Transference 104

Doney and Cannon (1997) explained that trust is developed and transferred from other parties, and that data reusers also developed their initial trust based on other people’s perceptions of the data’s trustworthiness. This is the process of transference. Participants based their trust upon reputation and recommendations. They accepted both as credible information and a research community’s positive acknowledgement and had high expectations of data with a good reputation or that came from reputable original investigators. This view was also based on the participants’ rational choice and thinking, which brings calculus-based trust.

Some participants relied on a person’s reputation to judge the trustworthiness of data or the sources of data. PP17 described data he used as “the” data, and also acknowledged the parent study’s reputation, saying, “The study is considered ‘the’ study.” Because the study was known for its “terrific study design” with “good sampling and measurement,” PP18 “felt good about the data.” For both PS04 and PS02, data from “renowned scholars” or “reputable” organizations

meant trustworthiness:

PS04: I haven’t met any of the researchers directly, but they were already renowned scholars in the field. So, most of the research with most of the articles written by them have high citation numbers. (…) [T]his was my decision whether these people were trustworthy.

PS02: [The data] is collected by a reputable and ethical agency like NIH. Basically that I don’t question [that] because they are all really trustworthy

organizations. (…) I blindly trust the data that it’s from a reliable [organization]. I don’t question the data.

Further, reputation implied positive characteristics: Reputable data implied a rigorous original study; reputable original investigators were competent and had integrity, while a reputable organization had the internal capability to conduct research. PS07 believed the “high level of integrity” of an original investigator because he was “world-famous.” To PP04, the fact that the data had been collected by reputable organizations like NIH or CDC made “[me] lay the

responsibility with that organization to collect the data properly (…) and as best quality.”

For data produced by institutions, such as government agencies (e.g., CDC, NIH, or USDA), the participants recognized the reputation of the institution as one whole party rather than individuals within the institution, which provided sufficient credentials:

PP02: It’s [the agency’s name]. It’s not like it’s Bob down the street.

reputation of those [individuals] that I had seen as authors of the [data]” (PP03). Participants pointed out the reasons for not checking the individuals involved in creating the data at the agencies or organizations. Because the agency’s reputation had been built through other data reusers, it was natural for participants to accept the reputation of organizations known for “their quality data” (PP07). At the same time, participants believed in the organization’s competence and that they “physically employed the trained people that are data collectors” (PP01), which made it unnecessary to see who worked for the project to create the data. The nature of trust resembled the concept of institutional trust identified in previous literature: trust in the

institutional structures, properties and competence (e.g. McKnight et al., 1998; Rousseau et al., 1998; Sitkin & Roth, 1993). When participants did not know the people responsible for data collection and did not have a direct relationship with members of the institutions, this

institutional trust could substitute for interpersonal trust (trust directly in the individuals who produced the data).

For data produced by individual researchers, the reputations of individuals—particularly the original investigators—was more important for participants than the reputations of

institutions. PP18 remembered the original investigator of the study as “a well-recognized expert in the field,” and everybody knew that the original investigator and his research “were really sort of groundbreaking.” This gave PP18 “a lot of respect for him and his research,” in addition to a “certain amount of trust” in the data.

However, reputations of academic institutions to which the individual researchers belonged (institutional trust) could still enhance the participants’ trust in data. They also substituted for interpersonal trust when participants found data from an unfamiliar source without a notable reputation, such as a junior researcher. PS02 referred to the institutions where

the individual researcher worked at a “very prestigious organizations (…) with established people,” and PS12 was “probably influenced a little bit by the reputation of that institution” because it was “a very high-powered, good research university,” which made PS12 think “[the data] must be good.” Still, institutional trust in this case only supplemented the participants’ trust development on data, and the reputation of the researcher’s institution “only gives a positive bias” and “doesn’t lead [to] a negative bias,” according to PS12: for instance, data from a non-R1 university did “not necessarily mean ‘Oh well, you shouldn’t trust that’” (PS12).

Direct recommendations on data from colleagues, advisors, or collaborators, were also part of the overall reputation that participants considered, as it was the opportunity to learn about other people’s perceptions of data trustworthiness. This is important because participants

“respect[ed] what other people have felt about data” (PP17). Sometimes, junior researchers sought a recommendation from senior researchers or their advisors:

PS04: I asked people or colleagues or advisors about data that they knew of that is up to date and that is relevant to my research questions. And this was one of the data [sets] that they mentioned: Ph.D. colleagues who [have] already graduated and are working as faculty. And, some of them have used this data or some of them just heard that this is good data to use for research. And my advisor also suggested that this is good data and you can use it well.

Often, recommendations from a party with an established authoritative relationship influenced the participants. PP13 planned to use data for a project when she wrote a proposal because one of her colleagues had used it for another project and recommended it. She explained that “we know him really well, and we trust his word about what the data can do.” PS07 said, when a senior

person “who’s a well-funded, well published, associate or full professor who [is] familiar with data analysis and research” advises him, he “typically take[s] it pretty seriously.”

For some participants, checking the reputation of data among the researchers around them was equally important to verify the reputation in the field, as they had received advice against using certain data. PS07 talked about “having people tell me, ‘Hey, don’t get involved in. (…) The data [are] a mess,’” and PS08 also had a “close colleague [who] warn[ed] me from something [about data].” Usually a negative recommendation was made privately among researchers who were in a close relationship, as PS12 described:

PS12: It’s not like shouting in the middle of the conference. (…) I mean, we had colleagues, and I can’t go into a lot of details, but I have had colleagues who told me about the data I should be worried about, and I wouldn’t use or recommend working with them.