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AFFILIATIVE COMMUNICATION ONLINE: A CONTENT ANALYSIS OF HOTEL RELATIONSHIP MAINTENANCE ON TWITTER

By

KARSTEN BURGSTAHLER

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FUFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN MASS COMMUNICATION

UNIVERSITY OF FLORIDA

2017

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© 2017 Karsten Burgstahler

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To my families

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ACKNOWLEDGEMENTS

First and foremost, I’d like to thank my chair, Dr. Mary Ann Ferguson. As I’m

brand new to the realm of deep academic research, her patience with me as I worked

through the details is so appreciated. I’m honored to be able to contribute to a theory

she laid the groundwork for. Thank you for your time and commitment.

I’d like to thank my committee members, Dr. Daniel Fesenmaier and Prof.

Deanna Pelfrey. Your insights have been invaluable, and I sincerely appreciate that you

took the time to help a graduate student tackling a project of this magnitude.

I’d like to thank my parents, who taught me that I could accomplish anything if I

was willing to put in the work and take risks – even if that meant I packed everything I

owned into a tiny Cobalt and drove it 900 miles to start again. It was the best decision

I’ve ever made, but it wouldn’t have been possible without your love and support.

I’d like to thank my fellow grad students. Coming in to grad school, I read so

many stories of vicious graduate departments where students are always in competition

with each other. Our group could not be any further from that. I’m eternally grateful for

all the support you’ve provided. Good luck with what comes next!

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TABLE OF CONTENTS Page

ACKNOWLEDGEMENTS ..................................................................................... 4 LIST OF TABLES .................................................................................................. 7 LIST OF FIGURES ................................................................................................ 8 ABSTRACT ........................................................................................................... 9 CHAPTER

1 INTRODUCTION ...................................................................................... 11

Travel in the New Millennium .................................................................... 11 Purpose of the Study ................................................................................ 13

2 REVIEW OF THE LITERATURE .............................................................. 16

Relationship Theory .................................................................................. 16 Constructs of Organizational-Public Relationships ................................... 20 Relationship Cultivation and Maintenance ................................................ 22 Openness .......................................................................................... 24 Access ............................................................................................... 25 Task Sharing ..................................................................................... 26 Networking ......................................................................................... 27 Assurances ........................................................................................ 28 Positivity ............................................................................................ 28 Affiliative Communication and Influence ................................................... 29 Affiliative Communication .................................................................. 29 Influence………………………………………………………………… ... 30 Influence Measurement…………………………………………………..32 Introduction to Social Media Use .............................................................. 33 Studies in Online Relationship Management ............................................ 34 Social Media/Relationship Maintenance in Travel and Tourism ............... 37 Hypotheses and Research Question ........................................................ 39

3 METHODOLOGY ..................................................................................... 43

Dependent Variables ................................................................................ 44 Content Analysis ....................................................................................... 45 Data Collection ......................................................................................... 46 Coding ...................................................................................................... 47 Data Analysis ............................................................................................ 50

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4 RESULTS ................................................................................................. 54 Descriptives .............................................................................................. 54 Influencer Scores ...................................................................................... 57 Indicator Prevalence ................................................................................. 57 Indicators in the Entire Tweet Sample ............................................... 57 Indicators in @reply Tweets .............................................................. 58 Hypotheses and Research Question ........................................................ 59 Summary of Significant Results ................................................................ 63

5 DISCUSSION ........................................................................................... 69

Relationship Maintenance/Influencer Correlations ................................... 70 Relationship Maintenance Indicator Correlations ..................................... 73 Implications............................................................................................... 74 Implications for Theory ...................................................................... 74 Implications for Industry ..................................................................... 76 Limitations of the Study ............................................................................ 78 Recommendations for Future Research ................................................... 80 Conclusion ................................................................................................ 82

APPENDIX A HOTEL SAMPLE WITH PARENT COMPANIES ......................................... 88 B CODEBOOK ................................................................................................ 91 LIST OF REFERENCES ..................................................................................... 94 BIOGRAPHICAL SKETCH ................................................................................ 100

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LIST OF TABLES

Table Page 3-1 List of sampled J.D. Power-ranked hotel chains categorized by class with Klout and Kred scores………………………………………………..........52 4-1 Division of relationship maintenance indicators in the complete tweet sample and @reply tweets……………………………………………………...65

4-2 Correlations for indicators in the entire tweet sample and @reply tweets…65 4-3 Regression analysis of the complete sample with Klout ..............................66 4-4 Regression analysis of @reply tweets with Klout ........................................66 4-5 Regression analysis of the complete sample with Kred ..............................67 4-6 Regression analysis of @reply tweets with Kred ........................................67 4-7 Correlations between indicators in the complete tweet sample ...................68

4-8 Correlations between indicators in @reply tweets.......................................68 A-1 Hotel sample with parent companies ...........................................................88 B-1 Codebook ....................................................................................................91

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LIST OF FIGURES Figure Page 2-1 Model of strength of relationship maintenance constructs of affiliative

communication ......................................................................................... 42 5-1 InterContinental @reply tweet to solve a customer problem .................... 84 5-2 InterContinental @reply tweet to respond positively to a customer

picture ....................................................................................................... 84

5-3 Super 8 @reply tweet to solve a customer problem .................................. 84 5-4 Revised model of strength of relationship maintenance constructs of

affiliative communication to Klout Influencer Scores ................................. 85 5-5 Revised model of strength of relationship maintenance constructs of

affiliative communication to Kred Influencer Scores ................................. 85 5-6 Revised model of strength of relationship maintenance constructs of

affiliative communication in @reply tweets to Klout Influencer Scores ...................................................................................... 86

5-7 Revised model of strength of relationship maintenance constructs of

affiliative communication in @reply tweets to Kred Influencer Scores ...................................................................................... 87

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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 Arts in Mass Communication

AFFILIATIVE COMMUNICATION ONLINE: A CONTENT ANALYSIS OF HOTEL RELATIONSHIP MAINTENANCE ON TWITTER

By

Karsten Burgstahler

May 2017

Chair: Mary Ann Ferguson Major: Mass Communication The studies of relationships and the dimensions that create them have long been

an area of study for public relations academics. As social media becomes an important

form of communication for many industries, academics have observed how

communication on these platforms reflects relationship theory. Studies have been

completed on how religious institutions (Waters et al., 2011) and institutions of higher

education (Beverly, 2013) use social media, but few studies have examined the

elements of relationship theory in the hospitality industry. The purpose of this study was

to determine if there were any associations between dimensions of relationship

maintenance on hotel Twitter accounts and influence on social media, and, if so, which

dimensions were more positively associated with influence.

The researcher used Hon and Grunig’s (1999) dimensions of relationship

maintenance, as well as the principles of affiliative communication, as theoretical

groundings for this study. The researcher and a research assistant observed 800

tweets, 20 tweets from 40 hotel chain Twitter accounts, and recorded the number of

relationship maintenance indicators contained within each tweet. The researcher

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combined the numbers for each hotel chain (n = 40) and ran correlational tests with

Klout and Kred, two companies that provide a numerical scale with which to measure

social media influence.

Results of the study showed that two dimensions of relationship maintenance

strategies (access and assurances) have significant negative correlations with brand

influence on social media, while two dimensions (positivity and networking) have

significant positive correlations to brand influence on social media. Two other

dimensions (openness and task sharing) did not appear with enough frequency for the

researcher to determine a significant positive or negative correlation to influence.

Based on the results, the researcher recommends hotel chains think critically

about the goals they want to accomplish with short-form communication -- as Twitter

provides little room for a brand to deliver its message. Access and assurances

strategies may help repair a broken relationship, but they do not engage large amounts

of followers to spread content. Conversely, brands should consider increasing two-way

communication – not just for resolving consumer problems, but for inviting positive

interaction as well – to build influence.

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CHAPTER 1 INTRODUCTION

This thesis seeks to determine to what extent hotel chains take advantage of the

growing popularity of social media to improve influence1. To reach this goal, this chapter

will provide background on travel trends before a more comprehensive look at public

relations and social media literature in the next chapter.

Travel in the New Millennium

Among those that have taken to social networking sites to connect with

audiences are the corporations responsible for providing an infrastructure to make

tourism possible, including hotel chains, airliners and rental car companies. Together,

travel and tourism were responsible for more than $7 trillion of the world’s total gross

domestic product in 2015 and employed more than 248 million people worldwide

(“Economic Impact Analysis,” 2016). Travel brands continue to grow; Hilton has plans

for approximately 65,000 new rooms worldwide between its Hilton and Hampton chains

(eHotelier, 2016)2 and budget airlines such as Allegiant continue to expand their fleets

(Mutzabaugh, 2016). As more Internet users become social media savvy, chances for a

company to face a crisis stemming from its social media management also increase.

Conversely, proper social media management gives a company another outlet to build a

stronger identity or reputation, one that establishes the company as mindful of

consumer needs. Twitter, one type of social media, gives corporations the chance to

1 For the purpose of this study, when the researcher refers to “influence,” he is discussing how influential a brand is with followers on social media, rather than how influential a brand is through advertising or in the marketplace.

2 Several sources used for statistics and references to news events in this thesis are not peer reviewed and are from websites that are not considered major news sources. These sources will be identified on first reference.

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cost-effectively establish what Grunig and Hunt (1985) called two-way symmetrical

communication, a process in which an organization opens a dialogue with consumers,

potentially leading to a change within both the publics and the organization.

The generation that makes up the most significant portion of Twitter users,

millennials (Duggan, 2015), spend nearly $200 billion a year on travel, a figure that grew

20% between 2013 and 2014 (Heller, 2016); 50% of millennials using Twitter mention

travel ideas among the reasons why they use social networking (Heller, 2016). Even

now companies are creating applications that make it easier to dig through the travel

information available on social media. For example, applications such as Gogobot and

Tripbirds direct users’ travel search queries to followers on Facebook and Twitter,

building on word-of-mouth by allowing friends to give advice and recommendations on

each others’ travel plans (Samiljan, 2016)3.

Travel companies are balancing the risks and rewards of communicating directly

on social networking sites, and some have such a large following that one damaging

instance on Twitter could cause significant harm to the corporate reputation. Travel and

tourism is an industry where a variety of factors, both internal and external, can cause a

crisis – for instance, the growth of bed bug infestations in hotel chains during the late

2000s (Dimmler, 2011), a recent computer outage for Southwest Airlines that grounded

flights for hours (Wattles & Marsh, 2016), the death of a child who was dragged into a

lake by an alligator at Walt Disney World in June (McLaughlin, Berlinger & Fantz, 2016)

or anxiety in the wake of the Orlando Pulse nightclub shooting (Daily Mail, 2016).

3 Source used for application examples

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Therefore, understanding how to best use the potentials of social media is a crucial part

of a company’s public relations strategy.

Purpose of the Study

While many of these companies appear to have a strategy as to what to say to

open successful, positive dialogue with their 140 characters, it’s useful to examine how

the words and phrases companies use on Twitter lead to stronger social media

influence. Developed in the early 1980s at the recommendation of Ferguson (1984),

relationship theory posits that the relationship between an organization and its publics,

referred to as an organization-public relationship (OPR), is made up of different

dimensions which, working in tandem, can create a stronger bond between both parties.

Variables such as trust between an organization and its public, as well as a commitment

from both parties to work together to achieve a goal, are common measures defined

among relationship researchers. Furthermore, researchers, including Hon and Grunig

(1999), have suggested there are communication processes involved in maintaining

these relationships once they have been formed. Hon and Grunig (1999) hypothesized

that openness – a company’s willingness to be transparent with its publics – and access

– how easily publics can find information from an organization – are among the factors

organizations use in relationship maintenance.

With the advent of new Internet platforms, researchers have sought to determine

how companies use their websites to cultivate and maintain relationships (ex. Williams

& Brunner, 2010, Ki, 2003). As social media arrived, Twitter’s introduction of a 140-

character limit for tweets constrained information shared – so companies needed to find

the best way to build or mend a relationship in a limited amount of space. Some

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researchers became interested in relationship maintenance in short-form

communication; Li (2015) adapted Hon and Grunig’s (1999) six proposed relationship

maintenance constructs: access, assurances, networking, openness, positivity and

sharing of tasks, to understand how Fortune 500 companies and brand leaders

maintained loyalty in Twitter relationships. The research proposed in this thesis is

designed to help organizations respond to crises or complaints on Twitter and to better

develop and maintain relationships to influence their publics. This thesis takes Li’s

research a step further to ask not only what types and degrees of relationship theory

constructs are present on Twitter, but also how they correlate with influence; it proposes

affiliative communication as the theory that suggests a correlation may exist (i.e. Lee &

Kim, 2014).

Social science research has shown how organizations use Twitter in retail and

for brand loyalty (Li, 2015), in the financial industry (Murray et al., 2014) and in higher

education (Beverly, 2013). While some studies explored whether the amount of content

in a tweet was related to the amount of interaction with publics (Beverly, 2013), this

thesis provides a launching point for other researchers to gain further insight into the

tweeting approaches of major corporations, as well as give practitioners research-

backed insights on how relationship theory informs short-form communication

strategies. The 24-hour news cycle is such that a mistake on social media can lead to

millions of dollars in lost revenue for an organization. Researching relationship

maintenance strategies used on Twitter can help brands that are highly engaged with

their consumers learn what strategies are most related to influencing consumers, and

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can subsequently suggest steps brands can take to improve their online

communication.

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CHAPTER 2 REVIEW OF THE LITERATURE

This chapter will explore the body of literature dedicated to relationship theory,

starting with an overview of major research topics within the field and moving

specifically to studies focused on defining constructs of organization-public relationships

and relationship cultivation and maintenance strategies that lead to social media

influence1, plus an overview of research completed on digital relationship maintenance

and social media in travel/tourism.

Relationship Theory

As public relations practitioners and scholars built the field into a discipline in the

mid-20th century, significant effort was dedicated to describing the fundamentals with

the first public relations textbook from Cutlip and Center (1952) – and later seeking to

establish public relations as a managerial function, as well as looking at the effects of

corporate internal communication on the receiver (Grunig, 1977). Historical research

suggests, before 1984, public relations scholars did not dedicate extensive research to

developing a unifying theory for the discipline (Meadows & Meadows, 2014). Ferguson’s

(1984) content analysis of Public Relations Review, the only public relations journal at

the time, aligns with these findings, showing only 4.1% of articles published between

1975 and 1984 focused on building theory in public relations. Meadows and Meadows

1For the purpose of this study, when the researcher refers to “influence,” he is discussing how influential a brand is with followers on social media, rather than how influential a brand is through advertising or in the marketplace.

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(2014) found that theory-based journal articles increased dramatically after 1984, rising

to 17% between 1985 to 1994 and to nearly 40% between 2005 to 2013.

The components of public relationships did not become a focus of study until

Ferguson (1984) recommended a paradigm shift to focus on the state and building

blocks of the relationships themselves as the unit of analysis, rather than describing the

organization, its publics, or the processes of communication, as the units of analysis.

Ferguson, a University of Wisconsin-Madison doctoral student studying with Glen

Broom (who had been a student of Scott Cutlip, an author of the first textbook),

proposed constructs, derived from interpersonal relationship theory, to predict the

effectiveness of organization-public relationships (OPR) (Personal Communication,

September 14, 2016). Ferguson (1984) argued that understanding OPR was essential

to differentiate the field from other related fields such as advertising and marketing and

to give the fledging academic area credibility as a discipline as well as open up new

research questions and theory development.

