Potential Power in a Quasi‐Competitive Market Open Access

This article explores the impact of potential power and actual power on negotiation outcomes in a quasi‐natural experimental setting. We analyze how the structure of a particular negotiation influences how power shifts in its actual and/or potential form. Building upon the premise that negotiations precede transactions that are themselves the basic units of measure for an economy (i.e., a market) (Commons 1934), our study examines how power shifts within a specific market setting.

We begin with the premise that the structure of a negotiation influences power dynamics. What if we view the structure in terms of how many buyers or sellers are present at the negotiation? It is this very shift in perspective to a bird’s‐eye view that positions our study primarily at the market level as we study explicit moves made by the negotiators as captured in their communicated offers. Limited to the boundaries of the negotiation table, when we pit the number of buyers against the number of sellers, we are able to derive the 2×2 table below (Table One), which presents four potential generic negotiation structures. Our focus is on the northeast quadrant as set against this larger landscape. One context that pits multiple buyers against one entrepreneur (seller) is angel investor syndicates that pool “finance(s) from individual investors to entrepreneurial ventures” (Mason, Botelho, and Harrison 2013: 4; see also Gregson, Mann, and Harrison 2013). Another is the ABC TV show Shark Tank, which is the focus of our study.

As scholars of management and entrepreneurship, one of our motivations for undertaking this research was the dearth of empirical studies—less than a handful—on the impact of potential power on negotiation outcomes (Brass and Burkhardt 1993; Mannix and Neale 1993). As entrepreneurs, we were also motivated by current regional efforts to improve the efficiency of entrepreneurial ecosystems (Audretsch, Belitski, and Cherkas 2021). We note that this is a two‐pronged effort addressing both the supply (entrepreneur) and demand (angel investor) components of a market, which we capture within each negotiation. We also note that this is a difficult effort because of the inherent inefficiency of the context due to the subjectivity surrounding these exchanges, suggesting that the more powerful hand at the negotiation table will exert greater influence on the firm’s current value and future strategy (Knight 1921; Fisher 1970; Wetzel 1983; Davidson 1996; Prowse 1998; Ibrahim 2008). Therein lies the challenge of angel investing, which we tackle via a “naturalistic study” as suggested by Donohue and Taylor (2007: 308).

A better understanding of how power shifts may give entrepreneurs and angel investors the confidence to engage in such exchanges more frequently, and may lead to the development of entrepreneurial ecosystems that have a more level playing field with respect to power differentials. Practically speaking, we think that accounting for power amounts to situational awareness. We argue that such awareness allows for an assessment of the bargaining table before one sits down, and allows the negotiator to calculate the power differentials at any given moment during the negotiation. Limited to angel investments, our findings suggest that exchange structures constructed as open forums that allow for both cooperation and competition over a finite amount of equity produce circumstances where potential power differentials favor the seller as long as the investor’s interest in the deal can be retained. In contrast, circumstances with only cooperation (e.g., angel syndicates) favor the buyers’ potential power if investments are pooled. However, matters are complicated because we also find that potential power is overshadowed by actual power (an offer) after we account for the size of the coalition tied to the offer; a further complication is added because coalitions are a double‐edged sword.

The object of the negotiation we study is represented by the firm, which will receive a capital investment directly from the investor, whereas the negotiation task is to divide ownership of the firm’s future cash flow in exchange for an immediate capital investment. We presume a main motivating factor of entrepreneurial capital requests is a firm‐level need (Fisher 1930). We also presume that a main motivating factor of angel investors is their expectation to generate a return on capital put at risk (March and Shapira 1987). We further presume that angel investor requests for equity ownership are tempered by the need to retain the entrepreneur’s motivation (in the form of retained equity) to implement the firm‐level strategy; while angels are hands‐on investors to a degree, their bandwidth is inherently limited by the size of their investment portfolio and the finite amount of time available in a day (Morrissette 2007; Wiltbank, Read, and Dew 2009). With respect to the actual task itself, we note that it can be reduced to a two‐issue negotiation at its core, where offers (equity/capital) and requests (capital/equity) are mirror images of one another. The firm’s value is derived by dividing capital by equity, as shown in Figure One.¹

