Mohammadtaghi Falsafi
email: mohammadtaghi.falsafi@itia.cnr.it

Rosanna Fornasiero
email: rosanna.fornasiero@itia.cnr.it

Umberto Dellepiane
email: umberto.dellepiane@act-operationsresearch.com

How to Make Industrial Symbiosis Profitable

Keywords: Industrial symbiosis, Flow management, Bi-level optimization

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

Introduction

Due to the increase of production costs, organizations are becoming more concerned to find new efficient approaches. Industrial symbiosis not only reduces the supply chain costs through geographical proximity but also suggests innovative ideas to increase the income of an organization thanks to closing the loop between the output (i.e. by-products and waste of a company) to be used as input for another. Industrial symbiosis (IS) is a type of collaborative networks for regional economic relationships providing a richer, more meaningful representation of local industry drivers and regional dynamics than do traditional methods.

In this paper, it is assumed that the Production Unit (PU) is the atomic decision unit within a collaborative network and we will treat each of them as a single decision maker. Thus, we are implicitly assuming that each single PU works with a high level of efficiency, but this does not seem restrictive since if this is not the case, at single PU level, it is possible to optimize the planning and scheduling and increase the efficiency level. Moreover, the decision autonomy of each PU has to be maintained.

Indeed, when the PU makes a decision, it has to consider also what is happening at network level. For this reason, we can introduce the concept of upper-level decisions. They are optimized with respect to network-level KPIs (Key Performance Indicators), which in turn have a twofold purpose: from one side to increase the wealth related to a symbiotic approach at cluster level while on the other maintain the single PU decision autonomy.

In section 2, a literature overview of the recent trends in the applicability of optimization techniques in different perspectives is presented. Section 3 explains the optimization model of the problem in this paper. The application of the model in a case study and the input data and the assumptions is described in section 4. In section 5, the results of applying the model to the case study with their analyses are demonstrated. Finally, section 6 gives a brief overview of what has been done in this paper and what is going to be done to expand the model.

Theoretical Background

Organizations can benefit from shared resources in an IS while they can still make decisions independently. However, there are some challenging issues that prevent organizations from easily accepting to apply it. Shared resources incur some limitations for a better collaboration among organizations [START_REF] Van Donk | A case of shared resources, uncertainty and supply chain integration in the process industry[END_REF]. In addition, according to Chertow [START_REF] Chertow | Industrial symbiosis: literature and taxonomy[END_REF], the most prominent reason that many organizations are not willing to participate in IS is that they do not see sufficient material flow to make it worth wide. Chertow also emphasizes the importance of the analysis of specific industries based on key materials.

Bi-level optimization is concerned with a class of problems characterized by a particular nested structure. Some decision variables of the inner level problem may act as parameters for the objective function of the second level problem and may also influence its constraints. The nesting of these problems may be deeper and involve more than two levels, and in this case, we refer to Multilevel Programming. More in detail, a problem called lower or inner problem is nested inside the feasible region of another problem, called upper level or outer problem.

Bi-level problems, or more generally multi-level problems, allow modelling the complex situations and decision dynamics. However, their capability of capturing the complex reality results in a problem hard to solve. For this reason, a compromise between consistency with the reality and modelling simplifications is often sought.

Bi-level problems are NP-hard problems. Several approaches to solving this kind of problem have been proposed in the literature. Possible drawbacks to this formulation are:

 The problem can have large dimension, and the proposed approaches may be negatively influenced;

 To assure the convergence of an algorithm, some assumptions have to be made, and they cannot be representative of the reality we intend to model. Why using a bi-level formulation for modelling the industrial symbiosis problem? For answering this question, we will use a parallelism with another sector that has drawn great benefit from the development of bi-level programming. Many Transportation Management problems, in fact have been well represented with bilevel formulations. For more details, readers can refer to [START_REF] Marcotte | Network design problem with congestion effects: A case of bilevel programming[END_REF] or [START_REF] Marcotte | Efficient implementation of heuristics for the continuous network design problem[END_REF] [START_REF] Marcotte | Network design problem with congestion effects: A case of bilevel programming[END_REF][START_REF] Marcotte | Efficient implementation of heuristics for the continuous network design problem[END_REF].

Usually, these formulations try to model, at the inner level, the choices or the behaviors of the network users while at the upper level they represent the decision problem faced by the manager of the transport system, which wants to improve the performance of the system.

Many other applications of bi-level optimization can be found in different application fields. For example, the management and the engineering sectors also obtained advantages from this kind of optimization. In particular, in the work of [START_REF] Neittaanmäki | Solving some optimal control problems using the barrier penalty function method[END_REF] [START_REF] Neittaanmäki | Solving some optimal control problems using the barrier penalty function method[END_REF], control problems with unilateral boundaryvalue are addressed (a typical problem with these characteristics could be represented by Optimal Shape Design problem). The bi-level structure is the result of a finite difference approximation of the original optimal control problem with boundary value. The resulting bi-level problem involves the minimization of a functional (different according to the particular problem) that depends on control variables and state variables. The state variables are the solution of an auxiliary quadratic problem which is characterized by parameters that depend on the control variables. Some features of this problem are the expensiveness of the functional evaluation and the large scale of the inner level problem. It is important to stress that these features are often common in many real two-level problems.

Indeed, an important motivation for the widespread use of bi-level models in the management field is the consistent presence of decentralized decision-making processes. This kind of decisions is particularly common within companies where different choices, which will affect each other, are taken by different parties in some points of the supply chain. Two papers which deal with decentralized decision process in supply chain are [START_REF] Patriksson | Stochastic mathematical programs with equilibrium constraints[END_REF] [START_REF] Patriksson | Stochastic mathematical programs with equilibrium constraints[END_REF] and Júdice and Faustino (1988) [START_REF] Júdice | The solution of the linear bilevel programming problem by using the linear complementarity problem[END_REF].

Recently bi-level applications have also appeared in financial studies, and examples can be found in the works of Carriòn et al. ( 2009) [START_REF] Carrion | A bilevel stochastic programming approach for retailer futures market trading[END_REF] and Conn and Vicente (2012) [START_REF] Conn | Bilevel derivative-free optimization and its application to robust optimization[END_REF].

Another Network Design problem has been faced in Ben-Ayed et al. (1992) [START_REF] Ben-Ayed | Construction of a real-world bilevel linear programming model of the highway network design problem[END_REF] where the upper-level decision maker (the manager of the system) would like to allocate some investments to strengthen an existent interregional highway network. Choosing an investment allocation, the manager of the system changes the users' preferences. In consistence with this dynamic, the users are the inner-level decision makers who have the goal of minimizing their travel costs.

Furthermore, to answer the previous question, we need to report also two important assumptions that we cannot relax:

1. each single PU works with a high level of efficiency 2. the decision autonomy of each PU has to be maintained Focusing on the last example and considering that the following assumptions cannot be relaxed, we understand the reason of the use of bi-level programming for modelling the symbiosis problem.

First of all, the inner level of the problem is the decisions related to the single Production Units (PUs) within each plant. These are optimized with respect to some proper KPIs. In this study, we will assume that the PUs are the atomic decision units within a collaborative network and we will treat each of them as a single decision maker.

Indeed, it is necessary to consider what happens at the network level. For this reason, we can introduce the concept of the upper-level decisions. These are optimized with respect to network-level KPIs, which in turn have a twofold purpose: from one side to increase the wealth related to a symbiotic approach while on the other maintain the single PU decision autonomy.

In order to make things work, coordination to improve the whole symbiotic network performance is necessary, measured in different ways, for example economic (profit or costs), environment impact (energetic, waste) or social. This coordination is provided by an organization that is a physical organization or more easily a virtual platform which is called the network coordinator. Of course, for maintaining the decision autonomy of each PU, a top-down approach (i.e. a centralized network coordinator) cannot be considered.

We then conceive a multi-layer architecture organized into two cooperating layers. At the company level, each industry performs its Supply Chain Optimization (SCO) to gain a high efficiency operative level optimizing its short-term and long-term decisions. These decisions do not take into account any of the decisions of the other industries in the cluster or in other words, are completely independent. To govern the whole cluster performance, we introduce the cluster-level optimization, whose aim is the increasing of the global cluster efficiency. This two-layer architecture is formulated by a bi-level optimization problem.

Methodology

The objective of the model is to integrate the two decision levels: decisions at PU level for managing flows in supply chain and at the network level for managing the industrial symbiosis (IS) among PUs. The aim of the model is to support the optimization of the flow in the cluster (IS) while maximizing the total benefit of each PU and dynamically maximize the benefit for the IS. Accordingly, the upper level of the bi-level model maximizes the total flow inside the IS among PUs, and the inner level maximizes the total benefit of each of the PUs.

Input parameters

𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑖𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑜𝑢𝑡𝑝𝑢𝑡 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠 𝑜𝑝 𝑎𝑡 𝑃𝑈 𝑖

The assumption is that finished products and by-products could be sold to both the market and other PUs in the IS with the same price (𝑖. 𝑒. 𝑆𝑃 𝑜𝑝∈𝑘 𝑖 𝑖 , 𝑆𝑃 𝑜𝑝∈𝑏 𝑖 𝑖 ). Also, the price and purchasing cost for a specific material is the same for all the PUs. The waste is normally disposed with the cost 𝐶𝑇 𝑤 𝑖 𝑖 , while it could be exchanged in the IS without any cost (𝑆𝑃 𝑜𝑝∈𝑤 𝑖 𝑖 = 0, 𝐶 𝑖𝑝∈𝑤 𝑗 𝑖 = 0).

The inventory level of each PU is the input inventory for input products and output inventory for output products. For each of them we assume an initial inventory level (i.e. 𝐼 𝑖𝑝 𝑖,0 and 𝐼 𝑜𝑝 𝑖,0 ).

