A Preliminary Analysis on the Effects of Propensity to Trust in Distributed Software Development

Editor's Notes

• #3: Trust is a complex matter to study since it involves both interpersonal relationships (e.g., cultural issues between trustee and trustor) and facets of human behavior (e.g., personal traits). To date several definitions of trust have been given. A widely used and concise definition is provided by Jarvenpaa et al. “The expectations of one (the trustor) that the others (the trustees) will behave as predicted” [1] In other words, positive trust emerges when others’ actions meet our expectation; otherwise, negative trust, or mistrust, arises. When we assess someone’s trustworthiness, we do it along judging others along several dimensions or personal characteristics called antecedents of trust, that is, the properties of others that trigger the evaluation when one assessing their trustworthiness. Therefore, sharing these properties/antecedents about someone fosters trust building, although this is also influenced by one’s intrinsic propensity to trust, a natural disposition towards trusting others in general propensity to trust (i.e., (a dispositional willingness to rely on others) Trust propensity is a general disposition to trust people in life (this is a consumer trait that is relatively stable. People differ in this trait; some are always distrusting others, whereas others believe that people can be trusted. Disposition to trust is a general, i.e. not situation specific, inclination to display faith in humanity and to adopt a trusting stance toward others (Mayer et al. 1995; McKnight et al. 1998), and is perceived as a personality trait (Grabner-Kräuter et al., 2003). This tendency is not based upon experience with or knowledge of a specific trusted party (ref), but is the result of an ongoing lifelong experience and socialization. As an antecedent of trust, disposition to trust is most effective in initial phases of a relationship when the parties are still mostly unfamiliar with each other (Mayer et al. 1995) and before extensive ongoing relationships provide a necessary background for the formation of other trust-building beliefs, such as integrity, benevolence and ability. ability (e.g., skills, knowledge), benevolence (e.g., courtesy, availability), integrity relates to a set of moral norms and trustee’s characteristics usually considered as good as, for example, honesty, fairness, loyalty and discretion, and predictability (e.g., reliability, consistent behaviors) are the personal characteristics of a trustee that facilitate the establishment of the trust relationship with a trustor. More specifically, the ability and predictability dimensions are assessed by means of cognitive elaboration of personal and professional information. At the same time, affective-based appraisal leads to trust building along the dimensions of benevolence and integrity is adherence to a set of principles (such as study/work habits) thought to make the trustee dependable and reliable, according to the trustor
• #4: Previous research on psycholinguistic has confirmed strong connections between language use and personality and that the personality traits can be successfully derived from the analysis of written text [13] such as emails [14]. In fact, Tausczik & Pennebaker [15] found that every trait in the Big-Five model is strongly and significantly associated with theoretically-appropriate patterns of word use, indicating.
• #5: Why is trust so important in SE and to SE researchers?
• #6: Connection strength = • prior interactions with core devs
• #7: One common limitation identified in prior empirical-research findings is that there is no explicit measure of ‘how much’ improving trust contributes to a project performance. Difficult to measure trust, it is sensed  self-reported data (read questionnaires) We seek for a more operative, quantitative measure, approximate trust by means of exchange of affective lexicon between a pair Performance is intuitively intended as a software project that delivers a quality product staying on budget and time schedule We approximate it as successful requests accepted PRs that cause the code base to grow, adding features, fixing bugs, one step closer to the project final goal
• #11: You can look at the paper to check the formula with the coefficient estimates. For a given PR k, there’s a +34% increase in the chance of being merged when the propensity to trust score of the integrator shifts from Low to High.
• #12: Big5 model: trust is just one facet of agreeableness Is it any reliable, we have seen with Sentiment Analysis tool how their lexicon is domain dependent. Minnesota Multiphasic Personality Inventory NRC lexicon