Reconceptualizing the elements of market orientation: A process-based view

IMM-07286; No of Pages 13 Industrial Marketing Management xxx (2015) xxx–xxx

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Industrial Marketing Management

Reconceptualizing the elements of market orientation: A process-based view Xiaodan (Dani) Dong a, Zelin Zhang b,⁎, Christian Andrew Hinsch c, Shaoming Zou d a

Wagner College, Campus Hall 215, 1 Campus Road, Staten Island, NY 10301, United States Marketing Department, School of Business, Renmin University of China, Mingde Building, Rm. 915, 59 Zhongguancun St., Haidian Dist., Beijing 100872, PR China c Grand Valley State University, Seidman College of Business, 3114 Seidman Center, Grand Rapids, MI 49501, United States d University of Missouri, Department of Marketing, Robert J. Trulaske, Sr. College of Business, 335 Cornell Hall, Columbia, MO 65211, United States b

a r t i c l e

i n f o

Article history: Received 10 July 2014 Received in revised form 4 December 2015 Accepted 11 December 2015 Available online xxxx Keywords: Market Intelligence Implementation Process (MIIP) Market orientation Disaggregation Moderated mediation Centralization International experience

a b s t r a c t Market Orientation (MO) was originally introduced with a reflective second-order scale, but much recent research has conceptualized MO as a formative second-order construct. However, either the reflective or the formative approach to measuring MO may have issues that obscure relationships between both the individual dimensions and their relationships with other variables. Thus, the current research disaggregates the MO construct into three sub-constructs in an effort to explore relationships between the three dimensions of MO and its implementation process within the firm. The proposed Market Intelligence Implementation Process (MIIP) model suggests both a direct path from intelligence generation to responsiveness and an indirect path through a company-wide focus on dissemination. The process model suggests that firms may select two distinctly different paths to responsiveness when applying market intelligence. Explicating this dual process model allows us to understand how firm characteristics impact the process of MO through the individual elements. If the three subconstructs do not vary in concert with each other, researchers cannot simply conclude that a firm characteristic (i.e., centralization or international experience) positively or negatively impacts MO's relationship to important marketing variables. The results indicate that for centralized and experienced firms, a high level of intelligence dissemination may actually hinder responsiveness. However, in decentralized and inexperienced firms, high levels of dissemination are linked to increased responsiveness. Using conditional process modeling, our study disaggregates the temporally distinct process of MO to reveal internal relationships among its dimensions. The current research also shows that the mediation of intelligence dissemination on the link between intelligence generation and responsiveness depends on the firm's levels of both centralization and international experience. © 2015 Elsevier Inc. All rights reserved.

1. Introduction In the current era of increasingly competitive global markets and constantly evolving customer demands, marketing orientation (MO) has emerged as a major influence on strategic decision-making (e.g., Jaworski & Kohli, 1993). Understanding the impact of MO has been found to be especially important in business to business (B2B) and industrial markets where the financial implications of customer loss can be particularly troublesome (Liao, Chang, Wu, & Katrichis, 2011). Market intelligence generation, dissemination, and response to markets have been widely accepted as the primary components of market orientation, and these activities have been routinely executed, though the exact relationships between the dimensions of MO have not been empirically shown. Numerous studies have found that factors like levels of international experience, competition, or other environmental

⁎ Corresponding author at: Marketing Department, School of Business, Renmin University of China, Mingde Building, Rm. 915, 59 Zhongguancun St., Haidian Dist., Beijing, 100872, PR China. E-mail addresses: [email protected] (X.(D.) Dong), [email protected] (Z. Zhang), [email protected] (C.A. Hinsch), [email protected] (S. Zou).

moderators, influence market orientation's impact on important variables like firm performance (e.g., Sandvik & Sandvik, 2003). However, it should be noted that many of these studies treat market orientation as a composite construct (either a formative or a reflective composite). This composite approach may hide existing relationships or even suggest non-existent relationships (Cadogan & Lee, 2013). According to Howell, Breivik, and Wilcox (2007), aggregation of multiple dimensions can be justified only when all dimensions show substantially similar relationships with antecedents or consequences. The multiple dimensions of MO can each have their own “nomological networks”, and so each dimension has the potential to have different consequences (Cadogan, 2012, p. 344). For example, intelligence generation or dissemination might have no impact on performance, while responsiveness does. Hult, Ketchen, and Slater (2005) found that intelligence generation and dissemination operates through responsiveness to drive the firm success. Thus, if the dimensions of MO are differentially impacted by other variables, or if each dimension affects other variables with a different strength or weight, these differences may not be visible if one addresses MO as a composite construct. Recent research has begun to explore the isolated effects of the individual dimensions of MO (e.g. Cadogan, 2012; Dong, Hinsch, Zou, & Fu,

http://dx.doi.org/10.1016/j.indmarman.2015.12.005 0019-8501/© 2015 Elsevier Inc. All rights reserved.

Please cite this article as: Dong, X.(D.), et al., Reconceptualizing the elements of market orientation: A process-based view, Industrial Marketing Management (2015), http://dx.doi.org/10.1016/j.indmarman.2015.12.005

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2013). Some studies have found that intelligence generation or dissemination, when studied in isolation, had no effect on firm performance (e.g. Carbonell & Rodríguez Escudero, 2010, Horng & Chen, 1998, Murray, Gao, Kotabe, & Zhou, 2007, Rose & Shoham, 2002), a finding that conflicts with much extant research using aggregated MO as an independent variable (IV) (Cadogan & Lee, 2013). This conflict provides an impetus for the exploration of the implementation process of market intelligence by studying the relationships that exist between the individual MO dimensions. Market orientation is a construct that defines a firm's focus or orientation. This orientation is indicated by the degree to which the firm generates intelligence, disseminates intelligence, and responds to consumers. In a pure MO conceptualization, the mix or combination of these variables is irrelevant as an increase in any variable would result in the firm becoming more market oriented. The current research suggests a logical order to the MO variables and conceptualizes these measures as a linked process, which is defined as the marketing intelligence implementation process (MIIP). The purpose of this study is to revisit market orientation by exploring the relationships between market intelligence generation, dissemination, and responsiveness as a process. Within the proposed process, intelligence generation is modeled as the independent variable (IV) and responsiveness is modeled as the dependent variable (DV). The multinational corporation's (MNC) strategic business unit (SBU) level of intelligence dissemination is modeled as a mediator (see Fig. 1). This model shows two paths to responsiveness, one directly from intelligence generation (the c path) and the other through the SBU's level of intelligence dissemination (the a × b path). This disaggregated, mediated model will allow for additional tests of potential moderating variables like centralization and international experience. The current research offers several important contributions. First, this study advances the theory of MO by investigating the process of market intelligence implementation. Researchers need to sufficiently understand how market intelligence is internally processed to better understand MO's impact on other variables. Compared with the conventional conceptualization of a composite MO, the disaggregation of MO provides an advanced avenue to scrutinize the more granular effects of each MO dimension. Each component performs a unique function in the process, and there is a logical connection between them. Thus, treating MO as a composite or simply separating the MO dimensions into three different constructs ignores the chronological interplay between each of the individual dimensions. Recognizing the independent logical role of each dimension in the MIIP can help us better understand how market intelligence contributes to performance. Secondly, based on a disaggregation of the traditional MO construct, the current research suggests that the level of intelligence dissemination can either depress or enhance the effect of intelligence generation

