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Information technology and privacy: conceptual muddles or privacy vacuums?

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Abstract

Within a given conversation or information exchange, do privacy expectations change based on the technology used? Firms regularly require users, customers, and employees to shift existing relationships onto new information technology, yet little is known as about how technology impacts established privacy expectations and norms. Coworkers are asked to use new information technology, users of gmail are asked to use GoogleBuzz, patients and doctors are asked to record health records online, etc. Understanding how privacy expectations change, if at all, and the mechanisms by which such a variance is produced will help organizations make such transitions. This paper examines whether and how privacy expectations change based on the technological platform of an information exchange. The results suggest that privacy expectations are significantly distinct when the information exchange is located on a novel technology as compared to a more established technology. Furthermore, this difference is best explained when modeled by a shift in privacy expectations rather than fully technology-specific privacy norms. These results suggest that privacy expectations online are connected to privacy offline with a different base privacy expectation. Surprisingly, out of the five locations tested, respondents consistently assign information on email the greatest privacy protection. In addition, while undergraduate students differ from non-undergraduates when assessing a social networking site, no difference is found when judging an exchange on email. In sum, the findings suggest that novel technology may introduce temporary conceptual muddles rather than permanent privacy vacuums. The results reported here challenge conventional views about how privacy expectations differ online versus offline. Traditionally, management scholarship examines privacy online or with a specific new technology platform in isolation and without reference to the same information exchange offline. However, in the present study, individuals appear to have a shift in their privacy expectations but retain similar factors and their relative importance—the privacy equation by which they form judgments—across technologies. These findings suggest that privacy scholarship should make use of existing privacy norms within contexts when analyzing and studying privacy in a new technological platform.

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Notes

  1. This negotiation over privacy norms is not synonymous with privacy as a commodity (Smith et al. 2011), a privacy calculus (Culnan and Armstrong 1999; Dinev and Hart 2006), or a second exchange (Culnan and Bies 2003), all of which assume individuals relinquish privacy in order to gain something in return. In other words, individuals are seen as giving up some measure of privacy to benefit from a transaction (e.g., customizing products or using electronic health records or having books suggested online). In this paper, the negotiation is over the privacy norm function; actors within a context negotiate what the privacy rules will be while retaining every expectation of privacy.

  2. In comparison, in experiments, factors are designed orthogonal to each other but manipulated one at a time; however, in a traditional survey, many factors are examined but are not necessarily orthogonal to each other (Appelbaum et al. 2006).

  3. Individuals regularly give access to information to people or organizations while keeping the same information from others. Alternatively, the restricted access version of privacy—where information that is inaccessible is private and that which is accessible is public –supports a dichotomy where information can be universally declared ‘public’ or ‘private.’ Many find the distinction to be false (Solove 2006; Nissenbaum 2004, 2009; Tufekci 2008) and “the idea of two distinct spheres, of the ‘public’ and the ‘private’ is in many ways an outdated concept” (Marwick et al. 2010). Or, as Nissenbaum states, “the crucial issue is not whether the information is private or public, gathered from private or public settings, but whether the action breaches contextual integrity” (2004, p. 134).

  4. Respondent fatigue was a factor for some respondent groups. Two dummy variables were created to signify vignette ratings with a sequence number over 30 and over 20. If the ordinal regression model demonstrated a significant impact on the rating task by either dummy variable, those associated vignette ratings were discarded for that model. The regression was rerun without the offending data. However, a larger design issue came from the respondents’ learning curve—presumably from the novelty of the survey design. Once the first two vignette ratings for each respondent (sequence numbers 1 and 2) were discarded for all respondents, the model fit criteria and parallel lines assumptions improved dramatically. all vignette ratings were discarded with a sequence number of 1 or 2 for the entire analysis.

  5. The rating task in the survey was a five-level ordinal scale (0–4) as is shown in the “Appendix”, however the distribution of the ratings was not normal around the mean. The top three levels (0–2) were combined to create a new scale with three levels coded 1–3. The user was not, however, given an option to answer “I don’t know” or “I need more information.” The user could skip a vignette and continue.

