Relationship of Self-Efficacy to
Stages of Concern in the Adoption of
Innovation in Higher Education
Dissertation presente...
Overview
• Need for the Study
• Literature Review
• Method
• Results
• Discussion
• Questions
2
Need for the Study
Problem Statement
• Cost of innovation adoption.
• A desire for a prescriptive innovation adoption
model or approach.
• Hi...
Purpose of the Study
• Quantitative, statistical research.
• Explore the relationship between various self-
efficacies and...
Research Questions
Innovation Identification
1.Which stage of Rogers’ (2003) Diffusion of
Innovations curve is the chosen ...
Research Questions
Self-Efficacy and SoC
3.What is the relationship between instructor
general self-efficacy and the level...
Benefits of the Study
1. To contribute results to an area of innovation
diffusion in higher education that has not yet
bee...
Literature Review
Innovation & Diffusion Models
10
Technology Acceptance
Model (TAM)
11
… the TAM can only explain “about 40%
of the variance in individuals’ intention
to us...
Innovation & Diffusion Models
12
Diffusion of Innovations
(Innovation-Decision Process)
5 Confirmation Seeking reinforcement or reversing the adoption
deci...
Transtheoretical Behavioral
Change Model (TBCM)
7 Termination Often excluded due to the impossibility of zero
temptation a...
CBAM Stages of Concern
(SoC)
IMPACT
6 Refocusing Exploring more benefits. Making major
changes or seeking replacement.
5 C...
Self-Efficacy (SE)
• Defined as intrinsic “beliefs about one’s
perceived capability … to attain designated
type of perform...
Method
Research Study Design
Examine the relationship between SoC and 3 types
of SE (general, college teaching, and teaching with...
Population Profile & Setting
• Mid-Atlantic, land-grant university.
• Population: Actively teaching faculty.
• 1,713 poten...
Instruments
One Questionnaire, five parts:
1.Demographic questions
2.Prieto’s College Teaching Self-Efficacy Scale
(CTSES)...
Procedure
• Google Applications for Education was the
innovation under study.
• Ran study 7 weeks (Dec 2012 to Jan 2013)
•...
Data Analysis Methodology
• Each set of SE responses was averaged
together for overall scores and std. dev. for
the group....
Data Analysis Methodology
For each of the three research questions pertaining
to SE and SoC, the nominal logistic regressi...
Research Question 1 Results
Which stage of Rogers’ Diffusion of Innovations
curve is the chosen innovation in?
•Google App...
Research Question 2 Results
What is the Stages of Concern profile for
instructors in the use of the innovation?
•Majority ...
Research Question 3 Results
What is the relationship between instructor
general self-efficacy and the levels of the
Stages...
Research Question 4 Results
What is the relationship between instructor
teaching self-efficacy and the levels of the
Stage...
Research Question 5 Results
What is the relationship between instructor
technology self-efficacy and the levels of the
Sta...
Discussion
• Analysis at this point of the innovation revealed
no relationships to Stages of Concern.
• Why?
• Google Apps...
Recommendations
• Run study at several different stages in the
life of an innovation.
• Use of another diffusion model.
• ...
Thank you:
Dr. Cennamo, Dr. Moore,
Dr. Evans, and Dr. Doolittle
Dissertation presented by
• Amber D. Marcu
March 20, 2012 ...
