Social Design Engineering Series

SDES-2018-8

Intentions behind disclosure to promote trust under shorttermism: An experimental study

Satoshi Taguchi
Doshisha University

Yoshio Kamijo
Research Institute for Future Design, Kochi University of Technology
School of Economics and Management, Kochi University of Technology

2nd October, 2018

School of Economics and Management
Research Institute for Future Design
Kochi University of Technology
KUT-SDE working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchCenter for Social Design Engineering at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed
and edited. They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressedin these papers are those of the author(s). It is expected that most working papers will be published in some other form.

Intentions behind disclosure to promote trust under short-termism:
An experimental study

Satoshi Taguchi
Professor, Graduate school of Commerce, Doshisha University
Karasuma Imadegawa, Kamigyo-ku, Kyoto 602-8580, Japan
Email: staguchi@mail.doshisha.ac.jp

Yoshio Kamijo
Professor, School of Economics and Management, Kochi University of Technology
2-22 Eikokuji, Kochi City, Kochi 780-8515, Japan
Email: yoshio.kamijo@gmail.com

Acknowledgement
We would like to thank all the participants at 37th European Accounting Association Annual
Congress (Tallinn), and 2014 American Accounting Association Annual Meeting (Atlanta).
Funding sources
This research was supported by JSPS KAKENHI Grant Number JP15H03403, JP16K13403. The
experiments in this study have been approved by the institutional research ethical committee ofDoshisha University.

1Intentions behind disclosure to promote trust under short-termism:
An experimental study
ABSTRACT
We experimentally examine the impact of varying intentions behind information disclosures ontrust and reciprocity between an investor and a manager during short-term transactions wherereputations cannot be established. To do so, we use a trust game with asymmetric information andconduct lab experiments, comparing one unintentional disclosure condition and two intentional
disclosure conditions. The results reveal that information disclosure promotes investments andreturns under all three conditions, even in short-term transactions. Further, compared with
unintentional disclosure, intentional disclosure fosters greater trust and reciprocity between
managers and investors. We also suggest that mutual trust can be developed even before reputation
and a long-term relationship are formed. Our study sheds light on the merits of intentional
disclosures from a short-term perspective and in particular, the practical importance of institutional
design for investors to acquire information.
(134 words)
Keywords: Disclosure; Short-termism; Experimental economics; Trust game; Gift exchange;
Intention2

I. INTRODUCTION
Disclosure is a major component in corporate financial reporting (Dye [2017], Sunder [2012]).
However, recent accounting scandals involving prominent companies including Enron, WorldCom,and HealthSouth have disrupted the fiduciary relationship between managers and investors in thecontext of information disclosure. Moreover, the recent financial crisis suggests that the rise of
short-termism 1 has negatively impacted manager–investor relationships (Stiglitz [2015]). With
such relationships becoming increasingly short-termed and given the loss of opportunities to build
reputation in the long term, it has become difficult for managers and investors to develop a trusting
relationship.
In the securities market, for instance, it is essential to build a trusting relationship between
managers and investors. The Securities and Exchange Commission (SEC) states that it strives topromote a market environment that is worthy of public trust. Where is the origin of trust andreciprocity? Many related studies suggest that information disclosure fosters trust and reputation ina long-term relationship (e.g., Lunawat [2013a, 2013b]). However, the literature remains open on
the effects of information disclosures on the trust between managers and investors during
short-term transactions 2 where reputations cannot be established.
Even in the context of short-term transactions, information disclosure has an importantfunction in that investors have better knowledge about companies and markets. Reducing
information asymmetry between an investor and a manager is expected to promote transactions.
However, the extent of their trust depends on the manner in which information is disclosed.
Therefore, we expect mutual trust between managers and investors to differ by disclosure process,even if the same information is disclosed. Consequently, the level of trust between managers andinvestors may vary by the information disclosure system.
In this study, we examine the effects of information disclosure on mutual trust andreciprocity in the context of short-term transactions. We focus on the entity who chooses todisclose information and whose intention underpins the discourse decision. If information
disclosure is mandatory, neither the investors nor the managers intend to disclose information. In
contrast, if managers voluntarily decide to do so, the disclosure occurs according to the managers’
intention. Many previous studies have discussed both mandatory and voluntary disclosure (e.g.,
Einhorn [2005], Graham, Harvey, and Rajgopal [2005], Healy and Palepu [2001], Jamal, Maier,and Sunder [2005], Li and Yang [2016]) and have mainly demonstrated the usefulness of voluntary
disclosure in a long-term relationship (e.g., Beaulieu [2014], Lunawat [2013a, 2013b]). We
1

Short-termism is a post-1980s model based on corporate governance that focuses on shareholders’short-term profits and returns as opposed to investments in long-term sustainability, innovation, and growth(Stiglitz [2015]). In this experiment, we interpret short-termism as the lack of opportunities to build
reputations.
2
It is important to consider the influence of a short-term perspective on economic behavior. Gigler, Kanodia,
Sapra, and Venugopalan [2014], for example, study this from a “real effects” perspective and show that pricepressure created by high reporting frequency induces managers to adopt a short-term perspective when
determining a firm’s investments.
3

examine if the same result holds even in short-term transactions, which occur in a more realistic
and severe environment. In addition, a disclosure system reflects an investor’s intentions. From ashort-term perspective, it is important for investors to independently obtain information and request
information disclosures from managers. In a short-term transaction, it is natural for investors torequest information disclosure given the drastic changes that economic and corporate activities
undergo. However, previous research has overlooked these aspects.
We adopt an experimental approach to examine the origin of trust and reciprocity. In
particular, we apply the key features of a classical trust game first developed by Berg, Dickhaut,and McCabe [1995]. The trust game is an appropriate method to measure trust and reciprocity
between managers and investors and has been used in numerous accounting studies (Basu,
Dickhaut, Hecht, Towry, and Waymire [2009], Davidson and Stevens [2013], Lunawat [2013a,
2013b]). We use a trust game with asymmetric information, that is, firm productivity is private
information known to managers (Lunawat [2013a]), and accordingly, consider the option of
information disclosure in the context of firm productivity.
We examine the impact of differences in the intention to disclose information on trust andreciprocity between managers and investors by eliminating the reputation effect. We compare thefollowing three conditions through lab experiments: random disclosure, where a computer
randomly determines whether to disclose information; voluntary disclosure, where managers can
opt to make a voluntary disclosure; and demanded disclosure, where investors request disclosuresfrom managers. Compared to empirical analyses conducted on the field, a laboratory offers tighter
controls on disclosure mechanisms. By carefully selecting relevant parameters, we can directlycompare actual behavior under the three disclosure systems, which are equally deterrent in theory.
In addition, we can measure investors’ and managers’ behavior in a laboratory without errors thatare otherwise observable in field data (for further discussions on experimental methodology, see,for example, Bloomfield, Nelson, and Soltes [2016], Camerer [2003], Libby, Bloomfield, andNelson [2002], and Luft and Shields [2009]).
We hypothesize that in the context of a “gift exchange” paradigm, intention behind
disclosure plays the role of a “gift” in the trust game. The literature on gift exchange is relevant
here because the settings seem to have many similarities (e.g., Akerlof [1982], Berg et al. [1995],
Fehr, Kirchsteiger, and Riedl [1993]). Applying the gift exchange paradigm to the voluntary
disclosure condition, we predict that an investor views the manager’s disclosure decision as a “gift”
and thus, reciprocates accordingly. Under the demanded disclosure condition, however, we predictthat the manager perceives the investor’s intentional request for disclosure as a personal concern
for him/herself (McAllister [1995]) and reciprocates accordingly. In other words, we examine how
differences in a relationship that is based on “gift exchange” affects trust and reciprocity between
managers and investors.
Our experiment shows that information disclosure promotes investments under all three
conditions. This is because information disclosure reduces the uncertainty of investment andeliminating information asymmetry promotes investments. This is the pure effect of information
4

disclosure common to all three conditions. Similar to the findings of previous studies using trust
games to explore long-term transactions, we find that information disclosure enhances trust
between a manager and an investor even in short-term transactions.
In addition, we observe that the disclosure effect differs by disclosure intention, as
expected: the levels of investment and returns in the case of intentional disclosure (voluntary anddemanded conditions) are higher than those for unintentional disclosure (random condition),
indicating that intentional disclosure fosters mutual trust between investors and managers during
short-term transactions. This is because intentional voluntary disclosure and intentional disclosure
request are recognized as a type of “gift” and the levels of investment and returns increase in linewith the “gift.” Furthermore, a comparison of intentional disclosures with each other reveals thatthe levels of investment and returns are higher under the demanded disclosure condition than thevoluntary condition. This result is in contrast to our expectations but can be reasonably understoodbecause an investor’s information request functions not only as a “gift” under the hypothesis of agift exchange between disclosure request and returns, but also as a priming to promote managers’
fair behavior (e.g., Andreoni and Rao [2011], Yamamori, Kato, Kawagoe, and Matsui [2008]).
This study makes the following contributions. First, it contributes to the literature on
information disclosure. There are various arguments about the type of information disclosure that is
considered desirable and thus far, considerable attention has been given to the reputation effect ofvoluntary disclosure in the long-term relationship (e.g., Lunawat [2013a, 2013b]). In contrast, ourstudy attempts to compare all three conditions by controlling for the reputation effect. Our
experimental results show that intentional disclosure fosters trust and reciprocity between managers
and investors even during short-term transactions and indicate the possibility of mutual trust being
formed even before developing one’s reputation or a long-term relationship. Therefore, our study
sheds light on the merit of intentional disclosure from a short-term perspective, particularly thepractical importance of institutional design that assists investors in acquiring information under, forexample, the demanded condition. To this effect, if economic conditions are expected to be
short-term in the future, the current Regulation Fair Disclosure, 3 among others, may lead tounintended consequences since it uniformly requests disclosures without intention. 4
Second, this study contributes to the literature on trust and reciprocity in social science andeconomics (Camerer [2003], Fehr, Hart, and Zehnder [2011], Fehr, Kirchsteiger, and Riedl [1993],
Fehr and Schmidt [1999], Kuang and Moser [2009], Luft and Shields [2009]). A traditional trust
game assumes the economic rationality of human beings and does not anticipate differences in trust
and reciprocity among the three disclosure conditions. However, the gift exchange hypothesis in
our study highlights differences in trust and reciprocity among the three conditions. That is, the3

