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Towards an empirical typology of buyer–supplierrelationships based on absorptive capacityElena Revilla a , Maria Jesús Sáenz b & Desirée Knoppen ca Operations Management, IE Business School , Madrid , Spainb MIT-Zaragoza International Logistics Program, Zaragoza Logistics Center , Zaragoza , Spainc Operations Management and Information Systems, EADA Business School , Barcelona ,SpainPublished online: 07 Jan 2013.

To cite this article: Elena Revilla , Maria Jesús Sáenz & Desirée Knoppen (2013) Towards an empirical typology ofbuyer–supplier relationships based on absorptive capacity, International Journal of Production Research, 51:10, 2935-2951,DOI: 10.1080/00207543.2012.748231

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Towards an empirical typology of buyer–supplier relationships based on absorptive capacity

Elena Revillaa, Maria Jesús Sáenzb and Desirée Knoppenc*

aOperations Management, IE Business School, Madrid, Spain; bMIT-Zaragoza International Logistics Program, Zaragoza LogisticsCenter, Zaragoza, Spain; cOperations Management and Information Systems, EADA Business School, Barcelona, Spain

(Received 10 February 2012; final version received 3 November 2012)

This paper develops a taxonomy of buyer–supplier relationships (BSRs), based on the supplier’s absorptive capacity(AC). AC encompasses three learning processes: exploration, assimilation, and exploitation. The aim is to develop a tax-onomy that can predict a firm’s performance with regard to innovation and operational efficiency. This research comple-ments the literature, which presently focuses on descriptive rather than predictive taxonomies. Data from 153 firms werecollected through survey research. Confirmatory factor analysis was used to assess the quality of data and calculate com-posite scores to be used in the cluster analysis to develop the BSRs patterns. Analysis of variance was used to explorethe relationships between BSR type and firm performance. Finally, semi-structured interviews aided interpretation of theproposed taxonomy. Findings support the identification of groups of dyads through different combinations of the learningprocesses underlying AC. The different combinations are typified through AC strength and AC reinforcement. Theresults provide evidence of a significant relationship between AC strength and firm performance. Surprisingly, we didnot find empirical support for the relationship between AC reinforcement and performance.

Keywords: absorptive capacity; learning processes; buyer–supplier relationships; performance; taxonomy

1. Introduction

Over the last couple of decades it has become almost axiomatic that innovation is necessary to thrive in an environmentwith increased global competitiveness and highly dynamic markets. Studies revealed that firms increasingly rely on exter-nal knowledge to foster innovation and enhance their performance (Ireland, Hitt, and Vaidyanath 2002, Zollo, Reuer, andSingh 2002, Chesbrough 2003, Laursen and Salter 2006). For instance, suppliers have become important sources of knowl-edge that complement a firm’s own internal knowledge (Powell, Koput, and Smith-Doerr 1996, Dyer and Singh 1998,Gulati 1998, Ahuja 2000, Eisenhardt and Martin 2000). However, many firms have encountered difficulties in applyingexternal knowledge in their own firms (Hult et al. 2000). Studies that increase our understanding of the connection betweeninternal and external knowledge sourcing remain underdeveloped (Easterby-Smith, Lyles, and Tsang 2008) despite a recentemphasis on the capacity to absorb knowledge as crucial for the firm’s success (Volberda, Foss, and Lyles 2010).

Cohen and Levinthal (1990) introduced the term ‘absorptive capacity’ (AC) to label the capabilities of the firm toinnovate and, thus, to be dynamic. They defined AC as ‘the ability of a firm to recognise the value of new, external infor-mation, assimilate it, and apply it to commercial ends’ (p. 128). A recent process-based definition holds that AC is afirm’s ability to utilise external knowledge through the sequential learning processes related to exploration, assimilation,and exploitation (Lane, Koka, and Pathak 2006). To date, however, few studies have analysed these learning processes indetail (Jansen, van den Bosch, and Volberda, 2005, Volberda, Foss, and Lyles 2010). As most studies have focused onintra-firm processes, external knowledge acquisition and use has been relatively neglected (Katila and Ahuja 2002).

Considering the growing importance of buyer–supplier relationships (BSRs) for the organisation, it is appropriate todiscuss the implications of AC in a relational context (Dyer and Singh 1998, Lane and Lubatkin 1998, Lichtenthal2009, Volberda, Foss, and Lyles 2010). As such, we focus our attention on the application of the AC construct to thecontext where external knowledge stems from selected suppliers or buyers. For instance, suppliers usually have a greaterexpertise and more comprehensive knowledge regarding the parts and components which may be critical to the develop-ment of a new product (Tsai 2009). Likewise, von Hippel (1988) found that more than two-thirds of the innovations inscientific instruments could arise from a customer’s idea. In some cases, buyers and suppliers come together to shareany business-related information and jointly explore new markets with new ideas (Sen et al. 2008). In general, firms

*Corresponding author. Email: dknoppen@eada.edu

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heavily depend on close interactions with their customers for obtaining critical knowledge on customers’ shifting needsin order to continuously close the gap between what customers want and what is available in the marketplace (Hult,Ketchen, and Arrfelt 2007, Esper et al. 2010).

An initial review of the literature on BSRs led to the identification of a main limitation. Most existing researchexamined the relationships between individual dimensions of the BSRs rather than focusing on relations as a whole(Flynn, Huo, and Zhao 2010, Wong, Wilkinson, and Young 2010). In order to examine the multidimensional characterof AC in a relational context, relationships need to be seen as simultaneous combinations of diverse learning processesthat fit together in various ways across different contexts (Cannon and Perreault 1999). Moreover, most existing BSRconfigurations do not provide empirical evidence linking BSR typologies to firm performance (Tangpong, Michalisin,and Melcher 2008) with some valuable exceptions (Vanpoucke, Boyer, and Vereecke 2009). This link has proven to beuseful in other fields, for instance manufacturing or supply chain strategy configurations have been related to firm per-formance (Hambrik 1983, Bozarth and McMermott 1998, Narasimhan, Kim, and Tan 2006). Consequently, the primaryobjective of this research is to develop taxonomy of how buyers and suppliers combine diverse types of learning pro-cesses to establish patterns of AC that jointly complement and extend the literature on BSRs and AC. To pursue thisobjective, we postulate that these patterns can be described in terms of AC strength and the reinforcing nature of theirlearning processes. A related research objective is to analyse and empirically test how the different patterns of interac-tions between the learning processes of AC influence firm performance.

