Exploring Interactive Knowledge Networking Across Insurance Supply Chains 1
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Exploring Interactive Knowledge Networking across Insurance Supply Chains1 1Jordi Capó, 2Josep Capó-Vicedo , 3Josefa Mula. 1CEGEA, Universitat Politècnica de València, Spain, [email protected] 2Department of Business Management, Universitat Politècnica de València, Spain, [email protected] 3 CIGIP, Universitat Politècnica de València, Spain, [email protected]
Abstract In this research we provide a conceptual model for improving knowledge management in multi-level supply chains in mutual and insurance industries. This approach uses social network analysis techniques to propose and represent a knowledge network for supply chains. This proposal improves the establishment of inter-organizational relationships into networks to exchange knowledge among the companies along the supply chain and to create specific knowledge by promoting confidence and motivation.
Key words: Social networking systems, social network analysis, collaborative technologies, knowledge platform, knowledge network, conceptual model.
1. Introduction In the insurance industry there have been some changes in customer expectations, which can be seen in the fact that they look for products that are adapted to their needs. In these cases, the real added value of an insurance company will depend on the capabilities of the agents involved in their supply chain, in order to be more adaptable and innovative to provide solutions to the specific circumstances that customers need. This implies an intensive knowledge generation (explicit and tacit), which drives to an obvious competitive advantage for the companies in the supply chain. For the companies of an insurance supply chain, cooperation with other similar sized or larger sized companies in this supply chain is a strategic alternative that enables them to obtain competitive advantages. Some authors (Gattorna and Walters, 1996; Christopher, 1998; Gunasekaran et al., 2001; Ozkul and Barut, 2009) recognize this need for cooperation and stress the establishment of closer, long-term relationships as a way to construct increasingly efficient and responsive supply chains. Indeed, collaboration between supply chain partners is receiving increasing attention in the SC literature (McCarthy and Golicic, 2002; Matopoulos et al., 2007). It is essential to base this collaboration on mutual trust, openness, shared risks and shared rewards to yield competitive advantages that result in better performance than to not consider collaboration (Hogarth-Scott, 1999). More and more companies collaborate in the supply chain because it offers market diversity, competitive pricing and shorter product life cycles (Soosay et al., 2008). Malhotra et al. (2001) maintain that SC partners engage in interlinked processes that enable rich information sharing and building information technology infrastructures to process the information obtained from partners, a scenario that creates new knowledge. Knowledge is one of the most decisive factors capable of offering competitive advantages for supply chain partners (Crone and Roper, 2001; Cheng et al. 2008; Wu, 2008). In this sense, the concept of collaborative networking is particularly timely in the insurance industry as it looks to strengthen the inter-organizational ties
1 Funding provided by the EU Marie Curie Actions—Industry-Academia Partnerships and Pathways (IAPP) Project entitled “KNOWNET— Engaging in Knowledge Networking via an interactive 3D Social Supplier Network (FP7-PEOPLE-2012- IAPP)” is gratefully acknowledged. between its suppliers and external agencies, for improving processes, accelerating innovation, fostering creativity, and sharing experiences and local knowledge amongst its supplier networks (Grant, 2014). For this collaboration to take place in an insurance supply chain, an environment guaranteeing a series of factors that allows knowledge sharing among the participating companies is necessary. In this paper, we propose a conceptual model to explore the potential and value of current social networking technology to support sustained knowledge sharing and generation across a multi- level supply chain in the insurance sector. A model is a representation for the purposes of understanding the basic mechanics involved in the companies of a supply chain performance. This understanding is often used to measure, manage and improve their processes. However, process models do not capture the tacit knowledge involved in those processes and source of sustainable competitive advantage for the company. These types of models are based on a relatively predictable view of companies’ environment. In these cases, both information and knowledge (treated usually as synonymous) can be expressed in the rule-based logic, data inputs, and data outputs that trigger pre-defined and pre-determined actions in pre- programmed modes. These models based upon the above logics, have focused on the re-use and replication of existing knowledge (which has been captured, organized and retrieved) over creation of new knowledge. These models could work well in predictable and stable environments. However nowadays, the insurance environment is unpredictable and unstable, and it requires a dynamic model (which takes into account tacit knowledge -difficult to formalize-) based upon on-going reinvention and creation of knowledge to adjust better to the changeable future. This proposed model promotes an integrated approach to the creation, capture, organization, access and use of insurance supply chains intangible assets. These assets include structured databases, textual information such as policy and procedure documents (explicit knowledge), and most importantly, the tacit knowledge and experience of suppliers and their employees. Processing of knowledge through the model will provide a framework for evaluating and incorporating new experiences and knowledge in an insurance supply chain and the dynamic representation of knowledge will provide a more realistic model integrated within human and social interactions. The aims and objectives of this model are to propose an interactive model, designed to support local innovation and learning where explicit, implicit and tacit knowledge and experience of suppliers and their employees can be shared. The model is based on Social Networks (SN) that takes full advantage of knowledge management benefits. It uses the social networks analysis (SNA) techniques as a modelling tool to better understand knowledge management in a multi-level supply chain, and a set of web based tools, such as forums, blogs, wikis, FAQs, public recommendations/suggestion pools and exercises and applications specially designed to utilise a range of learning processes (e.g. learning by doing, learning from others, and applications supporting the formation of communities of inquiry and promoting learning through social interaction. The findings of this research provide useful insights into how supply chains can reinforce their collaborative behaviours and activities to not only enhance their relationships, but to also achieve competitive advantages for the supply chain as a whole.
