Pamela Danese
email: pamela.danese@unipd.it

CONTINGENCY THEORY OF COLLABORATIVE PLANNING INITIATIVES IN SUPPLY NETWORKS

Keywords: SUPPLY CHAIN MANAGEMENT, INTEGRATION, NETWORKS, FORECASTING Supply chain management, integration, networks, forecasting Word count: 6485

There has been increased interest in supply chain (SC) collaboration in recent years, as a process that promotes inter-company cooperation in different business areas. This paper focuses on collaborative planning initiatives adopted to support demand and supply planning in supply networks. Since companies implement several different forms of collaborative planning initiatives, this paper intends to examine the relevant contingency effects that lead firms to choose a precise collaborative planning initiative. Ten cases were analysed to investigate the research question.

Results found indicate that specific contextual conditions -i.e. goals of the collaboration, demand elasticity, product diversity and supply network spatial complexity -can affect the level of the collaboration in collaborative planning initiatives. Three different levels of collaboration are identified (i.e. communication, limited collaboration and full collaboration) -depending on the level of integration (i.e. whether companies simply exchange data/information, or synchronize and jointly decide their plans) and multiplexity (i.e. the number of business areas involved in the collaboration). It emerges that, while the goals of the collaboration influence the level of integration between companies; the elasticity of demand can determine the level of multiplexity. Furthermore, the research found that product diversity (i.e. whether companies sell different products) and a high supply network spatial complexity could limit the level of multiplexity in the collaboration.

Supply chain (SC) collaboration has been described in the literature as a process that promotes: inter-organisational cooperation, joint work, openness, the creation of inter-company decision making routines, information and knowledge sharing, and customer-supplier intimacy (Mentzer et al. 2000, McCarthy and[START_REF] Mccarthy | Implementing collaborative forecasting to improve supply chain performance[END_REF]. Some authors also refer to this concept with the term SC integration, and highlight especially the importance of creating and coordinating processes seamlessly across the supply network [START_REF] Flynn | Information-processing alternatives for coping with manufacturing environment complexity[END_REF][START_REF] Handfield | Introduction to Supply Chain Management[END_REF][START_REF] Frohlich | Arcs of integration: an international study of supply chain strategies[END_REF][START_REF] Sahin | Information sharing and coordination in make-to-order supply chains[END_REF]. This means that companies should behave as a part of a unified system and coordinate with each other toward common objectives [START_REF] Mentzer | Collaboration -The Enablers, Impediments, and Benefits[END_REF][START_REF] Romano | Coordination and integration mechanisms to manage logistics processes across supply networks[END_REF][START_REF] Arshinder | Supply Chain Coordination: Perspectives, Empirical Studies and Research Directions[END_REF].

There are several areas in which SC collaboration can take place, such as new product development, demand management, order fulfillment, quality management, customer service management [START_REF] Cooper | Supply Chain Management: More Than a New Name for Logistics[END_REF][START_REF] Slack | Operations Management[END_REF]. Similarly to other studies [START_REF] Barratt | Exploring the experiences of collaborative planning initiatives[END_REF]Oliveria 2001, Larsen et al. 2003), this paper focuses on inter-company collaborative planning initiatives implemented to support demand and supply planning. These initiatives include a variety of integration practices between the supplier (or manufacturer) and the manufacturer (or customer) to jointly manage demand and supply plans, such as sales forecasts, delivery, purchasing or promotion plans. A number of collaborative-based techniques in this field are worthy of mention, such as Vendor Managed Inventory (VMI), Continuous Replenishment (CR), or Collaborative Planning Forecasting and Replenishment (CPFR) (Barratt and Oliveria 2001[START_REF] Danese | Collaboration forms, information and communication technologies, and coordination mechanisms in CPFR[END_REF][START_REF] Småros | Forecasting collaboration in the European grocery sector: Observations from a case study[END_REF]. These techniques lever on information sharing and joint planning to radically reduce inventories within the supply network while improving customer service. In recent years, we have witnessed a growing initiatives can be valuable to develop mechanisms for proactive managerial action. In fact it can suggest to managers how to select the most appropriate action to be taken when implementing the collaboration through the analysis of the context where it should be implemented.

The paper is organized as follows. First, it analyses existing literature on collaborative planning initiatives, and contingency factors that can influence the implementation of different collaborative planning initiatives. The following section introduces the research methodology and case profiles.

Then, the paper describes the analyses conducted to answer the research questions and develop the results. Results found are presented in the form of propositions. Research implications are then discussed. The article ends with conclusions and suggestions for future research.

COLLABORATIVE PLANNING INITIATIVES IN SUPPLY NETWORKS

Collaborative planning initiatives in supply networks can greatly vary; they can range from the simple passive exchange of data and information among companies, to the joint development of plans and decision-making, based on the analysis of information exchanged (ECR 2001[START_REF] Lee | Aligning supply chain strategies with product uncertainties[END_REF][START_REF] Småros | Forecasting collaboration in the European grocery sector: Observations from a case study[END_REF]). Moreover, collaborative planning initiatives can involve different business areas, as companies can collaborate to jointly establish promotions, or sales forecasts or order forecast plans, or all these processes together [START_REF] Barratt | Exploring the experiences of collaborative planning initiatives[END_REF]Oliveira 2001, Larsen et al. 2003). One of the most advanced collaborative planning initiatives is collaborative planning, forecasting and replenishment (CPFR) [START_REF] Barratt | Exploring the experiences of collaborative planning initiatives[END_REF]. CPFR programs concern collaboration where two or more companies jointly plan a number of promotional activities and work out synchronized forecasts, on the basis of which the replenishment processes are determined [START_REF] Larsen | Supply Chain Collaboration: Theoretical perspective and empirical evidence[END_REF]. According to several authors [START_REF] Aichlmayr | DC Mart: Who manages inventory in a value chain?[END_REF][START_REF] Ireland | Only the beginning of collaboration[END_REF][START_REF] Barratt | Exploring the experiences of collaborative planning initiatives[END_REF][START_REF] Seifert | Collaborative Planning, Forecasting and Replenishment: How to create a Supply Chain Advantage[END_REF], CPFR can be considered the natural evolution for companies already implementing other collaborative planning initiatives, such as Vendor Managed Inventory (VMI) or Continuous Replenishment (CR). VMI is a technique developed in the mid 1980s whereby the manufacturer (supplier) has the responsibility for managing customer's inventory, including the replenishment process. CR practice is similar to VMI, but in this case the manufacturer (supplier) can use POS data to predict customer's future sales and manage the replenishment process. At the heart of the CPFR process lies the aspiration to cover the gaps left by these collaborative planning initiatives.

As suggested by the CPFR model developed by the Voluntary Interindustry Commerce Standards committee (VICS), CPFR has a more comprehensive focus that includes promotional, sales and order forecast plans (VICS 2002). Moreover, collaboration deals with synchronizing the dialogue between the parties, through joint decisions and exception management.

The belief that collaborative planning initiatives can take a number of different forms across supply networks is widely diffused. [START_REF] Larsen | Supply Chain Collaboration: Theoretical perspective and empirical evidence[END_REF] state that collaborative planning initiatives can be implemented in various ways; as they can be differentiated both in terms of multiplexity of the collaboration -indicating the number of business areas involved (e.g. definition of promotional, sales forecast or order forecast plans) -and level of integration of business processes (e.g. degree of discussion, co-ordination/synchronisation).

A similar perspective emerges from the Efficient Consumer Response (ECR) Guide on CPFR (ECR 2001). It suggests that the VICS model has indeed a "modular" structure since, in some circumstances, it is not necessary to collaborate on promotional, sales and order forecast plans. For example, Levi Strauss & Co. incorporates only certain aspects of the CPFR business process in to its retail replenishment service, by creating joint order forecast plans and identifying exceptions (for instance, over/under stock situations, execution problems) [START_REF] Aviv | The effect of Collaborative Forecasting on Supply Chain Performance[END_REF]. Similarly, [START_REF] Danese | Managing business processes across supply networks: The role of coordination mechanisms[END_REF] report some interesting examples of pharmaceutical companies where manufacturing units and distribution centers jointly manage only order forecast plans and solve the exceptions.

CONTINGENCY FACTORS IN COLLABORATIVE PLANNING INITIATIVES

A theory in supply chain management (SCM) literature that is often used to explain differences in collaborative planning initiatives states that they depend on the existence of integration paths, which evolve from basic to more advanced forms of collaboration [START_REF] Spekman | An empirical investigation into supply chain management: A perspective on partnerships[END_REF], ECR 2001 considered the natural evolution for companies that already implement more basic forms of collaboration. Once a firm begins collaborating, it develops experience at cooperation and reputation as a partner. Over time, the firm develops capabilities to interact with other firms and strengthen trust with its partners, thus having the opportunity to enlarge its collaboration network or increase integration with its partners. For instance, [START_REF] Larsen | Supply Chain Collaboration: Theoretical perspective and empirical evidence[END_REF] suggest that CPFR can be classified into three levels -basic, developed and advanced -depending on the depth of collaboration; and argue that the basic CPFR is frequently the starting point for other collaborative initiatives. Similarly, the ECR Guide on CPFR suggests the slogan: "think big, start small, and scale intelligently" (ECR 2001, p.67). Thus, it is necessary to "start small", focusing on only a few processes in the early stage of the project's development.

