Partner selection for interfirm collaboration in ship design

Partner selection for interfirm collaboration in ship

design

Marina Z. Solesvik & Sylvia Encheva

It is a post-review prepublication version of the manuscript. Please refer to the Industrial Management and Data Systemsfor the proof-read final version of the manuscript. Link:

http://www.emeraldinsight.com/journals.htm?articleid=1863369&show=pdf

Cite as:

Solesvik, M. and Encheva, S. (2010). Partner selection for interfirm collaboration in ship design. Industrial Management & Data Systems, Vol. 110, No.5, pp. 701-717.

Marina Z. Solesvik

Bodø Graduate School of Business, 8049, Norway

Stord/Haugesund University College, Bjørnsonsgate 45, 5528 Haugesund, Norway E-mail: mzs@hsh.no

Tel. +47 48133882

Sylvia Encheva

Stord/Haugesund University College, Bjørnsonsgate 45, 5528 Haugesund, Norway E-mail: sbe@hsh.no

Abstract

Purpose – The purpose of this study is to apply a mathematical method of formal concept

analysis (FCA) to facilitate evaluation of potential partners, and to select the most appropriate partner for horizontal strategic alliances. Horizontal collaboration between ship design firms is important in relation to business cyclicality in the industry. The workload in ship design firms drops during the troughs of the shipbuilding cycle and increases dramatically during the peaks of the cycle.

Design/methodology/approach – The proposed method of partnership selection applies FCA, which is based on mathematical lattice theory. FCA allows firms to evaluate and select the best suitable partners for horizontal interfirm cooperation from several possible candidate firms. Utilization of FCA allows a firm to visually analyze a potential partner for a horizontal strategic alliance.

Findings –The contribution of this study to the literature is twofold. First, it contributes to the

literature on the application of FCA in management field. Second, this study contributes to the partner selection literature. The contribution of the study is an alternative quantitative method for partner selection based on FCA. FCA compliments qualitative approaches in the process of alternatives evaluation and decision-making regarding partner selection for horizontal collaboration.

Practical implications –Practitioners from ship design firms can use the FCA tool to facilitate decision-making relating to the screening of potential partners for horizontal cooperation with regard to pre-specified selected criteria.

Originality/value – FCA has been marginally applied to aid managerial decision making. The

FCA tool is valuable for practitioners from ship design firms to manage the selection of partners for horizontal collaboration. The FCA tool is associated with numerous advantages, notably, relative simplicity and versatility of visual analysis when compared with other mathematical approaches such as the AHP, the ANP, optimization modeling, and fuzzy set logic.

Key words Partner selection, Ship design, Horizontal interfirm collaboration, Formal concept analysis

1 Introduction

Bain’s (1956) industrial organization theory suggests that the owners and managers of firms are unable to influence industry conditions (i.e. demand-side factors), or the performance of their firms. Population ecology theorists assert the deterministic power of external environmental conditions on firm survival

(Hannan and Carroll, 1992). Two conflicting theoretical traditions have questioned the assumed dominant power of external environmental conditions. Both the resource-based view (RBV) of the firm (Barney, 1991, 2001) and Porter’s framework of competitive advantage (1991) suggest that actions made by a firm can be a source of sustainable competitive advantage. Porter (1991) views a firm’s assets as being built from performing activities (i.e. strategy) over time, or acquiring them from the external environment, or both (Spanos and Lioukas, 2001). A firm’s stock of resources reflects prior entrepreneurial and managerial choices, the latter related to the choice of strategy. The ability of entrepreneurs to establish and develop their firms is shaped by their ability to deal with uncertainty, and to assemble, command and leverage resources from both the internal and external environments. To exploit ‘created’ or ‘discovered’ opportunities, entrepreneurs need to draw upon their skills, experience and knowledge to make judgmental decisions about how to undertake actions that promote wealth creation. To ensure firm survival and to generate profits for their firms, entrepreneurs guided by their skills, experience and knowledge (and existing resources within their firms) have to acquire new resources and / or develop new competencies.