Researchers began to contribute different constructs and process models to

developing relationship theory during the next two decades. More scholars became

open to the idea, encouraged by Cutlip, Center and Broom’s (1985) definition of public

relations with its emphasis on relationships between publics and organizations: “the

management function that establishes and maintains mutually beneficial relationships

between an organization and the publics on whom its success or failure depends”

(Cutlip et al., 1985, qtd. in Smith, 2010). Following on Ferguson’s (1984) invited paper

presented at the annual AEJMC conference, James Grunig, who also studied at

Madison with Cutlip at the same time as Broom and was in 1984 a relatively new faculty

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member at the University of Maryland, developed an interest in exploring relationship

theory. Subsequently, J. Grunig, L. Grunig and Ehling (1992) argued that organizations

must build positive relationships if they want to effectively deliver their message to a

public. No matter how they operate, organizations must maintain relationships with

publics at some point in their life cycle, so determining effective methods of relationship

management is a crucial area of research (J. Grunig et al., 1992). Ehling (1992) further

argued that determining what leads to positive and negative relationships – which he

refers to as cooperation vs. conflict – can help determine a method by which a public

relations department can measure its monetary contribution to an organization.

Several researchers connected the idea of social exchange, the theory that

people seek out relationships that will be of the most benefit to them, to relationship

theory research. Thomlinson (2000) placed relationships in a social exchange model he

called the “comparison level for alternatives,” arguing that as long as there are

alternatives to a given relationship, both parties will expect their relationship to be as

beneficial as possible. Because better alternatives may exist to meet consumer needs,

it is up to organizations to recognize publics as ever changing and adapt to their needs

(Thomlinson, 2000). Grunig and Huang (2000) noted that companies need to take both

a short-term and long-term approach to relationship management -- that recognition of

needs in the short-term serves as a building block for a positive long-term relationship.

Other researchers applied these theories to subsections of public relations. Coombs

(2000) posited that these expectations played a role in crisis management, as a public’s

expectation of an organization will be shaped by the way it handles a crisis. Bridges and

Nelson (2000) applied social exchange to issues management, suggesting that

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understanding a public’s relationship expectations can help weigh the pros and cons of

a risky decision, as well as mitigate backlash if the decision does not pay off.

Despite Ferguson’s (1984) recommendation that scholars start building a public-

organization relationship theory by first adopting a conceptual definition to unify their

research, no clear definition of the term organization-public relationships had been

developed by the late ‘90s (Broom, Casey & Richie, 1997). In addition, while these

authors did not recommend a specific definition, they encouraged researchers to join

them in the search, as failure to provide a clear definition hindered researchers’ ability to

make clear inferences about the effectiveness of a relationship model (Broom, Casey &

Richie, 1997). In an effort to move the field forward at about the same time, Ledingham,

Bruning, Thomlinson and Lesko’s (1997) research tried to flesh out the attributes of an

OPR; although they did not provide a definition, they noted “public relations activities

can help achieve organizational goals by fostering loyalty in the organization-public

relationship through involvement, investment, and commitment to the community served

by that organization” (“Preliminary Conclusions,” para. 3). The authors noted that

studying relationships would give the field a leg separate from advertising and

marketing (Ledingham et al., 1997).

Huang (1998) introduced a formal definition about factors that lead to successful

relationships: “(OPRs are) the degree that an organization and its publics trust each

other, agree on (sic) one has rightful power to influence, experience satisfaction with

each other, and commit oneself to another” (p. 12 qtd. in Huang, 2001). In their

definition, Ledingham and Bruning (1998) emphasized that relationships cannot work if

one party does not understand how its actions have an effect on the other, and they

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identified the impacts of interest: “(OPRs are) the state that exists between an

organization and its key publics in which the actions of either entity impact the

economic, social, political, and/or cultural well-being of the other entity” (p.62). Despite

different foci, all of these definitions have a similarity: the assumption that both parties

contribute to relationship maintenance.

Between 1984 and the early 21st century, researchers also began to propose

constructs that, when measured, could determine the effectiveness of a relationship.

Only after agreeing on such constructs2 could a communication or public relations

department demonstrate its worth, according to Grunig & Huang (2000), making the

research crucial to the field’s continued growth.

Constructs of Organization-Public Relationships

In her 1984 paper, Ferguson noted that relationships were made up of attributes

that needed to be identified in order to successfully study relationships. Ferguson

(1984) listed dynamic/static, open/closed, organization/public satisfaction, power

distribution, shared goals and mutual understanding, agreement, and consensus.

Ferguson (1984) noted that the list was not exhaustive, and researchers may have a

difficult time convincing each other that their attributes were correct. Grunig, Grunig and

Ehling (1992) also proposed a set of organization-public relationship attributes, building

off Ferguson’s (1984) list by including openness, mutual understanding and mutual

satisfaction, while adding trust and credibility, which they said was an important element

2 In discussing these constructs, it is important to note that researchers have used different but related terms to describe the necessary constructs for elaboration of organization-public relations theory; for this reason, the words “attributes,” “constructs,” “dimensions, “elements” and “strategies” are used interchangeably unless noted otherwise.

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of interpersonal relationships. Grunig, Grunig and Ehling (1992) also introduced

Pfeffer’s (1978) theory of organizational legitimacy, the idea that relationships are

strengthened when a public sees an organization’s actions and public stances as in line

with each other, as an attribute.

Ledingham and Bruning also conducted research in the late ‘90s to determine

relationship effectiveness; their efforts led to a textbook in 2000, as well as five

relationship constructs whittled down from a list of 17. The two joined with several other

researchers to conduct studies in the late ‘90s in order to pinpoint which constructs

showed up most often through interviews and surveys with public relations practitioners.

In their work with Thomlinson and Lesko (1997), the researchers operationalized

Wood’s (1995) constructs – investment, commitment, trust and comfort (mainly comfort

in organizations being open with their publics) – to start with, before building up a list of

other constructs provided by members of a focus group. After putting their final 17

constructs in front of other focus groups, Ledingham and Bruning (1998) finalized five

constructs they believed to be most important to a successful OPR – trust, investment,

commitment, openness, and involvement. Ledingham and Bruning (2000) suggested

these five constructs would lead to satisfaction, rather than satisfaction standing alone

as its own dimension.

In an effort to build a scale organizations could use to measure how their publics

perceive relationships – and therefore provide hard data on the effectiveness of

selected constructs – Hon and Grunig (1999) developed measures of OPR constructs,

also picking up trust, control mutuality, commitment and satisfaction from prior studies

as important. They also looked at the type of relationship organizations sought to

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achieve, introducing exchange vs. communal relationships to the list. The difference is

in the expectations; in an exchange relationship, each party expects something of equal

or greater value from the other, while organizations involved in communal relationships

give freely without expectations, as both parties are “concerned for the welfare of the

other” (Hon & Grunig, 1999, p. 3). The researchers introduced a series of Likert-type

scale questions to observe their six constructs.

However, Hon and Grunig (1999) did not stop at the relationship determinants.

The authors also proposed constructs for measuring an organization’s effectiveness in

the process of cultivating or maintaining a relationship, using their original six

relationship constructs as a foundation for their proposal.

Relationship Cultivation and Maintenance

Relationship cultivation research is focused on defining the strategies that bring

relationships about or keep them viable, rather than looking at the constructs of a

relationship itself. In her literature review, Ki (2003) offered a definition of relationship

maintenance: “any effort used to sustain desired relationships between organizations

and publics” (p. 12).

Serious research into relationship maintenance began about eight years before

Hon and Grunig (1999) introduced their concepts. As public relations is a young field

academically, some public relations theories are rooted in other disciplines. Stafford and

Canary’s (1991) relationship cultivation strategies are examples – they came not from

public relations literature, but from interpersonal communication, in an attempt to

determine which methods were best for those trying to maintain a partnership or

marriage with a romantic interest. The researchers looked at couples in serious

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relationships, engagements or marriage and asked 7-point Likert-type scale questions

pertaining to the five constructs they found most prevalent in relationships: positivity (ex.

“asks how my day has gone”), openness (ex. “seeks to discuss the quality of our

relationship), assurances (ex. “implies that our relationship has a future”), networking

(ex. “likes to spend time with our same friends”) and sharing of tasks (ex. “shares in the

joint responsibilities that face us”) (p. 228). The researchers found that the longer a

couple was together, and the more steps they took toward binding commitment, the

deeper their involvement in the relationship strategies became (Stafford & Canary,

1991). Canary and Stafford (1994) built their maintenance constructs on a framework of

several propositions. First, relationships cannot survive without maintenance; second,

maintenance is easier when relationships are equitable; third, the relationship itself

determines the amount and type of maintenance used by each party; fourth,

maintenance tactics can stand alone or be used in cooperation with other tactics; fifth,

maintenance tactics are both interactive and non-interactive; and sixth, maintenance

occurs in both routine and strategic interactions (Canary & Stafford, 1994).

Hon and Grunig (1999) approached relationship maintenance as a second step

in their work, with the stated goal of providing practitioners with research-tested

techniques. In addition to Stafford and Canary’s (1991) constructs of assurances,

networking, sharing of tasks, positivity and openness, they proposed access – public

relations practitioners providing publics with clear channels to find information – as a

crucial dimension. In addition, Hon and Grunig (1999) further defined maintenance

strategies used in theories of conflict resolution as: integrative, where both parties work

to find a resolution that works for everyone; distributive, where the sides are in conflict

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with one trying to come out better off than the other; and dual concern, which

recognizes both parties have needs and can ultimately be engaged in symmetrical or

asymmetrical relationships based on the subsequent resolution.

Ultimately, Hon and Grunig’s (1999) work, with 893 citations on Google Scholar,

is the most widely recognized public relations application of Stafford and Canary’s

(1991) work, later sharpened in studies such as Ki and Hon (2003) and used as the

basis for studies such as Li (2015), upon which this thesis is based. Because Hon and

Grunig’s (1999) six relationship maintenance constructs provide a research-based

foundation for other scholars to build on, a more comprehensive definition of each –

along with its application to short-form communication -- is warranted.

Openness

One of Ferguson’s (1984) first proposed attributes, openness relates to how

willing organizations and their publics are to speak candidly with each other. Rather

than listing openness as its own construct, Hon and Grunig (1999) characterized it as

hand-in-hand with trust – either party to a relationship trusts the other, and in turn the

other party is encouraged to be more open. In their operationalization of openness,

Ledingham and Bruning (2000) were not only concerned with the company disclosing

information at the moment, but being open about future plans for the community as well;

this means both parties must dedicate time to discuss the relationship, as Canary and

Stafford (1994) suggested.

Sowa (2013) argued openness is also a balancing act. Publics value an

organization that can adapt when the climate they operate in changes, but being too

open can have a rebound effect -- so companies must know what information to share

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and what information needs to stay undisclosed for security reasons, or organizations

that overshare run a risk of become untrustworthy in the eyes of their publics (Sowa,

2013). In applying openness to Twitter, Li (2015) defined the realm of openness as

“provid[ing] information about any changes pertaining to finances, organizational

restructuring, and other organizational activities” (p. 191)

Access

Access refers to how willing an organization or its publics are to make

information and resources available to each other. Hon and Grunig (1999) emphasized

that access is a two-way street; publics are willing to provide practitioners data and

access to opinion leaders within their communities, and practitioners in turn make

company officials and data available to publics. Both parties should be satisfied with the

access when they do not feel the need to include a third party (Hon & Grunig, 1999).

Access is not limited to higher-ups, as organizations which post employees’ phone

numbers and email addresses on their websites allow anyone to contact public relations

representatives (Ki, 2003), and organizations that provide message boards for

consumers to post on and begin discussions give consumers another way to access

customer service representatives (Ki & Hon, 2006).

Li (2015) notes that this dimension is closely related to Grunig and Hunt’s (1985)

two-way symmetrical communication model, as both parties must be open and

responsive to the other for access to be considered a positive attribute of the

relationship. Li (2015) pointed to several different ways organizations could provide

access on Twitter, including ”posting questions, @reply/mention, providing phone

number/email address, and providing links to more information” (p. 191).

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Task Sharing

Literature found in Li (2015) and Hon and Grunig (1999) points to societal task

sharing as a corporate social responsibility-related dimension of relationship

maintenance – a chance for organizations to get out and get involved in the

communities of which they are citizens. Public relations researchers have adapted

Stafford and Canary’s (1991) research assumption that task sharing was an important

part of a successful long-term relationship, as well as Canary and Stafford’s (1994)

hypothesis that relationship maintenance is easier when both parties are willing to work

together. While Stafford and Canary (1991) focused on joint tasks in their definition, Hon

and Grunig (1999) noted that task sharing can apply to problems that only affect one

party as well; examples include “managing community issues, providing employment,

making a profit, and staying in business, which are in the interest of the organization,

the public, or both” (p. 15). Hon and Grunig (1999) pointed to a health care center that

teamed up with a hospital to provide healthcare to low income families as a specific

example of task sharing; Li (2015) pointed to cleaning up pollution as another.

To apply this dimension to modern technology, Ki (2003) recommended

organizations use their web pages to both provide information on completed or on-going

social responsibility initiatives and ask for assistance in completing other projects. Li

(2015) defined task sharing on Twitter as “performing corporate social responsibility by

addressing social concerns or organizational efforts that relate to the problems of

mutual interest between the organization and its publics, such as environmental

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activities, community activities, education activities, and volunteer efforts” (Li, 2015, p.

191).

Networking

Stafford and Canary (1991) argued that partners who have mutual interests are

likely to have stronger relationships, and as couples seek to find mutual friends to spend

time with, they engage in networking. Hon and Grunig (1999) applied this principle to

public relations, with tangential organizations that hold mutual interest for organizations

and their publics serving as “friends”. When organizations look to either connect their

publics with organizations related to their interests or become involved in initiatives not

run in-house, they may network with other organizations that can close the gap. Hon

and Grunig (1999) suggested that networking involves finding common ground with

publics, as organizations look to become involved with the organizations to which their

publics are already connected; Ki (2003) offered unions or environmentalists as

potential networking opportunities.

Ki (2003) also promoted a two-fold networking use of shared media and owned

media – to connect with like-minded organizations and to provide publics information

about any projects an organization may partner with other organizations to complete. Li

(2015) viewed the retweet function on Twitter as networking, defining networking as “an

organization’s efforts in building networks or coalitions with the same groups that their

publics do, such as environmentalists, unions, community groups, celebrities, and

opinion leaders” (p. 191).

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Assurances

Prior research into relationship constructs is split on the inclusion of assurances.

As a dimension of relationship maintenance, assurance is related to Pfeffer’s (1978)

theory of organizational legitimacy. An organization must demonstrate it can follow

through on the promises it makes to its publics, and assurances function as the building

block of organizational legitimacy (Hon & Grunig, 1999). As with the other constructs,

assurances can function in the opposite direction – publics working to convince

organizations that their causes or efforts should be taken seriously (Hon & Grunig,

1999). On Twitter, assurances tend to come up in replies to customer complaints, Li

(2015) notes: “Most tweets for customer service and tweets that generally address

availability and willingness to help, as well as those that emphasize on maintaining

relationships are assurance” (p. 191).