A fundamental criticism of this theory lies in the fact that it seems to suggest that collaborative planning initiatives are context-free, only dependant on the experience and knowledge acquired over time by companies. Instead, it is plausible to suppose that they can be seriously influenced by several contextual factors that can drive companies' choices of what type of collaborative planning initiative should be implemented, given certain business conditions. Several firms for instance can deliberately limit collaboration to basic practices (e.g. passive exchange of data and information), even if the collaboration has reached an advanced stage of maturity.

In line with these considerations, in SCM literature, several authors maintain that some contextual conditions can influence SC collaborations and the implementation of collaborative planning initiatives.

In particular, the level of uncertainty in the context is usually considered a fundamental driver of SC collaborations and collaborative planning initiatives [START_REF] Davis | Effective Supply Chain Management[END_REF][START_REF] Fisher | What is the Right Supply Chain for Your Product?[END_REF][START_REF] Lee | Aligning supply chain strategies with product uncertainties[END_REF], Sari 2008[START_REF] Welker | The influence of business conditions on supply chain information-sharing mechanisms: A study among supply chain links of SMEs[END_REF][START_REF] Wong | The influence of institutional norms and environmental uncertainty on supply chain integration in the Thai automotive industry[END_REF]. This is because a better collaboration reduces uncertainty, and this in turn leads to greater operational performances. Environmental uncertainty has often been linked to demand unpredictability [START_REF] Davis | Effective Supply Chain Management[END_REF], Chen et al. 2000, Germain et 2008), i.e. the degree to which a firm can anticipate and forecast market trends. High levels of demand unpredictability often arise from innovative products, and thus [START_REF] Fisher | What is the Right Supply Chain for Your Product?[END_REF] distinguishes between innovative and functional products. Similarly [START_REF] Lee | Aligning supply chain strategies with product uncertainties[END_REF] analyses demand fluctuations; while Sari (2008) considers variability of customer demand. Another important source of uncertainty considered is supply chain process variability which is linked to inconsistencies in the flow of goods [START_REF] Germain | Supply chain variability, organizational structure, and performance: The moderating effect of demand unpredictability[END_REF]. Finally, [START_REF] Welker | The influence of business conditions on supply chain information-sharing mechanisms: A study among supply chain links of SMEs[END_REF] distinguish between simple and complex contexts (measured in terms of delivery times, order winners, variety of demand, type of supply chain relationships, and product/process characteristics) and investigate the influence on the level of information sharing (see also Van Donk andVan der Vaart 2004, 2005).

Taking for granted that the level of environmental uncertainty is positively related to the level of collaboration between companies, research on how some contextual factors, sources of uncertainty, can impact on specific aspects of collaborative planning initiatives is still scarce. As before discussed, collaborative planning initiatives can depend from different choices: for instance, from the decision to collaborate on many or few business processes, or from the degree of involvement of actors in the collaboration (e.g. frequency of data exchange, joint decisions, etc.). Though previous studies clarify that some contextual variables (e.g. uncertainty) impact on the level of collaboration (i.e. low or high), they lack a precise explanation of how contextual factors can influence the different aspects that characterise collaborative planning initiatives (e.g. number and type of business areas involved, level of coordination and synchronisation, etc.).

RESEARCH METHODOLOGY AND CASE PROFILES

The aim of this research is to uncover contingency effects in collaborative planning initiatives, by investigating: what contextual variables are critical in influencing these initiatives and their effect.

A multiple-case study method was adopted to investigate the research question, as it is particularly helpful for identifying and describing critical variables, and for discovering linkages between them [START_REF] Stuart | Effective case research in operations management: a process perspective[END_REF]. In particular, the implementation of different collaborative planning initiatives and Gemünden (2003) call the portfolio level, i.e. the unit of analysis is the network involved in the collaboration. Taking a single firm as a starting point (namely the focal firm), it includes a special subset of the focal firm's supply network, composed of all the actors that collaborate with the focal firm according to a well-defined collaborative planning initiative (e.g. VMI or CPFR).

Table 1 provides, for each case selected, information on: members involved in the collaboration,

Case selection

The literal and theoretical replication issues guided the selection of the cases [START_REF] Yin | Case Study Research, Design and Methods[END_REF].

Companies representative of different types of collaborative planning initiatives were selected.

Table 2 summarises for each case the main characteristics of the collaborative planning initiative implemented, and classifies the collaborative planning initiatives into three levels of collaboration: communication, limited collaboration and full collaboration, depending on: a) multiplexity and b) level of integration (table 3). In this research, multiplexity refers to the number and type of business areas involved in the collaboration (e.g. management of sales forecasts; management of order forecasts; management of promotions) [START_REF] Larsen | Supply Chain Collaboration: Theoretical perspective and empirical evidence[END_REF]. The level of integration depends whether the collaborative planning initiative is based on mere data exchange (i.e. communication) or, in addition to data exchange, on joint decisions and agreements on plans (i. 2) collaborated with the distribution centers (DCs) on a limited collaboration level, while its collaboration with the other production/packaging plants consisted in just the exchange of data/information on stock levels and available capacity (i.e. communication level).

Data collection, reduction and analyses

All data were gathered through company visits made from 2006 through 2009. Triangulation was used to ensure research reliability by obtaining the same piece of information from different sources: semi-structured interviews, documentation, archival records and direct observations. Data collection focused on variables underlying this research (i.e. context and collaboration planning initiatives), complemented with other issues enabling the understanding of the observed pattern of use of collaborative planning initiatives, such as the history of use of the practices, and the difficulties experienced by the companies in using them.

Data reduction allowed the information to be summarised and characterised from the masses of material that case studies generated [START_REF] Mccutcheon | Conducting case study research in operations management[END_REF]. It consisted of the characterisation of each case across the research variables (context and collaborative planning initiatives). A set of items was used to characterise each variable, and each item was classified according to a well-defined rule specified in Table 4. Central in defining these rules was the comparison of data across the cases and literature. For characterising the variable "context", four items were considered. As argued above, several authors consider uncertainty as a crucial contextual factor that can influence collaborative planning initiatives [START_REF] Davis | Effective Supply Chain Management[END_REF][START_REF] Fisher | What is the Right Supply Chain for Your Product?[END_REF][START_REF] Lee | Aligning supply chain strategies with product uncertainties[END_REF], Sari 2008[START_REF] Welker | The influence of business conditions on supply chain information-sharing mechanisms: A study among supply chain links of SMEs[END_REF][START_REF] Wong | The influence of institutional norms and environmental uncertainty on supply chain integration in the Thai automotive industry[END_REF]. Studies mainly refer to demand uncertainty, measured as demand fluctuations and unpredictability [START_REF] Fisher | What is the Right Supply Chain for Your Product?[END_REF][START_REF] Lee | Aligning supply chain strategies with product uncertainties[END_REF][START_REF] Germain | Supply chain variability, organizational structure, and performance: The moderating effect of demand unpredictability[END_REF], Sari 2008). In this research, elasticity of demand was considered. In fact, demand uncertainty is usually magnified in those sectors where promotions periodically generate a peak in demand sales, since demand elasticity is high. The elasticity of demand was classified into high and low categories. By comparing cases, a clear distinction was found between some contexts where the average increase of customers' sales volume during promotions was less than 40% (assigned to a low-class of demand elasticity) and contexts where it was higher than 200% (assigned to a high-class of demand elasticity).

In addition, the goals of companies involved in the collaboration project were taken into account. In fact, supply chain process variability, which influences the level of environmental uncertainty (see [START_REF] Germain | Supply chain variability, organizational structure, and performance: The moderating effect of demand unpredictability[END_REF], can also depend on companies' order winners and goals. [START_REF] Welker | The influence of business conditions on supply chain information-sharing mechanisms: A study among supply chain links of SMEs[END_REF], for instance, distinguish contexts by considering not only the variety of demand and product/process characteristics, but also companies' order winners, and investigate their influence on the level of information sharing (see also Van Donk andVan der Vaart 2004, 2005). In accordance with literature, the goals of the collaboration were distinguished into "efficiency" and "responsiveness" [START_REF] Forrester | Industrial Dynamics[END_REF][START_REF] Disney | A Discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain[END_REF][START_REF] Småros | Forecasting collaboration in the European grocery sector: Observations from a case study[END_REF].

Finally, two further contextual factors were analysed that can influence collaborative planning initiatives: the position of companies within a supply chain and supply network spatial complexity.

