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Imitation-related performance outcomes in social trading: A configurational approach
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Imitation-related performance outcomes in social trading: A configurational approach☆ ⁎
Elisabeth S.C. Bergera, , Matthias Wenzelb, Veit Wohlgemuthc a
University of Hohenheim, Entrepreneurship (570 C), Wollgrasweg 49, 70599 Stuttgart, Germany European University Viadrina, Chair of Management and Organization, Große Scharrnstraße 59, 15230 Frankfurt (Oder), Germany c HTW Berlin, Business School (FB 3), Treskowallee 8, 10318 Berlin, Germany b
A R T I C L E I N F O
A B S T R A C T
Keywords: Imitation Social trading Resource-based view Online community Qualitative comparative analysis Fintech
This paper draws on the resource-based view and risk-related research to examine imitation-related configurations that explain performance outcomes in social trading. The study applies qualitative comparative analysis to examine 16,964 investment observations at eToro, the world's largest social trading platform. The results show that the experience and the imitation of traders, in combination with a low risk level, equifinally explain similar performance outcomes. The findings contribute to the literature on social trading and the resource-based view by exploring imitation as a valuable strategy, conceptualizing and empirically validating the role of risk in social trading, and drawing on qualitative comparative analysis to develop a more complex configurational understanding of the examined phenomenon.
1. Introduction Scholars and investors devote an increasing amount of attention to social trading. In social trading, investors manage their portfolio in online communities that offer specialized tools and methods for making financial investments. The nascent literature on social trading (e.g., Oehler, Horn, & Wendt, 2016; Pentland, 2013; Wohlgemuth, Berger, & Wenzel, 2016) explores the unique features of this emergent but promising form of investing that other streams of literature do not fully illuminate. One of these features is copy-trading, a functionality of social trading platforms that allows investors to “automatically, simultaneously, and unconditionally replicate other investors' trades” (Wohlgemuth et al., 2016, p. 4970). This feature promises inexperienced investors with below-average trading performance to improve their performance in financial markets by imitating the investment decisions of more experienced investors (Pentland, 2013). Prior research highlights copy-trading as a key feature of social trading (Wohlgemuth et al., 2016). However, the literature on this phenomenon does not fully illuminate the performance outcomes of imitationrelated configurations in social trading. This paper addresses this gap by drawing on the resource-based view (e.g., Barney, 1991; Peteraf, 1993) and risk-related research (e.g., Berger & Fieberg, 2016; Markowitz, 1952) to examine the following research question: Which
imitation-related configurations explain performance outcomes in social trading? For this purpose, the present paper uses qualitative comparative analysis (QCA) to analyze 16,964 investment observations at eToro, the largest social trading platform worldwide. The results show that the experience of traders and their imitation, in combination with similar risk preferences, equifinally explain similar performance outcomes. These findings contribute to social trading research (e.g., Oehler et al., 2016; Wohlgemuth et al., 2016) through an in-depth examination of the imitation-related configurations that explain performance outcomes in social trading. Furthermore, the study complements work on the resource-based view focusing primarily on the barriers to imitation (e.g., Jonsson & Regnér, 2009; Madhok, Li, & Priem, 2010), by examining the performance outcomes of configurations that relate to the doing of imitation. In addition, the study complements the few works that draw on configurational approaches to examine resource-based phenomena (e.g., Hervas-Oliver, Sempere-Ripoll, & Arribas, 2016; Ho, Plewa, & Lu, 2016; Lisboa, Skarmeas, & Saridakis, 2016) by using QCA to develop a more complex understanding of the performance outcomes of imitation-related configurations.
☆ The authors are grateful to eToro for providing data access and supporting this research project. The authors also thank Ana Zorio Grima from the University of Valencia and Miriam Büxenstein from the European University Viadrina for helpful comments on earlier versions of the manuscript. ⁎ Corresponding author. E-mail addresses: [email protected] (E.S.C. Berger), [email protected] (M. Wenzel), [email protected] (V. Wohlgemuth).
https://doi.org/10.1016/j.jbusres.2017.12.016 Received 19 June 2017; Received in revised form 8 December 2017; Accepted 9 December 2017 0148-2963/ © 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Berger, E.S.C., Journal of Business Research (2017), https://doi.org/10.1016/j.jbusres.2017.12.016
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2. Social trading and imitation
imitator's inability as a key barrier to imitation (Madhok et al., 2010). For example, King and Zeithaml (2001) argue that the complex embeddedness of resources in firms prevents potential imitators from understanding how these resources generate above-average returns. Similarly, Thomke and Kuemmerle (2002) show that potential imitators are less able to emulate resources the more these resources are interconnected with other resources. In turn, Grahovac and Miller (2009) identify costs of imitation as a central barrier to imitation; they show that the costs at which potential imitators are able to emulate another firm's resources may be higher than expected benefits and, therefore, prevent imitation. More recent works consider the unwillingness of imitators a key barrier to imitation (Jonsson & Regnér, 2009; Madhok et al., 2010). For example, Jonsson and Regnér (2009) show that potential imitators may be unwilling to imitate because of institutionalized norms, such as professional codes, that prevent them from doing so. In turn, Madhok et al. (2010) show that potential imitators may be unwilling to imitate because they may have alternatives available that are more profitable than emulation. While these studies provide insightful contributions to a better understanding of barriers to imitation that help firms with above-average returns sustain their competitive advantage, few studies examine the performance outcomes of imitation (De Carolis, 2003; Ethiraj & Zhu, 2008; Pacheco-de-Almeida & Zemsky, 2012; Posen, Lee, & Yi, 2012). An examination of imitation-related configurations that explain performance outcomes is particularly interesting in the context of social trading: the technological affordances of social trading platforms enable traders to imitate the decisions of other investors (Wohlgemuth et al., 2016) and attract inexperienced investors in particular, who aim to make more profitable trading decisions (Pentland, 2013). Accordingly, social trading platforms undermine the inability and unwillingness to emulate others' valuable resources as classical barriers to imitation, and render the examination of configurations that explain the performance outcomes accessible. Therefore, this paper builds on the resource-based view to examine configurations that explain performance outcomes in social trading.
