Description
Collaboration is working with each other to do a task. It is a recursive process where two or more people or organizations work together to realize shared goals, (this is more than the intersection of common goals seen in co-operative ventures, but a deep, collective, determination to reach an identical objective[by whom?][original research?]) — for example, an endeavor
IMM-06555; No of Pages 11
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Industrial Marketing Management
Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia
Maria Smirnova a,?, Stephan C. Henneberg b, Bahar Ashnai b, Peter Naudé b, Stefanos Mouzas c
a b c
Graduate School of Management, St Petersburg State University Volkhovsky pereulok 3, 199004, Saint Petersburg, Russia Manchester IMP Research Group, Manchester Business School, United Kingdom Department of Marketing, Lancaster University Management School, United Kingdom
a r t i c l e
i n f o
a b s t r a c t
This study aims to investigate the role of interfunctional collaboration between marketing and purchasing functions in industrial companies. Interfunctional collaboration is considered as a measure of the internal alignment and partnership between departments in the ?rm, which in turn contributes to the creation of sustainable advantages via improved external partnerships and facilitating demand chain integration. We test the impact of customer orientation as well as the interactions between departments (speci?cally marketing and purchasing) as collaboration antecedents, and analyze the direct impact of marketing–purchasing collaboration on business performance. The model is tested on a sample of 148 industrial companies in Russia with two key respondents in each ?rm, incorporating the purchasing as well as the marketing perspective. The results show that marketing–purchasing collaboration mediates the effects of interfunctional interaction as well as customer orientation on business performance. Alternative model testing shows that the direct effects of these antecedent constructs on performance are non-signi?cant in the context of Russian industrial companies. © 2010 Elsevier Inc. All rights reserved.
Article history: Received 14 September 2009 Received in revised form 5 March 2010 Accepted 15 April 2010 Available online xxxx Keywords: Interfunctional collaboration Marketing Purchasing Customer orientation Russia
1. Introduction In the current business environment characterized by increasing competitive pressures, externally oriented functions of the ?rm face serious challenges, especially by striking a sustainable balance between the aims of companies and their business partners (Achrol, 1997; Achrol & Kotler, 1999; Ford, Gadde, Håkansson, & Snehota, 2003). Customers become increasingly demanding, forcing companies to increase their ability to interact collaboratively. This trend recognizes the importance of sustaining the performance of both a focal company and its network partners (Clarke, 2006; Wind, 2008). Thus, multifaceted goals put emphasis on achieving both operational ef?ciency within the ?rm as well as market effectiveness through the optimal combination of resources and knowledge of the ?rm and its network partners (Cox, 2004; Mouzas, 2006). Such thinking is in line with arguments from the resource dependence theory (Casciaro & Piskorski, 2005a,b; Pfeffer & Salancik, 1978) and the literature on business networks (Anderson, Håkansson, & Johanson, 1994; Matthyssens, Vandenbempt, & Weyns, 2009; Möller & Halinen, 1999). Achievement of immediate, operational results within a ?rm may lead to both a dramatic decrease in the ?rm's ability to adjust to future challenges (Ford & McDowell, 1999; Ritter,
? Corresponding author. Tel.: +7 812 323 84 48; fax: +7 812 329 32 34. E-mail addresses: [email protected] (M. Smirnova), [email protected] (S.C. Henneberg), [email protected] (B. Ashnai), [email protected] (P. Naudé), [email protected] (S. Mouzas). 0019-8501/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2010.09.010
1999; Ritter & Gemünden, 2003), and a reduction in its ability to retain competitive advantages via successfully mobilizing other business partners (Ehret, 2004; Eng, 2006; Mouzas, 2006; Mouzas & Naudé, 2007). Achieving a balance between focal company goals and those of interaction partners in complex business networks has previously been linked to developing interfunctional collaboration within a ?rm, i.e. creating internal cooperative approaches and internal strategic partnerships (Campbell, 1998; Piercy, 2009). Existing research highlights the demand for improvement of interdepartmental collaboration: “the logic is that strategic external relationships (with customers, supplier and partners) should be mirrored in strategic internal relationships…” (Piercy, 2009, p.857). More specifically, a shifting focus on interfunctional collaboration can be perceived: from investigating collaborations between ‘neighbouring’ functions, e.g. marketing and sales, or purchasing and operations, to collaborations between boundary spanning functions (Piercy, 2009). Such functions are responsible for the coordination of external relationships, speci?cally customer relationships and supplier relationships (Ivens, Pardo, & Tunisini, 2009). However, while research has suggested the importance of the integration of boundary spanning functions such as purchasing and marketing (Sheth, Sharma, & Gopalkrishnan, 2009), there is not enough empirical evidence regarding this issue and its interplay with ?rm performance. Our study uses this research challenge as its starting point by focusing on marketing–purchasing collaboration. Marketing–purchasing collaboration represents a speci?c case of interfunctional collaboration which has been shown to be an important
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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component of the behavioral aspect of both market orientation (Auh & Menguc, 2005; Narver & Slater, 1990) and demand chain integration (Jüttner, Christopher, & Baker, 2007; Lambert & Cooper, 2000; Sheth, Sisodia, & Sharan, 2000). The challenge is therefore for companies to instigate marketing–purchasing collaboration: to develop internal con?gurations of different functions which will help them to identify, maintain and develop external business relationships with crucial partners in the business network (Jüttner et al., 2007; Kahn & Mentzer, 1998). Improving external business relationships requires several distinctive antecedents with regard to the marketing–purchasing collaboration within a ?rm. Firstly, in line with requirements identi?ed as part of the concept of market orientation, the company needs to develop a deep understanding of its business partners, especially buying companies (Da Silva, Davies, & Naudé, 2002; Danneels, 2003; Hsieh, Chiu, & Hsu, 2008; Kohli & Jaworski, 1990; Narver & Slater, 1990). Such customer orientation is linked to the essence of the marketing function (Awuah, 2008; Brady & Cronin, 2001; Deshpandé, Farley, & Webster, 1993) and is also in line with the demand chain integration concept (Jüttner, Gosell, & Christopher, 2006). In order to best serve customers in a mutually bene?cial way, a company needs to use and transform certain resources (Alderson, 1965; Alderson & Martin, 1965; Prenkert & Hallen, 2006). However, the company does not possess all of the resources and competences itself and is thus dependent on its supply network (Paulraj & Chen, 2007; Pfeffer & Salancik, 1978). Consequently, the second antecedent important for aligning external business relationships lies in the interactions of internal boundary-spanning functions (Eng, 2006; Henneberg, Mouzas, & Naudé, 2009; Piercy, 2009). Thus, the company needs to develop interfunctional interactions which, together with customer orientation, provide the collaboration between marketing and purchasing which is hypothesized to be bene?cial for ?rm performance. Besides our contribution of clarifying the antecedents and consequences of interfunctional marketing–purchasing collaboration, we speci?cally focus on the setting of Russian ?rms. The importance of internal marketing–purchasing collaboration represents a particular challenge for transitional economies, such as Russia, as ?rms have not had suf?cient time to develop either the competences to relate to multiple business partners in complex networks, nor the capabilities to foster interaction and collaboration between externally facing functions such as purchasing and marketing (Peng & Luo, 2000; Piercy, 2009). The levels of complexity and dynamism of the new economic environments in transitional countries are in fact characterized by challenges to established competition rules, resulting in “collapsing capabilities” (Atuahene-Gima, Li, & De Luca, 2006, p. 360). In case of Russia, after the collapse of the planned economy and the dissolution of existing economic ties between companies and whole value-creating systems, ?rms had to adapt to the newly formed business environment, while developing new internal capabilities at the same time. Lorentz and Ghauri (2010) observe that “despite the recent positive development in Russian market, the heritage of the centralized planning oriented command economy is still evident” (p. 243). Numerous studies have focused on the challenges of Russian business culture and networking practices, which often provide examples of inef?cient and opportunistic interactions (Jansson, Johanson, & Ramström, 2007; Kouchtch & Afanasiev, 2001; Menkhaus, Yakunina, & Herz, 2004; Salmi, 1996). Overcoming such obstacles, as Johanson (2007) states, required signi?cant time and resource investments, and was based on the development of decentralized and mutual planning capabilities by individual ?rms. In fact, “interpersonal networks are important in uncertain and unstable economic environments, as interpersonal trust mitigates risk and reduces the in?uence of turbulent macro-environmental changes” (Butler & Purchase, 2008, p. 531). Our research is embedded in these discussions and is aimed at providing insights via an empirical study of Russian industrial ?rms, identifying antecedents and performance outcomes of marketing– purchasing collaboration. In addition to informing current managerial practice in transitional economies, speci?cally Russia, and thus
providing insights into the functioning of often overlooked business networks outside Western countries (e.g. Anderson et al., 1994; Håkansson & Ford, 2002; Ritter, Wilkinson, & Johnston, 2004, for an exception using non-Western data see Chan, 2000; Peng & Luo, 2000), our research contributes to addressing some important research gaps, namely clarifying the role of interfunctional collaboration of marketing and purchasing as crucial example of boundary spanning functions. We thus contribute to the stock of knowledge on demand chain integration and business relationships in networks (Jüttner et al., 2007; Lambert & Cooper, 2000; Sheth et al., 2000). The paper is structured as follows: we ?rst present the theoretical basis of the study and key concepts to be considered, and then propose a nomological model describing the key research assumptions as well as the underlying hypotheses. We present the results from a dyadic dataset of 148 Russian companies and, using structural equation modeling, propose key implications for practitioners. Finally, we identify further areas for potential research. 2. Literature review and hypotheses development 2.1. The nature and consequences of interfunctional collaboration The role of collaboration between different ?rm functions is widely discussed in the research literature, e.g. as “an affective and volitional process where departments work together with mutual understanding, common vision, and shared resources to achieve collective goals” (Kahn & Mentzer, 1998, p. 55). Effective interfunctional collaboration has become an important strategic emphasis of successful ?rms (Morgan & Piercy, 1998). Such collaboration is to a large extent based on aligning organizational aims, values and priorities between functions, especially regarding working with external partners. This alignment allows for the creation of synergetic effects among departments, leveraging available resources and knowledge (Ellinger, 2000; Piercy, 2009). Development of internal collaboration is often in?uenced by increasing pressure from external partners, especially customers, and thus “involving sales and marketing personnel in supply strategy and alliance management also offers potential for more effective joint working on shared problems and new opportunities” (Piercy, 2009, p. 862). Internal collaboration is arguably built into the system of external collaboration within up- and down-stream channels (Ellinger, 2000; Kahn & Mentzer, 1998). The nature of internal collaboration is very close to the concept of integration of functions (Ellinger, 2000) and is based on developing trust, mutual respect, communication and information sharing, as well as shared responsibility in decision making and outcomes (Grif?n & Hauser, 1996; Kahn & Mentzer, 1996). Integration can be de?ned as the process of achieving unity of effort among the various organizational subsystems in the accomplishment of the company's task (Lawrence & Lorsch, 1967). Previous research suggests that especially internal norms, for example exempli?ed in interfunctional collaboration, will have a direct effect on the norms in interacting with external partners — thus a high level of interdepartmental openness and readiness to share information will have a positive effect on cooperating with external partners (Campbell, 1998; Ellinger, 2000). Kahn and Mentzer (1998) discuss three existing approaches to de?ning integration among the functions: an interaction-based approach in which communication between the departments, meetings and information ?ow is forcing successful integration; collaboration-based integration, in which teamwork, shared resources and goals are the main driving force; and composite integration, implying that both interaction and collaboration are two main elements of interdepartmental integration. This multi-construct approach is also supported by Biemans, Bren?i?, and Malshe (2010). Arguably the most often used conceptualization of internal collaboration relates to interfunctional coordination as a subconstruct of market orientation (Jaworski & Kohli, 1993; Kohli & Jaworski,