An emerging strategic entrepreneurship perspective has been presented, which focuses on the integration of entrepreneurial (i.e. opportunity-seeking behavior) and strategic (i.e. advantage-seeking) perspectives in developing and taking entrepreneurial actions designed to create wealth (Hitt et al., 2001). Strategic entrepreneurship, grounded in the RBV of the firm, recognizes the importance of accessing the resources and capabilities required to support opportunity-seeking behavior aimed at achieving competitive advantage (Ireland et al., 2003). Entrepreneurial actions can entail creating resources or combining existing resources in new ways to develop and commercialize new products / services, and to service new customers / markets (Hitt et al., 2001). Interfirm cooperation is a source of competitive advantage. The number of interfirm collaborative arrangements (including strategic alliances, joint ventures, R&D alliances, licensing agreements, outsourcing, and virtual enterprises) is increasing (Barney and Hesterly, 2008). Firms form various collaborative arrangements in order to gain and sustain competitive advantage (Dyer and Singh, 1998).

Interfirm cooperation is realized either vertically (between firms within the supply chain) or horizontally (between firms which are not members of the same supply chain). The cooperation between competitors in order to get

competitive advantage for cooperating parties is steadily growing as well (Chin

et al., 2008). Ship design firms form horizontal cooperative relations with other ship design firms by two main reasons. First, the formation of the horizontal strategic alliances is relating to the shipbuilding cycles. The ship design firms are the part of shipbuilding industry. Thus, the business cycles which are observed in this industry influence on the ship design firms as well. The shipbuilding cycle is defined as “a period of time between one production peak and the next” (Volk, 1994, p.22). The shipbuilding cycle consists of four phases: (1) peak; (2) recession; (3) trough; and (4) recovery. The average length of the shipbuilding cycle is eight years (Volk, 1994). In the period of trough, customers order insignificant number of new ships from the shipyards. Consequently, the number of new contracts in the ship design firms is small as well. Over the trough phase, the reduction in ship design firms’ production volumes reaches, on average, 50 per cent. In contrast, during the peaks of the shipbuilding cycle, the number of new contracts to build and design ships is large. In this period, ship design firms might lack qualified engineers to satisfy all new orders. One of the effective ways to cover this resource and competence gap is to form horizontal strategic alliances with other ship design firms which have a capacity or able to raise its capacity.

Second, ship design firms specialize on the design of several types of vessels or ships intended to operate in certain geographical areas. Furthermore, ship design is a single-piece production. In some cases, a ship design firm does not posses all competences in-house to design special purpose vessels since such orders are infrequent. Instead, managers of a ship design firm form project-based horizontal strategic alliances when they need a missing competence in order to design certain types of vessels. This paper focuses on how firms can use a new tool to select an appropriate partner(s) to ensure firm competitive advantage with reference to qualitative data collected. This paper would be interesting for practitioners from the ship design firms looking to optimize partner evaluation and selection for horizontal collaboration.

The failure rate associated with cooperative arrangements can be high (Bierly and Gallagher, 2007). Up to 75 per cent of strategic alliances fail (Liker and Choi, 2004). Consequently, there is a growing body of research related to factors influencing the success of interfirm cooperation. Relationship issues (commitment, reciprocity and trust) and non-relationship factors are linked to interfirm cooperation success (Child et al., 2005). The choice of the right partner is viewed as a crucial prerequisite for successful cooperation (Elmuti

and Kathawala, 2001; Espino-Rodrigez and Rodrigez-Dias, 2008; Gonzalez et al., 2006; Medcof, 1997).

Partner evaluation and selection of partners for horizontal collaboration is a complex process. Despite a body of studies focusing on the criteria for partner selection, there are gaps in the knowledge base relating to methods that can be used by entrepreneurs to select appropriate partners who can sustain a firm’s competitive advantage in the face of changing market, technological and institutional conditions. The novel contribution of this study is an application of mathematical method of formal concept analysis that can be used by practitioners and entrepreneurs to identify appropriate partners if firms select a horizontal interfirm cooperative arrangement to ensure competitive advantage. Several studies have employed quantitative techniques to support decision-making with regard to partner selection relating to interfirm cooperation. Among the most popular quantitative techniques are fuzzy set logic (Lin and Chen, 2004; Wang and Chen, 2007), the mixed integer linear programming