on responsiveness, depending on organizational characteristics and the model path that is chosen (with or without the mediation of dissemination). We will further address conflicting findings in the extant literature and identify boundary conditions that are not visible when using an aggregated MO construct. Thus, the current research informs the management of the MIIP in situations where SBU attributes or functions can be either minimized or cultivated to foster positive SBU outcomes. Third, this study uses conditional process modeling (CPM) to examine both the intelligence implementation process and factors that moderate the proposed mediation effect of intelligence dissemination. This method allows for an explication of both the direct and indirect effects of intelligence generation on responsiveness at different values of the moderators. Previous studies have not explored the intelligence implementation process at this level. Lastly, this study provides important managerial guidance. Drawn from the findings, managers can decide, with an understanding of deeply rooted organization characteristics, how to leverage intelligence generation and/or dissemination to maximize the SBU's level of responsiveness. A deep understanding of these relationships will help managers to avoid wasting resources and allow them to maximize the utility of their intelligence implementation activities. The current research shows how managers can develop a mix of market intelligence generation, dissemination, organization structure and experience to maximize responsiveness. The next section will review the market orientation and management strategy literature relating to the focal constructs. Then the conceptual model and hypotheses are developed. This will be followed by an outline of the data collection procedure and an analysis of the results. Lastly, the paper concludes with theoretical contributions, implications for managerial practice and future research. 2. Literature review and conceptual development 2.1. Theoretical background From the behavioral perspective, market orientation emphasizes activities that are related to the generation of market intelligence, dissemination of the intelligence across departments, and responsiveness to the market using this intelligence (Kohli & Jaworski, 1990). This multidimensional construct has been studied from a wide array of perspectives and in many different contexts. While the construct originated with work done in the United states (Kohli & Jaworski, 1990; Kohli, Jaworski, & Kumar, 1993; Narver & Slater, 1990), the construct has been studied in Asia (Chung, 2012; Taylor et al., 2008; Wang & Wei, 2005), Europe (Börjesson & Dahlsten, 2004; Megicks & Warnaby, 2008) including former Soviet bloc countries (Akimova, 2000) and

Fig. 1. Conceptual model.

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includes developing markets in Africa (Nwokah, 2008). The construct has also been applied to firms of all sizes (Demirbag, Lenny Koh, Tatoglu, & Zaim, 2006; Keskin, 2006; Laforet, 2008) and virtually any type of industry including logistics (Ellinger, Ketchen, Hult, Elmadağ, & Richey, 2008), automotive supply (Börjesson & Dahlsten, 2004; Chang, Mehta, Chen, Polsa, & Mazur, 1999), food production (Grunert, 2005; Nwokah, 2008), non-profits Gainer & Padanyi, 2005; Lonial, Tarim, Tatoglu, Zaim, & Zaim, 2008; Macedo & Carlos Pinho, 2006), retail (Elg & Paavola, 2008; Megicks & Warnaby, 2008), services (for a review see Esteban, Millán, Molina, & Martin-Consuegra, 2002), and industrial/B2B applications (Beverland & Lindgreen, 2007; Deshpandé, Farley, & Webster, 2000). Even though MO has been found to be “equally applicable to all organizations regardless of size, scope, or industry” Liao et al., 2011 p. 306), a large subset of the extant research on MO has focused on industrial and B2B markets because the benefits or consequences associated with a market orientation are often magnified in this context (Liao et al., 2011). Market Orientation was originally introduced as a reflective composite, and a number of researchers have investigated whether or how this single composite is related to other variables like firm performance. For example, hundreds of papers have investigated the antecedents and consequences of MO (see Kirca, Jayachandran, & Bearden, 2005 andCano, Carrillat, & Jaramillo, 2004 for reviews). Many researchers have since reconceptualized MO as a second-order formative composite (e.g., Cadogan & Lee, 2013). We understand that treating the dimensions of MO together as a composite construct may make for a parsimonious model. However, conclusions drawn from research using the composite construct may be questionable unless all dimensions operate on performance in the same way (Cadogan, 2012; Howell et al., 2007), and it is possible that each dimension might impact important dependent variables differently. Modeling MO as a composite can obscure potentially interesting effects or even suggest relationships that do not exist, because the individual dimensions may counteract with or neutralize each other and result in misleading relationships (Cadogan, 2012). While many studies unsurprisingly report a significant direct positive effect of MO on performance (see Kirca et al., 2005 or Cano et al., 2004 for a review), others reveal non-significant and even negative relationships (e.g. Agarwal, Erramilli, & Dev, 2003; Akyol & Akehurst, 2003; Cadogan, Sundqvist, Salminen, & Puumalainen, 2002). This discrepancy might suggest unaddressed moderators, flawed or imprecise measurement tools, or varying data gathering or analysis techniques. It is also possible that these conflicting findings result from the fact that the disaggregated components of MO might relate to these DVs in unique ways. The true effect of the MO dimensions may not be visible if a composite is used to explore these relationships. A recent stream of MO research advocates that MO should be investigated through a disaggregated approach (e.g., Cadogan, 2012). By breaking the MO construct into its three dimensions, these studies suggest varied effects for the three MO components. For example, responsiveness to the market has been linked to significant effects on performance, while the effects of intelligence dissemination and generation on performance have been shown to be inconclusive (Carbonell & Rodríguez Escudero, 2010; Horng & Chen, 1998). Based on these findings, we suggest that each dimension of MO can impact the firm in a different way, and that each dimension has the potential for different firm outcomes. Also, relationships between the three dimensions themselves may interact with other variables resulting in varied effects. Modeling MO as a single aggregate may veil existing relationships or obfuscate the true impact of MO Cadogan (2012). For instance, recent research (e.g., Dong et al., 2013) shows that intelligence generation and dissemination impact outcome variables (e.g., performance) through responsiveness. Here, responsiveness, which refers to actions taken in response to relevant market intelligence generated and/or intelligence subsequently filtered in a timely fashion (Jaworski & Kohli, 1993; Kwon & Hu, 2000), has been shown to link the other two MO dimensions to the performance of the firm, and it reflects the speed and

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coordination with which marketing decisions are implemented and periodically reviewed (e.g., Hult et al., 2005). Therefore, the current research conceptualizes the MIIP as the indicators of MO placed in a logical order. A firm can't respond to consumers until intelligence about these consumers is generated. Similarly, since intelligence generation must precede intelligence dissemination, intelligence generation is proposed as the beginning of the MIIP. Because intelligence generation or dissemination in isolation will not have an impact on the firm unless the firm takes action based on this intelligence, responsiveness is proposed as the outcome of the MIIP. Given the increasing levels of dynamism and uncertainty in many B2B operating environments (e.g., Son & Benbasat, 2007), it is not surprising that responsiveness has been found to be a key factor in promoting competitive success (Jayachandran, Hewett, & Kaufman, 2004). Particularly, responsiveness enables companies to reconfigure their processes to meet new market demand, take maximum advantage of information processing systems, and adopt new product and process technologies more quickly than their competition (Hoyt, Huq, & Kreiser, 2007). As one of the dimensions of MO, responsiveness has been shown to have a critical role in transferring market intelligence to performance (Dong et al., 2013). Based on MO theory, we define market intelligence generation as an SBU's collection and assessment of both customer needs/preferences and the forces that influence the development and refinement of those needs in an international B2B market. Intelligence generation typically includes activities like collecting information relating to trends and changes in international markets or identifying other forces that influence the needs and wants of customers or prospects. Market intelligence dissemination refers to the process and extent of market information exchange within the SBU itself, and dissemination can both span levels of the organization and progress horizontally across a single level. Evaluation of the MIIP model suggests that intelligence generation is an antecedent of responsiveness while intelligence dissemination occurs between generation and responsiveness. Temporally, intelligence generation should precede both dissemination and responsiveness, and dissemination is hypothesized to precede responsiveness. Intelligence generation may also impact responsiveness directly in firms where consumer responses are initiated directly from generated intelligence without a high level of dissemination (the full model will be discussed in the next section). To the best of our knowledge, no extant research has explored the implementation of market intelligence from the perspective of a process or addressed possible boundary conditions that impact the relationships between these three noted components. The centralization of the firm's decision-making authority has been frequently investigated in conjunction with MO (see Kirca et al., 2005 for a review). Several studies have shown that centralization is negatively correlated with both intelligence generation and intelligence dissemination (Deshpandé & Zaltman, 1982). A significant negative impact of centralization on the MO/performance relationship has also been well documented (e.g. Dong et al., 2013; Jaworski & Kohli, 1993; Kirca et al., 2005), which suggests that a decentralized structure should be advocated to facilitate the MO/performance relationship. However, centralization has also been shown to have a non-significant or even a positive impact on firm performance in other studies (i.e. Dalton, Todor, Spendolini, Fielding, & Porter, 1980). These mixed results suggest that centralization might impact the disaggregated components of MO differently. By disaggregating MO and developing the intelligence implementation process, the true impact of centralization on MO and its relationships to other variables may help explain past inconsistent findings (Cadogan, 2012). Along with the degree of centralization of decision making in the firm, how the firm interacts with the external environment also impacts a firm's market intelligence implementation strategy. The nuances of how to use information are particularly critical in an international context, where both market dynamics and environmental turbulence are