Upcoming SlideShare
Loading in …5
×

Relationship of Self-Efficacy to Stages of Concerns in the Adoption of Innovation in Higher Education

1,364 views

Published on

In this research, it was proposed that self-efficacy is the missing underlying psychological factor in innovation diffusion models of higher education. This is based upon research conducted in the fields of innovation-diffusion in higher education, technology adoption, self-efficacy, health and behavioral change. It was theorized that if self-efficacy is related to adoption, it could provide a quick-scoring method for adoption efficiency and effectiveness that would be easy to administer. The innovation-diffusion model used in this study was Hall and Hord\'s (1987) Concerns Based Adoption Model (CBAM) and it\'s Seven Stages of Concern (SoC) About an Innovation. The SoC measures a user\'s perception of"and concerns about"an innovation over time. The self-efficacies under study were general, teaching, and technology. The scales used in this research instrument were Chen\'s New General Self-Efficacy (NGSE), Prieto\'s College Teaching Self-Efficacy Scale (CTSES), and Lichty\'s Teaching with Technology Self-efficacy scale (MUTEBI), respectively. This research hoped to uncover a relationship between self-efficacies and a Stage of Concern in the adoption of an instructional technology innovation, Google Apps for Education, at a large university institution. Over 150 quantitative responses were collected from a pool of 1,713 instructional faculty between late Fall 2012 and early Spring 2013 semesters. The response group was not representative of the larger population. Forty-six percent represented non-tenure track faculty compared to the expected 19 percent. Analysis using nominal logistic regression between self-efficacy and Stages of Concern revealed that no statistically significant relationship was found. Of note is that nearly all participants could be classified as being in the early-stages of an innovation adoption, possibly skewing the overall results. Complete dissertation can be obtained from http://hdl.handle.net/10919/19340

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,364
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
56
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • ABSTRACT In this research, it was proposed that self-efficacy is the missing underlying psychological factor in innovation diffusion models of higher education. This is based upon research conducted in the fields of innovation-diffusion in higher education, technology adoption, self-efficacy, health and behavioral change. It was theorized that if self-efficacy is related to adoption, it could provide a quick-scoring method for adoption efficiency and effectiveness that would be easy to administer. The innovation-diffusion model used in this study was Hall and Hord’s (1987) Concerns Based Adoption Model (CBAM) and it’s Seven Stages of Concern (SoC) About an Innovation. The SoC measures a user’s perception of—and concerns about—an innovation over time. The self-efficacies under study were general, teaching, and technology. The scales used in this research instrument were Chen’s New General Self-Efficacy (NGSE), Prieto’s College Teaching Self-Efficacy Scale (CTSES), and Lichty’s Teaching with Technology Self-efficacy scale (MUTEBI), respectively. This research hoped to uncover a relationship between self-efficacies and a Stage of Concern in the adoption of an instructional technology innovation, Google Apps for Education, at a large university institution. Over 150 quantitative responses were collected from a pool of 1,713 instructional faculty at between the Fall 2012 and Spring 2013 semesters. The response group was not representative of the larger population. Forty-six percent represented non-tenure track faculty compared to the expected 19 percent. Analysis between self-efficacy and Stages of Concern revealed that no significant relationship was found. Of note is that nearly all participants could be classified as being in the early-stages of an innovation adoption, possibly skewing the overall results.
  • Cost – “Large-scale innovation adoption often puts the organizations at risk for huge financial losses. Examples from the IT industry can be easily cited: “Hewlett-Packard’s failure [to successfully implement an innovation] in 2004 had a financial impact of $160 million and Nike’s failure in 2000 cost $100 million in sales” (Venkatesh & Bala, 2008, p. 274). There exists a desire for a model that combines: “(a) a systemic view, with (b) attributes from Rogers’ (1962) Diffusion of Innovations, with (c) research into affective characteristics to create a (d) prescriptive model for innovation adoption” (p. 4) Closest matches are Technology Acceptance Model (TAM) and Concerns Based Adoption Model (CBAM). TAM identifies “perceptions of the technology innovation under study” (p. 5): Perceived usefulness and perceived ease of use. CBAM measures “faculty member’s concerns as they engage an innovation” (p.5): adopter-centric, how they perceive the adoption and how they are feeling about the innovation.
  • Dissertation pg. 12
  • Dissertation pg. 12
  • Dissertation pg. 13
  • Dissertation pg. 18
  • Innovation can be a process, procedure, a product, or ideology that presents the opportunity for change. Diffusion is defined by Rogers (2003) as “the process in which an innovation is communicated through certain channels over time among the members of a social system” (p.5). It is also a kind of “social change, defined as the process by which alteration occurs in the structure and function of a social system” (p. 6). 4 elements: The innovation, Communication channels, Time, and the Social system. 5 adopter categories: Innovators, Early adopters, Early majority, Late majority, Laggards. Prospectus page 58. Diffusion of Innovations (Rogers, 2003) – 5 innovation-decision process stages Technology Acceptance Model (TAM) (Davis, 1985; Venkatesh & Bala, 2008) – User’s perceptions of innovation usefulness and ease of use Transtheoretical Behavioral Change Model (TBCM) (Prochaska & DiClemente, 1982) – 7 stages of change Concerns Based Adoption Model (CBAM) - Stages of Concern (SoC) (Hall & Hord, 2001) – 7 stages of concerns
  • Dissertation page 49. Developed in 1985 when Fred Davis completed his thesis at MIT. This model “attempts to predict individual adoption and use of IT’s” (Venkatesh & Bala, 2008, p. 276), by focusing on a user’s perception of innovation usefulness and ease of use. In 2000, the suggestion to investigate intrinsic motivation and technology self-efficacy was given. Page 6-7: Unfortunately, when measured as an element for explaining how, why, or when an adopter will adopt an innovation, the TAM still falls short. Although it can measure some amount of “technology trepidation” an adopter may experience, it is not a true gauge of their potential for innovation adoption. In recent literature by Venkatesh and Bala (2008), the TAM can only explain “about 40% of the variance in individuals’ intention to use an IT and actual usage” (p. 276). In truth, the determinants responsible for users’ adoption are still unknown. Also, the TAM has been harshly criticized for its noticeable “lack of actionable guidance to practitioners” (Venkatesh & Bala, 2008, p. 274). While it provides insightful overviews of technology adoption and how to improve software design for favorable adoption, it has done little to explain the role of the adopter in terms of affective characteristics. This lends itself to being classified still as more of a descriptive rather than a prescriptive adoption model. Clear steps or precise explanations for when to provide certain types of solutions are also not provided in the TAM. This makes it difficult to put the theory of the model into practice. Lastly, the TAM still focuses more on the (technology) innovation itself rather than on the adopters of the innovation. Venkatesh and Bala (2008) themselves admit that the model and resulting processes are still descriptive and innovation-focused, not yet prescriptive and user predictive as hoped.
