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Social Network Analysis

Permanent Link: http://ufdc.ufl.edu/UFE0042652/00001

Material Information

Title: Social Network Analysis Intra-Organizational Communication and Human Resource Management
Physical Description: 1 online resource (63 p.)
Language: english
Creator: Haynes, Dwayne
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: analysis, centrality, communication, human, management, network, organizational, resource, social
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this research is to better understand intra-organizational communication within the social network of employees in a local agribusiness firm, and to investigate the statistical relationship between particular social network variables and human resource management demographics. The main method of research was an internet based survey that was designed and is supported by the Food and Resource Economics Department at the University of Florida. Respondents of the survey were asked if, and how well they knew the other employees in the firm. They were also asked how often they communicated with the other employees. A social network analysis program was used to evaluate the data once it had been compiled. This research revealed that XYZ Corporation uses key strategies that allow them to maximize efficiency with regard to communication. One of these strategies is the use of 'hybrid' employees throughout various departments. Another is the forging of a unique 'communication culture' that is tailored specifically for XYZ. Important results of this research are that demographic variables were not significantly related to centrality scores, indicating a high level of diversity within the firm. Also, while in-person communication and email communication were positively related to degree centrality, phone communication was the only method that yielded a negative relationship to all centrality scores.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Dwayne Haynes.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: House, Lisa O.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042652:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042652/00001

Material Information

Title: Social Network Analysis Intra-Organizational Communication and Human Resource Management
Physical Description: 1 online resource (63 p.)
Language: english
Creator: Haynes, Dwayne
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: analysis, centrality, communication, human, management, network, organizational, resource, social
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: The purpose of this research is to better understand intra-organizational communication within the social network of employees in a local agribusiness firm, and to investigate the statistical relationship between particular social network variables and human resource management demographics. The main method of research was an internet based survey that was designed and is supported by the Food and Resource Economics Department at the University of Florida. Respondents of the survey were asked if, and how well they knew the other employees in the firm. They were also asked how often they communicated with the other employees. A social network analysis program was used to evaluate the data once it had been compiled. This research revealed that XYZ Corporation uses key strategies that allow them to maximize efficiency with regard to communication. One of these strategies is the use of 'hybrid' employees throughout various departments. Another is the forging of a unique 'communication culture' that is tailored specifically for XYZ. Important results of this research are that demographic variables were not significantly related to centrality scores, indicating a high level of diversity within the firm. Also, while in-person communication and email communication were positively related to degree centrality, phone communication was the only method that yielded a negative relationship to all centrality scores.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Dwayne Haynes.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: House, Lisa O.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042652:00001


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1 SOCIAL NETWORK ANALYSIS: INTRA ORGANIZATIONAL COMMUNICATION AND HUMAN RESOURCE MANAGEMENT B y DWAYNE J. HAYNES A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENT S FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2010

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2 2010 Dwayne J. Haynes

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3 To my wife and family

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4 ACKNOWLEDGMENTS I would like to thank my parents, Michael and Ruthlyn Haynes, for their undying support scholastica lly, emotionally, and financially. From the earliest age until this very moment, they have never allowed me to forget the importance of education. This degree is as much theirs as it is mine. Their patience and understanding in the toughest of times is wha t carried me through and made me the man I am today. I cannot emphasize enough how much I appreciate all that they have sacrificed so that I may have the privilege of having such a good education. Thank you. I would like to thank the faculty in the FRE De partment that have helped me immensely on my journey as a Gator, specifically, Dr. House, Dr. Burkhardt, Dr. Schmitz, Dr. Moss, Jessica Herman and Jennifer Clark. Thank you all for giving me a chance, for believing in me, and for your continued and extensi ve support. I could not have made it this far without you. Lastly, and most certainly not least, I would like to thank my beautiful, intelligent, and caring wife Kemesha Haynes. Thank you for selflessly pulling all nighters with me when I had to study for finals and write term papers. Thank you for always keeping me focused on what was important when I could have so easily been distracted. And, most of all, thank you for loving me for who I am, standing by my side, and continuing to support me in all of my endeavors.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 6 LIST OF FIGURES ................................ ................................ ................................ .......... 7 ABSTRACT ................................ ................................ ................................ ..................... 8 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 10 1.1 Social Networks ................................ ................................ ................................ 10 1.2 Social Network Analysis ................................ ................................ .................... 10 2 LITERATURE REVIEW ................................ ................................ .......................... 13 2.1 Social Netwo rk Analysis ................................ ................................ .................... 13 2.2 Social Network Analysism within a HR Organizational Context ........................ 14 3 METHODS AND THEORY ................................ ................................ ..................... 20 3.1 Analytical Keywords and Principles ................................ ................................ .. 20 3.1.1 Similarities ................................ ................................ ............................... 20 3.1.2 Social Relations ................................ ................................ ....................... 20 3.1.3 Interactions ................................ ................................ .............................. 20 3.1.4 Flow ................................ ................................ ................................ ......... 21 3.2 Types of Networks ................................ ................................ ............................ 22 3.3 Freeman Centrality ................................ ................................ ........................... 24 3.4 XYZ Corporation: A Case Study ................................ ................................ ....... 26 3.5 Procedure ................................ ................................ ................................ ......... 27 3.6 Administering The Survey ................................ ................................ ................. 29 4 EMPIRICAL RESULTS ................................ ................................ ........................... 35 4.1 Initial Analysis ................................ ................................ ................................ ... 35 4.2 Network Structure and Analysis ................................ ................................ ........ 35 4.3 Centrality Scores ................................ ................................ ............................... 36 4.4 Regression Results ................................ ................................ ........................... 38 5 CONCLUSIONS ................................ ................................ ................................ ..... 56 LIST OF REFERENCES ................................ ................................ ............................... 61 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 63

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6 LIST OF TABLES Table page 2 1 Multidimensionality of Knowledge ................................ ................................ ....... 19 4 1 Communication Method: Frequency of Use ................................ ....................... 49 4 2 Demographic Information ................................ ................................ ................... 50 4 3 Demographic Code ................................ ................................ ............................. 52 4 4 Freeman's Centrality Measures ................................ ................................ .......... 53 4 5 Select Variables: Effect on Degree Centrality ................................ ..................... 54 4 6 Select Variables: Effect on Closeness Centrality ................................ ................ 54 4 7 Select Variables: Effect on Betweenness Centrality ................................ ........... 55 4 8 Demographics and Degree Centrality ................................ ................................ 55

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7 LIST OF FIGURES Figure page 3 1 Star Network ................................ ................................ ................................ ....... 31 3 2 Line Network ................................ ................................ ................................ ....... 31 3 3 Circle Network ................................ ................................ ................................ .... 31 3 4 Geodesic path closeness centrality for Knoke informat ion network .................... 32 3 5 Freeman degree centrality and graph centralization of Knoke information network. ................................ ................................ ................................ .............. 33 3 6 XYZ Communication Sur vey ................................ ................................ .............. 34 4 1 Knowledge Matrix ................................ ................................ ............................... 41 4.2 Know Very Well ................................ ................................ ................................ .. 42 4 3 Know Mo derately ................................ ................................ ................................ 43 4 4 Time Employed ................................ ................................ ................................ ... 44 4 5 Centrality Graph (Degree) ................................ ................................ .................. 45 4 6 Centrality Graph (Closeness) ................................ ................................ ............. 46 4 7 Centrality Graph (Betweenness) ................................ ................................ ......... 47 4 8 Degree Score by Time Employed ................................ ................................ ....... 48