In fact, these circumstances can determine the competences possessed by companies on final market dynamics and thus their contribution in defining supply and demand plans. The product-diversity item takes into consideration the position of companies within a supply chain, i.e. whether collaborating companies sold the same or a different product. Moreover, a clear distinction was found between local and international networks: the first ones characterised by an average physical distance of a few dozens or hundreds of kilometers between firm(s) in the upstream network and the markets served by the firm(s) in the downstream network; the latter by an average physical distance of thousands of kilometers.

Finally, the collaborative planning initiatives were distinguished in terms of levels of collaboration, classified as communication, limited collaboration and full collaboration (see table 3). Each level differs for level of integration and multiplexity.

Data reduction was used for both the within-case and cross-case analysis. Within-case analysis gave the researcher the possibility to become intimately familiar with each case as a stand-alone entity [START_REF] Voss | Case research in operations management[END_REF]. This facilitated the comparison of the ten cases. Cross-case analysis was conducted by structuring the data through two-variable matrices (see next section). An effective approach was to pick a group of cases and to search for similarities and differences with other groups [START_REF] Voss | Case research in operations management[END_REF].

ANALYSIS AND RESULTS

This section compares the ten cases in order to identify some possible links between the context and collaborative planning initiatives implemented.

As suggested by [START_REF] Yin | Case Study Research, Design and Methods[END_REF], given the high number of cases examined, analyses of cases and results found are here presented in terms of cross-case analysis, and thus information from individual cases is dispersed throughout this section. In particular, the discussion is based on the comparison of the ten cases, through two-variable matrices. Results are then summarized in the form of propositions. In the two-variable matrix of table 5, cases are classified according to the variables "level of the collaboration" and "goals of the collaboration" (efficiency vs. responsiveness). Evidence from cases and interviews suggested that interesting relationships could be found. In particular, as emerges from the visual pattern of table 5, it seems that when an efficiency strategy prevails, a collaboration based on the mere communication of data suffices to guarantee achievement of the goals. In contrast, when the priority of a company is to make its supply network more responsive, a deeper collaboration is then necessary. In particular, the collaborative planning initiative should be based on discussions, joint decisions, agreements on plans, and thus on a higher level of integration. Proposition 1: The level of integration depends on the goals of the collaboration. In particular:

• Proposition 1a: When the goal of the collaboration is efficiency, companies tend to limit the collaborative planning initiative to data exchange (i.e. communication level, characterised by low integration); However, given the goals of the collaboration, it seems that other factors play a crucial role in choosing the business processes that are to be involved in the collaborative planning initiative, and thus the level of multiplexity of the collaboration. In fact, the matrix of Table 5 does not help us to understand why companies, whose goal is responsiveness, choose to collaborate on a full-level rather than limited-level of collaboration. Differences in elasticity of demand, product diversity, and supply network spatial complexity help us to better understand this (Table 7). Moreover, thanks to a prompt communication and analysis of POS data, producers can better react in the event of demand changes.

Thus, the following proposition is suggested:

• Proposition 2: The level of multiplexity in collaborative planning initiatives depends on demand elasticity. In particular, when demand elasticity is high companies tend to collaborate on a full collaboration level.

However, from the comparison of cases, it emerges also that full collaboration is efficacious only when partners have an in-depth knowledge of the final market, and thus can contribute with their competence to improving promotion management and sales forecast plans. A comparison with cases L and F can help to better understand the influence of product diversity and spatial complexity on collaborative planning initiatives. Unlike cases H and I, case L companies involved in the collaboration sell and market different products. This seems to limit the opportunity for joint promotional and sales forecast plans to be established. Central company 7 produces and sells corrugated cardboards while its customers produced and sold food. In such situation, it is unfeasible for members positioned upstream in the network to participate in the definition of the promotional and sales forecast plans of its customers. Similarly, central company 4 (case F) does not collaborate with its DCs/distributors in jointly defining promotional plans and sales forecasts. In fact, in the managers' opinion, this would not offer particular benefits. The distributors, located worldwide, in fact have the possibility to collect information and data on the market they serve and to elaborate accurate sales forecasts without collaborating with the central company, located far from the DCs it replenishes.

Finally, the following proposition summarizes the impact of product diversity and spatial complexity on the level of multiplexity in a collaboration.

• Proposition 3: Product diversity and a high supply network spatial complexity limit the level of multiplexity in collaborative planning initiatives.

The proposed contingency model

Propositions 1, 2, and 3 explain how the context can influence the different collaborative planning initiatives that should be implemented. By simultaneously considering the impact of all the It emerges that companies collaborate on a full-collaboration approach (i.e. characterised by an high level of multiplexity and integration) when the main goal is to increase companies' responsiveness to demand changes, companies sell and market the same products, demand elasticity in the event of price variation is high, and spatial complexity among partners is low. These are all necessary conditions for collaborating on a full collaboration level. Instead, when the main goal is to increase companies' responsiveness, but one of the other conditions is not satisfied (e.g. companies sell different products, demand elasticity is low, or spatial complexity is high), companies should limit their collaboration to a limited-collaboration approach (i.e. low level of multiplexity and high level of integration). Finally, when companies aim to reduce their costs (e.g. inventory costs), in any case, collaboration should be limited to data communication -e.g. companies exchange data on order forecast plans, stock levels, sales plans, etc.

DISCUSSION

The value of contingency theory for interpreting collaborative planning initiatives

The contingency model developed in this research suggests that in some circumstances it is not necessary to increase the level of the collaboration by adopting a full-collaboration approach.

In this way, the present study complements the theory on integration paths, according to which companies should evolve from basic to more advanced forms of collaboration [START_REF] Spekman | An empirical investigation into supply chain management: A perspective on partnerships[END_REF][START_REF] Barratt | Exploring the experiences of collaborative planning initiatives[END_REF], ECR 2001, Larsen et al. 2003[START_REF] Seifert | Collaborative Planning, Forecasting and Replenishment: How to create a Supply Chain Advantage[END_REF]. Over time, firms should develop capabilities for interacting with other firms and strengthen trust with their partners, thus having the opportunity to increase collaboration with their partners. It is understandable that companies can arrive at the use of certain practices via a process of cumulative competence building and experimentation. However, this research demonstrates that, when implementing collaborative planning initiatives, the main objective of companies is not in any case to maximize the level of collaboration, as the type of products involved in the collaboration, and the characteristics of demand or the spatial complexity of the collaboration network can determine the more appropriate and convenient level of collaboration to be adopted.

In line with Das et al. 's study (2006) on the relationship between integration and performances, this research supports that collaborating with other partners can cause increased costs of coordination and inflexibility. It is convenient to increase the level of collaboration only when these costs are offset by more gains. For example, the results found demonstrate that when a producer is far from the markets it serves (i.e. high spatial complexity), it does not have an in-depth knowledge of the final market dynamics, and thus the benefit of collaborating with DCs in defining promotions and sale forecast plans is very low, compared to the increase in the coordination costs. Thus, coherently with Das et al. 's study (2006), it can be argued that an excess of investment in collaborative planning initiatives can be harmful, and that an ideal profile of collaboration can be identified. This research suggests the ideal configuration of collaborative planning initiative to be adopted under different contexts (see table 8).

The level of uncertainty as driver of collaborative planning initiatives: the role of goals and demand elasticity

With regard to the influence of environmental uncertainty on collaborative planning initiatives, research findings confirm that the goals of the collaboration and demand elasticity are important sources of uncertainty that drive companies towards more intense forms of collaboration [START_REF] Chen | Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times and information[END_REF][START_REF] Lee | Aligning supply chain strategies with product uncertainties[END_REF][START_REF] Germain | Supply chain variability, organizational structure, and performance: The moderating effect of demand unpredictability[END_REF][START_REF] Welker | The influence of business conditions on supply chain information-sharing mechanisms: A study among supply chain links of SMEs[END_REF]). However, this research also found that it is important to distinguish between these two different sources of uncertainty and how each of them impacts on the different aspects of collaborative planning initiatives. In fact, in this study, an important variable characterising collaborative planning initiatives is the level of collaboration which depends on: the level of multiplexity (i.e. number and type of business areas involved in the collaboration) and level of integration (i.e. communication vs. collaboration through joint decisions and synchronisation of plans).

From the analysed cases, it emerges that, while the goals of the collaboration influence the level of integration between companies (see proposition 1); the elasticity of demand can determine the level of multiplexity (see proposition 2).

When the goal of the collaboration is to improve efficiency, the collaborative planning initiative can be based just on the exchange of data and information (i.e. low level of integration), as this allows the bullwhip effect to be limited, thus reducing costs, as pointed out by several authors [START_REF] Cachon | Supply chain inventory management and the value of shared information[END_REF][START_REF] Yu | Benefits of information sharing with supply chain partnerships[END_REF][START_REF] Dejonckheere | The impact of information enrichment on the bullwhip effect in supply chains: A control engineering perspective[END_REF][START_REF] Wu | The impact of information sharing in a multiple-echelon supply chain[END_REF]. However, when companies want to increase their responsiveness to demand changes then a collaborative planning initiative based on the synchronisation of plans and exceptions management is necessary. The result is an increased level of integration between companies. This is consistent with the study of [START_REF] Småros | Forecasting collaboration in the European grocery sector: Observations from a case study[END_REF] which concluded that the desire to improve responsiveness is one of the major triggers of CPFR collaborations.