Social trading is an aspect of what is termed fintech, that is, the recent emergence of digital technology in financial services (Chishti & Barberis, 2016). Social trading is a way of making trades through specialized, investor-focused online communities (Wohlgemuth et al., 2016). All members of the trading community have access to the trading decisions that investors make on the platform. In addition, traders can communicate the strategies underpinning their investment decisions, exchange information, and follow other traders and their investment decisions (Oehler et al., 2016; Wohlgemuth et al., 2016). Social trading is different from other forms of financial trading in that social trading platforms facilitate copy-trading: the simultaneous and unconditional imitation of other investors' trades through automatic brokerage execution (Wohlgemuth et al., 2016). This feature enables investors on social trading platforms to benefit from the wisdom of exceptionally successful traders or the “wisdom of the crowd” (Pan, Altshuler, & Pentland, 2012, p. 203). Accordingly, typical transaction costs, such as for gathering information on investment alternatives or portfolio selection (Markowitz, 1952), are accrued by the followed trader, but not the follower. Therefore, social trading is very attractive for less experienced traders: the technical ability to directly imitate the investment decisions of experienced traders promises “average traders—who are often losers in the financial markets [to turn] into winners” (Pentland, 2013, p. 7). Nevertheless, while prior research acknowledges imitation through copy-trading as a unique opportunity and the most important feature of social trading (Wohlgemuth et al., 2016), previous studies pay little empirical attention to the performance outcomes of imitation-related configurations in social trading (Oehler et al., 2016). To gain a better understanding of the performance outcomes of imitation-related configurations in social trading, this paper now turns to the resource-based view, a stream of literature in which imitation is an important topic. 3. Resource-based view and imitation
4. A resource-based view on performance outcomes of imitationrelated configurations in social trading
The resource-based view (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984), also called resource-based theory (Barney, Ketchen, & Wright, 2011), is a theoretical approach in the management literature that considers firms as bundles of resources. Resource-based studies typically define resources as any and all kinds of assets, capabilities, processes, information, attributes (Barney, 1991), or alternative stocks of available factors that actors own or control (Amit & Schoemaker, 1993). Departing from theories that attribute the origin of differences in firm performance to attractive markets (e.g., Porter, 1980), the resourcebased view states that firm heterogeneity is the underlying reason for differences in performance and the key cause of enterprises achieving a competitive advantage (Barney, 1991; Peteraf, 1993; Rumelt, 1991). The resource-based view explains such heterogeneity by arguing that firms possess different resources. More specifically, the resource-based view suggests that, to generate above-average returns, firms must possess resources that are valuable, rare, inimitable, and non-substitutable (Barney, 1991). According to the resource-based view, imitation plays a key role in explaining performance outcomes: if competitors are able to imitate a resource, they “hurt a given firm's performance” (De Carolis, 2003, p. 28) because this resource becomes incapable of generating uniquely above-average returns but delivers average returns at best, given that several firms use the same resource to create the same value (Kraajenbrink, Spender, & Groen, 2010). Owing to this important role of emulation processes in explaining performance outcomes, imitation is the “lynchpin of resource-based theory” (King & Zeithaml, 2001, p. 75). Prior research primarily focuses on exploring barriers to imitation through which firms sustain above-average returns (Dierickx & Cool, 1989; Lippman & Rumelt, 1982). Most works highlight a potential
The resource-based view suggests that to generate above-average returns investors must accumulate valuable resources. This insight stems from the assumption that investors cannot freely purchase valuegenerating resources in factor markets but must develop and accumulate them over time through gaining experience (Barney, 1986, 1991; see also Priem & Butler, 2001a, 2001b). However, as the resource-based view outlines, resources will no longer yield above-average returns if others imitate them; instead, they generate average returns at best, given that others can draw on the same resources to create the same value (Kraajenbrink et al., 2010). Accordingly, from a resource-based perspective, experience in social trading is a key condition for explaining above-average returns on social trading platforms. To generate aboveaverage returns, investors must accumulate a competence in social trading by accumulating experience in conducting investments on social trading platforms over time. In turn, the resource-based view suggests that imitators are unable to generate above-average returns. This idea originates from the assumption that imitators cannot exceed the performance of those firms that they imitate; therefore, by imitating, investors can only achieve competitive parity at best (Madhok et al., 2010). However, especially inexperienced traders use the copy-trading functionality of social trading platforms (Pentland, 2013). As prior research suggests, these traders generate below-average returns if they make independent investment decisions; by relying on the copy-trading functionality, unskilled investors may offset their lack of experience in social trading by imitating the decisions of experienced investors (Pentland, 2013) and, thus, achieve similar (average) returns. Therefore, for inexperienced 2
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engage in risky investments equally well, and such choices explain their returns. This also implies that the risk level is independent of the substitutive choice between a reliance on experience or imitation. The risk level is thus an important additional complementing condition to explain average performance. Therefore, this study proposes that the configurations of experience or imitation, and risk-averse investment behavior explain average returns in social trading.
traders, achieving average returns is not an indication of underperformance but a performance improvement. In keeping with these arguments, this study proposes that experience and imitation are substitutes. Configurations that include one of the conditions explain equifinally the achievement of average performance. 5. Risk and return in social trading
6. Method and data
Like other forms of making investments (e.g., Bromiley, Rau, & Yu, 2017; Martin, Wiseman, & Gomez-Mejia, 2016), investment decisions in social trading genuinely involve risks. In investment contexts, risk refers to the possible divergence of actual future returns from expected returns (Sharpe, 1964). As risk-related research suggests, investors can reduce, and potentially even eliminate, investment-specific risks through diversification in investment portfolios (Berger & Fieberg, 2016; Markowitz, 1952; Slater & Zwirlein, 1992). The basic idea is that, as long as the different investments within the portfolio do not correlate completely positively, investment-specific risks—such as those related to a management decision that affects the firm's market share—will average out each other as one party's loss is the other party's gain. At least theoretically, firms can avoid investment-specific risks completely in case of a completely negative correlation of the investments. Therefore, prior research recommends risk-averse investors create a portfolio of negatively correlated investments (Markowitz, 1952). Although imitating other investors differs from more conventional investments in stocks, currencies, commodities, etc. in several respects, this form of making investments uses the same portfolio selection criteria for reducing investment-specific risks. The imitated investors differ in the extent to which they seek risks in creating their more or less diversified investment portfolios. These risk preferences constitute the foundation for the risk scores that social trading platforms calculate and display for each trader. Therefore, based on these risk scores, and similar to conventional investments, an investor can create a diversified portfolio of imitated investors according to the trader's risk preferences. The risk-related characteristics of the imitator's portfolio resemble a conventional portfolio of financial instruments as long as the portfolios involve the same financial instruments with the same weights. Therefore, imitators can create their own risk exposure through portfolio selection just like investors who make conventional trades. As in every investment decision, expected returns must increase with increased risks to make the additional risk worthwhile (Fama & MacBeth, 1973). In keeping with the “performative praxis” of rational decision-making (Cabantous & Gond, 2011), investors usually rely on the extensively applied capital asset pricing model (Lintner, 