In doing so, we offer four main contributions to the literature. First, despite the growing interest in AC, few studieshave empirically captured the richness and multidimensionality of AC (Volberda, Foss, and Lyles 2010). In order tofully understand the AC concept and explore its future extensions, this research uses a configuration approach, as sug-gested in previous studies (Tangpong, Michalisin, and Melcher 2008, Wong, Wilkinson, and Young 2010), and suggestsfour groups of BSRs, each one presenting a unique combination of attributes related to the three processes that consti-tute AC (exploration, exploitation and assimilation). Second, while existing BSR typologies have a strong descriptiveand prescriptive orientation, they lack an explanatory and predictive orientation, particularly in terms of performanceimplications (Tangpong, Michalisin, and Melcher 2008). This research focuses on a predictive orientation of BSR typesand provides empirical evidence linking BSR taxonomy and firm performance. Third, although most studies have seenAC as an explanation of innovation (Stock, Greis, and Fischer 2002), AC also seems to result in the reduction of unnec-essary duplication and costs, thereby improving firm efficiency (Malhotra, Gosain, and El Sawy 2005, Tu et al. 2006,Beckett 2008, Azadegan 2011). In fact, recent research argues that more complete measures should be considered inorder to obtain a comprehensive evaluation of performance outcomes of AC (Volberda, Foss, and Lyles 2010). Accord-ingly, this study complements extant research and analyses how the relationship between AC and performance differsnot only in terms of innovation but also in terms of operational efficiency. Finally, while most studies on BSRs havefocused on the buyer perspective (Tangpong, Michalisin, and Melcher 2008), the supplier perspective is equally impor-tant, given that suppliers usually are engaged in several dynamic supply chains, where they are expected to contributeto various customers in different settings. For initiatives such as supplier development and new product or servicedesign, it is important to understand whether the suppliers are capable of contributing to knowledge creation in the rela-tionship while also being subjected to significant delivery-time and pricing pressure by buyers (Stjernström and Bengts-son 2004). Consequently, this research provides empirical evidence from the supplier’s perspective.

The remainder of the paper is organised as follows. The next section provides the theoretical foundations of thestudy. We then describe the database, the measures used, and the assessment of the quality of the measures. This is fol-lowed by the description of the methodology used to identify the BSR typology. The subsequent presentation of theresults provides evidence of different patterns of AC in a relational context as well as the direct relationship betweenAC and performance. The final section deals with conclusions, limitations, and future research directions.

2. Theoretical foundations

2.1. Absorptive capacity

The origins of AC are found in the organisational learning literature of the 1980s (Volberda, Foss, and Lyles 2010). Theconcept promised to explain the process through which firms learn, develop, and assimilate new knowledge necessaryfor competitive advantage. The main argument of Cohen and Levinthal (1990) was that prior related knowledge deter-mines a firm’s level of AC. In other words, existing knowledge facilitates the use of new knowledge (Powell, Koput,and Smith-Doerr 1996). So, firms need some relevant prior knowledge to successfully absorb external knowledge(Mowery, Oxley, and Silverman 1996, Lord and Ranft 2000).

Given the cumulative nature of knowledge, Zahra and George (2002) proposed that AC should be defined as adynamic capability, an ‘asset of organisational routines and processes by which firms acquire, assimilate, transform, and

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exploit knowledge’ (2002, p. 186). According to Teece, Pisano, and Shuen (1997), dynamic capabilities represent thefirm’s latent abilities to renew, augment, and adapt its resources base over time. Learning processes guide the evolutionof dynamic capabilities (Eisenhardt and Martin 2000, Winter 2003). More precisely, a ‘knowledge evolution cycle’,including generative variation, internal selection, replication, and retention, is behind the development of dynamic capa-bilities (Zollo and Winter 2002). Because dynamic capabilities reside in processes rather than resources themselves,dynamic capabilities are difficult to observe unless they are put into use. As stated by Iansiti and Clark, ‘Dynamic capa-bility links capacity for action to the evolution of the associated knowledge base through the effective execution ofproblem-solving processes’ (1994, p. 563).

According to the dynamic capabilities perspective, it is possible to delineate AC into a multi-dimensional constructembedded in distinctive learning processes. By combining this process perspective with the definition by Cohen andLevinthal (1990) of AC, Lane, Koka, and Pathak (2006, p. 856) defined AC as ‘a firm’s ability to utilise externally heldknowledge through three sequential processes:

(1) Recognising and understanding potentially valuable new knowledge outside the firm through exploratorylearning.

(2) Assimilating valuable new knowledge through transformative learning.(3) Using the assimilated knowledge to create new knowledge and commercial outputs through exploitative learning.

As their definition indicates, we separate a firm’s AC in three dimensions: exploration, assimilation, and exploitation.Because each of these dimensions requires different organisational attributes, we believe that consideration of the multi-dimensionality of AC is vital for understanding how companies take advantage of knowledge obtained from selectedbuyers or suppliers.

2.2. Dimensions of absorptive capacity

The learning process associated to exploration – exploratory learning – refers to the acquisition of external knowledge.Exploration introduces the variations needed to provide a sufficient amount of choices to solve problems (March 1991).In particular, it refines the understanding of the environment and increases the ability to react appropriately to futurestimuli. Research on exploration stresses the importance of the external acquisition of new knowledge that facilitates therenewal of the firm’s knowledge base (Narasimhan, Rajiv, and Dutta 2006) and\ diminishes the risk of obsolescence(Eisenhardt and Martin 2000). In fact, exploratory learning arises when existing knowledge is insufficient to solve theproblem, creating the necessity to construct and acquire new knowledge. In these situations, past knowledge is criticalbecause it guides firms to recognise and assess the value of new external knowledge (Zahra and George 2002). Thisview supports the concept of knowledge accumulation by showing that a firm’s knowledge base influences the effectiverecognition and assessment of external knowledge.

The second dimension involves the use of transformative learning to assimilate valuable external knowledge. Itrefers to the ability to merge new knowledge with the existing knowledge base of the organisation. In other words, firmsassimilate knowledge by integrating it into their knowledge bases (Lenox and King 2004). More specifically, Lane,Koka, and Pathak (2006) associated assimilation with ‘the combination of new knowledge with existing knowledge,allowing the latter to be used in new ways’ (p. 855). Practically speaking, it implies significant intra-firm knowledgetransfers (Szulanski 1996). In fact, transformative learning changes the patterns of combined knowledge that form theknowledge base of the organisation. It involves the potential to reorganise patterns of knowledge embodied in productsand activities through the establishment of flexible relationships and thus through a loosely coupled structure with multi-ple and evolving patterns (Verona and Ravasi 2003).

