Table 2 - uploaded by Cody Buntain
Content may be subject to copyright.
Source publication
This paper explores whether trust, developed in one context, transfers into another, distinct context and, if so, attempts to quantify the influence this prior trust exerts. Specifically, we investigate the effects of artificially stimulated prior trust as it transfers across disparate contexts and whether this prior trust can compensate for negati...
Context in source publication
Context 1
... expected, participants over- whelmingly chose the trustworthy benevolent agent as the teammate when no additional information is available. Data on this activity is shown in Table 2 with mean phase-one winnings ¯ W 1 and teammate selection counts C. If teammate selection was independent of the trust established in the investment games, one would expect per-agent-type selection counts to be uniform, or that each agent would be selected as a teammate approximately 58 times, which is demonstrably false. The chi-squared test for independence supports this strong relation between agent type and teammate selection with χ 2 (2, n = 175) = 399.00, ...
Similar publications
Trusting and trustworthy environments are argued to promote collective action, as people learn to rely on their fellow citizens and believe that only few individuals will free ride. To test the causal validity of this mechanism, we propose an experimental design that allows us to create different trusting and trustworthy conditions simply by (i) ma...
Citations
... There is some, albeit empirical, evidence to show that trust is transferred through links and connections, even across different contexts [13,14]. It follows that establishing a network of links between trusted information sources would strengthen an Open Science infrastructure. ...
Collaboration and the sharing of knowledge is at the heart of Open Science. However, we need to know that the knowledge we find and share is really what it purports to be; and we need to know that the authors we hope to collaborate with are really the people they claim to be. In this paper, the author argues that a prerequisite for Open Science is trust and that persistent identifiers help to build that trust. The persistent identifier systems must themselves be trustworthy and they must be able to connect the user or their machine to the information they need now and into the future. Infrastructure is rather like plumbing: it goes unnoticed and unappreciated until it fails. This paper puts infrastructure for persistent identifiers in the spotlight as a core component of Open Science.
... "Travel with companions" is by origin an offline activity but, through the influence of the Internet, it has become a combination of both offline and online activities. Trust formed in other online communities such as transaction networks or organizations has been shown to be vital in shaping behavior (Buntain & Golbeck, 2015). Nevertheless, the question of how members transfer trust created in an online community to trust of offline action remains unknown. ...
... Trust sets the foundation of future actions that people take on their travel behaviors. To this end, this study enriches the literature on trust building (Buntain & Golbeck, 2015;Mayer et al., 1995;Morgan & Hunt, 1994) that is latent in the tourism context rather than in a conventional branding and organizational research domain. ...
This article explores how cycling tourists build trust in the process of transition from online community to offline travel companions. It uses data collected from participant observation and interviews of a cycling tourist group in China. The findings indicate that after building a social circle in an online community, the members of the “community” build trust by: identifying travel companions’ attitudes, values, knowledge, and experiences to build dispositional trust; identifying companions’ preferences, activities, or the patterns of cycling behavior to build institutional trust; identifying companions’ cycling experiences, occupations, and hobbies to build interpersonal trust. After the identification of travel companions and trust has developed, online community members make the decision to travel together as companions and their online social circle becomes a regulated group without hierarchy. A “triple-jump” explanatory model to explain the trust-building process and practical recommendations from these insights were outlined.
... Trust transfer refers to a cognitive process in which an individual's trust in a known target is transferred to an unknown target related to the known target [79]. Moreover, trust transfer has been studied in various settings, including different contexts [9], distributed computer systems [18], and e-commerce channels [79,83]. In the context of e-commerce, consumer can be transferred from a traditional media source (e.g., TV or newspaper) to the Internet [56], from a traditional sales channel to a Web-based channel [79], or from an online channel to a mobile channel [51,83]; trust in a salesperson can turn into trust in the salesperson's firm [19]. ...
