How does customer orientation (in)congruence affect B2B electronic commerce platform firms' performance?

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How does customer orientation (in)congruence affect B2B electronic commerce platform firms' performance? Yi Liua, Daniel Q. Chenb, Wei Gaoc,

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a

Antai College of Economics and Management, Shanghai Jiao Tong University, 1954 Huashan Rd, Shanghai, China M.J.Neeley School of Business, Texas Christian University, Fort Worth, TX 76109, United States of America c College of Economics and Management, Southwest University, 2 Tiansheng Rd, Chongqing, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Customer orientation B2B e-commerce Platform businesses Demand uncertainty Polynomial regression

Business-to-business (B2B) electronic platforms have become important channels for transforming traditional modes of transaction. The success of these platforms relies heavily on the platform firms' customer orientation (CO) practices, which are designed to attract both sellers and buyers. This study draws on the cross network effect theory to explore whether and how a B2B e-commerce platform firm's (in)congruent CO strategic initiatives toward sellers or buyers affect the firm's performance. In addition, the moderating effects of seller-side and buyer-side demand uncertainty on the relationship between CO (in)congruence and platform firm performance are investigated. The analysis of data collected from 185 B2B electronic platform firms in China reveals that CO incongruence is more beneficial to firm performance than CO congruence. Furthermore, when seller-side demand uncertainty is high, an increase in seller-focused CO incongruence (i.e., higher seller orientation than buyer orientation) or buyer-focused CO incongruence (i.e., lower seller orientation than buyer orientation) improves or impedes a B2B e-commerce platform firm's performance, respectively. However, when buyer-side demand uncertainty is high, an increase in either type of CO incongruence does not improve firm performance. These findings contribute to the literature on and practices of B2B e-commerce and customer orientation.

1. Introduction With the rapid development of information and communication technologies in recent years, a large number of third-party business-tobusiness (B2B) e-commerce platform firms have emerged (Muzellec, Ronteau, & Lambkina, 2015). A B2B e-commerce platform creates a typical two-sided market consisting of the platform firm and participating selling and buying businesses (Chakravarty, Kumar, & Grewal, 2014). As an intermediary firm, the platform firm facilitates buyerseller interactions by charging each side an appropriate transaction fee. Successful B2B platforms not only allow buyers to access richer market information and mitigate overall procurement costs by effectively searching and locating sellers offering an assortment of vital goods and services (Spulber, 1996), but also allow sellers to reduce marketing and communication costs and easily and efficiently access more buyers (Cenamor, Sjödin, & Parida, 2017; Lucking-Reiley & Spulber, 2001). Compared with traditional B2B exchanges, B2B e-commerce platforms have significant advantages, such as better customer solutions (Wei, Geiger, & Vize, 2019) and lower transaction costs (Lucking-Reiley & Spulber, 2001). As such, B2B e-commerce platforms have become an

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important marketing channel that effectively facilitates trade between selling and buying firms (Watson, Worm, Palmatier, & Ganesan, 2015), thereby significantly contributing to economic growth (Chakravarty et al., 2014). A platform's success depends on its ability to 1) attract two interdependent groups of buyers and sellers and 2) facilitate direct transactions between buyers and sellers who may not be able (or may incur higher costs) to transact via other channels. In addition, an essential challenge for platform firms is to increase their customer base (both on the seller and buyer sides) (Fang, Li, Huang, & Palmatier, 2015; Sriram et al., 2015; Zhu & Iansiti, 2012). Thus, a B2B e-commerce platform firm needs to implement appropriate strategies to effectively attract both customer groups, i.e., sellers and buyers. Because of the critical role of customer size to platform success, we choose to investigate the strategic value of customer orientation (CO; i.e., prioritizing customer interests) for B2B e-commerce platform firms. Previous research has acknowledged that CO is essential for traditional firms to acquire and retain customers (Frösén, Jaakkola, Churakova, & Tikkanen, 2016; Gatignon & Xuereb, 1997; Kohli & Jaworski, 1990; Narver & Slater, 1990; Zhou, Chi, & Tse, 2005). Firms

Corresponding author. E-mail addresses: [email protected] (Y. Liu), [email protected] (D.Q. Chen), [email protected] (W. Gao).

https://doi.org/10.1016/j.indmarman.2020.02.027 Received 26 April 2019; Received in revised form 14 February 2020; Accepted 29 February 2020 0019-8501/ © 2020 Elsevier Inc. All rights reserved.

Please cite this article as: Yi Liu, Daniel Q. Chen and Wei Gao, Industrial Marketing Management, https://doi.org/10.1016/j.indmarman.2020.02.027

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resources and that sellers and buyers may not be equally important for a platform's value creation (Chakravarty et al., 2014), we argue that the CNE theory offers a useful theoretical lens for interpreting the strategic choices of B2B e-commerce platform firms in terms of CO (in)congruence. We test our hypotheses with a unique survey dataset collected from 185 China-based B2B platform firms. The results of the subsequent polynomial regression analysis support most of our arguments. This study makes at least three contributions to the literature. First, we explore the effects of CO (in)congruence in the emerging context of B2B e-commerce platforms. Although CO has been recognized as a vital strategy for any exchange relationship (Kirca et al., 2005; Kumar et al., 2011; Lusch & Laczniak, 1987; Narver & Slater, 1990), and some research has addressed CO asymmetry in two-sided markets (Chakravarty et al., 2014), previous studies on the effects of the CO strategy on firm performance seldom take into account the level of resources of firms. In this study, we examine how platform firms with limited resources can improve their performance by choosing appropriate CO (in)congruence strategies. The findings of the study contribute to the CO literature and B2B platform research. Second, our study extends the information processing literature by exploring the effects of seller-side and buyerside demand uncertainty on the choice of CO strategies of platform firms. Organizational information processing theory has been widely used in strategy and operations studies, but most studies have focused on explaining how traditional manufacturers or service providers deal with customer demand uncertainty (Gardner, Boyer, & Gray, 2015; Narayanan, Jayaraman, Luo, & Swaminathan, 2011; Trautmann, Turkulainen, Hartmann, & Bals, 2009; Wong, Boon-Itt, & Wong, 2011). In contrast, few studies examine how B2B platform firms can effectively cope with seller-side and buyer-side demand uncertainty. Third, this study offers compelling explanations for the unique performance characteristics of platform firms in electronic channels. Previous studies of B2B marketing channels mainly focus on traditional offline channels and dyadic interfirm relationships (Watson et al., 2015). This study conceptualizes rapidly emerging two-sided B2B e-commerce platforms as a new distribution channel and explores how platform firms cultivate both customer sides to improve their performance. Its triadic perspective offers a new viewpoint on marketing channels.

