Perceptions of Academic Advisors Regarding Text Messaging ...

COMPUTER MEDIATED COMMUNICATION: PERCEPTIONS OF ACADEMIC

ADVISORS REGARDING TEXT MESSAGING IN HIGHER EDUCATION

by

Kathryn Elizabeth Looney, MBA, MAED, MSIR

A Dissertation

Submitted to Franklin University

in Partial Fulfillment of the Requirements for the Degree of

DOCTORATE OF BUSINESS ADMINISTRATION

February 2022

Brenda Jones, Ph.D., Committee Chair

Patrick Bennett, Ed.D., Committee Member

Yuerong Sweetland, Ph.D., Committee Member

Franklin University This is to certify that the dissertation prepared by

Kathryn Looney “Computer Mediated Communication: Perceptions of Academic Advisors

Regarding Text Messaging in Higher Education”

Has been approved by the committee as satisfactory completion of the dissertation requirements for the degree of

Doctor of Business Administration

02/25/2022 Brenda Jones (Feb 25, 2022 16:46 EST)

Dr. Brenda Jones, Committee Chair and Asst. Dean, Instruction and Department Chair, Communications, Behavioral & Natural Sciences, Franklin University

02/25/2022 Dr. Patrick Bennett, Committee Member and Vice President, Academic Quality & Planning and Dean, School of Education, Franklin University

Yuerong Sweetland 02/25/2022 Yuerong Sweetland (Feb 25, 2022 16:56 EST)

Dr. Yuerong Sweetland, Committee Member and Director of Assessment, Franklin University

Tim Reymann (Feb 26, 2022 15:26 EST)

Dr. Tim Reymann, DBA Program Chair Franklin University

Dr. Wendell Seaborne, Dean, Doctoral Studies Franklin University

02/26/2022

02/26/2022

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© Kathryn Elizabeth Looney (2021)

iv

Abstract

Higher Education Institutions (HEIs) need to stay abreast of advances in communication

technologies to be student centric, but institutional adoption of Short Messaging Service (SMS)

text varies widely and research on incorporation for advising is limited (Arnold et al., 2020;

IPEDS 2020; Santos et al., 2018). This quantitative study explored advisor use and perceptions

on values, motives, and institutional support of SMS texting as a communication channel with

students and the possible variables impacting those factors. Theoretical concepts in Customer

Relationship Management (CRM) and adaptive leadership guided the study as well as existing

survey research on Computer Mediated Communication (CMC) in higher education (Duran et

al., 2005). Survey responses from 402 advisors nationwide were analyzed through descriptive

and inferential statistics. SMS use was reported among all genders, experience levels, and

programmatic formats and advisors overall had a positive view of the communication channel.

Motives for use varied between subgroups within the sample and SMS was predominantly used

to gain access to richer mediums. A statistically significant association between learning

environment and SMS incorporation indicated that online advisors were more likely to use SMS

texting for student communication. A statistically significant difference was also identified

between median institutional support scores for SMS users and non-users with the directionality

indicating users were more likely from SMS supportive institutions. Furthermore, advisors

reported using SMS texting for both transactional and relational communication, even when their

institution did not support the channel with training, policies, or technology. The study sheds

light on the prevalence of SMS use and calls for leadership to gain greater awareness of their

local-level policies, industry-wide practices, and system integrated options in managing the

university-to-student connection. For HEIs to enable adaptive advising to experiment with

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interventions at scale and relationship building in student-centric mediums, it may help to

provide the framework conducive to SMS text for supplementing communication. Failing to

integrate CMC approaches into an institution’s structural approach to relationship management

prevents leaders from evaluating how, or even if, it is improving outcomes (Joslin, 2018). The

current study emphasizes how aligning strategy and software with end user needs can help

ensure university communication is within the purview of those measuring advisor impact on

intended business outcomes like engagement and retention.

vi

Dedication

For my mom who read to me.

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Table of Contents

Abstract..............................................................................................................................................iv

TableofContents...........................................................................................................................vii

ChapterOne:FoundationoftheStudy........................................................................................1

Problem Statement....................................................................................................................................................3

Purpose of the Study................................................................................................................................................4

Research Questions..................................................................................................................................................5

Problem Significance Background.....................................................................................................................6

Significance of the Study.......................................................................................................................................7

Definition of Terms..................................................................................................................................................8

Advisors....................................................................................................................................................................9

CustomerRelationshipManagement(CRM)...........................................................................................9

HigherEducationInstitution(HEI).............................................................................................................9

LearningEnvironment...................................................................................................................................10

Retention..............................................................................................................................................................10

StudentOutcomes.............................................................................................................................................11

Texting..................................................................................................................................................................12

Assumptions, Limitations, and Delimitations.............................................................................................12

Summary....................................................................................................................................................................15

ChapterTwo:LiteratureReview...............................................................................................17

Introduction..............................................................................................................................................................17

Literature Review...................................................................................................................................................18

HistoricalBackgroundofCollegiateRetention...................................................................................18

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SocialIntegration.............................................................................................................................................21

RetentionfromaBusinessPerspective....................................................................................................22

CustomerRelationshipManagement(CRM)........................................................................................25

SelectionofCustomerRelationshipManagement..............................................................................27

AcademicStaffPopulation...........................................................................................................................30

ConsiderationsforAdvisingCommunication.......................................................................................32

Non-TraditionalStudents.............................................................................................................................38

OnlineLearning.................................................................................................................................................39

StudentCommunicationPreferences.......................................................................................................43

GenerationalConsiderations.......................................................................................................................44

ComputerMediatedCommunication.......................................................................................................46

ShortMessagingServiceTexting...............................................................................................................48

TextBasedCommunicationControversies............................................................................................49

AdaptiveLeadership........................................................................................................................................51

AdvisorPerceptions.........................................................................................................................................53

Gaps in the Literature...........................................................................................................................................55

Summary and Conclusions.................................................................................................................................56

ChapterThree:Methodology......................................................................................................58

Introduction..............................................................................................................................................................58

ProblemStatement..........................................................................................................................................59

PurposeoftheStudy........................................................................................................................................59

ResearchQuestions..........................................................................................................................................59

Method.......................................................................................................................................................................59

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SurveyInstrument............................................................................................................................................61

Target Population...................................................................................................................................................62

Accessing the Population....................................................................................................................................63

HEIListservs.......................................................................................................................................................64

SocialMedia........................................................................................................................................................64

PublicContactInformation..........................................................................................................................65

Sample Size..............................................................................................................................................................66

Validity and Reliability........................................................................................................................................66

Data Collection........................................................................................................................................................69

Data Analysis Procedures...................................................................................................................................72

DataPreparation..............................................................................................................................................73

StatisticalAnalysis...........................................................................................................................................74

Summary....................................................................................................................................................................77

ChapterFour:DataCollectionandAnalysis...........................................................................79

Introduction..............................................................................................................................................................79

ProblemStatement..........................................................................................................................................80

PurposeoftheStudy........................................................................................................................................80

ResearchQuestions..........................................................................................................................................80

Survey Pilot..............................................................................................................................................................81

Description of the Sample...................................................................................................................................87

Statistical Analysis.................................................................................................................................................91

ResearchQuestion1........................................................................................................................................91


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Summary.................................................................................................................................................................103

ChapterFive:Results,Conclusions,andRecommendations...........................................106

Discussion of Findings......................................................................................................................................107

AdvisorsareUsingSMSTexting...............................................................................................................107

OnlineAdvisorsareMoreLikelytoUseSMSText.............................................................................108

SMSisUsedtoGainRicherMediumAccess.........................................................................................109

GendermayImpactSMSUse.....................................................................................................................112

AgemayImpactSMSUse............................................................................................................................113

ProgrammaticFormatmayImpactSMSMotives............................................................................114

SMSSupportedInstitutionsareMoreLikelytoText.......................................................................115

AdvisorsHavePositivePerceptionsofSMSTexting........................................................................117

Limitations.............................................................................................................................................................119

Theoretical and Practical Implications........................................................................................................121

TheoreticalImplications.............................................................................................................................121

PracticalApplication....................................................................................................................................123

Future Recommended Research....................................................................................................................125

Summary.................................................................................................................................................................126

References.....................................................................................................................................129

AppendixA:InformedConsentForm.....................................................................................149

AppendixB:PermissiontoUseandAdoptSurveyInstrument......................................150

AppendixC:Survey.....................................................................................................................151

AppendixD1:OrganizationsandAssociations....................................................................155

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AppendixD2:SocialMediaGroups.........................................................................................156

AppendixD3:OpenDirectories...............................................................................................157

AppendixE:OrganizationandAssociationCorrespondence..........................................158

AppendixF:IRBApprovalandCITITrainingCertificate..................................................170

AppendixG:SurveyInvitationMessage................................................................................172

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List of Figures

1 Student to Institution Connection……………………………………………………………29

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List of Tables

1 Content Evaluation Panel Ratings………………………………………...…………………82

2 Survey Item Content Validity Ratio…………………………………………………………83

3 Pilot Survey Feedback……………………………………………………………….………86

4 Data Dictionary………………………………………………………………………………89

5 Survey Respondents Overview………………………………………………………...…….90

6 Construct Key for Motives…………………………………………………………...………92

7 Frequency for SMS Texting Motives…………………………………………………..……92

8 Ranked means between groups………………………………………………………………94

9 Value Statements and Statement Code………………………………………………………94

10 Value Statement Agreement by Groups…………………………………………………..…95

11 Excel Spreadsheet Screenshot for Chi-Square………………………………………………97

12 Chi-Square Data and Results………………………………………………………………...98

13 Institutional Support Score………………………………………………………………..…99

14 Distribution of Institutional Support………………………………………………………..101

15 Wilcoxon Rank Sum Test Results……………………………………………………….102

16 Motives Ranked by Mean of Frequency……………………………………………………110

17 Ranked Motives by Age……………………………………………………………...……..114

18 Institutional Support by Subgroup…………………………………………………….……117

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Chapter One: Foundation of the Study

Academic success hinges on effective communication (Buell, 2004; Sunha et al., 2019;

WIARS, 2020). Communication between a Higher Education Institution (HEI) and the student

body is primarily mediated by a program, academic, or degree advisor, mentor, or counselor who

serves as the main, ongoing point of contact for promoting successful outcomes such as retention

(Brown, 2017; Donaldson et al., 2020; Joslin, 2018; Vianden & Barlow; 2015). Advances in

technology have undoubtedly altered how society interacts, making the communication style and

preferred platforms of learners today drastically different than the students of just a decade ago

(Arnold et al., 2020; Osam et al., 2017; Page et al., 2020). As younger and more digitally

proficient students enter the ranks of higher education in the United States, their exposure to

communications technologies will introduce a new style of dialogue to universities (Bikanga-

Ada et al., 2017). HEIs must acknowledge and adapt to contemporary communication platforms

and the preferred mediums of interactions to be student centric (Argüello & Méndez, 2019;

Mirriahi & Alonzo, 2015; Oregon et al., 2018).

While traditional methods of conversation, like the face-to-face academic advising

appointment, retain relevance, Computer Mediated Communication (CMC) is of increasing

importance and, like the undergraduates they serve, universities must transition into the digital

age (Junco et al., 2016). CMC mediums have been controversial in their effectiveness and the

literature review will expand on theoretical concepts of business communication to include social

presence (Short et al., 1976), hyperpersonal communication (Walther, 1996), and transactional

distance (Moore, 1992). One channel for CMC is Short Message Service (SMS) text messaging

which offers the type of targeted, succinct, and immediate feedback expected by younger and

digitally proficient students in Gen Z (Chicca & Shellenbarger, 2018). Texting campaigns to

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reduce informational barriers through individualized advising have also demonstrated a positive

impact on completion and persistence rates in first year college students (Castleman & Meyer,

2020).

Persistence and retention are vital for HEIs; when students withdraw before graduation,

there are multiple negative consequences both for the individual, in terms of lost time and

money, as well as society in a greater sense, through the financial impact of defaulted loans and

the lack of credentialed candidates entering the workforce. As a business problem for HEIs,

higher attrition rates equate to less tuition revenue and less return on investment for marketing

and enrollment costs (Ackerman & Schibrowsky, 2008). Best practices in retention have been

codified in earlier models such as the Institutional Departure Model (Tinto, 1975) and the

Undergraduate Dropout Process Model (Spady, 1971) which gave way to later theories

emphasizing the business and sociological aspects of academic persistence. The Student

Attrition Model (Bean, 1980) applies company-to-customer approaches in retention to the

university-to-student relationships. This model lends to the work of Ackerman and Schibrowsky

(2008) who substantiate social engagement as a positive correlate for both student and customer

satisfaction, thus reinforcing relationship building as important for both customers and students.

The dissertation research was guided by the theoretical concepts of Customer

Relationship Management (CRM) through CMC, particularly perceptions of SMS text messages

as a communication tool in the student-to-advisor relationship. CRM has been applied to higher

education for best practices in managing the student relationship, particularly for advising and

mentoring staff who are positioned to drive student decision-making (Hrnjic, 2016; Juan-Jordán

et al., 2018; Troxel, 2018). Using SMS to relationship build and promote institutional business

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outcomes have been observed as emerging approaches in higher education (Arnold et al., Page et

al., 2020).

A frequent failure in adopting the CRM orientation to student-centric communication is a

lack of managerial involvement in the strategy and promotion of practices (Hrnjic, 2016).

Resistance to change is another factor inhibiting technology initiatives and is further hindered

through a lack of senior management support (Granić & Marangunić, 2019; Skoumpopoulou et

al., 2018). Knowing when and how to use CMC to develop relationships requires adaptive and

flexible individuals, capable of recognizing changes in societal communication practices (Khan,

2017). This positions adaptive leadership as another theoretical construct guiding the current

research as it emphasizes new strategies to thrive in emerging and unknown environments

(Heifetz, 1994; Heifetz et al., 2009). The digital transition facing HEI professionals requires

more collaborative efforts to meet changing demands; adaptive leaders are able to take existing

theory and best practices and apply new approaches based on current needs (Dopson et al.,

2019).

Problem Statement

The primary problem prompting this study was a lack of adoption and research on the use

of SMS text messaging as a communications technology among academic advisors, why it is

used, and the motives and perceptions of that use for student interfacing in higher education.

HEIs must continuously acclimate to the unique characteristics, needs, and preferences of their

student body for communication to benefit student outcomes (Blessinger & Wankel, 2013;

Kerby, 2015; Manyanga et al., 2017). Communication impacts student satisfaction and socially

linking the university to the student body is largely established by academic advising staff

(Jensen, 2017; Vianden & Barlow, 2015; Yusoff et al., 2015). Failures in managing this

4

relationship and adapting to changes in the needs of the student body have led to higher attrition

rates (Troxel, 2018). Ackerman and Schibrowsky (2008) introduce economic validation for

retaining students to graduation through cost-benefit analysis of new and long-term student

revenue.

With continuous growth in online programs and changes in how society communicates

digitally, organizations need to weigh the strategic costs and benefits of incorporating technology

training and adoption of newer operating mediums (Kerby, 2015; Manyanga et al., 2017; Santos

et al., 2018; Uddin, 2020). CRM through a variety of digital mediums is one such call for

technology adoption for HEIs to better manage student communication (Juan-Jordán, 2018).

Skoumpopoulou, Wong, Ng, and Lo (2018) found that behavioral intent for HEI professionals

using newer operating mediums positively correlated with their perception of the technology

improving their work performance and management being helpful in learning functionality.

Universities may benefit from considering their organization’s attitudes regarding CMC and

explore alternative channels preferred by their customers/students as they may improve

business/university outcomes (Blessinger, & Wankel, 2013; Kerby, 2015). Careful scrutinization

of employee perceptions, motives, and values regarding SMS texting as a communications media

must precede business protocol for effective integration.

Purpose of the Study

The purpose of the non-experimental, quantitative study was to investigate the

perceptions of college and university academic advisors, mentors, and counselors in the United

States regarding institutional support for texting and motives for use of the communication

channel as well as the possible variables impacting use and perceptions. Zarges (2018) notes

how technology has grown increasingly crucial in the delivery of advising and staff need to be

5

involved in the decision-making process regarding what tools are best suited for student

interaction. Surveying the perceptions of academic advisors and current practices through the

higher education sector is an important first step in considering a technological investment. HEI

leaders should choose digital tools that support the stated goals and desired student outcomes of

the institution instead of trying to fit practices around the technology (Ireland et al., 2016). SMS

text messaging is one of the many CMC options, yet research in the field is limited, particularly

for mentoring focused exchanges with learners (Davidovitch & Belichenko, 2018; Ross, 2019;

Santos et al., 2018).

The current research asked if respondents use SMS text messaging with students,

perceptions on their institution’s support for the communication tool, motives for student

messaging, and their level of agreement with value statements regarding the platform. The

researcher aimed to meaningfully contribute to discourse in higher education by obtaining and

analyzing the perceptions of those advisors intended for the tools to promote CRM through CMC

technologies. The primary programmatic format of an advisor’s institution was identified and

responses were compared between online and ground campus learning environments. The extent

to which CMC, such as text messaging, is augmenting or substituting other communication

options may also be mediated by individual participant variables (Duran et al., 2005).

Understanding employee responses regarding perceived use, motives, and administrative support

for SMS texting, and how it may differ based on participant or institutional differences, is a

precursor in determining if and how institutions should pursue the channel as a formal approach

to student communication.

Research Questions

The research questions guiding the current study were as follows:

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RQ1: What perspectives and motives do academic advisors report regarding their use of

SMS text messaging with students?

RQ2: Does use of SMS text messaging with students differ between academic advisors

of online and ground campus environments?

RQ3: Are there any differences in institutional support of SMS as a platform for student

communication between advisors using and advisors not using SMS text messaging?

Problem Significance Background

Advances in technology have enabled a growing number of internet-based programs but

attrition in online formats has been more significant than that of traditional, brick and mortar

programs (Miller, 2017). Furthermore, the recent circumstances surrounding Coronavirus

(COVID-19) have forced many universities to transition classes to an online platform (Roache et

al., 2020). For students attending completely online, they may only be afforded opportunities to

engage with the university staff through Computer Mediated Communication (CMC). Redmond,

Abawi, Brown, Henderson, & Heffernan (2018) underscore engagement as a primary variable in

addressing retention and student satisfaction, problems exacerbated for internet-based students

(Lockard et al., 2015; Manyanga et al., 2017; Thomas, 2020; Uddin, 2020; Vadell, 2016).

Advisors can be the key connecting factor for online students as increased social presence and

engagement with the university have been demonstrated to improve student success (Aldosemani

et al., 2016). Advisors typically engage with learners throughout the student lifecycle,

developing long-term relationships better served to communicate as conduits for all collegiate

offerings and requirements (Vianden & Barlow, 2015).

Student engagement can come in many forms to include interaction with instructors,

peers, institutional departments, and the curriculum. The current study aims to concentrate on

7

academic advisor communication as the primary form of engagement for analysis. This

university-to-student channel for exchange may benefit from SMS texting, particularly in

facilitating new student onboarding, ongoing relationship management, and retention (Arnold et

al., 2020; Naismith, 2007). It is advisable for leaders to recognize the impact of CRM centric

communication and consider the adoption of newer technologies, such as SMS texting platforms,

when it is the preferred method of their students as customers (Blessinger & Wankel, 2013;

Kerby, 2015; Manyanga, et al., 2017). Accountable for the design and implementation of

business strategy, institutional leaders are responsible for an organized approach to managing

student support options. Integrating and continuously assessing CMC technology in an

institution’s structural approach to relationship management is essential in promoting desired

business outcomes such as retention and student success (Joslin, 2018; Zarges, 2018).

Significance of the Study

Initial assumptions guiding the study’s significance comprise various constructive uses of

texting based on employee and institutional characteristics. Short messaging approaches can

include a number of communication strategies such as nudge theory, which can improve desired

behaviors and has been supported as effective for improving university outcomes such as student

grades (Smith et al., 2018), financial aid application completion rates (Castleman & Page, 2016),

and relationship building (Arnold et al., 2020). Understanding employee perceptions could aid

in institutional adoption of best practices but research on SMS texting in higher education and

advisor use remains limited (Amador & Amador, 2017; Davidovitch & Belichenko, 2018;

IPEDS, 2020). If findings indicate varied use and views of texting based on the dynamics of the

employee or programmatic format (online or traditional), it could improve future initiatives in

expanding digital interfacing for student contact. This is important because to have advisor

8

acceptance, access, and training, new resources like texting software need to be formally adopted

in strategy and not merely used on an ad-hoc basis by a minority of the staff or through their own

initiative on a personal device (Joslin, 2018). When there are no standard operating procedures

for communications technology it prevents continuous evaluation of an approach’s impact on

student outcomes (Joslin, 2018).

Understanding predominant motives for use of SMS texting and employee perceptions of

administrative support for texting is a precursor to adoption of technology and acceptance of

change. Continuous quality improvement initiatives must be implemented and monitored with

an understanding of employee attitudes to gain commitment (Ahmad & Zhichao, 2018). The

primary reasons employees use SMS could impact their perceived benefits and limitations of the

platform. Exploring SMS use in relationship to administrative support could aid institutions in

change management training with regard to CMC practices, policies, and technology tools.

Intervening variable trends such as age, gender, or work experience could also assist universities

in understanding advisor dynamics and how they may change the prevalence or perception of

SMS use. Analysis of why academic advisors initiate texts may be useful in supporting

institutional selection of software based on the necessary functionality or primary uses.

Definition of Terms

To frame the research, it is critical to define central terms and identify parameters for

what the current research included and excluded for the context of frequently referenced

concepts. It is not uncommon for definitions to vary between colleges or for special

consideration to be taken when applying meaning to or calculating certain concepts (Brown,

2017; Hagedorn, 2006; NCES, 2020). The following operational definitions are thus provided to

reduce ambiguity and create a shared understanding of concepts referenced in the dissertation:

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Advisors

While institutions may have financial or enrollment advisors, with whom a student does

not routinely speak with, or faculty, who primarily instruct through the duration of one course,

this study will focus on staff members who work with students consistently from term-to-term.

Troxel (2018) notes how primary-role academic advisors work in a host of capacities ranging

from transactional, for instance course registration, to developmental, which includes more

outcomes-based communication for engagement and goal setting. Developmental

communication overlaps faculty mentor and student services counselor responsibilities who also

build long-term student relationships. The term advisor will be used to encompass all academic,

program, and degree advisors, mentors, and counselors who have long-term communication

suited to build rapport, discuss a broad range of university themes, and promote positive student

outcomes (Brown, 2017; Vianden & Barlow, 2015).

Customer Relationship Management (CRM)

The CRM approach to business integrates people, process, and technology in a manner

aimed to enhance company-to-customer communication for long-term social ties, satisfaction,

and brand loyalty (Calma & Dickson-Deane, 2020; Suntornpithug, 2012). CRM promotes

retention through knowing the customer, rapport building, and ongoing dialogue, particularly for

companies in which the product requires a significant investment or ongoing sale, such as a

collegiate level credential (Azhakarraja, 2020; Niven, 2012).

Higher Education Institution (HEI)

Although originally a designation for degree-granting universities in the United Kingdom

following the Further and Higher Education Act of 1992 (Sassen, 2018), the current research will

use the term to refer to postsecondary education providers, colleges, and universities in the

10

United States. HEIs will be limited to Title IV, collegiate level education providers accredited

within the United States and include both online and traditional programmatic learning

environments (CHEA, 2020; DAPIP, 2020).

Learning Environment

The programmatic format or learning environment of a school will refer to the status of

being online or on campus. While many colleges and universities are hybrid in some way, the

distinction in this research will be made as it pertains to the primary format in which the

academic staff participants’ students attend courses and are advised. If the majority of a

participant’s students are enrolled in non-traditional internet-based, distance, remote, or

asynchronous web-based courses, their students are typically limited to CMC (Vadell, 2016;

Weidlich & Bastiaens, 2018). This subgroup was identified in the current study as online

advisors. The second subgroup within learning environment or programmatic format will be

traditional, in which student-to-advisor communication focuses on ground campus or brick-and-

mortar classes, where face-to-face advisor meetings are more likely accessible.

