Blogging, communication, and privacy ... - Wiley Online Library

Blogging, Communication, and Privacy Management: Development of the Blogging Privacy Management Measure

Jeffrey T. Child Kent State University, School of Communication Studies, PO Box 5190, Kent, OH 44242. E-mail: [email protected] Judy C. Pearson North Dakota State University, Dean’s Office, College of Arts, Humanities, and Social Sciences, PO Box 5075, Fargo, ND 58105-5075. E-mail: [email protected] Sandra Petronio Indiana University-Purdue University, Indianapolis, Department of Communication Studies, IU School of Medicine, and the Charles Warren Fairbanks Center for Medical Ethics, Clarian Health Partners, 5801 Sunset Lane, Indianapolis, IN 46228. E-mail: [email protected]

This study applied Communication Privacy Management (CPM) theory to the context of blogging and developed a validated, theory-based measure of blogging privacy management. Across three studies, 823 college student bloggers completed an online survey. In study one (n = 176), exploratory and confirmatory factor analysis techniques tested four potential models. Study two (n = 291) cross-validated the final factor structure obtained in the fourth model with a separate sample. Study three (n = 356) tested the discriminant and predictive validity of the measure by comparing it to the self-consciousness scale. The Blogging Privacy Management Measure (BPMM) is a multidimensional, valid, and reliable construct. Future research could explore the influence of family values about privacy on blogging privacy rule management.

Introduction Blogging disclosures have become an important aspect of communication among college students.1 As many as four million college students maintain their own blog and readily disclose personal information on their sites (Lenhart & Received February 20, 2009; revised April 16, 2009; accepted April 19, 2009 1 Weblog

is the formal name for a blog. While Weblog and blog are used interchangeably in the scholarly literature, we try to limit confusion of multiple terms for the same thing by utilizing blog and blogging throughout the article. © 2009 ASIS&T • Published online 12 June 2009 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/asi.21122

Madden, 2005). For many, blogging has replaced going to the mall or playing kickball in the community neighborhood as a recreational activity (Bahney, 2006; Lenhart & Madden, 2005). MySpace.com, created in 2003, is one of the most popular locations for college students to maintain an active blog and features a more central avenue to blogging than Facebook.2 MySpace.com has “exploded to include some 60 million registered users, [and] has become the dominant social networking site, eclipsing others like Friendster.com, Tribe.net, Xanga.com and Meetup.com” (Bahney, 2006, para. 13). Although many college students regularly use this new technology, we know little about how disclosing private information on blogs influences relationships. A new measure that captures the way this technology is used or abused could help close this gap in knowledge (Alexander, Kang, & Kim, 2006; Meszaros, 2004). This study undertakes the development, testing, and validation of a theory-based measure that examines privacy rules employed by college students on their blogs to regulate privacy. To develop the measure, we depend on the evidenced-based theory of Communication Privacy Management (CPM) as a dialectical and rule-based theory about 2 Facebook

also continues to grow in popularity among college students today as a social networking website. However, while Facebook contains a wall posting feature it does not provide a primary location for users to engage in open-ended disclosure about daily pursuits in a reverse chronological order in the same way that other blogging-based websites do like MySpace.com, Blogger.com, and Xanga.com.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 60(10):2079–2094, 2009

