Do consumers value employee ownership? Evidence from an experimental auction

Philip Pablo Mellizo (Department of Economics, The College of Wooster, Wooster, Ohio, USA)

Journal of Participation and Employee Ownership

ISSN: 2514-7641

Publication date: 10 September 2018

Abstract

Purpose

The purpose of this paper is to evaluate how the public at large perceives employee ownership, and how public perceptions of employee ownership translate into consumer valuation of goods and/or services produced by employee-owned firms. To the extent that consumer interest regarding the governance and ownership structure of firms matters in their purchasing decision, an employee-owned certification label could be an instrument by firms to segment consumer demand, differentiate products and potentially realize a competitive advantage.

Design/methodology/approach

Three specific questions are evaluated using the fifth price, experimental Vickrey valuation auction. First, the author obtains estimates of willingness to pay (WTP) premia for a specific item (coffee) differentiated in a controlled setting by the certifications labels that signal various non-market attributes. Specifically, the author examines the WTP premium for coffee that is eligible for the Certified Employee-OwnedSM label, the Fair Trade CertifiedTM Certified label, as well coffee that qualifies for both labels. Second, the author introduces a treatment to evaluate how the provision of information produced by the third party certifiers affects WTP estimates. And third, the author exploits the use of a controlled setting to evaluate how passive sensory information (i.e. taste) may influence the WTP valuation of the labels.

Findings

WTP premia for coffee carrying only the EO label only increase by 67 cents relative to conventional coffee, which was not significantly different from zero. Bids for both FT and EO&FT labeled coffee were, however, positive ($1.22 and $2.17, respectively) and are also statistically significant. The circulation of information to subjects about the certification programs resulted in increased bids. These bid differences were statistically significant for FT and EOFT coffee, but again, not for EO labeled coffee. Finally, differences in tastes did not appear to drive significant differences in bidding behavior, suggesting that WTP consumer decisions are strongly influenced by non-market attributes.

Originality/value

Marketers, economists and others have an interest in determining the monetary value individuals place on non-market goods for a variety of reasons; from forecasting new product success to understanding consumer and individual behavior. Unfortunately, many currently available stated preference techniques suffer from hypothetical bias while revealed preference techniques rely on indirect measures. Experimental auctions mitigate some of these issues since they involve individuals exchanging real money for real goods in an active market. WTP valuation has been conducted on a wide variety or products, but none that capture consumer valuation of employee ownership.

Keywords

Citation

Mellizo, P. (2018), "Do consumers value employee ownership? Evidence from an experimental auction", Journal of Participation and Employee Ownership, Vol. 1 No. 2/3, pp. 162-190. https://doi.org/10.1108/JPEO-10-2017-0001

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

At least since the 1970s, dozens of opinion polls have been conducted to evaluate how the public perceives different aspects of employee ownership. In total, 30 of these polls are reported in Kruse and Blasi (1999), and overall, they show that the public generally tends to hold positive views of employee ownership, despite not being wholly familiar with the concept[1]. For example, in one poll, a large majority of respondents believe that workers in employee-owned firms both pay more attention to the quality of their work and work harder than workers in non-employee-owned firms. Further, the two-thirds of respondents from another poll say that they would favor working for an employee-owned firm if given the choice. Results from a separate poll, however, show the two-thirds of respondents reported knowing either “not too much” or “nothing” about employee-owned firms. On one hand, the reported unfamiliarity with employee-owned firms is surprising given prevalence of employee-owned firms in the US economy (e.g. Kruse et al., 2010), yet on the other, it is probably unrealistic to expect poll respondents to accurately recall their experiences with firms of a specific organizational type in the absence of salient, credible, distinguishing signals.

The recently founded Certified Employee-OwnedSM label provides, for perhaps the first time, a way for employee-owned firms to certifiably signal their firm-type customers, potential employees and other stakeholders. Similar to other certification programs that signal non-market attributes related to the production process (e.g. non-GMO, Organic, etc.) a principle aim of the Certified Employee-OwnedSM mark is to raise awareness by influencing the choice architecture of potential consumers introducing a new decision to made between “employee-owned” and “non-employee owned” products. The information signaled through the certification label could appeal to consumers that have an existing preference for (non)employee owned produced goods and services, and also raise awareness among others that have had limited exposure to the concept of employee ownership. Indeed, an employee-owned certification label could be an instrument by firms to segment consumer demand, differentiate products and potentially realize a competitive advantage. Preliminary market research of the Certified Employee-OwnedSM label suggests that this may be possible.

Specifically, in a large, nationally representative online survey, 35 percent of respondents expressed that they are more likely to buy a product bearing the Certified Employee-OwnedSM label, and 48 percent of consumers express a willingness to pay a premium for Certified Employee-OwnedSM products (Ownership Alliance, 2016). Findings from the same survey show “a long tail of respondents who would be willing to pay large premiums […] 28 percent of respondents would pay over 10 percent more, and 11 percent would pay over 30 percent more [for Certified Employee-OwnedSM labeled products].” As encouraging, as these findings are, a sizable body of research suggests that there are often wide divergences between responses to scenarios wherein respondents are asked to provide hypothetical valuation estimates, vs cases where consumers reveal their valuation of a good with real money on the line (e.g. Lusk and Shogren, 2007). While hypothetical valuations might help in establishing a theory of consumer preferences, the principal drawback is that respondents do not have an explicit incentive to be truthful with their response. Further, in the absence of a salient, consequential incentives, respondents might not think carefully when providing a stated, hypothetical valuation. One stark example from List and Gallet (2001) shows that hypothetical valuations can be inflated by up to 20 times in relative to non-hypothetical valuation estimates where real monetary stakes for respondents were on the line.