Retention

In the congressionally mandated annual report from the National Center for Education

Statistics, retention at a four-year institution is defined as the percent of full-time, degree-

seeking, first-time students in a bachelor’s program from the previous fall who are enrolled again

the following fall (NCES, 2020). The complexities of transfer students, degree levels,

competency-based schools, part-time status, and online programs with different term lengths

makes measuring retention difficult with a standard definition. Hagedorn (2006) exemplifies this

and gives numerous examples of how variability in enrollment patterns requires multiple

methods to calculate retention. Since the current study did not measure retention and was not

11

limited to a specific type or level of degree, retention was recognized as a more general objective

in higher education. Retention here, refers simply to the students who persist in their various

programs and do not elect to discontinue through administrative processes or lapse enrolment

(Lockard et al., 2019; Villano et al., 2018).

Student Outcomes

With the varying nature of institutions and the certificates, certifications, licenses, and

range of degree levels they offer, key performance indicators differ greatly across the higher

education sector (Li & Kennedy, 2018). Favorable student outcomes are shared but measured

differently as not all data points translate across the same for transfer students, online programs,

or competency-based schools (Links, 2018; Mu & Fosnacht, 2019; Schwebel, 2012). For

instance, all schools have a vested interest in students maintaining enrollment through

graduation; however, measuring retention is one of the numerous metrics that can differ between

universities and is impacted by a myriad of factors (Marsh, 2014; Miller; 2017; Yazid, 2018).

Measuring the impact of communication is beyond the scope of this research since a vast array of

variables contribute to each student outcome and advisors will be solicited for perceptions

nationwide. Accordingly, concepts such as retention, attrition, persistence, graduation rates,

learning outcomes, grades, completion rates, on time progress, continuing enrollment, or

engagement will not be delineated with any specific classification. Similar to the definition for

retention above, terms will be referenced independently through the context of existing studies.

Precise delimitations for these concepts will not be given since a myriad of variables are

involved in advising assessment and associated institutional objectives (Hagedorn, 2006; NCES,

2020); instead, generalizable concepts intended for advising practices will be recognized as

desirable student outcomes.

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Texting

While instant messaging may be available within the student portal or on social media

platforms, this research will identify texting as Short Message Service (SMS) texts a student

receives directly to a cellular phone on a private phone number. The word texting for the

purposes of this research will imply a message which a student can respond to and receive

unique feedback from an advisor on the other end. This will exclude “no reply” messages from

the school such as generic push notifications. Institutional adoption of texting as a technology

tool will not refer to internal communication between staff as a resource for exchange and imply

specifically student-facing messages in which the learner is the receiver of the message.

Assumptions, Limitations, and Delimitations

A recognized weakness of the study surrounds the circumstances of accessibility to the

target population resulting in a convenience sample. As described in the design and

methodology section in more detail, the researcher used a combination of avenues to access the

target population to include social media, organizational listservs (or electronic mailing lists),

and direct email to present the survey link. Accordingly, the respondents in the sample were

from institutions participating in higher education organizations and partnerships that allow for

doctoral research or have publicly available contact information for academic advising staff.

Total population sampling was not possible as contact information for the entirety of HEIs was

inaccessible; limiting a true random sampling, the current study was based on convenience or

opportunity sampling (Leedy & Ormrod, 2016). An advisor’s willingness to respond may have

been indicative of the institution’s acceptance of collaboration and the sharing of best practices.

On the other hand, schools that do not text at all or do not have a clear institutional policy on the

communication medium, may be less inclined to complete the survey. Subsequently, reported

13

rates of texting use may be artificially inflated since these schools might already possess climates

for innovative CMC and thus not represent the entire population.

The limitation of self-selection bias was thus present since participants may have chosen

to complete the survey based on a personal interest in the topic and advisors who were

indifferent to SMS texting in higher education may thus be underrepresented (Zikmund, 2003).

Since participants are not randomly selected, the researcher makes no pretense of categorizing a

representative subset of the overall target population (Leedy & Ormrod, 2016). Although

population subsets, such as online/traditional, private/public, and proprietary/non-profit will all

be invited to participate, the sample cannot be generalizable to the entire target population.

Descriptive statistics in RQ1 allows for meaningful presentation of observations for

interpretation, but it cannot support inferences about the larger population (Salkind, 2006). RQ2

and RQ3 incorporated inferential statistics, through chi-square and the Wilcoxon Rank Sum test

respectively, which enabled exploration for relationships among the selected variables (Zikmund,

2003). However, these nonparametric tests cannot explain why a potential relationship exists or

if it is truly the research variable influencing the observed data. Gall, Gall, and Borg (2005) note

that while a relationship may be established, a key limitation is that the cause may not be clear.

If, for instance, members from a population subgroup are found to text message more than

another, this study does not proport to explain that relationship. Differences in programmatic

learning environment and institutional administrative support for SMS, as well as demographic

data, may impact an advisor’s use of communications technology; however, the current study

would only recognize if differences exist among advisor variables, not why (Cooper &

Schindler, 2014).

14

The researcher also recognized various delimitations to set parameters on the current

study based on the position of the employee as a representative who works with students after

matriculation throughout the student lifecycle. Delimitations exist for the theoretical concepts as

the researcher was interested in retention, CRM, and student success driven communication, not

SMS texting for marketing purposes prior to enrollment. Practices in CRM incorporate

communication from attracting new customer to retaining existing ones and promoting referrals.

However, the current research does not seek to explore university-to-student communication

prior to matriculation in a learner’s program, it instead focuses on exchange after admissions

through the student lifecycle to graduation. For this reason, program, degree, or academic

focused staff were selected since they work long-term with students, not advisors serving short-

term in an enrollment or admissions capacity.

Not every position within a college may be suited for texting students because not all

employees need to incorporate rapport building conversations to motivate, retain, and develop

students over a continued period. This limits the population of interest as blanket application

across all functional departments for texting would likely not befit institutional needs.

Subsequently, in addition to excluding admissions and enrollment staff, other post-matriculation

advising roles were excluded. For instance, a career services advisor may only speak to the

student once or a finance specialist might be restricted from messaging sensitive information

relevant to their department. Since not all HEI positions are suited for SMS exchange, the roles

of academic advisors, counselors, and mentors were set as part of the study’s delimitations for

the target population as they serve CRM through CMC intent. Employees in these positions are

routinely encouraged to get to know students on a personal level and work with them for a

prolonged period of time, thus better enabling curriculum advice, course placement,

15

encouragement, and accountability in a manner that fits their preferences and unique learning

styles (Kleemann, 2005; Vianden & Barlow, 2015).

The research was a snapshot of current perceptions held by academic advisors, mentors,

and counselors from May through July of 2021. Although practices in CMC exist in various

formats, such as online chat forums or email, the specific avenue of CMC exchange for the study

was bound by messages specifically between academic advisors and students through SMS text

messaging. The target population was not limited to institutional type in terms of programmatic

format or status as private/public or proprietary/non-profit but was limited to United States

accredited institutions of higher education (DAPIP, 2020). The distinction of Title IV degree

granting was be bound by the National Center for Education Statistics’ reported list of 4,313

public and private, 2 and 4-year colleges (NCES, 2021).

Summary

The current study aimed to explore CMC in higher education, particularly SMS texts as a

communication tool in the student-to-advisor relationship. The target population of HEI

employees were surveyed regarding their use and motives of SMS text messaging

communication with students. This chapter has briefly introduced the background of the study

and the rationale behind researching advisor perspectives on the research topic, which will be

further expanded in the following literature review, Chapter Two. Relationship building for

student success and retention through CMC requires adaptive professionals capable of

communicating with new technologies to benefit organizational outcomes. Accordingly, CRM

and adaptive leadership were briefly introduced as central theories guiding the research and will

be explored further in the subsequent chapter. The research questions were listed along with the

three associated statistical approaches for quantitatively analyzing data for each and these

16

procedures will be detailed in Chapter Three. Lastly, Chapter One identified the significance of

the study, definitions of key terms, and the assumptions, limitations, and delimitations related to

the approach and methodology.

17

Chapter Two: Literature Review

Introduction

Chapter Two provides a synthesis of seminal authors and the contemporary research

influenced by their studies as scholarly discourse pertaining to the research topic. Both business

and education concepts will be introduced from the literature in order to frame the historical

background of the current study and the identified research questions. Many HEI performance

objectives have been aligned with student outcomes, to include retention, which will be reviewed

and presented as an ongoing business problem spurring the research (Bean, 1980; Hagedorn,

2006; Joslin, 2018; Lockard et al., 2019; Mu & Fosnacht, 2019; NCES, 2020; Spady, 1971;

Tinto, 1975). The issue of retention will bridge the concepts to business theory in Customer

Relationship Management (CRM), which will be delineated as a student centric approach to

managing student outcomes (Ackerman & Schibrowsky, 2008; Azhakarraja, 2020; Calma &

Dickson-Deane, 2020). HEI professionals who work with students throughout the student

lifecycle are best positioned to influence the university-to-student relationship and thus mentors,

counselors, and advisors are selected as the population of interest (Brown, 2017; NACADA,

2017; RLN, 2019; Uddin, 2020; Vianden & Barlow, 2015).

Building meaningful ties and promoting retention can be difficult in online settings where

all communication is mediated by the technology through which it is delivered (Ng, 2018;

Oregon et al., 2018). Accordingly, the literature review will introduce the complexities

associated with non-traditional students and the Computer Mediated Communication (CMC)

channel of exchange. CMC and asynchronous program formats have spurred the application of

business communication theories in education to include social presence (Short et al., 1976),

hyperpersonal communication (Walther, 1996), and transactional distance (Moore, 1992). This

18

leads to the introduction of Short Messaging Service (SMS) texting as a CMC approach for

sharing information and socially connecting the university to the student.

Since advisors are the end users, intended to bridge the gap between technology and its

intended purposes, it is important to understand their perspectives and varying levels of

technology acceptance (Granić & Marangunić, 2019; Ireland et al., 2016; Skoumpopoulou et al.,

2018). This positions adaptive leadership as essential in CRM application through CMC since it

represents a business problem without definitive, existing solutions (Heifetz, 1994; Heifetz et al.,

2009). Adaptive leadership will thus be introduced as foundational to the current study as the

theory recognizes employee perspectives during change (Donaldson, 2020). Contending views

among HEI regarding these concepts will be introduced, expanded on through the shifting digital

environment, and critically assessed. Finally, the gaps in the literature will be identified leading

to the current research and concluding Chapter Two.

Literature Review

Historical Background of Collegiate Retention

Higher education leadership has historically promoted a variety of key performance

indicators paralleling positive student outcomes to include retention, which is difficult to

measure with uniformity across institutions (NCES, 2020; Hagedorn, 2006). Gaytan (2015)

recognizes how the government has promotes retention by way of grants, and various institutions

have placed new projects and research focused on retention at the forefront of administrator

initiatives. When students withdraw before graduation, there are multiple negative consequences

both for the individual, in terms of lost time and money, as well as society in a greater sense,

through the financial impact of defaulted loans and the lack of credentialed candidates entering

the workforce (Lockard et al., 2019; Sunha et al., 2019; Vadell, 2016). As a business problem

19

for HEIs, higher attrition rates equate to less tuition revenue and less return on investment for

marketing and enrollment costs (Ackerman & Schibrowsky, 2008).

To reiterate from Chapter One, the current research does not to seek to quantify any

particular student outcome; however, the core objectives of advisor-to-student interaction, to

include retention, are foundational. HEI professionals now have digital options for interaction

which can facilitate communication. For instance, even if an advisor prefers face-to-face

meetings, SMS texting was found to increase the likelihood of traditional appointments

occurring (Junco et al., 2016). Acceptance of communications technology increases among HEI

professionals when it is perceived as easy to use, supported by management, and believed to

improve job performance (Granić & Marangunić, 2019; Skoumpopoulou et al., 2018). While the

variables for acceptance of technology will be expanded upon in subsequent sections, the last

factor, pertaining to the potential for improving advisor performance, is critical. In order to

explore improvement in the student-to-advisor communication structure, understanding core

advising objectives, in a broad sense, is first necessary. Retention is one such objective and, for

the purposes of the current research, will refer simply to those students who persist in their

various programs and do not elect to discontinue through administrative processes or lapse

enrolment (Lockard et al., 2019; Villano et al., 2018).

Many universities have admission policies based on student attributes and qualifications

prior to enrollment to increase metrics for student success, making demographic data one of the

many predictive variables researched in collegiate retention (Crose, 2015; Dornan, 2015; Millea,

2018). Since a key aim of online programs is to increase access to educational opportunities to

underserved segments of the population (Miller, 2017; Vadell, 2016; Sunha et al., 2019), limiting

new students based on precursor characteristic aligned with success seems counterproductive.

20

Although there is some value in identifying at-risk students from the onset (Dornan, 2015;

Uddin, 2020), retention initiatives for the dissertation research were instead geared toward best

practices in post-matriculation approaches for the entire student body, not bolstering admissions

standards. Effective student-facing communication is one such avenue and retention endeavors

from a social perspective will thus be a key focus.

Spady (1971), who introduces the Undergraduate Dropout Process Model, is a seminal

author in the field of retention known for incorporating sociological aspects to his research. The

author examines admissions credentials, academic records, application responses, and semi-

unstructured interview answers from 683 first-year students from the College of the University

of Chicago. Multiple regression analysis is applied to nine variables on the dependent variable

of dropout decision to include the participant’s prior education, collegiate potential, normative

congruence, cognitive development, friendship support, social integration, grades, level of

satisfaction, and institutional commitment. Expectedly, Spady’s (1971) findings corroborate

social integration and engagement as vital indicators for commitment. Surprisingly however,

intellectual growth was only marginally influenced by the variable of past academic

performance. This result was significant as it refutes earlier notions of admissions criteria

relating to cognitive abilities as a primary determinant.

Of greater significance to retention in the findings of Spady (1971) is a student

positioning toward program material, opportunities for contacts with college staff, and

extracurricular involvement stimulating critical thought. In fact, social integration for men, with

a beta weight of .282, came second only to Grade Point Average (GPA), with a beta weight of

.306, in significance to learner satisfaction. For females, social integration is even higher and

ranks first with a beta weight of .332 and, for both genders, has a greater impact when coming

21

from university-to-student interactions than from the student’s peer network. The impact of

student focused communication is evident, and the study helps organize the importance of

university-to-student interaction on retention and corroborates later research emphasizing social

engagement as a core function of advisors (Joslin, 2018; Mu & Fosnacht, 2019; Smith & Allen,

2006). However, the relative technology of Spady’s era has been dynamically altered with the

introduction of digital options, which can impact communication (Junco et al., 2016). Computer

Mediated Communication (CMC) and online learning will be discussed later but the broader

findings on social engagement are key components of successful advisor strategies in higher

education.

Social Integration

In contrast to Spady (1971) who explores both academic and social reasons for attrition

from surveying student participants, Tinto (1975) reviews over 100 studies in the field of student

retention and created the Institutional Departure Model. Tinto focused further on the social

reasons found in the literature, specifically the university-to-student relationship and sought to

understand student attrition through the lens of psychology and economic underpinnings such as

Durkheim’s (1961) theory on suicide and cost-benefit analysis in personal decision-making.

Tinto (1975) predicts insufficient amounts of moral and social integration that result in choosing

to leave a social system can be applied to collegiate withdraw. Academic integration is also

introduced, and the study attempts to distinguish between voluntary dropouts as many students

may go on to transfer to another institution, thus terminating their enrollment only temporarily.

The variables of transfer and temporary drop have been noted as a chief complication in

measuring retention nationwide (Hagedorn, 2006; Li & Kennedy, 2018; NCES, 2020).

Traditional variables such as family background, previous achievement, and commitment levels

22

are also present, but the Institutional Departure Model goes further by incorporating external

variables such as job demand which could lead to such temporary withdraws. For instance,

social and academic integration could be high, but a learner may still choose to leave based on

their cost-benefit perception of a high paying job offer prior to graduation. According to Tinto

(1975), the nature of individual drops is the result of interplay between a student’s commitment

to his or her academic goal and to the college itself.

Tinto (1975) notes how the social system for student engagement consists of the student’s

peer network and university staff but the latter has been found to have a greater impact on

retention since it increases both social and academic integration (Mu & Fosnacht, 2019;

Vreeland & Bidwell, 1966). More recent studies in the field of higher education tie in these

earlier social aspects to further scholarly dialogue on lowering attrition. For instance, Kerby

(2015) introduces a new model for voluntary dropout decisions that parallels the work of Tinto

(1975) and Spady (1971). The instrument is similar in student characteristics and emphasizes the

socialization process but also adds external factors such as the national climate and

contemporary education system characteristics. The author notes various reasons for temporary

drop or transfer unrelated to academic performance and calls for colleges creating resiliency in

the student body to better enable adaptation to changing external influences.

Retention from a Business Perspective

Bean (1980) introduces the Student Attrition Model, a novel theory of retention in which

organizational performance and behavior concepts are applied to institutions of higher education.

The author considers the same variables for workforce turnover analogous with student

persistence. In other words, employees quit their jobs for similar reasons that learners drop out

of their degree programs. Bean tailors an existing survey model from Price (1977), who

23

considers membership changes in social systems and workforce satisfaction. The variable of

turnover equates to Bean’s (1980) dependent variable of student attrition and other explanatory

variables are altered to fit the HEI setting. For example, factors such as salary of an employee

are modified in the survey instrument to comparable extrinsic rewards such as grade point

average for a student in higher education. Independent variables in the Student Attrition Model

are similar to preemployment demographics taken to predict employment retention; background

information on each student comprises pre-matriculation characteristics such as socioeconomic

status, hometown size, and high school performance. Organizational determinants include data

on university communication, faculty interaction, major areas of study, campus housing, advisor

relationship, and job opportunities. These factors are expected to influence the intervening

variables of satisfaction and institutional commitment, which in turn drive retention (Bean,

1980).

Bean (1980) distributes a 107-item questionnaire to freshman students enrolled in one

university’s composition program, and the study’s 66 percent return rate yielded 1,195 surveys.

Results are limited to students who are full-time status and first-time (non-transfer) students and

their responses are compared with the following term’s fall registration data to associate with the

research variable of retention. Statistical analysis was conducted to include path analysis and

multiple regression which demonstrate the comparative effect of each independent variable.

Institutional commitment in Bean’s (1980) research are found to be a primary predictive metric

which reinforces the earlier introduced theories of the Undergraduate Dropout Process Model

(Spady, 1970) and the Institutional Departure Model (Tinto, 1975). Other findings supported

student satisfaction as a more important factor for women than men and institutional quality and

opportunity as key variables for both genders.

24

Ackerman and Schibrowsky (2008) build upon the findings of Bean (1980) by

incorporating Customer Relationship Management (CRM) philosophies into HEIs. Their work

considers retention in higher education from a corporate perspective and implements tenets of

organizational performance, such as customer satisfaction and repeat business, in practical

application for a university. Commitment to an institution or business is a primary focus of

CRM intentions which sustain clients through engagement and communication (Azhakarraja,

2020; Calma & Dickson-Deane, 2020; Suntornpithug, 2012). Ackerman and Schibrowsky

(2008) introduce economic validation for universities to lower attrition in their strategic business

plan published in the Journal of College Student Retention. Their cost-benefit analysis of long-

term learner persistence leads to a call for CRM approaches to aid universities in learner

engagement, commitment, and satisfaction. The authors draw parallels between preserving

customers and maintaining students, accentuating the need to bond and shape long-term,

meaningful connections. Their compilation of existing research point to student and customer

approval ratings both being positively affected by social engagement.

Social contacts are highlighted as those extending beyond just the classroom. For

instance, conversations can occur within the environments of dorm rooms, sidewalks, dining

halls, or sporting events which may be difficult to reproduce in an online setting where all

contacts are mediated by technology (Aldosemani et al., 2016; Davidovitch & Belichenko, 2018;

Gilardi & Guglielmetti, 2011). Although retaining students has many more unique

considerations than CRM for retention of business customers, many of the same best practices

for communication are still applicable. Relationship building, responsiveness, and social

involvement contribute to both workplace and university retention; this is particularly true for

25

new students in their first year and first-generation students (Britto & Rush, 2013; Jensen, 2017;

Ware & Ramos, 2013).

For the purposes of the current research study, the relationship management framework

of best practices is paramount in understanding the purpose behind advisor, counselor, or mentor

driven interaction with the student body. Calma and Dickson-Deane (2020) note that student

satisfaction surveys cannot drive HEI initiatives in terms of quality management; however, the

authors do note applicability in some collegiate aims, particularly student communication. This

positions CRM as a guiding theoretical concept for the study and CMC as a key consideration

since digital avenues can alter the nature of communication, both of which will be discussed in

the following sections.

Customer Relationship Management (CRM)

The underpinning theories of the current research included CRM through SMS text

messaging to enhance positive student outcomes and adaptive leadership for considering

incorporation of this communication technology. Adaptive leadership will be discussed later to

frame the importance of recognizing employee perspectives during change which is a necessity

for CRM through CMC application (Donaldson, 2020). The CRM business approach integrates

people, process, and technology in a manner aimed to enhance company-to-customer interaction

for long-term social ties, satisfaction, and brand loyalty (Calma & Dickson-Deane, 2020;

Suntornpithug, 2012). As delineated in the key retention studies in the historical background,

Bean’s Student Attrition Model (1980) spurred the work of Ackerman and Schibrowsky (2008)

who build upon higher education exploration from a business perspective. Company-to-

customer considerations are transferred to the university-to-student scenario which takes

business management theory to application in the scholastic realm. CRM theory for HEIs

26

emphasizes intimate communication and building long-term relationships to improve retention

outcomes (Ackerman & Schibrowsky, 2008). School pride, persistence, and student satisfaction

are typically metrics collegiate providers strive toward which translates CRM business objectives

to HEI performance aims (Vianden & Barlow, 2015).

CRM promotes retention through knowing the customer, rapport building, and ongoing

dialogue, particularly for companies in which the product requires a significant investment or

ongoing sale, such as a collegiate level credential (Azhakarraja, 2020; Niven, 2012). Belonging,

connectedness, involvement, and responsiveness are critical components of social exchange for

the longevity of employees in a corporate setting (Weidlich & Bastiaens, 2018). Researchers in

higher education have also cited individualized staff-to-student communication as essential for

university key performance indicators such as retention rates, particularly for new students

(Britto & Rush, 2013). Dorman (2015) finds communication of resources and onboarding

emphasized in qualitative responses from HEI administrators as essential for at-risk freshman

students. Suvedi, Ghimire, Millenbah, and Shrestha (2015) also find students benefit from

advisor communication and higher approval ratings among first year students with more

interaction. The authors analyze survey data from 4,875 undergraduate students from Michigan

State University. On a five-point Likert scale, students reacted positively (4.14 ± 0.96) to

advising communication and, when segmented by grade level, freshmen had slightly more

positive perceptions than seniors on interaction variables such as my “advisor gives me accurate

information about degree requirements (p ˂ 0.01) and my major advisor helps me with academic

problems (p ˂ 0.01)” (Suvedi et al., 2015, p. 230).

The application of CRM in converting a business to being customer centric or a

university to being student centric are supported as beneficial for satisfaction and retention rates

27

as well (Hrnjic, 2016). Retention improves from individualized communication on skills related

to managing time, self-advocacy, and study tactics from a designated coach (Bettinger & Baker,

2014). Coaching and mentorship may come from a variety of HEI professionals in the student

experience, but the current research focuses on long-term assignments better positioned to

employ CRM methods. Troxel (2018) notes how advising is in essence an academic relationship

and an advisor’s position, by design, should influence student decision-making throughout the

student lifecycle to include the decision to persist in their respective program. Managing this

relationship through student-centric communication spurs the intended study and the selection of

CRM as the underpinning theoretical construct.