We build upon the inherent tension between “the haves” and the “have nots,” and employ a foundational premise in our understanding of power—its inverse relationship to dependence (Emerson 1962). We derive potential power from the number of options that a negotiator has for closing a deal, which is a function of the size of the market of active buyers, which is itself a function of how the negotiation is structured (Emerson 1962; Mechanic 1962; Pfeffer 1981; Brass and Burkhardt 1993). In contrast, actual power is derived from the actual exchange of offers (Mintzberg 1984; Kim, Pinkley, and Fragale 2005). Our empirical research question is: Which type of power (potential or actual) has a greater impact on negotiation outcomes during a negotiation that pits (n) buyers against (1) seller? Our theoretical research question is: How does power shift in a quasi‐competitive market?

As discussed below in the section on theoretical advancement, we find that there are a finite number of positions at each size of the market of active buyers, and any change in position occurs along a set pattern. These changes occur as offers are extended, as buyers enter or exit the market, or as coalitions are formed or dissolved. We advance the conceptualization of a power board as comprised of an elastic set of options as the negotiation unfolds. Each position on the board has an associated set of power descriptors for actual power, potential power, and coalition size. We investigate this trifecta of power variables in the section below on empirical validation.

Weber (1947) defined power as an individual’s ability to satisfy their will even when facing resistance. It is “getting the other to do something [they] would not do in the absence of influence [and] settle for an outcome of less than his or her maximum utility” (Greenhalgh, Neslin, and Gilkey 1985: 13). More broadly, power is a meta‐construct and the literature has identified different bases of power that range from perceptual (legitimacy power) to structural (positional power) (French and Raven 1959; Yukl and Falbe 1991). We may view the generic roles of either buyer or seller as a structural base of power, pointing to the finding by Bazerman, Magliozzi, and Neale (1985) that buyers consistently outperformed sellers—presumably due to their ability to buy and the seller’s need to sell (see Mannix and Neale 1993; Donohue and Taylor 2007). Legitimacy power is considered the most complicated form of power; it is based primarily on perceptions of the counterparty’s power—on what one thinks of the other (French and Raven 1959; Kim, Pinkley, and Fragale 2005).

Power is not a static phenomenon but rather a force. It is any “force that results in behavior that would not have occurred if the force had not been present” (Mechanic 1962: 351, emphasis in original). And one way that power moves, or shifts, is through a negotiation that “occurs whenever people cannot achieve their own goals without the cooperation of others” (Thompson, Wang, and Gunia 2010: 491; see also Weber 1947; Emerson 1962; Casciaro and Piskorski 2005).

We incorporate both behavioral aspects—a negotiator’s tactics and perception (Thompson and Luthans 1983; Brass 1984; Mintzberg 1984; Kim, Pinkley, and Fragale 2005)—and structural aspects of power in our analysis. Structural “sources of power reflect the potential of a social system rather than the particular attributes or behaviors of any particular individual or interaction…” (Brass and Burkhardt 1993: 444). Actual power is a function of the explicitly communicated offers (i.e., behavior); potential power is a function of the structural arrangements that allows us to derive the set of available options to secure a deal.

We define coalitions following Thibaut and Kelley (1959: 205): “two or more persons [or groups] who act jointly to affect the outcomes of one or more persons.” (See also Meyer [2013] for a more nuanced consideration of multiple perspectives on coalitions.)

Building upon our definition of potential power as a function of the available option set, the potential to form coalitions that exclude other independent entities from the opportunity expands the associated option set available to all parties. We can thereby view the introduction of coalitions as contributing to a greater degree of competition (Fisher 1970). Further, coalitions impact both potential and actual power because coalitions do not exist in thought alone, but are a function of an explicit offer that conveys both positional and legitimacy power.