In an effort to mitigate potential hypothetical bias in valuation estimates for goods bearing the Certified Employee-OwnedSM label, the present research therefore uses an experimental valuation auction. The main difference between an experimental auction and a survey is that in an experimental auction subjects are put in an active market that penalizes stated, hypothetical valuation estimates that diverge from the “true” preferences of the subject. While it is certainly possible that subjects misrepresent their “true” valuation in an experimental auction, an incentive compatible auction makes “untrue” bids privately costly.

In this paper, three questions regarding consumer valuation are examined in a controlled setting. First, as in many other experimental valuation studies, we obtain estimates of willingness to pay (WTP) premia for a specific item (coffee) differentiated in a controlled setting by the certifications labels that signal various non-market attributes. Specifically, we examine the WTP premium for coffee that is eligible for the Certified Employee OwnedSM label, the Fair Trade CertifiedTM label, as well coffee that qualifies as to carry both Certified Employee OwnedSM and Fair Trade CertifiedTM labels (throughout the document, we refer to Certified Employee OwnedSM and Fair Trade CertifiedTM labels as EO and FT). We collect valuation estimates of the Fair Trade CertifiedTM label because it is a ubiquitous mark with hundreds of licensing partners that can serve as basis of comparison for the Certified Employee OwnedSM label, which is, at the time of that this research was conducted, not yet seen on products in circulation. We chose coffee as the good to be used in our study since it is a product that is heavily labeled and marketed according to attributes alluding how it was produced[2].

Second, we evaluate whether the provision of public information on the respective certifiers for the Certified Employee OwnedSM and Fair Trade CertifiedTM labels has a systematic effect on changing WTP estimates. And finally, we exploit the use of a controlled setting to evaluate how passive sensory information (i.e. taste) may influence the WTP valuation of the labels.

In general, much of the research on employee ownership has focused on how broad-based asset ownership interacts with a wide variety of outcomes, including but limited to profitability, productivity and compensation (e.g. Blasi et al., 1996), turnover, absenteeism, loyalty, worker effort (e.g. Blasi et al., 2008), job satisfaction (e.g. Long, 1980), innovation (e.g. Garrett, 2010), firm survival (e.g. Park et al., 2004; Kurtulus and Kruse, 2017) and inequality (e.g. Carberry, 2010). Other programs have focused on factors that contribute to the incidence of employee-owned firm (e.g. Poutsma and Ligthart, 2017), outcomes that interact with unions (e.g. Hoffman and Brown, 2017), states (e.g. Williams, 2016), multi-national corporations (e.g. Poutsma et al., 2005), while still others have examined how broad-based employee ownership potentially address industrial externalities that affect the environment (Aguinis and Glavas, 2013), health outcomes (Erdal, 2000) and civic mindedness (e.g. Laessig, 2014). Comparatively less is known, however, regarding how the public at large perceives employee ownership, and how public perceptions of employee ownership translate into consumer valuation of goods and/or services produced by employee-owned firms. This research aims to advance our understanding of public perception of employee ownership by collecting valuation estimates using an experimental auction.

In the following section, a detailed description of the research methodology and the experimental design used in the study is given. Section 3 provides descriptive statistics from the sample, and in Section 4 the results are presented from the study. Section 5 concludes the paper.

2. Methodology and experimental design

Over the past 50 years, researchers in economics, psychology and marketing have developed several methods to elicit valuation of both market and non-market goods and services. Stated preference methods use questionnaires or comparative choice trials that ask individuals to state ones value for a good or service. The benefit of stated preference methods is that they can accommodate rich vignettes and alternative scenarios for consumers to consider as they simulate a choice they might make under the hypothetical circumstances. The drawback, of course, is that the choices observed are hypothetical. The absence of a “real,” non-hypothetical, stake risks bias in responses (e.g. Johansson-Stenman and Svedsäter, 2008; Lusk, and Schroeder, 2004; Murphy et al., 2005; List and Gallet, 2001).

In contrast to stated preference methods, revealed preference methods have been used to try to tease out valuation of different types of goods. For example, the number of bathrooms in a house is not traded independently in the marketplace; but by finding the difference in the price of, say, a 2-bathroom home vs an otherwise identical 1-bathroom home, we can indirectly determine people’s values for an extra bathroom. The benefit of revealed preference methods is that real choices are examined. The drawback is that the valuation is often indirect and the interpretation of empirical patters is susceptible to a number of confounding, unobservable factors.