Heflin, Kross, and Suk [2016] document the consequences of the Regulation Fair Disclosure concerning
the use of management earnings forecasts and their influence on a firm’s information environment.
4
In this regard, Bushee, Jung, and Miller [2017] indicate that selective access can lead to profitable trading
opportunities. Their empirical evidence suggests that selective access to management conveys benefits tocertain investors even in the post-Regulation FD period.
5intention behind information disclosure affects trust and reciprocity between managers andinvestors. This result extends the findings of Douthit and Stevens [2015], who experimentally
propose that an authority’s identity is an important factor affecting the interdependent relationshipbetween principals and agents. The present study also encourages future debate on the factors
promoting trust and reciprocity. The literature comprises extensive studies on social norms inaccounting and auditing systems (Cardinaels and Yin [2015], Davidson and Stevens [2013], Maas
and Rinsum [2013], Tayler and Bloomfield [2011], Wysocki [2011]). For example, using
Bicchieri’s [2006] model of social norm activation, Davidson and Stevens [2013] remark that acode of ethics with a certification activates social norms that control opportunistic behavior. Our
study, which shows that three types of disclosure systems lead to varying outcomes of trust andreciprocity between investors and managers, expands the extant understanding of social norms inaccounting and auditing systems. 5
The remainder of this paper is organized as follows. Section II proposes the model. Section
III reviews the related literature. Section IV presents our hypotheses. In Section V, we explain theexperimental design employed to test our hypotheses. Section VI describes our empirical results
and Section VII discusses the results of our additional analyses. Section VIII concludes the paper.
II. MODEL
Trust GameWe use a trust game developed by Berg et al. [1995] because it simply and accurately represents
the strategic relationship between managers and investors (Davidson and Stevens [2013]). 6 The
original trust game is a two-step sender–receiver game. In the first step, the sender/investor
receives some wealth (E) and determines the amount of money (M) to invest with areceiver/manager within the limit of the initial endowment (0 ≤ M ≤ E). 7 In the second step, thereceiver/manager is endowed with multiplier e (e > 1), which is a production technology or a firm’s
productivity. The manager puts the investment to productive use, thereby generating a strictly
5

In addition, several empirical studies on information disclosure focus either on investors’ actions (Barron
and Qu [2014], Elliott, Hobson, and Jackson [2011], Fanning, Agoglia, and Piercey [2015], Hales, Kuang,
and Venkataraman [2011], Han and Tan [2010], Hirst, Koonce, and Venkataraman [2008], Nelson and Rupar[2015], Thayer [201]) or managers’ actions (Dickhaut, Ledyard, Mukherji, and Sapra [2003], King [1996]).
As a result, thus far, strategic correlations between investors and managers have been neglected. Our study
addresses this gap in the literature by analyzing direct trust and reciprocity that emerge from strategic
correlations between investors and managers.
6
Davidson and Stevens [2013] present the three advantages of utilizing the trust game in Berg et al. [1995].
First, the trust game examines the same investment setting as that explored in the corporate governanceliterature (Bushman and Smith [2001]). Second, the underlying agency theory incorporated in the trust gameis the foundation of the corporate governance literature (Shleifer and Vishny [1997]). Third, the trust gamehas clear economic predictions that may be compared with and contrasted to behavioral predictions (Brown,
Evans, and Moser [2009]).
7
We consistently use the terms “investor” and “manager” in this study to describe the roles of the twoplayers. However, in our experiments, we used the labels ‘‘sender’’ and ‘‘receiver’’ to avoid the potential
effect of context-laden terms (Berg et al. [1995], Davidson and Stevens [2013]).
6

positive income (eM) for the firm. Information on productivity (multiplier e) is known to bothmanagers and investors. From the firm’s income, the manager determines the amount of money (K)to pay the investor as dividend within the limit eM (0 ≤ K ≤ eM). The residual amount at the end of
the second step is the net gain in this game; the sender’s gain is E – M + K and the receiver’s is eM– K. All information about the game is common knowledge, but the investor’s and manager’s
identities remain anonymous. This is a game of trust, that is, the sender explicitly puts money atrisk. The receiver either negatively or positively reciprocates the sender’s behavior (Dickhaut[2009]).
We predict the consequences using game theory. Using the principle of backward induction,
we assume the investor is aware that the manager is motivated to keep all the investor’s money;
therefore, the equilibrium prediction is that investors will not transfer any resources to the manager.
Despite this prediction, numerous experiments have observed that investors provide a
positive amount of money to managers, who then repay investors a part of the money earned (Berget al. [1995]). Thus, in certain cases, the experimental results contradict the prediction made using
game theory. 8 This highlights the need to not only develop a model but also conduct experiments.
Trust Game with Disclosure Option: Random, Voluntary, and Demanded Conditions
This study modifies the traditional trust game by focusing on multiplier e. We introduce
information asymmetry and the disclosure regime in the traditional trust game.
First, we introduce information asymmetry (Lunawat [2013a]). In the traditional trust game,information on productivity (multiplier e) is known to both managers and investors. In reality,
however, this information may be naturally considered as private to managers. Therefore, we
assume multiplier e is managers’ private information and there are two types of firm productivity:
high productivity (e = 5) and low productivity (e = 3). 9 The firm’s deterministic productivity is
contingent on the state of nature. Unlike the investor, the manager always observes the state ofnature.
Second, we consider the effects of the information disclosure option on firm productivity.
This type of information is important because it largely concerns firms’ future profitability, whichis related to investors’ investment decisions. In reality, such information disclosure may beconsidered a disclosure of management forecasts (Elliott, Hobson, and Jackson [2011], Graham etal. [2005], Hirst, Koonce, and Miller [2008]). Disclosures of management forecast are voluntary in
the United States but mandatory in Japan (Kato, Skinner, and Kunimura [2009]).
Executing the disclosure option makes firm productivity common knowledge. In otherwords, when the disclosure option is executed, managers and investors observe the realized value

8

See Bohnet [2010], Cox [2004], and Johnson and Mislin [2011] for explanations on the frequent deviationfrom equilibrium.
9
Prior studies (e.g., Basu et al. [2009]) assume that multiplier e is fixed and equal to three. Lunawat[2013b] assumes that e is variable and ranges from 1 to 5. For the sake of simplicity, we assume two values
for e: 3 and 5.
7

of firm productivity; however, when the option is not exercised, investors remain unaware of therealized value.
In line with the disclosure option, we define three conditions that differ by intention behind
information disclosure: random disclosure (control condition), voluntary disclosure, and demandeddisclosure. Under the random disclosure condition, a computer randomly determines whether toexercise the option. Under voluntary disclosure, managers have the option to make a voluntary
disclosure. As for the demanded disclosure condition, investors can request managers to make adisclosure. Our research extends Douthit and Stevens [2015], who experimentally propose that theauthority’s identity is an important factor affecting the interdependent relationship between
principals and agents. For simplicity, we assume there is no cost associated with the execution of
the option. 10 We verify which condition promotes investors’ trust and managers’ reciprocity. 11
Figure 1 presents the timeline of the game and the experimental conditions.
[Insert Figure 1 about here]

III. RELATED LITERATURE
Our setting draws on Lunawat [2013a, 2013b], who theoretically and experimentally examines therole of reputation building through voluntary disclosure using a trust game. The difference between
our study and those of Lunawat [2013a, 2013b] is that the latter follows repeated game theory to
explore the role of voluntary information disclosure as a reputation builder in a finitely repeatedtrust game using sequential equilibrium. Our model excludes reputation building through thevoluntary disclosure condition to conduct a fair comparison of the three conditions; the random anddemanded disclosure conditions have no scope for reputation building. Lunawat’s [2013a, 2013b]
voluntary disclosure model assumes that the manager decides whether to commit to disclosing
private information before he/she observes the realized productivity value and thus, the manager’s
disclosure serves as ex-ante commitment, which builds reputation in a repeated game. Conversely,
our voluntary disclosure model assumes that the manager decides whether to disclose private
information after he/she observes the realized productivity value in a single-period game. Our
voluntary model excludes the ex-ante commitment function of disclosure.
Further, our experimental design (Section V) adopts the random-matching protocol as thematching method for participants to eliminate reputation building in a repeated game. Therefore,10