Coalitions play a central role in our study, and to add greater clarity we briefly compare how coalitions form in the Shark Tank to angel investor syndicates. Modern angel syndicates typically have a coordinating body that conducts due diligence prior to inviting the entrepreneur to present to a group of angel investors who typically pool their investments (Mason and Harrison 2008). The due diligence process bestows legitimacy power because presumably there would be no invitation in the event of a negative due diligence outcome. In contrast, despite earlier screening from producers who “visit trade shows or reach out to companies directly and ‘cherry pick’ entrepreneurs to apply” (Levin 2019), the Sharks negotiate based upon the entrepreneur’s claims and make a decision based on the exchange of information (subjective, objective, visual, and physical) that will later be validated via due diligence. And while only ~80 to 100 applicants are aired in any given season out of an initial pool of ~40,000 (Levin 2019), negotiated agreements represent only an intention‐to‐fund (Smith and Viceicza 2018) and do not represent a guarantee of investment. We can view this part of the transaction as analogous to a residential real estate transaction where a contract is signed before the home inspection. Sharks must not only negotiate against the seller, but also against other potential teams of would be buyers to secure the rights to conduct due diligence. (There is typically no competition within angel syndicates, reducing the potential options to participating or abstaining.) This renders the entrepreneur in a weaker, pre‐legitimized power position when facing Sharks; a legitimacy that even residential real estate owners (sellers) enjoy via the benefit of public records (taxes) and comparable sales. While entrepreneurs on the Shark Tank lack any actual legitimacy power as conveyed by passing an initial due diligence process within angel syndicates, we note that they enjoy a higher potential power position based on the greater number of options to close a deal.

In Figure Two, we provide a simplified visual of the negotiation. We begin with the seller’s option set (see Table Two). For a market with five buyers that allows for coalitions, the seller’s option set contains thirty‐one dyadic options, where each dyad contains a seller and a coalition of buyers of various size: five single‐party coalitions, ten dual‐party coalitions, ten tri‐party coalitions, five quad‐party coalitions, and one coalition with all angel investors representing a quintet party. At each market size ranging from five buyers to a single buyer, the potential combination of all dyadic interactions is calculated via the standard combinatorics formulas [(n!)]/[(n–r)*(r!). We obtain the total potential option set by adding the various combinations at each size of the market. If we were to add a sixth buyer, the potential combinations increase to 63; a seven‐buyer market yields 127 outcomes.²

Note that the series of numbers on the right‐hand side of the table (127, 63, 31, 15, 7, 3, 1) follows a pattern named after the French mathematician Marin Mersenne (1588–1648), referred to as the Mersenne Prime Series.³ The formal function is y = (2^n) – 1, and we observe that this function, which was discovered many years ago, perfectly represents the potential options to close a deal driven by the pool of buyers.

The angel investor’s perspective is slightly different, but here also we observe a pattern based on the available options. For each market size, any individual angel investor will only participate in a fraction of the potential outcomes available to the entrepreneur. For example, within the seven‐buyer market, a single angel investor can participate in 1 of 7 single‐party coalitions, 6 of 21 dual‐party coalitions, 15 of 35 tri‐party coalitions, 20 of 35 quad‐party coalitions, 15 of 21 quintet‐party coalitions, 6 of 7 sextet coalitions, and 1 of 1 septet‐party coalition (see Table Three for details). As before, the summation of all potential coalition options for the buyer at each market size (n buyers) yields a pattern that is similar to but different from the function found for the seller: [y = 2^(n–1)].⁴

Comparing the potential outcomes for the buyer and seller, we note that within the bubble of the particular negotiation the seller has a larger number of options than does any single angel investor. We present this counter‐intuitive point in contrast to the angel investor’s global power advantage. We suggest that the entrepreneur’s ability to maintain investor interest is partially a function of the unique force exerted by the opportunity itself in terms of return expectations, and if the seller can maintain the level of interest in the market of active buyers such that none exit the negotiation, then the entrepreneur can retain its potential power advantage during the negotiation even if the angel investor retains its global power advantage (Emerson 1962).

Building upon the above, we are able to quantify (in percentages) the potential power of either the buyer or seller. We calculate these percentages as follows: For the seller, we account for the negative hit on power that occurs if the pool of buyers (and therefore the available options) shrinks due to lack of interest or the formation of coalitions; we do this by setting the remaining options available to a seller against the original number of buyers to assess how much potential power changed from its full potential position at the start of each negotiation. For example, if there are five buyers initially generating thirty‐one options, then reducing to a four‐buyer market yields fifteen options, or a potential power score of ~48.4 percent (15/31). If the number of active buyers shrinks to three, the options reduce to seven, yielding a power score of ~22.6 percent (7/31).