In consideration of the potential issues that arise with stated preference and conventional revealed preference valuation methods, many applied researchers have turned to experimental auctions to elicit valuations (e.g. Lusk and Shogren, 2007)[3]. As Lusk and Shogren suggest, the advantage of experimental auctions over stated preference methods is that subject participants are put in an active market environment with real economic consequences for each of their actions. Clearly, this does not eliminate the possibility that some people misrepresent their true valuations for one reason or another. When, however, the auction used to elicit private valuation of a good or service follows an incentive compatible auction mechanism misrepresentation and hypothetical bias in bids are penalized. For example, in the second-price Vickrey auction (one such incentive compatible mechanism), the winner of the auction is the person that had the highest bid. The price the winner of the auction pays, however, is the second-highest price. N-price Vickrey auctions are incentive compatible since the best thing that a participant could do is to bid one’s own value. That is, strategic bidding (i.e. bidding a value that is higher or lower than one’s own value) risk losses. Consider why this is true: if an auction participant bid more for a good than it is worth to her, she may end up having to pay more the good than what she wanted to pay in the first place. Conversely, if she bid less for a good than it is worth to her, she may end up losing out on the good even though she could have bought the good at the price she was actually willing to pay[4]. Experimental auctions therefore provide a straightforward way to identify differences in the direction and intensity consumer valuation driven by the desire for non-market attributes grounded in an incentive compatible mechanism wherein the dominant strategy for each bidder is to submit a bid that is equal to their value of the good.

The WTP estimates collected from an experimental auction are generally thought to be qualitatively externally valid to “real-world” contexts (in direction) but not necessarily quantitatively externally valid (i.e. in the precise magnitude). Kessler and Vesterlund (forthcoming) summarize this common perspective taken within the field well. Specifically, “[the] focus on quantitative external validity is misplaced for many (if not most) experimental studies, however, as the emphasis in these studies is to identify the direction rather than the magnitude of an effect […]. For example, the revenue difference between an English auction and a first-price sealed bid auction in the lab is not thought to be indicative of the quantitative difference one would find between any other set of English and first-price sealed bid auctions. Similarly, despite the clear objective of finding externally valid results, the experiments that tested various designs of FCC spectrum auctions were not aiming to identify magnitudes that would generalize to the field. Instead, they were run with the expectation that the general principles of behavior identified in lab auctions also would be present in the field. The emphasis on qualitative results is in part explained by the fact that all theoretical and empirical models require simplifying assumptions. In constructing these models, we eliminate any factors that we think are not central. A consequence of abstracting away from environments of interest is that we likely fail to capture the precise magnitude of the effect we expect to see in those environments.” The study reported herein, is conducted in this spirit.

Experimental design[5]

The participants for the valuation study were recruited by e-mail that went to students, faculty and staff members at the College of Wooster. The e-mail noted that a marketing study examining preferences for different types of coffee and chocolate was being conducted on campus. The resulting sample was composed of a combination of students and non-students (primarily staff members). We conducted the fifth-price Vickrey auction, in which participants were endowed with “conventional” (non-Employee Owned, non-Fair Trade) coffee and were given the opportunity to participate in an auction over coffee that qualifies for the Certified Employee-OwnedSM label coffee that qualifies for the Fair Trade CertifiedTM label and coffee that qualifies for both certification labels. At the time of this study was conducted, no products in circulation carried the newly founded Certified Employee-OwnedSM label. The coffee that carried the Certified Employee-OwnedSM used in the study was sourced from a company in Ohio that qualifies for carrying the label[6]. After an initial auction used to familiarize subjects with the rules and procedures of the Vickrey Auction, “homegrown” willingness to pay values were elicited in Auction 1[7]. Auction 2 elicited WTP values after information taken from the Certified Employee Owned and Fair Trade USA websites that describe their respective certifications were circulated to subject participants. Auction 3 elicited WTP valuations after passive sensory rankings information was recorded. Following Costanigro et al. (2014) and other valuation studies, we did not reveal winners or the winning bid prices until the very end of the study. The experimental design consisted of seven parts, described below. The full set of instructions and procedures are in Appendix 1.

Part 1: demographic and attitudinal information

As subjects arrived, they signed into the study and received a subject ID number that corresponded to a similarly numbered experiment station. Each station had an envelope with $16.05, a small candy bar, a downward facing pile of small bid sheets, a pen, a stapled packet that was also facing downward, and the study consent form[8]. Subjects were asked to read and sign the consent form and then to turn the stapled packet at their station over that contained a demographic and attitudinal questionnaire. Subjects were given 6 min to complete the questionnaire.

Part 2: practice auction

Following the convention of experimental valuation auctions, we first conducted a practice auction with candy bars in order to familiarize subjects with how the fifth-price Vickrey Auction worked. It is important to run a practice auction to minimize confusion with auction rules and bidding procedures[9]. Subjects were physically endowed with a small chocolate bar (the small chocolate was placed at each subject’s station prior the study) and then submitted bids for three different upgrade candy bars. That is, subjects participated in three simultaneous auctions (one for each upgrade candy bar). After all bids were made and collected, a random process (i.e. a public die roll) determined which of the three separate auctions was binding[10]. After the binding auction was made public, so too were the four winners (those with the four highest bids) as well as the price that they would pay for the upgrade candy bar (the fifth highest price) at the end of the study.

