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RESEARCH ARTICLE
PLATFORM-BASED FUNCTION REPERTOIRE, REPUTATION, AND SALES PERFORMANCE OF E-MARKETPLACE SELLERS1
Huifang Li International Institute of Finance, School of Management, University of Science and Technology of China,
Hefei 230026 CHINA, and Faculty of Management and Economics, Dalian University of Technology,
Dalian 116024 CHINA {[email protected]}
Yulin Fang and Kai H. Lim Department of Information Systems, College of Business, City University of Hong Kong,
Hong Kong SAR, CHINA {[email protected]} {[email protected]}
Youwei Wang Department of Information Management and Information Systems, School of Management, Fudan University,
Yangpu District, Shanghai 200433, CHINA {[email protected]}
In today’s emerging and competitive e-marketplaces, sellers must take competitive action to improve their sales performance. E-marketplace platform operators offer sellers a portfolio of platform-based functions that are intended to enhance competitiveness. However, little is known about how these platform-based functions can be used at the repertoire level to improve the sales performance of e-marketplace sellers. Extending the competitive repertoire theory to the e-marketplace context and integrating it with the e-commerce literature on reputation, we posit that a seller could improve sales performance by using these functions as a repertoire, featuring such structural characteristics as large volume, high complexity, and heterogeneity. We also posit that the performance impact of this repertoire approach to function use varies depending on seller reputation, manifested as customer rating. We empirically examined the hypotheses with a unique longitudinal dataset consisting of 43,992 seller-week observations from Taobao, one of the largest e-marketplaces in the world. Our analyses yield a set of interesting findings that unveil more nuanced theoretical relationships between different structural characteristics of the platform-based function repertoire and sales performance under different levels of seller reputation. We elaborate on how these findings contribute to the e-marketplace literature in the information systems field and the competitive action research in the strategy field. We also discuss implications for practice and make suggestions for future work.
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Keywords: Competitive action, reputation, performance, e-marketplace, platform-based function, competitive repertoire, complexity, heterogeneity, volume
1Bin Gu was the accepting senior editor for this paper. Chee-Wee Tan served as the associate editor. Youwei Wang was the corresponding author for this article.
The appendices for this paper are located in the “Online Supplements” section of MIS Quarterly’s website (https://misq.org).
DOI: 10.25300/MISQ/2019/14201 MIS Quarterly Vol. 43 No. 1, pp. 207-236/March 2019 207
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Introduction
E-marketplace platforms for online transactions between sellers and buyers have been proliferating and are becoming increasingly important in the era of e-commerce (Jiang et al. 2011). For instance, an equivalent of over 711.2 billion U.S. dollars of online retail transactions took place via e-marketplaces in China in 2016, rising 31.6% from 2015 (iResearch 2017). The growth of e-marketplaces has been accompanied by increasingly intense competition among sellers on transaction platforms. For example, more than 9 million sellers are competing on Taobao, China’s dominant e-marketplace. These sellers face the major challenge of maximizing sales performance in an emerging, yet crowded and competitive e-marketplace.
E-marketplace platforms, such as Taobao, have prepackaged a rich set of IT-enabled storefront functions that their sellers can use to customize their offerings. These functions may be used to facilitate pricing (e.g., time-limited discounts), mar- keting (e.g., luxury store interface), product presentation (e.g., zoom function), or customer service (e.g., seven-day money- back guarantee), among others (Su et al. 2015). We call these unique IT-enabled configurable functions, which are embed- ded in or supported by the e-marketplace platform, platform- based functions. Sellers are commonly found to compete by using platform-based functions to initiate many diverse and distinctive actions (Su et al. 2015). Thus, sellers are keen to understand whether and how these platform-based functions could improve sales performance in a highly nascent and competitive e-marketplace. This constitutes the key objective of our study.
Understanding the performance impact of platform-based functions has been of great interest to IS researchers in recent years. However, the results are not conclusive. A careful scrutiny of the IS literature suggests that existing studies mainly examine the performance effect of each of these individual functions, with mixed findings (Bockstedt and Goh 2011; Li et al. 2009; Ou and Chan 2014). One plausible reason could be that any single seller-enabled function can be easily observed and replicated, hence quickly losing its com- petitive edge. This implies that examining platform-based functions individually might generate limited insights. To this end, a few exploratory studies suggest that combined use, rather than individual use of these functions, might be neces- sary to provide tangible results for the sellers who operate in a highly competitive environment (Kirmani and Rao 2000; Li et al. 2009; Mathews 2004). However, there has been very little IS research that examines platform-based functions in a combinational fashion, although it has been common practice that sellers routinely use a repertoire of platform-based func- tions for competitive edge. This research gap provides us
with a unique opportunity to examine sales performance of platform-based functions as a repertoire (i.e., at the repertoire level), instead of the individual function level commonly seen in prior research. This leads to our first research question: To what extent is a seller’s use of platform-based functions as a repertoire related to sales performance in an e-marketplace?
To address the first research question, we examine how the structural characteristics of platform-based function repertoire relate to e-marketplace seller performance by extending the competitive repertoire theory (CRT) from the strategy field. The theory posits that firm performance in a highly compe- titive market can result from configuring competitive action repertoire with specific structural characteristics in terms of volume, complexity, and heterogeneity (Chen and Miller 2012; Ketchen et al. 2004; Smith et al. 2001). By extending the theory to the distinctive e-marketplace context, we posit that the structural configuration of platform-based function repertoire, as an important IT artifact that enables sellers’ competitive actions in the e-marketplace, can form the basis for competitive differentiation, thereby leading to superior performance.
While the CRT is appropriate for understanding the perfor- mance impact of platform-based functions, the theory was originally developed in the traditional industry context. In comparison, the e-marketplace is far more crowded, nascent, and risky (Bockstedt and Goh 2011; Pavlou and Gefen 2004). These characteristics create unique challenges for customers to pay attention to, make sense of, and lend credibility to the competitive actions of a particular seller (Biswas and Biswas 2004; Komiak and Benbasat 2008). Thus, to better under- stand the performance impact of platform-based functions as a repertoire in the unique e-marketplace context, we must incorporate firm-specific factors that effectively address these challenges for e-marketplace customers.
To address this need, we focus on the role of seller reputation, manifested as customer rating, and address our second ques- tion: How does seller reputation affect the relationships between the structural characteristics of platform-based function repertoire and e-marketplace seller performance? We selected seller reputation because it is highly instrumental for sellers to gain attention, understanding, and credibility from customers (Biswas and Biswas 2004; Komiak and Benbasat 2008), and reputation is directly observable in the form of customer rating in the e-marketplace. Further, al- though the direct impact of e-marketplace seller reputation on e-commerce performance has been widely examined (Chu et al. 2005; Jarvenpaa et al. 1999; Li et al. 2015; Wells et al. 2011), we know far less about the interaction of online repu- tation with other factors to affect performance. Motivated as such, we address our second research question by integrating
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the e-commerce literature on reputation with the CRT. We test our research model using a large dataset of 43,992 seller- week observations from Taobao, China’s largest e-marketplace.
This study makes several important research contributions (see Table 1 for a summary). First, we contribute to the e-marketplace literature by extending the CRT to examine the performance impacts of the structural characteristics of the platform-based function repertoires, the distinctive IT artifacts commonly used by e-marketplace sellers. Second, we offer a more context-specific understanding about the performance impact of platform-based function repertoire by drawing on the notion of seller reputation that is crucial to the e-marketplace context, thereby contributing to the e-marketplace literature. In doing so, we also contribute to the strategy literature by shedding light on the condition under which the CRT operates in the unique e-marketplace context. Third, we contribute to the understanding of seller reputation in the e-marketplace context by going beyond its well-studied direct performance impact in the e-commerce literature (Berger et al. 2010; Bockstedt and Goh 2011; Bolton et al. 2008; Forman et al. 2008; Li et al. 2009; Zhu and Zhang 2010) and revealing its indirect role in affecting seller perfor- mance. Fourth, we empirically reveal the aforementioned theoretical insights by analyzing a unique longitudinal dataset of Taobao sellers, including objective function usage and sales data that have not been tractable in prior research.
Theoretical Background
Reviewing the Performance Impact of Platform- Based Functions in E-Marketplaces
The e-marketplace is an Internet platform-based market through which both sides of an exchange—buyers and sellers—conduct transactions (Eisenmann et al. 2011). Here, platform serves as an intermediary, providing the infrastruc- ture and rules to bring together the two distinct user groups in the network, and facilitate transactions between them (Eisen- mann et al. 2006). E-marketplaces allow sellers to interact with customers by leveraging the IT infrastructure provided in the platform. In particular, the e-marketplace offers a variety of online functions to help sellers transact with buyers (Zhu and Iansiti 2012). For example, eBay’s “buy-it-now” function allows eBay sellers to decide whether and when to allow customers to purchase without going through the bidding process (Du et al. 2012). Taobao also offers its sellers a broad array of functions, such as luxury store inter- face, buy-it-now, bundling-related discounts, money-back guarantee, and credit card payment, to name a few (see Appendix A for screenshots of such functions).
The common feature among these functions is that they are all created by the platform, technically embedded in the IT- enabled platform architecture, and made available to sellers as IT-enabled functional settings that can be configured via the storefront interface hosted in the platform. For instance, eBay sellers activate the buy-it-now function in their storefront management screen to display this discount information in their storefront as well as in customers’ search results pages. We use platform-based functions to refer to such IT-enabled configurable functions that are (1) embedded in or supported by the platform, (2) activated at the seller’s discretion, and (3) externally observable so as to attract and retain customers. Platform-based functions, as a form of IT artifacts in the e-marketplace context, are different from Internet auction features (Li et al. 2009) and online attributes (Bockstedt and Goh 2011), in that the latter are not always at the seller’s discretion. For instance, online review is a platform-based Internet feature and an online attribute (Li et al. 2009), but we do not include it in the present study because it cannot be configured by sellers. This distinction is important because configurability is a prerequisite to understanding the competi- tive actions that sellers take by using platform-based func- tions, as we will discuss later. Sellers typically activate a range of functions to appeal to e-commerce customers (Su et al. 2015).
The extant e-commerce research has examined the individual impact of platform-based functions on seller performance but with inconclusive findings (see Table 2 for a summary of the literature). Some studies have found that platform-based functions can affect sales performance by serving as effective indicators of credibility or quality, but others have not. For instance, Gallien and Gupta (2007) found that the buy-it-now option improves sales, but Walia and Zahedi (2013) found the opposite—that the buy-it-now option has a strong negative impact on sales. Ou and Chan (2014) suggested that consu- mer protection schemes, a digital option made available by the platform, significantly affect sales performance, but Bockstedt and Goh (2011) found no significant impact.
One reason for the mixed findings observed among e-commerce studies that focus on the performance effect of individual platform-based functions could be that they do not take a holistic view of the simultaneous use of other functions in the repertoire. We argue that, although each of these platform-based functions is somewhat useful in enticing customers to make a purchase, individually, they may not necessarily strengthen seller performance in a crowded (hence competitive) and transparent e-marketplace because it is easy for competitors to observe and replicate the use of a specific function (Zhu 2004). A portfolio approach that examines how sellers configure the entire portfolio of platform-based func- tions would be more appropriate for the e-marketplace context
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Table 1. Preview of Study Contributions
Contribution State of the Literature
1. Enhancing our theoretical understanding of the extent to which platform-based function repertoire, as an important set of IT artifacts, affect seller performance by extending the CRT to the e-marketplace context.
There is scant research that understands the competitive moves of sellers in the highly competitive e-marketplace. The limited research focuses predominantly on the effect of individual platform- based functions, rather than taking these functions as a repertoire. Further, there are inconsistencies in prior studies on the impact of discrete functions on sales performance for e-marketplace sellers (Kirmani and Rao 2000; Li et al. 2009; Mathews 2004).
2. Providing a more nuanced understanding of the performance impact of platform-based function repertoire by establishing seller reputation as a moderator to address the unique characteristics of the e-marketplace. The results also help reconcile mixed findings in the competitive action literature.
Prior competitive action research has focused mainly on traditional markets, with little theoretical contextualization effort applied to the emerging e-marketplace context.
3. Enrich our understanding of the performance impact of online seller reputation, manifested by customer rating, by revealing its indirect, modera- tion effect on seller performance in e-marketplaces.
Previous e-commerce research has examined only the direct effect of reputation on various performance outcomes in the e-commerce context (Berger et al. 2010; Bockstedt and Goh 2011; Bolton et al. 2008; Forman et al. 2008; Li et al. 2009; Zhu and Zhang 2010).
4. Collect and analyze a unique longitudinal dataset of objective data on platform-based function usage and sales of Taobao sellers.
No prior research on the e-marketplace has used objective function usage data and sales performance data for e-marketplace sellers.
Table 2. Review of E-Marketplace Studies on Platform-Based Functions
Platform-Based Functions Theory Findings Citation
Buyout option Game theory
The buyout option is attractive to time-sensitive participant; temporary buyout options yield lower predicted revenue than do permanent buyout options.
(Gallien and Gupta 2007)
Buy-it-now option Customer protection scheme Money-back guarantee Credit card payment Third-party payment
Signaling theory These functions each can serve as effective credibility or quality indicators to shape bidding outcomes.
