Running head: INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS
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Your Life on a Screen: The Influence of Online Communication on Modern Relationships Denise Arellano, Danielle Grommersch, Jonathan Infranco, Lindsey Lippert Point Loma Nazarene University

Author Note Research conducted by Denise Arellano, Danielle Grommersch, Jonathan Infranco, Lindsey Lippert. Department of Communications, Point Loma Nazarene University. This paper was produced as a group research project for the Communications Research and Theories (COM465) course for the Fall semester of 2013. Correspondence for this paper should be addressed to Danielle Grommersch at dgrommersch116@pointloma.edu.
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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS Abstract Computer-mediated communication has increased in recent years, making it more socially acceptable as a means of interaction. This study explored the different ways that computer-mediated communication (CMC) develops and maintains relationships. Strong

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correlations were found between the effect on one’s degree of communication satisfaction and self-disclosure on their choice of mode of communication. Findings also indicated a correlation between one’s motivation, intention, and communicator style and the perceived level effectiveness of the transaction. The level of satisfaction of a CMC interaction can be determined by one’s communicator style, level of experience with the technology, and intention of the communication. In addition, it was also found when compared to males, females are more expressive in their self-disclosure, therefore giving them more satisfying and effective CMC interactions. Keywords: computer-mediated communication, communication satisfaction

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS The Influence of Online Communication on Modern Relationships

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With the rapid development of technology, interpersonal communication has expanded into new communicative channels. These new communicative channels are changing the nature of how humans communicate altogether. According to Pew Internet and American Life (2005), there are currently 70 million Americans that go online, and 58 million Americans that use email – while numbers are still increasing. Many organizations have adopted the modern communication technology (e.g., email) to improve their organizational productivity. Past email research reported that email improved performance effectiveness, decision effectiveness, decision quality, and quality of work (Ishii, 2005). Many face-to-face relationships are initially developed and maintained by utilizing a valuable resource, commonly known as computer-mediated communication (CMC) (Craig & Wright, 2013). According to Sidelinger, Ayash, Godorhazy, & Tibbles (2008), there is existing research pertaining to CMC being generally inconsistent with acknowledging online relationships as either normative or nonnormative forms of relations. The research also concluded that typologies included using forms of mediated communication as a relational maintenance behavior. For example, if a person calls his/her spouse from work, the mere act of calling to communicate plays the relational maintenance role, regardless of the content of the conversation. Whether by email or IM, computer mediation allows relational development in similar fashion as face-to-face communication. Communication ties are strengthened when more channels of communication are opened up in an effort to maintain the relationship (Marian, Fleuriet, & Estrada, 2012). It is also important to take into consideration if the amount of CMC is the same after a relationship has progressed from an online relationship to an offline one (Guerrero & Chaves, 2005).

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“It stands to reason that many forms of electronic communication might be used to forge many different relationships,” (Marian, Fleuriet, & Estrada, 2012, p. 35). An article by Caughlin & Sharabi (2013), found that text messaging is preferred for some conflict strategies, while faceto-face (FtF) communication is preferred for others. Having people choose text messaging over face-to-face communication to avoid a conflict implies that they will probably be talking with that person again at some point and having the text conversation is undoubtedly influential on the next FtF conversation. “People who are linked closely appear to rely on multiple means of communicating” (Caughlin & Sharabi, 2013, p. 878). Research done by Xiao-Yun, Yan, & Zhong-Ming (2009), found that another factor when using CMC is that there is less socioemotional communication that takes place and more task-related communication that occurs. Email is the primary communication feature that about 48 million Americans use each day (Anderson & Wang, 2005). In CMC, the discussion is generally more direct, concrete and specific. CMC, being a more direct mode of communication allows less room for emotional argument – which tends to add confusion. In a computer-mediated conversation a person will take a side quicker and decide easier because of its clarity. In order to discover the advantages of CMC, one must dive into the literature to analyze research previously performed on this topic. Review of Literature As people have transformed and adapted over the years, so have their techniques of communicating with one another. The majority of adults are applying this updated communication model to their interpersonal relationships, using various forms of CMC (Houser, Fleuriet, & Estrada, 2012). Using the web as an environment to host these conversations provides a context free of obstacles in the material world, such as time, space and cost (Cortese & Seo, 2012). Maintaining long-distance relationships online among individuals who are not