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8 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science SOCIAL NETWORK ANALYSIS: INTRA ORGANIZATIONAL COMMUNICATI ON AND HUMAN RESOURCE MANAGEMENT B y Dwayne J. Haynes December 2010 Chair: Lisa House Major: Food and Resource Economics The purpose of this research is to better understand intra organizational communication within the social network of employees in a local agribusiness firm, a nd to investigate the statistical relationship between particular social network variables and human resource management demographics. The main method of research was an internet based survey that was designed and is supported by the Food and Resource Economics Department at the University of Florida. Respondents of the survey were asked if, and how well they knew the other employees in the firm. They were also asked how often they communicated with the other employees. A social n etwork analysis program was used to evaluate the data once it had been compiled. This research revealed that XYZ Corporation uses key strategies that allow them to maximize efficiency with regard to communication. One of these strategies is the use of "hy brid" employees throughout various departments. Another is the forging of a unique "communication culture" that is tailored specifically for XYZ. Important results of this research are that demographic variables were not significantly related to centralit y

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9 scores, indicating a high level of diversity within the firm. Also, while in person communication and email communication were positively related to degree centrality, phone communication was the only method that yielded a negative relationship to all ce ntrality scores.

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10 CHAPTER 1 INTRODUCTION 1.1 Social Networks A social network is a set of socially relevant nodes connected by one or more relations or ties. Wasserman and Faust describe these 'nodes' as 'actors' in their 1994 paper. Actors are the uni ts that are connected by the relations whose patterns we study. These units are most commonly persons or organizations, but in principle any units that can be connected to other units can be studied as nodes (Marin and Wellman 2009). Nodes can be connecte d in various ways, depending on the type of network in question. It is important to note that there are several types of ties that may occur in a social network. There can be dyads or dyadic ties which are simply interactions or established links between t wo actors (Wasserman and Faust, 1994). There can also be triadic or three way ties among actors. These ties are what make up the social structure of the network. 1.2 Social Network Analysis What then, is social network analysis? Breiger (2004) defines soc ial network analysis as T he disciplined inquiry into the patterning of relations among social actors, as well as the patterning of relationships among actors at different levels of analysis (such as persons or groups)." C ross et al (2001) s tate that soci al network analysis is a means to systematically assess informational networks by mapping and analyzing relationships among people, teams, departments or even entire organizations. While the application of social network analysis is relatively modern, the actual roots of the basic perspective (on a fundamental level) are as old of sociology itself (Scott,1988). Even though several metaphors have been used over the years, the most prevalent, most

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11 accurate, and seemingly most accepted depiction of a social ne twork has been essentially a net or fabric. Scott (1988) offers the following: It was, perhaps, in classical German sociology that this viewpoint was most explicitly allied with the metaphor of a 'network' of sociology that this viewpoint was most explici tly allied with the metaphor of a 'network' of social relations, the social world being depicted as an intertwined mesh of connections through which individuals were bound together. The very language used was redolent of the production of fabrics and texti les, and the works of Toennies, Weber and, above all, Simmel abound with concepts embodying such language: chains of action 'interweave' and 'interlock' to form a tightly 'knit' social 'fabric'. The purpose of metaphor in science is to make the unfamiliar understandable by describing it as if it were analogous to a familiar object or process. The metaphor or a 'social network' served to make the complex and unfamiliar patterns of the social world comprehensible by relating them to well understood everyday c oncepts drawn from the produ ction and handling of textiles. At the fundamental level, Wellman (1983) declares that the foremost objective of social network analysis is to determine how the pattern of ties in a network provides significant opportunities and constraints to those in the network. These ties or relationships affect the access of people to resources such as information, wealth and power (Wellman, 1983). This seems to hold with respect to all networks (where there is a clear or supposed hierarchy) regardless of size, in that where one is in the network has great influence on who one knows and what one know s Aside from determining what knowledge is transferred and how that knowledge is transferred, social network analysis also allows for the measur ement of strengths or weaknesses of relationships. This ability to measure relationships helps define the behaviors that exist and the impact they might have on the capability of an individual to function (Hatala, 2006). Th is study proposes that while s ocial network analysis is still a relatively young field of study, it is an important a nalytical tool which yields important result s Through the use of this analysis, we will have a better understanding of not only the types of

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12 communication used in and b etween different departments of an agribusiness firm, but also will gain possible insight on the effectiveness of each type of communication. The main hypothesis is that social network analysis variables have a s ignificant impact on centrality Centrality is the measure of the extent to which a particular node or actor dominates a network (Wellman, 2008), is presented via as a numerical scores.

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13 CHAPTER 2 LITERATURE REVIEW A review of the literature suggests that the research on network s in a human resou rces context is still in its early stages. There are relatively few papers on this topic specifically H owever the ones that d o exist are relevant. Before reviewing the limited literature on SNA and HR, a brief review of papers regarding SNA in general wi ll be presented. 2.1 Social Network Analysis A general characterization of SNA is provided by Hatala ( 2006) below: SNA is a general set of procedures that uses indices of relatedness among individuals, which produces representations of the social structur es and social positions that are inherent in dyads and groups. These representations are important for describing the nature of the environment and the impact it has on the individu als who form the relationships. The general steps to actually conducting SN A are outlined by Hat ala, (2006) and are : Determining the type of analysis Defining relationships in the network using a theoretically relevant measure Collecting the network data Measuring the relations Determining whether to include actor attribute infor mation Analyzing the network data Creating descriptive indices Presenting the network data A more technical discussion o f social networks and how nodes interact is given by Borgatti et al (2009) H ere they present theoretical mechanisms that explain cons equences of social network variables. The first mechanism is referred to as the adaptation mechanism. "The adaptation mechanism states that nodes become

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14 if two nodes have ties to the same (or equivalent) others, they face the same environmental forces and are likely to adapt by becoming increasi ngly similar." (Borgatti et al 2009). The second and third mechanisms (binding and exclusion, respectively) are opposites. In the c ase of binding several nodes converge around a central node (or group of nodes) and form a cluster. These nodes cooperate together and can seem to act as one. Conversely, any nodes within the immediate area of the network that are not incorporated into t he cluster can be effectively excluded and deprived of any resources or information. 2.2 Social Network Analysis within a HR Organizational Context Over the years, many firms have begun to realize not only the importance, but also the advantages of using social network analysis (SNA). More specifically, in a society where time is equivalent to money, the less time searching for answers to a company's and client's needs and questions, the more efficient one become s and hence, the more money one make s In th eir 2002 paper, Cross, Parker, and Borgatti outline this idea, focusing on knowledge creation and sharing using research conducted by IBM's Institute for Knowledge Based Organizations. They state the following: In short, who you know has a significant imp act on what you come to know. Many people we work with have discovered the importance of attending to the human element in knowledge management programs and are initiating various programs to facilitate knowledge creation and use. Although we can design pr ograms to enhance organizational learning, knowledge transfer or innovation, it is often difficult to understand the impact of such interventions. We have found social network analysis (SNA) a set of tools for mapping important knowledge relationships betw een people or departments to be particularly helpful for improving collaboration, knowledge creation and knowledge trans fer in organizational settings.