(Li et al. 2009)
Directed search technology Recommendation system
Marketing literature on consumer behavior
The use of directed search technology has contrary effects on the sales of promoted and non-promoted products.
(De et al. 2010)
Customer protection scheme Signaling theory This function has no significant impact on auction outcomes.
(Bockstedt and Goh 2011)
Price bundling Consumer behavior literature
Price bundling significantly improves sales. (Zhu and Iansiti 2012)
Buy-it-now option Marketing mix model; competitive heterogeneity theory
Website media richness positively impacts on the number of bids and sales, while buy-it-now option has a strong negative impact on sales.
(Walia and Zahedi 2013)
Service requirement Service acquisition Service ownership Service retirement
Customer Service Life Cycle (CSLC)
IT-mediated service content functions represent a core area of website design, especially when considered in tandem with the type of transactional activity.
(Tan et al. 2013)
Customer protection scheme Money-back guarantee Repairing service
Signaling theory These functions, as institutional mechanisms, have significant and positive impact on sales performance.
(Ou and Chan 2014)
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where sellers face fierce competition and rapid replication of competitive moves (Bockstedt and Goh 2011; Kirmani and Rao 2000). Indeed, most e-marketplace sellers do not use a single function at any particular time; instead, they simul- taneously leverage multiple functions on their storefront, suggesting that this understudied perspective prevails in prac- tice (Ou and Chan 2014). Prior research has hinted at the importance of understanding platform-based functions as a portfolio. For instance, in an initial effort to reconcile the mixed findings in the extant literature, Li et al. (2009) suggest that the buy-it-now option as a quality indicator alone cannot yield performance impact; instead, it needs to be used in con- junction with other functions, such as third-party payment, to generate favorable outcomes. This work further sheds light on the importance of understanding how simultaneous usage of multiple platform-based functions can affect seller performance.
Inspired as such, we use platform-based function repertoire to refer to the set of platform-based functions deployed by a seller. We investigate how using a repertoire of such func- tions affects sales performance by drawing on the CRT—a theoretical perspective that has been under-utilized in the e-commerce literature—to conceptualize the use of these functions as instances of competitive actions in the e-marketplace and subsequently examine their performance impact on e-marketplace sellers.
Competitive Repertoire Theory
The CRT is an influential theoretical framework for under- standing how temporary competitive advantage can be explained by the structural characteristics of a firm’s compe- titive repertoire (Chen 1996; Chen and Miller 1994; Ferrier 2001; Ferrier and Lee 2002; Ferrier and Lyon 2004; Miller and Chen 1996a). The root of this theory is the configura- tional view, which focuses on how the constellations of firm elements, pulled together by a unifying strategic theme, con- tribute to firm performance (Bozarth and McDermott 1998; Miller and Whitney 1999). One firm can replicate another’s strategy and reverse engineer its technology. However, it is difficult, if not impossible, to replicate the portfolio of compe- titive moves that are underpinned by a thematic and syner- gistic configuration of strategy, technology, and routines; such configurations inside the firm constitute a vital source of competitive advantage (Miller and Whitney 1999). This view has recently been extended to the IS field. Sambamurthy et al. (2003) proposed that the configurations of competitive actions derived from dynamic capabilities and strategic pro- cesses contribute to firm performance. Gnyawali et al. (2010) examined the impact of the configurations of competitive actions by social network firms on their performance in the
digital age. More recently, in considering dynamic interac- tions among environmental turbulence, dynamic capabilities, and IT systems, El Sawy et al. (2010) pointed out that the configuration perspectives, with their holistic messy nature, are better suited to understanding the formation of firm competitive advantage.
Competitive action is defined as “any externally oriented, specific, observable competitive move initiated by a firm to enhance its relative competitive position” (Smith et al. 2001, p. 12), while competitive repertoire denotes the entire set of a firm’s competitive actions configured in a given period (Miller and Chen 1994). Although competitive actions can take many forms, they belong to several action categories depending on where they are along the value chain (Gnyawali et al. 2006). Action categories include such discrete strategic or tactical events as pricing, marketing, new product launch, capacity change, and service/operations (see Table 3 for the list of action categories summarized from the literature). Ac- tions of the same category are similar in fulfilling a particular need for the focal firm competing in the market. For instance, time-limited discount, buy-it-now option, and bundling all belong to the action category of pricing (Chi et al. 2010; Yu and Cannella 2007). Competitive repertoire is enacted when firms strategically configure their action set by deploying competitive actions from different action categories.
Anchored on this theoretical premise, the CRT explains firm performance by conceptualizing the structural characteristics of competitive repertoire that vary among firms (Danny and Chen 1996; Ferrier 2001; Ferrier et al. 1999; Miller and Chen 1996a). The theory specifies three key structural character- istics of competitive action repertoire, namely, volume, com- plexity, and heterogeneity (Chen and Miller 2012). Repertoire volume is the most commonly studied aspect of competitive behavior, denoting the “scale” or “number” of competitive actions across all the action categories carried out by a firm in a given time period (Ferrier et al. 1999; Miller and Chen 1996a). Repertoire complexity denotes the “scope” of a com- petitive repertoire, representing the range (narrow versus broad) of competitive actions in a category carried out by a firm in a given time period (Ferrier et al. 1999; Miller and Chen 1996a, 1996b; Young et al. 1996). Repertoire hetero- geneity denotes the “deviance” of a firm’s action repertoire from the industry norm (Miller and Chen 1996a); this struc- tural characteristic captures the extent to which a firm deploys different competitive actions on the basis of horizontal com- parison with the industry norm. The three characteristics are conceptually distinctive from each other and collectively model the usage patterns of a competitive action set. Specifi- cally, repertoire volume concerns the quantity of competitive actions regardless of their category, whereas complexity captures the diversity of the competitive actions in distinctive
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Table 3. Competitive Actions
Categories Descriptions Exemplary Actions
Pricing actions Announcing price cuts and sales incentives (Chi et al. 2010; Yu and Cannella 2007).
Price cuts, rebates, discounts
Marketing actions Advertising and promotion activities (Gnyawali et al. 2010; Yu and Cannella 2007).
Marketing campaigns, advertisement investments
New product actions Launching new products and product innovations (Chi et al. 2010).
Launching new versions of a product
Capacity- and scale-related actions
Changing the company’s capacity or output (Yu and Cannella 2007).
Buying production equipment
Service and operations actions
Changing the company’s distribution systems and after- sales service (Yu and Cannella 2007).
Setting up online distribution channels
Legal actions Altering the political and legal environment, either as an offensive or defensive action (Gnyawali et al. 2010).
Political lobbying, lawsuits against competitors
categories. For instance, a competitive repertoire that features a large number of competitive actions in one particular cate- gory is high on volume but low on complexity. The two structural characteristics of volume and complexity are distinct from repertoire heterogeneity: Whereas volume and complexity are inward looking, by capturing the frequency and diversity with which platform-based functions are deployed by a seller, heterogeneity tends to be outward looking, by accounting for how a seller’s platform-based function repertoire deviates from those of its rivals. Specifi- cally, different from the other two characteristics, repertoire heterogeneity is the difference that could exist between the platform-based function repertoire for a given seller and those of its rivals.2
The three properties of competitive repertoire together pro- vide a holistic picture of the competitive posture of a firm (Chen and Miller 2012, 2015; Ketchen et al. 2004; Smith et al. 2001). The firm that does more with its competitive repertoire along these three aspects can differentiate itself by creating a short window of temporal advantage over rivals, which amounts to overall superior performance (Chen and Miller 2012; Smith et al. 2001). The key basis for such differ- entiation in highly competitive marketplaces is not due to individual competitive actions, as they can be observed and replicated rapidly, but how the given set of competitive actions is configured as a whole in terms of its volume, complexity, and heterogeneity (Chen and Miller 2012; Smith et al. 2001).
Extending the Competitive Repertoire Theory to the E-Marketplace Context
To address our research questions, we extend the CRT to the e-marketplace context by (1) conceptualizing platform-based functions as IT-enabled competitive actions in the e-marketplace and (2) incorporating seller reputation as a firm-level factor moderating the performance impact of platform-based function repertoire.
Platform-Based Function Repertoire as the Basis for Competitive Differentiation in E-Marketplaces
We conceptualize e-marketplace sellers’ use of platform- based functions as competitive actions. This conceptuali- zation is appropriate because platform-based functions bear the key characteristics of competitive actions. As discussed earlier, platform-based functions are customer-oriented tools used at the seller’s discretion to appeal to potential customers and compete in a crowded e-marketplace. Moreover, these functions are all specific and observable to all the actors in the e-marketplace, including customers and other sellers.
It is noteworthy that platform-based functions are a set of predetermined and comparable functions provided by the plat- form operator. This raises the question of whether sellers using such comparable functions can engage in competitive differentiation. We argue that the single functions in the set of platform-based functions are not a basis for competitive differentiation because they are predetermined, accessible, and comparable across e-marketplace sellers; however, we posit that competitive differentiation lies in the various configurations of the platform-based function repertoire, as the CRT suggests. Here, the platform-based function reper- toire denotes the set of platform-based functions carried out2We thank the associate editor for this suggestion.
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by an e-marketplace seller in a given period. Specifically, unlike single competitive actions, repertoire is not easily replicated because, even if firms are aware of the actions of rivals, they differ in their motivation and capability/resources to respond to the actions (Chen and Miller 2012). For example, a complex set of competitive actions requires the firm to mobilize a broad array of resources (Ndofor et al. 2011), while heterogeneous competitive repertoire requires a firm to have slack resources (Miller and Chen 1996a). More- over, other contextual and firm factors, such as firm network position in the industry (Chi et al. 2010) and management team heterogeneity (Hambrick et al. 1996), could also deter- mine the configuration of the repertoire by motivating and enabling firms to take a repertoire of actions.
To the extent that using platform-based functions is a compe- titive action, it stands to reason that how e-marketplace sellers configure their platform-based function repertoire is also sub- ject to these motivation and resource considerations, thereby resulting in different configurations that create a basis for competitive differentiation. For instance, not all platform- based functions in an e-marketplace context are free to sellers; thus, a seller’s available financial resources directly affect how it configures its competitive action repertoire. Indeed, research has shown that sellers with less financial resource concerns prefer to attract customers through price-related activities (Zhu and Iansiti 2012).
Through further integrating the tenets of the CRT in the e-commerce context, we can argue that a seller’s competitive differentiation, enabled by its differentiated platform-based function repertoire, is somewhat difficult for rivals to quickly imitate. The underlying premise is that sellers differ in their ability to assimilate and configure a platform-based function repertoire, even though these platform-based functions are visible and accessible to all e-marketplace sellers. As the enterprise IT assimilation literature has suggested, organi- zations assimilate IT to different degrees due to technological, organizational, and environmental considerations (Zhu et al. 2006). In particular, the literature that emphasizes the organi- zational consideration points to the significant role of coor- dination between business and IT within an organization in affecting enterprise IT assimilation (Armstrong and Samba- murthy 1999). For instance, Chatterjee et al. (2002), in the context of web technologies assimilation, state that the extent of coordination between business and web-based functions significantly influences the extent to which firms assimilate web technologies for e-commerce initiatives. This highlights the importance of an organization’s business operational capa- city in differentiating the extent of related enterprise IT assimilation. By extending this logic to the e-marketplace context, where sellers use platform-based functions to manage their front end, we can argue that sellers must have the ability
to coordinate the corresponding back-end business operations when they configure their repertoire of platform-based func- tions. Although the deployment of the platform-based func- tion “price cut” by e-marketplace sellers might be seen as an easy and quick process, pricing as a competitive action actually requires the seller to possess the necessary resources to coordinate back-end operations, such as inventory manage- ment and order fulfillment, in back offices and possibly with external stakeholders (Dutta et al. 2002; Randall et al. 2006; Sahay 2007). In the same vein, sellers cannot deploy the zoom function, one of the platform-based functions in the product presentation category, unless they ensure the related resources for product presentation, such as professional pro- duction technicians and devices, are coordinated in the back end. To the extent that sellers differ in their ability to manage various back-end operations, it is reasonable to expect that the sellers differ in their ability to assimilate and configure a platform-based function repertoire. In other words, the need for coordination between the use of platform-based functions and the management of the corresponding back-end opera- tions makes it possible for a seller to sustain, at least with some longevity, the competitive differentiation created by its specific platform-based function repertoire.
The Role of Seller Reputation in the Unique E-Marketplace Context
The configuration of platform-based repertoire creates the basis for competitive differentiation between an e-marketplace seller and its rivals; however, we also posit that the temporal advantage resulting from differentiation will not lead to sales unless the competitive actions enacted through platform-based functions are favorably perceived by e-marketplace custo- mers. The competition repertoire literature addresses mainly the effect of competitive actions in traditional markets with rivals and/or investors as the major stakeholders (Ferrier et al. 1999; Gnyawali et al. 2006; Miller and Chen 1996a). How- ever, we consider it important to include customers as stake- holders due to the notable distinctions of the e-marketplace context. The inherent attribute of the e-marketplace—a two- sided market that matches sellers and buyers (i.e., customers)— suggests that the two sides of an exchange are more directly connected in the e-marketplace (via information technology) than in the traditional markets studied earlier, such as the airline, computer, in-vitro diagnostic substance, and auto- mobile manufacturing industries (Chu and Manchanda 2016; Lei et al. 2010; Ndofor et al. 2011; Yang 2011; Young et al. 1996). Indeed, all the platform-based functions in the e-marketplace are designed to appeal directly to customers.
