Adoption of IT by Faculty Members at a Midwestern University in the United States - Case Study Example

Quantitative Article Review Name Institution Abstract This paper critiques a peer-reviewed article on the adoption of information technology by faculty members at a midwestern university in the United States. The purpose of the study was to determine the factors for predicting the adoption level of technology by faculty members in the College of Education at a US mid-western university. The article used the Learning/Adoption Trajectory framework to guide data collection and analysis. A structured questionnaire was used to obtain technology adoption levels and predictive factors from 84 faculty members. Five predictive factors were believed the adoption level of technology by faculty members at the university namely, instructional courseware, non-traditional operating systems, data analysis and management tools, self-directed sources of information, collegial interaction and online sources. Results of the analysis revealed that most significant predictive factors for technology adoption levels were use of non-directed sources of information, collegial interaction and data analysis tools. The findings confirmed that these predictive factors had the ability to reinforce teaching and learning in higher education. In addition, the findings revealed the importance of focusing on the needs of the instructors with regards to their use of technology in their teaching practice. Brief Introduction This paper reviews a peer-reviewed article titled ‘Analysis of predictive factors that influence faculty members’ technology adoption level’ by Sahin and Thompson (2007). The purpose of the article was to assess the adoption of information technology by faculty members at a Midwestern university in the United States. Five predictive factors were believed the adoption level of technology by faculty members at the university namely, instructional courseware, non-traditional operating systems, data analysis and management tools, self-directed sources of information, collegial interaction and online sources. The article analyzed questionnaire responses from 43 faculty members and observed that only three factors were significant predictors of technology adoption by faculty (collegial interaction, data analysis tools and use of information sources that are self-directed. The paper analyzes the article to determine the objectivity, accuracy, and validity of the methodology, research design, philosophical framework, analysis methods and the findings. Table of Contents Abstract 2 Brief Introduction 3 Table of Contents 4 Research Questions 5 Purpose of the Study 5 Population and Sample 5 Research Design 6 Data Collection and Analysis 7 Presentation of Data 9 Philosophical Framework 11 Critique of Methodological Options 12 Conclusions 13 References 14 Research Questions The article did not have research hypothesis or questions. However, it provided an aim. The aim was to predict the factors affecting the technology adoption level among faculty members (Sahin & Thompson, 2007). The predictive factors would be determined from questionnaire responses in four areas: demographics, experience with computers, use of instructional hardware in teaching and technology learning. Purpose of the Study The purpose of the study was to determine the factors that could predict the adoption level of technology by faculty members in the College of Education at a US mid-western university (Sahin & Thompson, 2007). The purpose was driven by the impact of technology and computers on higher education. The authors observed that the availability and integration technology had the ability to reinforce teaching and learning in higher education. Consequently, they sought to determine whether technology was used to reinforce teaching at a Midwestern university and what factors predicted the adoption level of faculty members at the institution. The article used the Learning/Adoption Trajectory model to assess the adoption of technology by 87 faculty members in a university with a student population of 25,000 (Sahin & Thompson, 2007). Population and Sample The population comprised of 87 members of faculty at the College of Education (Sahin & Thompson, 2007). This population included lecturers/instructors, assistant professors, associate professors and professors. All of the faculty members were considered for the sample. The article did not describe a sampling method used to select participants since the population size was quite small. Male and female participants were selected. Only 43 out of the 84 (49 percent response rate) responded to the article’s questionnaire. The respondents were from all faculty levels that had an average of 9 years experience at the university. However, the article does not mention whether the authors obtained the consent of the faculty members’ prior to carrying out the research. Furthermore, there is no mention of consent from a review board to ensure that ethical guidelines were followed in the collection of data from human subjects (Sahin & Thompson, 2007). Research Design The article does not describe the research design selected for the study. There was no indication that the authors used a quantitative approach to guide data collection and analysis. Quantitative research uses different methods to ensure objectivity, generalizability of results, predictability and replicability (Creswell, 2003). These methods include randomized designs, randomized controlled trials, correlational design and quasi-experimental design. Correlation design would not have been suitable since the article did not aim to establish a relationship between the predictive factors and technology adoption by faculty members. Similarly, randomized design would not have been suitable since the article was not a clinical study on two groups of participants (control and intervention groups). A quasi-experimental approach could have been used to establish the relationship between the predictive factors and the level of technology adoption. Alternatively, a cross-sectional design would have enabled the authors to evaluate cross-sectional trends in the adoption of technology by faculty members in higher education institutions across the country. Conversely, the study could have used case study, ethnography or action research to guide the research. A case study would have enabled the authors to use real-life approach to investigate the research problem whereas action research would have enabled the authors to