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15 Aside from finding SNA merely helpful, we hold that it is indeed a necessary component in better under standing intra organizational communication. A similar study was conducted by Cross et al ( 2001 ) They were interested in better understanding social and technical means of improving a network's capacity to collectively recognize and act on new opportunit ies, as well as in discovering what made social relations effective in the creation of knowledge (Cross et al ., 2001). With respect to the latter area of focus, they identified several features that determined the effectiveness of social relations. These a re : 1) Knowledge 2) Access 3) Engagement 4) Safety Social relations knowledge is defined in two different ways. The first relates to the basic definition of the word (i.e., whether a person has some spec ific knowledge regarding a particular problem.). T he second definition focuse s on the person's "ability to help think through a tough issue. These people were tapped for advice in either defining or refining a complex problems and were considered good at identifying and making salient important dimensions of such problems" (Cross et al ., 2001). Having alternate definitions of knowledge can provide a more accurate and complete scope when attempting to classify and assess an employee's performance. It might be shown that an employee should be relocated to an other department in order to fully utilize all of the employee's knowledge and talents.

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16 Access refers to the availability of the knowledgeable employee. More specifically, the employee's wisdom is entirely useless if there are barriers to obtaining it. An example is if an employee constantly out of touch. This is especially important regarding time sensitive cases (e.g., a client needs an answer to an important question by 1 pm but the only person capable of answering doesn't arrive until 3 pm. Cross et al (2001) specify how important it is to understand a person's preferred response style and what medium is most effective for establishing contact. They hold that this alone will reduce frustration in the work place as well as allow employees to have mor e accurate expectations from each other regarding communication. Effective engagement can be characterized as a p r ocess comprised of two steps: eople would first ensure that they understood the other person's problem and then actively shape what they kne w to the problem at hand." (Cross et al 2001). This is particularly interesting in the sense that almost everyone encounters this situation on a day to day basis. For example, if an employee is in need of help, they must first find the knowledgeable perso n who is accessible to assist them. In order for there to be an effective engagement and useful flow of information, the knowledgeable person must understand what is being asked of him/her and efficiently as well as successfully disseminate the appropriate information back to the other employee. Cross et al (2001) identify this person as being an "effective teacher". The fourth and last feature of effective social relations is characterized by Cross et al (2001) as "safety". This is essentially the degr ee of trust that the person asking for information has in the knowledgeable person. More specifically, the requestor must feel comfortable admitting his or her own ignorance about the issue in question. In

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17 situations/organizations where there is a higher c omfort level among relationships, it is clear how there can be a higher flow of information and even a higher quality (e.g., employees that are not afraid of asking each other questions are more likely to ask for help more frequently which can lead to bett er productivity of the company overall as fewer mistakes are likely to be made). Table 2 1 shows the objectives and interventions associated with these key features and concepts. Borgatti and Cross delve deeper into the above mentioned concepts in their 2 003 paper. They investigate the reasons behind a person's decision to seek information from other people as well as the positive effects of doing so below: L earning someone's level of expertise or determining how to gain timely access to them affects the probability of seeking that person out for information in the future. At a collective level, the structure of these perceptual relations reflects learning and the potential of a network to identify and react to new issues or opportunities requiring coordi nated effort or integration of disparate expertise. As members of one region of a network become aware of and [are] able to leverage the expertise of those in other regions, they become individually capable of doing more while the entire network's potentia l to sense and respond to new opportunities is also enhanced. T hey add another concept labeled "value" to the previous ones. This concept is somewhat attached to the "knowledge" concept in that the person seeking the knowledge must positively evaluate the knowledge and skills of the person sought out in relation to the problem that the seeker is attempting to solve (Borgatti and Cross, 2003). Hansen (1999) focuses on the strength of the ties in the network in relation to how productive and efficient the net work is as. He holds that the complexity of the knowledge being transferred is directly proportional to the strength of the tie between the actors. In other words, the weaker the tie between actors, the less complex the knowledge in their information flow. Intuitively, this makes sense within a network

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18 because the more complex an idea being communicated is, the higher the level of understanding between the actors there must be in order to ensure effective engagement. Another effect of the strength of ties i s the speed of project completion. According to Hansen (1999), "Findings show that weak inter unit ties help a project team search for useful knowledge in other subunits but impede the transfer of complex knowledge, which tends to require a strong tie betw een the two parties to a transfer. Having weak inter unit ties speeds up projects when knowledge is not complex but slows them down when the knowledge to be transferred is highly complex." Consider, however, removing the complexity variable. It would appea r that in a HR context, the above hypothesis still holds. For example, consider a hypothetical factory. Where weaker ties are present among workers, there is a diminished likelihood for prolonged interaction between them. In the case where ties between wor kers are stronger, there is the increased probability for more frivolous communication and thus, a possibility for decreased production. Since we have removed the complexity factor, it is unlikely that extensive communication will be beneficial to producti on. The other side of this argument is that stronger ties would mean more cohesion within the firm as a whole and everything would work more smoothly and efficiently. This is mostly determined by the type of firm in question however

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19 Table 2 1. Multidim ensionality of Knowledge Aspects Objectives Technical interventions Social interventions Knowledge Increase awareness of who knows what and who is working on what within the company Skill profiling and corporate yellow pages Communities of practice, thema tic help desks manned by knowledge area specialists and knowledge fairs Access Add speed of access to knowledge sharing and target accessibility as a critical behavior Email and cell phones Peer feedback forums and periodic SNA Engagement Increase ease o f interaction, add a dimension to more conventional communication that engages people. Enhanced performance Increased awareness of skills, abilities and knowledge of co workers Synchronous technologies White boarding applications Video conferencing Pe er reviews Safety Allow safe relationships to develop over time Increase visibility of relationships that are not safe so they can be discussed by the group Any form of communication technology used throughout the company Face to face interactions such a s working sessions or SNA Source: Cross, Parker, Borgatti (2002)

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20 CHAPTER 3 METHODS AND THEORY 3.1 Analytical Keywords and Principles 3.1.1 Similarities Within social network analysis, there are several keywords and phrases that must be understood in order to effectively interpret research results. In their 2009 paper, Marin and Wellman begin with the term similarities This is in reference to an event where two nodes share the attributes frequently studied in variable based approach es. 3.1.2 Social Relations The term social relations is defined by Marin and Wellman (2009) to include kinship or other types of commonly defined role relations (e.g., friend, student); affective ties, which are based on network members' feelings for o ne another (e.g., liking, disliking); or cognitive awareness (e.g., knowing). This research focuses on the latter in that a major portion of the survey used in the research was dedicated to ascertaining how well the respondents knew each other. 3.1.3 Inte ractions Another term commonly used in social network analysis is interaction This is any behavior based tie between nodes such as communicating with, helping, inviting somewhere etc. (Marin and Wellman, 2009). This term was also a major part of the res earch with respect to the survey. The respondents were questioned about the types of interactions and the frequency of interactions that they had with each other.

