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Accepting technology as a solution: A quantitative study investigating the adoption of technology at colleges

Dissertation
Author: III Leroy G. Demuth
Abstract:
Adopting a new technology is an important decision for many academic institutions. Colleges have had to upgrade technology or face diminishing enrollment as students choose to attend other, more responsive institutions. New and imaginative approaches to technology have been developed and implemented by faculty, support staff, and students to prepare for changing roles they will encounter in the workplace. This study was an examination of the adoption of technology by faculty, support staff, and students to identify dependent variables concerning the probability that faculty, support staff, and students will be innovator, early, early majority, late majority, or laggard adopters. The study was based on critical concepts observed by Everett Rogers regarding the diffusion of innovations theory. The quantitative research design was a statistical study as the chosen variables could not be manipulated. The data collection was conducted at several college campuses in Pennsylvania and New York. A total of 534 faculty, support staff, and students from two colleges in the State of Pennsylvania and three colleges in the State of New York responded to the Survey of Technology Use-Consumer. Results showed that late majority adopters were not significant variables in estimating the adoption of technology.

v Table of Contents Acknowledgments iv List of Tables vii CHAPTER 1. INTRODUCTION 1 Introduction to the Problem 1 Background of the Study 1 Statement of the Problem 3 Purpose of the Study 3 Research Questions and Hypotheses 4 Nature of the Study 5 Significance of the Study 6 Definitions of Terms 6 Assumptions and Limitations 7 Organization of the Remainder of the Study 8 CHAPTER 2. LITERATURE REVIEW 10 Diffusion of Innovation Theory 10 Review of Relevant Literature 20 Summary 35 CHAPTER 3. METHODOLOGY 36 Theoretical Framework 36 Research Design Strategy 37 Variables 37 Population and Sampling Procedures 38

vi Sample Size 38 Instrument 39 Data Collection 42 Data Analysis 42 Limitations of Methodology and Strategies for Minimizing Impact 43 Ethical Considerations 43 Summary 44 CHAPTER 4. DATA COLLECTION AND ANALYSIS 45 Descriptive Analysis 45 Analysis by Hypothesis 57 Summary 63 CHAPTER 5. SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS 65 Summary of the Research Study 65 Summary 69 Conclusions 74 Recommendations 79 Summary of Chapter 82 REFERENCES 83 APPENDIX A. SURVEY OF TECHNOLOGY USE–CONSUMER 88 APPENDIX B. MEAN SCORES OF SURVEY ITEMS 92 APPENDIX C. MEAN SCORES OF TECHNOLOGY ADOPTER CATEGORIES 93

vii List of Tables Table 1. Distribution of Responses by State 46 Table 2. Distribution of Responses by Gender 46 Table 3. Distribution of Responses by Position 46 Table 4. Distribution of Responses by Discipline 47 Table 5. Distribution of Responses by Length of Time at the College 48 Table 6. Distribution of Responses by Age of Respondent 49 Table 7. Distribution of Responses by Education 50 Table 8. Distribution of Responses by Experience With Voice Recognition Software 50

Table 9. Distribution of Responses by Age of Respondent‘s Computer 51 Table 10. Descriptive Statistics for Sum Score 52 Table 11. Distribution of Demographic Information 53 Table 12. Frequency of Rogers‘s Diffusion of Innovation Theory Variables by Adopter Category 56

Table 13. Summary of Results for General Research Hypothesis 1 57 Table 14. Summary of Results for Specific Research Hypotheses 1 to 5 61 Table 15. Summary of Results for Regression Analysis 63 Table 16. Summary of Results of Hypotheses Testing 77

1 CHAPTER 1. INTRODUCTION Introduction to the Problem Adopting a new technology is an important decision for many academic institutions. Gardner (2000) maintained that rapid and decisive changes in available technology do not allow academic institutions to remain static. The focus of this study was on the current problem of adopting technology at colleges. Academic institutions find technology as a solution toward improving direct quality communication, and compensating for inadequate keyboard skills, ergonomic inefficiencies, and tasks that are labor-intensive. Academic institutions need to act more rapidly and radically to adopt technology (Gardner). Background of the Study Academic institutions have moved from lecture-based instruction to active, cooperative learning supported by technology. Deden (1998) argued changes are often made in direct response to the growing demand for graduates who work in teams, solve open-ended problems, and think critically. Deden suggested that the need for technology- savvy graduates has left faculty little choice but to expand instructional methods to include technology. Alley (1996) observed that technology is an important component to integrate into academic institutions. Student technological abilities vary from that of passive receivers to active participants and partners in learning and using technology.