For each product, there is a specific demand in the market (𝑑 𝑝 𝑚𝑘𝑡 ). This is a general demand regardless of the product type (finished product, by-product) of each PU. In other words, if two PUs produce the same product (finished product or byproduct), the market demand of that product for both of them is the same.

Illustrative Example

The above model has been applied to a simplified example of an IS case with five PUs, and it is partially taken from the paper of Gonela and Zhang (2014) [START_REF] Gonela | Design of the optimal industrial symbiosis system to improve bioethanol production[END_REF]. Each PU produces a set of finished products, by-products, and wastes. It is assumed that the waste is either disposed or transacted inside the IS among PUs. Furthermore, for some products, there is the possibility to transact with one or more PUs as well as the market, while some products could solely be purchased/sold to the market. Figure 1 shows the schema of the PUs and the potential interconnections among them. For each potential product, it is also specified if it is a finished product (FP), by-product (BP), or waste (W).

Fig. 1. Schematic view of PUs and potential interconnections

Table 1 shows the input data for the first two PUs. For each it is considered a set of inputs (ip) that could be supplied from the market or, potentially, from the PUs in the network. The purchasing cost is the same if the product is supplied from the market or it is a finished product or by-product of another PU inside the IS (𝐶 𝑖𝑝∈𝑙 𝑖 ∪𝑘 𝑗 ∪𝑏 𝑗 𝑖 ). Alternatively, if the input product is a waste of another PU inside the IS, it is transacted without any cost (𝐶 𝑖𝑝∈𝑤 𝑗 𝑖 = 0; ∀𝑤 𝑗 ). For producing each unit of finished product, a specific amount of each input product is consumed and a specific amount of by-product and waste is produced. Hence, it is defined by a coefficient which is specified in parenthesis in the column ip and op.

The three types of outputs are finished products, by-products, and waste. Finished product is the main output of the PU. For each PU, there are also one or more byproducts and wastes. Based on the configuration of the PU, it is capable of producing a certain amount of finished product (𝑃𝐶𝑎𝑝 𝑜𝑝∈𝑘 𝑖 𝑖 ). All the other parameters are the same as is explained in section 3. There are some products for which some dimensions are not applicable (n.a.). For example, in case of P1 (which could represent electricity) it is not possible to keep inventory for it.

Results and Discussion

Table 2 shows the first part of the results from applying the bi-level optimization. The decision variables are the flows to/from the market, the wastes that are disposed, and the final inventory level. The numbers in green represent the flows for each PU. For instance, the product P12 (𝑋 𝑃12 𝑚𝑘𝑡,𝑖 ) is the input for PU 4 and therefore, it is possible to have a flow greater than zero which is 2412. The results also show that some products do not interact with the market when considering the IS. For example, the output product P2 from PU 2 does not have any interaction with the market. In other words, it is entirely transacted within the IS. Figure 2 shows the flows between PUs inside IS. For example, 90 units of finished product P1 which is produced by PU 2, goes to the market and 4 and 5 units is sold to PUs 1 and 3 respectively. However, there is no transaction with the other potential PUs (i.e. 4,5). So, production amount of P1 in PU 2 is 99 units. 3 compares the results when the PUs operate as standalone and independently from the IS (SA) with the results obtained by the bi-level optimization in the IS. In other words, the standalone mode is the result of the inner function when the transaction flows within the cluster is set to zero (Y=0). In the standalone mode, production units 1,2,3, and 5 produce less than their production capacity (i.e. 60,99,150 and 100 respectively) to maximize their profit. Contributing with the cluster, the PUs 1,2, and 3 produce with the maximum production capacity, and therefore their utilization arrives at 100%. This difference stems from the fact that collaborating in the IS, companies can produce more than the market demand and transact the additional production within the cluster.

Regarding the economic impact, there is an increase in four PUs. The companies with more collaboration with the IS observe more increase in their profits. As Figure 2 represents, most of the connections in the cluster are related to PU 2. Consequently, this PU has the highest profit increase with around 42%. The increase of the profit is due to the free transaction of waste inside the IS while it is costly for the PU to dispose them. On the contrary, PU 5 with the lowest symbiotic interaction, its profit decreases slightly to approximately 4%.

Conclusion

The emergence of environmental and sustainability regulations is asking companies to define new collaboration mechanisms to close the loop of waste and by-product and to put them back into the system as an input for other companies. IS is one of the sustainable strategies that can help to reduce waste production and logistic costs. In IS, traditionally separate plants collocate in order to efficiently utilize resources, reduce waste and increase profits for the entire system as well as each player in the IS. This paper focuses on developing an innovative approach for supporting the definition of the IS configurations based on the total profit and on the profit of each PU. An innovative decision framework that combines two decision levels is proposed to determine the optimal configuration of IS and to design the optimal network flows of various products in the network. An empirical case has been conducted to study the efficiency and effectiveness of the proposed model, and the results suggest a significant increase in profitability for the single nodes and the rest of the players in the network when IS is applied. In this case, the collaboration is based on the possibility to share resources as a way to gain profit for all the partners in the network. The model is still under development and will be further improved introducing other decision variables (e.g. transportation costs) and enlarging the case study to a larger network.

Fig. 2 .

 2 Fig. 2. Optimization results -Flows to the other PUs inside IS Table3compares the results when the PUs operate as standalone and independently from the IS (SA) with the results obtained by the bi-level optimization in the IS. In other words, the standalone mode is the result of the inner function when the transaction flows within the cluster is set to zero (Y=0). In the standalone mode, production units 1,2,3, and 5 produce less than their production capacity (i.e. 60,99,150 and 100 respectively) to maximize their profit. Contributing with the cluster, the PUs 1,2, and 3 produce with the maximum production capacity, and therefore their utilization arrives at 100%. This difference stems from the fact that collaborating in the IS, companies can produce more than the market demand and transact the additional production within the cluster.Regarding the economic impact, there is an increase in four PUs. The companies with more collaboration with the IS observe more increase in their profits. As Figure2represents, most of the connections in the cluster are related to PU 2. Consequently, this PU has the highest profit increase with around 42%. The increase of the profit is due to the free transaction of waste inside the IS while it is costly for the PU to dispose them. On the contrary, PU 5 with the lowest symbiotic interaction, its profit decreases slightly to approximately 4%.

Table 1 .

 1 Input data

			i	𝑖𝑝	𝐶 𝑖𝑝∈𝑙 𝑖 ∪𝑘 𝑗 ∪𝑏 𝑗 𝑖	𝐶 𝑖𝑝∈𝑤 𝑗 𝑖	𝐻 𝑖𝑝 𝑖	𝐼𝐶𝑎𝑝 𝑖𝑝 𝑖	𝐼 𝑖𝑝 𝑖,0
			1	P1 (0.5) P2 (2)	5 3		n.a. 0	n.a. n.a.	n.a. n.a.	n.a. n.a.
				P5 (50)	1.5	n.a.	0.55	200	20
			2	P7 (1)	4		n.a.	1	5	2
				P6 (30)	0.02	0	n.a.	n.a.	n.a.
			op
	i	𝑘 𝑖	𝑏 𝑖	𝑊 𝑖	𝑃𝐶 𝑜𝑝∈𝑘 𝑖 𝑖	𝑆𝑃 𝑜𝑝 𝑖	𝑃𝐶 𝑜𝑝 𝑖	𝑖 𝐶𝑇 𝑤 𝑖	𝐻 𝑜𝑝 𝑖	𝐼𝐶𝑎𝑝 𝑜𝑝 𝑖	𝐼 𝑜𝑝 𝑖,0	𝑑 𝑝 𝑚𝑘𝑡
		P3			60	13	10		1	0.5	0	70
	1		P4 (0.5) P5 (30)			2.5 1.5	1 0.5		0.02 0.5	1 15	0.2 5	100 1500
				P6 (30)		0	0	0.002	n.a.	n.a.	n.a.	0
		P1			99	5	4		n.a.	n.a.	n.a.	110
			P2 (1)			3	1		n.a.	n.a.	n.a.	90
			P8 (30)			1	0.5		n.a.	n.a.	n.a.	4000
	2		P9 (50)			2.2	0.5		0.2	300	50	10000
				P10 (10)		0	0.5	1	n.a.	n.a.	n.a.	0
				P11 (10)		0	0	0.75	n.a.	n.a.	n.a.	0
				P12 (12)		0	0	0.005	n.a.	n.a.	n.a.	0

Table 2 .

 2 Optimization results -Flow from/to the market, amount of waste disposed, and Inventory level

	Decision			PU (i)			Decision			PU (i)
	variable	1	2	3	4	5	variable	1	2	3	4	5
	𝑋 𝑃8 𝑚𝑘𝑡,𝑖	0	0	0	0	0	𝑋 𝑃8 𝑖,𝑚𝑘𝑡	0	1100	0	0	0
	𝑋 𝑃7 𝑚𝑘𝑡,𝑖	0	85	0	0	0	𝑋 𝑃7 𝑖,𝑚𝑘𝑡	0	0	90	0	0
	𝑋 𝑃4 𝑚𝑘𝑡,𝑖	0	0	12.8	0	0	𝑋 𝑃13 𝑖,𝑚𝑘𝑡	0	0	0	360	0
	𝑋 𝑃1 𝑚𝑘𝑡,𝑖	26	0	0	144	14.3	𝑋 𝑃4 𝑖,𝑚𝑘𝑡

Table 3 .