Please cite this article as: Dong, X.(D.), et al., Reconceptualizing the elements of market orientation: A process-based view, Industrial Marketing Management (2015), http://dx.doi.org/10.1016/j.indmarman.2015.12.005

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high in magnitude (e.g., Cadogan, Cui, & Li, 2003). As such, the firm's level of international experience is likely to impact a firm's capacity to deal with a rapidly evolving business environment. Traditionally, past experience in global markets has been recognized as a factor linked to a firm's success. For example, Williams and Chaston (2004) suggest that international experience can positively impact a firm's success. However, Hultman, Katsikeas, and Robson (2011) found that international experience can also hinder a firm from quickly adapting to market changes. Therefore, we consider a firm's experience in an international context as another structural characteristic that might moderate the process of intelligence implementation. Much like centralization, a firm's experience in these markets will impact both the need for and utility of disseminating market intelligence. Based on the rationale that centralization can impose diverse effects on the disaggregated process of MO, we explore the possibility that international experience would display a similar pattern of moderating the MIIP. 2.2. Conceptual development 2.2.1. The market intelligence implementation process With market intelligence generation, the SBU seeks to keep abreast of the dynamic aspects of both its customer and competitor environments. This information is only meaningful and valuable if it is properly used to respond to market changes. Day (1994) and Hult et al. (2005) suggest that a firm's information-processing abilities are critical antecedents to firm responsiveness due to the acceleration of change, the explosion of available market data, and the importance of anticipatory action in the increasingly dynamic industrial marketplace. The relationship between responsiveness and performance has been well established (Dong et al., 2013; Hult et al., 2005), but few studies have addressed how responsiveness serves as a mediator between intelligence generation/dissemination and performance (Dong et al., 2013), and none have tested the implied mediated relationships between intelligence generation, intelligence dissemination, and responsiveness. Based on the conceptualization above, we contend that responsiveness serves as the dependent variable in the MIIP, because it reflects a firm's actions based on market information that is both generated and disseminated. Intelligence generation is a clear antecedent of responsiveness. Huber and Daft (1987) suggest that the generated intelligence should be efficiently disseminated to maximize its impact on the firm. Theoretically, the dissemination of market intelligence should function as a mediator between intelligence generation and responsiveness. However, all firms disseminate market intelligence to some extent; it is the depth, breadth, and level of formalization accompanying dissemination that impacts the intelligence implementation process (Maltz & Kohli, 1996). It is important to note that dissemination should be defined on a continuum, not a dichotomy. Some firms may intentionally limit intelligence dissemination to respond to customer needs promptly. For instance, certain luxury hotel chains are on the low end of the continuum as they require employees who are initially presented with a customer complaint (a specific form of customer intelligence) to resolve the complaint (a specific form of customer responsiveness) personally, without passing the complaint to other employees or even their supervisor. No intelligence is disseminated across departments/ units in this case. Past research has suggested that “intelligence dissemination may have a reduced role in small to medium-sized firms” (Rose & Shoham, 2002, p. 224) because the employees who generate intelligence would be the same personnel to respond the market. These examples indicate that employees do not necessarily have a single role in the intelligence implementation process; rather a single employee can take multiple roles potentially spanning intelligence generation and responsiveness. Depending on the organizational infrastructure, the level of formalized dissemination can be very limited such as in the extreme case noted here (Maltz & Kohli, 1996). B2B firms differ in their levels of formalized dissemination (e.g., Slater, Mohr, & Sengupta, 1995), and firms at the high end of the scale can have rules

and infrastructure in place that both mandate and allow for intelligence to spread broadly through the firm. The wide dissemination of market information to employees who cannot act on that information may be wasteful and time-consuming. For firms who realize the potential waste inherent in organization wide dissemination, there may simply be a direct effect between intelligence generation and responsiveness that is not driven by broad intelligence dissemination. Thus, we believe there might be two paths from intelligence generation to responsiveness. The indirect path is mediated by dissemination, the direct path links generation to responsiveness without significantly involving structured intelligence dissemination. Thus, we hypothesize that market intelligence generation impacts responsiveness through intelligence dissemination. However, a high commitment to dissemination across departments may not be needed for the creation of responsiveness in some firms. H1. Market intelligence generation positively impacts market responsiveness. H2. Market intelligence dissemination mediates the effect of market intelligence generation on responsiveness.

2.2.2. Centralization The MIIP model translates marketing intelligence into behaviors and actions designed to respond to market changes. Firms employ differing organizational structures, so the degree of centralization in the SBU's decision-making will likely impact the firm's responsiveness level as well. To fully understand the relationships between intelligence generation, dissemination and responsiveness, any disaggregation of the MO construct must also take the SBU's centralization of decision-making into account. Centralization of decision-making is one of the fundamental dimensions of organizational design (Egelhoff, 1988). The degree to which decision-making is centralized or decentralized is a key indicator of the manner in which an organization allocates its marketing resources. According to Holdaway, Newberry, Hickson, and Heron (1975), a firm's level of centralization has been defined as the distribution of decisionmaking authority through the SBU's hierarchy. This construct focuses on the locus of authority to make decisions affecting the organization. It determines whether the locus of decision-making authority resides in the higher (in centralized firms) or lower (in de-centralized firms) levels of the organization, or the level at which a decision would need to be approved before being implemented. A high degree of centralization prevents middle and lower-level managers and employees from exercising discretion in dealing with market demands. A firm's level of centralization can provide a foundation for achieving coordination and control of organizational activities, as it constrains and prescribes the behavior of organization members (Andrews, Boyne, Law, & Walker, 2009). Prior research (e.g., Claycomb, Iyer, & Germain, 2005) has indicated the importance of decentralized organizational structures as a means for delivering responsive and effective services. Decentralization has been found to be necessary for rapid decision-making, which is needed in constantly changing markets and for the continuous generation of new tactics (Pertusa-Ortega, Zaragoza-Sáez, & Claver-Cortés, 2010). High levels of decision participation (i.e. a decentralized structure) can maximize the points of contact between employees and customers, leading to more effective responses to customer needs. SBUs with lower levels of centralization would be expected to generate greater responsiveness to market dynamics as the decision-makers are often interacting directly with customers and competitors in the market. A high level of centralization has also been posited to cause inefficiency in the dissemination and use of marketing information, and may cause unnecessary errors as it retards decision-making with regard to market changes (Poppo, 1995). When an SBU intends to respond to market changes, a high degree of centralization can hinder interdepartmental