We adopt this assumption because we focus on the costless effect of disclosure intention and not the
impact of costly signals (Spence [1973]). Thus, we examine if the initiative to disclose information without adisclosure cost affects trust and reciprocity between managers and investors. However, when there is a cost
attached to the execution, we find that our model bears a similar structure to that of the disclosure costmodel (Verrecchia [1983]), which refers to several types of cost sharing.
11
Our study extends Hirst, Koonce, and Miller [1999], who experimentally examine how prior forecast
accuracy moderates the effects of the forecast form. They conclude that when prior forecast accuracy is high,the forecast form influences investor judgments. In our model setting, prior management forecast accuracy is“high” and thus, disclosure influences investors’ judgments.
8we compare three conditions at the same level and restrict our analysis to the effect of intentions
behind information disclosure. Our setting is also consistent with that in the real world, where amanager–investor relationship becomes short-term and opportunities for long-term reputationbuilding are lost (Stiglitz [2015]).
Our setting also extends Coletti, Sedatole, and Towry’s [2005] study, which examines trustand cooperation between two parties with varying levels of information asymmetry. The authors
show that trust and cooperation increase in a strongly controlled environment wherein information
asymmetry between two parties is reduced. The difference between our study and that of Coletti etal. [2005] is that the latter suppose the superiority of the strong control environment is the Nashequilibrium in game theory and that their result is a natural consequence of game-theoreticalprediction. By contrast, our study supposes that the equilibria of the three conditions predicted bygame theory are the same, and thus, the superiority of demanded (or voluntary) disclosure is not anatural consequence of game-theoretical prediction.
IV. HYPOTHESES
We propose the following three hypotheses for investors’ investment behavior and managers’
returns behavior.
First, we predict that information disclosure positively affects investment and returns. We
expect the levels of investment and returns to be higher when the disclosure option is exercisedthan when it is not. When information about productivity is disclosed, investment uncertainty
decreases and investments are promoted by eliminating information asymmetry. In addition, themanager will return dividends in good faith since the investor accurately knows about the amountof money the manager possesses. 12 Thus, we predict that information disclosure benefits both thesender and receiver in a trust game with information asymmetry. We term this the pure effect of
information disclosure common to all three conditions.
H1: Pure effect of information disclosure
The levels of investment and returns will be higher when the information disclosure option is
exercised rather than excluded under all three conditions.
Second, we hypothesize that, in the context of the “gift exchange” paradigm, information
disclosure or the intention behind it plays the role of a “gift” in a trust game. The gift exchange
paradigm posits that people reward kind behavior and punish unkind behavior even in situations
12

Koch and Normann [2008] indicate that recent bargaining experiments, such as a dictator game, evidence
that a key driving force in seemingly altruistic behavior is whether the responder can observe a proposer’s
action. When introducing information asymmetry about the size of pie to be divided by the proposer, atypical finding is that subjects are concerned others will perceive them as abiding by social norms (evenunder inter-subject anonymity) but otherwise selfishly exploiting their informational advantage (Croson[1996], Guth, Huck, and Ockenfels [1996], Kagel, Kim, and Moser [1996], Mitzkewitz and Nagel [1993],
Pillutla and Murnighan [1995], Rapoport and Sundali [1996], Straub and Murnighan [1995]).
9where standard economic theory predicts they would not (Akerlof [1982], Fehr et al. [1993]). In
line with trust games, the literature on gift exchange hypotheses would be relevant here because thesettings seem to share many similarities. Berg et al. [1995, p.138], for example, show that the trust
game demonstrates that people are willing to reward appropriate behavior and also that people act
in anticipation of a reward. 13 Therefore, in a traditional trust game, investors’ investment behavior
and managers’ returns behavior constitute the relationship of a “gift exchange.” We compare therelationship under the random condition, in which a computer unintentionally exercises thedisclosure option, with that of a gift exchange under the voluntary or demanded conditions, inwhich managers or investors intentionally exercise the disclosure option.
Applying the gift exchange paradigm to the voluntary disclosure condition, we derive thehypothesis of gift exchange behavior between voluntary disclosure and investment: the investor
perceives the manager’s disclosure decision as a kind “gift” and reciprocates accordingly. Thus,
under the voluntary disclosure condition, investors respond generously to managers’ “favorable”
actions by increasing their investments with managers who voluntarily disclose information. In
other words, when a manager voluntarily exercises the disclosure option, an effect attributable tointentional disclosure is expected to occur in addition to the pure effect of disclosure. We deem thisthe strategic effect of managers’ voluntary disclosure and distinguish it from the previous pureeffect of disclosure. Assuming that firms have the same productivity, we predict that investment
value is higher in the case of voluntary information disclosure than under the random disclosure
condition (H2a). 14 Furthermore, we know from previous studies (e.g., Berg et al. [1995]) that if
investment amount increases, the rate of returns from the manager is expected to rise accordingly(H2b).
H2a: Investors’ investment behavior under the voluntary condition
At the same level of productivity (i.e., multiplier e is unchanged), the amount of investment ishigher when the disclosure option is exercised under the voluntary disclosure condition than the
random disclosure condition.
H2b: Managers’ returns behavior under the voluntary condition

13

Rabin [1993] incorporates a “kindness” function in subjects’ utility to capture the following behavior: as
one’s counterpart increases his or her “kindness,” the utility maximizing response is to be kinder in return.
14
H2a is consistent with the findings for self-disclosure reciprocity effects in the social psychology
literature (e.g., Collins and Miller [1994], Cozby [1973], Jourard [1959], Sprecher et al. [2013]): that is, theperson receiving shared information positively responds by disclosing information him/herself. Further, H2a
is consistent with the findings of Slovic [1993], who experimentally shows the effects of voluntary “hostage
posting” by organizations (nuclear power plants) to restore public trust following adverse events. Using
scenario-based experiments, Nakayachi and Watabe [2005] demonstrate that voluntary hostage posting with
monitoring provisions and self-sanctions increases participants’ trust perceptions regarding organizations
that are guilty, whereas imposed or involuntary hostage posting does not result in more positive evaluations.
In our study, under the voluntary condition, exercising the disclosure option serves as “self-disclosure” or“hostage posting” and increases investors’ trust perceptions toward managers who take “favorable” actionand encourage investors to respond similarly.
10

At the same level of productivity (i.e., multiplier e is unchanged), the rate of returns is higher when
the disclosure option is exercised under the voluntary disclosure condition than the random
disclosure condition.
Third, we examine the effects of investors’ intention under the demanded disclosure
condition on the gift exchange relationship. We consider the hypothesis of gift exchange behavior
between disclosure request and returns, in which the manager perceives the investor’s intentional
disclosure request as a personal concern for him/herself and thus reciprocates accordingly. To thiseffect, Lewis and Weigert [1985] indicate that interpersonal trust has cognitive and affective
foundations and affect-based trust is an emotional form of trust, wherein one party exhibits genuine
care and concern for the welfare of another. McAllister [1995, p.53] shows empirical evidence
from organizational psychology literature that affect-based trust is associated with affiliative
citizenship behavior such as a personal interest to the individual. Thus, under the demandeddisclosure condition, managers respond generously to investors’ affiliative behavior by increasing
returns to investors who request for information disclosure. In other words, when an investor
exercises the disclosure option, an effect attributable to the intentional disclosure request is
expected to occur in addition to the pure effect of disclosure. We call this the strategic effect ofinvestors’ disclosure request and distinguish it from the pure effect of disclosure. Assuming thatfirms have the same level of productivity, we assume that the rate of returns is higher under thedemanded disclosure condition than the random disclosure condition (H3b). Further, previous
studies (e.g., Berg et al. [1995]) suggest that an increase in the rate of returns will cause investment
level to rise accordingly (H3a).
H3a: Investors’ investment behavior
At the same level of productivity (i.e., multiplier e is unchanged), the amount of investment ishigher when the disclosure option is exercised under the demanded disclosure condition than the
random disclosure condition.
H3b: Managers’ return behavior
At the same productivity (i.e., multiplier e is unchanged), the rate of return when the disclosure
option is exercised is higher under the demanded disclosure condition than the random disclosure
condition.
Figure 2 summarizes the gift exchange hypotheses presented in this section.
[Insert Figure 2 about here]
First, under the random condition, both the investment and returns constitute the giftexchange relationship (normal gift exchange hypothesis). This is because a computer randomly
decides to exercise the disclosure option and there is no intention behind it under the random
11condition, and therefore, managers and investors do not regard information disclosure as a kind
“gift.”
Second, under the voluntary condition, both the manager’s intentional voluntary
disclosure and the investor’s investments constitute the relationship (hypothesis for gift exchange
between voluntary disclosure and investment). This is because the investor perceives the manager’s
disclosure decision as a kind “gift” and reciprocates accordingly (the boxes in Figure 2 represent
this relationship). In addition, the correlation between the investment and the returns shown in theprevious studies is expected to hold (see the parentheses in Figure 2 for the relationship).
Finally, under the demanded condition, both the investor’s intentional disclosure request
and returns by the manager constitute the relationship (hypothesis for gift exchange between
disclosure request and returns). This is because the manager treats the investor’s intentional
disclosure request as a personal concern for him/herself and reciprocates accordingly (see the boxes
in Figure 1). It is also expected that the correlation between the investment and the returns shownin the previous studies holds (see the parentheses in Figure 2).