 3 Impacts of the model

	PU	Production capacity	Production SA	Utilization SA %	Profit SA	Production IS	Utilization IS %	Profit IS	Profit difference %
	1	60	49.83	83	1,260.6	60	100	1,684	+ 14.4
	2	99	90	91	777.6	99	100	1,926.8	+ 42.5
	3	100	89	89	124.2	100	100	155.3	+ 11.1
	4	360	360	100	4,665.7	360	100	5,564.7	+ 8.8
	5	150	138	92	128.2	143	95	117.7	-4.3


Salvatore Ammirato
email: salvatore.ammirato@unical.it

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

Marco Della Gala

Nicola Frega
email: nicola.frega@unical.it

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

Marco Della Gala
email: mdellagala@glos.ac.uk

Sustainable Development for Rural Areas: A Survey on the Agritourism Rural Networks

Keywords: Sustainable development, Agritourism, Collaborative network, Agritourism Rural Network, ICT

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

Introduction

Rural communities have been heavily impacted from a wide range of changes, mainly consisting in the loss of economic importance of agricultural activities, low wages, a dramatic fall in employment rates and a large amount of people that left rural areas for big cities. Such changes exposed rural communities to the risk of inexorable decline, from an economic, social and environmental point of view.

To overcome the present unsustainable development model, rural communities are gaining an increasing awareness on the importance of territorial specific resources (e.g. typical foods, landscape, monuments, history, traditions) as a way to produce a long lasting development for rural area as a whole. Agrifood producers and other organizations (e.g., suppliers of touristic service, craftsmen, local administrations) operating in rural areas have spontaneously started organizing in rural networks, as a way to answer the rural communities' needs in terms of socio-economic development, improving their revenue streams, developing new market niches and increase their competitiveness against large retail companies. Collaboration in the agrifood sector is characterized by a re-connection among producers and consumers that is known in literature as "re-localisation" whose aims are: "re-vitalisation of territory identity and rural community relations to local food and agriculture, linking with sustainable agriculture, economically viable and socially responsible practices" [START_REF] Marsden | The Condition of Rural Sustainability[END_REF] [START_REF] Volpentesta | Classifying short agrifood supply chains under a knowledge and social learning perspective[END_REF]. Collaboration based organization models which rise under the principle of re-localization are generally named Alternative AgriFood Networks (AAFNs) [START_REF] Volpentesta | Alternative Agrifood Networks as Learning Communities: Some Issues for a Classification Model[END_REF]. In this work we consider a particular form of AAFNs, namely Agritourism Rural Networks (ARNs), where farms, which deploy agritourism activities, represent a touchpoint between a network of rural actors (organizations, companies, public administrations) and tourists interested in enjoying the local territory [START_REF] Ammirato | The potential of Agritourism in revitalizing rural communities: some empirical results[END_REF]. This work aims to provide a deeper understanding of the ARN phenomenon evaluating its potentials in fostering socioeconomic development of rural areas. Agritourism is able to balance needs of rural tourists with those of rural communities, offering a real opportunity for the economic and social development while mitigating undesirable impacts on environment and other socio-cultural aspects. In particular, we report main results of an empirical survey carried out in 2016 on a sample of 105 farms offering agritourism activities and all located in the same region (Calabria, Italy). After analysing the extent of the agritourism phenomena in the region, the survey was aimed to evaluate the presence of ARNs and their characteristics. A specific section has been devoted to introduce ICT tools useful to support collaboration among the ARN's members and to exploit the collectable big amount of data in order to design new patterns of sustainable development.

Theoretical Background

Over the last decades, both at political and scientific level there has been an increasing consensus on the need of a transition to sustainable rural development strategies [START_REF] Watts | Making reconnections in agro-food geography: alternative systems of food provision[END_REF] [START_REF] Feagan | Consumer quest for embeddedness: a case study of the Brantford Farmers' Market[END_REF]. Such strategies are aimed to balance economic growth with cultural and natural resource conservation [START_REF] Sonnino | For a 'piece of bread'? Interpreting sustainable development through agritourism in Southern Tuscany[END_REF], taking into account the three pillars of sustainability which are known as the Triple Bottom Line (TBL) [START_REF] Elkington | Cannibals With Forks: the Triple Bottom Line of 21st Century Business[END_REF]. Several authors have argued that the agricultural sector represent a 'seedbed' for sustainable rural development and has the potential to foster the interrelationships between farms and people, both within rural areas and between rural and urban areas [START_REF] Miller | Diversification for sustainable development in rural and regional Australia: How local community leaders conceptualise the impacts and opportunities from agriculture, tourism and mining[END_REF][10]. To foster sustainable development pathways, scholars advocate the necessity to re-construct local agro-food systems [START_REF] Berti | Competitiveness of Small Farms and Innovative Food Supply Chains: The Role of Food Hubs in Creating Sustainable Regional and Local Food Systems[END_REF] leveraging on the concepts of re-territorialization, re-localization [START_REF] Volpentesta | Classifying short agrifood supply chains under a knowledge and social learning perspective[END_REF], diversification [START_REF] Volpentesta | A collaborative network model for agrifood transactions on regional base[END_REF] and on the adoption of organizational models which allows the reconnection between producer and consumers. Thanks to the renewed interest in local food production, new organizational networks, based on processes of synergic collaboration between farmers, consumers and other rural actors, have emerged in recent years [START_REF] Volpentesta | Alternative Agrifood Networks as Learning Communities: Some Issues for a Classification Model[END_REF]. These collaborative networks (that have been called AAFNs) aim at short-ening the physical and social distances between producers and consumers by minimizing the number of intermediaries in the food supply chain [START_REF] Kneafsey | Short food supply chains and local food systems in the EU. A state of play of their socio-economic characteristics[END_REF] [START_REF] Goodman | Editorial the quality ''turn'' and alternative food practices: reflections and agenda[END_REF]. AAFNs have the potential to positively affecting the sustainable development of rural areas along all the three pillars of sustainability in agricultural systems [START_REF] Sonnino | Sustainable food security: an emerging research and policy agenda[END_REF] [START_REF] Forssell | The sustainability promise of alternative food networks: an examination through alternative characteristics[END_REF].

Strictly related to the sustainable rural development and to the sustainable agriculture, there is the topic of sustainable rural tourism. Rural tourism represents a growing market offering to rural communities growth opportunities that arise from the emerging trends in tourism demand, which tent to pay more attention to the values of culture, food, and to the countryside. It can bring a valuable contribution to the sustainable development of rural areas. Its contribution can be expressed not only in financial terms, but also in terms of jobs, enhancement and revitalization of community pride, encouragement to the adoption of new working practices, and the injection of a new vitality into sometimes weakened economies [START_REF] Irshad | Rural Tourism -an overview[END_REF]. In particular, within rural tourism, agritourism offers to small farmers a chance for the economic diversification. In [START_REF] Sonnino | For a 'piece of bread'? Interpreting sustainable development through agritourism in Southern Tuscany[END_REF] agritourism is defined as "tourism activities exercised by farmers through the exploitation of their own farm according to logic of connection complementarity and nonprevalence" of tourism activities with respect to agricultural based activities. This definition is in line with the Italian Legislative System. In the scientific literature the term "agritourism" is often understood as a synonym for "farm tourism," "farm-based tourism," and "rural tourism" [START_REF] Tew | The perceived benefits of agritourism: The provider's perspective[END_REF]. In our work, while the term "rural tourism" refers simply to tourism services provided in a rural context, we refer to agritourism as "tourism services provided by agricultural entrepreneurs within their own farm, allowing also visitors to take part, directly or indirectly, in agricultural activities". It has been observed that agritourism offers many opportunities to small and medium farms, including increased farm gross income, the generation of cash flow and the creation of job opportunities. Moreover, it has the potential to affect positively the local rural development as a whole because its positive impacts extend beyond the farm gates through the interrelated activities with other local economic sectors [START_REF] Yang | Impacts and challenges in agritourism development in Yunnan, China[END_REF]. Strictly connected with the concept of agritourism, the concept of Agritourism Rural Network, ARN, has been first introduced in [START_REF] Ammirato | The potential of Agritourism in revitalizing rural communities: some empirical results[END_REF]. Within a rural community it is possible to identify particular kinds of Collaborative Networks, namely ARNs, whose members are agritourism farms and other rural organizations (craftsmen, artists, local public administration, transportation services, typical agrifood producers, etc.) that collaborate to offer tourists the opportunity to enjoy the local territory. Rural communities, by means of the ARNs, could achieve the common goal of sustainable local development in order to increase their general competitiveness in a larger area.

The aim of this regional study is to analysing the extent of the agritourism phenomena and, in particular, its potentials to trigger and support ARNs as means of sustainable development for rural areas. To pursue the above purpose, we operationalized it in three research questions which can be formulated as follows according to [START_REF] Ammirato | The potential of Agritourism in revitalizing rural communities: some empirical results[END_REF] and [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF].

 RQ1: Do farmers understand the importance of setting agritourism activities within their farm (i.e.: to what extend do farmers engage in agricultural activities)?  RQ2: Can the agritourism farm be a direct contact point between the rural community offer of products/services and the tourist request for relocalization?

 RQ3: Is it possible to highlight the presence of an agritourism rural network in supporting the activities of the agritourism farm?"