Selection of Customer Relationship Management

CRM was selected for the current research over existing theories in academic advising as

the approach to student communication should not be dictated by the medium, but rather the

medium (SMS texting) should be dictated by student-advisor needs, characteristics, and

circumstance. Not all communication may be appropriate through SMS messaging and different

styles of advising will use student SMS messages in a different manner. While staff members

may ascribe to varying models and theories of advising, the current study’s primary researcher

did not want perceptions on text messaging to be influenced by the expected nature of

communication. Initiating dialogue or replying to a student can take on many forms. For

instance, one respondent might only use what Crookston (1972) refers to as prescriptive advising

in SMS texts that direct students to book time with other departments or instruct them to sign up

for math tutoring. Another might use Walsh’s (1979) developmental approach by asking open

ended questions via text to younger students to get them to share thoughts on how their

programmatic requirements will aid in future professional aspirations. Some advisors may

28

follow Glennen’s (1976) intrusive advising model for texting to target uncommunicative or at-

risk students who are increasingly absent or reticent to ask questions. Some advisors may use

only supplemental text messages to reinforce other forms of communication such as applying

nudge theory to encourage meeting deadlines and recognizing progress that may have already

been expressed in the student portal or through email (Castleman & Page, 2016; Smith et al.

2018).

All of these methodologies could have merit and the style is of lesser interest in the

current research than the advising function served through texting. Correspondingly, a CRM

model was selected to enable differentiated approaches in CMC and relationship building that

can allow for a variety of theoretical advising practices. Shani and Chalasani (1992) note the

model as a cohesive strategy to build a network with each customer and to constantly improve

the relationship for mutual benefit of the company and consumer, through prolonged contacts

that are interactive, personalized, and add value. From the perspective of higher education,

positive student outcomes are certainly beneficial for both the university in terms of graduation

rates and the student in terms of obtaining a credential or employment qualifications.

Aside from the long-term, interactive, and value-added communication emphasized in the

definition above, the concept of an integrated effort to customer communication is also

accentuated. CRM allows for a more comprehensive view of how academic advisors serve to

give and receive information between the university, including all its functional departments, and

the student to manage the relationship holistically (Azhakarraja, 2020; Hrnjic, 2016;

Suntornpithug, 2012). The exchange is depicted in the research specific graphic below (Figure

1) and examples are given to demonstrate how advisors, mentors, and counselors might

incorporate communication for varying departments based on a number of previous studies.

29

Existing literature identifies functional department connections and effective advising practices

(Blessinger & Wankel, 2013; Britto & Rush, 2013; NACADA, 2017; O’Halloran, 2019; RNL,

2019) as well as studies that emphasize academic advisors as the key relationship connecting

these resources to the student (Ackerman & Schibrowsky, 2008; Aldosemani et al., 2016;

Brown; 2017; Troxel, 2018; Vianden & Barlow, 2015). The connection depicted below (Figure

2) draws from these studies and is designed to visually represent departmental components and

university resources, exemplify the associated communication, and emphasize the central role of

student-to-advisor communication in connection.

Figure 1

Student to Institution Connection

Regardless of the communication medium, advisors can uniquely customize their

message to improve student understanding and enthusiasm based on their in-depth knowledge of

that specific student (Vadell, 2016; Vianden & Barlow, 2015). CRM emphasizes getting the

right message in the right channel to the right person at the right time (Suntornpithug, 2012).

30

The long-term relationship and rapport academic advisors, counselors, and mentors have with

their students make them better suited to determine what information should be sent, to whom,

how, and when (Brown, 2017; Kleeman, 2005; Schwebel, 2012; Vianden & Barlow, 2015). The

black arrows running from both right to left and left to right (Figure 1) also represent the two-

way flow of information between the student and HEI. In essence the advisor speaks both

languages, translating the intimidating or foreign concepts of university policy and procedure

into something understandable that resonates with the student. Correspondingly, their personal

knowledge of the student also enables them to take unorganized student comments and concerns

and code them into strategic action items for the university to best reach that individual

(Almisad, 2015; Troxel; 2018). Strategic messaging to manage the student-to-university

relationship is further expanded in the following section which incorporates the relevant

literature substantiating academic advisors, mentors, and counselors as best positioned to

promote CRM tenets through CMC platforms.

Academic Staff Population

To reiterate from Chapter One, the target population for the current study did not

distinguish between primary-role advisors and other positions with overlapping responsibilities

and instead focused on the long-term relationships through which SMS texting might serve

communication objectives. While some transactional functions of student interaction, such as

course registration, may be limited specifically to academic advisors, the more developmental

functions, such as goal setting and overcoming education barriers, are responsibilities shared

with faculty advisors, program or degree mentors, and student services counselors (Troxel,

2018). The National Academic Advising Association recognizes the community of HEI

professionals with outcomes-based aims in advising to include faculty and administrators as well

31

(NACADA, 2017). Although customized, personal guidance from the university-to-to student is

desirable from any department, individuals with a holistic understanding of the student are better

situated for tailored guidance (Vadell, 2016; Vianden & Barlow, 2015). Accordingly, full-time

faculty, with roster turnover every term, were excluded for the purposes of the current research

but faculty mentors, who work with students for a longer duration, are included. The operational

definition of academic staff thus encompasses all academic, program, and degree advisors,

mentors, and counselors who have long-term communication suited to build relationships,

discuss a broad range of university themes, and promote positive student outcomes (Brown,

2017; NACADA, 2017; Troxel, 2018).

Unlike student services professionals, who may not work with every student, or faculty

and, as noted, may only have students for a term, advisors may be uniquely positioned to

establish the strongest university-to-student tie because of their prolonged contact. Vianden and

Barlow (2015) emphasize this concept and note advisors are relationship managers; their strong

interpersonal bonds can lead to student self-efficacy, emotional pride/commitment, loyalty, and

persistence. The authors administer an online survey incorporating the Student University

Loyalty Instrument (SULI) to undergraduates enrolled at three public universities to obtain a

random sample of 1,207 responses. Spearman’s rho correlation analysis is applied to student

perceived quality of academic advising and 11 SULI subsets. The strongest relationship (r =

0.78, p ≤ .001) was found between positive advising experiences and the quality of student

services. Other statistically significant associations are found between quality of academic

advising and loyalty (r = 0.31, p ≤ .001), student satisfaction (r = 0.19, p ≤ .001), and

interestingly the relationship is higher for quality of staff (r = 0.41, p ≤ .001) than quality of

instructors (r = 0.35, p ≤ .001). The research delineates how staff, such as institutional advisors,

32

can break down boundaries between learners and university authority to reduce confusing or

intimidating policies. The authors do not specifically address the medium of communication for

advising staff but do note how short CRM exchange can be applied. For instance, they state

“keeping track of students’ out-of-class engagement and sending simple messages inquiring

about students’ wellbeing (e.g., ‘How did that career fair go?’ ‘How is your grandmother doing

after her surgery?’) ties students more strongly to the advisor and the institution” (Vianden &

Barlow, 2015, p. 23).

Considerations for Advising Communication

Proactive Outreach

The short snippets of communication recommended in Vianden and Barlow’s (2015)

research above represent preemptive interest in a student’s personal, professional, and academic

pursuits. Cox-Davenport (2017) note how even a five minute “check-in” on a routine basis can

help keep students on track. Such proactive communication has been highlighted as a critical

field of retention focused efforts (NACADA, 2017; Oregon et al., 2018; Uddin, 2020). Earl

(1988) incorporates earlier proactive advising theories such as Tinto (1975) and uniquely

distinguishes the concept of intrusive advising as a purposeful and structured method of

proactive intervention. His definition recognizes the immediacy necessary in identifying

academic risk indicators and the impact of advisor communication in action-oriented outreach.

The concept of motivating students to seek help has also been explored as a correlate

with student outcomes. For example, Schwebel, Walburn, Klyce, and Jerrolds (2012) look at

student success, to include retention, resulting from varying levels of intrusive advising. The

research follows two groups of students over a four-year, undergraduate program, one group

received only traditional advising and university announcements. The other set of students were

33

preemptively contacted by their advisor. Participants who received intrusive advising did in fact

demonstrated higher levels of retention (60%), but it was not considered statistically significant

compared to the control group retention rate (55%).

Uddin (2020) classifies intrusive advising as a best practice for at risk students, noting

how the individuals most at risk for dropping out are also the least likely to reach out. The

author surveys 32 academic advisors nationwide in the field of Engineering Technology on open

ended questions to obtain participant’s views on advising models, new and at-risk learner

strategies, and recommended approaches for promoting retention. The advising model

predominantly adopted among respondents is intrusive/proactive advising which refutes

alternative findings emphasizing developmental advising (Brown, 2017; Leach & Wang, 2015;

Mu & Fosnacht, 2019). The author notes the small sample size (N=32) as a clear limitation of

the study, but another potential issue not identified in the research could be the wording of the

question pertaining to advising model selection as the survey instrument states to “describe the

model or method of advising (such as appreciative, intrusive) you use” (Uddin, 2020, p. 13). By

providing limited options, the survey question could influence selection based on the provided

information. To increase validity in the survey instrument, Uddin (2020) could have improved

this question by either providing no examples or giving participants a more comprehensive list of

techniques (Leedy & Ormrod, 2016). Because of this, prescriptive, developmental, coaching,

and learning-centered were all introduced as advising methods grouped in “other” but may have

been underreported (Uddin, 2020). Respondents from the research also reiterated getting to

know the student on a personal level regardless of the advising style, particularly during the

transition to college and discussing a broad range of topics from career exploration to study

tactics.

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Meeting Student Expectations

Knowing each student has been supported as essential, particularly when using

developmental and intrusive forms of advising (Lema & Agrusa, 2019). Despite conflicting

views on advising practices, there is a great deal of overlap between communication approaches

as one model may be used to support another overarching method or an advisor might change

methods depending on the student’s needs and preferences (Leach & Wang, 2015; Thomas,

2020). Hale, Graham, and Johnson (2009) survey students for their level of satisfaction with

academic advising between prescriptive and developmental approaches. Nearly all students

(95%) opted for developmental advising but, with regard to expectations, participants who

experienced congruence between the preferred and experienced styles demonstrated a

significantly higher satisfaction level (M = 3.20) than students who experienced incongruence

between their desired and the experienced style of academic advising (M = 2.52).

The Hale, Graham, and Johnson’s (2009) research relating to advising styles and student

satisfaction are additionally supported with the findings of Brown (2017) who deals with

perceptual differences between student and advisor practices. The author notes how variables,

like contact frequency, can negatively impact retention when student expectations are drastically

different than actual advisor communication practices. Brown addresses how advising in online

programs can be the strongest tie in connecting a remote student to the college or university.

Accordingly, learning what students prefer and seek out in online options can help institutions

both grow and maintain their student body. Brown (2017) presents the Winston and Sandor’s

Academic Advising Inventory (1984) to participants from a large, non-profit university to gather

quantitative data from both students and advisors. Survey questions relate to the regularity and

nature of student appointments and include variables to include the student’s preference for the

35

type of contact (phone, email, virtual meeting, etc.) or the advisor’s approach to discussing non-

class related opportunities, for example. The outcomes reinforce developmental advising in a

one-on-one setting and the researcher notes the importance of shared expectations in determining

mentoring communication. The author’s findings are validated with Whitsett, Lynn-Suell, and

Ratchford’s (2014) study who similarly apply the Winston Sandor (1984) model to highlight

how institutional practices need to align with student needs, preferences, and expectations.

Recommendations from the Academic Advising Inventory on nine faculty and 72 student

participants from University of Montevallo corroborate the concept of meeting student

preferences for more balance in academic and personal advising (Whitsett et al., 2014).

Contact Purpose and Frequency

Jensen (2017) researches mentoring at California State University using a mixed methods

approach and emphasizes how mentors provide personalized advice regarding barriers,

community, and available resources to help students on their transformational journey. The

exploratory study provides descriptive statistics for three primary questions pertaining to what

components of the mentoring program are used most frequently, what factors contribute to use,

and how technology impacts the program. Self-care, time management, emotional support, and

balancing personal/professional goals are found to be the predominant student needs among all

grouped demographics which segmented students by age, geographic setting, proximity to

campus, status as first-generation or not, and degree level. Some differences are revealed

between the divisions as well. For instance, rural students reported a higher need for help with

communication skills (64%) and met with their mentor more frequently (55% monthly) than

their urban counterparts (80% twice a semester or less).

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Since the communication frequency in Jensen’s (2017) research is not a variable directly

tied to academic success, there is no way of telling if the rural students higher meeting frequency

is based on academic struggles or on rural students merely desiring a greater degree of

communication than urban students. Responsible students might be more proactive in asking

questions and scheduling conversations with institutional staff, thus representing a difference

based on preference instead of need (Brown, 2017; Uddin, 2020). Other differences in contact

frequency are identified to include younger students (less than 40) as having fewer appointments

and, although email is reportedly preferred, the qualitative findings indicate this CMC platform

is associated with a burdensome and formal task. Focus groups support face-to-face meetings as

preferential for more meaningful contacts but SMS text messaging and phone calls as less

onerous in coordination of appointments, quick questions, and receiving accolades (Jensen,

2017). The communication technology facilitating advising will be discussed later but

increasing demand has been observable in the literature for students to have more mobile device

access to HEI professionals (Cameron & Pagnattaro, 2017; Cretu et al., 2020; Mirriahi &

Alonzo, 2015).

The frequency of meeting with an advisor has also been associated with academic

outcomes. Schwebel et al. (2012) present a randomized longitudinal study and discovered higher

retention rate (60%) in student groups when advising outreach is applied in comparison to the

control group (55%). More statistically significant findings are provided by Mu and Fosnacht

(2019) who survey 26,516 undergraduates nationwide from 156 degree granting colleges and

universities and find frequency of advisor meetings as a significant positive correlate with self-

reported gains and grades. Quantitative findings indicate that each advisor meeting was

37

associated with a standard deviation increase of 0.04 for self-reported gains and 0.01 increase in

grades.

Promoting Engagement

Similar to communication, cognitive engagement is also supported in the literature as a

predictive variable for retention and another broad aim of the advisor-to-student relationship

(Gilardi & Guglielmetti, 2011; Redmond et al., 2018; RNL, 2019). Crose (2015) discusses three

types of engagement (academic, cognitive, and faculty/peer interactions) and applies multiple

regression analysis to address which form of engagement is most predictive of withdraw and

student anticipated final grade for first year students in an online program. The author does not

find a statistically significant correlation for any of the variables with regard to overall student

success of the entire sample population; however, cognitive and peer/faculty engagement are

statistically significant predictors for retention in the sample’s population of non-traditional

students. Engagement could include time in the online learning platform, listening to online

lectures/webinars, or exchanges with other students in the class, yet the most predictive type is

found to be communication with institutional employees. Accordingly, Crose (2015)

recommends increasing opportunities for university-to-student communication to improve

performance outcomes but this can be difficult for online students working asynchronously.

One potential avenue to improving student outcomes like engagement is through

comprehensive academic advising. Joslin (2018) cites departmental silos and disjointed

initiatives as a serious threat to effective efforts in advising management and calls for focus as

campuses frequently have vastly different approaches to mentorship. The author associates the

positive links between advising practices and university goals such as retention, engagement, and

learning outcomes. One of the nine conditions reviewed from the National Academic Advising

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Association’s comprehensive initiative includes technology-enabled advising (NACADA, 2017).

Localized and arbitrary communication protocols can make CMC exchange with students look

vastly different between colleges or even teams within the same college (Joslin, 2018). This

makes the comparison of CMC practices between advisors of different institutions and program

types (online or traditional) a central them in the dissertation study.

Non-Traditional Students

The internet revolutionized collegiate education opportunities and the rapidly growing

number of online programs has increased access to obtaining a college degree, particularly for

non-traditional learners, but retention rates have failed to keep pace (Argüello & Méndez, 2019;

Miller, 2017; Vadell, 2016). Gilardi and Guglielmetti (2011) incorporate university-to-student

relationships by exploring student perceptions of quality in their first year and their retention but

expand on non-traditional learners. The authors note there is no universally accepted definition

but do describe these students as atypical or potentially impeded by variables uncommon to the

traditional student. For instance, this could include special considerations such as attending

remotely, part-time, or being a transfer student, in addition to personal circumstance such as

being in the military, a full-time worker, geographically distanced, a parent, or older than the

typical high school graduate entering college.

For the purposes of this study, the various non-traditional indicators resulting in a student

needing or preferring an online program are acknowledged; however, only the status of the

programmatic format (online or traditional learning environment) will be identified since the

academic staff, not student population, is being surveyed. If the majority of an advisor, mentor,

or counselor’s students are enrolled in internet-based, distance, remote, online, or asynchronous

web-based courses, they are referred to as online advisors and their students are typically limited

39

to CMC (Vadell, 2016; Weidlich & Bastiaens, 2018). This distinction is made to explore if there

is a difference in advisor use, motives, and perceptions regarding SMS text messaging between

learning environments. While email and discussion posts within the curriculum offer

asynchronous CMC exchange opportunities, SMS messaging may provide additional benefits in

some circumstances that call for speed of open rate (Naismith, 2007) or short, informal messages

(Cameron & Pagnattaro, 2017; Cretu et al., 2020). Since this programmatic difference can

drastically impact a student’s experience, it is critical to consider the advent of online learning

and some of the challenges to communication and retention found in the literature associated

with this population of non-traditional learners.

Online Learning

Addressing retention and other desirable student outcomes discussed in the literature is a

complex problem and made considerably more challenging with remote programs (Argüello &

Méndez, 2019; Vadell, 2016). Electronic-based education, or eLearning, grew exponentially

because of the freedom and flexibility online education afforded individuals in obtaining their

degree (Elcicek et al., 2018). Online programs are now accessible from tablet and cell phone

applications, making on-the-go learning opportunities more accessible and result in students

learning at all times of the day (Schmidt-Hanbidge et al., 2018). The dynamics of online

learning, generational communication differences, and varying levels of digital proficiency in the

student body could make CMC channels, such as SMS texting, an asset or liability. Online

education will be discussed first, but age and digital proficiency will be explored in the following

section on student communication considerations as these factors can also change how students

and HEI professionals engage.

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Nolan (2013) follows a small cohort of students (N = 56) at the Community College of

Vermont after they elect online as their primary programmatic format. The author notes how

measures such as phone calls and emails are being constantly upgraded to live chat and virtual

meetings. Nolan’s small pilot study consists of 56 participants of the total 155 online student in

the college’s fall 2011 cohort. The study reveals retention of online students to be roughly ten

percent lower than peers in traditional courses. Student surveys overwhelmingly indicate the

importance of advisor interaction, particularly for the onboarding of new students. Respondents

from the sample population also demonstrate a strong desire for an advisor who would stay with

them throughout their college experience. Surprisingly, only half (56%) of four-year public

universities practice specific advisor assignments and this protocol is more infrequent (31.8%) in

community colleges (Donaldson et al., 2020). Maintaining prolonged and rapport building

communication is emphasized within CRM approaches to student advising (Azhakarraja, 2020;

Calma & Dickson-Deane, 2020). These relationships enable customized advising based on

knowing the students and their needs which can be drastically altered based on both student

dynamics and programmatic format (Ng, 2018; Lema & Agrusa, 2019; Roache, et al., 2020).

Online Program Concerns

One of the primary concerns for an online program format relates to a sense of disconnect

between the student and university which, by design, distance advising should mitigate through

relationship management (Argüello & Méndez, 2019). The concept of isolation can be examined

by the degree of severity, or the level of what Moore (1992) coined as transactional distance.

Moore’s theory involves distance as a function of three features; the first two (course structure

and leaner autonomy) are typical established by faculty and students while the third (dialogue)

allows for advisor manipulation (Huang et al., 2016). Weidlich and Bastiaens (2018) refer to

41

transactional distance as “the perception of psychological distance between the student and his

peers, his instructor/teacher, and the learning content” (2018, p. 222). The authors note how

student satisfaction is tied to retention yet measures of satisfaction are limited by the technology

mediating connectedness to other students, faculty, and material, since exchanges occur through

the internet. The authors suggest the observed transactional distance between student and

learning modalities, from the student’s perspective, is established by technological learner

proficiency and platform functionality. Multiple linear regression is conducted on survey results

from 141 students enrolled in online education courses. Although the results do not aid in

understanding learning outcomes, they do support effective technologies to reduce perceived

transactional distance as a relevant means in increasing student satisfaction.

The findings of Weidlich and Bastiaens (2018) are further expanded with the research of

Oregon et al. (2018) because, not only must HEIs decrease isolation, they must also consider the

most appropriate application of technologies in doing so. As dialogue increases, transactional

distance for the student decreases; digital tools can enhance options for advisor-to-student

communication to increase that dialogue (Huang et al., 2016). Inaccessibility to such discourse

exacerbates the issue of isolation and generic, impersonal, and slow communication are cited as

chief reasons for dissatisfaction within the online setting (Gravel, 2012; Yusoff et al., 2015).

Many students have dropped from their program, reporting feelings of isolation and insufficient

feedback as their primary complaint (Oregon et al., 2018). Technology can break down barriers

of time and place which are common in online programs and thus alleviate such concerns either

directly or indirectly as a facilitator in coordinating more traditional forms of communication

(Argüello & Méndez, 2019). Exploring CMC options to increase dialogue is important from

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both the student and advisor side of messaging to aid in congruent expectations (Almisad, 2015;

Duran et al., 2005; Taylor et al., 2011).

Unique feedback from virtual advising is another concern since form template emails,

copy/paste content, and generic responses are prevalent, all of which further exacerbate the issue

of interest and engagement for online students (Bikanga-Ada et al., 2017). Non-traditional

learners have reported HEI personnel in online settings as difficult to work with and university

support services as “indifferent and unresponsive” (Miller, 2017, p. 113). Timely and unique

responses from an advisor can be a concern for online advising environments when, collectively,

students are working every day of the week and at all hours (Schmidt-Hanbidge et al., 2018).

Building meaningful ties and maintaining satisfied students is undoubtedly a more

significant challenge in online settings where all communication is mediated by the technology

through which it is delivered (Ng, 2018; Oregon et al., 2018). Ross, Crittenden, and Peterson

(2019) introduce a mixed methods approach to exploring student satisfaction with various

learning management systems and student preferences for feedback. The authors cite speed in

response times as one of the most essential aspects for younger generations and several

qualitative responses provide complaints about access to the university. One student noted the

frustrations in turnaround time via email which wasted multiple days merely asking a question

and waiting for clarification before an assignment could even be started (Ross et al., 2019).

Timely, individualized, and rapport building communication are all CRM tenets that can

factor into how a student perceives their institution. Ali, Uppal, and Gulliver, (2018) note how

failing to act in accordance with communication demands can negatively impact a student’s

opinion of the school administration. Gaytan (2015) recommends increased awareness that

delivering and catering to online programs must be accompanied by the same, if not a greater,

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degree of interaction to ensure students are receiving the same level of consideration and

personalized communication as their face-to-face counterparts. Effective application of CRM to

promote student outcomes hinges on awareness of issues in online education; altering

communication approaches to best manage the relationship must acknowledge how technology

alters social interactions (Azhakarraja, 2020; Castleman & Meyer, 2020; Ghemawat, 2017; Mu

& Fosnacht, 2019; Suntornpithug, 2012).

Student Communication Preferences

The manner in which society transmits messages is in large part dependent on personal

attributes and, while everyone is different, there are emerging themes in the literature relevant to

how and why SMS texting might be used by either party in the student/advisor relationship. One

example comes from Swanson, Renes, and Strange (2020) who explore preferences from the

student side in communicating both in general for unacademic related dialogue and with their

respective collegiate institution regarding academic matters. Participants ranged in age and

gender and universities differed in proprietary status and program delivery so there was a robust

level of representation within the study. Internet based surveys for 1,986 college students

indicated a preference for in-person exchange as the dominant method for non-academic

communication and email as the preferred option for academic communication. Although the

research merely presented preferences and cannot explain this difference, the fact students opted

for a CMC channel over face-to-face meetings for school related matters is noteworthy.