Instead, the level of multiplexity is mainly determined by the elasticity of demand. A high demand elasticity causes fluctuations in demand in the event of promotions. As a consequence, companies involved in the collaboration, as well as jointly defining the order forecast plans, often collaborate to jointly decide promotions in the stores and sales forecast plans, through an intense exchange of data and opinions on future demand. As a consequence, the level of multiplexity increases. This result is in line with several cases described in the SCM literature. For instance, Spartan Stores, a grocery chain, shut down its VMI project after 1 year due to vendors' inability to deal with product after the implementation of CR, argued that although CR had provided a better approach to replenishment, there was still a lot of work to do in relation to promotions [START_REF] Barratt | Exploring the experiences of collaborative planning initiatives[END_REF].

Product diversity and supply network spatial complexity as barriers to multiplexity in collaborative planning initiatives

Another result of this research concerns the role of product diversity and supply network spatial complexity in influencing collaborative planning initiatives. By analysing the effect of these variables, this study suggests that some factors can act as barriers to the collaboration and moderate the relationship between uncertainty and collaborative planning initiatives. In fact, in some cases (e.g. L and F), even if the goal of the collaboration was responsiveness to demand changes and demand elasticity was high, companies decided to limit multiplexity in the collaborative planning initiative, by jointly defining only order forecast plans. This means that, in some contexts, even if the environmental uncertainty is high, given certain contextual conditions, collaborative planning initiatives remain limited to less advanced forms.

In particular, proposition 3 suggests that product diversity and supply network spatial complexity can act as barriers, and thus limit the level of multiplexity in collaborative planning initiatives. In fact, upstream members can participate in the process of promotion and sales forecast definition, only if they have an in-depth knowledge of final market dynamics. Cases analysed demonstrate that this depends on the position of a company within the supply chain (e.g. a raw material supplier has usually no competence on final market dynamics), and from the physical distance between the company and market where products are sold. These results are in contrast with some SCM studies according to which advanced collaboration techniques (such as CPFR), once implemented in the downstream network, can be easily extended also upstream, independently of actors' position in the supply chain, or the geographical distance between companies and final market served (ECR 2001, VICS 2002). Instead, results found suggest that a company should collaborate in a different way upstream with suppliers or downstream with distribution centers/retailers, and thus, collaborative planning initiatives should not only vary across sectors/contexts because the environmental uncertainty differs, but also along a supply chain. Or if two companies operate in the same sector or in a similar context but the configuration of their collaboration network is international vs. local, the collaborative planning initiatives adopted will differ.

CONCLUSIONS AND FUTURE RESEARCH

Literature provides only a partial understanding of the reasons that lead a company to implement a well defined collaborative planning initiative. This article intends to advance research on this issue by proposing a contingency theory of collaborative planning initiatives. Using data from ten case studies, this research provides a set of propositions that analyse in detail the impact of context on collaborative planning initiatives. Three levels of collaboration are identified (i.e. communication, limited collaboration and full collaboration) -depending on the level of integration (i.e. whether companies simply exchange data/information, or synchronise and jointly decide their plans) and multiplexity (i.e. the number of business areas involved in the collaboration). They are found to be associated with the goals of the collaboration (efficiency vs. responsiveness strategy) and demand elasticity. Companies analysed attempted to implement a full collaboration approach, based on the joint management of promotions, sales and order forecast plans, when they intended to improve their responsiveness in the event of demand changes and they faced a high level of demand elasticity. Furthermore, the research found that product diversity (i.e. whether companies sell different products) and a high supply network spatial complexity could limit the level of multiplexity in the collaboration. In fact, in these contexts, the collaboration is usually limited to the order forecast definition process, since collaborating on promotions or sales forecast plans does not lead to particular benefits, since upstream members do not have competence on final markets dynamics. Finally, it was found that when companies' main goal is efficiency, collaborative planning initiatives should be limited to a communication level (i.e. companies exchange data and information but do not synchronise/discuss their plans). In fact, the cases analysed demonstrated that a collaborative planning initiative based on exceptions management, synchronisation and discussion of plans is especially useful only when companies aim to improve their responsiveness.

The links found between the contextual variables analysed and collaborative planning initiatives can provide managers with important levers for action. In fact, they help to determine which is the most appropriate form of collaborative planning initiative to be implemented. As previously discussed, one widespread SCM theory states that companies follow integration paths that evolve towards advanced collaborative planning initiatives. The risk of this theory is that it can lead us to consider advanced forms of collaboration, such as the CPFR, as the natural evolution for companies already implementing other collaborative planning initiatives. Instead, the links found between the above mentioned contextual variables and collaborative planning initiatives demonstrate that, under certain conditions, a company could decide to limit the collaboration to basic practices.

An additional important implication for managers, deriving from the adoption of the contingency perspective, is that it provides practitioners with a framework to understand the changes necessary in collaborative planning initiatives, as they anticipate changes in the environment and company strategy. By foreseeing the implications of these changes, the company will be in a position to make a series of planned changes in the collaborative planning initiative rather than being forced into reactionary, rushed changes when it finds that the old collaborative planning initiative does not fit with the new contingency factors.

Research findings provide insights that could be of interest to managers working in firms operating in different sectors and positioned in different supply network echelons (e.g. suppliers, manufacturers, distributors). However, the opportunity to use the contingency model proposed in this research as a managerial tool calls for the testing of results within larger samples of supply networks, whose central companies are representative of a broader range of industries and countries. In fact, although the replication logic adopted in this research permits analytical generalization, it is worth noting that the analysed case studies are limited to a relatively small Finally, it is worth noting that contingency-theory based studies have several limitations. In fact, the contingency perspective assumes that practices are adopted due to efficiency factors; but companies often deviate from contingency-determined patterns due to non-efficiency pressures that could also lead to the low use of efficient practices. For example, powerful external organizations (associations, governmental regulations, etc.) may exert political pressures discouraging or encouraging the use of certain practices. Hence, as suggested by [START_REF] Sousa | Contingency research in operations management practices[END_REF], institutional theory emerges as a promising theoretical perspective to explain deviations from contingency-determined patterns. Linked to this, a further shortcoming of contingency theory is that it does not contemplate the development of capabilities as an important source of performance. The capabilities paradigm is rooted in the resource-based view of the firm [START_REF] Barney | Firm resources and sustained competitive advantage[END_REF], and provides a relevant explanation for why some firms may deliberately choose not to adopt efficient practices and rather opt to invest in the generation of slack resources that are difficult to imitate, and thus

Communication

B

Central company 2 receives stock level data and sales forecast plans from fifty DCs. On the basis of this data the central system proposes the replenishment plans, suggesting dates to the central company for the deliveries of final products to each distribution center. The deliveries are decided in order for the stock level at the DCs' facilities to fall within a jointly established range (called VMI min-max range). Replenishment plans have then to be confirmed by the planners within both the central company and the DCs. If a DC does not confirm the plans, or asks for additional orders that fall within the frozen planning horizon, the central company proposes -on the basis of a what-if analysis -alternative delivery plans by estimating the impact of any order time/volume change on the plans of the downstream supply network members. This type of collaborative planning initiative can be classified as limited collaboration, as parties jointly develop the plans, but the collaboration is limited to order forecast definiton process.

Limited collaboration C Every Monday morning, company 2's planners send to two packaging material suppliers and to the active agent supplier the order forecast plan that includes a 5-month planning period. The suppliers consider the order forecasts that fall within the frozen period as firmed orders. Thus the collaborative planning initiative is at a communication level. Limited collaboration E MU decides the production and delivery plans for all the production and packaging plants (more than twenty) included within the supply network of the antibiotic packaged and distributed by company 3. The collaboration between the MU and production and packaging plants is mainly based on the exchange of data and information (i.e. communication level), as the MU reads stock levels and available capacity of production and packaging plants, and communicate production and delivery plans to them. Exceptions are not discussed nor shared, while plans are centrally decided by the MU.

Communication

F

Central company 4 receives stock level and sales forecast data from four distributors, each of which sells and distributes air conditioners in a specific market. Both company 4 and the distributors elaborate order forecast plans (i.e. deliveries of air conditioners to the distributors) that are then compared to identify exceptions. The exceptions are then solved to achieve a final common order forecast plan. This case is an example of limited collaboration.