The Research Design, Results and Discussion

To answer the research questions, we analysed:  secondary data from the Italian National Institute of Statistics -ISTAT [START_REF]ISTAT: Report on Agritourism activities in Italy[END_REF], and Confagricoltura, [22], related to agribusiness sector in Calabria for the year 2015;  results of a survey carried out among the Calabrian agritourism farms in the period 02 January 2016 to 31 January 2016 concerning activities they performed in 2015. Moreover, to give a better understanding of the evolution over time of the surveyed phenomenon, results of this study are compared with those obtained from a similar research conducted in 2012 [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF]. The period between the two surveys characterized by a strong economic crisis in the EU agricultural sector, thus putting in contrast results of the two surveys helped us to better understand the role of agritourism and ARNs in giving sustainable answers to the rural development question. At the end of 2015, 521 agribusinesses were authorized to offer agritourism activities, in Calabria [START_REF]ISTAT: Report on Agritourism activities in Italy[END_REF]. They represent the population of interest, P, of this study. In other Italian regions agritourism was much more widespread both in absolute and proportional terms. Tuscany and Trentino Alto Adige were the regions where agritourism was most diffused. In Tuscany, in the 2013 on 66.584 agribusinesses, 4.108 deployed agritourism activities (6,16%) while in Trentino Alto Adige on 34.693 farms, 3.506 of them offered agritourism activities (10.10%). At the national level, on 758.953 Italian agribusiness, only 22,238 (2,93%) offered agritourism activities [22]. In Calabria only 521 to 30.857 agribusinesses offered agritourism activities meaning that the rate (1,69%) was lower than the national one. In 2012 data were more promising in Calabria: on 20474 agribusinesses, 610 were authorized to offer agritourism activities (1,96%). After three years, the already limited percentage of agritourism on the total agribusinesses in Calabria was further decreased (from 1,96% to 1,69%). Moreover, while the overall number of agribusiness showed a growth rate of 8,62%, in the same three years, the number of agritourism farms decreased at a rate of 14,6% [START_REF]UNIONCAMERE: Rapporto economico sulla Calabria[END_REF] A questionnaire was set up containing 16 questions aimed to answer the research questions. The questionnaire has been delivered to managers of all the farms belonging to P. On 521 agritourism farms in P, 105 of them (20,1%) returned the filled questionnaires with the requested data. Such farms constituted the sample S of our survey; for each farm in S, we analysed their responses and statistical results are reported.

When requested to quantify the percentages of farm's yearly turnover and overall costs coming from the agritourism activities, 74% of respondents affirmed that more than 20% of turnover come from agritourism while 90% of respondents reported that agritourism costs represent more than 20% of the overall farm's costs. While data about turnover is essentially in line with the analogous data in [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF], surprisingly, percentage of agritourism costs on overall farm's costs is more than double in 4 years (from 44% to 90%). Details can be found in table 1. Moreover, when asked to point out trends of evolution of turnover and costs of agritourism activities on the overall farm's turnover and costs in 2015, with respect to the first year of activity of the agritourism, what emerges is:

 29% of respondents stated that in 2015 agritourism turnover is decreasing respect the first year of activity, 19% reported a stable trend of evolution, 34% reported a growth while 18% did not answer the question.  1% of respondents stated that in 2015 agritourism costs are decreasing respect the first year of activity, 27% stated a stable trend of evolution, 48% stated a growth while 24% did not answer the question. The small number and percentage of agritourism farms present in Calabria in 2015, together with the strong decrease of them in 4 years (-14,6% while the total number of agribusiness in the region remained unchanged in the same period) highlight that we cannot positively answer to RQ1. This result confirms the conclusion in [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF]. Data from the survey have a twofold interpretation. On the one side, investments in agritourism activities give farmers a good return in terms of turnover that is durable through time. On the other side, percentage of farm costs imputable to the agritourism costs have been increasing considerably in the last 4 four years and, generally, since the beginning of the agritourism activity.

To answer RQ2, respondents were enquired to indicate which of the activities in table 2 they offered to tourists as part of the agritourism products portfolio. Activities in table 2 go in the direction to make the agritourism a contact point between the tourist and the local territory. The aim of these questions is to understand if agritourism farms have potentials to be drivers for the development of ARNs. In table 2, results of the survey are compared with results in [START_REF] Li | The concept of smart tourism in the context of tourism information services[END_REF]. tasks) also in the form of "Educational farm" (e.g. milking cows, sheepdog display, feeding animals)

Data highlights that farmers well understand the importance to enlarge the product portfolio with territory-related activities that let tourists enjoy the cultural/naturalistic heritage of the region (also in the form of participation to farm life) as well as to buy typical agrifood products of the region (both self-produced and purchased from local producers). When put in contrast with data from the 2012, 2015 results become more important considering the growth rate in the offering of activities from surveyed farms. We can give a positive answer to RQ2; agritourism is no more conceived only as a means to sell products/services self-produced as in 2012 but it can be considered a sort of tourist's bridgehead to the rural network. This result is in contrast with result of the 2012 survey when "agritourism is still intended by farmers only as a means to sell products/services self-produced" [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF].

To answer RQ3, respondents were asked about the presence of formal cooperation agreements with other local service/product providers to carry out the agritourism activities and the percentage of agrifood products utilized in agritourism activities coming from local producers. Moreover, respondents were asked about the numbers of local workers engaged by the farm exclusively to carry out the agritourism activities. Results are present in table 3 together with similar data from the 2012 survey [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF]. The farm utilized only self-produced agrifood products for its agritourism activities.

27% 5%

Overall farms using products supplied from local producers. 71% 95%

Data highlights that ARNs are present and consolidated in Calabria, thus we can positively answer to RQ3. Local service/product providers definitely benefit from the agritourism presence considering that almost 90% of farms declare some kind of formal cooperation with them. Moreover, local products are purchased from 95% of agritourism farms.

ICT and Agritourism

On the basis of a recent literature and empirical exploration we can suggest how ICTs can provide noteworthy opportunities for organizations belonging to an ARN in term of reduction of operational and management costs [24][25]. ICTs have in fact a con-siderable impact in improving internal business processes (e.g. reservation management, back-office activities), facilitating business transaction and networking among partners in ARNs. From ARNs perspective, ICTs have not only a considerable impact in process efficiency, thus reducing operational costs, but represent also an effective advertising medium and a fundamental distribution channel in order to increase customers and visitors the (e.g. booking platforms). Moreover, social interactions between organizations in an ARN and tourists enable new learning opportunities, educating tourists about local tangible and intangible assets (monuments, typical foods, traditions, cultural heritage). Hence, the use of ICTs is able to engender trust and cooperation within a community and supports the exchange of local knowledge that can revitalise local/traditional knowledge and encourage sustainable territorial management.

ICT can be deployed in many business activities carried out by the ARN. When evaluating the business opportunities coming from tourism activities, farmers can be helped by the large amount of data available from a variety of sources. A good starting point can be offered by social media platforms which are taking an important role in supporting tourists in decision support both for planning and enjoy a journey experience. Data coming from the social activities of tourists are important to point out new trends in destination choices and preferences in tourism experience. According to [START_REF] Mariani | Facebook as a destination marketing tool: Evidence from Italian regional Destination Management Organizations[END_REF], a multidimensional analysis of such a data can be deployed to improve marketing strategies. The use of mobile devices, during the journey experience, generate another class of data very useful to depict and track tourist behaviors in different contextualized scenarios. Smartphones can interact with other smart "objects" to generate an "augmented tourism experience" within the local territory [START_REF] Ammirato | Rethinking tourism destinations: collaborative network models for the tourist 2.0[END_REF]. Internetof-Things technologies let farmers create and manage cyber-physical systems, CPS, in tourism activities in order to perform virtual interactions with visitors using sensors already present in their devices [START_REF] Rho | Cyber physical systems technologies and applications[END_REF]. Bluetooth beacons, NFC tags, GPS positioning, e.g., can help user to improve the information quality of the destination, to digital control and manage access to physical resources, to guide and support orienteering [START_REF] Nitti | IoT Architecture for a Sustainable Tourism Application in a Smart City Environment[END_REF]. In [START_REF] Smirnov | Cyber-physical infomobility for tourism application[END_REF], a service oriented architecture of CPS in tourism infomobility is proposed in order improve the tourism management activities. The concept of CPS, as integration of digital and physical processes, can lead to the achievement of valid smart tourism management to ensure its sustainability and efficiency. All these interactions can be analyzed both at farm and at ARN level to better tune the overall experience and plan an improved bundle of suggested activities within the ARN territory [START_REF] Li | The concept of smart tourism in the context of tourism information services[END_REF].

E-commerce platforms can be the main digital touchpoint through which tourist can buy ARN products and services. In planning phase of the tourism experience, tourists can book events and services to enjoy in situ. In experience phase, tourists can do the same and can buy food or ingredients used during cooking experiences; tourists can buy products previously delighted at farms site and eat them at home. In [START_REF] Vassalosa | Farmers' Willingness to Pay for Various Features of Electronic Food Marketing Platforms[END_REF], authors have analyzed and explored some case studies in introducing ICT to increase the value proposition of a touristic organization. ARNs should consider the influence and impact of social media (as part of tourism marketing strategy) for all involved actors in agritourism sector to determine the economic contribution of social media networks [START_REF] Zeng | What do we know about social media in tourism? A review[END_REF]. It is crucial, for farmers in ARN, get experienced in using social media tools to meet marketing objectives quickly and improve their competitiveness.

From a supply-chain point of view, ARN member might adopt many ICT solutions to support collaboration and cooperation. B2B e-commerce solution or digital multisided platforms (DMSPs) can be a valid support for supply chain management for meal and services provisioning. One goal can be an e-Marketplace framework in which all cooperative actions of ARN members can take place. One of the prerequirement to deploy such an innovative strategy can be the partial adoption of process automation (for example, using basic ERP solutions) to support farmer organization internal activities [START_REF] Yoo | The Role of Information and Communication Technologies (ICTs) in Marketing Tourism Experiences[END_REF].