In contrast, the buyer’s power position improves as the market shrinks by recalibrating the number of options each buyer has, setting it against the total options left remaining for the entrepreneur. For example, with five buyers, each buyer has sixteen options for closing a deal from the thirty‐one options available to the seller, resulting in a potential power score of ~51.2 percent. If the set of buyers shrinks down to two due to lack of interest or coalitions, then the buyers’ options reduce to two out of a total of three options available to the seller, yielding a potential power score of ~66.6 percent.

The above theorizing leads us to view this particular negotiation setting as a “power board” with a set number of positions at each configuration in terms of the number of options available to either actor given the size of the market of active buyers. These options are captured in the right side of Tables One and Two. The board adjusts to the dynamics at hand, and we note the equifinality in being assigned the same power score. We present three brief examples:

The three examples above present equivocality in the buyer’s high potential power position and the seller’s low potential power position. We note that this equifinality represents a third source of granularity to the observed negotiation positions that we do not delve into within this article; the other two sources are the underlying determinants of either the angel investors’ or the entrepreneurs’ behavior. (These factors strike at the question of why any shift in power occurred—we only assess how power shifted when it is observed via communicated offers.)

We make two observations: (1) the absolute difference in the option set through a market reduction event for either the buyer or seller follows a similar pattern represented by the function (2^n); and (2) while the respective power functions are inverses of one another, they are not perfectly inverse (Emerson 1962). The above leads us to the following three propositions, which are quantified in Table Four and Figures Three and Four:

We remind the reader that our article is primarily a theoretical discussion, and that we merely present an empirical analysis to support the implications of our theoretical contributions, which are partially reflected in propositions set forth above. While we remain within the larger umbrella of angel investing as captured in the negotiation task outlined earlier, we study only a sliver of this space, in which angel investors are billionaires and multimillionaires who operate in a public forum. Our use of this particular negotiation (the Shark Tank) allows us to study the phenomenon of power in its potential and actual forms.

One way to view Shark Tank is as a gathering for a public display of competition—a knightly joust. Another is as an educational tool for the public. From a practical perspective, the TV show provides a direct link to a target market of interested consumers for investments that total ~$68.0 million across 356 deals over thirteen seasons (Walker 2020).

According to a study by Pollack, Rutherford, and Nagy (2012), who corresponded with Barbara Corcoran (a Shark), the Shark Tank exchanges are unscripted and are representative of non‐TV‐show interactions.⁵ Thus, our study is generalizable to (n) versus (1) negotiations with the following characteristics: (1) there is competition across the set of buyers, (2) there is cooperation across the set of buyers, (3) buyers explicitly indicate if they are “out,” (4) all communication is conducted in a public forum, and (5) all parties have the ability to walk away.

In the interest of brevity, the remainder of this section includes only the following pertinent information from our empirical investigation: (1) hypotheses that are directly derived from our theoretical advancements, (2) the data structure, data coding, and our analysis of the data, (3) measures of our main investigated variables, and (4) the results and implications of our findings.

Our work has led to five hypotheses. We first assess if actual power or potential power has a stronger impact on negotiation outcomes. One may expect that actual offers have a stronger impact on the outcomes—“a bird in the hand” argument. Because entrepreneurs are the recipients of offers, we assess the degree of actual power accrued across two bases of power: positional power in terms of the number of unique offers and legitimacy power in terms of the number of buyers who are participating in those offers (French and Raven 1959; Yukl and Falbe 1991).

The counterargument is that potential power has a stronger impact than any actualization of power—a “two in the bush” argument. In the event that buyers remain active but silent, we might expect that this lingering potential contributes to increased uncertainty. This is the “law of anticipated reactions,” defined as “the effects of perceptions of power and expectations of its use without any actual use of power” (Zelditch and Ford 1994: 64). According to Zelditch and Ford, this “does not suppose that potential power is automatically converted into outcomes even if it is not used. It supposes only that perceptions and expectations are functional equivalents of use” (1994: 65).