Consumers' trust in the source of an online review message is largely determined by their trustworthiness beliefs about the source that can potentially influence their willingness to engage in a transaction. While extant research examined consumer trust in a message source (i.e., a reviewer), little attention has been paid to the role of trust transfer within the review platform in forming source trustworthiness perceptions in the e-WOM context; source trustworthiness refers to the extent to which the composer of a review message deserves trust. It is the primary aim of this research to understand how trust transfer between related targets affects a consumer's assessment of online reviews. We empirically examined consumers' e-WOM adoption mechanism from the trust transfer perspective. We constructed a conceptual model based on the elaboration likelihood model and the trust transfer theory, and tested the model using data collected from an online survey conducted on TripAdvisor.com. The findings revealed that trust is transferred from the review site to the community of reviewers and, then, to a specific reviewer, but not directly from the review site to a specific reviewer. It was also found that consumer trust in a specific reviewer and review helpfulness both contribute to review adoption. This study offers some implications from these findings.
... Game Configuration: The number of interactions in a game is five (as in [3,7,27]), which is a short enough duration to avoid participants becoming bored and yet still allows team members to adapt to teammates with predictable behavior. The size of team task is incremented by two in each interaction, i.e., the sequence of task sizes is ⟨6, 8, 10, 12, 14⟩. ...
Interacting with strangers and agents through computer networks has become a routine aspect of our daily lives. In such environments, reputation plays a critical role in determining our future interactions and satisfaction derived from them. This paper empirically investigates the effects of agent reputation on humantrust in andbehavior towards "peer'' level agent teammates over repeated interactions. We developed a team coordination game, the Game of Trust, in which a human player and an agent player repeatedly cooperate to complete team tasks without prior assignment of subtasks. Before the game begins, the agent player is introduced with either positive or negative reputation to the human player. The effects of agent reputation are evaluated by performing an extensive set of controlled experiments with participants recruited from Amazon Mechanical Turk, a crowdsourcing marketplace. We collect both teamwork performance data as well as surveys to gauge participants' trust in their agent teammates. The empirical results show that positive (negative) agent reputation led to greater (lower) human trust in agent teammates. Moreover, the interplay between the game expertise and expectation from agent teammate significantly affected the influence of reputation. These findings enhance our understanding of changes in human trust with respect to agent reputation towards achieving successful human-agent teamwork.
... Using repeated trust game, we tested the effect of trust score. Our experimental settings follow previous studies [Colombo and Merzoni, 2006;Avner Ben-Ner and Putterman, 2009;Dubois et al., 2012;Buntain and Golbeck, 2015] to test the effect of a new information given to users. ...
... We define three types of user profiles according to the values of send_proportion: low, medium and high. Similar to [Buntain and Golbeck, 2015], we define that a user with a low profile sends in average 20% of the maximum possible amount, while for a medium profile user the send_proportion is 50% and for a high profile user it is 80%. We also define a fluctuate profile user who first tries to behave well and then deviates. ...
Large-scale collaborative systems wherein a large number of users collaborate to perform a shared task attract a lot of attention from both academic and industry. Trust is an important factor for the success of a large-scale collaboration. It is difficult for end-users to manually assess the trust level of each partner in this collaboration. We study the trust assessment problem and aim to design a computational trust model for collaborative systems.We focused on three research questions. - What is the effect of deploying a trust model and showing trust scores of partners to users? We designed and organized a user-experiment based on trust game, a well-known money-exchange lab-control protocol, wherein we introduced user trust scores. Our comprehensive analysis on user behavior proved that: (i) showing trust score to users encourages collaboration between them significantly at a similar level with showing nick-name, and (ii) users follow the trust score in decision-making. The results suggest that a trust model can be deployed in collaborative systems to assist users. - How to calculate trust score between users that experienced a collaboration? We designed a trust model for repeated trust game that computes user trust scores based on their past behavior. We validated our trust model against: (i) simulated data, (ii) human opinion, and (iii) real-world experimental data. We extended our trust model to Wikipedia based on user contributions to the quality of the edited Wikipedia articles. We proposed three machine learning approaches to assess the quality of Wikipedia articles: the first one based on random forest with manually-designed features while the other two ones based on deep learning methods. - How to predict trust relation between users that did not interact in the past? Given a network in which the links represent the trust/distrust relations between users, we aim to predict future relations. We proposed an algorithm that takes into account the established time information of the links in the network to predict future user trust/distrust relationships. Our algorithm outperforms state-of-the-art approaches on real-world signed directed social network datasets.