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21 3.1.4 Flow According to Marin and Wellman (2009), flows are relations based on exchanges o r transfers between nodes. An example from this research project would be if a dyad were to share some type of information between them. In addition to the keywords, there are also several principles that are used by social network analysts. The following principles outlined by Wellman (1983) are the most clearly presented ones : Ties are often asymmetrically reciprocal, differing in content and intensity. Ties link network members directly as well as indirectly; this means that we must analyze ties within the context of larger network structures. The structuring of social ties creates nonrandom networks; hence network clusters arise. Cross linkages connect clusters as well as individuals. Asymmetric ties and complex networks distribute scarce resources diff erentially. An example of what it means for ties to be asymmetrical reciprocal follows: Persons A and B both took a survey and responded to the question "How well do you know this person? Person A's response in reference to person B was "Very Well" while person B's response to person A was "Slightly". It is reciprocal in the sense that they both indicated that they knew each other, however, it is asymmetrical because of the intensity or degree of knowing (i.e., slightly vs. very well). Principle two may b e explained as follows : While Persons A and B might have a dyadic relationship, person A also knows person C. At the same time, person B knows person D and person D knows person E etc. It is easy to see how several direct ties can lead to many indirect tie s in the network. The possibilities for indirect ties are abundant because each direct tie links two individuals and not just two roles (Wellman, 1983).

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22 P rinciple three introduces the idea of clustering. This happens when a dyad draws others with whom the y are linked into a cluster (or group) of ties in which most members are directly linked with each other (Abelson, 1979; Cartwright and Harary, 1979). There can be many clusters in a network or there can be very few. Clusters may be very tight or loose. Th is wide range of possibilities is due, in part, to the type of network that is in question. For example, two kinds of networks that will share significant differences are a family and a business. For obvious reasons, the ties and clusters in a family will be very different from those in a business. Principle four is rather self explanatory in that, if person A is in group A and person B is in group B, their dyadic link also joins their respective groups together. This is what is meant by cross linkage. Fin ally with principle five, we see how networks structure collaborative and competitive activities in order to secure scarce resources. In a system with finite resources; social networks compete for the access to such resources (Mullady, 2008) 3.2 Types of Networks Following the aforementioned definition of a social network (Wasserman and Faust, 1994) we focus on Hanneman and Riddle (2005) who provide a few examples of some simple networks below, aptly named for the shapes they take on: Starting with the "st ar" n etwork (Figure 3 1), we can immediately discern that node "A" is in a particularly interesting position in that it has the highest degree in th e network. By highest degree, is meant that A has the highest number of connections within the network (Mull ady, 2008). Subsequently, this means that it has the more opportunities and alternatives than the other nodes in the network (Hanneman and Riddle, 2005). The other nodes in the network share the same lower degree in that they

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23 are only tied to "A". An examp le of a degree measure would be "coreness". Coreness is the measure of how centrally located an actor or node is in a subset of the group (Mullady, 2008). In this particular network "A" would be the most centrally located node. Consider Figure 3 2 where th e line network is displayed. This type of network offers a different structure in that there is not a particular node that is in a more advantageous position than the others. For example nodes A and G are actually disadvantaged in the sense that they may only have ties with one other node, while nodes F, E D, C and B each have two ties. It is important to note that there is one node with a specific advantage with respect to "closeness". Closeness is the number of links that nodes go through to get to every one else. Therefore people with a large closeness score are on the inside of the group and have the shortest paths to all others -they are close to everyone else. They are in an excellent position to monitor the information flow in the network -they have the best visibility of what is happening in the network (Beng Chong and Daniel 2004). In this particular network, D would have the advantage as well as the highest closeness score because it is relatively closer to the rest of the nodes whereas A and G wo uld be have the lowest score and be at the greatest disadvantage. Focusing on Figure 3 3 or the "Circle" network, we notice that all actors share the same advantages and disadvantages. Notice how each of the actors are always "between" the other actors in the network. Betweenness is the measure of the extent to which a node lies on the geodesics between each other pair of nodes (Wellman, 2008). Geodesics refer to the shortest, straight line path between two nodes (Webst er's). With respect to Figure 3 1, we can see how node "A" is once again advantaged in that it is the only node between other nodes. This means that i f A wants to contact F, A may

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24 simply do so. If F wants to contact B, they must do so through A. This gives actor A the capacity to broker contac ts among other actors -to extract "service charges" and to isolate actors or prevent contacts (Hanneman and Riddle, 2005). Degree, Coreness, Closeness and Betweenness are all measures of centrality. Centrality is the measure of the extent to which a par ticular node or actor dominates a network (Wellman, 2008). Measures of centrality are very important properties to social network analysis. Being able to understand and apply these measures of centrality to research allows analysts to effectively compare n odes within their respective networks. 3.3 Freeman Centrality For the purposes of this research, when referring to degree centrality (or degree closeness and betweenness) we use Freeman's approach. This approach (modeled by Linton Freeman, a co author of the UCINET software used in this research) is shown below in Figure 3 4. A brief explanation on the importance of studying degree centrality is provided by Hanneman and Ridd le (2005) : Actors who have more ties to other actors may be advantaged positions. Because they have many ties, they may have alternative ways to satisfy needs, and hence are less dependent on other individuals. Because they have many ties, they may have access to, and be able to call on more of the resources of the network as a whole. B ecause they have many ties, they are often third parties and deal makers in exchanges among others, and are able to benefit from this brokerage. So, a very simple, but often very effective measure of an actor's centrality and p ower potential is their degre e. In this example, the first column (numbers 1 through 10) represent each actor in the network. Hanneman and Riddle (2005) explain briefly how to interpret the Freeman degree centrality in the following: Actors #5 and #2 have the greatest out degrees, an d might be regarded as the most influential (though it might matter to whom they are sending information, this measure does not take that into account). Actors #5 and #2

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25 are joined by #7 when we examine in degree. In the last two columns of the first panel of results above, all the degree counts have been expressed as percentages of the number of actors in the network, less one (ego). The next panel of results speaks to the "meso" level of analysis. That is, what does the distribution of the actor's degree centrality scores look like? On the average, actors have a degree of 4.9, which is quite high, given that there are only nine other actors. We see that the range of in degree is slightly larger (minimum and maximum) than that of out degree, and that there is more variability across the actors in in degree than out degree (standard deviations and variances). By the rules of thumb that are often used to evaluate coefficients of variation, the current values (35 for out degree and 53 for in degree) are moderat e. Clearly, however, the population is more homogeneous with regard to out degree (influence) than with regard to in degree (prominence). The last bit of information provided by the output above are Freeman's graph centralization measures, which describe t he population as a whole -the macro level. This is how the Freeman graph centralization measures can be understood: they express the degree of inequality or variance in our network as a percentage of that of a perfect star network of the same size. In th e current case, the out degree graph centralization is 51% and the in degree graph centralization 38% of these theoretical maximums. We would arrive at the conclusion that there is a substantial amount of concentration or centralization in this whole netwo rk. That is, the power of individual actors varies rather substantially, and this means that, overall, positional advantages are rather unequally distributed in this network. Closeness centrality approaches emphasize the distance of an actor to all others in the network by focusing on the distance from each actor to all others (Hanneman and Riddle, 2005). The Freeman Geodesic Path Approach was used in this research and an example of the outpu t is provided below in Figure 3 5. Hanneman and Riddle (2005), pro vide a brief interpretation of the centrality closeness measures here: We see that actor 6 has the largest sum of geodesic distances from other actors (inFarness of 22) and to other actors (outFarness of 17). The farness figures can be re expressed as near ness (the reciprocal of far ness) and normed relative to the greatest nearness observed in the graph (here, the inCloseness of actor 7). Summary statistics on the distribution of the nearness and farness measures are also calculated. We see that the distri bution of out closeness has less variability than in closeness, for