(Jarimo and Salo, 2008; Wu and Su, 2005), optimization modeling (Cao and Wang, 2007; Fuqing et al., 2006), the analytic hierarchical process (Mikhailov, 2002; Wang and Chen, 2007), and the analytic network process approaches

(Chen et al., 2008; Sarkis et al., 2007; Wu et al., 2009). Quantitative techniques are not substitutes for qualitative methods of decision-making (Ordoobadi, 2008; Wu et al., 2007). Thus, the purpose of this study is to highlight a new approach to partner selection using FCA. FCA can aid practitioners and entrepreneurs seeking to select an appropriate partner that matches their alliance goals. The aim of FCA is to compliment subjective perceptions of decision-makers when they evaluate an appropriate partner for the horizontal alliance.

The paper is structured as follows. Prior research using mathematical methods to identify partners for interfirm cooperation is discussed in section 2. The theoretical foundations and basic notions of FCA are then discussed in section 3. The methodology of building a concept lattice from a qualitative data set is highlighted. In section 4, a decision support procedure for partner selection based on FCA is proposed and illustrated with regard to a horizontal cooperative agreement relating to a ship design firm. Results are then discussed and compared with the prior studies in section 5. Finally, conclusions, limitations of the present study and managerial implications of FCA are discussed. Appendix contains an example of solution methodology used in formal concept analysis.

2 Theoretical background

Partner evaluation and selection follows the opportunity recognition stage in the life cycle of a cooperative arrangement. The next stages relate to the functioning of a collaborative arrangement and its eventual termination (Pidduck, 2006). The performance of an interfirm collaborative arrangement depends on the selection of a partner who contributes with particular skills, competencies and resources (Berg and Friedman, 1980). The decision-making process underlying the choice of an alliance partner has been researched from several theoretical lenses relating to the resource-based view of the firm, the knowledge-based view, game theory, the organizational learning perspective, the resource-dependence perspective, transaction costs economics, and the competence-based view of the firm. Trust and commitment between partners can also shape the formation of alliances. However, the presence of trust between parties is not a sufficient condition for successful interfirm cooperation (Bierly and Gallaher, 2007).

Analysis of prior studies focusing on interfirm cooperation revealed that there is a body of studies that support the use of quantitative techniques relating to partner selection for horizontal collaboration. Table I summarizes the research questions, theoretical perspectives and the key findings of prior studies.

Table I. Summary of interfirm collaboration studies focusing upon partner selection methods Source Research question/purpose Theoretical perspective Key findings Cavusgil and Evirgen (1995)

Discusses the application of expert systems relating to partner selecting for an international joint venture.

Expert systems

Partner selection tool is used to formalize comparison and evaluation of possible partners. New managers shape partner selection.

Mikhailov (2002)

Discusses the relevance of fuzzy programming with regard to partnership selection. Fuzzy logic approach, analytical hierarchy process (AHP)

“A novel Fuzzy Preference Programming method was elaborated for partner selection purposes in new temporary collaborative arrangements. Choice of collaborative partner is articulated as a multiple criteria decision-making problem under uncertainty” (p. 400).

Fuqing et al.

(2006)

Apply the multi-objective optimization model to select cooperative partners for virtual enterprises.

M ulti-objective optimization

model

During the first stage inefficient candidates are removed. The generic algorithm is applied during the second stage to calculate probabilities and to isolate possible candidates.

Cao and Wang (2007)

Improve the one-stage partner selection model.

Combinato-rial optimization

model

Two-stage partner selection model. The first stage relates to a pilot study focusing on multiple potential partners. During the second stage, the best suitable partner is selected. Sarkis et al.

(2007)

Discusses a partner selection tool to facilitate formation of virtual enterprise. Analytical network process (ANP) approach Multi-attribute decision-making methodology is presented for partner evaluation and selection.

Wang and Chen (2007)

Discusses improvements to the fuzzy preference programming method for partnership selection.

Fuzzy logic approach,

AHP

The Fuzzy Preference Programming method is improved by deriving decision matrices.