Conclusion

Overall, Calabrian farmers do not understand the importance of setting agritourism activities within their farm. In this sense, they look less far-sighted than farmers in other Italian regions, like Tuscany and Trentino Alto Adige. Such result is more serious considering that survey results confirm the potential of agritourism activities in improving the revenue streams of Calabrian farms. Anyway, among the Calabrian farmers there is a leading group (the 521 ones that offer agritourism activities) who exploit the potential of agritourism in giving sustainable development to the farm and in being a contact point between the tourist and the local territory through the presence of consolidated ARNs. The ARNs can be means of sustainable development for local areas also in order to react to the economic crisis. This conclusion is in contrast with [START_REF] Ammirato | The Agritourism as a means of sustainable development for rural communities: a research from the field[END_REF] who stated that, in 2012, "Local suppliers, through their long-term contracts with the farm, offer agritourists their goods and services having an indirect contact with them (mediated by the farm)". Survey results demonstrate that, in 2015, local suppliers can have a direct contact with tourists selling them their products and services, even if this is mediated through the agritourism.

A change in the ARNs structure is evident respect to the 2012 survey:  farms which declare some kinds of formal cooperation with other local service/product providers to carry out the agritourism activities increase from 40% in 2012 to 89,5% in 2015.  farms using products supplied from local producers increase from 71% in 2012 to 95% in 2015.  farms selling products of other local suppliers through the agritourism surprisingly increase from 13,46% to 68,5%.  Percentage of respondents who declare a yearly turnover coming from agritourism activities more than 20% of the overall farm turnover remains almost the same in 2012 and 2015 among while % of respondents who declared that overall farm's costs coming from agritourism activities are more that 20% of the overall farm costs grow up from 44% to 90%.  The mean value of the number of local workers engaged by the farm exclusively to carry out the agritourism activities decreased from 5 to 1,53. It seems that farmers reacted to the economic crisis improving collaboration with local suppliers, so strengthening the connection within the ARN, and reducing the number of direct employers. Unfortunately, in the same period when such forms of outsourcing were set up, an overall increase in the agritourism costs was evident. Whether it was the outsourcing the motivation of the cost increase or vice-versa, the question remains unclear. What is certain is that the increase of operational costs is a problem for agritourism farms. At the same time, it is evident how the growth of tourist number would have a direct impact on improving farm's turnovers and on increasing the number of ARN customers. Lastly, agritourism farm owners should increase service value and their competitiveness exploiting the potential of ICT, both to consolidate the relationships with ARN partners and to meet and anticipate travelers' needs and expectations. Further questions on this topic will be investigated in future research.

Table 1 .

 1 Percentages of farm's yearly turnover and overall costs coming from the agritourism activities

	% of	% of Yearly turnover	% of overall farm's costs com-
	respondents	coming from agritourism	ing from agritourism activities
		activities
		2012	2015	2012	2015
	< 20 %	33 %	27 %	56 %	10 %
	Between 20 % and 50 %	29 %	61 %	23 %	69 %
	> 50%	38 %	12 %	21 %	21%

Table 2 .

 2 Activities offered to tourists and percentage of agritourism which offer them

	Activity	agritourism farms	agritourism farms
		offering the activi-	offering the activi-
		ty in 2012	ty in 2015
	Meal provision	94,23%	96,19
	Selling of self-produced food	71,15%	93,33
	Selling of products of other local farms	13,46%	68,5%
	Visits to touristic/cultural/naturalistic places close	53,8%	67,7%
	to the agritourism (Excursion)
	Active participation in the life of the farm (e.g.	48,5%	60,9%
	'pick-your-own' facilities, participation in farm

Table 3 .

 3 Agritourism connections with the local territory

	2012	2015

Acknowledgments. This work has been partially supported by a Marie Skłodowska-Curie Individual Fellowship of the European Union's Horizon 2020 Programme, 'Situated learning Opportunities Fostered by Ict adoption in AAFN -SOFIA' (G.A. 702884). This paper only reflects the authors' views and funding agencies are not liable for any use that may be made of the information contained herein


Samia Chehbi-Gamoura

Ridha Derrouiche

Big Valuable Data in Supply Chain: Deep Analysis of Current Trends and Coming Potential

Keywords: Big Valuable Data, Analytics, Supply Chain Management, SCOR model, Mind-Map cartography

Today, Big Data Analytics (BDA) are definitely the key basis of competitiveness for enterprises in their Supply Chains. The outburst of data captured, accumulated and analyzed is impacting the value-added-chain at all levels from manufacturers to customers. In this paper, we develop a structured methodology to provide a deep analysis of Big Data Analytics methods across the Supply-Chain Operations Reference (SCOR) model processes. An exhaustive literature review is illustrated to afford a comprehensive Mind-Map cartography with a BDA-SCOR matching matrix. The proposed approach points to a number of research concerns that need to be addressed by research community. Outcomes of this study may highlight relevant guidelines for upcoming works of both academics and industrials. It highlights the need for collaborative Big Data to manage SCM more intelligently. Our objective is to provide an effective analysis to understand how Big Data Analytics become even more valuable for better Supply Chain Management.

Introduction

There is no doubt; Big Data Analytics (BDA) is progressively becoming inevitable practice to extract more and more business values [1]. The main motive of Supply Chain (SC) managers is certainly to possess meaningful insights that allow them to better forecast, predict, reveal hidden patterns, and then to gain wider edge of competitiveness [START_REF] Bendoly | Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics[END_REF]. Henceforth, academics and industrials are aware that conventional databases are not capable to process such Big Data [START_REF] Hazen | Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications[END_REF]. Analytics are reforming the way Supply Chain Management (SCM) handle acquired data internally; such as transactional information systems, and externally such as social networks, mobile devices, and IoT (Internet of Things) sensors [START_REF] Satpute | A Review of: Cloud Centric IoT based Framework for Supply Chain Management in Precision Agriculture[END_REF].

Beyond considerate what Big Data earns, companies now are focusing on how analytics might be used to obtain business meaningful insights [START_REF] Ketter | Competitive benchmarking: an IS research approach to address wicked problems with big data and analytics[END_REF]. Given the variety and complexity of very large unstructured and high-dynamic datasets, this task is not trivial and may be very challenging [START_REF] Hazen | Back in business: Operations research in support of big data analytics for operations and supply chain management[END_REF].

Accordingly, we propose this paper with multifold purposes: (a) Foremost, it carried out a profound literature review across past and present patterns in academic publications. The bibliographical analysis is conducted by observing contributions, synthetizing knowledge, drawing tendencies, and making causality relationships by classifying intakes and highlighting further research opportunities. (b) Secondly, it provides a Mind-Map cartography of using BDA methods in Supply-Chain Operations Reference (SCOR) model as a clear synopsis of current research trends. (c) Lastly, this study draws future research trajectories and sheds light on the gaps that need to be overcome. Scholars can leverage this paper as a reference for future research opportunities, and SC specialists can use it as a methodology to benchmark BDA methods and measure impact in valuable information for SC.

This paper is structured as follows. We begin by introducing BDA methods and SCOR model reminder. We then develop the proposed methodology of bibliography analysis. Finally, we conclude by underlining the current trends of SCM-BDA researches, the untaken breaks, and the expected potential in the near future.

Related Works and Research Gaps

Analytics are ranged into several collections of methods, sub-methods and techniques that diverge in their objectives, prerequisites and applicability [START_REF] Bi | Big data analytics with applications[END_REF]. Hence, prior to hastily study applicability of these methods in SCM, there is a prerequisite to first understand their settings and features. The next sub-section offers a brief overview.

Big Data Analytics Methods and Techniques

The word 'value' in the context of BD denotes extraction of worthy information by investigating the accumulated data through transformation and processing. Aligned with [START_REF] Zhao | An optimization model for green supply chain management by using a big data analytic approach[END_REF], BDA are emerging as the 'next big thing' and is defined as a collection of technologies that handle, incorporate, and report massive data by transforming it into new insights not attainable with traditional technologies.

In one hand, academics are aware that decision makers progressively view in BDA an imperative driver of modernization and a substantial source of value creation for competitive benefit [START_REF] Cao | Linking business analytics to decision making effectiveness: A path model analysis[END_REF]. In fact, since 2014, there is an increasing emerging of research works: case studies ( [START_REF] Chong | Predicting RFID adoption in healthcare supply chain from the perspectives of users[END_REF], [START_REF] Dubey | The impact of big data on world-class sustainable manufacturing[END_REF], [START_REF] Wu | Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties[END_REF], etc.), original works ( [START_REF] Zhang | An optimization method for shopfloor material handling based on real-time and multi-source manufacturing data[END_REF], [START_REF] Rehman | Big data reduction framework for value creation in sustainable enterprises[END_REF], [START_REF] Giannakis | A multi-agent based system with big data processing for enhanced supply chain agility[END_REF], etc.), surveys ( [START_REF] Emani | Understandable big data: A survey[END_REF], [START_REF] Chen | Data-intensive applications, challenges, techniques and technologies: A survey on Big Data[END_REF], etc.), and a variety of literature papers ([18, [START_REF] Schoenherr | Data science, predictive analytics, and big data in supply chain management: Current state and future potential[END_REF], [START_REF] Duan | Big data analytics and business analytics[END_REF], etc.). Likewise, industrials are attentive to the potential of analytics in management of SC. Indeed, the worldwide leader in research and advisory Gartner's® has mentioned in [START_REF] Gartner | Survey Analysis: What Does Increased Adoption of Big Data Mean to Supply Chain Strategists? Research Analysis[END_REF] that BDA appear among the Top 25 SC research topics for 2016. Forbes®, also, argued in [START_REF] Forbes | 51% Of Enterprises Intend To Invest More In Big Data[END_REF] that no single research topic in the last decade has as much effect on incumbent investments for industrials.