1965; Sharpe, 1964) or the extended Fama and French (1993, 2015) models to compute a hurdle rate, that is, the benchmark for an expected minimum return that makes an investment attractive. The hurdle rate is the sum of the return a riskless investment generates and the risk premium. The risk premium is the price for the risk the investor takes. By using such “technologies of rationality” (Jarzabkowski & Kaplan, 2015), analytically skilled investors evaluate investments with an expected return equal or below the return of a less risky investment as inefficient and, therefore, do not pursue them. However, high risk does not necessarily result in higher returns, but instead produces a wider potential spread of returns. In fact, popular hurdle rate models, such as the capital asset pricing model (Lintner, 1965; Sharpe, 1964), even operationalize risk as statistical spread measures, such as standard deviation or variance. That spread makes risky investments more likely to yield non-average (i.e., above-average or below-average) returns, whereas risk-averse investments are more likely to produce average returns. Accordingly, prior research suggests that, in addition to experience and imitation, risk-taking plays a primary role in explaining performance outcomes in social trading. According to this research, investors who do not rely on imitation and traders who rely on imitation can manage the extent to which they
This study proposes a set-subset relationship between the performance of traders who make investments on social trading platforms and the combination of the traders' risk level, their experience in social trading, and the degree of imitating the investment decisions of other traders. In this study, QCA offers an appropriate research method in that it makes it possible to capture both the complexity and the asymmetric nature of the phenomenon (Misangyi et al., 2017), while also accounting for potential equifinal paths explaining average performance (Ragin, 1987, 2008). QCA captures the complexity of social trading by accounting for the interdependencies between aspects of the investment behavior, for instance the conjoint effect of a low risk strategy in combination with imitation or experience, rather than the net effects. The asymmetry assumption is especially relevant when studying performance in social trading, as imitation might lead to average returns, but to achieve above-average returns, applying imitation-related configurations might not be effective. Accordingly, these outcomes require separate analysis (Schneider & Wagemann, 2012). Equifinality is especially relevant in the context of copy-trading as any large group of traders is likely to be heterogeneous and display different investment strategies that could all lead to high, low, or average performance. Additionally, QCA considers calibrated data rather than non-interpreted variables (Ragin, 2000). In the current study, this is advantageous because the investors who trade on social trading platforms also rely on calibrated categorizations that the platform operator provides, such as risk indicators. eToro provided data on the investment activities undertaken on its platform to the authors. eToro is the world's largest social trading platform and a particularly suitable database for research on social trading and copy-trading (Wohlgemuth et al., 2016). The specifics of eToro's business model are available on the firm's website. The data include the investment activities of all 642,288 community members active between January 2013 and May 2015. This is the entire population of eToro investors, which reduces the possibility of sampling errors. The data cleansing involved eliminating non-active traders, that is, investors who trade less than once per fortnight. This procedure also involves eliminating the trading of complete newcomers with an activity cut-off below 90 days and incomplete cases. The resulting sample contains 16,964 cases. Based on insights from prior literature, this study aims to analyze imitation-related configurations that explain average performance in social trading as the outcome. Performance refers to the quarterly gain. The platform operator states that eToro calculates this value based on the modified Dietz formula, a measure of an investment portfolio's historical performance that accounts for external flows (Dietz, 1966). The membership criteria to calibrate the data for this dichotomous outcome result from arithmetical calculations. The range between 0.5 standard deviations above and below the adjusted mean determines the membership criteria for average performance, while values outside this range indicate non-average returns. To understand the paths that lead to average performance, this study considers three causal conditions based on prior literature: low risk level, experience in social trading, and imitation. The low risk level expresses the extent to which investors undertake risky investments. 3
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The platform operator determines this score on a scale of one to ten by measuring the volatility of the financial instruments. eToro also provides the traders with a categorization that helps them interpret this score. Accordingly, this study calibrates risk scores of two or below as hardly risky investment behavior and six or above as very risky investment behavior. Experience represents the expertise in social trading, more specifically, on the focal social trading platform, that an investor accumulates over time. Given that the cleansed data sample only includes active traders, the time spent on the social trading platform constitutes a viable proxy for accumulated experience (see Barney, 1991) in social trading. Therefore, in line with Wohlgemuth et al. (2016), activity for one year indicates full membership in the experience set and 90 days of activity marks non-membership in terms of experience. The condition imitation expresses the degree to which traders simultaneously copy the investment decisions of other traders. The condition refers to the share of the portfolio that a trader devotes to copying other traders. Assigning more than half of the portfolio to imitating others indicates full membership in imitation terms, whereas no investment in copy-trading represents non-membership. Table 1 presents the descriptive statistics for the uncalibrated outcomes and conditions, and also the membership criteria for the calibration.
Table 2 Results necessity analysis.
Low risk level ~Low risk level Experience ~Experience Imitation ~Imitation
→ AVERAGEOUTCOME The two configurations both individually and jointly offer a high consistency of 0.88. As the coverage level indicates, they can jointly explain 36% of the outcome average performance. Configuration 1 explains average performance with the subset of low risk level and high level of imitation (raw coverage: 0.28, consistency 0.88). Configuration 2 requires the presence of low risk and a high level of imitation to explain average performance (raw coverage: 0.33, consistency 0.88). The presence or absence of imitation in configuration 1 and experience in configuration 2 does not matter. Fig. 1 presents the complex solution of the sufficiency analysis. The parsimonious solution indicates that low risk level is a core condition in both configurations (Fiss, 2011). The analysis of the non-outcome does not Table 1 Descriptive statistics (not calibrated) and calibration criteria. Calibration criteria
Average performance Low risk level Experience Imitation
Crossover
− 0.1
0.64
Dichotomous (1 = 0.12–− 0.39; else = 0)
5.6 488.1 0.5
2.28 262.49 0.41
2.00 365 0.50
4.00 180 0.20
0.87 0.62 0.72 0.59 0.73 0.64
These findings make several contributions to prior research: they (1) deepen understanding of the performance outcomes of imitation-related configurations in the nascent stream of literature on social trading, (2) extend the resource-based view, and (3) showcase the relevance of QCA in developing a more complex understanding of resource-based phenomena. This paper now discusses these contributions in more detail. The first contribution pertains to research on social trading. This research begins to grasp the uniqueness of this relatively recent phenomenon, especially with regard to copy-trading as an unconventional form of making investments (e.g., Oehler et al., 2016; Pan et al., 2012; Pentland, 2013; Wohlgemuth et al., 2016). The present study extends prior research by exploring this unique feature in depth. Specifically, this paper examines the performance outcomes of configurations that involve copy-trading, which as others (Oehler et al., 2016) argue, merit more research attention. In this vein, this study presents empirical evidence supporting the idea that imitation enables traders to achieve returns that are comparable to those of experienced investors. In addition, this study specifies the role of risk in imitation-related performance outcomes on social trading platforms. In particular, this paper conceptualizes that the risk-related portfolio selections of imitators are similar to those of experienced traders. Imitators may equally manage risks by creating more or less diversified investment portfolios that primarily consist of experienced traders and their investment portfolios instead of traditional investments. Therefore, copy-trading is not more or less risky than other ways of making investments. Based on this conceptualization, this study empirically validates the notion that imitators and experienced investors with equal risk preferences can achieve a similar performance. The second contribution relates to the resource-based view. Whereas prior research mainly focuses on examining the ability-based and normative barriers to imitation (e.g., Jonsson & Regnér, 2009; King & Zeithaml, 2001; Madhok et al., 2010; Thomke & Kuemmerle, 2002), this study examines the performance outcomes of imitation. Specifically, this paper examines the performance outcomes of imitation-related configurations in social trading, a context in which ability-based and normative barriers to imitation are non-existent, and finds that the experience of skilled traders and the imitation of their investment decisions equifinally explain average returns in social trading in combination with risk aversion. In part, this finding confirms prior research, which suggests that imitated resources generate average returns (e.g., Kraajenbrink et al., 2010), but the result goes further. Unskilled traders who under-perform in financial markets (Pentland, 2013) can offset a lack of competence by imitating competent traders and achieve returns that are as high. Accordingly, by examining the under-researched
