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The online expert service (OES) market is thriving by providing convenient professional services such as telemedicine consultation, legal advice, and financial planning. However, the practical phenomena, namely information asymmetry and the existence of naive consumers in the OES industry, motivate us to investigate how consumers' two-dimensional heterogeneity and the expert's agency pricing strategy with effort costs affect the OES platform's information disclosure decision and corresponding market outcomes. By building a game-theoretic model, we consider the monopolistic OES platform in which the expert provides service to a mass of consumers, who can be either sophisticated or naive. The platform, as the designer of the disclosure framework, is thus an additional player in the seller-consumer game. Our results show that, when the marginal cost is intermediate, the platform strategically manipulates consumers' valuation beliefs with partial disclosure to increase profitability without significant loss of market coverage. Furthermore, when naive consumers exist, the platform counter-intuitively discloses more information compared to that of all sophisticated consumers. Moreover, interestingly, when the marginal cost is low-to-intermediate, more naive consumers bring about more disclosure and thus obtain more demand for high-end sophisticated consumers, making both the expert and platform better off. [ABSTRACT FROM AUTHOR]

A stochastic clearing system is studied from a game-theoretic perspective in the paper where the server is subject to a Poisson-generated catastrophe and a follow-up repair process. Whenever a fatal shock (catastrophe) occurs, all customers are cleared from the system and the server fails. A repair is rendered immediately to fix the server with an exponential repair time. During the repair process, no customers are allowed to enter the system. Customers are strategic and they have the right to decide whether to join the system or balk based on a linear reward-cost structure with two types of rewards: A service reward for those customers that receive service and a compensation for those customers that are forced to abandon the system due to a catastrophe. During the service process, the server takes a working vacation after serving all the customers in the system. Our study is the first attempt to provide models to jointly characterize and analyse the queueing system with working vacation and catastrophes, with an emphasis on game-theoretic modeling of such a service system. The customer's equilibrium strategy and social benefit of the system under four different information scenarios are obtained. In particular, we find that customers obey the follow-the-crowd (FTC) property in almost observable condition, which provides managerial insight on the operations management perspective. Numerical experiments are presented to show the effects of system parameters and information levels on the equilibrium joining behavior of customers. [ABSTRACT FROM AUTHOR]

The return cost burdens both consumers and merchants, leading merchants to emphasize information disclosure during pre-sales of new products. This study analyzes the effects of information disclosure methods on consumer valuation and merchants' pre-sale strategies across four scenarios: no advance selling, no information disclosure, traditional information disclosure, and blockchain-enabled information disclosure. We examine optimal pricing and inventory strategies under each scenario, assessing the value of information disclosure and the merchant's optimal advance selling strategy. We find that traditional information disclosure could lead to various pre-sale pricing outcomes based on consumer valuation distribution bounds, trust levels, and merchant return losses. Blockchain-based disclosure influences merchants to implement premium pricing, which may render blockchain unnecessary. Offering pre-orders is beneficial with any information disclosure method. If consumers have varied product understandings, merchants benefit more from using an information disclosure strategy rather than just offering returns; otherwise, the reverse is true. The paper provides theoretical insights into optimal pre-sale information disclosure practices. [ABSTRACT FROM AUTHOR]

With the frequent occurrence of online public opinion events, the problem of product stigma is becoming increasingly serious. Enterprises must use effective quality information disclosure strategies to reduce losses affecting market sales and profit. Therefore, this paper aims to address the supply chain structure composed of one product manufacturer and one component manufacturer under the influence of stigma. It constructs a decision optimization model under three scenarios: no information disclosure, the product manufacturer disclosures information, and the component manufacturer disclosures information, and uses Stackelberg game theory to solve and analyze the model. Furthermore, we use numerical examples to verify the model results, and provide management suggestions for enterprises. The research results show that enterprises suffering from product stigma should actively implement information disclosure strategies to reduce their profit losses, and the lower the stigma level, the better the effect of information disclosure will be; when the stigma level becomes more serious, enterprises should take timely steps to reduce the sales price of products, the sales price of components, and the efforts to disclose information; for industries that value confidentiality of product information, although the implementation of information disclosure by the component manufacturer can require less effort for information disclosure, the two enterprises will suffer higher economic losses. [ABSTRACT FROM AUTHOR]