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geographically close may require a higher level of trust and commitment (Anderson & EmmersSommer, 2006). Social Information Processing Theory (SIP) explains how people communicate online; developing and managing relationships without the use of nonverbal cues. The Social Presence Theory says that a medium’s social effects are mostly caused by the degree of social presence it affords to its users. The degree to which a communication medium can accept nonverbal cues is known as “social presence” (Cortese & Seo, 2012). Social presence theory says that different communication media changes levels of experience for individuals who are participating in communication (Bubas, 2001). Media Richness Theory represents a framework to describe a communications medium by its ability to reproduce the information sent to it (Bubas, 2001). Social Information Processing theory can be characterized within three assumptions. Despite the reduction of cues in CMC, it still leads them to develop strong impressions from the communicators’ relationship and motives. Communicators using CMC as their mode of communication are able and willing to modify communication attempts in exchanging social information successfully by the use of whatever cues CMC has provided. These cues may be content, linguistic styles, and even emoticons. Relationship processes through CMC are relatively slower than those in FtF communication. It is arguable that CMC promotes and increases self-disclosure quicker than FtF communication (Tidwell & Walter, 2002). Motivations Behind the Use of CMC Motivation is a great factor for people using CMC rather than face to face. Anderson & Wang (2005), argue that many people prefer to communicate and develop a relationship first through CMC; which is the breaking ground for many to construct a relationship. According to

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS Cho (2007), enjoying entertainment, gaining information, and maintaining interpersonal relationships are motivations for Internet usage. There are many types of communicative

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channels in the Internet such as blogging, IM, email, etc. just to name a few. Although there are many channels within the CMC, outlet each channel has its own language, motivations, behaviors, and management. According to Ying, & Gongcheng (2012), the behavior and motivation for blogging was decomposed into the orientation of interaction with others and the orientation to gather information. A more recent study by Liu, Liao, and Zeng (2007) addressed expectancy theory analyzing motivations for blogging; the study discovered that rewards played an important role in motivating bloggers. The rewards that bloggers most valued was pouring out feelings and connecting with people. Those bloggers with high motivation for rewards had a higher level of blogging intention and are willing to spend more time maintaining their blog (Ying & Gongcheng, 2012). The age of individuals using CMC has drastically decreased; approximately 94.99% of young adolescents connect to social networks between the ages of 10 and 15 years old (Colás, González, & de Pablos, 2013). Individuals use the internet because young people have a social need to share experiences and be recognized by others; thus establishing new social relationships. Colás, González, & de Pablos (2013) found that the teenagers motivation for CMC was knowing what their friends said about their picture uploads. This made them feel good when sad, and they felt positive about interactions with new friends. All of these factors resulted in fitting the model by Notley (2009) to the extent that the motivations to use social networks falls within the sphere of personal interests, as well as social needs of relational nature.

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS Intentions in Communicating Online Bo, Wei, & Dahui (2011), wrote an article explaining the use of email to enrich