  • Innovation can be a process, procedure, a product, or ideology that presents the opportunity for change. Diffusion is defined by Rogers (2003) as “the process in which an innovation is communicated through certain channels over time among the members of a social system” (p.5). It is also a kind of “social change, defined as the process by which alteration occurs in the structure and function of a social system” (p. 6). 4 elements: The innovation, Communication channels, Time, and the Social system. 5 adopter categories: Innovators, Early adopters, Early majority, Late majority, Laggards. Prospectus page 67. Diffusion of Innovations (Rogers, 2003) – 5 innovation-decision process stages Technology Acceptance Model (TAM) (Davis, 1985; Venkatesh & Bala, 2008) – User’s perceptions of innovation usefulness and ease of use Transtheoretical Behavioral Change Model (TBCM) (Prochaska & DiClemente, 1982) – 7 stages of change Concerns Based Adoption Model (CBAM) - Stages of Concern (SoC) (Hall & Hord, 2001) – 7 stages of concerns
  • From Rogers (2003), Innovation-Decision Process (5 stages) Knowledge Persuasion Decision Implementation Confirmation
  • Dissertation pg 52 Borrows heavily from Rogers and the theory of therapeutic content, processes of change, and Bandura’s “comprehensive model of change in which effective therapy is seen as producing a cognitive restructuring in the individual’s sense of self-efficacy” (p. 61). TBCM has the inclusion and testing for self-efficacy assumed. For this model, SE “is currently known to be the best predictor of behavior, and Marcus and Owen (1992) suggest that SE may actually be clear indicators of stage change readiness” (or being ready to change to the next stage) (p. 62).
  • Dissertation pg 33. Figure 4. The Stages of Concern About an Innovation. (George, et al., 2006, p. 8). Dissertation Summary pg42. CBAM SoC is categorical, not a lock-step, linear process.
  • Dissertation pg 44. “ In several research studies, high self-efficacy corresponds to a higher resolve and sustaining period in the maintenance stage than those with low self-efficacy scores ” (p. 62). And different types of self-efficacy can indicate differing levels of success (hence why looking at 3 different types).
  • Prospectus page 74.
  • Data based on Office of Institutional Research and Effectiveness Fall 2011. http://www.surveysystem.com/sscalc.htm Somewhere in the 5-10% (200-400 ppl) range would be ideal for statistically significant data. Although, what may be more likely is Confidence Level: 95% with a Confidence Interval of 10 with a pop of 4000 = 94 participants. An excellent return would be to have a Confidence Level: 95% with a Confidence Interval of 5 with a pop of 4000 =351 participants. This study is Confidence Level: 95% with a Confidence Interval of 7.65 with a pop of 1713 = 150 participants. Not too bad. The confidence interval (also called margin of error) is the plus-or-minus figure usually reported in newspaper or television opinion poll results. For example, if you use a confidence interval of 4 and 47% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer. The confidence level tells you how sure you can be. It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain. Most researchers use the 95% confidence level. When you put the confidence level and the confidence interval together, you can say that you are 95% sure that the true percentage of the population is between 43% and 51%. The wider the confidence interval you are willing to accept, the more certain you can be that the whole population answers would be within that range. For example, if you asked a sample of 1000 people in a city which brand of cola they preferred, and 60% said Brand A, you can be very certain that between 40 and 80% of all the people in the city actually do prefer that brand, but you cannot be so sure that between 59 and 61% of the people in the city prefer the brand.