2. Theoretical Framework
2.1 Social Capital and Social Networks Many scholars have worked on defining and establishing social capital as a theory. Some authors have traced the evolution of social capital research as pertaining to economic development and identify four distinct approaches: communitarian, networks, institutional and synergy (Woolcock and Narayan, 2000). In fact, there is no recognized and established definition of social capital. Several scholars have conceptualized it as a set of social resources embedded in relationships (Loury, 1977; Burt, 1992). Other scholars, however, have espoused a broader definition of social capital, including not only social relationships, but also the norms and values associated with them (Coleman, 1990; Portes and Sensenbrenner, 1993; Putnam, 1995). A more precise definition can be found in Westlund and Bolton (2003: 79), who define space bound social capital as spatially-defined norms, values, knowledge, preferences, and other social attributes or qualities that are reflected in human relations. In network terms, this may be expressed as meaning qualities, capacity, objectives and the number of nodes (actors) and qualities, capacity, objectives and the number of links in primarily informal, spatially demarcated social networks. Although to some extent relational and social capital can be considered interchangeable concepts, in our view relational capital can be understood as a part or one of the dimensions of social capital. As we understand it, relational capital includes the nature of the ties (strength) and its outcomes (common norms and values, such as trust). According to Kale, Singh and Perlmutter (2000) relational capital is based on mutual trust and interaction at the individual level between alliance partners. Another definition of relational capital is provided by Capello (2002), who referred to the mutual trust, respect and friendship that reside at the individual level between alliance partners. In the context of the industrial district, relational capital is defined as the stock of relations that a firm can entertain with others. A social network is a set of actors connected by a set of ties (Borgatti and Foster, 2003). The elements of the network are often referred to as vertices, nodes or actors and the size of a network is the total number of nodes and contacts that compound the network (Martinez-Lopez et al., 2009). The actors can be people, teams, organizations, concepts, etc. Ties connect pairs of actors and can be directed (i.e., potentially one-directional, as in giving advice to someone) or undirected (as in being physically proximate) and can be dichotomous (present or absent, as in whether two people are friends or not) or valued (measured on a scale, as in strength of friendship) (Borgatti and Foster, 2003). Social network analysis (SNA) provides researchers a descriptive and statistical method to understand how supply chain components are positioned, connected and embedded within the supply chain system by using both node- and network-level measures (Wasserman and Faust, 1994). SNA has emerged as a key technique in modern sociology, anthropology, geography, physical sciences, social psychology, information science and organisational studies. Social network theory views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors. The emphasis lies on the relationships and the ties between actors within the network and the structure of the network and the quality of the relations are the main determinants of its usefulness to its participating individuals (Caniels and Romijn, 2008). SNA aims to describe the interactions between individuals within a group (Anderson and Jay, 1985; Granovetter, 1985; Wellman, 1988) and to understand the collective behaviour of a group (Laumann and Pappi, 1976). Thus, the links or connections among the elements are usually referred to as edges or contacts (Martinez-Lopez et al., 2009). When we focus our attention on a single focal actor, we call that actor “ego” and call the set of nodes that ego has ties with “alters” (Borgatti and Foster, 2003). The lack of ties among an actor’s alters is named structural holes (Burt, 2009). While board interlocks are ties among organizations through a member of one organization sitting on the board of another (Borgatti and Foster, 2003). Related to SNA in the context of supply chain systems, it is necessary to highlight the seminal work by Borgatti and Li (2009), which provides an overview of social network concepts in order to be applied an a supply chain context. Recently, Bellamy and Basole (2013) identify a growing recognition of the significant benefits a network analytic lens provide to understand, design and manage supply chain systems.