Part 3: blind-sensory evaluation

Once the practice auction in Part 2 was complete, the attention of subject participants in the study was directed toward a table in front of their station where four half-pound bags of coffee were placed. Each bag of coffee was in a plain, 8-ounce coffee bag either labeled “A,” “B,” “C” or “D.” The subjects were given 5 min to passively observe/smell the contents of the different bags of coffee knowing only that each of the four coffee samples was a “medium roast” and “ground for brewing.” We opted for a smell/passive observation test in favor of a blind-taste test of the coffee to simulate the conditions a potential customer might face in a retail or wholesale environment. It is almost always not the case that customers are able to taste and compare different coffees prior to purchase[11]. Subjects then ranked their preferences for the different types of coffee from 1 to 4, with a rank of “1” signaling their favorite and “4” as their least favorite. These rankings were then set aside by subjects until Part 6 of the study where coffee types were revealed to all subjects.

Part 4: auction 1: “Homegrown” values

Once participants completed the sensory test and ranked their preferences for the different types of coffee, subjects received a new set of instructions that reminded them of how the Vickrey auction worked. Subjects were then told that they owned (i.e. factually endowed) with a half-pound bag of “conventional” non-EO, non-FT coffee[12]. The subjects then participated in simultaneous, fifth-price Vickrey auctions for a half-pound bags coffee that qualify for the EO label, the FT label and both the EO and FT labels. The purpose of this initial coffee auction was to collect “homegrown values,” or those values that people bring into an experiment for goods that carry these labels prior to any manipulation (Lusk and Shogren, 2007). We collected bid sheets for the simultaneous auction on the bid sheet “Coffee Auction Set 1” before handing out a second set of instructions for Part 5 of the study. (See Appendix 2 for example bid slips).

Part 5: auction 2: label information sheets

Prior to the second coffee auction, subjects received information sheets taken from the respective “about” link posted on the Certified Employee Owned and Fair Trade USA websites[13]. The purpose of this manipulation was to give subjects information issued by the third-party certifiers in their own words, and to test if this information systematically affected changes in the WTP for products eligible to carry the EO and/or FT labels (akin to an information treatment in McFadden and Huffman, 2017)[14]. Subjects were allotted 6 min to read the information sheets and to then submit a second set of bids on the bid sheet labeled “Coffee Auction Set 2.” These bid sheets were then collected and recorded as Part 6 while Part 6 of the study was under way.

Part 6: auction 3: sensory information

Subject participants were asked to look at the coffee-rankings sheet that they filled out in Part 3 (described above) of the study. The identities of the four different types of coffee from the blind-sensory observation were revealed and subjects were asked to notice how their prior preference ordering aligned with the identities of the coffee. All subjects then entered bids on the bid sheet labeled “Coffee Auction Set 3” that was then collected by a member of the research team.

Part 7: binding auction determination

At this point in the study, the research team had collected three different sets of three simultaneous auctions for EO, FT and EO + FT labeled coffee. An initial die-roll determined which of the three auction sets was binding. A second die roll determined which good was being auctioned. After the binding auction was randomly determined, auction losers collected their half-pound bag of ground “conventional” (non-EO, non-FT) coffee and their endowments of unspent money. Winners of the binding auction were announced by subject number. Winners then paid fifth highest bid (which was made public information) and their bag of conventional coffee in exchange for the coffee they won in the auction. We concluded the study by collecting payment for the practice (candy bar auction) following the same procedure (Table I).

3. Descriptive statistics and data overview

Table II shows that the 53 subjects in the sample, while younger on average than the population of Ohio (25.4 vs 34 for the state), were more highly educated (60.38 percent with at least an undergraduate degree vs 26.8 percent for Ohio)[15]. The sample also reported similar gross household incomes relative to Ohio residents with 64.15 percent of participants reporting a gross household income greater than $35,000 compared to 62 percent statewide. Close to two-thirds (64 percent) of the subjects in the sample were female while 43 percent of the sample reported as primary shoppers in their respective household. Most of the participants were also regular coffee drinkers, with 52.83 percent drinking at least one cup of coffee a day[16].

Table III summarizes how much subjects in our study agreed or disagreed with a series of statements designed with the intention of measuring perceptions EO and FT products (see Appendix 1. Items 11–30 correspond those in Table III). The sample means suggest that customer attitudes toward EO firms and/or FT products are seen in a favorable light with average scores around 6.00 (1=strongly disagree, 9=strongly agree) without a large amount of variation. These attitudinal data were used in a regression analyses that evaluate whether these items explain bidding behavior in each of the different auction rounds. None of the items were statistically significant predictors (the regression output is not shown in the paper).

Table IV shows a general summary of the subjective sensory ranking from the passive observation scores from Part 3 of the experiment. It suggests that subjects tended to express a blind preference for what was later revealed as EO+FT coffee. We make this claim using the aggregate Best-Worst (BW) multi-criteria decision making (MCDM) method[17]. In BW designs, the overall ranking of a product is estimated by finding the number of times each product has been rated the best (i.e. ranked 1) and worst (i.e. ranked 4), and then subtracting number of worst rankings from best rankings. The overall product ranking of BW choices in a sample of N judges is scored by the aggregating the individual BW scores for each product, followed by sorting on the summed score. Using the BW criterion, subject participants found the EO+FT labeled coffee to be most desirable during the blind-sensory test, followed by FT, conventional, and finally EO coffee. Kendall’s coefficient of concordance (W score) provides a measure of the extent that judges agreed on the same ranking of products that ranges from 0 (no agreement in rankings) to 1 (full agreement in rankings)[18]. Kendall’s W scores 0.60 for ranking agreement on EO&FT rated as “best” and EO as “worst,” which suggests a fair amount of conformity on opinions.

