges need to be addressed to fully support collaborative processes in the context of a virtual factory.

In this paper, we look at the compliance of running collaborative business processes with data constraints. The paper proposes a new way to describe data constraints using descriptive logic (DL) and Linear Temporal Logic (LTL). The traces of the running processes are used to check whether the current collaborative business processes are compliant with the data constraints described in DL and LTL. The structure of the paper is as follows: related work is presented in Section 2. Section 3 introduces an exemplary business process and related traces. DL and LTL are used to express the data constraints in Section 4. Section 5 shows how to present compliance properties as well as related verification algorithms. Future work and conclusion are presented in Section 6.

Related Work

Pesic et al. propose DECLARE, a declarative constraint based specification language and model compliance checking in relation to ordering requirements [START_REF] Pesic | Constraint-based workflow management systems: shifting control to users[END_REF], [START_REF] Pesic | DECLARE: Full support for loosely-structured processes[END_REF] Whereas Wynn extends YAWL language with reset nets to determine correctness of business processes with cancellation and OR joins, other work by Elgammal et al. and Taghiabadi present compliance frameworks for managing the compliance of a business process during its life cycle. The constraints are organised according to patterns like control flow, resources, temporal and data [START_REF] Wynn | Business process verificationfinally a reality![END_REF], [START_REF] Elgammal | Formalizing and appling compliance patterns for business process compliance[END_REF]. Despite the fact that the frameworks comprehensively cover all perspectivies of the business process, the proposed languages employ complex mathematics and logics that are not intuitive for ordinary end users.

Data specific constraint checking approaches check compliance between the model and data requirements. Knuplesch et al. propose a graph method for modelling compliance rules and address verification through structured compliance checking based on compliance rules and data checking based on abstracted data [START_REF] Knuplesch | On Enabling Data-Aware Compliance Checking of Business[END_REF] The resultant graph based approach is data constraint based. Borrego and Barbara enhance earlier work of Declare to include data requirements compliance checking [START_REF] Borrego | Conformance checking and diagnosis for declarative business process models in data-aware scenarios[END_REF]. In this paper we extend our earlier work for supporting compliance verification in collbaorative business processes [START_REF] Kasse | Process Driven Access Control and Authorisation Approach[END_REF]- [START_REF] Kasse | A comparative survey of Business Process Verification Methods and Tools[END_REF]. Related work remains limited in various ways; the compliance management framework by Elgammal et al results into a compliance request language in which constraints can be specified by ignores their verification. Taghiabadi' s compliance approach caters for verification for control flow and data constraints. However, its application to collaborative environments is not demonstrated, it is also a domain specific approach and so is Declare language.

Wynn et al's work based on YAWL is geared towards control flow verification to achieve model soundness. Data constraints are not considered by the authors. Moreover, non of these works presents mechanism easily comprehensible for nonexpert end users. This limits their application. Our work leverages previous work by supporting specification of data constraints and verifying for their compliancy with collaborative business processes using an approach that empolys syntactic and semantic mechanism close to natural language. Besides, we provide a coarse grained approach in which we cater for data constraints in terms of accessibility, authentication and privacy by means of access control and authorisation. This is a valuable contribution in the wake of revised compliance requirements of the 2008 general data protection regulation.

An Exemplary Business Process and Related Traces

We adopt an abstracted industry based use case, the Pick and Pack business process proposed in [START_REF] Kasse | The Need for Compliance Verification in Collaborative Business Processes[END_REF]. In this case, customers submit orders online after registering on the system. Stores' staffs check order details, and proceed to process the order as Fig. 1 illustrates.

Fig1. Pick and Pack business process

Order processing involves activities summarised in Table 1 and assigned roles: Based on the role assignments, the following resource assignment conditions apply;

1. Pickers cannot participate in the verification of orders. 2. Packers can also do the verify order task. 3. Pickers can participate in hand over task at peak times 4. Supervisors oversee other employees and can execute any task. Supervisors delegate or share rights of task execution to other staff, e.g. supervisors can delegate pickers to pack items. Relatedly, the constraints governing data access are summarized;

1. Supervisors have full data access and can grant access to other staff. 2. Basic data must be accessible and available for staff to execute tasks that do not need much restriction and control. E.g. order list data. 3. Access control and authorization is observed for restricted data. For example, customer personal data, financial data among others. 4. For security of data and system, users must be authenticated by the system. For illustration purposes, the business process is considered in terms of activities while traces are used as a mechanism to derive process execution to facilitate checking compliance to constraints. Table 2 lists sample traces based on the use case.