We argue that one cannot assume that e-marketplace custo- mers will respond favorably to sellers’ competitive actions, as
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the e-marketplace has several distinctive characteristics that affect how customers notice, interpret, and trust competitive actions. First, the e-marketplace is very crowded because Internet technology brings together numerous sellers and buyers with no temporal and geographical boundaries (Bockstedt and Goh 2011). Unlike the traditional industries, in which there are just a few major providers, studied in prior competitive action research, a quick search for any product in the e-marketplace reveals hundreds, if not thousands, of relevant sellers (Su et al. 2015). Such a high level of compe- tition makes publicity (visibility to customers) a challenge for e-marketplace sellers, as customers have limited time and short attention spans.
Second, although eBay, as the pioneer of the e-marketplace, has been in operation for two decades, new e-marketplaces continue to proliferate across the globe, including Taobao.com, JD.com, Rakuten.com, Newegg.com, and Bonanza.com, to name just a few. These e-marketplaces can be considered nascent, given that their structure and rules of competition are still evolving as e-marketplace operators make ongoing efforts to integrate new digital options and institute new market rules into the platform to facilitate the transactions between sellers and buyers (Mithas et al. 2013). Such ongoing changes in the platform structure and rules make the emergent e-marketplaces less structured (i.e., having a shortage of recurrent and institu- tionalized patterns of business relations and actions that, in turn, leads to market ambiguity; Bennett and Lemoine 2014; Gnyawali et al. 2010; Santos and Eisenhardt 2009). Here, ambiguity is defined as lack of clarity about the meaning and implications of particular events and actions (Santos and Eisenhardt 2009). Ambiguity could lead to confusion and multiple interpretations (Santos and Eisenhardt 2009), thereby requiring the participants of the markets (e.g., customers) to exert more cognitive effort to make sense of the meaning and implications of market actions (e.g., a seller’s usage of various platform-based functions; Li and Karahanna 2015; Rosen and Purinton 2004).
Third, the absence of face-to-face encounters in the e-marketplace amplifies information asymmetry between sellers and buyers (Wells et al. 2011) and leaves customers with more concerns about sellers’ opportunistic behaviors (Pavlou and Gefen 2004). The lean nature of the online medium imposes additional unique risks because neither product characteristics nor seller identity can be fully assessed during the transaction, making it easier to cheat (Pavlou and Gefen 2004). Such information asymmetry makes it difficult for customers to trust sellers and favorably respond to their competitive actions.
In such a crowded, nascent, and uncertain context, sellers must develop firm-specific solutions to overcome these con-
textual challenges. Reputation is defined as the prestige accorded to sellers on the basis of how they have performed in the past; it is often used in e-commerce to overcome cus- tomer perceptions of uncertainty by providing assurance and signaling seller/product quality (Jarvenpaa et al. 1999). Repu- tation is based on specific assessments of relevant attributes, such as a seller’s ability to produce quality products (Rindova et al. 2005). It denotes an expectation of future behavior that is based on past demonstrations of that behavior. In the e-marketplace context, seller reputation is widely manifested as customer rating on key attributes of seller past perfor- mance, such as product, delivery, and after-sales service quality (Wang et al. 2013).
The extant e-commerce literature has extensively studied the performance effect of online reputation in the form of customer rating. Most suggest a direct positive effect of customer rating on seller performance through enhanced credibility (Kim et al. 2004) and customer willingness to purchase (Chu et al. 2005; Jarvenpaa et al. 1999; Li et al. 2015; Wells et al. 2011). However, little scholarly attention has been paid to the possible indirect effects of reputation on seller performance (e.g., the moderating role of reputation). This provides us with a unique opportunity to extend the e-commerce literature on reputation by understanding how it jointly works with sellers’ competitive actions.
In this study, we extend the CRT to the e-marketplace context by incorporating seller reputation, in the form of customer rating, as a contingency factor, so as to investigate the moder- ating effect of reputation on the relationship between competitive repertoire and performance. The basis for this integration lies in the proven effect of reputation on mitigating the contextual issues specific to the e-marketplace context. As discussed earlier, the performance impact of competitive action repertoire in an emerging market could be contingent on how visible, interpretable, and credible are the actions of the firm (Gnyawali et al. 2010; Ndofor et al. 2011; Zhang et al. 2011). We argue that reputation serves as an extrinsic and readily observable cue capable of addressing these concerns in the e-marketplace context by helping to improve visibility to online customers (Bockstedt and Goh 2011), reduce ambiguity about the firm (Bolton et al. 2008; Boulding and Kirmani 1993), and enhance credibility (Biswas and Biswas 2004; Komiak and Benbasat 2008).
Research Model and Hypotheses
We build a research model to explain the performance impact of the platform-based function repertoire of e-marketplace sellers with different levels of reputation (manifested as cus-
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Figure 1. Research Model
tomer rating) by integrating the CRT and the e-commerce literature on reputation (see Figure 1). Specifically, we draw on the CRT to propose that platform-based function reper- toires exhibit the structural characteristics of volume, com- plexity, and heterogeneity. These structural characteristics directly affect the sales performance of the e-marketplace seller, but these direct effects are contingent upon the seller’s reputation.
Platform-Based Function Repertoire Volume
Building on the CRT, we define platform-based function repertoire volume as the total number of times that a seller uses platform-based functions to support competitive actions in a given period. We argue that the more often a seller uses platform-based functions, the better sales performance will be. As discussed earlier, the CRT suggests that a large volume of competitive actions forms a base for competitive differentia- tion by enabling a seller to gain a temporal advantage over rivals (D'Aveni 1994; Ferrier et al. 1999; Gnyawali et al. 2010; Young et al. 1996). It does so by indicating that the seller competes aggressively; hence, it is more likely to dis- rupt the market status quo and create advantage by impressing customers (Chen and Miller 2012; D'Aveni 1994; Ferrier et al. 1999; Gnyawali et al. 2010; Smith et al. 2001; Young et al. 1996). In particular, a large volume improves firm publicity because it is seen to be more active in the market (Gnyawali et al. 2010). More publicity raises the firm’s profile with cus- tomers, so it is more likely to be called upon when customers want to make a purchase. Moreover, a large volume also indicates that the firm is resourceful and competitive, as it has
accumulated necessary capability through frequent competi- tive actions, thereby helping to build customer trust and appeal (Gnyawali et al. 2010). Taken together, and to the extent that competitors are not able to quickly replicate the numerous actions, action volume enables the focal firm to gain a short-term window of advantage.
By extending this logic to the e-marketplace context, we argue that an e-marketplace seller with a large platform-based function repertoire volume could generate better sales per- formance for at least three reasons. First, this seller competes aggressively in its sector with a large volume of actions and, hence, is more likely to disrupt the market status quo through more frequent actions, which attracts customers. For instance, announcing price cuts and sales incentives has been reported as frequently used competitive actions to demonstrate compe- tition aggressiveness (Chi et al. 2010; Yu and Cannella 2007). The vast literature in economics and marketing consistently finds that consumers respond to frequent price cuts by pur- chasing more, leading sellers to expand sales (Hendel and Nevo 2006). Similarly, a seller that frequently uses various forms of price-cutting functions in e-marketplaces demon- strates that it is aggressively competing for customer purchases.
Second, the seller with a large platform-based function reper- toire volume is more likely to be seen as resourceful and capable because repertoire deployment requires resource possession and organizational support from the seller. As discussed, when sellers deploy their repertoire of platform- based functions, they need to have resources to support the corresponding organizational capacity that coordinates the
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back-end business operations and enables them to be in line with the platform-based function deployed. For instance, the deployment of price-cutting functions requires the seller to have the ability to ensure that back-end operations, such as accurate demand prediction, inventory management, and order fulfillment, are planned and coordinated (Randall et al. 2006), representing the resourcefulness of the seller. In addition, prior research has found that sellers who attract customers through frequent price-related activities are seen to have fewer profit concerns and, hence, are more likely perceived as being resourceful (Zhu and Iansiti 2012). The impression of resourcefulness is important in the e-marketplace context, because it provides a cue for customers to trust the seller, an important condition of online purchases (Gnyawali et al. 2010).
Third, a large volume of action in function repertoire makes the seller more publicly visible by increasing the rate at which it is indexed in the product/seller search criteria in the e-marketplace, thereby making the seller more accessible to customers. For instance, the platform records seller actions associated with platform-based functions (e.g., activating a consumer protection scheme or a buy-it-now option). If cus- tomers search the e-marketplace for these options, the seller would be more likely to be identified. The more accessible a seller, the more likely customers are to visit its storefront and to make a purchase. Taking these together, we expect a positive relationship between platform-based function reper- toire volume and the sales performance of e-marketplace sellers.
H1a: Platform-based function repertoire volume of an e-marketplace seller is positively related to its sales performance.
We argue that the effect of platform-based function repertoire volume on sales performance is contingent on a seller’s repu- tation in the e-marketplace such that when online reputation is higher, the positive performance effect of platform-based repertoire volume is weaker. The logic is that reputation, manifested as customer rating, is a strong mechanism for enhancing the visibility and credibility of an e-marketplace seller (Bockstedt and Goh 2011). Thus, when reputation is higher, the importance of frequent use of platform-based functions to mitigate the issue of visibility and credibility becomes lower. Specifically, when customer rating is higher, the seller is already well publicized because it is displayed at the top of search results ranked by customer rating. As such, the impact of frequently used platform-based functions on sales performance through enhancing visibility is weaker. Similarly, when customer rating is higher, although the seller can still frequently use platform-based functions to carry out competitive actions and disrupt the market status quo for
short-term advantage, there is less performance impact by sig- naling credibility through demonstrating abundant resources and strong competitiveness via frequent actions because customer rating has already effectively signaled credibility (Bolton et al. 2008; Boulding and Kirmani 1993).
Conversely, when customer rating is low, it is more important for the seller to frequently use platform-based functions to exert competitive actions to gain more publicity in the crowded e-marketplace. Similarly, when customer rating is low, it is more important for the seller to enhance its credi- bility to customers by demonstrating its strong competitive- ness and capability through frequent use of platform-based functions to enable competitive actions. Thus, we hypothesize
H1b: An e-marketplace seller’s reputation (cus- tomer rating) negatively moderates the positive relationship between platform-based function reper- toire volume and sales performance of the seller, such that when reputation is higher, the positive relationship is weaker.
Platform-Based Function Repertoire Complexity
Building on the CRT, we define platform-based function repertoire complexity as the scope of platform-based function categories used by a seller in a given period. Platform-based functions can be classified into different categories based on the aspects of the value chain they address. Functions from the same category are able to fulfill similar needs of the seller to compete in the e-marketplace while functions from dif- ferent categories differ in their corresponding capacity. For instance, time-limited discounts and product bundling are dif- ferent platform functions, but both are categorized as tradi- tional pricing actions, that is, the announcing of price cuts and sales incentives (Chi et al. 2010; Yu and Cannella 2007). This pricing-oriented function category differs from other function categories in, for instance, after-sales service with a consumer protection scheme. High complexity means that a seller uses platform-based functions that draw from a broad array of function categories.
We argue that the more complex the platform-based function repertoire, the better the sales performance of the seller. As discussed, the CRT suggests that a complex set of competitive actions forms a base for competitive differentiation over rivals by having a positive impact on multiple aspects of the firm’s value chain, improving the firm’s ability to outperform its competitors and to meet the needs of customers (D’Aveni 1994; Miller and Chen 1996a). The same literature suggests
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that firms in nascent, ambiguous markets could benefit from a complex competitive actions repertoire because the nascent market does not indicate which action is more effective and favors firms that “hit the right spot” by attempting diverse actions.
By extending the CRT, we argue that sellers that configure a broad set of platform-based functions are better able to address customer needs in multiple categories. For instance, while pricing functions offer lower prices, marketing func- tions offer customers better access to products. Thus, in a given time period, a diverse set of platform-based functions endows the focal seller with a temporal advantage in at- tracting customers and in persuading them to buy. In par- ticular, a seller who carries out a broad set of function categories is able to meet the diverse needs of customers in the nascent e-marketplace by attempting diverse actions, thereby enhancing the probability of deploying the most effective action. Because the use of multiple categories of platform-based functions entails close coordination with a variety of corresponding back-end operational capacities of the seller, as discussed, the competitive differentiation at- tained through repertoire complexity may not be immediately replicated by rivals. This leads to a temporary advantage for the focal seller. Based on these theoretical arguments, we believe that sellers with more complex platform-based func- tion repertoires can achieve better sales performance than can those with less complex platform-based function repertoires. We thus posit
H2a: Platform-based function repertoire complexity of an e-marketplace seller is positively related to its sales performance.
