Guidelines for the Methods and Results paper Below are the step-by-step instructions for completing the paper due in Lesson 12. Follow these steps, and use the knowledge you've gained this semester to analyze the data. You do not need to write up the implications of your findings. That will be included in the paper due in the next lesson. Step 1: Start by cleaning the data. You have practiced this in the lab assignments. Be sure to look for any data entry issues and any missing data. Decide how best to handle the missing data. Step 2: Identify your variables. For our iPhone project, we were working with these research questions and hypotheses: RQ1: In general, how satisfied are iPhone users with their iPhone? (variable 5: GEN_SATISFIED) RQ2: What are the top five most commonly used social media that iPhone users use on their cell phone? H1: people who are willing to purchase another iPhone in the future have higher level of customer loyalty/commitment and customer satisfaction than those who are not willing to do so. (Please note that you need to combine items 1418 into a single variable called consumer commitment, and then please use the score of the single variable for analysis. To compute the consumer commitment variable, simply take the mean score of the 5 items, which are question 14, 15, 16, 17, and 18. To learn how to compute a mean score in SPSS, please view this video: https://www.youtube.com/watch? v=ReWZeNQXoGY) (Please note that both the commitment and the satisfaction scale need to be computed and used to test this hypothesis, you can find instructions below) H2: females are more likely to search for information on products they may buy on their cell phone than males. (Products they may buy is the first variable in question 8 called PRODUCTS) Please find below two more hypotheses, and you can get 5 points of extra credit for correctly testing and reporting each hypothesis. Extra Credit Hypothesis: H3: iPhone owners with high social media use on their phones have greater product loyalty than iPhone users with low social media use on their phones H4: iPhone owners who search for personal information more than business information have greater product satisfaction than iPhone owner who use their phones to search for business information more than personal information Our seven variables for the study are: 1. 2. 3. 4. 5. 6. Social media types Social media use Product (or customer) loyalty/commitment Product (or customer) satisfaction Types of Internet searches (products, events, news, etc.). Types of communication via cell phone (this includes texting, calling, emailing, etc.) to connect with family and friends 7. Level of phone use to connect with family and friends (are people high vs low users of the phone to connect with family and friends) Step 3: Identify the questions in the survey that fit with each of the variables. What questions did we ask that provide information for each of the variables? For example, what questions did we ask to find out whether people are willing to purchase another iPhone in the future? At this point, it would be a good idea to print out the survey instrument and write on it, or you can type on an electronic version. Also, you might want to print out the Variable View screen in SPSS and make notes about which of the SPSS variables fit with each of the variables in our study. Some are very clear and some are not. Spend some time making the connections. It will make future steps easier. Step 4: Check reliability and combine measures for scales. In our survey instrument, we used two scales - one for loyalty/commitment and one for satisfaction. For loyalty/commitment we asked five questions that need to be combined into one score per participant. And, for satisfaction, we asked three questions that need to be combined. However, before we combine the measures, we need to be sure that they are reliable. Earlier in the semester we discussed the importance of reliability in our measures. When we combine multiple measures (questions) to give us a single variable, we need to be sure that the measures are closely related. To test for this, we use Cronbach's alpha. Let's look at how we run this, using loyalty/commitment as an example. In SPSS you open Analyze Scale Reliability Analysis A box will pop up. Select all of the items that make up one variable. In this case, select all of the COMMIT measures (there are five of them), and send them to the Items box. Near the bottom of the box you will see a place to type the name of the scale. In this case, we are looking at loyalty/commitment. So type that in the box. And then, click OK. The results of the test will appear. Please note that this analysis was done based on last year's data. You will need to redo the analysis using this year's dataset that you all collected and then report your findings. These results tell you that 93 of the 94 cases were used in the analysis. Your Cronbach's alpha score is . 923, and you included 5 measures (N of items) in the analysis. So, how do you interpret the alpha score? The score can be anywhere between .1 and 1.0. However, you want to see a score or .7 or above. Scores between .7 and .8 are acceptable, between .8 to .9 are good, and between .9 and 1 are very strong. This score is very strong. What does that mean? It means you can be confident that the items are all measuring the same concept. So, you can combine them into a single variable. Here's how you do that. As you saw in the SPSS videos, you can create a new variable by selecting Transform Compute Variable. In this case, you will name the new variable Loyalty_Commit. Next, in the Function Group box, select Statistical and you will be given a number of options in the Functions and Special Variables box. Click on Mean. That will populate the correct format in the Numeric Expression box. Notice above how you must select each of the measures of COMMIT and send them over to the Numeric Expression box. Be sure they are in the parentheses, and there is a comma between each. When you are finished, and the screen looks like the one above, click OK. Check the Variable View tab, and you should see the new variable Loyalty_Commit. Now, try following the same process for satisfaction. Keep track of the reliability score, because you will need it when you write up the methods. Step 5: Write up your method. You will title this section of the paper Methods. Begin the section by stating the method that you used in the study (survey) and how the sample was identified and recruited to participate. Next, discuss each of the variables in the study. For each of the seven variables, give a one-sentence description of what it is, then give an example of a measure used. Provide citations for the measures that we used. Refer to the document titled Variables for the iPhone Survey in the Course