Given that not all new knowledge can be immediately exploited and sometimes has to be maintained for years untilit is finally applied in new products, firms must actively manage knowledge retention to keep assimilated knowledge‘alive’ (Rothaermel and Deeds 2004, Lane, Koka, and Pathak 2006, Marsh and Stock 2006). In this respect, transforma-tive learning is essential to retain knowledge over time (Garud and Nayyar 1994, Sabberwal and Becerra-Fernandez2003). The failure to retain knowledge may have effects that are as negative as the complete lack of assimilated knowl-edge (Argote, McEvily, and Reagans 2003, Marsh and Stock 2006).

After assimilating external knowledge, exploitative learning determines potential applications and matches knowl-edge with those applications, converting knowledge into new products, processes, or strategies (Tsai 2001). Severalauthors associate exploitative learning with matching knowledge and markets (Lenox and King 2004, Rothaermel andDeeds 2004, Lichtenthaler 2009). Accordingly, market knowledge is a critical component of prior knowledge intransmuting and applying assimilated knowledge (van den Bosch, Volberda, and de Boer 1999). Thus, potential negative

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effects of dysfunctional rigidity are likely diminished by the exploitation of assimilated external knowledge (March1991, Leonard-Barton 1992). Exploitation leads to a deeper understanding of concepts (Katila and Ahuja 1995) andfacilitates the development of routines that allow the deconstruction of activities in an efficient order so that unnecessarysteps can be eliminated (Eisenhardt and Tabrisi 1995). Accordingly, the renovated knowledge base of the organisationleads to refinement, efficiency, productivity, and process control (March 1991).

2.3. Absorptive capacity in a BSR context

In line with Lane and Lubatkin (1998) and Dyer and Singh (1998), we posit that AC is not an absolute phenomenon atthe firm level, but rather relative to the dyad in which the firm learns. In other words, the locus of capability formationis at the firm level, because it is the individual firm and not the dyadic relationship that implements and institutionalisesnew ideas, practices, or material artefacts. But a firm may have multiple ACs, each related to a particular buyer or sup-plier. Learning embodied in an AC related to a specific BSR can be applied to another BSR, but there is a need fordyad-specific learning applied to adaptation, which takes time and effort. In other words there is a quality of ‘stickiness’in the knowledge created in dyads (Szulanski 1996).

Additionally, we separate the buyer perspective on BSRs from the supplier perspective in order to simplify anddisentangle the multiple dimensions of AC, in line with Tangpong, Michalisin, and Melcher (2008). There is littleempirical work documenting the impact of a BSR from a supplier viewpoint (Stjernström and Bengtsson 2004).Consequently, we focus on the development of a supplier perspective to the BSR taxonomy.

2.4. A taxonomy of buyer–supplier relationships

In order to examine the multi-dimensional character of AC, we use a configuration approach that establishes pat-terns or profiles, capturing in that sense the complexities of organisational reality (Ketchen and Shook 1996) andthus facilitating a holistic analysis of the phenomenon under investigation (Miller 1986, Ward, Bickford, and Leong1996, Flynn, Huo, and Zhao 2010). Instead of the pairwise relationships that the conventional econometric researchfocuses on, our approach focuses on relations as simultaneous combinations of multiple dimensions (Cannon andPerreault 1999).

The configuration approach is typically divided into the development of typologies and the development of taxono-mies. Much research focuses on developing typologies, or ideal types, each of which reflects a particular combination oforganisational attributes (Doty and Glick 1993, Chandra, Grabis, and Tumanyan 2007), although no existing firms mayfit exactly the suggested ideal type (Bozarth and McDermott 1998). A taxonomy, on the other hand, does not defineideal types, but rather attempts to classify firms into mutually exclusive and exhaustive groups a posteriori throughempirical work (Miller and Roth 1994, Narasimhan, Kim, and Tan 2006, Lai, Zhao, and Wang 2007, Martín-Peña andDíaz-Garrido 2008). The identified groups in turn allow other patterns, which may have not been part of a theoreticaltypology, to emerge (Flynn, Huo, and Zhao 2010).

This research builds upon the learning processes of AC to classify BSRs into mutually exclusive and exhaustivegroups. In other words, BSRs can be viewed as a pattern of practices related to knowledge exploration, assimilation,and exploitation. We argue that the diverse dimensions of AC can ultimately be collapsed into two dimensions: ACstrength and AC reinforcement. AC strength is the level or extent to which AC learning processes are taking place. Incontrast, AC reinforcement is the extent to which the three learning processes that integrate AC complement each other.Processes are described as complementary when they build upon each other and are mutually reinforcing; in otherwords, when there is a positive correlation between them (Milgrom and Roberts 1995, Cassiman and Veugelers 2006).According to the process-based definition of AC, the three learning processes are not mutually exclusive but rather inter-dependent and mutually supportive (Lichtenthaler 2009).

We, therefore, deconstruct the complex three-dimensional map of AC into two major dimensions: AC strength andAC reinforcement. These two dimensions provide a taxonomy that organises and consolidates all information about groupconfigurations, making it easier to process and comprehend the differences in the composition of the three learning pro-cesses that characterise AC. Additionally, the literature is notably lacking a taxonomy of BSRs (Tangpong, Michalisin,and Melcher 2008) that incorporates the three complementary learning processes that make up AC (Volberda, Foss, andLyles 2010). Thus, we propose:

H1. A taxonomy of buyer-supplier relationships can be developed based on the three dimensions of absorptive capacity (explo-ration, assimilation, and exploitation).

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2.5. Impact of BSRs patterns on performance

Just knowing that there are AC differences within BSRs is not particularly compelling. What makes this of interest is thatthese divergences significantly and differently affect performance outcomes. Hence we examine how BSR configurationsmay have different performance implications. Selecting the appropriate performance measure is challenging due the inher-ent complexity and interdependence of firms constituting a supply chain (Flynn, Huo, and X. Zhao 2010). While researchconsistently shows that AC improves firm performance, most studies have analysed the impact of AC on only a narrowrange of performance measures related to innovation (Zahra and George 2002, Malhotra, Gosain, and El Sawy 2005,Yeoh 2009). Innovation, described as change that can occur by implementing a new process, introducing a new product,or addressing a new market (Jansen, Van den Bosch, and Volberda 2006, Koufteros, Cheng, and Lai 2007, Sanders2008), requires the supplier in a BSR context to integrate dissimilar knowledge from the buyer (Cohen and Levinthal1990). Thus, AC contributes to innovation by enhancing the breadth and depth of relevant knowledge available to thesupplier and increasing the level of willingness to explore new ideas and develop new products or services.

