Personality & technical knowledge management systems research: Data collection & analysis [Personality & TKMS series]
This is part 18 of a series of articles featuring edited portions of Dr. Maureen Sullivan’s PhD dissertation.
Data collection
The target population was administered a survey via an internet survey tool and given 20 days to complete the survey. A description of the study and the survey link was posted on LinkedIn, and in the IEEE newsletter and SIKM Leaders Yahoo! Group for potential participants to access. This participant selection strategy allowed the researcher to obtain responses from a variance of IT, KM, academia and psychology professionals.
Participants were selected based on the following criteria:
- consultants, researchers, employees or managers that directly use technical KMSs in their daily work, and
- at least 18 years of age or older, and
- having used a technical KMS with the past one year.
Participants must have also been members of approximately twenty-six online networking groups and professional knowledge management systems, academia and IS groups.
Participants were given the option to access the survey twenty-four hours per day and advised to complete the survey during non-business hours. In addition, informed consent information was distributed to participants upon requesting their participation in the study.
Once the data was retrieved from the internet survey tool, it was entered into the Statistical Package for Social Science (SPSS) for analysis and reporting. If the number of participants was not sufficient, the process would have been repeated.
Data analysis
The data analysis process included the coding and cleaning of the data collected from the survey. Statistical calculations were then performed to analyze the data collected.
Coding
The survey instrument measuring personality traits, used a five-point Likert scale, with anchors ranging from very inaccurate to very accurate. Similarly, the survey instrument portion measuring technology acceptance (UTAUT) used a five point Likert scale with anchors ranging from strong disagreement to strong agreement.
Each point was assigned a numerical value and this numerical value was used to record the responses to each survey question. Each survey question was given a variable name. Additionally, each respondent was given a unique ID. All of this information was entered into a spreadsheet for loading into SPSS.
Cleaning
The data from the spreadsheet was loaded into SPSS. Frequencies on all of the variables were run. Based on the selected variables, the mean, median, mode, and standard deviation were determined. These tasks allowed the researcher to validate the data and eliminate any surveys that were not valid (e.g., missing data or incorrect data entry.
Statistical procedures
Once the data was cleaned or fixed, and then other frequencies tests were performed. Additional examinations of the output included the review of the descriptive statistics. The technology acceptance factors in the IPIP-B5 and the UTAUT identified in this research were captured in a Likert scale (1 to 5). The overall scores for each of these factors were calculated by averaging the scores from each item.
Linear regression was performed to address each null hypothesis using the testing procedures defined by Howell1 and Stevens2. First, the participants‘ data were screened for outliers. The participants‘ residuals were standardized, and the resulting z-scores were utilized to identify outliers in the data. A participant is considered an outlier when standardized residual is greater than 3.
The next step was to assess model linearity and homoscedasticity using a plot of standardized residuals. Finally, the regression coefficients statistics were calculated to determine if the variable was a significant predictor of perceived usefulness.
Next edition: Personality & technical knowledge management systems research: Results.
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