Chen et al. (2008)

Discusses an iterative review approach relating to partner selection for a strategic alliance.

ANP Relative weights need to be assigned to criteria considered relating to partner selection.

Wu et al.

(2009)

Discusses how the ANP approach can be used to select partners for a strategic alliance.

ANP The ANP approach considers tangible and intangible factors that can shape the partner selection decision.

2.2 The analytic hierarchy process (AHP)

Several quantitative approaches have been proposed to identify appropriate strategic alliance partners. Mikhailov (2002) applied the analytic hierarchy process to assess uncertain weights relating to partner selection criteria and uncertain scores of alternative partners. The following procedure is used in the AHP method for making decisions: (1) problem structuring; (2) assessment of local priorities; and (3) calculation of global priorities (Saaty, 1994). Following this procedure, the partner selection is conducted with regard to four steps (Mikhailov, 2002). First, a hierarchy of alternative partners and criteria for selection are constructed using the AHP. Then, weights of each criterion are derived from pairwise comparison matrices.

By applying fuzzy preference programming during the third step, scores relating to each potential partner are generated. The global priorities for all potential partners are calculated during the fourth step. The partner having the

highest global priority is recommended to be a cooperating partner. This approach is associated with simplicity, precision and consistency (Wang and Chen, 2007). The drawback of Mikhailov’s (2002) approach is that it uses exact values to estimate decision-maker’s attitude to alternative partners and selection.

Herrera-Viedma et al. (2004) discussed improvements relating to the fuzzy preference programming method. Wang and Chen (2007) improved Mikhailov’s fuzzy preference programming method with regard to the AHP. They derived decision matrices instead of the exact values of the decision-maker’s opinion. This improved method reduced the number of pairwise comparisons to be considered. The AHP is associated with drawbacks. Notably, it is difficult to judge the relations among the selected factors considered.

2.3 The analytic network process (ANP)

The analytic network process addresses the latter disadvantage because it considers interrelationships between factors in the decision-making model. Sarkis et al. (2007) applied the ANP, which is rooted in the idea of Markov chains, to aid partner selection. They employed a five-step approach. First, a network of factors affecting the final decision was elaborated. Four factors were selected, namely, cost, quality, time and flexibility to select one partner for each link in the value chain relating to logistics, design, manufacturing and services. Second, using inputs from managers, factors were compared with regard to an elicit pairwise comparison. Third, the relative ranking of factors was determined with reference to the pairwise comparison matrix. Fourth, a super matrix was computed. Fifth, the weights relating to alternative partners were calculated.

Four criteria for alliance partner selection were highlighted by Chen et al.

(2008) relating to corporate compatibility, technology capability, resources for R&D, and financial conditions. Depending on the motivation for establishing a strategic alliance, the weights for each criterion were estimated. Then, an initial super matrix, comprising the relative weights was formed in order to articulate effects of interdependence between the motivations for alliance formation, and criteria at different levels of the hierarchical structure. During the next stage, the relative importance of the attributes relating to each criterion was analyzed. Finally, each potential partner was compared with regard to all selected attributes, and with regard to the ‘highest suitability index’ the most appropriate alliance partner was identified.

Wu et al. (2009) used the ANP with regard to strategic alliance partner selection. They suggest that partner selection is the most significant stage in creating a successful partnership. Wu et al. (2009) evaluated tangible and intangible factors regarding potential partners using the ANP. The major complexity in applying the ANP for partner selection is the difficulty to estimate interrelationships among factors. Decision-makers have sometimes limited information regarding potential partners. Optimization models can be used to reduce the latter problem.

2.4 Optimization models

Fuqing et al. (2006) applied a multi-objective optimization model to select partners. Several partners were selected to fulfill sub-projects that had to be done according to a schedule. The processing time for each sub-project was considered. This approach assumes the concept of inefficient candidates. The first step relates to the identification and removal of inefficient candidates. A generic algorithm is used rather than a traditional mathematical model. The important decision at this step is to set the values for each parameter. With the help of a generic algorithm the probabilities to ‘succeed’ are calculated for each possible partner. The partner with the ‘highest probability to succeed’ is selected to fulfill a sub-project.