  • Appendix C Demographics – pg. 124-127 Teaching SE – pg. 127 – 132 Technology SE – pg. 133 – 134 General SE – pg. 134 – 135 SoC – pg. 135 – 143 Appendix D – SoC Profiles pg. 144-149
  • Averages reflect the “norms” for the group. Std. dev. explains how much spread there is from the “norm.” Appendix D. When conducting statistical analysis that looks for predictors in a relationship between variables, it is known that ordinal or nominal regression works well for prediction. If we take the stance that the user's stage (in the Stages of Concern) is an ordinal (in a progressive order) variable, one can look at non-parametric methods. (Non-parametric methods are used for studying populations that take on a ranked order and may be necessary when data have a ranking but no clear numerical interpretation.) However, it is more appropriate to treat the user's stage as a nominal variable (not in any particular order, like abstract categories) due to the fact that the seven Stages of Concern are not always linear stages of progression. In fact, while the Stages of Concern does have seven specific stages, there is also a categorization of the stages due to the type of concerns the users embody. Figure 6 (page 85) illustrates the Stages of Concern and the three categorizations they fall into.
  • Averages reflect the “norms” for the group. Std. dev. explains how much spread there is from the “norm.” Appendix D. When conducting statistical analysis that looks for predictors in a relationship between variables, it is known that ordinal or nominal regression works well for prediction. If we take the stance that the user's stage (in the Stages of Concern) is an ordinal (in a progressive order) variable, one can look at non-parametric methods. (Non-parametric methods are used for studying populations that take on a ranked order and may be necessary when data have a ranking but no clear numerical interpretation.) However, it is more appropriate to treat the user's stage as a nominal variable (not in any particular order, like abstract categories) due to the fact that the seven Stages of Concern are not always linear stages of progression. In fact, while the Stages of Concern does have seven specific stages, there is also a categorization of the stages due to the type of concerns the users embody. Figure 6 (page 85) illustrates the Stages of Concern and the three categorizations they fall into.
  • Dissertation pg. 92 W-shaped profiles = personal concerns interfere with the desire to learn more about the innovation. It demonstrates a “common problem for which users understood and accepted that they must use—and to some degree master—the innovation, but they were unclear as to the purpose of the innovation, why they must use it, or how it is being used by others.” Generally speaking, it’s a poorly managed innovation and at risk of rejection if it isn’t the only option available. Tailing-up group profile = “When a profile tails up at the end (Stage 5, 6), it suggests that the individual wants to see what other people are doing and thinks they have ideas that are better than the proposed innovation. This also suggests resistance to adopting the innovation.”
  • Dissertation pg. 94. “ a nominal logistic regression analysis was used. The group average from the NGSE scale (X) was compared in a whole model test to the three SoC categories SELF, TASK, and IMPACT (Y).” p-value = 0.9670 > 0.05 = “suggests that general self-efficacy is not related to a stage for a user.”
  • Dissertation pg. 95 “ a nominal logistic regression analysis was used. The group average from the NGSE scale (X) was compared in a whole model test to the three SoC categories SELF, TASK, and IMPACT (Y).” p-value = 0.6416 > 0.05 = “suggests that teaching self-efficacy is not related to a stage for a user.”
  • Dissertation pg. 95 “ a nominal logistic regression analysis was used. The group average from the NGSE scale (X) was compared in a whole model test to the three SoC categories SELF, TASK, and IMPACT (Y).” p-value = 0.0980 > 0.05 = “suggests that teaching with technology self-efficacy is not related to a stage for a user.”
  • Dissertation pg. 100 “ This indicates that for these individuals with high Stage 0 or 1 concerns, there would be a: ‘general awareness of the innovation and interest in learning more details about it [They do] not seem to be worried about the himself or herself in relation to the innovation. Any interest is impersonal, substantive aspects of the innovation, such as its general characteristics, effects, and requirements for use’ (George, et al., 2006, p. 8).” A poorly managed innovation would result in users becoming “stuck” at certain levels. Stuck = frustration, irritation, and becomes a blocker to adoption – may fully result in rejection of the innovation.
