Academia.eduAcademia.edu

Figure from:Individual Decision-Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability and Perceived Task-Technology Fit Perspective

See full PDFdownloadDownload figure
of the population selection consisting of only female subjects are shown in Table 71.

Table 71 of the population selection consisting of only female subjects are shown in Table 71.

Related Figures (88)

The following section explores reoccurring themes found in literature related to
The following section explores reoccurring themes found in literature related to
Table 2. List of Complexity Frameworks (Adapted from Gill and Hicks, 2006).
Table 2. List of Complexity Frameworks (Adapted from Gill and Hicks, 2006).
Decision-Making Performance
Decision-Making Performance
Figure 1. Proposed Research Model. these propositions and the conceptual model through an empirical study presented in
Figure 1. Proposed Research Model. these propositions and the conceptual model through an empirical study presented in
Lee and Bednarz (2009) emphasize that most of the spatial ability tests utilized in
Lee and Bednarz (2009) emphasize that most of the spatial ability tests utilized in
Figure 2. Overview of Scale Development Procedure from MacKenzie et al. (2011). studies (e.g. Balog, 2011; Brusset, 2012).
Figure 2. Overview of Scale Development Procedure from MacKenzie et al. (2011). studies (e.g. Balog, 2011; Brusset, 2012).
Table 7. Proposed Substrata of GRA.
Table 7. Proposed Substrata of GRA.
The SPGMR substratum is defined as ‘the self-perceived ability to commit
The SPGMR substratum is defined as ‘the self-perceived ability to commit
Table 10. Initial Self-Perceived Geospatial Memorization and Recall Measurement Items. The SPGV substratum is defined as ‘the self-perceived ability to form mental
Table 10. Initial Self-Perceived Geospatial Memorization and Recall Measurement Items. The SPGV substratum is defined as ‘the self-perceived ability to form mental
Table 11. Initial Self-Perceived Geospatial Visualization Measurement Items.
Table 11. Initial Self-Perceived Geospatial Visualization Measurement Items.
Figure 3. First-Order Reflective, Second-Order Formative Measurement Model including Propositions. to test its relationship to GRA.
Figure 3. First-Order Reflective, Second-Order Formative Measurement Model including Propositions. to test its relationship to GRA.
Table 17. Pretest A Reliability Statistics. Pretest B necessarily have a familiarity with geographic concepts.
Table 17. Pretest A Reliability Statistics. Pretest B necessarily have a familiarity with geographic concepts.
(Ringle et al., 2005). For this analysis the measurement items of each first-order
(Ringle et al., 2005). For this analysis the measurement items of each first-order
Figure 4. Pretest B, Path Analysis including SPGS. it is a construct similar to the GRA measure, so it can be used to establish discriminate
Figure 4. Pretest B, Path Analysis including SPGS. it is a construct similar to the GRA measure, so it can be used to establish discriminate
Highest Item Loadings shown in bold. Table 19. PLS Factor Analysis Loadings.
Highest Item Loadings shown in bold. Table 19. PLS Factor Analysis Loadings.
Table 20. Latent Variable Correlation. and 0.9528, while there is also a high correlation between each of the substrata.
Table 20. Latent Variable Correlation. and 0.9528, while there is also a high correlation between each of the substrata.
Table 21. Path Coefficients. Next, construct reliability and validity were established. The Average Variance
Table 21. Path Coefficients. Next, construct reliability and validity were established. The Average Variance
Table 22. Construct Reliability. for this construct. See Table 22 for more detail.
Table 22. Construct Reliability. for this construct. See Table 22 for more detail.
are presented in Table 23 along with factor loadings. updated path coefficients, is presented in Figure 6. The reduced items for each substratum
are presented in Table 23 along with factor loadings. updated path coefficients, is presented in Figure 6. The reduced items for each substratum
Table 24. PLS Latent Variable Correlation. high correlation between each of the substrata.
Table 24. PLS Latent Variable Correlation. high correlation between each of the substrata.
Table 25. Path Coefficients.
Table 25. Path Coefficients.
Table 26. Construct Reliability.
Table 26. Construct Reliability.
