![new model against the traditional model. Further they compare
the
models in an illustrative empirical study in which they study the
growth
of an Internet auction platform. The results show that this
separation of
effects is beneficial because the existing customers on both
sides of the
market can influence the acquisition and dropout of other
customers
asymmetrically. The paper thus makes an important contribution
that
should impact the way how researchers and business
practitioners
measure network effects in two-sided markets.
KEYWORDS
Two-sided markets;
electronic commerce; online
intermediaries; customer
churn; customer acquisition;
platform economy
Motivation
In two-sided markets, an intermediary provides a platform for
interactions between two
distinct customer populations [35, 38]. For example, the
intermediaries Amazon, Taobao.
com, and eBay use their platforms to enable transactions
between sellers and buyers; and
the intermediary Monster.com brings together employers and
employees. These two-sided
markets are not an entirely new phenomenon: In medieval times,
for example, city
councils provided marketplaces as platforms for farmers to offer](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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- 1. Estimating Network Effects in Two-Sided Markets Oliver Hinza, Thomas Ottera, and Bernd Skierab aFaculty of Business and Economics, Goethe University Frankfurt, Frankfurt am Main, Germany; bFaculty of Business and Economics, Goethe University Frankfurt (& Professorial Fellow at Deakin University, Australia), Frankfurt am Main, Germany ABSTRACT The proliferation of the Internet has enabled platform intermediaries to create two-sided markets in many industries. Time-series data on the number of customers on both sides of the markets allow platform intermediaries for estimating the direction and magnitude of network effects, which can then support growth predictions and subsequent information technology (IT) or marketing investment decisions. This article investigates the conditions under which this estimation of same- side and cross-side network effects should distinguish between its impact on the number of new customers (i.e., acquisition) and existing customers (i.e., their activity). The authors propose an influx- outflow model for doing so and conduct a simulation study to benchmark the
- 2. new model against the traditional model. Further they compare the models in an illustrative empirical study in which they study the growth of an Internet auction platform. The results show that this separation of effects is beneficial because the existing customers on both sides of the market can influence the acquisition and dropout of other customers asymmetrically. The paper thus makes an important contribution that should impact the way how researchers and business practitioners measure network effects in two-sided markets. KEYWORDS Two-sided markets; electronic commerce; online intermediaries; customer churn; customer acquisition; platform economy Motivation In two-sided markets, an intermediary provides a platform for interactions between two distinct customer populations [35, 38]. For example, the intermediaries Amazon, Taobao. com, and eBay use their platforms to enable transactions between sellers and buyers; and the intermediary Monster.com brings together employers and employees. These two-sided markets are not an entirely new phenomenon: In medieval times, for example, city councils provided marketplaces as platforms for farmers to offer
- 3. their products to buyers. Yet, the rise of what Shapiro and Varian [40] label the “network economy” has resulted in a plethora of two-sided markets due to the widespread use of the Internet ([3, 5, 13]; for comprehensive overviews on both online and offline two-sided markets, see Parker and Van Alstyne [32]). Such markets facilitate different kinds of network effects: Cross-side network effects describe the situation whereby the presence of many sellers attracts more buyers to the market (e.g., eBay) and vice versa [26, 42]. In contrast, same- side network effects capture the interplay within one customer population. Same-side and cross-side effects can some- times go in different directions: For example, more buyers make an auction platform less CONTACT Oliver Hinz [email protected] Faculty of Business and Economics, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 4, Frankfurt am Main 60323, Germany JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 2020, VOL. 37, NO. 1, 12–38 https://doi.org/10.1080/07421222.2019.1705509 © 2020 Taylor & Francis Group, LLC https://crossmark.crossref.org/dialog/?doi=10.1080/07421222.2 019.1705509&domain=pdf&date_stamp=2020-02-19 attractive for buyers because of the heightened competition, but