V. EXPERIMENTAL DESIGN
To test our hypotheses, we conduct a trust game experiment with the disclosure option described in
the previous section. We adopt a 3 × 1 experimental design in which the intention to disclose

information is manipulated among participants under three conditions: random disclosure (control
condition), voluntary disclosure, and demanded disclosure.

We performed this experiment in three waves: February 2013, April 2014, and April 2018.
The laboratory experiment is programmed using Z-Tree software (Fischbacher [2007]) and it was
conducted at Doshisha University’s experimental lab. Participants are enrolled students recruitedfrom the campus through the laboratory’s website. We recruited a total of 246 graduate andundergraduate students 15 as participants. We conducted 20 sessions of computerized experiments:
five for the random condition, eight for the voluntary condition, and seven for the demanded
condition. The random condition was included in the third wave (April 2018) and the disclosure
probability in the random condition was determined with reference to the actual average disclosure
rate estimated during the first and second waves under the voluntary and demanded conditions. In
particular, we set the disclosure probability under the random condition to 75%.
Participants were 20.77 years old on average (SD = 2.04). The maximum and minimum
ages were 34 and 19 years respectively, and 57.8% of the participants were male. We incentivized15

The use of students as surrogates for employed adults and professionals has long been an issue inbusiness research (Dickhaut, Livingston, and Watson [1972]). However, several studies have suggested thatbusiness students are appropriate proxies for professionals when assessing basic traits or perceptions (Ward[1993]). Remus [1986] and Greenberg [1987] address the student-as-surrogates issue by simultaneously
studying business students and employed adults. Both researchers conclude that the results show nodifferences. Further, Geiger and Smith [2010] argue that the use of business students as surrogates for
employed professionals is appropriate.
12

participation through monetary rewards. The allocation of participants to the conditions was
random: 78, 84, and 84 subjects participated under the random, voluntary, and demanded condition,
respectively. Since we adopted a between-subject design, no individual participated in more than
one experimental session.
Each subject participated in one session comprising 20 decision-making rounds.
Participants were assigned the role of an investor or a manager, which was randomly predeterminedby the computer at the beginning of the experiment. The roles remained unchanged throughout the
20 rounds. Under the random condition, half the participants (39 participants) were assigned therole of an investor and the other half that of a manager. Similarly, under the voluntary anddemanded conditions, half the participants (42 participants) were assigned the role of an investor,
and the other half that of a manager. In the experimental instructions, the investor was labeled a“sender” and the manager was labeled a “receiver” (see Appendix). The participants interact
anonymously through a computer network.
We informed all participants that their partner would be randomly determined by acomputer at the beginning of each of the 20 rounds. We adopted random matching as a matching
protocol for the participants to eliminate reputation building in a repeated game. This was because,
as previously explained, we intended to compare the three conditions with a focus on the impact of
the intention behind information disclosure.
The participants were separated by dividers in each experimental session. At the beginningof each session, the experimenter read aloud an initial set of instructions (see Appendix) while theparticipants followed along using their own instruction copies. The explanation of the gamestructure for the participants used neutral terminology. Once the instructions were read, participants
were asked to respond to the questions about the experiment. Participants were required to answer
all the questions correctly before they started an experimental task. In this way, we ensured that all
the participants accurately understood the experiment’s details.
The feedback information at the end of each round was as follows: his/her own action,
partner’s action, and his/her own payoff. During all the treatments, participants received no
information, individually or in aggregate, about the results of the other pairs of subjects.
The experiment’s parameters were standardized for both conditions as follows. The value of
initial endowment E was 10. Productivity (multiplier e) in each period was low (e = 3) or high (e =5) with a 50% probability. To control for the realizations of productivity across sessions, we chose
one set of the 20-period productivity realizations before the first experiment and applied it to every
session.
At the end of the experiment, participants were asked to fill out an exit questionnaire thatgathers demographic information and personal perceptions. The duration of each session, including
the reading of the instructions, was about 90 minutes. The participants received a JPY 1,000
show-up fee plus their earnings from the game in cash. The average earnings were JPY 2,269: JPY
2,136 for the random condition, JPY 2,235 for the voluntary condition, and JPY 2,426 for thedemanded condition. Table 1 summarizes the experiment design.
13

[Insert Table 1 about here]

VI. RESULTS
Manipulation Checks and Summary Statistics
To test the effectiveness of our experimental manipulation and controls, we asked participants to
respond to two questionnaires: ex-ante check questions and an exit questionnaire. Once theinstructions were read, participants answered the ex-ante check questions about the experimental
structure. All 246 participants responded to the check questions correctly. Thus, the experimentdetails were clear to and well-understood by all the participants.
The participants responded to numerous statements in the exit questionnaire, designed totest the effectiveness of our experimental manipulation and controls using a seven-point Likert-type
scale, where 1 = strongly disagree and 7 = strongly agree. The manipulation and controls tests
measure the mean difference from the neutral response of 4 (for all, p < 0.01). The participants
admitted to not knowing their partner during the experiment, indicating that we effectively
controlled for reputation effects (p < 0.01). In sum, the manipulation and controls were effective forour experiment.
Table 2 reports the descriptive statistics for each experimental condition. Panels A, B, and C,
present the descriptive statistics for the rate of disclosure, investors’ investment behavior, andmanagers’ returns behavior, respectively.
[Insert Table 2 about here]
Panel A in Table 2 presents the rate of disclosure by condition and productivity level(multiplier e). It reveals that, under the voluntary disclosure condition, the rate of disclosure at highproductivity (e = 5) and low productivity (e = 3) are 83.6% and 71.2%. Thus, under the voluntary
disclosure condition, managers do not exercise disclosure when the firm’s productivity is low (e =3). This result is consistent with that of Kothari, Shu, and Wysocki [2009], who empirically showthat managers delay the release of bad news to investors. Further, they conclude that managers’
tendency to withhold bad news can stem from a standard agency problem wherein managers’
disclosure preferences are not aligned with those of shareholders. 16 In addition, Panel A shows thatwhen intentional disclosures are compared with each other, the disclosure rate under the demanded
condition (88.0%) is higher than that for the voluntary condition (77.4%). Thus, the execution of
disclosure options differs by demanded and voluntary conditions. In the former, investors request
information disclosures more proactively because they cannot directly observe if the firm’s
16

Baginski et al. [2018] empirically evidence that, as a result of career concerns, managers delay thedisclosure of bad news.
14

productivity is high or low. Conversely, under the voluntary condition, managers do not exercise
disclosure, particularly when the firm’s productivity is low.
Panel B of Table 2 indicates that, under all three conditions, the levels of investment when
the disclosure option is exercised are higher than those when it is not. Panel B also shows that theinvestment levels are the highest (5.96) under the demanded condition. Panel C of Table 2
highlights an identical tendency that, under all three conditions, the levels of ROI are higher when
the disclosure option is exercised than when it is not. In addition, Panel C shows that the levels of
ROI are the highest (1.08) under the demanded condition. Contrary to the sub-game perfectequilibrium prediction of zero investment and zero returns, the levels of investment and returns
under all three experimental conditions are greater than zero. This is consistent with the results ofprevious studies that experimentally examine behavior using trust games (Berg et al. [1995],
Johnson and Mislin [2011]).
Results for H1: Pure Effect of Information Disclosure
In the following analyses to test H1, H2, and H3, we treat each subject as an independent
observation, computing averages for each subject. We first analyze the pure effect of information
disclosure under all three conditions. Table 3 presents the parametric and nonparametric tests for
H1.
[Insert Table 3 about here]
The Mann–Whitney U values in Panel A (Table 3) reveal that, under all three conditions,
there are significant differences in the investment levels between the cases of disclosure andnon-disclosure. This result provides support for H1. Similar results hold for the ROI levels (Panel B
of Table 3).
Results for H2: Strategic Effect of Managers’ Voluntary Disclosure
We analyze the strategic effect of managers’ voluntary disclosure and compare thevoluntary disclosure condition with the random disclosure condition. Table 4 presents theparametric and nonparametric tests for H2.
[Insert Table 4 about here]
Table 4 shows that, in all cases, both the levels of investment and ROI are higher under thevoluntary condition than the random condition. In the case of H2a, the statistically significant
difference is confirmed in the following case: when the disclosure option is exercised andproductivity is low (e = 3), the mean investment level is higher under the voluntary condition (4.16)
than the random condition (2.83) (t = 2.28, p = 0.026; U = 1,046, p = 0.032). This result partially
supports H2a. As for H2b, the statistically significant difference is confirmed in the following case:15