2.2 Knowledge Management in Supply Chains There are different ways of understanding and classifying knowledge, and most focus on knowledge types: tacit, explicit, individual, organizational, etc. Nonetheless, there are many other factors to consider, among which the interdependence between knowledge and the organizational context stands out (Zheng et al., 2010). This is especially important because generation of new knowledge occurs in this context, and each context requires not only a different form of KM, but also different support systems (Capó-Vicedo et al., 2011). With supply chains it is necessary to form a relationship or deal with organizations with very different experiences, languages and contexts. This implies new organizational ideas, plus an environment of trust and collaboration between the enterprises in the SC, which facilitates knowledge creation, and distribution. To obtain advantages from knowledge sharing, it is of strategic importance that firms understand the factors affecting partners’ knowledge sharing behaviours (Cheng et al. 2008). Along these lines, Grant (2001) indicates that there are certain occasions involving collaboration with another enterprise that help achieve better integration and diffusion of knowledge than internal collaboration. The reason for this is that the creation of informal relationships usually takes place between different enterprises. These relationships are based on common interests and the will to share experiences, and are much more effective than the more formal enterprise processes used for knowledge integration and transfer. Companies in a supply chain can use knowledge of social networks to identify internal collaboration opportunities (Carter et al., 2007) and to obtain management improvements such as working together easier with the rest of the supply chain members, generate confidence between the companies in the supply chain and collaborative learning, among others. Therefore it is necessary to propose new models for improving the understanding of the generation and transfer of knowledge processes between the partners of a supply chain. These models should be approached at a network or multi- level supply chain, rather than in a dyadic supply chain (Chen and Paulraj, 2004; Mueller et al., 2007; Johnsen et al., 2008).
3. Proposal of a Conceptual Model
3.1 Establishment of a Dynamic Knowledge Network in the Supply Chain Knowledge management in a supply chain is possible with a series of met conditions. These conditions come in two large groups: conditions relating to the industrial sector and conditions relating to each supply chain (Capó-Vicedo et al., 2011). Regarding the conditions relating to the industrial sector, it is necessary to bear in mind that the sector to which enterprises belong influences the form of knowledge management. This is because industrial processes can differ considerably for each sector, which implies certain differences in the nature of the knowledge transmitted. In fact, several authors in the literature deal with the particularities of knowledge management in a specific industrial sector (Mentzas et al., 2006; Venters et al., 2005; Egbu et al., 2005; Newell et al., 2006; Fong and Kwok, 2009; Javernick-Will and Scott, 2010). Regarding the specific conditions relating to each supply chain, the particular context in which enterprises operate has a strong influence on the knowledge management carried out in it. As mentioned above, a series of met requisites are necessary to achieve positive interaction among the different enterprises in order to generate knowledge creation and an interchange process. This process demands a degree of similarity among the management systems, culture, language, objectives, etc., which is not always the case. In line with all this, several authors describe the influencing facts that achieve suitable knowledge generation and transmission throughout the supply chain. These characteristics become especially important in the case of an Insurance supply chain because, if they do not exist, it is impossible to generate, acquire, transfer and combine knowledge among them; therefore, achieving customer satisfaction is also impossible. Conducting business in agile industries such as insurance, which depend on complex processes of multiple individuals exchanging information, knowledge, ideas, and insights, interaction, via social networks for example, could potentially deliver a huge set of efficiencies and opportunities for rethinking core supply chain and internal processes (Grant, 2014). Business in the services industry traditionally requires the input, participation and decisions of many stakeholders. For example, risk managers, actuaries, IT and marking/distribution staff often collaborates in product development. Despite this need, and some minor developments in collaborative knowledge sharing, firms in these industries are not seen as conducive to fostering knowledge sharing and generating collaborations across their supply chains in a proactive way (Dawson, 2004). Insurers are beginning to look to incorporate collaboration technologies into their operating models, to improve process efficiency and knowledge sharing (Josefowicz, 2011, Kontzer, 2002), and the use of social media to assist in the coordination of knowledge sharing and other business activities is only starting to be explored. This can allow companies to stay close to the changing desires of their customers and the changing trends in the market. However, the use of such approaches and technologies presents a new set of challenges to these organizations, who are not used to managing knowledge transfer in this way. Included in these challenges are monitoring appropriate content for sharing or archiving issues, measuring the needs of these new tools, integrating these new tools into existing workflow, communication and archiving systems and understanding the motivations prompting people to share knowledge or participate in virtual communities, in an industry that has typically always used private communication channels (Grant, 2014). All the above-mentioned conditions can be combined as a single