the way people regulate disclosure and privacy (Petronio, 2002). To contextualize this study, the extant research about computer-mediated interactions is provided. Review of the Literature Early computer-mediated communication (CMC) research proffered largely negative perspectives about mediated interactions and personal relationships. CMC was shown to produce higher levels of loneliness, more social isolation, and fewer satisfying interpersonal relationships (Kraut, Patternson, Lundmark, Kiesler, Mukopadhyay, & Scherlis, 1998). The cues-filtered-out (CFO) perspective argues that one explanation for the cause of such outcomes based in how mediated interactions do not facilitate relational outcomes as well as face-to-face (FtF) interaction because CMC is a lean medium transmission source (Short, Williams, & Christie, 1976). Indeed, media richness theory proposes that there should be a match between the capacity of a communication medium and its utilization for information because media vary in the extent of interactive feedback, the encoding and decoding of both nonverbal and verbal cues, and the personalization of messages allowed through the medium (Daft & Lengel, 1984, 1986). Walther (1992, 1994, 1996) demonstrated how the unique characteristics of CMC allow for the development and maintenance of interpersonal relationships in ways that are different from, but just as fulfilling, as FtF relationships. The social information processing (SIP) model (Walther & Burgoon, 1992) posits that social and relational development can effectively occur through CMC with more time than is necessary in FtF interactions because CMC messages take longer to transmit, decode, and trust than FtF messages. The model and research from the SIP model supports that, given enough time, CMC relationships can achieve the same, and sometimes more positive, outcomes than do FtF relationships (Hian, Chuan, Trevor, & Detenber, 2004). CMC facilitates relational development when anticipation, desire, or need for future interactions exists through the medium (Walther, 1996; Walther & Burgoon, 1992). The hyper-personal perspective (Walther, 1996) describes circumstances whereby more meaningful interactions occur through CMC than FtF communication. Walther (1996) contends that CMC can be considered hyper-personal “when users experience commonality and are self-aware, physically separated, and communicating via a limited-cues channel that allows them to selectively self-present and edit; to construct and reciprocate representations of their partners and relations without the interference of environmental reality” (p. 33). Bloggers engage in selective self-presentation strategies on their blogs, tailored to anticipated interaction with strangers, friends, or both types of audiences (Becker & Stamp, 2005; Qian & Scott, 2007; Tice, Butler, Muraven, & Stillwell, 1995). Bloggers maintain various and desired levels of anonymity on their blogs (Qian & Scott, 2007; Westerman, Heide, Klein, & Walther 2008). Finally, bloggers construct versions of reality for their lives and personal relationships on 2080

their blogs (through both text and image) that may be different from lived experiences (Trammell & Keshelashvili, 2005; Vasalou & Joinson, 2009). Given these features, blogging interactions qualify as a type of hyper-personal communication because of the communication and privacy management options afforded by the interaction medium for managing identity (Walther, 1996). While the previous research reviewed has advanced greater understanding of relational development that occurs through CMC channels, the current study extends this literature by examining specific disclosure and privacy management processes used by the blogging population. This study develops and establishes a reliable measure that assesses the choices bloggers make when regulating access to their privacy. This study applies the evidenced-based propositions of CPM to the content of blogging in an effort to develop a measure that charts how people implement different privacy rules that preserve individual privacy boundaries or allow more public access (Petronio, 2002). In particular, CPM details six propositions about privacy access, discussed in the next section. Communication Privacy Management of Blogging CPM’s predictions serve as an effective theoretical framework for describing blogging disclosures and privacy management (Petronio, 2002). Through the use of a boundary metaphor, CPM proposes a scheme to understand the way that people manage private information both personally and in conjunction with others. Six propositions underpin CPM theory. Proposition 1: From a behavioral perspective, CPM argues that people believe their private information belongs to them.3 Proposition 2: Because people believe that they own their private information, they also believe that they have the right to control the flow of that information. Proposition 3: In order to control the flow of private information, people develop and use privacy rules based on criteria important to them. The theory stipulates rule-based factors that impact the management of both individual privacy boundaries (cultural, gendered, motivational, contextual, and risk-benefit ratio criteria) and collective privacy boundaries (permeability, ownership, and linkage rules). Proposition 4: When individuals grant access to their private information through disclosure or other means, that information enters into collective ownership, which represents an extension of the privacy boundary. Within that collective ownership, the discloser expects an acceptance of responsibility for the information. The recipient is made a co-owner of the information 3 The research-based CPM principles define private information according to the beliefs people articulate. That is, when people believe information belongs to them, it is private. This perceptional understanding, based in a behavioral perspective, is different from the legal orientation to privacy found in the United States. Essentially, the legal definition in the US does not concede that privacy is a property. However, in the research conducted on people’s perceptions of privacy, from a behavioral point of view, individuals define private information as something they own and should have control over.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY—October 2009 DOI: 10.1002/asi