when the disclosure option is exercised and productivity is high (e = 5), the mean ROI level ishigher under the voluntary condition (1.17) than the random condition (0.95) (t = 2.17, p = 0.033;
U = 1,017.5, p = 0.035). This result partially supports H2b.
In sum, our results partially support H2a and H2b, that is, the strategic effect of managers’
voluntary disclosure. The findings indicate that the manager’s disclosure intention affects the giftexchange relationship. In other words, the investor views the manager’s disclosure decision a kind
“gift” and reciprocates accordingly.
Results for H3: Strategic Effect of Investors’ Disclosure Request
Here, we examine the strategic effect of investors’ disclosure request and compare the demandeddisclosure condition with the random disclosure condition. Table 5 presents the parametric andnonparametric tests for H3.
[Insert Table 5 about here]
For H3a, Table 5 shows that the mean investment level when the disclosure option is
exercised is higher under the demanded condition (6.14) than the random condition (4.17) (t = 3.70,
p = 0.000; U = 1,173, p = 0.001). This result supports H3a. As for H3b, Table 5 shows that themean ROI level when the manager discloses information is higher under the demanded condition(1.27) than the random condition (0.82) (t = 6.26, p = 0.000; U = 1,398, p = 0.000).
Thus, our results support H3a and H3b, that is, the strategic effect of investors’ disclosure
request. Further, the findings indicate that investors’ intention to request a disclosure affects the giftexchange relationship. The manager views such a request as a personal concern for him/herself andreciprocates accordingly.
As described above, we analyze H1, H2, and H3, where we treat each subject as anindependent observation, computing averages for each subject. As a result, we obtain results that
support all hypotheses. However, given the short-term setting of our study, a more robust analysis
that controls for the effects of both periods and individuals is warranted. In particular, ourexperiment comprises 20 decision-making periods, and thus, it is necessary to account for changes
in the variables throughout all the periods. Further, an analysis of returns behavior must control for
not only the effects of both periods and individuals but also the impact of the choices of disclosure
and investment. This is because repayment is likely a function of both treatments (multiplier anddisclosure rule) and subjects’ responses to those treatments (choice of disclosure and choice ofinvestment). We analyze this in the next section.
VII. DISCUSSION: ADDITIONAL ANALYSIS
Comparison of Three Conditions by Period16In this section, we expand the viewpoint presented in this study and compare all three conditions.
Figure 3 presents the mean levels for investment and ROI for each condition by period.
[Insert Figure 3 about here]
Figure 3 shows that, despite the influence of differences in productivity (e) throughout thechart, the first and second halves of the round do not differ for all conditions. In other words, thelearning effect has a limited influence and our experimental setting to control for the reputation
effect functions successfully.
In addition, Figure 3 shows that, throughout all the rounds, the levels of investment andROI under the voluntary and demanded conditions, where managers and investors intentionallyexercise their disclosure, were higher than those under the random condition, where a computer
unintentionally opts for disclosure. In particular, the levels of investment and ROI are the highest
under the demanded condition.
Analysis using Linear Mixed-Effects Models
In this subsection, we use the linear mixed-effects (LME) models to control for the influence of
periods and individuals (Bolker, Brooks, Clark, Geange, Poulsen, Stevens, and White [2009])
because our experiment is designed to induce repeated measures. The LME model allows forflexible specifications for the effect of a disclosure rule in terms of the observed fixed effects andfor those of individuals in terms of a random effect.
Suppose rit is a response variable for individual i in period t, for example, senders’investment or receivers’ ROI and xit is the vector of variables for subject i in period t, such as anexperimental condition dummy, profitability (e), a period, or disclosure dummy for fixed effects 𝜷.
Then, we estimate the response variable rit of individual i in period t as𝑟𝑖𝑖 = 𝜷 ∙ 𝒙𝒊𝒊 + 𝑢𝑖 + 𝜀𝑖𝑖 ,where ui is the individual-specific random effects across individuals and 𝜀𝑖𝑖 is an unobservableerror term across individuals and periods. 17 We assume independent and normally distributed
random terms denoted by u and 𝜀.

[Insert Table 6 about here]

First, we analyze investors’ investment behaviors using the LME models. Table 6 reports
the estimation results for several models in which investment level is the dependent variable andthe experimental conditions dummy and other variables such as profitability and period are
independent variables. Model 1 estimates the average effect of experimental conditions on
investment level relative to the baseline condition (random) and shows that while the voluntary17

17

We run a regression using Stata 14.0 with the command mixed.

dummy is not statistically significant, the demanded dummy is statistically significant at the 1%
level. However, if we incorporate the interaction terms for disclose dummy and experimental
conditions (Model 2), the interaction term for voluntary dummy becomes positive and statisticallysignificant. This indicates that the effect of experimental conditions on investment level differs
between the disclosed and non-disclosed cases. We run similar regressions using the disclosed(Model 3) and non-disclosed (Model 4) samples. For the disclosed sample, we observe large
average effects by both the demanded and voluntary conditions on investment level relative to thebaseline condition. However, such effects are not observable in the non-disclosed sample. Thisfinding strongly supports H2a and H3a.
[Insert Table 7 about here]
Next, we examine the effects of disclosure decisions and the intention behind it on
managers’ returns behavior. Table 7 presents the estimation results for LME models, where ROI is
the dependent variable. The experimental condition dummies and the disclosure decision are theindependent variables in Model 1, and the interaction terms for these are added in Model 2. In bothmodels, the disclose and demanded dummies are positive and statistically significant at the 1%
level, although the coefficients related to the voluntary dummy are not. In other words, while thedemanded condition positively affects managers’ returns behavior, the voluntary dummy does not.
However, as in the case for senders’ behavior, returns behavior may differ between the disclosed
and non-disclosed case. We examine this in the subsequent discussion.
[Insert Table 8 about here]
Table 8 reports the estimation results for the LME model, where ROI is the dependent
variable and the sample is restricted to disclosed data. In addition to the experimental condition
dummies, we incorporate investment (Model 1) as well as the interaction term for investment andthe experimental condition dummies (Model 2) as independent variables. We find a positive andsignificant effect by the demanded dummy on ROI on both the intercept and slope of investment,
which is consistent with the observations in Table 7. However, we also observe differences
between voluntary and random with respect to the investment slope in Model 2, which is in contrast
with the findings in Table 7. Managers who voluntarily disclose information reciprocate toinvestments stronger than those who randomly do so. In Models 3 and 4, we conduct a similar
analysis using e * investment instead of investment and obtain the same result. This indicates that
ROI level is statistically higher under the voluntary condition with intentional voluntary disclosure
than under the random condition after controlling for the effects of investments and firm
productivity. This result strongly supports H2b.
In sum, even if we use the LME models to control for the influence of periods andindividuals, we obtain the same results as those from the analysis presented in the previous section.
18

This indicates that our results are robust. In particular, our experimental results show that
intentional disclosure fosters trust and reciprocity between managers and investors even in
short-term transactions where reputations cannot be established. Further, when comparing intention
disclosure with each other, the levels of investment and returns are higher under the demandeddisclosure condition than the voluntary condition. This result suggests that the gift exchange
relationship between investors’ disclosure request and managers’ returns is the most importantfactor in enhancing mutual trust. Therefore, investors’ affective trust (Lewis and Weigert [1985],
McAllister [1995]) in the manager is key in the manager–investor relationship.

Analysis on Efficiency and Fairness of Distribution
In this subsection, we analyze the efficiency and fairness of distribution under the three conditions.
Figure 4 depicts the average profit per period by condition.
[Insert Figure 4 about here]
Figure 4 demonstrates that receivers’ profits are greater than those of the sender and under all
conditions, the total profits are higher when the disclosure option is exercised, which can beconsidered a logical consequence of disclosure promoting investments under all conditions. As forthe distribution of total profits increasing by disclosure, we find a difference in each condition. In
particular, under the intentional disclosure conditions (voluntary and demanded conditions), theprofits of a player with the disclosure option significantly differ between the cases of information
disclosure and non-disclosure. Table 9 presents the statistical analysis.
[Insert Table 9 about here]
Table 9 shows that, under the voluntary condition, where the receiver has the disclosure option, thereceiver’s mean profit level in the disclosure case (15.28) is significantly higher than that in the
non-disclosure case (7.02) at the 1% level (parametric and nonparametric tests). On the other hand,for senders who do not have a disclosure option under the voluntary condition, only the parametric
test shows significant differences in profit levels between the disclosure (10.76) and non-disclosure(9.67) cases at the 5% level.
In addition, Table 9 shows that, under the demanded condition, where the sender has thedisclosure option, the sender’s mean profit level is significantly higher in the disclosure case(12.53) than in the non-disclosure case (8.66) at the 1% level (parametric and nonparametric tests).
As for receivers, who do not have a disclosure option under the demanded condition, the parametricand nonparametric tests show no significant difference in the profit levels between the disclosure(16.38) and non-disclosure (14.50) cases.
19