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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1990), using a behavioral operationalization (Narver & Slater, 1990). It refers to the degree to which the functions and departments within the ?rm communicate with each other and work cooperatively (Kohli & Jaworski, 1990). The danger of low internal alignment has been addressed by Atuahene-Gima et al. (2006), who stress the phenomenon of ‘internal stickiness’, which occurs when accumulated market knowledge at the ?rm level stays isolated within one department. The elements that lead to interfunctional collaboration can become a source of competitive advantage (Blomqvist & Levy, 2006; Eng, 2006; Kent, 1996). Menguc and Auh (2005) argue that such collaboration develops social capital within the company which in itself enables better access to, and use of, resources (Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998). Reagans and Zuckerman (2001) postulate the same effect of interfunctional collaboration but see diversity as the main driver. While interfunctional collaboration has been discussed extensively in the literature, one of the shortcomings of existing empirical research is that it is mostly based on single informants, capturing only one speci?c functional perspective. Internal dyadic or triadic studies are rare. Furthermore, existing research mostly focuses on collaborations between neighboring functions, e.g. between marketing and other customer- or internal resource-related departments, such as the interaction of marketing with sales (Gosselin & Bauwen, 2006), manufacturing (Alegre & Chiva, 2004), sales, communication and product development (Möller & Rajala, 1999), administration, production and R&D (Ford & Saren, 2001; Kahn & Mentzer, 1998; Souder & Moenaert, 2007), human resource management (Chimhanzi, 2004), or ?nance (Srivastava, Shervani, & Fahey, 1998). However, while the importance of interactions between ‘boundary-spanning functions’ such as purchasing on the one hand, and marketing on the other, has been advocated (Piercy, 2009), there does not exist enough empirical research analyzing the speci?c interplay between these two crucial departments. Most research focuses on inter-organizational purchasing and marketing interactions, such as in the context of Just-in-Time business relationships (Gunesekaran, 1999; O'Neal, 1989), neglecting the intra-organizational collaboration issues. Another shortcoming of existing studies is that they have used mainly Western countries for their investigation. The issue of interfunctional alignment is arguably pivotal for ?rms in transitional economies. These economies, given their relatively short history of being a marketdriven, are typi?ed by lack of information about potential partners and low information disclosure readiness, resulting in both higher instability in business relationships and readiness to adopt opportunistic behavior (Håkansson & Ford, 2002; Ford & Håkansson, 2006; Johanson, 2007). These factors imply that there exists more risk in managing external interactions and relationships than in established market economies. Thus, internal collaboration among functions becomes important due to the need to compensate internally for the instability of external relationship interactions (Powell, 1992). 2.2. Consequences of marketing–purchasing collaboration Given that interfunctional collaboration is one of the major behavioral characteristics of a ?rm enabling sustainable competitive advantage (Narver & Slater, 1990), and that marketing and purchasing are two major functions in ?rms engaged in business-to-business activities (Ford, 2002), it is important to understand how marketing and purchasing collaborate and learn from each other to enhance effectiveness and goal achievement (Jüttner et al., 2007; Williams, Giunipero, & Henthorne, 2006). None of the existing studies have examined the antecedents and consequences of interfunctional collaboration between the marketing and the purchasing functions as the two main organizational activity areas which link the ?rm to outside stakeholders (customers and suppliers, respectively). Marketing and purchasing interactions have so far only been discussed from a supply chain perspective (Paulraj & Chen, 2007; Trkman, Štemberger, Jaklic, & Groznik, 2007). However, several marketing studies have pointed to the cross-functional nature of
marketing capabilities of the ?rm with the focus on supply chain coordination (Day, 1994; Jüttner et al., 2007; Sheth et al., 2000). Therefore, marketing and purchasing collaboration becomes the focal construct of our study in the context of the Russian economy. This construct is based on the concept of demand chain integration which argues for linking both the supply chain management and marketing perspectives, taking the customer as a starting point of the analysis (Jüttner et al., 2007). This marketing–purchasing collaboration can therefore be conceptualized as a part of the ?rm's strategic market sensing capabilities (Day, 1994; Foley & Fahy, 2004), i.e. the ability to identify potential opportunities and align the ?rm's activities with capabilities and resources of upstream (supplier) and downstream (customer) partners in the market. Such activities have been argued to provide a higher probability of value innovation, contributing to the creation of sustainable advantages for the ?rm (Day & Van den Bulte, 2002; Teece, Pisano, & Shuen, 1997). This is in line with ?ndings that effective ?rms achieve higher interfunctional collaboration among different organizational activity areas (Lawrence & Lorsch, 1986). Therefore, it can be posited that: Hypothesis 1. Increased marketing – purchasing collaboration impacts positively on business performance. 2.3. Antecedents of marketing–purchasing collaboration While interaction has frequently been considered in the literature in terms of activities between business partners (Håkansson, 1982), a similar perspective can be applied to interdepartmental issues (Lim & Reid, 1992; Ruekert & Walker, 1987). Interaction has been argued to be part of interdepartmental collaboration, e.g. Kahn and Mentzer (1998) de?ne it as information exchange processes between departments. Ruekert and Walker (1987) posit communication interactions as a driver of interfunctional collaboration. Similarly, Morgan and Piercy (2009) suggest that collaboration between departments depends on the level of communication and interaction. As Kahn and Mentzer (1998) argue, interaction is often an important component of interdepartmental collaboration. However, they warn that it is not a suf?cient, as it alone will not ensure successful interdepartmental relationships. Nevertheless, an interaction view emphasizes communication (e.g. meetings or information ?ows) between departments (Grif?n & Hauser, 1992; Ruekert & Walker, 1987). In fact, much of the marketing literature highlights that effective interfunctional collaboration is predicated on interaction, and thus, prescribes marketing's increased contact with other departments (Grif?n & Hauser, 1992; Moenaert, Souder, DeMeyer, & Deschoolmeester, 1994; Urban & Hauser, 1993). Other sources argue that the role of interaction is a composite of existing communication, connectedness and con?ict (Menon, Jaworski, & Kohli, 1997). The positive relationship between interfunctional interaction and collaboration will therefore exist in cases of aligned aims and interests between the departments, which means in turn an absence of interdepartmental con?ict (Morgan & Piercy, 1998). Empirical studies regarding the effect of aspects of interfunctional interaction on collaboration have produced mixed results. Rodriguez, Perez, and Gutierrez (2005) as well as Meunier-FitzHugh and Piercy (2006) consider interfunctional communication as an antecedent of collaboration, as well as of performance (Biemans et al., 2010; Hitt, Hoskisson, & Nixon, 1993; Kahn, Reizenstein, & Rentz, 2004). Empirically these studies were able to support the impact of interactions on collaboration, and of collaboration on performance, but not the effect of interactions on performance (Kahn & Mentzer, 1998). However, for marketing and R&D collaboration within a ?rm, Song, Neeley, and Zhao (1996) show that interaction positively impacts both information sharing and communication, which are then in turn positively associated with collaboration.
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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Interfunctional collaboration in our study relates to the marketing and purchasing function. Some existing research provides evidence that interaction is an important antecedent for collaboration involving the marketing function: “Careful management of marketing's interaction with other departments…can help ?rms become adaptive, effective and ef?cient” (Cadogan, Sundqvist, Salminen, & Puumalainen, 2005, p. 520). We follow the above argument and model interfunctional interaction (between marketing and purchasing) as being an antecedent of interfunctional collaboration, i.e. collaboration mediates the effect of interactions on ?rm performance, with interfunctional interaction itself not having a direct impact on performance. Higher interaction is therefore expected to foster further collaboration but does not necessarily have a direct impact on ?rm success: Hypothesis 2. Marketing–purchasing interaction impacts positively on marketing–purchasing collaboration. Marketing and purchasing have different perspectives on business relationships. This may hinder key aspects of interfunctional collaboration, for example the sharing and exchange of knowledge and ideas (Day, 1994; Nonaka, 1994; Nonaka, Umemoto, & Senoo, 1996). Marketing–purchasing collaboration can be an area of con?ict due to differences in motivation and interests (Humphreys, Williams, & Goebel, 2009), which has been explained via mutual resource dependence (Goebel, Marshall, & Locander, 2003) or overall organizational con?ict (Barclay, 1991). For the purpose of our study, we posit the central role of ‘market-sensing’ in creating interdepartmental alignment of aims and facilitating collaboration between marketing and purchasing. This is due to the fact that the extant literature discusses the particular role of understanding markets (something which is particularly done by marketing) in aligning departments and functions within the ?rm (Day, 1994; Jaworski & Kohli, 1993; Kohli & Jaworski, 1990). For example, Kahn and Mentzer (1998) argue that the integration of departments is to a large extent the responsibility of customer-facing units. It is the marketing department that is responsible for creating interfunctional collaboration, and enabling the rest of the company to become customer oriented. The collaboration among the departments will be based on the goals that are received and aligned among the departments (Kahn & Mentzer, 1998). The aims set at the strategic level of the ?rm will have direct impact on the level of involvement of departments in the collaboration process (Morgan & Piercy, 1998). Thus, marketing needs to disseminate market information and customer-related knowledge among the departments (Day, 1994; Kohli & Jaworski, 1990; Nonaka et al., 1996). Narver and Slater (1990) point to the fact that every department and function can create higher value for customers. Indeed, the contribution of other functions should be incorporated into the ?rm's marketing strategy (Narver & Slater, 1990; Wind & Robertson, 1983). It is therefore important for both marketing and purchasing to understand customer demands in order to create a basis for superior value creation. Thus, investigating interfunctional collaboration between marketing and purchasing departments relates speci?cally to the strategic balancing of departmental interests around the issue of customer orientation (Williams et al., 2006). Again this follows the logic of the
demand chain integration concept (Heikkila, 2002; Jüttner et al., 2007) which suggests that the customer should be the starting point of any analysis, i.e. proposing a marketing and customer-oriented approach to interactions regarding supply chain management (Baker, 2003). We therefore hypothesize that: Hypothesis 3. Customer orientation impacts positively on marketing–purchasing collaboration. Customer orientation, while mostly associated with knowledge residing in the marketing department, needs to be distributed within the whole company to impact on collaboration and business performance. However, taking the purchasing department as an example, this function typically has suboptimal resources to achieve knowledge about the target customers as well as the wider demand network. To achieve such customer knowledge, the purchasing department needs to have interactions with the owner of such knowledge, i.e. the marketing department. Thus, it can be posited that: Hypothesis 4. Marketing–purchasing interaction impacts positively on customer orientation. Based on these four hypotheses which link the four main constructs, the overall nomological model is introduced in Fig. 1. The focal concept of marketing–purchasing collaboration is modeled as a mediator between the antecedent constructs of customer orientation on the one hand, and marketing–purchasing interaction on the other. Interfunctional collaboration between the marketing and purchasing departments in turn is related to the company's business performance. 3. Research design 3.1. Data collection The empirical dataset to test our nomological model relates to Russian companies across different industries. To pre-test the constructs as well as existing measurement scales, several qualitative studies were undertaken. A speci?c aim of these studies was to understand whether the focal concepts which had been developed from the research literature could be used in the context of the transitional Russian economy, as these are mainly based on data from mature economies. For this purpose, an initial open round-table discussion was organized with thirty-seven CEOs, marketing and purchasing directors to discuss the issue of business relationships and interfunctional collaborations, including customer and supplier interactions in a business-to-business context. During these discussions, the managers indicated that they perceived customer knowledge dissemination throughout the ?rm to be one of the key sources of market development and a driver of innovation in Russia. Furthermore, this discussion also identi?ed the potential issue of misalignment between marketing and purchasing departments. Having established that the core constructs were understood and meaningful in a Russian context, some further interviews (as with all other empirical phases, these interactions were done in Russian) were
Customer orientation
H3
Marketing Purchasing collaboration
H1
Business performance
H4
Marketing Purchasing interaction
H2
Fig. 1. Nomological model.