and has implied responsibilities for managing the received information. For bloggers, information disclosed on the blog becomes co-owned by all of the people who have access to it. Therefore, anyone who sees information posted on a blog may relay that posted information to someone else who is not a part of the collective boundary or they may choose to protect the blogger’s privacy by not discussing it with anyone else. Proposition 5: Once the information becomes co-owned and collectively held, the parties negotiate collectively agreed upon privacy rules for third-party dissemination. These rules may be explicit or implicit within the conversation about the private information. CPM posits the existence of three main types of privacy rules. Permeability rules help determine how much (breadth and depth) others can know about information. Negotiations of ownership rules guide the coowners in determining how much control they have over the co-owned private information. Finally, linkage rules for private information consider who else can know the information. Proposition 6: Given that people do not consistently, effectively, or actively negotiate privacy rules for collectively held private information, there is a possibility of boundary turbulence. Boundary turbulence occurs when violations, disruptions, or unwanted mistakes are made in the way that co-owners control and regulate the flow of private information to third parties (e.g., Petronio, 1991, 2002, 2006, 2007; Petronio & Durham, 2008). Since blogging entails intentionally inviting others into a private sphere, this study focuses on the collective boundary phenomenon, examining the way bloggers navigate and potentially select ways to regulate their privacy boundaries. No measures currently exist that capture the boundary management process for bloggers from a CPM theoretical perspective. Consequently, this study seeks to take the necessary first step in developing needed valid and reliable measures to understand the way that CPM predictions play out in the world of bloggers. Hence, this research provides a way to measure the three privacy rule operations used when bloggers allow others to co-own private information. Permeability. Permeability rules refer to the amount, breadth, and depth of disclosure. Typically, when people want a significant amount of control over private disclosures they create boundary structures that reduce the possibility of information leakage or they establish boundaries with low permeability possibilities. When people exercise less control, they have a higher degree of permeability (Petronio, 2002). Choosing more or less control of information is often based on contextual requisites, goals, or personal needs (Petronio & Reierson, 2009). The avoidance of a topic clearly signals restrictions on boundary permeability (Caughlin, 2002; Caughlin & Afifi, 2004; Golish & Petronio, 2002). Bloggers might regulate privacy by avoiding certain topics, even if others raise those topics in the course of Web interactions. Permeability rule strategies are also apparent when blogger audiences reciprocate the sharing of very private or intimate disclosure. Thus, blogger audiences can

shape and alter boundary permeability needs through the co-construction process and full consideration of ownership rules (Petronio, 2002). Ownership. The second measure developed gives a way to assess privacy ownership rules.4 Ownership rules capture the extent to which the original owner of private information assumes that co-owners have control to make independent decisions about further disclosure of private information posted on a blog. Thus, ownerships rules are negotiated for people who have been granted access to private information. Synchronization of rules controlling the collective boundary are jointly established and include who has rights to distribute the information and how much they can tell others not invited as a part of the collective boundary of the blog, such as parents or peers (Petronio, 2000, 2002). Rules governing ownership are easily observed when they are violated (i.e., the CPM concept of boundary turbulence). When boundary turbulence erupts, the individuals involved discover they must change or readjust their privacy rules to guard against unwelcome intrusions by others who have inappropriately gained access to information. For example, college students may discover their parents have access to their blog through computer surveillance technology and adapt their permeability or linkage rules in light of discovering such information. A fundamental issue of blogging boundary turbulence concerns how individuals consider co-ownership of private information when deciding what to reveal on their blogs. When individuals create their blogs, they initialize privacy settings through the use of privacy customization tools, which specify ownership and control parameters. Assessing the way these tools are used can give insights into the extent to which people consider the issue of co-ownership. Some bloggers freely share the rights and privileges for personal information disclosed on their blog (Lenhart & Madden, 2005). Other bloggers directly specify restrictions to blog information use and distribution. Linkage. Finally, privacy linkage rules identify who else should be privy to private information (Petronio, 2002). Consequently, this third process involves how people negotiate the selection of others (aside from the current co-owners) allowed to be included in the collective privacy boundary. For example, a blogger may utilize their blog primarily to discuss a significant and personal healthcare need (such as cancer or HIV/AIDS) and allow similar others to be linked into the collective boundary. Establishing a linkage means 4 There are multiple domains in which privacy plays a role in society, including our interpersonal interactions, our cognitions, and our legal rights. The term “ownership” is a theoretical term from CPM theory examining privacy management and disclosure primarily from a perceptual, interpersonal, and communicative viewpoint (Petronio, 2002). As such, the CPM term of ownership carries no legal connotations. Solove (2002, 2003) provides a complete discussion of privacy and ownership from a legal standpoint. Future research should examine how individuals’ interpersonal or communicationbased notions of privacy differ from legal considerations, given the nature of how CMC transcends national borders.