Positivity

Stafford and Canary (1991) found that when couples take a positive interest in

each other’s lives and work to resolve problems in a manner that would not damage the

partnership, relationships are strengthened – and research determined that these

results were more significant for younger couples in serious relationships but not yet

married. In applying this research to public relations, Hon and Grunig (1999) noted that

positivity comes when either party in an OPR tries to improve relations with the other

and provides reasons why the other party should want to strengthen the relationship,

while Li (2015) focused on the tone of the message. On Twitter, positivity “indicators

include using a positive and cheerful tone, positing smiling face signs, using positive

exclamations, and showing of humor” (p. 191).

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These six maintenance strategies can be used in tandem through affiliative

communication to build brand loyalty. This concept will be discussed further in the next

section.

Affiliative Communication and Influence

Affiliative communication serves as the concept that ties this thesis’ independent

variables to its dependent variable.

Affiliative Communication

Much of the literature explores affiliative communication as a leadership style (i.e.

Gagnon, Vough, and Nickerson, 2012; Lee and Kim, 2014). One study posits that

leaders develop affiliative communication skills and gain the loyalty of their followers by

(1) opening themselves to their followers, (2) establishing trust with their followers and

(3) sharing tasks with their followers (Gagnon et al., 2012). These researchers speak of

a different sort of task sharing than Hon and Grunig (1999) did, referring to it asking for

input and including it in the decision-making process, rather than a disclosure of

corporate social responsibility practices (Gagnon et al., 2012). All three of these

measures line up with Hon and Grunig’s (1999) construct of openness.

Lee and Kim (2014) observed affiliative communication from social media users’

perspectives, finding that no matter a user’s communication competency, the desire for

an affiliative relationship drives users to communicate via Twitter. However, the

researchers also discovered that for those with lower levels of communication

competency (for example, more introverted users), there was a difference between the

desire to further develop a user’s network and the desire to maintain already

30

established relationships (Lee & Kim, 2014) – suggesting that networking may not be as

strong a relationship maintenance construct as Hon and Grunig’s (1999) five others.

Based on the literature, the thesis proposes a conceptual definition of affiliative

communication – it is a style of relationship maintenance involving two or more parties

where one party (A) reaches out to start a dialogue with the other parties and (B)

ensures their concerns are being taken into account. If enacted successfully, it is

believed that affiliative communication can lead to influence – this thesis’ dependent

variable and the next section’s focus.

Influence

Literature on influence has yet to determine either a single definition or clear

components of the construct. Sheldrake (2011) looks at influence as a two-step system:

first, an organization provides content for audiences to interact with; then, consumers

take the content and share it with friends, or use it to shape decision-making.

Practitioners and researchers must be careful not to confuse popularity and influence;

influence uses a brand’s popularity to lead to a measureable outcome (Sheldrake,

2011). Cha, Haddadi, Benevenuto, & Gummadi (2010) investigated social media

influence as “an individual’s potential to lead others to engage in a certain act” (p. 11).

From a psychology perspective, Cialdini (2009) approached influence as something

practiced by “compliance” experts, or those responsible for getting audiences to agree

to a message or request. Cialdini (2009) determined six principles that make up a

successful influencer campaign. These principles include consistency, or an

organization following through on its promises; liking, or cultivating a favorable identity

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in the eyes of public; and authority, or branding an organization as an expert such that

audiences turn to them for their opinion or leadership (Cialdini, 2009).

Social media gives users a number of tools to demonstrate their interaction with

a brand3; research shows organizations have a basic understanding of how their

outreach affects brand influence. In one study, researchers specifically looked at Twitter

and categorized social media influence into three distinct areas: indegree influence, or

number of followers; retweet influence, or number of retweets; and mention influence, or

the number of times someone include the person’s/organizations’ Twitter handle in a

tweet (Cha et al., 2010). In another sudy, researchers separated influence factors into

two categories: long-lasting and dynamic (Rao et al., 2015). Dynamic factors require

social media users to only engage with a brand/user for a short period; examples

include likes, comments and retweets (Rao et al., 2015). Long-lasting factors require a

commitment that goes deeper than interacting with a single post; examples include

following or subscribing to an organization/person (Rao et al., 2015). These dynamic

and long-lasting factors are part of the Klout Influence Score measurement, further

discussed in the next section and used as a dependent variable in this thesis.

Based on the literature, the researcher proposes a conceptual definition of

influence: it is the construct that forms when an organization develops a strong enough

relationship with a consumer that the consumer values that organization’s input in the

decision-making process. Consumers can also influence organizational decision-making

through methods such as political pressure; a decrease in sales of Ivanka Trump-

3 In the literature, “brand” and “organization” are used interchangeably.

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branded merchandise, driven by social campaigns such as #GrabYourWallet, have led

a series of companies to drop her products (Kulp, 2017).

Influence Measurement

In order to determine the influence of different businesses on social media, two

organizations – Klout and Kred – have developed measurement tools that rank

businesses based on different aspects of their social media profiles. Klout, developed in

2008, assigns an influencer score of 0-100 to any person or organization with a public

Twitter account (Stevenson, 2012). In addition to dynamic and long-lasting factors such

as likes and retweets (Rao et al., 2015), Klout scores consider factors such as

frequency of updates and the Klout scores of an individual’s friends and followers

(Stevenson, 2012). Klout scores have become popular in several industries; Stevenson

(2012) describes a job candidate who lost out on a position because his Klout score

was too low, as well as a hotel in Las Vegas that upgraded customers with higher Klout

scores to suites. Klout also provided the researcher a numerical outcome variable with

which to compare independent relationship maintenance variables. Kred launched in

2012 as a Klout competitor. While it has not had the media attention Klout received

upon its launch, the brand sets itself apart by offering users a transparent list of factors

that determine their scores (“Kred Scoring Guide,” n.d.). Kred provides users a score

both for their influence, through measures such as retweets and favorites, and for their

outreach, by examining how well the brand or influencer responds to others on their

social network (“Kred Scoring Guide,” n.d.).

As social media can serve as a means of organization-public relationship

maintenance helping to form influence through affiliative communication, it provides new

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ground for research into the effects of affiliative communication on influence. This thesis

will investigate that connection on Twitter. For a deeper understanding of how

relationship management strategies have been tested on both regular websites and

social networking websites, this literature review will now turn to previous studies

concerning online relationship maintenance after an overview of social media use.

Introduction to Social Media Use

In 2005, 10% of all Internet users were active on social media websites; 7% of

those were adults (Perrin, 2015). Within 10 years, those numbers multiplied

significantly. By 2015, 76% of all Internet users were active on social media (including

65% of all adults) (Perrin, 2015). Use continues to grow, as more senior citizens join

social networking sites while groupings such as Caucasians, African-Americans and

Hispanics use social networking sites at generally the same rate (Perrin, 2015). Overall,

there are 2.3 billion active social media users (Smith, 2016)4, approximately 30% of the

world’s population.

Many brands will take a post on social media and move it to a more private form

of communication; for example, on the social media website Twitter, Samiljan (2016)

observed tweets from customers at American Airlines and Norwegian Cruise Lines that

customer service representatives replied to via email. While the customers’ issues were

resolved, the resolution was not readily visible to most social networking users. Other

organizations reply directly on Twitter and allow users to see how they have responded

4 Used for social media statistics only

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to an issue. In Martin (2014)5, a Twitter conversation between American Airlines and a

customer demonstrates this principle; the customer complains about a delayed flight

and tags the airline, and rather than ignoring it, the airline tweets back flight information

and gives a reason for delay, as well as an apology for the inconvenience – all within

view of other customers.

Studies in Online Relationship Maintenance

As social networking sites have provided quick outlets for organizations to reach

their publics, public relations scholars have examined what strategies practitioners are

using to take full advantage of technological developments. Scholars have conducted

general research on businesses both on Twitter (Rybalko & Seltzer, 2010; Li, 2015) and

on corporate websites (Ki & Hon, 2006) as well as across different sub-disciplines

including international public relations (Men & Tsai, 2012) and religious organizations’

communication strategies (Waters et al., 2011). Not all of these scholars used Hon and

Grunig’s (1999) six relationship maintenance strategies, choosing to adopt other

authors’ models. Li (2015) provided the first guide for specifically studying Hon and

Grunig’s (1999) strategies of openness, positivity, access, awareness, task sharing and

networking on Twitter.

Rather than focus on a specific style of company, Li (2015) drew from

organizational rankings to choose a sample, drafting companies highly ranked for

customer loyalty on the Brand Key Customer Loyalty Engagement Index, as well as

companies ranked on the Forbes 500 list. Li (2015) drew 20 tweets from a random

5 Used to give an example of social media relationship maintenance only

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sample of 40 tweets from each brand for a total of 400 tweets, and content analyzed the

tweets for evidence of Hon & Grunig’s (1999) six relationship maintenance strategies. In

her results, Li (2015) noted that loyalty-leading brands were more apt to perform

customer service-related relationship maintenance behaviors than Fortune 500

companies in general that did not appear to have particular strategies they

predominately followed. The study found companies most often invoked the access

strategy in discussions with consumers, followed by assurance and positivity,

respectively (Li, 2015).

Ki and Hon (2006) followed a similar pattern but focused on corporate websites

before the social media boom of the mid-2000s. In an extension of Ki’s (2003) thesis, Ki

and Hon (2006) examined the websites of Fortune 500 companies to observe which

approaches companies used the most. The authors found that openness strategies

were found most often on corporate websites, as companies used websites to circulate

press releases and report earnings information; however, much of this dialogue was

one-way (Ki & Hon, 2006). Access came second, with sharing of tasks coming in last; Ki

& Hon (2006) concluded that companies had not mastered the possibilities that come

with an owned media outlet like a website.

Rather than adopt the above six relationship maintenance strategies, Rybalko

and Seltzer (2010) turned to Kent and Taylor’s (1998) principles of dialogic

communication: ease of interface, conservation of visitors, generation of return visits,

providing useful information to a variety of publics and maintaining a dialogic loop

defined as providing users opportunities to ask questions and provide feedback

(Rybalko and Seltzer, 2010, p. 337). The researchers drew 10 tweets each from Twitter

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accounts of 93 Fortune 500 companies and found that companies most often used

Twitter to open and maintain a dialogic loop with consumers, indicating a high level of

question and answer tweets between the organization and its publics. Generation of

return visits came second and usefulness of information came last; the authors noted

that while companies often used Twitter to provide links to their main website, they did

not provide links to related company information for further reading or the company’s

other social media accounts (Rybalko and Seltzer, 2010).

Men and Tsai (2012) used a mix of dialogic principles and maintenance

strategies in their study of 100 corporations, 50 from the United States and 50 from

China. The authors looked at three different categories – openness, information

dissemination and interactivity, and involvement – on Facebook for the American

corporations and on Renren, Facebook’s foreign counterpart, for the Chinese

corporations (Men and Tsai, 2012). The researchers found organizations in America

were more likely to provide interactive content such as surveys to encourage users to

stay on their Facebook page, while Chinese organizations were more likely to engage in

conversations with publics; American companies were also more likely to promote their

brand through the Facebook page, while Chinese organizations were more likely to

promote tips and fun posts tangentially related to their brand rather than the brand itself

(Men and Tsai, 2012).

Waters, Friedman, Mills and Zeng (2011) moved away from Fortune 500

companies and focused on religious organizations, examining 270 religious

organizations’ websites across nine states. The authors argued that religious

organizations must realize the diverse range of users who could cross their website and

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seek out ways to cultivate a relationship with them that may not reflect traditional

ministry, as users may research churches online first rather than immediately attending

a service (Waters et al., 2011). Ultimately, positivity came up as the most often used

maintenance strategy; however, the scholars gave it a more unlimited definition than Li

(2015) – “any attempt to make a visit to a Web site more efficient and effective” (Waters

et al, 2011, p. 94). The authors concluded the sites generally provided basic contact

information for the church but nothing deeper that would allow potential churchgoers to

explore the church further on the Internet (Waters et al, 2011).

While much Twitter relationship maintenance research has been committed to a

general sample of companies such as those on the Fortune 500 list, little has been

completed for subcategories such as travel and tourism. The final section of this chapter

will explore the existing social media and relationship literature from travel and tourism

scholars.

Social Media/Relationship Maintenance in Travel and Tourism

After an examination of prior research, it appears the only relationship

maintenance dimension study completed for travel and tourism is Zhu and Han (2014),

who looked at Hon and Grunig’s (1999) maintenance strategies through the lens of both

state-run and travel agency-run tourism websites. After performing a content analysis of

all 50 official state tourism sites and 45 of the top travel agencies listed on Yahoo, the

researchers determined that access is the most commonly used maintenance strategy

for both styles of tourism websites, while sharing of tasks was not often used on either

type of site (Zhu & Han, 2014). In a comparison of the two types of sites, the

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researchers found that state websites were better at demonstrating positivity than travel

agencies but did not speculate why (Zhu & Han, 2014).

As in other fields, researchers are still examining the ways social media has

affected the travel industry. Xiang and Gretzel (2010) provided an argument for the

importance of social media in the field, noting their research showed high search engine

optimization on Google for social media sites, giving consumers their first impressions

of a travel destination when completing an online search. Despite these findings,

practitioners may hesitate to focus on a social media campaign out of fear of negative

publicity, as Ayeh, Leung, Au, and Law (2012) found in a survey of those in the travel

industry. Those who did engage in social media used it as a visual medium, posting

pictures of the destination, or as a forum for dialogue, seeking to start discussion or

obtain positive word of mouth, but not as a place to directly sell the consumers on

products (Ayeh et al., 2012).

Other studies examined the way tourism brands such as hotel chains used both

social media, like Twitter, and ranking websites, like TripAdvisor, to reach out to

consumers, but did not take the relationship maintenance perspective. Sotiriadis and

van Zyl (2013) found that users were more likely to trust online information if the source,

whether it be a corporation on Twitter or a reviewer on a site like TripAdvisor,

demonstrated proven expertise on a topic. Thus, organizations may respond to positive

reviews with the intent to increase the legitimacy of the poster (Sotiriadis & van Zyl,

2013); as the authors note, “a successful (social media) strategy will provide forums for

destination, opportunities for comments, suggestions and feedback” (p. 120). Law, R.

Leung, Lo, D. Leung, Hoc, and Fong (2015) found that while organizations have

39

recognized a changing tide in technology and have worked to adapt, a personal touch is

still important in travel communications -- making an argument for organization-owned

social media to co-exist with travel agents rather than replace them. Law et. al (2015)

concluded, “a pragmatic approach would be to take advantage of the Internet and treat

it as an opportunity instead of a threat” (p. 448).

Hypotheses and Research Questions

Based on the research found while reviewing the relevant literature, this thesis

will examine four hypotheses and one research question. The findings suggest that

leaders who are transparent, look to build trust and seek input from their peers are

successful at affiliative communication (Gagnon et al., 2012). These three qualifications

describe Hon and Grunig’s (1999) openness construct. The research suggests that

organizations that follow those same guidelines on Twitter will be successful at affiliative

communication. The research did not provide evidence to support a hypothesis that any

of the other constructs – positivity, networking, task sharing, access and assurances –

would have a stronger positive association with Klout and Kred influencer scores.

Therefore,

H1a: Prominence of openness will be more positively associated with influence

than prominence of positivity.

H1b: Prominence of openness will be more positively associated with influence

than prominence of networking.

H1c: Prominence of openness will be more positively associated with influence

than prominence of task sharing.