On the other hand, Okhuysen and Eisenhardt (2002) investigate how learning routines facilitate operational effi-ciency. Empirical literature shows that AC is a crucial determinant of an organisation’s aptitude for effective implemen-tation of time-based manufacturing practices (Tu et al. 2006). Malhotra, Gosain, and El Sawy (2005) more preciselysuggested that the learning processes of AC impact not only innovation but also operational efficiency such as cost cut-ting. They found that the initial focus of developing processes and information technology for building AC in supplychains has been the improvement in day-to-day operational processes between supply chain partners that permit them tounlock potential efficiency gains. Thus, when supplier firms have greater AC, it increases their performance in bothinnovation and operational efficiency. This idea is consistent with the knowledge-based view, as it suggests that perfor-mance differences result from variances in the availability and use of knowledge resources (Grant 1996, Kogut and Zan-der 1992). Thus, more complete measures of benefits suggest a model where AC is viewed as boosting both innovationand operational efficiency. Consequently, we propose the following hypothesis:

H2. Suppliers belonging to those BSR types that show higher levels of AC strength will obtain grater benefits in both innova-tion and operational efficiency.

In order to guarantee better performance through AC, supplier firms should simultaneously pursue the adoption ofexploration, assimilation, and exploitation. Exploration makes it possible to seize new market opportunities by ensuringthat a firm’s new products or relational strategies incorporate emerging ideas that differentiate them from a competitor’soffer, thus likelier to be deemed superior by customers (Katila and Ahuja 2002, Atuahene-Gima and Murray 2007).However, exploration needs to be combined with existing knowledge through assimilation in order to be used for com-mercial purpose and to improve performance. The complementarities of the three learning processes inherent to ACenhance a supplier’s potential capacity to identify valuable external knowledge from its buyers and to efficiently com-bine it with prior knowledge in such a way that innovation, as well as efficiency, are improved. Although exploration,assimilation and exploitation strive for different objectives and have little in common in their day-to-day operation, acentral focus and common interest should permeate each capability: the need to create superior customer value and, as aconsequence, better performance. By contrast, if suppliers make a trade-off and focus on one learning process at theexpense of the other, problems and tensions will inevitably arise. Suppliers that engage in exploration and excludeexploitation are likely to suffer the cost of experimentation without gaining the benefits, while suppliers that engage inexploitation to the exclusion of exploration are likely to become trapped in a suboptimal equilibrium (March 1991).Likewise, suppliers that fail to combine new knowledge with their existing knowledge base because of limited transfor-mative learning could suffer from the cost of knowledge acquisition without gaining the benefits of exploitation (Zahraand George 2002, Lichtenthaler 2009). Accordingly, we offer the following hypothesis:

H3. Suppliers belonging to those BSR types that show higher levels of AC reinforcement will obtain grater benefits in bothinnovation and operational efficiency.

3. Research method

3.1. Sample characteristics and data collection

Survey-based research (Saris and Gallhofer 2007) was used as the main empirical research methodology of this study.The questionnaire was designed and developed from a thorough literature review and qualitative preliminary researchwork (15 semi-structured interviews with boundary spanners from both sides of four different dyads, as well as observa-tion of dyadic meetings). The questionnaire was then validated through a pre-test that was carried out with three

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academics, seven supply chain executives and two senior consultants in the field of supply chain management (SCM).These interviews allowed us to purify our survey items and rectify any potential deficiency. Minor adjustments weremade on the basis of specific suggestions.

The target frame consisted of the 250 most important suppliers (in terms of volume and revenue) of the same focalbuyer, a Spanish subsidiary of a multinational retail chain with a local turnover of more than a billion Euros and 9000employees. We sent the survey to the sample members along with a cover letter from the focal buyer, explaining thepurpose of the study and assuring the anonymity of the respondents. The targeted respondents were executives incharge of the supply chain with the focal buyer, and it was assumed that individual views on BSR issues will be afunction of the respondents’ organisational roles (Ring and van de Ven 1994, Paulraj, Lado, and Chen 2008). The useof key informants as data sources is standard in behavioural organisation research (Venkatraman and Ramanujan1986). Moreover, it is a widely adopted approach in the field of operations management to survey one of both firmsthat constitute a dyad (e.g. Paulraj, Lado, and Chen 2008). The data collection process yielded 153 usable responses,for a response rate of 61%. A missing value analysis was completed, with a result of 1.5% overall. Table 1 shows theprofile of the sample, which reflects the diversity that exists among the participating firms, based on the number ofemployees, nationality, and annual sales.

In order to test the non-response bias, we conducted a complementary secondary data analysis based on ORBIS, aglobal database that has information on over 60 million companies. Non-response bias was assessed based on chi-squaredifferences for respondents and non-respondents for ROA (χ2 = 1.01, p > 0.05), industry (χ2 = 3.319, p > 0.05) andemployees (χ2 = 7.792, p > 0.05), and was found to be insignificant. This result suggests that participating firms wererepresentative of the population from which they were drawn.

Since we collected the information from a single respondent within a single firm, common method bias could pres-ent a problem. The potential for common method bias was assessed based on Harman’s test as described in Podsakoffet al. (2003). It consists of loading all of the variables into an exploratory factor analysis and examining the un-rotatedfactor solution. Results revealed four distinct factors with Eigen values above 1.0, together explaining more than 70%of the variance. The first factor accounted for only 26% of the variance. Since a single factor did not emerge and thefirst factor did not account for most of the variance, common method bias might not be an issue in the data.

3.2. Measures

Although there is extensive literature about the AC construct, little evidence can be found of the measurement of thethree AC processes, with the exceptions of Lichtenthaler (2009) and Tu et al. (2006). We build upon and adapt the mea-sures provided by these two studies (see Table 2).

Supply chain performance is measured in terms of innovation and operational efficiency (see Table 2). Innovation ismeasured by three items related to improvements in the existing product and developments in new products and strate-gies owing to teamwork between the buyer and supplier. These items were adapted from Koufteros, Cheng, and Lai

Table 1. Profile of the sample.

Number of employees Frequency %

0–15 34 26.0%16–50 41 31.3%51–100 29 22.1%>101 27 20.6%Total 131 100.0%

Country Frequency %Spain 115 81.6%Non Spain 26 18.4%Total 141 100.0%

Annual sales Frequency %0–5 million 46 40.8%5–10 million 22 19.5%10–30 million 23 20.4%>30 million 22 19.5%Total 113 100.0%

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(2007) and Sanders (2008). Operational efficiency is adopted from previous studies that capture operational performanceusing multiple items (Dahlstrom, McNeilly, and Speh 1996, Malhotra, Gosain, and El Sawy 2005). This scale is relatedto improvements in terms of higher productivity and lower costs owing to teamwork between the buyer and supplier.