Previous studies claim some students, particularly younger students, may be more willing to

share when typing over speaking and the ability to respond asynchronously enables preparation

which can in turn reduce intimidation (Almisad, 2015; Cameron & Pagnattaro, 2017; Jensen,

2017). In the subsequent rankings specific to academic communication, texting on a cellular

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device ranked third in preferences, following only in-person and email options. This placed

SMS text messaging as the preferred method over all remaining technology platforms introduced

in the study’s options to include phone calls, social media, live chat, and video conference

(Swanson et al., 2020).

Although the authors do not specifically consider the preferences of college students in

relationship to their age, the findings do indicate a partiality to using the same options for

academic and non-academic communication among participants which might be attributed to

familiarity with technology (Swanson et al., 2020). Knowing what CMC avenues society

primarily uses in life may thus be indicative of what they prefer to use in an academic setting

because users are accustomed to the technology. Ease of use aligns with technology acceptance

models, which will be introduced later, and makes understanding the dynamics of today’s learner

important in determining if SMS texts are valuable for the advisor relationship (Granić &

Marangunić, 2019). It may subsequently be fitting to explore how age can contribute to digital

communication preferences through reviewing generational tendencies in CMC.

Generational Considerations

Millennials are significant technology consumers and digitally proficient; as true digital

natives, Generation Z is adept to communicating entirely online (Chicca & Shellenbarger, 2018).

This population of student, born roughly in the mid-1990s to 2010, are coming into the college

age range and increasingly prefer short, instant, and informal messaging (Cretu et al., 2020).

Younger students are on social media, text to maintain relationships, and expect immediate

reactions befitting of SMS messaging since responses tend to highlight salient details in a

succinct manner with a known contact (Almisad, 2015; Ware & Ramos, 2019). Cameron and

Pagnattaro (2017) emphasize how Gen Z students are often more comfortable sharing learning

45

complications through SMS texting rather than over the phone or through email. This could

drastically alter how the generation tends to learn and problem solve. Generation Z students

have been noted as less likely to need an orientation period with platforms but may have greater

expectations for engaging and timely responses from university staff (Samuels-Peretz et al.,

2017). The eight-second attention span of the Generation Z student should not necessarily be

viewed as a negative characteristic for learning; it enables them to sort through and assess large

quantities of information in a short time span (Cameron & Pagnattaro, 2017).

Despite the noteworthy characteristics and tendencies above, the concept of generational

differences is refuted by other authors in both the communication and technology fields.

Shepherd (2020) takes 244 online surveys based on the Felder-Soloman Index of Learning Styles

to identify differences between generational divisions. Findings for all research questions

exhibit no significant variance between preferences among the aged-based cohorts. Jauregui et

al. (2020) also criticize generational classifications as stereotyping. The authors denounce the

concept of the Millennial learner as it fails to acknowledge the unique attributes of individuals

and the inherent diversity among age groups. Lai and Hong (2015) extend this argument to

technology adoption. In their survey of 799 undergraduate and 81 postgraduate students,

findings contest the concept of respondent generation as a determinant for use of CMC

platforms. Despite the lack of differentiation between generations, the study did reveal a

significant amount of time spent on digital technologies among participants. This is also

supported by Mirriahi and Alonzo (2015) who survey 171 students ranging from first year

undergraduate to graduate students and found 97 percent of participants reported daily use of text

messaging and 64 percent responded they would like to use this platform more for educational

purposes.

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The contending views on generational differences make the philosophy a controversial

method for prescribing HEI communication approaches. Subsequently, the current research does

not condone limiting advisor dialogue to any particular platform based on the demographic of

age or generation. Nevertheless, and despite conflicting views on generational grouping, the

research pointing to increased digital proficiency and technology use of younger students still

has merit in terms of promoting connection and engagement through multiple forums and is

pervasive in the scholarly literature (Brett, 2011; Cameron & Pagnattaro, 2017; Chicca &

Shellenbarger, 2018; LaBowe, 2011; Newport, 2014; Samuels-Peretz et al., 2017; Thurlow,

2003). Universities must endeavor to meet learners where they are, in platforms they are

familiar with which requires awareness of changing societal norms. Engaging students through

SMS texting is seldom referenced for HEI communication, particularly through the lens of

advisor driven efforts (Arnold et al., 2020; Amador & Amador, 2017; Davidovitch &

Belichenko, 2018; Hrnjic, 2016; Page et al., 2020).

Computer Mediated Communication

CMC comes in many forms to include email, social networking, cellphone calls,

teleconferences, texting, online forums, and discussion boards to name a few but is generally

recognized as interface between individuals through computer or mobile devices (Bernhold &

Rice, 2020; Duran et al., 2005; Olaniran, 2003). Lessons in CRM can be applied when parallels

are drawn between customers in business with students in higher education; the era of students

who grew up with emerging digital communication are already accustomed to CMC

communication in social platforms (Juan-Jordán et al., 2018). As institutions are continuously

adapting to newer generations, HEIs are faced with many options to consider in adopting digital

communication to promote individualized and prompt feedback as a method of building

47

connectedness (Ross et al., 2019). While some students may prefer digital exchange, others may

be limited to it based on programmatic format. By virtue of possessing a variety of

communication channels at one’s disposal, students can selectively decide the best medium for

their communication objectives yet online students, because of their proximity to advisors, may

be limited to CMC platforms (Bernhold & Rice, 2020). Restricting opportunities for dialogue,

one of Moore’s (1992) three aforementioned variables for transactional distance, can in turn

inhibit social connection between the student and university (Huang et al., 2016).

Building meaningful ties and maintaining satisfied students can be a challenge through

CMC restricted avenues (Ng, 2018; Oregon et al., 2018). Digital mediation of messaging may

inhibit the intent of communication, particularly when the message sender or receiver lacks

technological proficiency in digital tools or when culture differences are at play (Olaniran, 2003;

Weidlich & Bastiaens, 2018). Bernhold and Rice (2020) demonstrate this in how online and

offline communication may differ in nonverbal cues like eye contact, gestures, or tone and thus

make the message more vulnerable to misunderstanding. The authors differentiate CMC

channels by their media richness. For instance, because of their visual and audio cues, face-to-

face mediums may be optimal; however, phone calls are considered to have a higher level of

media richness than email since they still contain audio cues whereas email is strictly limited to

verbiage.

In a survey of 299 adults in the United States, Dunaetz, Lisk, and Shin (2015) conduct

correlational and regression analyses on preferences for media richness which include the

demographic variable of age. The authors found no significant association between a preference

for media richness and age although the number of communication channels used was found to

decreases with age. This supports the earlier literature refuting generational classification for

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student preferences (Shepherd, 2020) but also reinforces the importance of providing multiple

avenues to communicate for younger students to augment connection (Brown, 2017; Lema &

Agrusa, 2019). Although not every message is appropriate for CMC mediums, technology can

enhance connection between individuals; it is meant to supplement and reinforce, not replace,

other forms of communication and is scalable based on recognition of student needs (Lema &

Agrusa, 2019). For the purposes of the current research, SMS text messaging was the specific

CMC modality explored within the range of CMC mediums.

Short Messaging Service Texting

SMS text messaging has the potential to provide a varied communication approach to

save the university time and money through efficient employee practices (Castleman & Page,

2016). Instead of two half hour phone appointments, an advisor might text a veteran student and

spend the entire hour with a new or struggling student, thus applying scalability in CMC

approaches to maximize advisor efforts (Lema & Argrusa; 2019). Texting may even be the

channel that makes a face-to-face meeting or phone call conversation more likely to occur (Junco

et al., 2016). While the same benefits might be accomplished with email, research underscores

the immediacy and higher open rate of text over email (Naismith, 2007). When compared to

other CMC options, Brett (2011) recognizes an implicit culture of immediacy among student

perceptions associated with SMS text messaging.

Instant messaging as a university provided medium may also be a student’s desired

format for interaction and existing literature highlights that students report a greater level of

satisfaction when they are given the opportunity to elect their own feedback method (Bikanga-

Ada et al., 2017). LaBowe (2011) collects 485 responses from undergraduate students in the

2010 fall semester and self-reported data on cellular phone behaviors and attitudes indicate age

49

as the strongest predictive variable in SMS text usage with younger students more likely to use it

as a platform for communicating. This research considers all student communication and not

specifically communication with contacts from their college or university. Although channel

selection may vary based on the intended goal of the message sender, Mirriahi and Alonzo

(2015) also find that 64 percent of 171 surveyed undergraduate and graduate students desired

text messaging as a platform for higher education purposes. Knowing what form of interaction

students prefer and catering to their preferences is important as student satisfaction has been

supported as a positive correlate on retention (Crose, 2015; Weidlich & Bastiaens, 2018).

Satisfaction and social connection are core objectives of CRM theory which sustain clients

through engagement and communication (Azhakarraja, 2020; Calma & Dickson-Deane, 2020;

Suntornpithug, 2012).

Text Based Communication Controversies

The application of SMS texting in higher education can be a divisive topic as some

employees believe it dilutes academic dialogue and others simply refuse to learn mobile text

application for student use (Naismith, 2007). Working with ground campus students, it might be

an advisor’s preference to opt for face-to-face meetings as much as possible. This approach is

grounded in what Short, Williams & Christie (1976) first label as social presence. The authors

evaluate attitudes regarding various communication channels by comparing how sociable,

sensitive, warm, and personal different options were perceived between traditional and computer

mediated mediums. The social presence theory contends that CMC avenues will exchange less

socioemotional content as typed messages are impersonal and lack auditory and visual cues

(Short et al., 1976). From this perspective, SMS texting seems at odds with CRM approaches

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since social presence is arguably limited through CMC, thus preventing strong interpersonal

relationships (Oztok & Brett, 2011).

Challenging the concepts of CMC limiting social presence, Walther (1996) first

introduces the concept of hyperpersonal communication. This theory disputes the limiting effect

of digital dialogue introduced by Short et al. (1976) and even goes a step further in arguing that

media channels are actually capable of surpassing face-to-face communication in developing

interpersonal relationships. Walther’s (1996) research provides multiple advantages for CMC to

include being selective and controlled in the composition of messages. The sender and receiver

are able to engage in more profound exchange because of the lagging response time. Text

messaging enables editing of content, allowing the sender to pause and mindfully construct

content (Walther, 1996). This supports the previously introduced research regarding the benefits

of SMS text or email as new students may be intimidated by communicating with HEI

professionals and prefer asynchronous communication to enable researched responses (Britto &

Rush, 2013; Dornan, 2015; Suvedi et al., 2015). The ability to share learning complications

through digital forums is also supported by Cameron and Pagnattaro (2017) who assert that

Generation Z students may feel less comfortable revealing concerns in a traditional setting.

Getting to know students on a personal level in order to cater to their needs is defining of CRM

and why texting is advocated as an option for communication (Azhakarraja, 2020; Hrnjic, 2016;

Suntornpithug, 2012).

Naismith (2007) follows a trial of StudyLink, an email service that converts university

emails to texts for student communication, at the University of Birmingham. Like the

dissertation research, communication does not focus on academic engagement in curriculum

dialogue but administrative in nature between students and academic staff for coordination and

51

information exchange. Focus groups were small and consisted of less than 30 students in

multiple cohorts, two program secretaries, and seven tutors. The author notes how text messages

are associated with taking-action and thus enable the sender to prompt a desired behavior.

Promoting action and influencing decisions enables advisors to improve student outcomes

aligned with their core objectives, such as improving retention (Troxel, 2018).

Other aspects of SMS text messaging can be both a help and a hinderance and require

careful consideration of the circumstance to be deemed appropriate. One such example is that of

message timing. SMS texting enables reallocation of attention in that individuals can

communicate asynchronously, returning to respond at their convenience and thus permit

discretionary reply times (Walther, 1996). While this benefit is also present with other forms of

CMC in higher education such as email (Druan et al., 2005, Smith et al., 2018), texting provides

the less formal and more immediate avenue that may be preferred (LaBowe, 2011; Naismith,

2007, Ross et al., 2019). This support’s Naismith’s (2007) findings from students participating

in texting trials who report satisfaction rates for content, speed, and accessibility of the channel

more favorably than email. However, the expectation of immediacy in texting can also be a

liability as students have identified it as a personal intrusion when sent at inappropriate times

(Brett, 2011). While all students are different, the customer centric communication practices of

CRM endorse knowing the individual’s preferences in the application of digital tools (Juan-

Jordán, 2018).

Adaptive Leadership

Knowing if, when, and how to use CMC to develop relationships requires adaptive and

flexible individuals, capable of recognizing changes in societal communication practices (Khan,

2017). Institutions have endeavored to incorporate newer technologies to be more attractive to

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potential students and improve efficiency, both of which can make them more competitive within

their sector (Skoumpopoulou et al., 2018). CRM through a variety of digital mediums is one

such call for technology adoption for HEIs to better manage student communication, to include

SMS texting (Juan-Jordán, 2018). The value of SMS text application will fluctuate between

different students and situations (Brett, 2011, Cameron & Pagnattaro, 2017). The need to modify

communication approaches thus positions adaptive leadership as a theoretical construct guiding

application of CRM as it emphasizes new strategies to thrive in emerging and unknown

environments (Heifetz, 1994; Heifetz et al., 2009). The digital transition facing HEI

professionals requires collaboration and adjustment to meet changing demands; adaptive leaders

are able to take existing theory and best practices and apply new approaches based on current

needs (Dopson et al., 2019).

The theory of adaptive leadership originates with Heifetz (1994) who distinguishes two

types of challenges; the first involves technical issues for which leaders possess a known solution

while the second, adaptive challenges, involve a disparity between values and circumstance.

These problems lack a known solution since they stem from environmental changes and thus

require learning, experimentation, and alterations in the attitudes and procedures of individuals

within an organization (Heifetz, 1994). Current trends in digital communication are drastically

altering the attitudes and protocols for exchange (Chicca & Shellenbarger, 2018; Ghemawat,

2017; Junco et al., 2016). CMC represents an adaptive challenge for HEIs and, with adaptive

leadership, the intent is not the application of authority towards a resolution. Heifetz et al.,

(2009) explain the intent behind this theory is instead to place the responsibility of problem

solving to the very individuals who need to learn and shift with the circumstance. Since CMC is

an emerging field in higher education, experimentation with channels for student engagement

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with the university through technology are abundant (Arnold et al., 2020; Aldosemani et al.,

2016; Amador & Amador, 2017; Blessinger & Wankel, 2013; Castleman & Meyer, 2020;

Davidovitch & Belichenko, 2018; Huang et al., 2016; Junco et al., 2016; Oregon et al., 2018;

Page et al., 2020; Roache et al., 2020; Weidlich & Bastiaens, 2018).

A common reason HEIs struggle in adopting the CRM orientation to student-centric

communication is a lack of managerial involvement in the strategy and promotion of practices

(Hrnjic, 2016). Resistance to change is another factor inhibiting technology initiatives and is

further hindered through a lack of senior management support (Granić & Marangunić, 2019;

Skoumpopoulou et al., 2018). Miller (2007) discusses personnel and professional development

of HEI professionals; she contends the philosophies and attitudes of collegiate employees can

alter student outcomes and university leaders need to promote persistence promoting behaviors.

For Khan (2017), the application of adaptive leadership to solve problems in higher education

means recognizing changes in societal communication patterns in the external environment and

considering how employee actions can benefit business outcomes. For SMS texting, as a

medium of CMC, to have a positive impact on student outcomes like retention, perspectives of

the intended users must be explored which spurred the current study.

Advisor Perceptions

Understanding differences between advisor attitudes and approaches can be a predictor of

student success for their caseload, leading to a need to understand perceptions on change, such as

the adoption of digital communication tools. This is important because to have advisor

acceptance, access, and training, new resources need to be formally adopted in strategy, not

merely used on an ad-hoc basis by a minority of employees through their own initiative or

personal device (Joslin, 2018). When administrations lack policy, advisors may avoid text

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messaging altogether or offer it from their private cell phone number which prevents leadership

from appropriately measuring scope of use or impact with any degree of accuracy. If CMC

avenues, such as SMS text, are not integrated into an institution’s structural approach to

relationship management, it is impossible to measure how, or even if, academic staff are using

the platform which prevents continuous evaluation of SMS exchange’s impact on student

outcomes (Joslin, 2018).

Based on perceptions of texting, such as ease of use, functionality, or institutional

support, some advisors may be less likely to incorporate this CMC channel (Granić &

Marangunić, 2019). Such attitudes toward communications technologies spurred the current

research as employee uses and motives should be considered prior to exploring alternative or

supplemental platforms (Skoumpopoulou et al., 2008). Understanding employee perceptions

could aid in institutional adoption of best practices but research on SMS texting in higher

education and academic staff use remains limited (Amador & Amador, 2017; Davidovitch &

Belichenko, 2018; IPEDS, 2020). Understanding employee perceptions and predominant uses of

SMS texting is a crucial first step in exploring communications technology acceptance and

change. Continuous quality improvement initiatives must be implemented and monitored with

an understanding of employee attitudes to gain commitment (Ahmad & Zhichao, 2018).

The primary reasons employees use SMS texting could impact their perceptions of the

platform. Exploring predominant reasons for not using SMS text could aid institutions in change

management training with regard to CMC practices and policies. Intervening variable trends

such as advisor age, gender, or work experience could also assist universities in understanding

advisor dynamics and how they may change the prevalence of use. Analysis of why academic

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advisors initiate texts could further alter the institutional selection of software based on the

necessary functionality or primary purposes in messaging.

Gaps in the Literature

Institutions of higher education need to continuously adapt to meet learners where they

are, in contemporary platforms they are familiar with, yet research on collegiate use of instant

messaging or SMS texting for communication remains limited (Amador & Amador, 2017;

Davidovitch & Belichenko, 2018; Page et al., 2020). Existing discourse on the prevalence of

digital proficiency among younger and more digitally experienced students is abundant (Brett,

2011; Cameron & Pagnattaro, 2017; Chicca & Shellenbarger, 2018; LaBowe, 2011; Newport,

2014; Samuels-Peretz et al., 2017; Thurlow, 2003). Scholarly literature relating to student

outcomes, such as retention, is also established (Dornan, 2015; Price, 1977; Spady, 1971; Tinto,

1975; Vadell, 2016). Engagement and student satisfaction have both been positively correlated

with persistence (Gilardi & Guglielmetti, 2011; Star & Collette, 2010). Communication impacts

student satisfaction and socially linking the university to the student body is largely established

by academic advising staff (Jensen, 2017; Vianden & Barlow, 2015; Yusoff et al., 2015).

Authors contributing to retention discourse advocate for colleges and universities to

adopt newer technologies (Arnold et al., 2020; Blessinger & Wankel, 2013; Kerby, 2015;

Manyanga et al., 2017). CRM philosophies are applied to higher education but mobile CRM,

such as relationship building through SMS text, is still an emerging field (Ackerman &

Schibrowsky, 2008; Suntornpithug, 2012; Page et al, 2020). In making a case for instant

messaging, authors substantiate the modality as beneficial for student success (Castleman &

Meyer, 2020; Santos et al., 2018; Brett, 2011). Yet, who is adopting the medium and their

perceived value of SMS texting remains unclear since texting can be on an ad hoc basis and does

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not constitute a commonly reported institutional characteristic amongst HEIs (IPEDS, 2020).

CRM through CMC requires adaptive leaders capable of experimenting with new technologies to

benefit organizational outcomes, but this is still an emerging field in higher education (Khan,

2017). Technology platforms supporting the university-to-student relationship have been

introduced but advisor perspectives in the literature are deficient for SMS texting as a CRM tool

(Duran et al., 2006; Jensen, 2017; Junco et al., 2016; Swanson et al., 2020).

Summary and Conclusions

The literature review has introduced a synthesis of scholarly literature presenting the

business problem of student retention for HEIs. Ackerman and Schibrowsky (2008) highlight

the negative financial consequences of student withdrawal prior to degree attainment. Historical

approaches to mitigating attrition were introduced from early authors (Bean, 1980; Spady, 1971;

Tinto, 1975) to those incorporating newer technologies to enhance student engagement (Amador

& Amador, 2017; Castleman & Meyer, 2020; Davidovitch & Belichenko, 2018; Huang et al.,

2016; Junco et al., 2016; Roache et al., 2020; Weidlich & Bastiaens, 2018). Universities require

continuous adaptation to meet students where they are, on contemporary communication

platforms they desire, but research on institutional use of texting for academic advisor

communication remains limited (Amador & Amador, 2017; Davidovitch & Belichenko, 2018).

Allowing preferred modalities of communication for remote students could aid in satisfaction

and engagement, variables thought to improve retention rates, a focal key performance indicator

for any institution and many advisors (Aldosemani et al., 2016).

Customer Relationship Management (CRM) was outlined as a student centric approach to

managing student communication and outcomes (Azhakarraja, 2020; Calma & Dickson-Deane,

2020). Academic mentors, counselors, and advisors, who work with students throughout the

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student lifecycle, were identified as best positioned to incorporate the long-term relationship

aspects of CRM practices (Brown, 2017; NACADA, 2017; RLN, 2019; Uddin, 2020; Vianden &

Barlow, 2015). Building and maintaining this rapport to drive student action is made even more

complex through online education where all messaging is mediated by the technology through

which it is delivered (Ng, 2018; Oregon et al., 2018). This leads to the introduction of Short

Messaging Service (SMS) texting as a CMC approach for sharing information and socially

connecting the university to the student, yet the channels limited media richness has led to

controversy regarding effectiveness (Moore, 1992; Short et al., 1976; Walther, 1996).

The dissertation study highlights the importance of analyzing advisor perspectives as they

are the intended SMS text message users to bridge the gap between technology and its intended

purposes (Ireland et al., 2016; Skoumpopoulou et al., 2018). Knowing when and how to use

CMC to develop relationships requires adaptive and flexible individuals, capable of recognizing

changes in societal communication practices (Khan, 2017). Adaptive leadership was accordingly

introduced as a theoretical construct since adaptation to the changing realm of digital

communications technology is an emerging field of HEI research (Heifetz, 1994; Heifetz et al.,

2009).

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Chapter Three: Methodology

Introduction

Collegiate education providers need to stay abreast of technological advances in

Computer Mediated Communication (CMC) to remain student centric, but institutional adoption

of text messaging varies widely and research on incorporation of the platform for academic staff

is limited (Amador & Amador, 2017; Davidovitch & Belichenko, 2018; Duran et al., 2005;

IPEDS 2020; Santos et al., 2018). Chapter Two introduced the literature pertaining to the

various constructs and philosophies in higher education and business related to the current study.

Central theories applied in framing the research problem included Customer Relationship

Management (CRM) through CMC to promote positive student outcomes and adaptive

leadership to benefit successful incorporation of communication technology. Chapter Three

introduces the methodology, research design, and procedures used for data collection, security,

preparation, and analysis corresponding to the current study.

The primary researcher aimed to describe advisor reported use, institutional support, and

perspectives regarding Short Messaging Service (SMS) text for academic communication with

students. Accordingly, a survey design was adopted to fit the needs of descriptive and inferential

research (Creswell, 2009; Zikmund, 2003). Since participant perspectives were collected in

nominal and continuous type data, quantitative analysis was possible through multiple statistical

approaches (Harpe, 2015; Ott & Longnecker, 2016). Chapter Three will introduce techniques

used to tailor and assess the survey instrument for reliability, ethicality, and validity as well as

the data analysis approaches selected to fit the research needs. Descriptive statistics were used to

investigate RQ1 and inferential statistics, through nonparametric tests, were used to investigate

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RQ2 and RQ3. The target population will be described, and lastly, methods used to access

survey participants will be delineated.

Problem Statement





The purpose of the non-experimental, quantitative study was to investigate the perceptions of

college and university academic advisors, mentors, and counselors in the United States regarding

institutional support for texting and motives for use of the communication channel as well as the

possible variables impacting use and perceptions.