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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conducted with forty top managers (these were selected from the same sample frame that was later used for the quantitative phase of the research; after the interviews, these ?rms did not participate in the quantitative stage). These interviews tested the construct descriptions as well as an initial draft of the questionnaire with measurement models for each construct. As a result, some question wordings were slightly changed, and the ?nal questionnaire for the quantitative study was prepared. Our sample was based on a list of industrial companies (from the SKRIN database, comprising a variety of sectors), covering the whole of Russia. We randomly contacted 502 ?rms. The respondents were mostly top management, speci?cally heads of marketing or purchasing. Two key respondents per company were used, one covering the marketing perspective and the other the supply perspective of the company. A total of 186 companies agreed to participate (response rate of 37.1%). The data was collected by face-to-face interactions with the relevant respondents; research assistants were used for conducting the interviews. This method was used as mailed questionnaires typically do not yield suf?cient response rates in the Russian business environment. The questionnaire for both key respondents contained the same questions. We thus always attempted to collect two different internal perspectives on the same constructs, controlling for two diametrically opposed outside-facing functions in the ?rm, as well as minimizing the potential for common methods bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). A total of 38 ?rms identi?ed the same person (usually the CEO) as being the most competent respondent covering supply as well as marketing perspectives in the ?rm, thus providing only single respondent data sets. These ?rms were excluded from further analyses in order not to violate the common method bias assumption. Thus, the ?nal sample consisted of 148 ?rms providing a dyadic data set (a response rate of 29.5%) with a total of 296 respondents. The respondents were also asked to indicate how knowledgeable they perceived themselves to be in addressing the questions in our survey. The level of perceived respondent knowledgeability (on a 5-point Likert scale) averaged 4.7, indicating the respondents had relevant knowledge for our study. Tables 1a and 1b provide some socio-demographic information on the respondents for both the purchasing and marketing sides. 3.2. Construct measures The measurement models for each construct were based on 5point Likert scales (virtually all were anchored in “strongly agree” and “strongly disagree”). The item pool for each measurement model was taken from existing research. We used translation/back-translation to ensure that the question wording in Russian was equivalent to that of the source publication. The ?nal questionnaire consisted of 19 items for the four constructs. All measurement models use re?ective scales (Diamantopoulos & Siguaw, 2006; Diamantopoulos & Winklhofer, 2001; MacKenzie, Podsakoff, & Jarvis, 2005). Scale properties were evaluated using traditional psychometric approaches. Reliability and unidimensionality of the scales was tested, and item-to-total correla-
Table 1b Sample description companies. Key industries (%) Machinery Production of construction materials Food industry Appliance machinery Chemical industry Light industry Woodworking industry Packaging Other Annual sales 2006 (m USD) Less than 1.5 1.5–3 3–15 More than 15 Number of employees Less than 50 From 50 to 100 From 100 to 500 From 500 to 1000 More than 1000 Note: Percentages may not add up to 100 due to rounding.
18.1 13.9 13.9 11.1 9.7 7.6 6.9 5.6 13.1 12.2 30.1 28.5 29.2 8.8 12.2 38.1 17.9 23.0
Table 1a Sample description respondents. Marketing side Male respondents (%) Female respondents (%) Average age (years) Average duration of employment in this ?rm (years) Average duration of employment on the position (years) CEO and board level (%) Functional director level (%) 62.2 37.8 41.7 10.6 5.5 61.3 38.7 Supply side 69.0 30.9 42.3 10.3 5.6 77.0 23.0
tions were assessed for all the scales (Anderson & Gerbing, 1988; Fornell & Larcker, 1981). Following an exploratory factor analysis (EFA), four items were dropped due to cross-loadings on other constructs, leaving 15 items used in the path estimations. Table 2a shows the ?nal item loadings. For these 15 items the CFA using AMOS 7.0 to cross-validate the factor structure (Mishra, Heide, & Cort, 1998) shows good model ?t (Jöreskog & Sörbom, 1988; Kline, 2005) with ?2 = 1.107 (p = 0.237), RMR = 0.042, GFI = 0.926, CFI = 0.993, RMSEA = 0.027 (p = 0.906). The construct of customer orientation was measured using four items from the original Narver and Slater (1990) scale. Marketing–purchasing interaction was measured using four existing items from Kahn and Mentzer (1998). The same article also provided four items for marketing– purchasing collaboration. Finally, we measure business performance as the ?rm's pro?tability, relative market share, and sales growth in comparison to competitors. The respondents were asked to compare the performance of their ?rm over the last three years vis-à-vis their main competitors (Vorhies & Harker, 2000). The items for this construct were measured on a ?ve-point Likert-type scale anchored in ‘much worse’ and ‘much better’. To avoid common method bias, we used dyadic data in the model test, i.e. we obtained predictor (independent) and criterion (dependent) construct scores from different respondents (Podsakoff et al., 2003). We used the purchasing side respondents for the construct of customer orientation and marketing–purchasing interaction, and marketing side respondents for the constructs of marketing–purchasing collaboration and business performance. The rationale for this lies in the fact that the marketing department is the one responsible for creation of interfunctional alignment in the ?rm (Kahn & Mentzer, 1998; Verhoef & Lee?ang, 2009). Therefore the assessment of the ?rm's customer orientation and of the marketing–purchasing interaction by the purchasing side respondents is capturing the effects of the activities created by the marketing department (and not the assumed or intended effectiveness of these activities). At the same time, interfunctional collaboration between marketing and purchasing, and business performance, relate to marketing-perceived measures; thus, the dependent constructs in the nomological model are assessed by the responsible function (i.e. marketing), while its antecedents are measured by purchasing. This dataset will be referred to as the initial or ‘mixed dataset’. Furthermore, we also test this dataset against a dyadic dataset, using average scores for all items based on the average values from the answers provided by both the marketing and the purchasing respondent for the same item (Van Bruggen, Lilien, & Kacker, 2002). This dataset will be called the ‘dyadic dataset’ later.
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
6 Table 2a EFA results (mixed dataset, original model). Item Component 1 M–P Collaboration 6 (M) M–P Collaboration 2 (M) M–P Collaboration 4 (M) M–P Collaboration 1 (M) Customer Orientation 4 (P) Customer Orientation 2 (P) Customer Orientation 3 (P) Customer Orientation 1 (P) M–P Interation 3 (P) M–P Interation 1 (P) M–P Interation 2 (P) M–P Interation 4 (P) Business Performance 2 (M) Business Performance 1 (M) Business Performance 3 (M) % of variance 0.870 0.870 0.863 0.845 2
M. Smirnova et al. / Industrial Marketing Management xxx (2010) xxx–xxx Table 3 AVE and squared construct correlation matrix (initial dataset). Construct 3 4 1. 2. 3. 4. Customer orientation Marketing–purchasing interaction Marketing–purchasing collaboration Business performance 1 0.66 0.18 0.16 0.05 2 0.57 0.15 0.00 3 4
0.69 0.05
0.71
0.856 0.848 0.798 0.753 0.812 0.780 0.766 0.646 0.892 0.882 0.843 15.9
21.3
19.5
16.8
Note: Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization, rotation converged in 5 iterations. (M) indicates answers from marketing respondents, (P) from purchasing respondents.
As Table 2b shows, all standardized factor loadings for the used items are above 0.7 (Nunnally & Bernstein, 1994) except in the case of one item for marketing–purchasing interactions. In all cases the average variance extracted (AVE) exceeds the critical level of 0.5 (Fornell & Larcker, 1981), and the composite reliabilities (CR) are higher than 0.85. Discriminant validity has been con?rmed applying the Fornell and Larcker (1981) criterion (Table 3). 4. Hypotheses testing 4.1. Initial nomological model test To test the hypotheses in our model, we used covariance-based structural equation modeling (SEM) with AMOS 7.0, after we tested the dataset for normal distribution. For our initial model tests we used the
mixed purchasing–marketing dataset (see Appendix A for construct means, standard deviations, and construct correlations). Using the Maximum Likelihood (ML) indicator, the model shows a good ?t with the data (Bollen, 1990; Hu & Bentler, 1995). All the structural paths were supported at signi?cant con?dence levels. No additional paths were proposed by the SEM modi?cation indices. The overall ?t measures and signi?cance levels are: ?2 =1.125 (p =0.202), with the non-signi?cant ?t indicating that the model's covariance structure is not signi?cantly different from the observed covariance matrix, RMR=0.047, GFI=0.923, CFI=0.991, RMSEA=0.029 (p =0.890). As Fig. 2 shows, both antecedent constructs have highly signi?cant and positive path coef?cients in relation to the focal construct of marketing– purchasing collaboration. Thus, Hypotheses 2 and 3 are supported: Customer orientation positively impacts on interdepartmental collaboration with a coef?cient of 0.291 (p b 0.01), as does marketing–purchasing interaction (0.257, p b 0.01). In turn, marketing–purchasing interaction strongly affects customer orientation (0.425, p =0.00), as expected by Hypothesis 4. Marketing–purchasing collaboration has a relatively strong and signi?cant positive effect on ?rm performance (0.233, p =0.01). Hypothesis 1 is therefore also supported. 4.2. Alternative model tests Three additional model tests were conducted to ascertain if the original model could be improved upon. We identify these as the reversed, dyadic, and saturated approaches, and discuss each brie?y in turn. Firstly, in our initial nomological model we used the mixed dataset with antecedent constructs based on respondents from the purchasing perspective, and outcome constructs based on the marketing perspective. For the reversed model, we tested the ?t of a ‘reversed’ dataset, i.e. the antecedent constructs are made up from marketing respondents, while the outcome construct scores are based on purchasing respondents. As such a make-up of the dataset would have no valid conceptual grounding, it would be expected that the reversed dataset ?ts the model worse than the mixed dataset. The ?2 =1.383 (p =0.017) now becomes signi?cant, and the slightly worse ?t indicators support the assumption that the reversed model does not ?t the data well (RMR=0.070, GFI=0.915, CFI=0.976, RMSEA=0.051 (p =0.452)). The second variant on the dataset used utilizes dyadic data. Noting that each item was answered by two respondents from the same company (a purchasing and a marketing manager), the data can be combined to form a dyadic score for each item, representing not the perceptions of a single manager but a more balanced perspective relating to the organization. Several ways to integrate dyadic data exist. For the purpose of this study, we used average scores (Deshpandé, Farley, & Webster, 2000; Van Bruggen et al., 2002). The resulting dyadic dataset provided the item scores shown in Table 4. Cronbach's alpha for the four constructs are improved slightly compared to the mixed dataset, as does the composite reliability. The estimates of the CFA demonstrate good model ?t, with RMR = 0.042, GFI = 0.913, CFI = 0.977, RMSEA = 0.054 (p = 0.364); however, the ?t is not as good as for the mixed data. Table 5 provides the AVEs and squared correlation matrix, indicating in all cases AVEs exceeding the critical level of 0.5, with the squared correlations being lower than the AVEs (Fornell & Larcker, 1981). Again using ML estimation procedures, we test the model using this dyadic dataset (see Fig. 3). All hypotheses are supported, with all pathways showing slightly stronger positive path coef?cients, all at
Table 2b Measurement statistics (mixed dataset). Construct/items Standardized factor loading Cronbach's CR alpha 0.90 0.89
Customer orientation (purchasing) We closely monitor and assess our level 0.83 of commitment in serving customer's needs. Business strategies are driven by the goal 0.91 of increasing customer value. Our competitive advantage is based 0.73 on understanding customer needs. Our business objectives are driven 0.77 by customer satisfaction. Marketing–purchasing interaction (Purchasing) Meetings 0.75 Committees/task forces 0.77 Phone conversations 0.88 Emails 0.60 Marketing–purchasing collaboration (marketing) Achieve goals collectively 0.82 Have a mutual understanding 0.89 Share ideas, information and/or resources 0.77 Work together as a team 0.86 Business performance (marketing) Growth of comparative market share 0.91 Sales growth 0.85 Business pro?tability 0.76
0.82
0.84
0.89
0.90
0.88
0.88
Note: CR = composite reliability; all items are measured on a 5-point Likert scale, anchored in ‘strongly agree’ and ‘strongly disagree’ (except items for business performance which were anchored in ‘much worse’ and ‘much better’).