  • Pp 104
  • Relationship of Self-Efficacy to Stages of Concerns in the Adoption of Innovation in Higher Education

    1. 1. Relationship of Self-Efficacy to Stages of Concern in the Adoption of Innovation in Higher Education Dissertation presented by • Amber D. Marcu March 20, 2013 from 8:00am-10:00am
    2. 2. Overview • Need for the Study • Literature Review • Method • Results • Discussion • Questions 2
    3. 3. Need for the Study
    4. 4. Problem Statement • Cost of innovation adoption. • A desire for a prescriptive innovation adoption model or approach. • Higher education models lack research into underlying psychological factors. • A desire to understand the Higher Ed. Adopter; • Affective characteristics, Psychological factors; • Parallels between innovation adoption models suggest self-efficacy should be studied. • Could self-efficacy be indicative of adoption? 4
    5. 5. Purpose of the Study • Quantitative, statistical research. • Explore the relationship between various self- efficacies and different stages of concern of an innovation adoption. • CBAM Stages of Concern Questionnaire • SoC Profile • Three Self-efficacies: 1.General, 2.College teaching, and 3.Teaching with Technology. 5
    6. 6. Research Questions Innovation Identification 1.Which stage of Rogers’ (2003) Diffusion of Innovations curve is the chosen innovation in? Identify Stages of Concern (SoC) 2.What is the Stages of Concern profile for instructors in the use of the innovation? 6
    7. 7. Research Questions Self-Efficacy and SoC 3.What is the relationship between instructor general self-efficacy and the levels of the Stages of Concern (SoC)? 4.What is the relationship between instructor teaching self-efficacy and the levels of the Stages of Concern (SoC)? 5.What is the relationship between instructor technology self-efficacy and the levels of the Stages of Concern (SoC)? 7
    8. 8. Benefits of the Study 1. To contribute results to an area of innovation diffusion in higher education that has not yet been fulfilled; 2. To determine if results from this study are consistent with those found in a sister innovation diffusion discipline, behavioral change; 3. To possibly suggest which (if any) self- efficacy may be a predictor of adoption categories (and ultimately successful adoption of an innovation).8
    9. 9. Literature Review
    10. 10. Innovation & Diffusion Models 10
    11. 11. Technology Acceptance Model (TAM) 11 … the TAM can only explain “about 40% of the variance in individuals’ intention to use an IT and actual usage” (p. 276). In truth, the determinants responsible for users’ adoption are still unknown.
    12. 12. Innovation & Diffusion Models 12
    13. 13. Diffusion of Innovations (Innovation-Decision Process) 5 Confirmation Seeking reinforcement or reversing the adoption decision. 4 Implementation Putting the innovation into practice. 3 Decision Engaging in activities leading to a choice. [Rejection or adoption] 2 Persuasion Developing an attitude (positive or negative) toward the innovation. [Perceived characteristics of the innovation] 1 Knowledge Exposure to the innovation and knowing how the innovation works. [Social system variables] 13
    14. 14. Transtheoretical Behavioral Change Model (TBCM) 7 Termination Often excluded due to the impossibility of zero temptation and 100% self-efficacy. 6 Relapse Resumption of old behavior. (Evaluate trigger, motivation, and coping strategies) 5 Maintenance Continued commitment to sustaining new behavior. (6 months – 5 years) 4 Action Practicing new behavior. (3 – 6 months) 3 Preparation Some experience with making the change and testing out the new behavior. (Planning to act within 1 month. 2 Contemplation Ambivalent about change. (Not considering change within the next month.) 1 Pre-contemplation Not considering any change. (Ignorant of change.)14
    15. 15. CBAM Stages of Concern (SoC) IMPACT 6 Refocusing Exploring more benefits. Making major changes or seeking replacement. 5 Collaboration Coordinating and Cooperating with others. 4 Consequence Concerned about the innovation’s impact on students. TASK 3 Management Using the innovation with support from resources. SELF 2 Personal Uncertain, unclear, unsure. Considering personal conflicts. 1 Informational Not worried. Gaining awareness of the innovation. 0 Unconcerned Unaware or unconcerned about the innovation.15
    16. 16. Self-Efficacy (SE) • Defined as intrinsic “beliefs about one’s perceived capability … to attain designated type of performances and achieve specific results” (Pajares, 1996, p. 546). • TBCM already includes and assumes SE. • High SE corresponds to higher stage resolve. • High SE corresponds to higher sustaining period. • Marcus & Owen (1992) suggest that SE may actually be clear indicators of stage change readiness. 16