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H1d: Prominence of openness will be more positively associated with influence

than prominence of access.

H1e: Prominence of openness will be more positively associated with influence

than prominence of assurance.

Lee and Kim (2014) found that more introverted users were more likely to be

interested in what their friends were doing than in trying to make connections with or

following what friends-of-friends were doing. Hon and Grunig’s (1999) networking

construct is focused on organizations trying to connect with other organizations or

opinion leaders in which their publics are interested, rather than talking about the

organizations’ internal efforts. The research did not provide evidence to support a

hypothesis that any of the other constructs – positivity, task sharing, openness, access

and assurances – would have a weaker positive association with Klout and Kred

influencer scores. Based on these findings, the researcher suggests an organizations’

use of the networking construct is not as effective for influence as is use of the other

constructs. Therefore,

H2a: Prominence of networking will be less positively associated with influence

than prominence of positivity.

H2b: Prominence of networking will be less positively associated with influence

than prominence of task sharing.

H2c: Prominence of networking will be less positively associated with influence

than prominence of access.

H2d: Prominence of networking will be less positively associated with influence

than prominence of assurances.

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One logically derived hypothesis can be tested if H1 and H2 are supported:

H3: In the hierarchy of relationship maintenance constructs used to build

organizational influence, networking < (task sharing, positivity, access, or assurances) <

openness.

Overall, research on affiliative communication (i.e. Gagnon et al., 2012, Lee &

Kim, 2014) suggests an organization’s efforts to participate in affiliative communication

with publics - seeking to build trust with publics and resolving any problems in a manner

that leaves consumers with a positive attitude - leads to organizational influence.

Therefore, the researcher suggests:

H4: The greater prominence of all relationship maintenance strategies in

affiliative communication on social media will be positively related to higher

organizational influence.

These hypotheses are accompanied by one research question:

RQ1: How are the six types of relationship maintenance strategies used in

tweets by hotel chains related to overall social media organizational influence?

Together, these hypotheses suggest the model pictured in Figure 2-1.

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Figure 2-1. Model of strength of relationship maintenance constructs of Affiliative Communication (width of the line represents strength of correlation)

Networking

Brand Influence

Access

Task Sharing

Positivity

Assurances

Openness

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CHAPTER 3

METHODOLOGY Although some research focused on relationship maintenance online has been

completed, and scholars have looked at how travel brands respond to consumer

sentiment on websites like TripAdvisor ®, little research has studied tourism relationship

maintenance behaviors on a short-form communication medium like Twitter. Twitter,

which allows its users to post updates restricted, with a few exceptions, to 140

characters or less, is one of several sites that have benefited from the social media

boom. Twenty-three percent of all Internet users are active on the site, and use skews

younger, with 32% of 19-29 year olds on the site compared to 13% of 50-69 year olds

(Duggan, 2015). Use is also greater in urban vs. rural areas (30% to 15%), and 38% of

its users log on daily (Duggan, 2015). Overall, there are 320 million active Twitter users

(Smith, 2016). Many major businesses have one or more Twitter accounts through

which the communications team posts updates for followers and can provide direct

contact between customer service and consumers.

Some previous studies have observed affiliative communication on social media

from the public perspective, but not from the organizational perspective. Rather than

focusing on how the audience interprets a message, or how the organization intends to

send a message, this thesis uses variables from studies like Ki and Hon (2006) and,

largely, Li (2015), to examine the actual message displayed to the public. Different

relationship maintenance constructs were observed for their prominence –how

frequently an indicator of a particular construct is present in a tweet. Xiang and Gretzel

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(2010) demonstrated why the content of a social media page is important – it’s often the

first thing a consumer sees when researching a brand.

Dependent Variables

The researcher sought to measure the dependent variable construct – influence1,

based on an organization’s use of relationship maintenance constructs in affiliative

communication. As defined in Chapter 2, influence is the construct that describes an

organization’s strong relationship with a consumer such that the consumer values that

organization’s input in the decision-making process. The researcher looked to online

social media influence brand-ranking systems to find one that would provide a

commonly accepted ranking of hotel social media influence.

Klout vs. Kred: The dependent variable was measured by both an

organization’s Klout and Kred influencer scores. Each ranking system has positives and

negatives. While Kred only covers a brand’s Facebook and Twitter interactions, Klout

covers data from Facebook, Twitter, Instagram, LinkedIn, Google+ and Tumblr, among

others (Kellogg, 2013)2. Kred provides a broader view of an organization’s social media

influence, factoring the brand’s last 1,000 days of interactions; Klout focuses in on a

brand’s most recent interactions, factoring the brand’s last 90 days of interactions

(Kellogg, 2013). Kred is ultimately more researcher-friendly, as it publicly lists the

elements that add together to create its score; Klout uses 400 different elements to

determine its score (Kellogg, 2013), but it publicly discloses very few of these elements

1 For the purpose of this study, when the researcher refers to “influence,” he is discussing how influential a brand is with followers on social media, rather than how influential a brand is through advertising or in the marketplace.

2 Source used for background information on influencer programs only

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– for example, the Twitter elements it publicly discloses are followers, comments,

replies, mentions, retweets and views (Rao et al., 2015)

Because each dependent variable represents a different type of observation of

influence for each hotel brand, two different measures increased measurement validity.

It is also important to note that because of Klout’s 90-day range vs. Kred’s 1,000-day

range, any differences in correlation could suggest a change in a brand’s relationship

maintenance strategies over time.

Content Analysis

In order to effectively observe the research questions and test hypotheses

proposed in Chapter 2, the researcher completed a quantitative content analysis of

tweet elements – including sentences, phrases and punctuation – in a sample of tweets

from all Twitter accounts belonging to the hotel chains that had earned both a Klout and

Kred influencer score. Rose, Spinks and Canhoto (2015) defined quantitative content

analysis as “the classification of parts of a text through the application of a structured

coding scheme from which conclusions can be drawn about the message content”

(“Quantitative Research Designs,” para. 30). The authors presented two different goals

for content analysis; description, wherein a researcher examines a message and

explains the content within it, and prediction, wherein the researcher analyzes a

message to determine its intended effect (Rose et al., 2015). As this thesis examined

tweets to determine correlations between prominence of relationship maintenance

constructs and the dependent variable, influence, this study is a predictive content

analysis.

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Similar to Li (2015), content analysis is the appropriate research method for this

thesis to examine the words included in the tweet. Consumers do not have access to an

organization’s thought process behind developing a tweet, only the published tweet, so

content analysis of a tweet ensures the researcher studies only information visible to

consumers. Using a coding sheet and one research assistant, the researcher or the

research assistant analyzed phrases to determine which relationship maintenance

strategies are represented most prominently in each tweet.

Data Collection

During initial hotel brand selection, the researcher used J.D. Power and

Associates’ rankings to obtain an extensive list of hotel chains. The researcher first

determined which Twitter accounts were viable for research among the 71 hotel chains

ranked by J.D. Power and Associates and removed chains with Twitter accounts that a)

had not provided a tweet within the last week in order to ensure the account was active,

b) had individual franchise accounts rather than a centralized account, as looking at one

hotel would not be generalizable to the entire chain or c) did not have a strong enough

social media presence to have earned both a Klout and a Kred score. This eliminated

30 hotel chains, leaving 40 observable chains. The researcher collected 1,600 tweets

from these chains (40 per chain). The chains are listed in Table 3-1, categorized by

hotel class as determined by the J.D. Power rankings.

On November 22, the researcher downloaded the 40 most recent tweets from

each account using MassMine. MassMine is a data scraping tool that allows users to

search for tweets categorized by, for example, hashtags and account handles. Users

can then set parameters for how many tweets they would like MassMine to collect from

47

the specified categories (Van Horn & Beveridge, 2016). The researcher instructed

MassMine to collect tweets from the Twitter handles of the hotel chains listed in Table 3-

1. As MassMine does not translate its readout into sortable text, the researcher

uploaded the tweets into the data cleaning software OpenRefine. The software

converted the data back into its original English readout and parsed it an Excel

spreadsheet for searchability.

Previous studies have differed concerning how many tweets are examined and

how large of a random sample of tweets are drawn. Rybalko and Seltzer (2010) chose

to pull 10 tweets at random from an organization’s previous 20 tweets; Li (2015)

doubled the frame, pulling 20 tweets at random from an organization’s previous 40

tweets. To keep the number of tweets available to draw from per organization constant

while pulling from the largest sample of tweets possible, the researcher followed Li’s

(2015) method. The researcher entered the numbers 1-40 into a random number

generator and recorded the first 20 unique numbers the generator provided. The

researcher then labeled each organization’s tweets 1-40, from newest to oldest, and

derived the sample from the tweets that corresponded with the random number

generator listing. The researcher also compiled a list of backup numbers from the

random number generator in the event a tweet was not valid (i.e. the tweet

corresponding with a randomly selected number was a retweet, rather than an original

tweet).

Coding

Coding for this thesis drew directly from Li (2015). Li (2015) examined tweets

gathered from Fortune 500 companies and loyalty leaders within the industry and coded

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those based on Hon and Grunig’s (1999) constructs of relationship maintenance. Within

five of the six measures, Li (2015) categorized the level of maintenance as ordinal with

high, medium, low and non-existent categories. Li’s (2015 measurements only provide

ordinal data, so it is unclear what gap exists between none, low, medium and high. In

order to create interval and ratio variables that allow the use of more powerful statistics,

the researcher converted five of Li’s (2015) six ordinal variables into ratio variables (Li

(2015) already included ratio indicators for the positivity construct). The number of

indicators – words, phrases and punctuation within a tweet that signal the use of a

relationship maintenance construct – was counted, with no limit; a list of construct

indicators is available in Table 3-1. Li (2015) pre-tested her measures using Scott’s pi

and found the intercoder reliability for nearly all of the measures to be above .8, with the

exception of networking, at .6; she adjusted the definition accordingly and retrained the

coders.3

The researcher trained a coder using 40 tweets, 10% of the anticipated number

of randomly selected tweets each coder will work with, that the researcher had already

coded. The researcher used Cohen’s kappa to determine intercoder reliability as a

matter of convenience, as SPSS provides a calculation for Cohen’s kappa. After

calculating intercoder reliability, the researcher discovered there were not enough

indicators of certain relationship maintenance strategies present in the tweets to

achieve the 0.8 reliability threshold. The researcher and coder achieved 0.91 reliability

on positivity, 0.87 reliability for access and 0.86 reliability for networking. However,

3While Li (2015) adjusted her definition, she ultimately found networking was not a significant construct and expressed in her limitations that the lower reliability could be to blame.

49

reliability came to -0.42 for openness and 0.56 for assurances. Intercoder reliability also

came to 1 for task sharing, but the researcher and coder only found one instance of the

indicator. Because there were so few indicators for task sharing and openness, and

because reliability was low for openness, these indicators were disregarded for

observation. While the researcher and research assistant did not achieve the .7

threshold for acceptable intercoder reliability on the assurances strategy as determined

by Cohen’s kappa, the researcher and research assistant had greater than 90%

agreement on assurance indicators. Based on the agreement level, correlations

between assurances and Klout and Kred influencer scores were still considered.

The researcher and coder observed 800 tweets, 380 for the researcher and 380

for the research assistant (plus the 40 tweets both the researcher and research

assistant completed for intercoder reliability; the researcher used his responses as the

official data for these hotels -- Aloft and Loews Hotels) with 20 tweets from each hotel

chain account. The researcher divided the tweets based on alphabetical order; the

researcher took the first 380 tweets starting with the hotel that was first in alphabetical

order. The research assistant took the next 380 tweets.

The hotels listed in the index provided the researcher a number of examples that

range from value to luxury brands, increasing the ability to generalize to hotel chains

across the cost spectrum. As this study seeks to expand Li’s (2015) research, her

codebook was adapted and updated for this research. The complete new codebook is

available in Appendix B

50

Data Analysis

Once tweets were collected, and the researcher and research assistant had each

coded their assigned tweets, scores for Klout and Kred were entered into the data set

for each organizations’ tweets (Klout and Kred scores are available in Table 3-1). In

order to obtain the Klout and Kred scores, the researcher downloaded two Chrome

extensions. For Klout, the ranking organization provides an extension for users to find

other’s Klout scores. Once downloaded, the plugin displays Klout scores next to a

user’s Twitter name on their account. The researcher visited each hotel chain’s account

to obtain this information. For Kred, the researcher downloaded an extension called

“Twitter tools,” which could provide a user’s Kred scores if activated once on said

Twitter user’s page. The researcher visited each hotel chain’s account to obtain this

information.

Using SPSS data analysis software, the researcher used Pearson’s r to

determine if there was a correlation between the prominence of relationship

maintenance strategies in tweets and the Klout or Kred scores, and, if so, which

strategies have stronger positive correlations to Klout and Kred compared to the others.

The researcher tested to see if there was a positive correlation between greater

prominence of relationship maintenance strategies and higher organizational Klout and

Kred influence scores.

Once all relationship maintenance indicators had been added to the SPSS file,

the researcher ran correlation and regression tests on the tweets using two different

classifications. First, the researcher examined the sample as a whole and looked at

correlations based on indicators present in all 800 tweets, 20 per brand.

51

In addition, in order to observe two-way communication on Twitter, the

researcher examined @reply tweets. In order for a tweet to qualify as an @reply, the

tweet had to be in response to a customer’s tweet mentioning the company, and the

tweet needed to include the handle of the original tweeter. @reply tweets are a subset

of the complete sample; ultimately, 519 of the 800 observed tweets were considered

@reply. @reply tweets are conversations between the brand and a smaller number of

consumers, usually one or two, as opposed to a conversation between the brand and all

of its followers. Therefore, the organization’s goal may be different in those tweets,

leading to a higher or lower use of a specific relationship maintenance indicator to

achieve that goal, as well as different influence correlations.

52

Table 3-1. List of sampled J.D. Power-ranked hotel chains categorized by class with Klout and Kred scores

Hotel Class Hotel Chain Followers Following Klout Score (1-100)

Kred Score

(1-1000) Economy Motel 6 2,621 525 51 796

Red Roof Inn 3,545 554 57 757

Super 8 2,251 177 46 725

Midscale Wingate by Wyndham

3,865 909 42 707

Upper Midscale

Country Inn 8,161 1,094 56 844

Drury Hotels 3,645 1,831 45 741

Fairfield Inn & Suites

9,999 601 51 764

Hampton Inn 54.9K 9,526 61 922

Holiday Inn 106K 49.2K 68 939

Holiday Inn Express

70.8K 36K 60 918

Upscale Aloft 31.5K 1,505 58 859

Coast Hotels 6,645 3,061 49 767

Courtyard by Marriott

68.6K 7,462 61 948

Crowne Plaza 69.3K 3,678 67 936

Doubletree by Hilton

98.3K 12.5K 63 956

Hilton Garden Inn

22.3K 2,509 45 851

Hotel Indigo 39.2K 9,437 61 799

Radisson 23.1K 407 56 872

Springhill Suites

11.4K 1,238 50 782

Upper Extended

Stay

Homewood Suites

13.8K 3,392 56 888

53

Residence Inn 18.8K 896 52 877

Staybridge Suites

23.7K 17.7K 77 778

Upper Upscale

Delta Hotels 13.7K 4,777 51 794

Embassy Suites

46.2K 5,715 60 869

Hilton 255K 5,800 70 970

Hyatt Regency 9,888 536 55 731

Kimpton 49K 34.1K 66 916

Marriott 247K 9,939 69 958

Omni Hotels 50.6K 15.5K 77 896

Renaissance Hotels

123K 3,865 82 935

Sheraton 62.1K 4,252 65 907

Westin 62.5K 2,613 63 945

Luxury Fairmont Hotels 160K 6,562 78 962

Four Seasons 239K 6,562 69 980

InterContinental Hotels

123K 6,848 83 921

Loews 43.7K 5,022 82 929

JW Marriott 14.9K 2,308 62 849

Ritz-Carlton 190K 426 71 975

W Hotels 95.2K 2,137 63 926

Waldorf Astoria 18.9K 2,047 64 865

Table 3-1 cont.