Finally, Table V shows sample means bids for each good and auction round. These bids preview the main findings of the study. On average, bids increased in Auction 2 after information sheets describing the certifications in the words of the certifiers were circulated. The relatively high standard deviation in all three auctions suggests a fair amount of heterogeneity in the sample.

4. Tests of hypotheses and results

The aim of this study is three-fold. First, as in many other valuation studies, we obtain estimates of WTP premia for a specific item (i.e. coffee) differentiated by the certifications labels that signal various non-market attributes. Specifically, we examine the WTP premium for coffee that is eligible for the EO, FT, and EO+FT labels. Second, we evaluate whether the provision of information using language from the third-party certifiers affects WTP estimates. Finally, we exploit the use of a controlled setting to evaluate how passive sensory information (i.e. taste) influences the WTP valuation. We take each of these questions in turn. Table VI summarizes the results from the experiment.

What are the WTP premia for Certified Employee-OwnedSM, Fair Trade CertifiedTM, and Certified Employee-OwnedSM and Certified Employee-OwnedSM and Fair Trade CertifiedTM coffee?

To evaluate the WTP premia, we use WTP estimates from Coffee Auction Set 1. Recall, that in Coffee Auction Set 1, subjects were endowed with a half-pound bag of non-EO, non-FT coffee and then participated in simultaneous, fifth-price Vickrey auctions for a EO label, FT label and EO+FT labeled coffee. The purpose of this initial coffee auction was to collect “homegrown values,” or those valuations that people bring into an experiment prior to any manipulation (Lusk and Shogren, 2007). Column 1 in Table VI reports Tobit regression results that account for left-censored bids at zero. We suppress the constant term and the coefficients represent the marginal effects.

We find that expected bids increase by 67 cents relative to bids made for the conventional coffee. Despite the positive sign, however, the bid difference only meets 20 percent level of significance (rather than the more customary standard of a 10, 5 or 1 percent level) that the bid difference is statistically different from zero. That is, chances are from four to one that consumers have a baseline willingness to pay more for products carrying the EO mark. Column 1 in Table VI also shows the expected bids for both FT and EO+FT are positive and respectively significant at the 5 and 1 percent level of significance. Expected bids increase by $1.22 for FT labeled coffee relative to conventional, and expected bids increase by $2.18 for EO+FT labeled coffee. The difference in bids between the FT and EO+FT marks was evaluated using a Wald test to evaluate whether the increase from $1.22 to $2.18 (96 cents) was statistically significant from zero. This 96 cent difference in willingness to pay is significant at the 20 percent level of significance again showing that odds are 4 to 1 that consumers have a baseline willingness to pay for EO labeled products.

How does the provision of information produced by 3rd party certifiers (i.e. the certified EO organization and fair trade USA) affect WTP estimates?

To evaluate if the provision of information about the certification labels affects WTP premia, we examine the difference in bids from Coffee Auction Set 1 to the bids in Coffee Auction Set 2. Recall that prior to the second auction round, subjects received information sheets taken from the Certified Employee Owned and Fair Trade USA websites. The purpose of this manipulation was to give subjects information issued by the third-party certifiers in their own words, and to examine whether the provision of this information systematically affected changes in the WTP estimates. The regression output from Column 2 in Table VI uses OLS to regress the difference of bids from Auction Set 2 and Auction Set 1 (dependent variable DiffA2A1) for the different types of labeled coffee.

The results show, that on average, the information treatment had a positive effect on bids for all three types of goods. Specifically, bids increased by an average of $0.10, $0.20 and $0.16 respectively, for EO, FT and EO+FT labeled coffee after the information sheets were circulated. Notice, however, that these differences are only significantly different than zero only the cases of FT and EO+FT coffee (90 percent level of significance). The difference in bids for EO coffee was not significantly different from zero. These general findings are confirmed by non-parametric Wilcoxon test of the equality of matched pairs of observations. The null hypothesis that distribution of bids on each good between homegrown (Set 1) and information treatment (Set 2) are the same is rejected for EO+FT coffee (Prob>|z|=0.0107), FT coffee (Prob>|z|=0.0018), but not for EO coffee (Prob>|z|=0.1890). Though the information sheets did make a difference in subject bids for the Certified Employee-OwnedSM label, they were not significant.

Does passive sensory information (i.e. taste) influence the WTP valuation of certification labels?

At the conclusion of the practice auction (over candy bars), and prior to the final coffee auction, subjects were directed toward a table in front of their station where four half-pound bags of coffee were placed. Each bag of coffee was in a plain, 8-ounce coffee bag either labeled “A,” “B,” “C,” or “D.” The subjects were given 5 min to passively observe/smell the contents of the different bags of coffee knowing only that each of the four coffee samples was a “medium roast” and “ground for brewing.” The subjects then ranked their preferences for the different types of coffee from 1 to 4, where 1 = their favorite and 4 was their least favorite. The identities of the four different types of coffee from the blind-sensory observation were not revealed until just before the final auction. The subjects were asked to notice how their prior preference ordering aligned with the identities of the coffee and then enter bids on the bid sheet labeled “Coffee Auction Set 3.”