- 4. more attractive for sellers because of the increase in demand. Companies typically have access to data — in particular, time- series data — on the development of the number of customers on the two market sides, which can help companies estimate the direction and magnitude of network effects. Such knowledge can support growth predictions, as well as the information technologies (IT) and marketing investment decisions that follow. Yet, measuring network effects remains a troublesome task, and the literature to date has examined, at best, 2 × 2 = 4 kinds of network effects, that is, a same-side and a cross- side network effect for each of the two market sides. However, network effects arise from a variety of mechanisms. For example, on the one hand, a larger number of customers can lead to a wider range of offerings or more word-of -mouth within and across both market sides, which can increase the attractiveness of the market. On the other hand, the same situation can also lead to a decrease in attractiveness because of stronger competition among customers on one market side. Furthermore, such effects can differ for new and existing customers. For example, word-of-mouth generated by existing customers (hereafter called the installed base) might affect the acquisition of new customers more strongly than the activity of existing customers. As another example, disclosing a large number of buyers on an auction platform might attract new buyers because such a large number serves as an indicator of the
- 5. attractiveness of the market, but existing buyers might churn because of the expected increase in competition that is as a result of a higher number of buyers. The research to date (as we will show in Table 1) has mainly investigated the sum of these two effects by assessing the net change in the number of customers on one side of the market. Thus, instead of examining changes in the number of newly acquired customers and the number of churning customers separately, they simply examine the sum of both, that is, the change in the number of total customers. More technically speaking, the market grows on both sides because of an influx (which constitutes the number of new customers) and shrinks because of an outflow (which constitutes the dropout, or churn, of existing customers) [19]. However, investments in IT can have asymmetric effects on influx and outflow; thus, jointly estimating them may inaccurately summarize both effects because the growth in the number of new and existing customers may differ across time. Yet, it is important to have knowledge of the separate effects because organizations usually assign different units to acquire and retain customers on the two market sides [4]. In this paper, we develop a new model, the influx-outflow model, which allows for asymmetric network effects1; that is, dropout and acquisition present different effects on
- 6. each market side. This model is unique because it is the first to conceptually and empirically estimate eight network effects (two kinds of same-side network effects, two kinds of cross- side network effects, and two kinds of effects on influx and outflow). We show under which circumstances this model should be preferred over the standard model (hereafter labeled the “net change model”), which does not distinguish between network effects on the acquisition and dropout of customers. We use a simulation study and an empirical study to compare the influx-outflow model with the net change model, finding that the former performs signifi- cantly better, on average, with respect to estimating the true parameters. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 13 Ta b le 1. em p ir ic al st ud ie
- 52. i.e ., ch ur n fr om th e m ar ke t. 14 HINZ ET AL. Related Work Network effects exist if an additional user in a market (alternatively called a “platform”) affects the value that existing customers derive from that market. If that value increases, then network effects are positive, and vice versa. Products such as phones and e-mail constitute one-sided markets that exhibit a positive network effect because the value of those products for a user increases with the number of interactions that occur with other users of the platform. Two-sided markets are interorganizational information systems that provide two user populations (e.g., buyers and sellers) with rules and processes
- 53. to identify potential users with whom to interact, select a specific trading partner, and execute transactions [9]. Through these interactions, user populations create value [38]. Unlike one-sided mar- kets, two-sided markets offer interactions between two distinct user populations [14]. Usually, a user interacts only with users of the other market side (e.g., transactions between a buyer and a seller), although they can also influence their own market side both positively (e.g., by providing advice) or negatively (e.g., by increasing competition) [8, 46]. Thus, researchers usually examine four network effects in two-sided markets: one same-side network effect for both market sides, as well as two cross-side network effects. As Table 1 depicts, researchers have intensively studied network effects in recent years. Mainly finding that the estimated cross-side network effects are positive, scholars have then derived their impact on demand [17, 31, 36] and prices [6, 7]. Chu and Manchanda [10] contend that previous works have often concentrated on the benefits (or costs) that users realize from the addition of users from either the same or the other market side, but not simultaneously from both sides. As a consequence, many studies estimate cross-side net- work effects but do not consider same-side network effects [6] or they instead rely on a proxy such as lagged sales for a potential same-side network effect [41]. However, more recent research underscores the strong influence of same-side