Constraint Expression in Description Logic and LTL

To achieve constraint expression, descriptive logic (DL) [START_REF] Baader | Basic Description Logics[END_REF] and LTL are adopted. While using DL, the business process is the domain of discourse with activities and constraints as concepts. The intention is to support expression of constraint requirements in a way close to natural language for easy intuition by non-experts yet expressive enough to support reasoning. DL is known for knowledge base representation and building in knowledge management systems [START_REF] Baader | Basic Description Logics[END_REF]. Its application in business process design however has not received much attention with fewer applications in [START_REF] Del-Río-Ortega | Defining and analysing resource-aware process performance indicators[END_REF], [START_REF] Cabanillas | Specification and automated design-time analysis of the business process human resource perspective[END_REF]. The limitation is due to lack of known syntax to express unique business process requirements.

To enhance its expressiveness, we adopt logical operators and quantifier operators from temporal logic. Temporal logic is a formal method founded on mathematics. Models are specified and checked for correctness against a set of properties expressed as event orderings in time [START_REF] Lowe | Specification of communicating processes: Temporal logic versus refusalsbased refinement[END_REF]. Role representation establishes a link between the constraints and the activities while the role restrictions impose specific existential and value restrictions of a constraint over the activity. We use unary predicates to represent sets of individual constraints while binary predicates denote relationships between individual constraints. We further use composite predicates to denote relationships between different constraints.

Expressions of Data Requirements as Constraints in DL and LTL

Data constraints are based on data patterns [START_REF] Russell | Workflow data patterns[END_REF] and represented as unary predicate expressions using DL and logical operators and quantifiers from LTL. Task execution requires access to data. E.g., 'delivery' task needs access to customer address and contact to be accessible. Further still, actions that a user can do must be preauthorized i.e. action to read, write, modify. Thus, task data assignment 𝑇𝐷 is composed of a task, data object (𝑜), value (𝑣) and action (𝜕). 𝑇𝐷assignment is achieved by a function 𝑓: 𝑎 → 𝑜, 𝑣, 𝜕 which maps data item attributes like value and action to a task. Fig. 2 exemplifies task data assignment and the required attributes. 3 presents some examples of expressions as derived for data constraints in; All /some has authenticity and privacy constraints

Verification Algorithms for Compliance to Data Constraints

Data constraints are based on Boolean conditional evaluations where the condition is either true or false. Depending on the outcome of the conditional evaluation assessed against predefined access policies, access is granted or denied. To check for compliance, a constraint is satisfied if a trace is true for the given conditions, and the constraint is violated if trace is otherwise. To that effect, the following specifications and definitions are useful for the data constraints compliance checking algorithm.

Given a set of activities a1, a2 and a3 whose execution by role actor (r1) requires product catalogue data (Pcd). Access to this data is constrained by access and availability. If the assignment is true per the executed behavior, then the trace (𝜎) satisfies (⊨) the constraint.

If 𝜎 = 𝑇𝑟𝑢𝑒) then 𝜎 ⊨ 𝐴𝑢𝑡ℎ𝑒𝑛𝑡𝑖𝑐𝑎𝑡𝑖𝑜𝑛 (2)
The definition specifies access control by authentication granted for Pcd data with actions to read and write for role actor (r1) who executes activities a1, a2 and a3.

Satisfaction of the authentication constraint is achieved if the trace of the executed events exhibits the specified behavior. Otherwise, a violation is detected for the constraint.

If 𝜎 = 𝑇𝑟𝑢𝑒) then 𝜎 ⊨ 𝑃𝑟𝑣 (3)
The definition specifies Privacy constraint for accessing Pcd data where action to read private data is to be granted for the resource actor r1 who executes activities a1, a2 and a3. During verification using the privacy compliance verification algorithm, the constraint is checked if it is satisfied before access is granted to private data. If trace is true to the specification, then the constraint is satisfied and thus compliance achieved. Otherwise, it is a violation detected for the privacy constraint.