2 Technology allows students to obtain a large amount of information through the Internet, electronic mail (e-mail), voice recognition software, and electronic bulletin boards. Busacco (2001) contended the Internet allows students to access faculty, support staff, and other fellow students globally 24 hours a day. In addition, students can equip themselves with study resources over the Internet from professional and consumer Web sites, online journals, and chat rooms concerning topics related to specific academic disciplines. The use of increased technology in organizations has caused employers to view the hiring of technologically efficient employees as a problem (Busacco, 2001). Organizations use technology to become more efficient and provide increased amounts of information for use in decision making. The word diffusion was defined by Rogers (1995) as innovation. Rogers contended, ―Diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system‖ (p. 5). Communication involves spreading information to accomplish a task. Rogers reported, ―Diffusion is a special type of communication, in which the messages are about a new idea‖ (p. 6). DeSieno (1995) summarized possible benefits of adopting technology into higher education. First, technology provides easily accessible information and can be focused on the immediate needs of the student. Second, technology supports student interaction by encouraging responsible learning. Third, technology helps students learn fundamental concepts, freeing faculty to work with students on complex aspects of coursework. Fourth, technology allows for effective access to information and improves feedback. Other benefits posited by DeSieno include a reduction in the overall cost of education.

3 There are numerous benefits; however, not all faculty, support staff, or students are quick to adopt technology into the learning environment. This study was based on Rogers‘s (1995) diffusion of innovation theory, the purpose of which is to facilitate an understanding of diffusion and social change. This theory provides an explanation for the adoption of a new innovation, identified by Rogers as a process of social change. Diffusion is understood as the way innovation is communicated in many channels over time among individuals within a social system. Statement of the Problem This study was designed to determine whether Rogers‘s diffusion of innovation theory adoption curve occurred as a result of technology use among faculty, support staff, and students at universities in New York and Pennsylvania. This study was an attempt to determine whether faculty, support staff, and students are able to implement technology in the learning environment. The problem in this study was to explore the potential usefulness of determining whether the adoption of technology by faculty, support staff, and students is likely or unlikely. Purpose of the Study The purpose of this study was to test Rogers‘s diffusion of innovation theory curve to determine whether Rogers‘s categories of innovator, early, early majority, late majority, and laggard adopters could be applied to information technology application in universities. The population for the study included data from faculty, support staff, and students at universities. The Survey of Technology Use–Consumer (Institute for Matching Person & Technology, 2008) was used to collect data for this study. Faculty,

4 support staff, and students were provided with an opportunity to observe a technology video demonstration as a way to test the theory of diffusion of innovation, and to respond to the survey. The study applied critical concepts put forth by Rogers as they pertain to the diffusion of innovation theory. Research Questions and Hypotheses The following research questions and their null and alternative hypotheses guided this study: 1. R1: Can Rogers‘s theory predict the pattern of diffusion of faculty, support staff, and students to adopt new technology at colleges?

a. H 0 1: Rogers‘s theory cannot predict the adoption at colleges.

b. H a 1: Rogers‘s theory can predict the adoption at colleges.

c. Statistics: T test, correlation analysis, and regression analysis

2. R2: Can Rogers‘s diffusion of innovation theory curve predict technology use for the likelihood of innovator, early, early majority, late majority, and laggard adopters at colleges?

a. H 0 2.1: Rogers‘s diffusion of innovation theory curve of innovator adopters can predict the likelihood of using technology at colleges.

b. H a 2.1: Rogers‘s diffusion of innovation theory curve of innovator adopters cannot predict the likelihood of using technology at colleges.

c. H 0 2.2: Rogers‘s diffusion of innovation theory curve of early adopters can predict the likelihood of using technology at colleges.

d. H a 2.2: Rogers‘s diffusion of innovation theory curve of early adopters cannot predict the likelihood of using technology at colleges.

e. H 0 2.3: Rogers‘s diffusion of innovation theory curve of early majority adopters can predict the likelihood of using technology at colleges.

f. H a 2.3: Rogers‘s diffusion of innovation theory curve of early majority adopters cannot predict the likelihood of using technology at colleges.