with a consistent CO strategy can outperform their rivals, because they better understand customer needs, forecast market demand, and prioritize tasks to create customer value (Danneels, 2003; Olson, Slater, & Hult, 2005; Theoharakis & Hooley, 2008). As a result, CO can generate competitive advantages and is a key driver of firm performance (Han, Kim, & Srivastava, 1998; Hult & Ketchen, 2001; Jaworski & Kohli, 1993; Kirca, Jayachandran, & Bearden, 2005; Kumar, Jones, Venkatesan, & Leone, 2011; Luo, Hsu, & Liu, 2008; Zhou, Brown, Dev, & Agarwal, 2007). However, most CO studies focus on traditional dyadic exchange relationships. The study of Chakravarty et al. (2014) is among the first to modify the CO concept and apply it to the B2B ecommerce triadic (seller-platform-buyer) context. In particular, they propose that CO in platform firms should be examined along two dimensions: total CO (toward both the seller side and buyer side) and CO asymmetry (favoring one side over the other side). Chakravarty et al. (2014) provide valuable insights into the strategic choices of Internet-based two-sided market makers. However, it remains unclear how B2B e-commerce platform firms can benefit from their CO strategies. For example, they find that only total CO, but not CO asymmetry, had a direct positive effect on firm performance. One of the reasons Chakravarty et al. fail to uncover a direct link between an unbalanced CO strategy (i.e., CO asymmetry) and firm performance may be that the CO structure in their study is examined without considering the resource constraints of B2B e-commerce platform firms. The literature suggests that many B2B electronic platform firms, unlike those of the traditional platform economy, are small businesses with fewer resources (Leong, Pan, Newell, & Cui, 2016; Muzellec et al., 2015). Thus, B2B e-commerce platform firms may not be able to implement a total CO strategy (i.e., invest freely on both the seller side and buyer side when needed) and may have to decide how to allocate limited resources between seller-focused or buyer-focused CO. Therefore, we submit that an unbalanced CO strategy is more relevant for B2B e-commerce platforms and that resource constraints must be taken into account when evaluating the potential organizational effects of this unbalanced CO strategy. In addition, demand uncertainty may influence the strategic value of CO. Unlike firms operating exclusively in a dyadic trading system, B2B e-commerce platform firms must cope with higher seller-side and buyer-side demand uncertainty, which refers to the magnitude and frequency of changes in the demand of sellers and buyers, including changes in their number, concentration, composition, needs, and behavior (Grewal, Chakravarty, & Saini, 2010). This demand uncertainty may lead to a high turnover of market participants and difficulty in anticipating customer needs (Grewal et al., 2010), further complicating the decision-making of B2B platform firms in pursuing appropriate CO strategies. To fill these research gaps, this study adopts a congruence perspective (Ahearne, Haumann, Kraus, & Wieseke, 2013; Zhang, Wang, & Shi, 2012) and introduces the concepts of CO (in)congruence, defined as the extent of (in)consistency in CO between the resource allocation efforts of a B2B e-commerce platform firm for its sellers and buyers. In particular, we focus on understanding the effects of CO (in)congruence on firm performance in the context of seller-side and buyer-side demand uncertainty (see Fig. 1) and address two interrelated research questions. First, how does (in)congruence in CO toward sellers and buyers affect the performance of a B2B e-commerce platform firm? Second, how do seller-side demand uncertainty and buyer-side demand uncertainty moderate the effects of CO (in)congruence on firm performance? To answer these questions, we combine the CO literature with the theory of cross-network effects (CNEs) (Chu & Manchanda, 2016). CNEs are highly relevant to our research questions as they show that platform participants (i.e., buyers or sellers) from either side can benefit from the growth and evolution of the other side (i.e., sellers or buyers) (Boudreau & Jeppesen, 2015; McIntyre & Srinivasan, 2017). Considering that most B2B e-commerce platform firms have limited

2. Theoretical background and hypotheses This study focuses on two-sided B2B electronic platform firms,1 a specific form of multi-sided platforms that have rapidly proliferated with the rise of the Internet and information technologies over the last three decades. Examples of successful B2B electronic platforms include eWorldTrade and ThomasNet in the U.S. and Alibaba and Ganglian Holdings in China. In this section, we begin with a brief review of the CO literature, followed by an introduction of some of the unique characteristics of B2B e-commerce platform firms. We then apply the logic of CNE and the perspective of information processing to develop hypotheses related to the CO (in)congruence strategies of B2B platform firms. 2.1. Customer orientation Customer desires, concerns, and opinions have become the main driver of many strategic business decisions of contemporary firms (Deshpandé, Farley, & Webster Jr, 1993; Kohli & Jaworski, 1990). CO is a well-studied concept in the marketing strategy literature that refers to a set of organizational beliefs and behaviors prioritizing customer interests (Deshpandé et al., 1993; Rindfleisch & Moorman, 2003). The welfare of customers is taken into account by listening to their voices and delivering solutions adapted to their interests (Deshpandé et al., 1 In this study, we use the terms “B2B electronic platform firms,” “B2B ecommerce platform firms,” and “B2B platform firms” interchangeably.

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Fig. 1. The conceptual model.

2.2. B2B platform firms and cross-network effects

1993). CO involves all activities related to information generation and dissemination and appropriate responses to the needs and preferences of current and future customers. Thus, the adoption of a CO strategy is assumed to lead to better firm performance (e.g., Jaworski & Kohli, 1993; Kirca et al., 2005; Kumar et al., 2011; Narver & Slater, 1990). However, our review of the CO literature reveals at least two research gaps. First, there are inconsistent findings regarding the effects of CO on various firm performance variables. While some authors find a general positive relationship (e.g., Hult & Ketchen, 2001), others report no relationship at all (e.g., Smirnova, Naudé, Henneberg, Mouzas, & Kouchtch, 2011) or even negative effects (e.g., Voss & Voss, 2000). Some authors report that CO may be positively related to certain firmlevel outcomes, but negatively related to other variables (Im & Workman, 2004). Others suggest that the effects of CO on performance are contingent on environmental factors (Im & Workman, 2004). For example, Gatignon and Xuereb (1997) find that in markets with high demand uncertainty, CO positively relates to firm innovation performance, but in markets with low demand uncertainty, this relationship is negative. Thus, the nature of the relationship between CO strategies and firm performance should be further explored. The second gap is related to the research context. Most research explores the effects of CO on firms transacting in a dyadic relationship (Kirca et al., 2005; Zhao & Cavusgil, 2006; Ziggers & Henseler, 2016). However, few studies examine the application of CO in a platform context, in particular the B2B e-commerce platform environment (Chakravarty et al., 2014). B2B platform firms face more difficulties than traditional firms in a dyadic relationship when designing CO strategies. B2B platform firms must implement a CO strategy for both sellers and buyers, because their success depends on the continued participation of both sides. However, they face the challenge of properly allocating limited resources between both sides to optimize their own performance. Thus, the findings in the CO literature are not readily applicable to the B2B e-commerce context, and the question of how B2B e-commerce platform firms should implement CO strategies effectively remains unanswered. In particular, the problem of insufficient resources encountered by most B2B platform firms is largely overlooked.

To explore the favorable CO strategies of B2B platform firms, we discuss some of the unique characteristics of platform-based firms. In a traditional B2B dyadic supply chain, a focal business (i.e., a manufacturer) works with one type of downstream customers (i.e., its buyers). The needs of all buyers drive the CO strategy of the manufacturer. In general, the suppliers of the focal business do not directly transact with its customers (Chakravarty et al., 2014; Wathne & Heide, 2004). In contrast, a B2B electronic platform can be seen as a triadic relationship, involving the platform firm and its two-sided users: sellers and buyers. The platform itself does not make direct transactions with its two categories of users. Instead, its role is to enable direct interactions and transactions between buyers and sellers (de Matta, Lowe, & Zhang, 2017). Ideally, both the selling and buying sides rely exclusively on the platform to search, screen, recruit, manage, and monitor a pool of high quality participants from the other side of the platform. Thus, the economic value of the platform is a function of the volume and quality of platform participants on both sides. However, the platform firm generally does not have control over various decisions (e.g., production, pricing, service decisions) that are essential to the volume and frequency of transactions between its two sides of users. Thus, an effective CO strategy is essential for a platform firm to retain its customers (Chakravarty et al., 2014). One possible approach for B2B e-commerce platform firms is to consider CNEs (or indirect network effects), a prevailing characteristic of platforms, when designing their CO strategies (Boudreau & Jeppesen, 2015; Chu & Manchanda, 2016; McIntyre & Srinivasan, 2017). The benefits of platform participants depend on the total number of participants on the other side of the same platform (Chu & Manchanda, 2016), which is a common CNE logic in two-sided markets. In addition, both sides of the network can mutually benefit from the size and characteristics of the other side (Boudreau & Jeppesen, 2015; McIntyre & Srinivasan, 2017). For instance, sellers are more likely to participate in a B2B platform with a sufficiently large buyer base. Similarly, buyers usually prefer to join a B2B platform with a larger number of sellers. Taking as an example of Zhaogang.com (also known as stealsearch. com), the largest steel trading platform in China, industrial buyers (e.g., 3