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
M. Smirnova et al. / Industrial Marketing Management xxx (2010) xxx–xxx
7
Customer orientation (P) 18.0%
.425 (0.000)
.291 (0.003)
Marketing Purchasing interaction (P)
.257 (0.009)
Marketing Purchasing collaboration (M) 21.4%
.233 (0.010)
Business performance (M) 5.4%
Note : (P) indicates construct being measured via purchasing respondents, (M) indicates construct being measured via marketing respondents
Fig. 2. Path estimation original model (mixed dataset).
p = 0.000 signi?cance levels. Furthermore, the dyadic dataset also provides higher explained variances for the dependent constructs (e.g. for marketing-purchasing collaboration 43.8% for the dyadic dataset, up from 21.4% for the mixed dataset). However, the ?t indices show a slightly worse ?t than for the mixed dataset: RMR= 0.048, GFI = 0.908, CFI = 0.973, RMSEA = 0.058 (p = 0.246). Furthermore, ?2 = 1.501 (p =0.002) is signi?cant, indicating a ?t which can be optimized. Therefore, our mixed dataset arguably ?ts the model better than the dyadic version. Finally, a more saturated, i.e. enriched model was tested by adding two hypotheses linking the antecedent constructs directly to the dependent construct of business performance to qualify the impact of marketing– purchasing collaboration as a mediator construct. Note that this model had again two sub-variants, using ?rst the mixed and then the dyadic datasets. Two hypothetical pathways were tested: The ?rst pathway relates to the assertion by Kahn and Mentzer (1998) that not just interfunctional collaboration but also interfunctional interactions have a direct impact on ?rm performance. The second hypothetical pathway relates to the fact that customer orientation, has been found to have a direct and positive impact on company outcomes (Appiah-Adu & Singh, 1998; Baker & Sinkula, 1999; Han, Kim, & Srivastava, 1998; Slater & Narver, 1994). We
Table 4 Measurement statistics (dyadic dataset). Construct/item Customer orientation (dyadic) We closely monitor and assess our level of commitment in serving customer's needs. Business strategies are driven by the goal of increasing customer value. Our competitive advantage is based on understanding customer needs. Our business objectives are driven by customer satisfaction. Marketing–purchasing interaction (dyadic) Meetings Committees/task forces Phone conversations Emails Marketing–purchasing collaboration (dyadic) Achieve goals collectively Have a mutual understanding Share ideas, information and/or resources Work together as a team Business performance (dyadic) Growth of comparative market share Sales growth Business pro?tability Standardized factor loading 0.86 0.91 0.80 0.81 0.81 0.79 0.85 0.81 0.51 0.92 0.91 0.91 0.79 0.88 0.91 0.90 0.91 0.81 0.91 0.93 0.83 Cronbach's CR alpha 0.92 0.91
therefore also test the direct effect of customer orientation on business performance. These two tentative pathways directly link the antecedent constructs and the dependent construct of business performance, without the mediating in?uence of the construct of marketing–purchasing collaboration. The enriched model therefore looks at the relative impact that the antecedent constructs have in terms of their direct vis-à-vis their mediated impact on company success. Fig. 4a shows the path estimations for this model using the mixed dataset. The model ?t is improved with ?2 = 1.107 ( p = 0.237), RMR = 0.042, GFI = 0.926, CFI = 0.993, RMSEA=0.027 (p =0.906). While the paths initially hypothesised in Hypotheses 1–3 are still signi?cant (the path coef?cients change only slightly), while Hypotheses 4 and the two new pathways are not signi?cant. Thus, marketing–purchasing interaction has no direct effect on ?rm performance, thereby replicating the empirical results found in Kahn and Mentzer (1998). More interesting is the rejection of the link between customer orientation and business performance: for Russian industrial companies, customer orientation does not impact directly on performance. Although the path estimation coef?cient is positive as expected, it is moderately strong at 0.202 but not signi?cant with p =0.056. Thus, any positive effect of a deep understanding of customer needs and wants, and driving customer satisfaction and value, is mediated via the construct of interfunctional alignment, in our case the collaboration of the marketing and the purchasing function. However, the link between marketing– purchasing collaboration and business performance (path estimate of 0.189) is now not signi?cant (p =0.066). Altogether, the model improves the explained variance of the criterion constructs. We also tested the enriched model with the same six pathways with the dyadic dataset (Fig. 4b). While the overall model ?t is adequate with RMR=0.042, GFI=0.913, CFI=0.977, RMSEA=0.054 (p =0.364), the ?2 =1.430 (p =0.006) is again problematic. In this model the path between customer orientation and performance becomes signi?cant, i.e. customer orientation is positively and signi?cantly linked with business performance (0.336, p b 0.01). The additional path increases the explained variance of the business performance construct to 17.3%. However, the other new pathway again cannot be supported as the path coef?cient is not signi?cant. Furthermore, while the other path coef?cients in the model change only marginally, the link between marketing–purchasing collaboration and business performance is again not signi?cant (0.162, p =0.161). This means that while the original model in Fig. 2 corroborated the mediating effect of marketing–purchasing collaboration, this effect is statistically not signi?cant in either of the enriched models. This ?nding,
Table 5 AVE and squared correlation matrix (dyadic dataset). Variable 1. 2. 3. 4. Customer orientation Marketing–purchasing interaction Marketing–purchasing collaboration Business performance 1 0.72 0.25 0.34 0.16 2 0.57 0.32 0.04 3 4
Note: CR = composite reliability; all items are measured on a 5-point Likert scale, anchored in ‘strongly agree’ and ‘strongly disagree’ (except items for business performance which were anchored in ‘much worse’ and ‘much better’).
0.76 0.10
0.77
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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M. Smirnova et al. / Industrial Marketing Management xxx (2010) xxx–xxx
Customer orientation (dyadic) 25.2%
.502 (0.000)
.402 (0.000)
Marketing Purchasing interaction (dyadic)
.361 (0.000)
Marketing Purchasing collaboration (dyadic) 43.8%
.331 (0.000)
Business performance (dyadic) 11.0%
Note : (dyadic) indicates construct being measured as the average values of the scores from purchasing and marketing respondents
Fig. 3. Path estimation original model (dyadic dataset).
plus the signi?cant ?2 provide some dif?culties in interpreting the results of the dyadic enriched model vis-à-vis other models, especially due to the fact that ?2 indices are dif?cult to interpret for structural equation models with relatively small samples (Fornell & Larcker, 1981; Kaplan, 1988). The overall best ?t between model and dataset is therefore provided by the original model with the mixed dataset (see Fig. 2). However, the other models provide additional interpretation options. 5. Conclusions and implications 5.1. Theoretical conclusions and limitations This study examined the extent to which purchasing and marketing managers collaborate in order to achieve superior business performance. We used data from 148 paired respondents from Russian industrial companies so as to overcome the problems commonly associated with
common methods bias. As hypothesized, our results provide evidence for the role that both antecedents play in determining marketing– purchasing collaboration, as well as for the mediating effect that customer orientation has between marketing–purchasing interaction and marketing–purchasing collaboration. Furthermore, our empirical results demonstrate that marketing–purchasing collaboration has a signi?cant and positive effect on business performance. This ?nding supports recent suggestions that there exist dual relationships between how active and in?uential a marketing department is, and the development of a customer orientation (Verhoef & Lee?ang, 2009). Our results are in line with ?ndings by Han et al. (1998) which show that having a customer orientation positively impacts both directly and indirectly on performance, with the indirect (mediated) path being the dominant one. However, this effect was not robust in our study as the extended model did not corroborate this positive relationship. This may indicate that some of the hypothesized relationships which are based on a
a
.202 (0.056)
Customer orientation (P) 18.1%
.425 (0.000)
.288 (0.003)
Marketing Purchasing interaction (P)
.288 (0.008)
Marketing Purchasing collaboration (M) 21.4%
.189 (0.066)
Business performance (M) 8.5%
-.106 (0.319) Note : (P) indicates construct being measured via purchasing respondents, (M) indicates construct being measured via marketing respondents. Non-significant path estimates are indicated by dotted lines
b
Customer orientation (dyadic) 25.3%
.503 (0.000) Marketing Purchasing interaction (dyadic) .363 (0.000)
.336 (0.003)
.397 (0.000) Marketing Purchasing collaboration (dyadic) 43.4% -.064 (0.568)
.162 (0.161)
Business performance (dyadic) 17.3%
Note : (dyadic) indicates construct being measured as the average values of the scores from purchasing and marketing respondents
Fig. 4. a. Path estimation enriched model (mixed dataset). b.Path estimation enriched model (dyadic dataset).
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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nomological model that was developed from research mostly pertaining to developed/Western economies do not yet adequately describe the transitional state of the Russian economy but refer to an ‘end-state’ of development of market-based exchanges in an economy. As such, this quali?es Slater and Narver's (1994) ?ndings that the business environment does not affect the link between customer orientation and business performance. Tests with different datasets show the robustness of these results but also indicate questions for further research. Direct relationships between customer orientation and marketing–purchasing interaction on business performance were not generally supported, thus underlining the importance of marketing–purchasing collaboration as a mediating construct in achieving business performance. However, some of the alternative models provide an indication that there exists a direct link between customer orientation and ?rm performance (the path estimates on several data models were only just not signi?cant) which complements the mediated effect that customer orientation has via fostering more interfunctional alignment. These ambivalent results suggest that additional research needs to be undertaken in order to understand the development of these direct vis-à-vis indirect (mediated) relationships of customer orientation to company performance, e.g. by a longitudinal study which takes into account the changing transitional characteristics of the Russian economy. Furthermore, intra-industry datasets may ?nd different relationships depending on the developmental stage of these industries in adapting to market-based exchanges. This would require larger datasets to be gathered, where the effects of control variables such as industry, company size, or the length of time that a company has been in business can be assessed. Our analyses also show that different datasets using multiple respondents can be used to cross-validate the testing of the nomological model for robustness. These tests outline some limitations of our research, namely, the fact that although a dyadic dataset improves the explained variance within the overall model, the ?t indices, especially ?2, become problematic. Furthermore, we use an average score method to derive dyadic data. Further research is necessary which also uses the ?t and directionality information inherent in dyadic data (Deshpandé et al., 1993; Van Bruggen et al., 2002). 5.2. Managerial implications An important managerial implication of our work is that we have demonstrated that in order to achieve improved business performance, companies need to improve both their understanding of their business partners (especially their counterparts' buyers) through developing a customer orientation, and the internal relationships between purchasing and marketing. These internal and external facing capabilities are considered to be sources of particular risk in more recently developing economies such as Russia, where under earlier conditions of being a more centrally planned economy, such competencies were not required. We used a cross-industry dataset from Russia to understand these issues with regard to whether or not they impact on overall business performance. The study has identi?ed both interaction between the marketing and purchasing functions and also customer orientation as being antecedents of the level of collaboration between the same two functions, which in turn in?uences business performance. The empirical results of this study provide strong support for the hypotheses that have been developed. If managers operating in a Russian environment have the objective of improving their business performance – as they clearly do, given the freedom that they now have to do so – then they do need to focus on internal collaboration between their marketing and purchasing departments, given the linkages between the two. As customer-facing resources (information, access, etc.) are usually controlled by the marketing department, this makes it necessary for the marketing department to be proactive in fostering such interfunctional alignment. Moreover, companies need to focus on improving both customer orientation and the
interactions between the marketing and purchasing departments, given the direct effect that these have on collaboration, plus the indirect effect of interaction via customer orientation. Our original thinking in tackling this research project was that Russian management practices, in some sense, lagged behind those in more mature economies, given the fact that their transition to freer managerial practices have only relatively recently taken place. What we ?nd interesting is that the robustness of our results provide only weak this thinking: the results that we report in this paper have tended to re?ect what is found in traditional western economies, albeit, less clearly developed. As in the west, there is evidence to support the mediating role that marketing–purchasing collaboration plays in achieving superior performance. Unlike the west, there seems no direct link between customer orientation and superior business performance. This indicates to us that Russian managers can and indeed should consider traditional business practices used by Western companies and employ them in Russia's transitional economy. However, the fact that no direct link between customer orientation and ?rm performance was found was unexpected. As this relationship is linked to an underlying concept of marketing, and also linked to the concept of market orientation, further studies need to explore this counter-intuitive ?nding. Appendix A
Means, standard deviations and correlations of the constructs (mixed dataset). Constructs 1.Customer orientation 2.Marketing – purchasing interaction 3.Marketing – purchasing collaboration 4.Business performance Mean Standard deviation 4.55 3.73 4.03 0.67 0.59 1.16 0.89 0.78 1 2 3 4
1 0.425** 1 0.399** 0.382** 1 0.233** 0.052** 0.229** 1
**p b 0.01. Note: all constructs are scored on a scale of 1 to 5, except for business performance which is scored on a scale of ? 2 to + 2.
Means, standard deviations and correlations of the constructs (dyadic dataset). Constructs 1.Customer orientation 2.Marketing – purchasing interaction 3.Marketing – purchasing collaboration 4.Business performance Mean Standard deviation 4.51 3.58 4.11 0.72 0.57 0.99 0.77 0.69 1 2 3 4
1 0.503** 1 0.580** 0.562** 1 0.398** 0.196** 0.321** 1
**p b 0.01. Note: all constructs are scored on a scale of 1 to 5, except for business performance which is scored on a scale of ? 2 to + 2.