However, since both buyers and sellers have a potential option set, the question is: Whose potential power is more impactful? Following Zelditch and Ford (1994), we first test if the seller’s potential power has a stronger impact on outcomes than the seller’s actual power (H2a and H2b). Next, we compare the buyer’s potential power to the seller’s potential power (H3). If we incorporate the finding of Bazerman, Magliozzi, and Neale (1985) that buyers outperform sellers, we would expect that the buyer’s potential power is stronger than the seller’s potential power:

Earlier we noted that coalitions are accelerants that create motion in power. However, it is not clear just what motion means for power that is partially perceptual and partially objectively observable. On the one hand, according to our own theorizing, the formation of a coalition shrinks the available option set for both the buyer and the seller. Further, this reduction benefits the buyer to a greater degree because each incremental reduction increases the proportion of options of any single buyer while reducing the seller’s absolute option set. Assuming that coalitions can be weaponized, we test the following:

On the other hand, coalitions cannot exist in thought alone and the act of their formation conveys actual power to the seller in the form of both legitimacy and positional power (French and Raven 1959; Yukl and Falbe 1991). Coalitions, therefore, are a double‐edged sword, leading us to present a counterargument:

We view Shark Tank as a quasi‐natural experiment that lends itself to analysis due to the consistency of its transactions. The entrepreneur’s introduction always segues to a dialogue with the investors in which an offer may or may not be communicated. Each pitch begins with five potentially interested buyers, and ends in various ways based on offer/exit activity. Each negotiation reflects a snapshot of behavior of the larger quasi‐competitive market (Commons 1934; Sutton 1980). Though we study the “final” negotiation outcomes from the show, we acknowledge that these agreements are not binding contracts (Ibrahim 2008), but rather declarations of intent‐to‐fund (Smith and Viceisza 2018). As such, the coded interactions are a glimpse at only a sliver of angel investor behavior, and any deal merely grants the buyer the right to conduct due diligence.

Our data collection included the following steps. We purchased four seasons (2009, 2011, 2013, and 2015) of Shark Tank from Amazon in a digital format. We transcribed all of the televised episodes in those seasons into scripts that identify the name of each speaker. This 400‐hour process of raw transcription yielded 343 cases (each representing a unique business), of which 147 resulted in a negotiated agreement on both components of capital ($) and equity (%). Taking only those cases that reached negotiated agreements into account should reduce any potential bias due to the Hollywood factor.

We tried to adhere to one main premise of price theory—that all subjective utility is captured in the price of an asset. Articulation of a price implies that a negotiated agreement has been reached and that the price is not a mere fantasy for one party while not even contemplated by the other. Therefore, we excluded 196 cases from the analysis that fall into one of two groups: 160 cases that did not result in any offer from any buyer, and 36 cases that had offers but did not result in a negotiated agreement. We suggest that future research elaborate on how power shifted across those cases that resulted in impasses. Such investigation is outside the bounds of our study and we expect that it will need to delve into the underlying determinants of power to understand the space left behind by silence (the impasse).

The dataset can be conceptualized as unique cases along the left‐most column (the rows) and the associated characteristics for each case broken into three categories: (1) pertaining to the embryonic firm, (2) pertaining to the objectively observable factors of the entrepreneurial team, and (3) pertaining to the opening salvo of negotiation positions that could potentially impact cognition (Oksoy 2020). We followed established statistical practice in standardizing all of our noncategorical variables.⁶ We included relevant control variables following Boulton, Shohfi, and Zhu (2019),⁷ and conducted a stepwise regression measuring the distance between a common starting point for power positions and a final resting point that varies according to each negotiated agreement.

The dependent variables represent the two issues of the negotiation task: the final capital position as captured in dollars (DV1), and the final equity position as captured in equity percentages (DV2).

The first and second independent variables are operationalizations of actual power. Actual positional power (IV1) is derived from the number of unique offers secured by an entrepreneur and follows Emerson’s (1962) power‐dependence theory (Yukl and Falbe 1991). Actual legitimacy power (IV2) is derived from the number of unique angel investors that are involved in the explicitly communicated offers (French and Raven 1959). These two notions are inherently connected (Ma, Rhee, and Yang 2013; Oksoy 2020). For example, if two angel investors extend two different offers, the actual positional power (IV1) score is two, and the actual legitimacy power (IV2) score is also two. If there was only one offer with two angel investors, actual positional power would be one and the actual legitimacy power would remain at two.