Approaches of BDA are numerous and may be alienated into several methods and sub-methods; to be employed solely or combined jointly [START_REF] Chen | Data-intensive applications, challenges, techniques and technologies: A survey on Big Data[END_REF]. Each method/submethod uses one or a mixture of statistical or mathematical techniques, up on the expected business value. According our literature review, the main root methods that concerned by SC are: Exploratory Data Analysis (EDA) [START_REF] Zhao | An optimization model for green supply chain management by using a big data analytic approach[END_REF], Confirmatory Data Analysis (CDA) [START_REF] Dubey | The impact of big data on world-class sustainable manufacturing[END_REF], Qualitative Data Analysis (QDA) [START_REF] Luthra | Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry[END_REF], and Real-Time Analysis (RTA) [START_REF] Vera-Baquero | Real-time business activity monitoring and analysis of process performance on big-data domains[END_REF]. Based on the underlying methods, sub-methods and techniques can also be categorized into several groups, but given the breadth of these techniques, an exhaustive list exceeds the extent of a single paper. Though, a relevant sub-list which has already been used in the field of SC covers mainly: regression [START_REF] Papadopoulos | The role of Big Data in explaining disaster resilience in supply chains for sustainability[END_REF], optimization [START_REF] Dutta | Managing a big data project: the case of ramco cements limited[END_REF], induction [START_REF] Tan | Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph[END_REF], fuzzy logic [START_REF] Chang | IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis[END_REF], classification [START_REF] Chong | Predicting RFID adoption in healthcare supply chain from the perspectives of users[END_REF], aggregation [START_REF] Wu | Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties[END_REF], and so on.

Supply Chain Operations Reference (SCOR) Model

In order to expand in competiveness of companies, SCOR model was developed by Supply Chain Council (SCC) for designing and enhancing SCs. It offers standard frame for deciding, organizing and fulfilling SC processes [START_REF]Supply Chain Operations Reference Model: Release 11[END_REF]. Nevertheless, it is traditionally used in benchmarking studies [START_REF] Jamehshooran | Assessing supply chain performance through applying the SCOR model[END_REF]. SCOR model governs five processes; they are consecutively named and briefly described in table 1.

Table 1. Brief definition of processes in SCOR Model (SCOR model V.9.0 [START_REF]Supply Chain Operations Reference Model: Release 11[END_REF])

Plan

Process that includes planning activities.

Source

Process that includes procurement activities.

Make

Process that includes transformation of contents or services.

Deliver

Process that includes fulfilment of customer orders.

Return

Process that includes reverse movements of goods from customers.

Based on pre-defined processes, SCOR can detect where action is required. It can also help to arrange more accurate projections. Accordingly, we base our study through SCOR processes. The next section describes step-by-step our methodology.

3 Proposed Methodology

Data Collection

By using Harzing's Publish or Purish V.5® [31], we queried out the more relevant and recent publications in the topic of BDA and SCM (from 2014 to 2017). Parameters of Harzing's serach request are itemized in table 2. Once the information were sorted, we then cleansed and filtered the academic database by deleting unwanted columns (ranking, types, ISSN) and rows (programs, books, technical reports, white papers, patents, literature reviews, unrelated researches, etc.). We kept only used columns (cites, authors, title, year, source, publisher, URL, countries) and rows (journals).

Analysis and Findings

By using the sorted list of last step, we reviewed each contribution and recorded a matrix that matched information with respect to BDA methods and SCOR processes. Discussion of our relevant findings and empirical results are in following sections.

Finding1: SCOR-BDA matching matrix. For each row (reference), we analyzed BDA methods, sub-methods and techniques (section 2.1), and then recorded matching cells with regard to SCOR processes according to table 1. Table 3 below illustrates the SCOR-BDA matrix as applied to the collected bibliographical dataset.

Finding 2: Mind-Map Cartography of SCOR-BDA. We constructed cartography by using Mind-Mapping technique [START_REF]mindmapping[END_REF] with Coggle® tool [33]. The branching out in (Appendix, Fig. 3) illustrates that all defined methods are employed in all SCOR processes but not with the same techniques. For instance, the FUzzy Logic technique (FUL) is used in Data Statistical-inference Methods (DSM) in 'Make' process but not in 'Source' process. Another ascertainment; the process 'Deliver' is the most prolific branch regard to methods and techniques already addressed in literature.

Table 3. SCOR-BDA matching matrix according to the bibliographical dataset. As seen in Fig. 1, there is an exponential increase in the number of contributions for all SCOR processes with an overall exponential variation of +0.064 (R²=~0,90). A few publications was issued in early 2014 (equal and less than 11%), and at the end of 2015 (~11%-~23%). However, in just two consecutive years later (2016 and 2017), publication rates have almost doubled for each year and each process (for instance; in plan process, the rate jumped from 11% to 26%, and then to 53%). This demonstrates that BDA tools have emerged as one of the fastest growing fields in SCM in recent years. On the other hand, as we observe an unequal growing interest for processes, we then broke down the total numbers by processes. As we observe in Fig. 2, all SCOR processes have adopted BDA, however with mixed paces. The evolution rate is sorted to make processes in a consecutive order: (1) Plan (+0,238), (2) Deliver (+0,100), (3) Return (+0,092), (4) Make (+0,056) and lastly (5) Source (+0,052).

Subsequently, we assume that: the more operations are oriented inside the company (manufacturer), the less analytics are exploited. In other words, researchers need valuation of the data that are positioned at company's connectivity with the Big Valuable Data in Supply Chain 233 outside (mainly Deliver, Return and Plan). This indicates a lack of collaboration between companies in the process of valuing their data. We can explain this by the fact that these companies compete to extract as much value from data, but unfortunately each company acts separately. This point is very significant as it raises the need for collaborative Big Data to exploit it intelligently in SCM.

Furthermore, from a rational point of view, BDA are applied to processes differently because of the nature of these processes themselves. In instance, it seems normal to use analytics in Plan process as it is the most uncertain activity and then which necessities advanced techniques to obtain pertinent results. While for Make process, which is less unstable and better setup and maintained, analytics are less pertinent.

Conclusion: Discussion and Future Trends

It is true that the proposed approach may have some limits such as restriction in dataset collection. Indeed, we have collected data by the way of Harzing's Publish or Purish® tool which is itself limited in search queries and cannot combine searching engines. Despite this, the study allowed us to have a deep overview of the BDAs and their positioning in the SC processes.

The proposed approach offers a matching matrix and a Mind-Map cartography for BDA-SCOR benchmarking. Both tools can be largely taken up by academics and SC specialists to intuitively integrate them in orienting understanding. Furthermore, the empirical results presented in this paper argue that future researches should embrace more largely BDA, to continue redefining the focus of the contemporary management of SC. However, as the relevant literature shows, using BDA must not be hastily due to a fashion effect. SC specialists must establish a comprehensive schema to identify the objective of using analytics, the reason to expand volumes of exploited data and expressly the expected business value by using analytics.

Otherwise, the usage of BDA methods and techniques in the field of SC can still be significantly improved in at least three aspects: (a) more attention needs to be focused to BDA in manufacturing (Make process) and procurement (Source process). (b) A faultless and feasible deployment of BDA should be set up between all SC actors, thus, valuation of massive data would be conducted more accurately in terms of collaboration with decreasing of underlying costs, and timely truthful information can be provided for decision-makers (c) Upstream of the data processing step, this data should be collected, arranged, grouped, and summarized jointly by all SC actors. Rather than just accumulating and processing data, managers need to structure, and link their efforts to create a coherent picture of valuable data across overall the chain. This leads us to scrutinize more collaborative and intelligent Big Data utilization.

Appendix

Finding 3 :

 3 Overview of SCOR-BDA evolution. Bars diagrams in Fig.1; summarize the total number of publications for each process in SCOR, in the current three years (2014-2017).

Fig. 1 .Finding 4 :

 14 Fig. 1. Normalized total numbers of publications in BDA-SCOR matrix according to the bibliographical dataset (2014-2017) Finding 4: Detailed view of SCOR-BDA evolution. Fig. 2 sorts the number of contributions for each process in the matrix BDA-SCOR of the selected dataset.

Fig. 2 .

 2 Fig. 2. Variations of BDA in SCOR processes according to the collected dataset (2014-2017)

Fig. 3

 3 Fig. 3 bellow illustrates the Mind-Map cartography according the collected dataset.

Fig. 3

 3 Fig. 3 MindMap of BDA-SCOR according to the bibliographical dataset (available under public access on [67])

Table 2 .

 2 Search request in Harzing's Publish or Purish V.5®[31]

	Query Date	2017-03-22
	Maximum papers number	100
	Any of the words	{Big Data Analytics, Supply Chain, Management, Value}
	Publication type	Journal
	Since year	2014
	Publisher	All
	Search engine	Google Scholar Engine
	Location	Title, abstract, key words


Marika Iivari
email: marika.iivari@oulu.fi

Minna Pikkarainen
email: minna.pikkarainen@oulu.fi

Timo Koivumäki
email: timo.koivumaki@oulu.fi

How MyData is Transforming the Business Models for Health Insurance Companies

Keywords: Business model, MyData, personal data, occupational health, insurance business

This paper discusses the potential impacts of MyData in healthcare business, more precisely occupational health insurance companies, and how the coming of MyData will transform the business models and the whole logic of value creation and capture of health insurance businesses. These companies have traditionally acted alone and relied on organization-centric business models. Through an empirical study, we demonstrate how these organizations are now heading towards acting as active members of collaborative health service ecosystems.

