The role of propensity to trust and the five factor model across the trust process

Alarcon, Gene M.; Lyons, Joseph B.; Christensen, James C.; Bowers, Margaret A.; Klosterman, Samantha L.; Capiola, August

doi:10.1016/j.jrp.2018.05.006

Cited by 44 publications

(35 citation statements)

References 66 publications

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“…People exhibit some extent of trust by sharing a portion of money with strangers (Johnson & Mislin, ), but they are heterogeneous regarding their propensity to trust (Cesarini et al, ; Krueger et al, ). Individual variations in propensity to trust have been related to many factors, including genetic polymorphisms (Krueger et al, ; Nishina, Takagishi, Inoue‐Murayama, Takahashi, & Yamagishi, ), plasma oxytocin levels (Zhong et al, ), and personality attributes (Alarcon et al, ; Unoka, Seres, Áspán, Bódi, & Kéri, ), such as individual‐level collectivism and individualism (Shin & Park, ; Van Lange, Rockenbach, & Yamagishi, ; Zeffane, ). Here, we examined whether individual variations in the trust propensity can be predicted from intrinsic brain imaging measures.…”

Section: Introductionmentioning

confidence: 99%

Connectome‐based model predicts individual differences in propensity to trust

Xia

et al. 2019

Human Brain Mapping

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Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task‐based fMRI; however, little progress has been made in predicting its variations by task‐free fMRI based on whole‐brain resting‐state functional connectivity (RSFC). Here, we combined a one‐shot trust game with a connectome‐based predictive modeling approach to predict propensity to trust from whole‐brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes—caudate, amygdala, lateral prefrontal cortex, temporal–parietal junction, and the temporal pole—which are part of domain‐general large‐scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain‐behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data‐driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders.

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Section: Introductionmentioning

confidence: 99%

Connectome‐based model predicts individual differences in propensity to trust

Xia

et al. 2019

Human Brain Mapping

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“…As trust tends to be combined with uncertainties and risksharing, its developing process is influenced by various factors such as mutual values, kindness, abilities, openness, interactive experiences, communications, familiarity, and personalities (Alarcon et al, 2018;Jones & Shah, 2016;Schoorman, Mayer, & Davis, 2007). In particular, Pavlou (2002) highlights organizational members' collective assess based on their confident expectations in the process of interorganizational trust-building.…”

Section: Trust-building Within Industrial Clustersmentioning

confidence: 99%

Do Industrial Clusters Still Matter to Trust-Building in the Internet Era? A Network Embeddedness Perspective

Wei

Rayman‐Bacchus

et al. 2019

SAGE Open

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Despite the considerable amount of research regarding trust as one of the most important characters and competitive advantages for an industrial cluster, few empirical studies have examined whether geographic proximity still leads to higher levels of trust in the current Internet era. This article explores this issue of trust from a network embeddedness perspective. Based on the data from Zhongshan gas appliance cluster, we employ structural equation modeling to examine the extent to which relational embeddedness, structural embeddedness, and positional embeddedness have any impact on trust-building in the cluster. Our findings reveal that high clique trust and low aggregate trust coexist in the cluster, showing that the effect of geographical proximity on trust has been weakened and is no longer a sufficient condition for trust-building. This study also gives some suggestions on how to improve the trust within a cluster through network governance.Lez Rayman-Bacchus is a research follow in the Business School, University of Winchester, United Kingdom. He also founded the Centre for Corporate Responsibility at London Metropolitan University, and the international symposium on Corporate Responsibility and Sustainable Development. His research interests include the practice of strategy and international management, currently focused on Chinese SME attitudes and behavior in industrial network clusters, and that of Middle Eastern businesses. He has a background in both industry and academia. He spent more than 15 years with high technology multinational companies working in business development and marketing, before moving into research and teaching on strategy, corporate responsibility and sustainability, corporate ethics, and corporate governance. He has published more than about 30 papers as well as one academic book.GuangMing Xiang is pursuing master's degree in finance at the School of Management and Economics in UESTC.

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“…The interpersonal trust literature has demonstrated that trust beliefs influence trust intentions and subsequent behaviors [21]. However, there is additional variance that is unaccounted for as beliefs, intentions, and behaviors do not correlate highly enough in the literature to indicate convergent validity of the two constructs [22]. As such, although readability, organization, and source have been related to trustworthiness perceptions of the code, no research to date has explored the influence of these three factors on intentions to reuse code.…”

Section: Code Trustworthinessmentioning

confidence: 99%

“…Researchers have found that personality influences the cognitive responses to stressors [27]. Personality also influences the formation of trust perceptions [22,28]. Specifically, personality influences initial perceptions of others, but as participants have more interaction with a referent, personality plays less of a role.…”

Section: Personalitymentioning

confidence: 99%

“…Propensity to trust is a stable personality trait that reflects one's general expectancy of the trustworthiness of others and a general willingness to trust others [13,29]. Propensity to trust has demonstrated a relationship with trust behaviors, trust intentions, and trust beliefs [2,8,22]. Although propensity to trust has demonstrated a relationship within interpersonal trust intentions and behaviors [21], it remains to be seen if this personality trait influences intentions to reuse code.…”

Section: Personalitymentioning

confidence: 99%

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The Influence of Personality on Code Reuse

Ryan¹,

Walter²,

Alarcon³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The ubiquity and necessity of computer software requires programmers to reuse extant code to keep up with increasing software demands. Researchers have started to investigate the underlying psychological processes and the programmer characteristics affecting code reuse. The present study investigated the role of programmer personality (propensity to trust, suspicion propensity) on willingness to reuse code. Programmers were recruited through Amazon's Mechanical Turk. Programmers completed propensity to trust and suspicion personality inventories and were subsequently presented with 18 pieces of computer code containing transparency and reputation manipulations. The results demonstrated that propensity to trust did not influence willingness to reuse code. However, facets of suspicion propensity did affect reuse willingness. Programmers lower in trait mal-intent perceptions and higher in cognitive activity were more likely to report they would reuse code. Implications and applications are discussed.

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The role of propensity to trust and the five factor model across the trust process

Cited by 44 publications

References 66 publications

Connectome‐based model predicts individual differences in propensity to trust

Connectome‐based model predicts individual differences in propensity to trust

Do Industrial Clusters Still Matter to Trust-Building in the Internet Era? A Network Embeddedness Perspective

The Influence of Personality on Code Reuse

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