Responses had to be given on a five-point Likert scale (1 = totally disagree and 5 = totally agree). The first order-constructs were purified as recommended by Churchill (1979) by examining CITC for each item. As a general rule,items with a CITC score of less than 0.60 were removed. Table 2 displays the selected items per construct. After purifi-cation of the items, Cronbach’s alpha was calculated as a first assessment of the reliability of the scales (we are awarethat reliability has to be assessed after validity, and will calculate another reliability measure in the next section). A cut-off value of 0.70 is widely accepted, and all scales showed reliability above 0.8, as shown in Table 2. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was calculated. This score is outstanding for the operational effi-ciency scale, very good for the assimilation and exploitation scales, and average for the exploration and innovation per-formance scales.

3.3. Confirmatory factor analysis to test the measurement models

To verify that items tapped into their stipulated construct, a confirmatory factor analysis (CFA) (Bollen 1989) with aid ofLisrel (Jöreskog 1969) was employed to determine the validity of the constructs. Two separate CFAs were conducted:one corresponding to the three correlating learning processes of AC, and one for the two correlating performance mea-sures. Convergent validity is commonly established by acceptable fit indices and significant loadings from all scale itemson the hypothesised constructs (Anderson and Gerbing 1988, Hu and Bentler 1998). The fit indices of the performancemeasures were acceptable, and analysis of modification indices (MI) and expected parameter changes (EPC) (SarisSatorra, and van der Veld 2009) did not result in suggested changes for the performance measures. The root mean squareerror of approximation (RMSEA) of the dimensions of AC, however, is above the cut-off value of 0.07 (initial fit indicesare: χ2 = 117.15; DF = 62; χ2/DF = 1.89; RMSEA = 0.081) and subsequent analysis of MI and EPC suggests a correlated

Table 2. Measurement items and descriptives of the scales.

Range of CITC Cronbach α KMOExploration 0.6620 to 0.7190 0.851 0.798

Explor1 – With the buyer we share changes in the preferences of our clientsExplor2 – With the buyer we share changes in the market structures (mergers and acquisitions, alliances, etc)Explor3 – With the buyer we share changes in technologyExplor4 – With the buyer we share changes in the strategies and policies of your organisation

Assimilation 0.6630 to 0.8130 0.875 0.839Assim1 – With respect to our culture, the supervisors and subordinates communicate frequently among themselvesAssim2 – With respect to our culture, new ideas are often communicated between internal departmentsAssim3 – With respect to our culture, employees frequently are supportive of each otherAssim4 – With respect to our culture, employees share ideas freely with each otherAssim5 – With respect to our culture, our employees are willing to accept changes

Exploitation 0.6940 to 0.7830 0.875 0.878Exploit1 – With the buyer we share cost and benefits that result from common programmes for improvementExploit2 – With the buyer we share cost reduction programmesExploit3 – With the buyer we share quality improvements programmesExploit4 – With the buyer we share the production planningExploit5 – With the buyer we jointly plan new market creation

Operational efficiency 0.7410 to .9350 0.953 0.902Op1 – In regard to supply orders our relationship with the buyer has allowed us to considerably lower production costsOp2 – In regard to supply orders our relationship with the buyer has allowed us to considerably lower indirect costsOp3 – In regard to supply orders our relationship with the buyer has allowed us to considerably lower labour costsOp4 – In regard to supply orders our relationship with the buyer has allowed us to a lower total costsOp5 – In regard to supply orders our relationship with the buyer has allowed us to more efficient use of the financial resources

Innovation 0.6950 to 0.7860 0.864 0.723Inn1 – Our relationship with the buyer has helped us to develop new strategies to compete in the marketInn2 – Our relationship with the buyer has helped us to develop new products for our marketsInn3 – Our relationship with the buyer has helped us to introduce improvements in the existing products

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error between the items assim1 and assim2. The suggested correlated error makes theoretical sense; i.e. these two itemsshare a unique component that has nothing to do with the other three items, but rather with ‘cross-functional and cross-hierarchical communication’. In other words, the assimilation dimension has two sub-dimensions in turn: one sub-dimen-sion referring to communication behaviour aspects of assimilation and the other referring to attitudinal aspects of assimi-lation. Therefore, we introduce the modification in the model. As a result, the parameter estimation is 0.23 for thecorrelated error, and the RMSEA is now below the cut-off point of 0.07. Further analysis of MI/EPC does not suggestany further changes. The loadings and fit indices of the final measurement model are reported in Table 3.

Discriminant validity is demonstrated by the absence of cross-loadings and by correlations between the first-orderfactors that are significantly smaller than one. More precisely, the correlation between strategic and operational perfor-mance is 0.64, and the correlations between the learning dimensions are 0.37 (exploration and assimilation), 0.39(exploitation and assimilation) and 0.79 (exploration and exploitation). Additionally, convergent validity was assessedthrough the average variance extracted (AVE; Anderson and Gerbing 1988) which ranges between 59 and 80%, asshown in Table 3.

In the next step, we have calculated composite scores (CS) for each of the five first-order constructs. Working withcomposite scores reduces the number of parameter estimates and thus improves the sample size-to-estimator ratio, lead-ing to more robust testing (Shah and Meyer Goldstein 2006). Composite scores were calculated based on the regressionweights of the final model (distinguishing two sub-dimensions of the assimilation factor) provided by Lisrel, which arethe weights that optimise the quality of the composite score (i.e. the correlation between the composite score and thelatent construct it is intended to measure). The quality of the CS is reported in the last column of Table 3 and is calcu-lated in line with Saris and Gallhofer (2007) with the formula:

qcs ¼X

i

ðk�i weightiÞ=rcs

where qcs = quality of CS; Σi = summation over all items constituting a CS, λi = standardised loading of item i, weighti= regression weight of item I; σcs = standard deviation of CS.

The composite scores were added to the SPSS file that was the basis for the subsequent cluster analysis. The advan-tage of our approach is twofold: the cluster analysis is more robust (Shah and Meyer Goldstein 2006) and it takes intoaccount measurement error (Saris and Gallhofer 2007).