Limited collaboration

G

Company 4 elaborates and sends its raw material order forecast plans to four suppliers, producing aluminium (two suppliers), and copper (two suppliers). Each supplier uses this data to organize product deliveries, and plan its production. Thus collaboration is limited to a mere data communication (i.e. communication level). Every year, central company 5 and customers involved in the collaboration jointly establish a promotional plan (e.g. promotions to be made in the shops, in what periods, how many stock-keeping units (SKUs) will be included), that is reviewed every 3 months. Then, by using customers' sales data and promotional plan, both the central company and each customer estimate the sales forecast plans (i.e. demand of final customers). Discrepancies in the plans are discussed to obtain a common sales forecast plan. By considering stock level data and common sales forecast plan, both the central company and each customer elaborate an order forecast plan. Again, by comparing the plans, exceptions (e.g. significant differences) are identified and solved. Hence, the collaborative planning initiative is at a full collaboration level, as companies jointly define promotional, and sales and order forecast plans. Initially, company 5 collaborated on a full collaboration level with few partners, but, in 2005, decided to extend the collaboration to several other customers.

Full collaboration I

Similalrly to case H, the collaborative planning initiative is at a full collaboration level. The joint promotional plan is established every year. It mainly concerns decisions on promotional events (i.e. promotional period and SKUs to be involved). This plan is then reviewed and detailed during the year. Every week, on Friday, suppliers and company 6 elaborate independent sales forecast plans, by using supermarkets's POS data of the last two years, and promotional plans. Afterwards, suppliers' and company 6's sales forecasts are compared. They can't differ more than a certain percentage. Otherwise, an exception occurs. Every Monday, company 6 and its suppliers try to solve the exceptions found by analyzing POS data. Similarly, companies collaborate in defining order forecast plans. Every Tuesday, each company elaborates its order forecast plan, and every Wednesday companies collaborate to solve order forecast exceptions. The collaboration initially involved three suppliers; then was extended to include several other partners. The collaboration allowed to increase company 1's "visibility", thus limiting the "bullwhip" effect and minimizing inventories of products within company 1's warehouse.

Case B Strategy of responsiveness: Company 2 is the sole responsible for the whole production process of injectable cephalosporins and for their distribution throughout the world. For this reason, for company 2, being responsive in case of demand changes is essential.

Limited collaboration: Final deliveries plans are jointly defined by the DCs and central company 2, by taking into account also the additional orders that fall within the frozen planning horizon

Responsiveness is the result of an high level of integration between the central company and DCs, based on a two-way interaction and frequent discussions to reach an agreement on final delivery plans.

  networks was examined. The inter-organisational level of analysis here is what Ritter

  central company's and headquarters' location and products. ************************************* Insert Table 1 about here *************************************

  e. collaboration). ************************************* Insert Table 2 about here ************************************* ********************************** Insert Table 3 about here ************************************* It is worth noting that three of the central companies contacted collaborated differently in the upstream and downstream networks, and this provided the opportunity to examine different collaborative planning initiatives. For instance, central company 3 (table

  ********************************** Insert Table 4 about here *************************************

  ************************************* Insert Table 5 about here ************************************* The richness of data collected helped to better understand the rationale behind this link. Cases A and B are useful to explain the meaning of the relationship found between level of integration and goals of the collaboration (table 6). ************************************* Insert Table 6 about here ************************************* The following proposition summarizes what emerged from cross-case analysis, and from information collected during the interviews.

  ************************************* Insert Table 7 about here ************************************* High demand elasticity is an important distinguishing feature of cases H and I, indicated in

  located in Belgium -interact with local suppliers to define promotions and the sales forecast plans of company 6's supermarkets located in Belgium, on the basis of which order forecast plans are established. It seems that the contribution of partners positioned upstream in the network when defining promotional and sales forecast plans is significant only when they have a thorough knowledge of market dynamics.

  , some contexts can be identified and an ideal configuration of collaborative planning initiatives for each of these can be defined (table8). ************************************* Insert Table 8 about here *************************************

  represent a source of competitive advantage. Future research should examine the relative explanatory power and interplay of contingency, institutional, resource-based or other theoretical arguments in best SC collaboration practice adoption and use.

  sales forecasts and stock level data from about thirty DCs. The delivery plans, elaborated by the MU, are proposed to the DCs that can confirm the plans or ask for modifying, anticipating or postponing the orders. Similarly to case B, DCs' stock levels have to fall within a range jointly established by the MU and the DCs. When order forecast exceptions occur (e.g. a DC asks for anticipating an order), the MU, on the basis of what-if analyses, can propose alternative delivery plans, thanks to the flexibility due to the jointly agreed stock level range. As the MU and DCs jointly define the order forecast plans and solve order forecast exceptions, this type of information processing can be classified as limited collaboration.

  to company 7 their corrugated cardboards gross requirement plans. By considering customers' stock level of corrugated cardboards, both the central company and customers elaborate deliveries of corrugated cardboards to be made. Exceptions are identified through the comparison of plans. The collaborative planning initiative is at limited collaboration level, as the collaboration concerns just the order forecast definition

  ,

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  decided to launch a collaborative planning initiative with the aim of improving service level during promotions. In both cases, the collaborative planning initiative follows the procedure suggested by the CPFR technique (see table 2), and thus partners in the collaboration network jointly define promotional, sales and order forecast plans. Managers agree that this type of collaboration significantly contributes to improving forecast accuracy during promotions as lots of information from different sources is considered and discussed when elaborating sales and order forecast plans.

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7

with the acronym HDE. In cases H and I, product shelf prices can significantly influence customer behavior, hence demand elasticity in the event of price variations is very high. In company 6's stores (case I), sales volume could increase by up to 300 percent during a promotion; in the stores of company 5's customers (case H) it varied between 200 and 300 percent. As stated by the supply chain manager of central company 6:

Table 7

 7

shows, for instance, that cases H and I are similar to cases L and F in terms of demand elasticity (i.e. HDE), but differ in product diversity and spatial complexity. In fact, in cases H and I, partners sell the same product (i.e. SP) and are located in the same country (i.e. low spatial complexity -LSC). These contextual factors favor the development of a certain knowledge of the final market, even if a partner in the collaboration does not sell its products directly to final consumers. Central company 5 (case H), located in Brussels, for instance, sells and markets consumer and soft goods to retailers located in Belgium who then distribute the products in the Belgian region. Thus the spatial complexity is indeed low. The same considerations can be drawn for the collaborative planning initiative between company 6 and its suppliers. Company 6 is a food retailer that collaborates with its suppliers following the CPFR technique. Managers within

  sample and only a few industries. Future research is needed to confirm or refine the domain of applicability of the research findings by ascertaining whether they replicate in other industries.

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Table 1 .

 1 Overview of the cases

	Page 29 of 34	International Journal of Production Research
			Headquarters and
	Case	Supply network members involved in the collaboration	central company	Products	Interviewees
			location
					Logistics Operations manager and planners
	A	Central company 1: manufacturing unit (MU) producing starter batteries Several distribution centers (DCs) (independent and owned)	Headquarters: Italy MU: Italy	Starter batteries	(company 1); external consultant involved in the implementation of the collaboration project; area managers (company 1), factory manager
					(DC)
	B C	Central company 2: MU producing injectable cephalosporins Owned DCs located worldwide and directly replenished by company 2 Central company 2 Owned and independent suppliers	Headquarters: UK MU: Italy	Medicines	Logistics Director and production planners (company 2); product managers (DC) Logistics Director and buyers (company 2); factory managers and planners (suppliers of labels and active agents)
	D E	Central company 3: MU producing an anaesthetic and responsible for the final packaging of an antibiotic Owned and independent DCs Central company 3 Production and packaging plants (owned and independent)	Headquarters: UK MU: Italy	Medicines	Logistics Director and planners (company 3); product team's members (DC) Logistics Director (company 3); factory managers (production and packaging plants)
	F G	Central company 4: MU producing and distributing air conditioners Distributors Central company 4 Suppliers of engines and copper	Headquarters: Sweden MU: Italy	Sales manager, Comfort & Refrigeration Business Unit managers and product manager (company 4); factory managers (distributors) conditioners Factory managers and planners (company 4); Air factory managers and planners (suppliers of engines and copper)
	H	Central company 5: sales company(SC) (located in Belgium) selling and marketing consumer and soft goods Large customers (retailers)	Headquarters: Sweden SC: Belgium	Consumer and soft goods	Customer Supply Chain Manager and sales managers (company 5); external consultant involved in CPFR implementation; supply chain manager (retailer)
	I	Central company 6: subsidiary of a food retailer located in Belgium, responsible for establishing promotional plans for the local supermarkets and for managing the replenishment of the Belgian DC Three suppliers producing fats and margarines; candy bars and feminine hygiene products	Headquarters: Belgium Subsidiary: Belgium	Food	Supply chain manager (company 6); external consultant involved in CPFR implementation; factory manager and planners within supplier plant producing fats and margarines
	L	Central company 7: MU producing corrugated cardboards for product transport Some customers (food producers)	Headquarters: Belgium MU: Belgium	Corrugated cardboards	Chief Supply Chain Officer and planners (company 7); factory manager and planners within a customer's plant

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Table 2 .

 2 Collaborative planning initiativesThe collaboration is mainly based on the exchange of data and information (i.e. communication level). Company 1 can read and extract DCs' stock and sales data. By using this data, the MU forecasts what retailers will require to the DCs, and elaborates the order forecast plans of the DCs, by taking into account DCs' stock levels. However, sales and order forecast plans elaborated by central company 1 are not communicated nor shared with DCs.