Cao and Wang (2007) used a combinatorial optimization model. They suggested the two-stage selection model. During the first stage, several potential partners are engaged in a pilot study. Whilst during the second stage, a firm’s managers select the best partner for cooperation. This two-stage selection method enables more information to be collected relating to potential partners, which is an advantage over the one-stage selection models.

2.5 Expert systems

Cavusgil and Evirgen (1995) proposed the use of an expert system PARTNER method for partner selection. An expert system refers to computer-assisted tools that use the knowledge of one or more experts for analysis and solving problems (Mentzer and Gandhi, 1992). PARTNER uses Geringer’s (1991) classification of partner-related and task-related criteria for partnership selection. PARTNER consists of two modules relating to CEVED (Candidate EValuation EDitor) and CEVAL (Candidate EVALuator). CEVED is used to develop a knowledge base depending on the rationale for a cooperative venture, whilst CEVAL is used to evaluate alternative partners for cooperation. The

expert system has several managerial implications. First, it formalizes the process of partner evaluation and comparison, and generates a standard summary of potential partners. Second, the expert system promotes a learning process. Entrepreneurs and managers can learn essential criteria of partner selection and key issues of the selection process.

3 Formal concept analysis

Information can be transformed into knowledge applying concepts since people operate with concepts in order to understand and handle realities. Formal concept analysis was developed to promote better communication between the developers of lattice theory and its end users (Wille, 1982). FCA is defined as “a method to visualize data and its inherent structures, implications and dependencies” (Wormuth and Becker, 2004, p.4).

3.1 Contexts and concepts

FCA is based on philosophical notion of a concept. A concept is defined as a cognitive element of meaning constructed from other elements which act as a concept’s characteristics (Stanford Encyclopedia of Philosophy, 2005). The extension encompasses all objects belonging to the concept, whilst the intension covers all attributes applicable to selected objects (Wolff, 1993).

A context is defined as a triple (G, M, I) where G and M are sets and I < G ×

M (Wolff, 1999). The elements of G and M are called objects and attributes

respectively. I is a relation between G and M. The relation I is also labelled the incidence relation of the context (Carpineto and Romano, 2004). If the sets are finite, the context can be specified with a help of a cross-table. An example of a simple context is shown in Table II. An example demonstrating formal concept analysis is given in Appendix.

A concept of a context (G, M, I) is defined as a pair (G', M'), where a set of objects whose members of M’ have in common is G'. The set of attributes that the members of G' have in common is M' (du Boucher-Ryan and Bridge, 2006). From Table II, ({g1, g3}, { m1})is a concept of the context, where g1 and g3 are the members of the extension andm1is a member of intension.

Table II. The formal context M: attributes m1 m2 m3 G : o bject s g_g1 × × 2 × g3 × g4 × 3.2 Concept lattices

A lattice is a network-like classification structure that can be generated automatically from a term-document indexing relationship. Lattices are defined as “partially ordered sets where any two elements have a unique supremum and an infimum” (d’Orey, 1996, p.350). A network structure outperforms a hierarchical classification structure. The former structure considers many paths to a particular node while the latter structure restricts each node to possess only one parent. Hence, the lattice navigation provides an alternate browsing-based approach, which can overcome the weakness of hierarchical classification browsing (Cheung and Vogel, 2005). The set of all concepts of the context (G, M, I) is a complete lattice. It is known as the concept lattice of the context (G, M, I). The concept lattice that corresponds to the context in Table II is depicted in Figure 1.

--- Take in Figure 1 here ---

4 Illustrative Example

This illustrative example is intended to show the utilization of the technique of FCA for alliance partner selection. This example relates to a ship design firm that intends to sign a contract to develop a new type of platform supply vessel to be used in Arctic waters. The ship design firm has broad experience in

designing platform supply vessels, but has little competence in the design of the ships that operate in the Arctic. Consequently, the ship design firm seeks a partner firm that has the latter competencies. We need to have a context, which is defined as a binary relation between objects and attributes. There are five prospective partner firms (i.e. firms 1, 2, 3, 4 and 5). All five firms have necessary competencies in the design of vessels for Arctic navigation. Other characteristics of the prospective partner firms are presented in Table III using the following abbreviations:

P – Reputation.