5 g. H 0 2.4: Rogers‘s diffusion of innovation theory curve of late majority adopters can predict the likelihood of using technology at colleges.

h. H a 2.4: Rogers‘s diffusion of innovation theory curve of late majority adopters cannot predict the likelihood of using technology at colleges.

i. H 0 2.5: Rogers‘s diffusion of innovation theory curve of laggard adopters can predict the likelihood of using technology at colleges.

j. H a 2.5: Rogers‘s diffusion of innovation theory curve of laggard adopters cannot predict the likelihood of using technology at colleges.

k. Statistics: T test, correlation analysis, and regression analysis

3. R3: Do faculty and support staff fit the adoption curve of students to best estimate Rogers‘s diffusion of innovation theory of technology at colleges?

a. H 0 3: Faculty and support staff do fit the adoption curve of students on Rogers‘s diffusion of innovation theory of technology at colleges.

b. H a 3: Faculty and support staff do not fit the adoption curve of students on Rogers‘s diffusion of innovation theory of technology at colleges.

c. Statistics: T test, correlation analysis, and regression analysis

This study was an examination of technology adoption by faculty, support staff, and students at universities. Faculty, support staff, and students provided information about gender, position, discipline, length of time at the college, age, education, use of technology, and approximate age of the computer they were using at the time of the study. Nature of the Study Technology has been adopted at colleges and universities for the purpose of more responsive communications within the organization and incorporation in the learning environment. Past investigators have studied technology adoption, but their studies contain insufficient information directly related to the purpose of the present study. When

6 a behavior is consistent with existing norms, individuals deem it appropriate and are likely to behave in a similar manner; thus, the more individuals who adopt and use new technology, the more likelihood that others will follow (O‘Reilly & Caldwell, 1985). This study followed the quantitative method and implemented a researcher-administered paper survey to collect data about technology adoption at colleges in the States of Pennsylvania and New York. The study used close-ended responses. Follow-up e-mail communication was implemented when a need for any clarification arose. Significance of the Study This study was designed to determine whether faculty, support staff, and students at colleges were likely or unlikely to adopt new technology. This study contributed new knowledge to current research on technology adoption. In addition, a novel approach to the data collection may inspire future researchers to explore other findings resulting from this research study. Definitions of Terms The following terms are presented for clarification. The general subject of the study was the adoption of technology by faculty, staff, and students at universities. The specific subject was the extent to which the population under study implemented technology in the learning environment. Adoption. ―The decision to make full use of an innovation‖ (Rogers, 1995, p. 21). Diffusion. ―The process by which an innovation is communicated through certain channels over time and among members of a particular social system‖ (Rogers, 2003, p. 5).

7 Early adopter: Respect. ―The early adopter is respected by his or her peers, and is the embodiment of successful, discrete use of new ideas‖ (Rogers, 2003, p. 283). Early majority adopter: Deliberate. ―The early majority may deliberate for some time before completely adopting a new idea‖ (Rogers, 2003, p. 284). Innovator: Venturesome. ―The salient value of the innovator is venturesomeness, due to a desire for the rash, the daring, and the risky‖ (Rogers, 2003, p. 283). Jobs-to-be-done theory. ―Products are successful when they connect with a circumstance—with a job that customers find themselves needing to get done‖ (Christensen et al., 2004, p. 281). Laggard: Traditional. ―Laggards tend to be suspicious of innovations and of change agents. Their innovation-decision process is relatively lengthy, with adoption and use lagging far behind awareness-knowledge of a new idea‖ (Rogers, 2003, p. 284). Late majority adopter: Skeptical. ―Innovations are approached with a skeptical and cautious air, and the late majority do not adopt until most others in their system have already done so‖ (Rogers, 2003, p. 284). Learning. ―Characterized by the exchange of ideas, thoughts, and feelings between and among people, resulting in new ways of viewing the world or in new ways of acting‖ (Lauzon, 1992, p. 33). Assumptions and Limitations The following assumptions and limitations were pertinent to the proposed study. Assumptions 1. It was assumed that all research respondents would respond honestly to the survey.