	Case

Table 2 .

 2 Collaborative planning initiatives

	Case

Table 3 .

 3 Summary characteristics of three levels of collaboration collaboration, full collaboration is characterized by an increased number of areas in which companies collaborate. The collaboration includes the joint development of promotional, sales and order forecast plans, and sales/order forecast exception management, as suggested by CPFR technique (high level of multiplexity and high level of integration).

			Characteristics	Level of integration and multiplexity
			Companies collaborate simply by exchanging data and information with	Low level of integration
			trading partners. The types of data exchanged can differ. For example, a
			company can receive order forecast plans from its customers.
		Communication	Alternatively, a company can receive stock level and consumption data (or sales forecasts) from its customers and decide customers' order plans
	LEVEL OF COLLABORATION	Limited collaboration	(e.g. VMI or CR). In all cases the collaboration is simply a sort of data communication. Indeed parties do not jointly develop promotional, sales or order forecast plans (i.e. low level of integration). Limited collaboration differs from communication by taking the collaboration a little further than mere data exchange. Parties jointly develop order forecast plans and manage exceptions (e.g. discrepancies in the plans). The collaboration is limited to order forecast definition process (i.e. low level of multiplexity and high level of integration).	High level of integration Low level of multiplexity
			Compared to limited High level of integration
				High level of multiplexity
		Full
		collaboration

Table 4 .

 4 Data reductionStrategy of efficiency (companies aim to reduce costs (e.g. investments in stocks) without penalizing service levels) or strategy of responsiveness (the main purpose is to make the supply network more reactive to demand changes)Product diversitySame products (SP) (companies involved in the collaboration sell the same products)or different products (DP) (companies sell different products)

	Variable	Characterisation	Rating
				Elasticity of demand	Low (LDE) (less than 40%) or high (HDE) (more than 200%)
		Context		Goals
				Supply network spatial	Low (LSC) (few dozens or hundreds of kilometers); high (HSC) (thousands of
				complexity	kilometers)
	Collaborative	planning	initiative	Level of collaboration	Communication, limited collaboration, full collaboration

Table 5 .

 5 Relationship between the level of the collaboration and goals

		Level of collaboration and integration
		Communication	Limited Collaboration	Full Collaboration
		Low level of integration	High level of integration	High level of integration
			CASE B
	Responsiveness		CASE D CASE L	CASE I CASE H
	Goal	CASE A	CASE F
	Efficiency	CASE C CASE G
		CASE E

Table 6 .

 6 Links between goals and level of the collaboration in cases A and B

	Goals	Level of the collaboration	Main conclusion
	Case A Strategy of efficiency: In the last years	Communication level: In the 2002,
	the pressure for reducing component	company 1 launched a collaborative
	prices in the automotive industry has	planning initiatives, based on the
	significantly increased and led company	communication of distribution centers'
	1 to pursue a strategy aimed at	(DCs) data (sales and stock levels) to
	containing production costs.	company 1.

Table 7 .

 7 Relationship between product diversity, elasticity of demand, spatial complexity, and multiplexity in the collaborative planning initiatives

					Product diversity, demand elasticity, spatial complexity
					(SP, HDE, LSC)	DP or LDE or HSC
	Level of	collaboration and	multiplexity	Full collaboration High multiplexity	CASE H (SP, HDE, LSC) CASE I (SP, HDE, LSC)

Table 8 -

 8 Contexts and collaborative planning initiatives Goal of the collaboration: efficiency Companies exchange data and information (e.g. POS data) wth the aim of minimizing inventories. Goal of the collaboration: responsiveness -Elasticity of demand: high -Spatial complexity: low -Product diversity: same product Companies collaborate to jointly manage promotional, sales and order forecast plans. Collaborating on promotions and sale forecast plans is necessary, because demand elasticity is high. However, companies in the upstream network can participate in the process of promotion and sales forecast definition, only if they have an in-depth knowledge of final market dynamics (i.e. when the spatial complexity is low and companies participating in the collaboration sell the same product).

			Page 34 of 34
	Contexts	Collaborative planning initiatives	Level of integration and multiplexity
	-Low level of integration
	-Goal of the collaboration: responsiveness	Parties jointly develop order forecast plans and manage	High level of integration
	-Elasticity of demand: low, or spatial	exceptions. Collaborating on sales forecast plans and	Low level of multiplexity
	complexity: high, or product diversity:	promotions is not a priority, since demand elasticity is
	different products	low. Moreover product diversity or a high supply network
		spatial complexity limit the collaboration to order forecast
		definition process.
	-High level of integration
			High level of multiplexity

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uk International Journal of Production Research

http://mc.manuscriptcentral.com/tprs Email: ijpr@lboro.ac.ukInternational Journal of Production Research

ACKNOWLEDGMENTS

Research funded by University of Padova -Project CPDA083831


Marina Isidori

Emanuela Caterino

Emma Criscuolo

Veronica Fatigati

Giovanni Liguori

Alfredo Parrella

Gómez-Arroyo S Cortés-Eslava

ANTIMUTAGENIC AND ANTIGENOTOXIC EFFECTS OF VEGETABLE MATRICES ON THE ACTIVITY OF PESTICIDES

Keywords: Pesticides, vegetables, Ames Test, SOS Chromotest, GC 29

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

Pesticides are peculiar compounds because, in addition to their intentional effects, they are toxic for 32 several non-target organisms included humans and their toxicity has limited species selectivity. In 33 several studies and reviews about the risk of occupational exposure to pesticides, they have been 34 implicated in causing or promoting cancers, leukemias, and lymphomas due to their properties of 35 inducing mutations, chromosomal alterations or DNA damage [START_REF] Viel | Bladder cancer among French farmers: does exposure to pesticides in 379 vineyards play a part?[END_REF]Maroni et 36 al. 2006;[START_REF] Provost | Brain tumours and exposure to pesticides: a case-control study in southwestern France[END_REF][START_REF] Maele-Fabry | Risk of leukemia among pesticide 349 manufacturing workers: A review and meta-analysis of cohort studies[END_REF]. Some of them interfere with normal 37 neurotransmitter function (Calamandrei et al. 2006;Eyer et al. 2008;[START_REF] Slotkin | Developmental neurotoxicity of low dose 371 diazinon exposure of neonatal rats: effects on serotonin systems in adolescence and adulthood[END_REF]) and others 38 act as enzymatic and/or endocrine disruptors [START_REF] Petrelli | Reduction in fertility in male greenhouses workers exposed to 358 pesticides[END_REF]Hoogduijn et al. 39 2006). But, exposure to pesticides may be a significant risk factor also for food consumers for their 40 frequent occurrence as residues in fruit and vegetables even at very low concentrations (ng-µg/g 41 food). Many studies showed that chronic exposure to low levels of pesticides in fruits and 42 vegetables is associated with genetic toxicity (De Marco et al, 2000;Feretti et al. 2007). Among the 43 adverse effects of pesticides, genotoxicity is of particular concern because of the irreversibility of 44 this process. Plant systems are able to bio-concentrate pesticides as well as other xenobiotics but 6 in distilled water. Finally, the mixtures were co-incubated with ascorbic acid (3000 µM) and gallic 134 acid (400 mg/L) at room temperature for 1h on mechanical shaker before mutagenesis/genotoxicity 135 testing to evaluate if the activity of pesticides could be influenced by the presence of those 136 metabolites.