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and/or inter unit communication (Pertusa-Ortega et al., 2010), especially in a multinational context. In these cases, the cultural distance present in many SBU's operating environments likely magnifies the impact of decision-making structure. Therefore, we suggest that in SBUs with a low level of centralization, responsiveness will be more likely to be positively impacted by intelligence generation through intelligence dissemination. H3. The lower the SBU's level of centralization, the higher the impact of intelligence generation on market responsiveness through intelligence dissemination. Generated intelligence can be widely disseminated throughout the organization, where varied organization members may participate in the process of spreading and dispersing information in an effort to enact responsive behaviors. However, firms vary in the degree to which they disseminate generated intelligence. For example, Apple Inc. has very minimal dissemination after collecting market information. Each unit that generates market intelligence disseminates this intelligence to the top management team instead of spreading or communicating it to other units. These units respond to the market based on directives from top managers. In this case, the intelligence can reach decision-makers without wide dissemination throughout the firm (Johnson, Li, Phan, Singer, & Trinh, 2012). The structural property of centralization allows the firm to more tightly control and coordinate organizational behaviors. In a highly centralized firm, disseminating information throughout the organization may be a waste of time and managerial resources. If only a few managers in the organization have the authority to make decisions, companywide dissemination may not be necessary for the proper utilization of marketing intelligence. In these cases, extensive dissemination may serve only to delay the decision-making process and could even derail the firm's response to market changes as lower level employees may seek unnecessary clarification or information rather than following the direction of centralized decision makers (Eisenhardt, 1989). A high degree of centralization may actually increase the speed and decisiveness of a marketing response in certain situations, particularly in environments with high levels of uncertainty (Argote, 1993). For example, in order to increase control over a threatening situation, some firms adopt a more centralized structure to minimize the need for broad intelligence dissemination in an effort to speed the firm's response (Bourgeois, McAllister, & Mitchell, 1978; Staw et al., 1981; Pfeffer and Leblebici (1973) found that a stressful environment with a high degree of market competition, coupled with a rapid degree of environmental change, was associated with a more hierarchical firm structure and increased control of decision-making. Centralized firms, in this situation, do not have to disseminate market information companywide, and the tightened structure can greatly reduce delays associated with intelligence transfer throughout the firm. Intelligence used for decision-making need only be distributed among a few decision makers, because dissemination serves no purpose when the information is presented to personnel who have no authorization to take action. Through avoiding much of the dissemination process, responsiveness to dynamic markets can be undertaken more quickly and efficiently as they bypass many of the more time consuming dissemination activities. When speed is essential, a centralized structure would allow SBUs to circumvent many intelligence dissemination activities with a more direct link between intelligence generation and response. Thus, the direct relationship between intelligence generation and responsiveness may be more pronounced in these cases. Theoretically, these SBUs may bypass much of the “organization wide” dissemination that is often advocated in the MO literature. H4. The higher the SBU's level of centralization, the higher the direct effect between intelligence generation and responsiveness to market information.

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These hypotheses, if supported, will help to explain mixed findings in the extant research regarding centralization and its relationship with the aggregated market orientation construct (Eisenhardt, 1989; Kirca et al., 2005). 2.2.3. International experience International experience refers to the amount of experience management has accumulated operating internationally. An SBU can gain international experience via direct involvement in international transactions, operating in many foreign markets, interacting with foreign suppliers or distributors, and so on (Cavusgil, Zou, & Naidu, 1993). Inexperienced SBUs and experienced SBUs tend to display different decision-making processes. More experienced SBUs are likely to utilize an internal formula that has been applied in the past as the basis for an organizational decision (Lu, 2002). SBUs without experience are often uncertain about the market, as no “formulae” have been developed for making marketing decisions. One consequence of inexperience is that firms often search for industry best practices or other models on which to base its operational decisions (DiMaggio & Powell, 1983). The range of options for an inexperienced firm are virtually limitless while a more experienced firm is likely to deploy one of a few strategies that have been employed in the past to address changes in the market. In addition, SBUs with lengthy experience have typically established the system or standards to process market intelligence (Erramilli, 1991), while inexperienced SBUs may be still exploring how to be responsive to market intelligence. As suggested in H1 and H2, not all firms engage in high levels of both intelligence generation and dissemination. Inexperienced SBUs may be able to increase their ability to respond to market change by disseminating intelligence more broadly throughout the organization. Lesserexperienced SBUs which already consider many options for a potential response may benefit from the flexibility to disseminate this information organization wide. The dissemination process may act as a screening process that filters poor ideas while exposing viable ideas from a sea of potential alternatives. However, high levels of dissemination may not be appropriate for more experienced SBUs, even though this has been frequently advocated by proponents of MO. Well-established institutional processes that have been developed over time can efficiently direct the flow of information to the decision makers. The tried and true internal decision-making formulae gained through international experience can be implemented to correspond with market changes. In SBUs with high levels of experience, broad dissemination of market information may waste time and squander resources that may be better applied in other areas. Therefore, we hypothesize a moderated mediation relationship as increasing levels of international experience depress the relationship between intelligence generation and responsiveness through intelligence dissemination. H5. The lower the SBU's level of international experience, the higher the impact of intelligence generation on market responsiveness through intelligence dissemination. An experienced SBU using a tested formula or analysis pattern as well as a focused dissemination pattern (not broad dissemination) is expected to make a response decision quickly, as experience has taught the SBU to efficiently deliver the information to the right personnel. Anecdotal evidence suggests that experienced SBUs and management teams are better able to transform market intelligence directly to behaviors (Erramilli, 1991). Williams and Chaston (2004) found that managers who had experience living or working overseas demonstrated higher levels of both intelligence-gathering activity and incorporation of intelligence into their decision-making processes. SBUs with more experience in international business likely have a greater appreciation of the differences between markets, and are more capable of responding to the idiosyncrasies of each market with effective marketing strategies

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(Cateora, 1990). These findings suggest that firms can respond quickly to generated intelligence without broad dissemination if decisionmakers have high levels of international experience. Not all generated intelligence is of value to the SBU, a fact that is magnified when intelligence is disseminated widely throughout the firm. It is imperative that market intelligence is put in the hands of the individuals who have both the ability to process this information as well as the authority to respond to dynamic markets. International experience comes into play because SBUs with high levels of experience have likely developed the ability to get generated market intelligence into the hands of those who need it without distracting employees who are not equipped to process this information and/or cannot act to respond to market changes. In this situation, experience becomes extremely important since using intuition, understanding industry norms, and copying competitors all require years of experience to do well. H6. The higher the SBU's level of international experience, the higher the direct effect between intelligence generation and responsiveness to market information.

2.3. Summary of hypotheses The hypotheses outlined above posit a causal order to MIIP, a process that incorporates the elements of the widely used market orientation construct. The idea that intelligence generation must temporally precede intelligence dissemination, along with the concept that responsiveness to market changes will be informed by both intelligence generation and intelligence dissemination is fundamental to these hypotheses. However, the more important aspects of this research arise from the exploration of how an SBU's structural characteristics impact the MIIP. The hypotheses above suggest that the outcome of the MIIP (i.e. responsiveness to market changes) can result from two distinct paths depending on the structural characteristics of the firm. In SBUs with high levels of centralization and/or international experience, responsiveness can flow directly from intelligence generation, without high levels of formal intelligence dissemination. However, for SBUs with low levels of centralization and/or international experience, responsiveness to the market will be produced through wide dissemination of market intelligence. It is interesting to note that both moderators are hypothesized to have similar effects on the MIIP even though past research has shown them to be negatively correlated (Gates & Egelhoff, 1986). However, the fundamental reasoning for both moderators (i.e., centralization and international experience) is the same in the moderated mediation model proposed above. High levels of intelligence dissemination waste time and resources if they are employed in an improper organizational context. This concept underlies both sets of hypotheses even though SBU decision-making is expected to become less centralized as international experience grows.

consumer goods industries (n = 24) such as pharmaceutical preparations, soap and other detergents, perfumes, cosmetics and other toilet preparations. SBUs from consumer goods industries are basically manufacturers or wholesalers dealing with retailers, and who had little or no direct consumer contact. These business units are divisions of SBUs designed to sell distinct sets of products to identifiable sets of business customers, and to compete with a well-defined set of competitors across the globe (Jacobson, 1992). In addition, the above hypotheses were tested using the data from consumer goods firms and industrial goods firms separately. The results showed the same pattern of effects, though the relationships were not significant in the consumer goods analysis probably because the sample size was too small (n = 24). The unit of analysis is the business unit, which is defined as a division of a multinational corporation designed to sell a distinct set of products to an identifiable set of business customers, and to compete with a welldefined set of competitors across the globe (Jacobson, 1992). In SBUs, business units function as autonomously operating multinational organizations, even though they are technically under the umbrella of SBUs. For example, to consider an SBU like Virgin at the corporate level would be meaningless, because it is only by breaking the firm down into the diverse business units (i.e. airlines, mobile telecom, resorts, finance, European, Asian, American, etc.) where meaningful conclusions can be drawn. These business units often act as distinct multinational businesses in the global marketplace, with distinctive resources and strategies. Within the 23 identified industries, BUs were selected from Dun and Bradstreet's America's Corporate Families Index and The Directory of Corporate Affiliations. Three criteria were used to select the BUs. First, to facilitate data collection, the BU (that is, a multinational division of an SBU) had to be based in the United States, although the parent company could be based elsewhere. Second, the BU had to have at least 200 employees. Third, total annual sales of the BU had to total at least $20 million. Overall, 434 BUs qualified for the study. Middle level and above managers served as informants. Overall 126 BUs returned the completed questionnaires, representing a response rate of 29%. An assessment of potential nonresponse bias was conducted by comparing the responding BUs with the nonresponding BUs, the early-responding BUs with the lateresponding BUs, as well as consumer goods firms with industrial goods firms, in terms of annual sales and employee size (Armstrong & Overton, 1977; Rogelberg & Stanton, 2007). No statistically significant difference was found between the responding BUs and the nonresponding BUs, between the early-responding BUs and the lateresponding BUs, as well as between industrial goods BUs and consumer goods BUs. As our survey topic in question is not related to excessive workload or busyness, the nonresponse should be regarded as passive nonresponse, where systematic bias is unlikely. The nonrespondents should not differ from respondents with regard to the variables we are investigating in this case. Thus, it can be concluded that there is no evidence to suggest the existence of nonresponse bias. 3.2. Questionnaire and measures