... Trust requires the truster vulnerable, taking a risk that the trustee will act in their best interest (Buntain and Golbeck 2015). The strong communities trust in Sidodadi and Sumur Kumbang is implemented by their willingness to replace coffee and cocoa with MPTS. ...
Qurniati R, Febryano IG, Zulfiani D. 2017. How trust influence social capital to support collective action in agroforestry development?. Biodiversitas 18: 1201-1206. Trust is an important factor for strengthening social capital. Trust in forest community is achieved through collective action in small scale agroforestry. It was challenging process regards the low on social capital in communities. This study intended to analyse and explain how trust influences social capital in encouraging collective action in agroforestry development. The study conducted in Sidodadi and Sumur Kumbang Lampung Province, from October-December 2016. The area was selected based on disparities of sea level of villages. The data collected through in-depth interviews with several members of farmer group who responsible for agroforestry. The research found that trust between farmer group member remains high. However, trust as a source of social capital was not supported collective action since farmer group institution is weak. Therefore, to support agroforestry development, social capital should be increased through the development of good networks (bridging) to reach collective goal i.e. community welfare and sustainable forest management
... Trust requires the truster vulnerable, taking a risk that the trustee will act in their best interest (Buntain and Golbeck 2015). The strong communities trust in Sidodadi and Sumur Kumbang is implemented by their willingness to replace coffee and cocoa with MPTS. ...
Trust is an important factor for strengthening social capital. Trust in forest community is achieved through collective action in small scale agroforestry. It was challenging process regards the low on social capital in communities. This study intended to analyse and explain how trust influences social capital in encouraging collective action in agroforestry development. The study conducted in Sidodadi and Sumur Kumbang Lampung Province, from October-December 2016. The area was selected based on disparities of sea level of villages. The data collected through in-depth interviews with several members of farmer group who responsible for agroforestry. The research found that trust between farmer group member remains high. However, trust as a source of social capital was not supported collective action since farmer group institution is weak. Therefore, to support agroforestry development, social capital should be increased through the development of good networks (bridging) to reach collective goal i.e. community welfare and sustainable forest management. © 2017, Society for Indonesian Biodiversity. All rights reserved.
... The second information block we wanted to visualize in TRIVIA denotes the product type, price and time of the transaction the feedback refers to. Although transaction context has been recognized to play an important role for trust evaluation in literature [33,34], only a very limited number of trust models formalizes the influence of context for reputation assessment [35]. The non-consideration of the transaction context, however, might lead to the value imbalance problem, where a malicious seller can build a high reputation selling cheap products while cheating on the expensive ones [16]. ...
Reputation systems are an essential part of electronic marketplaces that provide a valuable method to identify honest sellers and punish malicious actors. Due to the continuous improvement of the computation models applied, advanced reputation systems have become non-transparent and incomprehensible to the end-user. As a consequence, users become skeptical and lose their trust toward the reputation system. In this work, we are taking a step to increase the transparency of reputation systems by means of providing interactive visual representations of seller reputation profiles. We thereto propose TRIVIA - a visual analytics tool to evaluate seller reputation. Besides enhancing transparency, our results show that through incorporating the visual-cognitive capabilities of a human analyst and the computing power of a machine in TRIVIA, malicious sellers can be reliably identified. In this way we provide a new perspective on how the problem of robustness could be addressed.
... 1) Fluctuating user behaviors: We define three types of user profiles according to the values of send proportion: low, medium and high. Similar to [28], we define that a user with a low profile sends in average 20% of the maximum possible amount, while for a medium profile user the send proportion is 50% and for a high profile user it is 80%. We also define a fluctuate profile user who first tries to behave well and then deviates. ...
This paper examines what influences trust in mobile social commerce environment. Drawing on trust-based acceptance model (i.e. cognitive and emotional trust) and online review features (i.e. profile photo, linguistic style, and reported experience), we examine how these factors affect trust in mobile social commerce. Hypotheses were tested using survey data. The results of our model showed that there are significant influences of profile photo, reported experience, cognitive, and emotional trust towards trust in ms-commerce. This work contributes to existing literature by examining the roles of previous trust in mobile payments and online reviews on trust in mobile social commerce.