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permission is given to another to become a co-owner of private information. When such a linkage occurs, the new confidant is expected to assume responsibility for protecting the information (Petronio, 2002). Once inside the collective boundary, new co-owners often contribute to the shared information, further extending the level of collectiveness. For bloggers, linkage could measure the extent to which people expend more conscious effort on using their blog as a network facilitator to link to others or how they disallow linkages. As a set, these intertwined privacy rule processes help determine the parameters of privacy management. An underlying force that drives these processes is the characterization of privacy management as a dialectical tension between openness and closedness. People need both personal control over their private information and they need to grant others access. CPM theory helps account for many situations in which privacy is at the core of decision-making and helps untangle situations that appear to be paradoxical (Petronio, 2002). In validating a measure of blogging and privacy management, we utilized a self-awareness scale, as previous research supports a positive relationship between CMC engagement and self-awareness (Doherty & Schlenker, 1991; Joinson, 2001; Matheson, Reno, & Kenny, 1992; Zanna, 1988). A selfawareness scale seems an appropriate comparison, given that personal blogs provide an exclusive forum for individuals to explore their own self perceptions (Matheson & Zanna, 1988).

A measure specifying privacy rule use and implementation has substantial theoretical and practical value. Three separate studies were conducted to create, validate, cross-validate, and examine the predictive validity of such a measure. Study One Method

Focus of the Study

Participants. College student bloggers represent the target sample of this study. To be defined as a blogger for this study, three requirements needed to be met: first, the participant needed to be a college student; second, the participant needed to have a personal blog site; and third, the participant needed to update their blog weekly, at a minimum. Thus, individuals with inactive blogs were excluded from the sample. A convenience sample of 176 college student ages 18–19 years old completed the online survey. An additional five individuals initiated the study but did not progress beyond the first page and were consequently removed from the sample. From the sample, 125 of the participants (72%) were female and 49 (28%) were male. A slightly larger proportion of the participants were 19 years old (111 participants, 65%) versus 18 years old (59 participants, 35%). Participants had access to approximately two computers in their home (M = 2.2, SD = 1.6), and both of those computers had Internet access (M = 2.1, SD = 1.6). Participants blogged an average of 4 hours per week (SD = 5.1) and had been blogging for ≈2 years (M = 22.9 months, SD = 16.6). Additional information about participants’demographics and blogging habits can be found in Table 1.

This research aims to develop a measure that relies on Petronio’s CPM theory to investigate how college students manage privacy as bloggers. Privacy and technology are intertwined, and CPM theory serves as a pivotal base from which to study this relationship (Metzger, 2007). As Petronio (2002) notes, “Everywhere we look, there are technological issues that impact privacy” (p. 224). Examining college students’ privacy rules on their blogs allows a glimpse of how they manage disclosures. Becoming a member of an online community depends in part on the user’s willingness both to disclose and to regulate private information. Wolak, Mitchell, and Finkelhor (2006) examined changes in college students’ disclosures on the Internet within the past 5 years and noted significant increases in personal disclosures. This is partly due to the increased use of the new communication technologies. What is unknown is the nature of privacy management and the impact it has on the lives of college students. CPM suggests that revealing private information affects the sense of security and well-being a person might feel (Petronio, 2002). Since disclosure of private information is central to blogging, it is important to know how college students traverse the Web by managing the tensions between balancing the need for contact and the need for privacy. Because no measure is available, the development of an instrument is the first step in gaining such an understanding.

Procedures. Survey participants were solicited to complete an online survey in a variety of ways. First, introductory public speaking students at a midsized, midwestern university received an e-mail invitation to participate in the research project and their instructors verbally reinforced the survey in class. Next, all students at the same institution were e-mailed an invitation to participate through the general university listserv. Finally, two instructors at another local area university e-mailed the survey link to their students. The survey, posted on surveymonkey.com, was anonymous and took ≈15 minutes to complete. The survey progressed through a series of nine Web pages. Radio buttons were used on the survey to minimize the number of mouse movements and clicks required by students to complete the study. Forced responses to survey questions were not utilized. Participants provided their names and e-mail addresses at the end of the study on a separate form to enter a prize drawing to win a $25.00 cash prize. Because scale development was the primary purpose of the study, several steps were taken to prepare for survey administration. Before administration of the survey, Petronio evaluated the item pool for consistency and relevance of each item with the intended construct measured, clarity and conciseness of each item to the latent construct, and completeness of the items in addressing all relevant aspects of the

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TABLE 1.