54

CHAPTER 4 RESULTS

In order to keep sample sizes the same from each hotel (20 randomly selected

tweets from 40 hotels), be consistent with Li’s (2015) methodology and ensure the

researcher could generalize the results to the population, the researcher and coder

ultimately coded 800 tweets from a total of 1600 tweets downloaded. This chapter

begins with a breakdown of basic statistics for indicator prevalence, followed by a

review of correlations for each research question and hypothesis.

Descriptives

The researcher observed the official Twitter accounts of 40 different hotel chains.

Because the researcher needed a comprehensive list of hotel chains rather than

specific destinations, the J.D. Power and Associates’ 2016 North America Hotel Guest

Satisfaction Survey was the best available ranking from which to develop a purposive

sample. J.D. Power separates hotels into eight different categories: economy/budget,

extended stay, midscale, upper midscale, upscale, upper extended stay, upper upscale

and luxury. However, because not all hotels ranked on J.D. Power’s scales have active

central Twitter accounts, the researcher does not have hotels from all rankings. The

sample included three budget hotels, no extended stay hotels, one midscale hotel, six

upper midscale hotels, nine upscale hotels, three upper extended stay hotels, ten upper

upscale hotels and eight luxury hotels.

55

The researcher obtained information on the average number of accounts each

brand followed (m= 6,943; s.d.= 10,508.7) and the brand’s followers (m= 62,403; s.d.=

69,713). The hotel class statistics1 are as follows:

Economy/Budget: The researcher calculated the average number of accounts

the brands followed (m = 419; s.d. = 209.8) and brand followers (m = 2,806; s.d. =

666.5). The sample included economy/budget hotels Motel 6, Red Roof Inn and Super

8. Red Roof Inn followed the most users (554), while Super 8 followed the fewest users

(177). Red Roof Inn had the most followers (3,545) and Super 8 had the fewest

followers (2,251).

Extended Stay: Because no ranked extended stay hotels had active central

Twitter accounts, the researcher did not observe tweets from this class.

Midscale: The research observed tweets from Wingate, the only midscale hotel

with an active central Twitter account. Wingate followed 909 accounts and had 3,865

followers.

Upper Midscale: The researcher calculated the average number of accounts the

brands followed (m = 16,375; s.d. = 20,992.3) and brand followers (m = 42,251; s.d. =

41,789.3). The sample included upper midscale hotels Country Inn, Drury Hotels,

Fairfield Inn & Suites, Hampton Inn, Holiday Inn and Holiday Inn Express. Holiday Inn

followed the most users (49,200), while Fairfield Inn & Suites followed the fewest (601).

Holiday Inn had the most followers (106,000) and Drury Hotels had the fewest followers

(3,645).

1 Statistics for all observed hotels are available in Chapter 3 and Appendix A.

56

Upscale: The researcher calculated the average number of accounts the brands

followed (m = 4,644; s.d. = 4,183.8) and brand followers (m = 41,149; s.d. = 30,955.1).

The sample included upscale hotels Aloft, Coast Hotels, Courtyard by Marriott, Crowne

Plaza, Doubletree by Hilton, Hilton Garden Inn, Hotel Indigo, Radisson and Springhill

Suites. Doubletree by Hilton both followed the most users (12,500) and had the most

followers (98,300). Radisson followed the fewest users (407), while Coast had the

fewest followers (6,645).

Upper Extended Stay: The researcher calculated the average number of

accounts the brands followed (m = 7,329; s.d. = 9,068) and brand followers (m =

18,767; s.d. = 4,950). The sample included upper extended stay hotels Homewood

Suites, Residence Inn and Staybridge Suites. Staybridge Suites both followed the most

users (17,700) and had the most followers (23,700). Residence Inn followed the fewest

users (896) and Homewood Suites had the fewest followers (13,800).

Upper Upscale: The researcher calculated the average number of accounts the

brands followed (m = 8,710; s.d. = 9,846) and brand followers (m = 91,899; s.d. =

89,351). The sample included upper upscale hotels Delta Hotels, Embassy Suites,

Hilton, Hyatt Regency, Kimpton, Marriott, Omni Hotels, Renaissance Hotels, Sheraton

and Westin. Kimpton followed the most users (34,100), while Hilton had the most

followers (255,000). Hyatt Regency both followed the fewest users (536) and had the

fewest followers (9,888).

Luxury: The researcher calculated the average number of accounts the brands

followed (m = 3,304; s.d. = 2,488) and brand followers (m = 110,588; s.d. = 77,189).

The sample included luxury hotels Fairmont Hotels, Four Seasons, Loews, Hotel

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Intercontinental, JW Marriott, Ritz-Carlton, Waldorf-Astoria and W Hotels. Hotel

Intercontinental followed the most users (6,848), while Ritz-Carlton followed the fewest

users (429). Four Seasons had the most followers (239,000), while JW Marriott had the

fewest followers (14,900).

Influencer Scores

As Klout scores can shift as brands lose or gain consumer interaction or

following, the researcher collected scores on Dec. 9, 2016, less than two weeks after

the body of tweets to be observed had been collected. As of that date, Klout scores for

the hotel chains observed averaged 61.6 (s.d. = 10.7) on a scale of 0-100. Wingate by

Wyndham had the lowest score, at 42. Intercontinental Hotels ranked highest, with a

Klout score of 83. In order to strengthen measurement validity, the researcher added

Kred influencer scores on Jan. 24, 2017. As of that date, Kred scores for the hotel

chains observed averaged 868.9 (s.d. = 80.5) on a scale of 1-1000. Wingate ranked

lowest, with a Kred score of 707. Ritz-Carlton ranked highest, with a Kred score of 980.

Indicator Prevalence

A total of 1,555 relationship maintenance indicators were present within the 800

observed tweets. Indicators were divided by category as shown in Table 4-1.

Indicators in the entire tweet sample

Of the indicators present in the entire tweet sample, positivity was the most

commonly present one, with 639 indicators (41% of total indicators; m = 16; s.d. = 6.8).

Total positivity indicators among the tweets sampled ranged from zero (Super 8) to 28

(Aloft & Red Roof Inn). Assurances came in second, with 380 indicators (24%$ of total

indicators; m = 9.5; s.d. = 10.4). Total assurances indicators among the tweets sampled

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ranged from zero (seven brands) to 31 (Hampton Inn). Networking followed closely

behind, with 347 indicators (22% of total indicators; m = 8.7; s.d. = 7.8). Access followed

in fourth, with 172 indicators (11% of total indicators; m = 4.3; s.d. = 5.4). Ten brands

had zero instances of access indicators within their sample. Wingate had the most, with

19 access indicators.

Indicators in @reply tweets

The researcher and coder also categorized the tweets by @replies for further

study. Ultimately, 519 tweets (63%) were classified as @replies. Within those tweets,

the researcher and coder found 1,049 indicators.

Positivity came in first, with 531 indicators (51% of total indicators; m = 13.28;

s.d. = 6.8). Total positivity indicators among the @reply tweets sampled ranged from

zero (Super 8) to 25 (DoubleTree). Assurances came in second, with 372 (35% of total

indicators; m = 9.3; s.d. = 10.4). Seven brands had zero assurance indicators in their

@reply tweets, while all 39 of Hampton’s indicators came from @replies. Access came

in third, with 74 indicators (7% of total indicators; m = 1.85; s.d. = 5.4). Total access

indicator among the @reply tweets sampled ranged from 22 brands with zero indicators

to 14 (Wingate). Networking came in fourth, with 60 indicators (6% of total indicators; m

= 1.48; s.d. = 3.2). Twenty-three brands had zero networking indicators in their @reply

tweets, while Four Seasons had the most (18).

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Hypotheses and Research Question

The table below demonstrates means, standard deviations and correlations drawn

between the communication indicators and the two measures of influence2 (Klout and

Kred), as well as the same measures observed through @reply tweets:

H1a-H1e: Prominence of openness will be more positively associated with

influence than prominence of positivity/access/assurances/networking.

The data did not provide enough information for the research to make

conclusions for for H1a-H1e. While the literature suggested openness would be the

strategy that is most strongly correlated to influence, openness indicators appeared

infrequently within the tweet sample (14 indicators within the 800 tweets observed). Any

comparisons of the relationships between openness and influence and between the four

observed strategies would be inconclusive because of this lack of data.

H2a: Prominence of networking will be less positively associated with influence

than prominence of positivity.

The researcher was unable to find a significant relationship to support this

hypothesis because none of the correlations for positivity with influence were significant,

so values cannot be estimated from these data. As noted in H1b, the significant positive

correlation between networking and influence will be discussed later; however, neither

Klout nor Kred produced significant correlations with positivity; therefore, the researcher

cannot reject the null hypothesis.

2 For the purpose of this study, when the researcher refers to “influence,” he is discussing how influential a brand is with followers on social media, rather than how influential a brand is through advertising or in the marketplace.

60

H2b: Prominence of networking will be less positively associated with influence

than prominence of task sharing.

The researcher was unable to find a significant relationship to support this

hypothesis because so few indicators of task sharing were presented within the tweet

sample that the researcher was unable to make any conclusions concerning the

strategy. For this reason, the research cannot reject the hull hypothesis.

H2c: Prominence of networking will be less positively associated with influence

than prominence of access.

Networking was significant on Klout for the entire sample (r = .28) and so was

access (r = -.41), but because networking had a positive association with Klout and

access had a negative association with Klout, the research cannot reject the hull

hypothesis. When considering only @reply tweets, both strategies had significant

correlations on Klout (For networking, r = .36; for access, r = -.33) and Kred (For

networking, r = .28; for access, r = -.32). However, because networking has a positive

association with influence, while access has a negative association with influence, the

researcher still cannot reject the null hypothesis.

H2d: Prominence of networking will be less positively associated with influence

than prominence of assurances.

Networking was significant on Klout for the entire sample (r = .28) and so was

assurances (r = -.31), but because networking had a positive association with Klout and

assurances had a negative association with Klout, the research cannot reject the hull

hypothesis. When considering only @reply tweets, both strategies had significant

correlations on Klout (For networking, r = .36; for assurances, r = -.33). However,

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because networking has a positive association with influence, while assurances has a

negative association with influence, the researcher still cannot reject the null

hypothesis.

H3: In the hierarchy of relationship maintenance constructs used to build

organizational influence, networking < (task sharing, positivity, access, or

assurances) < openness.

Because the researcher and research assistant found so few indicators of

openness and task sharing within the complete sample (14 and 3, respectively), the

researcher cannot reject the null hypothesis.

H4: The greater prominence of relationship maintenance strategies in affiliative

communication on social media will be related to higher organizational influence.

Because this hypothesis is not true on all accounts, the researcher cannot reject

the null hypothesis.

RQ1: How are the six types of relationship maintenance strategies used in tweets

by hotel chains related to overall organizational influence?

The results of this content analysis show that the six types of relationship

maintenance strategies affect influence in different ways – not all positive. Ultimately,

too few indicators of openness or task sharing were present in the sample tweets to

confirm whether they have an effect on hotel social media influence scores. Two

correlations were deemed significant using both Klout and Kred scores – a negative

correlation between access and influencer score across the entire tweet sample

(significant at the .05 level on Klout, and the .01 level on Kred), and a positive

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correlation between networking and influencer score in @reply tweets (significant at the

.05 level on Klout and Kred).

The researcher also performed a regression analysis on the data, instructing

SPSS to calculate the data with positivity only (Model 1), positivity and access (Model

2), positivity, access and assurances (Model 3) and positivity, access, assurances and

networking (Model 4). The analysis in tables 4-2, 4-3, 4-4 and 4-5 provides further

evidence as to how predictive the access and @networking indicators can be. Table 4-2

suggests both access and assurances play significant roles in explaining the variance of

Klout influencer scores when considered within the entire sample. The addition of

access in particular over positivity causes a large shift in the R- value, from .08 to .42.

Access also appears to play a large role in determining variance with the Kred score

(Table 4-5) – as the R-value shifts from .16 to .41 with significance at the .02 level.

Within the @reply tweets, the strongest predicator of influencer score – on both Klout

and Kred – appears to be networking. The R-value jumps from .44 to .57 in the Klout

sample, and from .37 to .49 in the Kred sample (both values are significant, at .01 and

.04, respectively). These regressions provide further support for the zero-order

correlations above.

Other significant correlations appeared on either one influencer score scale or

the other; research showed a negative correlation between assurances and the Klout

influencer score in both the entire sample and @reply tweets, both significant at the .05

level. On the other hand, research with Kred shows a significant positive correlation

between positivity and Kred influencer scores in @reply tweets, significant at the .05

level.

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Correlations can also be drawn between the indicators themselves, as shown in

Tables 4-7 and 4-8. In both the entire sample and @reply tweets only, as access

indicators increase, positivity indicators decreased, and vice-versa. Networking and

assurances also showed a significant negative correlation in the entire sample, as did

networking and positivity.

In answer to RQ1, correlations between maintenance strategies and influence

scores demonstrated that two indicators have significant positive correlations to social

media influence within the Twitter sample – positivity in the entire sample on Kred,

networking within the entire sample on Klout, and positivity and networking within

@reply tweets on Kred. Two indicators have significant negative correlations to social

media influencer within the Twitter sample – access and assurances. Access is

significant in both the entire sample and @replies with Klout and Kred (the only indicator

to be significant in all four observations), and assurances is significant in the entire

sample and @replies on Klout.

Summary of Significant Results

Although the researcher hypothesized positive correlations between relationship

maintenance strategies and influencer scores, most of the significant results showed a

negative correlation between a particular strategy and another and between strategies

and influence scores. Across the entire sample (and on both influencer score scales),

the correlation between influence score and access suggests that as hotel chains place

more access relationship indicators in a tweet – such as providing phone numbers or

emails for users to reach customer service – their social media influence decreases.

The correlation was significant in all four observations – (r = -.41 on Klout and Kred in

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the complete sample; for @reply, r = -.33 on Klout and r = -.32 on Kred). When only the

subsample of @reply tweets are taken into account, both influencer score scales

suggest that inclusion of networking indicators – such as using hashtags the public can

contribute to, or tagging another user in a tweet – will increase a brand’s influence. This

correlation appears to be moderately powerful, as r = .36 with Klout (.28 on Kred) even

with very few networking indicators in @reply tweets (m = 1.48).

As the thesis anticipated in Chapter 1, tweets give organizations little room to

present their message, so their choice of words and punctuation will determine which

indicators are most prominent. In both the entire sample and when only @reply tweets

were observed, the negative correlations suggested the presence of positivity indicators

occurs with fewer access indicators and vice versa. Within the entire sample,

networking and positivity, as well as assurances and networking, showed significant

negative correlations. These correlations will be further discussed in Chapter 5.