3. Method 3.1. Data collection Data were collected using a cross-sectional mail survey of business units (BUs) in global industries. Global industries were selected as the context of the study because of their fast changing and dynamic nature (Roth, Schweiger, & Morrison, 1991). Since global industries are characterized by a high level of intra-industry trade (Porter, 1986), using a trade ratio of 30:70 (that is, 30% intra-industry and 70% interindustry) as the minimum limit to control for the global nature of industries 23 global industries were identified. These included industrial goods industries (n = 102) such as oil and gas field machinery & equipment, textile machinery, and ball and roller bearings, as well as

In order to develop the questionnaire, existing scales were identified to measure intelligence generation, intelligence dissemination, responsiveness, centralization of decision-making, and international experience. We also interviewed several business professionals to confirm that the adapted scales effectively measured the relevant constructs for the specific context. 3.2.1. Independent variable Market intelligence generation was measured using four items in a 7-point Likert format (from strongly agree to strongly disagree) developed by Jaworski and Kohli (1993). These items measured what Jaworski and Kohli described as the generation of market information on customer needs and market development.

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3.2.2. Mediator Market intelligence dissemination was measured using four items in a 7-point Likert-type scale (from strongly agree to strongly disagree) proposed by Jaworski and Kohli (1993). This scale assessed the degree to which intelligence involving both customers and competitors is formally disseminated throughout the organization. 3.2.3. Moderators The measure of centralization was adapted from three items in a 7-point Likert format (from strongly agree to strongly disagree) developed by Jaworski and Kohli (1993). These items measured the hierarchies of authority in the SBU. The measure of international experience was adapted from Cavusgil and Zou (1994). We used three items in a 7-point Likert-type scale (from strongly agree to strongly disagree). These items measured the degree of involvement in international business of managers in the SBU. 3.2.4. Dependent variable The measure of responsiveness was adapted from a 7-point, Likerttype scale (from strongly agree to strongly disagree) developed by Jaworski and Kohli (1993). These five items characterized the SBUs' responsiveness to market changes. 4. Results 4.1. Confirmatory Factor Analysis (CFA) We performed a CFA in EQS 6.0 to assess the measurement model of factors. All variables were measured using multiple items. Construct reliability and convergent and discriminant validity were assessed through this analysis. The results of the standardized factor loadings, R-square, the corresponding t-statistics, coefficient α, and the model fit indices are presented in Table 1. We followed the model fit evaluation procedure recommended by Bagozzi and Yi (1988). First, as the data

7

revealed a relatively high value in kurtosis, we used elliptical reweighted least square to estimate the model (Bagozzi & Yi, 1988), and the model fit indices showed some improvement. Second, the model converged properly without any anomalies in the results (e.g. improper solutions, condition codes). Third, because the chi-square test was significant(χ2 (125) = 173.870, p = .002), we examined additional model fit indexes, which showed excellent fit (CFI = .966, IFI = .967, BBNFI = .867, and RMSEA = .056) Bagozzi & Yi, 1988). Because several problems have been identified with the chi-square test, such as unknown power, inadequate measurement of goodness of fit, and sensitivity to sample size (Fornell & Larcker, 1981), we concluded that our model fit the data well. Fourth, we examined the convergent validity of the factors. The standardized factor loadings of all items were significant; their variances were positive and significant as well; in addition, the average variance extracted (AVE) ranged from .43 to .55 (see Table 1). Fifth, the coefficient α evidenced that each scale had acceptable reliability. Sixth, we tested discriminant validity by comparing one- and two-factor models of each pair of 5 factors in smaller CFA models, and the results showed a structure with 5 unique factors. To test for potential common method variance bias we applied Harman's one-factor test to the data. Items for all reflective constructs were subjected to an exploratory factor analysis. The unrotated factor analysis extracted five principal components and showed that the items were not loading on a single, common methods factor (Podsakoff & Organ, 1986). Further, the most prominent factor accounted for less than one-third of the variance present. As an additional test, we ran the partial correlation test by partialing out the first principal component. The results indicated that many significant partial correlations remained between the variables. Based on these tests, we concluded that common method variance bias is not a concern in this data. To determine if multicollinearity was a problem, variance inflation factors were computed for each of the variables. The highest VIF was

Table 1 Confirmatory factor analysis. Constructs/items Market intelligence generation We often meet with customers worldwide to find out what products and services they will need in the future. We do a lot of in-house market research. We frequently poll worldwide end users to assess the quality of our products and services. Departments get together periodically to discuss global market trend and development. Market intelligence dissemination We frequently circulate reports and newsletters that provide information about our customers across the world. Data on our customers are regularly disseminated to all country subsidiaries. Information about our competitors is regularly disseminated to all country subsidiaries. When one department or subsidiary finds out something important about our competitors, it will promptly alert other departments or subsidiaries. Centralization There can be little action here until top management approves a decision. Even small matters have to be referred to top management for an answer. Any course of action a country subsidiary takes has to have top management's approval. International Experience Our management possesses a great deal of international business experience. We have had a long history of international business involvement. Managers in our business unit have worked in a at least one foreign market before. Responsiveness It takes us a long time to decide how to respond to our major competitors' new campaign. Even if we came up with a great marketing plan, we probably would not be able to implement it in a timely fashion. When we find that a competitor has a new campaign, we will not respond until we feel its impact on our global competitive position. For one reason or another, we tend to ignore changes in our customers' product or service needs. χ2 (d.f. = 125, =.002) BBNFI CFI IFI MFI RMSEA

Standard loading

R2

t-Value

.64 .60 .72 .65

.41 .36 .51 .43

.66 .81 .72

.44 .66 .52

6.63 6.15

.51

.26

4.61

.83 .85 .62

.69 .72 .38

.82 .79 .63

.68 .68 .39

.89 .82

.79 .68

9.81

.68

.47

7.75

.51 173.870 .959 .966 .967 .824 .056

26

AVE

Cronbach's α

.43

.745

.47

.772

.60

.811

.56

.781

.55

.810

5.22 5.99 5.58

8.51 6.41

7.19 6.06

5.34

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X.(D.) Dong et al. / Industrial Marketing Management xxx (2015) xxx–xxx

less than 1.6 indicating that muticollinearity did not unduly influence our results. 4.2. Conditional process analysis Taken together, the above hypotheses suggest that the MIIP operates as a moderated mediation model (Hayes, 2013; Preacher, Rucker, & Hayes, 2007), in which intelligence dissemination mediates the effect of intelligence generation (IG) on responsiveness. The above hypotheses suggest that centralization and international experience moderate both the direct relationship between IG and responsiveness and the indirect effect through intelligence dissemination. Our hypotheses were tested based on the conceptual model depicted in Fig. 1. From this model, we know that the conditional indirect effect of IG on RESP through IDi = ai(b1i + b2i CE + b3i IX), and the conditional direct effect of IG on RESP = c1′ + c4′ CE + c5′ IX. The diagram represents two equations, one for ID and one for RESP: ID ¼ i1 þ ai IG þ eIDi ;