Comparative demographic information about participants from the three studies. Study 1

Study 2

Study 3

n

%

n

%

n

%

125 49

72% 28%

168 96

64% 36%

255 98

72% 28%

Live at home with parents

57

35%

89

34%

169

48%

Ethnicity White/Caucasian Asian Hispanic Black/African American American Indian Other

157 5 8 2 1 2

90% 3% 4.5% 1% .5% 1%

217 16 11 5 3 8

84% 6% 4% 2% 1% 3%

267 25 19 26 0 11

77% 7% 6% 8% 0% 3%

Family of origin Married or other two-parent household Single-parent mother household Non-family household Single-parent father household

139 18 14 4

80% 10% 8% 2%

221 27 9 4

85% 10% 3% 2%

278 46 13 11

80% 13% 4% 3%

Computer and Internet use at current residence Computer and Internet access in bedroom Computer access only in bedroom Neither computer not Internet access in bedroom

126 8 37

72% 5% 22%

206 14 44

78% 5% 17%

319 7 27

90% 2% 8%

87 30 16 7 2 26 6

49% 17% 9% 4% 1% 15% 3%

107 38 35 14 7 26 2

49% 14% 13% 5% 3% 10% 1%

182 35 25 31 28 40 2

52% 10% 7% 9% 8% 11% 1%

Biological sex Women Men

Blogging domains used by participants* Myspace.com Facebook.com Xanga.com Livejournal.com Blogger.com Two different domains Three or more different domains

Note. ∗ Blogging domains not reported by more than one person are not reflected in the table.

construct (DeVellis, 2003; Netemeyer, Bearden, & Sharma, 2003). Administration of the measure was designed to establish reliability, validity, and consistency of factor measures. Once the surveys were collected, factor structure, reliability, and validity were established through exploratory factor analysis, computation of alpha reliability coefficients, and confirmatory factor analysis techniques. Measures. Preliminary blogging privacy management measure. Blogging privacy rules were assessed via participants’ responses to 33 items designed to measure the three aspects of boundary coordination processes, as per CPM theory (Petronio, 1991, 2002), i.e., boundary permeability, boundary ownership, and boundary linkage. The initial 33 items measure the three different types of boundary coordination processes of CPM theory (Petronio, 1991, 2002) in application to blogging disclosures. Participants responded to the questions using a seven-point scale ranging from “never true” to “always true.” All three dimensions measure the degree to which privacy blogging rules for each area were coordinated to establish more restrictions to private information or greater public informational access. Higher scores on

each of the submeasures indicate greater public information access or more disclosures and less privacy. Items representing the privacy boundary permeability process (α = 0.75) included six statements such as, “When I face challenges in my life, I feel comfortable talking about it on my blog,” and “I often tell intimate, personal things about myself on my blog without hesitation.” Higher scores on the submeasure mean permeability rules functioned in a more public manner, where greater breadth and depth of disclosure occurs on the blog. Items representing the blogging privacy boundary ownership process (α = 0.70) included six items such as, “I don’t blog about certain topics because I worry about who has access” and “I usually am slow to talk about recent events because people might talk.” Negatively worded items were reverse-coded. Higher scores indicate that an individual freely shares the rights and privileges for personal information disclosed on their blog and they are less concerned about who is reading the information posted. Finally, items representing the privacy boundary linkage process (α = 0.70) included six items, such as “I create a profile on my blog so that other bloggers can link to me with similar interests” and “I try to let people know my main interests on my blog so that I can find friends.” Higher scores


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TABLE 2.

Component loadings for rotated solution of blogging privacy rule measure. Component

Items

1

Component 1: Blogging boundary permeability (α = 0.75) 1. When I face challenges in my life, I feel comfortable talking about them on my blog. 2. I like my blog entries to be long and detailed. 3. I like to discuss work concerns on my blog. 4. I often tell intimate, personal things on my blog without hesitation. 5. I share information with people whom I don’t know in my day-to-day life. 6. I update my blog frequently.