LOWRISKLEVEL (IMITATION + EXPERIENCE )
Fullmember
0.37 0.63 0.84 0.16 0.64 0.36
8. Discussion and conclusion
QCA enables scholars to analyze necessary and sufficient conditions (Schneider & Wagemann, 2012). As Table 2 shows, the necessity analysis identifies no necessary condition for the outcome. That is, low risk level, experience, and imitation are not necessary conditions for explaining performance outcomes in social trading because the consistency level is below the cut-off of 0.9 (Skaaning, 2011). The sufficiency analysis results in two configurations that equifinally explain average performance. Based on the gaps in the consistency scores (Schneider & Wagemann, 2012), the study applies a consistency cut-off of 0.81 and a frequency cut-off of 431 and to identify these sufficient configurations. Fig. 1 visualizes the results of the analysis. In Boolean algebra, the sufficiency analysis reads as:
S.D.
Coverage
produce any results. Furthermore, the analyses for above-average and below-average performance do not yield sufficient configurations. In order to probe the robustness of these findings, this study applied additional post-hoc analyses using different frequency (10, 113, 200) and consistency cut-offs (0.7; 0.75; 0.8) (Skaaning, 2011), all resulting in the same configurations as are presented in Fig. 1 with similar consistency and coverage levels.
7. Results
Mean
Consistency
Nonmember
6.00 90 0.00
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Fig. 1. Results of sufficiency analysis.
(Reckwitz, 2002; Wenzel & Koch, in press). Third, this study does not explore how inexperienced, unskilled traders make investment decisions on social trading platforms. In this context, the practice of risk management (e.g., Hardy & Maguire, 2016) is particularly relevant: inexperienced traders may (or may not) use established models and tools for managing risks in different ways (e.g., Jarzabkowski & Kaplan, 2015). Future research might explore in some depth how traders manage their investment portfolios on social trading platforms. Fourth, future research could extend its purview beyond the immediate achievement of results on social trading platforms. For instance, by comparing the outcomes that investors achieve on social trading platforms with other forms of investments, for example, index funds that reflect benchmarks (see Oehler et al., 2016). Such examinations can situate the traders' experience, risk preferences, and performance outcomes in a broader market context. Fifth, different economic market conditions, such as a financial crisis, might influence the performance outcomes of imitation, experience, and risk aversion. This study encourages future research to examine performance outcomes under such conditions. Sixth, this study explains the achievement of average returns, but not the achievement of above-average returns, which leads to a possible developmental path that is worth investigating. Traders who are active on social trading platforms, but initially lack sufficient experience, might start by imitating other investors to achieve average returns; when these traders accumulate experience over time, they may increasingly substitute copy-trading and conduct their own trades more successfully. This dynamic suggests that, although copy-trading is the key feature of social trading platforms, these platforms indirectly can do more in that they might help inexperienced traders to develop skills in conducting trades in financial markets, while achieving average returns from the outset.
performance outcomes of imitation (e.g., Ethiraj & Zhu, 2008; Pachecode-Almeida & Zemsky, 2012; Posen et al., 2012), this study explores imitation as a surprisingly valuable strategy to improve performance to a certain level. The third contribution pertains to the use of QCA in the field of resource-based theory. Whereas most studies in this field rely on conventional quantitative methodologies that map linear relationships between the characteristics of resources and outcome variables (e.g., Kraajenbrink et al., 2010; Newbert, 2007), only very few studies draw a more complex picture of these relationships by applying a configurational approach (e.g., Hervas-Oliver et al., 2016; Ho et al., 2016; Lisboa et al., 2016). This paper complements and extends these studies by examining the imitation-related configurations that explain performance outcomes in social trading. Imitation and experience can lead to average performance as substitutes. QCA also enables the inclusion of counterfactuals in order to identify core conditions. In this study, a low risk level is a core condition in both configurations explaining average performance. The “coreness” of this condition might result from the prominent presentation of the risk level of traders on their profiles. As followers can effortlessly observe the risk score, they can easily find a “risk appetite” match in traders they would like to imitate. Relatedly, in contrast to variance-based results that linear regressions foster, this study highlights the equifinality of different conditions in explaining performance outcomes in social trading. In doing so, this study heeds recent calls to apply configurational approaches to the examination of management-related phenomena (e.g., Berger & Kuckertz, 2016; Misangyi et al., 2017; Woodside, 2013). The limitations of this study offer several opportunities for future research. First, although social trading has commonalities with conventional trading and strategic decision-making in firms, this study should spur further research on the performance outcomes of imitationrelated configurations in other contexts. Specifically, the uniqueness of copy-trading as an unconditional, simultaneous replication of investment decisions indicates that imitation processes outside of this context may be more problematic, for example, when temporal lags erode the value of imitating successful strategies. Future research may explore the specific practices (Vaara & Whittington, 2012) of imitation, as well as their consequential outcomes across different contexts. Second, while this study adopts the most salient conditions from the literature on social trading, the resource-based view, and risk, future research may continue to explore the under-studied performance outcomes of imitation-related configurations by including other conditions through the use of different theoretical lenses, such as social identification theory (Tajfel & Turner, 1986; Turner & Reynolds, 2010), dynamic capabilities (Teece, 2007; Wohlgemuth & Wenzel, 2016), business models (Priem, Wenzel, & Koch, in press; Zott, Amit, & Massa, 2011), or practice theory
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Imitation-related performance outcomes in social trading: A configurational approach☆ ⁎
Elisabeth S.C. Bergera, , Matthias Wenzelb, Veit Wohlgemuthc a
University of Hohenheim, Entrepreneurship (570 C), Wollgrasweg 49, 70599 Stuttgart, Germany European University Viadrina, Chair of Management and Organization, Große Scharrnstraße 59, 15230 Frankfurt (Oder), Germany c HTW Berlin, Business School (FB 3), Treskowallee 8, 10318 Berlin, Germany b
A R T I C L E I N F O
A B S T R A C T
Keywords: Imitation Social trading Resource-based view Online community Qualitative comparative analysis Fintech
This paper draws on the resource-based view and risk-related research to examine imitation-related configurations that explain performance outcomes in social trading. The study applies qualitative comparative analysis to examine 16,964 investment observations at eToro, the world's largest social trading platform. The results show that the experience and the imitation of traders, in combination with a low risk level, equifinally explain similar performance outcomes. The findings contribute to the literature on social trading and the resource-based view by exploring imitation as a valuable strategy, conceptualizing and empirically validating the role of risk in social trading, and drawing on qualitative comparative analysis to develop a more complex configurational understanding of the examined phenomenon.