The need to develop environmentally respectful business activities has led academics to propose the circular economy (CE) as a more sustainable production and consumption model than the linear model, where interest groups demand greater disclosure of information that makes it possible to evaluate the business transition towards the CE. Also, the planetary boundaries framework provides thresholds for different natural processes that are affected by business activities, so in this research, we link the CE to planetary boundaries by designing a score, and we analyse the presence of female directors and the role women play in the presentation of CE information that attends to planetary boundaries. We also examine the effect that national cultures characterized by high power distance and masculinity can have on their performance. We use a sample of 832 companies around the world from 2011 to 2020. The results show that women directors promote the presentation of CE information at a global level and disaggregated by CE issues, framed within planetary boundaries—a relation that is reinforced in societies with high power distance. [ABSTRACT FROM AUTHOR]

When two people coincidentally have something in common (such as a name or birthday), they tend to like each other more and are thus more likely to offer help and comply with requests. This dynamic can have important legal and ethical consequences whenever these incidental similarities give rise to unfair favoritism. Using a large-scale, longitudinal natural experiment, covering nearly 200,000 annual earnings forecasts over more than 25 y, we show that when a CEO and a securities analyst share a first name, the analysts financial forecast is more accurate. We offer evidence that name matching improves forecast accuracy due to CEOs privately sharing pertinent information with name-matched analysts. Additionally, we show that this effect is especially pronounced among CEO-analyst pairs who share an uncommon first name. Our research thus demonstrates how incidental similarities can give way to special treatment. Whereas most investigations of the effects of similarity consider only one-shot interactions, we use a longitudinal dataset to show that the effect of name matching diminishes over time with more interactions between CEOs and analysts. We also point to the findings of an experiment suggesting that favoritism born of sharing a name may evade straightforward regulation in part due to peoples perception that name similarity would exert little influence on them. Taken together, our work offers insight into when private disclosures are likely to be made. Our results suggest that the effectiveness of regulatory policies can be significantly impacted by psychological factors shaping the context in which they are implemented.

This paper considers a retail platform selling a product to consumers and voluntarily discloses product quality information by using blockchain technology (BCT). Consumers decide whether to deliberate before making purchase decisions. We develop an analytical model to explore the effects of information disclosure strategies on the retail platform's pricing and consumers' deliberation decisions. Some interesting results are obtained. First, regardless of the information disclosure strategy, consumers' deliberation and purchasing decisions depend on the deliberation cost and product price. Consumers will only deliberate if the price is at the medium level and the deliberation cost is not too high. Second, when the disclosure cost is high, the retail platform either stimulates or prevents consumer deliberation; however, when the disclosure cost is at a medium interval, the retail platform's pricing decision depends on its information disclosure strategy. Specifically, when disclosing quality information, the retail platform will implement the stimulated deliberation (SD) or prevented deliberation (PD) strategy and will never adopt the sold at a low price (SL) strategy; however, when quality information is withheld, the retail platform will only adopt the PD strategy. Third, we identify the conditions under which the retail platform should disclose quality information when facing different disclosure costs. [ABSTRACT FROM AUTHOR]

Blockchain technology is commonly used in many industries. One current application is that providing supply chain transparency, sellers can disclose product information to consumers for authentication and certification. To examine the supply chain blockchain based transparency‐level strategy and its interact with different refund policies, in a two‐echelon supply chain, we consider a supplier decides on the transparency level and wholesale price, and a retailer decides on retail price and provides full refund (policy F), or partial refund (policy P), or no refund (policy N) policy to consumers. We find the refund policy choice and the transparency‐level strategy have a mutual influence. A lenient refund policy (a higher refund) can generate more demand, which makes the supplier to provide a high transparency level, whereas a high consumer's transparency awareness also promotes the retailer to choose a more lenient refund policy. We find Pareto improvement exists under a cost‐sharing strategy, and the retailer is willing to share part of the adoption cost only when the efficiency of improving the transparency level is moderate. Otherwise, the retailer adopts blockchain technology only when there is no cost sharing. Further, different shipping cost bearers can change the sensitivity of refund policy choice, and the supplier prefers to provide a high transparency level when the retailer covers the shipping cost. When the retailer becomes more socially responsible, the supplier is more willing to provide a higher transparency level, and the retailer is more willing to provide a partial refund policy. [ABSTRACT FROM AUTHOR]

In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT.