Further, we argue that the effect of platform-based function repertoire complexity on sales performance is contingent on a seller’s reputation in the e-marketplace, such that when online reputation is higher, the positive performance effect of platform-based repertoire complexity is stronger. The key logic is that reputable sellers are more likely to be considered by e-marketplace customers, which makes their deployment of platform-based functions more effective; in contrast, the same set of platform-based functions if deployed by low repu- tation sellers would have little or no impact on their sales performance. The rationale is that a seller’s reputation, mani- fested as customer rating, affects the willingness of cus- tomers, who are known as “cognitive misers” (Fiske and Taylor 1984), to spend their cognitive resources in processing the seller’s diverse appeals made through a complex platform- based function repertoire. Specifically, e-marketplace sellers’ storefronts, where platform-based functions are exhibited, are the cognitive landscape through which customers make pur- chase decisions by making sense of the seller and its various
offerings, as enabled by the platform-based functions (Rosen and Purinton 2004). Prior literature has established that pur- chase decision making in e-commerce entails cognitive resources from customers (Hong et al. 2004; Jiang and Ben- basat 2007b; Li and Karahanna 2015). Similarly, in the e-marketplace context, although a seller’s diverse competitive actions might enhance the likelihood of appealing to cus- tomers, this diversity also increases the likelihood that customers expend more cognitive resources to process the diverse information needed for their purchase decision (Jiang and Benbasat 2007a). In other words, when a seller under- takes a repertoire with multiple categories of platform-based functions, customers are exposed to diverse information cues and are thereby more likely to face the need to exert cognitive effort in their decision-making process (Li and Karahanna 2015). For instance, if more categories of functions are enacted, e-marketplace customers would need to assess offerings about price, payment method, after-sales services, and so on when making their purchase decision; as such, they would need to expend more cognitive effort to process diverse information than when being exposed to few categories of functions.
Humans are believed to be cognitive misers, however, with a tendency to economize on cognitive effort by simplifying complex decisions (Fiske and Taylor 1984). To do so, they first use relatively simple and the most relevant decision algo- rithms to screen and filter the alternatives and then evaluate the short-listed alternatives through more complex algorithms (Gensch 1987). Such a need for simplified algorithms for decision making is more pertinent in nascent markets in which customers face high ambiguity, as decision making in am- biguous environments is more cognitively taxing (Rindova et al. 2010). Following this logic, customers in e-marketplaces would need simple decision algorithms to screen the sellers before expending cognitive effort on the more complex deci- sion process (e.g., processing the seller’s diverse appeals, as seen in a complex platform-based function repertoire).
Firm reputation serves as a simple but powerful decision algorithm that reduces the complexity of decision making that customers face during transactions (Jensen and Roy 2008). Similarly, customer rating, as an indicator of seller reputation in the e-marketplace context, is recognized as a salient extrin- sic cue that customers use to stratify sellers and to reduce cognitive effort in decision making. Seller reputation is par- ticularly important for customers to reduce cognitive effort in a nascent e-marketplace because their decision making in an ambiguous environment would require more cognitive effort. As cognitive misers, customers in the e-marketplace rely on seller reputation to efficiently form a definitive perception of a seller’s standing and to decide whether to assess the seller further, using more complex algorithms that would involve
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more cognitive effort. When a seller’s reputation is high, customers are more likely to shortlist the seller for further evaluation and, therefore, are more willing to expend cogni- tive effort in evaluating the overall value offered by the seller via the diverse platform-based functions. As such, we expect that platform-based function repertoire complexity could have a stronger impact on sales performance for sellers with higher customer rating. Conversely, when a seller’s reputation is low, customers are less likely to shortlist the seller for further evaluation, as they would prefer more reputable alternatives. As such, customers are less willing to process the diverse appeals made by the seller through its platform-based function repertoire. This leads to a weaker impact of platform-based repertoire complexity on sales performance. Thus, we propose
H2b: An e-marketplace seller’s reputation (cus- tomer rating) positively moderates the relationship between its platform-based function repertoire com- plexity and sales performance, such that when reputation is higher, the relationship is stronger.
Platform-Based Function Repertoire Heterogeneity
Building on the CRT, we define platform-based function repertoire heterogeneity as the extent to which the set of platform-based functions undertaken by a seller in a given period deviates from those of its rivals. A stream of competi- tive repertoire research has demonstrated that competitive repertoire heterogeneity improves firm performance. First, a firm’s set of distinctive actions signals an aggressive attempt to break away from the norms of everyday competition (D’Aveni 1994), creating a base for competitive differen- tiation. To the extent that it takes time for rivals to catch up, the focal firm enjoys a temporal advantage. Second, heterog- eneous actions may enable the firm to deliver distinctive appeals to stakeholders, resulting in better performance (Chen and Miller 1994; Chen et al. 1992; D’Aveni 1994; Ndofor et al. 2011).
By extending the CRT, we argue that sellers that configure a heterogeneous set of platform-based functions are able to differentiate themselves from rivals by offering customers something that they are unlikely to attain elsewhere. In other words, platform-based functions that are less used by rivals could endow the focal seller with a distinctive value proposi- tion that attracts customers. For instance, all else equal, if a seller enables the “cash on delivery” function as a payment option, while most of its competitors accept only prepayment, the focal seller is more likely to have a heterogeneous platform-based function repertoire that differs from its rivals,
which could help the seller to gain a distinct competitive edge by attracting more customers to purchase. Such a heterog- eneous platform-based function repertoire would not be immediately replicable by its rivals, even if all of the func- tions are readily accessible by all of the e-marketplace sellers. This is because, as discussed, use of platform-based functions would require corresponding back-end operational capacity to be in place. To the extent that rivals may differ in their ability at the back-end, a distinctive temporary advantage for the focal seller would be created through a heterogeneous platform-based function repertoire. Hence, we propose
H3a: Platform-based function repertoire hetero- geneity of an e-marketplace seller is positively related to its sales performance.
While we hypothesize an overall positive relationship be- tween platform-based function heterogeneity and sales performance based on the original theory, we argue that this relationship could vary significantly, depending on seller reputation, such that when seller reputation is high, this rela- tionship is stronger. The logic for this moderation effect is that customer rating serves as an extrinsic cue to signal the credibility of the seller to customers. Credibility is of particu- lar importance to e-marketplace customers due to the uncer- tain and risky nature of e-marketplaces (Wells et al. 2011). We posit that this credibility signal is important for mitigating the uncertainty inherent in the heterogeneous actions that the seller enacts through platform-based functions.
As elaborated earlier, a seller’s competitive actions that deviate from the industry norm may enhance performance by offering distinctive value to customers. However, the strategy literature also suggests that deviations from an industrial norm may be seen as suspicious and erode firm credibility in the eyes of customers, which might, in turn, compromise firm performance (DiMaggio 1991; Scott 1987). This credibility issue is particularly salient in markets characterized by uncer- tainty, such as e-marketplaces, because the high information asymmetry between sellers and customers in the e-marketplace compels opportunistic behaviors (Pavlou and Gefen 2004). Following the same logic, a heterogeneous platform-based function repertoire may particularly alarm e-marketplace cus- tomers because such deviations from the industry norm would reinforce the risk perception prevailing in e-marketplaces. Customer concerns about seller credibility are common in the e-marketplace context due to its risky nature and could pre- vent customers from responding favorably to the seller.
When customer rating is high, the seller is perceived as being credible (Pavlou and Gefen 2004), and this could mitigate concerns arising from the seller’s abnormal competitive actions through platform-based function repertoire, thus
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increasing the likelihood that the customer would make a pur- chase that results from the appeals made in the seller’s actions. By contrast, when customer rating is low, the seller is not perceived as credible, and customers would be sus- picious of the seller’s abnormal platform-based functions, which, in turn, offsets the distinctive appeals that would otherwise attract customers to purchase.3 Hence, in this situation, the effect of platform-based function repertoire heterogeneity on sales performance would be weaker. Thus, we hypothesize
H3b: An e-marketplace seller’s reputation (cus- tomer rating) positively moderates the relationship between its platform-based function repertoire heterogeneity and sales performance, such that the relationship is stronger when customer rating is higher.
Research Method
Sampling
To test the research model, we collected a longitudinal dataset of e-marketplace sellers in the cosmetics industry on Taobao.com, which is the largest e-marketplace in China (and arguably in the world) and widely known as a platform for small and medium businesses to run online stores (Xu et al.
2017).4 Taobao, as an e-marketplace, is highly competitive and crowded and offers sellers a variety of platform-based functions, making it an appropriate context in which to test our research model. We chose the cosmetics industry because this is a major industry on Taobao with intense competition among sellers. This intense competition encourages sellers to use platform-based functions to gain a competitive edge. We selected a single industry to prevent cross-industry confounds on seller performance. Economists have long since confirmed the existence of industry effects (Schmalensee 1985); thus, including multiple industries in our study without proper control could have introduced aggregation bias. Using single- industry data is a control that can eliminate industry con- founds (Sharp et al. 2013), and it is common practice in the strategic management field. A recent review study reveals that “nearly half of the most impactful empirical articles pub- lished in strategy over the previous decade were conducted in a single-industry setting” (Sharp et al. 2013, p. 48). Hence, we apply a single-industry approach to remove bias and to generate more accurate findings.5
Alibaba, the holding company of the e-marketplace, worked with us to collect a sample of sellers over a 39-week window from the central database using a random sampling approach, stratified by the scale of the sellers. Then, we used two cri- teria to ensure that the e-marketplace sellers included in our final sample were appropriate for testing our theory. First, we focused on sellers who have used the platform-based func- tions at least once in the period of observation. This criterion ruled out sellers who have not used such functions. Second, we targeted sellers who had operated businesses continually during the observation period. This filtering exercise resulted in a final sample of 43,992 seller-week observations for 1,128 sellers over a period of 39 weeks.
Identification and Classification of Platform-Based Functions
A prerequisite for measuring the structural characteristics of function repertoire is to identify the platform-based functions and collect data on their use. In prior research, a common approach to identifying competitive actions has been to code publicly available information through structured content
3While our measure on heterogeneity captures the objective deviation of platform-based function repertoire from the norm by leveraging the unique strengthen of the secondary data, its performance effect, as we theorize, builds on the assumption about customers’ perceptions of such deviation and their subsequent reaction to it. We believe that it is reasonable to assume that e-marketplace customers as a whole are able to sense deviated use of platform-based functions given the high level of availability and transparency of function usage information in the e-marketplace that are of high relevance to customer purchase decision. This assumption about customer as a stake- holder acting knowledgeably in markets with transparency is quite common in economics theories (Granados et al. 2010; Stigler 1961). This said, we also acknowledge that customers, when disaggregated, may vary in their per- ception about this deviation. On this note, we believe that it is reasonable to assume customers could be approximate to this “objective truth” of deviated function use through past shopping experience in similar contexts and/or a quick search across a number of rivals, both being common practices for online customers striving to make informed decisions (Brown and Goolsbee 2002). Moreover, specific to the e-marketplace context, customer’s ability to approach this objective truth has been greatly enhanced through the ad- vanced filtering options prevailing in contemporary e-marketplaces designed to minimize customers’ search efforts. For instance, customers can quickly detect whether use of a particular function by a seller is unusual among its rivals by seeing how many sellers remain visible from the original seller pool after filtering by this particular function (Chen and Yao 2016). This process makes them better aware of unusual competitive actions.
4In this study, e-marketplace sellers refer mainly to the trading parties that have registered on the e-marketplace platform as retailers that connect manu- facturers and end customers.
5We also tested the model with another sample collected from the apparel industry on Taobao. Overall, the results were consistent with those for the cosmetics industry, lending further empirical support to our research model (see Appendix C).
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analysis; however, this approach is coarse and relies heavily on the availability of media reports (Ferrier et al. 1999; Gnyawali et al. 2010; Miller and Chen 1996a). The Taobao platform keeps track of seller use of platform-based functions. We took advantage of our direct access to the Taobao central database and obtained the unique platform-based function usage data over the observation period, thus ensuring the accuracy and completeness of our observations. Specifically, weekly usage data for all of the platform-based functions (22 in total at the time of data collection) were obtained for each seller in the sample over the 39-week observation period (see Appendix B for descriptions of the 22 functions). We verified the completeness of this list by cross-checking with Taobao seller storefronts. We also confirmed that all of the platform- based functions met the criteria defined earlier in the paper. Note that the value of the usage of a platform-based function in a particular week was recorded as “1” in the database if the function was activated in that week and “0” otherwise.
We subsequently classified the functions into different cate- gories to measure the complexity and heterogeneity of the platform-based function repertoire. Following prior research, we first adopted Ferrier et al.’s (1999) well-established frame- work that classifies competitive actions in traditional indus- tries into six action categories (see Table 3) (Chen and Miller 2012; Ferrier 2001; Ndofor et al. 2011; Roberts and Grover 2012; Smith et al. 2001; Yu and Cannella 2007; Zhang et al. 2011). Given that platform-based functions are designed to appeal to customers, it is not surprising that our initial assess- ment of the framework against the identified functions suggested that pricing, marketing, and service operations (e.g., after-sales services) were most applicable in the e-marketplace context. Viewing these categories through the lens of the customer service life cycle (CSLC) model that is unique to the e-commerce context (Tan et al. 2013),6 we found that they correspond to the requirement stage (i.e., pre-transaction, such as pricing and marketing) and the ownership stage (i.e., post-transaction, such as after-sales services), but the acquisition stage (i.e., facilitating transac- tion) is missing from the original competitive action frame- work. While transaction facilitation is not important in tradi- tional industries, it is instrumental in e-commerce. Thus, we keep the CSLC model as a complement to the original com- petitive action framework to guide the subsequent coding exercise.