4. Analysis and results

There were two stages in our analysis: identification of the patterns/profiles of AC and the comparison of performanceoutcomes across the groups. In the first case, we employed cluster analysis to classify BSRs based on their profile ofexploration, assimilation, and exploitation. A major issue of the clustering technique is determining the number of clus-ters. In this research, we have applied Ward’s hierarchical method, using the Euclidean distance and an agglomerationschedule to determine the number of clusters and the initial seeds (centres of the groups) to be used in a second K-means, non-hierarchical analysis that provided the final categorisation of the firms. he decision on the number of clusterswas guided by an agglomeration coefficient, which displayed the squared Euclidean distance between the cases andgroups of cases (see Table 4). The agglomeration coefficient shows quite large increases from 5 to 4 clusters, from 4 to3 clusters and from 3 to 2 clusters. Considering the percentage change in the clustering coefficient, this led us to deter-mine that the appropriate number of clusters was 4 (Flynn, Huo, and Zhao 2010). This final result shows clear differ-ences between clusters 2 and 4, while the distance between centres of clusters 1 and 3 is considerably smaller. Thethree dimensions of AC have discriminatory power, which supports H1 (see ANOVA test, Table 5).

Table 3. Further descriptives of the scales (based on CFA).

Construct Standardised Loadings Fit indices AVE Q of CS

Exploration 0.75/0.80/0.82/0.75 CHI2 = 91.60 70.0% 0.681Assimilation 0.63/0.77/0.82/0.84/0.68 58.7% 0.826Exploitation 0.79/0.78/0.80/0.73 DF = 61CHI2/DF = 1.51 62.4% 0.663

RMSEA = 0.057Operational efficiency 0.91/0.90/0.92/0.97/0.76 CHI2 = 30.32 80.0% 0.911Innovation 0.77/0.87/0.85 DF = 19 68.8% 0.694

CHI2/DF = 1.60RMSEA = 0.062

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Table 5 characterises the clusters based on the final centres. While cluster 2, which comprises 45 BSRs, is character-ised by highest levels of exploration, assimilation, and exploitation, cluster 4, with 17 dyads, is characterised by thelowest levels in the same AC dimensions. We respectively labelled them as strong and weak AC patterns. Cluster 1 andcluster 3 present intermediate levels of AC compared with cluster 2 and cluster 4. However, it is important to highlightthat the contrast between cluster 3 and 1 stems rather from the degree of mutual reinforcement of the learning dimen-sions. The 36 BSRs that integrate cluster 3 show a uniform level of the three AC dimensions, and accordingly wedescribed them as balanced AC patterns. On the opposite side, the 47 dyads of cluster 1 show different levels of explo-ration, assimilation, and exploitation, as did clusters 2 and 4, and therefore we labelled them as medium AC patterns(see Table 5 and Figure 1).

Following Flynn, Huo, and X. Zhao (2010), canonical discriminant analysis was used to identify the underlyingdimensions that defined the clusters. Table 6 shows that the first function had eigenvalue value larger than 1, explaining82% of the variance. The second function had an eigenvalue value close to 1. Together, these two functions esplain the99.6% of the variance.

Table 7 reveals that all three processes of AC were important in forming function 1, which divided the clusters intolow AC, medium AC, and high AC. Thus, function 1 represents AC strength, and it is the greatest differentiatorbetween supply chain relationships. Function 2 reflects the degree to which the three learning processes that integrateAC reinforce each other, indicated by the positive loading of assimilation and the negative loading of exploitation. Itdivides the cluster into those with greater and less AC reinforcement. Function 2 especially discriminates cluster 1 andcluster 3. Cluster 1, made up of BSRs with medium AC, shows an assimilation level that is very high when comparedto the rest of the processes of AC. However, cluster 3, BSRs with balanced AC, presents similar levels of exploration,assimilation, and exploitation.

Figure 2 indicates that the clusters were differentiated from each other by the discriminating functions representingAC strength and AC reinforcement. Accordingly, BSRs can be clustered into groups with different levels of AC strengthand reinforcement.

Next, we used ANOVA and Tukey comparison tests in order to identify significant differences across the clusters interms of operational efficiency and innovation. Table 8 shows descriptive statistics (mean and deviation values) and theANOVA test for the segmented configurations. As indicated by the ANOVA test, results demonstrate significant differ-ences in the levels of both types of performance as a result of variations in AC configurations in BSRs. Furthermore, it

Table 4. Analysis of agglomeration coefficients.⁄

Number of clusters Agglomeration coefficient Change in coefficient in the next level (%)

10 85,960 10.3259 94,835 10.6668 104,951 12.3857 117,949 11.4906 131,502 15.0565 151,300 13.4064 171,584 27.5163 218,797 36.8732 299,475 46.8921 439,904

Note: ⁄Hierarchical cluster based on Ward method and Euclidean distance.

Table 5. Cluster results for absorptive capacity.

Absorptivecapacity

Mean (SD) of cluster group

Cluster 1:Medium AC

Cluster 2:Strong AC

Cluster 3:Balanced AC

Cluster 4:Weak AC Total F (ANOVA)

Exploration 2.44 (0.70) 3.43 (0.66) 3.08 (0.62) 1.04(0.90) 2.74 (1.01) 54.301 (0.00)⁄⁄⁄

Assimilation 4.00 (0.62) 4.83 (0.38) 3.06 (0.54) 2.55 (1.03) 3.85 (1.01) 86.778 (0.00)⁄⁄⁄

Exploitation 1.58 (0.55) 2.91 (0.60) 2.65 (0.52) 0.44 (0.59) 2.12 (1.00) 104.644 (0.00)⁄⁄⁄

N 47 45 36 17 145

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is worth mentioning that discrimination amongst clusters is more pronounced regarding innovation than regarding opera-tional efficiency.

Through a deeper analysis of potential differences, Table 8 reveals that distinctions in terms of innovation are partic-ularly salient between the BSRs characterised by strong AC (cluster 2) and the remaining BSRs, and between those that

Table 7. Standardised canonical discriminant.

Function coefficients

Function 1 Function 2

Exploration 0.447 �0.077Assimilation 0.645 0.765Exploitation 0.605 �0.574

Figure 2. Cluster centroids.

Figure 1. Taxonomy of BSRs based on AC.

Table 6. Discriminant analysis.

Function Eigenvalue Percentage of variance Cumulative percentage Canonical correlation

1 4.077 82.0 82.0 0.896⁄⁄⁄

2 0.878 17.7 99.6 0.684⁄⁄⁄

3 0.018 0.4 100.0 0.132

Note: ⁄p < 0.05; ⁄⁄p < 0.01; ⁄⁄⁄p < 0.001.

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show a Weak AC (cluster 4) and the remainder of the clusters. BSRs typified by a strong AC are the best performing(highest mean value) in terms of innovation. BSRs that display medium or balanced levels of AC (clusters 1 and 3) canbe considered homogeneous in terms of this performance outcome. The lowest level of this performance indicator isshown by those BSRs that present a Weak AC. Table 8 also shows significant differences in terms of operational effi-ciency between the BRS identified as weak in terms of AC and the rest of the clusters. These dyads are worst perform-ing (lowest mean value) in terms of operational efficiency. Clusters 1 and 3 perform similarly in terms of operationalefficiency. Thus, these findings support H2 but not H3. In other words, while our empirical evidence supports that ACstrength influences performance, it cannot confirm that AC reinforcement determines performance.