- 54. network effects in the market. The few studies that have investigated both types of network effects (see Table 1) focus on their implications for sales [42] or revenue [2]. Our study builds upon Chu and Manchanda [10] by exploring the impact of same- and cross-side network effects on a platform’s growth, while adding several important aspects. First, Chu and Manchanda [10] examined network effects in a consumer-to-consumer (C2C) market, whereas we investigate a business-to-consumer (B2C) market. Second, and more importantly, we distinguish between network effects that affect the acquisition of new users and those that affect the activity of existing users. Results from other settings highlight the importance of this distinction: Iyengar et al. [23] analyzed peer effects among physicians (which is similar to a one-sided market) and found that such effects have a different impact on trial (comparable to our understanding of acquisition) and repeat purchases (comparable to the activity of existing users). Although they mainly studied social contagion processes, the distinction they made between the drivers might also be applicable to markets with network effects. Finally, our study examines the circumstances under which this distinction between different network effects presents benefits for plat- form operators. Table 1 underscores that most studies in the area of two-sided markets do not distinguish between network effects on the acquisition of new
- 55. customers and the dropout JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 15 of existing customers. Interestingly, studies such as Ackerberg and Gowrisankaran [1] assumed that users will endlessly utilize a technology or platform once they have adopted it. As such, their participation increases the network effects without any temporal limita- tion, which is a strong assumption according to Wattal et al. [44] . Researchers typically make this assumption when their data is solely at the market level, such as with sales data of video game consoles [27]. Clements and Ohashi [11] are among the few who consider the possibility, at least in a robustness test, that the installed base depreciates at an annual rate of 5 percent. If analysts have access to individual customers’ transactional data — which is increas- ingly the case — then they can consider the dropout of individual customers. Chu and Manchanda [10] did so for one of the two market sides. They used an activity-adjusted proxy for the number of sellers, but continued to use the cumulative number of registra- tions as a proxy for the number of buyers, even though buyers who registered years ago might have already churned or become inactive. Nevertheless, the authors’ use of an activity-adjusted proxy for one market side is a substantial step forward for accommodat-
- 56. ing dropouts. Table 1 shows that previous research in the area of two-sided markets provides important insights, but does not distinguish between the eight kinds of network effects outlined herein. The insights of Iyengar et al. [23] indicate that a subtler distinction might be required in a setting in which different features affect the processes that determine growth. For example, some features of a two-sided platform can create initial trust that motivates new customers to join the platform. These features are highly important for the acquisition of new customers, but less so for existing customers. Other features, mean- while, may only affect existing customers who already use the platform. If a model to predict platform growth does not separate between these effects, then the estimation results can be biased and lead to less effective management decisions. In the next section, we discuss the importance of separating the network effects in two-sided markets and then analyze the conditions under which a separation should be preferred over a joint consideration. Theoretical Considerations for Separating Network Effects in Two-Sided Markets and Results of a Simulation Study Analysis of Importance of Separating Network Effects in Two- Sided Markets Empirically inferring network effects requires econometric and
- 57. causal identification, as well as supporting statistical information. When conclusions depend upon the statistical significance of measured effects — as they should — the amount of statistical information in the system under study is important. Typically, platform intermediaries possess only a limited number of observations (e.g., weekly observations of the number of customers on both sides of the market; changes in the number of customers on a weekly basis). As a result, the length of the observation period is limited. Thus, in empirical, non-experimental studies of network effects, the data are, by definition, outside the analyst’s control. However, in the context of two-sided markets, the analyst has a choice between separately or jointly modeling the influx of new buyers (sellers) and the outflow of existing buyers (sellers). Jointly modeling them requires just 16 HINZ ET AL. two equations (one for the net change of buyers and one for the net change of the sellers) that characterize the market dynamics and equilibrium market size. Modeling them separately — our suggested approach — also yields the number of buyers and sellers, but it requires four separate equations: two equations for the influx of new buyers and the outflow of existing buyers, and two for the influx and outflow of sellers.