Project folder on Angel. Not every variable has a citation, but find as many as you can. Report the reliability for the two scale variables, meaning you will tell the reader what you found when you ran a Cronbach's alpha for the measures. After discussing variables, tell the reader about any other items that were used in the survey (demographic questions). Take a look at the paper that I uploaded to Angel as an example if you feel stuck. I think it will give you some ideas of how to write this up. Step 6: Begin your Results section. This section should be titled Results, and it will tell the reader what you found. You begin the results section by stating the number of people who were asked to participate in the survey and the number who agreed to participate. In total, the class invited 140 people to participate, and you know by the number in the SPSS file how many accepted. So what is the response rate? State that in your results section. Next, run descriptive statistics on the demographic information and report the results. This disclosure helps the reader decide whether your sample reflects characteristics of the population. Following the demographics, you will answer your research questions and test your hypotheses one at a time. You begin with the first research question or hypothesis and summarize what it stated. Then, tell the reader what test you used to analyze the data, and report the results using APA style. Our first research question is: RQ1: In general, how satisfied are iPhone users with their iPhone? We asked questions in the survey that will help you answer this question. A simple descriptive analysis should be all you need. Take the first extra credit hypothesis as another example, but again, the analyses below are based on last year's data, and you can follow the instruction to redo the analysis using this year's data: H3: iPhone owners with high social media use on their phones have greater product loyalty than iPhone users with low social media use on their phones This one is a little tricky because we want to compare high social media users with low social media users, but we don't have a categorical variable for that. What we do have is the number of minutes that each person spent on social media yesterday. Could we use that to create two groups? Yes we can, and here is how we do it. (NOTE: This was in the ANOVA video that corrupted, so I am spelling it out here in detail since you didn't get to see it earlier.) The first thing we want to do is divide the two groups. Using a frequency count we can find the median (middle score) and then put everyone below that number in the LOW group and everyone above the number in the HIGH group. Go to Analyze Descriptive Statistics Frequencies. Then, choose the variable that you want to group. In this case, we want to divide the responses to Social Media into two groups. Send that variable to the Variable(s) box. Before you click on OK, click on the Statistics button. And you will get the option to identify cut points for two groups. In the future, you could cut a variable into more groups. Click Continue and OK, and you will get a frequency and a midpoint for the range. See, below, the top box tells you that the 50th percentile is \"45.0000\" - that means that people who said they used their phones for social media from 0 to 45 minutes were in the lower half of the range, and people who used their phones from 46 to 600 minutes were in the top half of the range. Let's create a new variable that will tell us who is in the lower half and who is in the upper half. Here's how we do it. Go to Transform Recode into a Different Variable. You will see a screen like the one below. From the list of variables, choose the SocialMedia variable, and send it to the Numerical Variable box. Then, create a name for the new variable in the Name box. I have chosen SM_two_groups. After you type it, click Change, and it will populate in the Numeric Variables Output Variables box. Now, click on the Old and New Values button. You will see a screen like the one below. What we want to do is assign everyone who spent 0 to 45 minutes to the low social media use category, and we want to assign everyone who spent more than 45 minutes to the high social media use category. We'll assign the value of 1 to the low category, and the value of 2 to the high category. On the left we will indicate the values from the SocialMedia variable that we want to convert and on the right we will indicate the new values that we want to assign. So, let's start with the low social media group. Click on the Range option, and tell SPSS who should be in the low group (0 to 45). On the right, we will indicate the value for our low group (which is 1). Click the Add button, and the value will show up in the Old New box. See how that looks below. Next we create the high social media user group by entering the upper range of use (45 to 1000...you can enter 600 instead of 1000 since that was the highest). For the new value, we will assign a 2. Click Add, then click Continue, and then click OK. Check the Variable view, and you should see the new variable SM_two_groups. Now you can use that variable to compare two groups. Run your analysis to test the hypothesis #3 using this new variable as your grouping variable. Write up the results that you find. Did you support your hypothesis with the data? That means, did you find that your statement was correct? Or is the hypothesis not supported because the data are not significant? Don't write up interpretation here. In other words, you don't need to tell the reader what Apple should do based on the findings. You will write that in the next paper, due next week. Step 7: Continue to answer your research questions and test your hypotheses one at a time. You will continue to do this until you have answered all research questions and tested all hypotheses. At that point, you are finished with the methods and results sections. Feel free to run more than one statistical test to answer your research questions, if you feel that will provide a more comprehensive answer. Be sure your paper is double spaced. When you are finished, upload your paper to the drop box. Good luck!! $FL2@(#) IBM SPSS STATISTICS 64-bit MS Windows 24.0.0.0 ####=###########W#########Y@18 Jul 1621:55:06 ###########################STUDENT ###StudentID ########################SURVEYNU####Participant Survey Number ########################OWNIPHON####Do you own an iPhone? ########################PRIMPHON####Is it your primary phone? ########################LONGIPHOb###How long, in terms of years, have you owned an iPhone, including current and any previous iPhones? ########################GENERATI1###Which generation of iPhone do you currently use? ########################GEN_SATI4###In general, how satisfied are you with your