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26 example. This is also reflected in the graph in centralization (71.5%) and out centralization (54.1%) measures; that is, in distances are more un equally dist ributed than are out distances 3. 4 XYZ Corporation: A Case Study The following is a brief description and background of the firm that was used in the research specific to this paper. The study was conducted on XYZ Corporation (the name of the company has been altered to protect their anonymity). It is a bacteriological and chemical research company and was founded in 1976. It is one of the largest full service laboratories in the U.S. and serves the global food industry. Just a few of their services include: Nutrition labeling solutio ns, product development and performance solutions, product safety and quality solutions as well as foreign material identification. XYZ conducts daily chemical, physical, and microbiological analyses for its customer base of over 2000 food companies. This includes mostly large (but also small) fast food chains, mainstream chain restaurants, food retail and wholesale firms, food processing firms, packing firms, commercial farms, and some companies in foreign countries (Jaramillo, 2004). As of mid 2010, XYZ has 54 employees distributed in the following departments: Administration Food Microbiology Research Microbiology Food Chemistry Research Chemistry Product Performance Services Administration/Other

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27 The "Administration/Other" category is a not necessarily a department per se Rather, it contains employees that are hybrids between administration and one or more of the other departments. With so many departments and employees, it is the job of Human Resource management to understand how members of their resp ective departments interact with those in their departments as well as with those in other departments. This comprehension of social networking by upper management is discussed by John Paul Hatala in his 2006 paper Social Networking Analysis in Human Reso urce Development: A New Methodology. He states: For HRD practitioners and researchers to improve the interactivity between individuals that leads to increased performance and effectiveness, it is necessary to identify techniques that measure the relation s between people within a given environment. Social network theory involves a body of methods, measurement concepts, and theories that provide an empiric al measure of social structure. The following outline s the necessary steps taken in order to study th e social network structure of XYZ Corporation. 3. 5 Procedure The research began with locating a suitable social network. Since the focus of the research was to be on an agribusiness firm, XYZ Research was the optimal selection. There was already an estab lished relationship between the Food and Resource Economics Department of UF and XYZ. Also, XYZ was in very close proximity to UF campus allowing for the availability and ease of onsite access. Once a social network was selected for the study, a survey wa s drafted. XYZ's employee list chemists, microbiologists, sales representatives, and office support staff. These are all very busy people. The survey instrument needed to allow for the collection

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28 of complete and meaningful information, without taking too m uch time from employees. To accomplish the goals of this survey, Qualtrics Survey Software was used to design, construct, and test the survey. It also provided us an online medium in which to disseminate the survey among XYZ's employees. In formulating t he survey we selected questions that were imperative in analyzing the intra organizational communication within a firm. The initial question in the survey served to identify the nodes (i.e., determine which employee was taking the survey at the given time) in XYZ Corporation. The survey was designed in such a way that when this question was answered, a specific number would be assigned to the respondent that would be his/her identification number for later data analysis. The next question was designed to d etermine social relations between em ployees, mean ing, how well the respondent knows (if he/she knows at all) the other employees. The following Likert scale (House 2008) was used in the survey: 1. Do not know 2. Know slightly 3. Know moderately 4. Know well 5. Know very well It is important to note that for the purposes of our research, we advised the respondents to select "Do not know" if they were only familiar with someone's name or knew of the person in question but never interacted with them. This was done to ensure that there was at least some form of communication between employees that indicated that they "knew" each other, as in a work situation, they may have been aware of names of other employees, but not interact with them.

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29 The next question focused on the t ypes of communication methods used, as well as how often the methods were used. The respondents were using an average two week period as the guideline for their answers. We specified an "average" two week period because we wanted to know what typical resul ts would be. The respondents were provided with four methods of communication in which to choose from: Email, In person, Phone or other. The 'other' category encompassed more modern methods of communication including: Text messages, Skype Instant messagin g, Facebook etc. The remaining questions in the survey captured demographic information, including age, race/ethnicity, level of education and gender. Other information collected included their work information including what department they worked in, ho w long they had been employed by XYZ and whether they were full or part time employees. This was done in order to have the most complete profile of the employees. Once the survey was des igned (Figure 3 6), the next step was to get approval from the Univ ersity of Florida Institutional Review Board. This was to ensure that we were legally allowed to conduct the research and to certify that there would be no risks or benefits to possible respondents for participating. The actual document (IRB Consent Form) had to be signed by the prospective respondents before we could administer the survey. 3.6 Administering the Survey The survey was administered through two methods. The first method (the more common method) involved an onsite interview with XYZ employees. The interviews took place in a closed conference room where the principal investigator ensured all IRB forms were signed and dated. The principal investigator administered the survey to each employee one at a time while recording answers in the online sur vey software.

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30 About 95% of respondents were surveyed using this method. The other 5% (who were not available for the onsite interview) were called and the survey was administered over the phone. One employee took the survey on their own through the online site after discussing the instructions with the principal investigator.

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31 Figure 3 1 Star Network Figure 3 2 Line Network Figure 3 3 Circle Network

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32 Figure 3 4 Geodesic path closeness centrality for Knoke information network Source: Han neman and Riddle, 2005

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33 Figure 3 5 Freeman degree centrality and graph centralization of Knoke information network. Source: Hanneman and Riddle, 2005.

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34 Figure 3 6. XYZ Communication Survey

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35 CHAPTER 4 EMPIRICAL RESULTS 4.1 Initial Analysis Once the data was collected, it was analyzed through the use of several software programs including Microsoft Excel UCINET for Windows Net D raw and SAS All of the responses to the survey were tabulated in Excel In particular, the responses to the second ques tion (how well the other employees were known) were ordered into an as ymmetric full matrix (Figure 4 1). The use of a symmetric matrix would have rendered our results useless (it would have falsely shown that all employees reported knowing each other equal ly well, which is clearly not the case). The responses to question three (how often each method of communication was u sed) were compiled into Table 4 1. This table summarizes the frequency of use of each method (email, in person, phone or other) within a typical two week period for each employee. Demographic information from the last question was compiled into Table 4 2, and the values in this table can be interpreted using the accompanying Table 4 3 4.2 Network Structure and Analysis With UCINET and Netd raw we used the values Figure 4 1 in conjunc tion with the values in Table 4 2 to construct a visual representation of the company and its employees that is consistent with the "fabric like" model so commonly referred to in so cial network analysis (Figure 4 2). We can see how each department is its own cluster and that all of the departments are focused around one central area Administration. While it is interesting to actually visualize this, it is what we would expect within any company: that administrati on is the