- 58. In the following, we discuss the advantages and disadvantages of modeling jointly versus separately. We show that jointly considering the decisions may result in a loss of statistical information under rather general conditions because summarizing the number of new and lost buyers into a net change in the number of customers may decrease the signal to noise ratio in the data. For example, if a measured cause exerts its influence such that a larger number of new buyers tend to coincide with a larger number of lost buyers and vice versa, then the systematic variance in the net change will be low. At the same time, the error variance may increase by forming net changes. Thus, joint consideration generally obfuscates the independent influence of causes on the number of new and lost buyers. Consider the situation where the installed base of buyers attracts even more new buyers (e.g., because of positive word-of-mouth created by existing buyers’ positive experience with the platform), but buyers on the platform actually compete for the same product, as is the case in an auction. In this example, an increase in the number of acquired buyers and lost buyers may balance out to suggest that network effects are not important in this market. Formally, we investigate the following proposition: Proposition 1: The influx-outflow model better estimates network effects if the influx and outflow of one market side correlate positively (i.e., they even out).
- 59. Example: Influx = 2 new customers, Outflow = 2 lost customers, thus no change in the number of customers. In this example, the influx-outflow model would be superior. In contrast, if network effects positively (negatively) influence influx, but negatively (positively) influence outflow, then their joined effect will be better measured by analyzing net changes. An example would be same-side network effects that may simultaneously decrease the influx and increase the outflow of buyers, such as a gaming platform that links game publishers and gamers. Existing gamers’ positive word-of-mouth attracts new gamers, and the resulting increase in the number of gamers makes the platform more valuable because gamers have more gamers to play with; this increase in value reduces the outflow of gamers. Stated differently, a joint consideration leads to more variance in the network effect of interest, leading to proposition 2: Proposition 2: The net change model better estimates network effects if influx and outflow of one market side correlate negatively. Example: Some variable causes expected Influx = 2 of new customers and expected Outflow = 2 of lost customers to change to Influx = 4 and Outflow = 0, i.e., a change by 2 customers each, and in opposite directions. The resulting change in the number of customers of course increases from 0 to +4 customers. In this example, the net change model would be superior.
- 60. Note that the influx-outflow model and the net-change model are identical in the limiting case of a deterministic growth-process. In this case, we can exactly solve for the parameters of either model and compute the exact net-change model parameters from the influx-outflow model parameters by differencing deterministic influx-outflow equations, assuming known JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 17 functional forms. However, the influx-outflow parameters generally cannot be recovered from the net-change parameters. The practically important difference between the two formula- tions arises when the growth-process is not deterministic. In this case, depending on the relationship between unobservables affecting growth and the link between observed variables and growth, either formulation may be more statistically efficient, that is, result in more reliable inference given the data. In the following, we prove propositions 1 and 2 for the case of linearly additive error structures. While a general proof is beyond the scope of this paper, we first note that the empirical literature measuring network effects heavily relies on linear models. Second, we generalize beyond linear additivity in our simulation study because we also use multiplicative error terms. Proof For non-positive correlations between unobservables affecting
- 61. influx (εIn) and outflow (εOut) (on either market side), the variance of the error process in the difference between Influx and Outflow is larger than the variance of the individual error processes by elementary covariance algebra: var Δεð Þ ¼ var εIn � εOutð Þ ¼ var εInð Þ þ var εOutð Þ � 2cov εIn; εOutð Þ. At the same time, the covariance between an explanatory variable x (affecting influx linearly with coefficient δIn and outflow with coefficient δOut) and the difference between influx and outflow, i.e., δIn � δOutð Þvar xð Þ will decrease relative to the covariance with influx δInvar xð Þ(outflow δOutvar xð Þ), whenever δInδOutð Þ > 0, i.e., x increases (or decreases) both influx and outflow. Now, because the statistical information about parameters is a function of the signal (explained variance) to noise (unexplained variance) ratio the net change model will be less statistically efficient, i.e., will yield less reliable estimates of the influence of x, proving proposition 1. However, for covariates Buyerst�1 and Sellerst�1, we often have δInδOutð Þ < 0. For example, more buyers in the past may increase the inflow of (new) sellers, while decreas- ing the outflow of (existing) sellers. Thus, before considering unexplained variance from unobservables, the net-change model results in a stronger signal about the influence of the installed base of buyers that proves proposition 2. Thus, determining whether the influx- outflow approach or the net change model is more efficient depends on the data- generating values in the error process and the mean structure.