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relationships, maintain and expand their social networks. Past uses for CMC were used for task efficiency; however, things have shifted and the current intent behind CMC is to build and maintain relationships online. Anecdotal and academic evidence supports the claim that individuals use CMC to keep in touch with friends and family around the world (Bo, Wei, & Dahui, 2011). “CMC and mobile phone use can be positively related to one’s life satisfaction when used for maintaining contact with close friends” (Yu, 2010, p. 18). About 37% of teenagers agree that they write messages using IM to write things they wouldn’t necessarily say in a FtF conversation, sometimes our intent to communicate something might be a bit bolder online than it would ever be in person (Anderson & Wang, 2005). One aspect of CMC that is especially attractive to teenagers is the ability to blog, and sending photos and videos out to their friends (Subrahmanyam, 2008). Even though older adults are more likely to use CMC, younger people are more likely to develop positive feelings towards the use of CMC (Pornsakulvanich, 2007). Degree of Experience and Its Influence on CMC Wrench & Punyanunt-Carter (2007), discuss an individual’s level of experience and knowledge of CMC. They look closely to find specific influences on usage and competency patterns. “CMC competence starts with the notion that people must be motivated to be competent in a CMC environment, possess specialized knowledge and technical know-how, and learn the conventions, rules, and roles that affect CMC interactions” (Wrench & PunyanuntCarter, 2007, p. 355). Studies have shown that relationships developed online, are no less developed than FtF relationships (Schiffrin, 2010). “A competent CMC user shows attentiveness

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or interest and concern for one’s CMC interaction partner. By interaction management, the user attracts a CMC partner by engaging a partner actively and controls the time and relevance of communication. Expressiveness or filling the CMC interaction with emotion is a skill of a competent CMC user. Composure is another skill associated with competence, including displaying confidence, mastery, and comfortableness as a CMC interactant” (Wrench & Punyanunt-Carter, 2007, p. 356). Communicator Style and Self-Disclosure Academic and business researchers have concluded that the development and use of effective communication strategies is a critical skill set for all managers. These skills have been directly linked to both individual effectiveness (e.g., opportunities for promotion, special assignments, team effectiveness) and to organizational effectiveness and bottom line performance. One study recorded mobile phone use, and said “quality and strength of interpersonal relationships are affirmed by the length of the mobile phone conversation and frequency of text messaging” (Bakke, 2009, p. 1). This parallels with current ways that individuals use the computer as a means of communication. Today’s university students, the millennials, have been characterized as being technologically sophisticated and capable of multitasking, yet seriously deficient in oral, written, and interpersonal communication skills. As business educators, our responsibility is to address these shortcomings in ways that are both appealing to the student and ultimately effective in the global marketplace. We believe that educating students about the concepts of style-typing and style-flexing will help them become more effective communicators by deepening their understanding of the communication process (Hartman & McCambridge, 2011).

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While communication style typing and style flexing are not the only communication tools that could be emphasized when educating millennials, they encompass many of the essential skills that address the millennial’s communication challenges. By understanding and employing the practice of style typing and style-flexing, a sense of camaraderie among communicators is built and some of the barriers to effective communication can be eliminated. Millennials will benefit from developing a broadened understanding of the multiple styles of communication that are personally available to them. Additionally, they will benefit from learning that others might very likely have a different preferred style (Hartman & McCambridge, 2011). Therefore, in order to become more effective communicators, millennials should use multiple styles to supplement their toolkit with these available resources, knowledge of the different styles, and the ability to apply that knowledge as they endeavor to communicate with others. Developing style-typing and style-flexing skills will serve as building blocks for millennial’s subsequent interpersonal skill development in key areas such as: audience analysis, active listening, conflict management and negotiation, effective team building. Style typing and style flexing require individuals to attend not only to their own preferred communication practices but also to those of the individuals with whom they are interacting, serving, and leading (Hartman & McCambridge, 2011). According to Walther (2011), some CMC interpersonal relationships develop more rapidly than FtF communication. When the sender is able to carefully form an identity and maintain relationship online, closeness develops. Through CMC, one can edit messages, personal profiles, and create an overall positive persona. The receiver has a tendency to form a more positive ideal of the identity of the sender, and the intensity of self-disclosure will increase (Anderson & Emmers-Sommer, 2006).