Regression 3 reported in Table VI regresses the difference in WTP from Auction 1 (Homegrown) and WTP from Auction 3 (taste preferences revealed) on the three coffee types. The results from the regression analysis show that the differences in Auctions 1 (Homegrown valuation) and 3 (taste preferences revealed) were not statistically significant, suggesting that taste was not a significant driver of average bids, which is notable in consideration of the high ratings given to the EOFT labeled coffee in the blind-sensory test, and the low ratings given to the EO labeled coffee. The results suggest that taste mattered little in driving bid differences, which underscores the importance consumers place on product attributes in valuation of largely homogenous products.

Results summary

In this valuation study, we conducted the fifth-price Vickrey auction, in which participants were endowed with conventional (non-EO, non-FT) coffee and were given the opportunity to participate in an auction over coffee that qualifies to carry the Certified Employee-OwnedSM label, the Fair Trade CertifiedTM label, as well as coffee eligible for carrying both labels. After an initial auction used to familiarize subjects with the rules and procedures of the Vickrey Auction, homegrown willingness to pay values were elicited in Auction 1. Auction 2 elicited WTP values after information sheets taken from the respective Certified Employee Owned and Fair Trade Certified websites were circulated to subject participants. Auction 3 elicited WTP valuations after passive sensory rankings information was recorded. The purpose of the study was to obtain estimates of WTP premia, evaluate whether the provision of information produced by the third-party certifiers (i.e. the Certified EO organization and Fair Trade USA) about what is behind their respective certifications has a systematic effect on changing WTP estimates and to evaluate how passive sensory information influences the WTP valuation of the labels.

Regarding WTP premia, homegrown bids (measured as marginal effects) for coffee carrying only the EO label only increase by 67 cents relative to conventional coffee, which was significantly different from zero at the 20 percent level of significance. The expected Tobit bids for both FT and EO&FT labeled coffee were, however, positive ($1.22 and $2.17, respectively) and are also statistically significant at the 10 percent level of significance.

The circulation of information to subjects about the certification programs resulted in increased bids. These bid differences were statistically significant for FT and EOFT coffee, but again, not for EO labeled coffee.

Finally, differences in tastes did not appear to drive significant differences in bidding behavior, suggesting that certification labels do indeed contribute to consumer decisions, and that they are willing to pay for non-market attributes.

5. Conclusion

Product certifiers like Certified Employee-OwnedSM redress information deficits that often exist between buyers and sellers of goods and services. In the present study, we find that these labels also increase a consumer’s likeliness to pay more for products carrying the Certified Employee-OwnedSM mark.

The general concept of “employee ownership” is intuitive on one level, yet the various forms it takes in practice likely muddies public perception. Continued public education efforts ranging from the traditional forms of popular media (e.g. Beyster and Romero, 2012; Blasi et al., 2013), to efforts in the classroom, the boardroom and also to consumers through certification labels will be required to make broad-based employee ownership a part of the zeitgeist.

The experimental design we use and the results we report offer several opportunities for future research. Although we can estimate the impact and value of the third-party information about the certification program in general, another design approach, such as that used in Costanigro et al. (2014) would use “scientific information” from the academic record that documents claims made about Employee-Owned firms, or Fair Trade certified products. Also, given that at this time, no products carry the Certified Employee-OwnedSM label, and that the Fair Trade USA has hundreds of licensed partners, an experiment design that uses Certified Employee-OwnedSM and a less ubiquitous label would provide an additional layer of understanding baseline consumer perceptions of employee-owned companies.

Experiment procedure

Step Task Instrument
1 Demographic and attitudinal information Subject questionnaire
2 Practice auction 5th Price Vickrey Auction
3 Passive sensory observation Sensory Score Sheets
4 Auction 1: “homegrown” values 5th Price Vickrey Auction
5 Auction 2: information sheets 5th Price Vickrey Auction
6 Auction 3: sensory information 5th Price Vickrey Auction
7 Binding auction determination Random Process (Die rolls)

Summary statistics for demographic variables

Obs Mean SD Min. Max.
Age 53 25.41509 11.12245 18 61
Gender Freq. Percent Cum.
 Male 18 33.96 33.96
 Female 34 64.15 98.11
 Other 1 1.89 100.00
Ethnicity Freq. Percent Cum.
 White 34 64.15 64.15
 Asian 11 20.75 84.91
 Native Am 1 1.89 86.79
 Black 3 5.66 92.45
 Hispanic 2 3.77 96.23
 Other 2 3.77 100.00
Income Freq. Percent Cum.
 Less $20,000 11 20.75 20.75
 20,000–34,999 4 7.55 28.30
 35,000–49,999 4 7.55 35.85
 50,000–74,999 16 30.19 66.04
 75,000–99,999 3 5.66 71.70
 100,000–150,000 14 26.42 98.11
 More 150,000 1 1.89 100.00
Education Freq. Percent Cum.
 Secondary 10 18.87 18.87
 Some college 21 39.62 58.49
 Bachelor 12 22.64 81.13
 Some graduate 4 7.55 88.68
 Graduate 6 11.32 100.00
Primary household shopper Freq. Percent Cum.
 No 30 56.60 56.60
 Yes 23 43.40 100.00
Drink coffee how often Freq. Percent Cum.
 Less than once/week 13 24.53 24.53
 Once/week 4 7.55 32.08
 3–4 times/week 8 15.09 47.17
 Once/day 15 28.30 75.47
 More than once/day 13 24.53 100.00
Brew coffee how often Freq. Percent Cum.
 Less than once/week 19 35.85 35.85
 Once/week 5 9.43 45.28
 3–4 times/week 12 22.64 67.92
 Once/day 15 28.30 96.23
 More than once/day 2 3.77 100.00