Research Questions







Method

The methodology and design of the research has multiple characteristics that will be

introduced and assessed for feasibility and applicability in this chapter. The first categorization

to note is the non-experimental nature of the current research; respondents will not be influenced

through application of a variable and should answer based only on known information to include

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their opinions and personal practices at their respective institutions (Salkind, 2006). The

research design was also descriptive in nature because it aimed to describe the current status of

something; no attempt was made to manipulate the opinions and attitudes of the participants and

the researcher was only interested in describing advisor perceptions as they naturally existed

(Creswell, 2009). Finally, the research design constitutes survey research as advisors were asked

to provide their feedback through an online questionnaire as detailed in the subsequent sections

(Willard, 2020). The aforementioned characteristics are key considerations for protections

involving human research participants which will be discussed later (eCRF, 2021; HHS, 2020).

The decisions to include both online and traditional programmatic learning environments

and not select a research site or regionalize the study pertain to the broader scope of the research

problem and applicability of considerations. Despite the wide variety of institutional

characteristics and approaches to student interfacing, retention is not a unique problem among

HEIs (Li & Kennedy, 2018; Hagedorn, 2006 Mu & Fosnacht, 2019). Retaining students through

effective communication practices represents a business problem relevant to every school and

requires leadership solutions (Calma & Dickson-Deane, 2020; Zikmund, 2003). For this reason,

the researcher aims to collect collegiate advising community feedback from a diverse array of

HEIs. The extensive inclusion of nationwide higher education organizations and associations for

sampling will be discussed in detail in subsequent sections but it is important to note the sample

is not random. The sample will be a non-probability, convenience sample since participants are

not randomly selected and the researcher makes no pretense of categorizing a representative

subset of the overall target population (Leedy & Ormrod, 2016). Although generalizations to

larger populations can only be made with true random samples, analytic generalizations can be

used to translate particulars of the data to broader constructs or theory (Polit & Beck, 2010).

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Accordingly, findings on advisor perceptions are anticipated to contribute to understanding

broader theory application of CRM through CMC and be used to inform strategy and change

through adaptive leadership.

Survey Instrument

The components of the current survey instrument stem from the original work of Duran,

Kelly, and Keaten (2005). Like the researcher, the authors of the original study, were also

interested in HEI employee use and perceptions of CMC tools for student exchange, but their

focus was on the use of email instead of texting as the communication modality and surveyed

faculty members instead of advisors. The research included 259 faculty participants from two

unnamed universities divided with 124 respondents from a private school and 135 from a public.

The researcher obtained consent from the primary author (Appendix B) and altered the tested

survey instrument of the Duran et al. (2005) study by replacing the communication tool of email

with text messaging and participant role of faculty with academic staff. Additional alterations

were applied based on shortcomings denoted in the original research and the inclusion of options

specific to text messaging considerations. For instance, the original authors cited issues with the

first two research variables of email frequency and number of messages initiated by the faculty

member. Duran et al. (2005) do not specify how many but noted some faculty participants

included mass messages with generic course information, not only the personalized emails as

intended, which the researchers recognized as a weakness requiring clarification. To mitigate the

issue of mass verses personalized messages, the current study excludes the frequency related

components and instead focuses questions pertaining to perceptions of overall use and primary

motives for SMS texts.

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The original research of Duran et al. (2005) on faculty perceptions of email use was a

mixed methods study. Exploratory research is typically qualitative since the dimensions of the

problem are unknown and attempting to be uncovered (Zikmund, 2003). Motives for student

communication with advisors and mentors is not an ambiguous problem in the field of higher

education and outlined in detail in the literature review (Aldosemani et al., 2016; Blessinger &

Wankel, 2013; Britto & Rush, 2013; NACADA, 2017; O’Halloran, 2019; Page et al., 2020;

RNL, 2019; Troxel, 2018; Vianden & Barlow, 2015). The current study thus does not seek to

understand why messages are exchanged between students and their respective advisor but rather

if those exchanges are taking place through the medium of SMS messaging, predominant

motives for messaging, value of the channel, and the level of administrative support perceived by

the HEI professionals of various subgroups. Accordingly, the dissertation research intended to

exclude any short answer options, thus eliminating the qualitative component of response coding

for common themes found in one research question of the original study (Duran et al., 2005).

Lastly, another research dynamic was added since the target and sample populations of

the current research was not limited to traditional, ground campus universities as the original

authors present (Duran et al., 2005). Inclusion of both programmatic learning environments

allowed for inferential statistics in RQ2 for participants according to either online/internet-based

or traditional/ground campus programs. Since some institutions offer both types of courses, as

well as hybrid courses, participants were directed to select the predominant learning environment

by indicating if the majority of their students were in online or ground campus programs.

Target Population

The target population included all academic, degree, and program advisors, counselors,

and mentors in accredited, Title IV degree-granting, undergraduate and graduate programs in the

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United States and, as indicated in the study’s key terms, referred to simply as advisors. The

distinction of Title IV degree granting was bound by the National Center for Education

Statistics’ reported list of 4,313 public and private, two and four-year colleges (NCES, 2021).

HEIs were not limited to institutional type in terms of programmatic learning environment or

status as private/public or proprietary/non-profit but were limited to United States accredited

HEIs (CHEA, 2020; DAPIP, 2020). By using Google Forms, individuals remained completely

anonymous under IRB standards since no personal information was required, IP addresses were

not taken, and answers could not be associated with individuals (HHS, 2020). Other qualifying

or demographic information was requested during the survey to include the participant’s status as

an advisor, age, gender, and years of experience. Such information did not constitute identifiable

private information since the participant’s identity could not be ascertained by the researcher or

be derived from the provided responses (eCRF, 2021). Since the survey link was provided to

advisors nationwide and made available on social media sites reaching thousands of members,

participants could not be associated with their answers and thus ensured anonymity.

Accessing the Population

The researcher used a combination of avenues to access the target population to include

social media, organizational listservs (or electronic mailing lists), and direct email to present the

survey link. The study was not regionalized within the United States as membership in these

organizations span the country and the researcher wanted to obtain participation from

nontraditional schools and recruit for respondents on social media irrespective of national

geographical boundaries. Since many organizations required IRB approval prior to the

consideration of survey dissemination or posting, some consented to promote the study but many

avenues to the target population were later solidified after IRB approval. After adding post IRB

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approval entities, the consolidated list of organizations, groups, and associations willing to

facilitate sharing the survey link were resubmitted for IRB approval (Appendices D1-D3).

HEI Listservs

The policy for assisting in academic research fluctuates as it pertains to the nature of

sharing as well as the corresponding organization or group. For instance, obtaining access to

HEI listservs typically follows the most rigorous approval standards and requires IRB approval

prior to application (Appendix E). The researcher obtained approval from some entities prior to

IRB approval for listserv access to include the Association for the Assessment of Learning in

Higher Education (AALHE) and the Association for University and College Counseling Center

Directors (AUCCCD) (Appendix E). Additional listserv access was viable through state and

regional associations such as California Community College Student Affairs (CCCSA), Missouri

College Personnel Association (MCPA), and the South Carolina Personnel Association (SCPA)

who responded with approval for access to survey their members (Appendix D1 & E). Since the

researcher’s degree granting institution is a member of the Association of Independent Colleges

and Universities of Ohio, this represented another approved outlet for dissemination (AIUCO,

2020). Some United States wide HEI organizations did not share their listserv for direct email

but were willing to disseminate a survey link on behalf of the researcher. For instance, American

College Personnel Association (ACPA) policy did not allow for the release of e-mail addresses

of members; however, ACPA did provide the service of sending an e-mail message with the

survey link and follow-up announcement (ACPA, 2021).

Social Media

The researcher obtained consent to share a doctoral dissertation research link on the

NACADA Facebook group (Appendix E) and, although not all 12,000 advising colleagues were

65

members on social media, this avenue provided access to the 1,200 Facebook group members

without needing more than group administrator approval (NACADA, 2020). A similar approach

was taken with a host of other professional organizations such as the Council for the

Advancement of Standards in Higher Education, the American College Personnel Association,

and the University Professional and Continuing Education Association just to name a few.

In addition to sharing on organizational pages, such as the above examples, there were

also Facebook groups which required membership approval but allowed for open posting once

granted access (Bhutta, 2012). The researcher obtained membership in the following HEI related

groups that possessed academic staff as group members: American Association of University

Women, American College Counseling Association, Community College Student Affairs

Professionals, Student Affairs Professionals, Student Affairs Training and Development, The

Admin: A Place for Student Affairs Professionals, NACADA: Advising Community for

Wellbeing & Advisor Retention, and Academic Affairs Professionals. While the response rate of

social media accessed surveys was expected to be lower, these sixteen groups comprise a total of

87,880 members (Appendix D2). Although logistical permissions were not required for

Facebook group postings, the primary researcher did ensure approval receipt from the HEI

professionals administrating these pages as a courtesy per IRB recommendations (Appendix E).

Public Contact Information

In addition to organizational listservs and social media outlets, the researcher also

incorporated direct requests for participation from publicly available contact information of a

variety of institutions online through openly accessible website information. For instance, The

Ohio State University had ten listed academic counselors, two managers, and the department

director posted on their external directory (OSU, 2020). Through emailing the group inbox or

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these open advisor emails, the researcher requested dissemination or participation to a number of

members in the target population in this manner (Appendix, D).

Sample Size

In determining the sufficient sample size for the population, Leedy and Ormrod (2016)

recommend that if the population is 100 or fewer, to survey the entire population; if the

population consists of roughly 500, half should be sampled; if the size of the population is 1500,

20% should be sampled; and in populations larger than a certain point (roughly 5000), the

population size can be considered irrelevant and the authors support a sample size of 400 as

sufficient for quantitative statistical methods. Salkind (2006) recognizes the decision of a sample

size should ensure it is adequate enough to answer the research question accurately but not so

large the sampling process is uneconomical or inefficient. In survey research, it is desirable to

obtain as large a size as possible to increase statistical power (Ott & Longnecker, 2016). The

target of 400 qualified survey responses was established and deemed appropriated for the

selected statistical methods of the research.

Validity and Reliability

Reliability is the degree to which measures provide error free, consistent results

(Zikmund, 2003). Validity in quantitative research pertains to the extent selected measures

depict or deal directly with the intended topic (Mertler & Charles, 2008). In other words,

validity is the extent to which the study is measuring what it is supposed to measure. The best

way to ensure validity is to avoid potential threats which occur when investigators use

inadequate definitions and measures of variables (Zikmund, 2003). There are various methods

for testing the survey as a reliable and valid research instrument. For example, Creswell (2009)

recommends field or pilot testing a survey as such trials can establish content validity of an

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instrument and aid in improving questions, organization, format, and scales. Ott and Longnecker

(2016) introduce various forms of validity to include construct validity or the adequacy of the

operational definitions.

For the current study, the key terms and target population were the result of consolidating

from other authors and the application of standards recognized in US higher education (CHEA,

2020; DAPIP, 2020; Hagedorn, 2006; NACADA, 2020, NCES, 2021; Troxel, 2018). Face

validity, or professional consensus of a scale’s accuracy, and criterion validity, or the ability to

correlate with other measures, were both enhanced by the researcher’s use of an existing survey

instrument (Zikmund, 2003). Research questions pertaining to the motives for CMC messaging

were altered to fit SMS texting but still incorporated options established for communication by

Duran et al. (2005). Since the survey was altered, two statistical methods were incorporated,

CVR and Cronbach’s alpha, to further establish validity and reliability.

CVR was applied to establish content validity. To do this, the survey instrument was first

administered to subject matter experts in the field of higher education. The pilot test included

the dissertation committee, collegiate colleagues, and mentors in the primary researcher’s

network to ensure respondents understood questions introduced and followed the survey link as

intended. The researcher included a ten-member panel including five advisors to review the test

from the perspective of the target population. The other five pilot test participants were

administrative leaders meant to review for construction in accordance with higher education

language and best practices. The procedure followed Lawshe’s (1975) model for item inclusion

by asking members to rate each survey question as essential, useful but not essential, or not

necessary. The pilot survey also included a blank field below each question for participants to

comment on the wording or clarity of each instrument item.

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To substantiate internal consistency, Cronbach’s alpha was incorporated to establish

reliability in the three survey scales used and will be discussed in Chapter Four (Ott &

Longnecker, 2016). Reliability was also ensured by presenting the questionnaires in the same

manner each time so that there were no external influences in participant submissions (Zikmund,

2003). Participant error and bias could not be fully controlled because factors such as

technology proficiency or time of day were impossible to manage since the survey was provided

through email and social media, thus taken asynchronously through the internet (Stern et al.,

2017). However, researcher error and bias were mitigated since online surveys enable the exact

same experience for every participant as questions were provided electronically and thus

identically introduced without nonverbal ques and recorded exactly as elected by the respondent

without researcher influence (Zikmund, 2003).

Validity was also considered from internal and external perspectives; internal validity

relates to the researcher’s ability to draw conclusions about casual relationships from the data

while external validity is the quality of generalizing beyond the data to other subjects or groups

in the larger target population (Zikmund, 2003). Subsequently, the primary weakness of the

research design was that it could not be conducted through random sampling. Since not all

advisors partake in social media or are associated with the various HEI organizations willing to

share in research efforts, not every individual in the total population had an equal chance of

being selected (Stern et al., 2017). While random sampling is the means to obtain a

representative sample, no method of sampling can guarantee perfect representation (Mertler &

Charles, 2008).

The sample for the research was a non-probability, convenience sample since participants

were not randomly selected and the researcher made no pretense of categorizing a representative

69

subset of the overall target population (Leedy & Ormrod, 2016). Polit and Beck (2010) note that

although generalizations to the larger population can only be made with true random samples,

analytic generalizations can be used to translate particulars of the data to broader constructs or

theory. The authors note how, with quantitative research, adding to sample size can both

enhance the ability to generalize and improve the statistical power. Despite the nature of

convenience sampling, the researcher made every effort to enable a broad array of population

members and obtain a large sample size to service external validity (Ott & Longnecker, 2016).

This was done through incorporating 26 HEI associations and organizations to disseminate and

share the survey link with as many individuals from the target population as possible (Appendix

D1).

Data Collection

Internet recruitment of self-selected participants in a web survey enabled fast access to a

significant number of participants (Stern et al., 2017). Data collection for the current research

was completely electronic and consist of closed-ended questions administered via a Google

Forms survey link (Appendix C). This type of question may have aided in return rates since they

are fast and straightforward for participants to answer (Zikmund, 2003). Closed-ended questions

also provided quantitative benefit since data was easier to consolidate, code, present, and

statistically analyze (Creswell, 2009). Google Forms did not take participant URLs to maintain

complete anonymity and was selected over other survey software, such as Survey Monkey or

Qualtrics, as it enabled unlimited responses, advanced question formatting, and skip logic

functionality without a paid subscription (Taylor & Doehler, 2014). A link to the survey

instrument was emailed directly to colleges and universities agreeing to participate in the study,

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organizational listservs granting research consent, and posted in online higher education

professional forums that allow for doctoral studies or consented to sharing.

The Google Forms platform provided computer-interactive surveys and was first piloted

on professional peers and mentors after approved by the committee and IRB (Ott & Longnecker,

2016). The informed consent information (Appendix A) was provided at the onset and the

answer field required participant agreement to proceed per requirements of research involving

human participants (eCRF, 2021). Skip logic was used to move SMS texting users and non-

users to the appropriate questions and thus saved respondents’ time in avoiding inapplicable

questions. As a matter of best practices, demographic information was placed at the end of the

survey which has been supported as early engagement to reduce boredom and improve response

rates (Niederhauser & Mattheus, 2010). The measure of age and years of experience were also

presented as blanks to fill-in, instead of ranges, as this enabled the means to be analyzed instead

of frequency distribution (Bailey, 2016).

Through the research process, no data was falsified, the original ideas and findings of

others have been acknowledged, and all steps have been honestly and thoroughly discussed with

the researcher’s dissertation chair and approved by the committee in advance. Leedy and

Ormrod (2016) note the majority of ethical issues in research procedures tend to fall under one of

four following categories: harm to living things, privacy considerations, informed consent and

voluntary participation, and honesty in the academic setting. To ensure privacy, no personal

identifiers were taken such as the participant’s name or personal information, only broad

categorizations regarding their position, characteristics about their institution, and their

perceptions in response to the given research questions.

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Written consent for surveys must be obtained in scholarly research and delineate the

nature of the project and participation expectations (Cooper & Schindler, 2014). Institutional

employees contributing their responses were told the survey was on a voluntary basis, it was

anonymous, questions were completely internet based, and their involvement should have took

no more than five or ten minutes. In accordance with the United States Department of Health

and Human Services’ Office for Human Research Protections, additional components were

included such as benefits and explanation of the research topic to comprehensively meet all

checklist requirements (HHS, 2020). With respect to the research topic, participants were given

a general idea of the dissertation study’s purpose but not information specific enough to

influence their responses (Leedy & Ormrod, 2006). Details on privacy, data security, voluntary

participation, and contact information were presented in the informed consent (Appendix A) and

provided a virtual click to accept button for advisors to indicate their willingness to participate in

the study (Leedy & Ormrod, 2006).

No data were collected until the researcher obtained approval from the Institutional

Review Board (IRB). The current study was submitted for consideration of procedures, to

include the aforementioned components of informed consent and anonymity, as well as impact

on participants and pilot tested on professional peers and personal mentors within the

researcher’s network of contacts. The survey was filled out online, thus did not fall outside of

the scope of normal activities, questions were close ended, and data taken only included

qualifying yes/no questions, basic demographic information, and opinion-based answers

(Creswell, 2009). The current study was categorized as unobtrusive since questions were not

invasive or upsetting and individuals were not being scrutinized in any way (Gall et al., 2005).

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Data Analysis Procedures

The first three survey questions obtained informed consent and qualified the participant

for the study based on their role as a collegiate advisor and their institution as accredited in the

United States. The fourth survey item distinguished one of two subgroups for comparison by

asking for the programmatic learning environment of their students (RQ2 data). Item five

obtained respondent’s perceptions on their institution’s support for use of SMS text messaging in

student communication. This survey item introduced six statements regarding various way in

which an institution’s administration could prevent, enable, or even encourage SMS as an option

for advisor communication avenues. Each statement was followed by a five-point Likert scale

for their level of agreement ranging from strongly disagree to strongly agree from which values

were added together. The comprehensive score for administrative support thus enabled the

information to be used as continuous data (Harpe, 2015). Accordingly, the construct of

institutional support was operationalized as a compilation of respondents’ Likert scale responses

to the following items presented for survey item five:

• The administration of my institution supports advisor use of text messaging in

student-facing communication.

• My immediate supervisor encourages me to use text messaging as an option in

student communication.

• My institution provides the technology tools necessary to support text messaging for

student contact.

• The policies and procedures for incorporating text messaging with students are clear

at my institution.

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• I receive adequate training from my administration on how to use text messaging with

students.

• My institution’s leadership has clarified how texting can improve my performance as

an advisor.

The sixth survey question asked participants if they use SMS texting for student

communication and divided the population into the second recognized subgroup for analysis of

users and non-users (data used in all three RQs). Online surveys enabled automatic display of

the appropriate question order based on prior responses which further decreased completion time

and potentially improved response rates (Mavletova & Couper, 2014). Participants affirming use

of SMS text continued on to items pertaining to that channel’s use with students, while skip logic

routed advisors answering in the negative past these questions (Appendix C).

Data Preparation

To prepare survey responses for statistical analysis, data were be scored, cleaned, and

screened. To score the data, all nominal response fields with two categories, such as yes or no to

SMS use, were converted to 0 and 1 and/or a simple title representative of the group. Responses

for all continuous data questions, such as Likert scale responses, were replaced with a 1 for

strongly disagree to 5 for strongly agree. These the scores were added together for all cases

within groups to represent the various survey item themes. Scores were used differently

depending on the statistical method associated with each research question and its associated

survey items. For instance, motives for SMS use and perceptions of the medium were all used

only for descriptive statistics and thus scores were added to identify predominantly ranked

motives. This was also the case for RQ3 as the items were added together to and an

administrative support score was associated with each participant. Conversely, numeric

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responses were not added together among all cases and instead placed directly in Statistical

Analysis Software (SAS) and Microsoft Excel since analysis required association with two

variables (Zikmund, 2003).

In addition to scoring the data, cleaning and screening was also necessary and the

features of Google Forms enabled many considerations to be addressed prior to data collection

(Ott & Longnecker, 2016). Incomplete and non-qualified cases were excluded from analysis;

instead of deleting such cases, the researcher formatted the online questionnaire to avoid

submission of such responses. Required fields were included for all survey items to prevent

missing data. Also, skip logic was incorporated to direct advisors to the end of the survey when

they did not qualify as part of the target population based on responses regarding their status as

an advisor, mentor, or counselor and their institution’s status as an accredited US, degree

granting college or university.

Statistical Analysis

Descriptive and inferential statistics were used to identify trends, associations, and

relationships among collected data. Since participant responses were assigned a numeric score

based on Likert scale responses or considered nominal data, the methodology of the study took

on a quantitative approach which allowed for a greater number of responses to be analyzed

quickly from a broader scope of participants (Leedy & Ormrod, 2016). By providing options for

various statements on a Likert scale, responses could be assigned a value of one through five,

added together for each item on the scale, and considered continuous data for analysis (Harpe,

2015; Ott & Longnecker, 2016). Gradations such as this were identified as likely more

indicative of the respondent’s stance on the construct and enabled averaging a mean score for use

in RQ1 and RQ3 (Creswell, 2009). By placing numeric information in charts and graphs, the

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researcher observed prominent trends in advisor perceptions as well as data distribution which

prevented the parametric tests from being conducted in the statistical analysis of RQ3 through t

Tests (Mertler & Charles, 2008). These outcomes are later discussed in Chapters Four and Five.

Descriptive statistical analysis was applied for RQ1 and allowed for meaningful

presentation of data for interpretation, but it could not support inferences about the larger

population (Salkind, 2006). RQ1 was addressed using information from survey items four

through twelve; the first three survey items were excluded since they pertained only to informed

consent and qualification as part of the target population. Descriptive statistics included

calculation of central tendencies (mean, median, and mode), standard deviation, predominant

frequency distributions, variance, and cross tabulation (Willard, 2020). CVR was applied to

establish construct validity and reliability as well as identify the number of dimensions provided

in the survey regarding perceived motives for SMS use. Non-parametric tests were included for

analysis of RQ2 through chi-square and RQ3 through Wilcoxon Rank Sum. Additionally,

demographic and intervening variables were selected to identify differences between groups

which followed the original work of the Duran et al. (2005). For instance, responses regarding

motives for SMS messages to students were displayed as ranked frequency distributions for the

entirety of responses as well as examined by subgroups based on programmatic format

(online/traditional groups) and demographic information of advisors (gender, experience, and

age).

RQ2 included survey items four and six (Appendix C) and evaluated only nominal data in

yes/no to use of SMS messaging with students and online/traditional to programmatic

environment which called for nonparametric approaches (Zikmund, 2003). Since two variables

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were observed for each respondent data set in RQ2, a chi-square test of independence was

adopted to analyze the following question and hypotheses:



H0: There is no statistically significant association between learning environment and

SMS use.

H1: There is a statistically significant association between learning environment and

SMS use.

Lastly, RQ3 potentially allowed for parametric tests since continuous data could be used

from the six constructs in survey item five (institutional support for SMS) and include two

groups (SMS users/non-users) for comparison based on survey item six. RQ3 explored only

questions being asked of the entire population of participants to enable a larger sample

encompassing all received surveys. T tests were originally established to compare the mean of

continuous data regarding level of reported institutional support for SMS. Discussed later in

Chapter Four, the data distribution did not meet the necessary assumptions in that the means of

both samples had to be normally distributed. Accordingly, a Wilcoxon Rank Sum test was

adopted to replace t Tests to assess the two groups regarding the following research question and

hypothesis:



H0: There is no statistically significant difference between institutional support for SMS

texting users and non-users.

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H1: There is a statistically significant difference between institutional support for SMS


This statistical approach enabled exploration for relationships among multiple variables

and information on the degree of the relationships could be extracted (Zikmund, 2003).