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Table 4-1. Division of relationship maintenance indicators in the complete tweet sample and @reply tweets

Indicator Categories Indicators in the complete sample (n = 1,555)

Indicators in @reply tweets (n = 1,049)

Positivity 639 (41.1%) 531 (50.6%)

Access 172 (11.1%) 74 (7.1%)

Assurances 380 (24.4%) 372 (35.5%)

Networking 347 (22.3%) 60 (5.7%)

Table 4-2. Correlations for influence for relationship indicators in the entire tweet sample and @reply tweets

Indicator Mean Std.

Dev.

Klout Correlation Kred Correlation

Positivity 16 6.8 .01 .16

Access 4.1 5.1 -.41** -.41**

Assurances 9.3 10.4 -.31* -.05

Networking 8.7 7.8 .28* .13

@Positivity 13.3 6.8 .10 .30*

@Access 1.6 4.3 -.33* -.32*

@Assurances 9.3 10.4 -.33* -.04

@Networking 1.5 3.2 .36* .28*

* One star denotes significance at the .05 level (1-tailed) **Two stars denote significance at the .01 level (1-tail)

66

Table 4-3. Regression analysis of the complete sample of Klout on relationship indicators Model R R

Squared Adjusted R

Square Std. Error

R Square Change

F Change Sig F Change

1 .08 .01 -.02 10.83 .01 .32 .58 2 .42 .18 .13 10.00 .17 7.49 .01** 3 .56 .31 .25 9.30 .13 6.88 .01** 4 .57 .32 .14 9.34 .01 .01 .42

**Two stars denotes significance at the .01 level (1-tailed) Model 1, Positivity; Model 2, Positivity + Access; Model 3, Positivity + Access + Assurances; Model 4, Positivity + Access + Assurances + Networking

Table 4-4. Regression analysis of @reply tweets of Klout on relationship indicators Model R R

Square Adjusted R Square

Std. Error

R Square Change

F Change

Sig F Change

1 .10 .01 -.02 10.82 .01 .37 .55 2 .33 .11 .06 10.41 .1 4.06 .05* 3 .44 .19 .13 10.04 .08 3.77 .06 4 .57 .32 .25 9.33 .13 6.72 .01**

*One star denotes significance at the .05 level (1-tailed) **Two stars denote significance at the .01 level (1-tailed) Model 1, Positivity; Model 2, Positivity + Access; Model 3, Positivity + Access + Assurances; Model 4, Positivity + Access + Assurances + Networking

67

Table 4-5. Regression analysis of the complete sample of Kred on relationship indicators

Model R R Square

Adjusted R Square

Std. Error

R Square Change

F Change

Sig F Change

1 .16 .02 -.00 80.58 .024 .93 .34 2 .41 .17 .12 75.51 .14 6.27 .02* 3 .42 .17 .10 76.23 .01 .31 .58 4 .43 .19 .10 76.55 .02 .71 .41

*One star denotes significance at the .05 level (1-tailed) Model 1, Positivity; Model 2, Positivity + Access; Model 3, Positivity + Access + Assurances; Model 4, Positivity + Access + Assurances + Networking

Table 4-6. Regression analysis of @reply tweets of Kred on relationship indicators

Model R R Square

Adjusted R Square

Std. Error

R Square Change

F Change

Sig F Change

1 .30 .09 .07 77.78 .09 3.79 .06 2 .37 .14 .09 76.85 .05 1.92 .17 3 .37 .14 .07 77.76 .00 .14 .71 4 .49 .24 .15 74.13 .10 4.61 .04*

*One star denotes significance at the .05 level (1-tailed) Model 1, Positivity; Model 2, Positivity + Access; Model 3, Positivity + Access + Assurances; Model 4, Positivity + Access + Assurances + Networking

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Table 4-7. Correlations among indicators in the complete tweet sample and with influencer scores

Positivity Access Assurances Networking

Positivity N/A

Access -.48** N/A

Assurances .01 -.06 N/A

Networking -.30* -.01 -.50** N/A

Klout .01 -.41** -.31* .28*

Kred .16 -.41** -.05 .13

*One star denotes significance at the .05 level (1-tailed) **Two stars denote significance at the .01 level (1-tailed)

Table 4-8. Correlations among indicators in @reply tweets and with influencer scores

@Positivity @Access @Assurances @Networking

@Positivity N/A

@Access -.44** N/A

@Assurances .20 .11 N/A

@Networking .05 .18 -.24 N/A

Klout .10 -.33* -.33* .36*

Kred .30* -.32* -.04 .28*

*One star denotes significance at the .05 level (1-tailed) **Two stars denote significance at the .01 level (1-tailed)

69

CHAPTER 5

DISCUSSION Twitter has given consumers unprecedented access to the brands they

encounter in their daily lives. Research has shown that brands recognize that and use it

to their advantage, often providing consumers further information so that conversations

may start online and continue offline through the access strategy (Li, 2015). Prior to Li’s

study, few researchers considered the outcomes of organizations enacting Hon &

Grunig’s (1999) relationship maintenance strategies on short-form communication

outcomes. While Li (2015) determined which strategies were most often enacted, her

research stopped short of determining the outcome of using such strategies.

The research also differed with LI (2015) in terms of indicator calculation. While

Li coded for the presence of at least one relationship maintenance strategy in a tweet,

this research counted all indicators present in the tweet, even if there was more than

one of the same indicator type. Therefore, the results are not directly comparable. Li

(2015) found that organizations most often send tweets that include an access indicator;

she found “nine out of ten tweets had at least one indicator of access” (p. 193).

Assurances and positivity came in second and third. This research found positivity to be

the most widely used relationship maintenance strategy; positivity made up 41% of all

measured indicators. Access had the fourth highest number of indicators, at 11% of the

complete sample.

While the researcher cannot claim to explain what about the six strategies in

particular leads to an increase or decrease in influence, the results of this thesis

suggest that some strategies, such as access and networking, have a significant

70

correlation to a brand’s influence1 – although not in a way the hypotheses anticipated.

The purpose of this thesis was to determine how one subset of business – the hotel

industry – practiced relationship maintenance on Twitter, and whether there was a

correlation between that and its social media influence. The study diverted from earlier

results in some areas; the results show organizational use of relationship maintenance

strategies on social media varies quite a bit from owned media sources such as

corporate websites. For example, Ki & Hon (2006) determined brands most often enact

openness on their websites; the results of this thesis suggest hotel brands rarely

implement the openness or task sharing relationship strategies on Twitter.

This chapter will discuss the significant findings of this thesis – first, the

correlations between relationship maintenance strategies and influencer scores, then

the correlations among relationship maintenance strategies. It will discuss implications

for public relations theory understanding and the public relations industry, and provide

guidance for future research.

Relationship Maintenance/Influencer Correlations

The findings suggest that if a brand wants to build influence on social media as

measured by Kred or Klout, its best option is to turn to the networking strategy within

two-way communication, or @reply tweets. Presence of networking in @reply tweets –

even when very few networking indicators were present – correlated to a higher

influencer score on both Kred and Klout.

1For the purpose of this study, when the researcher refers to “influence,” he is discussing how influential a brand is with followers on social media, rather than how influential a brand is through advertising or in the marketplace.

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@reply tweets do not have to be a brand replying to a complaint – based on the

researcher’s qualitative observations during coding, brands reviewed in this study

appeared to often use one of two methods to build relationships in @reply tweets: (1)

provide content, such as photos or videos, that promoted interaction with the

organization’s general follower base or (2) maintain relationships one consumer at a

time by answering questions or responding to compliments.

Saffer, Sommerfeldt and Taylor (2013) provided evidence greater use of @reply

tweets could lead to stronger relationship maintenance. The researchers observed Hon

& Grunig’s (1999) organization-public relationships dimensions on Twitter accounts with

both high interactivity with followers, or greater use of two-way communication, and low

interactivity with followers, or greater use of one-way communication (Saffer et al,

2013). The researchers found their subjects perceived brands with higher interaction to

have more indicators of Hon & Grunig’s (1999) dimensions (Saffer et. al, 2013). By

practicing two-way communication with consumers and demonstrating the brand pays

attention to the marketing its consumers do on its behalf – such as when a user shares

a picture of a resort for all their own followers on social media – social media managers

practice Cialdini’s (2009) dimension of “liking” – cultivating a favorable image with an

audience – therefore building influence. Cialdini (2009) argued that consumers are more

apt to listen to those who they have developed a friendly image of. Even businesses

that don’t have one-on-one relationships with their consumers can practice “liking” using

methods such as paying compliments to consumers (as seen in Figure G-3) or

promoting cooperation between the organization and consumers (as seen in Figure G-

2). Cialdini’s (2009) liking dimension can also be seen in the positive correlation

72

between positivity and Kred scores in the entire tweet sample. When companies display

a positive attitude on social media, such as thanking a customer for nice words or

greeting a customer, they cultivate a favorable image.

In regards to networking, Ki (2003) noted the strategy involves organizations

helping users find common ground with other users. An organization can increase

influence by providing networking strategy indicators in positive @reply tweets, as

indicated by the positive correlations discovered in this research. For instance,

organizations can create a hashtag users can coalesce on to provide pictures of a hotel,

or tag another user that may be interested in the @reply content.

At the same time, the results suggest that certain indicators within two-way

communication can be associated with lower levels of influence – particularly

assurances and access. As mentioned for networking, not all @reply tweets are

designed to solve a customer’s problem. Consider the difference in the two tweets

below in Figures G-2 and G-3 (included at the end of the chapter). Figure G-2 includes

two indicators: one assurance (“Please email details with your booking and confirmation

information) and one access (IHGService@ihg.com). Figure G-3 includes three

indicators: two positivity (“Thanks for sharing” and inclusion of an exclamation mark

after “place”) and one networking (#InterContinentalLife). While these are both @reply

tweets, their goals are different: the first is designed to maintain a relationship with one

customer in particular. The second opens two-way communication with one consumer,

but it also invites others to join the conversation with the hashtag. The second tweet

invokes the positivity strategy suggested by Cialdini’s (2009) liking dimension and

makes a tweet designed for one customer part of a larger conversation. Tweets that

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require assurances and access indicators to accomplish their goals often shut out the

hotel brand’s followers at large. While Hon and Grunig (1999) noted that access can be

an important component of successful two-way communication, the researcher’s

observations suggest not all two-way communication on Twitter is created to build

influence. Some chains used their Twitter account to solve customer problems only – for

instance, Super 8 (Figure G-4, at the end of the chapter), where every tweet observed

had the exact same wording (and influencer scores were among the lowest measured).

The researcher’s observations also suggest that the difference in why hotel

chains use access indicators, as opposed to why they use the other relationship

maintenance indicators, could contribute to the negative correlation. Access is the only

strategy focused on guiding users away from social media to solve a problem; the other

indicators encourage engagement with the tweet, potentially leading to replies, retweets

and likes – part of the Kred and Klout influence calculations.

The correlations and regressions, with the influencer scores observed by

themselves, suggest that positive two-way communication using networking indicators

may be one of the strongest ways for the hotel industry to build influence as measured

by Klout and Kred on social media.

Relationship Maintenance Indicator Correlations

As organizations generally only have 140 characters to communicate a message

on Twitter, indicator strategies must be prioritized to reach the organization’s desired

goal. By determining the correlations between the different relationship maintenance

communication strategy indicators, the researcher found several significant results

suggesting brands are less likely to use certain indicators with each other.

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The strongest negative correlation existed between networking and assurances.

Part of this correlation could be explained by the contrast in indicator purposes. The

researcher’s qualitative observations suggested that while assurances were almost

entirely contained to @reply tweets focused on fixing customer problems, networking

existed in tweets designed to expand an organization’s follower base. Significant

correlations found in this study are positive between networking and influence, and

negative between assurances and influence. It would make sense that brands choose

different strategies based on the endgame of their tweet.

A similar argument could be made for the second strongest negative correlation,

between positivity and access. The researcher’s qualitative observations suggested

access was typically used for one of two reasons, both of which guided customers away

from social media: (1) providing a link to more information about a hotel on an official

website or (2) providing a method by which consumers can reach customer service

representatives; neither helped expand the audience specifically on social media.

Positivity did not seek to take customers off social media, but rather to make their social

media experience with the organization more pleasant. The negative correlations

suggest that the intended goals of using either the positivity or access strategies divert

enough that organizations choose to include one or the other.

Implications

Implications for Theory

This thesis adds to the growing body of research that suggests two-way

communication – in this case, in coordination with networking strategies -- is the most

effective form of public relations.

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This thesis also gives researchers a starting line for completing deeper research

into hospitality public relations. Much of the research into relationship maintenance

strategies online until this point has focused on owned media (ex. Ki & Hon (2006),

Waters et al. (2011) such as corporate websites, where interactivity between the brand

and the consumer is mainly restricted to one page or email address where a consumer

can provide feedback. Ki & Hon (2006) found openness to be the most practiced

strategy on corporate websites. Openness was virtually non-existent in this study’s

sample – many of the indicators present suggested brands use Twitter to highlight the

relationship between the brands and their followers. Because space is a limited

resource on Twitter, brands use it to create dialogue with consumers, rather than list

corporate accomplishments or take the time necessary to be transparent about a

business decision – one of the primary purposes of the openness strategy, as defined

by Li (2015) and Ledingham and Bruning (2000).

The argument assumed that different relationship maintenance strategies could

work in concert to improve an organization’s influence; however, the results suggest

different strategies are not often used with others. Researchers should consider the

relationships among strategies, rather than just the presence of specific strategies in an

organization’s public relations outreach, when adopting Hon and Grunig’s (1999)

strategies. The research initially hypothesized that openness would be most strongly

correlated with influence, while networking would be the least strongly correlated with

influence; there were not enough indicators of openness to determine whether it had a

significant correlation with Klout and Kred influencer scores. Based on these results, a

76

revised versions of Figure G-1 are pictured at the end of the chapter (Figure G-5 for

Klout, Figure G-6 for Kred.

As positivity and networking within @replies have the strongest positive

correlations to influencer score, it could be hypothesized that brands that use these

strategies in tandem within @reply tweets could see higher levels of influence; however,

brands within this sample did not appear to follow this method, as the two strategies

were weakly correlated within @reply tweets (r = .05, no significance). A revised version

of the Figure G-1 model, based on correlations garnered from the @reply subsample, is

pictured in Figures G-7 and G-8.

Implications for Industry

Because 76% of adults who use the internet are active on social media (Perrin,

2015), it is important for brands to realize the power social media can hold for their

public relations efforts. While the research does not support specific courses of action,

the results support the widely held public relations belief that two-way communication is

an ideal organizational-public interaction. However, the results also show the way

organizations employ two-way communication is also important. As demonstrated by

the Motel 6 tweets, organizations that only respond to problems on Twitter are missing

the potential to build their social media influence. Hotel brand social media managers

could engage their followers after compliments, or provide feedback for an image a

follower posted. For example, Loews Hotels, one of the highest ranked hotel chains on

both Klout and Kred, routinely responds to customers who post photos of the Loews

resort in which they are staying. They also repost photos and tag the photographer. If

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the photographer replies to Loews after the repost, then Loews has successfully

completed Sheldrake’s (2011) two-step process for developing influence.