ð1Þ

RESP ¼ i2 þ c01 IG þ c02 CE þ c03 IX þ c04 IG  CE þ c05 IG  IX þb1i IDi þ b2i IDi  CE þ b3i IDi  IX þ eRESP :

ð2Þ

*IG = intelligence generation, ID = intelligence dissemination, CE = centralization, IX = international experience, RESP = responsiveness From Eq. (1), grouping terms involving IG and factoring out IG, the effect of IG on ID is θIG → ID = ai. Using the same procedure on Eq. (2), the effect of ID on RESP is θID → RESP = b1i + b2i CE + b3i IX. The indirect effect of IG on RESP through ID is the product of these two conditional effects, meaning it is conditional – the conditional indirect effect – and defined as θIG→ID θID→RESP ¼ ai ðb1i þ b2i CE þ b3i IXÞ:

ð3Þ

The indirect effect of IG is a function of both CE and IX. The direct effect of IG is a function of both CE and IX as well, but the coefficients show different directions. θIG→RESP ¼ c01 þ c04 CE þ c05 IX

ð4Þ

Conditional process analysis is required with the current model because the indirect effect of IG on RESP is conditional on the level of the moderators IX and CE. The same process occurs with respect to ID's relationship with RESP. That is to say that both the direct effect of IG on RESP and the mediation mechanism (through ID) differs in size or strength as a function of the moderators (Hayes, 2013, p. 334). Instead of relying on conventional hypothesis tests to determine mediation (i.e. Baron & Kenny, 1986; Sobel, 1982), conditional process analysis utilizes a bootstrapping technique to calculate conditional indirect effects

in the form of a confidence interval for the product of the “a path” (X–M) and the “b path” (M–Y). Confidence intervals for this path (a × b) that exclude zero are evidence of a mediation effect (see Fig. 1). In addition, moderated mediation would be indicated when there is evidence for mediation at some levels of the moderator (i.e. the a × b confidence interval excludes zero) but no evidence for mediation at other levels of the mediator (i.e. the a × b confidence interval includes zero) (Preacher, Rucker, & Hayes, 2007). Conditional process analysis also facilitates spotlight analysis with respect to the moderators' effects on the proposed direct and indirect paths. Effect sizes and p-values are calculated to show when the direct effect occurs at the quintile levels of the moderators. 4.3. Results Table 2 displays the conditional process model coefficients, standard errors, p-values, and model summary information. Using traditional hypothesis testing, H1 and H3–H6 are supported. However, conditional process modeling (CPM) provides a more detailed explanation of the hypothesized moderation effects along with a nuanced test of mediation. Process modeling repeatedly samples from the pool of data and generates both conditional direct effects (for the link between IG and Resp in the presence of the moderators CE and IX) and conditional indirect effects (for the link between IG and Resp through ID in the presence of moderators CE and IX). The moderation effect was statistically significant in the coefficients for the product terms, c4′, c5′, b2i, and b3i. This is evidence that the direct and indirect relationships between intelligence generation and SBU responsiveness depend on the SBU's levels of centralization and/or international experience. Both relationships were conditional. According to Hayes (2013), with the evidence that both the ID → RESP path and IG → RESP were moderated by CE and IX, we must next estimate the conditional indirect effect for various values of CE and IX at different percentile values of the moderators. Table 3 displays these results at the quintile levels of the moderators. Looking first at the indirect effects, the effect size (i.e. the product of the “a path” from IG → ID and the “b path” from ID → Resp) steadily decreases as both CE and IX increase. The effect size is significantly different from zero in most cases as the 95% confidence interval excludes zero. This is true moderated mediation as the mediated effect switches valence. At low levels of CE and/or IX, the mediated effect is positive. At high levels of CE and/or IX, the effect through ID is negative. This not only supports H2, but this is also strong support for hypotheses 3 and 5; as SBUs become more centralized and/or gain more international experience, less responsiveness is generated through company-wide dissemination of information. The last three columns of Table 3 “spotlight” the direct effect of IG on Resp at different levels of the moderators. At very low levels of CE and IX, the impact of IG on Resp is negative and significant. As the levels of

Table 2 Model coefficients for the conditional process model. Consequent Intelligence dissemination (M) Antecedent Intelligence generation Intelligence dissemination Centralization International experience Intelligence generation × centralization Intelligence dissemination × centralization Intelligence generation × international experience Intelligence dissemination × international experience Constant

Coeff. ai

i1

SE

Responsiveness (Y) p

.071 b.001 – – – – – – – – – – – – – – .344 b.001 2 R = .416 F(1, 124) = 88.183, p b .001

.662 – – – – – – – 1.342

Coeff c1′ b1i c2′ c3′ c4′ b2i c5′ b3i i2

SE

−2.021 2.354 −.193 −.166 .285 −.346 .254 −.213 4.867

p

.527 .590 .371 .280 .100 .099 .060 .075 2.250 R = .390 F(8, 117) = 9.367, p b .001

b.001 b.001 .604 .5548 .005 b.001 b.001 .006 .033

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X.(D.) Dong et al. / Industrial Marketing Management xxx (2015) xxx–xxx

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Table 3 Coefficients for the indirect path and direct path (centralization on hold). Centralization

Indirect effect

Direct effect

Percentile

Value

International experience

ai(b1i + b2i CE + b3i IX)

95% bias-corrected Bootstrap CI

θIG → RESP = c1′ + c4′CE + c5′ IX

seθIG → RESP

p

10th 10th 10th 10th 10th 25th 25th 25th 25th 25th 50th 50th 50th 50th 50th 75th 75th 75th 75th 75th 90th 90th 90th 90th 90th

2.600 2.600 2.600 2.600 2.600 3.200 3.200 3.200 3.200 3.200 3.600 3.600 3.600 3.600 3.600 4.200 4.200 4.200 4.200 4.200 5.000 5.000 5.000 5.000 5.000

2.333 3.667 4.667 5.333 6.667 2.333 3.667 4.667 5.333 6.667 2.333 3.667 4.667 5.333 6.667 2.333 3.667 4.667 5.333 6.667 2.333 3.667 4.667 5.333 6.667

.634 .446 .305 .211 .023 .496 .308 .167 .073 −.115 .404 .216 .075 −.019 −.207 .267 .079 −.062 −.156 −.345 .083 −.105 −.246 −.340 −.528

.273 to 1.015a .168 to .703a .103 to .512a .028 to .404a −.201 to .247a .208 to .820a .105 to .510a .020 to .297a −.056 to .220a −.337 to .077a .136 to .711a .051 to .400a −.042 to .201a −.145 to .108a −.443 to −.015a .013 to .563a −.107 to .249a −.222 to .070a −.333 to −.015a −.620 to −.115a −.246 to .371a −.385 to .104a −.508 to −.055a −.628 to −.125a −.909 to −.227a

−.688 −.349 −.095 .074 .413 −.517 −.178 .076 .245 .584 −.403 −.064 .190 .359 .698 −.232 .107 .360 .530 .868 −.004 .334 .588 .758 1.096

.232 .181 .159 .156 .179 .200 .144 .120 .119 .154 .186 .128 .105 .107 .148 .181 .126 .109 .115 .159 .202 .163 .156 .163 .203

.004⁎ .057⁎ .551⁎ .636⁎ .023⁎ .011⁎ .217⁎ .530⁎ .043⁎ b.001⁎ .033⁎ .616⁎ .074⁎ .001⁎ b.001⁎ .201⁎ .401⁎ .001⁎ b.001⁎ b.001⁎ .983⁎ .042⁎ b.001⁎ b.001⁎ b.001⁎

a The indirect effect is significant at the 95% bias-corrected bootstrap confidence interval. ⁎ The direct effect is significant at .05 p-value.

the moderators increase, the direct effect consistently increases and becomes positive and significant. This is a clear support for hypotheses 4 and 6. As SBUs gain international experience and/or become more centralized, more responsiveness is generated directly from intelligence generation, without high levels of intelligence dissemination (see Fig. 2). Table 4 is a mirror of Table 3 with the anchor switched from centralization to international experience. A final point arises from the fact that high levels of international experience and high levels of centralization are expected to be inversely related (Gates & Egelhoff, 1986). That is, in fact, what was found using the current dataset (see correlation at Table 5). These variables were significantly and negatively correlated (Pearson correlation = −.21, p = .018) which highlights the purpose of utilizing the MIIP in the first place. Market orientation is all about generating information about the market and getting this information to individuals in the

firm who use this information to make both strategic and tactical decisions. Prescribing a single “standard” level of intelligence dissemination is as foolish as prescribing a single approach to treating cancer. Depending on the type of cancer and the patient's situation, proper approaches could include chemotherapy, surgery, monitoring, or any combination of these. The “right” level of intelligence dissemination depends on at least two moderating variables, and these relationships will not be uncovered without disaggregating the MO construct. 4.4. Comparing rival models The hypothesized model was structured based on a very specific theoretical conceptualization. There is a variety of ways to structure a model with these constructs, and other models may fit equally well. The following outlines a competing model assessment that was

Fig. 2. Effect size for quintiles of each moderator.