The entrepreneur’s potential power (IV3) is calculated using the function [y = (2^n) – 1]; the “n” represents the number of active buyers in the negotiation and this function identifies the remaining potential outcomes available for a single entrepreneur to secure a deal. This is an objectively measured variable, representing the structural arrangements of the negotiation (Pfeffer 1981; Brass 1984; Brass and Burkhardt 1993). The potential power for the entrepreneur = remaining available options/total initial option set, or $2Remaining#ofBuyersinMarket‐12Original#ofBuyersinMarket‐1$⁠.

The angel investor’s potential power (IV4) is calculated using the function [y = 2^(n–1)], where again, “n” represents the number of active buyers in the negotiation.⁸ This is also an objective measure. The potential power for buyer = remaining option set for a single angel investor/remaining option set for the entrepreneur, or $2Remaining#ofBuyersinMarket‐12Remaining#ofBuyersinMarket‐1$⁠.

The fifth independent variable is the coalition size (IV5) associated with the final negotiation round. Coalitions both reduce the option set and convey actual power in both its positional and legitimacy manifestations. The operationalization of coalition size is therefore very similar to the operationalization of actual positional power and actual legitimacy power; and yet, it remains slightly different. Three examples clarify the difference. Consider a single offer with a coalition size of two: actual positional power is one and actual legitimacy power is two. However, if there are two different coalitions of each size two, the actual legitimacy power becomes four and the actual positional power is two. Or consider two offers, one with a two‐party coalition and the other a single offer; positional power is two, legitimacy power is three, and coalition size is two. Therefore, while we only focus on the coalition size of the final round, the actual bases of power are aggregated for the entire negotiation period—banked, so to speak.

The figures below depict the hypothesized relationships (Figure Five), and the impact that our power variables have on final negotiation positions of capital and equity (Figure Six). These figures succinctly tell the story that the results reveal to us. We present the results of our stepwise regression in the models contained in Tables A1 and A2, included in the Appendix.

For the direct effect on capital (DV1), we note that the entrepreneur’s potential power (IV3) is more significant than either base of actual power; though actual positional power (IV1) is also significant (model #4). Introducing the significance of the angel investor’s potential power (IV4) renders the entrepreneur’s potential power (IV3) as insignificant, though actual positional power (IV1) remains significant (model #5). The introduction of coalitions (IV5) renders both potential power variables as insignificant while enhancing the significance of the actual power variables—both actual positional (IV1) and legitimacy power (IV2) become highly significant (model #6). Therefore, the accelerating effect that coalitions have on power dynamics are observed, as well as the actualizing effect of coalitions that renders potential power insignificant. This suggests that coalitions are a double‐edged sword, and that they enhance both actual and potential power dimensions asymmetrically.

We note peculiarities with respect to the directional impact of the variables on the capital position. While it makes sense that actual positional power (IV1) has a positive impact on capital after accounting for the legitimizing effect that coalitions provide, actual legitimacy power has a strong negative impact on capital (model #6). Equally curious are the results for the potential power variables, where the entrepreneur has a consistently negative effect while the angel investor has a consistently positive effect. As one possible explanation, we might suggest that the capital issue of the negotiation task can be equated to an “ante” in poker and is reflected in the firm‐level need that has brought everyone to the table in the first place; it is therefore not influenced by power but rather fixed to the strategy.

For the direct effect on equity (DV2), we observe a similar story where initially the entrepreneur’s potential power is more significant than either actual power variables (IV1 and IV2) (model #10). All three become insignificant when we introduce the angel investor’s potential power (IV4) (model #11). As before, when we introduce the coalition size (IV5), both potential power variables become insignificant, whereas both actual power variables become significant (model #12). While we observe the same directionality as before, the equity‐centric relationships make more sense because we need to flip our perspective: the entrepreneur’s request/offer is the mirror image of the angel investor’s offer/request. For equity outcomes, we observe coalition size as a net benefit to the buyer but at a cost of improving the entrepreneur’s legitimacy power—a double‐edged sword. Further, the directionality for potential power’s impact on equity makes sense in that potential power is a benefit to both actors, with the buyer’s potential power having a stronger impact, lending support to the finding of Bazerman, Magliozzi, and Neale (1985) that buyers outperform sellers.