1. Introduction Scholars and investors devote an increasing amount of attention to social trading. In social trading, investors manage their portfolio in online communities that offer specialized tools and methods for making financial investments. The nascent literature on social trading (e.g., Oehler, Horn, & Wendt, 2016; Pentland, 2013; Wohlgemuth, Berger, & Wenzel, 2016) explores the unique features of this emergent but promising form of investing that other streams of literature do not fully illuminate. One of these features is copy-trading, a functionality of social trading platforms that allows investors to “automatically, simultaneously, and unconditionally replicate other investors' trades” (Wohlgemuth et al., 2016, p. 4970). This feature promises inexperienced investors with below-average trading performance to improve their performance in financial markets by imitating the investment decisions of more experienced investors (Pentland, 2013). Prior research highlights copy-trading as a key feature of social trading (Wohlgemuth et al., 2016). However, the literature on this phenomenon does not fully illuminate the performance outcomes of imitationrelated configurations in social trading. This paper addresses this gap by drawing on the resource-based view (e.g., Barney, 1991; Peteraf, 1993) and risk-related research (e.g., Berger & Fieberg, 2016; Markowitz, 1952) to examine the following research question: Which
imitation-related configurations explain performance outcomes in social trading? For this purpose, the present paper uses qualitative comparative analysis (QCA) to analyze 16,964 investment observations at eToro, the largest social trading platform worldwide. The results show that the experience of traders and their imitation, in combination with similar risk preferences, equifinally explain similar performance outcomes. These findings contribute to social trading research (e.g., Oehler et al., 2016; Wohlgemuth et al., 2016) through an in-depth examination of the imitation-related configurations that explain performance outcomes in social trading. Furthermore, the study complements work on the resource-based view focusing primarily on the barriers to imitation (e.g., Jonsson & Regnér, 2009; Madhok, Li, & Priem, 2010), by examining the performance outcomes of configurations that relate to the doing of imitation. In addition, the study complements the few works that draw on configurational approaches to examine resource-based phenomena (e.g., Hervas-Oliver, Sempere-Ripoll, & Arribas, 2016; Ho, Plewa, & Lu, 2016; Lisboa, Skarmeas, & Saridakis, 2016) by using QCA to develop a more complex understanding of the performance outcomes of imitation-related configurations.
☆ The authors are grateful to eToro for providing data access and supporting this research project. The authors also thank Ana Zorio Grima from the University of Valencia and Miriam Büxenstein from the European University Viadrina for helpful comments on earlier versions of the manuscript. ⁎ Corresponding author. E-mail addresses: [email protected] (E.S.C. Berger), [email protected] (M. Wenzel), [email protected] (V. Wohlgemuth).
https://doi.org/10.1016/j.jbusres.2017.12.016 Received 19 June 2017; Received in revised form 8 December 2017; Accepted 9 December 2017 0148-2963/ © 2017 Elsevier Inc. All rights reserved.
Please cite this article as: Berger, E.S.C., Journal of Business Research (2017), https://doi.org/10.1016/j.jbusres.2017.12.016
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2. Social trading and imitation
imitator's inability as a key barrier to imitation (Madhok et al., 2010). For example, King and Zeithaml (2001) argue that the complex embeddedness of resources in firms prevents potential imitators from understanding how these resources generate above-average returns. Similarly, Thomke and Kuemmerle (2002) show that potential imitators are less able to emulate resources the more these resources are interconnected with other resources. In turn, Grahovac and Miller (2009) identify costs of imitation as a central barrier to imitation; they show that the costs at which potential imitators are able to emulate another firm's resources may be higher than expected benefits and, therefore, prevent imitation. More recent works consider the unwillingness of imitators a key barrier to imitation (Jonsson & Regnér, 2009; Madhok et al., 2010). For example, Jonsson and Regnér (2009) show that potential imitators may be unwilling to imitate because of institutionalized norms, such as professional codes, that prevent them from doing so. In turn, Madhok et al. (2010) show that potential imitators may be unwilling to imitate because they may have alternatives available that are more profitable than emulation. While these studies provide insightful contributions to a better understanding of barriers to imitation that help firms with above-average returns sustain their competitive advantage, few studies examine the performance outcomes of imitation (De Carolis, 2003; Ethiraj & Zhu, 2008; Pacheco-de-Almeida & Zemsky, 2012; Posen, Lee, & Yi, 2012). An examination of imitation-related configurations that explain performance outcomes is particularly interesting in the context of social trading: the technological affordances of social trading platforms enable traders to imitate the decisions of other investors (Wohlgemuth et al., 2016) and attract inexperienced investors in particular, who aim to make more profitable trading decisions (Pentland, 2013). Accordingly, social trading platforms undermine the inability and unwillingness to emulate others' valuable resources as classical barriers to imitation, and render the examination of configurations that explain the performance outcomes accessible. Therefore, this paper builds on the resource-based view to examine configurations that explain performance outcomes in social trading.