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Katsikeas, & Jung, 2016; Vogel, Rodell, & Lynch, 2016) and propose two distinct components of CO from a congruency perspective: CO congruence and CO incongruence. The concepts of CO congruence and CO incongruence are chosen to distinguish them from total CO and CO asymmetry (Chakravarty et al., 2014). In particular, we consider the problem of resource constraints and define CO (in)congruence as the extent of (in)consistency in CO between a B2B e-commerce platform's resource allocation efforts in practicing customer orientation toward both sellers and buyers, when the total available amount of resources for CO is constant. According to this definition, CO congruence describes a strategy in which a platform firm equally allocates a fixed amount of resources available to buyers and sellers. Conversely, CO incongruence refers to a strategy in which the platform disproportionately distributes a fixed amount of resources to sellers and buyers. In summary, CO incongruence and CO asymmetry are conceptually similar but operationally different notions in that CO incongruence considers the level of resource availability when exploring platforms' unbalanced CO strategies. Consequently, we identify two forms of CO incongruence: sellerfocused CO incongruence and buyer-focused CO incongruence (i.e., higher or lower seller orientation than buyer orientation, respectively). Thus, when the level of resources is constant, the degree of CO incongruence can be considered as a two-sided continuum, ranging from low incongruence to high incongruence (see Fig. 2). The left half of Fig. 2 captures the scenario in which seller orientation is higher than buyer orientation, while the right half reflects the opposite.

distributors and traders of steel products) value the many steel manufacturers and products available for 24/7 search. Sellers (e.g., steel manufacturers) also benefit from the large customer base. In particular, by joining a platform, sellers may get access to more buyers and may sell more products at lower costs. In addition, buyers can approach a large number of sellers and enjoy a wider selection of products at more competitive prices. Therefore, platform firms can leverage their CNEs to attract buyers and sellers, gaining significant competitive advantages (Song, Xue, Rai, & Zhang, 2018). Although the CNE theory is a powerful conceptual tool for examining platform-mediated firms, little empirical research has been conducted on network effects, particularly those related to B2B e-commerce (Chu & Manchanda, 2016). Based on the CNE concept, we explore valid approaches for B2B ecommerce platform firms to design their CO strategies when constrained by resources. We suggest that the mutual dependence of platform participants on both sides provides opportunities for platform owners to grow their businesses via incongruent CO strategies toward selling and buying firms. In the following section, we argue that an incongruent CO strategy is more appealing than a congruent strategy. 2.3. (In)congruence in platform CO strategies Chakravarty et al. (2014) are among the first to examine the CO of B2B e-commerce platforms. They propose a two-dimensional understanding of CO: total CO and CO asymmetry. Total CO captures the extent to which a platform firm aims to understand, serve, and meet the needs of all customers. CO asymmetry describes a CO strategy aimed at understanding, serving, and meeting the needs of one side more than the other, which reflects an unbalanced strategy of platform owners, who focus their efforts more on one particular side of the marketplace (Chakravarty et al., 2014). Based on the power dependency theory, Chakravarty et al. (2014) reason that both total CO and CO asymmetry strategies are desirable, but find that only total CO directly contributes to platform firms' performance. One limitation of the Chakravarty et al. (2014) study is that the authors only consider the dependency of platform firms on their buyers and sellers, ignoring the fact that most platforms are constrained by limited resources (Leong et al., 2016; Muzellec et al., 2015). This can result in two problems. First, although a total CO strategy is compelling, it is almost impossible for B2B platform firms, particularly at the early stage of their business, to implement CO strategies toward both customer sides (and perhaps even toward one side) without restriction. Therefore, total CO is not an affordable strategic choice. Second, without considering the total amount of resources available for CO strategies, the operationalization of the CO asymmetry construct of Chakravarty et al. (2014), i.e., an absolute score difference between buyer orientation and seller orientation, is somewhat misleading. In particular, comparing the variance of CO asymmetry across platforms of different sizes is problematic. Consider the following scenario in which two platform firms, A and B, both implement a buyer-focused CO strategy (i.e., invest more resources toward buyer orientation than seller orientation). Specifically, the two firms respectively spend $1000 and $11,000 on seller orientation. In addition, they invest $7000 and $17,000 in buyer orientation, respectively. Although the two firms show an equivalent level of CO asymmetry, without taking into account the total amount of resources available for CO, we have no reference point to compare the effects of these two CO practices on firm performance. The operationalization of CO asymmetry (Chakarvarty et al., 2014) is thus less likely to capture the true value of an unbalanced CO strategy for B2B platform firms. Hence, this study examines whether and how a sensible unbalanced CO strategy can add value to platform firms when considering resource availability. Specifically, we seek an empirically rigorous alternative to operationalize such a strategy. We follow recent developments in the marketing and organizational behavior literature by taking a polynomial regression approach2 (e.g., Ahearne et al., 2013; Menguc, Auh,

2.4. CO (in)congruence and B2B e-commerce platform performance As described earlier, B2B e-commerce platforms are two-sided markets characterized by CNEs (McIntyre & Srinivasan, 2017). Specifically, the installed customer base on the seller (or buyer) side can facilitate customer growth on the buyer (or seller) side. In other words, without the active participation of either side, the other side remains reluctant to join the platform, thus rendering it defunct (Cennamo & Santalo, 2013; McIntyre & Srinivasan, 2017; Sriram et al., 2015). The results of previous studies indicate that platform firms focusing on one customer side of the platform can improve their market efficiency (e.g., Armstrong, 2006; Chu & Manchanda, 2016; Rochet & Tirole, 2003; Sriram et al., 2015). Based on this logic, we suggest that in order to best utilize their limited resources to attract both customer sides, B2B platform firms can use an incongruent CO strategy to take advantage of CNEs. Platform firms pursuing a seller-focused CO incongruence strategy devote a greater proportion of their resources to understanding and meeting the needs of potential sellers, thus signaling their seriousness toward sellers (Anderson & Weitz, 1992; Rokkan, Heide, & Wathne, 2003). This incongruent CO strategy can help a platform attract more quality sellers and create a bonding effect to build a large seller base. More importantly, this extensive strategy and commitment to sellers can motivate sellers to generate more business and better serve their buyers through the platform (Chakravarty et al., 2014). Accordingly, based on the CNE logic (i.e., the size and nature of the selling side affect those of the buying side;Boudreau & Jeppesen, 2015; McIntyre & Srinivasan, 2017), potential buyers are more likely to join the same platform to engage in transactions with its large seller base to obtain high quality products at a good price. Thus, a seller-focused CO incongruence strategy can fully leverage seller-initiated CNEs to attract and acquire a large number of sellers and buyers. Consequently, the platform's performance is likely to increase. Similarly, platform firms implementing a buyer-focused CO incongruence strategy spend more time and effort listening to their buyers, 2 We provide the details of the polynomial regression in the Research Methodology section.

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CO CO toward Sellers / CO toward Buyers

tow ard Sel ler s

ers uy B d

ar

CO

tow

CO toward Sellers > CO toward Buyers

CO toward Buyers > CO toward Sellers

High CO Incongruence

CO Congruence Low CO Inongruence

High CO Incongruence

Note：CO=Customer Orientation

Fig. 2. A continuum view of CO incongruence.

understanding their needs, and finding solutions to meet their interests. This strategy can directly appease and attract potential buyers to a platform, thereby forming a large base of active buyers (Chakravarty et al., 2014). Furthermore, increasing its buyer-focused strategy and commitments signals that the platform prioritizes the interests of its buyers (Deshpandé et al., 1993; Rindfleisch & Moorman, 2003), which may encourage buyers to use the same platform to purchase products/ services more frequently. As a result, more potential sellers are likely to be drawn to the platform to open their online stores to approach its large number of active buyers and sell more products to them (Chakravarty et al., 2014). Therefore, following the same CNE logic (i.e., the size and nature of the buyer side affect those of the seller side) (Boudreau & Jeppesen, 2015; McIntyre & Srinivasan, 2017), a buyerfocused CO incongruence strategy can take full advantage of buyerinitiated CNEs to attract and acquire a large number of buyers and sellers to make transactions through the platform, contributing to the growth of the platform. Based on the above arguments, in either case of an incongruent CO strategy (i.e., seller-focused or buyer-focused), we expect an increase in the level of participation from both sides of the market created by a B2B platform. As such, platform-mediated transactions between both sides are likely to flourish with an incongruent CO strategy. We acknowledge that a B2B platform firm also has a third choice, implementing a congruent CO strategy, i.e., it can allocate its already limited resources evenly to achieve an equal level of seller orientation and buyer orientation. However, in this scenario, the platform firm runs the risk of not meeting the business needs of either side of customers due to a lack of resources. As a result, neither the CNEs on the seller side nor those on the buyer side will be fully deployed by the platform firm to appease or acquire more customers. Therefore, with a higher degree of CO congruence, the potential benefits of CO are less likely to be realized (Chakravarty et al., 2014; Chu & Manchanda, 2016). Hence, we propose that CO incongruence is a valid path to enhance a platform firm's performance.