Introduction

Within the past years, the use of data in the healthcare sector has become increasingly important. People are embracing a future healthcare system that allows them to control and share their personal health information for receiving improved personalized care. The adoption of cloud technologies and mobile devices, for instance, enable novel ways to generate, access, and manage personal health data. People voluntarily agree vast amounts of personal data to be stored and utilized by companies in exchange of services. For the use of personal health data, the MyData paradigm has therefore emerged to address to strengthen digital human rights while simultaneously opening new opportunities for businesses to develop personal datadriven services. MyData refers to an approach that seeks to transform the current organization-centric system to a human centric-system to use personal data as a resource that individuals themselves can access, control and share based on mutual trust. MyData both enables and requires active collaboration among healthcare businesses for fulfilling the human-centric service perspective through technological solutions. A shared MyData infrastructure enables decentralized management of personal data, improves interoperability, eases to comply with tightening data protection regulations, and allows individuals to change service providers without proprietary data lock-ins [START_REF] Poikola | MyData -A Nordic Model for human-centered personal data management and processing[END_REF].

The continuing growth in personal data is thus paving way for data-driven business solutions. Sharing individual's data between actors is crucial especially in preventive healthcare services. This kind of collaboration is seen as a way to differentiate from competition, requiring new kinds of collaborative business models. Because MyData is only becoming for business use through technological and regulatory developments, there is a clear research gap in studying how MyData-based collaboration is projected in business models. The aim of this study is to increase knowledge on the business potential of MyData in the field of occupational healthcare, in the case of health insurance companies. Sharing and use of data between health professionalsincluding insurance companiescould contribute to increased health and wellbeing through preventive healthcare, and result in e.g. lowered insurance costs, bringing added value to the individual client. How MyData eventually impacts the insurance business, is thus a very topical and relevant question. Building on business model literature, the research question of this study calls, how MyData is transforming health insurance companies' business models?

The paper first discusses the theoretical foundations on business models in datadriven business. It then dives deeper into MyData as a human-centric approach to healthcare. Research methodology and the empirical case are described next. The study ends with discussing research results, findings and conclusions.

Toward Data-Driven Business Models

One of the buzzwords of contemporary business is the concept of business model [START_REF] Zott | The Business Model: Recent Developments and Future Research[END_REF], [START_REF] Onetti | Innovation and Entrepreneurship: Business Models for New Technology-Based Firms[END_REF]. Previous literature has described and defined business models in various ways, such as a structure, an architecture or a business frame; a representation of a firm's relevant interactions and activities [START_REF] Wirtz | Business Models: Origin, Development and Future Research Perspectives[END_REF]. Although scholars are debating on a unanimous definition for the concept, the common view nevertheless is that business models act as pathways to fulfill unmet needs, profitability and the promise of service ([4] that will lead to competitive advantage [START_REF] Teece | Business Models, Business Strategy and Innovation[END_REF], [START_REF] Zott | The Business Model: Recent Developments and Future Research[END_REF]. Thus, business models are to "create and capture value in an inimitable way and through rare and valuable resources that are utilized efficiently" [START_REF] Ahokangas | Cloud computing and transformation of international e-business models[END_REF]. This means that a business model is a system of specific activities conducted to satisfy the perceived needs of the market, as well as specifying who does what (whether it is the firm or its partners), and how do these activities link to each other. From collaborative perspective, a business model also acts as a system of interconnected activities that determine the way the company does business with its customers, partners and vendors [START_REF] Zott | Business Model Design: An Activity System Perspective[END_REF].

Business models are often imposed by technological innovation that creates the need to bring discoveries to market, and the opportunity to respond to unmet customer needs [START_REF] Teece | Business Models, Business Strategy and Innovation[END_REF]. Stemming from this background, the concept of data-driven business model has emerged to address connectivity issues, the Internet of Things, and Big Data [START_REF] Pujol | Data-driven business models in the digital age: The impact of data on traditional businesses[END_REF]. [START_REF] Hartmann | Big Data for Big Business? University of Cambridge Service Alliance[END_REF] define data-driven business models as business models that rely on data as a key resource. According to [START_REF] Hartmann | Big Data for Big Business? University of Cambridge Service Alliance[END_REF], the source for this data can be either internal or external, the offering can consist of data itself, information or non-data product or service. Revenues can consist of, e.g. sale, licensing or subscriptions, but their definition does not consider data sharing and re-use [START_REF] Pujol | Data-driven business models in the digital age: The impact of data on traditional businesses[END_REF], as implied in MyData paradigm. According to research conducted by [START_REF] Pujol | Data-driven business models in the digital age: The impact of data on traditional businesses[END_REF], in current data-driven business, data sharing is still uncommon, to which this research this research contributes to contribute to from business model perspective.

Still, using data has become as a necessity for many organizations in order to remain competitive or survive in their field [START_REF] Brownlow | Data and Analytics -Data-Driven Business Models: A Blueprint for Innovation[END_REF]. In healthcare, the most successful services should place the sensing and supporting technologies around the needs of individuals in a manner that is highly personalized and makes the person as a driver of his own health and wellbeing. The key challenges of integrating personal data are both data availability from different silos, and consumer protection laws that currently hinder data usage especially in the health sector. Recently, open source solutions around modern web interfaces or database solutions have started to break the data silos from different sectors. This has resulted in "API Economy" [START_REF] Anuff | Almost everyone is doing the API economy wrong[END_REF], which means that companies separately create revenues through application programming interfaces (APIs) -either licensing, use-for-fee or other monetization models -very much on personal data sets. An aggregator model on the other hand emphasizes the controlling role of a central organization. In open business environment, a shared MyData infrastructure enables decentralized management of personal data, makes it easier for companies to comply with tightening data protection regulations, and allows individuals to change service providers without proprietary data lock-ins [START_REF] Poikola | MyData -A Nordic Model for human-centered personal data management and processing[END_REF]. MyData model means that organizations are moving from traditional, technology and aggregator models towards a human-centric data management approach. In the traditional "structureless" API economy, there is no clear infrastructure or platform in place for controlling and organizing the use of data in a logical manner. Organizations do not systematically collaborate, and the ecosystem is governed by closed business models. Aggregating data control would make life easier for organizations and individuals, but different aggregators do not have a built-in incentive to develop interoperability between them. In this model, there is an ecosystem in place, however, it is a closed system, dominated by large corporations. Compared to the aggregation model, MyData is a resilient model because it is not dependent on a single organization, but works as a shared open infrastructure [START_REF] Poikola | MyData -A Nordic Model for human-centered personal data management and processing[END_REF], thus relying on open innovation approach. MyData can been seen as a way to convert data from closed silos into an important, reusable resource. It can be used to create new services that help individuals to manage their lives. The providers of human-centric services can therefore create new data sharing based business ecosystems and new business models, leading to economic growth to the society as whole [START_REF] Poikola | MyData -A Nordic Model for human-centered personal data management and processing[END_REF].

Thus, business models can be seen as the focal firm's boundary-spanning transactions with external parties [START_REF] Zott | The Business Model: Recent Developments and Future Research[END_REF]. Indeed, collaboration of the focal firm with its network can be considered as one of the main functions of the business model. This approach is well captured in the MyData paradigm, yet it brings a lot of challenges for organizations to realign their current strategies and business models. As [START_REF] Ahokangas | The Practice of Creating and Transforming a Business Model[END_REF] state, the transformation of an existing business brings special challenges on business models. Business model transformation is about transforming an existing organization through repositioning the core business and adapting the current business model into the altered market place [START_REF] Ahokangas | Cloud computing and transformation of international e-business models[END_REF], [START_REF] Ahokangas | The Practice of Creating and Transforming a Business Model[END_REF]. With the emergence of data sharing and the control of individuals over their health data will transform healthcare business. This means shifting away from the transactional fee-for-service model towards a strategic valuebased care [START_REF] Kaiser | Harvard Business Review Turning Value-Based Health Care into a Real Business Model[END_REF]. It provides an opportunity to "better understand their true customer, the patient-consumer; tailor products to meet their needs; and to capture a high share of distinct customer subsets who will pay for and be loyal to their brand" [START_REF] Numerof | 3 strategies for changing the health insurance business model[END_REF].

However, transforming an organization requires a lot of commitment from the management, as the old ways of doing things may become a challenge [START_REF] Giannopoulou | Turning open innovation into practice: open innovation research through the lens of managers[END_REF]. The activities and logic related to the new business model may be incompatible with the status quo [START_REF] Chesbrough | Business Model Innovation: Opportunities and Barriers[END_REF]. Therefore, business models should always be evaluated and calibrated against the business context in order to find an optimal fit with the environment [START_REF] Teece | Business Models, Business Strategy and Innovation[END_REF]. Business models become fully comprehensible for firms only through action in the business context where they emerge [START_REF] Ahokangas | The Practice of Creating and Transforming a Business Model[END_REF].

According to [START_REF] Numerof | 3 strategies for changing the health insurance business model[END_REF], the main actionable strategies driving the transformation of health insurance companies start with 1) developing partnerships with right parties, moving away from volume towards limited partnerships and innovative treatment pathways. 2) Predictive care paths, when correctly executed, are the true offerings for future hospitals and physicians. Insurance businesses can play a key role in building such collaborations that have the power to achieve measurably better health outcomes at lower overall costs. In the 3) systematic transformation, payers will have a significant role to play in bridging the divide that is between providers and patients [START_REF] Numerof | 3 strategies for changing the health insurance business model[END_REF]. Thus, it is important to be aware that business model creation in start-ups is a different process from business model transformation within established firms [START_REF] Ahokangas | The Practice of Creating and Transforming a Business Model[END_REF].

It is also important to acknowledge that a firm does not have to bind itself to a single business model, but should, in fact, experiment with several simultaneously [START_REF] Trimi | Business model innovation in entrepreneurship[END_REF]. During the transformation process, it is not clear what the new business model will be like, but by experimenting the data needed to justify the transformation can be gained. The search for a new business model thus often requires a period of coexistence for the current and the new model(s) [START_REF] Chesbrough | Business Model Innovation: Opportunities and Barriers[END_REF]. However, although the business model as an actionable concept includes an underlying assumption of a process, academic research has not widely addressed the issues related to business model transformation [START_REF] Ahokangas | The Practice of Creating and Transforming a Business Model[END_REF].