2

Desc. stats. 3

M

SD

0.72 0.70 0.68 0.61 0.54 0.43

0.10 0.09 −0.08 0.35 0.21 0.02

0.13 0.03 0.02 0.16 0.24 0.15

3.02 2.84 2.43 2.10 2.34 4.13

1.79 1.53 1.61 1.37 1.67 1.83

0.11 −0.12

0.70 0.64

−0.07 0.08

2.97 3.72

1.83 2.12

−0.06 0.27 0.18 0.28

0.61 0.51 0.46 0.45

0.06 −0.03 0.17 −0.01

2.35 4.49 3.36 3.25

1.56 1.92 2.14 1.96

0.03 −0.02 0.30 0.03 0.19 0.21

0.02 −0.07 −0.12 0.08 0.14 0.27

0.75 0.65 0.61 0.54 0.48 0.44

3.52 3.31 3.41 3.05 2.69 2.05

2.01 1.97 2.01 1.98 1.89 1.62

Component 2: Blogging boundary ownership (α = 0.70) 1. I have limited the personal information posted on my blog.* 2. I use shorthand (e.g., pseudonyms or limited details) when discussing sensitive information so others have limited access to know my personal information.* 3. If I think that information I posted really looks too private, I might delete it.* 4. I usually am slow to talk about recent events because people might talk.* 5. I don’t blog about certain topics because I worry who has access.* 6. Seeing intimate details about someone else, makes me feel I should keep their* information private. Component 3: Blogging boundary linkages (α = 0.70) 1. 2. 3. 4. 5. 6.

I create a profile on my blog so that other bloggers can link to me with similar interests. I try to let people know my best interest on my blog so I can find friends. I allow people with a profile or picture I like to access my blog. I comment on blogs to have others check out my blog. I allow access of my blog through any of these: directories, key word searches, or weblog rings. I regularly link to interesting websites to increase traffic on my blog. Note. *Denotes the item has been recoded (reverse-scored).

indicate an individual expends more conscious effort on the use of a blog as a network facilitator. Analysis. Several steps were taken to create and test this measure. First, 33 items reflecting the three privacy rule conditions on blogs were constructed. A principal components factor analysis determined which of the 33 items were most useful in measuring the three dimensions of blogging privacy rule conditions. A three-factor solution with varimax rotation was imposed a priori, in agreement with the CPM theoretical framework suggested by the empirical work on privacy rules and collective boundary coordination (Petronio, 1991, 2000, 2002). A three-factor solution with oblique rotation also was conducted and the results produced the same factor structure as the varimax rotation. Oblique factor rotation assumes the underlying factors are correlated (Tabachnick & Fidell, 2001). Since the structure was no different with varimax or oblique factor rotation, the varimax rotation results were used in the analysis.5 5 Items that did not load higher than .4 on any factor or items that loaded approximately equally on more than one factor were dropped (Pedhazur & Schmelkin, 1991; Tabachnick & Fidell, 2001). Generally, a factor loading was considered acceptable if the primary factor loading was at least double any secondary factor loadings. Three items reflected a secondary factor loading that was slightly more than half the primary factor loading. However, the differential was still of considerable size in each case. The three items were also kept because the primary loadings were theoretically consistent with the other items measured for the factor.

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Next, the LISREL 8.80 structural equation modeling program examined how well structural coefficients conformed to a theoretically hypothesized base model (Bollen, 1989; Byrne, 1998; Joreskog & Sorbom, 1996). A maximum likelihood Confirmatory Factor Analysis (CFA) was conducted with the program. The CFA techniques tested four potential models for comparison. The CFA assessed the fit of the model and tested the validity of the measure. The threefactor model incorporated items of the three latent variables (boundary linkages, boundary permeability, and boundary ownership) as measuring blogging privacy management. Results Exploratory factor analysis. To test the factor structure of the blogging privacy management measure in Study One, a principal components factor analysis was undertaken. Three criteria were used to determine the number of factors to rotate the a priori hypothesis that the measure was not unidimensional, the scree test, and the interpretability of the factor solution as consistent with CPM theory. The scree plot indicated that the initial hypothesis of the measure as multidimensional was correct. Based on the plot and CPM theory, three factors were rotated using a varimax rotation solution. Table 2 provides the component loadings for items with the rotated three-factor solution. Table 2 also contains the mean and standard deviations for each item associated


TABLE 3.

Item removed from the BPMM due to multiple, low, or inconsistent factor loadings. M

SD

1.42 5.19 4.74 3.99 4.49 2.67

1.21 1.86 1.88 2.47 2.00 1.99

1. I am certain that all the information I reveal on my blog remains under my control. 2. When I reveal personal information on my blog I expect it won’t be repeated anywhere. 3. Once information is on my blog, it seems I have no control over what happens to it.*

5.08 2.94 5.24

1.96 1.86 1.79

Boundary linkage items 1. When responding to other people’s blogs, I never leave my name to restrict access to my own blog.* 2. Typically, I don’t share my blog address.* 3. People who know me personally also have access to my blog. 4. I’m comfortable allowing open access to my blog because I feel my postings are largely anonymous. 5. No one can read my blog without my permission.* 6. When people post comments about my entries it makes me nervous to respond.*

4.78 4.09 5.24 3.07 4.23 5.67

1.97 2.03 1.81 1.77 2.41 1.63

Boundary permeability items 1. 2. 3. 4. 5. 6.