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS The mode of communication and the motivation of people choosing to communicate through technology is not the only elements that come into play when measuring the effect of CMC. According to Anderson, & Wang (2005), self-disclosure is an essential and crucial element on relationship maintenance. There are three different dimensions that are proposed of self-disclosure, which

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contributes to different levels of self-disclosure within a relationship. The three dimensions are breadth, the number of topics one is comfortable to discuss; which leads to the second dimension of depth, measured in intimacy and emotions. The last dimension is frequency, how often selfdisclosure occurs in a conversation. Other sub dimensions of self-disclosure are valence, whether the message being disclosed have good or bad implications; duration how long one or both people spend in self-disclosing in one single conversation; and reciprocity stating whether each person in the conversation is self-disclosing at the same level (Anderson & Wang, 2005). Craig & Wright (2012), suggest that it is important “to study self-disclosure in CMC relationships, due to the relatively limited contextual cue and largely asynchronous nature of communication in most CMC contexts.” Anderson & Wang (2005), stated that long distance relationship disclose greater in face-to-face communication rather through CMC. In another study done by Anderson & Wang (2005), in Long distance relationships, he found that people self-disclose less to avoid bringing up any sensitive or problematic issues during long distance communication rather than in FtF communication. While in another study it suggests that people are more likely to self-disclose via CMC that through FtF communication (Archer, 1980; Johnson, 2001; & Rheingold, 1993). Online self-disclosure is often times more open and more expressive because there is a certain element of anonymity. When speaking FtF with another person there is a slight barrier

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that goes up to censor what we express; however, the sensor is less up online. We are willing to be more vulnerable to our anonymous cyberspace friends (Bareket-Bojmel & Shahar, 2001). Relational uncertainty exists in all relationships, and that appears to be the motivation behind the increase in self-disclosure “during the process of relational development [and] maintenance, especially among people who are geographically separated or who frequently on media for relational maintenance” (Craig & Wright, 2013). An increase in online self-disclosure tends to lead relationships to perception of greater predictability (Tidwell & Walther, 2002). The way people self-disclose is dependent on whether the receiver is being attentive and the way the sender is expressing the self-disclosed message. In Anderson & Wang’s study (2005) they have found that 43.6% of long distance relationships/friendships maintained their relationship because of the use of the Internet and self-disclosure. CMC Effectiveness Vs. FtF Interactions According to Pew (2000) study, 55% of internet users have said that their email exchanges have improved and enhance relationships with family members; 66% said email had maintained their relationship with friends; and that women make this statements more than men. CMC is not only a communication tool that allows only the conversation of two, but also invited the conversation of more than two people to be in a single conversation (Anderson & Wang, 2005). It has evolved in the sense that you don’t have to have a face-to-face conversation to have an interaction with more than one person. Anderson and Wang say that this new age of information will reshape our relationships and the way in which we communicate with one another (Anderson & Wang, 2005). This reshaping of relationships incorporates new ideas and conversations between multiple individuals, resulting in collaborations. Bubas explores CMC theories and phenomena,

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including factors that influence collaboration over the internet. He says that because CMC lacks nonverbal cues, uncertainty is increased as well as the possibility for misunderstanding. Social presence is lost in CMC and it could lead to depersonalized and short, business-like communication rather than FtF interactions that show friendliness and emotion (Bubas, 2001). Five factors that can support virtual team effectiveness include having a supportive organizational culture, some characteristics of the task itself, technology use, team member characteristics supported by training and development, and work and team processes (Cohen & Gibson, 2003). Relatively simple organization work or team tasks can be completed virtually without the presence of higher-level collaboration skills and trust (Jarvenpaa & Leidner, 1999), but the highest levels of effectiveness require these characteristics, along with leadership and systems support (Klein & Kleinhanns, 2003). Organizational level support, for example, could include norms developed to enhance a positive virtual teamwork culture (Whitener, Brodt, Korsgaard, & Werner, 1998). Formation activities such as establishing easy communication, understanding the technology to be used and technology training, and creating explicit startup norms and expectations for team members are more critical in virtual team start-up than in collocated teams (Gibson & Cohen, 2003). This initial work provides the common grounding needed to bridge differences between team members and helps in developing a basic operating structure that aids in creating stability and shared expectations so that tasks can be completed. In certain situations and with certain tasks, virtual teams have created superior performance (Jarvenpaa, Rao, & Huber, 1988; Maynard, 2006; Schmidt, Montoya-Weiss, & Massey, 2001). In other situations; however, especially when the team is under time constraints, virtual team performance has been found to be less than face-to-face team performance (Graetz, Boyle, Kimble, Thompson, & Garlock,