To conduct a more in-depth analysis, we create a scatterplot for each condition. Figure 5
presents the scatterplot for efficiency and fairness.
[Insert Figure 5 about here]
Figure 5 shows that information disclosure improves efficiency, denoted on the horizontal
axis. On the other hand, the distribution of fairness on the vertical axis differs greatly between theinformation disclosure and non-disclosure cases, particularly under the intentional disclosure
conditions (voluntary and demanded conditions). Therefore, further statistical analyses are requiredon the relationship between information disclosure and fairness. Table 10 presents the statisticalanalyses.
[Insert Table 10 about here]
Panel A of Table 10 shows that, under all conditions, the mean efficiency levels are
significantly higher in the disclosure case than in the non-disclosure case at the 1% level (under thevoluntary and demanded conditions) and the 10% level (under the random condition).
In addition, Panel B of Table 10 indicates that fairness increases when an information
disclosure is made. Further, it shows differences in the effects of an information disclosure on
fairness between intentional (voluntary and demanded conditions) and unintentional (random
condition) disclosure conditions. The null hypothesis is not rejected in the disclosure case under thevoluntary and demanded conditions, although it is rejected in the disclosure case under the random
condition at the 5% level (two-tailed, one-sample t-test). Thus, under the intentional disclosure
conditions, information disclosure increases fairness.
In the context of efficiency and fairness, our experimental results show that information
disclosure promotes investments and enhances efficiency. Moreover, they indicate that intentional
information disclosure particularly promotes fairness between investors and managers. Therefore,
intentional disclosure can be deemed a mechanism to improve efficiency and fairness.
Demanded vs. Voluntary Condition
In this subsection, we further analyze the results for the demanded condition. Our experimental
results show that both investment amount and ROI levels are the highest under the demanded
condition. Why is their performance high under the demanded condition? We consider thefollowing hypothesis to address this question: We hypothesize that disclosure requests under thedemanded condition promotes fair responses by managers. We assume that investors’ information
requests function not only as a ‘gift’ in the hypothesis for gift exchanges between disclosure
requests and returns, but also as a priming for managers to promote fair behavior.
In this regard, previous studies show that communication promotes empathy (e.g.
Andreoni and Rao [2011], Kandul [2016], Yamamori et al. [2008]). Andreoni and Rao [2011]20

employed a dictator game experiment to study the effects of communication on resulting
allocations. They found that when the recipient spoke, giving increased, indicating that asking is
powerful. They concluded that communication, especially the power of asking, greatly influences
feelings of empathy and pro-social behavior. Yamamori et al. [2008] studied a dictator game with a“voice” option in which recipients were allowed to communicate numerical “minimal offer
requests” to dictators, and examined the effects of communication in the absence of coordination
aspects and personal identification. Their experimental results reveal that a recipient’s voice cansignificantly affect the allocation of pie: if the minimum offer that a recipient is willing to receive isless than or equal to half the total amount, the dictators tend to increase their offers as the minimum
offer rises. If we consider these results in our settings, we expect managers to believe that theyshould act more equitably given the investors’ request for information disclosure.
Moreover, this effect may have a positive influence on not only managers’ returns but
also investment amount through investors’ expectations of managers’ fair behavior. Since such aneffect cannot be assumed under the other conditions, both investment amount and ROI level are thehighest under the demanded condition.
[Insert Table 11 about here]
To verify this, we focus on the frequency of managers’ equitable distribution. Table 11
presents the frequency and the statistical tests. Panel A shows that the rate of frequency for
managers’ equitable distribution is statistically the highest at the 1% level under the demanded
condition (X-squared = 19.61, p-value = 0.000). Panel B indicates that a manager tends to repay
more than or equal to half the firm’s income (eM), particularly when the disclosure option is
exercised under the demanded condition. The three-sample test for the equality of proportions
highlight the statistical difference among the three conditions (X-squared = 20.66, p-value =0.000).
[Insert Table 12 about here]
To check the robustness of our findings on mangers’ fair behavior under the demanded
condition, we run a regression analysis that controls for an individual’s repetitive responses and thevarying investment levels across the experimental conditions. We conduct a multi-level, mixed
effects logistic regression, extending the LME model to binary responses. Table 12 presents theestimation results. We set the voluntary condition as the baseline condition and incorporate
investment level and its interaction term with the experimental condition as independent variables.
This is because the traditional logic of trust and reciprocity in a trust game implies that the fair
reply increases with a rise in investment level. Model 1 shows that, as investment level increases,
the probability of fair returns is higher under the demanded condition than the voluntary condition.
21

The same result holds when we use a slightly different definition of managers’ fair reply (Model2).
As described above, our experimental results on the demanded condition, that is, thefrequency of managers’ equitable distribution, is the highest and it is concentrated when thedisclosure option is exercised, suggesting that investors’ disclosure requests under the demanded
condition positively affects the promotion of the fairness norm among managers. This tendency is
supported by the regression analysis. This effect is one of the explanations for the highperformance under the demanded condition.
VIII. CONCLUSIONSIn this study, we examine the effects of information disclosure on mutual trust and reciprocity inthe context of short-term transactions. We focus on the entity choosing to disclose information andthe intention behind the decision. Several studies have mainly demonstrated the usefulness ofvoluntary disclosure in a long-term relationship (e.g., Lunawat [2013a, 2013b]). We examine
whether the same result holds during short-term transactions, which is a more realistic and severe
environment. In addition, we account for a disclosure system that reflects investors’ intensions.
We hypothesize that in the context of the “gift exchange” paradigm, the intension behind
disclosure plays the role of a “gift” in the trust game. The literature on gift exchange is relevant
here because the settings seem to share numerous similarities (Akerlof [1982], Berg et al. [1995],
Fehr et al. [1993]).
Our experiment highlights that information disclosure promotes investments under all three
conditions. This is because information disclosure reduces investment uncertainty and investments
are promoted by eliminating information asymmetry. Thus, similar to the findings of previous
studies using a trust game to explore long-term transactions, we find that information disclosure
enhances trust between a manager and an investor even in the short-term transaction.
In addition, we observe that the disclosure effect differs by disclosure intention, as expected.
The levels of investment and returns are higher in an intentional disclosure case (voluntary anddemanded conditions) than in an unintentional disclosure case (random condition), suggesting that
intentional disclosure fosters mutual trust between investors and managers during short-termtransactions. This is because an intentional voluntary disclosure and intentional disclosure request
are recognized as a kind “gift” and the levels of investment and returns increase in line with the“gift.” Further, when comparing types of intentional disclosure, the levels of investment and returns
are higher under the demanded disclosure condition than the voluntary condition. This is because
investors’ information requests function not only as a “gift” under the hypothesis of a gift exchange
between disclosure request and returns, but also as a priming to promote fair behavior among
managers (e.g., Andreoni and Rao [2011], Yamamori et al. [2008]).
Despite its contributions, this study is naturally subject to several limitations. Some ofthese limitations are inherent to the use of a controlled laboratory experiment with student
participants and relate to the generalizability of our findings to real-world disclosure settings. For
22

example, to manipulate the effects of intentions underpinning disclosures, we used a modified trust
game in which a computer, a manager, or an investor can exercise the disclosure option. The
cautious approach is recommended when extrapolating laboratory results to real-world situations
since our experimental settings are highly controlled.
Second, this study did not consider the content of accounting information. That is, theeffects of disclosure intention may vary by the nature of disclosed content such as CSR information(Martin and Moser [2016], Moser and Martin [2012]). We leave the in-depth analysis of this issue
to future studies.
The third and final limitation of this study is the quality of information. The quality of
disclosed information has remained constant in this experiment. However, if we allow managers tochange the disclosure quality, particularly under the voluntary disclosure condition, higher quality
may be disclosed. To ensure the robustness of this research, it is necessary to conduct experimentsthat allow for the selection of quality. 18

18

In connection with this point, experimental research that takes into account the level of readability ofqualitative information can also be considered in the future. As related studies, Asay, Elliott and Rennekamp[2017], Asay and Hales [2018], and Asay, Libby, and Rennekamp [2018] may be useful.
23

APPENDIX.
INSTRUCTIONS USED FOR THE EXPERIMENT 19
1. RolesThe following decision-making problem requires two roles: the Sender and the Receiver. Your rolein this game will be randomly determined by the computer at the start of the game and will remainunchanged until the end of the game.
2. Rules for decision-making
At the start of the game, pairs consisting of a Sender and a Receiver are formed. Decisions are
made by each pair. The timeline of the game is as follows.
The timeline
Stage 1: Receiver’s (sender’s [demanded condition]/ a computer’s [random condition]) decision
Stage 2: Sender’s decision
Stage 3: Receiver’s decision
Stage 1: Receiver’s (Sender’s [demanded condition]/ a computer’s [random condition])) decision
The Receiver (Sender/ Computer) decides whether to disclose the information (request thedisclosure of information[demanded]) on the parameter e to the Sender (Receiver). When theinformation is disclosed, the Sender learns the value of e in that round. If the information is notdisclosed, the Sender will not know the value of e.
Stage 2: Sender’s decision
The Sender is given 10 points. The Sender decides how many of those points to send to the
Receiver.
Stage 3: Receiver’s decision
The points sent by the Sender to the Receiver are multiplied by e by the computer. The Receiverdecides how many of the points received should be returned to the Sender. The points arecalculated in the following way:
Points earned by the Sender= [points remaining on hand] + [points received from the Receiver]
Points earned by the Receiver= [points received from the Sender] × e – [points returned to the Sender]
3. Matching
19

The underlined text in italics varies depending on the conditions (the demanded or random conditions

appear within parentheses).
24

The sequence of decision-making described above is repeated 20 times. Pairs of Senders andReceivers are determined randomly in each round by the computer. Thus, the decision-making
process is not conducted repeatedly between the same participants.
4. Parameter eParameter e takes the value 3 or 5, which is determined randomly in each round by the computer.
The value of e is known only to the Receiver. However, in instances where the information isdisclosed, the Sender finds out the value of e.

25

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30

31

Figure 1. Game timeline
Note: This figure illustrates the timeline of the trust game with the disclosure option. In step 1, the state of nature
determines the firm’s productivity (multiplier e). It is equally possible for the realized value of productivity to behigh (e = 5) or low (e = 3). Only the manager observes the realized value. In step 2, a computer (under the
random condition), the manager (under the voluntary condition), or the investor (under the demanded condition)
determines whether to exercise the disclosure option. Steps 3–5 are the same as those in the traditional trust game.
After step 5, managers and investors are randomly re-matched and thereafter, steps 1–5 are repeated for a total of
20 rounds.