2.5. The moderating effects of demand uncertainty The above hypotheses imply that when the level of resources is finite, a more effective CO strategy for B2B platforms is to disproportionally allocate their resources between seller- and buyer-oriented efforts. A closely related question is the following: under what circumstances should B2B platforms be more seller-oriented, and vice versa? To answer this question, we use the organizational information processing perspective to integrate the concept of demand uncertainty, which is highly relevant to the success of B2B electronic markets (Grewal et al., 2010), into our theoretical development. Demand uncertainty indicates a market characterized by turbulent changes in the composition of its customers and their preferences (Grewal, Comer, & Mehta, 2001; Kohli & Jaworski, 1990). In the context of B2B e-commerce platforms, high customer turnover, coupled with difficulty in understanding or anticipating the needs of sellers and buyers, creates uncertainty. Therefore, demand uncertainty for B2B ecommerce firms reflects the extent of frequent changes arising from unpredictability and volatility in the surrounding environment, such as changes in the composition, needs, and behavior of sellers and/or buyers (Grewal et al., 2010). Accordingly, we distinguished two types of demand uncertainty faced by B2B platform firms: seller-side and buyer-side demand uncertainty. Seller-side demand uncertainty describes the extent to which sellers frequently enter and exit the platform and exhibit various needs and behaviors. When demand uncertainty from its sellers is high, the platform firm may have difficulty meeting the needs of its sellers and anticipating their behavior (Grewal et al., 2010). Accordingly, the firm must seek and obtain more information to understand sellers' concerns. Organizational information processing theory (OIPT) (Tushman & Nadler, 1978) suggests that firms must also acquire appropriate information processing capabilities to match information processing requirements due to uncertainty (Tushman & Nadler, 1978). As discussed earlier, CO strategies seek to obtain and use more information from customers to satisfy their needs (Ziggers & Henseler, 2016). Thus, high seller-side demand uncertainty requires more seller-oriented initiatives from the platform firm to enhance its information processing capabilities and address the increasing information processing requirements due to this uncertainty. This increase in seller orientation efforts indicates that the platform firm pays considerable attention and devotes more resources to serve and satisfy its sellers (Chakravarty et al., 2014; Danneels, 2003; Olson et al., 2005; Theoharakis & Hooley, 2008), which may attract more sellers to enter the market created by the platform. Hence, based on our reasoning for H1, the positive effects of CO incongruence, in the form of higher seller orientation, on firm

H1a. A CO incongruence strategy, in the form of seller-focused CO incongruence (i.e., higher seller orientation than buyer orientation), is positively related to the performance of B2B e-commerce platform firms. H1b. A CO incongruence strategy, in the form of buyer-focused CO incongruence (i.e., higher buyer orientation than seller orientation), is positively related to the performance of B2B e-commerce platform firms.

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focused CO incongruence may not only reduce the platform firm's buyer-related information processing capabilities to meet buyers' needs, but also cause a mismatch between the firm's seller-related information processing capabilities and information processing requirements. Therefore, based on OIPT, we propose the following hypotheses:

Table 1 Demographic information. Firm size (no. of employees)

Frequency

Percentage

≤ 100 101–300 301–500 501–1000 ≥ 1000

100 59 13 4 9

54.0 31.9 7.0 2.2 4.9

Firm age (years) ≤5 6–10 11–15 ≥ 16

116 33 22 14

62.7 17.8 11.9 7.6

Industry Steel Chemical Textile Machinery Electronics Construction materials Multiple industries Othersa

10 12 7 24 11 8 66 47

5.4 6.5 3.8 13.0 5.9 4.3 35.7 25.4

H3. When buyer-side demand uncertainty is high, a) the positive effects of the buyer-focused CO incongruence strategy on the performance of B2B e-commerce platform firms are stronger; and b) the positive effects of the seller-focused CO incongruence strategy on the performance of B2B e-commerce platform firms are weaker. 3. Research methodology 3.1. Sample and data collection To test our hypotheses, we commissioned a China-based nationwide market research firm to collect data from B2B electronic platform firms in China. No database of B2B platform firms existed at the time of data collection, so we first compiled a sampling frame of B2B electronic platform firms in China through an extensive Internet search. We carefully inspected the websites of all of the identified firms to ensure their suitability for our research. In addition, during the selection process of targeting key respondents of these firms, we also asked them to name other suitable B2B platform firms in their industries to help us expand the sampling frame. In the end, we obtained a population of 586 B2B platform firms in China. Next, we collaborated with the market research company to contact these platform firms by phone to acquire the names and information of the people responsible for strategic decision-making in their firms. These respondents were mostly founders, general managers, chief inspectors, and department managers. During the telephone conversations, we explained the purpose of our research and invited them to participate in our survey, promising to provide a summary of the findings. To improve respondent validity and minimize potential common method bias, we invited two key respondents from each platform firm to complete the questionnaire. In the end, we obtained usable responses from 185 B2B electronic platform firms, representing eight industrial sectors, namely steel, chemical, construction materials, textile, electronics, machinery, multiple industries, and others. As shown in Table 1, 54% of the B2B electronic platform firms in our sample had fewer than 100 employees, and another 31.9% had more than 100 but fewer than 300 employees. In other words, more than 85% of the firms in our sample were small or medium-sized businesses, consistent with the finding of previous research that the majority of B2B e-commerce platform firms are small businesses who are most likely constrained with resources (Leong et al., 2016; Muzellec et al., 2015). Using late responses as representatives of non-responses, a comparison of the early 25% with the late 25% of the respondents revealed no significant differences between the two groups in terms of industry type (i.e., steel, chemical, textile, machinery, electronics, construction materials, multiple industries, and others) (χ2(7) = 4.160, p = .761 > 0.05) and firm size (number of employees) (t = −1.421, p = .158 > 0.05). Therefore, non-response bias did not seem to be a major concern for our dataset.

a Note: The “others” category includes the coal, petroleum, agricultural, medical, plastic, glass, and automobile industries.

performance will be amplified when seller demand uncertainty is high. In contrast, the positive effects of CO incongruence, in the form of higher buyer orientation, on firm performance are likely to diminish when seller demand uncertainty is high. Based on OIPT (Tushman & Nadler, 1978), when platform firms face high seller-side demand uncertainty, there is less need for them to devote more resources to increasing their buyer-side information processing capabilities to meet buyer-side information processing requirements. Furthermore, increasing buyer-focused CO incongruence at the expense of seller-focused CO incongruence may not only reduce platform firms' information processing capabilities concerning sellers' needs, but also result in overinvestment in information processing capabilities to address buyers' needs. According to the tenet of OIPT, such a mismatch between information processing capabilities and information processing requirements reflects a suboptimal CO buyer-focused incongruence strategy. In other words, the positive impact of a buyer-focused incongruence strategy under the circumstance of high seller demand uncertainty is most likely declined. Therefore, we propose the following hypotheses: H2. When seller-side demand uncertainty is high, a) the positive effects of the seller-focused CO incongruence strategy on the performance of B2B e-commerce platform firms are stronger; and b) the positive effects of the buyer-focused CO incongruence strategy on the performance of B2B e-commerce platform firms are weaker. For platform firms, buyer-side demand uncertainty indicates a high turnover of buyers and greater difficulty in understanding or forecasting their needs (Grewal et al., 2010). Based again on the logic of matching organizational information processing capabilities with information processing requirements, we hypothesize that buyer-side demand uncertainty calls for a more incongruent buyer-focused CO strategy for platforms to better serve the needs of their buyers and thereby create value (Chakravarty et al., 2014). Thus, we propose that the positive effects of CO incongruence, in the form of higher buyer orientation, on firm performance will be intensified when buyer-side demand uncertainty is high. Using arguments similar to those leading to Hypothesis 2b, we suggest that the positive effects of CO incongruence (in the form of higher seller orientation) on firm performance are dampened when buyer-side demand uncertainty is high. In particular, given resource constraints, a seller-focused CO incongruence at the expense of buyer-