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1998; Hollingshead, McGrath, & O’Conner, 1993). Possibly, the variance in performance may be dependent on the efforts and knowledge expended by leaders when establishing n the virtual or face-to-face teams, or perhaps the variance is dependent on the amount of experience with virtual work among the virtual team members, or possibly due to other social factors. Uncertainty still needs to be resolved on any team, and this takes more time on the virtual team compared with the face-to-face team (Berry, 2011). Communication Satisfaction in Online Relationships Communication is the cornerstone for effective relationships, but having and maintaining communication satisfaction within a relationship is a long, difficult process (Thomas, BoothButterfield, & Booth-Butterfield, 1995). According to Anderson & Emmers-Sommer (2006), when an individual is content and satisfied with his or her relationship they have reached “relationship satisfaction.” There are various factors to how communication satisfaction can be achieved. Burleson, Kunkel, & Birch (1994), discovered that the similarity in communication values suggested relationship satisfaction. Interpersonal variables are also additional predictors suggesting marital satisfaction in a study done by Neimeyer (1984). In online communication, however, there are other predictors that take lead in communication maintenance and satisfaction. Wright (2004), examined maintenance strategies used in online non-romantic relationships and found that openness and positivity were the two most common strategies. When an individual feels highly committed with an online partner and is anticipating future interaction in an offline basis, then the perceived commitment of meeting in an offline context influences satisfaction. Rusbult & Buunk (1993) found that online couples that showed high relationship satisfaction were also more intimate. Their high levels of selfdisclosure led to higher levels of commitment, resulting in higher satisfaction.

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Following Walther’s suggestion that online interactions are prone to a cyclical process in which participants have the ability to selectively edit messages and choose the information shared to present themselves in a positive and over-attributed sense. This cyclical process influences and predicts how an online relationship will shape their similarities, commitment, intimacy, trust, confidence, and communication satisfaction; which are all positively related to form overall relationship satisfaction (Gottman, 1999). Research Questions Although all of these variables are predictors of successful FtF relationships it is hard to predict the same variables and the affect in online communication and relationships. There is a broader sense when it comes to online communication because of its huge arena of vagueness and misinterpreted messages. It is important to examine variables to predict significance in the level of satisfaction in online relationships, because a large portion of relationship satisfaction can be predicted by the stability of a relationship (Rohlfing, 1995). Therefore, we have configured the following research questions: RQ1: Does technology poses a positive or negative effect on communication in comparison to traditional face-to-face methods? RQ2: To what extent does computer-mediated communication influence communication effectiveness in modern relationships? RQ3: To what extent does computer-mediated communication influence communication satisfaction in modern relationships? RQ4: Is there a statistically significant difference between the way in which males and females view and use computer-mediated communication?

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS Method Participants The participants for this study were 160 students at Point Loma Nazarene University. The respondents ranged from freshmen to senior in class rank, varied in age, and included multiple races and both sexes. Upon receipt of the survey participants were informed of their