iPhone? ########################VOICECALq###What was the combined number of voice calls and voicemails you made and received on your mobile phone yesterday? ########################BIZ_VOIC####How many were for business? ########################PERSONAL####How many were personal? ########################TEXT e###Yesterday, what was the combined number of text messages you sent and received on your mobile phone? ################# ### ##BIZ_TEXT####How many were for business? ########################PERSON_T####How many were personal? ########################PRODUCTSI###Did you search for this type of information on your cell phone yesterday? ########################NEWS_8 I###Did you search for this type of information on your cell phone yesterday? ########################EVENTS_8I###Did you search for this type of information on your cell phone yesterday? ########################BIZ_INFOI###Did you search for this type of information on your cell phone yesterday? ########################ART_8 I###Did you search for this type of information on your cell phone yesterday? ########################INFO_PP I###Did you search for this type of information on your cell phone yesterday? ########################SPORTS_8I###Did you search for this type of information on your cell phone yesterday? ########################SCHOOL_8I###Did you search for this type of information on your cell phone yesterday? ########################TRENDS_8I###Did you search for this type of information on your cell phone yesterday? ########################OTHER_8 I###Did you search for this type of information on your cell phone yesterday? ########################TIME q###Yesterday, how much time (in minutes) did you spend searching online and reading the results on your cell phone? ########################MORE_TIMa###Did you spend more time searching and reading information for business/work or for personal use? ########################FACEBOOK####Facebook########################MYSPACE ####Myspace ########################VINE_10 ####Vine########################TWITTER ####Twitter ########################YOUTUBE ####Youtube ########################LINKEDIN####Linkdin ########################INSTAGRA ###Instegram ########################TUMBLR_1####Tumblr ########################GOOGLEPL####Google+ ########################PINTERES ###Pinterest 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your cell phone?########################PHONE f###Yesterday, how much time (in minutes) did you spend connecting with family and friends on your phone? ########################COMMIT1 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how likely are you to use the Apple brand in spite of competitors' deals? ########################COMMIT2 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "I prefer the Apple brand to other brands?" ########################COMMIT3 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "I will buy additional products from Apple?" ########################COMMIT4 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "I will recommend the Apple brand to my friends and family?" ########################COMMIT5 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "I will continue to use this brand because I am satisfied with this?" ########################SATIS1 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "I am happy with the Apple organization?" ########################SATIS2 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "Both the organization and customers benefit from their relationship?" ########################SATIS3 ###On a scale of 1-5 from Strongly Disagree to Strongly Agree, how strongly do you agree with the statement, "Customers enjoy dealing with the organization?" ########################PURCHASE2###Would you purchase another iPhone in the future? ########################AGE ####What is your age? ########################GENDER ####What is your gender? ########################EDUCATIO)###What is your highest level of education? ########################INCOME ,###What is your total household income level? ########################RACE !###What is your race and ethnicity? ##############?#Yes #######@#No ##########################?#Yes #######@#No ##########################?#iPhone #######@ iPhone 3G #######@ iPhone 3GS #######@#iPhone 4 #######@ iPhone 4S #######@#iPhone 5 #######@ iPhone 5C ###### @ iPhone 5S ######"@#iPhone 6 ######$@ iPhone 6S ######&@ I don't know ##########################?#Very satisfied #######@#Somewhat satisfied #######@#Neutral#######@#Somewhat dissatisfied #######@#Very dissatisfied ##########################?#Yes #######@#No ##############################?#Yes #######@#No ##########################?#Yes #######@#No ##########################? #Yes #######@#No ##########################?#Yes #######@#No ############*#################?#Yes #######@#No ##########################?#Yes #######@#No ##########################? #Yes #######@#No ##########################?#Yes #######@#No ##########################?#More 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#######@#Neutral#######@#Agree #######@#Strongly Agree ########2#################?#Strongly Disagree #######@#Disagree #######@#Neutral#######@#Agree #######@#Strongly Agree ########3#################?#Strongly Disagree #######@#Disagree #######@#Neutral#######@#Agree #######@#Strongly Agree ########4#################?#Strongly Disagree #######@#Disagree #######@#Neutral#######@#Agree #######@#Strongly Agree ########5#################?#Strongly Disagree #######@#Disagree #######@#Neutral#######@#Agree #######@#Strongly Agree ########6#################?#Strongly Disagree #######@#Disagree #######@#Neutral#######@#Agree #######@#Strongly Agree ########7#################?#Yes #######@#No ########8#################? #Under 18 #######@#18-24 #######@#25-34 #######@#35-44 #######@#45-54 #######@#55-64 #######@#65-74 ###### @#75+ ########9#################? #Female #######@#Male #######@#Other ########:#################?