3.2. Measurement items Well-established multi-item scales from previous research were adapted to measure the constructs of our conceptual model. Table 2 provides the details of all measurement items, based on a 7-point Likert scale. Specifically, the items measuring CO were adapted from Chakravarty et al. (2014). We followed Chakravarty et al. (2014) in defining CO toward sellers (or buyers) as the extent to which a platform firm establishes its business objectives based on seller (or buyer) satisfaction. The constructs were measured by assessing the platform 6

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technology skills of the platform firm, because of their critical role in ensuring reliable platform operations (Chakravarty et al., 2014; Grewal et al., 2010). We also controlled the platform firm's ability to provide effective training to its participants. Most potential sellers and buyers may not be familiar with the transaction model and rules of a particular electronic platform. Therefore, the extent of training provided by the platform (e.g., how to perform transactions in the platform) may affect firm performance (Grewal et al., 2010). In addition, the number of platform employees, a good indicator of firm size, can influence the performance of the platform firm. Therefore, it was also controlled. Finally, we controlled potential industrial heterogeneity by including industry fixed effects.

Table 2 Measurement scales. Items

Loading

Customer orientation toward sellers (Cronbach's alpha = 0.830, CR = 0.887, AVE = 0.663) 1. Our business objectives for the marketplace are primarily driven by 0.771 seller satisfaction. 2. We constantly measure our level of commitment to serving seller 0.819 needs. 3. We give close attention to servicing sellers. 0.843 4. Our marketplace strategy for competitive advantage is based on 0.821 understanding of seller needs. Customer orientation toward buyers (Cronbach's alpha = 0.883, CR = 0.920, AVE = 0.742) 1. Our business objectives for the marketplace are primarily driven by 0.850 buyer satisfaction. 2. We constantly measure our level of commitment to serving buyer 0.850 needs. 3. We give close attention to serving buyers. 0.885 4. Our marketplace strategy for competitive advantage is based on 0.860 understanding of buyer needs.

3.3. Common method variance To reduce potential common method variance, we applied ex ante and ex post approaches, namely research design and statistical analysis (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). First, we protected the anonymity of the respondents and improved the wording of the items. Second, we asked two key respondents from each B2B e-commerce platform firm to complete the questionnaire. We checked inter-rater agreement between the two respondents using rwg, a commonly used inter-rater agreement index for Likert-type scales (Brown & Hauenstein, 2005; LeBreton, Burgess, Kaiser, Atchley, & James, 2003). The lowest average rwg for our constructs was 0.899, which was much higher than the generally accepted cutoff value of 0.70 (James, Demaree, & Wolf, 1984). This indicates that the two respondents from each platform firm provided consistent evaluations of the constructs. The scores of both respondents were averaged before further analysis to reduce the effects of possible bias from individual respondents. Third, we used the marker variable method recommended by Lindell and Whitney (2001). The lowest positive correlation in the correlation matrix was 0.003, which we used to estimate common method variance. After adjusting the observed correlations, the correlations between the independent and dependent variables retained their statistical significance, indicating that common method variance was not a serious issue.

Seller-side demand uncertainty (Cronbach's alpha = 0.874, CR = 0.914, AVE = 0.727) 1. Our seller demands vary a lot. 0.818 2. We are often surprised by our sellers' behavior. 0.906 3. A lot of seller firms join and/or leave our platform. 0.853 4. We are often puzzled by actions of our sellers. 0.832 Buyer-side demand uncertainty (Cronbach's alpha = 0.850, CR = 0.900, AVE = 0.692) 1. Our buyer demands vary a lot. 0.831 2. We are often surprised by our buyers' behavior. 0.869 3. A lot of buyer firms join and/or leave our platform. 0.876 4. We are often puzzled by actions of our buyers. 0.745 Performance (Cronbach's alpha = 0.932, CR = 0.946, AVE = 0.747) 1. Return on investment relative to objective. 2. Sales relative to objective. 3. Profits relative to objective. 4. Growth relative to objective. 5. Market share relative to objective. 6. General success. Reputation (Cronbach's alpha = 0.825, CR = 0.878, AVE = 0.590) 1. We have a good reputation in the industry. 2. Our opinion is valued in the industry. 3. We are perceived as a firm with high level of integrity in the industry. 4. Firms in the industry respect us. 5. Our current customers seek our opinion out. Training (Cronbach's alpha = 0.786, CR = 0.875, AVE = 0.701) 1. The training we offer satisfies the seller and buyer firms. 2. We offer an adequate level of technical support to firms participating in our platform. 3. Sellers and buyers firms have many opportunities for participating in training sessions.

0.861 0.885 0.868 0.876 0.879 0.813 0.767 0.796 0.760

3.4. Measurement validation

0.801 0.714

We used confirmatory factor analysis to assess the reliability and validity of the multi-item constructs in our model. The results indicated that the measurement model showed a good fit with the data (χ2 = 686.422, df = 499, Comparative Fit Index = 0.943, TuckerLewis Index = 0.936, root mean square error of approximation = 0.045, and standardized root mean square residual = 0.057). As reported in Table 2, Cronbach's alpha and composite reliability (CR) for each construct exceeded the 0.700 threshold, suggesting adequate reliability (Fornell & Larcker, 1981). All factor loadings were significant, and the average variance extracted (AVE) for each construct was above the cutoff value of 0.500, indicating good convergent validity (Bagozzi & Yi, 1988). We measured discriminant validity by comparing the square root of the AVE for individual constructs with the correlations between all pairs of variables. As the results in Table 3 show, the square root of the AVE for each construct was greater than the highest correlation between all pairs of variables, indicating satisfactory discriminant validity (Fornell & Larcker, 1981).

0.827 0.837 0.847

Information technology skills (Cronbach's alpha = 0.824, CR = 0.885, AVE = 0.657) 1. We are experienced with IT. 0.750 2. We have strong technical IT skills. 0.835 3. We have adequate knowledge about IT. 0.840 4. We have adequate managerial IT skills. 0.815

firm's commitment to meeting the needs of its sellers (or buyers) and how it developed its marketing strategy based on these needs. The measurement items for seller-side and buyer-side demand uncertainty were adapted from Grewal et al. (2001) and Grewal et al. (2010). These items measured the extent of frequent changes in the demand, composition, number, and behavior of sellers (and buyers). The measurement items to evaluate the performance of B2B e-commerce platform firms were adapted from Grewal et al. (2010), taking into account sales, profits, market share, growth, and return on investment. In addition, we included a set of control variables relevant to the performance of B2B e-commerce platforms. First, we controlled for the platform firm's reputation as it is strongly correlated with its performance (Grewal et al., 2010). Second, we controlled for the information

3.5. Analytical approach To test our hypotheses, we used the polynomial regression approach. As discussed in the Theoretical Development section of this paper, CO incongruence differs from CO asymmetry (Chakravarty et al., 2014) in terms of operationalization. CO asymmetry was measured using the difference score approach (Kumar, Scheer, & Steenkamp, 1995), commonly used in early (mis)fit research, but criticized for presenting methodological problems in terms of reliability, 7

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The bold numbers in the diagonal row are square roots of the AVEs. Note: ⁎p < .05, ⁎⁎p < .01 (two-tailed), N.A. = not applicable. Industry1 = Steel, Industry2 = Chemical, Industry3 = Textile, Industry4 = Machinery, Industry5 = Electronics, Industry6 = Construction materials, Industry7 = Multiple industries.