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rights to privacy and told that they were not obligated to complete the survey. The ages of each respondent was recorded as well as their sex. Ages of participants ranged from 18 to 24. For race/ethnicity participants were given the choices of American Indian/ Alaskan Native, Asian/ Pacific Islander, Black/ African American, Hispanic/ Latino, White/ Caucasian, and Other. Procedure This research was conducted through the distribution of a survey containing forty-six Likert-type questions. The survey questions were all obtained from previously developed and thoroughly tested and deemed reliable questionnaires regarding user use and satisfaction with computer-mediated communication (CMC). The source of the survey questions was a questionnaire entitled “CMC COMPETENCE” which measures “how people use various computer-mediated communication (CMC) technologies for conversing with others” (Spitzberg, 2002, p. 1). After finalizing the survey it was sent to the Institutional Review Board (IRB) at Point Loma Nazarene University for approval and were then distributed within the targeted population. The questions in the survey measured several different aspects of each respondent’s uses and successes with computer-mediated communication. These aspects, or independent variables, includes mode of communication, self-disclosure: attentiveness, self-disclosure: expressiveness, motivation, level of experience and knowledge with the technology, intention of the

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS communicated message, effectiveness of the message, and communicator style. Responses ranged on a scale of 1-5, with one being totally untrue, two being mostly true, three being

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undecided, four being mostly true, and five being totally true. Once all of the 160 surveys were distributed and completed the results were anonymously entered into SPSS, a computer based survey and statistical results program. Surveys that were either incomplete or biased were discarded. Negatively valence questions were reversed in their scoring so as to not skew the results. The appropriate statistical tests were run on the data in order to produce the most accurate and appropriate results to determine the effects of each aforementioned independent variable on the dependent variable, computer-mediated communication. The statistical tests that were run on the data include t-Test, regression analysis, summated variables, and a correlation matrix. “The objective of simple linear regression is to predict a person’s score on a dependent variable from knowledge of their score on an independent variable.” (Jaeger, 1990, p.333) Using the output created from these statistical tests we created statistical tables in order to organize and compare the results to make them usable in determining the effects of each independent variable on the dependent variable. Instrumentation The research chose to focus its attention on the impact of several independent variables on one dependent variable, the satisfaction of computer-mediated communication interactions. These independent variables consisted of mode of communication, self-disclosure: attentiveness, self-disclosure: expressiveness, motivation, level of experience and knowledge with the technology, intention of the communicated message, effectiveness of the message, and communicator style.

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After the results were entered into the SPSS data software, many statistical measurements were produced. There were three demographic measurements regarding age, sex, and race. Our descriptive statistics measure and explain the mean, standard deviation, range, and Chronbach’s alpha score for each independent variable (Refer to Table 1). Table 1 Variables Motivation Effectiveness Communicator Style Communication Satisfaction Mode of Communication Intention Self-Disclosure: Expressiveness Level of Experience Self-Disclosure: Attentiveness Age Mean 3.21 3.50 3.79 3.78 3.89 3.39 3.55 3.61 3.91 19.36 Std Dev .78 .65 .75 .64 .73 .81 .71 .73 .64 1.38 Alpha .75 .72 .76 .83 .81 .80 .76 .70 .75 Range 1.17-5.0 1.8-5.0 1.2-5.0 1.2-5.0 1.8-5.0 1.0-5.0 1.33-5.0 1.25-5.0 1.83-5.0 18.0-24.0

Communication Satisfaction. Communication satisfaction is defined as “the degree to which an individual is content and satisfied with his or her relationship.” (Anderson & EmmersSommer, 2006) The questions regarding communication satisfaction were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this
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variable is “I enjoy my CMC interactions with others.” The alpha recorded for this dependent variable is 0.832, making it statistically significant. Mode of Communication. In this case, the mode of communication refers to the technological vehicle used in the transaction of communication. The questions regarding mode of communication were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I choose which medium to send messages by based on how lively the interaction needs to be.” The alpha recorded for this independent variable is 0.812, making it statistically significant. Self-Disclosure: Attentiveness. This measure includes the breadth, depth, frequency, reciprocity, valence, and duration of the communication. (Anderson & Wang, 2005) The questions regarding self-disclosure: attentiveness were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I send comforting messages to others when I sense they are down.” The alpha recorded for this independent variable is 0754, making it statistically significant. Self-Disclosure: Expressiveness. This measure includes the breadth, depth, frequency, reciprocity, valence, and duration of the communication. (Anderson & Wang, 2005) The questions regarding self-disclosure: expressiveness were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I am expressive in my CMC conversations.” The alpha recorded for this independent variable is 0.757, making it statistically significant. Motivation. Motivation is one’s reasoning for a preference to communicate and develop relationships through CMC rather than face to face. (Anderson & Wang, 2005) The questions regarding motivation were taken from a survey compiled by Spitzberg (2002). An example of