8 2. It was assumed that the Survey of Technology Use–Consumer would retain nearly equivalent content, validity, and reliability indicated in the research literature, and would be representative of the variable acculturation of innovative technology.

3. It was assumed that the respondents completing this survey would understand the questions being asked and have experience with innovation technology.

4. It was assumed that faculty, support staff, and students would be efficient in using technology.

5. It was assumed that faculty would be aware of relevant uses of technology in seminars and workshops on campus and in other forms of communication.

6. It was assumed the respondents would become confident to use more technologies after participating in this study.

Limitations 1. Faculty, support staff, and students may have been exposed to computer and projector technology malfunctions on the days of this study.

2. Local community innovative technology experts were not allowed to participate in this study.

3. The sample for the proposed study was limited to faculty, support staff, and students of at least 18 years of age at the colleges.

4. Despite the confidentiality and anonymity provisions of the research, respondent responses may have been biased due to their feeling they must respond in a socially acceptable manner on the survey.

5. The study results were limited by the honesty of the respondents to survey questions that may have been influenced by extraneous factors that cannot solely be controlled, including but not limited to (a) personal events occurring on campus resulting in negative emotion bias, (b) time-of-day variations, (c) amount of time in between college classes available to complete survey questions, and (d) individual experiences of the respondents‘ use and adoption of technology.

Organization of the Remainder of the Study This dissertation comprises five chapters. Chapter 1 presented an introduction to the study, the background of the study, statement of the problem, purpose of the study,

9 research questions and hypotheses, nature of the study, significance of the study, definitions of terms, assumptions and limitations, and organization of the remainder of the study. Chapter 2 consists of a thorough review of the literature arranged by major themes and topics on diffusion of innovation theory in relation to the problem identified. Chapter 3 presents the theoretical framework for the study and the study‘s research design strategy, variables, population and sampling procedures, sample size, instrument, data collection, data analysis, limitations of methodology, strategies for minimizing impact, and ethical considerations. Chapter 4 is an analysis of the data collection utilizing the methodology instrumentation indicated as descriptive analysis and analysis by hypotheses in chapter 3. Finally, chapter 5 comprises conclusions, summaries, recommendations of contributions of the research study developed from the data presented in chapter 4, and implementations and recommendations for future research in the field of interest.

10 CHAPTER 2. LITERATURE REVIEW The purpose of this study was to identify whether faculty, support staff, and students were adopters of technology at colleges. This chapter consists of literature relevant to technology adoption in higher education and is divided into three main sections. The first section is a presentation of the diffusion of innovation theory in higher education and technology, and includes a discussion of research and support of technology for the study, including summaries of adoption technology studies in higher education. The second section presents a review of relevant literature. Scholarly books, seminal journal articles, and research documents were examined for this literature review through the Capella University library. Additional databases searched included EBSCOhost and ProQuest. The online databases of Google Scholar also provided information for the search of the pertinent literature. Bibliographic and reference listings were accessed from appropriate titles discovered within the review process. Approximately 55 current scholarly articles pertaining to business, technology, educational institutions, colleges, innovation, and diffusion were reviewed. Diffusion of Innovation Theory The increasing use of technology in organizations motivates employees to embrace technology while at the same time regard it as a problem. Organizations use technology to provide increased amounts of information in decision making and to improve efficiency. The advent of word processing was the first application of

11 technology in the workplace to improve efficiency, and was initially viewed by some as only a secretarial tool. Nevertheless, diffusion of innovation was the outcome of implementing this technology in the workplace. As defined by Rogers (1995), ―diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system‖ (p. 5). This definition communicates a new idea about innovation. Communication involves spreading information to accomplish a task. Rogers asserted that ―diffusion is a special type of communication, in which the messages are about a new idea‖ (p. 6). Rogers‘s diffusion of innovation theory curve treats technology as a serious social system problem. Stages of Diffusion of Innovation Rogers‘s (1995) diffusion of innovation theory is focused on the idea that new innovation devolves from creation. The innovation process goes through channels that are created over time among members of an organization (Clarke, 1999). The process stages include knowledge, persuasion, decision, implementation, and confirmation. The knowledge stage includes explaining the function of an innovation. The decision stage seeks commitment on innovation adoption, then implementation occurs. The last stage is confirmation, which is usually based on outcomes (Clarke). Examples of Diffusion of Innovation Clarke (1999) gave examples of how different innovations are accepted at different intervals. One example is the Sony Walkman. Clarke identified this innovation as one that attracted much attention. Some other innovations have great potential but are waiting until society has a need—for example, the fax machine. It was invented but not