  6. In order to examine respondent level factors with OLS regression equations for each respondent, a minimum number of ratings per respondents was required. Therefore, all respondents were dropped who answered less than 20 vignettes and removed their ratings from the larger data set of vignette ratings. Therefore, the number of respondents used in this study is 471 (rather than 811) and the total number of vignette rated is 15,108 (rather than 21,187).

  7. For ordinal variables, the outcome is at or below given outcome Y j . Ordinal dependent variables– such as the traditional Likert scale rating task used here—do not necessarily meet the assumptions required of traditional OLS models (O’Connell 2005; Kennedy, 2003) which impacts analysis below.

  8. Each respondent equation is based on between 20 and 40 rated vignettes (N = number of vignettes). Each OLS regression equation was performed using clustered and unclustered regression with no significant difference. In addition, neither the undergraduate status or gender were statistically significant determinants of the number of vignettes rated.

  9. Ideally, experience with each technology platform would also be used here, however, experience with email or Facebook was not collected in the survey. Undergraduate status may be an imprecise proxy for experience with Facebook.

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Authors and Affiliations

  1. School of Business, The George Washington University, Funger Hall, 2201 G St, NW, Washington, DC, 20052, USA

    Kirsten Martin

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  1. Kirsten Martin
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Correspondence to Kirsten Martin.

Appendix

Appendix

Sample vignettes

In general

[NAME] is a [MEMBERSHIP] college student [SPACE]. [LOCATION A] [NAME] [LOCATION B] from a fellow team member talking about [CONTENT]. [ACCESS]. The next day, [NAME] shared the information with [DISTRIBUTION].

Sample 1:

Ryan is a senior college student on an assigned project team for a required class. While on Facebook, Ryan received a newsfeed from a fellow team member talking about problems with his mom. Ryan was not sure that his teammate realized that he saw the information. The next day, Ryan shared the information with other students on the project team, including the professor.

Sample 2:

Kevin is a new college student on a varsity athletic team. While on Facebook, Kevin saw a wall post from a fellow team member talking about a date that went horribly wrong. Kevin was not sure that his teammate realized that he saw the information. The next day, Kevin shared the information with other members of the team.

Vignette factors

 

 

Attributes

Dimensions

Operationalized

1

Space

0

Well defined—athletic team

On a varsity athletic team

1

Ill defined—randomly assigned group

On an assigned project team for a required class

2

Access

0

Give willingly

 

1

Coerced

[NAME]’s teammate only shared the information reluctantly after being chided by other students on the team.

2

Overheard

[NAME] was not sure that his teammate realized that he heard/received the information.

3

Content

0

Public

Housing decisions for next semester

1

Role based

Who is going to start for the next game/how the projects were assigned

2

Personal I

A date that went horribly wrong

3

Family

Problems with his mom

4

Private

An embarrassing medical condition

4

Location

0

Verbal inside role-based space

While in the locker room/study room…heard

1

Verbal outside role-based space

While in the cafeteria…heard

2

Email

While checking his messages….received an e-mail

3

Facebook newsfeed

While on Facebook…received a newsfeed

4

Facebook wall post

While on Facebook…saw a wall post

5

Distribution of information

0

Distributed within group

Other members of the team

1

Distributed to team leaders

Other members of the team including the coach

2

Distributed to captains

Other members of the team including the team captains

3

Distributed outside group

Students not on the team

6

Membership

0

New

New

1

Senior

Senior

Question 1: Should [NAME] have shared the information with others?

Absolutely should share

OK to share

Absolutely should not share

0

 

1

 

2

 

3

 

4

 

 

 

 

 

 

 

 

 

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Martin, K. Information technology and privacy: conceptual muddles or privacy vacuums?. Ethics Inf Technol 14, 267–284 (2012). https://doi.org/10.1007/s10676-012-9300-3

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