N - Competence in Nupas software. A - Competence in AutoCAD software. D - Competence in new product development. K - Knowledge of partner’s internal standards.

C - Complementarity of partner’s resource contribution. T - Trust between the top management teams.

M - Competence in using modular product architecture.

E - Experience in computer-aided design’s technology applications. B - Competence in strength and buoyancy calculations.

Table III. Context – characteristics of candidate partner firms

P N A D K C T M E B F 1 x x x x x F 2 x x x x x F 3 x x x x x x F 4 x x x x x x F 5 x x x x x

So, we made a cross-table where some cells contain crosses ( ) that show a relation. The relation states which a firm (object) has certain resource or competence (attribute). Crosses represent the relation between objects and attributes.

It is worth noting that these ten partner selection criteria are purely illustrative for this example. The paper does not focus on partner related criteria which is a separate domain of research in alliance partner selection literature. Partner selection criteria are related to the goals of a strategic alliance. The most

frequently cited classification of partner selection criteria is proposed by Geringer (1991). Geringer (1991) classified the selection criteria for a strategic alliance to partner-related criteria and task-related criteria. The partner-related criteria include reputation, strategic fit, trust between the top management teams, financial stability of the partner, and position within the industry. The task-related criteria include knowledge of the local and international markets; competence in new product development; knowledge of partner’s culture and internal standards; competencies in special software use; links with major buyers, suppliers and distribution channels; political influence and other criteria relating to the industry and alliance goals.

In order to make connections in cross-tables more transparent, data are presented graphically. A concept lattice is visualized with reference to a Hasse diagram (or line diagram). Nodes relate to formal concepts and edges relate to the subconcept (i.e. superconcept relations between formal concepts). A concept lattice allows the investigation and interpretation of relationships between concepts, objects, and attributes. Each edge represents a concept. The concepts are arranged hierarchically (i.e. the closer a concept is to the supremum (the topmost node), the more attributes belong to it). Moving from one vertex to a connected vertex that is closer to the supremum means moving from a more general to a more specific description of the attributes, if an object occurs in both concepts.

Concepts are presented by the labels attached to the nodes of the lattice in the Figure 2. The meaning of the used notations is as follows:

Node number 5 has a label I = {A, N}, E = {F1, F2, F4}. This means that firms F1, F2, F4 have two characteristics in common, A and N (i.e. competencies in AutoCAD and Nupas software).

Node number 11 has a label I = {A, C, E, N}, E = {F2, F4}. This means that firms F2 and F4 have four characteristics in common, A, C, E and N

(i.e. competencies in AutoCAD and Nupas software, complementarity of partner’s resource contribution, and experience in computer-aided design’s technology applications).

Node number 14 has a label I = {A, B, N}, E = {F1, F4}. This means that firms F1 and F4 have three characteristics in common, A, B and N

(i.e. competencies in AutoCAD and Nupas software and competence in strength and buoyancy calculations).

--- Take in Figure 2 here ---

Nodes 8, 11, 13, and 14 represent the most interesting concepts with respect to choice of partners and characteristics. These four concepts show couples of partners to choose from, provided that certain characteristics are of importance. An ascending path between two edges represents a subconcept-superconcept relationship. As an example, we can take concepts represented in node 5 and node 14. Increasing the number of firms by one, i.e. F2 leads to decreasing the amount of common attributes, i.e. only attributes A and N are left in the concept contained in node 5. Logical connections in concept lattices often result in critic and self-correction of existing models.

5 Discussion

The contribution of this study to the existing literature is twofold. First, the paper contributes to the partner selection literature. The methodological contribution of the study to the partner selection literature is a quantitative method for partner selection for horizontal collaboration based on formal concept analysis. Numerous studies have discussed alternative mathematical techniques to enable decision-makers to identify appropriate partners for interfirm collaboration (see Table I). FCA proposes the analysis of qualitative information. In the presented illustrative example, the data set relates to information on characteristics of potential partners firms, which can be converted into a concept lattice for a subsequent analysis.