N.A. 0.357 0.480 N.A. −0.158⁎ 0.043 0.204 N.A. −0.053 −0.187⁎ 0.060 0.237 N.A. −0.097 −0.082 −0.288⁎⁎ 0.130 0.337 N.A. −0.077 −0.050 −0.042 −0.148⁎ 0.038 0.191 N.A. −0.052 −0.102 −0.066 −0.056 −0.196⁎⁎ 0.065 0.247 N.A. −0.063 −0.047 −0.092 −0.060 −0.051 −0.178⁎ 0.054 0.227 0.811 0.090 0.045 −0.032 −0.103 −0.095 0.010 0.077 −0.132 0.171⁎ 4.855 0.776 0.864 0.327⁎⁎ 0.292⁎⁎ 0.339⁎⁎ 0.268⁎⁎ 0.068 −0.007 0.038 −0.047 −0.014 0.015 0.010 0.057 4.944 0.969 0.861 0.150⁎ 0.300⁎⁎ 0.165⁎ 0.154⁎ 0.348⁎⁎ 0.301⁎⁎ −0.077 0.080 −0.110 −0.085 0.077 0.031 0.108 −0.098 0.142 4.916 0.909 0.814 0.417⁎⁎ 0.159⁎ 0.297⁎⁎ 0.280⁎⁎ 0.170⁎ 0.408⁎⁎ 0.256⁎⁎ 0.115 0.098 −0.125 −0.059 −0.057 −0.057 0.080 0.113 0.136 4.845 0.708 1. Customer orientation toward sellers 2. Customer orientation toward buyers 3. Seller-side demand uncertainty 4. Buyer-side demand uncertainty 5. Performance 6. Reputation 7. Training 8. Information technology skills 9. Platform firm size 10. Platform firm age 11. Industry1 12. Indusrty2 13. Industry3 14. Industry4 15. Industry5 16. Industry6 17. Industry7 M SD

0.853 0.481⁎⁎ 0.123 0.025 0.065 0.121 0.012 0.083 0.104 −0.120 −0.116 −0.077 0.062 0.036 0.031 3.924 1.182

0.832 0.186⁎ 0.020 0.154⁎ 0.217⁎⁎ −0.055 0.120 −0.033 0.016 0.003 −0.113 0.119 0.098 0.053 4.229 1.196

0.768 0.318⁎⁎ 0.235⁎⁎ 0.188⁎ 0.098 0.112 0.026 −0.017 0.041 0.008 0.020 −0.100 5.054 0.635

0.837 0.263⁎⁎ 0.145⁎ 0.106 −0.054 −0.186⁎ 0.030 −0.021 0.077 0.005 0.100 4.962 0.715

N.A. 0.330⁎⁎ 0.182⁎ −0.056 −0.111 −0.035 0.047 −0.045 0.061 1.720 1.030

N.A. 0.134 −0.094 0.077 −0.007 0.128 0.005 0.004 6.005 5.166

12 8 5 2 1 Variable

Table 3 Descriptive statistics and correlations.

3

4

6

7

9

10

11

13

14

15

16

17

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discriminant validity, spurious correlations, and variance restriction (Edwards & Parry, 1993). Polynomial regression is an effective approach to analyze the effects of (in)congruence measures that can avoid the drawbacks of the difference score approach (Edwards & Parry, 1993). Thus, this approach has been widely used in a growing number of studies testing (mis)fit and (in)congruence (e.g., Ahearne et al., 2013; Edwards & Cable, 2009; Menguc et al., 2016; Vogel et al., 2016; Zhang et al., 2012). In particular, unlike the difference score approach used by Chakravarty et al. (2014) to operationalize CO asymmetry, polynomial regression allows researchers to control the total level of CO toward sellers and buyers (i.e., COS + COB) when estimating and comparing the effects of CO (in)congruence (Edwards, 2001; Edwards & Parry, 1993). This approach suited our research context with platform firms with limited resources to conduct CO strategies. Therefore, polynomial regression was used to test our hypotheses and model. We describe the estimation process in more detail below. In polynomial regression, the dependent variable is estimated by integrating the simple and squared components of the congruence measure and the interaction between both components in the equation. Thus, the effects of CO (in)congruence on platform firm performance were represented by the following equation:

Z = a0 + a1 COS + a2 COB + a3 COS 2 + a4 COS × COB + a5 COB2 + e (1) where Z represents the performance of a platform firm, COS represents CO toward sellers, and COB represents CO toward buyers. We centered COS and COB at their scale midpoints before calculating the higher order terms (Edwards & Cable, 2009; Edwards & Parry, 1993; Menguc et al., 2016). To explore the polynomial effects, the estimated coefficient for each polynomial term was used to compute the slope and curvature along the (in)congruence line (Edwards & Parry, 1993). Based on the suggestions provided by Edwards and Parry (1993), the slope and curvature of the incongruence line could be computed by substituting –COS for COB in Eq. 1:

Z = a0 + a1 COS − a2 COS + a3 COS 2 − a4 COS × COS + a5 COS 2 + e (2) After rearranging and gathering similar terms, the equation becomes:

Z = a0 + (a1 − a2) × COS + (a3 − a4 + a5) × COS 2 + e

(3)

The slope of the surface along the incongruence line is represented by the quantity [qslope = a1 − a2]. The curvature of the surface is represented by the quantity [qcurvature = a3 − a4 + a5]. To test the moderating effects of seller-side and buyer-side demand uncertainty, we followed the method used in Vogel et al.'s (2016) polynomial analyses. We examined the polynomial moderating effects by adding a moderator and the interaction term of the moderator with all polynomial terms. After estimating the moderating effect model, we calculated the slope and curvature of the surface at the moderator's low levels (i.e., substituting values one standard deviation below the mean of the moderator) and high levels (i.e., substituting values one standard deviation above the mean of the moderator) (Cohen, Cohen, West, & Aiken, 2003). Specifically, the moderator and the interaction term were added to the equation as shown below:

Z = b0 + b1 COS + b2 COB + b3 COS 2 + b4 COS × COB + b5 COB2 + b6 M + b7 COS × M + b8 COB × M + b9 COS 2 × M + b10 COS × COB × M + b11 COB2 × M + e

(4)

where Z represents the performance of a platform firm, COS represents CO toward sellers, COB represents CO toward buyers, and M represents the moderator (i.e., seller-side demand uncertainty or buyer-side demand uncertainty). Similarly, all higher order terms were created at 8

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their scale midpoints (Edwards & Cable, 2009; Edwards & Parry, 1993; Menguc et al., 2016). Based on the suggestions provided by Edwards and Parry (1993), the slope and curvature of the incongruence line could be computed by substituting –COS for COB in Eq. 4:

Table 5 Slope and curvature for incongruence line. A. Polynomial effects of CO incongruence Polynomial effects

Slope of Surface 90% CI qslope −0.076 [−0.679, 0.538]

Performance

Z = b0 + b1 COS − b2 COS + b3

COS 2

− b4 COS × COS + b5

COS 2

+ b6

B. Moderating effects of seller-side demand uncertainty Seller-side demand Slope of Surface uncertainty qslope 90% CI Low −0.441 [−1.125, 0.222] High 1.391 [0.533, 2.256]

M + b7 COS × M − b8 COS × M + b9 COS 2 × M − b10 COS × COS × M + b11 COS 2 × M + e

(5)

After rearranging and gathering similar terms, the equation becomes:

Z

C. Moderating effects of buyer-side demand uncertainty Buyer-side demand Slope of Surface 90% CI uncertainty qslope Low −0.244 [−1.016, 0.524] High 0.507 [−0.430, 1.671]

= b0 + [b1 − b2 + (b7 − b8) × M ] × COS + [b3 − b4 + b5 + (b9 − b10 + b11) × M ] ×

COS 2

+e

(6)

The slope of the surface along the incongruence line is calculated by the quantity [qslope = b1 − b2 + (b7 − b8) × M]. The curvature of the surface is calculated by the quantity [qcurvature = b3 − b4 + b5 + (b9 − b10 + b11) × M].

Curvature of Surface qcurvature 90% CI 0.396 [0.016, 0.829]

Curvature of Surface qcurvature 90% CI 0.727 [0.206, 1.464] −0.279

[−0.882, 0.461]

Curvature of Surface qcurvature 90% CI 0.675 [0.016, 1.412] 0.113

[−0.521, 0.699]

Note: N = 185; 90% bias-corrected confidence intervals produced from 1000 bootstrapped estimates.