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS the questions used in the survey to measure this variable is “I never get nervous using CMC.” The alpha recorded for this independent variable is 0.752, making it statistically significant. Level of Experience and Knowledge. Experience in this circumstance refers to the

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extent and frequency of one’s use of CMC. (Wrench & Punyanunt-Carter, 2007) The questions regarding level of experience and knowledge were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I am usually the one who initiates new topics and/or agendas.” The alpha recorded for this independent variable is 0.695, making it statistically significant. Intention. Intention refers to an individual’s reasoning behind the sending of a message. The questions regarding intention of the communicated messages were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I get my ideas across clearly when I use CMC.” The alpha recorded for this independent variable is 0.800, making it statistically significant. Effectiveness. Effectiveness is the degree to which the intention of the message, determined by the sender, is interpreted as such by the receiver. The questions regarding effectiveness of the communication were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I generally get what I want out of my CMC interactions.” The alpha recorded for this independent variable is 0.715, making it statistically significant. Communicator Style. A person’s communicator style is concerned with the specific way in which they send their communicative messages to others. The questions regarding communicator style were taken from a survey compiled by Spitzberg (2002). An example of the questions used in the survey to measure this variable is “I never say things in CMC that

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hurt/offend the other person.” The alpha recorded for this independent variable is 0.832, making it statistically significant. Other findings include, that when compared to each other, every independent variable shows a statistically significant correlation to each other. This can be seen in Table 2. This can be interpreted to mean that when participating in computer-mediated communication your communication satisfaction, mode of communication, level of attentiveness and expressiveness, motivation, level of experience, and communicator style all have an effect on one another. Table 2 Correlation Matrix (N=160) Variable 1. Motivation 2. Effectiveness 3. Communicator Style 4. Comm. Satisfaction 5. Mode of Comm. 6. Intention 7. Self-Disc: Expressiveness 8. Level of Experience 9. Self-Disc: Attentiveness 1 2 3 4 5 6 7 8 9

.54** .22** .46** .21** .2* .26** .34** .25**

.38** .55** .35** .47** .35** .32** .32**

.47** .32** .33** .22** .21** .31**

.37** .4** .36** .41** .4**

.31** .42** .52** .38** .35** .48** .52** .36** .58** .53**

Notes. * correlation is significant at p < .05; ** correlation is significant at p < .01 Results Sample Characteristics The sample included 67 males and 93 females, for a total of 160 participants (n=160). The mean age was 19 years old including n=59 (18 years old), n=36 (19 years old), n=31 (20 years old), n=22 (21 years old), n=8 (22 years old), n=3 (23 years old), n=1 (24 years old). The demographics for this sample were representative of their population with respondents being
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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS 80.6% White/Caucasian (n=129), 9.4% Hispanic/Latino (n=15), 4.4% Asian/Pacific Islander (n=7), 3.1% Black/African American (n=5), 1.9% responding as Other (n=3), and 0.6% American Indian/Alaskan Native (n=1). The other demographic measured by this survey was sex, also representative of the population with respondents being 58.1% female (n=93) and