#Some high school #######@#High school degree #######@ Some college 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################################################################################ ################################################################################ ################################################################################ ################################################################################ ################################################################################ ##################\\###STUDENT=Student SURVEYNU=SURVEYNUMBER OWNIPHON=OWNIPHONE PRIMPHON=PRIMPHONE LONGIPHO=LONGIPHONE GENERATI=GENERATION GEN_SATI=GEN_SATISFIED VOICECAL=VOICECALLS BIZ_VOIC=BIZ_VOICECALLS_6 PERSONAL=PERSONAL_CALLS_6 TEXT=TEXT BIZ_TEXT=BIZ_TEXT_7 PERSON_T=PERSON_TEXT_7 PRODUCTS=PRODUCTS_8 NEWS_8=NEWS_8 EVENTS_8=EVENTS_8 BIZ_INFO=BIZ_INFO_8 ART_8=ART_8 INFO_PP=INFO_PP_8 SPORTS_8=SPORTS_8 SCHOOL_8=SCHOOL_8 TRENDS_8=TRENDS_8 OTHER_8=OTHER_8 TIME=TIME MORE_TIM=MORE_TIME FACEBOOK=FACEBOOK_10 MYSPACE=MYSPACE_10 VINE_10=VINE_10 TWITTER=TWITTER_10 YOUTUBE=YOUTUBE_10 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ghggh#####@e@ghegfhhgmeeineddddeeeffeffeffeffefeeffeeffdeeeeehiiiihhhefeg ehmeefneededeeeffefffffefeeeeefeeeffdeeeeeiihhiihhefehhhmeem######? egdgxdxeefffeffffsfeffeefeffeeffeeeee#####r@######n@hihihhiiefegfhmeehoeddd deeeffeffeff######n@efeeeeeefeeffd######i@######i@eeefeii#####v@iiiiigege hghmeemehdhxs#######@ieeeeeeefeffeff#####p@fefeffeefedfef#####@j@#####@j@e eihehi#####@j@geieffgjhmeene#######@gdgxdxeeeefeeeeffeffeeeefeeeffdeefeii iiiiiiiegfjffmeeemeddddddfefefffffffefffefeffeeffdeeefedhiihhiiheffhih##### Public Relations Journal Vol. 2, No. 3, Summer 2008 2008 Public Relations Society of America Admiring the Organization: A Study of the Relational Quality Outcomes of the Nonprofit Organization-Volunteer Relationship Denise Bortree & Richard Waters1 The contribution of volunteer hours to nonprofit organizations plays a critical role in the success of an organization; however little research in public relations has been conducted on the nature of the relationship between volunteers and nonprofits. This study measured the organization-public relationship between these two partners using the four relational quality outcomes originally proposed by Linda Hon and James E. Grunig a decade ago. In addition, the study introduced the measurement of admiration as an outcome in the organization-public relationship. Analysis showed that in the volunteer-nonprofit relationship, admiration is the strongest predictor of the overall rating of the relationship. Further analysis showed that while organization type is rarely a significant factor in volunteers' rating of the relationship, volunteer involvement with the organization does impact significantly on all five outcomes. Introduction In 2005 the Bureau of Labor Statistics reported that more than 65 million people volunteer their time to nonprofit organizations annually. A key task of nonprofit organizations is identifying and motivating these volunteers to donate their time and energy for worthwhile causes (Allen, 2006). Once an organization has recruited volunteers, the task then turns to managing volunteers in a manner that meets the needs of both the volunteer and the organization. Research has identified a number of effective strategies that nonprofit organizations can use to increase volunteer retention. These include regular supervision and communication, screening procedures such as an interview, annual recognition activities, written policies and job descriptions for volunteers, and volunteer professional development opportunities (Hager & Brudney, 2004; Brudney, 2005). These strategies rarely involve a two-way dialogue between organization and public to solicit information from volunteers to help shape their experiences. The study Denise Bortree, Ph.D., is Assistant Professor of Communication in the College of Communication at Penn State University, dsb177@psu.edu. Richard Water, Ph.D., is Assistant Professor in the Department of Communication in the College of Humanities and Social Sciences at North Carolina State University. A previous version of this paper was presented at the Association for Education in Journalism and Mass Communication conference in August 2007. Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 presented here applies relationship management theory to the volunteer-nonprofit organization relationship by measuring the relationship between the two partners. Literature Review Public relations is commonly defined as the management of relationships between an organization and its significant publics. Measurement of the organizationpublic relationship (OPR), a relationship that develops between an organization and its key stakeholders, has made significant advances since Ferguson (1984) first suggested that the relationship act as the unit of study in public relations (Bruning & Ledingham, 2000; Hall, 2006; Hon & Brunner, 2002, Ki & Hon, 2007a,b). The quality of the relationship is commonly measured along four relational dimensions drawn from the interpersonal literature: trust, commitment, satisfaction and control mutuality (balance of power) (Hon & Grunig, 1999). Trust A public's trust in an organization relates to the degree to which the organization keeps its word (Ledingham & Bruning, 1998). The scale developed by Hon and Grunig (1999) measures trust along three dimensions: integrity, dependability and competence (Hon & Grunig 1999). In the OPR, integrity is defined as the belief that both parties involved in the relationship are fair and just; dependability is judged as the degree to which relational partners do what they say they will do; and competence gauges the degree to which parties have the abilities to do what they say they will do. In the volunteer-nonprofit relationship, trustworthiness is a critical factor in the volunteers' decision to help advance the organization's mission (Spitzer & MacKinnon 1993). By listening to volunteers' suggestions and demonstrating social accountability, nonprofit organizations build trust with their volunteer public. This leads to a greater likelihood of seeing volunteers stay with an organization for an extended period of time because they not only understand the nonprofit but also feel it is capable of accomplishing its mission. Commitment Previous public relations research has determined that an individual's level of commitment to an organization helps define the future of the relationship. Commitment, in essence, is driven by the individual's attitude toward the organization. (Bruning & Galloway, 2003). Commitment, commonly defined as \"the extent to which one party believes and feels that the relationship is worth spending energy to maintain and promote\" ( Hon & Grunig, 1999, p. 20) in the public relations literature, can be measured along the dimensions of both attitude and behavioral intention. Unlike the other relationship outcome, commitment hints toward future behavior. For nonprofit organizations, volunteers represent an active public. When nonprofits dedicate time and resources to cultivate relationships with their volunteers, this stakeholder group can be motivated to become more involved and committed to 2 Admiring the Organization - Public Relations Journal - Vol. 2, No. 3, 2008 the relationships. Even though individuals have diverse motivations for giving their time and energy to nonprofits, ultimately they share a commitment to the organization because of its mission (Omoto & Snyder, 2002). Satisfaction Satisfaction can also be used to assess how the two sides of the OPR view each other. Ferguson (1984) originally suggested that this variable was important in studying organizational relationships and that understanding what drives stakeholder satisfaction could influence public relations decisions, Hon and Grunig (1999) view a satisfying relationship as \"one in which the benefits outweigh the costs\" (p. 3). Drawing from relationship marketing research, satisfaction has been shown to be a powerful variable that can be used to predict an individual's willingness to maintain relationships with consumer and social organizations (Dwyer & Oh, 1987). Ledingham and Brunig (2000) suggest that an individual's amount of satisfaction can be increased if organizations invest time and resources needed to foster growth in the relationship. Driggers and Dumas (2002) offered specific suggestions on how libraries can best manage their volunteers to maximize the likelihood that they give more of their time. These suggestions included developing specific job descriptions for volunteers so individuals know what is expected of them, conducting thorough interviews and reference checks of potential volunteers, and offering regular reviews of work so volunteers know their work is valued by the organization. By demonstrating interest in their work, nonprofits can produce feelings of satisfaction with their volunteers. Control Mutuality Hon and Grunig (1999) identified one additional dimension of the OPR, which they termed \"control mutuality.\" This component of the OPR seeks to evaluate which party has more power over the other. Power exists in any relationship, and its distribution impacts the perceptions and actualities of an individual's relationship with an organization. Power is often misunderstood in the nonprofit organization-volunteer relationship. Many assume that because volunteers are willing to work for organizations without pay that they retain the power because they can walk away from the relationship. However, organizations also have a significant amount of power. Many volunteers want to assist in resolving community issues that interest them, and they need the organizations to help fulfill that desire (Clary & Snyder, 1999). Additionally, organizations often offer professional development training and opportunities for volunteers to enhance their resumes (Mowen & Sujan, 2005). For a healthy relationship with its volunteers, an organization needs to balance the levels of power with them. The organization may need to be assertive and fire a volunteer, but it may also need to be willing to compromise with its volunteer base to see a project succeed. 3 Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 Admiration A fifth dimension of the organization-public relationship, not found in the work of Hon and Grunig, was tested in the study presented here. The concept of admiration, the degree to which relationship partners esteem one another, has been studied in the interpersonal communication literature as a measure of the quality of an interpersonal relationship (Furman & Buhrmester, 1992). The adaptation of this construct to the study of the OPR would provide a way for organizations to assess the degree to which they are esteemed by key publics and the degree to which key publics esteem the organization. The four relationship quality outcomes as identified by Hon and Grunig (1999) do not consider this aspect of the relationship, yet it holds promise for its contribution to the overall quality of the relationship. Defined as the degree to which one likes or approves of the behavior of a partner (Holladay & Kerns, 1999), admiration in the interpersonal communication literature has been measured for its impact on satisfaction in a relationship (Agne & White, 2004). The term admiration has not been defined or measured in the public relations relationship literature, but this construct has the potential to add an important dimension to the study of the OPR. In the professional world, organizations seek to be admired by their key publics. Fortune magazine annually publishes a list of the most admired companies ranked by industry experts for their admirable qualities including innovation, quality of management, people management, financial soundness, use of corporate assets, long-term investment, social responsibility and product/services quality (Fortune, 2008). This list of qualities suggests an overlap between the definition of admiration in the interpersonal literature (esteeming a partner and approving of a partner's behavior) and the concept of admiration of an organization. A highly-admired corporation would gain approval of its behaviors in the categories named above. The presence of admiration in the volunteer-nonprofit organization relationship would likely improve the volunteer's perception of the relationship. Volunteers seek out organization that they admire and expect that their donation of time and their work be valued by the organization. Volunteers donate their time to organizations for a number of reasons, including learning new skill, need for activity and self-enhancement (Mowen & Sujan, 2005). However, the strongest motivator for volunteering is altruism or value motives, defined by Clary et al (1998) as the desire to help others and improve the lives of the less fortunate. Volunteers seek opportunities that match with their values. A study of AIDS volunteerism found that a strong motivation for volunteering within that community was personal values related to the disease and a desire to help those who are affected by it (Omoto & Snyder, 2002). If volunteers admire the mission of the organization with which they volunteer and share its values toward the community, then their perception of the relationship would benefit. At the same time, admiration of volunteers by the organization would likely impact the relationship as perceived by the volunteer. The literature demonstrates that admiration plays a significant role in bolstering morale for paid-staff (Kerson, 1980) and would likely do the same for volunteers. 