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Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
doc_823043263.pdf
Collaboration is working with each other to do a task. It is a recursive process where two or more people or organizations work together to realize shared goals, (this is more than the intersection of common goals seen in co-operative ventures, but a deep, collective, determination to reach an identical objective[by whom?][original research?]) — for example, an endeavor
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Contents lists available at ScienceDirect
Industrial Marketing Management
Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia
Maria Smirnova a,?, Stephan C. Henneberg b, Bahar Ashnai b, Peter Naudé b, Stefanos Mouzas c
a b c
Graduate School of Management, St Petersburg State University Volkhovsky pereulok 3, 199004, Saint Petersburg, Russia Manchester IMP Research Group, Manchester Business School, United Kingdom Department of Marketing, Lancaster University Management School, United Kingdom
a r t i c l e
i n f o
a b s t r a c t
This study aims to investigate the role of interfunctional collaboration between marketing and purchasing functions in industrial companies. Interfunctional collaboration is considered as a measure of the internal alignment and partnership between departments in the ?rm, which in turn contributes to the creation of sustainable advantages via improved external partnerships and facilitating demand chain integration. We test the impact of customer orientation as well as the interactions between departments (speci?cally marketing and purchasing) as collaboration antecedents, and analyze the direct impact of marketing–purchasing collaboration on business performance. The model is tested on a sample of 148 industrial companies in Russia with two key respondents in each ?rm, incorporating the purchasing as well as the marketing perspective. The results show that marketing–purchasing collaboration mediates the effects of interfunctional interaction as well as customer orientation on business performance. Alternative model testing shows that the direct effects of these antecedent constructs on performance are non-signi?cant in the context of Russian industrial companies. © 2010 Elsevier Inc. All rights reserved.
Article history: Received 14 September 2009 Received in revised form 5 March 2010 Accepted 15 April 2010 Available online xxxx Keywords: Interfunctional collaboration Marketing Purchasing Customer orientation Russia
1. Introduction In the current business environment characterized by increasing competitive pressures, externally oriented functions of the ?rm face serious challenges, especially by striking a sustainable balance between the aims of companies and their business partners (Achrol, 1997; Achrol & Kotler, 1999; Ford, Gadde, Håkansson, & Snehota, 2003). Customers become increasingly demanding, forcing companies to increase their ability to interact collaboratively. This trend recognizes the importance of sustaining the performance of both a focal company and its network partners (Clarke, 2006; Wind, 2008). Thus, multifaceted goals put emphasis on achieving both operational ef?ciency within the ?rm as well as market effectiveness through the optimal combination of resources and knowledge of the ?rm and its network partners (Cox, 2004; Mouzas, 2006). Such thinking is in line with arguments from the resource dependence theory (Casciaro & Piskorski, 2005a,b; Pfeffer & Salancik, 1978) and the literature on business networks (Anderson, Håkansson, & Johanson, 1994; Matthyssens, Vandenbempt, & Weyns, 2009; Möller & Halinen, 1999). Achievement of immediate, operational results within a ?rm may lead to both a dramatic decrease in the ?rm's ability to adjust to future challenges (Ford & McDowell, 1999; Ritter,
? Corresponding author. Tel.: +7 812 323 84 48; fax: +7 812 329 32 34. E-mail addresses: [email protected] (M. Smirnova), [email protected] (S.C. Henneberg), [email protected] (B. Ashnai), [email protected] (P. Naudé), [email protected] (S. Mouzas). 0019-8501/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2010.09.010
1999; Ritter & Gemünden, 2003), and a reduction in its ability to retain competitive advantages via successfully mobilizing other business partners (Ehret, 2004; Eng, 2006; Mouzas, 2006; Mouzas & Naudé, 2007). Achieving a balance between focal company goals and those of interaction partners in complex business networks has previously been linked to developing interfunctional collaboration within a ?rm, i.e. creating internal cooperative approaches and internal strategic partnerships (Campbell, 1998; Piercy, 2009). Existing research highlights the demand for improvement of interdepartmental collaboration: “the logic is that strategic external relationships (with customers, supplier and partners) should be mirrored in strategic internal relationships…” (Piercy, 2009, p.857). More specifically, a shifting focus on interfunctional collaboration can be perceived: from investigating collaborations between ‘neighbouring’ functions, e.g. marketing and sales, or purchasing and operations, to collaborations between boundary spanning functions (Piercy, 2009). Such functions are responsible for the coordination of external relationships, speci?cally customer relationships and supplier relationships (Ivens, Pardo, & Tunisini, 2009). However, while research has suggested the importance of the integration of boundary spanning functions such as purchasing and marketing (Sheth, Sharma, & Gopalkrishnan, 2009), there is not enough empirical evidence regarding this issue and its interplay with ?rm performance. Our study uses this research challenge as its starting point by focusing on marketing–purchasing collaboration. Marketing–purchasing collaboration represents a speci?c case of interfunctional collaboration which has been shown to be an important
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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component of the behavioral aspect of both market orientation (Auh & Menguc, 2005; Narver & Slater, 1990) and demand chain integration (Jüttner, Christopher, & Baker, 2007; Lambert & Cooper, 2000; Sheth, Sisodia, & Sharan, 2000). The challenge is therefore for companies to instigate marketing–purchasing collaboration: to develop internal con?gurations of different functions which will help them to identify, maintain and develop external business relationships with crucial partners in the business network (Jüttner et al., 2007; Kahn & Mentzer, 1998). Improving external business relationships requires several distinctive antecedents with regard to the marketing–purchasing collaboration within a ?rm. Firstly, in line with requirements identi?ed as part of the concept of market orientation, the company needs to develop a deep understanding of its business partners, especially buying companies (Da Silva, Davies, & Naudé, 2002; Danneels, 2003; Hsieh, Chiu, & Hsu, 2008; Kohli & Jaworski, 1990; Narver & Slater, 1990). Such customer orientation is linked to the essence of the marketing function (Awuah, 2008; Brady & Cronin, 2001; Deshpandé, Farley, & Webster, 1993) and is also in line with the demand chain integration concept (Jüttner, Gosell, & Christopher, 2006). In order to best serve customers in a mutually bene?cial way, a company needs to use and transform certain resources (Alderson, 1965; Alderson & Martin, 1965; Prenkert & Hallen, 2006). However, the company does not possess all of the resources and competences itself and is thus dependent on its supply network (Paulraj & Chen, 2007; Pfeffer & Salancik, 1978). Consequently, the second antecedent important for aligning external business relationships lies in the interactions of internal boundary-spanning functions (Eng, 2006; Henneberg, Mouzas, & Naudé, 2009; Piercy, 2009). Thus, the company needs to develop interfunctional interactions which, together with customer orientation, provide the collaboration between marketing and purchasing which is hypothesized to be bene?cial for ?rm performance. Besides our contribution of clarifying the antecedents and consequences of interfunctional marketing–purchasing collaboration, we speci?cally focus on the setting of Russian ?rms. The importance of internal marketing–purchasing collaboration represents a particular challenge for transitional economies, such as Russia, as ?rms have not had suf?cient time to develop either the competences to relate to multiple business partners in complex networks, nor the capabilities to foster interaction and collaboration between externally facing functions such as purchasing and marketing (Peng & Luo, 2000; Piercy, 2009). The levels of complexity and dynamism of the new economic environments in transitional countries are in fact characterized by challenges to established competition rules, resulting in “collapsing capabilities” (Atuahene-Gima, Li, & De Luca, 2006, p. 360). In case of Russia, after the collapse of the planned economy and the dissolution of existing economic ties between companies and whole value-creating systems, ?rms had to adapt to the newly formed business environment, while developing new internal capabilities at the same time. Lorentz and Ghauri (2010) observe that “despite the recent positive development in Russian market, the heritage of the centralized planning oriented command economy is still evident” (p. 243). Numerous studies have focused on the challenges of Russian business culture and networking practices, which often provide examples of inef?cient and opportunistic interactions (Jansson, Johanson, & Ramström, 2007; Kouchtch & Afanasiev, 2001; Menkhaus, Yakunina, & Herz, 2004; Salmi, 1996). Overcoming such obstacles, as Johanson (2007) states, required signi?cant time and resource investments, and was based on the development of decentralized and mutual planning capabilities by individual ?rms. In fact, “interpersonal networks are important in uncertain and unstable economic environments, as interpersonal trust mitigates risk and reduces the in?uence of turbulent macro-environmental changes” (Butler & Purchase, 2008, p. 531). Our research is embedded in these discussions and is aimed at providing insights via an empirical study of Russian industrial ?rms, identifying antecedents and performance outcomes of marketing– purchasing collaboration. In addition to informing current managerial practice in transitional economies, speci?cally Russia, and thus
providing insights into the functioning of often overlooked business networks outside Western countries (e.g. Anderson et al., 1994; Håkansson & Ford, 2002; Ritter, Wilkinson, & Johnston, 2004, for an exception using non-Western data see Chan, 2000; Peng & Luo, 2000), our research contributes to addressing some important research gaps, namely clarifying the role of interfunctional collaboration of marketing and purchasing as crucial example of boundary spanning functions. We thus contribute to the stock of knowledge on demand chain integration and business relationships in networks (Jüttner et al., 2007; Lambert & Cooper, 2000; Sheth et al., 2000). The paper is structured as follows: we ?rst present the theoretical basis of the study and key concepts to be considered, and then propose a nomological model describing the key research assumptions as well as the underlying hypotheses. We present the results from a dyadic dataset of 148 Russian companies and, using structural equation modeling, propose key implications for practitioners. Finally, we identify further areas for potential research. 2. Literature review and hypotheses development 2.1. The nature and consequences of interfunctional collaboration The role of collaboration between different ?rm functions is widely discussed in the research literature, e.g. as “an affective and volitional process where departments work together with mutual understanding, common vision, and shared resources to achieve collective goals” (Kahn & Mentzer, 1998, p. 55). Effective interfunctional collaboration has become an important strategic emphasis of successful ?rms (Morgan & Piercy, 1998). Such collaboration is to a large extent based on aligning organizational aims, values and priorities between functions, especially regarding working with external partners. This alignment allows for the creation of synergetic effects among departments, leveraging available resources and knowledge (Ellinger, 2000; Piercy, 2009). Development of internal collaboration is often in?uenced by increasing pressure from external partners, especially customers, and thus “involving sales and marketing personnel in supply strategy and alliance management also offers potential for more effective joint working on shared problems and new opportunities” (Piercy, 2009, p. 862). Internal collaboration is arguably built into the system of external collaboration within up- and down-stream channels (Ellinger, 2000; Kahn & Mentzer, 1998). The nature of internal collaboration is very close to the concept of integration of functions (Ellinger, 2000) and is based on developing trust, mutual respect, communication and information sharing, as well as shared responsibility in decision making and outcomes (Grif?n & Hauser, 1996; Kahn & Mentzer, 1996). Integration can be de?ned as the process of achieving unity of effort among the various organizational subsystems in the accomplishment of the company's task (Lawrence & Lorsch, 1967). Previous research suggests that especially internal norms, for example exempli?ed in interfunctional collaboration, will have a direct effect on the norms in interacting with external partners — thus a high level of interdepartmental openness and readiness to share information will have a positive effect on cooperating with external partners (Campbell, 1998; Ellinger, 2000). Kahn and Mentzer (1998) discuss three existing approaches to de?ning integration among the functions: an interaction-based approach in which communication between the departments, meetings and information ?ow is forcing successful integration; collaboration-based integration, in which teamwork, shared resources and goals are the main driving force; and composite integration, implying that both interaction and collaboration are two main elements of interdepartmental integration. This multi-construct approach is also supported by Biemans, Bren?i?, and Malshe (2010). Arguably the most often used conceptualization of internal collaboration relates to interfunctional coordination as a subconstruct of market orientation (Jaworski & Kohli, 1993; Kohli & Jaworski,