We classified the available platform-based functions by fol- lowing the coding procedure documented in Tan et al. (2013). Two independent academic experts in e-commerce were first briefed on the definitions and categories of the competitive
action framework and the list of platform-based functions on Taobao. They were then instructed to code the functions against the framework independently. Based on the content analysis of the transaction platform, 18 platform-based func- tions were coded. These functions represent three cate- gories—pricing, marketing, and after-sales service— consistent with our initial assessment. Specifically, 10 func- tions that focus on price cuts and sales incentives (e.g., cumulative quantity discount, time-limited discount) were categorized into the pricing category. Four functions were classified into the marketing category, and four functions were classified into the category of aftersales service-oriented functions (ASOFs) (e.g., seven-day money-back guarantee) (see Table 4 for classification results). We adopted the reliability index to test coding reliability (Perreault and Leigh 1989). The result indicates a high degree of coding reliability (0.878).
The coders also were told to elicit the remaining four func- tions that could not be properly classified in accordance with the CRT and discuss how best to categorize them based on the CSLC. First, sellers can use two functions—zoom and alter- native photos—in the CSLC requirement stage to mitigate the lack of “touch and feel” (De et al. 2013). Thus, they were categorized as product presentation-oriented functions. Second, two functions—credit card payment option and cash- on-delivery option—provide online customers with diverse payment options in the CSLC acquisition stage. They were categorized as Payment-Oriented Functions. Although these two additional categories are generally unobservable and less relevant as competitive actions in traditional industries, they are highly relevant in the emerging e-commerce context and correspond well to the CSLC. Taken together, we classified the 22 functions into five categories. Table 4 summarizes the resulting five categories, and Appendix B depicts the full list of functions grouped by the resulting categories.
Finally, for triangulation, we employed another two indepen- dent coders to place the functions into the five categories.7
The results imply a high degree of agreement between the two coders (hit ratio: 0.909; inter-coder Kappa: 0.897). We also confirmed the comprehensiveness and relevance of the list and categories of platform-based functions by seeking feed- back from both sellers and platform providers.
6 We thank one of the anonymous reviewers for this suggestion.
7The number of categories is approximately what was reported in prior research. For example, Yu and Cannella (2007) and Chi et al. (2010) identi- fied six categories of competitive actions in the global automobile industry, and Zhang et al. (2011) identified seven categories of competitive actions for IT product providers. Thus, the category scheme provides a conservative, yet reasonable, range of actions to measure complexity and heterogeneity.
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( ) ( ) ( ) ( ) ( ) ( ) 2 2 2 2 2
1 1 2 2 3 3 4 4 5 5 ,
5
i i i i i t i
f f f f f f f f f f S f f
− + − + − + − + − =
Table 4. Platform-Based Functions
Categories Definitions
Pricing-Oriented Functions
Allows sellers to specify the degree of, and time period for, product discounts, e.g., time-limited discount, buy-it-now option, price bounding (Bockstedt and Goh 2011; Luo et al. 2012).
Marketing-Oriented Functions
Enhances retailer visibility in the e-marketplace, e.g., sponsored searching advertisements, hyperlink advertisements, purchasing agency community, luxurious shop interface (Bockstedt and Goh 2011; Luo et al. 2012).
Product Presentation- Oriented Functions
Provides detailed product information to mitigate the lack of “touch and feel” in the e-marketplace, e.g., zoom, alternative photos (De et al. 2013).
Payment-Oriented Functions
Provides online customers with diverse payment options in the acquisition stage of CSLC, e.g., credit card payment option, cash-on-delivery option.
After-Sales Service- Oriented Functions
Supports the customer along and after the sales process, e.g., money-back guarantee (Ba 2001; Ba and Pavlou 2002; Fang et al. 2014).
Measurement
To measure seller performance, we obtained proprietary data from Taobao on the daily average revenue over a week for all of the sampled sellers during the observation period. Unlike prior research that can infer sales only through sales rank (Amblee and Bui 2011; Brynjolfsson et al. 2003; Chevalier and Mayzlin 2006) or auction bids (Bockstedt and Goh 2011), our study is uniquely positioned to examine substantive seller performance in terms of actual dollars with actual revenue data.
We measured platform-based function repertoire volume by the number of platform-based functions in use in a week by a seller, consistent with prior research (Smith et al. 2001). Platform-based function repertoire complexity was measured as the degree to which a seller carried out a diverse repertoire of competitive action types by using an established formula as follows:
2
1 N
NT α
α −
The Nα represents the number of functions belonging to the α th
type, NT is the total number of functions across all types discussed above, and Nα/NT denotes the proportion of func- tions in the αth type (Ferrier et al. 1999). This formula was developed based on the Herfindahl index, initially used to measure industry diversification and then routinely used in competitive action research (Ferrier et al. 1999). A high score indicates that a seller used a broad range of platform-based functions; conversely, a low score reveals that a seller favored just a few types of platform-based functions.
Platform-based function repertoire heterogeneity captures the extent to which a seller differed from the industry norm in
terms of function types used. It was measured by adopting the existing formula commonly used in the competitive action literature as follows:
The fij represents the number of functions in each function type j undertaken by a seller i in the past one week, i = 1, 2, 3, …, 1128, f̄ j = Σ(f1j, f2j, …, f1128j)/1128 (where j = 1, 2, 3, 4, 5) (Ferrier et al. 1999; Miller and Chen 1996a). A high repertoire heterogeneity score indicates that the platform- based functions that a seller deployed are very different from its rivals, while a low score denotes that the seller’s platform- based functions are similar to its rivals. Table 5 lists the major variables and their measures.
Reputation was measured using a customer-generated rating that averages the values of three types of detailed seller ratings (DSR) that are commonly used by Taobao customers to rate sellers, namely, product quality DSR, distribution DSR, and service DSR. This measure has been used in the literature (Wang et al. 2013).
To rule out possible alternative explanations, we added several control variables, which could also affect sales per- formance. Because the price of the products sold in a store could affect sales (Ou and Chan 2014), we controlled for the price effect by using the number of products in different price intervals to represent an overall price level of products of a seller. Specifically, we included the number of products in low-price (25th percentile of price rank or below), mid-price (25th – 75th percentile), and high-price (75th percentile or above) intervals. Such price intervals are recorded by the transaction platform and defined by the platform based on the
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Table 5. Measurements of All Variables
Variables Measurements
Sales Performance The average actual transaction revenue per day over the past one week.
Number of Low-Price Products The number of products in the low-price interval [0, 25%].
Number of Mid-Price Products The number of products in the mid-price interval [25%, 75%].
Number of High-Price Products
The number of products in the high-price interval [75%, 100%].
Response Speed of Instant Message
The average response speed of instant message (i.e., AliWangWang) in the past week.
Delivery Time The average time difference between paying and delivering in the past week.
Platform-Based Function Repertoire Volume
The total usage times of all functions for a seller in the past week (Smith et al. 2001).
Platform-Based Function Repertoire Complexity
2
1 N
NT α
α −
The Nα represents the number of functions in the α
th category, NT is the total number of functions across all categories discussed above, and Nα/NT represents the proportion of functions in the αth category (Ferrier et al. 1999).
Platform-Based Function Repertoire Heterogeneity
( ) ( ) ( ) ( ) ( ) ( )2 2 2 2 2
1 1 2 2 3 3 4 4 5 5 ,
5
i i i i i
t i
f f f f f f f f f f S f f
+ + + + =
− − − − −
The fij represents the number of functions in each function type j that were undertaken by seller i in the past week, i = 1, 2, 3, …, 1128; f̄ j = Σ(f1j, f2j, …, f1128j)/1128 (where j = 1, 2, 3, 4, 5) (Ferrier et al. 1999; Miller and Chen 1996a).
Reputation The average of the values of three types of detailed seller ratings (DSR), i.e., product quality DSR, distribution DSR, and service DSR.
price rank of the entire industry. We believe that these three- tier price intervals capture the industry-level distribution of products across different price intervals for a seller.8 We also controlled for response speed of instant message, an inter- active channel for communication between seller and customer (Wang et al. 2013), and delivery time (Luo et al. 2012), both of which are known to encourage transactions in the e-marketplace.
Data Analysis
Model Specification
We developed a fixed-effects model (FE) to analyze the longitudinal observation dataset (Jabr and Zheng 2014; Oh and Oetzel 2011; Tang et al. 2012) because the result of the Hausman test that we ran suggested that estimates of the FE are consistent, while the estimates of a random-effects model are not (Hausman 1978). We specified the following FE:
yit = αi + β1Vit + β2Cit + β3Hit + β4Rit + β5VitRit + β6CitRit + β7HitRit + Σ(k = 8, 9, 10, 11, 12)βkXk, it + εit
where yit represents the logarithm of dependent variable sales performance for seller i at time t; αi(i = 1, 2, ..., 1128) is the unknown intercept for each entity (1128 seller-specific intercepts); β1, β2, …, β12 denote the coefficients for all vari- ables; V denotes volume, C denotes complexity, H denotes heterogeneity, R denotes reputation, and X denotes other control variables, namely, number of low-price products, number of mid-price products, number of high-price products, response speed of instant message, and delivery time; and εit is the error term. As these five control variables are very
8We obtained a sample of 522 cosmetic sellers, randomly drawn from the Taobao platform, with their complete list of products and product prices. The results of our analysis show that these sellers’ average price and the number of low-price products, the number of mid-price products, and the number of high-price products are strongly correlated at -0.38, -0.18, and 0.50, respec- tively (significant at p < 0.001). Thus, the three variables that we used in our study represent collectively a reasonable proxy for the sellers’ price level. Further, we contend that there are pros and cons for both measures. Although average price is a more common measure of the price effect, it does not account for the number of products available for sale at different price levels (so that there is the chance that more products are sold at an above-average or below-average price). In contrast, although the number of products at different price Intervals might be rare as a control variable, it aggregates both price category and the number of product listings in each category. This noted, we acknowledge that average price could be a more usual control variable for pricing effect, and we encourage its inclusion in future research.
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different from the independent variables in terms of means and standard deviations, log transformation is used to improve the model fit (Chen et al. 2015).
Serial Correlation, Heteroscedasticity, and Multicollinearity
Serial correlation (Bhargava et al. 1982) and heteroscedas- ticity (Baltagi and Li 2006) are the two common challenges in panel data analysis (Jabr and Zheng 2014). To check for serial correlation, we used Wooldridge’s (2002) test to examine whether there is first-order serial correlation. The results indicated the presence of first-order autocorrelation in our panel dataset (F(1, 1127) = 79.632, p < 0.0001). To check for heteroscedasticity, we performed the Breusch-Pagan test to examine whether the errors are homoscedastic (Breusch and Pagan 1979). The test result indicates the presence of heteroskedasticity (χ² = 3.70, p < 0.1). These two issues concerning the variance-covariance matrix of the error terms in a panel data model attack one specific error term specification assumption in the plain panel data models, but they do not compromise the consistency of fixed-effect esti- mation, although they may lead to inefficiency in the estimation of the standard deviation (Wooldridge 2002). As such, we adopted an FE with robust standard errors to account for these two issues by using a flexible form of variance covariance matrix (Arellano and Bond 1991).
We also checked for multicollinearity to ensure that it is not a major concern. The highest correlation coefficient is 0.65, below the critical threshold of 0.7 (Gnyawali et al. 2010) (see Table 6). The highest variance inflation factor (VIF) is 2.60, far below the threshold of 10 (Jabr and Zheng 2014) (see Table 7), indicating that multicollinearity is not a major issue in this study.
Results
We used the step-wise approach by first adding control vari- ables to the model (Model 1). We then added three indepen- dent variables, the moderator, and their interaction terms, one at a time. Table 8 presents the FE regression results for all variables with robust standard errors, and the dependent variables are all sales performance. The results show that number of low-price products is negatively related to sales performance (β = - 0.035, p < 0.025). As expected, response speed of instant nessage has a positive effect (β = 0.074, p < 0.001). Furthermore, reputation positively impacts the sales performance of e-marketplace sellers (β = 0.024, p < 0.001) (Model 5). These results are all within our expectations,
further enhancing our confidence to the econometric model for the subsequent hypotheses testing.
H1a, H2a, and H3a respectively predict positive relationships between platform-based function repertoire volume, com- plexity, and heterogeneity with sales performance. As shown in Model 2, the impact of repertoire volume on sales per- formance (H1a) is positively significant (β = 0.055, p < 0.001), supporting H1a. Model 3 shows that the relationship between repertoire complexity and performance (H2a) is significant and positive (β = 0.074, p < 0.025), supporting H2a. Contrary to our expectation, the relationship between repertoire heterogeneity and sales performance (H3a) is negative and significant (β = -0.260, p < 0.025), shown in Model 4. Thus, H3a was not supported.
Regarding the moderation effects, H1b stating that reputation negatively moderates the relationship between repertoire volume and sales performance was supported (β = -0.009, p < 0.001), as shown in Model 6. We plotted this moderating effect in Figure 2. As predicted, at low levels of reputation (mean – standard deviation), sales performance increases more rapidly when repertoire volume increases. However, at high levels of reputation (mean + standard deviation), sales performance increases marginally as repertoire volume in- creases. Model 7 shows that H2b, which hypothesizes that reputation positively moderates the relationship between repertoire complexity and sales performance, was not sup- ported (β = 0.023, t = 0.895).