In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers' information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers' personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions. The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of environmental information disclosure on firm pollution behavior by leveraging an exogenous shock which allow Chinese environmental non-governmental organizations (ENGOs) to disclose pollution information. Using a difference-in-differences approach comparing firms in disclosed cities versus undisclosed cities, we find ENGO information disclosure significantly reduces manufacturing firm COD emissions. We explore several potential mechanisms and find that the reduction effect was primarily driven by "higher-level government supervision" rather than "public supervision." Heterogeneous analysis reveals significant effects only for firms facing high reduction pressure, low governance costs, weak local protection, and in heavy-polluting industries. Our findings provide insights into supplementing inadequate regulations with informal institutions to achieve environmental aims within a decentralized economy. [ABSTRACT FROM AUTHOR]

The information regarding agricultural products is crucial to consumers. Blockchain technology theoretically offers a solution to the trust issues surrounding agricultural products by ensuring the credibility of information sources, facilitating trust transmission, and improving information management. Consequently, information plays a pivotal role in consumers' purchasing decisions. This paper investigates strategies for adopting blockchain technology within an agricultural supply chain comprising two competing platforms. Additionally, it analyzes the level of consumer trust in disclosed information and the influence of blockchain adoption costs on strategy implementation. [ABSTRACT FROM AUTHOR]

Environmental information disclosure (EID) is integral to government environmental policies and corporate social responsibilities. The current research presents a theoretical model that analyses the connection between EID, green finance, and green technological innovation (GTI). The required data was collected through a structured questionnaire, and final data analysis was performed using 230 valid responses. Structural equation modeling (SEM) combined with artificial neural networks (ANN) is used in the present framework to analyze constructs' linear and non-linear relationships. The empirical analysis found that government EID significantly improved the value of green securities (GS) and green credit (GC), aided considerably by enterprises' openness about environmental practices. Green securities and GC are also used, which has a good impact on the development of GTI. Green financing is critical when linking environmental disclosure with green technologies in businesses. The results reveal the mediating role of GC and GS in the relationship between the two aspects of EIDs (EEID and GEID) and GTI, providing a new perspective on how EID influences GTI through financial mechanisms. The findings contribute to a more comprehensive understanding of the intricate interplay between EID, green finance, and GTI, providing valuable insights for policymakers, businesses, and investors working toward sustainable development. [ABSTRACT FROM AUTHOR]

This paper examines the impact of investor-paid rating agency on stock price crash risk. The findings reveal a substantial 127.6% reduction in the stock price crash risk for stocks tracked by investor-paid rating agency compared to those without such tracking. As for the mechanism, the following of investor-paid rating agency reduces earnings management, induces more negative information disclosure, and improves information disclosure quality. The impact of investor-paid rating agency is more pronounced in firms with poorer corporate governance. Further analysis indicates that the impact of investor-paid rating agency increases with the frequency of rating tracking and the rating difference between issuer-paid rating agency and investor-paid rating agency, while stock market reaction induced by investor-paid rating agency has little effect on the baseline result. Moreover, the tracking of investor-paid rating agency facilitates the information flow between the bond market and stock market, and improves analyst forecast performance. In summary, we suggest that investor-paid rating agency tracking acts as a valid passive monitoring mechanism to alleviate principal-agent problems and provide information on firms' downside risk. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to address the challenges of self-adaptive privacy in cloud computing environments (CCE). Under the Cloud-InSPiRe project, the aim is to provide an interdisciplinary framework and a beta-version tool for self-adaptive privacy design, effectively focusing on the integration of technical measures with social needs. To address that, a pilot taxonomy that aligns technical, infrastructural, and social requirements is proposed after two supplementary surveys that have been conducted, focusing on users' privacy needs and developers' perspectives on self-adaptive privacy. Through the integration of users' social identity-based practices and developers' insights, the taxonomy aims to provide clear guidance for developers, ensuring compliance with regulatory standards and fostering a user-centric approach to self-adaptive privacy design tailored to diverse user groups, ultimately enhancing satisfaction and confidence in cloud services. [ABSTRACT FROM AUTHOR]

Purpose: Communication privacy management (CPM) theory is a major theory explaining the tensions between disclosing and concealing private information in interpersonal communication. By considering differences in interpersonal and humantechnology information disclosure and drawing on existing work related to privacy and technology, this article presents CPM theory as a broad theoretical framework for human-technology privacy boundary management. Method: This research employed a speculative theoretical approach by drawing on existing literature and synthesizing it to both apply and extend CPM theory's propositions to human-technology privacy boundary management. Results: CPM theory can be applied to understand the dynamics of humantechnology information disclosure and should incorporate technological literacy as a key consideration in human-technology privacy boundary management. Legal ties characterize human-technology privacy boundary coordination instead of social ties. Additionally, in human-technology information disclosure contexts, CPM theory should provide guidance regarding managing third parties that may gain access to information. Conclusion: CPM theory is the most comprehensive framework for how individuals manage privacy boundaries, be it in interpersonal or human-technology contexts. By considering technology as a property of technological actors instead of an actor itself, CPM theory in human-technology contexts becomes a flexible theoretical framework for understanding information disclosure and privacy boundary management, both for existing technologies (e.g., social media, online shopping platforms, artificial intelligence, Internet of Things) and future technologies. [ABSTRACT FROM AUTHOR]