Summary statistics for attitudinal data

Survey Item Mean SD Min. Max. Mode
11 Workers in EO firms more fairly compensated 6.55 1.16 4 9 7
12 EO firms strengthen the local economy 6.69 1.52 3 9 7
13 EO firms place a higher value on customer support 6.33 1.64 1 9 7
14 EO firms more committed to social responsibility 6.64 1.57 2 9 7
15 EO firms produce higher quality products 5.88 1.83 2 9 7
16 EO firms lower negative environmental impact 5.77 1.85 1 9 5
17 EO firms are more trustworthy 5.81 1.81 1 9 5
18 Workers in EO firms have better work-life balance 5.73 1.74 1 9 5
19 Workers in EO firms more satisfied with their job 6.43 1.43 2 9 7
20 FT results in more producer income 6.43 1.78 2 9 6
21 FT strengthen the local economy 6.37 1.68 2 9 6
22 FT products give better customer support 6.01 1.53 2 9 5
23 FT products committed to social responsibility 7.03 1.58 2 9 8
24 FT products higher quality 5.73 1.66 1 9 5
25 FT products lower negative environmental impact 6.03 1.59 3 9 5
26 FT products more trustworthy 6.00 1.62 2 9 6
27 Primary suppliers work under safer conditions 6.24 1.69 1 9 6
28 Primary suppliers more satisfied with their job 6.07 1.65 1 9 6
29 Buy lowest cost item irrespective of labels 5.39 2.93 1 9 9
30 Nutritional concern irrespective of labels 6.88 1.93 1 9 7

Notes: Attitudinal responses on a nine-point Likert scale (strongly disagree=1; strongly Agree=9). In total, 53 survey respondents. Survey items listed are abbreviated. Full questions can be found in the Appendix

Subject sensory rankings (blind-passive observation)

Freq. % Cum.
EO&FT
1 20 37.74 37.74
2 15 28.30 66.04
3 8 15.09 81.13
4 10 18.87 100.00
FT
1 14 26.42 26.42
2 11 20.75 47.17
3 13 24.53 71.70
4 15 28.30 100.00
Conventional
1 11 20.75 20.75
2 15 28.30 49.06
3 20 37.74 86.79
4 7 13.21 100.00
EO
1 8 15.09 15.09
2 13 24.53 39.62
3 12 22.64 62.26
4 20 37.74 100.00
Best (B) – Worst (W) Blind Rankings (R)
Best Worst B-W Rank
EO and FT 20 10 10 1
Conventional 11 7 4 2
FT 14 15 −1 3
EO 8 20 −12 4

Note: Kendall’s W=0.60 (for ranking agreement on EO and FT as Best and EO as Worst)

Sample means of auction bids

Variable Obs Mean ($) SD Min. Max. Bids of zero
AUCTION SET 1
Homegrown EO 53 1.62 2.84 0 16.05 19
Homegrown FT 53 1.88 2.91 0 16.05 14
Homegrown EOFT 53 2.51 3.08 0 16.05 8
AUCTION SET 2
Information EO 53 1.72 2.95 0 16.05 20
Information FT 53 2.08 2.77 0 16.05 9
Information EOFT 53 2.67 3.06 0 16.05 4
AUCTION SET 3
TasteRevealed EO 53 1.20 1.75 0 7.50 23
TasteRevealed FT 53 2.02 2.80 0 16.05 15
TasteRevealed EOFT 53 2.41 3.05 0 16.05 10

Experimental auction results

(1) (2) (3)
DepVar Auction 1 Bid DiffA2A1 Bid DiffA3A1 Bid
EO 0.669 (0.525) 0.100 (0.137) −0.426 (0.337)
FT 1.223** (0.494) 0.203* (0.106) 0.139 (0.209)
EOFT 2.178*** (0.475) 0.158* (0.0888) −0.104 (0.169)
Obs 159 159 159
Pseudo/AR2 0.0288 0.019 0.003
Reg Tobit OLS OLS

Notes: The dependent variables in Model 1 are “homegrown” bids from Auction 1. Models 2 and 3 show Tobit regressions to account for censored at bids at zero (because negative bids were not allowed). The dependent variables in Models 2 and 3 are the respective differences between Auctions 2 and 1 bids, and Auctions 3 and 1 bids. Constant terms were suppressed. Robust standard errors in parentheses. *p<0.10; **p<0.05; ***p<0.01

Notes

1.