137 Mutagenesis / genotoxicity testing 138 Ames test was performed on Salmonella typhimurium strain TA98 for frame-shift mutations and 139 TA100 and TA 1535 for base-pair substitution to detect direct mutagenic compounds. The plate 140 incorporation assay was conducted without metabolic activation (S9) to detect direct mutagenic 141 compounds [START_REF] Maron | Revised methods for the Salmonella mutagenicity test[END_REF]. Preliminary tests of mutagenesis/genotoxicity were carried 142 out to select proper test doses independently for each sample tested. Sodium azide (2, 1 and 0.5 143 µg/plate) and 2-nitrofluoren (1, 0.5 and 0.25 µg/plate) were used as positive controls for TA100, 144 TA1535 and TA98, respectively. Results obtained showed that these positive controls behaved as 145 expected and that consequently all tests could be considered valid.The number of His+ revertants 146 was counted (colony counter Cardinal, Perceptive Instruments, Suffolk, UK). Results were 147 expressed as mutation ratios (MRs) between the number of colonies on test plates and the number 148 of colonies on solvent control plates. A compound was considered mutagenic when the MR was 149 more than double and a dose-response effect was clear (US. EPA, 1983). 150 151 E. coli PQ37 was used in the colorimetric assay SOS-Chromotest. This strain exhibits sfiA::lacZ 152 fusion and has a deletion of the normal lac region, so that β-galactosidase activity is strictly 153 dependent on sfiA expression. The assay is quantitative [START_REF] Quillardet | The SOS Chromotest: a review[END_REF]. The phenolic content of the selected vegetables was determined using the Folin-Ciocalteu method 171 as described by Rekha and Vijayalakshmi (2008). The vegetable sample was homogenized, mixed 172 with distilled water (1:10) and shaked at 26°C for 2 h. The extract was centrifuged at 3000 rpm for 173 15 min, 0.25 mL of Folin-Ciocalteu (1:2) and 1 mL of 10% Na 2 CO 3 were added to supernatant 174 solution. The mixture was incubated at room temperature for 2 h and the absorbance was measured 175 at 760 nm using a Unicam, Helios Alfa spectrophotometer. Gallic acid was used as a standard. A 2. Seven samples (38.8%) did not contain any pesticide but five out of 200 eighteen (27.7%) showed two or three pesticides simultaneously present. Five vegetables were not 201 in compliance with Italian directive on Maximum Residue Limits (MRL). Among the pesticides 202 found at higher concentrations, azynphos methyl, was detected only into aubergine and indoxacarb 203 in a tomato sample was found at concentrations three fold higher than the allowed limit. 2) were subjected to Ames Test and SOS Chromotest. Sample 3 was chosen 249 because it contained penconazol, a pesticide not allowed in Italy that, even though it was found 250 neither mutagenic nor genotoxic (Table 3 andTable 5) it could determine any effect when in 251 presence of other pesticides. Sample 7 was chosen because azynphos methyl residue exceeded 252 three fold the maximum concentration allowed. The sample number 13 was selected because all 253 residues in this cherry tomato were at concentrations higher than the respective limits. The results of 254 genotoxicity tests on vegetables are shown in Table 6. Neither mutagenic nor genotoxic effects 255 were found in the vegetable extracts tested. The mutagenic/genotoxic activity found in the previous 256 tests for the pure pesticides, some of them highly active such as iprodione and indoxacarb, was Chromotest. The analysis of data from Ames test on S. typhimurium TA98 and TA100 for pesticide 275 mixtures (Table 7) showed mutagenic ratios > 2 only at 100% for mixture B. The results of 276 mixture A indicated a MR >2 only at 100% for TA98 while for TA100 at 100% a toxic effects of 277 the pesticides was found. A clear dose related effect was shown but the mixtures were not 278 considered positive for those two strains because there were not two consecutive dose levels with 279 responses at least twice that of the negative control. On the contrary, data obtained from the strain 280 1535 showed a strong positive mutagenic effect also according to the statistical analysis for both the 281 mixtures indicating that they induced direct mutations for pair base substitution. The mixture B was 282 the most active with a MR = 2.33 already at 25% of the mixture dilution. Finally, the pure 283 pesticides, found positive for mutagenesis, when in mixtures showed some interactions determining 284 the decrease of activity on the strain TA98 while the mutagenic effect was maintained for TA1535.

285

As reported in Table 8, the mixtures were not able to induce the SOS system, they exhibited a clear 286 dose-response relationship and a toxic effect was shown for not diluted mixtures of pesticides 287 (100%). On the basis of these results, to demonstrate that the high decrease in activity of the 2006). The green pepper exhibited the highest 300 antioxidative activity followed by aubergines and cherry tomatoes but no further data were found 301 for aubergines in the literature. Figures 1 and2 compare the mutagenic activity of azinphos methyl 302 (for S. typhimurium TA100 and TA1535) and the mixtures A and B (for S. typhimurium TA1535) to 303 the same activity when the pesticides were pre-incubated with gallic acid or ascorbic acid. The 304 inhibitory effect of the antioxidants on the activity of pesticides was high and significantly different 305 indicating that antioxidants positively influence the decrease in mutagenesis of pesticides. The same 306 trend was shown for the genotoxic activity of azinphos methyl (Figure 3) that was suppressed going 307 from IF = 2.71 to 1.14 and 1.12 with ascorbic acid and gallic acid, respectively. The present study has shown that vegetables cultivated and distributed in South Italy may contain 311 mixtures of pesticides at concentrations higher than the respective MRLs. It is to be considered that 312 the MRL expressed in mg kg -1 of food, is the amount that may be taken daily without evident health 313 risk but it is referred to adults whose weight is comprised between 50 and 80 kg. Table 3: Mutagenic activity of the tested pesticides in Salmonella strains TA98, TA100 and TA1535. In bold the positive MRs.

± SD= standard deviation obtained from three independent experiments. nm= no mutagenic effect. ns= no significative number of revertants compared to the control. a MR (Mutagenic ratio): number of revertants/plate compared to the negative control.

F o r P e e r R e v i e w O n l y

Table 4 : Mutagenic activity of λ-Cyhalothrin in Salmonella strains TA 100, TA 98 and TA 1535. In bold the positive MRs.

± SD= standard deviation obtained from three independent experiments. a MR (Mutagenic ratio): number of revertants/plate compared to the control.

  properties of the pure pesticides utilized in the present study are given in 84 Table1. All pesticides, 4-nitroquinoline-N-oxide (4-NQNO), 2-nitrophenyl-β-galacto-pyranoside 85 (ONPG), ρ-nitrophenyl phosphate disodium (PNPP), citric acid, gallic acid, ascorbic acid, Folin

  of E. coli PQ37 was diluted and incubated for 2h at 37°C to a bacterial density 156 of 2 x 10 8 bacteria/mL. Then, 1mL of the culture was diluted in 9 mL of fresh medium. Fractions 157 (600 µL) were distributed to test tubes containing 20 µL of the sample and negative (solvent) and 158 positive controls (4-nitroquinoline-N-oxide). After shaking for 2h at 37°C, two aliquots (300 µL) each tube for β-galactosidase and alkaline phosphatase assay with 4-160 nitrophenyl-β-D-galactopyranoside (ONPG) and 4-nytrophenyl phosphate (PNPP) solutions, 161 respectively. The β-galactosidase and alkaline phosphatase activity were measured as o-nitrophenol 162 and 4-nitrophenyl concentration, respectively, by photometric measurement at 405 nm. The 163 genotoxic activity was identified as the ratio R= β/ρ where β represents β-galactosidase activity and 164 ρ, phosphatase alkaline activity. The results were expressed as induction factor (IF) for each 165 dilution of compound, defined as IF= R/R 0 , in which R 0 is the spontaneous ratio measured in the 166 solvent control. A sample was considered genotoxic when the IF was more than double with a clear 167 dose-effect relationship (Ruiz and Marzin 1997). Also SOS Chromotest was carried out in the 168 absence of S9 fractions of liver homogenate from rat. 169 Determination of Polyphenols 170

  176solvent control was prepared for each dilution of sample and positive control. Each sample was 177 analyzed three times. Results were expressed as microgram gallic acid equivalent per g Oyaizu described byRekha and Vijayalakshmi (2008) was used to determine the 181 reducing activity. The sample was homogenized and mixed with distilled water (1:100) and 182 centrifuged at 1500 rpm for 20 min. The extract (0.5 mL) was supplemented with 0.5 mL 183 potassium ferricyanide (1%) and 0.5 mL of sodium phosphate buffer (0.02 M, pH 7) and the 184 mixture was incubated at 50°C for 20 min. Then, 0.5 mL of trichloroacetic acid (10 %) was added 185 was left at room temperature for 15 min and then was centrifuged at 1000 rpm for 186 5 min. The supernatant (1.5 mL) was mixed with 0.2 mL ferrichloride (0.1 %) and incubated at 187 room temperature for 5 min. Vitamin C was used as standard. The absorbance was measured at 700 188 nm (Unicam, Helios Alfa spectrophotometer). The results were expressed as an equivalent performed three time (three independent assays). The results of all assays were 192 analyzed using analysis of variance (one-way ANOVA ) and difference among means were 193 compared to negative control for critical F at p ≤ 0.05 or p ≤ 0.01. The level of significance is given 194 in the respective tables. Furthermore, the coefficient correlation (R) was estimated with linear 195 regression statistic method and compared to Student's t distribution for p ≤ 0.05 or p ≤ 0.01. different classes were detected in the samples of vegetables examined and all 199 results are reported in Table

204

  Cypermethrin, chlorpyriphos methyl and indoxacarb were found, concurrently, into a sample of 205 cherry tomato at concentrations higher than the respective MRLs, as well as λ-cyhalothrin in yellow 206 pepper and fenazaquin in cherry tomato. One sample of green pepper contained penconazol (0.02 207 mg/kg), a compound not allowed in Italy. Looking at these findings we chose the twelve pesticides, 208 representative of different chemical classes, to be subjected to mutagenesis/genotoxicity testing to 209 determine whether compounds commonly used in South Italy against pests can damage DNA. the pesticides inducing or not direct mutagenic activity in the Ames test are shown 211 in Table3. All pesticides were examined at concentrations around values detected in the 212 contaminated vegetables. Chlorothalonil, chlorpyriphos ethyl and penconazol were negative for all 004 µg/mL. The other pesticides were not able to determine a significant SOS pesticides tested, azinphos methyl, cyprodinil, fludioxonil and iprodione were 240 positive to Ames test as well as stimulated SOS DNA repair system in E. Coli PQ37. chlorothalonil, 241 chlorpyriphos ethyl, cypermethrin and penconazol were not mutagenic for Salmonella neither 242 genotoxic for E. coli. 243 244 Our main objective was to investigate whether the contaminated vegetables maintained the same 245 genotoxic activity detected for pure pesticides or they had any antimutagenic/antigenotoxic effect. 246 For this reason bioassays were performed on food extracts. Of 18 vegetables analyzed by GC, 247 sample number 3 (green pepper), sample number 7 (aubergine) and sample number 13 (cherry 248 tomato) (see Table