4 Admiring the Organization - Public Relations Journal - Vol. 2, No. 3, 2008 It follows that the level of admiration that volunteers have for an organization and its mission, as well as the level of admiration that volunteers believes the organization has for them would contribute to the overall perception of the volunteernonprofit organization. Given this study's aim to determine how well these dimensions measure the nonprofit-volunteer relationship, the following two research question were created: RQ1: To what extent do volunteers give nonprofit organizations favorable evaluations of the five relationship dimension - trust, commitment, balanced power, satisfaction, and admiration? RQ2: To what degree does admiration predict the level of quality in the volunteer-nonprofit relationship? The Association of Fundraising Professionals identifies six categories of nonprofit organizations: arts and humanities, education, healthcare, human services, public/society benefit, and religious organizations. It is possible that some of these organizations engender more admiration than others. To explore how admiration and the other relational quality outcomes influence the organization-public relationship across nonprofit categories, the following research question is proposed. RQ3: Are there significant differences in trust, balanced power, commitment, satisfaction and admiration between organization types? Finally, because public relations literature suggests that individuals will evaluate their relationships differently based on levels of involvement with that organization, a third research question was created to determine if the dimensions of the OPR could be used to predict which volunteers are more involved with the organization: RQ4: Can an individual's work with a nonprofit organization (as determined by the number of hours volunteered) be predicted by his or her evaluation of the relationship using the five dimensions? Methodology Data in this study were collected through intercept surveys administered to participants in volunteer fairs at two large Florida cities by students enrolled in a nonprofit management minor. Students were given extra credit for recruiting adults to complete the survey. Of the 300 adults asked to complete the surveys, 144 completed usable surveys, resulting in a survey completion rate of 48%. Though a purposive nonprobability sample limits generalizability of results, using intercept surveys at a volunteer fair gave the researchers access to volunteers from many types of nonprofit organizations. This strategy was deemed more appropriate for answering the research 5 Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 questions than sampling from an individual organization from which a random sample would have been possible. The survey administered in this study used Hon and Grunig's (1999) four outcome scales and four measures based on Furman's (2006, personal communication) measures of admiration. Participants were asked an additional question to assess the overall quality of the relationship they experienced with their volunteer organization. Organizations identified by the volunteers were classified into six categories using the Association of Fundraising Professionals' classification system: arts and humanities, education, healthcare, human services, public/society benefit, and religious organizations. Information was collected about volunteers' gender, age, race, and number of volunteer hours worked at the organization per month. The volunteer hours were then classified into two groups (high and low involvement) based on calculating the cutoff points from the hours reported by the participants. The five relationship qualities were measured with 9-point Likert-type scale questions: four questions for control mutuality (balanced power) and admiration, five for commitment and satisfaction and six for trust (Appendix A). One additional measure was used for the overall quality of the relationship. Following previous OPR studies (e.g., Ki, 2007), participants were asked to evaluate their overall relationship with the nonprofit organization they volunteer using a 9-point scale that ranged from very positive to very negative. The indices were found to be reliable with Cronbach's alpha values ranging from .80 to .86 (see Table 1). Results The participants in the study represented a wide variety of backgrounds. The respondent group was 57% female and 43% male. Most (70%) of respondents said they were Caucasian; 15% said African-American, 14% said Latino, and 1% said Asian. The mean age of the participants was 23 years old, ranging from a low of 18 years to a high of 85. Finally, the participants volunteered an average of 17.4 hours per month at nonprofit organizations (SD = 15.01). Research question one sought to determine how individuals evaluated the relationship with the nonprofit organizations for which they volunteer. On a 9-point scale, the organizations earned scores greater than seven on all outcomes, scoring well above the mid-point of the scale. The data indicate that the volunteers tended to perceive the relationship positively on all of the relationship dimensions (Table 1). 6 Admiring the Organization - Public Relations Journal - Vol. 2, No. 3, 2008 Table 1: Volunteers' Evaluation of their Relationship with Nonprofits on a 9-point Scale. Overall Mean SD Cronbach's Trust 7.16 1.20 0.80 Commitment 7.03 1.46 0.83 Satisfaction 7.35 1.25 0.86 Control Mutuality 7.06 1.38 0.84 Admiration 7.66 1.20 0.85 The second research question asked the degree to which admiration predicted the quality of the overall volunteer-nonprofit organization relationship. To address this question, a multiple regression analysis was run for predictors of trust, balanced power, commitment, satisfaction, and admiration for the outcome measure of overall rating of relationship. Results showed that three relational qualities were significant predictors of the overall relationship - admiration, satisfaction, and power balance - with admiration acting as the strongest predictor, F (4,139) = 102.13, p < .001 (see Table 2). Together the three relational qualities explain 69% of the variance in overall rating of relationship. Table 2: Stepwise regression of Relationship Outcome Indices for Overall Relationship using admiration. Unstandardized Standardized t-value p-value Coefficient (B) Coefficient () Constant -0.02 -0.05 0.963 Admiration 0.52 0.45 6.35 < .001 Satisfaction 0.31 0.28 3.71 < .001 Control Mutuality 0.19 0.19 2.62 0.010 R = .83, R2 = .69, F (4,139) = 102.13, p = .000, n = 143 The third research question asked whether volunteers would rate nonprofit organization types differently in relational quality outcomes. To examine this question, an ANOVA was run for six nonprofit organization types - religion, education, healthcare, arts/culture, human services, and public/society benefit - for five dependent variables trust, power balance, commitment, satisfaction and admiration. The results of the ANOVAs were significant for trust F(5, 138) = 2.52, p = .033, commitment F (5, 138) = 5.33, p < .001 and admiration F (5, 138) = 2.29, p = .049 (see Table 3). Post hoc testing with Bonferri Test showed that healthcare organizations were the only type to be rated significantly differently from others. For trust, healthcare organizations were rated significantly higher than religious organizations and public/society benefit organizations, for commitment they were significantly higher than all other organizations, and for admiration they were rated significantly higher than public/ 7 Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 society benefit