However, comparisons could not explain why the relationship exists or if it is truly the research

variable influencing the observed data. Gall, Gall, and Borg (2005) note that while a relationship

may be established, a key limitation is that the cause may not be clear.

Summary

Communication between HEIs and their respective students is primarily mediated by an

advisor, mentor, or counselor who serves as the main, ongoing point of contact for promoting

successful outcomes such as retention (Brown, 2017; Donaldson et al., 2020; Joslin, 2018;

Vianden & Barlow; 2015). The current research has viewed this communication through the

lens of CRM and recognizes how advances in technology alter the manner in which society

interacts through SMS texting (Osam et al., 2017). HEI leaders and administrators should

consider contemporary communication platforms as well as technology options consistent with

advisor and student needs and expectations (Argüello & Méndez, 2019; Mirriahi & Alonzo,

2015; Oregon et al., 2018). The methodology chapter outlined an actionable plan for survey

research to investigate advisor perspectives on SMS texting with students. The nature and

selection of quantitative methods, to include descriptive and inferential statistics were discussed

for their appropriateness and feasibility. The web-based survey design was adopted to fit the

needs of descriptive research and the data collection plan was consistent with IRB regulation for

ethical treatment of research participants (Creswell, 2009; HHS, 2020; Zikmund, 2003). The

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target population was described and methods for accessing survey participants was introduced

(Appendices D1-D3 & E).

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Chapter Four: Data Collection and Analysis

Introduction

The dissertation research was guided by the theoretical concepts of Customer

Relationship Management (CRM) through Computer Mediated Communication (CMC),

particularly perceptions of Short Message Service (SMS) texts as a communication tool in the

student-to-advisor relationship. The CRM approach to business integrates people, process, and

technology in a manner aimed to enhance company-to-customer communication for long-term

social ties, satisfaction, and brand loyalty (Calma & Dickson-Deane, 2020; Suntornpithug,

2012). CRM has been applied to higher education for best practices in student retention,

particularly for advising and mentoring staff who are positioned to drive student decision-making

(Hrnjic, 2016; Juan-Jordán et al., 2018; Troxel, 2018).

SMS text messaging offers the type of targeted, succinct, and immediate feedback

expected by Generation Z and digitally proficient students but may not be appropriate for every

interaction (Chicca & Shellenbarger, 2018). Knowing when and how to use CMC to develop

relationships requires adaptive and flexible advisors, capable of recognizing changes in societal

communication practices (Khan, 2017). This positions adaptive leadership as another theoretical

construct that guided the research as it emphasizes leveraging new strategies to thrive in

emerging and unknown environments (Heifetz, 1994; Heifetz et al., 2009).

The researcher aimed to describe advisor reported use, institutional support, and

perspectives regarding SMS text for academic communication with students. The data collection

method followed a survey design through online questionnaire to fit the needs of descriptive and

inferential research (Creswell, 2009; Zikmund, 2003).

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Problem Statement





The purpose of the non-experimental, quantitative study was to investigate the perceptions of

college and university academic advisors, mentors, and counselors in the United States regarding

institutional support for texting and motives for use of the communication channel as well as the

possible variables impacting use and perceptions.

Research Questions







To address the research questions, an eleven-item survey was adopted, the components of

which stemmed from the original work of Duran, Kelly, and Keaten (2005). Similarly, the

original study focused on HEI employee use and perceptions of CMC tools for student exchange.

However, email was the primary communication channel of interest instead of texting, and

faculty members were surveyed instead of advisors. The current survey was pilot tested;

Lawshe's (1975) CVR approach was applied to test and help improve the content validity of the

instrument. Accordingly, Chapter Four provides the process, outcomes, and alterations made as

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a result of the pilot study before detailing the full research data collection, sample characteristics,

statistical analysis, and discussion.

Survey Pilot

According to Lawshe (1975), item selection can be derived from a content evaluation

panel and should consist of a balanced number of incumbents and supervisors to serve as Subject

Matter Experts (SMEs) for optimal results. After sending the pilot survey to six leaders in higher

education and eight members in the target population, ten full survey submissions were obtained

(n=10) with exactly half advisors and half supervisors based on the reported student load. The

online questionnaire presented to the pilot participants was composed of the research instrument

with a question pertaining to the usefulness of the question after each item. Respondents were

asked to rate each survey item as essential, useful but not essential, or not necessary, and

provided a blank field for commenting on the wording, clarity, concerns, or recommendations for

each instrument item.

The data was coded and cleaned by converting Google Forms submissions to Microsoft

Excel, deleting incomplete surveys, removing comments for later consideration, and scoring each

item based on a rating of essential (3), useful but not essential (2), and net necessary (3). These

values can be seen below with the ten participants anonymously labeled in the left column and

their item ranks spanning from informed consent on the far left through the eleven questions

from left to right (Table 1). The total score for each can be easily compared with unanimous

agreement, for instance, on the first item of informed consent being essential on the instrument

(score of 30/30), whereas question nine was deemed less essential (25/30).

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Table 1

Content Evaluation Panel Ratings

Wilson et al. (2012) note that, despite the trichotomy of Lawshe’s rating scale,

distinctions are only made between essential and not essential. Thus, the method for calculating

the critical value of the Content Validity Ratio (CVR) treats responses as discrete binominal

data. The CVR method was used to obtain the “minimum number of experts required to agree an

item ‘essential’ for a given panel size, such that the level of agreement exceed that of chance”

(Ayre & Scally, 2014, p. 81). The content evaluation panel ratings (Table 1) denote the

converted binomial data by item number. Essential (rating of three) was represented as an x,

while ratings of two (useful, but not essential) and one (not essential) were left blank. Essential

scores (x) are presented below along with calculation of the CVR (Table 2). Lawshe’s (1975)

equation for item inclusion is formulated with 𝐶𝑅𝑉 =%&'(

)*)

,/. where 𝑛0 is the number of

incumbents and/or experts rating the item as essential and N is the total number of participants in

the content evaluation panel. Again, using the informed consent (denoted with IC) as example,

all panel exerts gave the rating of essential so the first row of data (Table 2) was unanimously

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marked across the line and the CVR equation was written as 12'(34* )

12/.= 1. This process was

duplicated for the eleven items following the informed consent and presented below (Table 2).

Table 2

Survey Item Content Validity Ratio

In the CVR calculation, “values range between −1 (perfect disagreement) and +1 (perfect

agreement) with CVR values above zero indicating that over half of panel members agree an

item [as] essential” (Ayre & Scally, 2014, p. 79). Data obtained in the research pilot sample

were analyzed (Table 2) with CVR calculated in the far-right column. Informed consent for the

research, expectedly, resulted in a CVR score of 1.0, as did items one (qualifying position) and

seven (value statements). Items two (qualifying institution), five (use of text), and six (motives

for use) also scored close to unanimous agreement with a CVR of 0.8. Lawshe recommends

retaining items that have CVR equal to or higher than 0.8 but also notes that “any item,

performance on which is perceived to be ‘essential’ by more than half of the panelists, has some

degree of content validity” (1975, p. 567). Accordingly, items three (learning environment), four

(institutional support for SMS), and eleven (age demographics) were also considered acceptable

for inclusion in the research pilot despite having a CVR score of 0.6.

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Items eight (years of experience), nine (student load), and ten (gender demographics) all

obtained rating of more essential than not but resulted in lower CVR scores of 0.4, 0.2, and 0.2

respectively. Items eight and ten were not directly involved in the three primary research

questions of the study but were intended for use in secondary analysis. “It should be pointed out

that the use of the CVR to reject items does not preclude the use of a discrimination index or

other traditional item analysis procedure for further selecting those items to be retained in the

final form of the test” (Lawshe, 1975, p. 568). These variables were substantiated as valid and

reliable constructs in the study of CRM and CMC through existing research. Statistical analysis

of gender has been incorporated by leading authors in the field of education on retention research

to include Spady (1971), finding a higher correlation with social integration for women, and

Bean (1980), finding student satisfaction a more predictive metric for women. Gender has also

been incorporated as a variable for more recent research regarding technology acceptance in

students (Weidlich & Bastiaens, 2018), faculty (Bailey, 2016; Marrs, 2013), and advisors

(Brown, 2017; Leach & Wang, 2015) in higher education.

The original study by Duran et al. (2005) also used gender demographics in secondary

analysis on CMC exchange and found females to have a significantly higher rate of received

emails (M = 16.85, SD = 30.80) than male instructors (M = 13.27, SD = 12.85). Aside from

gender, the second of the three variables with a lower CVR score is that of advising experience

in years. The variable of experience was not part of the original survey instrument although

construct and content validity for this variable have also been extensively established in the

literature ranging from research on technology acceptance to advising models (Donaldson et al.

2020; Granić & Marangunić, 2019; Hart-Baldridge, 2020; Marrs, 2013; Martin et al., 2020;

Skoumpopoulou, 2018). Subsequently, the researcher elected to retain items eight and ten of the

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survey despite their CVR scores lacking sufficiency for inclusion as gender and experience are

both codified in the literature and appropriate for secondary demographic analysis.

The third disputed survey question from the content evaluation pilot was item nine, which

related to the advisor’s assigned student load in number of students. With the varying degrees to

which institutions incorporate SMS text messaging, the original intent was to explore for a

possible relationship between use and views with the size of advisor case load. For instance,

Hart-Baldridge (2020) incorporates the variable of “number of advisees per term” in researching

advisor perspectives; however, the author had a research site, so all university participants had

similar responsibilities as they were recruited from the same school.

For the current research, the variable of case load becomes convoluted since the study is

not regionalized. The researcher thus cannot hold variables constant, such as full or part-time

status, primary advisor or faculty mentor roles, degree level advised, program type advised, and

other possible intervening variables that may alter case load from one HEI professional to

another. The comments, consolidated below, also indicated some confusion in item nine with,

for example, one respondent asking, “per academic year?” and another recommending a

specified timeframe (Table 3). Of the ten participating advisors and higher education SMEs,

three rated this item as useful but not essential and one rated it as not necessary, resulting in a

CVR ratings of 0.2, the overall lowest score. Based on the panel rating and the respondent

feedback, the researcher elected to eliminate item nine regarding case load from the survey

instrument.

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Table 3

Pilot Survey Feedback

Based on the comment box for each item, any requests for clarification or

recommendations accompanying the rating were consolidated (Table 3). Items without any

feedback were not listed, and all comments were included with the exception of those rating each

line of an item when all lines were marked as essential. Some comments provided personal

feedback on the use of SMS text with students that were insightful but did not alter the item

inclusion or wording. Other comments were taken into consideration and associated edits were

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made to the Google Forms link and survey (Appendix A). First, items one through three were

changed from multiple choice to drop down boxes solely for aesthetic purposes. Secondly, the

option of “unsure” was added to item two regarding the recognized status of the institution and

any participants responding with this or “no” were later scrubbed from the data as non-members

of the target population. Third, the word “current” was added to item three so advisors

responded to the primary learning environment (online or traditional) of their students at the

current point in time. This alteration was made because more programs may have moved online

following precautions and instructional protocols surrounding Coronavirus (COVID-19) (Roache

et al., 2020). The fourth and last change was to separate, by adding additional lines, to item six

for advisor SMS messaging motives based on pilot participant feedback. “Finance and

registration reminders and notifications” were made into two distinct lines instead of one, “direct

student to another university service or department” was similarly separated, and “ensure receipt

of another message such as email” was added resulting in the final survey (Appendix C).

Description of the Sample

The post-pilot survey was approved for instrument edits with the IRB and opened

through Google Forms in May of 2021 for three months. Additional organizations and

associations requiring IRB verification were added to Appendices D1-D3 and E. Over 50

associations, organizations, and institutions facilitated sharing the survey through listserv emails,

posts, and newsletter insertions; 42 social media posts were made by the researcher in higher

education forums; and emails were sent to advisors from 31 colleges and universities with

publicly available contact information (Appendix D1-D3). Response rates for listserv access to

the population were highest with a significant number of submissions in the first wave. This

consisted of emails and posts made directly by the following organizations on the researcher’s

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behalf: the American College Personnel Association (ACPA), the Association for the

Assessment of Learning in Higher Education (AALHE), Community College Student Affairs

Professionals (CCSAP), the Association of Independent Colleges and Universities of Ohio

(AICUO), and the South Carolina Personnel Association (SCPA). Subsequent access through

social media was lower based on daily posts and uptick results; however, there is no way of

analyzing which avenues were truly most beneficial since the survey was anonymous. Since

some respondents did not qualify based on the study parameters, the survey was kept open to

exceed 400 responses. In accordance with the proposal, the researcher added approved

Facebook Groups (Appendix D2) and individually emailed advisors from public, institutional

directories (Appendix D3) until the target sample size of 400 qualified participants was reached.

The online survey was closed on 30 July, 2021 with 427 total advisor responses. Those

not consenting, not serving as collegiate level advisors, or not employed with Title IV schools

were disqualified resulting in 402 qualifying surveys for analysis. The data was cleaned by

removing responses with a negative answer to either informed consent, working in the prescribed

position, or Title IV institutional status (Appendix C) and then removing the corresponding

columns altogether. Each response was then assigned an identification number of one through

402.

The data was scored in accordance with Chapter Three; each question was given a short

title and each response a numeric or letter code for utilization in Statistical Analysis Software

(SAS). For instance, the institutional format of either online/internet-based or traditional/ground

campus was changed to “format” with corresponding ordinal values of “o” or “t”. Coding for

Likert scale statements followed in the same manner with a five-point numeric value for

frequency or agreement. For example, the statement “my institution provides the technology

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tools necessary to support text messaging for student contact” was changed to “support:

resources” with corresponding “strongly disagree” coded as “1” and “strongly agree” as “5”.

The coded data can be translated using the data dictionary below (Table 4) where short questions

align with the instrument content (Appendix C) and each response option is assigned one letter

or a numeric value.

Table 4

Data Dictionary

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Based on the data dictionary, the “replace all” feature was used in Microsoft Excel for

each construct and response to obtain the final spreadsheet of coded data for upload to SAS

(Appendix H). To describe the sample, frequency tables were produced demonstrating 106

advisors were in online/internet based institutional formats (o), making up 26.368% of the

population, and 296 were traditional/ground campus based (t), making up 73.632% of the

population. For the demographic variable of gender, 311 participants or 77.363% were female

(f), 79 or 19.651% were male (p), and 12 or 2.985% preferred not to say (p). Lastly, 207

advisors (51.493%) reported yes to the incorporation of SMS text messaging with students (y)

and 195 (48.507%) did not use SMS (n). These divisions of the sample based on format, gender,

and SMS use are observed below (Table 5) and through distribution tables (Appendix I).

Table 5

Survey Respondents Overview

Summary statistics were used to examine ratio data for the entire data set as well as for

the identified categories (Table 5). The demographic question pertaining to age provided an

optional blank space for value entry. Of the full sample (n=402), 20 participants opted to not

provide their age, resulting in a mean of 41.696, standard deviation of 10.432, and variance of

108.83 (n=382). The field for years of experience was required in the survey and thus used in

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concert with other variables to examine the dataset. For the overall dataset, the average years of

experience or mean was 11.395 with a standard deviation of 7.99 (n=402). For SMS users

(n=207), the mean was 11.430 and standard deviation 7.812. For non-users (n), the mean was

11.350 with a standard deviation of 8.124 (n=195). Similar summary statistics were run for

experience by gender and experience by institutional format. The average age for females was

41 with an average of 11 years of experience, males averaged 44 years of age with 13 years of

experience, and those preferring not to report their gender averaged 41 years of age with an

average of nine years of experience.

To substantiate internal consistency, a reliability coefficient was used to examine

correlations among individual items within the three survey instrument scales of SMS motives,

institutional support for SMS, and perceived value of SMS. Cronbach’s alpha is a statistical

procedure commonly used to see if multiple-question Likert scale items in a questionnaire are

reliable and how closely related a set of test items are as a group (Ott & Longnecker, 2016). The

Cronbach’s alpha test demonstrated high levels of internal consistency with 0.917 for SMS

motives, 0.932 for perceived value of SMS, and 0.930 for institutional support for SMS

(Appendix I).

Statistical Analysis

Research Question 1

Descriptive statistics were used to quantitatively describe the sample features regarding

RQ1: What perspectives and motives do academic advisors report regarding their use of SMS

text messaging with students? Sections of the survey addressed previously (Table 4)

incorporated demographics and institutional format to encapsulate reported motives for SMS

messages and perspectives based on value statements. For motives, participants reporting use of

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SMS were asked to rate the level of frequency with regard to fourteen motives for initiating text

messaging with students. The various motivations pertaining to the function of SMS messaging

are seen below where each is associated with a motive number (Table 6).

Table 6

Construct Key for Motives

The five-point Likert scale for motives was based on frequency with “never” scored as a

“1” and “very frequently” a “5”.

Table 7

Frequency for SMS Texting Motives

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The descriptive statistics for RQ1 indicate which motives are predominant for the overall

dataset where motives one (request student contact), 13 (ensure receipt of another message such

as email), two (make an appointment), and eight (provide encouragement or recognition) score

the highest with respective means of 3.391, 3.343, 3.271, and 3.092. Motives demonstrating the

least predominant frequency were twelve (finance reminders), five (cite/explain policy), and six

(problem with student behavior or performance). Analyses were conducted on motives for SMS

use by gender and then institutional format as a classification variable. On the basis of gender,

female respondents (n=311) had overall lower mean scores than males (n=79), signifying a lower

frequency in use for the 14 motives (Table 8). Females also predominantly rated the frequency

of motive one (request student contact) highest with a mean of 3.317, while for males the highest

mean of 3.953 was for motive 13 (ensure receipt of another message such as email).

The associated data for all motives by gender (Appendix I) was used to rank the means

from largest to smallest (Table 8). The same procedure was then reproduced with the alternation

of the classification variable to institutional format. For survey submissions with advisors

primarily in a traditional, ground campus format (n=296), the most and least predominant

motives remained similar in order to the overall dataset. However, in online formats (n=106),

the most predominant motive became 13 (ensure receipt of another message such as email) with

a mean of 3.915 and motive 7 (relationship building) was ranked comparatively higher at 3.789.

Other variations in the predominance of means can be viewed below (Table 8) and online

respondents demonstrate a higher overall mean score for each motive indicating a greater

frequency in SMS use for the 14 motives which will be discussed further in Chapter Five.

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Table 8

Ranked means between groups

Unlike motives for SMS use, the statements regarding perspectives of the communication

modality were presented to all survey participants regardless of use or non-use. To obtain

advisor views on text messaging as an institutional tool for student interface, four value

statements were provided. Participants were asked to rate their level of agreement with each

based on a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5).

The output (Appendix I) can be interpreted using the statement code for each full value statement

(Table 9).

Table 9

Value Statements and Statement Code

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After obtaining a mean for each statement through summary statistics procedures, the

classification of SMS use, institutional format, and then gender analyses were conducted in three

separate procedures to obtain a score for each group (Table 10). A higher level of agreement for

each statement is associated with a higher perceived value of SMS messaging since each of the

four lines present a positive view on the communication tool. The mean score for all four

statements was above three, indicating an overall perceived benefit.

Table 10

Value Statement Agreement by Groups

Value statements one and four ranked highest with respective mean scores of 4.09 and

4.072. Value statement one was ranked highest for the entire population as well as for subgroups

of SMS non-users (mean=3.733), traditional advisors (mean=4.074), and females (mean=4.103)

whereas value statement four ranked highest for online advisors (mean=4.302) and males

(mean=4.19). For the subgroup of SMS users, value statements one and four tied with the

highest mean scores of 4.425 for both. The value statement agreement by groups (Table 10) also

includes an average of value statement means; expectedly, the mean score for each statement is

lower for non-users (n=195) with an average of 3.508 than that of users (n=207) with an average

of 4.34 and the entire sample (n=402) with an average of 3.936.

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Research Question 2

Inferential statistics were applied to data from survey items four and six (Appendix C) to

analyze RQ2: Does use of SMS text messaging with students differ between academic advisors

of online and ground campus environments? RQ2 evaluated only nominal data in yes/no options

to use of SMS messaging with students and online/traditional to programmatic format which

calls for nonparametric approaches (Zikmund, 2003). Since variables are categorical for each

respondent in RQ2 data, a chi-square test of independence was adopted to analyze the following

null and research hypotheses:


SMS use.


SMS use.

A chi-square test of independence test was run in both the statistical software and

calculated in Excel Spreadsheets to compare for accuracy. In the Excel based calculation (Table

11), response data were totaled for both categorical variables: 296 advisors from traditional

formats (T), 106 were online (O), 207 responded yes to the use of SMS texting (Y), and 195

responded they were not SMS users (N). To get an expected probability for each, these totals

were divided by the number of responses (n=402) resulting in column C, row two through five

data. The next step was to multiply respectively for all four combinations of two bivariate

categories. For instance, traditional (T) of 0.7363 was multiplied by SMS users (Y) of 0.5149 to

get column F, row 8 which calculated to 0.3792. These values (column F) were multiplied by

the number of responses (402) to obtain the expected value for the chi-square analysis.

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Table 11

Excel Spreadsheet Screenshot for Chi-Square

The next step demonstrated in the Excel based procedure was to subtract the expected

from the actual number of responses. The actual survey data (column B, rows eight through

eleven) for the dataset are as follows: 136 traditional, SMS users (T, Y); 160 traditional non-

users (T, N); 71 online, users (O, Y); and 35 online, non-users (O, N). The formula for chi-

square is 𝑥. = ∑ (:;'<;)*

<, where O is the actual observed value, E is the expected, and n is the

number of observations (Ott & Longnecker, 2016). The expected value subtracted from the

actual value resulted in column H. Squaring the four values (column I), dividing by the expected

value (column J), and totaling the results (cell J12) produces the final chi-square statistic of

13.82653 observed on the bottom right (Table 11).

The chi-square statistic was verified by running table analysis from the statistics options

under the tasks and utilities toolbar of the statistics software. A screenshot of the procedural

setup inputs (Appendix I) includes the selections of “SMS use” for row analysis and “format” for

column analysis from the available spreadsheet variables. The distribution pattern was again 136

traditional, SMS users (33.83% of the population); 160 traditional non-users (39.8%); 71 online,

users (17.66%); and 35 online, non-users (8.71%). The resulting output below (Table 12) also

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provides a chi-square statistic of 13.8265, along with 1 degree of freedom, and the p value of

0.0002.

Table 12

Chi-Square Data and Results

Since the p value (0.0002) is less than the significance level (alpha = 0.05), the following

null hypotheses is rejected:


SMS use.

The results of the chi-square test of independence thus support the following research

hypothesis:


SMS use.

The directionality of this association indicates online advisors being more likely to use

SMS texting for student communication than traditional advisors.

Research Question 3

The third research question also involved inferential statistics but, since Likert scale

questions could be converted to continuous data, parametric tests could be attempted. Harpe

(2015) recommends “individual rating items with numerical response formats at least five

categories in length may generally be treated as continuous data” (p. 382). Survey item four

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(institutional support) provides a five-point Likert scale along with five categories making up the

total SMS support score detailed below. Accordingly, the six components of survey items four

(institutional support) and survey item five (SMS use) were used to address RQ3: Are there any

differences in institutional support of SMS as a platform for student communication between

advisors using and advisors not using SMS text messaging?

To analyze RQ3, the original data was edited to add an additional column for total

institutional support of SMS as a student-facing communication platform. The value for each

respondent’s support score was established by adding the six contributing ways in which an

institution could support SMS as a communications platform. Institutional support score (Table

13) lists the full survey statements as well as the short title which was recognized as the column

heading in Excel. Each statement was followed by a five-point Likert scale for the respondent’s

level of agreement and scored from strongly disagree (1) to strongly agree (5). For the purposes

of the current study, the operational definition of institutional support is a compilation of

manners in which institutions enable and encourage SMS communication between the student

and advisor. Institutional support is thus the cumulative value for agreement on the six support

types demonstrated below (Table 13).