32

Figure 2. Gift exchange hypotheses
Note: This figure describes the gift exchange hypotheses presented in this section. For the random condition, theparentheses indicate the gift exchange relationship between investment and returns (normal gift exchange
hypothesis). As for the voluntary condition, the boxes denote the gift exchange relationship between the
manager’s intentional voluntary disclosure and the investor’s investment (hypothesis for gift exchange between
voluntary disclosure and investment). In addition, the parentheses highlight the relationship of the correlation
between the investment and the returns shown in the previous studies. Finally, for the demanded condition, theboxes show the gift exchange relationship between the investor’s intentional disclosure request and the returns by
the manager (hypothesis for gift exchange between disclosure request and returns). Further, the parentheses
highlight the relationship of the correlation between the investment and the returns shown in the previous studies.

33Panel A: Investment

Panel B: ROI

Figure 3. Mean levels of investment and ROI for each condition by period
Note: Panel A of Figure 3 shows the average investment levels for each condition by period. Panel B presents theaverage ROI levels for each condition by period. Each experimental session comprises 20 rounds. Productivity(multiplier e) in each period is low or high with a probability of 50%. Asterisks are appended to the upper rightcorner of the rounds in which productivity is high (e = 5).

34

Figure 4. Average profit per period by condition
Note: The bar graph shows the average profit per period by condition. Sender denotes the average profit ofsenders per period and Receiver is the average profit of receivers per round. Total is the average profit of thesender–receiver pairs per round. The error bars indicate the standard errors of the profits. This figure is based onpooled data because total profits cannot be individually calculated.

35

Figure 5. Scatterplot for efficiency and fairness by condition
Note: This figure is the scatterplot for the efficiency and fairness of distribution by condition. D and ND in thetitle of each scatterplot mean disclosure and non-disclosure. The horizontal axis denotes the index representing
efficiency, which is calculated as follows: Efficiency = total profits per period. The index is said to be efficientwhen it approaches 30 (e = 3) or 50 (e = 5 and non-disclosure). The vertical axis shows the index representing
fairness, which is calculated as follows: Fairness = [receiver’s profits] / [total profits]. This index ranges between
0 and 1 and is said to be fair when it approaches 0.5. However, if the index approaches 1 (or 0), profits tend to bebiased toward the receiver (or sender). The solid lines are regression lines.

36

C

Participants

S

R

Rounds

S-obs. R-obs.

Random

78

39

39

20

780

780

Voluntary

84

42

42

20

840

840

Demanded

84

42

42

20

840

840

Total

246

123

123

2,460

2,460

Table 1. Experimental design
Note: This table presents the experimental design. The items in this table are as follows: condition (C), number ofparticipants, senders (S), receivers (R), rounds, and total number of observations for senders (S-obs.) andreceivers (R-obs.) in the experiment. In the random disclosure condition, a computer randomly decides whether toexercise the disclosure option. The random disclosure condition involves 78 participants forming 39 pairs across
20 rounds, thus providing 780 investor (receiver) observations. In the voluntary disclosure condition, the manager
decides whether to exercise the disclosure option. This condition includes 84 participants forming 42 pairs across
20 rounds, thus resulting in 840 investor (receiver) observations. In the demanded disclosure condition, theinvestor decides whether to exercise the disclosure option. This condition involves 84 participants forming 42
pairs across 20 rounds, and consequently, 840 investor (receiver) observations.

Panel A: Rate of disclosure
Total
e=3
e=5

RandomVoluntary

Demanded

Rate

72.2%

77.4%

88.0%

Obs.

780

840

840

Rate

73.1%

71.2%

88.1%

Obs.

390

420

420

Rate

71.3%

83.6%

87.9%

Obs.

390

420

420

Panel B: Investments

Total
e=3
Disclosure
e=537

RandomVoluntary

Demanded

Mean

3.93

4.54

5.96

S.D.

3.70

3.35

3.33

Obs.

780

840

840

Mean

2.85

4.22

5.56

S.D.

3.30

3.30

3.52

Obs.

285

299

370

Mean

5.54

5.77

6.64

S.D.

3.58

3.24

2.98

Total

Obs.

278

351

369

Mean

4.18

5.06

6.10

S.D.

3.69

3.35

3.31

Obs.

563

650

739

Mean

3.27

2.77

4.93

S.D.

3.66

2.69

3.34

Obs.

217

190

101

RandomVoluntary

Demanded

Mean

0.61

0.81

1.08

S.D.

0.79

0.81

0.82

Obs.

780

840

840

Mean

0.43

0.57

0.81

S.D.

0.61

0.60

0.57

Obs.

285

299

370

Mean

0.88

1.12

1.48

S.D.

0.90

0.90

0.90

Obs.

278

351

369

Mean

0.65

0.87

1.14

S.D.

0.80

0.82

0.82

Obs.

563

650

739

Mean

0.50

0.64

0.61

S.D.

0.76

0.76

0.67

Obs.

217

190

101

Non-disclosure

Panel C: Return on investment

Total

Disclosure

e=3

e=5

Total

Non-disclosure

Table 2. Descriptive statistics per experimental condition
Note: This table presents the descriptive statistics for each experimental condition. The experiment follows a
3 × 1 factorial design. The rate of disclosure in Panel A is the rate at which the disclosure option is exercised by
a computer (random condition), a manager (voluntary condition), or an investor (demanded condition). Obs.

indicates the number of observations. Investment in Panel B is the level of the investment determined by theinvestors. S.D. is standard deviation. Return (ROI) in Panel C denotes return on investment, which is defined as
the level of managers’ returns (K) divided by the investor’s investment (M). Basu et al. [2009], for example, use
ROI as a proxy for managers’ returns behavior.

38

Panel A: investment behavior test
Tests of differences
t-testMann–Whitney U

p-value
Condition

Disclose

Non-Disclose

Random

4.17

3.40

[39]

[39]

4.98

2.55

[42]

[42]

6.13

4.52

[42]

[42]

Voluntary
Demandedt(two-tailed)p-valueU(two-tailed)

1.28

0.206

918.00

0.071

3.99

0.000

1,337.50

0.000

2.79

0.007

893.500.006

Panel B: Returns behavior test
Tests of differences
t-testMann–Whitney U

p-value
Condition
RandomVoluntary
Demanded

Disclose Non-Disclose
0.66

0.52

[39]

[39]

0.88

0.55

[42]

[42]

1.15

0.52

[42]

[42]

t

(two-tailed)p-valueU(two-tailed)

1.29

0.200

933.50

0.084

3.12

0.003

1,105.50

0.003

6.23

0.000

1,314.00

0.000

Table 3. Hypothesis test for H1
Note: This table presents the parametric and nonparametric tests for H1, where we treat each subject as anindependent observation computing averages for each subject. Investment behavior in Panel A is measured as theaverage investment levels. The returns behavior in Panel B is measured as the average ROI levels, which is thelevel of managers’ returns (K) divided by the investor’s investment (M). The square brackets denote the numberof observations.

39

Tests of differences
t-testMann–Whitney U

p-value
H2a:
Investment

H2b: ROI

Voluntary

Random

4.99

4.17

1.53

0.130

969.50

0.156

e=3

4.16

2.83

2.28

0.026

1,046.00

0.032

e=5

5.71

5.57

0.24

0.810

819.00

1.000

0.95

0.82

1.67

0.099

933.00

0.200

e=3

0.67

0.56

1.27

0.207

821.00

0.307

e=5

1.17

0.95

2.17

0.033

1,017.50

0.035Disclose all

Disclose alltp-value(two-tailed)U(two-tailed)

Table 4. Hypothesis test for H2
Note: This table presents the parametric and nonparametric tests for H2, wherein we treat each subject as anindependent observation computing averages for each subject. The investment behavior for H2a is measured as
the average investment levels. The returns behavior for H2b is measured as the average ROI level, which is thelevel of managers’ returns (K) divided by the investor’s investment (M).

Tests of differences
t-testMann–Whitney U

p-value
Demanded Random
H3a:Disclose all

Investment
H3b: ROIt(two-tailed)p-valueU(two-tailed)

6.14

4.17

3.70

0.000

1,173.00

0.001

e=3

5.59

2.83

4.64

0.000

1,230.00

0.000

e=5

6.70

5.57

1.95

0.055

993.00

0.100

1.27

0.82

6.26

0.000

1,398.00

0.000

e=3

0.98

0.56

5.32

0.000

1,232.00

0.000

e=5

1.58

0.95

5.90

0.000

1,342.00

0.000

Disclose all

Table 5. Hypothesis test for H3
Note: This table presents the parametric and nonparametric tests for H3, where we treat each subject as anindependent observation computing averages for each subject. The investment behavior for H3a is measured as
the average investment levels. The returns behavior for H3b is estimated as the average ROI levels, defined as thelevel of managers’ returns (K) divided by the investor’s investment (M).