4. Results

We now describe the results of hypotheses testing. As Table 5 (Panel A) and Fig. 3 (Panel A) show, the slope of the surface along the CO incongruence line was not significant (qslope = −0.076, 90% CI = [−0.679, 0.538]), suggesting that there was no significant difference in engagement between the two forms of CO incongruence (i.e., whether seller orientation was higher or lower than buyer orientation). In addition, the curvature of the surface along the CO incongruence line was significantly positive (qcurvature = 0.396, 90% CI = [0.016, 0.829]). This positive curvature indicated that firm performance

A summary of the polynomial regression analysis results is presented in Table 4. Table 5 and Fig. 3 offer more details of our statistical findings. We first report our findings regarding the effects of the control variables (see Table 4). The results show that platform reputation, information technology skills, and platform firm size had a significantly positive influence on platform performance. However, platform training, platform firm age, and industry characteristics had no significant influence on platform performance. Table 4 Results of Polynomial Regression. Variable Customer orientation toward sellers (COS) Customer orientation toward buyers (COB) COS2 COS × COB COB2 Seller-side demand uncertainty (SSU) Buyer-side demand uncertainty (BSU) COS × SSU COB×SSU COS2 × SSU COS × COB×SSU COB2 × SSU COS × BSU COB×BSU COS2 × BSU COS × COB×BSU COB2 × BSU Reputation Training Information technology skills Platform firm size Platform firm age Industry1 Industry2 Industry3 Industry4 Industry5 Industry6 Industry7 R2 △R2

Model 1

Model 2 ⁎

0.522 (0.300) −0.012 (0.201) −0.093 (0.137) −0.200 (0.133) 0.084 (0.088) −0.010 (0.149) 0.101 (0.073) 0.459⁎ (0.200) −0.316⁎ (0.156) −0.318⁎⁎ (0.113) 0.175 (0.139) 0.068 (0.072)

0.116 (0.277) 0.192 (0.187) 0.123 (0.140) −0.246⁎ (0.127) 0.027 (0.086) 0.024 (0.067) 0.065 (0.068)

0.276⁎ (0.128) 0.149 (0.116) 0.278⁎⁎ (0.095) 0.203⁎⁎ (0.070) −0.012 (0.015) −0.093 (0.258) 0.453 (0.279) 0.156 (0.408) 0.091 (0.224) −0.012 (0.285) 0.027 (0.612) 0.046 (0.151) 0.295

0.293⁎⁎ (0.125) 0.127 (0.111) 0.288⁎⁎ (0.097) 0.221⁎⁎⁎ (0.069) −0.015 (0.015) −0.154 (0.260) 0.516⁎ (0.285) 0.175 (0.414) 0.039 (0.222) −0.023 (0.307) −0.155 (0.635) 0.054 (0.156) 0.344 0.049⁎

Model 3 0.283 (0.269) 0.224 (0.210) 0.104 (0.145) −0.321⁎ (0.164) 0.023 (0.100) 0.017 (0.069) 0.031 (0.155)

0. 211 (0.243) −0.103 (0.189) −0.169 (0.122) 0.102 (0.129) 0.036 (0.078) 0.255⁎ (0.130) 0.136 (0.118) 0.257⁎⁎ (0.096) 0.198⁎⁎ (0.072) −0.016 (0.015) −0.045 (0.278) 0.476 (0.307) 0.191 (0.418) 0.078 (0.225) −0.009 (0.295) 0.043 (0.668) 0.027 (0.153) 0.319 0.024

Note: ⁎p < .05, ⁎⁎p < .01, ⁎⁎⁎p < .001 (one-tailed). Standard errors are provided in parentheses. Industry1 = Steel, Industry2 = Chemical, Industry3 = Textile, Industry4 = Machinery, Industry5 = Electronics, Industry6 = Construction materials, Industry7 = Multiple industries. 9

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Fig. 3. The shape of the response surface along the incongruence line.

However, increasing buyer-focused incongruence (i.e., lower seller orientation than buyer orientation) was negatively associated with the performance of the B2B platform firms. Thus, both H2a and H2b were supported. Similarly, we measured the slope and curvature of CO incongruence in relation to platform firm performance by considering the moderating effects of buyer-side demand uncertainty. Table 5 (Panel C) shows that when buyer-side demand uncertainty was low, the slope of the surface along the CO incongruence line was not significant (qslope = −0.244, 90% CI = [−1.016, 0.524]) and the curvature of the surface was significant and positive (qcurvature = 0.675, 90% CI = [0.016, 1.412]). Therefore, as shown by the U-shaped curve in Panel D of Fig. 3, when buyer-side demand uncertainty was low, platform firm performance increased as CO incongruence increased. However, as shown in Panel E of Fig. 3, when buyer-side demand uncertainty was high, neither the slope of the surface along the incongruence line (qslope = 0.507, 90% CI = [−0.430, 1.671]) nor the curvature of the surface along the incongruence line was significant (qcurvature = 0.113, 90% CI = [−0.521, 0.699]). Thus, the results did not support either H3a or H3b.

improved as the divergence between seller orientation and buyer orientation grew (i.e., the extent of CO incongruence moved toward either end of the X axis in Fig. 2), regardless of the dominant form of CO incongruence. Thus, both H1a and H1b were supported. From a different perspective, the results suggested that firm performance decreased as the gap between seller and buyer orientation decreased (i.e., the extent of CO incongruence moved toward the center point of the X axis in Fig. 2), regardless of the dominant form of CO incongruence. In other words, the more congruent a firm's CO strategy is, the worse its performance may be. This result also confirmed H1a and H1b. H2a and H2b concern the moderating effects of seller-side demand uncertainty. As shown in Panel B of Fig. 3 and Table 5 (Panel B), when seller-side demand uncertainty was low, the surface of the CO incongruence line was U-shaped, supported by a non-significant slope (qslope = −0.441, 90% CI = [−1.125, 0.222]) and a positive significant curvature (qcurvature = 0.727, 90% CI = [0.206, 1.464]). These results indicate that as CO incongruence increased, platform firm performance also increased. Conversely, Table 5 (Panel B) shows that when seller-side demand uncertainty was high, the slope of the surface was significant and positive (qslope = 1.391, 90% CI = [0.533, 2.256]) and the curvature of the surface was not significant (qcurvature = −0.279, 90% CI = [−0.882, 0.461]). These results indicate the presence of a directional effect. As illustrated in Panel C of Fig. 3, increasing seller-focused CO incongruence (i.e., higher seller than buyer orientation) was positively related to firm performance.

5. Discussion While B2B e-commerce marketplaces have stimulated economic growth, research on how these relatively new firms can acquire twosided customers with different features has only emerged recently 10

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results reported in previous CO studies examining the direct effects of CO on firm outcomes. Second, our study extends organizational information processing theory to the context of B2B electronic platforms. Although OIPT has been widely used in strategy and operations studies, most studies focus on how traditional offline firms build information processing capabilities to match information processing requirements due to environmental uncertainty (Gardner et al., 2015; Narayanan et al., 2011; Trautmann et al., 2009; Wong et al., 2011). In the context of B2B ecommerce, platform firms face demand uncertainty from both sellers and buyers, complicating the CO strategies used by these firms. In this study, we proposed that an incongruent CO strategy helps a platform firm develop appropriate information processing capabilities to cope with information processing requirements due to demand uncertainty. The results show that a seller-focused CO incongruence strategy is more beneficial to the performance of B2B e-commerce platforms when seller-side demand uncertainty is high. However, a buyer-focused CO incongruence strategy may not be as effective with high buyer-side demand uncertainty. These results help explain why and how platform firms should adjust their CO structure to different types of demand uncertainty to improve their performance, offering new insights for the information processing literature. Third, this study's triadic perspective enriches the literature on marketing channels. In particular, the study reveals the unique performance characteristics of platform firms in a two-sided market. As illustrated in the Literature Review section, most research has been conducted in the context of traditional offline channels and/or dyadic interfirm relationships (e.g., Dong, Fang, & Straub, 2017; Fang, Palmatier, & Steenkamp, 2008; Grewal, Kumar, Mallapragada, & Saini, 2013; Kashyap & Murtha, 2013; Palmatier, 2008; Palmatier, Dant, & Grewal, 2007). In an effort to expand this line of research, we conceptualize rapidly emerging two-sided B2B e-commerce platforms as a new distribution channel (Watson et al., 2015) and explore how platform firms can cultivate both customer sides to improve their performance. Although effective CO strategies are critical to the success of these online marketplaces, future studies should leverage the findings of this study to examine other useful strategic initiatives for this new type of market maker.