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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1990), using a behavioral operationalization (Narver & Slater, 1990). It refers to the degree to which the functions and departments within the ?rm communicate with each other and work cooperatively (Kohli & Jaworski, 1990). The danger of low internal alignment has been addressed by Atuahene-Gima et al. (2006), who stress the phenomenon of ‘internal stickiness’, which occurs when accumulated market knowledge at the ?rm level stays isolated within one department. The elements that lead to interfunctional collaboration can become a source of competitive advantage (Blomqvist & Levy, 2006; Eng, 2006; Kent, 1996). Menguc and Auh (2005) argue that such collaboration develops social capital within the company which in itself enables better access to, and use of, resources (Nahapiet & Ghoshal, 1998; Tsai & Ghoshal, 1998). Reagans and Zuckerman (2001) postulate the same effect of interfunctional collaboration but see diversity as the main driver. While interfunctional collaboration has been discussed extensively in the literature, one of the shortcomings of existing empirical research is that it is mostly based on single informants, capturing only one speci?c functional perspective. Internal dyadic or triadic studies are rare. Furthermore, existing research mostly focuses on collaborations between neighboring functions, e.g. between marketing and other customer- or internal resource-related departments, such as the interaction of marketing with sales (Gosselin & Bauwen, 2006), manufacturing (Alegre & Chiva, 2004), sales, communication and product development (Möller & Rajala, 1999), administration, production and R&D (Ford & Saren, 2001; Kahn & Mentzer, 1998; Souder & Moenaert, 2007), human resource management (Chimhanzi, 2004), or ?nance (Srivastava, Shervani, & Fahey, 1998). However, while the importance of interactions between ‘boundary-spanning functions’ such as purchasing on the one hand, and marketing on the other, has been advocated (Piercy, 2009), there does not exist enough empirical research analyzing the speci?c interplay between these two crucial departments. Most research focuses on inter-organizational purchasing and marketing interactions, such as in the context of Just-in-Time business relationships (Gunesekaran, 1999; O'Neal, 1989), neglecting the intra-organizational collaboration issues. Another shortcoming of existing studies is that they have used mainly Western countries for their investigation. The issue of interfunctional alignment is arguably pivotal for ?rms in transitional economies. These economies, given their relatively short history of being a marketdriven, are typi?ed by lack of information about potential partners and low information disclosure readiness, resulting in both higher instability in business relationships and readiness to adopt opportunistic behavior (Håkansson & Ford, 2002; Ford & Håkansson, 2006; Johanson, 2007). These factors imply that there exists more risk in managing external interactions and relationships than in established market economies. Thus, internal collaboration among functions becomes important due to the need to compensate internally for the instability of external relationship interactions (Powell, 1992). 2.2. Consequences of marketing–purchasing collaboration Given that interfunctional collaboration is one of the major behavioral characteristics of a ?rm enabling sustainable competitive advantage (Narver & Slater, 1990), and that marketing and purchasing are two major functions in ?rms engaged in business-to-business activities (Ford, 2002), it is important to understand how marketing and purchasing collaborate and learn from each other to enhance effectiveness and goal achievement (Jüttner et al., 2007; Williams, Giunipero, & Henthorne, 2006). None of the existing studies have examined the antecedents and consequences of interfunctional collaboration between the marketing and the purchasing functions as the two main organizational activity areas which link the ?rm to outside stakeholders (customers and suppliers, respectively). Marketing and purchasing interactions have so far only been discussed from a supply chain perspective (Paulraj & Chen, 2007; Trkman, Štemberger, Jaklic, & Groznik, 2007). However, several marketing studies have pointed to the cross-functional nature of
marketing capabilities of the ?rm with the focus on supply chain coordination (Day, 1994; Jüttner et al., 2007; Sheth et al., 2000). Therefore, marketing and purchasing collaboration becomes the focal construct of our study in the context of the Russian economy. This construct is based on the concept of demand chain integration which argues for linking both the supply chain management and marketing perspectives, taking the customer as a starting point of the analysis (Jüttner et al., 2007). This marketing–purchasing collaboration can therefore be conceptualized as a part of the ?rm's strategic market sensing capabilities (Day, 1994; Foley & Fahy, 2004), i.e. the ability to identify potential opportunities and align the ?rm's activities with capabilities and resources of upstream (supplier) and downstream (customer) partners in the market. Such activities have been argued to provide a higher probability of value innovation, contributing to the creation of sustainable advantages for the ?rm (Day & Van den Bulte, 2002; Teece, Pisano, & Shuen, 1997). This is in line with ?ndings that effective ?rms achieve higher interfunctional collaboration among different organizational activity areas (Lawrence & Lorsch, 1986). Therefore, it can be posited that: Hypothesis 1. Increased marketing – purchasing collaboration impacts positively on business performance. 2.3. Antecedents of marketing–purchasing collaboration While interaction has frequently been considered in the literature in terms of activities between business partners (Håkansson, 1982), a similar perspective can be applied to interdepartmental issues (Lim & Reid, 1992; Ruekert & Walker, 1987). Interaction has been argued to be part of interdepartmental collaboration, e.g. Kahn and Mentzer (1998) de?ne it as information exchange processes between departments. Ruekert and Walker (1987) posit communication interactions as a driver of interfunctional collaboration. Similarly, Morgan and Piercy (2009) suggest that collaboration between departments depends on the level of communication and interaction. As Kahn and Mentzer (1998) argue, interaction is often an important component of interdepartmental collaboration. However, they warn that it is not a suf?cient, as it alone will not ensure successful interdepartmental relationships. Nevertheless, an interaction view emphasizes communication (e.g. meetings or information ?ows) between departments (Grif?n & Hauser, 1992; Ruekert & Walker, 1987). In fact, much of the marketing literature highlights that effective interfunctional collaboration is predicated on interaction, and thus, prescribes marketing's increased contact with other departments (Grif?n & Hauser, 1992; Moenaert, Souder, DeMeyer, & Deschoolmeester, 1994; Urban & Hauser, 1993). Other sources argue that the role of interaction is a composite of existing communication, connectedness and con?ict (Menon, Jaworski, & Kohli, 1997). The positive relationship between interfunctional interaction and collaboration will therefore exist in cases of aligned aims and interests between the departments, which means in turn an absence of interdepartmental con?ict (Morgan & Piercy, 1998). Empirical studies regarding the effect of aspects of interfunctional interaction on collaboration have produced mixed results. Rodriguez, Perez, and Gutierrez (2005) as well as Meunier-FitzHugh and Piercy (2006) consider interfunctional communication as an antecedent of collaboration, as well as of performance (Biemans et al., 2010; Hitt, Hoskisson, & Nixon, 1993; Kahn, Reizenstein, & Rentz, 2004). Empirically these studies were able to support the impact of interactions on collaboration, and of collaboration on performance, but not the effect of interactions on performance (Kahn & Mentzer, 1998). However, for marketing and R&D collaboration within a ?rm, Song, Neeley, and Zhao (1996) show that interaction positively impacts both information sharing and communication, which are then in turn positively associated with collaboration.
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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Interfunctional collaboration in our study relates to the marketing and purchasing function. Some existing research provides evidence that interaction is an important antecedent for collaboration involving the marketing function: “Careful management of marketing's interaction with other departments…can help ?rms become adaptive, effective and ef?cient” (Cadogan, Sundqvist, Salminen, & Puumalainen, 2005, p. 520). We follow the above argument and model interfunctional interaction (between marketing and purchasing) as being an antecedent of interfunctional collaboration, i.e. collaboration mediates the effect of interactions on ?rm performance, with interfunctional interaction itself not having a direct impact on performance. Higher interaction is therefore expected to foster further collaboration but does not necessarily have a direct impact on ?rm success: Hypothesis 2. Marketing–purchasing interaction impacts positively on marketing–purchasing collaboration. Marketing and purchasing have different perspectives on business relationships. This may hinder key aspects of interfunctional collaboration, for example the sharing and exchange of knowledge and ideas (Day, 1994; Nonaka, 1994; Nonaka, Umemoto, & Senoo, 1996). Marketing–purchasing collaboration can be an area of con?ict due to differences in motivation and interests (Humphreys, Williams, & Goebel, 2009), which has been explained via mutual resource dependence (Goebel, Marshall, & Locander, 2003) or overall organizational con?ict (Barclay, 1991). For the purpose of our study, we posit the central role of ‘market-sensing’ in creating interdepartmental alignment of aims and facilitating collaboration between marketing and purchasing. This is due to the fact that the extant literature discusses the particular role of understanding markets (something which is particularly done by marketing) in aligning departments and functions within the ?rm (Day, 1994; Jaworski & Kohli, 1993; Kohli & Jaworski, 1990). For example, Kahn and Mentzer (1998) argue that the integration of departments is to a large extent the responsibility of customer-facing units. It is the marketing department that is responsible for creating interfunctional collaboration, and enabling the rest of the company to become customer oriented. The collaboration among the departments will be based on the goals that are received and aligned among the departments (Kahn & Mentzer, 1998). The aims set at the strategic level of the ?rm will have direct impact on the level of involvement of departments in the collaboration process (Morgan & Piercy, 1998). Thus, marketing needs to disseminate market information and customer-related knowledge among the departments (Day, 1994; Kohli & Jaworski, 1990; Nonaka et al., 1996). Narver and Slater (1990) point to the fact that every department and function can create higher value for customers. Indeed, the contribution of other functions should be incorporated into the ?rm's marketing strategy (Narver & Slater, 1990; Wind & Robertson, 1983). It is therefore important for both marketing and purchasing to understand customer demands in order to create a basis for superior value creation. Thus, investigating interfunctional collaboration between marketing and purchasing departments relates speci?cally to the strategic balancing of departmental interests around the issue of customer orientation (Williams et al., 2006). Again this follows the logic of the
demand chain integration concept (Heikkila, 2002; Jüttner et al., 2007) which suggests that the customer should be the starting point of any analysis, i.e. proposing a marketing and customer-oriented approach to interactions regarding supply chain management (Baker, 2003). We therefore hypothesize that: Hypothesis 3. Customer orientation impacts positively on marketing–purchasing collaboration. Customer orientation, while mostly associated with knowledge residing in the marketing department, needs to be distributed within the whole company to impact on collaboration and business performance. However, taking the purchasing department as an example, this function typically has suboptimal resources to achieve knowledge about the target customers as well as the wider demand network. To achieve such customer knowledge, the purchasing department needs to have interactions with the owner of such knowledge, i.e. the marketing department. Thus, it can be posited that: Hypothesis 4. Marketing–purchasing interaction impacts positively on customer orientation. Based on these four hypotheses which link the four main constructs, the overall nomological model is introduced in Fig. 1. The focal concept of marketing–purchasing collaboration is modeled as a mediator between the antecedent constructs of customer orientation on the one hand, and marketing–purchasing interaction on the other. Interfunctional collaboration between the marketing and purchasing departments in turn is related to the company's business performance. 3. Research design 3.1. Data collection The empirical dataset to test our nomological model relates to Russian companies across different industries. To pre-test the constructs as well as existing measurement scales, several qualitative studies were undertaken. A speci?c aim of these studies was to understand whether the focal concepts which had been developed from the research literature could be used in the context of the transitional Russian economy, as these are mainly based on data from mature economies. For this purpose, an initial open round-table discussion was organized with thirty-seven CEOs, marketing and purchasing directors to discuss the issue of business relationships and interfunctional collaborations, including customer and supplier interactions in a business-to-business context. During these discussions, the managers indicated that they perceived customer knowledge dissemination throughout the ?rm to be one of the key sources of market development and a driver of innovation in Russia. Furthermore, this discussion also identi?ed the potential issue of misalignment between marketing and purchasing departments. Having established that the core constructs were understood and meaningful in a Russian context, some further interviews (as with all other empirical phases, these interactions were done in Russian) were
Customer orientation
H3
Marketing Purchasing collaboration
H1
Business performance
H4
Marketing Purchasing interaction
H2
Fig. 1. Nomological model.