Table 27. PLS Factor Loading and Cross Loading. Using the two-step procedure outlined by Gefen and Straub (2005), discriminant
Table 27. PLS Factor Loading and Cross Loading. Using the two-step procedure outlined by Gefen and Straub (2005), discriminant
Table 28. Inter-Construct Correlations and Square Root of AVE. The analysis performed allowed for construct reliability and validity, discriminate
Table 28. Inter-Construct Correlations and Square Root of AVE. The analysis performed allowed for construct reliability and validity, discriminate
2005). The path coefficients of the first-order constructs (substrata) on the second-order
2005). The path coefficients of the first-order constructs (substrata) on the second-order
Figure 7. Test, Path Analysis. in Table 30.
Figure 7. Test, Path Analysis. in Table 30.
Table 30. PLS Factor Analysis Loadings and Weights.
Table 30. PLS Factor Analysis Loadings and Weights.
Table 31. Latent Variable Correlation. correlation (greater than 0.63) between each of the substrata.
Table 31. Latent Variable Correlation. correlation (greater than 0.63) between each of the substrata.
Table 32. Path Coefficients. are below 0.9, indicating no common method bias (Bagozzi et al., 1991).
Table 32. Path Coefficients. are below 0.9, indicating no common method bias (Bagozzi et al., 1991).
Table 34. PLS Factor Loading and Cross Loading.
Table 34. PLS Factor Loading and Cross Loading.
Table 35. Inter-Construct Correlations and Square Root of AVE Collinearity
Table 35. Inter-Construct Correlations and Square Root of AVE Collinearity
Table 36. SPGON > GRA: Gender Group Differences. The second test addressed the effects of SPGMR on GRA based on gender. This
Table 36. SPGON > GRA: Gender Group Differences. The second test addressed the effects of SPGMR on GRA based on gender. This
Table 37. SPGMR > GRA: Gender Group Differences.
Table 37. SPGMR > GRA: Gender Group Differences.
Table 38. SPGV ~ GRA: Gender Group Differences.
Table 38. SPGV ~ GRA: Gender Group Differences.
Table 39. Final GRA Measurement Items. Table 40. Final SPGS Measurement Items.
Table 39. Final GRA Measurement Items. Table 40. Final SPGS Measurement Items.
Prior research has shown that technologies such as location-based services and
Prior research has shown that technologies such as location-based services and
Table 41. Perceived Enjoyment Measurement Items. study, which were adapted from Lee et al. (2006).
Table 41. Perceived Enjoyment Measurement Items. study, which were adapted from Lee et al. (2006).
Table 42. Perceived Usefulness Measurement Items. from Davis (1989), Davis et al. (1989), Venkatesh and Davis (2000), and Fagan et al. Heijden (2004), and Fagan, Neill and Wooldridge (2008). These measurement items are
Table 42. Perceived Usefulness Measurement Items. from Davis (1989), Davis et al. (1989), Venkatesh and Davis (2000), and Fagan et al. Heijden (2004), and Fagan, Neill and Wooldridge (2008). These measurement items are
Table 43. Perceived Ease-of-Use Measurement Items.
Table 43. Perceived Ease-of-Use Measurement Items.
Table 44. Attitude Measurement Items. Lee et al. (2006). These items are shown in Table 44.
Table 44. Attitude Measurement Items. Lee et al. (2006). These items are shown in Table 44.
Table 45. Behavioral Intent Measurement Items. Venkatesh and Davis (2000). These items are shown in Table 45.
Table 45. Behavioral Intent Measurement Items. Venkatesh and Davis (2000). These items are shown in Table 45.
Upon completion of the data collection, an analysis was performed using
Upon completion of the data collection, an analysis was performed using
Table 47. Descriptive Statistics of Demographic Variables. The measurement model explains the relationship between each of the constructs
Table 47. Descriptive Statistics of Demographic Variables. The measurement model explains the relationship between each of the constructs
Table 48. Item Reliability. Generally, acceptable thresholds have stated that items should load at greater than all values appear excellent, which demonstrates measurement item reliability.
Table 48. Item Reliability. Generally, acceptable thresholds have stated that items should load at greater than all values appear excellent, which demonstrates measurement item reliability.
Table 49. Item Loadings and Cross Loadings, highest loadings shown in bold.
Table 49. Item Loadings and Cross Loadings, highest loadings shown in bold.
Table 51. Inter-Construct Correlations and Square Root of AVE. Next, validity is examined using the AVE and latent variable correlations. When a