40

Dependent variable

Investment

SampleFull

Full

Disclosed

Non-Disclosed

Variables

(1)

(2)

(3)

(4)

0.536

−0.3393.018***−0.655(0.532)(0.575)(0.763)(0.588)1.801***

1.266**

5.022***

0.950(0.533)(0.612)(0.747)(0.659)1.464***

0.873***(0.132)(0.203)

Base line: random
Voluntary
Demanded

Disclose
Voluntary * Disclose

1.171***(0.293)

Demanded * Disclose

0.714**(0.363)e1.373***(0.0973)

Voluntary * e−0.572***(0.133)

Demanded * e−0.769***(0.129)−0.00784−0.0106

0.00771−0.0402**(0.00871)(0.00871)(0.00916)(0.0180)

2.954***

3.410***−1.385**3.820***(0.404)(0.420)(0.561)(0.462)

Observations

2,460

2,460

1,952

508

Number of individuals

123

123

123

111

Individual random effect

Yes

Yes

Yes

Yes

Log likelihood

−5912

−5904

−4564

−1212

PeriodConstant

Standard errors are in parentheses.
*** p < 0.01, ** p < 0.05, * p < 0.1
Table 6. LME models’ results for investors’ investment behaviors
Note: This table shows the LME results for investors’ investment behaviors. Investment level is the dependent
variable and the baseline condition is random. Voluntary and Demanded are dummy variables indicating the41experimental conditions. Disclose is a dummy that takes the value of 1 if e is disclosed and 0 otherwise. Full,
Disclose, and Non-Disclose, respectively, mean that we use the sample of all, disclosed, and non-disclosed data.

Depending variable

ROI

SampleFull

Full

Variables

(1)

(2)

0.09460.0158(0.110)(0.131)0.376***−0.0462(0.110)(0.138)0.314***

0.145**(0.0394)(0.0631)

Baseline: Random
Voluntary
Demanded

Disclose

0.109

Voluntary * Disclose(0.0905)
Demanded * Disclose

0.505***(0.0996)

Period−0.00542**(0.00256)−0.00535**(0.00254)

Constant

0.642***

0.768***(0.0879)(0.0956)

Observations

2,029

2,029

Number of individuals

123

123

Individual random effect

Yes

Yes

Log likelihood

−2154

−2140

Standard errors are in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1
Table 7. LME models’ results for managers’ returns behaviors
Note: This table presents the LME result for managers’ returns behaviors. ROI is the dependent variable. Pleaserefer to the note appended to Table 6 for a description of the independent variables.

42

Dependent variable

ROI

SampleDisclosed

Disclosed

Disclosed

Disclosed

Variables

(1)

(2)

(3)

(4)

Baseline: Random
Voluntary
Demanded

Investment

0.106

0.134

0.109

0.122

(0.116)(0.113)(0.113)(0.110)0.375***

0.362***

0.341***

0.314***(0.115)(0.113)(0.113)(0.110)0.0636***0.0103(0.00534)(0.00872)

0.0194***0.00597***(0.00135)(0.00224)

Voluntary * Investment

0.0808***(0.0123)

Demanded * Investment

0.0835***(0.0127)e * Investment

0.0201***e * Investment * Voluntary(0.00317)e * Investment * Demanded

0.0214***(0.00327)ePeriod0.240***

0.244***(0.0146)(0.0143)−0.00900*** −0.0101*** −0.0123***−0.0135***(0.00251)(0.00247)(0.00271)(0.00267)−0.0242(0.108)−0.0526(0.106)0.999***

0.999***(0.0860)(0.0841)

Observations

1,644

1,644

1,644

1,644

Number of individuals

123

123

123

123

Individual random effects

Yes

Yes

Yes

Yes

Log likelihood

−1539

−1510

−1660

−1632

Constant

Standard errors are in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1

43Table 8. LME models’ results for managers’ returns behavior using subsamples limited to
information disclosure
Note: This table presents the LME models’ results for managers’ returns behavior using a subsample limited to
information disclosure. ROI level is the dependent variable and investment is mean centered. Please see the note
appended to Table 6 for a description of the independent variables.

Tests of differences
t-testNon-DisMann–Whitney U

p-value

Condition

Role

Disclose

RandomSender

9.54

9.35

0.57

0.568

820.500.420

Receiver

14.39

11.18

2.52

0.014

1,038.50

0.005

Sender

10.76

9.67

2.33

0.023

1,100.50

0.050

Receiver

15.28

7.02

9.72

0.000

1,500.50

0.000

Sender

12.53

8.66

6.93

0.000

1,151.00

0.000

Receiver

16.38

14.50

1.52

0.135

903.500.312

Voluntary
Demandedclosetp-value(two-tailed)U(two-tailed)

Table 9. Difference in profits between information disclosure and non-disclosure cases perrole by condition
Note: This table presents the parametric and nonparametric tests for differences in the profits between theinformation disclosure and non-disclosure cases per role by condition, wherein we treat each subject as anindependent observation computing averages for each subject. Sender denotes senders’ average profits andReceiver is receivers’ average profit.

Panel A: Parametric and non-parametric tests of efficiency
Tests of differences
t-testNon-

Mann–Whitney U

p-valuet

p-value

Condition

Disclose

disclose

Random

23.82

20.44

1.73

0.089

967.500.020

Voluntary

26.14

17.56

5.63

0.000

1,450.50

0.000

Demanded

28.91

22.57

3.40

0.001

961.500.000(two-tailed)

Panel B: Parametric test of fairness
Condition

Disclose

Non-disclose

Random0.45 **

0.38 ***

Voluntary

0.50

0.34 ***

Demanded

0.50

0.52

44

U

(two-tailed)Table 10. Statistical tests for efficiency and fairness by condition
Note: This table presents statistical tests for efficiency and fairness by condition, wherein we treat each subject as
an independent observation computing averages for each subject. Panel A presents the parametric andnonparametric tests for the differences in the index representing Efficiency, which is calculated as follows:
Efficiency = total profits per period. This index is said to be efficient when it approaches 50. Panel B shows theparametric test (one-sample, t-test) for the index representing Fairness, which is calculated as follows. Fairness =[receiver’s profits] / [total profits]. This index ranges between 0 and 1 and is said to be fair when it approaches
0.5. If the index approaches 1 (or 0), however, profits tend to be biased toward the receiver (or sender). We
conduct a two-tailed t-test using the null hypothesis that the true mean is equal to 0.5. *** p < 0.01, ** p < 0.05, *p < 0.10.

Panel A: Frequency of managers’ equitable distribution by condition
Condition
Frequency rate

RandomVoluntary

Demanded

9.29%

11.44%

17.10%(54/581)(82/717)(125/731)

3-sample test for equality***of proportions

X-squared = 19.61, p-value = 0.000

Panel B: Breakdown of frequency by condition
Condition
Disclose
Non-disclose

RandomVoluntary

Demanded

75.93%

84.15%

97.60%(41/54)(69/82)(122/125)24.07%

15.85%

2.40%(13/54)(13/82)(3/125)

3-sample test for equality***of proportions

X-squared = 20.66, p-value = 0.000

Table 11. Frequency of managers’ equality distribution and its statistical tests
Note. This table discusses the frequency of managers’ equitable distribution and its statistical tests. Both Panel A
and Panel B are based on pooled data. We exclude the sample in which the investment amount is equal to 0.
Panel A shows the frequency rate for managers’ equitable distribution by condition. When a manager’s
repayment is greater than or equal to half the firm’s income (eM), it is known as managers’ equitable distribution.
The rate is calculated as follows: rate = [frequency of managers’ equitable distribution] / [number of total
observations]. For the statistical test, we conduct a three-sample test for the equality of proportions. Panel B
indicates the breakdown rate for the frequency of managers’ equitable distribution by condition. Disclose and
Non-Disclose mean disclosure and non-disclosure. The rate is calculated as follows: rate = [frequency ofmanagers’ equitable distribution when information is (or is not) disclosed] / [frequency of managers’ equitable
distribution]. Here as well, we use a three-sample test to statistically examine the equality of proportions. ***p <0.01, ** p < 0.05, * p < 0.10.

45Strict Definition

Weak Definition

Fair Reply Dummy 1

Fair Reply Dummy 2

(1)

(2)

-0.313-0.442(0.497)(0.438)0.0980-0.114(0.477)(0.437)0.399***

0.186***(0.0573)(0.0440)0.252***

0.294***(0.0928)(0.0734)-0.338***-0.341***(0.0755)(0.0631)0.584**

0.361*(0.268)(0.213)-0.335***-0.437***(0.0863)(0.0756)-0.00658-0.00842(0.0150)(0.0128)-2.275***-0.920*(0.558)(0.474)

Observations

2,029

2,029

Number of groups

123

123

Individual random effect

Yes

Yes

Log likelihood-565.9-709.1

DEPENDENT VARIABLE
VARIABLES
Base Line: Voluntary
DemandedRandomInvestment
Demanded * Investment
Random * Investment

DiscloseeperiodConstant

Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Table 12. Results of multi-level, mixed-effects logistic regressions for managers’ fair reply
Note: This table shows the results of the multi-level, mixed logistic regression for managers’ fair reply. The fair
reply dummy is the dependent variable. When a manager’s repayment is greater than or equal to ⌊𝑒𝑒/2⌋, it is

known as a weak definition of fair reply, and when a manager’s repayment is greater than or equal to ⌈𝑒𝑒/2⌉, it46

is known as a strict definition of fair reply. 20 The baseline condition is voluntary. Random and Demanded are
dummy variables indicating the experimental conditions. Investment is mean centered. Please see the note
appended to Table 6 for a description of the other independent variables.

⌊𝑥⌋ is the maximum integer that is less than or equal to x. ⌈𝑥⌉ is the minimum integer that is greater than orequal to x.
47
20