Effective communication is an important aspect of providing quality healthcare. Radiology staff may be called upon to communicate results, even when it is difficult. Simple, efficient communication models can be learned to reduce clinician distress and support patients and families through stressful times. These practical tips ensure essential communication elements are met, including assessing patient and family perspectives, clearly delivering medical information, responding with empathy, and collaborating around next steps. Models such as "Ask-Tell-Ask," "SPIKES," and "NURSE" can be used to disclose results, communicate serious news, and respond to emotion. Lastly, clinicians can also utilize "I wish... I worry... I wonder" statements to align with a patient's hopes and acknowledge a family's concerns, ultimately allowing healthcare professionals to support and guide families through challenging situations. [ABSTRACT FROM AUTHOR]

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using an online experimental survey, we test how Fact Sheets presenting standardised information on retirement income products – Average Annual Income, an Income security Product Rating, Potential Income Shape, Access to Capital, and Death Benefits – influence product knowledge, perceptions and choices from a menu of annuity, phased withdrawal and hybrid products. Product choice is significantly influenced by annual income and income security, and the annuity and hybrid annuity products are most preferred in Fact Sheet versions where income security is most salient. We find no significant differences in knowledge of Fact Sheet information by version, with Access to Capital least understood in all versions. JEL Classification: D14, D83, D91 [ABSTRACT FROM AUTHOR]

This article investigates whether firms commit ex-ante to higher levels of investment transparency and the impact of transparency on stock returns and information asymmetry. We construct a novel measure of investment transparency based upon the extensiveness of profitability forecasts and cost disclosures in new project-level capital investment announcements. Using cross-sectional regression, we find that prior to announcement greater investment transparency is associated with lower information asymmetry. We further show that rather than reporting information strategically on a project-by-project basis, managers commit ex-ante to a disclosure policy that influences the disclosure level of new project announcements, and that a firm-level commitment to fuller disclosure reduces information asymmetry in the days surrounding the announcement. Using event study methodology, we also find that investors react more positively to announcements displaying greater investment transparency. JEL Classification: G14, D83, G31, G30, G38 [ABSTRACT FROM AUTHOR]

This study examined the effects of personal information types and brand familiarity on Japanese consumers' perceived brand trust, privacy concerns, and anxiety about personal information disclosure to marketers. It also investigated how the cultural dimensions of Japanese consumers affect the relationships between privacy concerns, perceived brand trust, and personal information disclosure outcomes. The results of an online experiment (n = 771) showed that neither personal information requested nor brand familiarity affected Japanese consumers' anxiety about personal information disclosure. However, cultural factors, namely uncertainty avoidance and independent self-construal, moderated the relationship between brand trust and anxiety regarding personal information disclosure to marketers. [ABSTRACT FROM AUTHOR]

We study optimal information disclosure via an ambiguous persuasion approach in a two-player contest. The designer can precommit to an ambiguous device to influence the uninformed contestant's belief about his opponent's private type. We fully characterize the optimal ambiguous information structures when players are maxmin expected utility (MMEU) maximizers. Depending on the prior, it is optimal to either induce ambiguity or fully conceal information. We provide a necessary and sufficient condition under which an effort-maximizing organizer can benefit strictly more from using ambiguous persuasion than from using the optimal Bayesian device. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Law Review is the property of Angle Publishing Co., Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid development of artificial intelligence technology allows robots to have social functions. In the case of human individuals interacting directly with a robot with artificial intelligence, if the individual can perceive the same or similar feelings as they have when interacting with a real human, the robot can be considered to have social presence. Trust is an important factor that affects human–robot collaboration. This research explores the influence of the character and information disclosure of robots on trust in human–robot collaboration as well as the mediating role of social presence. This study uses the Columbia Card Task to design a human–robot cooperative experiment platform. During the experiment, robots provide different levels of character (introversion vs. extroversion) and information disclosure (high disclosure vs. low disclosure). The results show that the character of robots has a significant impact on emotional trust: the higher the level of extroversion is, the stronger the level of human emotional trust. Furthermore, the level of information disclosure by robots has a significant impact on cognitive trust: the higher the level of information disclosure is, the stronger the level of cognitive trust. Social presence has a mediating role in the effect of character on emotional trust and the impact of information disclosure on cognitive trust. The research results can provide suggestions for improving the acceptance of social robots in human–robot collaboration and improving the quality and efficiency of collaborative human–robot task decision-making. Research on robots' character and information disclosure can provide a theoretical basis for related researchers and developers. [ABSTRACT FROM AUTHOR]