organizations. This indicates that the strength of the overall volunteernonprofit organization is not significantly different between nonprofit organization types, with few exceptions. Table 3: One-Way ANOVA on Evaluation of the Volunteers' Relationship with six organization types Nonprofit Organization. Source of df F-score p-value Variation Trust 5, 138 2.52 0.03 Commitment 5, 138 1.07 0.38 Satisfaction 5, 138 1.71 0.14 Control Mutuality 5, 138 5.33 0.001 Admiration 5, 138 2.29 0.05 The fourth research question sought to explore whether a volunteer's evaluation of the nonprofit-volunteer relationship using all five outcomes could be used to predict a volunteer's level of involvement with the organization as determined by the number of hours volunteered per month. To examine the predictive nature of the relationship dimensions, discriminant analysis was used to compare the five OPR index scores (trust, commitment, satisfaction, control mutuality (balanced power) and admiration) with the classification level of volunteer hours (high or low). Table 4 presents the results of the discriminant analysis. Table 4: Discriminant Analysis of Overall Relationship with Nonprofit Organization. Group 1 (n = 74) Group 2 (n = 70) b Wilks' F (1, 142) Mean Std. Dev. Mean Std. Dev. Constant -6.79 Trust 0.95 0.61 90.94* 6.43 1.06 7.93 0.79 Satisfaction -0.22 0.77 42.97* 6.76 1.33 7.96 0.78 Commitment 0.45 0.66 73.38* 6.21 1.42 7.90 0.88 Control -0.04 0.80 36.70* 6.46 1.44 7.70 0.97 Mutuality Admiration -0.17 0.82 30.92* 7.17 1.29 8.19 0.83 R = .65, Wilks' of function = .58, 2 = 76.76, df = 5, p<.001, centroids = (-.83, .88) *p < .001 8 Admiring the Organization - Public Relations Journal - Vol. 2, No. 3, 2008 As Table 4 shows, the most important variables that led to group prediction when considered individually were trust and commitment. These variables have the lowest Wilks' values, meaning that 61% and 66% of the variance in these variables is not explained by the group differences, respectively. The group differences explained even less variance for the remaining variables. When examining the interaction of the OPR variables, trust and commitment were the variables that differentiated between the two groups based on the value of the standardized coefficients. However, all of the variables were used to create the model to predict an individual's level of involvement with the organization. To calculate the discriminant function score, the following formula was created: Discriminant Function Score = -6.79 + .95(trust) -.22(satisfaction) + .45(commitment) - .04(balanced power) - .17(admiration) The canonical correlation of the discriminant function, R = .65, means that there is a moderate to strong correlation between all of the independent variables together and the discriminant function score. The function's Wilks' value means that 58% of the variance in the discriminant function score is not explained by the differences between the highest and lowest involved volunteers. Based on the Chi-square test, the Wilks' of the function was statistically significant (2 = 76.08, df = 4, p <.001). The mean discriminant scores for each of the dependent variable categories in the function or group centroids reveal that the function is fairly discriminating given the distance between the two points. The group centroids for the groups of this function are -.82 and .87. Because the function was statistically significant, the model can be tested to see if it can properly predict group membership. Table 4 presents how many cases were correctly classified against those that were not. As the table shows, the model was very accurate in predicting group membership for individuals who volunteered a large amount of hours per month at nonprofit organizations as 58 out of 67 cases. The model also was able to predict most of those who did not volunteer a large number of hours to the organizations. Of the 75 cases predicted to have low volunteer hours, only 16 were predicted incorrectly. 9 Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 Table 5. Classification Matrix of Discriminant Analysis Function. Predicted Original Group 1 Group 2 (High Volunteer Hours) (Low Volunteer Hours) Group 1 (High 58 16 Volunteer Hours) Group 2 (Low 9 61 Volunteer Hours) 2 = 62.07, df = 1, p <.001 Overall, the success rate of this model at predicting the group membership was 83% (119 of 144 cases correctly predicted). To determine if this hit rate was statistically significant, the t-value had to be calculated, and it was found to be significant (t = 6.33, df = 142, p < .001). Discussion The results of this study indicate that the nonprofit organization-volunteer relationship is viewed positively by the volunteers. While this overall finding is not surprising given the abundance of studies highlighting the willingness of the public to donate time and energy to causes and nonprofit organizations addressing social issues (e.g., Clary & Snyder, 1999; Lysakowski, 2003), the true benefits of this study are found with deeper examination of the results and their impact on the overall organizationpublic relationship. A new relational quality outcome was introduced in this study, admiration, and it proved to be the strongest predictor of the overall rating of relationship in the volunteer-nonprofit relationship. This indicates that volunteer's respect for an organization and the respect nonprofit organizations show toward their volunteer has a strong impact on the way the volunteer perceives the relationship between the two partners. Other outcomes, satisfaction and control mutuality (balanced power) contributed to the overall rating of relationship as well. These three outcomes appear to have the greatest impact on volunteer-nonprofit relationship. The difference in quality of relationship across nonprofit organization type was measured in this study, and little difference was found. Only one organization type showed significant difference from the others. Healthcare was ranked higher in trust, commitment and admiration. This seems to suggest that most of the variance in quality of relationship comes from other factors besides organization type. One factor that did contribute to the difference in quality of relationship as measured by the five quality outcomes was the level of involvement with a nonprofit organization. All of the relationship outcomes were statistically important in predicting which volunteers were more likely to donate their time to help organizations carry out 10 Admiring the Organization - Public Relations Journal - Vol. 2, No. 3, 2008 their programs and services. It is not surprisingly that people would be committed to an organization that they willingly expend their time and energy to help see community issues they care about resolved. The remaining four variables touch on management