In contrast to existing mathematical methods, FCA emphasizes the identification of structural similarities between potential partners rather than statistical or mathematical manipulations (Carpineto and Romano, 2004). Compared to pure mathematical methods of data analysis (i.e. the AHP, the ANP and optimization models), the FCA partner selection method is an effective tool due to its “simplicity, elegance and versatility” (Carpineto and Romano, 2004: xi). FCA is also associated with the important advantage relating to the visualization of the sets of formal concepts (Priss, 2006). However, visualization of concept lattices is comprehensible for

decision-makers only when lattices are not too big. The Toscana, ConExp and ConImp software packages (Burmeister, 2000) can be used to overcome this drawback and to conduct a FCA. They are user-friendly and can easily transform data sets.

Furthermore, the paper contributes to the partner selection literature by focusing on horizontal collaboration. The prior research on partner selection has not usually differentiated between horizontal and vertical collaboration, considering general issues of partner selection for strategic alliances (e.g. Lin and Chen, 2004; Wu, Shih and Chan, 2009). It is important differentiate between these two types of alliances since approach to selection of partners from firms linked vertically in the value chain and from competing firms are different.

Second, this study contributes to the application of the formal concept analysis, responding to a call for further application of this method in management field (Carpineto and Romano, 2004). The technique has been widely applied since the 1980s with regard to a broad spectrum of applications. FCA has been utilized in several disciplines such as psychology, sociology, economic sciences (Wille, 2005), marketing, linguistics (Priss, 2009), knowledge discovery and data mining (Valtchev et al., 2004), and information retrieval (Kötters, 2009). It has also been applied to visualize and analyze data obtained from interviews concerning employee selection criteria reported by large firms (Ganter and Zickwolff, 1990). Earlier, this method has been marginally applied in management. Concept lattices facilitate decision making and exploration of conceptual structures during partner evaluation and selection.

The study highlights the broader potential application of FCA. The tool needs to be widely considered and used by entrepreneurs and practitioners seeking to evaluate and select the ‘most suitable’ partner(s) when interfirm cooperation is viewed as an appropriate way to ensure a firm's competitive advantage.

6 Conclusions

Technological, market and institutional change can lead to uncertainty. Entrepreneurs and decision-makers seeking to reduce uncertainty and to gain access to resources, competencies, skills and knowledge they do not possess,

may select a horizontal collaborative strategy to generate sustained competitive advantage. One of the major strategic choices influencing the success of cooperation is the selection of the ‘most appropriate’ partner. The purpose of this study was to propose an alternative method to assist evaluations of potential partners for horizontal interfirm cooperation. The paper contributes to the formalization of the partner selection process relating to horizontal interfirm collaboration.

This study examines earlier studies that have proposed both qualitative and quantitative methods of evaluation and selection partners for strategic alliances. Quantitative techniques used in prior research to facilitate partner selection are analyzed in this study. They include fuzzy set logic, expert systems, linear programming, the analytical hierarchy process, and the analytical network process approach. A quantitative method for partner selection based on FCA

has been highlighted. FCA can be applied to facilitate decision-making. The study presented the methodology of formal concept analysis. An illustrative example demonstrates how the FCA tool might be practically utilized to evaluate candidate firms for horizontal collaboration.

FCA approach has not been widely used in the management field. Thus, this paper makes a novel contribution to the application of formal concept analysis. FCA has the potential to be more widely used by decision-makers seeking a quick and versatile approach to identify the ‘most appropriate’ partner. Concept lattices facilitate decision-making as well as the exploration of conceptual structures during partner evaluation and selection. The FCA tool is associated with numerous advantages, notably, relative simplicity and versatility of visual analysis when compared with other mathematical approaches such as the AHP, the ANP, optimization modeling, and fuzzy logic. FCA, therefore, can be considered either on its own, or in conjunction with other techniques, to identify the ‘most appropriate’ partner.

Inevitably, FCA is associated with some limitations. Notably, when the number of objects and attributes increases there is a potential for the concept lattice to become too complicated. Visual representation of complicated data clustering can be difficult to interpret. Another limitation is relating to the inability to collect ‘complete information’ regarding all potential partners during the selection process. Criteria for selecting a partner for interfirm collaboration need to be justified and then appropriately conceptualized and measured with regard to appropriate data. The limitation of this study is that it has not focused on partner selection criteria which are important in the partner

selection process.