In this study, we examine the peer effect on climate risk information disclosure by analyzing A-share listed companies in China. We find that industry peers influence target firms’ climate risk information disclosure through active (passive) imitation resulting from cost–benefit considerations (institutional pressures). Leader companies are more likely to be emulated by within-industry follower companies and target firms prefer to learn from similar within-industry firms. Executive overconfidence and performance pressure negatively affect target firms’ willingness to emulate their peers. Finally, the peer effect of climate risk information disclosure demonstrates a regional aspect. Our findings have implications for reasonable climate risk information disclosure at the micro level and effective regulation to move toward achieving carbon peak/neutrality at the macro level.

Ad exchanges, where real-time auctions for display ad impressions take place, have historically emphasized user targeting, and advertisers sometimes did not know on which sites their ads would appear; in other words, they had no ad context. More recently, some ad exchanges have been encouraging publishers to provide context information to ad buyers that would allow them to adjust their bids for ads at specific sites. This article explores the empirical effect of a change in context information provided by a private European ad exchange. Analyzing this as a quasi-experiment using difference in differences, the authors find that average revenue per impression rose when the exchange provided subdomain information to ad buyers. Thus, ad context information is important to ad buyers, and they will act on it. Revenue per impression rises for nearly all sites, which is what auction theory predicts will happen when rational buyers with heterogeneous preferences are given more information. Exceptions are sites with thin markets prior to the policy change; consistent with theory, these sites do not show a rise in prices. This study adds evidence that ad exchanges with reputable publishers, particularly smaller volume, high-quality sites, should provide ad buyers with context information, which can be done at almost no cost. [ABSTRACT FROM AUTHOR]

Drawing from a content analysis of publicly traded companies' privacy notices, a survey of managers, a field study, and five online experiments, this research investigates how consumers respond to privacy notices. A privacy notice, by placing legally enforceable limits on a firm's data practices, communicating safeguards, and signaling transparency, might be expected to promote confidence that personal data will not be misused. Indeed, most managers expected a privacy notice to make customers feel more secure (Study 1). Yet, consistent with the analogy that bulletproof glass can increase feelings of vulnerability despite the protection offered, formal privacy notices undermined consumer trust and decreased purchase interest even when they emphasized objective protection (Studies 2, 3, and 5) or omitted any mention of potentially concerning data practices (Study 6). These unintended consequences did not occur, however, when consumers had an a priori reason to be distrustful (Study 4) or when benevolence cues were added to privacy notices (Studies 5 and 6). Finally, Study 7 showed that both the presence and conspicuous absence of privacy information are sufficient to trigger decreased purchase intent. Together, these results provide actionable guidance to managers on how to effectively convey privacy information (without hurting purchase interest). [ABSTRACT FROM AUTHOR]

The environmental, social, and governance (ESG) rating method is a powerful tool that can help investors to judge the investment value of companies based on the information disclosure. However, mainstream ESG rating methods ignore the distinction between companies with incomplete information disclosure and companies without information disclosure, which decreases the initiative and enthusiasm of companies to disclose information. In this study, a self-disclosure ESG (SDESG) rating method is proposed to evaluate companies’ ESG performance capabilities. First, based on the fuzzy set, fuzzy data is defined and applied to the SDESG rating method. Second, analogous to the academic reward system of a university, a reward mechanism of disclosure is used in the SDESG rating method. Finally, the effectiveness and reliability of the SDESG rating method are demonstrated through Refinitiv’s case. The results show that the SDESG rating method can distinguish companies with incomplete information disclosure from companies without information disclosure and allow companies that proactively disclose information to obtain better ESG scores under each industry. The implications of the study would increase companies’ enthusiasm to disclose information and maintain transparency within a company. [ABSTRACT FROM AUTHOR]

Information disclosure has become a ubiquitous component of public policy. The virtues of information disclosure partly rest on citizens acting on disclosed information. Through a survey experiment, this article evaluates citizens' responses to local environmental information from the Toxics Release Inventory, a major environmental information disclosure program in the U.S. The results suggest that individuals adjust their concern for self and family and sense of personal obligation to act based on new information relative to their prior knowledge. The information, however, has no impact on policy preferences and behavioral intentions. While citizens do not completely reject new information, the findings underscore the challenges of using information disclosure to motivate meaningful behavioral changes. They also contribute to the understanding of the relationships between information, political knowledge, attitudes, and preferences. [ABSTRACT FROM AUTHOR]