Social trading is an aspect of what is termed fintech, that is, the recent emergence of digital technology in financial services (Chishti & Barberis, 2016). Social trading is a way of making trades through specialized, investor-focused online communities (Wohlgemuth et al., 2016). All members of the trading community have access to the trading decisions that investors make on the platform. In addition, traders can communicate the strategies underpinning their investment decisions, exchange information, and follow other traders and their investment decisions (Oehler et al., 2016; Wohlgemuth et al., 2016). Social trading is different from other forms of financial trading in that social trading platforms facilitate copy-trading: the simultaneous and unconditional imitation of other investors' trades through automatic brokerage execution (Wohlgemuth et al., 2016). This feature enables investors on social trading platforms to benefit from the wisdom of exceptionally successful traders or the “wisdom of the crowd” (Pan, Altshuler, & Pentland, 2012, p. 203). Accordingly, typical transaction costs, such as for gathering information on investment alternatives or portfolio selection (Markowitz, 1952), are accrued by the followed trader, but not the follower. Therefore, social trading is very attractive for less experienced traders: the technical ability to directly imitate the investment decisions of experienced traders promises “average traders—who are often losers in the financial markets [to turn] into winners” (Pentland, 2013, p. 7). Nevertheless, while prior research acknowledges imitation through copy-trading as a unique opportunity and the most important feature of social trading (Wohlgemuth et al., 2016), previous studies pay little empirical attention to the performance outcomes of imitation-related configurations in social trading (Oehler et al., 2016). To gain a better understanding of the performance outcomes of imitation-related configurations in social trading, this paper now turns to the resource-based view, a stream of literature in which imitation is an important topic. 3. Resource-based view and imitation
4. A resource-based view on performance outcomes of imitationrelated configurations in social trading
The resource-based view (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984), also called resource-based theory (Barney, Ketchen, & Wright, 2011), is a theoretical approach in the management literature that considers firms as bundles of resources. Resource-based studies typically define resources as any and all kinds of assets, capabilities, processes, information, attributes (Barney, 1991), or alternative stocks of available factors that actors own or control (Amit & Schoemaker, 1993). Departing from theories that attribute the origin of differences in firm performance to attractive markets (e.g., Porter, 1980), the resourcebased view states that firm heterogeneity is the underlying reason for differences in performance and the key cause of enterprises achieving a competitive advantage (Barney, 1991; Peteraf, 1993; Rumelt, 1991). The resource-based view explains such heterogeneity by arguing that firms possess different resources. More specifically, the resource-based view suggests that, to generate above-average returns, firms must possess resources that are valuable, rare, inimitable, and non-substitutable (Barney, 1991). According to the resource-based view, imitation plays a key role in explaining performance outcomes: if competitors are able to imitate a resource, they “hurt a given firm's performance” (De Carolis, 2003, p. 28) because this resource becomes incapable of generating uniquely above-average returns but delivers average returns at best, given that several firms use the same resource to create the same value (Kraajenbrink, Spender, & Groen, 2010). Owing to this important role of emulation processes in explaining performance outcomes, imitation is the “lynchpin of resource-based theory” (King & Zeithaml, 2001, p. 75). Prior research primarily focuses on exploring barriers to imitation through which firms sustain above-average returns (Dierickx & Cool, 1989; Lippman & Rumelt, 1982). Most works highlight a potential
The resource-based view suggests that to generate above-average returns investors must accumulate valuable resources. This insight stems from the assumption that investors cannot freely purchase valuegenerating resources in factor markets but must develop and accumulate them over time through gaining experience (Barney, 1986, 1991; see also Priem & Butler, 2001a, 2001b). However, as the resource-based view outlines, resources will no longer yield above-average returns if others imitate them; instead, they generate average returns at best, given that others can draw on the same resources to create the same value (Kraajenbrink et al., 2010). Accordingly, from a resource-based perspective, experience in social trading is a key condition for explaining above-average returns on social trading platforms. To generate aboveaverage returns, investors must accumulate a competence in social trading by accumulating experience in conducting investments on social trading platforms over time. In turn, the resource-based view suggests that imitators are unable to generate above-average returns. This idea originates from the assumption that imitators cannot exceed the performance of those firms that they imitate; therefore, by imitating, investors can only achieve competitive parity at best (Madhok et al., 2010). However, especially inexperienced traders use the copy-trading functionality of social trading platforms (Pentland, 2013). As prior research suggests, these traders generate below-average returns if they make independent investment decisions; by relying on the copy-trading functionality, unskilled investors may offset their lack of experience in social trading by imitating the decisions of experienced investors (Pentland, 2013) and, thus, achieve similar (average) returns. Therefore, for inexperienced 2
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engage in risky investments equally well, and such choices explain their returns. This also implies that the risk level is independent of the substitutive choice between a reliance on experience or imitation. The risk level is thus an important additional complementing condition to explain average performance. Therefore, this study proposes that the configurations of experience or imitation, and risk-averse investment behavior explain average returns in social trading.
traders, achieving average returns is not an indication of underperformance but a performance improvement. In keeping with these arguments, this study proposes that experience and imitation are substitutes. Configurations that include one of the conditions explain equifinally the achievement of average performance. 5. Risk and return in social trading
6. Method and data
Like other forms of making investments (e.g., Bromiley, Rau, & Yu, 2017; Martin, Wiseman, & Gomez-Mejia, 2016), investment decisions in social trading genuinely involve risks. In investment contexts, risk refers to the possible divergence of actual future returns from expected returns (Sharpe, 1964). As risk-related research suggests, investors can reduce, and potentially even eliminate, investment-specific risks through diversification in investment portfolios (Berger & Fieberg, 2016; Markowitz, 1952; Slater & Zwirlein, 1992). The basic idea is that, as long as the different investments within the portfolio do not correlate completely positively, investment-specific risks—such as those related to a management decision that affects the firm's market share—will average out each other as one party's loss is the other party's gain. At least theoretically, firms can avoid investment-specific risks completely in case of a completely negative correlation of the investments. Therefore, prior research recommends risk-averse investors create a portfolio of negatively correlated investments (Markowitz, 1952). Although imitating other investors differs from more conventional investments in stocks, currencies, commodities, etc. in several respects, this form of making investments uses the same portfolio selection criteria for reducing investment-specific risks. The imitated investors differ in the extent to which they seek risks in creating their more or less diversified investment portfolios. These risk preferences constitute the foundation for the risk scores that social trading platforms calculate and display for each trader. Therefore, based on these risk scores, and similar to conventional investments, an investor can create a diversified portfolio of imitated investors according to the trader's risk preferences. The risk-related characteristics of the imitator's portfolio resemble a conventional portfolio of financial instruments as long as the portfolios involve the same financial instruments with the same