Table 13

Institutional Support Score

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T tests were intended to compare the mean of continuous data regarding level of reported

institutional support for SMS between two groups to test the following null and research

hypothesis:

H0: There is no statistically significant difference between institutional support for SMS


H1: There is a statistically significant difference between institutional support for SMS


Under the tasks and utilities toolbar, t Tests were selected in the statistical software and

the new data set with total support score was uploaded and selected as the analysis variable. A

two-sample, two-tailed test was run and with the two support means being the basis for

hypothesis testing. The assumptions of this test were assessed. The first assumption in a two-

sample t test is that the means of both samples are normally distributed (Ott & Longnecker,

2016). The entirety of the statistical results (Appendix I) and test for normality (left) and sample

distribution (right) results can be seen below (Table 14).

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Table 14

Distribution of Institutional Support

The test of normality showed that mean support of both SMS users and non-users were

not normally distributed (Table 14). Therefore, a two-sample t-test was not an appropriate

analysis for this research question. Nonparametric tests were then run by selecting secondary

statistical methods. The alternative method applied was a Wilcoxon Rank Sum test which

determines whether two populations are statistically different based on ranks (Ott & Longnecker,

2016). The following null and research hypothesis were tested:

H0: There is no statistically significant difference between median institutional support

scores for SMS texting users and non-users.

H1: There is a statistically significant difference between median institutional support

scores for SMS texting users and non-users.

The assumptions of this test were met since the survey submissions are independent, are

at least ordinal in level of measurement, and the total support score can be applied as a

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continuous variable (Harpe, 2015). The Wilcoxon Rank Sum output data are displayed below

(Table 15) along with the overall and group medians for institutional support.

Table 15

Wilcoxon Rank Sum Test Results

The median for the entire sample (n=402) was 15. The median of SMS non-users

(n=195) was 12, while the median of SMS users (n=207) was 19. The p score (0.0001) of the

Wilcoxon Rank Sum test was again less than the significance level. Since the p value was less

than the significance level (alpha = 0.05), the null hypotheses was rejected. The results of the

Wilcoxon Rank Sum test thus reject the null hypothesis (H0) and support the research hypothesis

(H1) that there is a statistically significant difference between median institutional support scores

for SMS texting users and non-users. The directionality of the mean and medians both indicate a

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statistically significant difference with SMS users being more likely to have a higher score for

total institutional support for SMS.

Summary

Chapter Four has outlined the data collection phase of the research consistent with the

Chapter Three methodology plan, introduced the survey pilot results, and analyzed the three

research questions investigating advisor use and perspectives pertaining to SMS texting with

students. The survey design stemmed from the original work of Duran, Kelly, and Keaten

(2005) on CMC in higher education. Since the instrument was modified, a pilot survey was first

conducted to confirm content validity through the application of Lawshe’s (1975) model for item

inclusion. The process, outcomes, and alterations made as a result of SME feedback and item

ratings were delineated. The final instrument (Appendix C) was approved by the IRB and

opened on Google Forms from May through July of 2021.

In accordance with the organizations and associations approved for research support

(Appendix D1), social media groups (Appendix D2), and publicly available HEI directories

(Appendix D3), the survey link was posted, shared, or emailed to obtain 427 total responses.

Responses not qualifying as members of the target population were removed, resulting in 402

final responses for analysis. Survey submissions were scored using the data dictionary (Table 4)

and the resulting 30 columns and 402 rows of coded data were analyzed (Appendix H).

Summary statistics were used to examine ratios, averages, and frequency distributions (Table 5).

Of the 402 qualified respondents, 106 advisors were in predominantly online/internet-based

formats and 296 were traditional/ground campus advisors; 311 participants were female, 79 were

male, and 12 preferred not to say; 207 reported yes to the incorporation of SMS text messaging

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with students and 195 reported non-use; the average age was 41; and the average years of

experience was 11.

The quantitative approaches to descriptive and inferential statistics used to address the

three research questions were identified for their feasibility and appropriateness. Descriptive

statistics were used to respond to RQ1: What perspectives and motives do academic advisors

report regarding their use of SMS text messaging with students? Sections of the survey

pertaining to demographic data, institutional format, SMS use, age, and years of experience were

elected for summary statistical outputs (Appendix I). The analysis and discussion of RQ1

identified the mean, mode, median, and standard deviation of scores for motives and value

statements. Mean scores were used to rank both variables and distinguish predominant

responses. RQ1 found that advisors are using SMS messaging, use varies between subgroups

within the sample, and advisors on average have a positive view of the communication platform.

Frequency and agreement rankings, and the differences between subgroups, will be discussed

further in Chapter Five as well as the.

Inferential statistics were applied to data from survey items four and six (Appendix C) to

analyze RQ2: Does use of SMS text messaging with students differ between academic advisors

of online and ground campus environments? A chi-square test of independence test was

conducted and, since the p value (0.0002) was less than the significance level (alpha = 0.05), the

null hypotheses was rejected. The results of the chi-square test of independence thus supported

the research hypothesis that there is a statistically significant association between learning

environment and SMS use with online advisors being more likely to use SMS texting for student

communication.

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The six components of survey items four (institutional support) and survey item five

(SMS incorporation) were used to address RQ3: Are there any differences in institutional support

of SMS as a platform for student communication between advisors using and advisors not using

SMS text messaging? The test of normality showed that mean support of both SMS users and

non-users were not normally distributed (Table 14). Therefore, a two-sample t-test was not

appropriate since the necessary assumptions of the statistical method were not met.

Nonparametric approaches were conducted as an alternative and the Wilcoxon Rank Sum

test was applied. This approach determines if two samples are selected from populations with

the same distribution by assessing differences in sample median (Ott & Longnecker, 2016). The

p score of 0.0001 was less than the significance level (alpha = 0.05) so the null hypotheses was

rejected. The findings of RQ3 thus also support the research hypothesis that is a statistically

significant difference between median institutional support scores for SMS texting users and

non-users. The directionality of the mean and medians both indicate a statistically significant

difference with SMS users being more likely to have a higher score for total institutional support

for SMS. The outcomes of each research question will be further discussed in Chapter Five with

the results, conclusions, and recommendations based on these findings.

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Chapter Five: Results, Conclusions, and Recommendations

The purpose of the current, non-experimental study was to investigate the perceptions of

higher education advisors in the United States regarding SMS text messaging with students.

This quantitative study explored advisor use and perceptions on values, motives and institutional

support of SMS texting as a communication channel and the possible variables impacting those

factors. Chapter Four provided the data collection and analysis phases of the study. Chapter

Five provides additional observations regarding trends in the data and a discussion of the

statistical outcomes based on the following research findings and themes:

1. Advisors are using SMS texting

2. Online advisors are more likely to use SMS text

3. SMS is used to gain access to richer mediums

4. Gender may impact SMS use

5. Age may impact SMS use

6. Programmatic format may impact SMS motives

7. SMS supported institutions are more likely to text

8. Advisors have positive perceptions of SMS texting

After discussing these themes, Chapter Five reviews research limitations before

introducing theoretical implications of findings through the lens of adaptive leadership and

communication theories. Adaptive leadership is applied, along with Customer Relationship

Management (CRM), to frame practical implications for leaders in higher education. The

business decision for an HEI to incorporate SMS requires first considering how and why to use it

and then continuously assessing the impact on advising outcomes such as student persistence

(Joslin, 2018; Zarges, 2018). Implications for future recommended research are outlined for

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related studies and potential approaches. Lastly, the chapter will conclude with a summary of

research outcomes.

Discussion of Findings

Advisors are Using SMS Texting

Increasing demand is observable in the literature for students to have more mobile device

access to HEI professionals (Cameron & Pagnattaro, 2017; Cretu et al., 2020; Mirriahi &

Alonzo, 2015). The current study found that advisors across a broad range of institutions are

choosing to use SMS texting with students (51.49%) to accommodate mobile connectivity. SMS

text messaging use was reported from advisors ranging in age from 18 to 75 years old (Appendix

H). Use was found among advisors from different genders, experience levels, and programmatic

formats in the current sample which included the United States broadly. Some college and

university advisors still reported use even when they perceived their school to lack the associated

policy, tools, or training for the communications platform. For instance, despite the fact that

over half the participants reported using SMS text messaging (51.49%), 221 participants

(54.98%) reported (2) disagree or (1) strongly disagree with the statement: My institution

provides the technology tools necessary to support text messaging for student contact. In fact,

even among SMS users, 75 of 207 advisors (36%) reported disagreement with the institutional

support statement regarding technology tools. This indicates that, despite the lack of an

institutionally designated platform for text messaging, advisors are still incorporating it on their

own.

To effectively assess the impact of advisor messages on student outcomes, interaction

and engagement data need to be collected which calls for standardized operating systems

(Zarges, 2018). If HEIs lack SMS-related policy and resources, advisors may avoid text

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messaging altogether or not properly annotate the method of communication which prevents

leadership from measuring the scope of use or effect with any degree of accuracy. The finding

of use among any advisors with low institutional support scores reiterates the concern of Joslin

(2018) with regard to ad hoc applications and some institutional employees choosing to text

students from a personal device. If protocols for capturing the advisor-student interaction do not

exist, or texting software does not interface with a university’s data management system, it is

difficult to determine if or how this form of digital communication might impact an HEI’s goals.

Findings in the current study call for HEI leaders to consider standardization of technology tools

and policies which will subsequently be discussed in the practical implications section using

tenets of CRM and adaptive leadership.

Online Advisors are More Likely to Use SMS Text

One of the primary concerns for an online format relates to a sense of disconnect between

the student and university which, by design, distance advising should mitigate through

relationship management (Argüello & Méndez, 2019). Building meaningful ties and maintaining

satisfied students is undoubtedly a more significant challenge in online settings where all

communication is mediated by the technology through which it is delivered (Ng, 2018; Oregon

et al., 2018). In the current study, chi-square analysis supported a statistically significant

association between programmatic format and SMS use with online advisors being more likely

to use SMS texting for student communication.

A recognized limitation of this finding is that it cannot explain why a relationship exists

or if it is truly the research variable influencing the observed data. Gall et al. (2005) note that

while an association may be established, a key limitation is that the cause may not be clear.

While online advisors were found to be more likely to use SMS messages, this study does not

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purport to explain that relationship. Regardless of the study’s limitation, the association between

learning environment and advisor SMS use is noteworthy and should be considered when

transitioning a program to an online format. Gaytan (2015) recommends increased awareness

that delivering and catering to online programs must be accompanied by the same, if not a

greater, degree of interaction to ensure students are receiving the same level of consideration and

personalized communication as their face-to-face counterparts. Effective application of CRM to

promote student outcomes hinges on awareness of issues in online education; altering

communication approaches to best manage the relationship must acknowledge how technology

effects social interaction (Azhakarraja, 2020; Castleman & Meyer, 2020; Ghemawat, 2017; Mu

& Fosnacht, 2019; Suntornpithug, 2012).

SMS is Used to Gain Richer Medium Access

One of the key outcomes derived from RQ1 is a better understanding of motives for SMS

use with students and how those motives may vary between advisors. Theoretical comparisons

and practical applications, discussed later in this chapter, can arise from the overall motive

ranking as well as differences between subgroups. Participants reporting use of SMS were asked

to rate the level of frequency with regard to fourteen motives for initiating text messaging with

students. The various motivations pertaining to the advising function of SMS messaging are

below (Table 16) ranked from highest to lowest based on the mean of frequency usage.

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Table 16

Motives Ranked by Mean of Frequency

The richness of a communication channel pertains to the extent to which it recreates the

same level of information the sender originally expressed (Guffey & Loewy, 2018).

Interestingly, the top three motives (request student contact, ensure receipt of another message

such as email, and make an appointment) all relate to using SMS as a basis for gaining access to

richer channels for exchange. These were also the only three motives geared toward facilitating

another form of communication and not based directly on the function of advising itself. For

instance, “make an appointment” could be the use of text to coordinate a phone call for

discussing academic goals, whereas “discuss academic or career goals” implies the advising aim

occurred through the channel of SMS outright. Bernhold and Rice (2020) differentiate

communication channels by their level of richness; because of their visual and audio cues, face-

to-face mediums provide the richest form of advisor-to-student interaction. Other CMC

channels, such as phone calls are considered to have a higher level of media richness since they

contain audio cues. Emails, which are limited to written communication content, are still

considered richer than text messaging but also may be checked less frequently (Guffey &

Loewy, 2018).

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Text messages to request contact or make an appointment are frequently geared toward

using a leaner channel to obtain student interfacing opportunities to richer options and the

findings support this as the primary motive for SMS in the sample. The leaner channel of SMS

could facilitate videoconferencing or phone calls, which might later provide students with levels

of media richness closer to a traditional in-office advising appointment than email (Swanson et

al., 2020). Based on the top three mean scores in the current study, advisor motives for texting

students was primarily as a conduit for further exchange. This supports the earlier work of Lema

and Agrusa (2019) who claim student texts are to supplement and reinforce, not replace, other

forms of communication. RQ1 findings also correspond to the focus group results of Jensen

(2017), in which participants supported face-to-face meetings as preferential for more

meaningful contacts but SMS text messaging as less onerous in the coordination of

appointments, quick questions, and receiving accolades.

The SMS motives with the least predominant frequency also align with theoretical

expectations of media richness and channel selection; with text messaging having no

audio/visual cues, it was indeed used less for certain message types. Explaining policy and

discussing problems with a student’s behavior or performance present scenarios in which an

advisor would plausibly require more interactive feedback. Social presence theory contends that

CMC avenues exchange less socioemotional content as typed messaging are impersonal and lack

auditory and visual cues (Short et al., 1976). For these conversations, richer communication is

made possible through the current study’s top three ranking motives but, expectedly, advisors

were less likely to use SMS specifically for discussing the actual matters of policy, problems,

and behavior.

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The motive rankings of RQ1 thus correspond with conclusions from Junco et al. (2016)

that, while face-to-face meetings may be preferable, SMS texting can be used to increase the

likelihood of appointments occurring. With the business need of improving student persistence

and retention, SMS may enable more opportunities for richer communication thought to aid these

performance indicators in advising. This finding also reinforces more recent research from

Argüello and Méndez (2019) on the beneficial use of technology as a facilitator in coordinating

more traditional forms of exchange.

Gender may Impact SMS Use

In terms of subgroup differences based on demographics, the findings of RQ1 indicate

why and how often an advisor incorporates SMS texts varies slightly with gender. Female

respondents had overall lower mean scores than males and predominantly rated the frequency of

motive one (request student contact) highest, while motive 13 (ensure receipt of another message

such as email) ranked highest among males. Since requesting student contact could encourage a

phone call, virtual meeting, or a traditional office appointment, the advisor is encouraging a

richer channel where nuances like voice inflection, tone, and possibly even facial expression are

possible. On the other hand, motive 13 references receipt of an email, another CMC medium

with only written words and thus a less rich medium. The gender variances here indicate

females tend to opt for encouraging richer channels for social integration and connection while

males opt for efficiency of information delivery that may not enable the same degree of

emotional cues.

The incongruences in SMS motives between genders align with findings of seminal

authors regarding different male and female prerogatives for retention-based communication in

higher education. For instance, Bean (1980) supported student satisfaction as a more important

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factor for women than men. Similarly, Spady (1971) identified social integration as the highest

weighted variable for women in significance to learner satisfaction and, it had a greater impact

coming from university staff than student peers. In the current study, women predominantly use

SMS messaging to request student contact, which could conceivably be to discuss educational

topics in a richer communication channel. Their primary motives for use thus reinforce gender

variations in prioritization of student satisfaction and social integration found to be more

important variables in higher education for women than men (Bean, 1980; Spady, 1971).

Accordingly, the practices of men and women should be considered in SMS training and

adoption as use may vary by gender. This is supported by the literature as gender may also

influence technology acceptance in students (Weidlich & Bastiaens, 2018), faculty (Bailey,

2016; Marrs, 2013), and advisors (Brown, 2017; Leach & Wang, 2015) in higher education.

Age may Impact SMS Use

Some demographic differences found in the current study contradict existing literature on

age-related expectations. Dunaetz et al. (2015) explored preferences for media richness, finding

the number of communication channels used was found to decrease with age. The findings of

their study might lead to an expectation that older SMS users may only use the channel to obtain

access to traditional channels and less for independent messages. Interestingly, this was not the

case. While motive 13 (ensure receipt of another message such as email) was still in the top

three for both categories, the other predominant motives varied. Requesting contact and making

an appointment were still top motives for those under 40 years of age. However, for advisors

over 40, motives seven (relationship building) and eight (provide encouragement or recognition)

became the other predominant motives, ranking second and fourth respectively (Table 17).

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Table 17

Ranked Motives by Age

Programmatic Format may Impact SMS Motives

Outside of demographic subgroups, differences in mean rankings for SMS motives were

also apparent in the division between online and traditional advisors. Having a variety of

communication channels, advisors can selectively decide the best medium for their

communication objectives yet online students and advisors, because of their proximity, may be

limited to CMC platforms (Bernhold & Rice, 2020). Restricting opportunities for dialogue can

increase transactional distance and inhibit social connection between the student and university

(Huang et al., 2016; Moore, 1992). Retention and social engagement, core functions of advisors,

have accordingly been emphasized as hurdles for CRM in online settings (Joslin, 2018; Mu &

Fosnacht, 2019; Smith & Allen, 2006).

In the current study, survey submissions with advisors primarily in a traditional, ground

campus format most frequently used SMS as a means to facilitate richer communication

channels. However, in online formats, motives eight (provide encouragement or recognition)

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and seven (relationship building) jumped to the second and fourth positions respectively, ranking

higher even than motive two (making an appointment). Findings from RQ1 thus indicate SMS

motives are not solely geared toward augmenting more media-rich channels in online settings.

Troxel (2018) notes how an advisor’s position, by design, should influence student decision-

making through effective relationship management and motives reported in the current sample

indicate use associated with this CRM purpose. The implications of online advisor reported

motives will be discussed later in relation to communication theory and social presence.

SMS Supported Institutions are More Likely to Text

Given the data distribution of the current sample, nonparametric approaches were applied

to address RQ3 regarding the association between advisor use and institutional support of SMS.

For the purposes of the research, the operational definition of institutional support was identified

as the compilation of manners in which institutions enable and encourage SMS communication

between the student and advisor. Institutional support was scored as the cumulative value for

agreement on the following six SMS support statements:

1. My immediate supervisor encourages me to use text messaging as an option in student

communication.

2. The administration of my institution supports advisor use of text messaging in student-

facing communication.

3. My institution provides the technology tools necessary to support text messaging for

student contact.

4. The policies and procedures for incorporating text messaging with students are clear at

my institution.

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5. My institution’s leadership has clarified how texting can improve my performance as an

advisor.

6. I receive adequate training from my administration on how to use text messaging with

students.

The results of Wilcoxon Rank Sum test supported that there is a statistically significant

difference between median institutional support scores for SMS texting users and non-users. The

directionality of the mean indicated SMS users were more likely to have a higher score for total

institutional support for the communication channel. This finding further confirms existing

technology acceptance models in that advisor use of SMS could be impacted by organizational

and managerial support. Resistance to change inhibits technology initiatives and is further

hindered through a lack of senior management support (Granić & Marangunić, 2019;

Skoumpopoulou et al., 2018).

Findings from the current study found SMS users to have higher scores than non-users,

particularly for those support variables related to the involvement of leadership. The two highest

ranking mean scores among users were for supervisor encouragement at 3.94 and administration

support at 3.85 (Table 18). This supports Hrnjic’s (2016) assertions that a frequent failure in

adopting the CRM orientation to student-centric communication is a lack of managerial

participation and encouragement in the strategy and promotion of practices.

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Table 18

Institutional Support by Subgroup

The lowest scoring support mean for non-users was alignment at 1.63 (Table 18), which

references the following statement: My institution’s leadership has clarified how texting can

improve my performance as an advisor. This further substantiates the research of

Skoumpopoulou, Wong, Ng, and Lo (2018) who found behavioral intent for HEI professionals

using newer operating mediums was positively correlated with their perception of the technology

improving their work performance and management being helpful in learning functionality.

Advisors Have Positive Perceptions of SMS Texting

The long-term relationship and rapport academic advisors have with their students make

them better suited to determine what information should be sent, to whom, how, and when

(Brown, 2017; Kleeman, 2005; Schwebel, 2012; Vianden & Barlow, 2015). This makes advisor

perceptions critical in selecting what communication channels are necessary for maintaining the

student relationship. Zarges (2018) notes how technology has grown increasingly necessary in

maintaining student relationships and advisors need to be involved in the decision-making

process regarding the suitability of communication tools. The current study reinforces the notion

of analyzing advisor views as they are the technology users intended to bridge the gap between

the HEIs and their students (Ireland et al., 2016; Skoumpopoulou et al., 2018). Promoting action

and influencing decisions enables advisors to improve student outcomes aligned with their core

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objectives, such as improving retention (Troxel, 2018). Findings from RQ1 of the current study

indicate an overall positive view of SMS messaging as a platform for student interface. The

entirety of participant responses resulted in value statements one (mean=4.09), two (mean=3.82),

three (mean=3.76), and four (mean=4.07) all having mean scores higher than three. Advisors

thus had on average a positive view of SMS texting based on the following four statement:

1. Text messaging is a beneficial technology tool for increasing student communication.

2. Adding text messaging to my contact options improves my ability to build and manage

student-to-advisor relationships.

3. The ability to communicate with students through text message advances university goals

and my performance as an advisor.

4. The ability to communicate with their university through text messaging will be

increasingly important for students in the future.

Gaining commitment to an institution or business is a primary focus of CRM, which

sustains clients and retains students through engagement and communication (Azhakarraja,

2020; Calma & Dickson-Deane, 2020; Suntornpithug, 2012). CRM emphasizes getting the right

message in the right channel to the right person at the right time and promotes a more

comprehensive view of how advisors give and receive information holistically (Azhakarraja,

2020; Hrnjic, 2016; Suntornpithug, 2012). Since value statement one pertains to a positive view

of SMS text as a tool for increasing connection, the participant responses reveal that even

traditional advisors find value in the platform for the purpose of enhancing communication.

Value statement two pertains to the addition of SMS as another option in relationship

management. As demonstrated previously through advisor motives, SMS may be used to

encourage richer forms of communication. An overall higher level of agreement in the current

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study supports expanding digital communication options for advisors as desirable. By virtue of

possessing a variety of communication channels at their disposal, a channel can be selectively

chosen to best fit the needs of the student (Bernhold & Rice, 2020). A positive view of

enhancing university goals also supports the existing literature in that SMS has the potential to

provide a varied communication approach to save HEIs time and money through efficient

employee practices (Castleman & Page, 2016; Lema & Argrusa, 2019).

Limitations

A recognized weakness of the study is a lack of direct and comprehensive access to the

target population resulting in a convenience sample. As detailed in Chapter Three, the researcher

used a combination of avenues to access advising staff. Advisors are routinely encouraged to get

to know students on a personal level and work with them for a prolonged period of time, thus

better enabling curriculum advice, course placement, encouragement, and accountability in a

manner that fits their preferences and unique learning styles (Kleemann, 2005; Vianden &

Barlow, 2015). Survey participants were obtained through social media, organizational listservs

(or electronic mailing lists), and direct email. Accordingly, advisors came from institutions

participating in higher education organizations and partnerships that allow for doctoral research

or have publicly available contact information. Total population sampling was not possible as

contact information for the entirety of HEIs was inaccessible; limiting a true random sampling

approach, the current study was based on convenience or opportunity sampling (Leedy &

Ormrod, 2016).

The limitation of self-selection bias was also present since participants may have chosen

to complete the survey based on a personal interest in the topic and advisors indifferent to SMS

texting in higher education may thus be underrepresented (Zikmund, 2003). Since respondents

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were not randomly selected and decided voluntarily to participate, the researcher makes no

pretense of categorizing a representative subset of the overall target population (Leedy &

Ormrod, 2016). Although population subsets, such as online/traditional, private/public, and

proprietary/non-profit were all invited to participate, the sample is not generalizable to the entire

target population.

Limitations also exist in the quantitative methods selected for analyzing the data.