5. Discussion and conclusions

5.1. Discussion and conclusions: based on the quantitative analyses

Consistent with past studies on AC (e.g. Lane, Koka, and Pathak 2006, Lichtenthaler 2009), our data supported the dis-tinction of three correlating dimensions of AC: exploration, assimilation, and exploitation. Moreover, our study extendsprior work by empirically analysing how these learning dimensions combine to fit together in various ways. Thestrength of this approach lies in discovering the complementarities and conflicts among the interdependent dimensions,rather than in establishing multiple relationships between individual dimensions of the phenomenon under research(Miller 1986). Additionally, the study shows that BSRs with strong, medium, and weak AC do not have a uniform levelof all learning processes. These BSRs, rather, show high levels of assimilation compared with the other two dimensionsof AC. This confirms that the suppliers did already redesign their operations in order to optimise intra-organisationalknowledge integration and continuously match customers’ desires with market offerings (Esper et al. 2010). Likewise,the exploitation learning process systematically reaches the lowest level, suggesting that suppliers still encounter difficul-ties when trying to add novel processes or material artefacts to ongoing exploitation of known processes or materialartefacts. This could be explained by pressure from the buyer to meet cost and quality standards of ongoing exchange,therefore postponing change.

When we compare our research with previous studies on AC configurations in a supply chain context (Malhotra,Gosain, and El Sawy 2005), we find important differences. Malhotra, Gosain, and El Sawy (2005) do not directlyaddress the varying nature of the AC construct, but rather focus on how firms in supply chain partnerships configuretheir integrative inter-organisational process mechanisms and IT infrastructures to build AC. Thus, our research differsfrom previous studies on BSRs in the sense that it identifies a taxonomy based on AC using a data-driven, analyticalapproach. We found that there were significant differences regarding the three learning processes of AC across the fourgroups. While one group of BSRs is characterised by strong AC, showing the highest levels of exploration, assimilation,and exploitation, another presents the lowest levels of the same learning processes and is typified as weak AC. Betweenthose BSRs that show an intermediated level of AC strength, we also differentiate another two groups: those that exhibita balance between the learning underlying the three AC dimensions and those that show a lack of equilibrium. We thuscomplement and extend the AC literature in a BSR context.

Our analysis also provides evidence of a significant relationship between type of BSR and both operational effi-ciency and innovation. We found that BSRs typified as strong AC outperformed other BSRs on both performance indi-cators. On the opposite side, BSRs labelled as weak AC show the poorest performance indicators. This result isconsistent with literature that views AC as an explanation of competitive advantage (Cohen and Levinthal 1990, Dyer

Table 8. Results of cluster analysis and ANOVA results for performance measures.

OutcomesCluster 1:

Medium ACCluster 2:Strong AC

Cluster 3:Balanced AC

Cluster 4:Weak AC Total F(ANOVA) Levene’s Test Brown-Forsythe

Operationalefficiency

1.92 (0.93) 2.33 (0.79) 2.19 (0.82) 1.09 (1.17) 2.02 (0.96) 8.559 (0.000)⁄⁄⁄ 2.971 (0.034)⁄ 7.508 (0.000)⁄⁄⁄

Innovation 2.92 (0.78) 3.71(0.64) 3.09 (0.65) 2.29 (1.28) 3.13 (0.90) 15.709 (0.000)⁄⁄⁄ 5.246 (0.002)⁄⁄ 11.877 (0.000)⁄⁄⁄

N 47 45 36 17 145Main group

differences (Tukey test):Operational

efficiency(1–4)⁄⁄; (2–4)⁄⁄⁄; (3–4)⁄⁄⁄

Innovation (1–2)⁄⁄⁄; (1–4)⁄; (2–3)⁄⁄; (2–4)⁄⁄⁄; (3–4)⁄⁄

Note: ⁄p < 0.05; ⁄⁄p < 0.01; ⁄⁄⁄p < 0.001.

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and Singh 1998), innovation (Stock, Greis, and Fischer 2002), and operational efficiency (Malhotra, Gosain, and ElSawy 2005). This suggests that the higher the levels of AC, the more likely it is that a supplier can leverage buyerknowledge to build internal competences and capabilities, permitting it to meet more accurately buyer needs and toimprove the performance out of the relationship. Likewise, we provide empirical evidence on firms’ challenges to har-monise seemingly contradictory requirements in order to simultaneously pursue operational efficiency and innovation.The parallels in the growing ambidexterity literature that point out the need to pursue efficiency and innovation simul-taneously are noteworthy. Take for example the work of Andriopoulos and Lewis (2009) that considers the trade-offbetween operational outcomes and innovation, showing that firms who focus too much on operational short term bene-fits can fall into a competence trap.

Table 8 also shows that innovation presents higher values than operational efficiency across four configurations.While this finding supports the classical assumption that AC fosters innovation (Malhotra, Gosain, and El Sawy 2005,Zahra and George 2005, Yeoh 2009) it also emphasises the growing importance of BSRs for organisations. Since BSRliterature and practice have been mainly focused on operational benefits, this result highlights the strategic value ofBSRs when the organisation is able to leverage the supply chain partner’s knowledge and reach more strategic goals.Accordingly, corporate emphasis on knowledge and knowledge-based capabilities as means to create value and achievesuperior performance demands supply chains that go beyond the exchange of materials and information. Supply chainsshould move towards complex, collaborative value networks, where partners work and experiment together on problemsolving and promoting inter-firm learning, sharing risks and benefits (Malhotra, Gosain, and El Sawy 2005).

Surprisingly our analysis does not provide evidence that the effect of the three learning process underlying AC iscumulative, since the relationship between AC reinforcement and performance was not supported by the data. The dif-ferences in impact on both types of performance from BSRs labelled as balanced AC versus those presented as mediumAC were not found significant, although keeping the hypothesised direction. In that sense, we confirm the results fromFlynn and Flynn (2004), who concluded, based on their empirical analysis, that the development of cumulative capabili-ties is a complex endeavour. Lichtenthaler (2009), on the other hand, was able to empirically confirm the complementar-ity of the different learning dimensions. Consequently, there still is a missing link between the complementarity ofexploration, assimilation, and exploitation and its impact on BSR performance. This gap indicates the need for futureresearch.