See Kruse and Blasi (1999) for the questions and results of 30 different US public opinion polls taken between 1975 and 1997 on issues related to employee ownership and/or profit sharing. For a recent poll that shows support for employee ownership across the political spectrum, see Public Policy Polling (2015) www.nceo.org/assets/pdf/articles/PPP_results_employee_ownership.pdf.

2.

For example, common certifications seen on retail coffee include organic, local, rainforst alliance, shade grown, B-Corp, Kosher, direct exchange, among others.

3.

See Lusk and Shogren (2007) for an extensive review of 113 experimental auctions applied to study wide variety of valuation questions ranging from the consumer valuation of food safety, non-GMO goods, value of biodiversity and conservation, bison meat, health risk, omega 3 fatty acid and metal content, value of food coloration, among other applications.

4.

See Lusk and Shogren (2007) or any standard game theory textbook for an exposition of an nth-price auction.

5.

To remain consistent with accepted research on consumer valuation, we apply the experimental valuation paradigm used in Costanigro et al. (2014) that evaluated direct and complementary effects of local and organic food labels on WTP estimates.

6.

The employee owned, non-fair trade coffee was sourced from an Ohio coffee roaster and supplier that is listed on the ESOP online business directory (https://esopb2b.com) and has also appeared on the Ohio Employee Ownership Center “Top 50 ESOPs in Ohio” lists (www.oeockent.org). Certified Employee Owned verified that the company was indeed eligible to carry the Certified Employee Ownedsm label.

7.

The literature on valuation refers to homegrown values as those values that individuals bring into an experiment for real-world goods prior to any experimental treatment or manipulation.

8.

Subjects were given $16.05 (two $5 bills, five $1 bills, three quarters, two dimes, one nickel, five pennies) to eliminate the need to make change during payment.

9.

Because the data collected from the practice auction is only for teaching subjects the procedures used in the live auction, the data collected on preferences for different types of chocolate are not presented or analyzed.

10.

This process for collecting data, called the strategy method, has subjects make decisions in all possible situations knowing that any one of them could be “live.” The procedure optimizes data collected on each subject without compromising incentive compatibility since each auction has the same likelihood of being selected at random.

11.

Further, the chemistry of coffee taste is highly sensitive to various aspects of its preparation that we would not have been able to properly control for in our study, including, for example, the water quality used in its preparation (Navarini and Rivetti, 2010), the interaction of bean type with the method of preparation (Navarini et al., 2004) and the temperature at which coffee is served (Zellner et al., 1988).

12.

It is important to note that subjects were not physically endowed with coffee in any of the coffee auctions to mitigate biased bids owing to the endowment effect – or the behavioral regularity that individuals ascribe more value to goods in their possession (e.g. Kahneman et al., 1991). A series of experiments in Reb and Connolly (2007) show that “factual ownership” wherein subject are endowed with a good that they never physically possess does not affect ones monetary valuation of the good. Once subjects physical possess a good they own, however, monetary valuation is strongly influenced by the endowment effect.

13.

http://fairtradeusa.org/what-is-fair-trade (Fair Trade USA Information Sheet) and www.certifiedeo.com/eo/about (Certified Employee Owned Information Sheet).

14.

Treatments that introduce various types of information are common in valuation studies. For example, Menkhaus et al. (1992) manipulate the information pertaining to certain types of retail packaging on food in some treatments. Costanigro et al. (2014) introduce a “scientific information” treatment on the use of pesticides in organic and non-organic certified apples in a valuation study on Organic and Local labels.

15.

Sample size for the study determined using calculations for within sample (paired responses) using the formula n=((zα+zβ)2)(σ2)) /Δ2 where we assumed an economically relevant minimal difference bids across products to be $0.67 (Δ), a standard deviation (σ) of $1.50, 95% test of significance (z0.05=1.96) and 90% power (z0.90=1.28). See Lusk and Shogren (2007), pp. 55-56 as a reference for sample size determination of bid comparisons for different goods conducted on the same group of people.

16.

Unreported regression analysis shows that preferences are balanced in the sample. That is, no demographic category systematically drives differences in bids in all three treatments.

17.

Several MCDM methods, including Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) among others have been developed and are employed in different research protocols. See Rezaei (2015) for a discussion on these methods.

18.

See for example, Gibbons and Chakraborti (2011) for a discussion of Kendall’s W and other non-parametric methods.

Appendix 1. Experiment instructions

Appendix 2. Example bid slips

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Acknowledgements

The author of this paper has not made their research data set openly available. Any enquiries regarding the data set can be directed to the corresponding author.

The author would like to thank to Shephan Kroll, Emily Foley, Erik Olsen, Fidan Kurtulus, Thomas Dudley, and the participants at the 2017 Beyster Summer Symposium for valuable feedback on early versions of this research. The author also grateful to the three anonymous referees for providing excellent comments that improved the manuscript and also to Charlie Holt and Mary Spencer for their help in conducting the experiment sessions and the Kauffman Fund for sponsoring the data collection. The project received IRB approval (Protocol No. 2017/05/1) at the College of Wooster. All errors are the author’s own.

Corresponding author

Philip Pablo Mellizo can be contacted at: pmellizo@wooster.edu