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X.(D.) Dong et al. / Industrial Marketing Management xxx (2015) xxx–xxx

Table 4 Coefficients for the indirect path and direct path (international experience on hold). International experience Percentile 10th 10th 10th 10th 10th 25th 25th 25th 25th 25th 50th 50th 50th 50th 50th 75th 75th 75th 75th 75th 90th 90th 90th 90th 90th

Indirect effect

Direct effect

Value

Centralization

ai(b1i + b2i CE + b3i IX)

95% Bias-corrected Bootstrap CI

θIG → RESP = c1′ + c4′CE + c5′ IX

seθIG → RESP

p

2.333 2.333 2.333 2.333 2.333 3.667 3.667 3.667 3.667 3.667 4.667 4.667 4.667 4.667 4.667 5.333 5.333 5.333 5.333 5.333 6.667 6.667 6.667 6.667 6.667

2.600 3.200 3.600 4.200 5.000 2.600 3.200 3.600 4.200 5.000 2.600 3.200 3.600 4.200 5.000 2.600 3.200 3.600 4.200 5.000 2.600 3.200 3.600 4.200 5.000

.634 .496 .404 .267 .083 .446 .308 .216 .079 −.105 .305 .167 .075 −.062 −.246 .211 .073 −.019 −.156 −.340 .023 −.115 −.207 −.345 −.528

.273 to 1.015a .208 to .820a .136 to .711a .013 to .563a −.246 to .371a .168 to .703a .105 to .510a .051 to .400a −.107 to .249a −.385 to .104a .103 to .512a .020 to .297a −.042 to .201a −.222 to .070a −.508 to −.055a .028 to .404a −.056 to .220a −.145 to .108a −.333 to −.015a −.628 to −.125a −.201 to .247a −.337 to .077a −.443 to −.015a −.620 to −.115a −.909 to −.227a

−.688 −.517 −.403 −.232 −.004 −.349 −.178 −.064 .107 .334 −.095 .076 .190 .360 .588 .074 .245 .359 .530 .758 .413 .584 .698 .868 1.096

.232 .200 .186 .181 .202 .181 .144 .128 .126 .163 .159 .120 .105 .109 .156 .156 .119 .107 .115 .163 .179 .154 .148 .159 .203

.004⁎ .011⁎ .033⁎ .201⁎ .983⁎ .057⁎ .217⁎ .616⁎ .401⁎ .042⁎ .551⁎ .530⁎ .074⁎ .001⁎ b.001⁎ .636⁎ .043⁎ .001⁎ b.001⁎ b.001⁎ .023⁎ b.001⁎ b.001⁎ b.001⁎ b.001⁎

a The indirect effect is significant at the 95% bias-corrected bootstrap confidence interval. ⁎ The direct effect is significant at .05 p-value.

conducted to compare model power and fit with respect to rival models (see Table 6). Rival model 1 conceptualizes MO as a formative composite with three dimensions as reflective indicants, where each dimension reflects the MO construct. The model fit is acceptable, but not at the same level as the hypothesized model. In addition, many of the expected relationships did not achieve statistical significance with the inclusion of the moderators. Based on the results, one might mistakenly conclude that market orientation, centralization and international experience have no significant impact on an SBU's performance. Rival model 2 treats each dimension of MO as a separate construct where each construct affected the dependent variable independently and simultaneously. This model allowed for the testing of whether each dimension impacts the outcome variable independently, but where no internal relationships (or processes) were indicated. Responsiveness and intelligence generation both displayed significant effects on performance, but the effect of intelligence dissemination was nonexistent. These findings confirm previous studies that suggest that these three dimensions may have varied effects on performance (Carbonell & Rodríguez Escudero, 2010; Horng & Chen, 1998). Therefore, it is imperative to investigate the individual effects of each dimension. The results indicated that the predictive power of the model was stronger when the three dimensions were treated as separate constructs (R2 = .55, F (9, 116) = 18.49, p b .01). The final proposed model hypothesized a process where the individual dimensions of MO interact with firm characteristics to hypothetically Table 5 Correlation coefficients.

IG ID CE IX RESP ⁎ p b .05. ⁎⁎ p b .01.

IG

ID

CE

IX

.542⁎⁎ −.178⁎ .460⁎⁎ .355⁎⁎

−.240⁎⁎ .288⁎⁎ .299⁎⁎

−.193⁎ −.552⁎⁎

.210⁎

RESP

impact a dependent variable. The results showed that intelligence dissemination mediated the effect of intelligence generation on both responsiveness and performance. Interestingly, moderators (i.e., centralization and international experience) that did not have an impact within the Rival 1 model did show significant effects in this process model. The hypothesized model has both better fit and explanatory power than its rivals. This brief model comparison supports the idea that the MIIP model explains more of the variance in predicting performance and exposes hidden relationships that are not visible using the other conceptualizations. Thus, the hypothesized model outperforms the other rival models. 5. Discussion The present study focused on the idea that market intelligence implementation is a process. Depending on an SBU's structural characteristics, some firms had intelligence generation affect responsiveness primarily through high levels of formal intelligence dissemination, while in other firms intelligence generation primarily impacted responsiveness directly, without high levels of formal intelligence dissemination. Two characteristics of the SBU were identified as moderators that interacted with the MIIP. Conditional process modeling showed that centralization and international experience both depressed the indirect effect of intelligence generation on responsiveness through intelligence dissemination. At the same time, both moderating variables enhanced the direct effect of intelligence generation on responsiveness. These results are somewhat counterintuitive and provide possible explanations for the inconsistent results of some prior market orientation studies. For example, some studies have suggested that intelligence generation and/or dissemination did not have a positive effect on performance (e.g., Horng & Chen, 1998) while many studies reported a significant and positive effect. The current research has advanced MO theory by developing a conceptual process of MII. Understanding how these elements are linked along with boundary conditions can help explain some of the mixed results reported in the extant market orientation literature. The theoretical implications of the current research are threefold. First, this study disaggregated MO and emphasized the importance of

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Table 6 Rival models. Rival model 1

Rival model 2

Proposed model

MO as a formative composite with the three dimensions as reflective indicants

Each of the three dimensions of MO as separate constructs

Proposed MIIP

Path

Effect estimate

Path

Effect estimate

Path

Effect estimate

MO → PS CE → PS IX → PS MO × CE → PS MO × IX → PS

.62 b.01 −.15 −.23 .11

IG → PS ID → PS RESP → PS CE → PS IX → PS IG × CE → PS ID × CE → PS IG × IX → PS ID × IX → PS

.78⁎ −.44 .73⁎⁎

IG → ID ID → RESP IG → RESP CE → RESP IX → RESP IG × CE → RESP ID × CE → RESP IG × IX → RESP ID × IX → RESP RESP → PS R2 = .59 F(9, 116) = 18.49 p b .01

.58⁎⁎ 1.61⁎⁎

R2 = .41 F (5, 120) = 18.08 p b .01

R2 = .55 F (9, 116) = 18.49 p b .01

.16 .10 −.54 .34 −.85 .62

−1.61⁎⁎ −.41 −.21 .23⁎⁎ .24⁎⁎ .23⁎⁎ −.17⁎ .82⁎⁎

MO: market orientation; PS: performance satisfaction; IG: intelligence generation; ID: intelligence dissemination; RESP: responsiveness; CE: centralization; IX: international experience. ⁎ p b .05. ⁎⁎ p b .01.