Data Constraints Verification Algorithms

In this section, four algorithms are introduced, namely access and availability, authentication and privacy data constraints compliancy verification algorithms. At the end, an overall data constraint compliancy verification algorithm is presented.

Access and Availability Constraint Verification

Verifying Access and Availability data constraint ensures that basic non-exclusive data is accessible and available with less restriction to enable accomplishment of basic tasks. Algorithm 1 is composed to the effect. Violation occurs if role actors or tasks are denied access to data or where the permitted action type differs from the initial assignment, e.g. modify action type instead of read action type.

Violation of AA constraint as per Algorithm 1 exists when tasks or their actors (r, e.ac) are denied access. The violation results into to a deadlock or livelock. Deadlock occurs if running activities are denied access to data necessary for the process to progress, while livelock occurs when a task denied data access stays in waiting mode stagnating process execution. Another form of violation occurs when a task executes without necessary data resulting into wrong outcomes which compromise data integrity.

Verifying Compliancy with Authentication Data Constraint

Authentication Algorithm 2 verifies for compliance by checking that role actor credentials match the credentials stored in a database of authorized actors and their access privileges over tasks. Two forms of authentication errors lead to violations, i.e.;

• Access leakage which occurs when non-authenticated users gain access to data. This is traced from running or finished events • Deadlocks which occur when users are authorised to execute activities but access to data is denied for technical or logical reasons e.g. improper configurations.

When data constrained by authenticity constraint exists outside the constraint it leads to access leakage since it will be accessible by users without authentication or if it is accessed by non-authenticated role actors. Similarly, where data is not accessible to authenticated actors leads to a deadlock since they cannot progress with the current work being executed.

Verifying Compliancy with Privacy Data Constraints

Privacy constraint is enforced by means of access control and authorization. Authorization involves the process of validating that the authenticated user is granted permission to access the requested resources. Privacy as a data constraint restricts access to data regarded private as defined by GDPR. Data restricted from public access is enforced by authorization. Algorithm 3 checks whether the process is complying with the privacy data constraint. Violation to privacy constraint is checked targeting two forms of errors; deadlocks and privacy breach.

• Deadlocks occur when the executing events authorised to access data are denied access for technical or logical reasons e.g. improper configurations, • Breach to privacy i.e. non-authorised activities eventually access private data and execute.

When data constrained by privacy constraint exists outside the constraint, it leads to a leakage since it is accessible by non-authorized actors. Similarly, where authorized data is not visible in 'seen' and 'finished', it implies denied access as a form of violation.

The overall compliance verification algorithm 4, is a general algorithm that invokes algorithms 1, 2 and 3 to check whether the entire business process complies with above mentioned data constraints.

Conclusion and Future Work

Regardless of the industrial sector, compliance is a major concern not only to keep pace with changing regulations but to address the rising concerns of security, product and service quality and data privacy which are fundamental for implementing industry 4.0. With the EU GDPR in force, concerned organizations (European or otherwise) must meet its requirements by reviewing and realigning their business processes. It is necessary for software to be designed accordingly to reduce overheads from organizational measures used in the interim. In this spirit, we propose a new way to check the compliance of current running business processes. DL and LTL are used to describe the constraints related to data. Related algorithms are presented to detect the potential violations, i.e. data access and availability violation, data authentication violation, and data privacy violation. The research of collaborative process model verification covered also control flow and resource constraint verifications. For page limitation, we only present data constraint verification in this paper. Further research related to data constraint verification will carry out the practical implementation and evaluations as the next step.

  . The language is limited to control flow checking. Similar work is presented by Awad et al. and Wynn et al. Awad et al, propose a BPMN-Q language which extends BPMN to search for segments of a process model affected by changes and verify their compliancy in terms of control flow.