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41.9% male (n=67). Further analysis showed no statistically significant differences based on age and race. However, sex did have a statistically significant difference on respondent’s uses and success with CMC. Research Question 1 In this research we attempted to determine if technology poses either a positive or negative effect on communication in comparison with traditional face-to-face (FTF) methods. This research question was tested by running a stepwise multiple regression with mode being the dependent variable against all other independent variables. We found that two independent variables- communication satisfaction and self-disclosure: attentiveness- were statistically significant predictors of our dependent variable, mode of communication. The equation resulted in R²= 0.302, p < .01 (Refer to Table 3). Table 3 Stepwise Regression of Mode of Communication (N=160) Variables Self-Disc: Attentiveness Comm. Satisfaction (Constant) R2 .27 .30 R2cha .27 .03 b .6 .22 1.06 t 7.64*** 2.71**

Note. Standard Error = .62; Adjusted R2 = .03 *p < .05. **p < .01. ***p < .001.

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We can interpret this to mean that approximately 30% of the variance in scores on choice of different modes of communication can be explained by the level of communication satisfaction and the amount of attentiveness given to the needs of the other person. Research Question 2 In this research we attempted to determine if demographics, satisfaction, the mode of communication, and/or one’s communicator style have any influence, and to what degree, on the effectiveness of the communication. This research question was tested by running a stepwise multiple regression against all other independent variables with the exception of communication satisfaction, with communication effectiveness being the dependent variable. We found that three of the independent variables- motivation, intention, and communicator style- were statistically significant predictors of our dependent variable, effectiveness. The equation resulted in R²= .454, p < .01 (Refer to Table 4). We can interpret this to mean that approximately 45% of the variance in scores on communication effectiveness can be explained by the motivation behind the communication transaction, the intention of the communicated message, and the communicator’s style. Table 4 Stepwise Regression of Effectiveness (N=160) Variables Motivation Intention Communicator Style (Constant) R2 .29 .43 .45 R2cha .29 .14 .03 b .45 .3 .15 .87 t 8.03*** 6.11*** 2.81**

Note. Standard Error = .49; Adjusted R2 = .44 *p < .05. **p < .01. ***p < .001.

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS Research Question 3 In this research we attempted to determine user’s degree of satisfaction with

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computer-mediated communication (CMC) and the effect that has on their choice of mode, level of self-disclosure, motivation, amount of experience and knowledge, intention, and communicator style. This research question was tested by running a stepwise multiple regression test against all other independent variables with the exception of effectiveness, with communication satisfaction being the dependent variable. We found that four of the independent variables- communicator style, motivation, level of experience/knowledge, and intention- were statistically significant predictors of our dependent variable, communication satisfaction. The equation resulted in R²= .426, p < .01 (Refer to Table 5). We can interpret this to mean that the communicator’s style, the motivation behind the communication transaction, the level of experience and knowledge of the communicator, and the intention of the sent message can explain approximately 43% of the variance in scores on communication satisfaction. Table 5 Stepwise Regression of Communication Satisfaction (N=160) Variables Communicator Style Motivation Level of Experience Intention (Constant) R2 .22 .35 .4 .43 R2cha .22 .14 .05 .03 b .4 .31 .21 .14 .95 t 6.6*** 5.78*** 3.53** 2.61**

Note. Standard Error = .49; Adjusted R2 = .41 *p < .05. **p < .01. ***p < .001.

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INFLUENCE OF ONLINE COMMUNICATION ON RELATIONSHIPS Research Question 4

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In this research we attempted to determine if sex, either male or female, had an effect on one’s satisfaction and success when using computer-mediated communication. In order to compare males and females in their responses regarding all of the independent factors that affect their computer-mediated communication tested this research question, a t-Test was run to determine that there is a statistically significant difference between males and females in regards to their amount of self-disclosure: expressiveness (Refer to Table 6). Females tend to be more expressive in their messages when using computer-mediated communication than males. Table 6 t-Test Comparing Sex Differences Among Variables

Variables M Motivation Effectiveness Communicator Style Comm. Satisfaction Intention Self Disc: Expressiveness Level of Expertise Self Disc: Attentiveness Mode of Communication *p