(Chakravarty et al., 2014; Sawhney, Verona, & Prandelli, 2005). This study explored the effects of B2B e-commerce platform firms' CO (in) congruence strategies and their boundary conditions. The empirical results based on the data collected from 185 China-based B2B e-commerce platform firms supported most of our hypotheses. In particular, we found that CO incongruence is generally more beneficial for B2B ecommerce platform firms and enables them to perform better than CO congruence (H1a and H1b). In addition, when seller-side demand uncertainty is high, increasing seller-focused incongruence (i.e., higher seller orientation than buyer orientation) or buyer-focused incongruence (i.e., lower seller orientation than buyer orientation) improved or impeded firm performance, respectively (H2a and H2b). One possible explanation for the rejection of both H3a and H3b is that our data analysis suggests, as shown in Table 3, that buyer-side demand uncertainty (MeanBSU = 4.229) is generally higher than sellerside demand uncertainty (MeanSSU = 3.924). This is consistent with common sense. In practice, buyers can join or leave a B2B electronic platform much more easily than sellers, because sellers typically face a higher switching cost to participate in a new B2B platform (Lin, Cheng, Wang, & Chang, 2012). This result indicates that there may be other factors (beyond the platform's knowledge, e.g., sellers' product offers and pricing changes, buyers' market turbulence, among others) influencing buyers' decision to join an online marketplace when buyer-side demand uncertainty is high. Thus, a buyer-focused CO incongruence strategy (i.e., lower seller orientation than buyer orientation) may not be as effective as a seller-focused CO incongruence strategy. In other words, from the perspective of e-commerce platform firms, it may be more efficient to devote more resources to increasing their seller-focused CO activities to indirectly acquire buyers by leveraging seller-side CNEs, even when buyer-side demand uncertainty is relatively high. 5.1. Theoretical implications By showing the strategic effects of various CO implementation practices of B2B e-commerce platform firms with limited resources, the results of this study empirically support the use of CNEs to examine the performance of B2B platforms. In addition, this study offers important theoretical contributions to advance the literature on CO, OIPT, and marketing channels in the context of the emerging B2B platform economy. First, our research enriches the CO literature by investigating how B2B e-commerce platform firms can leverage effective CO strategies toward both sellers and buyers to improve their performance. While strong evidence suggests that CO is critical to firm performance (Han et al., 1998; Kirca et al., 2005; Luo et al., 2008; Narver & Slater, 1990), most studies have been conducted in the context of a dyadic interfirm relationship. In the context of triadic B2B e-commerce or two-sided online marketplaces, platform firms as a market maker must acquire sellers and buyers simultaneously (Chakravarty et al., 2014). Therefore, the results of previous research on CO are not readily applicable to the B2B e-commerce platform context. In this study, we examined the potential effects of CO strategies, taking into account the trade-off of simultaneously focusing limited CO efforts on both sellers and buyers. To capture the nature of CO in the context of B2B platforms, this study introduced the concepts of CO (in)congruence, reflecting the complex structure of platform firms' CO practices. Although Chakravarty et al. (2014) conduct a pioneering study of CO asymmetry (which is conceptually the same as CO incongruence), they operated the notion of CO asymmetry differently from our study (i.e., they do not consider the resource constraints of platform firms). Applying the polynomial regression approach, our study offers a more comprehensive understanding of CO, i.e., from firms directly transacting in a dyadic interfirm relationship to B2B platform firms serving as two-sided online marketplaces. We provide strong evidence of why an unbalanced CO strategy is a more favorable choice for B2B platforms when the level of available resources is fixed. Our results also help explain the mixed

5.2. Practical implications By exploring the effects of CO (in)congruence on the performance of platform firms, the findings of this study have implications for platform firm managers pursuing CO strategies with limited resources. First, we show that CO is an essential strategy that can be implemented by B2B e-commerce platform firms to improve their performance. Platform firm owners or managers should be aware of CNEs and the fact that CO incongruence is generally a more effective strategy than CO congruence. We suggest that B2B e-commerce platform firms should disproportionately allocate their limited organizational resources to sellers or buyers. Specifically, when there is no significant difference between seller-side or buyer-side demand uncertainty, B2B platform firms should focus their limited resources on CO initiatives to attract one customer side (either sellers or buyers), rather than maintaining a balanced CO strategy. For example, they can either choose to make more efforts to identify the needs of sellers and offer better customer solutions to attract sellers, or launch targeted CO initiatives to understand and meet the needs of buyers. Based on the CNE theory and our research findings, if a significant number of customers on one side are ready to join the marketplace, potential customers on the other side are also likely to participate. Second, B2B e-commerce platform firm managers should carefully assess contingencies that may influence the effects of CO incongruence on firm performance. In particular, when facing high seller-side demand uncertainty, platform firms should optimize their resource allocation by increasing the appropriate level of CO toward sellers and 11

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References

decreasing it toward buyers. As explained above, the increase in CO toward sellers helps a platform firm develop its information processing capabilities to better understand and anticipate sellers' needs, which is essential for the firm to meet information processing requirements due to high seller-side demand uncertainty. When sellers' needs are well understood and satisfied, the seller base will attract interested buyers. As a result, the platform business will grow. Third, it is important to note that increasing CO toward one customer side does not imply that platform firms can ignore the demand and requirements of the other side. Instead, our study presents an initial strategic choice for platforms to develop their businesses. B2B e-commerce platform firms must understand that both customer sides (i.e., the seller and buyer sides) are critical to their performance and that they should not give up on either side. Without the participation of either sellers or buyers, platforms would fail. Platform managers should note that the ultimate goal of the CO incongruence strategies proposed in this study is to enable online B2B platform firms to attract and serve customers on both sides. Therefore, platform firms should carefully choose their CO incongruence strategies in different situations to acquire and retain a large base of active sellers and buyers.

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5.3. Limitations and directions for future research Although our study has important implications, it also has some limitations. First, our conceptual model was tested using cross-sectional survey data. Therefore, longitudinal studies are demanded in future to further validate and extend our research results. Second, because we focused on the strategic allocation of resources in relation to CO in B2B e-commerce platform firms, we collected data from online platform firms, which is consistent with previous B2B e-commerce platform studies (Chakravarty et al., 2014; Grewal et al., 2010). However, as mentioned earlier, online B2B marketplaces represent dynamic triadic relationships (between platform firms, sellers, and buyers). Therefore, future studies using triadic datasets should provide new information to validate our theory. Third, we examined the moderating effects of seller-side and buyer-side demand uncertainty. Future research should explore other contingency factors (e.g., regulatory environment, market concentration, competitive pressure, etc.) that are also relevant for B2B e-commerce platform firms when making strategic decisions. Fourth, previous studies have addressed that power, as a contextual factor, may influence the effects of strategic choices (Vendrell-Herrero, Bustinza, Parry, & Georgantzis, 2017). In this study, we didn't consider the impact of a platform's power over buyers and/or sellers. Future studies may explore whether and how a platform's power affects the relationship between its CO strategies and its performance. 6. Conclusion A B2B e-commerce platform is a market-making intermediary fostering exchanges between sellers and buyers (Watson et al., 2015). One of the challenges of platform firms is to simultaneously acquire buyers and sellers (Chakravarty et al., 2014; Fang et al., 2015; Sriram et al., 2015; Zhu & Iansiti, 2012). We propose a conceptual model and find that in terms of CO strategy, incongruence is better than congruence. The findings of the empirical analysis suggest that when seller-side demand uncertainty is high, a seller-focused CO incongruence strategy promotes platform firm performance, while a buyer-focused CO incongruence hinders it. Acknowledgements This study was supported by the National Natural Science Foundation grants of P.R. China (71832008, 71572109), the Program for Changjiang Scholars and Innovative Research Team in University (IRT13030). 12

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