unit: companies in an insurance supply chain must establish relationships to create a dynamic network that eliminates learning barriers to allow knowledge to flow freely throughout the network. The key to obtain competitive advantages lies in the capacity of the companies in the supply chain to acquire and absorb knowledge, to exploit it to develop new products and processes, and to learn from the best business practices. To go about this, it is important to strengthen ties among supply chain members. Companies must change their mind-set and create a new business culture to encourage knowledge exchange in order to share and use the tacit knowledge possessed by their employees throughout the supply chain. This process uses cross-functional and cross-organizational groups, which come together regularly to address different operational issues, and to break down and overcome inter- company barriers (Soosay et al., 2008). Total implication from all the agents is also necessary to create a climate of collaboration and mutual confidence. This is only possible by means of more stable and durable relationships to establish equal relationships. Instead of the classical models of buyer-supplier relationships which assume a hierarchy wherein customers specify and demand suppliers to conform or acquiesce, new organizational forms are necessary like those based on transparency (Lamming et al., 2005; 2006) in order to exchange sensitive information and knowledge in a supply chain, or in the extended enterprise and social networking (Kinder, 2003) as conduits of knowledge. Therefore, an organizational structure that eliminates barriers is necessary for the creation, transfer and diffusion of knowledge (Kinder, 2003; Lamming et al., 2005; Walter et al., 2007). In this context, a SN should be created between the companies in the supply chain. The current definitions in the literature consider SN to be networks of collaborating companies (Carter et al., 2007; Borgatti and Li, 2009). Each company is a node that contributes what it knows best (its core competence) to the network. Each network member establishes good communication with not only other members, but also the environment beyond the network. For cooperation purposes, it is crucial to understand the activities of others as they provide a context for the node’s own activity. The most important aspect of an SN is mutual trust among members. The need for flexibility and fast-changing organization implies information having to flow through the network nodes. All SN members must have access to information to make the right decisions. Evidently, this reinforces the idea of collaboration; neither leaders nor followers exist. In the following section a proposal for a conceptual model is presented. This model will take into account the requirements identified previously. The intention is to create an integrated model through a knowledge sharing culture, to recognize the value of intellectual capital and to understand that the competition depends not on the differential possession of physical assets, or even of information, but on the ability to deploy and exploit knowledge. 3.2 Conceptual Model for Interactive Knowledge Networking across Insurance Supply Chains The concept of collaborative networking is particularly timely in an industry that seeks to strengthen the inter-organizational ties between insurers, suppliers and external agencies, for improving processes, accelerating innovation, fostering creativity, sharing experiences, and generating ideas amongst the supplier network (Grant, 2014). An integrated model for interactive Knowledge Management in Insurance Supply Chains should consider the aspects listed below (Figure 1): 1. It’s necessary to develop, build and test an interactive collaborative platform -Supplier Social Network (SSN), designed to support innovation and leaning where both explicit and tacit knowledge and experience of suppliers and their employees can be shared. Each node of this network has to transform itself in a learning organization. A Change Team has to be carefully selected for leading the transformation inside them. One member of the change team will be involved in the Project management node of the network. They will facilitate the needed steps to promote the continuous change inside their own organization and in the network. 2. A same language has to be used in order to share data, information and knowledge. This entity is named “The Language Register”. 3. The organizational configuration, which will permit to understand the mechanism of creation of knowledge, will be the “Network functioning Model”. 4. Each node of the network has to be immersed in the knowledge dynamic. The aim is to transfer the individual knowledge into organizational knowledge. 5. Identify the appropriate tools that will help the total process and choose the indicators necessary to evaluate the system efficiency. Figure 3. Proposal of a Conceptual Model for interactive Knowledge Networking in Insurance SC
The proposed conceptual model is shown in Figure 1, and it has the next characteristics: It considers the two main knowledge dimensions (explicit and tacit) It allows the creation, conversion of knowledge through the company’s different levels (individuals, teams and organization) and through the entire supply chain It allows the capture, store, transmission and utilization of knowledge in the supply chain It is supported by IT It provides a way of measuring the results obtained using indicators
The main objective of the proposed model is to be able to turn a poorly connected supply chain, with isolated nodes, and where the main insurance company maintains an outside position (As Is Model), into a much denser network, with more and better connections in which the company has a central position, acting as a broker in the supply chain (To Be Model). The proposed model has five components, which enable knowledge management in insurance supply chains. The general goal is to create a tangible manifestation of the organization’s knowledge (Intangible Assets) that gives and increase the market value of the supply chain (Intellectual Capital).