weights. Therefore, imitators can create their own risk exposure through portfolio selection just like investors who make conventional trades. As in every investment decision, expected returns must increase with increased risks to make the additional risk worthwhile (Fama & MacBeth, 1973). In keeping with the “performative praxis” of rational decision-making (Cabantous & Gond, 2011), investors usually rely on the extensively applied capital asset pricing model (Lintner, 1965; Sharpe, 1964) or the extended Fama and French (1993, 2015) models to compute a hurdle rate, that is, the benchmark for an expected minimum return that makes an investment attractive. The hurdle rate is the sum of the return a riskless investment generates and the risk premium. The risk premium is the price for the risk the investor takes. By using such “technologies of rationality” (Jarzabkowski & Kaplan, 2015), analytically skilled investors evaluate investments with an expected return equal or below the return of a less risky investment as inefficient and, therefore, do not pursue them. However, high risk does not necessarily result in higher returns, but instead produces a wider potential spread of returns. In fact, popular hurdle rate models, such as the capital asset pricing model (Lintner, 1965; Sharpe, 1964), even operationalize risk as statistical spread measures, such as standard deviation or variance. That spread makes risky investments more likely to yield non-average (i.e., above-average or below-average) returns, whereas risk-averse investments are more likely to produce average returns. Accordingly, prior research suggests that, in addition to experience and imitation, risk-taking plays a primary role in explaining performance outcomes in social trading. According to this research, investors who do not rely on imitation and traders who rely on imitation can manage the extent to which they
This study proposes a set-subset relationship between the performance of traders who make investments on social trading platforms and the combination of the traders' risk level, their experience in social trading, and the degree of imitating the investment decisions of other traders. In this study, QCA offers an appropriate research method in that it makes it possible to capture both the complexity and the asymmetric nature of the phenomenon (Misangyi et al., 2017), while also accounting for potential equifinal paths explaining average performance (Ragin, 1987, 2008). QCA captures the complexity of social trading by accounting for the interdependencies between aspects of the investment behavior, for instance the conjoint effect of a low risk strategy in combination with imitation or experience, rather than the net effects. The asymmetry assumption is especially relevant when studying performance in social trading, as imitation might lead to average returns, but to achieve above-average returns, applying imitation-related configurations might not be effective. Accordingly, these outcomes require separate analysis (Schneider & Wagemann, 2012). Equifinality is especially relevant in the context of copy-trading as any large group of traders is likely to be heterogeneous and display different investment strategies that could all lead to high, low, or average performance. Additionally, QCA considers calibrated data rather than non-interpreted variables (Ragin, 2000). In the current study, this is advantageous because the investors who trade on social trading platforms also rely on calibrated categorizations that the platform operator provides, such as risk indicators. eToro provided data on the investment activities undertaken on its platform to the authors. eToro is the world's largest social trading platform and a particularly suitable database for research on social trading and copy-trading (Wohlgemuth et al., 2016). The specifics of eToro's business model are available on the firm's website. The data include the investment activities of all 642,288 community members active between January 2013 and May 2015. This is the entire population of eToro investors, which reduces the possibility of sampling errors. The data cleansing involved eliminating non-active traders, that is, investors who trade less than once per fortnight. This procedure also involves eliminating the trading of complete newcomers with an activity cut-off below 90 days and incomplete cases. The resulting sample contains 16,964 cases. Based on insights from prior literature, this study aims to analyze imitation-related configurations that explain average performance in social trading as the outcome. Performance refers to the quarterly gain. The platform operator states that eToro calculates this value based on the modified Dietz formula, a measure of an investment portfolio's historical performance that accounts for external flows (Dietz, 1966). The membership criteria to calibrate the data for this dichotomous outcome result from arithmetical calculations. The range between 0.5 standard deviations above and below the adjusted mean determines the membership criteria for average performance, while values outside this range indicate non-average returns. To understand the paths that lead to average performance, this study considers three causal conditions based on prior literature: low risk level, experience in social trading, and imitation. The low risk level expresses the extent to which investors undertake risky investments. 3
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The platform operator determines this score on a scale of one to ten by measuring the volatility of the financial instruments. eToro also provides the traders with a categorization that helps them interpret this score. Accordingly, this study calibrates risk scores of two or below as hardly risky investment behavior and six or above as very risky investment behavior. Experience represents the expertise in social trading, more specifically, on the focal social trading platform, that an investor accumulates over time. Given that the cleansed data sample only includes active traders, the time spent on the social trading platform constitutes a viable proxy for accumulated experience (see Barney, 1991) in social trading. Therefore, in line with Wohlgemuth et al. (2016), activity for one year indicates full membership in the experience set and 90 days of activity marks non-membership in terms of experience. The condition imitation expresses the degree to which traders simultaneously copy the investment decisions of other traders. The condition refers to the share of the portfolio that a trader devotes to copying other traders. Assigning more than half of the portfolio to imitating others indicates full membership in imitation terms, whereas no investment in copy-trading represents non-membership. Table 1 presents the descriptive statistics for the uncalibrated outcomes and conditions, and also the membership criteria for the calibration.
Table 2 Results necessity analysis.
Low risk level ~Low risk level Experience ~Experience Imitation ~Imitation
→ AVERAGEOUTCOME The two configurations both individually and jointly offer a high consistency of 0.88. As the coverage level indicates, they can jointly explain 36% of the outcome average performance. Configuration 1 explains average performance with the subset of low risk level and high level of imitation (raw coverage: 0.28, consistency 0.88). Configuration 2 requires the presence of low risk and a high level of imitation to explain average performance (raw coverage: 0.33, consistency 0.88). The presence or absence of imitation in configuration 1 and experience in configuration 2 does not matter. Fig. 1 presents the complex solution of the sufficiency analysis. The parsimonious solution indicates that low risk level is a core condition in both configurations (Fiss, 2011). The analysis of the non-outcome does not Table 1 Descriptive statistics (not calibrated) and calibration criteria. Calibration criteria
Average performance Low risk level Experience Imitation
Crossover
− 0.1
0.64
Dichotomous (1 = 0.12–− 0.39; else = 0)
5.6 488.1 0.5
2.28 262.49 0.41
2.00 365 0.50
4.00 180 0.20
0.87 0.62 0.72 0.59 0.73 0.64