    17. 17. Method
    18. 18. Research Study Design Examine the relationship between SoC and 3 types of SE (general, college teaching, and teaching with technology). •1 survey instrument • Questionnaire, 5 parts •Population • Large university • Actively teaching faculty •Statistical analysis • Chi-square goodness-of-fit test • Logical and nominal regression •A pilot study was conducted a year prior. 18
    19. 19. Population Profile & Setting • Mid-Atlantic, land-grant university. • Population: Actively teaching faculty. • 1,713 potential participants • Sample Population • 150 responses analyzed (~9% response rate) • 51% Female, 46% Male, 2.7% unknown • Goodness of Fit • Unusually high number of non-research faculty • Sample does not perfectly represent population 19
    20. 20. Instruments One Questionnaire, five parts: 1.Demographic questions 2.Prieto’s College Teaching Self-Efficacy Scale (CTSES) 3.Lichty’s Teaching with Technology Self Efficacy Scale (MUTEBI) 4.Chen’s New General Self-Efficacy (NGSE) Scale 5.Hall & Hord’s Stages of Concern Questionnaire (SoCQ) 20
    21. 21. Procedure • Google Applications for Education was the innovation under study. • Ran study 7 weeks (Dec 2012 to Jan 2013) • Participants contacted via email. • Lists obtained from director of the LMS and director of instructional development • Email invitation includes consent form & link to online survey. • Email reminder sent in early January. 21
    22. 22. Data Analysis Methodology • Each set of SE responses was averaged together for overall scores and std. dev. for the group. • Descriptive statistics stratified by demographic variables. • Chi-square goodness-of-fit test • How well does the sample reflect population? • SoCQ profiles • “Peak” concerns represented in categories 22
    23. 23. Data Analysis Methodology For each of the three research questions pertaining to SE and SoC, the nominal logistic regression was run where: 1.X = General SE, Y = 3 Categories of the 7 Stages of Concern; 2.X = Teaching SE, Y = 3 Categories of the 7 Stages of Concern; 3.X = Teaching with Tech SE, Y = 3 Categories of the 7 Stages of Concern. 23
    24. 24. Research Question 1 Results Which stage of Rogers’ Diffusion of Innovations curve is the chosen innovation in? •Google Apps for Education is a new and emerging innovation. •Only innovators and early adopters so far • 24% = “non-users” • 62% = “novice” or “intermediate” • 14% = “old hand” 24
    25. 25. Research Question 2 Results What is the Stages of Concern profile for instructors in the use of the innovation? •Majority of users have or had used Google Apps in some capacity. •Peak Scores • 95.2% = SELF (Stages 0-2) • 2.1% = TASK (Stage 3) • 2.7% = IMPACT (Stages 4-6) •No primary peak scores in Stage 4 or 6 •“W” shaped and “tailing up” profiles 25
    26. 26. Research Question 3 Results What is the relationship between instructor general self-efficacy and the levels of the Stages of Concern (SoC)? •Nominal logistic regression. •X = General SE group average, Y = 3 Categories (SELF, TASK, IMPACT) •p-value = 0.9670 •General self-efficacy is not related to the levels of the Stages of Concern 26
    27. 27. Research Question 4 Results What is the relationship between instructor teaching self-efficacy and the levels of the Stages of Concern (SoC)? •Nominal logistic regression. •X = Teaching SE group average, Y = 3 Categories (SELF, TASK, IMPACT) •p-value = 0.6416 •Teaching self-efficacy is not related to the levels of the Stages of Concern 27
    28. 28. Research Question 5 Results What is the relationship between instructor technology self-efficacy and the levels of the Stages of Concern (SoC)? •Nominal logistic regression. •X = Teaching with Technology SE group average, Y = 3 Categories (SELF, TASK, IMPACT) •p-value = 0.0980 •Instructor technology self-efficacy is not related to the levels of the Stages of Concern 28
    29. 29. Discussion • Analysis at this point of the innovation revealed no relationships to Stages of Concern. • Why? • Google Apps for Education is an early-stage innovation • Adoption primarily by “innovators” & “early majority” users. • Majority of users in “SELF” category. • Even “mandated” Gmail users may not perceive themselves as Google Apps users. • Possibly a poorly-managed innovation. 29
    30. 30. Recommendations • Run study at several different stages in the life of an innovation. • Use of another diffusion model. • Examine other psychological factors. 30
    31. 31. Thank you: Dr. Cennamo, Dr. Moore, Dr. Evans, and Dr. Doolittle Dissertation presented by • Amber D. Marcu March 20, 2012 from 8:00am-10:00am Questions?

    ×