H3b stating that reputation positively moderates the relation- ship between repertoire heterogeneity and sales performance was supported (β = 0.298, p < 0.025), as shown in Model 8.9
We plotted this moderating effect in Figure 3. To our sur- prise, the plot reveals that the sign of the direct relationship turned opposite at high versus low levels of seller reputation. Specifically, at high levels of reputation (mean + 1 standard deviation), sales performance is positively related to reper- toire heterogeneity. However, at low levels of reputation (mean – 1 standard deviation), sales performance is negatively related to repertoire heterogeneity.
Robustness Check for Endogeneity
A concern in this study is the potential endogeneity of the three independent variables, namely, volume, complexity, and heterogeneity of platform-based function repertoire, which may
9Because repertoire heterogeneity exerts a negative and significant effect on the sales performance of e-marketplace sellers, a positive moderating impact of seller reputation would reinforce this negative relationship.
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Table 6. Correlation Coefficients of All Variables
Variables Mean S.D. 1 2 3 4 5 6 7 8 9 10
1. Sales Performance 2414.46 9016.15 1
2. Number of Low-Price Products 78.72 128.27 .18 1
3. Number of Mid-Price Products 124.26 152.08 .24 .65 1
4. Number of High-Price Products 58.81 141.36 .32 .08 .48 1
5. Response Speed of Instant Message
23.99 22.94 .12 -.08 -.11 -.04 1
6. Delivery Time 0.81 0.87 -.02 -.09 -.04 .12 .03 1
7. Repertoire Volume 5.32 3.19 .41 .29 .27 .12 -.01 -.07 1
8. Repertoire Complexity 0.64 0.20 .15 .07 .05 .07 -.01 -.02 .38 1
9. Repertoire Heterogeneity 0.54 0.27 .21 .12 .12 .02 -.06 -.05 .60 -.05 1
10. Reputation 4.55 1.07 .05 .19 .24 .15 -.16 .02 .11 .05 .05 1
Notes: The unit of Sales Performance is China Yuan (CNY). All the correlations are significant at p<0.01, except for -0.01 (ns).
Table 7. VIF Values
Variable VIF 1/VIF
Number of Mid-Price Products 2.600 0.385
Number of Low-Price Products 2.020 0.495
Number of High-Price Products 1.510 0.661
Platform-Based Function Repertoire Volume 1.290 0.776
Platform-Based Function Repertoire Complexity 1.120 0.890
Reputation 1.100 0.911
Platform-Based Function Repertoire Heterogeneity 1.100 0.912
Response Speed of Instant Message 1.040 0.959
Delivery Time 1.030 0.968
Mean VIF 1.420
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Table 8. Results of Fixed-Effects Model with Robust Standard Errors
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Ln(Number of Low Price Products)
-0.035** (0.017)
-0.046** (0.017)
-0.047** (0.017)
-0.047** (0.017)
-0.052** (0.017)
-0.052** (0.017)
-0.052** (0.017)
-0.053** (0.017)
Ln(Number of Mid-Price Products)
0.031 (0.021)
0.016 (0.021)
0.016 (0.021)
0.011 (0.021)
0.005 (0.021)
0.004 (0.021)
0.004 (0.021)
0.003 (0.021)
Ln(Number of High Price Products)
0.025 (0.016)
0.025 (0.015)
0.025 (0.015)
0.025 (0.015)
0.024 (0.015)
0.023 (0.015)
0.023 (0.015)
0.025 (0.015)
Ln(Response Speed of Instant Message)
0.074*** (0.006)
0.068*** (0.006)
0.069*** (0.006)
0.065*** (0.005)
0.068*** (0.008)
0.068*** (0.008)
0.068*** (0.008)
0.069*** (0.008)
Ln(Delivery Time) -0.006 (0.018)
-0.003 (0.017)
-0.003 (0.017)
-0.002 (0.017)
-0.003 (0.017)
-0.003 (0.017)
-0.003 (0.017)
-0.003 (0.017)
Repertoire Volume (H1a) 0.055***
(0.008) 0.059***
(0.008) 0.078***
(0.012) 0.076***
(0.012) 0.078***
(0.012) 0.078*** (0.012)
0.076*** (0.012)
Repertoire Complexity (H2a) 0.074**
(0.110) 0.228**
(0.125) 0.223**
(0.125) 0.219**
(0.125) 0.212** (0.122)
0.208** (0.122)
Repertoire Heterogeneity (H3a)
-0.260** (0.07)
-0.252** (0.07)
-0.228** (0.07)
-0.227** (0.07)
-0.235** (0.07)
Reputation 0.024***
(0.006) 0.014**
(0.008) 0.014** (0.008)
0.019** (0.008)
Volume * Reputation (H1b) -0.009*** (0.003)
-0.009*** (0.003)
-0.009*** (0.003)
Complexity * Reputation (H2b)
0.023 (0.036)
0.034 (0.036)
Heterogeneity * Reputation (H3b)
0.298** (0.032)
Constant 5.899***
(0.05) 6.012***
(0.051) 6.012***
(0.051) 6.043***
(0.051) 6.078***
(0.052) 6.082***
(0.052) 6.082*** (0.052)
6.090*** (0.052)
F test 40.860*** 36.400*** 32.350*** 29.720*** 26.400*** 24.570*** 23.640 *** 21.513***
R-square 9.03% 25.23% 25.24% 25.32% 25.38% 27.03% 27.09% 28.12%
R-square Change – 16.20% 0.01% 0.08% 0.06% 1.65% 0.06% 1.03%
Notes: The dependent variable is Ln(SalesPerformance); Number of observations = 43,992; Number of sellers = 1,128; Robust Standard Errors in parentheses. *p < 0.05, **p < 0.025, ***p < 0.001, one-tailed tests.
Figure 2. Interaction between Repertoire Volume and Reputation
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Figure 3. Interaction between Repertoire Heterogeneity and Reputation
be affected by sales performance. To address this problem, we adopted an error component two-stage least squares (EC2SLS) regression using instrument variables (IV). The parameters of this method are estimated in two stages: (1) regress the endogenous variable (xk) on all of the exogenous variables (x1, …, xk-1, z), where (x1, …, xk-1) are control variables in the original equation and z is the instru- ment variable, and then (2) use the predicted values of xk from the first stage as a regressor in the original equation (Woold- ridge 2002). Following the guidelines presented in past studies (Brynjolfsson and Hitt 1996; Ghose 2009; Hitt 1999; Hitt and Brynjolfsson 1996; Luo et al. 2012), we used lagged platform-based repertoire volume, complexity, and hetero- geneity as the instrument variables for the current platform- based repertoire volume, complexity, and heterogeneity, respectively. The rationale of using the lagged variable at time (t-1) as an instrument variable is that (1) it should be highly related to the endogenous variable at time t, and (2) it is predetermined and, thus, not correlated to the error terms (εit) at time t. The lagged variable is often used as instrument variable in the literature that uses panel data (Groves et al. 1994). The results show no noteworthy difference from the baseline results with regard to all of the hypotheses (see Table 9), indicating the robustness of our results.
Discussion
The study contributes to the e-marketplace literature by taking the first step toward understanding the role of platform-based function repertoire in affecting the sales performance of e-marketplace sellers. Specifically, we build on the CRT and the online reputation literature to develop a research model that examines how the structural characteristics of platform- based function repertoire affect the sales performance of an e-marketplace seller, contingent upon its reputation, mani-
fested as customer rating. Four of six hypotheses are sup- ported (summarized in Table 10), providing evidence for most of our theoretical arguments. In this section, we reflect on the theoretical implications of our study and discuss why some hypotheses were not supported, and offer recommendations for practice and future research.
Research Implications
First and foremost, our study confirms that both platform- based function repertoire volume and complexity are posi- tively related to the sales performance of e-marketplace sellers, indicating that these two characteristics of platform- based function repertoire do enhance seller performance. This is consistent with our expectations, providing evidence for the theory that we advanced earlier in the paper. Further, the results confirm that seller reputation weakens the positive relationship between repertoire volume and sales performance such that, when seller reputation is high, the positive effect of repertoire volume on sales performance is weaker, verifying empirically our theoretical account of the contingent effect of seller reputation.
Unexpectedly, the moderating effect of seller reputation on the relationship between platform-based function repertoire complexity and sales performance is not significant. One plausible explanation is that, our empirical context, the Taobao platform structure, may have become less of a novelty after a few years in development. We make this speculation based on two considerations. First, while sellers’ actions enacted through platform-based functions on Taobao are diverse across five categories, the functions remain limited in number and unchanged in nature, suggesting a relatively stable platform structure. Second, Taobao might have made an effort to manage its nascence by deliberately making these
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Table 9. EC2SLS Estimates of Sales Performance
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Ln(Number of Low Price Products)
0.097*** (0.006)
0.096*** (0.006)
0.095*** (0.006)
0.096*** (0.006)
0.096*** (0.006)
0.096*** (0.006)
0.096*** (0.006)
Ln(Number of Mid-Price Products)
-0.060* (0.008)
-0.061* (0.008)
-0.068* (0.008)
-0.064* (0.008)
-0.064* (0.008)
-0.064* (0.008)
-0.064* (0.008)
Ln(Number of High Price Products)
0.315* (0.005)
0.316* (0.005)
0.312* (0.005)
0.315* (0.005)
0.315* (0.005)
0.315* (0.005)
0.315* (0.005)
Ln(Response Speed of Instant Message)
0.181*** (0.006)
0.180*** (0.006)
0.172*** (0.006)
0.167*** (0.006)
0.167*** (0.006)
0.167*** (0.006)
0.167*** (0.006)
Ln(Delivery Time) -0.043* (0.012)
-0.043* (0.012)
-0.043* (0.012)
-0.042* (0.012)
-0.042* (0.012)
-0.042* (0.012)
-0.042* (0.012)
Repertoire Volume (H1a) 0.209***
(0.003) 0.212***
(0.003) 0.249***
(0.004) 0.251***
(0.004) 0.251***
(0.004) 0.251***
(0.004) 0.251***
(0.004)
Repertoire Complexity (H2a)
0.252*** (0.069)
0.614*** (0.074)
0.615*** (0.074)
0.617*** (0.074)
0.618*** (0.073)
0.613*** (0.073)
Repertoire Heterogeneity (H3a)
-0.600*** (0.043)
-0.604*** (0.043)
-0.607*** (0.044)
-0.608*** (0.044)
-0.630*** (0.045)
Reputation 0.035***
(0.007) 0.033**
(0.008) 0.033**
(0.008) 0.023*
(0.090)
Volume * Reputation (H1b) -0.002* (0.004)
-0.003* (0.004)
-0.003* (0.004)
Complexity * Reputation (H2b)
-0.011 (0.060)
0.003 (0.060)
Heterogeneity * Reputation (H3b)
0.130** (0.064)
Constant 4.709***
(0.030) 4.720***
(0.030) 4.764***
(0.031) 4.763***
(0.031) 4.762***
(0.031) 4.762***
(0.031) 4.758***
(0.031)
Wald χ² 14187.33*** 14211.56*** 14564.05*** 14680.17*** 14674.52*** 14693.23*** 14657.56*** R-square 26.87% 26.92% 27.35% 27.39% 27.40% 27.40% 27.53%
R-square Change – 0.05% 0.43% 0.04% 0.01% 0.00% 0.13%
Notes: The dependent variable is Ln(Sales Performance); Number of observations = 43,992; Number of sellers = 1,128; Robust Standard Errors in parentheses. *p < 0.05, **p < 0.025, ***p < 0.001, one-tailed tests.
Table 10. Summary of Results
Hypotheses Supported
H1a Repertoire Volume º Sales Performance (+) Yes
H1b Repertoire Volume × Reputation º Sales Performance (-) Yes
H2a Repertoire Complexity º Sales Performance (+) Yes
H2b Repertoire Complexity × Reputation º Sales Performance (+) No
H3a Repertoire Heterogeneity º Sales Performance (+) No
H3b Repertoire Heterogeneity × Reputation º Sales Performance (+) Yes
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platform-based functions structurally easier for customers to understand, so that the diversity of functions does not come with complexity as the downside. As such, customer rating, as a mechanism to reduce customer cognitive effort for eval- uating a seller, is less necessary in this situation; hence, we find a nonsignificant moderation effect of seller reputation. If our conjecture is true, customer rating, as a firm-level mechnism to reduce cognitive effort for reacting to actions by e-marketplace sellers, might still be important when platforms are more nascent. Future research should investigate this conjecture by contrasting our finding here with results in other more nascent e-marketplaces.
Another interesting finding is that there is a significant and negative relationship between platform-based function reper- toire heterogeneity and sales performance, contrary to our original hypothesis. The original CRT suggests that firms could gain a competitive edge by exerting competitive actions that deviate from the industry norm (Chen and Miller 1994; Chen et al. 1992; D'Aveni 1994; Ndofor et al. 2011); our findings reveal that the opposite is true in the e-marketplace context. Note that a negative performance impact of reper- toire heterogeneity also was reported in several previous competitive action studies (Miller and Chen 1996a; Zhang et al. 2011). To better understand the nature of this unexpected relationship, we turn to the positive moderation effect of seller reputation (H3b) that was supported by our data. The inter- action plot (Figure 3) reveals that, when seller reputation is high, the effect of repertoire heterogeneity on sales perfor- mance is indeed positive, just as we hypothesized. However, when seller reputation is low, the effect of repertoire hetero- geneity on sales performance turns out to be strongly negative.