These results allow us to advance two additional propositions. We find it interesting that actual legitimacy power appears to be more important than the number of independent offers (for equity). Further, we note that legitimacy power strengthens as coalition size increases, which also improves the buyer’s potential power. As such, a tension exists between actual and potential power. Coalition size acts as a double‐edged sword cutting along an actual and potential edge; as coalition size increases, the sword cuts deeper. Our fourth proposition is, therefore, as follows:

We remind the reader that our findings pertain to a market context comprised of 147 negotiation instances where the specific negotiation task and negotiation structure are consistent across each instance (see Commons 1934). A negotiated agreement on the two components of capital and equity sets the value of a firm; we may also refer to this value as a cost (Fisher 1930). Our theorizing and empirical findings suggest that variation in cost is a result of power, even though an open question remains as to how, and/or if, such power‐related frictions hinder or facilitate market efficiency Dahlman (1979). This leads us to our final proposition:

We note the importance of how market interactions are structured with respect to the influence of power differentials on negotiated outcomes (see Mannix and Neale 1993). Our research furthers our understanding of the efficiency of markets as influenced by the underlying logic of power, and we relate our findings to two seminal papers that also explored how free markets operate: Bazerman, Magliozzi, and Neale (1985) and Fisher (1970). Our findings help resolve some open questions in the buy/sell negotiation and institutional economics literatures; this is an important discussion as regions across the United States analyze the efficiency of their entrepreneurial ecosystems.

Bazerman and colleagues conducted experiments reflecting how slivers of the free market operate by designing exchange structures in which “negotiators can make transactions with multiple opponents in a fixed amount of time” (Bazerman, Magliozzi, and Neale 1985: 295).⁹ The authors noted: “[D]espite the existence of a completely symmetrical simulation, buyers outperformed sellers.... We do not have a clear explanation of this result” (310). We present evidence as to why the role of the buyer may present an advantage in a negotiation—the buyer’s potential power does not drop below ~50 percent, even though the seller’s potential positional power does indeed drop down to 1 percent. Further, the buyer’s potential power remains fairly stable at ~50 percent while the seller’s potential power can run the gamut and drops in increments of half.

Bazerman and his team also noted that “as the number of participants in a market increases, the nature of the competitive situation increases the potential of all subjects, since there are more potential opponents” (Bazerman, Magliozzi, and Neale 1985: 310–311). Our findings suggest that as the number of buyers increases in a market, and assuming that all buyers can operate independently with adequate purchasing power for each opportunity, the potential power of any single buyer approximates to 50 percent, implying the lack of any potential power differential across the set of buyers from a structural sense (Blau 1964; Wrong 1968). Our findings confirm that increased competition creates an inherent check on the potential power of any individual buyer.

Fisher (1970) added a meta‐level discussion to the implications of microeconomic activity in his discussion of the invisible hand, while Bazerman and colleagues explored the dynamics of microeconomic activity through their experiments mimicking free markets. Fisher (1970) uses the term quasi‐competitive to account for exchanges within a low‐information context that inhibit an understanding of firm value. We consider the Shark Tank specifically, and angel investing more generally, to occur in a quasi‐competitive market (Fisher 1970; Arrow 1974; Sutton 1980).

Fisher (1970) begins his paper by noting that most scholarly attention in this area has focused on how “prices behave in different markets taken as a whole, rather than on a model of individual behavior” (195), and his primary focus is on price as a whole. Our study addresses not only the roles of individual buyers and sellers (see Bazerman, Magliozzi, and Neale 1985), but also how capital ($) and equity (%)—the two components of price—operate. Our study presents power as representing the invisible hand—what Fisher refers to as an invisible “auctioneer” (195).

And yet, as important as Fisher’s study is for our own, our study differs in one fundamental manner. What Fisher calls the “adjustment process” (197) involves—in the words of John R. Commons (1934)—“routine transactions.” Without these routine transactions between a firm and its environment (i.e., sales), there would be no feedback for any adjustment to occur. If we follow Fisher and position ourselves at an extreme end of complexity, where an angel investor is evaluating only an idea with no routine transactions (i.e., pre‐revenue), then any adjustment in price is a function of shifts in the subjective expectations of a distant future that is itself unknowable (Knight 1921). As Fisher notes, when the market is in disequilibrium, the price is expected to move. “The Invisible Hand is a little too invisible in this, the center of its activities” (1970: 196). Our study can be viewed as focusing directly on this invisible hand of the market. We simply acknowledge that the one hand is dependent upon the other hand(s) at the negotiation table, and that one of those hands is typically more powerful than the other(s). Power is one factor that guides the hands and alters the price of a firm via its impact on the two components of firm value: capital and equity.