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36 center of the social network structure. It is from this position where administration can most efficiently and effectively operate and manage the company. As we will see later in the paper, this is the reason for the higher centrality scores repo rted for those employees within administration. It is important to note that this F igure 4 2 only includes responses indicating that employees knew each other "very well". As w e inclu ded more responses (i.e., take into account those who reported "knowing s lightly", "knowing moderately", "knowing well" etc.), the figure has increasingly more ties, making it difficult to determine what connections are actually present. For this reason, all but the following figure (Figur e 4 3) will feature only the "know very well" ties. Figure 4 3 has ties included for "know moderately", "know well" and "know very well". This can be interpreted to mean how many employees indicated knowing each at least moderately. To visualize the impact of how long a person has worked at X YZ on their position within the network, the node sizes were adjusted based on the "ti me employed" variable (Figure 4 4). Again, with the exception of a few, the employees in administration tend to have the larger nodes, indicating that they have been emp loyed the longest. 4.3 Centrality Scores The centrality scores of the employees were calculated using UCINET (focusing on degree, closeness and betweenness). These scores were consolidated into Table 4 4 For the purposes of this paper, we will be focusing on the "in" measures (where applicable), the indication of how other people rated how well they knew the subject in the measure.

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37 The degree scores ranged from 59 to 168 with a mean of 111.57 over the 42 observations. The degree network centralization cont ains insight on how the network is structured. While a high network centralization percentage (e.g., the star network depicted in Figure 3 1) signifies a very centralized network, it is not always optimal. In fact, Krebs (2010) describes this case of a ver y centralized network: A very centralized network is dominated by one or a few very central nodes. If these nodes are removed or damaged, the network quickly fragments into unconnected sub networks. A highly central node can become a single point of failu re. A network centralized around a well connected hub can fail abruptly if that hub is disabled or removed. Hubs are nodes with high degree and betweenness centrality. A less centralized network has no single points of failure. It is resilient in the face of many intentional attacks or random failures -many nodes or links can fail while allowing the remaining nodes to still reach each other over other network paths. Networks of low centralization fail gracefully. In XYZ's case network degree centralizatio n was 28.2%. The degree centrality scores for those in administration are higher when compared to the other employees. Out of all of t he administration employees, 75% had scores above the mean for the company. Closeness measures how connected each employee is to all other employees a higher score means that the employee has many avenues within the company to send and receive information. This is regarded as having a high "flow". The closeness scores ranged from 56.16 to 100 with a mean of 75.99. Once again, 75 % of employees in administration had scores higher than the company average and there was a network centralization score of 49.81 % The last centrality score identified is betweenness. The betweenness scores ranged from 0.49 to 54.76 with an average of 14.5. Roughly 70 % of administration

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38 employees scored above the average company score. The network centralization percentage was 2.41. The centrality results for degree, closeness and betweenness are shown graphically in Figures 4 5, 4 6 and 4 7 respectiv ely. In each of these figures, the node size is adjusted to represent the relative size of their network scores. 4.4 Regression Results Regression analysis was used to determine the effect of several social network variables on the centrality scores of th e employees in XYZ. In particular an ordinary least squares model was used and the following equation was estimated: and d. Table 4 5 shows the results of the regression when the dependent variable was the degree score and independent variables were the following: Hybrid Administration Time employed Email sum Phone sum In person sum Away (accounting for offsite employees ) The hybrid variable represents a department in XYZ where employees are a combination of administration and some other department. Time employed represents how long the employees worked for XYZ in terms of months. Email, phone and In person sums represen t the total amount of interactions for each respective method of communication. The away variable represents if employees worked on site or not.

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39 The hybrid, time employed and email sum variables were all significant at the 1% level and all positively relat ed to degree scores. The away variable was significant at the 1% level but, as expected, was negatively related to degree scores. The phone sum variable was significant at the 5% level and was negatively related to degree scores. The in person sum variable was significant at the 5% level and was only slightly positively related to degree scores. The administration variable was not significant Table 4 6 depicts results for the case when the dependent variable was changed to closeness, ceteris paribus The h ybrid, time employed and email sum variables were all significant around the 1% level and all positively related to centrality scores. The away variable was significant at the 1% level but, as expected, was negatively related to centrality scores. The phon e sum variable was significant at the 10% level and was slightly negatively related to centrality scores. The in person sum variable was significant at the 10% level and was only slightly positively related to centrality scores. The administration variable was not significant. Table 4 7 has the dependent variable as betweenness, ceteris paribus The hybrid variable was significant at the 1% level and was positively related to betweenness scores, Time employed and email sum variables were significant at 5 an d 10 % t levels respectively, and positively related to centrality scores. The away variable was significant at the 5% level and was negatively related to centrality scores. The phone sum variable was not significant. The in person sum variable was signific ant at the 5% level and was only slightly positively related to centrality scores. The administration variable was not significant.

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40 Table 4 8 features the dependent variable as degree once again; however, this time the independent variables only include th e gender and ethnicity variables which were not significant. Figure 4 8 depicts a scatter plot and a best fit line correlating degree score by time employed.

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41 Figure 4 1 Knowledge Matrix

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42 Figure 4.2. Know Very Well

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43 Figure 4 3 Know Moderately

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44 Figure 4 4 Time Employed

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45 Figure 4 5 Centrality Graph (Degree)

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46 F igure 4 6. Centrality Graph (Closeness)

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47 Figure 4 7. Centrality Graph (Betweenness)

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48 Figure 4 8 Degree Score by Time Employed

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49 Table 4 1. Communication Method: Frequency of Use ID Email Sum In Person Sum Phone sum 1 Other sum Total Sum A1 449.5 1 221 230 901.5 A10 605 890 355 0 1850 A11 171.5 575 169 0 915.5 A12 0 1204 80 0 1284 A13 133 807 161 3 1104 A14 460 497 28 0 985 A15 51 353 86 0 490 A16 1120 1323 1410 20 3873 A2 201 232.5 124 0 557.5 A3 1230 1125 1315 50 3720 A4 487 407 260 0 1154 A5 560 1864 260 0 2684 A6 355 845 356 5 1561 A7 115 169 163 0 447 A8 0 239 4 0 243 A9 532 0 325 0 857 FC1 146 863 189 0 1198 FC2 0 830 0 0 830 FC3 0 1418 237 0 1655 FC4 0 817 1 0 818 FC5 119 785 54 0 958 FC6 1 1100 11 0 1112 FC7 0 714 1.5 0 715.5 FC8 0 55.5 0 0 55.5 FMB1 8 952 0 8 968 FMB2 233 1334.5 122 61 1750.5 FMB3 0 462 0 0 462 FMB4 0 851 6 5 862 FMB5 2 1340 0 8 1350 FMB6 238 934 49 56 1277 FMB7 3 655 0 0 658 FMB8 0 900 0 2 902 FMB9 0.5 865 0 0 865.5 PP1 7 205 1 120 333 PP2 0 652 0 0 652 PP3 10 773 0 19 802 1 Note: Phone communications also include the use of the intercom within XYZ