12 rapidly implemented until people were ready to adopt it. Clarke suggested that most innovations slowly penetrate an organization, after which adoption grows more quickly. Clarke also recognized that, quite often, this quick adoption slows afterward. Research on Diffusion of Innovation Diffusion of innovation research was first started in 1903 by seminal researcher Gabriel Tarde, who first plotted the S-shaped diffusion curve. In the 1940s, Ryan and Gross continued interest in diffusion of innovation using the S-shaped curve. Everett Rogers then reevaluated and continued in the adopter categories in the 1960s. Diffusion of innovation research has increased over the past decades due to its versatility. Various research studies have had similarities, such as the S-curve, which provides this versatility. A study conducted by Smith (2000) of diffusion of innovation in an organization resulted in an adoption rate of 14%. Generally, adoption rates are relevant to how quickly people accept technology. Abandonment of newly adopted technology in Smith‘s study was as high as 90%. Smith stated that costly abandonment and adoption rates revealed that organizations do not know how to motivate its employees to adopt new technology. Grubler (1996) found organizations struggled with adoption of technology to increase productivity. A study conducted by Karahanna, Straub, and Chervany (1999) recounted pre-adopters‘ and post-adopters‘ views on diffusion. The results demonstrated that potential adopters vary in behaviors, attitudes, and norms. Potential adopters are pressured by the norms of the organization, and the intention to use technologies is determined by attitude (Karahanna et al.). Potential adopters either reject the innovation, adopt the innovation, or are forced to use the innovation because a decision to use it has been made at a higher level (Rogers, 1983).

13 Bayer and Malone‘s (2003) research revealed that diffusion of innovation is necessary for an organization or individual to be successful. Bayer and Malone concluded, however, that a single implementation approach or strategy is not best for all new technologies. Characteristics of Diffusion of Innovation Clarke (1999) characteristics of diffusion of innovation include 1. Relative advantage, which is the degree that an innovation improves ways of doing jobs

2. Compatibility or consistency required within an organization based on past needs and experiences

3. Complexity, which is the extent to and way in which an innovation is implemented within an organization

4. Trialability, which is a prototype of an innovation introduced into the workplace

5. Observability, which is how visible the results are of an innovation

Clarke (1999) defined various categories as innovator, early, early majority, late majority, and laggard adopters. This categories were characterized as venturesome, respectable, deliberate, skeptical, and traditional respectively (Clarke). Kappelman (2003), in his recent article, ―The Big Picture: We‘ve Only Just Begun to Use IT Wisely,‖ contended, We‘re still in the beginning of the Information Age; where it goes is up to us. Enterprise architecture isn‘t the business any more than a map is the highway or blueprints are the building. But maps, blueprints, and enterprise architecture are tools to help us efficiently and effectively get where we want to go. Without them, we‘re lost. (p. 2)

14 Innovations are adopted in phases. Implementation plans are developed to meet specific needs within an organization and fit specific characteristics of the innovation. Tarde (1903) defined the innovation-decision process as a series of steps that include 1. First knowledge 2. Forming an attitude 3. A decision to adopt or reject 4. Implementation and use 5. Confirmation of the decision Rogers‘s 1995 study on the diffusion of innovation was conducted in many areas of business. His research was applied to farming, medicine (Glanz & Rimer, 1995), justice (Travis, 1998), and others. The steps of characteristics from Rogers‘s diffusion of innovation have been important for organizations since the 1960s. Champions of Innovation Rogers (2003) explained the role of champion in his recent book on the diffusion of innovation. Rogers defined a champion as ―a charismatic individual who throws his/her weight behind an innovation, thus overcoming indifference or resistance that the new idea may provoke in an organization‖ (p. 14). A supervisor does not have to become a champion; however, a new idea needs a champion to survive in an organization. Smith (2003) supported Rogers‘s statement that rank makes no difference in a champion. He reported that hierarchical lines diminish ways to make people feel that they are part of a team. Dessler (2003) argued that gaining everyone‘s support requires the diminishing of lines of authority to enhance effectiveness in diffusion of innovation.