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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conducted with forty top managers (these were selected from the same sample frame that was later used for the quantitative phase of the research; after the interviews, these ?rms did not participate in the quantitative stage). These interviews tested the construct descriptions as well as an initial draft of the questionnaire with measurement models for each construct. As a result, some question wordings were slightly changed, and the ?nal questionnaire for the quantitative study was prepared. Our sample was based on a list of industrial companies (from the SKRIN database, comprising a variety of sectors), covering the whole of Russia. We randomly contacted 502 ?rms. The respondents were mostly top management, speci?cally heads of marketing or purchasing. Two key respondents per company were used, one covering the marketing perspective and the other the supply perspective of the company. A total of 186 companies agreed to participate (response rate of 37.1%). The data was collected by face-to-face interactions with the relevant respondents; research assistants were used for conducting the interviews. This method was used as mailed questionnaires typically do not yield suf?cient response rates in the Russian business environment. The questionnaire for both key respondents contained the same questions. We thus always attempted to collect two different internal perspectives on the same constructs, controlling for two diametrically opposed outside-facing functions in the ?rm, as well as minimizing the potential for common methods bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). A total of 38 ?rms identi?ed the same person (usually the CEO) as being the most competent respondent covering supply as well as marketing perspectives in the ?rm, thus providing only single respondent data sets. These ?rms were excluded from further analyses in order not to violate the common method bias assumption. Thus, the ?nal sample consisted of 148 ?rms providing a dyadic data set (a response rate of 29.5%) with a total of 296 respondents. The respondents were also asked to indicate how knowledgeable they perceived themselves to be in addressing the questions in our survey. The level of perceived respondent knowledgeability (on a 5-point Likert scale) averaged 4.7, indicating the respondents had relevant knowledge for our study. Tables 1a and 1b provide some socio-demographic information on the respondents for both the purchasing and marketing sides. 3.2. Construct measures The measurement models for each construct were based on 5point Likert scales (virtually all were anchored in “strongly agree” and “strongly disagree”). The item pool for each measurement model was taken from existing research. We used translation/back-translation to ensure that the question wording in Russian was equivalent to that of the source publication. The ?nal questionnaire consisted of 19 items for the four constructs. All measurement models use re?ective scales (Diamantopoulos & Siguaw, 2006; Diamantopoulos & Winklhofer, 2001; MacKenzie, Podsakoff, & Jarvis, 2005). Scale properties were evaluated using traditional psychometric approaches. Reliability and unidimensionality of the scales was tested, and item-to-total correla-
Table 1b Sample description companies. Key industries (%) Machinery Production of construction materials Food industry Appliance machinery Chemical industry Light industry Woodworking industry Packaging Other Annual sales 2006 (m USD) Less than 1.5 1.5–3 3–15 More than 15 Number of employees Less than 50 From 50 to 100 From 100 to 500 From 500 to 1000 More than 1000 Note: Percentages may not add up to 100 due to rounding.
18.1 13.9 13.9 11.1 9.7 7.6 6.9 5.6 13.1 12.2 30.1 28.5 29.2 8.8 12.2 38.1 17.9 23.0
Table 1a Sample description respondents. Marketing side Male respondents (%) Female respondents (%) Average age (years) Average duration of employment in this ?rm (years) Average duration of employment on the position (years) CEO and board level (%) Functional director level (%) 62.2 37.8 41.7 10.6 5.5 61.3 38.7 Supply side 69.0 30.9 42.3 10.3 5.6 77.0 23.0
tions were assessed for all the scales (Anderson & Gerbing, 1988; Fornell & Larcker, 1981). Following an exploratory factor analysis (EFA), four items were dropped due to cross-loadings on other constructs, leaving 15 items used in the path estimations. Table 2a shows the ?nal item loadings. For these 15 items the CFA using AMOS 7.0 to cross-validate the factor structure (Mishra, Heide, & Cort, 1998) shows good model ?t (Jöreskog & Sörbom, 1988; Kline, 2005) with ?2 = 1.107 (p = 0.237), RMR = 0.042, GFI = 0.926, CFI = 0.993, RMSEA = 0.027 (p = 0.906). The construct of customer orientation was measured using four items from the original Narver and Slater (1990) scale. Marketing–purchasing interaction was measured using four existing items from Kahn and Mentzer (1998). The same article also provided four items for marketing– purchasing collaboration. Finally, we measure business performance as the ?rm's pro?tability, relative market share, and sales growth in comparison to competitors. The respondents were asked to compare the performance of their ?rm over the last three years vis-à-vis their main competitors (Vorhies & Harker, 2000). The items for this construct were measured on a ?ve-point Likert-type scale anchored in ‘much worse’ and ‘much better’. To avoid common method bias, we used dyadic data in the model test, i.e. we obtained predictor (independent) and criterion (dependent) construct scores from different respondents (Podsakoff et al., 2003). We used the purchasing side respondents for the construct of customer orientation and marketing–purchasing interaction, and marketing side respondents for the constructs of marketing–purchasing collaboration and business performance. The rationale for this lies in the fact that the marketing department is the one responsible for creation of interfunctional alignment in the ?rm (Kahn & Mentzer, 1998; Verhoef & Lee?ang, 2009). Therefore the assessment of the ?rm's customer orientation and of the marketing–purchasing interaction by the purchasing side respondents is capturing the effects of the activities created by the marketing department (and not the assumed or intended effectiveness of these activities). At the same time, interfunctional collaboration between marketing and purchasing, and business performance, relate to marketing-perceived measures; thus, the dependent constructs in the nomological model are assessed by the responsible function (i.e. marketing), while its antecedents are measured by purchasing. This dataset will be referred to as the initial or ‘mixed dataset’. Furthermore, we also test this dataset against a dyadic dataset, using average scores for all items based on the average values from the answers provided by both the marketing and the purchasing respondent for the same item (Van Bruggen, Lilien, & Kacker, 2002). This dataset will be called the ‘dyadic dataset’ later.
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
6 Table 2a EFA results (mixed dataset, original model). Item Component 1 M–P Collaboration 6 (M) M–P Collaboration 2 (M) M–P Collaboration 4 (M) M–P Collaboration 1 (M) Customer Orientation 4 (P) Customer Orientation 2 (P) Customer Orientation 3 (P) Customer Orientation 1 (P) M–P Interation 3 (P) M–P Interation 1 (P) M–P Interation 2 (P) M–P Interation 4 (P) Business Performance 2 (M) Business Performance 1 (M) Business Performance 3 (M) % of variance 0.870 0.870 0.863 0.845 2
M. Smirnova et al. / Industrial Marketing Management xxx (2010) xxx–xxx Table 3 AVE and squared construct correlation matrix (initial dataset). Construct 3 4 1. 2. 3. 4. Customer orientation Marketing–purchasing interaction Marketing–purchasing collaboration Business performance 1 0.66 0.18 0.16 0.05 2 0.57 0.15 0.00 3 4
0.69 0.05
0.71
0.856 0.848 0.798 0.753 0.812 0.780 0.766 0.646 0.892 0.882 0.843 15.9
21.3
19.5
16.8
Note: Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization, rotation converged in 5 iterations. (M) indicates answers from marketing respondents, (P) from purchasing respondents.
As Table 2b shows, all standardized factor loadings for the used items are above 0.7 (Nunnally & Bernstein, 1994) except in the case of one item for marketing–purchasing interactions. In all cases the average variance extracted (AVE) exceeds the critical level of 0.5 (Fornell & Larcker, 1981), and the composite reliabilities (CR) are higher than 0.85. Discriminant validity has been con?rmed applying the Fornell and Larcker (1981) criterion (Table 3). 4. Hypotheses testing 4.1. Initial nomological model test To test the hypotheses in our model, we used covariance-based structural equation modeling (SEM) with AMOS 7.0, after we tested the dataset for normal distribution. For our initial model tests we used the
mixed purchasing–marketing dataset (see Appendix A for construct means, standard deviations, and construct correlations). Using the Maximum Likelihood (ML) indicator, the model shows a good ?t with the data (Bollen, 1990; Hu & Bentler, 1995). All the structural paths were supported at signi?cant con?dence levels. No additional paths were proposed by the SEM modi?cation indices. The overall ?t measures and signi?cance levels are: ?2 =1.125 (p =0.202), with the non-signi?cant ?t indicating that the model's covariance structure is not signi?cantly different from the observed covariance matrix, RMR=0.047, GFI=0.923, CFI=0.991, RMSEA=0.029 (p =0.890). As Fig. 2 shows, both antecedent constructs have highly signi?cant and positive path coef?cients in relation to the focal construct of marketing– purchasing collaboration. Thus, Hypotheses 2 and 3 are supported: Customer orientation positively impacts on interdepartmental collaboration with a coef?cient of 0.291 (p b 0.01), as does marketing–purchasing interaction (0.257, p b 0.01). In turn, marketing–purchasing interaction strongly affects customer orientation (0.425, p =0.00), as expected by Hypothesis 4. Marketing–purchasing collaboration has a relatively strong and signi?cant positive effect on ?rm performance (0.233, p =0.01). Hypothesis 1 is therefore also supported. 4.2. Alternative model tests Three additional model tests were conducted to ascertain if the original model could be improved upon. We identify these as the reversed, dyadic, and saturated approaches, and discuss each brie?y in turn. Firstly, in our initial nomological model we used the mixed dataset with antecedent constructs based on respondents from the purchasing perspective, and outcome constructs based on the marketing perspective. For the reversed model, we tested the ?t of a ‘reversed’ dataset, i.e. the antecedent constructs are made up from marketing respondents, while the outcome construct scores are based on purchasing respondents. As such a make-up of the dataset would have no valid conceptual grounding, it would be expected that the reversed dataset ?ts the model worse than the mixed dataset. The ?2 =1.383 (p =0.017) now becomes signi?cant, and the slightly worse ?t indicators support the assumption that the reversed model does not ?t the data well (RMR=0.070, GFI=0.915, CFI=0.976, RMSEA=0.051 (p =0.452)). The second variant on the dataset used utilizes dyadic data. Noting that each item was answered by two respondents from the same company (a purchasing and a marketing manager), the data can be combined to form a dyadic score for each item, representing not the perceptions of a single manager but a more balanced perspective relating to the organization. Several ways to integrate dyadic data exist. For the purpose of this study, we used average scores (Deshpandé, Farley, & Webster, 2000; Van Bruggen et al., 2002). The resulting dyadic dataset provided the item scores shown in Table 4. Cronbach's alpha for the four constructs are improved slightly compared to the mixed dataset, as does the composite reliability. The estimates of the CFA demonstrate good model ?t, with RMR = 0.042, GFI = 0.913, CFI = 0.977, RMSEA = 0.054 (p = 0.364); however, the ?t is not as good as for the mixed data. Table 5 provides the AVEs and squared correlation matrix, indicating in all cases AVEs exceeding the critical level of 0.5, with the squared correlations being lower than the AVEs (Fornell & Larcker, 1981). Again using ML estimation procedures, we test the model using this dyadic dataset (see Fig. 3). All hypotheses are supported, with all pathways showing slightly stronger positive path coef?cients, all at
Table 2b Measurement statistics (mixed dataset). Construct/items Standardized factor loading Cronbach's CR alpha 0.90 0.89
Customer orientation (purchasing) We closely monitor and assess our level 0.83 of commitment in serving customer's needs. Business strategies are driven by the goal 0.91 of increasing customer value. Our competitive advantage is based 0.73 on understanding customer needs. Our business objectives are driven 0.77 by customer satisfaction. Marketing–purchasing interaction (Purchasing) Meetings 0.75 Committees/task forces 0.77 Phone conversations 0.88 Emails 0.60 Marketing–purchasing collaboration (marketing) Achieve goals collectively 0.82 Have a mutual understanding 0.89 Share ideas, information and/or resources 0.77 Work together as a team 0.86 Business performance (marketing) Growth of comparative market share 0.91 Sales growth 0.85 Business pro?tability 0.76
0.82
0.84
0.89
0.90
0.88
0.88
Note: CR = composite reliability; all items are measured on a 5-point Likert scale, anchored in ‘strongly agree’ and ‘strongly disagree’ (except items for business performance which were anchored in ‘much worse’ and ‘much better’).