The contrasting performance impacts of platform-based reper- toire heterogeneity at high and low levels of seller reputation are very intriguing. First, this finding empirically verifies the corresponding moderation hypothesis that we proposed on seller reputation (H3b), lending support to our theory regarding the moderation role of seller reputation on the heterogeneity–performance relationship. Second, it sheds light on a plausible yet important boundary condition for predicting the direction of the heterogeneity-performance relationship, thus offering a reasonable account for the unex- pected negative direct effect we reported earlier. Specifically, this finding suggests that repertoire heterogeneity is beneficial to performance only when the seller is reputable; in contrast, heterogeneity could be highly detrimental when the seller has a poor reputation. Based on this observation, one plausible reason that the overall direct effect turns negative in our study is that our sample varies greatly on seller reputation; in this context, the negative performance impact of heterogeneity from low-reputation sellers subsumes the positive perfor- mance impact from reputable sellers.
Taking this finding further and relating it back to the original competitive repertoire literature, we sense that the path- changing boundary condition of seller reputation on the performance impact of repertoire heterogeneity might have a lot to do with the unique characteristics of the e-marketplace. Unlike the traditional industries from which the CRT origin- ates, the e-marketplace is often seen as an uncertain and risky place in which customers are wary of the opportunistic behavior of sellers (Wells et al. 2011). As such, customers may not readily assume seller credibility in the e-marketplace (Pavlou and Gefen 2004), unlike in the traditional airline, automobile, steel, and pharmaceutical industries. Rather, they need to be assured through clear credibility cues (Pavlou and Gefen 2004). Against this backdrop, platform-based hetero- geneity, as an extension of competitive repertoire hetero- geneity to the e-marketplace context, could easily reinforce serious perceptions of uncertainty or risk in the eyes of customers, particularly for sellers with poor reputation. This is because the seller actions, albeit distinctively appealing, are out of step with industry norms. As such, heterogeneous actions might discourage customers from transacting, espe- cially for sellers with poor reputation. This context-specific explanation may also account for similar negative perfor- mance impacts detected in previous competitive action studies (Miller and Chen 1996a; Zhang et al. 2011), which could be a subject for future research. In this sense, we reason that positive customer rating, as a salient credibility cue to elim- inate customer risk concerns in the e-marketplace context, is powerful in mitigating the negative side of heterogeneous actions and helping customers focus instead on their distinc- tive appeal. Taken together, the interesting finding regarding the role of platform-based repertoire heterogeneity under different conditions of seller reputation represents an impor- tant step toward revealing nuanced insights into how competitive action repertoire exerts effects in the unique e-marketplace setting.
Our work makes several major theoretical contributions. First, our study contributes to the e-marketplace literature by advancing scholarly understanding of the role of platform- based functions in improving seller performance from a reper- toire perspective. The extant e-marketplace literature has examined platform-based functions as IT artifacts that enhance seller performance, with a primary focus on the impact of individual functions (Bockstedt and Goh 2011; Li et al. 2009; Ou and Chan 2014). Further, there are mixed findings in prior e-commerce research on the performance impact of individual functions, implying that this is an inade- quate approach (Kirmani and Rao 2000; Li et al. 2009; Mathews 2004). By extending the CRT to the e-marketplace context (Chen and Miller 2012; Ketchen et al. 2004; Smith et al. 2001), our study takes a novel approach to examining the performance impact of platform-based functions by elevating
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these IT-enabled functions to the repertoire level, thereby offering a new theoretical explanation of how and why platform-based functions (as an IT artifact repertoire) affect e-marketplace seller performance. In particular, we add to the e-marketplace literature by demonstrating that volume and complexity, as structural characteristics of a platform-based function repertoire, are meaningful antecedents to e-marketplace seller performance.
We also contribute to this stream of e-commerce literature by specifying seller reputation as an important factor in influ- encing the impact of platform-based function repertoire, thus further clarifying the contextual boundary of these theoretical relationships. For instance, we find that the positive impact of repertoire volume varies, depending on seller reputation in the e-marketplace. More importantly, we add to the literature with our novel finding that heterogeneity exerts a strong and negative effect on performance when seller reputation is low and a positive effect when seller reputation is high. This finding is specific to the e-marketplace context and is quite unlike that predicted by the original competitive action litera- ture (Chen and Miller 1994; Chen et al. 1992; D’Aveni 1994; Ndofor et al. 2011). In doing so, we provide a more situated and nuanced theoretical understanding of the role of platform- based function repertoire in the e-marketplace context.
Second, our study contributes to the CRT by extending it to the e-marketplace context. Prior competitive action research has focused mainly on traditional industries (Chen et al. 1992; D'Aveni 1994; Ferrier et al. 1999; Gnyawali et al. 2006; Young et al. 1996). This extension contributes to the CRT by appropriating competitive actions as sellers’ use of platform- based functions and developing IT-specific arguments on how such functional usage at the repertoire level forms the basis for competitive differentiation, a theorizing effort that inte- grates the strategy and IS literature. Further, given the distinct characteristics of the e-marketplace, we incorporate seller reputation (customer rating) (Biswas and Biswas 2004; Bockstedt and Goh 2011; Komiak and Benbasat 2008) as a theoretically meaningful boundary condition of the theory in the e-marketplace context by drawing on the extant online reputation literature. This study enriches the theoretical ac- count of the CRT in explaining the performance impact of competitive actions by sellers in markets characterized as crowded, nascent, and uncertain.
Identifying seller reputation as a boundary condition of the theory also reveals possible ways to reconcile some mixed findings in the competitive action literature. For instance, empirical evidence for the performance effect of repertoire heterogeneity in the strategy literature is highly mixed. Several studies have found that heterogeneity of a firm’s com- petitive repertoire strongly improves performance (Ndofor et
al. 2011), while other studies have found the relationship to be not significant (Ferrier et al. 1999) or even negative (Miller and Chen 1996a; Zhang et al. 2011). Our research provides a possible explanation for such inconsistency by establishing seller reputation as a moderator, thus opening an avenue for future research to examine reputation in the traditional contexts in which the theory originates.
Third, our study adds to the e-commerce literature by comple- menting the predominant focus on the direct performance effect of online seller reputation in prior e-commerce research (Berger et al. 2010; Bockstedt and Goh 2011; Bolton et al. 2008; Forman et al. 2008; Li et al. 2009; Zhu and Zhang 2010) through addressing the indirect, moderation effect of online reputation on seller performance. As such, the results provide a point of departure to further our understanding of this important concept in the e-commerce literature by integrating it with other theoretical perspectives to explain e-marketplace seller performance. Future research could further investigate the indirect role of seller reputation in e-commerce.
Finally, we make meaningful empirical contributions to e-marketplace research in two ways. We obtain and analyze a unique longitudinal dataset that contains objective data on platform-based function usage and sales of one of the largest e-marketplaces in the world. This empirical effort adds to the methodological portfolio of the competitive action literature, which, to date, has mainly collected action data through subjective content analysis using publicly available media reports. We also, in the operationalization of platform-based function repertoire in view of the CRT, identify new IT- enabled function categories that are specific to IS and unique to the e-marketplace context, namely, product presentation- oriented functions and payment-oriented functions. These new IT-enabled function categories, together with the other functions that enable more traditional competitive actions, constitute a solid base to operationalize the structural charac- teristics of platform-based function repertoire and allow our study to yield novel insights into the performance impacts of the repertoire.
Practical Implications
This study has important practical implications for both e-marketplace sellers and e-marketplace platform providers. First, our findings provide guidance to e-marketplace sellers on how to properly configure their platform-based function repertoires by leveraging their existing platform infrastruc- ture. Specifically, according to our findings, sellers could benefit from having a large volume and diversity of platform- based functions. Whenever possible, sellers should strive to
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frequently use platform-based functions and use as many different types of them as possible, as it will make a positive impact on sales performance. For instance, sellers could make use of functions in all of the five function categories in the Taobao e-marketplace (see Table 4) to maximize reper- toire complexity and use more functions in each function category to maximize repertoire volume. Moreover, our findings imply that sellers without a high reputation in the form of customer ratings should exert more effort to deploy a greater volume of platform-based functions because the performance impact of function volume is stronger for these sellers. In contrast, our findings also imply that sellers should be very cautious about having their platform-based function repertoire deviate from common practices in the same in- dustry, unless such sellers are considered highly reputable (i.e., have high customer ratings). This recommendation is supported by our finding that deploying unusual actions could be highly detrimental to sellers if they do not have high customer ratings because customers will likely be suspicious about the actions and the sellers.
Second, our findings also provide practical guidance to e-marketplace platform providers (e.g., Taobao) in terms of designing platform-based functions and advising sellers on how to use them as a repertoire. For instance, our findings suggest that platform providers can design and provide more configurable platform-based functions for some sellers to differentiate themselves from their rivals, so as to attain performance advantage. Further, platform providers can analyze the function usage data and profile sellers in terms of their structural characteristics of platform-based function repertoire as compared to the industry norm. Such informa- tion could be provided to sellers as value-added services for a fee because it can provide decision support to sellers in regard to their repertoire configuration.
Limitations and Future Research
Like all studies, there are limitations to our research. First, we collected data from one industry (cosmetics) in one e-marketplace (Taobao), albeit validated with data from a second industry (apparel) from the same e-marketplace (Appendix C). Although there are advantages to testing a model using single-industry data, this approach may raise the question in regard to the degree to which empirical findings from this study can be extended beyond these two industries. This requires us to be mindful of the contextual boundaries of these two industries and to understand their possible impli- cations for the generalizability of the research model. For instance, sellers in both industries sell mainly experience goods that appeal to female customers. This raises the issue of whether the research model holds across types of goods
and genders of customers. The e-commerce and marketing literature has generally suggested that e-commerce customers’ browsing and purchasing behavior might differ between search goods and experience goods (Hong and Pavlou 2014; Huang et al. 2009) and that male and female customers may differ in their responses to cues presented at seller storefronts, such as product attribute information (Darley and Smith 1995), sales promotion (Mazumdar and Papatla 1995), and trust signals (Awad and Ragowsky 2008). Our review of these studies, however, suggests that these differences are more likely to be in terms of a matter of effect magnitude and not of significance or direction. In this regard, we believe that our research model, which builds on the premise of customer responses to platform-based functions and online reputation, should hold across genders of customers and types of goods.
That said, to test for generalizability, future research could replicate our work by examining different industries and with different customer segments (e.g., gender). In addition, our theory was empirically examined in the Taobao e-marketplace in China. Future research could also examine the research model in other e-marketplaces, such as eBay or Amazon, in which the platform operator also provides a set of functions for sellers to configure, as well as in different cultural/country settings. In particular, future research could examine the research model in a newly formed, likely more nascent, e-marketplace to observe the extent to which the data lend support to the moderation effect of customer rating on the relationship between repertoire complexity and performance, a hypothesized relationship that hinges on the nascence of the e-marketplace. Furthermore, future researchers are advised to reexamine the platform-based functions in other e-marketplaces when they test the theory. This is because, although we coded platform-based functions based on the competitive action literature and the CLSC cycle applicable to the general e-commerce context, the platform functions and corresponding types may manifest in different forms in different e-marketplaces. Future research should recategorize platform-based functions by applying this theoretically justified coding process to other empirical e-marketplace settings.
Second, we used revenue rather than profit variables (e.g., return on assets) to measure performance. We took this approach because enticing customer purchases (therefore, sales) is so important to e-marketplace sellers, and because cost data were unavailable. While we did incorporate cost in our theorizing on the role of platform-based function reper- toire usage, we acknowledge that profitability, which requires explicit inclusion of both revenue and cost, could be another crucial performance indicator for future research.
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Third, the model testing based on our data yields a significant, albeit limited increase in explained variance for sales perfor- mance. This might be due to two reasons. One, performance could be influenced by many factors. It is common to find a significant yet relatively small effect on performance in the research about competitive action (Madsen and Walker 2017; Marcel et al. 2011). Two, although our functional usage data are rare and valuable, their measure on usage is lean (used or not used in a given week). This leanness might reduce the explanatory power on performance at the empirical level. Although even small effects can hint at important model relationships (Chin et al. 2003), which is the case in our study, our interpretation of these significant results should still be made with caution. Future research is urged to collect richer functional usage data and test these effects under other e-marketplace settings.
Fourth, we focused on the static relationships between structural characteristics of platform-based function repertoire and sales performance. Future research could analyze the evolution of competitive repertoire and its impact on sales performance over time to yield more insights into the dynamic interplay between competitive repertoire and performance in the e-marketplace context.
Fifth, our empirical setting is constrained by a fixed set of platform-based functions that fall into a limited number of function categories or functions at the nascent stage of devel- opment. This might explain why our hypothesis regarding the moderation effect of seller reputation on repertoire complexity was not supported. This setting offers a relatively conserva- tive context in which to test our model; however, future research could retest this in other e-marketplaces that offer more types of platform-based functions or are more nascent in their development stage.