15 Dessler also stated that everyone‘s support is not necessary to diffuse innovation; at the same time, it is meaningless without employee support. Communication of Diffusion The main elements in the diffusion of innovation theory are communication channel, time, and social system. Innovation is an idea, a practice, or an object viewed as a new approach. Communication is a process that allows participants to develop and share information for a mutual understanding. Time is the rate at which the process of change occurs. Social systems focus on information related to members of a group faced with adoption. Diffusion theory incorporates methods to examine ways that innovation is adopted and why it is adopted at different rates. The diffusion of innovation theory is a communication-based theory primarily grounded in Rogers‘s (1995) synthesis in more than 1,500 case studies. Rogers stated that individuals are classified based on how quickly their adoption of innovation occurs. A bell-shaped distribution curve of innovation and potential for acceptance included innovator (2.5%), early (13.5%), early majority (34%), late majority (34%), and laggard (16%) adopters in his study. Innovators are identified as appreciative of new ideas, and they are usually at the forefront. Early adopters are usually the first to adopt an innovation after the innovators. Early majority adopters are viewed as average persons within a system who adopt new ideas once the early adopters do so. Late majority adopters are skeptical and often adopt new ideas after the average person within an organization. Laggard adopters are the last to adopt innovation in a social system (Rogers). The diffusion of innovation theory states that adoption rate takes place over

16 time, with innovations going through a gradual growth, followed by a dramatic growth, gradual stabilization, and—finally—decline. Innovations (new ideas, practices, or objects) take a lengthy period of time to implement, ranging from months to years from the time they become available to the time they are widely adopted (Rogers, 1995). Sociologists believe innovation occurs within diffusion. The diffusion of innovation theory includes adoption of a new idea (Rogers, 1983). The diffusion of innovation theory encompasses diffusion and social change. Adopters are affected by the time it takes for a social system to incorporate information. This theory is the explanation for adoption of a new innovation, otherwise identified by Rogers (1995) as a process for social change. Innovation research by Rogers (1995) did not originally include the topic of information systems, although researchers identified this theory as useful as a process for technology adoption. Rogers stated that individuals can be classified based upon how quickly adoption occurs for an innovation. Among others, Rogers identified two adopter categories as early adopters and late majority adopters. This study also found these two categories to be significant in predicting diffusion of innovation. Rogers described specific tendencies related to adoption. First, a person may wait to try out a new innovation. Second, a person is either comfortable using new technology, or waits until the technology is established, as is the case for the early adopter and late majority adopter respectively. A person‘s threshold to adopt technology varies on certain levels of Rogers‘s diffusion of innovation theory. The diffusion of innovation theory explains why innovations are adopted at different rates. Innovation is as an idea, a practice, or an object perceived as new by a

Full document contains 102 pages
Abstract: Adopting a new technology is an important decision for many academic institutions. Colleges have had to upgrade technology or face diminishing enrollment as students choose to attend other, more responsive institutions. New and imaginative approaches to technology have been developed and implemented by faculty, support staff, and students to prepare for changing roles they will encounter in the workplace. This study was an examination of the adoption of technology by faculty, support staff, and students to identify dependent variables concerning the probability that faculty, support staff, and students will be innovator, early, early majority, late majority, or laggard adopters. The study was based on critical concepts observed by Everett Rogers regarding the diffusion of innovations theory. The quantitative research design was a statistical study as the chosen variables could not be manipulated. The data collection was conducted at several college campuses in Pennsylvania and New York. A total of 534 faculty, support staff, and students from two colleges in the State of Pennsylvania and three colleges in the State of New York responded to the Survey of Technology Use-Consumer. Results showed that late majority adopters were not significant variables in estimating the adoption of technology.