Comparing the Effectiveness of Post-Scandal Apologies From Nonprofit and Commercial Organizations: An Extension of the Moral Disillusionment Model

There is a double standard in public responses to scandals: Nonprofits are penalized more harshly than commercial organizations for the same transgression (the “moral disillusionment effect”). However, previous research—focused on commercial organizations—has sometimes shown that a positive reputation can insure organizations against the negative effects of scandals. In light of this, we asked whether a second double standard exists when it comes to trust repair: Can nonprofits regain trust and consumer support more quickly than commercial organizations after apologizing? Two experiments ( combined N = 805), considering responses to sexual exploitation and fraud scandals, replicated and extended the moral disillusionment effect. Trust and consumer support were partially restored following an apology (and even a statement acknowledging the scandal without apologizing), but the rate of repair was the same for nonprofits and commercial organizations. Nonprofit managers should therefore implement internal controls to prevent violations and issue public responses when scandals emerge.

Implicit theories of malleability in machines influence the perception and trust repair of intelligent agent

After an intelligent agent makes an error, trust repair can be attempted to regain lost trust. While several ways are possible, individuals' underlying perception of malleability in machines--implicit theory-- can also influence the agent's trust repair process. In this study, we investigated the influence of implicit theory of machines on intelligent agents' apology after the trust violation. A 2 (implicit theory: Incremental vs. Entity) X 2 (apology attribution: Internal vs. External) between-subject design experiment of simulated stock market investment was conducted (N = 150) via online. Participants were given a situation in which they had to make investment decisions based on the recommendation of an artificial intelligence agent. We created an investment game consist of 40 investment opportunities to see the process of trust development, trust violation, and trust repair. The results show that trust damaged less severely in Incremental rather than Entity implicit theory condition and External rather than internal attribution apology condition after the trust violation. However, trust recovered more highly in Entity-External condition. We discussed both theoretical and practical implications.

Beyond the crisis: trust repair in an interorganizational network

This article extends understanding of trust repair by explaining in more detail the dynamics of trust at the network-level. Building on organizational-level trust repair research, the article explains how two periods of trust repair – catharsis and catalysis – contribute to trust repair of an interorganizational network. In addition, the article describes how changes to network-level trust in an interorganizational network changes the governance form of the network making the interorganizational network more durable and stable.

Trust repair in human-agent teams: the effectiveness of explanations and expressing regret

The role of intelligent agents becomes more social as they are expected to act in direct interaction, involvement and/or interdependency with humans and other artificial entities, as in Human-Agent Teams (HAT). The highly interdependent and dynamic nature of teamwork demands correctly calibrated trust among team members. Trust violations are an inevitable aspect of the cycle of trust and since repairing damaged trust proves to be more difficult than building trust initially, effective trust repair strategies are needed to ensure durable and successful team performance. The aim of this study was to explore the effectiveness of different trust repair strategies from an intelligent agent by measuring the development of human trust and advice taking in a Human-Agent Teaming task. Data for this study were obtained using a task environment resembling a first-person shooter game. Participants carried out a mission in collaboration with their artificial team member. A trust violation was provoked when the agent failed to detect an approaching enemy. After this, the agent offered one of four trust repair strategies, composed of the apology components explanation and expression of regret (either one alone, both or neither). Our results indicated that expressing regret was crucial for effective trust repair. After trust declined due to the violation by the agent, trust only significantly recovered when an expression of regret was included in the apology. This effect was stronger when an explanation was added. In this context, the intelligent agent was the most effective in its attempt of rebuilding trust when it provided an apology that was both affective, and informational. Finally, the implications of our findings for the design and study of Human-Agent trust repair are discussed.