Descriptive statistics in RQ1 allow for meaningful presentation of observations for interpretation

but cannot support inferences about the larger population (Salkind, 2006). Inferential statistics in

RQ2 and RQ3 enable exploration for relationships among the selected variables; however, the

nonparametric tests cannot explain why a relationship exists or if it is truly the research variable

influencing the observed data (Zikmund, 2003). Differences in programmatic learning

environment and institutional administrative support for SMS, as well as demographic data, may

impact an advisor’s use of communications technology; however, the current study only

recognized if differences existed among advisor variables, not why (Cooper & Schindler, 2014).

Lastly, the research was limited to a snapshot of current perceptions held by academic

advisors during the May to July of 2021 survey timeline. Although practices in CMC exist in

various formats, such as online chat forums or email, the specific avenue of CMC exchange for

the current study was bound by messages specifically between academic advisors and students

through SMS text messaging. The target population was not limited to institutional type in terms

of programmatic format or status as private/public or proprietary/non-profit but was limited to

United States accredited institutions of higher education (DAPIP, 2020). The distinction of Title

IV degree granting was bound by the National Center for Education Statistics’ reported list of

4,313 public and private, 2 and 4-year colleges (NCES, 2021). These guardrails in the research

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will be applied for their limitations in the associated implications section as well as the

recommended directions for future research.

Theoretical and Practical Implications

In addition to the earlier demographic comparisons between current findings and existing

literature, there are also key themes for implications. The following sections use concepts in

CMC theory and adaptive leadership to expand on theoretical implications. Adaptive leadership

will also be used, along with best practices in CRM, to frame the practical implications for HEI

leaders resulting from the research.

Theoretical Implications

According to Short et al. (1976), social presence theory would lead to the expectation that

advisors would use SMS more frequently to make an appointment than to build a relationship.

The SMS motives with the most and least predominant frequency for the overall sample did in

fact align with theoretical expectations of media richness and message type. However, the

finding that online advisors more frequently use SMS for relationship building, than frequency

with which they request an appointment (in which to relationship build through a richer medium)

is not explained by the theory. Challenging the concepts of CMC limiting social presence,

Walther’s (1996) concept of hyperpersonal communication could be applied to understand this

finding although more research would be required.

The hyperpersonal theory disputes the limiting effect of CMC dialogue introduced by

Short et al. (1976) and argues digital media channels are actually capable of surpassing face-to-

face communication in developing interpersonal relationships. Walther’s (1996) research asserts

the sender and receiver are able to engage in more profound exchange because the lagging

response time allows for controlled and mindfully constructed content. The finding that online

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advisors in the current study more frequently used digital media for relationship building could

be grounded in Walther’s (1996) concepts, particularly when engaging new students. The

finding may also be rooted in a lack of alternative options or student preference; learners are

sometimes intimidated by communicating with HEI professionals in richer mediums and prefer

asynchronous communication, particularly before a relationship has established (Britto & Rush,

2013; Dornan, 2015; Suvedi et al., 2015). Regardless of the cause, 74% of users in the current

study reported some degree of SMS use for relationship building which supports more recent

studies finding that individualized text messaging can be used for advising relationships in

higher education (Arnold et al., 2020).

CRM through SMS text messaging represents an adaptive challenge, and, with adaptive

leadership, the goal is not the application of authority towards a resolution. The intent is rather

to place the responsibility of problem-solving to the very individuals who need to learn and shift

with the circumstance (Heifetz et al., 2009). For Khan (2017), solving problems in higher

education with an adaptive approach means recognizing societal communication pattern changes

in the external environment and considering how employee actions can benefit organizational

outcomes. Findings from the current study indicate that advisors are already using SMS texting

although university administrations may not widely support the use of this communication

channel. Lower levels of administrative support through SMS policies, supervisor

encouragement, technology tools, and training were found even among participants who reported

use.

Theoretical application of adaptive leadership means allowing for an environment

enabling advisor experimentation with communication approaches. Since CMC is an emerging

field in higher education, experimentation with channels for student engagement with the

123

university through technology are abundant (Aldosemani et al., 2016; Amador & Amador, 2017;

Blessinger & Wankel, 2013; Arnold et al., 2020; Castleman & Meyer, 2020; Davidovitch &

Belichenko, 2018; Huang et al., 2016; Junco et al., 2016; Oregon et al., 2018; Page et al., 2020;

Roache et al., 2020; Weidlich & Bastiaens, 2018). The current study indicates that advisors are

texting for a variety of reasons and have an overall positive view of the communication channel.

Practical implications for HEI leaders thus inherently include a call for SMS incorporation into

university strategy as the channel is already used and positively perceived by advisors. Instant

messaging as a university provided medium may also be a student’s desired format for

interaction and existing literature underscores that students report a greater level of satisfaction

when they are given the opportunity to elect their own feedback method (Bikanga-Ada et al.,

2017). Furthermore, although institutional support was found to be associated with SMS use,

advisors reported varying levels of support in their school’s leadership, training, policies, and

technology.

Practical Application

The digital transition facing HEI professionals requires efforts to meet changing student

expectations; adaptive leaders are able to take existing theory and best practices and apply new

approaches based on current needs (Dopson et al., 2019). Accountable for the design and

implementation of business strategy, institutional leaders are responsible for an organized

approach to managing student support options. Zarges (2018) notes the importance of advising

assessment and the use of communication data to evaluate student impact on retention and

educational outcomes. Joslin (2018) reiterates how failing to integrate CMC approaches into an

institution’s structural approach to relationship management prevents leaders from evaluating

how, or even if, it is improving outcomes. The current study emphasizes how aligning strategy

124

and software with demand can help ensure university communication is within the purview of

those needing to measure its impact on intended business outcomes like engagement and

retention.

Findings from the current study indicate advisors are using SMS texting for both

transactional and relational communication, even when their institution does not support the

channel. Accordingly, one practical application of the current study is for HEI leadership to gain

an awareness of their local level of use and consider adopting specific policies and system

integrated SMS platforms to better assess communication practices. Instead of arbitrary use by a

minority of the staff through their own accord, Joslin (2018) emphasized the adoption of formal

operating procedures to enable continuous evaluation. Considering if and how to support SMS

texting to communicate with students is thus a practical outcome for HEI leaders in the

development of business strategy. RQ3 demonstrated advisor use was associated with

institutional support. Accordingly, if leaders want to enable adaptive advisors to experiment

with interventions at scale and relationship building in student-centric mediums, it may help to

provide the framework conducive to SMS text.

Many universities are choosing to offer online degree options and the recent

circumstances surrounding Coronavirus (COVID-19) have forced many to transition classes to

an online platform (Roache et al., 2020). With continuous growth in online programs,

organizations need to consider the advising approaches in place to support those changes and

work collaboratively to address student needs (Kerby, 2015; Manyanga et al., 2017; Santos et al.,

2018; Uddin, 2020). Accordingly, a practical implication of RQ2 is the call for traditional

institutions to consider the policies and training made available for SMS texting. This also

includes considering the practices of their online counterparts, who had higher levels of SMS

125

use, before transitioning a program to an online format. A higher prevalence in use does not

necessarily mean online schools are subject matter experts in practical application. Nevertheless,

more experiences may be available to evaluate SMS texting’s impact through a site study

targeting an online institution over a traditional campus.

Future Recommended Research

Current trends in digital communication are drastically altering the attitudes and

protocols for HEI professionals in managing the student relationship (Chicca & Shellenbarger,

2018; Ghemawat, 2017; Junco et al., 2016). The current study demonstrates a positive view of

SMS messaging among advisors, but student perspectives are still lacking in the literature. Ali,

Uppal, and Gulliver, (2018) note how failing to act in accordance with communication demands

can negatively impact a student’s opinion of the school administration. Exploring student views

of two-way SMS for advising, not merely university mass messaging for updates, is

recommended to consider learner perspectives. The inconsistencies in SMS motives from RQ1

also provide another research opportunity to understand if and why some advisors prioritize

relationship building through leaner communication channels.

Digital communication is here to stay and, just as best practices are emphasized for more

traditional methods, similar policies and access should be considered for these emerging

mediums (Guffey & Loewy, 2018). This requires an in-depth look at what adaptive leaders are

doing and how they might involve advising practitioners in the process (Zarges, 2018). The

current study involved advisors across a diverse array of institutional type since the population

was targeted through social media and email to HEI organizations on a broad scale. Expectedly,

the level of institutional support varied between advisors, indicating some schools lack a clear

approach to SMS as a communication channel while others have embraced it. An advisor texting

126

from a personal device may be far less effective than an integrated system with visibility among

different university functional departments. Accordingly, future research is recommended to

explore impacts of a structured approach. The more recent work of Page, Castleman, and Meyer

(2020) introduces this type of site study regarding the impact of text messaging to mitigate

student attrition due to admissions requirements but more exploration is needed on effectiveness

in ongoing student relationships and long-term CRM focused retention.

Lastly, the quantitative nature of the current study enabled for the analysis of broader

perceptions but cannot contribute to specific SMS approaches in message content. A natural

extension of the study would be to repeat the same broad spectrum of advisors while enabling a

more robust opportunity for providing best practices in digital communication. Qualitative

research, to include advisor interviews or open-ended survey questions, on texting for CRM

purposes would be a valuable addition to the body of knowledge. As indicated in the literature

review, best practices for synchronous communication exist for advising as well as asynchronous

approaches to more formal types of digital exchange. However, verbiage from an email cannot

merely be copied and pasted to another digital media channel; approaches need to meet

expectations of the medium. If advisors are going to offer SMS text as an avenue for exchange,

similar best practices for the channel’s use in higher education should be explored.

Summary

Institutions have endeavored to incorporate newer technologies to be more attractive to

potential students and improve efficiency, making these colleges and universities more

competitive in their sector (Skoumpopoulou et al., 2018). CRM through a variety of digital

mediums is one such call for technology adoption among HEIs to better manage student

communication, including SMS texting (Juan-Jordán, 2018). The current study surveyed higher

127

education institution advisors’ perceptions of this medium and was tested for reliability and

validity through CVR and Cronbach’s alpha. Of the 402 qualified submissions, 106 advisors

were in predominantly online/internet-based formats and 296 were traditional/ground campus

advisors; 311 advisors were female, 79 were male, and 12 preferred not to say; 207 reported yes

to the incorporation of SMS text messaging with students and 195 reported non-use; the average

age of participants was 41; and the average years of experience was 11. Descriptive statistics

were used to depict the sample features regarding motives and perspectives academic advisors

report regarding their use of SMS text messaging with students. Mean scores were used to rank

responses for SMS motives and value statements between different groups.

Overall, advisors had a positive view of the communication channel and, although motive

rankings varied by subgroups, the predominant motives were geared toward reinforcing richer

mediums. The extent to which CMC, such as text messaging, is augmenting or substituting other

communication options has varied based on demographic data which is also supported in the

current study (Duran et al., 2005). Inferential statistics through chi-square analysis supported a

statistically significant association between learning environment and SMS use with online

advisors being more likely to use SMS texting for student communication. Based on RQ2,

online advisors are more likely to text and, based on RQ1, online advisors are more likely to text

for relationship building. RQ3 findings support a statistically significant difference between

median institutional support scores for SMS texting users and non-users. In the current sample

of advisors, SMS users were more likely to have a higher score for total institutional support for

SMS.

The current research reinforces the need for college and university leaders to be adaptive

in addressing the inequities of access in online education and be cognizant of existing practices

128

when taking a program online. Advisors from all subgroups are using SMS to gain access to

richer channels for communicating with students and have a favorable view of the medium.

SMS use was still found among advisors who perceived their HEI to show less support for the

platform. This sheds light on the prevalence of texting and the need to standardize

communication options. The association between policies, training, leadership, and technology

support also indicates HEIs may want to consider their overarching strategy towards SMS

communication for advisors to embrace incorporation.

129

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Appendix A: Informed Consent Form This is a voluntary, anonymous survey conducted by doctoral candidate Kathryn Looney to explore perceptions of Computer Mediated Communication. The research is in higher education, leadership, and relationship management as part of a doctoral dissertation with Franklin University. Before agreeing to participate, it is important that you read the following information: Purpose: This research intends to explore characteristics of the institution and advisors in relation to use of text messaging as a medium for communication with collegiate level students. Procedure: The survey will take approximately five minutes to complete. Questions will ask for your basic demographic information, college/university characteristics, and close-ended perceptions statements with Likert scale response options. Risks: This survey does not pose any additional risk than expected of everyday advising and engagement on your computer or laptop. If you feel uncomfortable at any time, you can withdraw from the study altogether without consequence. Benefits: Aside from the knowledge that you have contributed to continuing education and academic exploration, there are no other benefits of participating. Privacy: Your responses in this research are anonymous. The researcher will not know your IP address and will not collect identifying information such as your name or the name of your institution. Questions About the Study: Please contact Kathryn Looney at [email protected] if you have any questions, concerns, or comments. Review for the Protection of Participants: This study was approved by the Franklin University Institutional Review Board (IRB) and dissertation committee for ethical and responsible research involving human participants. The IRB can be contacted at [email protected] and the dissertation chair can be reached at [email protected] Electronic Consent: Clicking Yes to the question indicates that you have read all of the above and that you confirm all of the following:

● The study has been explained to you and you have had an opportunity to contact the researcher with any questions.

● You have been informed of the possible benefits and the potential risks of the study. ● You understand that you do not have to take part in this study, it is voluntary ● There is no penalty in refusing to participate or your decision to withdraw. ● You understand your rights as a research participant. ● You understand you may print a copy of this form for your records.

I have read and understood the above consent form and desire of my own free will to participate in this study.

Yes (___) No (___)

150

Appendix B: Permission to Use and Adopt Survey Instrument

151

Appendix C: Survey

Informed consent block text (Appendix A): Yes/No to consent (no-end of survey)

1. Do you currently serve in a role academically advising, mentoring, or counseling

collegiate level students? Yes/No (no-end of survey)

2. Is your institution recognized by the Department of Education as degree granting,

Title IV, and accredited? Yes/No/Unsure (no or unsure-end of survey)

3. In what learning environment do the majority of your current students primarily

attend classes? Online (Internet Based)/Traditional (Ground Campus)

4. Rate your level of agreement for the below statement using the following scale:

Strongly Disagree, 2: Disagree, 3: Neutral/Not Sure, 4: Agree, 5: Strongly Agree

The administration of my institution supports advisor use of text messaging in

student-facing communication.

1 2 3 4 5

My immediate supervisor encourages me to use text messaging as an option in

student communication.

1 2 3 4 5

My institution provides the technology tools necessary to support text messaging for

student contact.

1 2 3 4 5

The policies and procedures for incorporating text messaging with students are clear

at my institution.

1 2 3 4 5

152

I receive adequate training on from my administration on how to use text messaging

with students.

1 2 3 4 5

My institution’s leadership has clarified how texting can improve my performance as

an advisor.

1 2 3 4 5

5. Do you incorporate SMS text messaging as a means for student-facing

communication? Yes/No (yes- survey will continue to question 7, no- skip to 9)

6. Rate the level of frequency with regard to the below motives for why you initiate text

messaging with students using the following scale:

Never, 2: Infrequently, 3: Sometimes, 4: Frequently, 5: Very Frequently

Request student contact

1 2 3 4 5

Make an appointment

1 2 3 4 5

Make an announcement

1 2 3 4 5

Registration notifications

1 2 3 4 5

Cite/explain policy

1 2 3 4 5

Problem with student’s behavior or performance

1 2 3 4 5

153

Relationship building

1 2 3 4 5

Provide encouragement or recognition

1 2 3 4 5

Direct student to a university service

1 2 3 4 5

Discuss academic or career goals

1 2 3 4 5

Portal navigation or technical assistance

1 2 3 4 5

Finance reminders

1 2 3 4 5

Ensure receipt of another message such as email

1 2 3 4 5

Coordinate communication with another department

1 2 3 4 5

7. Rate your level of agreement with the statements below using the following scale:

Strongly Disagree, 2: Disagree, 3: Neutral or Not Sure, 4: Agree, 5: Strongly Agree

Text messaging is beneficial technology tool for increasing student

communication.

1 2 3 4 5

Adding text messaging to my contact options improves my ability to build and

manage student-to-advisor relationships.

154

1 2 3 4 5

The ability to communicate with students through text message advances

university goals and my performance as an advisor.

1 2 3 4 5

The ability to communicate with their university through text messaging will be

increasingly important for students in the future.

1 2 3 4 5

8. Please indicate your years of experience in advising/mentoring/coaching in an

academic setting: (__) two-digit blank

9. Please provide the following demographic details:

Gender: drop down box with male, female, and prefer not to say

Age: (__) two-digit blank

155

Appendices D1-3: Organizations/Associations, Social Media Groups, and Open Directories

Appendix D1: Organizations and Associations

Organization or Association Website MembersAcademic Affairs Professionals AAP https://www.facebook.com/groups/828330657273323 1,700

American Association of University Women

AAUW https://www.aauw.org/ 39,898

American College Counseling Association

ACCA http://www.collegecounseling.org/ 2,150

American College Personnel Association

ACPA https://www.myacpa.org/ 11,413

Association for University and College Counseling Center Directors

AUCCCD https://www.aucccd.org/ 930

Association of Independent Colleges and Universities of Ohio

AICUO http://www.aicuo.edu/ 276

California College Personnel Association

CCPA https://ca.myacpa.org/ 501

California Community College Student Affairs

CCCSAA https://www.cccsaa.org/ 359

Community College Student Affairs Professionals

CCSAP https://www.facebook.com/groups/577733269096629 1,500

Missouri Academic Advising Association

MACADA https://macada.wildapricot.org/ 223

Missouri College Personnel Association

MOCPA http://mo.myacpa.org/ 403

NACADA: Advising Community for Wellbeing & Advisor Retention

https://www.facebook.com/groups/nacadawbar 932

School Counselors Connect https://www.facebook.com/groups/1612117982356296 15,600

South Carolina Personnel Association SCCPA http://sc.myacpa.org/ 522

Student Affairs Professionals https://www.facebook.com/groups/SAPros 35,700 Student Affairs Training and

Developmenthttps://www.facebook.com/groups/455265548356941 3,200

The Admin: A Place for Student Affairs Professionals

https://www.facebook.com/groups/1649502385281379 2,800

The Student Affairs Collective https://www.facebook.com/SACollective 7,421 NACADA - Academic Advising

Administration Advising Communityhttps://www.facebook.com/groups/advisingadministration 1,600

California College Personnel Association

CCPA https://www.linkedin.com/groups/2295236/ 647

South Carolina College Personnel Association

SCCPA https://www.linkedin.com/groups/4144033/ 145

Illinois College Personnel Association ICCPA https://www.linkedin.com/groups/3989517/ 228 Military Educators https://www.linkedin.com/groups/2858287/ 1,030

Career College Central https://www.linkedin.com/groups/2063352/ 6,657 Association for Continuing Higher

EducationACHE https://www.linkedin.com/groups/1080867/ 2,425

Leadership for Student Success & Higher Education

https://www.linkedin.com/groups/5187932/ 2,927

156

Appendix D2: Social Media Groups

Higher Ed Learning Collective: 37.0K members

Research Papers, Research Thesis & Assignments: 39.6K members

Research & Development: 4.0K members

Doctoral Research Forum: 12.6K members

Higher Education Professionals: 4.3K members

Research and Researchers in Higher Education and Student Affairs: 3.8K members

Research Scholars: 204.8K members

Innovations in Higher Education Teaching and Learning: 3.6K members

Leadership Educators in Higher Education: 3.8K members

NACADA Nerds: Research, Writing, and Advising Scholarship: 1.2K members

Academic Positions: 70.2K members

Academic Services: 4.9K members

NACADA - Academic Advising Administration Advising Community: 1.7K members

The Admin: A Place for Student Affairs Professionals: 2.8K members

American College Counseling Association (ACCA): 2.2K members

AAUW: advancing gender equity: 7.9K members

157

Appendix D3: Open Directories

University Directory WebsitePurdue University https://cla.purdue.edu/students/advising/directory.html

University of South Florida https://www.usf.edu/undergrad/students/advising-offices.aspx

University of North Texas https://directory.untdallas.edu/directory?title=&tid=1

San Diego State University https://evaluations.sdsu.edu/academic_success/major_adviser_directory

Ball State University https://www.bsu.edu/academics/advising/contactus/facultyandstaff

University of New Brunswick https://www.unb.ca/fredericton/studentservices/academics/advisors.html

University of Houston https://uh.edu/provost/students/advising/

Northern Arizona University https://in.nau.edu/university-advising/

Utah State University https://advising.usu.edu/exploratory/directory/index

University of North Georgia https://ung.edu/directory/?name=&department=Academic+Advising&campus=All+Campuses

University of Illinois https://las.uic.edu/advising/major-minor-advising/

University of North Carolina https://advising.uncc.edu/directory/aim-mentors

University of Oregon https://advising.uoregon.edu/content/work-advisor

University of Missouri-Kansas https://cas.umkc.edu/current-students/academic-advisors-directory.html

Central Washington University https://www.cwu.edu/academic-advising/education-and-professional-studies-directory

Appalachian State University https://advising.appstate.edu/staff

East Carolina University https://advising.ecu.edu/advisor-contact-list/

The University of Arizona https://advising.arizona.edu/advisors/college

Michigan State University https://education.msu.edu/resources/students/student-affairs/#advisors

University of Arkansas https://fulbright.uark.edu/advising-center/directory.php

Eastern Kentucky University https://cbt.eku.edu/people/College_Advising

University of Nevada https://www.unlv.edu/healthsciences/advising/directory

New York University https://www.sps.nyu.edu/homepage/student-experience/resources-and-services/Advising.html

Blue Ridge Community College https://www.brcc.edu/directory/advising-center.html

University of Cincinnati https://cahs.uc.edu/current-students/academic-advising/advising-directory.html

University of Delaware https://lerner.udel.edu/faculty-staff-directory/groups/undergraduate-advising/

Florida Inernational University https://acs.fiu.edu/offices-services/advising/index.html

Idaho State Univrsity https://www.isu.edu/advising/

Old Dominion University https://www.odu.edu/success/academic/advising

University of Denver https://www.du.edu/studentlife/advising/

Liberty University https://www.liberty.edu/online/academic-advisors/

University of Hawaii https://manoa.hawaii.edu/undergrad/caa/directory/

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Appendix E: Organization and Association Correspondence

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Appendix F: IRB Approval and CITI Training Certificate

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Appendix G: Survey Invitation Message

Email Invite Hello esteemed advising professionals, I am a Doctoral candidate at Franklin University and am researching computer mediated communication for my dissertation, specifically text messaging, in higher education. Your responses to this survey will help gain a better understanding of advisor, counselor, and mentor perspectives toward interaction with students using this medium. Please feel free to share this link with your peers/collogues and thank you for your time! The survey is very brief and will only take about 2-5 minutes to complete. Please click the link below for the survey site (or copy and paste the link into your browser). https://forms.gle/DDC2N7Np2vJnJ6eP8 Your participation in the survey is entirely voluntary, all of your responses will be anonymous, and submitting does not require any personal identifying information. Thank you, Kate Looney

Social Media Invite

For advisors, counselors, and mentors in higher education: please consider taking a few minutes to share your perspectives on texting students in the brief (2-5 minute) anonymous survey below. I am a Doctoral candidate and your responses will help gain a better understanding of uses/views toward this medium for my dissertation. Your participation is VERY appreciated. Please feel free to share this link with your peers/collogues and thank you for your time! https://forms.gle/DDC2N7Np2vJnJ6eP8

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Appendix H: Coded Data

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Appendix I: SAS Screenshots

RQ1

Distribution Table Analysis: Institutional format

SMS use

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Gender

Summary Statistics: Experience

Experience by users and non-users

Experience by institutional format

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Experience by Gender

SMS use by Gender

Age

Age by gender

Age by institutional format

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Motives for SMS use

Motives by gender

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Motives by institutional format

Value Statements

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RQ2 Chi-square with data input view included

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RQ3 T tests with data input view included

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