Methodology, Data Collection and Analysis

As this study seeks to gain an in-depth understanding of the mechanisms of change in an organizational setting, an action-based research methodology was applied for data collection [START_REF] Ballantyne | Action research reviewed: A market-oriented approach[END_REF]. [START_REF] Daniel | The role of dynamic capabilities in e-business transformation[END_REF] suggest that action research is a valuable method to study dynamic and turbulent environments. As MyData paradigm shift is still to come, the method enables researchers to get close to the business reality as of now, and thus foster the development of deep and rich insights of the complexities within (datadriven) decision-making [START_REF] Carson | Action research and action learning[END_REF] in the context of MyData.

The data utilized in this study is part of a wider research project on healthcare service ecosystem, Digital Health Revolution DHR2. The primary data was collected from ten in-depth interviews with insurance company representatives and stakeholders related to the insurance business during 2016 (Table 1). We intentionally selected both insurance players and their stakeholders in order to understand the business of insurance players from different perspectives. In addition, early 2017, the data collected from the interviews was further elaborated during a joint 3h workshop with insurance companies and their stakeholder ecosystem to validate the identified impact of MyData on business models. All the interviews and workshop material was recorded and transcripted. In the data analysis, statements were identified, sorted and structured to identify impacts of MyData to healthcare insurance companies. The business model wheel [START_REF] Ahokangas | Cloud computing and transformation of international e-business models[END_REF] was used as a tool to analyze the data derived in order to thematically identify the potential impact and use of MyData on healthcare insurance business, as this tool helps to identify the points of action and collaboration in a simplistic manner. The template addresses the following elements: 1) what, comprising offering, value proposition, customer segments, and differentiation, 2) how, covering key operations, basis of advantage, mode of delivery, and selling and marketing, 3) why, describing base of pricing, way of charging, cost elements, and cost drivers, and 4) where are all these items located, internally or externally to the firm, as each part of the business model can be executed through collaborating with outside partners [START_REF] Ahokangas | Cloud computing and transformation of international e-business models[END_REF]. This template is depicted in Figure 2.

Findings

In exploring how MyData will potentially impact the business models of health insurance companies, we thematically categorized our interview findings and mapped them together with the themes discussed during the joint workshop. The results are summarized in Figure 2, and discussed in more detail below through business model elements, where collaboration is addressed in all components.

Fig. 2. MyData-driven health insurance business model.

Business Opportunities of MyData

New type of access to human-centric data provides a novel possibility for insurance companies to take a bigger role in preventive healthcare field. The aim for insurance companies is to help their end customers to live more healthy and safe life, which will also support insurance business to decrease the compensation costs related to chronic disease and accidents. In this new field, insurance companies see that "Our role is not anymore just to buy compensation, it is more to help to make sure that everything is fine with individual". At a concrete level, insurance companies consider that "Mydata approach will offer us new opportunities to give better and updated information for example about the value of their property or risks for future accidents and the like." But, MyData is seen to enable also a more general approach to wellbeing, as "As soon as end users buy from us we can start to offer the services that helps them to improve their health and life style". This is based on some initial work insurance companies have conducted in the field, such as "We have noticed in our research that it is important to offer bonus or some price for people when they are changing their life style"… "smoking is a good example, if you get 3000 if you stop it perhaps people will do it". This indicates that in the future system the insurance companies can be characterized more as a service providers than as a player that buys compensations of How MyData is Transforming the Business Models for Health Insurance 321 general risks or issues that already have occurred.

Value and Competitive Advantage of MyData for Insurance Business

What. MyData was seen to enable extended and novel offerings based on collaborative use of data: "the data sharing would make it possible that both insurance company and doctor sees the same information and we could serve the individuals better". Also new players will emerge to collect and analyze data. First, insurance companies aim to use data to achieve close to real-time customer insights to better align themselves with customers for better services. Value could be captured especially in situations when a person has been using one service provider for 10 years and then decides to change. "that could be the case in which the end user could do some effort to be able to transfer information easily". Secondly, insurance companies could base the costs of insurance on real, not estimated, situations. This means that people with high-risk profile will have bigger costs whereas those who are living healthy life could get some compensation. Costs would be based on a person's lifestyle and activity level, which is not currently possible due to legal regulations. Thirdly, with MyData, insurance companies could offer a feeling of safety, such as using data from sensors and devices to detect likelyhood for potential accidents. Early risk detection services can be an opportunity for the insurance business. "… if we could use the sensor and personal data with the permission of end user to check that there is something wrong with the car tire and it is better to fix it before a long journey".

Insurance services can also be customized based on the data. For example, in many cases the insurance companies are supporting groups in employee organizations. "The use of Mydata approach will especially change the role of employer organizations in the occupational health business sector during the next 10 years of time". Indeed, employer organizations were seen as a core player who would benefit from the transformation to MyData enabled healthcare most: "In the new Mydata based model, the employee organizations should be able to better take into account the coping, energy level, wellbeing and health of their own employees" Other important players in this new business model could be banks, food stores, aviation industry, utilities and housing companies.

How. Utilizing collaborative networks were identified as the key strategic approach in MyData, as it is not possible to build open access to data open business or innovation models." "We have opened the interfaces and helped developers to build interfaces and open data sources." "we have organized hackathons that targeted to give developers a possibility to use their data as a basis for new application development." However, insurance companies mentioned that there is a key player, an operator, missing in this field who could take the responsibility of data sharing and offer the needed collaboration interfaces. Supporting customers to decide which data to share is important in Mydata transformation. Without an operator in place, it might be difficult for insurance companies to get access to the personal data without legal problems. Insurance companies have interest to lead this but their challenge is that it could be seen scary from citizen perspective.

Insurance companies aim to develop rapid data usage as source of competitive advantage: "the faster we can use the data either as a service or information or to do better pricing the better we can manage in the business compared to our competitors". Combining personal data with environmental data like cars or housing, insurance players could maximize the probability of customers finding products that they want to buy. It was also mentioned in the interviews that data usage is not only a competitive advantage but must-to-have for insurance players in the future if they want to survive: "The basic model in which we just send bills and compensations does not work anymore in the current digital world. If we cannot use the data we will stay behind in the insurance market". Why. From revenue perspective, the individual was highlighted as the most important player in the future MyData-driven business: In the new insurance business model, individuals can get discounts of their insurance if they are improving their lifestyle. At the same time the assumption was that the insurance companies should pay less compensations on chronic diseases and accidents. However, insurance companies do not yet have evidence that the costs actually decrease if data is better used. One approach could be reciprocical data sharing among within the collaborative network that includes also the end customer:"I think some players are ready also to buy the data from individuals". Equally, "You need to buy if you want to get the valuable services based on your data". Help is needed from other players such as individuals, developer organizations and data operators. Who owns the data and has the right to use or sell the data within MyData -based collaborative networks is a key issue.

Discussion and Conclusions

It seems that data-driven business models will be mandatory in future insurance business. It will open new opportunities for new services and therefore help insurance players to stay as a significant player in the preventive healthcare business. It was evaluated that the key players who will buy new MyData based services are individuals and employee organizations who will clearly financially benefit from new data-driven services. The way to achieve MyData transformation is to open the interfaces and organize hackathons to help developers to build solutions. This means that in order to attract and retain customers, insurance companies can offer lowered prices for those who voluntarily share their health data. This results in lowered income in the form of insurance payments (the higher the risk indicators, the more one has to pay), but equally lowers the amounts of compensations paid to individuals. Thus in general both losses and profits will decrease. The results of the study thus indicate that the use of personal data, and the coming of MyData will dramatically transform the business models of health insurance companies from transaction-based to service based business. This has also important policy implications for data regulations and legislation, as consent and control on the use of personal data is a central aspect of MyData, in terms of how for-profit companies can utilize it for business gain.

Through addressing an emergent phenomenon, this study contributes to business model literature, and especially on data sharing within data-driven business models.

Thus, this study also contributes to data based aspects of sharing economy discussion as well. The main limitations relate to empirical validity. As MyData yet is a still a paradigm, the results of this study still address the potential use and implications, and cannot be validated through large scale empirical studies. Similarly, as the project took place among occupational healthcare sector, the implications on revenue models and competitive advantages for organizations involving also public institutions and healthcare provides. Hence, larger scale future scenario work would be useful to validate the business potential of MyData, especially from regulations and legislation points of view. We are yet to see, if the findings of this study will become the reality of health insurance business soon enough. In the meanwhile, further research on the design and orchestration on networks around MyData would be extremely valuable, especially from the point of view of MyData operator business. Moreover, the voice of the individual consumers from user driven innovation perspective could contribute to human-centric data management.

Fig. 1 .

 1 Fig. 1. MyData model (adapted from [1].

Table 1 .

 1 Data collection of the study.

	Company	Key business area	Interviewee	Duration
	SME	Technology provider	CEO	106 min
	Health provider	Healthcare	Development Director	45 min
	Insurance player	Banking, finance, healthcare	Chief actuary	60 min
	SME	Wellness training	CEO and Director of Intl. Growth 75min
	SME	Wellness training	Two personal trainers	45 min
	Insurance player	Insurance	Business developer	35 min
	Insurance player	Insurance	Manager	45 min
	Large company	Mobile network operator	Innovation Manager	45 min
	Large company	Technology provider	Head of Ecosystems Research	73 min
	SME	Technology provider	CEO	56 min

Acknowledgments. The authors would like to acknowledge DHR2 -Digital Health Revolution -project consortium.