We have provided a way to measure the power differentials within a negotiation, measurements that we derive from the observable structure of the negotiation. An understanding of this underlying logic allows both practitioners and scholars to control for aspects within negotiations that are inherently messy, and this provides the observer or practitioner with a net positive position. We present the path along which power shifts for both a buyer and a seller; the situational awareness to which we refer is the actor’s position on their respective curves (their geolocations) (see Figures Five and Six). Situational awareness also translates to an understanding of the table before one sits down, and we emphasize the importance of understanding how coalitions form and dissolve due to their role as accelerants of power dynamics.

Our findings suggest that entrepreneurs should consider presenting angel investors with a more favorable opening position in order to develop an actual power base, which we find to be more important than any potential power base. In contrast, we suggest that angel investors take a more patient approach to allow the market to shrink, allowing for the entrepreneur to at least experience an initial power hit associated with a market reduction.

Finally, for the developer of the entrepreneurial ecosystem, we raise the question of how power differentials influence the efficiency of markets and suggest the need for further analysis across a range of open questions that impact the efficiency of ecosystems. While power differentials may be inevitable, which degree of imbalance leads to the most efficient system? Does coalition formation in a larger market (e.g., one with ten buyers) have the same influence as in smaller markets? What is the effect of the type of information—subjective vs. objective—that is conveyed at various power positions? How can we construct negotiation structures that produce high joint outcomes (Mannix and Neale 1993)?¹⁰ And of course, we need an investigation into all underlying determinants of why an offer is extended, not extended, accepted, or rejected.

The novelty of the issues uncovered in this article pertaining to how power shifts and how it can be measured allows us to propose an application to improve the entrepreneurial ecosystem. One can easily envision an online platform where teams of bidders vie for the entrepreneur’s approval with perhaps a time limit and an open record of past offer activity. Naturally, the negotiator’s power score could also be on display, while the entrepreneur may select the size of the competitive market when setting up negotiation parameters within the system. We contrast this envisioned potential online investing platform against contemporary exchange structures such as angel investor syndicates and crowdfunding platforms. Crowdfunding may be viewed as angel syndicates for “the masses,” while syndicates are private invite‐only affairs. Under both circumstances, coalition formation in the absence of competition effectively reduces the set of buyers to a monolith of varying size. We note how such circumstances increase the proportion of options available for a single buyer in relation to the total options available for the entrepreneur. This is so because any single buyer is not concerned with being excluded from a deal when the syndicate pools capital on individual investments (Mason and Harrison 2008; Agrawal, Catalini, and Goldfarb 2016). The buyer not only enjoys a global power advantage due to the ability to buy, but also has an improved local power position when at the table. And yet, these circumstances allow for the entrepreneur to enter the negotiation in a pre‐legitimized state, enjoying an elevated actual power position.

For negotiation teachers, we present the mathematical building blocks for capturing power differentials and dynamics within a given negotiation structure. We envision a variety of designed experiments or classroom exercises that manipulate the various moving parts as found in prior buy/sell negotiation studies (e.g., Bazerman, Magliozzi, and Neale 1985; Northcraft and Neale 1987; Mannix and Neale 1993).¹¹

For scholars, we have noted that the pattern depicting a seller’s potential option set is represented by the function y = (2^n) – 1. Originally referred to as the Mersenne Prime Series, this function applies perfectly to help us better understand how power shifts in a multiple (n) buyer versus single‐seller quasi‐competitive bargaining setting. The individual buyer also has their own similar but distinct curve as captured in the function y = 2^(n–1). As such, we note that while power and dependence are inverses of one another, they are not perfect inverses. Finally, we extend bargaining theory, recognizing that coalition size can both benefit and hinder buyers and sellers; it is a double‐edged sword that cuts along both actual and potential power dimensions.

The authors thank Michael Stein and Kevin Hawley for providing us with moments of clarity as we progressed along with the story. The authors also thank Melissa Manwaring and Lakshmi Balachandra from Babson College for their encouragement, guidance, and valuable discussions, which have significantly improved the paper. The authors reserve a final thank you for the extensive feedback provided by an anonymous reviewer.