This does not mean that the indicators negatively correlated to influence are

necessarily bad for an organization to use. Indicators, specifically assurances – with a

significant negative correlation to Klout – can accomplish other goals; assurance helps

an organization achieve Pfeffer’s (1978) theory of organizational legitimacy, or helps

consumers understand that the organization will follow up on its promises. For example,

in a crisis an organization may choose often to enact the assurance strategy. If an

organization’s goal is to build influence, however, the research suggests a focus on

networking strategies.

Influencer measurement is also an important consideration for social media

managers. The researcher examined both Klout and Kred scores. Kred has limitations

for organizations looking to build a strong social networking influence across a range of

platforms, as it only observes Facebook and Twitter. Klout also gives users a more

specific window of how their influence campaigns are shaping up, as it only measures

interactions within the last 90 days – perfect for managers looking to see short-term

growth. The two differed in that some relationship maintenance strategies were

significant on one measurement scale and not the other. The researcher attributes

these differences to the scales’ differing methodologies; Klout and Kred measure

different lengths of time (Kred measures 1000 days) and probably use different

indicators to determine the score (Kred publishes its indicators on its website, while

Klout’s are a black box).

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While each had positives and negatives, the researcher would ultimately

recommend organizations consider Kred, mainly because it is transparent about how an

organization’s influencer score is calculated – “by assessing how frequently you are

retweeted, replied, mentioned and followed on Twitter” (“Kred Scoring Guide,”n.d., para.

4). Posts, mentions, likes, share and event invitations on Facebook are also included, if

the user chooses to connect his or her Facebook profile to his or her Kred account

(“Kred Scoring Guide,” n.d.). This makes it easier for brands to set benchmarks for

social media growth, such as improved numbers of followers or retweets.

Limitations of the Study

This study is not without its limitations. First and foremost, the researcher can

only report correlations between strategies and influencer scores. While this chapter

provides potential explanations for the correlations, it cannot definitely say what about

the strategies causes influence to increase. For example, networking has a significant

positive correlation to influencer scores. However, the “why” was not studied, i.e. what

about the act of including networking indicators causes influence to increase. Based on

Cialdini (2009), the researcher would hypothesize that brands that enact the networking

strategy, with methods such as tagging other users or using hashtags, show that they

understand how to speak the language of Twitter. This, in connection with the use of

@reply tweets, could help the brand develop a more positive identity with its followers,

building Cialdini’s (2009) “liking” influence dimension.

While this thesis can serve as giving general recommendations to theory and the

public relations industry, scholars looking to follow up on it would need to do more

primary research to understand comprehensively the findings; specifically, research that

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focuses on human subjects, such as a survey or a focus group. Researchers could

distribute a survey with Likert-type scale statements based on each of the relationship

maintenance indictors (ex. For networking, “Brands that use trending hashtags in their

tweets are more influential than those that do not”) to determine which indicators for

each strategy are most powerful.

Second, in keeping the amount of content to be analyzed at a reasonable level,

the research did not study the full power of the Kred/Klout influencer scores. As

described in Chapter 3, Klout measures data from a variety of social media platforms,

but beyond basic metrics such as retweets, it does not disclose what it measures on

said platforms. Kred measures Facebook and Twitter; it also measures an account’s

last 1,000 days of interactions, as opposed to Kred’s 90 day measurement. The

research only considered an organization’s Twitter accounts. If the research were to be

expanded to include Facebook, the research might show a difference in correlations

between relationship maintenance strategies and influencer scores.

Third, influence scores are constantly evolving based on each tweet an

organization sends, unlike measurements such as the once-a-year J.D. Power Guest

Satisfaction Survey – so this thesis provides a snapshot in time (mainly tweets sent by

hotel chains in November 2016), but influencer scores may have changed days, or even

hours, after the researcher completed the study. Although brands appear to have

consistent patterns of relationship maintenance strategy use, a sample of tweets taken

today might have a different correlation based on shifting influence scores. The

correlations presented in this thesis should be considered a snapshot of how

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relationship maintenance strategies are related to influence scores, rather than a stable

long-term portrait.

Finally, the researcher began this process during the Obama administration,

which had dramatically different policies on international tourism. As the new Trump

Administration has expressed a desire to limit the movement of large populations into

America, hotel chains may exist in a different business climate a year from now than

they presently do. This would not only have an effect on populations that are restricted

from visiting America, but also could create a hostile tourism climate where even allies

are afraid to visit (Rizzo, 2016). The hospitality industry is already feeling the effects of

the new administration’s stances; searches for flights from countries around the world to

America are down, with the exception of Russia (Hughes, 2017). While this thesis will

remain a snapshot of hotel relationship maintenance in late 2016, this change in

business climate could have an effect on scholars’ understanding of relationship

maintenance in general – something for researchers to keep in mind when they are

looking to replicate this study.

Recommendations for Future Research

There are plenty of avenues for social scientists to continue the research found in

this thesis. First, researchers should consider a primary study such as a survey to

determine what about the relationship maintenance strategies used on Twitter drives

fans to engage. While the study shows a positive correlation between networking and

influencer score, what about networking is the foundation for that correlation? Rather

than simply measure how many indicators are present in a tweet, researchers could

keep track of which specific words and symbols were present; for instance, researchers

81

could consider whether the presence of an exclamation mark in a tweet has a stronger

positive correlation to influencer score than a “thanks” in a tweet (both positivity

indicators). While this method would not give the psychological theory behind

relationship maintenance, it could help brands determine which words and phrases they

should use in a tweet for maximum influence.

Second, researchers could expand the scope of the study by looking at different

segments within the travel/tourism industry. The researcher restricted this thesis to hotel

chains; however, future researchers could also look at airlines and rental car agencies

active on Twitter. Researchers could then compare and contrast strategies within the

field, and determine if different industry segments use Twitter with more influence than

others do. Researchers could also observe hotels with de-centralized Twitter accounts.

The research did not study the major hotel chain Best Western, as the chain lacked an

account run by the corporate office. Instead, managers run their own Twitter accounts

for their franchise. Other chains, like InterContinental, had a centralized account as well

as individual accounts for specific hotels. A comparison of strategies between the two

styles could give hotel chains a better idea of the level at which social media accounts

should be run – at corporate or at the location.

Third, researchers could examine consumer responses to organizational tweets.

This would be accomplished by focusing on an organization’s @reply tweets and

content analyzing the responses, specifically to tweets where the organization is

utilizing the access or assurance strategies. The researcher only focused on the

organization’s words, but looking at any follow-up tweets could determine whether the

82

organization’s response was effective in assuring the customer or providing the

customer access to the help and information they need.

Finally, the researcher had to drop the study’s original dependent variable –

loyalty – because he was unable to secure a complete list of loyalty rankings. If

researchers were able to obtain a methodology and scores from an organization such

as Brand Keys Loyalty Index, a study similar to this using the dependent variable loyalty

could have different results. Because the researcher grouped these variables under the

affiliative communication umbrella, future researchers could determine if different

strategies of affiliative communication have stronger correlations to relationship

maintenance strategies on social media than others.

Further research, such as a survey, could also be used to study other dependent

variables, such as satisfaction with online interaction; consumers could be surveyed as

to their experience with asking a question of an organization on Twitter. Researchers

could also conduct a content analysis of conversation chains on Twitter; for instance, if

a customer were to send an organization a complaint on Twitter and the organization

were to respond, the customer may follow up to the organization’s response.

Researchers could observe these tweets to determine if consumer respond positively or

negatively to the organization’s response.

Conclusion

While this thesis failed to support the hypotheses concerning communication on

Twitter, the results ultimately show several significant relationships – mainly negative.

The results suggest hotels looking to build influence on social media should network

with their publics and participate in two-way communication. It is important for brands to

83

determine the particular function for each of their social media platforms, because the

strategy they enact can lead to varying levels of success based on the outcome they

want to achieve.

In brief, the researcher’s observations suggest practitioners looking to build

influence on their hotel chain’s Twitter account should:

• Create a hashtag, or consistently follow trending hashtags, to join into

conversations with followers

• Reply to tweets – both complaints and compliments

• Start a conversation, and tag other users in the conversation

• Use positive terms – thank customers, wish them a good day and use

exclamations

As stated earlier, the researcher cannot say for certain the “why” behind these

strategies; however, the bulleted suggestions are among the most frequently used

indicators of the strategies that were positively associated with influencer scores.

There is still plenty of research to be completed before hotels could use this

research to enact a tried-and-true social media strategy, but the research contained in

this thesis can serve as background work for any researcher looking to further

investigate relationship maintenance on social media. Technology will continue to

develop; crises will continue to develop. This means more data will become available for

researchers to pinpoint what style of communication is most effective for building

mutually beneficial relationships on short-form communication platforms such as

Twitter. The future is bright for relationship theory research, and the researcher is happy

to be able to contribute.

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Figure 5-1. InterContinental @reply tweet to solve a customer problem

Figure 5-2. InterContinental @reply tweet to respond positively to a customer picture

Figure 5-3. Super 8 @reply tweet to solve a customer problem

85

Figure 5-4. Revised model of strength of relationship maintenance constructs of affiliative communication to Klout Influencer Scores (Width of line represents strength of

correlation; b = standardized regression coefficients; r = correlation)

Figure 5-5. Revised Model of strength of relationship maintenance constructs of affiliative communication to Kred Influencer Scores (Width of line represents strength of

correlation; b = standardized regression coefficient; r = correlation)

Access

Klout Influence

Assurances

Task Sharing

Positivity

Openness

Networking

Beta= -.294 r = -.31

Beta = -.476 r = -.41

Access

Kred Influence

Assurances

Task Sharing

Networking

Openness

Beta = .409 r = -.41

Beta = .141 r = .28

Positivity

86

Figure 5-6. Revised model of strength of relationship maintenance constructs of affiliative communication in @reply tweets to Klout Influencer Scores (Width of line

represents strength of correlation; b = standardized regression coefficient; r = correlation)

Klout Influence

@Networking

@Positivity

@Task Sharing

@Access

@Assurances

@Openness

Beta = -.393 r = -.33

Beta = .391 r = .36

Beta = -.176 r = -.33

87

Figure 5-7. Revised model of strength of relationship maintenance constructs of affiliative communication in @reply tweets to Kred Influencer Scores (Width of line

represents strength of correlation; b = standardized regression coefficient; r = correlation)

Kred Influence

@Networking

@Positivity

@Openness

@Assurances

@Access

@Task Sharing

Beta = .143 r = .30

Beta = .343 r = .28

Beta = -.324 r = -.32

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APPENDIX A HOTEL SAMPLE WITH PARENT COMPANIES

Table A-1. Hotel sample with parent companies

Hotel Class

Hotel Chain Parent Company

Followers Following Total # of

Tweets

Joined Date

Economy Motel 6 G6 Hotels 2,621 525 4,935 07/11

Red Roof Inn Red Roof Inn

3,545 554 3,630 01/10

Super 8 Wyndham 2,251 177 1,012 06/13

Midscale Wingate by Wyndham

Wyndham 3,865 909 2,534 06/09

Upper Midscale

Country Inn Carlson 8,161 1,094 10.2K 01/09

Drury Hotels Drury 3,645 1,831 3,252 03/09

Fairfield Inn & Suites

Marriott 9,999 601 4,566 05/12

Hampton Inn Hilton 54.9K 9,526 20K 06/09

Holiday Inn IHG 106K 49.2K 9,536 05/09

Holiday Inn Express

IHG 70.8K 36K 6,030 10/12

Upscale Aloft Starwood 31.5K 1,505 4,713 06/10

Coast Hotels Coast 6,645 3,061 2,413 10/08

Courtyard by Marriott

Marriott 68.6K 7,462 74.7K 05/09

Crowne Plaza IHG 69.3K 3,678 6,551 05/11

Doubletree by Hilton

Hilton 98.3K 12.5K 23.4K 01/09

Hilton Garden Inn

Hilton 22.3K 2,509 11.5K 10/10

Hotel Indigo IHG 39.2K 9,437 9,684 07/08

Radisson Carlson 23.1K 407 17.8K 02/10

Springhill Suites

Marriott 11.4K 1,238 4,726 04/12

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Table A-1 cont. Hotel Class

Hotel Chain Parent Company

Followers Following Total # of

Tweets

Joined Date

Upper Extended

Stay

Homewood Suites

Hilton 13.8K 3,392 14.2K 01/10

Residence Inn Marriott 18.8K 896 6,448 04/12

Staybridge Suites

IHG 23.7K 17.7K 4,954 06/11

Upper Upscale

Delta Hotels Marriott 13.7K 4,777 16K 04/09

Embassy Suites

Hilton 46.2K 5,715 13.1K 04/09

Hilton Hilton 255K 5,800 37.5K 08/09

Hyatt Regency Hyatt 9,888 536 2,211 09/15

Kimpton Kimpton 49K 34.1K 48.4K 09/08

Marriott Marriott 247K 9,939 55.9K 04/08

Omni Hotels Omni 50.6K 15.5K 15.8K 02/09

Renaissance Hotels

Marriott 123K 3,865 12.3K 04/09

Sheraton Starwood 62.1K 4,252 12.3K 06/10

Westin Starwood 62.5K 2,613 9,384 06/09

Luxury Fairmont Hotels Fairmont 160K 6,562 16.4K 08/08

Four Seasons Four Seasons

239K 6,562 53.7K 12/08

InterContinental Hotels

IHG 123K 6,848 13K 06/09

Loews Loews 43.7K 5,022 54.5K 04/09

JW Marriott Marriott 14.9K 2,308 4,970 08/11

Ritz-Carlton Marriott 190K 426 78.3K 04/09

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Table A-1 cont. Hotel Class

Hotel Chain Parent Company

Followers Following Total # of

Tweets

Joined Date

W Hotels Starwood 95.2K 2,137 8,753 06/09

Waldorf Astoria

Hilton 18.9K 2,047 8,171 04/11

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APPENDIX B CODEBOOK

When deciding on which conceptual definition to use, the researcher began with

Li (2015) to keep the study as close to hers as possible. If Li’s definition did not suffice,

the researcher turned to Hon & Grunig’s (1999) definitions.

Table B-1. Codebook Conceptual definition of maintenance constructs from Li (2015) and Hon and Grunig (1999)

Operational definition: attributes representing construct based on Li (2015)

Positivity “Anything the organization or public does to make the relationship more enjoyable for the parties involved” (Hon & Grunig, 1999, p. 14)

Indicators may include: (1) Posting smiling/happy emojis (2) Using positive exclamations (3) Showing humor (4) Expressing greetings (5) Expressing thanks

Networking “Organizations’ building networks or coalitions with the same groups that their publics do, such as environmentalists, unions, or community groups.” (Hon & Grunig, 1999, p. 15)

Indicators may include: (1) Identifying partnerships with networking groups (2) Identifying partnerships with networking

individuals (3) Offering details of cooperated programs (4) Providing a retweet of networking

groups/individuals (5) Linking to networking individuals (6) Using a trending hashtag others in the network

are using Openness “An organization’s efforts to make the information process more transparent.” (Li, 2015, p. 201)

Indicators may include: (1) Providing information about any changes

pertaining to finances (2) Providing information about organizational

restructuring (3) Advocating feedback about organizational

activities (4) Demonstrating that feedback will be used as

part of decision-making.