These findings make several contributions to prior research: they (1) deepen understanding of the performance outcomes of imitation-related configurations in the nascent stream of literature on social trading, (2) extend the resource-based view, and (3) showcase the relevance of QCA in developing a more complex understanding of resource-based phenomena. This paper now discusses these contributions in more detail. The first contribution pertains to research on social trading. This research begins to grasp the uniqueness of this relatively recent phenomenon, especially with regard to copy-trading as an unconventional form of making investments (e.g., Oehler et al., 2016; Pan et al., 2012; Pentland, 2013; Wohlgemuth et al., 2016). The present study extends prior research by exploring this unique feature in depth. Specifically, this paper examines the performance outcomes of configurations that involve copy-trading, which as others (Oehler et al., 2016) argue, merit more research attention. In this vein, this study presents empirical evidence supporting the idea that imitation enables traders to achieve returns that are comparable to those of experienced investors. In addition, this study specifies the role of risk in imitation-related performance outcomes on social trading platforms. In particular, this paper conceptualizes that the risk-related portfolio selections of imitators are similar to those of experienced traders. Imitators may equally manage risks by creating more or less diversified investment portfolios that primarily consist of experienced traders and their investment portfolios instead of traditional investments. Therefore, copy-trading is not more or less risky than other ways of making investments. Based on this conceptualization, this study empirically validates the notion that imitators and experienced investors with equal risk preferences can achieve a similar performance. The second contribution relates to the resource-based view. Whereas prior research mainly focuses on examining the ability-based and normative barriers to imitation (e.g., Jonsson & Regnér, 2009; King & Zeithaml, 2001; Madhok et al., 2010; Thomke & Kuemmerle, 2002), this study examines the performance outcomes of imitation. Specifically, this paper examines the performance outcomes of imitation-related configurations in social trading, a context in which ability-based and normative barriers to imitation are non-existent, and finds that the experience of skilled traders and the imitation of their investment decisions equifinally explain average returns in social trading in combination with risk aversion. In part, this finding confirms prior research, which suggests that imitated resources generate average returns (e.g., Kraajenbrink et al., 2010), but the result goes further. Unskilled traders who under-perform in financial markets (Pentland, 2013) can offset a lack of competence by imitating competent traders and achieve returns that are as high. Accordingly, by examining the under-researched
LOWRISKLEVEL (IMITATION + EXPERIENCE )
Fullmember
0.37 0.63 0.84 0.16 0.64 0.36
8. Discussion and conclusion
QCA enables scholars to analyze necessary and sufficient conditions (Schneider & Wagemann, 2012). As Table 2 shows, the necessity analysis identifies no necessary condition for the outcome. That is, low risk level, experience, and imitation are not necessary conditions for explaining performance outcomes in social trading because the consistency level is below the cut-off of 0.9 (Skaaning, 2011). The sufficiency analysis results in two configurations that equifinally explain average performance. Based on the gaps in the consistency scores (Schneider & Wagemann, 2012), the study applies a consistency cut-off of 0.81 and a frequency cut-off of 431 and to identify these sufficient configurations. Fig. 1 visualizes the results of the analysis. In Boolean algebra, the sufficiency analysis reads as:
S.D.
Coverage
produce any results. Furthermore, the analyses for above-average and below-average performance do not yield sufficient configurations. In order to probe the robustness of these findings, this study applied additional post-hoc analyses using different frequency (10, 113, 200) and consistency cut-offs (0.7; 0.75; 0.8) (Skaaning, 2011), all resulting in the same configurations as are presented in Fig. 1 with similar consistency and coverage levels.
7. Results
Mean
Consistency
Nonmember
6.00 90 0.00
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Fig. 1. Results of sufficiency analysis.
(Reckwitz, 2002; Wenzel & Koch, in press). Third, this study does not explore how inexperienced, unskilled traders make investment decisions on social trading platforms. In this context, the practice of risk management (e.g., Hardy & Maguire, 2016) is particularly relevant: inexperienced traders may (or may not) use established models and tools for managing risks in different ways (e.g., Jarzabkowski & Kaplan, 2015). Future research might explore in some depth how traders manage their investment portfolios on social trading platforms. Fourth, future research could extend its purview beyond the immediate achievement of results on social trading platforms. For instance, by comparing the outcomes that investors achieve on social trading platforms with other forms of investments, for example, index funds that reflect benchmarks (see Oehler et al., 2016). Such examinations can situate the traders' experience, risk preferences, and performance outcomes in a broader market context. Fifth, different economic market conditions, such as a financial crisis, might influence the performance outcomes of imitation, experience, and risk aversion. This study encourages future research to examine performance outcomes under such conditions. Sixth, this study explains the achievement of average returns, but not the achievement of above-average returns, which leads to a possible developmental path that is worth investigating. Traders who are active on social trading platforms, but initially lack sufficient experience, might start by imitating other investors to achieve average returns; when these traders accumulate experience over time, they may increasingly substitute copy-trading and conduct their own trades more successfully. This dynamic suggests that, although copy-trading is the key feature of social trading platforms, these platforms indirectly can do more in that they might help inexperienced traders to develop skills in conducting trades in financial markets, while achieving average returns from the outset.
performance outcomes of imitation (e.g., Ethiraj & Zhu, 2008; Pachecode-Almeida & Zemsky, 2012; Posen et al., 2012), this study explores imitation as a surprisingly valuable strategy to improve performance to a certain level. The third contribution pertains to the use of QCA in the field of resource-based theory. Whereas most studies in this field rely on conventional quantitative methodologies that map linear relationships between the characteristics of resources and outcome variables (e.g., Kraajenbrink et al., 2010; Newbert, 2007), only very few studies draw a more complex picture of these relationships by applying a configurational approach (e.g., Hervas-Oliver et al., 2016; Ho et al., 2016; Lisboa et al., 2016). This paper complements and extends these studies by examining the imitation-related configurations that explain performance outcomes in social trading. Imitation and experience can lead to average performance as substitutes. QCA also enables the inclusion of counterfactuals in order to identify core conditions. In this study, a low risk level is a core condition in both configurations explaining average performance. The “coreness” of this condition might result from the prominent presentation of the risk level of traders on their profiles. As followers can effortlessly observe the risk score, they can easily find a “risk appetite” match in traders they would like to imitate. Relatedly, in contrast to variance-based results that linear regressions foster, this study highlights the equifinality of different conditions in explaining performance outcomes in social trading. In doing so, this study heeds recent calls to apply configurational approaches to the examination of management-related phenomena (e.g., Berger & Kuckertz, 2016; Misangyi et al., 2017; Woodside, 2013). The limitations of this study offer several opportunities for future research. First, although social trading has commonalities with conventional trading and strategic decision-making in firms, this study should spur further research on the performance outcomes of imitationrelated configurations in other contexts. Specifically, the uniqueness of copy-trading as an unconditional, simultaneous replication of investment decisions indicates that imitation processes outside of this context may be more problematic, for example, when temporal lags erode the value of imitating successful strategies. Future research may explore the specific practices (Vaara & Whittington, 2012) of imitation, as well as their consequential outcomes across different contexts. Second, while this study adopts the most salient conditions from the literature on social trading, the resource-based view, and risk, future research may continue to explore the under-studied performance outcomes of imitation-related configurations by including other conditions through the use of different theoretical lenses, such as social identification theory (Tajfel & Turner, 1986; Turner & Reynolds, 2010), dynamic capabilities (Teece, 2007; Wohlgemuth & Wenzel, 2016), business models (Priem, Wenzel, & Koch, in press; Zott, Amit, & Massa, 2011), or practice theory
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