5.2. Post-hoc qualitative analysis

To provide more detailed information on the identified clusters, we conducted a post-hoc qualitative analysis of aselected dyad for each of the four clusters and evaluated whether these dyads have certain particularities related to theirrespective AC creation. This analysis helped to interpret the managerial implications stemming from discrimination offour clusters in terms of AC strength and reinforcement. The criterion for selecting each of these four dyads was thatthe supplier constituted the main source of supply for the buyer in the correspondent product category. We held a semi-structured interview at the supplier and buyer side of the dyad, resulting in a total of eight interviews. Questions wereasked on each of the dimensions of AC as well as on critical incidents within the relationship (regarding the ‘who, how,why, when and what’ of a remarkable event, either of a positive or negative nature).

Suppliers characterised by medium levels of AC, represented by cluster 1, show levels of assimilation which arehigher than the other two learning dimensions. These organisations are more inward- than outward-looking in the con-text of the dyad and do not pay a lot of attention to knowledge sourcing from the particular customer. The buyer andsupplier of the selected dyad of this cluster have a dominant position in their respective sectors and have consolidatedthis position by sourcing external knowledge from other partners. Consequently, they are accustomed to and capable ofcombining new knowledge with their existing knowledge base, but they have simply not done so within the context ofthe selected dyad. This suggests that either the opportunities related to AC creation with the specific buyer are smallcompared with the opportunities related to learning with other buyers or that practical obstacles led the companies topostpone learning with the particular partner. For instance, companies may chose to create AC with relatively small part-ners, given their high commitment to joint learning (Tsai 2009) and flexibility to adapt to the idiosyncrasy of the partner.The companies from the selected dyad are relatively big and therefore more difficult to move in that regard. Thus, thereis a potential for further creation of AC which is not currently realised (Yeoh 2009).

Companies engaged in BSRs represented by cluster 2 have the highest scores on all three AC dimensions. Bothfirms from the selected dyad of this cluster focus on how to take most advantage of efforts of learning from and withthe selected buyer/supplier. The supplier from the selected dyad stated in that regard about the buyer, ‘The strategy with[buyer] is to learn and test the innovation required by the final customer … They are the mirror where future marketbehaviour can be observed.’

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The supplier’s strategic definition of the benefits of joint exploration of new ideas or products with the buyer facil-itates the assimilation process, once the ideas are generated in boundary-spanning teams and internally proposed forapproval. Moreover, top management of the supplier acknowledges the opportunity to transfer to other customers thelessons learned. The companies of the selected dyad assume that operational benefits, in terms of cost savings andmore efficient use of their resources, can be obtained by opening up to each other and sharing knowledge, whichimplies openness from the boundary spanners to explore innovative ideas for the long-term benefit of the relationship.This confidence in the buyer’s goodwill did not result from specific policies or contractual agreements but rather froma shared discourse on the importance of the relationship, created jointly and over time. The management of potentiallydivergent stakeholder interests is crucial when companies jointly engage in learning and innovation journeys (Hardy,Lawrence, and Grant 2005).\

The BSRs included in cluster 3 engage in moderate and balanced levels of the AC dimensions. The supplier fromthe selected dyad of cluster 3 has increased its business significantly together with the development of the buyer. Duringthis period of growth the supplier has focused its external knowledge and its exploitation of novel ideas on this particu-lar buyer. The intermediate assimilation process is also dedicated to ideas stemming from this particular buyer. Conse-quently, all three dimensions are balanced. The buyer perceives a high operational performance of the dyadicrelationship, and the supplier perceived the relationship as a prosperous ground for innovation. Interestingly, the supplierdoes not have a relatively high level of the assimilation process (compared with the levels of exploration and exploita-tion) as companies in the other clusters have, which could be explained by their over-emphasis on projects with thisone specific customer (Knoppen, Sáenz, and Johnston 2011).

Finally, suppliers structuring their BSRs as in cluster 4 appear to focus their resources on deploying standard busi-ness interfaces with their buyers (Malhotra, Gosain, and El Sawy 2005). Boundary spanners within this kind of relation-ship limit their effort to the transactional exchanges, do not proactively suggest avenues for improvement and do notexpect the partner to do so. This lack of reciprocal motivation for relationship development leads to low levels of jointdecision making, low service levels and, consequently, low levels of operational performance. The suppliers and thebuyers of the dyads constituting this cluster do not perceive a strategic use of potential joint efforts. They rather per-ceive their exchange relationship as the simplest expression on the continuum of potential BSRs. As a large supplier ofthe lighting industry suggested: ‘If I were the buyer and they were my supplier, I would act in the same way they do:they ask for the product and I deliver it, as simple as that.’

Within this kind of BSR, the inspiration for innovation comes from sources external to the dyad. The focus in thatregard has been on product innovation, and consciousness of the potential of operational process innovation within thecontext of the specific dyad is low (Azadegan 2011).

6. Limitations and future research

This study offers four main contributions to the literature. First, it empirically captures the richness and multidimension-ality of AC, following several calls from the literature (e.g. Volberda, Foss, and Lyles 2010). Second, it develops a tax-onomy of BSRs with predictive power and provides empirical evidence linking BSR taxonomy and firm performance.In that sense it complements literature with a predominant descriptive and prescriptive orientation (Tangpong, Michali-sin, and Melcher 2008). Third, although most studies have seen AC as an explanation of innovation, we demonstrate itsdual impact on innovation and operational efficiency (Malhotra, Gosain, and El Sawy 2005, Tu et al. 2006, Azadegan2011). Finally, while most studies on BSRs have focused on the buyer perspective, the supplier perspective is equallyimportant in this research, given that suppliers usually are engaged in several dynamic supply chains, where they areexpected to contribute to various customers in different settings (Stjernström and Bengtsson 2004).

Future research is needed to complement the supplier’s perspective from this study with the buyer’s perspective, inorder to obtain a complete picture of learning processes within and between buying and supplying firms leading toabsorptive capacity creation. Moreover, our database included dyads regarding one focal buying company active in theretail sector. The strong point of this approach is that exogenous variability due to the context of the buying company isreduced, increasing the comparability of the dyads. But future research is needed with buying companies from other sec-tors. Finally, we could not confirm the complementarity of learning processes (Lichtenthaler 2009). Consequently, therestill is a missing link between the complementarity of exploration, assimilation, and exploitation and its impact on BSRperformance. This area should guide future research.

Overall, we hope to have provided a stimulating perspective on BSR; more precisely, we hope to have providedadditional insights on how to manage and collaborate with selected buyers or suppliers in order to create dynamic capa-bilities and improve performance.

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