Food

  257 completely suppressed. The MRs (ratio between the number of revertants of the test plates and the 258 number of revertants of the solvent control plates) was often less than 1, indicating that the food 259 extracts reduced the number of revertants by comparison with the negative control, acting as 260 protective factors for mutagenesis. The same findings were achieved for the SOS Chromotest. 261 These results differ from those of Feretti et al (2007) that in a study on the extracts from pesticide-262 Page 10 of 29 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk the Allium cepa assay, found genotoxicity in the most extracts except in 263 those not contaminated by pesticides. The Ames test and SOS Chromotest were also performed on 264 the mixtures of pesticides found in sample number 3 (green pepper) (mixture A) and in sample 265 number 13 (cherry tomato) (mixture B), to evaluate whether interactions could occur between 266 pesticides. For this reason, the mixtures were analyzed starting from the respective maximum 267 concentration of each pesticide found in that vegetable and maintaining the same concentration ratio 268 between the pesticides (only dilutions of the mixture were done) to compare their effects with those 269 of all constituents without testing all possible combinations of the individual pesticides. The 270 genotoxicity of the mixtures could not be described with respect to synergistic, antagonistic or 271 potentiating effects of pesticides because no dose-effect curves for each of the single pesticide were 272 done. This approach just allowed us to establish if the effects of the combined pesticides could 273 justify the decrease in genotoxicity showed by vegetables contaminated in the Ames test and SOS 274

  the vegetables, showed in Table6, could depend on the presence of natural 289 products such as vitamins, or secondary metabolites as flavonoids, we determined the content of 290 polyphenols and the reducing activity of the vegetables because these may contribute to overall 291 antioxidant activities of food. Subsequently, the mixtures A and B and azynphos methyl, the only 292 pesticide found in the sample of aubergine (7), were co-incubated with standard antioxidants 293 (ascorbic acid and gallic acid) and then subjected to Ames test and SOS Chromotest, for the strains 294 for which were previously found positive. The data presented in Table9, concerning the hydrophilic 295 portion of the content in polyphenols and the total reducing activity of the contaminated vegetables 296 indicated that the presence of antioxidants was high. The content in polyphenols expressed as µg 297 GAE/g (gallic acid equivalents/vegetable) fell into the range of values reported for cherry tomatoes 298 in previous studies (Toor and Savage, 2005; Brat et al. 2006) while our results were higher for 299 green pepper when compared to those of Brat et al. (

  b R : dose/response relationship evaluated for non-toxic concentrations One-Way ANOVA used to determine differences among means in negative control and sample. *p≤ 0.05 **p≤ 0

  Figure 2

  Figure 3

Table 1 :

 1 Characteristics and properties of the tested pesticides.

	The results of this

.84) Chlorpyriphos methyl (0.67) Indoxacarb (0.34)

				0.50
				0.50
				0.10
	14	Cherry Tomato	n.r.
	15	Cherry Tomato	Fenazaquin (0.78)	0.50
	16	Lettuce	Iprodione (2.31) Cyprodinil (1.14)	10.0 10.0
			Fludioxonil (0.85)	10.0
	17	Lettuce	n.r.
	18	Lettuce	Fludioxonil (0.13) Cyprodinil (0.47)	10.0 10.0

Table 2 :

 2 Concentrations and Maximum Residue Limits of pesticides occurred in the tested vegetables. In bold the pesticides found at concentrations higher than the respective MRL.

	n.r. = no residues

Table 5 :

 5 Genotoxic activity for E. coli PQ37 of the tested pesticides. In bold the positive IFs.

			Mean		Dose	Mean		Dose	Mean
			Revert./plate ±SD	a MR	(µg/mL)	Revert./plate ±SD	a MR	(µg/mL)	Revert./plate ±SD	a MR
	Negative Control		43 ± 8	-	-	169 ± 11	-	-	17 ± 3	-
		-	-	-	5	385 ± 13**	2.28	5	156 ± 25**	4.89
	Sodium azide	-	-	-	10	556 ± 25**	3.29	10	320 ± 19**	10.00
		-	-	-	20	809 ± 36**	4.79	20	496 ± 39**	15.50
		2.5	163 ± 16**	3.80	-	-	-	-	-	-
	2-Nitrofluoren	5	387 ± 13**	9.00	-	-	-	-	-	-
		10	516 ± 31**	12.00	-	-	-	-	-	-
		0.6 x 10 -1	67 ± 4**	1.56	0.6 x 10 -1	225 ± 21**	1.33	2.5 x 10 -1	20 ± 4	1.20
	λ-Cyhalothrin	1.2 x 10 -1 2.5 x 10 -1	88 ± 12** 103 ± 7**	2.05 2.39	1.2 x 10 -1 2.5 x 10 -1	268 ± 24** 304 ± 7**	1.58 1.80	5 x 10 -1 1.00	29 ± 6* 104 ± 8**	1.70 6.10
		5 x 10 -1	201 ± 8**	4.67	5 x 10 -1	375 ± 4**	2.21	2.00	71 ± 13**	4.20
		1.00	322 ± 41**	7.49	1.00	551 ± 26**	3.26
	b R		0.99**			0.99**			1.00**

± SD= standard deviation obtained from three independent experiments. * Significant at p ≤ 0.05. a IF (induction factor) = R/R0. b R: dose/response relationship evaluated for non-toxic concentrations. Page 22 of 29 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk

Table 6 :

 6 Mutagenic and genotoxic activity of three contaminated vegetables expressed as Mutagenic Ratio and Induction Factor.

	Page 23 of 29	Food Additives and Contaminants
		(3): N° sample contaminated by Chlorpyriphos ethyl; Iprodione; Penconazol
		(7): N° sample contaminated by Azynphos methyl
		(13): N° sample contaminated by Cypermetrin; Chlorpyriphos methyl; Indoxacarb

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Table 7 :

 7 Mutagenic activity of pesticide mixtures found in contaminated vegetables.Mixture A: chlorpyriphos ethyl (0.40 µg/mL), iprodione (0.73 µg/mL), penconazol (0.02 µg/mL). Mixture B: cypermethrin (0.84 µg/mL), chlorpyriphos methyl (0.67 µg/mL), indoxacarb (0.34 µg/mL). ± SD= standard deviation obtained from three independent experiments. Sample's dose/response relationship evaluated for non-toxic concentrations. a MR (Mutagenic ratio): number of revertants/plate compared to the control. One-Way ANOVA used to determine differences among means in negative control and sample *p ≤ 0.05 **p ≤ 0.01

		GENOTOXICITY
		(SOS CHROMOTEST)
	Mixture dilutions in %	a I.F.
		100	1.31 ± 0.16
	Mixture A	50 25	1.83 ± 0.11 1.11 ±0.15
		12.5	1.04 ± 0.10
		100	1.31 ± 0.18
	Mixture B F r o	50 25 12.5	1.99 ± 0.20 1.47 ± 0.22 0.41 ± 0.12
	P
	e
	e r
		R
		e
		v i e
		w
		O n l
			y

b R :

Table 8 :

 8 Genotoxic activity of the pesticide mixtures found in contaminated vegetables. Mixture A: Chlorpyriphos ethyl (0.40 µg/mL); Iprodione (0.73 µg/mL); Penconazol (0.02 µg/mL) Mixture B: Cypermethrin (0.84 µg/mL); Chlorpyriphos methyl (0.67 µg/mL); Indoxacarb (0.34 µg/mL)

	a IF (induction factor) = R/R0

Table 9 :

 9 Polyphenol content and reducing activity of three contaminated vegetables. Polyphenol content is expressed as microgram gallic acid equivalent per g of vegetable Reducing activity is expressed as an equivalent amount of ascorbic acid (µM).

		POLYPHENOL CONTENT (µg/g)	REDUCING ACTIVITY (µM)
	Aubergine	430.1 ± 48.2	3017.0 ± 103.1
	Green pepper	668.5 ± 48.3	4150.3 ± 337.3
	Cherry tomato	225.4 ± 8.6	1480.3 ± 140.3

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Rekha C R, Vijayalakshmi G. 2008. Biomolecules and nutritional quality of soymilk fermented 367 with probiotic yeast and bacteria. Applied Biochemistry and Biotechnology 151: 452-463.

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