address the practical concerns of faculty members’ regarding their technology adoption in instruction. An ethnographic approach would have guided the authors as they immersed themselves into the lives of the faculty members that use technology for instructional purposes. Although the study did not mention the design used, it provides clear phases such as the introduction that explains the purpose of the study, theoretical models, methodology involving sampling, instrumentation and data collection, reporting of the results and conclusions/implications (Creswell, 2003). Data Collection and Analysis The article used a structured questionnaire with a Likert scale. The questionnaire was divided into five parts. The first part sought to obtain demographic data on the respondents including gender, experience, supervision status and academic rank (Sahin & Thompson, 2007). The second part sought information on the respondents’ computer experience including application of tools, use of operating systems and adoption of instructional courseware in teaching. The third part sought information on the respondents’ use of instructional hardware in their teaching practice while the fourth part sought information on the methods of technology learning (such as use of assistance sources or instructional media). Lastly, the questionnaire sought information on the faculty members’ level of technology use based on the phases of the Learning/Adoption Technology model such as adopter, re-affirmer, learner, leader, and co-learner (Sahin & Thompson, 2007). The questionnaire was valid because it had been tested and applied in a previous study by Hagenson (2001). Hagenson designed a questionnaire to investigate the diffusion and integration of technology in teaching by educators at a mid-western university. He based the questionnaire on Learning Adoption Trajectory model for analysis, which is similar to the approached used in the article. However, the use of structured questions has certain disadvantages. For instance, poorly worded questions affect the quality of responses (Bryman, 2012). This means that poorly worded questions would affect the type of feedback obtained from the faculty members. The faculty could have confused or misunderstood the wording that could have led to incorrect responses. Secondly, structured questions do not provide an opportunity to probe for further information because the responses were limited to the Likert choices. This could have hindered objectivity of the study (Eisner, 1992). The authors therefore could not probe the respondents for further information on their technology adoption. Bryman (2012) propose that researchers could enhance the reliability of their study by asking correct questions, providing clear instructions and eliminating any ambiguous instructions or questions. The article used quantitative analysis techniques such as means, percentages, regression, correlation tests, and univariate statistics were used in the study (Sahin & Thompson, 2007). The dependent variable, and stage variable, was the technology use level by the faculty at the College of Education. The prediction model was tested using multiple linear regression method. Multiple linear regression was appropriate because it helped the authors to determine whether the instructional courseware variable could predict technology use level by the participants. It is through this regression method that the authors established the significance of the variable. The article also used factor analysis to combine five variables on instructional use of technology. Factor analysis was appropriate because it helped the authors to reduce the variables to an adequate number. Furthermore, factor analysis guided the authors in creating a component matrix and correlation matrix to show the degree of correlation between the variables. The authors used Bartlett’s test and KMO statistics to identify the fit of the variables. Univariate analysis of variance was also used to determine the relationship between the technology adoption level and the demographics of the participants in the study (Sahin & Thompson, 2007). The Statistical Package for Social Sciences software was used to perform the quantitative analysis to predict the adoption levels for technology use. Presentation of Data The data was presented in an organized manner. The authors created sub-headings for each variable and explained the results of each analysis in detail. The first subheading explained the significance of demographic factors on the level of technology use while the second subheading demonstrated the relationship between instructional courseware and technology use. The second subheading described results of quantitative analysis on instructional courseware components such as simulation, tutorials, drills and integrated learning systems. Within the subheading, the authors describe the findings of multiple linear regressions to demonstrate the relationship between the variable and technology use level. The residual plot illustrates this relationship to demonstrate normal distribution and consistency of the linear relationship between the variables. In the third subheading, Sahin and Thompson (2007) compared the relationship between the tool applications variable and the technology use level. The study confirmed the reliability of the variable’s components (such as desktop publishing, statistics package, database and text editing) using Cronbach alpha method. The sub-section explained the relationship and variation between these elements and the technology use level. Similar descriptions of reliability were given in subsequent subheadings for individual variables. Furthermore, each sub-section provides detailed information on the regression and significance between the independent and dependent variables. The use of tables and charts was appropriate for the study. The figures helped the reader to interpret the findings more easily. For instance, figure 2 on the adoption level shows that the majority of the participants were adopters and co-learners of technology. This information was easy to interpret from the bar chart rather than a paragraph with a similar description. The tables helped the reader to compare the results for each variable. For instance, table 2 helps the reader to compare the demographic characteristics and the level of technology use. From the table, it is clear that number of students being taught, academic year, and gender were not significant determinants of technology adoption by the participants (Sahin & Thompson, 2007). Lastly, the discussion provided useful information, supported by literature, on the importance of three predictors for technology use level among the participants. These predictors were collegial communication, use of self-directed sources of information and data analysis tools. Each predictor was discussed in a paragraph to show its relevance to the topic. From the discussion section, it is apparent that the three variables are significant for addressing the participants’ needs for technology use. The discussion also highlights the impact of lack of technological compatibility with the participants’ needs on the level of technology adoption (Sahin & Thompson, 2007). The article provides a succinct conclusion that outlines the theoretical framework, the results and the relationship between the independent variables and the dependent variables. In addition, the article concludes that the findings reveal the importance of developing support networks and encouraging the adoption of data analysis tools to improve the use of technology by faculty members (Sahin & Thompson, 2007). Philosophical Framework The philosophical framework guiding the research was influenced by three models, which include the Concerns Based Adoption Model (CBAM), the Diffusion of Innovations model and the Learning/Adoption Trajectory model. The Diffusion of Innovations framework focused on five variables affecting technology adoption namely the perceived attributes, the social system, the communication channel, the change agent efforts, and the innovation-decision model. The Learning/Adoption Trajectory model was applied in the study because it examines the dynamic adoption of technology. The model was adequate for the study because it looked at technology adoption from a cyclical approach and was adopted for the education sector. The model had been successfully used in a previous study by Hangenson (2001) that evaluated technology use by faculty in higher education institutions in previous studies and was seen adequate for this study (Sahin & Thompson, 2007, p.170). The Learning/Adoption Trajectory model was adequate because described five levels of technology adoption. These levels were learner, adopter, re-affirmer, co-learner, and leader. The first stage (learner) represented the faculty member as a learner that acquired knowledge on technology to perform instructional tasks. The second stage (adopter) described the teacher as an experimenter that learned technology before using it in the classroom. The model describes the adopter as a teacher that shares his/her experiences with peers. The third stage (co-learner) described the faculty member or teacher as developing a relationship between the instructional technology and curriculum. The fourth stage (re-affirmer) describes the teacher as one who creates awareness of the learning outcomes of technology use in teaching and who identifies new ways of using instructional technology to impact student learning and performance. The fifth stage (leader) describes the teacher as an action researcher that assess technology use in teaching practice, collects data on this adoption and identifies ways of improving teaching practice using technology (Sahin & Thompson, 2007). The Learning/Adoption framework was adequate for the study because it formed the theoretical underpinning for data collection and analysis. The framework helped the authors to determine the association between the technology adoption stage of faculty members’ and their responses to the questionnaire. Critique of Methodological Options The article does not indicate a specific methodology used for the study. However, a critique of the methodological options used in this article can include an assessment of the research design, sampling, data collection and data analysis (Harwell, 2011). The research design was not sufficiently explained in the article. However, the authors provided details on the choice of the Learning/Adoption Trajectory model in the research design and the integration of the model in the data collection process. The sampling process was well-explained. The article described the population, the selection of faculty at the College of Education for the sample and the number of responses to the research instrument. The authors stated that they had a response rate of 49 percent but did not explain or justify why that percentage was sufficient for the data collection. The article could have cited literature that justified continuation of a research study that had less than 50 percent response rate. The article provided detailed information on the choice of data collection instrument. The authors explained how the Learning/Adoption Trajectory model was adopted in the survey and the structured nature of the responses. The article also provided sufficient information on the segmentation of the questionnaire stating the purpose and the data expected from each survey category. Lastly, information on data analysis was sufficient. This is because the article explained the type of analysis conducted on the data, reasons for each analysis method and predictions of the outcomes. For instance, the article explained that factor analysis would be used to lower the number of research variables in different sections of the survey. This explanation helped the reader understand the purpose for factor analysis in the study. Conclusions The findings in the article aligned with the topic and the purpose of the study. The findings showed that three factors could predict the level of technology use by faculty at the College of Education. The findings were relevant to the research topic and confirmed that the Learning/Adoption Trajectory framework was sufficient in addressing the factors that influenced the adoption levels of technology for instruction. References Bryman, A. (2012). Social research methods. New York, NY: Oxford University Press. Creswell, J.W. (2003). Qualitative, quantitative and mixed methods (2nd ed.). Thousand Oaks, CA: Sage. Eisner, E. (1992). Objectivity in educational research. Curriculum Inquiry, 22(1), 9-15. Harwell, M. R. (2011). Research design in qualitative/quantitative/mixed methods. In C. Conrad & R. Serlin (Eds.), The SAGE Handbook for Research in Education: Pursuing Ideas as the Keystone of Exemplary Inquiry (2nd edn). New York: Sage. Sahin, I., & Thompson, A. (2007). Analysis of predictive factors that influence faculty members’ technology adoption level. JI of Technology and Teacher Education, 15(2), 167-190. Read More