Table 51. Inter-Construct Correlations and Square Root of AVE. Next, validity is examined using the AVE and latent variable correlations. When a
perform an assessment of the effects as shown in Figure 9. SIQTIMCAllt at alpila OF U.LUUT, © SIQHiiiCalll al dipild OF U.VO Figure 9. Initial Nomological Network of GRA, along with Path Coefficients and R Square Values. relationships was analyzed. The first step of analyzing the structural model was to
perform an assessment of the effects as shown in Figure 9. SIQTIMCAllt at alpila OF U.LUUT, © SIQHiiiCalll al dipild OF U.VO Figure 9. Initial Nomological Network of GRA, along with Path Coefficients and R Square Values. relationships was analyzed. The first step of analyzing the structural model was to
Upon completion of the assessment of the measurement model an analysis of the
Upon completion of the assessment of the measurement model an analysis of the
Table 53. Path Coefficients. Discussion
Table 53. Path Coefficients. Discussion
These results are summarized in Table 54.
These results are summarized in Table 54.
Table 55. Summary of Geospatial Decision Performance Research. Note: Only measures directly relevant to this study are shown in Table 55; see
Table 55. Summary of Geospatial Decision Performance Research. Note: Only measures directly relevant to this study are shown in Table 55; see
Prior research has shown conflicting results when measuring the effects of spatial literature review, objective measures of decision-making are widely-used measures of
Prior research has shown conflicting results when measuring the effects of spatial literature review, objective measures of decision-making are widely-used measures of
Figure 11. Experiment Workflow.
Figure 11. Experiment Workflow.
Table 56. Summary of Geospatial Decision Performance Research.
Table 56. Summary of Geospatial Decision Performance Research.
Figure 12. “Apartment Finder” Experiment Tool Developed using GISCloud and Bing Maps. variations in the treatment. Figure 12 presents an overview of the apartment finder tool.
Figure 12. “Apartment Finder” Experiment Tool Developed using GISCloud and Bing Maps. variations in the treatment. Figure 12 presents an overview of the apartment finder tool.
Table 57. Descriptive Statistics of Experiment Subjects.
Table 57. Descriptive Statistics of Experiment Subjects.
Table 61. Cronbach’s alpha and Composite Reliability.
Table 61. Cronbach’s alpha and Composite Reliability.
Table 62. Measurement Item Loadings. Following the establishment of indicator reliability, convergent validity was tested
Table 62. Measurement Item Loadings. Following the establishment of indicator reliability, convergent validity was tested
Table 64. Measurement Item Cross-Loadings.
Table 64. Measurement Item Cross-Loadings.
Table 65. Latent Variable Correlations and Sq. of AVE (shown in bold).
Table 65. Latent Variable Correlations and Sq. of AVE (shown in bold).
Table 66. R’ Values of Endogenous Latent Variables.
Table 66. R’ Values of Endogenous Latent Variables.
Figure 13. Visual result of SEM-PLS Algorithm (using SmartPLS).
Figure 13. Visual result of SEM-PLS Algorithm (using SmartPLS).
significance results of the path coefficients.
significance results of the path coefficients.
Figure 14. Visual result of SEM-PLS Algorithm (using SmartPLS).
Figure 14. Visual result of SEM-PLS Algorithm (using SmartPLS).
the visual results of the SmartPLS bootstrapping algorithm.
the visual results of the SmartPLS bootstrapping algorithm.
the endogenous latent variables are shown in Table 69. Heterogeneity A and T, yet only an approximately medium relevance of PTTF. The R’ and Q values.
the endogenous latent variables are shown in Table 69. Heterogeneity A and T, yet only an approximately medium relevance of PTTF. The R’ and Q values.
Next, female participant values were retrieved. The path coefficients and t-values
Next, female participant values were retrieved. The path coefficients and t-values
Findings and Discussion
Findings and Discussion
relationships within the research model. Figure 15. Research Model with Relationship Significance.
relationships within the research model. Figure 15. Research Model with Relationship Significance.
Table 73. Hypotheses Test.
Table 73. Hypotheses Test.
Figure 16. Conceptual Model.
Figure 16. Conceptual Model.
Figure 17. Current Nomological Network of GRA Construct.
Figure 17. Current Nomological Network of GRA Construct.