Fig. 2

 2 Fig. 2 Task and data assignment attributes Using DL constructs, the expression for 𝑇𝐷 is derived as; 𝑇𝐷 = 𝑎 → (𝑜 ⊓ 𝑣 ⊓ 𝜕) and formalized as; 𝑎 → (𝑜 ∧ 𝑣 ∧ 𝜕) in LTL. Based on Fig.2, the use case, it follows that; 𝑇𝐷 = [𝐷𝑒𝑙𝑖𝑣𝑒𝑟 𝑂𝑟𝑑𝑒𝑟 → (𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟_𝑎𝑑𝑑𝑟𝑒𝑠𝑠 ⊓ 𝐵𝐻14𝐴𝐴 ⊓ 𝑅𝑒𝑎𝑑)] Table3presents some examples of expressions as derived for data constraints in;

Definition 1 : 1 )

 11 Accessibility and Availability (AA) 𝜎 ∈ (((𝑎 1 , 𝑎 2 , 𝑎 3 ), 𝑟 1 ): (𝑃𝑐𝑑. [𝑅𝑒𝑎𝑑]): 𝐴𝐴 (If 𝜎 = 𝑇𝑟𝑢𝑒 then 𝜎 ⊨ 𝐴𝐴 The definition specifies accessibility and availability constraints for Pcd data object with action read granted to r1 for execution of activities a1, a2 and a3. During verification, the data compliance verification algorithm checks for compliance to the constraint for the data object, action by the user and tasks. If the outcome shows that the trace is true, then the availability and accessibility constraint is satisfied. Otherwise, it is a violation detected for the AA constraint. Definition 2: Authentication 𝜎 ∈ (((𝑎 1 , 𝑎 2 , 𝑎 3 ), 𝑟 1 ): (𝑃𝑐𝑑. [𝑇𝑟𝑢𝑒/𝐹𝑎𝑙𝑠𝑒]): 𝐴𝑢𝑡ℎ𝑒𝑛𝑡𝑖𝑐𝑎𝑡𝑖𝑜𝑛

Definition 3 :

 3 Privacy (Prv) 𝜎 ∈ (((𝑎 1 , 𝑎 2 , 𝑎 3 ), 𝑟 1 ): (𝑃𝑐𝑑. [𝑅𝑒𝑎𝑑]): 𝑃𝑟𝑣

Table 1 .

 1 Process activities and role assignments

	Activity	Description	Role
	Select Order	Order is chosen from a pool of pending orders.	Picker
	Pick items	Order items are picked	Picker
	Verify order	Right order in terms of items and quantities	Verifiers
	Pack order	Order is packed	Packers
	Hand over /	Orders ready for picked up or delivery by agent	Customer
	Deliver order		service

Table 2 .

 2 Exemplified log showing events, activities, constraints and process instances

			Constraints	Process
	Event	Activity	Accessible Data Time (units)	instance
	𝑒 1	Select order	OrderList	Duration [ 6]	𝑃 1
	𝑒 2	Hand over order	Customer orderlist	Delay[10]	𝑃 1
	𝑒 3	Select order	Orderlist	Between[10]	𝑃 1
	𝑒 4	Pick items	ProductList	Duration[15]	𝑃 1
	𝑒 5	Pickup or delivery	Contactlist	Duration[20]	𝑃 1
	𝑒 6	Select order	Customer address	Duration[10]	𝑃 1

Table 3 .

 3 Data Constant Expression in DL

	DL data unary expressions		Description
	∀data → visible /∃data → visible		All/ some data is visible
	∀data → valid /∃data → valid		All /some is valid data
	∀data → ¬available /∃data → ¬available	All /some data not available
	∀data → accessible /∃data → accessible	All /some data is accessible
	∀data → privacy /∃data → privacy	All /some data classified as
			private
	∀data → authentic /∃data → authentic	All /some data classified as
			private
	DL data binary expressions		Description
	∀data → (visible ⊓ accessible)	/∃data →	All/ some data has visible and
	(visible ⊓ accessible)		accessible constraints
	∀data → (authentic ⊓ privacy)	/∃data →
	(authentic ⊓ privacy)

Acknowledgements: This research is partially funded by the State Key Research and Development Program of China (2017YFE0118700) and it is part of the FIRST project which has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 734599.