I think my parents read my blog regularly. I would be upset if my friends shared what’s written on my blog.* Everyone knows there’s a rule that you don’t share information found on a friend’s blog with an adult.* I think my parents know about my blog. Reading intimate details about someone else makes me reluctant to share on my blog.* I don’t discuss private information about my family on my blog.*

Boundary ownership items

Note. *Denotes the item has been recoded (reverse-scored).

with the corresponding submeasure. Analysis of the scree plot revealed that only minimal gains in the item variance explained occurred beyond a three-factor solution. The first factor, blogging boundary permeability, accounted for 19.54% of the item variance. The second factor, blogging boundary ownership, accounted for 9.23% of the item variance. The final factor, blogging boundary linkages, accounted for an additional 6.76% of the item variance. Collectively, the three-factor solution accounted for 35.53% of the variance in items. The remaining 15 items of the initial scale, summarized in Table 3, were removed due to either low or multiple factor loadings. Six items loaded primarily on the first factor, boundary permeability (BP). Cronbach’s alpha was computed to examine the internal consistency of using the six items as a summative scale and the items maintained adequate reliability (α = 0.76). Six items contained high loadings on the second factor, boundary ownership (BO). The six items also maintained an acceptable level of reliability (α = 0.70). Six items loaded on the final factor, boundary linkages (BL). These six items also maintained an acceptable level of internal consistency and reliability as a summative measure (α = 0.70). Overall, the 18-item blogging privacy management scale maintained good reliability (α = 0.80). The higher reliability of the collective measure may be due to the increase in the number of items. The remaining 15 items with low or multiple factor loadings were subsequently removed from the blogging privacy management measure. Confirmatory factor analysis. CFA techniques were employed to test the adequacy of a unitary versus a multidimensional, three-factor measure of blogging privacy management. Four structural models were compared with Study One data to test the factor structure and the convergent and

divergent validity of the measure. In assessing the goodness of fit of a hypothesized model to actual data, several values were provided with the analysis. LISREL provides global tests of adequacy of the entire model and tests the statistical significance of all coefficients specified in the model. The χ2 goodness of fit statistic tests the adequacy of the model’s fit to the data. A nonsignificant chi-square (χ2 ) value indicates a good fit of the data to the model specified. The χ2 goodness of fit statistic is strongly influenced by small sample sizes (Bollen, 1989; Byrne, 1998). The χ2 to degrees of freedom ratio provides a more reliable goodness of fit index, taking into account the sample size. A χ2 to degrees of freedom ratio value less than five indicates a good fit of the data to the hypothesized model. Other fit indices reported with a CFA model include the root mean square error of approximation (RMSEA), the goodness of fit index (GFI), the adjusted goodness of fit index (AGFI), and the comparative fit index (CFI). RMSEA values less than 0.05 reflect excellent fit of the data to the model, 0.05 to 0.08 reflect a reasonably good fit, 0.08 to 0.10 reflects a moderate fit, and RMSEA values of 0.10 and above reflect a poor fit of the hypothesized model to the data (Byrne, 1996). GFI, AGFI, and CFI values can range from zero to 1.00, and higher values in each area represent a better fitting model. An excellent fit would be indicated by values 0.90 and above for the GFI, AGFI, and CFI fit indices. The first model examined all 18 observed variables (items) as loading on only one blogging privacy rule factor. The second model included the three first-order factors consistent with CPM theory and the EFA results (BP, BO, and BL). In the second model the three first-order factors were not allowed to correlate. The third model allowed the three first-order factors to correlate. Finally, the fourth model involved the three first-order boundary rule factors loading on one second-order


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TABLE 4.

Summary of fit indicators for the blogging privacy rule measure (Study 1).

Models 1. 2. 3. 4.

Model one (single factor) Model two (three uncorrelated factors) Model threea (three correlated factors) Model foura (three first-order factors, one second order factor)

χ2

df

P

χ2 /df

RMSEA

GFI

AGFI

CFI

372.44 301.70 225.61 219.04

135 135 129 129