investigating the interplay between the different activities associated with market intelligence and market orientation. The complexity of this mechanism is worth probing for an important reason. The number of extant studies focusing on or using MO as a primary theory is immense. If the MIIP impacts MO outcomes, extant studies which viewed the construct as a composite may result in incomplete or even misleading results. The disaggregated dimensions may display results that are different in strength or possibly even orthogonal to effects found using the composite MO construct. Conditional process modeling indicated that intelligence dissemination acts as a mediator between intelligence generation and responsiveness. In some SBUs, the generated intelligence impacted responsiveness primarily through the level at which that intelligence was formally dispersed throughout the organization, while in the other SBUs, responsiveness was impacted by generated intelligence directly (i.e. not through structured dissemination). The approach undertaken in the current research provides a way to uncover possible hidden relationships, as well as new avenues to revisit insignificant results using the aggregated approach. Second, based on the results using the disaggregated approach, the current research suggests that centralization and international experience did differentially impact the direct and indirect relationships between intelligence generation and responsiveness. In the indirect path, high levels of centralization depressed the effect of intelligence dissemination on responsiveness. Theoretically, this occurred because more centralized SBUs should not broadly disseminate market intelligence to employees who cannot act on this information. Wide dissemination in these situations was, at best, wasteful and could potentially distract or cause lower level employees to second-guess direction from the true decision makers. Thus, low levels of dissemination led to greater responsiveness in SBUs with centralized structures and vice versa. The current research showed that centralization was not necessarily a bad thing when SBUs dedicated fewer resources to intelligence dissemination. Our hypothesis was supported that centralization enhanced the effect of intelligence generation on responsiveness through the direct path, where dissemination was limited, even if it depressed the impact of intelligence generation on responsiveness through high levels of dissemination. Thus, for the SBUs employing limited dissemination, centralization helped expedite the intelligence transfer from the point of generation to the point of action. Based on the disaggregation of market orientation, international experience also displayed differential effects on both the direct and the indirect paths to responsiveness. Experience has been typically viewed as a good thing in international context (e.g., Williams & Chaston, 2004). However, in the indirect effect path (with dissemination as the

mediator), higher levels of international experience depressed the effect of intelligence generation on responsiveness. More experienced SBUs likely had established “formulae” for responding to market intelligence. These formulae resulted in greater efficiencies using the direct path from intelligence generation to responsiveness, when they did not have to broadly disseminate the intelligence. On the other hand, firms with little international experience showed greater responsiveness through a broad, formal dissemination of generated intelligence. In the indirect path, when more personnel were involved in the dissemination and evaluation of the information, ideas for responsive behaviors might come from virtually anywhere in the firm. The flexibility derived from allowing these responses is an important factor in determining the effectiveness of the response for firms with little experience in this area. In the direct relationship, we also found that the more international experience, the stronger the direct effect of intelligence generation on responsiveness. When market information did not have to be distributed companywide, experienced SBUs responded to market changes more swiftly. Due to a high level of uncertainty and threat in dynamic markets, international experience allowed these SBUs to deliver market information quickly to the hands of decision makers. Dispersing information through additional channels simply lengthened the transfer process by involving more participants who were not in a position to make responsive decisions. More importantly, understanding industry norms, responding intuitively, and copying competitors all require experience to do well. As a final check of the robustness of the model, we conducted a model comparison of the hypothesized model and two more traditional MO conceptualizations. Compared with the rival models, the hypothesized model showed a higher level of predictive power and better model fit. MO dimensions are disaggregated in the temporal MIIP, moderation effects are more likely to be visible. In this way, the MIIP may help explain extant inconsistent or insignificant findings in MO literature. We strongly recommend that future studies adopt the MIIP instead of simply using composite MO or disaggregated MO in their studies. Managerially, the findings suggest that managers should incorporate findings from past aggregated MO research selectively. Intelligence generation, dissemination, and responsiveness may not have a generic, predictable impact in all firms. Our results show that certain organizational characteristics, (i.e. a centralized structure or higher levels of international experience) may determine whether a firm should broadly disseminate information, because this practice will simply waste resources and potentially hinder responsiveness in the wrong situation. Following the traditional instinct to apply all components of MO can be costly for the firm with the wrong mix of structural characteristics.

Please cite this article as: Dong, X.(D.), et al., Reconceptualizing the elements of market orientation: A process-based view, Industrial Marketing Management (2015), http://dx.doi.org/10.1016/j.indmarman.2015.12.005

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X.(D.) Dong et al. / Industrial Marketing Management xxx (2015) xxx–xxx

Thus, the firm must deeply understand its structural properties and combine them with the right path in the MIIP. On the other hand, SBUs with a decentralized structure and/or limited international experience, will likely benefit from allocating resources to all dimensions of MO, particularly intelligence dissemination. The exploration of MII as a process allows us to scrutinize where conditional factors impact the relationships between the elements of MO. Instead of telling all firms to “be more market oriented” by engaging in intelligence generation, dissemination and responsiveness, the current results imply that a more nuanced, situation-specific approach may be more successful. The advocacy of market orientation needs more thoughtful consideration in business practice, and it should consider the SBU's specific structural properties. There are limitations to the current research. First, the current research investigated only two moderators to the market orientation process. Future research should address other variables worth probing such as cultural distance, type of industry, or strategic flexibility with respect to the disaggregated market orientation process. This would potentially allow for the explication of additional boundary conditions that might impact the successful utilization of a market orientation, and we would encourage future studies to explore variables that might interact with the process. Second, future research should further address both antecedents (e.g., organizational learning) and consequences (e.g., performance) of the disaggregated MO process. The current research focused on the MIIP, a process that is primarily within the firm. Future research using this model will likely uncover unknown external relationships, possibly between the MIIP elements and either antecedents or consequences. Thirdly, responsiveness is essentially a reactive behavior. Some practitioners and researchers strongly encourage being proactive in market orientation, which has been frequently advocated in new product development (Blocker, Flint, Myers, & Slater, 2011; Tsai, Chou, & Kuo, 2008). Future research might establish how proactive adjustment might work in the MIIP when compared to a reactive response. Aknowledgment This work was supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China [No. 13XNF053]. References Agarwal, S., Erramilli, M. K., & Dev, C. S. (2003). Market orientation and performance in service firms: role of innovation. Journal of Services Marketing, 17(1), 68–82. Akimova, I. (2000). Development of market orientation and competitiveness of Ukrainian firms. European Journal of Marketing, 34(9/10), 1128–1148. Akyol, A., & Akehurst, G. (2003). An investigation of export performance variations related to corporate export market orientation. European Business Review, 15(1), 5–19. Andrews, R., Boyne, G. A., Law, J., & Walker, R. M. (2009). Centralization, organizational strategy, and public service performance. Journal of Public Administration Research and Theory, 19(1), 57–80. Argote, L. (1993). Group and organizational learning curves: individual, system and environmental components. British Journal of Social Psychology, 32(1), 31–51. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research (JMR), 14(3). Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173. Beverland, M. B., & Lindgreen, A. (2007). Implementing market orientation in industrial firms: a multiple case study. Industrial Marketing Management, 36(4), 430–442. Blocker, C. P., Flint, D. J., Myers, M. B., & Slater, S. F. (2011). Proactive customer orientation and its role for creating customer value in global markets. Journal of the Academy of Marketing Science, 39(2), 216–233. Börjesson†, S., & Dahlsten, F. (2004). Management action in developing market orientation: a report from a customer knowledge project at Volvo cars. Journal of Change Management, 4(2), 141–154. Bourgeois, L. J., McAllister, D. W., & Mitchell, T. R. (1978). The effects of different organizational environments upon decisions about organizational structure. Academy of Management Journal, 21(3), 508–514.

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Please cite this article as: Dong, X.(D.), et al., Reconceptualizing the elements of market orientation: A process-based view, Industrial Marketing Management (2015), http://dx.doi.org/10.1016/j.indmarman.2015.12.005