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Conceptual definition of maintenance constructs from Li (2015) and Hon and Grunig (1999)

Operational definition: attributes representing construct based on Li (2015)

Task Sharing “Performing corporate social responsibility by addressing social concerns or organizational efforts that relate to the problems of mutual interest between the organization and its publics” (Li, 2015, p. 201)

Indicators may include: (1) Addressing social concerns or

organizational efforts that relate to environmental activities

(2) Addressing social concerns or organizational efforts that relate to community activities

(3) Addressing social concerns or organizational efforts that relate to education activities

(4) Addressing social concerns or organizational efforts that relate to volunteer efforts

(5) Addressing social concerns or organizational efforts that may not fit into the above categories

(6) Providing an evaluation of corporate internal teams involved in addressing social concerns or organizational efforts

(7) Providing an evaluation of programs related to concerns listed above

(8) Offering a general statement related to corporate responsibility

Assurances Attempts by parties in a relationship to demonstrate the other party’s concerns are important to them and that building relationships is a priority (Li, 2015; Hon & Grunig, 1999)

Indicators may include: (1) Answering a customer’s question (2) Forwarding an inquiry to another

department (3) Providing a statement demonstrating the

organization is committed to maintaining relationships

(4) Seeking more information from the customer

(5) Providing a statement asking for questions for customers.

(6) Identifying social media representative responding to tweet

Table B-1 cont.

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Conceptual definition of maintenance constructs from Li (2015) and Hon and Grunig (1999)

Operational definition: attributes representing construct based on Li (2015)

Access “An organizations’ efforts to foster communication and to provide communication channels or media outlets with other users.” (Li, 2015, p. 202)

Indicators may include: (1) Posting FAQs (2) Providing phone numbers (3) Providing email addresses (4) Providing a link for more information

Table B-1 cont.

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LIST OF REFERENCES Ayeh, J., Leung, D., Au, N., & Law, R. (2012). Perceptions and strategies of hospitality

and tourism practitioners on social media: an exploratory study. In Fuchs, M., Ricci, F., & Cantoni, L. (Eds.), Information and Communication Technologies in Tourism 2012:

(p. 1-12). Vienna, Austria & New York, NY: SpringerWeinNewYork Beverly, J.A. (2013). Public relations models and dialogic communication in the

Twitterverse: An analysis of how colleges and universities are engaging their public through Twitter (Doctoral Dissertation). University of Southern Mississippi, Hattiesburg, Mississippi.

Bridges, J.A. & Nelson, R.A. (2000). Issues Management: A Relational Approach. In Ledingham, S.D. & Bruning, J.A. (Eds.), Public Relations as Relationship Management: A Relational Approach to the Study and Practice of Public Relations (p. 95-116). Mahwah, N.J.: L. Erlbaum Associates.

Broom, G., Casey, S. & Richie, J. (1997). Toward a concept and theory of organization- public relationships. Journal of Public Relations Research 9(2), p. 83-98. Canary, D.J. & Stafford, L. (1994). Maintaining relationships through strategic and

routine interaction. In Canary, D.J. and Stafford, L. (Eds.), Communication and Relational Maintenance (p. 3-22). San Diego, CA: Academic Press.

Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010). Measuring user influence on Twitter: the million follower fallacy. Paper presented at the Association for the Advancement of Artificial Intelligence Conference on Weblogs and Social Media, George Washington University, Washington, D.C. Cialdini, R. (2009). Influence: Science and Practice. New York, NY: William Morrow Coombs, W. T. (2000). Crisis management: advantages of a relational perspective. In

Ledingham, S.D. & Bruning, J.A. (Eds.), Public Relations as Relationship Management: A Relational Approach to the Study and Practice of Public Relations (p. 73-94). Mahwah, N.J.: L. Erlbaum Associates.

Cutlip, S. & Center, A. (1952). Effective Public Relations: 1st ed., New York, NY: Prentice Hall. Cutlip, S., Center, A. & Broom, G. (1985). Effective Public Relations: 6th ed., Englewood Cliffs, N.J.: Prentice Hall.

95

Daily Mail (2016). Orlando massacre raises fears for vital tourism industry. http://www.dailymail.co.uk/wires/afp/article-3643213/Orlando-massacre-raises-fears-vital-tourism-industry.html

Dimmler, E. (2011). Bedbug crisis not abating, specialists warn. CNN, http://www.cnn.com/2011/HEALTH/02/01/bedbugs.summit/ Duggan, M. (2015). The demographics of social media users. Pew Research Center, http://www.pewinternet.org/2015/08/19/the-demographics-of-social-media-users/ “Economic Impact Analysis,” n.d. World Travel and Tourism Council, http://www.wttc.org/research/economic-research/economic-impact-analysis/ Ehling, W. (1992). Estimating the value of public relations and communication to an Organization. In J.E. Grunig (Ed.), Excellence in Public Relations and

Communication Management (p. 617-638). Hillsdale, N.J.: L. Erlbaum Associates.

EHotelier (2016). “Which hotel chains have the most projects under construction?” http://ehotelier.com/featured/2016/03/17/148775/ Ferguson, M.A. (1984). Building theory in public relations: Interorganizational

relationships. Paper presented at the convention of the Association of for Education in Journalism and Mass Communication, Gainesville, FL.

Gagnon, S., Vough, H.C., & Nickerson, R. (2012). Learning to lead, unscripted:

Developing affiliative leadership through improvisational theatre. Human Resource Development Review 11(3). p. 299-325

Grunig, J.E. (1977). Evaluating employee communications in a research operation. Public Relations Review, 3(4), p. 61-82. Grunig, J.E. & Huang, Y. (2000). From organizational effectiveness to relationship

indicators: Antecedents of relationships, public relations strategies, and relationship outcomes. In Ledingham, S.D. & Bruning, J.A. (Eds.), Public Relations as Relationship Management: A Relational Approach to the Study and Practice of Public Relations (p. 23-54). Mahwah, N.J.: L. Erlbaum Associates.

Grunig, J.E. & Hunt, T. (1984). Managing Public Relations. New York, NY: Holt, Rinehart and Winston. Grunig, L.A., Grunig, J.E., & Ehling, W. (1992). What is an effective organization? In

J.E. Grunig (Ed.), Excellence in Public Relations and Communication Management (p. 61-90). Hillsdale, N.J.: L. Erlbaum Associates.

96

Heller, M. (2016). Millennial expectations are reshaping travel industry. The G Brief, http://thegbrief.com/articles/millennial-expectations-are-reshaping-travel-industry-602

Hon, L.C. & Grunig, J.E. (1999). Guidelines for measuring relationships in public relations. Institute for Public Relations, Gainesville, FL. Huang, Y. (1998). Public relations strategies and organization-public relationships.

Paper presented at the annual conference of the Association for Education in Journalism and Mass Communication, Baltimore, MD.

Huang, Y. (2001). OPRA: A cross-cultural, multiple-item scale for measuring

organization-public relationships. Journal of Public Relations Research 13(1), p. 61-90.

Hughes, T. (2017). Searches for U.S.-bound flights fall since Trump’s inauguration.

USA Today, http://www.usatoday.com/story/travel/flights/2017/02/08/flight-searches-travel-ban-hopper/97608790/?hootPostID=f4c3ae851ffa05d0426c9fb9e3b8f6ac

Kent, M.L. & Taylor, M. (1998). Building dialogic principles through the world wide web. Public Relations Review 24(3), p. 321-334. Kellogg, K. (2013). What is Klout? What is Kred? 3 ways to wield social influence scores

for improved online interactions. Bruce Clay Inc., https://www.bruceclay.com/blog/what-is-klout/

Ki, E.J. (2003). Relationship maintenance strategies on websites (master’s thesis). Gainesville, FL: University of Florida. “Kred Scoring Guide.” Kred, n.d., http://home.kred/rules/. Kulp, P. (2017). More and more stores decide Trump is bad for business. Mashable, http://mashable.com/2017/02/13/retailers-trump-products/#j.2FPghAtkqq. Ledingham, J.A. & Bruning, S.D. (1998). Relationship management in public relations: constructs of an organization-public relationship. Public Relations Review 24(1). p. 55-65 Ledingham, J.A. & Bruning, S.D. (2000). A longitudinal study of organization-public

relationship constructs: Defining the role of communication in the practice of relationship management. In Ledingham, S.D. & Bruning, J.A. (Eds.), Public Relations as Relationship Management: A Relational Approach to the Study and Practice of Public Relations (p. 55-70). Mahwah, N.J.: L. Erlbaum Associates.

97

Ledingham, J.A., Bruning, S.D., Thomlinson, T.D. & Lesko, C. (1997). The applicability of Interpersonal relationship constructs to an organizational context: toward a theory of relational loyalty; a qualitative approach. Journal of Organizational Culture, Communications and Conflict 1(1), p. 23.

Lee, E-J. & Kim, Y.W. (2014). How social is Twitter use? Affiliative tendency and communication competence as predictors. Computers in Human Behavior 39. p. 296-305

Lenhard, W. & Lenhard, A. (2014). Hypothesis Tests for Comparing Correlations. https://www.psychometrica.de/correlation.html.

Liao, Y. Y. (2015). Customer satisfaction. In Wherry, F.B. & Schor, J.B. (Eds.), The

Sage Encyclopedia of Economics and Society (p. 537-540). Thousand Oaks, CA.: Sage.

Law, R., Leung, R., Lo, A., Leung, D., Hoc, L., & Fong, N. (2015). Distribution channel in

hospitality and tourism. International Journal of Contemporary Tourism Management, 27(3), p. 431-452

Li, Z. (2015). Relationship maintenance on Twitter: implications from loyalty leaders. Journal of Communication Management 19(2), p. 184-202. Martin, G. (2014). How to complain to airlines on Twitter (and what you’re doing wrong).

Forbes, http://www.forbes.com/sites/grantmartin/2014/10/24/how-to-complain-to-airlines-on-twitter-and-what-youre-doing-wrong/#1bc20b27314e

McLaughlin, E., Berlinger, J. & Fantz, A. (2016). Disney gator attack: 2-year-old boy

found dead. CNN, http://www.cnn.com/2016/06/15/us/alligator-attacks-child-disney-florida/

Meadows, C. & Meadows, C.W. (2014). The history of academic research in public

relations: Tracking research trends over nearly four decades. Public Relations Review, 40(5), p. 871-873.

Men, L.R. & Tsai, W-H. S. (2012). How companies cultivate relationships with publics

on social networking sites: Evidence from China and the United States. Public Relations Review, 38(5), p. 723-730.

Morgan, R.M. & Hunt, S.D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing 58(3), p. 20-38. Murray, L., Durkin, M., Worthington, S., & Clark, V. (2014). On the potential for Twitter

to add value in retail bank relationships. Journal of Financial Services Marketing, 19(4), 277-290.

98

Mutzabaugh, B. (2016). Expansion for 2016: Allegiant adds 3 new cities, 19 routes. USA Today, http://www.usatoday.com/story/travel/flights/todayinthesky/2016/01/12/expansion-2016-allegiant-adds-3-new-cities-19-routes/78673718/

Perrin, A. (2015). Social Media Usage: 2005-2015. Pew Research Center, http://www.pewinternet.org/2015/10/08/social-networking-usage-2005-2015/ Pfeffer, J. (1978). Organizational Design. Arlington Heights, IL: AHM Rao, A, Spasojevic, N., Li, Z. and DSouza, T. (2015). Klout score: measuring influence

across multiple social networks. Paper published online, https://arxiv.org/pdf/1510.08487.pdf.

Rizzo, C. (2016). Will tourism to the U.S. slow under Trump? Travel + Leisure, http://www.travelandleisure.com/travel-tips/american-tourism-donald-trump Rose, S., Spinks, N., and Canhoto, A (2015). Management Research: Applying the Principles. New York, NY: Routledge Rosenblatt, K. (2016). Former GMA Weatherman Sam Champion launches furious

Twitter rant at American Airlines…. Dailymail.com, http://www.dailymail.co.uk/news/article-3619091/Champion-airport-Former-Good-Morning-America-weatherman-Sam-Champion-goes-Twitter-rant-American-Airlines-swapped-people-plane-flight-heavy.html

Rybalko, S. & Seltzer, T. (2010). Dialogic communication in 140 characters or less: How

Fortune500 companies engage stakeholders using Twitter. Public Relations Review 36(4), p. 336-341.

Saffer, A., Sommerfeldt, E. & Taylor, M. (2013). The effects of organizational Twitter interactivity on organization-public relationships. Public Relations Review, 39 (3), p. 213-215.

Sheldrake, P. (2011). The Business of Influence: Reframing Marketing and PR for the Digital Age. West Sussex, UK: John Wiley & Sons. Samiljan, T. (2016). How social media is changing the travel industry. Travel + Leisure,

http://www.travelandleisure.com/slideshows/how-social-media-is-changing-the-travel-industry

Smith, B. (2010). Whither the public relations role? Exploring the influence of integrated communications on public relations. Institute for Public Relations, Gainesville, FL Smith, K. (2016). Marketing: 96 amazing social media statistics and facts for 2016.

99

Brandwatch, https://www.brandwatch.com/2016/03/96-amazing-social-media-statistics-and-facts-for-2016/

Sotiriadis, M.D. & van Zyl, C. (2013). Electronic word-of-mouth and online reviews in

tourism services: the use of Twitter by tourists. Electronic Commerce Research, 13(1), p. 103-124

Sowa, B. (2013). Openness. In Heath, R.L. (Ed.), Encyclopedia of Public Relations (

p. 627-629). Thousand Oaks, CA: Sage. Stafford, L. & Canary, D.J. (1991). Maintenance strategies and romantic relationship

type, gender, and relational characteristics. Journal of Social and Personal Relationships 8(2), p. 217-242.

Thomlinson, T.D. (2000). Interpersonal primer with implications for public relations. In

Ledingham, S.D. & Bruning, J.A. (Eds.), Public Relations as Relationship Management: A Relational Approach to the Study and Practice of Public Relations (p. 177-204). Mahwah, N.J.: L. Erlbaum Associates.

Van Horn, N.M. & Beveridge, A. (2016). MassMine: Your Access to Data. The Journal of Open Source Software 1(8). Waters, R.D., Friedman, C.S. Mills, B. & Zeng, L. (2011). Applying relationship

Management theory to religious organizations: an assessment of relationship cultivation online. Journal of Communication and Religion 34(1), p. 88-104.

Wattles, J. & Marsh, R. (2016). Hundreds of Southwest flights delayed after ground

stop. CNNMoney, http://money.cnn.com/2016/07/20/news/companies/southwest-airlines-grounded/index.html?sr=fbCNN072016companies0649PMStory.

Williams, K.D. & Brunner, B.R. (2010). Using cultivation strategies to manage public

relationships: A content analysis of non-profit organisations’ websites. PRism 7(2).

Xiang, Z. & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), p. 179-188. Zhu, L. & Han, G. (2014). Maintaining organization-public relationships on tourism

websites through relationship maintenance strategies. Public Relations Review, 40(5), p. 847-849.

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BIOGRAPHICAL SKETCH

Karsten Burgstahler was born and raised in Decatur, IL. After attending Southern

Illinois University for his undergraduate studies, he graduated in 2012 with a degree in

news-editorial journalism. After working for a year at the Journal Gazette Times-Courier,

in Charleston, IL., he decided to pursue his master’s degree in public relations at the

University of Florida. He graduated with his Master of Arts in Mass Communication in

May 2017. His professional goal is to work in hospitality public relations, whether it be

in-house or working with hospitality clients at an agency.