Supplier Social Network The main component of the proposed conceptual model is an interactive Supplier Social Network framework (SSN), designed to support innovation and learning where both explicit and tacit knowledge and experience of suppliers and their employees can be shared. SSN is a collaborative platform or social network sites for automatically collecting data or knowledge mapping in organizations. The main objective of SSN is to automatically generate and share some of the data versus the traditional approaches adopted in order to provide more reliability of SNA results. The SSN will consist of a set of web based tools, such as forums, blogs, wikis, FAQs, public recommendations/suggestion pools and exercises and applications specially designed to utilise a range of learning processes (e.g. learning by doing, learning from others, and applications supporting the formation of communities of inquiry and promoting learning through social interaction. Specifically, the SSN platform will bring together supply chain members in a highly interactive real-time 3D environment, who be able to communicate quickly and effectively with sound and image and will promote the sharing and adoption of good ideas, practices, etc. Specifically, the combination of exercises, applications and social interaction tools, ensures a holistic knowledge-sharing platform which encourages contact between supply chain partners, encourages knowledge transfer across and between supply chain partners, develops reciprocity and cooperation among supply chain partners, uses active learning techniques, gives prompt feedback, reduces ‘misinterpretation’ of information, communicates high expectations and respects diverse talents and ways of learning and knowledge.
4. Conclusions In this research we have proposed a conceptual model for exploring interactive knowledge networking across insurance supply chains. It has suggested a new organizational form based on SN to make the creation, transfer and sharing of knowledge possible in this particular case. Finally, it has proposed a knowledge network model represented within SNA techniques to gain a better understanding of the knowledge creation and transfer process. The first stage is to look at the main conditions and requisites to achieve real knowledge management in the specific case of insurance supply chain. One conclusion drawn is that collaboration is essential between supply chain members, which come in the form of inter-organizational networks to encourage knowledge exchange and creation. Some fundamental factors are mutual confidence among members, a similar way of thinking, etc. Another conclusion drawn is that each company not only focuses on its own processes, but also views the global process of the entire SN working as a single body. They can compete against other SNs and satisfy clients’ needs. This personnel exchange allows for personal and physical communication among people in the various supply chain companies, thus creating a social network. This research shows how establishing these inter-organizational relationships into networks leads to knowledge exchange among the companies under study, and to the creation of new specific knowledge by promoting confidence and motivation and by establishing alliances, team spirit and better coordination and communication among the enterprises involved. This implies a higher degree of innovation, fewer losses, improved efficiency in transactions and in production itself, and to increased competitiveness among the companies concerned. We have identified some potential extensions of this work as further research: (1) further research of this proposal in other supply chains of similar or different sectors is forthcoming; (2) an additional quantitative analysis to reinforce the validity of the data and results could be integrated; (3) complementary conceptual modelling tools, such as flow charts and IDEF models, could be used as communication tools among SC users; (4) a decision system incorporating the SNA models could be developed. Finally, and considering the social capital concept for the explicatory model of the network, we propose a research work that focuses on the distinction between simple relationships and more intense relationships which imply social components such as trust (Bernardes, 2010). In this sense, it would be interesting to propose variables to measure the relationships between agents not only as dummy variables, but which also take into account the degree of intensity in the relationship.
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