concerns particularly in the nonprofit sector. Previous public relations studies have found that trust is an important component of the OPR, and this study confirmed that trust is vital for the nonprofit organization-volunteer relationship. Trust is necessary on both sides of the relationship. Nonprofit organizations need to screen volunteers to ensure they are capable of carrying out the work needed and that they are dedicated to helping carry out the mission of the organization. Likewise, volunteers need to feel that they are trusted as well. As this study found, trust is one of the most significant variables in predicting which volunteers are likely to dedicate more time to volunteer work for the organization. To build trust, nonprofit management literature encourages organizations to recognize the unique talents that volunteers possess and to take advantage of the skills that volunteers have by assigning them to work on challenging projects rather than having them perform mundane office tasks (Driggers & Dumas, 2002). However, trust alone will not build the relationship. Volunteers indicated that satisfaction also was important. By incorporating small changes into their volunteer management practices, nonprofit organizations can not only increase organizational efficiency, but they can also make their volunteers feel more satisfied with their relationship with the organization. As previously stated, volunteers have many diverse reasons for becoming involved with nonprofits. Rather than accepting anyone who wants to volunteer, nonprofits should provide applications and conduct thorough interviews with potential volunteers. This screening process will allow the organization to better understand the individual's motivation to volunteer for the organization. The screening process also allows organizations to place that individual in volunteer situations where they are most likely to have those motivations met. For example, college students often use the volunteer experience to develop new social networks upon arriving in new cities while young professionals volunteer to develop new skills that will benefit future careers (Grube & Piliavin, 2000). Organizations can increase the levels of satisfaction in their volunteers by asking individuals to perform work that helps reinforce those underlying motivations. The balance of power, or control mutuality, was also found to be significant in predicting the amount of time a volunteer gives to an organization. The discriminant analysis test revealed that there was a significant difference in how those in the higher levels of volunteering group viewed the balance of power compared to those with lower levels of volunteer work. Volunteer coordinators must work to make sure that volunteers do not feel that they are simply being used by the organization, but through active listening to volunteers' suggestions, frank evaluations of the work with 11 Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 appropriate appreciation, and involving volunteers in meetings and the decisionmaking process can help lead to feelings of balanced power. The strong influence of admiration on the overall rating of relationship by volunteers coupled with the fact that it is a predictor of high and low involvement suggests a number of things. First, volunteers need to be working with an organization whose mission they admire. This means, attempts should be made to match volunteer interests and values to volunteer opportunities. Second, while admiration for the organization's work may draw volunteers to a nonprofit, the organization needs to continue to earn the admiration by the way it treats its volunteers. By treating volunteers with respect and by communicating the pride that the organization feels in the work of its volunteers, organization can work toward maintaining that level of admiration. Third, organizations need to continue to communicate their goals to volunteers so they can share in furthering the organization's mission. Conclusion This study sought to apply the model of the organization-public relationship to the volunteer-nonprofit relationship as well as introduce a new measure of the organization-public relationship, admiration. The identification of this outcome as the strongest predictor of the overall relationship in this relationship type suggests that it may be a predictor in other relationships too. One can easily imagine how admiration for an organization might impact consumer-organization relationships, membershiporganization relationships, donor-organization and even activist-organization relationships. Further investigation of this outcome with other relationship types is warranted. Limitations Though the study found support for the measurement of admiration in the organization-public relationship, the study had a few limitations that need to be addressed. Although the purposive sampling design allowed the researchers to target individuals who were active volunteers, their results should not be generalized beyond the participants in the study. It is possible those who chose to participate in the study were significantly different than those who did not. As well, volunteers who attend volunteer fairs may not be representative of other volunteers. The use of a purposive sample would introduce the likelihood of other types of bias in the data as well. Inferential statistics run on non-probability data should be regarded cautiously. Future Research While admiration was found to be a significant factor in the nonprofit organization-volunteer relationship, it remains to be determined how this concept impacts other publics. Future studies should examine how admiration impacts donors, investors, customers, and other organizational publics. Future studies will also help refine admiration measurement so it can be applied across public relations specializations. Additionally, more research needs to be conducted on organizations 12 Admiring the Organization - Public Relations Journal - Vol. 2, No. 3, 2008 that utilize volunteers. An in-depth analysis of one organization would provide additional insight into how organizations develop relationships with their volunteers, and studies across organizations can generate greater understanding of which relationship cultivation strategies are most important for volunteers. Then, scholars would be in a position to practical advice for maintaining the volunteer-nonprofit relationship. 13 Bortree & Waters - Public Relations Journal - Vol. 2, No. 3, 2008 References Agne, R. R., & White, C. H. (2004). The nature of facework in discussion of everyday problems between friends. Southern Communication Journal, 70(1), 1-14. Allen, K. (2006). From motivation to action through volunteer friendly organizations. The International Journal of Volunteer Administration, 24(1), 41-