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50 Table 4 1. Contin ued ID Email Sum In Person Sum Phone sum 2 Other sum Total Sum PP4 267 730 166 20 1183 RC1 1.5 213.5 0 10 225 RC2 19 250 25 6 300 RM1 263 156.2 44.7 0 463.9 RM2 411 1253 131 0 1795 RM3 0 831 0 0 831 TOTAL 8199 30471.2 6355.2 623 45648.4 Table 4 2. Demographic Information ID Time employed Department Age Race Education Employment Gender Away A1 2 7 4 1 4 1 1 1 A10 216 7 3 1 1 1 2 1 A11 60 7 4 3 4 1 2 0 A12 30 7 8 2 1 1 1 0 A13 60 7 6 5 5 1 2 0 A14 150 1 7 1 2 1 2 0 A15 312 1 9 1 1 1 2 0 A16 5 1 2 1 3 1 1 0 A2 120 7 5 4 4 1 1 0 A3 60 1 5 1 3 1 1 0 A4 84 1 4 1 5 1 2 0 A5 42 1 8 1 4 1 1 0 A6 360 1 8 2 2 1 1 0 A7 6 1 3 1 2 2 1 0 A8 180 1 11 1 5 2 1 0 A9 12 7 7 1 2 1 1 1 FC1 120 2 6 2 3 1 2 0 FC2 54 2 7 4 3 1 2 0 FC3 90 2 7 1 2 1 1 0 FC 4 30 2 3 1 2 1 1 0 FC5 84 2 7 1 3 1 2 0 FC6 10 2 2 1 3 1 2 0 FC7 30 2 3 1 3 1 1 0 FC8 12 2 2 1 2 2 1 0 FMB1 42 4 2 2 2 2 2 0 2 Note: Phone communications also include the use of the intercom within XYZ

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51 Table 4 2. Continued ID Time employed Department Age Race Education Employment Gender Away FMB2 108 4 5 3 2 1 1 0 FMB3 4 4 2 1 2 2 2 0 FMB4 24 4 3 3 3 1 2 0 FMB5 30 4 2 1 3 1 2 0 FMB6 108 4 4 1 3 1 1 0 FMB7 3 4 2 1 2 2 1 0 FMB8 22.8 4 2 3 3 2 2 0 FMB9 4 4 2 3 2 1 2 0 PP1 2 6 2 4 3 2 2 0 PP2 8 6 3 4 3 2 2 0 PP3 2 6 2 1 3 2 2 0 PP4 24 6 5 1 4 2 2 0 RC1 9 3 4 5 5 2 1 0 RC2 27 3 2 1 3 1 1 0 RM1 114 5 10 1 5 2 1 0 RM2 30 5 3 1 3 1 2 0 RM3 216 5 7 1 1 1 2 0

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52 Table 4 3. Demographic Code Departments Age Race Education Employment Gender Away 1=Administration 1= 16 to 19 1= W hite 1=H igh S chool 1= Full time 1= M al e 1= L ives away 2=Food Chemistry 2= 20 to 24 2=B lack 2=Some College/2 year 2= Part t ime 2=F emale 0= L ives locally 3= Research Chemistry 3= 25 to 29 3= H ispanic 3= B achelors 4=Food Microbiology 4= 30 to 34 4=A sian 4=M asters 5=Research Microbiology 5= 35 to 39 5=O ther 5=PhD 6=Product Performance 6= 40 to 44 7=Admin/Other 7= 45 to 49 8 = 50 to 54 9= 55 to 59 10= 60 to 64 11= >65

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53 Table 4 4. Freeman's Centrality Measures ID Degree Closeness Betweenness A1 76 63 1 A10 130 85 17 A11 168 100 42 A12 157 87 44 A13 152 87 18 A14 138 82 18 A15 156 95 55 A16 96 73 6 A2 127 89 15 A3 146 89 36 A4 154 93 23 A5 146 89 42 A6 144 87 26 A7 77 65 1 A8 121 82 11 A9 85 66 5 FC1 129 79 13 FC2 59 56 2 FC3 141 89 17 FC4 92 63 2 FC5 137 87 15 FC6 85 64 5 FC7 98 68 8 FC8 74 60 1 FMB1 97 69 7 FMB2 165 98 35 FMB3 67 59 0.5 FMB4 101 68 6 FMB5 83 63 3 FMB6 151 89 35 FMB7 70 60 0.5 FMB8 94 66 5 FMB9 71 60 7 PP1 70 61 2 PP2 84 68 3 PP3 66 61 3 PP4 145 93 30 RC1 88 72 9 RC2 95 69 6

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54 Table 4 4. Continued ID Degree Closeness Betweenness RM1 103 67 1 RM2 128 85 23 RM3 120 79 10 Table 4 5. Select Variables: Effect on Degree Centrality Table 4 6. Select Variables: Effect on Closeness Centrality Variable Parameter Estimated P value Hybrid 18.1 <.01 Admin istration 0.4 0.933 Time Employed 0.07 <.01 Email Sum 0.03 0.011 Phone Sum 0.02 0.093 In person Sum 0.01 0.081 Away 27.6 0.001 Variable Parameter Estimated P value Hybrid 47.6 <.001 A dmin istration 1.45 0.9 01 Time Employed 0.2 <.001 Email Sum 0.08 0.0 04 Phone Sum 0.06 0.0 32 In person Sum 0.02 0.020 Away 73.5 < .001

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55 Table 4 8. Demographics and Degree Centrality Variable Parameter Estimated P value Ethnicity 5.30 0. 557 Gender 2.8 3 0. 761 Table 4 7. Select Variables: Effect on Betweenness Centrality Variable Parameter Estimate d P value Hybrid 20.01 0.001 Admin istration 6.43 0.3 Time Employed 0.06 0.01 Email Sum 0.027 0.07 Phone Sum 0.02 0.11 In person Sum 0.01 0.01 Away 25.5 0.01

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56 CHAPTER 5 CONCLUSIONS The purpose of this research was to better understand intra organizational communication within an agribusiness firm. Social network analysis was used to investigate the sta tistical relationship between network position and human resource management demographics. XYZ Corporation provided the necessary medium in which to conduct this research. Even though social network analysis is still in its early stages of development (es pecially from a human resource standpoint), it still can provide great insight on how communication impacts productivity and the managing of human capital. A firm can only be as productive and efficient as its employees. Therefore, it is important to study how the employees and (on a bigger scale), how a firm's departments communicate, exchange information, and learn from each other. XYZ is a prime example of a firm that understands the importance of intra organizational communication for several reasons. Cross et al (2001) note the importance of simply allowing employees to choose which method of communication they are most comfortable using. Something so seemingly trivial has rather large implications in the workplace environment. This is especially impo rtant in XYZ in that the types of jobs vary greatly from department to department leading to a unique, established "communication culture". For example, in cases where employees are working mostly in labs collecting and analyzing samples (e.g., Food Chemis try and Food Microbiology), we can see that emails are r arely used (Table 4 1). The preferred method of communication seems to be In Person. Conversely, we can see that those in the administrative department use email extensively to communicate with other employees. There is an understanding between

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57 employees of how to efficiently get into contact with each other, regardless of the department. Wellman (1983) states that cross linkages connect individuals and in doing so, they inherently connect clusters (o r for our purposes, departments). It was fascinating to view this principle working within XYZ, as they have several "hybrid" employees that straddle the line between administration and other departments. They perform duties in their respective departments and also act as liaisons between their departments, allowing for the smooth and efficient flow of information. This strategic placement of "knowledge" (i.e., the hybrid employees), without a doubt, aids in productivity at XYZ in the sense that relatively little to no time is wasted looking for answers. Regarding the second area of focus regarding this research project, we will now look at the statistical relationships between social network analysis variables and human resource demographics. Specifically addressing this research, the term "human resource demographics" includes the standard demographic variables of age, race, gender and education, with the addition of department, time employed employment, and away (Table 4 2). The results of the regressi on analysis showed that demographics (age, race, gender and education) were never significantly related to the employees' network scores. This is a strong point for XYZ because it shows workplace diversity and signifies that regardless of race, age, gender or education, employees have an equal opportunity to grow within the compan y. This is evidenced in Table 4 8 where none of the aforementioned variables have any significance in the model.