Sixth, in our study, firm-specific back-end operational capa- city constitutes a notable mechanism in our theorization about the temporary competitive advantage created by a platform- based function repertoire. We did not control for this variable in our analysis due to data constraints. Although we reason that this lack of control would not affect our empirical results with regard to the main hypotheses, it is a limitation of our study that future research should consider addressing. Simi- larly, due to data constraints, we do not have an average price for each seller as a control. Future research could further examine the research model with average price as an alter- native control variable for the number of products at different price categories that we used in this study. Moreover, our theoretical reasoning is based on the assumption that the objective measures of platform-based function repertoire, such as complexity and heterogeneity, are more or less per- ceived by customers as such. While we believe this is a
reasonable assumption as noted in the hypotheses develop- ment section, our study is not able to empirically examine this assumption due to the objective nature of the data. Finally, following the competitive repertoire literature, our research does not explicitly model rival reactions. Future research could investigate competitive dynamics among rival sellers over time by incorporating alternative streams of competitive action research that examines dyadic interactions between rivals (Boyd and Bresser 2008) and sequences of competitive actions that constitute competitive attacks (Rindova et al. 2010). For instance, it would be interesting to account for rival action speed and to understand the extent to which it would erode a focal seller’s advantage, possibly by building on related competitive action literature (Derfus et al. 2008).
Concluding Remarks
Sellers routinely leverage on platform-based functions with the intent to boost sales performance in nascent, increasingly crowded, and highly uncertain e-marketplaces. However, our theoretical understanding in this arena is not adequate. Our study provides a clearer understanding of the extent to which platform-based functions, aggregated at the repertoire level, affect sales performance by extending the IS-novel CRT to the e-marketplace context. We also clarify the boundary of the set of theoretical relationships with regard to platform- based function repertoire developed in our study by incor- porating seller reputation, manifested as customer rating, as a key context-specific condition in the e-marketplace setting. Our research model is verified through a large-scale longi- tudinal dataset obtained from one of the world’s largest e-marketplaces and has strong implications for both theory and practice. We not only add to the growing e-marketplace literature by offering a repertoire perspective to explain seller performance, but also enrich the competitive action literature by extending it to a new context. Future research and practice can extend this line of inquiry by examining the model in different industries and different e-marketplaces contexts (possibly with different platform-based functions) and by more explicitly investigating the competitive dynamics among rivals and over time.
Acknowledgments
The authors are grateful to the senior editor, the associate editor, and the anonymous reviewers for their invaluable guidance and insight- ful comments. The first author would like to thank the research interest group at the Department of Information Systems, City Uni- versity of Hong Kong. The work described in this article was primarily supported by grants from the National Natural Science Foundation of China (NSFC) [Project No. 71571155, 71601027,
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71371056, 71490721, and 91746302] and the Hong Kong Research Grants Council [Project No. CityU 11502116]. It was also sup- ported by the NSFC [Project No. 71431002, 71421001 and 71772022] and China Postdoctoral Science Foundation [Project No. 2016M600206].
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About the Authors
Huifang Li is an associate professor at the International Institute of Finance, School of Management, University of Science and Technology of China (USTC), China. She obtained her Ph.D. from USTC. She also received research training of high quality from Department of Information Systems, City University of Hong Kong when she studied and worked there as a research assistant and Postdoctoral Fellow. Her research interests include competitive dynamics and product returns in e-marketplaces.
Yulin Fang is a professor and director of the MSc. Business Information Systems (BIS) Program in the Department of Informa- tion Systems, City University of Hong Kong. He obtained his Ph.D. at the Richard Ivey School of Business, Western University, Canada. His research is focused on digital innovation, knowledge manage- ment, social media, and e-commerce. He has published in major IS and business journals, such as MIS Quarterly, Information Systems Research, Journal of Management Information Systems, Journal of the AIS, Strategic Management Journal, Journal of Operations Management, Journal of Management Studies, and Organizational
MIS Quarterly Vol. 43 No. 1/March 2019 235
Li et al./Platform-Based Functions of E-Marketplace Sellers
Research Methods. He has served as an associate editor for MIS Quarterly, a senior editor for Information Systems Research and Information Systems Journal, and a coeditor-in-chief for Information Technology & People.
Kai H. Lim is Yeung Kin Man Chair Professor of Information Technology Innovation and Management and director of the Research and Ph.D. Program at the Information Systems Depart- ment, City University of Hong Kong. His research interests include e-health, cross-cultural issues related to information systems man- agement, IT-enable business strategy, e-commerce, social media, mobile commerce and human–computer interactions. He has served as a senior editor for MIS Quarterly (2011-2016; two terms) and on the editorial boards of Information Systems Research and Journal of the AIS. His research has appeared in MIS Quarterly, Information Systems Research, Journal of Management Information Systems, and Journal of the AIS. Kai served on the faculty of Case Western Reserve University and the University of Hawaii. He has won num- erous teaching and research awards, and is one of the top-ranked
teachers in the CityU’s EMBA program. He has conducted execu- tive training in Beijing, Guangzhou, Shanghai, and Hong Kong, and is an Honorary Professor of Fudan University, China.
Youwei Wang is a professor in the Department of Information Management and Information Systems, School of Management, Fudan University, China. He obtained his Ph.D. from Northeastern University, China. He was a visiting scholar in the University of Washington (Seattle, USA) in 2016, Western University (London, Canada) in 2007, and McMaster University (Hamilton, Canada) in 2005. His current research interests include e-commerce, mobile business, sharing economy, and machine learning. He is serving as a senior editor of Electronic Commerce Research and Applications. He has published three books and more than 20 papers in academic journals such as Decision Support Systems, IEEE Transactions on Systems, Man, and Cybernetics, IEEE Transactions on Engineering Management, International Journal of Electronic Commerce, Information Technology & People, European Journal of Marketing, among others.
236 MIS Quarterly Vol. 43 No. 1/March 2019
RESEARCH ARTICLE
PLATFORM-BASED FUNCTION REPERTOIRE, REPUTATION, AND SALES PERFORMANCE OF E-MARKETPLACE SELLERS
Huifang Li International Institute of Finance, School of Management, University of Science and Technology of China,
Hefei 230026 CHINA, and Faculty of Management and Economics, Dalian University of Technology,
Dalian 116024 CHINA {[email protected]}
Yulin Fang and Kai H. Lim Department of Information Systems, College of Business, City University of Hong Kong,
Hong Kong SAR, CHINA {[email protected]} {[email protected]}
Youwei Wang Department of Information Management and Information Systems, School of Management, Fudan University,
Yangpu District, Shanghai 200433, CHINA {[email protected]}
Appendix A
Examples of Platform-Based Functions in E-Marketplace
Notes: 1. Buy-it-now option; 2. Money-back guarantee; 3. Credit card payment; 4. Cash on delivery; 5. Customer protection scheme
Figure A1. Platform-Based Functions in E-Marketplace (Taobao)
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Notes: 6. Price bundling; 7. Cumulative quantity discount with free postage
Figure A2. Platform-Based Functions in E-Marketplace (Taobao)
Notes: Luxury store interface
Figure A3. Platform-Based Functions in E-Marketplace (Taobao)
Notes: 9. Coupons; 10. Time-limit discount; 11. Platform VIP
Figure A4. Platform-Based Functions in E-Marketplace (Taobao)
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Appendix B
Categories of Platform-Based Functions in E-Marketplace
Platform-Based Functions
Definitions
An online function provided by the Taobao platform that allows sellers to:
Pricing- Oriented Functions
Time-Limited Discount Specify the degree of and the time period for product discount. Buy-it-Now Option Specify the degree of price reductions within extremely short periods. Cumulative Quantity Discount with Price Cuts
Set price reductions based on the quantity purchased, whereby buyers receive discounts when their purchasing amounts reach a certain value.
Cumulative Quantity Discount with Free Postage
Set price reductions based on the quantity purchased, whereby buyers receive free postage when their purchasing amount reaches a certain value.
Cumulative Quantity Discount with Credits
Set price reductions based on the quantity purchased, whereby buyers receive credits when their purchasing amount reaches a certain value.
Cumulative Quantity Discount with Gift
Set price reductions based on the quantity purchased, whereby buyers receive a gift when their purchasing amount reaches a certain value.
Shop VIP Set price reductions based on the quantity purchased, whereby buyers receive special privileges from the shop, usually a price discount.
Platform VIP Set price reductions based on the quantity purchased, whereby buyers receive special privileges (usually price discount) from all the shops that support the functions.
Bundling Set price reductions based on the quantity purchased, whereby buyers receive a price discount if they buy a couple of goods simultaneously.
Coupons Set price reductions based on the quantity purchased, whereby buyers receive a discount, either a specified amount or a percentage, when they hold a virtual voucher.
Marketing- Oriented Functions
Pay for Performance Use paid advertising provided by the transaction platform, and be charged according to advertising effectiveness.
Hyperlink Advertisement Use product spreaders who help sellers promote their products and charge according to effectiveness.
Purchasing Agency Community
Accept orders placed in some bricks-and-mortar stores that are authorized by and affiliated to the platform.
Luxurious Shop Interface Use personalized online shop interface provided by Taobao. Product Presentation- Oriented Functions
Zoom Function Use zoom technology that shows the details of the products.
Pictures of Real Products
Use real photos taken by sellers (rather than copied from manufacturers or other third parties).
Payment- Oriented Functions
Credit Card Support payment through credit card.
Cash on Delivery Support payment by cash when buyers receive the goods.
Aftersales Service- Oriented Functions
Money-Back Guarantee within 7 Days
Reimburse within 7 days after purchasing without reason.
Three Times Compensation for Fake Products
Reimburse three times the monetary value of the product if it is found to be fake.
Consumer Protection Scheme
Join the consumer rights protection plan issued by the platform.
Free Repair within 30 Days
Offer maintenance service for free within 30 days after purchasing.
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Appendix C
Empirical Results for Apparel Industry
Table C1. Results of Fixed-Effects Model with Robust Standard Errors in Apparel Industry
Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Ln(Number of Low Price Products)
-0.037* (0.019)
-0.063*** (0.018)
-0.062** (0.018)
-0.060** (0.018)
-0.068*** (0.018)
-0.067*** (0.018)
-0.067*** (0.018)
-0.065*** (0.018)
Ln(Number of Mid-Price Products)
0.017 (0.022)
-0.025 (0.021)
-0.027 (0.021)
-0.025 (0.022)
-0.035 (0.022)
-0.036 (0.022)
-0.036 (0.022)
-0.035 (0.022)
Ln(Number of High Price Products)
0.102*** (0.017)
0.120*** (0.017)
0.115*** (0.017)
0.115*** (0.017)
0.120*** (0.017)
0.118*** (0.017)
0.118*** (0.017)
0.117*** (0.017)
Ln(Response Speed of Instant Message)
0.076*** (0.008)
0.072*** (0.008)
0.073*** (0.008)
0.072*** (0.008)
0.077*** (0.008)
0.077*** (0.008)
0.076*** (0.008)
0.075*** (0.008)
Ln(Delivery Time) 0.048**
(0.020) 0.052**
(0.020) 0.052**
(0.020) 0.053** (0.020)
0.051** (0.020)
0.050** (0.020)
0.050** (0.020)
0.050** (0.020)
Repertoire Volume (H1a) 0.121***
(0.012) 0.114***
(0.012) 0.121*** (0.014)
0.119*** (0.014)
0.126*** (0.014)
0.125*** (0.014)
0.126*** (0.014)
Repertoire Complexity (H2a)
0.485* (0.161)
0.381* (0.187)
0.372* (0.186)
0.306* (0.184)
0.308* (0.183)
0.309* (0.183)
Repertoire Heterogeneity (H3a)
-0.099* (0.086)
-0.118* (0.086)
-0.139* (0.087)
-0.139* (0.087)
-0.154* (0.087)
Reputation 0.048***
(0.008) 0.024**
(0.009) 0.030**
(0.010) 0.047***
(0.011)
Volume * Reputation (H1b) -0.016*** (0.003)
-0.011** (0.004)
-0.025*** (0.006)
Complexity * Reputation (H2b)
0.118 (0.058)
0.111 (0.075)
Heterogeneity * Reputation (H3b)
0.238*** (0.047)
Constant 5.465***
(0.093) 5.679***
(0.092) 5.691***
(0.092) 5.679 *** (0.092)
5.717*** (0.092)
5.729*** (0.092)
5.728*** (0.092)
5.721*** (0.092)
F test 31.47*** 38.4*** 34.52*** 30.55*** 29.25*** 28.66*** 26.82*** 25.38***
R-square 7.18% 25.69% 25.70% 25.72% 25.77% 25.91% 26.00% 26.21%
R-square Change – 18.51% 0.01% 0.02% 0.05% 0.14% 0.09% 0.21%
Notes: The dependent variable is Ln(SalesPerformance); Number of observations = 42,480; Number of sellers = 1,062; Robust Standard Errors in parentheses. *p < 0.05, **p < 0.025, ***p < 0.001, one-tailed tests.
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Figure C1. Interaction between Repertoire Volume and Reputation in Apparel Industry
Figure C2. Interaction between Repertoire Heterogeneity and Reputation in Apparel Industry
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