Additional research attention is warranted to explore the merits of FCA. Notably, additional examples of partner selection relating to horizontal cooperation need to be presented relating to simple and complex context for interfirm cooperation. Goals and resource profiles of potential partners need to be considered across a broader array of objects and attributes. Future studies are warranted to explore the applicability of FCA with reference to partner selection for vertical cooperation. Comparative studies of the quantitative approaches used to evaluate and select partners for strategic alliances are also needed.

The proposed method also has important implications for practitioners in ship design industry who need to evaluate potential partners for horizontal collaboration. FCA is one of many tools that can be used by decision-makers and does not exclude qualitative approach for selecting an appropriate partner. The proposed method allows analyzing effectively available data regarding candidates, evaluating and comparing different candidates following several criteria. The results of this study should encourage managers to identify criteria which are important when they select partners for horizontal collaboration and formalize the process of partner selection. Thus, managers should develop skills that ensure selection of the appropriate collaborating partners for their firms. The findings should encourage managers to test FCA methodology in the real-life settings.

Acknowledgements

The authors would like to thank the two anonymous reviewers and Professor Paul Westhead from Durham University for their valuable comments, which were of substantial help in the development of this paper.

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Appendix

In appendix, we explain formal concept analysis in details. Formal concept analysis is a method of data analysis that provides a graphical representation of knowledge and information. In this example objects are types of ships and attributes are types of cargo that the vessels are able to transport. Let us consider a group of vessel types (ferry, oil tanker, cruise ship, and ore/bulk/oil (OBO) ship), and a group of cargo types (passengers, oil, and cars). Here a set

of ships is referred as objects, and a set of cargo types is referred as attributes. Table IV demonstrates what type of cargo can be carried by given ships. Table IV is a formal context which corresponds to Table II.

Table IV. The formal context

M: set of attributes

Passengers Oil Cars

G: set of objects Ferry × ×

Oil tanker ×

Cruise ship ×

Ore/bulk/oil (OBO) ship

×

A concept is a set of objects possessing common attributes. There are several concepts in Table IV. For example, ({ferry, cruise ship}, {passengers}) is one concept, where ferry and cruise ships are the members of the extension and

passengers is a member of intension. Another concept is ({tanker, OBO ship},

{oil}), where tanker and OBO ship are the members of the extension and oil is a member of intension. All concepts from this formal context are presented in Table V.

Table V. A set of concepts

Concept 1 ({all objects}, {ø})

Concept 2 ({ferry, cruise ship}, {passengers}) Concept 3 ({tanker, OBO ship}, {oil})

Concept 4 ({ferry}, {passengers, cars}) Concept 5 ({ø}, {all attributes})

The next step is to build a concept lattice corresponding to the context in Table IV. Figure 3a is a Hasse diagram showing the concept lattice. In Figure 3a all nodes are marked. Each node in a concept lattice corresponds to a concept. In this example, no entity posses all three attributes. In other words, no ship is

designated to carry all three types of cargo. That is why the lowermost node is empty. In Figure 3b, the same concept lattice is depicted, but in a more concise form.

In this example, ({ferry}, {passengers, cars})≤({ferry, cruise ship},

{passengers}). This is subconcept-superconcept relations. The concept ({ferry,

cruise ship}, {passengers}) is the entry-level concept for entity cruise ship. In the same vein, ({ferry}, {passengers, cars}) is the entry-level concept of feature

cars.

--- Take in Figure 3 here

---m3 g1 m1 g3 m2 g2, g4

Figure II. Concept lattice relating firms and their characteristics relating to the context in Table III.

({all objects}, {ø})

({ferry, cruise ship}, {passengers})

({ferry}, {passengers, cars})

({ø}, {all attributes}) ({tanker, OBO ship}, {oil})

a

Figure 3. (a) and (b) A concept lattice corresponding to the context in Table IV Oil Oil tanker OBO ship Cars Ferry Passengers Cruise ship b