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58 However, v ariables related to their human resource status (time employed, department, whether or not they worked in or away from the main location) were significant. Length of time an employee was with the company is positively related to all n etwork centrality scores (Table 4.4 ). In cases where long time employees had lower centrality scores, other factors could be involved. For instance, time employed might not be capturing the following: 1. Physical structure of work space (e.g., hypothetical employee A works in the back corner of the lab, in a physical building separat e from other employees making constant communication unlikely), and therefore reducing the possible centrality score. 2. Language barrier (e.g., English not being the first language of an employee might reduce the amount of communication between them and othe rs). However, given that race and ethnicity were not significant, this is not as likely. 3. Personality of employee (e.g. some employees might naturally be soft spoken and frugal with words making centrality scores lower) It is important to note that in thes e particular cases, the lower centrality score by no means indicates that these employees are less productive or efficient. Management would need to evaluate these employees to determine if other explanations exist. In the case of XYZ, after presenting the results to the CEO, it was determined that the first and third issues were the likely explanations for employees with a lower score than their time employed indicated they might have. In person communication is positively related betweenness and degree b ut it is only marginally related to closeness. This may indicate that direct contact with a person (i.e., face to face interaction) is a great way for an employee to put him or herself in the middle of a network. When people see your face and can connect i t with a name and a personality, they are more likely to remember you than if the initial contact was through any other method.

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59 With respect to email communication, it was not related to betweenness; however, it was positively related to degree and close ness. This may mean that while email correspondence doesn't necessarily seem to move a person closer to the center of a network, it could allow one to maintain relational ties with those one know s In other words, it seems to have more of a maintenance fun ction. When phone communication was significant (66% of the time), it was negatively correlated to all centrality scores. This may signify that this method of communication is an impersonal way to convey information. While the overall research project w as interesting and insightful, there were certain challenges C hallenges inherent within social network analysis include finding a suitable network (company) that allows access to their employees and interview them. Finding an appropriate network was only half the battle however Being that XYZ is such a busy research firm, simply finding time to allow us to conduct our survey was somewhat difficult : everyone is on a different and tight schedule with deadlines looming. Aside from that, were fortunate to ha ve a firm with an optimal number of employees. This was especially important when it came to our survey design The more people an employee indicate d knowing, the more time was required for that employee to answer questions about them. A positive thing about how the survey was conducted was that the principal investigator was always side by side with the employees being interviewed (with the exception of the two or three employees who work offsite, and in those cases, the principal investigator was on th e phone with the employees). It is understood that taking surveys are not the favorite pastime for many people, and we do not always finish them

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60 or focus all of our attention on them while doing them. Having the principle investigator onsite essentially "f orces" the respondents to finish the survey free from distraction. Overall, social network analysis proved to be a useful tool within the human resource context and revealed some interesting statistical relationships. Future research may be used to prove how important it is to understand how intra organizational communication impacts a efficiency and productivity

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61 LIST OF REFERENCES Abelson, R. P. 1979. Social Clusters and Opinion Clusters. In Pau l Holland and Samuel Leinhardt, ed Perspectives on Social Network Research New York: Academic Press, pp. 239 56 Breiger, R.L. 2004. The Analysis of Social Networks In Melissa Hardy and Alan Bryman ed. Hand book of Data Analysis London: SAGE Publications, pp. 505 26 Cartwright, D., and Harary, F. 197 9. Balance and Clusterability: An Overview. In Pau l Holland and Samuel Leinhardt.ed. Perspectives on Social Network Research New York: Academic Press, 25 50 Cross, R., Parker, A., and Borgatti, S.P. 2002. A bird's eye view: Using social network analysis t o improve knowledge creation and sharing. IBM Institute for Knowledge Based Organizations. IBM Corporation. Cross, R., Parker, A., and Prusak, L. Borgatti, S.P. 2001. Knowing What We Know: Supporting Knowledge Creation and Sharing in Social Networks. Org anizational Dynamics 30 ( 2 ):100 120 Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside Accessed on August 15 th 2010 from htt p://faculty.ucr.edu/~hanneman/ Hatala, J.P., 2006. Social Network Analysis in Human Resource Development: A New Methodology. Human Resource Development Review 5 (1) : 45 71 House, L.A. 2008. Notes presented during Survey Research Methods course University of Florida. Gainesville Fl August Krebs V., 2010. Social Network Analysis, A Brief Introduction. Accessed on October 31st, 2010 from http://www.orgnet.com/sna.html Marin, A., Wellman, B. Forthcoming Social Network Analysis: An Introduction Peter Carrington and John Scott ed Handbook of Soc ial Network Analysis London: Sage, 2010 Mullady, J. 2008. Influence of Social Networks and Trust on Food Product Adoption. MS thesis, University of Florida. Jaramillo, P. 2004. Strategic Analysis and Recommendations for XYZ Research Corporation. A Cas e Study Qualtrics, 2010. Survey Software Scott, J. 1988. Social Network Analysis. Sociology 22 (1):109 27

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62 Wasserman, S., and Faust, K. 1994. Social Network Analysis: Methods and Applications Cambridge, ENG and New Yor k: Cambridge University Press. Webster's Online Dictionary. Accessed on August 21, 2010. http://www.websters dictionary online.com/definitions/geodesic%20line?cx=partner pub 0939450753529744%3Av0qd01 tdlq&cof=FORID%3A9&ie=UTF 8&q=geodesic%20line&sa=Search# 1034 Wellman, B. 2008. Networks for Newbies PowerPoint presentation given at the International Sunbelt Social Network Conference St Petersburg FL, January Wellman, B. (1983). Network Analysis: Some Basic Principles Sociological Theory 1:155 200.

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63 B IOGRAPHICAL SKETCH Dwayne Haynes was born in Manhattan New York. He lived in Queens, New York until the age of 15 Soon after finishing his first year at Benjamin Cardozo High School his father's job transferred to Tampa, Fl where the Haynes family would start life anew. In Tampa, Dwayne finished his high school career at Paul R. Wharton High School in the top 3% of his class. H is next goal was to be a Florida Gator so Dwayne applied, was accepted, and graduated four years later with an undergrad degree i n Food and Resource Economics. He was married shortly thereafter and is now looking forward to the PhD program at the University of Florida continuing in Food and Resource Economics.