- 62. Finally, classical statistical inference for parameters in four equations rather than two increases the risk of false positives and false negatives, assuming everything else equal. For example, repeated application of a particular criterion for statistical significance may result in “significance by chance.” A more parsimonious description of the system (using only two equations for modeling net changes) is more easily handled in a classical framework. Thus: Proposition 3: The net change model is superior because it has a lower risk to false positively detect non-existing network effects, which is relevant if same- side network effects are not present on at least one market side. In the following sections, we further investigate our propositions in a simulation study with a multiplicative error structure and an empirical study. 18 HINZ ET AL. Simulating Two-Sided Markets Setup of Simulation Study To test our theoretical considerations, we implemented a large- scaled simulation in C# and R. To this end, we created 84,672 markets by systematically varying the strength of the different network effects and the error level, as shown in Table 2.
- 63. We assume that a decision-maker or data scientist uses weekly data from the past year (from T – 52 to T) to calibrate both the net change and the influx-outflow models, with the aim of forecasting the development of the installed base (i.e., the number of customers on both market sides) over the next 52 weeks (from T to T + 52). We then compared the models’ performance. The results help us better understand when differences between the modeling approaches occur and under which circumstances one approach outperforms the other. The number of sellers in each of the 104 weeks (two years) is given by: Sellerst ¼ Sellerst�1 þ InfluxSellerst � OutflowSellerst (1) with InfluxSellerst ¼ ðδ1 � Buyerst�1Þ � ð1 þ E1Þ þ ðδ2 � Sellerst�1Þ � ð1 þ E2Þ (2) OutflowSellerst ¼ ðδ3 � Buyerst�1Þ � ð1 þ E3Þ þ ðδ4 � Sellerst�1Þ � ð1 þ E4Þ ; (3) where δ1 is the cross-side network effect from (existing) buyers on the number of acquired (new) sellers; δ2 is the same-side network effect from (existing) sellers on the number of acquired (new) sellers; δ3 is the cross-side network effect from (existing) buyers on the outflow of sellers, and δ4 is the same-side network effect from sellers on the outflow of sellers. E1–4 constitute errors given by random numbers that
- 64. are ~N(0, x) distributed. We systematically varied x to determine the influence of different sizes of the error on the prediction accuracy. Table 2. Experimental design of simulation study. Experimental Factors Number of Factor Levels Values for Each Factor Level Number of sellers in t = 0 1 50 Number of buyers in t = 0 1 500 Parameter of impact of buyers on seller influx δ1 2 .001/.0015 Parameter of impact of sellers on seller influx δ2 6 –.03/–.02/– .01/0/.01/.02 Parameter of impact of buyers on seller outflow δ3 2 .001/.0015 Parameter of impact of sellers on seller outflow δ4 6 .03/.02/.01/0/-.01/-.02 Parameter of impact of sellers on buyer influx δ5 2 .01/.015 Parameter of impact of buyers on buyer influx δ6 7 –.003/– .002/–.001/0/.001/.002/.003 Parameter of impact of sellers on buyer outflow δ7 2 .01/.015 Parameter of impact of buyers on buyer outflow δ8 7 –.003/– .002/–.001/0/.001/.002/.003 Random error (E1–E4, each drawn separately) 3 Low/Medium/High Number of replications 1 Number of simulated markets 1⋅ 1⋅ 2⋅ 6⋅ 2⋅ 6⋅ 2⋅ 7⋅ 2⋅ 7⋅ 3⋅ 1 = 84,672 JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 19 The number of buyers is given by: Buyerst ¼ Buyerst�1 þ InfluxBuyerst � OutflowBuyerst (4)
- 65. with InfluxBuyerst ¼ ðδ5 � Sellerst�1Þ � ð1 þ E5Þ þ ðδ6 � Buyerst�1Þ � ð1 þ E6Þ (5) OutflowBuyerst ¼ ðδ7 � Sellerst�1Þ � ð1 þ E7Þ þ ðδ8 � Buyerst�1Þ � ð1 þ E8Þ ; (6) where δ5 is the cross-side network effect from (existing) sellers on the number of acquired (new) buyers; δ6 is the same-side network effect from (existing) buyers on the number of acquired (new) buyers; δ7 is the cross-side network effect from (existing) sellers on the outflow of buyers, and δ8 is the same-side network effect from (existing) buyers on the outflow of buyers. Again, E5–8 …