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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Customer orientation (P) 18.0%
.425 (0.000)
.291 (0.003)
Marketing Purchasing interaction (P)
.257 (0.009)
Marketing Purchasing collaboration (M) 21.4%
.233 (0.010)
Business performance (M) 5.4%
Note : (P) indicates construct being measured via purchasing respondents, (M) indicates construct being measured via marketing respondents
Fig. 2. Path estimation original model (mixed dataset).
p = 0.000 signi?cance levels. Furthermore, the dyadic dataset also provides higher explained variances for the dependent constructs (e.g. for marketing-purchasing collaboration 43.8% for the dyadic dataset, up from 21.4% for the mixed dataset). However, the ?t indices show a slightly worse ?t than for the mixed dataset: RMR= 0.048, GFI = 0.908, CFI = 0.973, RMSEA = 0.058 (p = 0.246). Furthermore, ?2 = 1.501 (p =0.002) is signi?cant, indicating a ?t which can be optimized. Therefore, our mixed dataset arguably ?ts the model better than the dyadic version. Finally, a more saturated, i.e. enriched model was tested by adding two hypotheses linking the antecedent constructs directly to the dependent construct of business performance to qualify the impact of marketing– purchasing collaboration as a mediator construct. Note that this model had again two sub-variants, using ?rst the mixed and then the dyadic datasets. Two hypothetical pathways were tested: The ?rst pathway relates to the assertion by Kahn and Mentzer (1998) that not just interfunctional collaboration but also interfunctional interactions have a direct impact on ?rm performance. The second hypothetical pathway relates to the fact that customer orientation, has been found to have a direct and positive impact on company outcomes (Appiah-Adu & Singh, 1998; Baker & Sinkula, 1999; Han, Kim, & Srivastava, 1998; Slater & Narver, 1994). We
Table 4 Measurement statistics (dyadic dataset). Construct/item Customer orientation (dyadic) We closely monitor and assess our level of commitment in serving customer's needs. Business strategies are driven by the goal of increasing customer value. Our competitive advantage is based on understanding customer needs. Our business objectives are driven by customer satisfaction. Marketing–purchasing interaction (dyadic) Meetings Committees/task forces Phone conversations Emails Marketing–purchasing collaboration (dyadic) Achieve goals collectively Have a mutual understanding Share ideas, information and/or resources Work together as a team Business performance (dyadic) Growth of comparative market share Sales growth Business pro?tability Standardized factor loading 0.86 0.91 0.80 0.81 0.81 0.79 0.85 0.81 0.51 0.92 0.91 0.91 0.79 0.88 0.91 0.90 0.91 0.81 0.91 0.93 0.83 Cronbach's CR alpha 0.92 0.91
therefore also test the direct effect of customer orientation on business performance. These two tentative pathways directly link the antecedent constructs and the dependent construct of business performance, without the mediating in?uence of the construct of marketing–purchasing collaboration. The enriched model therefore looks at the relative impact that the antecedent constructs have in terms of their direct vis-à-vis their mediated impact on company success. Fig. 4a shows the path estimations for this model using the mixed dataset. The model ?t is improved with ?2 = 1.107 ( p = 0.237), RMR = 0.042, GFI = 0.926, CFI = 0.993, RMSEA=0.027 (p =0.906). While the paths initially hypothesised in Hypotheses 1–3 are still signi?cant (the path coef?cients change only slightly), while Hypotheses 4 and the two new pathways are not signi?cant. Thus, marketing–purchasing interaction has no direct effect on ?rm performance, thereby replicating the empirical results found in Kahn and Mentzer (1998). More interesting is the rejection of the link between customer orientation and business performance: for Russian industrial companies, customer orientation does not impact directly on performance. Although the path estimation coef?cient is positive as expected, it is moderately strong at 0.202 but not signi?cant with p =0.056. Thus, any positive effect of a deep understanding of customer needs and wants, and driving customer satisfaction and value, is mediated via the construct of interfunctional alignment, in our case the collaboration of the marketing and the purchasing function. However, the link between marketing– purchasing collaboration and business performance (path estimate of 0.189) is now not signi?cant (p =0.066). Altogether, the model improves the explained variance of the criterion constructs. We also tested the enriched model with the same six pathways with the dyadic dataset (Fig. 4b). While the overall model ?t is adequate with RMR=0.042, GFI=0.913, CFI=0.977, RMSEA=0.054 (p =0.364), the ?2 =1.430 (p =0.006) is again problematic. In this model the path between customer orientation and performance becomes signi?cant, i.e. customer orientation is positively and signi?cantly linked with business performance (0.336, p b 0.01). The additional path increases the explained variance of the business performance construct to 17.3%. However, the other new pathway again cannot be supported as the path coef?cient is not signi?cant. Furthermore, while the other path coef?cients in the model change only marginally, the link between marketing–purchasing collaboration and business performance is again not signi?cant (0.162, p =0.161). This means that while the original model in Fig. 2 corroborated the mediating effect of marketing–purchasing collaboration, this effect is statistically not signi?cant in either of the enriched models. This ?nding,
Table 5 AVE and squared correlation matrix (dyadic dataset). Variable 1. 2. 3. 4. Customer orientation Marketing–purchasing interaction Marketing–purchasing collaboration Business performance 1 0.72 0.25 0.34 0.16 2 0.57 0.32 0.04 3 4
Note: CR = composite reliability; all items are measured on a 5-point Likert scale, anchored in ‘strongly agree’ and ‘strongly disagree’ (except items for business performance which were anchored in ‘much worse’ and ‘much better’).
0.76 0.10
0.77
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
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Customer orientation (dyadic) 25.2%
.502 (0.000)
.402 (0.000)
Marketing Purchasing interaction (dyadic)
.361 (0.000)
Marketing Purchasing collaboration (dyadic) 43.8%
.331 (0.000)
Business performance (dyadic) 11.0%
Note : (dyadic) indicates construct being measured as the average values of the scores from purchasing and marketing respondents
Fig. 3. Path estimation original model (dyadic dataset).
plus the signi?cant ?2 provide some dif?culties in interpreting the results of the dyadic enriched model vis-à-vis other models, especially due to the fact that ?2 indices are dif?cult to interpret for structural equation models with relatively small samples (Fornell & Larcker, 1981; Kaplan, 1988). The overall best ?t between model and dataset is therefore provided by the original model with the mixed dataset (see Fig. 2). However, the other models provide additional interpretation options. 5. Conclusions and implications 5.1. Theoretical conclusions and limitations This study examined the extent to which purchasing and marketing managers collaborate in order to achieve superior business performance. We used data from 148 paired respondents from Russian industrial companies so as to overcome the problems commonly associated with
common methods bias. As hypothesized, our results provide evidence for the role that both antecedents play in determining marketing– purchasing collaboration, as well as for the mediating effect that customer orientation has between marketing–purchasing interaction and marketing–purchasing collaboration. Furthermore, our empirical results demonstrate that marketing–purchasing collaboration has a signi?cant and positive effect on business performance. This ?nding supports recent suggestions that there exist dual relationships between how active and in?uential a marketing department is, and the development of a customer orientation (Verhoef & Lee?ang, 2009). Our results are in line with ?ndings by Han et al. (1998) which show that having a customer orientation positively impacts both directly and indirectly on performance, with the indirect (mediated) path being the dominant one. However, this effect was not robust in our study as the extended model did not corroborate this positive relationship. This may indicate that some of the hypothesized relationships which are based on a
a
.202 (0.056)
Customer orientation (P) 18.1%
.425 (0.000)
.288 (0.003)
Marketing Purchasing interaction (P)
.288 (0.008)
Marketing Purchasing collaboration (M) 21.4%
.189 (0.066)
Business performance (M) 8.5%
-.106 (0.319) Note : (P) indicates construct being measured via purchasing respondents, (M) indicates construct being measured via marketing respondents. Non-significant path estimates are indicated by dotted lines
b
Customer orientation (dyadic) 25.3%
.503 (0.000) Marketing Purchasing interaction (dyadic) .363 (0.000)
.336 (0.003)
.397 (0.000) Marketing Purchasing collaboration (dyadic) 43.4% -.064 (0.568)
.162 (0.161)
Business performance (dyadic) 17.3%
Note : (dyadic) indicates construct being measured as the average values of the scores from purchasing and marketing respondents
Fig. 4. a. Path estimation enriched model (mixed dataset). b.Path estimation enriched model (dyadic dataset).
Please cite this article as: Smirnova, M., et al., Understanding the role of marketing–purchasing collaboration in industrial markets: The case of Russia, Industrial Marketing Management (2010), doi:10.1016/j.indmarman.2010.09.010
M. Smirnova et al. / Industrial Marketing Management xxx (2010) xxx–xxx
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nomological model that was developed from research mostly pertaining to developed/Western economies do not yet adequately describe the transitional state of the Russian economy but refer to an ‘end-state’ of development of market-based exchanges in an economy. As such, this quali?es Slater and Narver's (1994) ?ndings that the business environment does not affect the link between customer orientation and business performance. Tests with different datasets show the robustness of these results but also indicate questions for further research. Direct relationships between customer orientation and marketing–purchasing interaction on business performance were not generally supported, thus underlining the importance of marketing–purchasing collaboration as a mediating construct in achieving business performance. However, some of the alternative models provide an indication that there exists a direct link between customer orientation and ?rm performance (the path estimates on several data models were only just not signi?cant) which complements the mediated effect that customer orientation has via fostering more interfunctional alignment. These ambivalent results suggest that additional research needs to be undertaken in order to understand the development of these direct vis-à-vis indirect (mediated) relationships of customer orientation to company performance, e.g. by a longitudinal study which takes into account the changing transitional characteristics of the Russian economy. Furthermore, intra-industry datasets may ?nd different relationships depending on the developmental stage of these industries in adapting to market-based exchanges. This would require larger datasets to be gathered, where the effects of control variables such as industry, company size, or the length of time that a company has been in business can be assessed. Our analyses also show that different datasets using multiple respondents can be used to cross-validate the testing of the nomological model for robustness. These tests outline some limitations of our research, namely, the fact that although a dyadic dataset improves the explained variance within the overall model, the ?t indices, especially ?2, become problematic. Furthermore, we use an average score method to derive dyadic data. Further research is necessary which also uses the ?t and directionality information inherent in dyadic data (Deshpandé et al., 1993; Van Bruggen et al., 2002). 5.2. Managerial implications An important managerial implication of our work is that we have demonstrated that in order to achieve improved business performance, companies need to improve both their understanding of their business partners (especially their counterparts' buyers) through developing a customer orientation, and the internal relationships between purchasing and marketing. These internal and external facing capabilities are considered to be sources of particular risk in more recently developing economies such as Russia, where under earlier conditions of being a more centrally planned economy, such competencies were not required. We used a cross-industry dataset from Russia to understand these issues with regard to whether or not they impact on overall business performance. The study has identi?ed both interaction between the marketing and purchasing functions and also customer orientation as being antecedents of the level of collaboration between the same two functions, which in turn in?uences business performance. The empirical results of this study provide strong support for the hypotheses that have been developed. If managers operating in a Russian environment have the objective of improving their business performance – as they clearly do, given the freedom that they now have to do so – then they do need to focus on internal collaboration between their marketing and purchasing departments, given the linkages between the two. As customer-facing resources (information, access, etc.) are usually controlled by the marketing department, this makes it necessary for the marketing department to be proactive in fostering such interfunctional alignment. Moreover, companies need to focus on improving both customer orientation and the
interactions between the marketing and purchasing departments, given the direct effect that these have on collaboration, plus the indirect effect of interaction via customer orientation. Our original thinking in tackling this research project was that Russian management practices, in some sense, lagged behind those in more mature economies, given the fact that their transition to freer managerial practices have only relatively recently taken place. What we ?nd interesting is that the robustness of our results provide only weak this thinking: the results that we report in this paper have tended to re?ect what is found in traditional western economies, albeit, less clearly developed. As in the west, there is evidence to support the mediating role that marketing–purchasing collaboration plays in achieving superior performance. Unlike the west, there seems no direct link between customer orientation and superior business performance. This indicates to us that Russian managers can and indeed should consider traditional business practices used by Western companies and employ them in Russia's transitional economy. However, the fact that no direct link between customer orientation and ?rm performance was found was unexpected. As this relationship is linked to an underlying concept of marketing, and also linked to the concept of market orientation, further studies need to explore this counter-intuitive ?nding. Appendix A
Means, standard deviations and correlations of the constructs (mixed dataset). Constructs 1.Customer orientation 2.Marketing – purchasing interaction 3.Marketing – purchasing collaboration 4.Business performance Mean Standard deviation 4.55 3.73 4.03 0.67 0.59 1.16 0.89 0.78 1 2 3 4
1 0.425** 1 0.399** 0.382** 1 0.233** 0.052** 0.229** 1
**p b 0.01. Note: all constructs are scored on a scale of 1 to 5, except for business performance which is scored on a scale of ? 2 to + 2.
Means, standard deviations and correlations of the constructs (dyadic dataset). Constructs 1.Customer orientation 2.Marketing – purchasing interaction 3.Marketing – purchasing collaboration 4.Business performance Mean Standard deviation 4.51 3.58 4.11 0.72 0.57 0.99 0.77 0.69 1 2 3 4
1 0.503** 1 0.580** 0.562** 1 0.398** 0.196** 0.321** 1
**p b 0.01. Note: all constructs are scored on a scale of 1 to 5, except for business performance which is scored on a scale of ? 2 to + 2.
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